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Code · REGISTER · 2005-08-15 · Centers for Medicare & Medicaid Services (CMS), HHS · Notices

Notices. Final rule

121,338 words·~552 min read·/register/2005/08/15/05-15419

A research copy — for the controlling text, always check the official state or federal source. Not legal advice.

BILLING CODE 4810-31-P 70 156 Monday, August 15, 2005 Rules and Regulations Part II Department of Health and Human Services Centers for Medicare & Medicaid Services 42 CFR Part 412 Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for FY 2006; Final Rule DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Medicare & Medicaid Services 42 CFR Part 412 [CMS-1290-F] RIN 0938-AN43 Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for FY 2006 AGENCY:
Centers for Medicare & Medicaid Services (CMS), HHS. ACTION: Final rule. SUMMARY: This final rule will update the prospective payment rates for inpatient rehabilitation facilities for Federal fiscal year 2006 as required under section 1886(j)(3)(C) of the Social Security Act (the Act). Section 1886(j)(5) of the Act requires the Secretary to publish the classification and weighting factors for the inpatient rehabilitation facilities case-mix groups and a description of the methodology and data used in computing the prospective payment rates for that fiscal year.
In addition, we are implementing new policies and are changing existing policies regarding the prospective payment system within the authority granted under section 1886(j) of the Act. DATES: These regulations are effective October 1, 2005. The updated IRF prospective payment rates are applicable for discharges on or after October 1, 2005 and on or before September 30, 2006 (FY 2006). FOR FURTHER INFORMATION CONTACT: Pete Diaz,
(410)786-1235. Susanne Seagrave,
(410)786-0044. Mollie Knight,
(410)786-7948 for information regarding the market basket and labor-related share. August Nemec,
(410)786-0612 for information regarding the tier comorbidities. Zinnia Ng,
(410)786-4587 for information regarding the wage index and Core-Based Statistical Areas (CBSAs). SUPPLEMENTARY INFORMATION: Table of Contents I. Background A. General Overview of the Current Inpatient Rehabilitation Facility Prospective Payment System (IRF PPS) B. Requirements for Updating the Prospective Payment Rates for IRFs C. Operational Overview of the Current IRF PPS D. Summary of the FY 2006 Proposed Update to the IRF PPS II. Provisions of the Proposed Regulations III. Analysis of and Responses to Public Comments IV. Research to Support Refinements of the Current IRF PPS V. Refinements to the Patient Classification System A. Changes to the IRF Classification System 1. Development of the IRF Classification System 2. Description and Methodology Used To Develop the IRF Classification System in the August 7, 2001 Final Rule a. Rehabilitation Impairment Categories b. Functional Status Measures and Age c. Comorbidities d. Development of CMG Relative Weights e. Overview of Development of the CMG Relative Weights B. Changes to the Existing List of Tier Comorbidities 1. Changes to Remove Codes That Are Not Positively Related to Treatment Costs 2. Changes to Move Dialysis to Tier One 3. Changes to Move Comorbidity Codes Based on Their Marginal Cost C. Changes to the CMGs 1. Changes for Updating the CMGs 2. Use of a Weighted Motor Score Index and Correction to the Treatment of Unobserved Transfer to Toilet Values 3. Changes for Updating the Relative Weights VI. FY 2006 Federal Prospective Payment Rates A. Reduction of the Standard Payment Amount to Account for Coding Changes B. Adjustments to Determine the FY 2006 Standard Payment Conversion Factor 1. Market Basket Used for IRF Market Basket Index a. Overview of the RPL Market Basket b. Methodology for Operating Portion of the RPL Market Basket c. Methodology for Capital Proportion of the RPL Market Basket d. Labor-Related Share 2. Area Wage Adjustment a. Revisions of the IRF PPS Geographic Classification b. Current IRF PPS Labor Market Areas Based on MSAs c. Core-Based Statistical Areas (CBSAs) d. Revisions of the IRF PPS Labor Market Areas i. New England MSAs ii. Metropolitan Divisions iii. Micropolitan Areas e. Implementation of the CBSA-Based Labor Market Areas f. Wage Index Data 3. Teaching Status Adjustment 4. Adjustment for Rural Location 5. Adjustment for Disproportionate Share of Low-Income Patients 6. Update to the Outlier Threshold Amount 7. Budget Neutrality Factor Methodology for Fiscal Year 2006 8. Description of the Methodology Used to Implement the Changes in a Budget Neutral Manner 9. Description of the IRF Standard Payment Conversion Factor for Fiscal Year 2006 10. Example of the Methodology for Adjusting the Federal Prospective Payment Rates VII. Quality of Care in IRFs VIII. Miscellaneous Comments Within the Scope of the Proposed Rule IX. Miscellaneous Comments Outside the Scope of the Proposed Rule X. Provisions of the Final Regulations XI. Collection of Information Requirements XII. Regulatory Impact Analysis Acronyms Because of the many terms to which we refer by acronym in this final rule, we are listing the acronyms used and their corresponding terms in alphabetical order below. ADC Average Daily Census AHA American Hospital Association AMI Acute Myocardial Infarction BBA Balanced Budget Act of 1997 (BBA), Pub. L. 105-33 BBRA Medicare, Medicaid, and SCHIP [State Children's Health Insurance Program] Balanced Budget Refinement Act of 1999, Pub. L. 106-113 BIPA Medicare, Medicaid, and SCHIP [State Children's Health Insurance Program] Benefits Improvement and Protection Act of 2000, Pub. L. 106-554 BLS Bureau of Labor Statistics CART Classification and Regression Trees CBSA Core-Based Statistical Areas CCR Cost-to-charge ratio CMGs Case-Mix Groups CMI Case Mix Index CMSA Consolidated Metropolitan Statistical Area CPI Consumer Price Index DSH Disproportionate Share Hospital ECI Employment Cost Index FI Fiscal Intermediary FIM Functional Independence Measure (FIM TM is a registered trademark of UDS <sup>MR</sup> ) FIM-FRGs Functional Independence Measures-Function Related Groups FRG Function Related Group FTE Full-time equivalent FY Federal Fiscal Year GME Graduate Medical Education HCRIS Healthcare Cost Report Information System HIPAA Health Insurance Portability and Accountability Act HHA Home Health Agency IME Indirect Medical Education IFMC Iowa Foundation for Medical Care IPF Inpatient Psychiatric Facility IPPS Inpatient Prospective Payment System IRF Inpatient Rehabilitation Facility IRF-PAI Inpatient Rehabilitation Facility-Patient Assessment Instrument IRF-PPS Inpatient Rehabilitation Facility-Prospective Payment System IRVEN Inpatient Rehabilitation Validation and Entry LIP Low-income percentage MEDPAR Medicare Provider Analysis and Review MSA Metropolitan Statistical Area NECMA New England County Metropolitan Area NOS Not Otherwise Specified NTIS National Technical Information Service OMB Office of Management and Budget OSCAR Online Survey, Certification, and Reporting PAI Patient Assessment Instrument PLI Professional Liability Insurance PMSA Primary Metropolitan Statistical Area PPI Producer Price Index PPS Prospective Payment System RIC Rehabilitation Impairment Category RPL Rehabilitation Hospital, Psychiatric Hospital, and Long-Term Care Hospital Market Basket TEFRA Tax Equity and Fiscal Responsibility Act TEP Technical Expert Panel I. Background We received approximately 55 timely items of correspondence on the Inpatient Rehabilitation Facility Prospective Payment System for FY 2006 proposed rule (70 FR 30188). Summaries of the public comments and our responses to those comments are set forth below under the appropriate section heading of this final rule. A. General Overview of the Current Inpatient Rehabilitation Facility Prospective Payment System (IRF PPS) Section 4421 of the Balanced Budget Act of 1997
(BBA)(Pub. L. 105-33), as amended by section 125 of the Medicare, Medicaid, and SCHIP [State Children's Health Insurance Program] Balanced Budget Refinement Act of 1999
(BBRA)(Pub. L. 106-113), and by section 305 of the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000
(BIPA)(Pub. L. 106-554), provides for the implementation of a per discharge prospective payment system (PPS), through section 1886(j) of the Social Security Act (the Act), for inpatient rehabilitation hospitals and inpatient rehabilitation units of a hospital (hereinafter referred to as IRFs). Payments under the IRF PPS encompass inpatient operating and capital costs of furnishing covered rehabilitation services (that is, routine, ancillary, and capital costs) but not costs of approved educational activities, bad debts, and other services or items outside the scope of the IRF PPS. Although a complete discussion of the IRF PPS provisions appears in the August 7, 2001 final rule, we are providing below a general description of the IRF PPS. The IRF PPS, as described in the August 7, 2001 final rule, uses Federal prospective payment rates across 100 distinct case-mix groups (CMGs). Ninety-five CMGs were constructed using rehabilitation impairment categories, functional status (both motor and cognitive), and age (in some cases, cognitive status and age may not be a factor in defining a CMG). Five special CMGs were constructed to account for very short stays and for patients who expire in the IRF. For each of the CMGs, we developed relative weighting factors to account for a patient's clinical characteristics and expected resource needs. Thus, the weighting factors account for the relative difference in resource use across all CMGs. Within each CMG, the weighting factors were “tiered” based on the estimated effects that certain comorbidities have on resource use. The Federal PPS rates were established using a standardized payment amount (previously referred to as the budget-neutral conversion factor). The standardized payment amount was previously called the budget neutral conversion factor because it reflected a budget neutrality adjustment for FYs 2001 and 2002, as described in § 412.624(d)(2) of our regulations. However, the statute requires a budget neutrality adjustment only for FYs 2001 and 2002. Accordingly, for subsequent years we believe it is more consistent with the statute to refer to the standardized payment as the standardized payment conversion factor, rather than refer to it as a budget neutral conversion factor (see 68 FR 45674, 45684 and 45685). Therefore, we will refer to the standardized payment amount in this final rule as the standard payment conversion factor. For each of the tiers within a CMG, the relative weighting factors were applied to the standard payment conversion factor to compute the unadjusted Federal prospective payment rates. Under the current system, adjustments that accounted for geographic variations in wages (wage index), the percentage of low-income patients, and location in a rural area were applied to the IRF's unadjusted Federal prospective payment rates. In addition, adjustments were made to account for the early transfer of a patient, interrupted stays, and high cost outliers. Lastly, the IRF's final prospective payment amount was determined under the transition methodology prescribed in section 1886(j) of the Act. Specifically, for cost reporting periods that began on or after January 1, 2002 and before October 1, 2002, section 1886(j)(1) of the Act and as specified in § 412.626 provide that IRFs transitioning into the PPS would receive a “blended payment.” For cost reporting periods that began on or after January 1, 2002 and before October 1, 2002, these blended payments consisted of 66 2/3 percent of the Federal IRF PPS rate and 33 1/3 percent of the payment that the IRF would have been paid had the IRF PPS not been implemented. However, during the transition period, an IRF with a cost reporting period beginning on or after January 1, 2002 and before October 1, 2002 could have elected to bypass this blended payment and be paid 100 percent of the Federal IRF PPS rate. For cost reporting periods beginning on or after October 1, 2002 (FY 2003), the transition methodology expired, and payments for all IRFs consist of 100 percent of the Federal IRF PPS rate. We established a CMS Web site that contains useful information regarding the IRF PPS. The Web site URL is *http://www.cms.hhs.gov/providers/irfpps/default.asp* and may be accessed to download or view publications, software, and other information pertinent to the IRF PPS. B. Requirements for Updating the Prospective Payment Rates for IRFs On August 7, 2001, we published a final rule entitled “Medicare Program; Prospective Payment System for Inpatient Rehabilitation Facilities” in the **Federal Register** (66 FR at 41316), that established a PPS for IRFs as authorized under section 1886(j) of the Act and codified at subpart P of part 412 of the Medicare regulations. In the August 7, 2001 final rule, we set forth the per discharge Federal prospective payment rates for fiscal year
(FY)2002 that provided payment for inpatient operating and capital costs of furnishing covered rehabilitation services (that is, routine, ancillary, and capital costs) but not costs of approved educational activities, bad debts, and other services or items that are outside the scope of the IRF PPS. The provisions of the August 7, 2001 final rule were effective for cost reporting periods beginning on or after January 1, 2002. On July 1, 2002, we published a correcting amendment to the August 7, 2001 final rule in the **Federal Register** (67 FR at 44073). Any references to the August 7, 2001 final rule in this final rule include the provisions effective in the correcting amendment. Section 1886(j)(5) of the Act and § 412.628 of the regulations require the Secretary to publish the classifications and weighting factors for the IRF CMGs and a description of the methodology and data used in computing the prospective payment rates for the upcoming FY. On August 1, 2002, we published a notice in the **Federal Register** (67 FR at 49928) to update the IRF Federal prospective payment rates from FY 2002 to FY 2003 using the methodology as described in § 412.624. As stated in the August 1, 2002 notice, we used the same classifications and weighting factors for the IRF CMGs that were set forth in the August 7, 2001 final rule to update the IRF Federal prospective payment rates from FY 2002 to FY 2003. We have continued to update the prospective payment rates each year in accordance with the methodology set forth in the August 7, 2001 final rule. We published a proposed rule in the **Federal Register** (70 FR 30189) to update the IRF Federal prospective payment rates from FY 2005 to FY 2006, and we proposed revisions to the methodology described in § 412.624. C. Operational Overview of the Current IRF PPS As described in the August 7, 2001 final rule, upon the admission and discharge of a Medicare Part A fee-for-service patient, the IRF is required to complete the appropriate sections of a patient assessment instrument, the Inpatient Rehabilitation Facility-Patient Assessment Instrument (IRF-PAI). All required data must be electronically encoded into the IRF-PAI software product. Generally, the software product includes patient grouping programming called the GROUPER software. The GROUPER software uses specific Patient Assessment Instrument
(PAI)data elements to classify (or group) the patient into a distinct CMG and account for the existence of any relevant comorbidities. The GROUPER software produces a 5-digit CMG number. The first digit is an alpha-character that indicates the comorbidity tier. The last 4 digits represent the distinct CMG number. (Free downloads of the Inpatient Rehabilitation Validation and Entry (IRVEN) software product, including the GROUPER software, are available at the CMS Web site at *http://www.cms.hhs.gov/providers/irfpps/default.asp* ). Once the patient is discharged, the IRF completes the Medicare claim (UB-92 or its equivalent) using an alphanumeric CMG code and sends it to the appropriate Medicare fiscal intermediary (FI). (Claims submitted to Medicare must comply with both the Administrative Simplification Compliance Act (ASCA), Pub. L. 107-105, and the Health Insurance Portability and Accountability Act of 1996 (HIPAA), Pub. L. 104-191. Section 3 of ASCA requires the Medicare Program, subject to subsection (H), to deny payment under Part A or Part B for any expenses for items or services “for which a claim is submitted other than in an electronic form specified by the Secretary.” Subsection
(h)provides that the Secretary shall waive such denial in two types of cases and may also waive such denial “in such unusual cases as the Secretary finds appropriate.” See also, 68 FR 48805 (August 15, 2003). Section 3 of ASCA operates in the context of the Administrative Simplification provisions of HIPAA, which include, among others, the transactions and code sets standards requirements codified as 45 CFR part 160 and 162, subparts A and I through R (generally known as the Transactions Rule). The Transactions Rule requires covered entities, including covered providers, to conduct covered electronic transactions according to the applicable transaction standards. See the program claim memoranda issued and published by CMS at *www.cms.hhs.gov/providers/edi/default.asp* ( *http://www.cms.hhs.gov/provider/edi/default.asp* ) and listed in the addenda to the Medicare Intermediary Manual, Part 3, section 3600. Instructions for the limited number of claims submitted to Medicare on paper are located in section 3604 of Part 3 of the Medicare Intermediary Manual. The Medicare Fiscal Intermediary
(FI)processes the claim through its software system. This software system includes pricing programming called the PRICER software. The PRICER software uses the CMG code, along with other specific claim data elements and provider-specific data, to adjust the IRF's prospective payment for interrupted stays, transfers, short stays, and deaths and then applies the applicable adjustments to account for the IRF's wage index, percentage of low-income patients, rural location, and outlier payments. D. Summary of the FY 2006 Proposed Update to the IRF PPS In the FY 2006 proposed rule (70 FR 30188), we proposed a number of refinements to the IRF PPS case-mix classification system (the CMGs and the corresponding relative weights) and the case-level and facility-level adjustments. The refinements that we proposed were based on analyses by RAND using calendar year 2002 and FY 2003 data. Several new developments warranted proposing these refinements, including—(1) The availability of more recent 2002 and 2003 data;
(2)better coding of comorbidities and patient severity;
(3)more complete data;
(4)new data sources for imputing missing values; and
(5)improved statistical approaches. Our proposals included the following key changes: The FY 2006 IRF PPS proposed rule (70 FR 30188, 30234 through 30241) included a proposal to adopt OMB's Core Based Statistical Area
(CBSA)market area definitions in a budget neutral manner. This geographic adjustment is made using a 1-year lag of the pre-reclassification hospital wage index (FY 2001 hospital wage data). The FY 2006 proposed rule (70 FR 30188, 30222) also included a proposal to implement a payment adjustment to account for changes in coding. We proposed to reduce the standard payment amount by 1.9 percent to account for changes in coding following implementation of the IRF PPS. The analysis conducted by CMS's contractor found that the real change in the case-mix was between negative 2.4 percent and positive 1.5 percent, with the rest of the change (between 1.9 percent and 5.8 percent) attributable to coding changes. CMS proposed to reduce the standard payment amount by the lowest of these estimates. In addition, in the FY 2006 proposed rule (70 FR 30188), we proposed modifications to the case mix groups, tier comorbidities, and relative weights. The proposed rule included a number of adjustments to the IRF classification system that are designed to improve the system's ability to predict IRF costs. The new data indicate that moving or eliminating some comorbidity codes from the tiers, redefining the case mix groups, and other minor changes to the system could improve the ability of the classification system to ensure that Medicare payments to IRFs continue to be aligned with the costs of care. In addition, the FY 2006 IRF PPS proposed rule (70 FR 30188, 30241) contained a proposal to implement a new teaching status adjustment for IRFs, similar to the one recently adopted for inpatient psychiatric facilities. We proposed to implement the teaching status adjustment in a budget neutral manner. The FY 2006 IRF PPS proposed rule (70 FR 30188, 30222) also contained a proposal to revise the market basket. We proposed to use a new market basket reflecting the operating and capital cost structures for rehabilitation, psychiatric, and long term care hospitals to update IRF payment rates. The proposed new market basket excludes cancer hospitals and children's hospitals. For the FY 2006 proposed rule (70 FR 30188), we proposed a market basket increase for FY 2006 of 3.1 percent. In the FY 2006 proposed rule (70 FR 30188, 30244 through 30246), we also proposed to update the rural adjustment (from 19.1 percent to 24.1 percent), the low-income patient adjustment (from an exponent of 0.484 to an exponent of 0.636), and the outlier threshold amount (from $11,211 to $4,911). We proposed to implement the changes to the rural and low-income percentage updates in a budget neutral manner. Lastly, in the FY 2006 proposed rule (70 FR 30188), we estimated that the proposed changes would increase costs to the Medicare program for IRF services in FY 2006 by $180 million over FY 2005 levels. The estimated increased cost to the Medicare program was due to the estimated IRF market basket of 3.1 percent, the 1.9 percent reduction to the standard payment amount to account for changes in coding that affect total estimated aggregate payments, and the update to the outlier threshold amount. We proposed to make the changes to the IRF labor-related share and the wage indices, the case mix groups, tier comorbidities, and relative weights, the new IME adjustment, the updated rural adjustment, and the updated LIP adjustment in a budget neutral manner. Thus, these proposed changes would have no overall effect on estimated costs to the Medicare program. II. Provisions of the Proposed Regulations In the FY 2006 proposed update to the IRF PPS (70 FR 30188), hereinafter referred to as the FY 2006 proposed rule, we proposed to make revisions to the regulations to implement the proposed PPS for IRFs for FY 2006 and subsequent fiscal years. Specifically, we proposed to make conforming changes in 42 CFR part 412. These proposed revisions and others are discussed in detail below. A. Section 412.602 Definitions In § 412.602, we proposed to revise the definitions of “Rural area” and “Urban area” to read as follows: Rural area means: For cost-reporting periods beginning on or after January 1, 2002, with respect to discharges occurring during the period covered by such cost reports but before October 1, 2005, an area as defined in § 412.62(f)(1)(iii). For discharges occurring on or after October 1, 2005, rural area means an area as defined in § 412.64(b)(1)(ii)(C). Urban area means: For cost-reporting periods beginning on or after January 1, 2002, with respect to discharges occurring during the period covered by such cost reports but before October 1, 2005, an area as defined in § 412.62(f)(1)(ii). For discharges occurring on or after October 1, 2005, urban area means an area as defined in § 412.64(b)(1)(ii)(A) and § 412.64(b)(1)(ii)(B). B. Section 412.622 Basis of Payment In this section, we proposed to correct the cross references in paragraphs (b)(1) and (b)(2)(i). In paragraph (b)(1), we proposed to remove the cross references “§ 413.85 and § 413.86 of this chapter” and add in their place “§ 413.75 and § 413.85 of this chapter.” In paragraph (b)(2)(i), we proposed to remove the cross reference “§ 413.80 of this chapter” and add in its place “§ 413.89 of this chapter.” C. Section 412.624 Methodology for Calculating the Federal Prospective Payment Rates In this section, we proposed to make the following revisions: • In paragraph (d)(1), remove the cross reference to “paragraph (e)(4)” and add in its place “paragraph (e)(5).” • Add a new paragraph (d)(4). • Redesignate paragraphs (e)(4) and (e)(5) as paragraphs (e)(5) and (e)(6). • Add a new paragraph (e)(4). • Revise newly redesignated paragraph (e)(5). • Revise newly redesignated paragraph (e)(6). • Add a new paragraph (e)(7). • In paragraph (f)(2)(v), remove the cross references to “paragraphs (e)(1), (e)(2), and (e)(3) of this section” and add in their place “paragraphs (e)(2), (e)(3), (e)(4), and (e)(7) of this section.” D. Additional Changes *We also proposed the following changes:* • Reduce the standard payment amount by 1.9 percent to account for coding changes. • Revise the comorbidity tiers and CMGs. • Use a weighted motor score index in assigning patients to CMGs. • Update the relative weights. • Update payments for rehabilitation facilities using a market basket reflecting the operating and capital cost structures for the RPL market basket. • Provide the weights and proxies to use for the FY 2002-based RPL market basket. • Indicate the methodology for the capital portion of the RPL market basket. • Adopt the new geographic labor market area definitions as specified in § 412.64(b)(1)(ii)(A)-(C). • Use the New England MSAs as determined under the proposed new CBSA-based labor market area definitions. • Implement a budget neutral 3 year hold harmless policy for FY 2005 rural IRFs redesignated as urban in FY 2006. • Use FY 2001 acute care hospital wage data in computing the FY 2006 IRF PPS payment rates. • Implement a teaching status adjustment. • Update the formulas used to compute the rural and the LIP adjustments to IRF payments. • Update the outlier threshold amount to maintain total estimated outlier payments at 3 percent of total estimated payments. • Revise the methodology for computing the standard payment conversion factor (for FY 2006 only) to make the CMG and tier changes, the teaching status adjustment, and the updates to the rural and LIP adjustments in a budget neutral manner. III. Analysis of and Responses to Public Comments As stated above, we received approximately 55 timely items of correspondence containing multiple comments on the FY 2006 proposed rule (70 FR 30188) from providers, health industry organizations, the Medicare Payment Advisory Commission, and others. In general, commenters expressed some concerns about our proposals in light of other changes occurring in the IRF PPS at this time and suggested that we wait to implement the proposals until other recent IRF policy changes are fully implemented. However, many commenters supported the proposed changes to the facility-level adjustments. Summaries of the public comments received on the proposed provisions and our responses to those comments are provided in the appropriate sections of the preamble of this final rule. IV. Research To Support Refinements of the Current IRF PPS As described in the August 7, 2001 final rule, we contracted with the RAND Corporation to analyze IRF data to support our efforts in developing the CMG patient classification system and the IRF PPS. Since then, we have continued our contract with RAND to support us in developing potential refinements to the classification system and the PPS. RAND has also developed a system to monitor the effects of the IRF PPS on patients' access to IRF care and other post-acute care services. 1. History of RAND's Research on the IRF PPS In 1995, RAND began extensive research, sponsored by us, on the development of a per-discharge based PPS using a patient classification system known as Functional Independence Measures—Function Related Groups (FIM-FRGs) for IRFs. The results of RAND's earliest research, using 1994 data, were released in September 1997 and are contained in two reports available through the National Technical Information Service (NTIS). The reports are: Classification System for Inpatient Rehabilitation Patients—A Review and Proposed Revisions to the Function Independence Measure—Function Related Groups, NTIS order number PB98-105992INZ, and Prospective Payment System for Inpatient Rehabilitation, NTIS order number PB98-106024INZ. In July 1999, we contracted with RAND to update its earlier research. The update included an analysis of Functional Independence Measure
(FIM)data, the Function Related Groups (FRGs), and the model rehabilitation PPS using 1996 and 1997 data. The purpose of updating the earlier research was to develop the underlying data necessary to support the Medicare IRF PPS based on CMGs for the November 3, 2000 proposed rule (65 FR at 66313). RAND expanded the scope of its earlier research to include the examination of several payment elements, such as comorbidities, facility-level adjustments, and implementation issues, including evaluation and monitoring. Then, to develop the provisions of the August 7, 2001 final rule (66 FR 41316, 41323), RAND did similar analysis on calendar year 1998 and 1999 Medicare Provider Analysis and Review (MedPAR) files and patient assessment data. We have continued to contract with RAND to help us identify potential refinements to the IRF PPS. The refinements we proposed to make to the IRF PPS, and which we are finalizing in this final rule, are based on the analyses and recommendations from RAND. In addition, RAND sought advice from a technical expert panel (TEP), which reviewed their methodology and findings. 2. Data Files Used for Analysis of the Current IRF PPS RAND conducted updated analyses of the patient classification system, case mix and coding changes, and facility-level adjustments for the IRF PPS using data from calendar year 2002 and FY 2003. This is the first time CMS or RAND has had data generated by IRFs after the implementation of the IRF PPS that are available for data analysis. Public comments and our responses on RAND's research to support the proposed refinements are summarized below: *Comment:* Several commenters expressed concerns about basing the refinements that we proposed in the FY 2006 proposed rule (70 FR 30188) on analyses of calendar year 2002 and FY 2003 data, which do not reflect IRF case mix changes currently taking place in response to our recent enforcement of the classification criterion, commonly known as the “75 percent rule.” These commenters suggested that we wait for analysis of future data (CY 2005 or beyond) to become available before implementing refinements to the IRF PPS. *Response:* As discussed in the August 7, 2001 final rule (66 FR 41316), we used RAND's analysis of calendar year 1998 and 1999 Medicare Provider Analysis and Review (MedPAR) files and patient assessment data to develop the initial classification system and prospective payment amounts for the IRF PPS. These data were from a period of time before the IRF PPS when IRFs' reimbursement was based on costs, subject to certain limits, rather than on prospective payment amounts. Furthermore, we used the best available 1998 and 1999 data from a time period that also preceded enforcement of the 75 percent rule requirements. Today, we have 2002 and 2003 data that represents all Medicare-covered IRF cases in a post-PPS environment and, therefore, portrays a recent and complete picture of IRFs' patient populations. In addition, the IRF payment system has undergone a major transformation since the 1998 and 1999 data in the form of a change from a cost-based payment system to a PPS that became effective with the cost reporting periods beginning on or after January 1, 2002. Because of this transformation, we believe the data we have on which to base refinements to the IRF PPS will help ensure that IRF PPS payments accurately reflect the costs of care in an IRF. This is because these data allow RAND to obtain precision in their analyses, and ensures that the data are not over- or under-representing particular types of facilities or patients. We believe it is appropriate and necessary to implement refinements to the IRF PPS at this time, based on the best available data we have from calendar year 2002 and FY 2003. Since analysis of this data indicates that we have an opportunity at this time, through the proposed refinements, to improve the alignment between IRF payments and the cost of care, we believe it is important to proceed with the refinements discussed in this final rule. However, we agree with the commenters that we should continue to collect the best available data we can to monitor the IRF PPS and ensure that IRF payments are appropriately aligned with costs of care and that Medicare patients continue to have appropriate access to IRF services. We will, whenever necessary, use the best data available in the future to propose appropriate refinements that will further improve the alignment between IRF payments and the costs of care. Thus, to the extent changes in case mix occur due to enforcement of the 75 percent rule, these changes should appear in later data that we will use to propose refinements in the future. *Comment:* Several commenters noted that 98 IRF providers in RAND's analysis data affiliated with HealthSouth decided to omit home office cost data from the 2002 and 2003 cost reports that were filed with us. The commenters questioned whether this omission might have affected the results of RAND's analysis and, therefore, our proposed policies. *Response:* After publication of the FY 2006 proposed rule (70 FR 30188), we learned that 98 providers in our data file that were affiliated with HealthSouth omitted home office cost data from the 2002 and 2003 cost reports that were filed with us and that RAND used in the analysis of the FY 2006 proposed rule (70 FR 30188). These data were a voluntary omission on the part of these providers, but nevertheless affect some of the distributional policies (that is, the proposed teaching status adjustment, the proposed changes to the rural and LIP adjustments, and the proposed change to the outlier threshold) contained in the proposed rule. However, because RAND used the hospital-specific relative value method (that is, the methodology that effectively controls for inter-hospital variation while estimating the relative costs of different types of patients within each hospital) for all of the proposed changes to the classification system described in section V of this final rule (that is, the proposed changes to the tier comorbidities, the proposed changes to the CMG definitions, the proposed weighted motor score methodology, the proposed change to the coding of the transfer-to-toilet item, and the proposed update of the relative weights), these proposed changes would not have been affected by the omission of the home office cost data. In other words, RAND examined the relative costs of patients within each IRF, so the fact that the omission of HealthSouth's home office costs caused total costs to be understated in the cost report data would not have mattered for the proposed classification system changes described in section V of this final rule. In addition, the omission of the home office cost data would have no effect on the proposed 1.9 percent reduction to the standard payment amount (discussed in section VI.A of this final rule) because cost report data were not used in the analysis that supports this proposed reduction. Although the omission of the home office cost data, in theory, could have had some effect on the estimates of the proposed FY 2002-based RPL market basket (discussed in section VI.B.1 of this final rule), our Office of the Actuary conducted some preliminary analyses of the effects on the market basket calculation and, based on these analyses, determined that these effects would likely be small. Home office costs represent only one of many cost categories (including, but not limited to, salaries, benefits, professional liability insurance, and pharmacueticals) that are used to develop the cost category weights. We believe the absence of HealthSouth home office costs in this market basket has a minor impact on the distribution of these weights and, by extension, the final market basket update itself. Thus, we did not believe it was necessary to recalculate the market basket. Finally, since the facility-level adjustments we proposed in the FY 2006 proposed rule (70 FR 30188) were calculated using regression analysis based on the relative total costs associated with care in different types of IRFs (that is, urban/rural, teaching/non-teaching, low DSH percentage/high DSH percentage), the omission of HealthSouth's home office costs had some effect on the results of these analyses. The largest example is for the cost differential between urban and rural facilities in our analysis. Since the providers that omitted the home office cost data were largely urban facilities, their lower reported total cost data caused the differential between urban and rural facilities to be larger in the initial analyses. The same was true, to a lesser extent, with the teaching status adjustment and the LIP adjustment. Furthermore, the omission of the home office cost data caused overall reported costs to be lower in these facilities and, therefore, affected the cost-to-charge ratios computed for these facilities for FYs 2002 and 2003. We used these cost-to-charge ratios to determine the proposed update to the outlier threshold amount. Therefore, analysis of the data indicates that the outlier threshold amount we proposed in the FY 2006 proposed rule (70 FR 30188) was affected by the omission of the home office cost data. Given that the facility-level adjustments, such as the rural, LIP, and teaching status adjustments, and the outlier threshold amount for all IRFs were likely affected by the decision of this one large for-profit chain provider to omit home office cost data from the FY 2002 and FY 2003 cost reports, we believe it is appropriate for us to recalculate the values for these adjustments and for the outlier threshold using data that accounts for the omitted home office costs. Thus, we obtained the FY 2004 HealthSouth home office cost statement and, from this cost report statement, compiled the home office cost data for each of the individual HealthSouth IRF providers listed. Of the 98 providers that omitted home office cost data for FYs 2002 and 2003, 92 of the providers have had home office cost data reported on the FY 2004 home office cost statement; and six providers did not have any home office cost information for FY 2004. We considered several options with respect to incorporating the missing HealthSouth home office costs into the data RAND used to conduct the analyses for this final rule. First, we considered the option of removing all of the HealthSouth cost report data from the analysis and re-computing the facility-level adjustments (that is, the rural adjustment, the LIP adjustment, and the teaching status adjustment) and the outlier threshold without the HealthSouth cost report data. Dropping all of the cost report data for 98 of the 1,188 facilities in RAND's analysis file, especially when they are large urban facilities, would seem to skew the data even further because we would be leaving out a substantial amount of cost report data connected with one specific type of IRF provider (i.e., urban IRFs). Leaving out the data for these facilities would make other types of IRFs that are left in the data appear to have more of an effect on the regression analysis than they actually do. Since we were hoping to reduce the bias in the data, rather than increase the bias, we generally rejected this option. The second option we considered was to update the analysis using FY 2004 data for all providers and re-compute the facility-level adjustments and the outlier threshold using the FY 2004 cost report data. Unfortunately, the FY 2004 data have only recently been submitted by all IRF providers, and it would have been impossible for RAND and CMS to have completed all the necessary re-analysis of all of the proposed policies with the FY 2004 cost report data for all IRF providers in time for the proposed policies to be implemented in FY 2006. The third option we considered was to use the FY 2004 home office cost data that we were able to obtain from the HealthSouth home office cost statement for 92 of the 98 HealthSouth IRF providers, standardize all of the other cost report data from FY 2003 for the 98 HealthSouth providers and the other non-HealthSouth providers using the most recent market basket for FY 2004, and fill in the FY 2004 home office cost data for the 92 HealthSouth providers for which we had data. This option enabled us to meet the October 1 implementation date of our updates as well as to make those updates and payment adjustments as accurate as possible. Next, we considered two options for treating the six HealthSouth facilities for which we did not have FY 2004 home office cost data: We considered leaving those six IRFs' cost data as is, without adding any home office cost data since we had none from FY 2004 to add. The other option we considered for treating these six facilities was to take the average home office costs as a percentage of total costs for the 92 facilities (which came to approximately 13 percent) and use this as an estimate of home office costs for the 6 facilities. We chose the second of the two options, which meant that we inflated total costs for those six facilities by the average of about 13 percent, because it seemed inappropriate to ignore the fact that cost data was missing for these six facilities and 13 percent appeared to be a reasonable estimate of home office costs generally for IRFs (from the general analysis we were able to perform). Because we believe the data file that results from the third option is more complete than the data RAND previously used to compute the proposed facility-level adjustments and the proposed outlier threshold amount for the FY 2006 proposed rule (70 FR 30188), we used the data from the third option described above to re-compute the values for the teaching status adjustment (described in more detail in section VI.B.3 of this final rule), the rural adjustment (described in more detail in section VI.B.4 of this final rule), the LIP adjustment (described in more detail in section VI.B.5 of this final rule), and the outlier threshold amount (described in more detail in section VI.B.6 of this final rule). Because the values of these adjustments have changed, we also re-computed the budget neutrality factors and, thus, the standard payment conversion factor. *Comment:* Several commenters requested that we make IRF claims data, IRF-PAI data, patient-specific CMG data, and cost report files available to the public so that the public would have the opportunity to recreate the analyses used in developing the proposed refinements for the FY 2006 proposed rule (70 FR 30188). *Response:* The data files mentioned by the commenters are generally available (and were generally available during the comment period for the FY 2006 proposed rule (70 FR 30188)) to the public through CMS's standard data distribution systems. More information on CMS's data distribution policies is available on CMS's website at *http://www.cms.hhs.gov/researchers/statsdata.asp* . *Comment:* A few commenters requested that we make available RAND's research using FY 2003 data. They noted that 3 of the 4 reports published on RAND's website for public access are based on analysis of calendar year 2002 data. One of RAND's publicly available reports is based on analysis of FY 2003 data. *Response:* We asked RAND to use the best available, most current data possible for the analyses contained in the FY 2006 proposed rule (70 FR 30188) and this final rule. This was generally FY 2003 data. The updated analysis is generally not contained in RAND's reports, and RAND has indicated to CMS that they have no plans to publish the updated analyses (using the FY 2003 data) after publication of the final rule. However, RAND informed us that, in all of the FY 2003 analyses for the FY 2006 proposed rule (70 FR 30188) and for this final rule, they used the identical methodologies presented in the reports available on RAND's website and reviewed by RAND's technical expert panel. The only change was that RAND used updated data from FY 2003 (and FY 2004 HealthSouth home office cost data, as discussed above). Thus, interested parties should examine the reports available on RAND's website for the detailed methodology used to develop the proposed and final revisions. In addition, interested parties may contact RAND directly for more information regarding the analysis of FY 2003 data. *Comment:* One commenter asked whether a large number of short period cost reports for periods ending in 2001 might have affected RAND's research findings and, if so, how RAND handled this issue in the data. *Response:* We were unable to find any reasons for the unusually large number of short period cost reports the commenter is indicating for cost report periods ending in 2001. However, since some of RAND's analysis for this final rule was based on calendar year 2002 data, and the majority of RAND's analysis for this final rule was based on FY 2003 data, we do not believe that a spike in the number of short period cost reports in 2001 would have had an effect on RAND's analyses. V. Refinements to the Patient Classification System A. Changes to the IRF Classification System 1. Development of the IRF Classification System Section 1886(j)(2)(A)(i) of the Act, as amended by section 125 of the Medicare, Medicaid, and SCHIP Balanced Budget Refinement Act of 1999 requires the Secretary to establish “classes of patient discharges of rehabilitation facilities by functional-related groups (each referred to as a case-mix group or CMG), based on impairment, age, comorbidities, and functional capability of the patients, and such other factors as the Secretary deems appropriate to improve the explanatory power of functional independence measure-function related groups.” In addition, the Secretary is required to establish a method of classifying specific patients in IRFs within these groups as specified in § 412.620. In the August 7, 2001 final rule (66 FR at 41342), we implemented a methodology to establish a patient classification system using CMGs. The CMGs are based on the FIM-FRG methodology and reflect refinements to that methodology. In general, a patient is first placed in a major group called a rehabilitation impairment category
(RIC)based on the patient's primary reason for inpatient rehabilitation, (for example, a stroke). The patient is then placed into a CMG within the RIC, based on the patient's ability to perform specific activities of daily living, and sometimes the patient's cognitive ability and/or age. Other special circumstances, such as the occurrence of very short stays, or cases where the patient expired, are also considered in determining the appropriate CMG. We explained in the August 7, 2001 final rule that further analysis of FIM and Medicare data may result in refinements to CMGs. In the August 7, 2001 final rule, we used the most recent FIM and Medicare data available at that time (that is 1998 and 1999 data). Developing the CMGs with the 1998 and 1999 data resulted in 95 CMGs based on the FIM-FRG methodology. The data also supported the establishment of five additional special CMGs that improved the explanatory power of the FIM-FRGs. We established one additional special CMG to account for very short stays and four additional special CMGs to account for cases where the patient expired. In addition, we established a payment of an additional amount for patients with at least one relevant comorbidity in certain CMGs. 2. Description and Methodology Used To Develop the IRF Classification System in the August 7, 2001 Final Rule a. Rehabilitation Impairment Categories In the first step to develop the CMGs, the FIM data from 1998 and 1999 were used to group patients into RICs. Specifically, the impairment code from the assessment instrument used by clients of UDSmr and Healthsouth indicates the primary reason for the inpatient rehabilitation admission. This impairment code is used to group the patient into a RIC. Currently, we use 21 RICs for the IRF PPS. b. Functional Status Measures and Age After using the RIC to define the first division among the inpatient rehabilitation groups, we used functional status measures and age to partition the cases further. In the August 7, 2001 final rule, we used 1998 and 1999 Medicare bills with corresponding FIM data to create the CMGs and more thoroughly examine each item of the motor and cognitive measures. Based on the data used for the August 7, 2001 final rule, we found that we could improve upon the CMGs by making a slight modification to the motor measure. We modified the motor measure by removing the transfer to tub/shower item because we found that an increase in a patient's ability to perform functional tasks with less assistance for this item was associated with an increase in cost, whereas an increase in other functional items decreased costs. We describe below the statistical methodology (Classification and Regression Trees (CART)) that we used to incorporate a patient's functional status measures (modified motor score and cognitive score) and age into the construction of the CMGs in the August 7, 2001 final rule. We used the CART methodology to divide the rehabilitation cases further within each RIC. (Further information regarding the CART methodology can be found in the seminal literature on CART (Classification and Regression Trees, Leo Breiman, Jerome Friedman, Richard Olshen, Charles Stone, Wadsworth Inc., Belmont CA, 1984: pp. 78-80).) We chose to use the CART method because it is useful in identifying statistical relationships among data and, using these relationships, constructing a predictive model for organizing and separating a large set of data into smaller, similar groups. Further, in constructing the CMGs, we analyzed the extent to which the independent variables (motor score, cognitive score, and age) helped predict the value of the dependent variable (the log of the cost per case). The CART methodology creates the CMGs that classify patients with clinically distinct resource needs into groups. CART is an iterative process that creates initial groups of patients and then searches for ways to divide the initial groups to decrease the clinical and cost variances further and to increase the explanatory power of the CMGs. Our current CMGs are based on historical data. In order to develop a separate CMG, we need to have data on a sufficient number of cases to develop coherent groups. Therefore, we are removing these codes from the tiers that increase payment. c. Comorbidities Under the statutory authority of section 1886(j)(2)(C)(i) of the Act, we proposed to make several changes to the comorbidity tiers associated with the CMGs for comorbidities that are not positively related to treatment costs, or their excessive use is questionable, or their condition could not be differentiated from another condition. Specifically, section 1886(j)(2)(C)(i) of the Act provides the following: The Secretary shall from time to time adjust the classifications and weighting factors established under this paragraph as appropriate to reflect changes in treatment patterns, technology, case mix, number of payment units for which payment is made under this title and other factors that may affect the relative use of resources. The adjustments shall be made in a manner so that changes in aggregate payments under the classification system are a result of real changes and are not a result of changes in coding that are unrelated to real changes in case mix. A comorbidity is a specific patient condition that is secondary to the patient's principal diagnosis or impairment that is used to place a patient into a RIC. A patient could have one or more comorbidities present during the inpatient rehabilitation stay. Our analysis for the August 7, 2001 final rule found that the presence of a comorbidity could have a major effect on the cost of furnishing inpatient rehabilitation care. We also stated that the effect of comorbidities varied across RICs, significantly increasing the costs of patients in some RICs, while having no effect in others. Therefore, for the August 7, 2001 final rule, we linked frequently occurring comorbidities to impairment categories in order to ensure that all of the chosen comorbidities were not an inherent part of the diagnosis that assigns the patient to the RIC. Furthermore, in the August 7, 2001 final rule, we indicated that comorbidities can affect cost per case for some of the CMGs, but not all. When comorbidities substantially increased the average cost of the CMG and were determined to be clinically relevant (not inherent in the diagnosis in the RIC), we developed CMG relative weights adjusted for comorbidities (§ 412.620(b)). d. Development of CMG Relative Weights Section 1886(j)(2)(B) of the Act requires that an appropriate relative weight be assigned to each CMG. Relative weights account for the variance in cost per discharge and resource utilization among the payment groups and are a primary element of a case-mix adjusted PPS. The establishment of relative weights helps ensure that beneficiaries have access to care and receive the appropriate services that are commensurate to other beneficiaries that are classified in the same CMG. In addition, prospective payments that are based on relative weights encourage provider efficiency and, hence, help ensure a fair distribution of Medicare payments. Accordingly, as specified in § 412.620(b)(1), we calculate a relative weight for each CMG that is proportional to the resources needed by an average inpatient rehabilitation case in that CMG. For example, cases in a CMG with a relative weight of 2, on average, will cost twice as much as cases in a CMG with a relative weight of 1. We discuss the details of developing the relative weights below. As indicated in the August 7, 2001 final rule, we believe that the RAND analysis has shown that CMGs based on function-related groups (adjusted for comorbidities) are effective predictors of resource use as measured by proxies such as length of stay and costs. The use of these proxies is necessary in developing the relative weights because data that measure actual nursing and therapy time spent on patient care, and other resource use data, are not available. e. Overview of Development of the CMG Relative Weights As indicated in the August 7, 2001 final rule, to calculate the relative weights, we estimate operating (routine and ancillary services) and capital costs of IRFs. For this final rule as we indicated in the FY 2006 proposed rule (70 FR 30188), we use the same method for calculating the cost of a case that we outlined in the August 7, 2001 final (66 FR at 41351 through 43153). We obtained cost-to-charge ratios for ancillary services and per diem costs for routine services from the most recent available cost report data. We then obtain charges from Medicare bill data and derived corresponding functional measures from the FIM data. We omit data from rehabilitation facilities that are classified as all-inclusive providers from the calculation of the relative weights, as well as from the parameters that we use to define transfer cases, because these facilities are paid a single, negotiated rate per discharge and therefore do not maintain a charge structure. For ancillary services, we calculate both operating and capital costs by converting charges from Medicare claims into costs using facility-specific, cost-center specific cost-to-charge ratios obtained from cost reports. Our data analysis for the August 7, 2001 final rule showed that some departmental cost-to-charge ratios were missing or found to be outside a range of statistically valid values. For anesthesiology, a value greater than 10, or less than 0.01, is found not to be statistically valid. For all other cost centers, values greater than 10 or less than 0.5 are found not to be statistically valid. In the August 7, 2001 final rule, we replaced individual cost-to-charge ratios outside of these thresholds. The replacement value that we used for these aberrant cost-to-charge ratios was the mean value of the cost-to-charge ratio for the cost-center within the same type of hospital (either freestanding or unit). For routine services, per diem operating and capital costs are used to develop the relative weights. In addition, per diem operating and capital costs for special care services are used to develop the relative weights. (Special care services are furnished in intensive care units. We note that less than 1 percent of rehabilitation days are spent in intensive care units.) Per diem costs are obtained from each facility's Medicare cost report data. We use per diem costs for routine and special care services because, unlike for ancillary services, we could not obtain cost-to-charge ratios for these services from the cost report data. To estimate the costs for routine and special care services included in developing the relative weights, we sum the product of routine cost per diem and Medicare inpatient days and the product of the special care per diem and the number of Medicare special care days. In the August 7, 2001 final rule, we used a hospital specific relative value method to calculate relative weights. For the FY 2006 proposed rule (70 FR 30188) and this final rule, we used the following basic steps to calculate the relative weights as indicated in the August 7, 2001 final rule (at 66 FR 41316, 41351 through 41352). The first step in calculating the CMG weights is to estimate the effect that comorbidities have on costs. The second step required us to adjust the cost of each Medicare discharge
(case)to reflect the effects found in the first step. In the third step, the adjusted costs from the second step were used to calculate “relative adjusted weights” in each CMG using the hospital-specific relative value method. The final steps are to calculate the CMG relative weights by modifying the “relative adjusted weight” with the effects of the existence of the comorbidity tiers (explained below) and normalizing the weights to 1. Our methodology for determining the IRF classification system remains unchanged from the August 7, 2001 final rule. B. Changes to the Existing List of Tier Comorbidities 1. Changes To Remove Codes That Are Not Positively Related to Treatment Costs While our methodology for this final rule for determining the tiers remains unchanged from the August 7, 2001 final rule, as we indicated in the FY 2006 proposed rule (70 FR 30188), RAND's analysis indicates that 1.6 percent of FY 2003 cases received a tier payment (often in tier one) that was not justified by any higher cost for the case. Therefore, under statutory authority section 1886(j)(2)(C)(i) of the Act, as we proposed in the FY 2006 proposed rule (70 FR 30188) we are implementing several technical changes to the comorbidity tiers associated with the CMGs. Specifically, the RAND analysis found that the first 17 diagnoses shown in Table 1 below are no longer positively related to treatment cost after controlling for CMG. The additional two codes were also problematic. According to RAND, code 410.91 (AMI, NOS, Initial) was not specific enough to be differentiated from other related codes and code 260, Kwashiorkor, was found to be unrealistically represented in the data according to the RAND technical expert panel. With respect to the eighteenth code in Table One, (410.X1) Specific AMI, initial), we note that RAND found there is no clinical reason to believe that this code differs in a rehabilitation environment from all of the specific codes for initial AMI of the form 410.X1, where X is an numeric digit. In other words, this code is indistinguishable from the seventeenth code in Table One (410.91 AMI, NOS, initial). Following this observation, RAND tested the other initial AMI codes as a single group and found that they have no positive effect on case cost. Thus, as we indicated in the FY 2006 proposed rule (70 FR 30188), we proposed to remove “AMI, NOS, initial” from the tier list because it is not positively related to treatment cost after controlling for the CMG. In addition, for similar reasons, we proposed in the FY 2006 proposed rule (70 FR 30188) to remove “Specific AMI, initial from the tier list since it is indistinguishable from “AMI, NOS, initial.” As we proposed in the FY 2006 proposed rule (70 FR 30188), with respect to the last code in Table One (Kwashiorkor), we are removing this code from the tier list as well. This comorbidity is positively related to cost in our data. However, RAND's technical expert panel
(TEP)found the large number of cases coded with this rare disease to be unrealistic and recommended that it be removed from the tier list. Table 1 contains two malnutrition codes, and as we proposed in the FY 2006 proposed rule (70 FR 30188), we are removing these two malnutrition codes. As we stated in the FY 2006 Proposed Rule (70 FR 30188), removal of these codes where use is concentrated in specific hospitals is particularly important because these hospitals are likely receiving unwarrantedly high payments due to the tier one assignment of these cases. Thus, because we believe the excess use of these two comorbid conditions is inappropriate based on the findings of RAND's TEP, they will be removed. The data indicate large variation in the rate of increase from the 1999 data to the 2003 data across the conditions that make up the tiers. The greatest increases were for miscellaneous throat conditions and malnutrition, each of which were more than 10 times as frequent in 2003 as in 1999. The growth in these two conditions was far larger than for any other condition. Many conditions, however, more than doubled in frequency, including dialysis, cachexia, obesity, and the non-renal complications of diabetes. The condition with the least growth, renal complications of diabetes, may have been affected by improved coding of dialysis. As we proposed in the FY 2006 proposed rule (70 FR 30188), we are finalizing changes to our initial list of diagnoses that deal with tracheostomy cases. These rare cases were excluded from the pulmonary RIC 15 in the August 7, 2001 final rule. The new data indicate that they are more expensive than other cases in the same CMG in RIC 15, as well as in other RICs. Therefore, we believe the data demonstrate that tracheostomy cases should be added to the tier list for RIC 15 in order to receive a higher payment. Finally, the new data indicate that DX V55.0, “attention to tracheostomy” should be part of this condition as these cases were and are as expensive as other tracheostomy cases. Thus, since “attention to tracheostomy” is as expensive as other tracheostomy cases, it is logical to group such similar cases together. Therefore, we are finalizing our proposal to remove the RIC 15 exclusion for code V55.0 (attention to tracheostomy) so that code V55.0 can receive appropriate payment for the additional costs it incurs. As we stated in the FY 2006 proposed rule (70 FR 30188), we believe that the data provided by RAND support the removal of the codes in Table 1 below because they either have no impact on cost after controlling for their CMG or are indistinguishable from other codes or are unrealistically overrepresented. Therefore, we are finalizing our proposed policy to remove these codes from the tier list. Table 1.—List of Codes To Be Removed From the Tier List ICD-9-CM code Abbreviated code title Condition 235.1 Unc behav neo oral/phar Miscellaneous throat conditions. 933.1 Foreign body in larynx Miscellaneous throat conditions. 934.1 Foreign body bronchus Miscellaneous throat conditions. 530.0 Achalasia & cardiospasm Esophegeal conditions. 530.3 Esophageal stricture Esophageal conditions. 530.6 Acquired esophag diverticulum Esophageal conditions. V46.1 * Dependence on respirator Ventilator status. 799.4 Cachexia Cachexia. V49.75 Status amputation below knee Amputation of LE. V49.76 Status amputation above knee Amputation of LE. V49.77 Status amputation hip Amputation of LE. 356.4 Idiopathic progressive polyneuropathy Meningitis and encephalitis. 250.90 Diabetes II, w unspecified complications, not stated as uncontrolled Non-renal complications of diabetes. 250.93 Diabetes I, w unspecified complications, uncontrolled Non-renal complications of diabetes. 261 Nutritional Marasmus Malnutrition. 262 Other severe protein calorie deficiency Malnutrition. 410.91 AMI, NOS, initial Major comorbidities. 410.X1 Specific AMI, initial Major comorbidities. 260 Kwashiorkor Malnutrition. * V46.11 and V46.12 were not in existence when the data used in the analysis was collected. Since these codes are subcategories of code V46.1 (the code we proposed to remove from the tiers that make additional payment), they will be removed from the comorbidity tiers as well. We received numerous comments on the proposed changes to the existing list of tier comorbidities which are summarized below: *Comment:* One commenter remarked that kwashiorkor should be omitted from the list of comorbidities to be deleted from the list of comorbidities that increase the payment rate of the CMG because some of the software packages used by the industry allow this code to be used for the coding of the inpatient's comorbidities. *Response:* We disagree with the commenter. Kwashiorkor is a severe malnutrition of infants and young children, primarily in tropical and subtropical regions, caused by deficiency in the quality and quantity of protein in the diet. It is characterized by anemia, edema, potbelly, loss of pigment in the skin, hair loss or change in hair color, hypoalbuminemia, and bulky stools containing undigested food. In addition, an inpatient with this condition most likely would not be able to receive the three hours of intensive rehabilitation that is a qualifying guideline to be an inpatient within an IRF. While protein deficiencies may be noted in patients within an IRF, by definition, the incidence of Kwashiorkor could not be as high as reported. Also, as previously stated, RAND's TEP reported that the data indicate large variation in the rate of increase across conditions. However, coding of malnutrition increased by more than 10 times, and RAND found the large number of cases coded with this rare disease to be unrealistic and recommended that it be removed from the tier list. Consequently, kwashiorkor will be eliminated from the list of comorbidities that increase the payment rate of the CMG. *Comment:* One commenter wrote that code V46.1 is listed in the proposed list of codes to be removed from the tier list. Since this code contains two other codes, the commenter wanted to know if it is our intention to remove both codes in this category, namely V46.11 (Dependence on respirator, status) and V46.12 (Encounter for respirator dependence during power failure) or just one of these codes. *Response:* First, we want to explain how codes V46.11 and V46.12 became codes that are used to increase the CMG payment rate. In the August 7, 2001 final rule (66 FR 41316), we published Appendix C that listed the ICD-9-CM comorbid condition codes which are used to increase the CMG payment rate. The ICD-9-CM codes of the comorbid conditions are recorded by the IRF's staff on the IRF-PAI, and that data as well as some other data recorded on the IRF-PAI is used to classify an inpatient into a CMG payment rate. One of the codes we published as part of Appendix C was V46.1. Each year the codes used in the ICD-9-CM coding system undergo a review resulting in updates to some of the existing codes. In accordance with a review that updated the ICD-9-CM coding system V46.11 and V46.12 were added to the ICD-9-CM coding system as subcategories of V46.1. We believe that the comorbid condition represented by the code V46.11 or V46.12 is a derivative of the comorbid condition represented by the code V46.1. Therefore, in 2005 we updated the CMG grouper software which resulted in the CMG payment being increased by the same amount if the IRF-PAI data of an inpatient included codes V46.1, or V46.11, or V46.12. The analysis that our data contractor performed, using certain data after the IRF PPS was implemented, shows that the comorbid condition represented by code V46.1 does not have an effect upon treatment cost after controlling for the CMG. Therefore, code V46.1 and its derivative codes that comprise it (V46.11 and V46.12) will be removed from the list of codes that are used by the IRF PPS to increase the CMG payment rate. *Comment:* Several commenters urged us to consider not removing codes V49.75, V49.76, and V49.77 from the list of comorbidity codes that increase the CMG payment because of concerns with the complexity of a patient with an amputation. *Response:* After controlling for the CMG, RAND found that these codes do not impact cost. Further, IRFs do not incur additional costs to treat these comorbidities after controlling for the CMG. This means that the CMG to which the inpatient is assigned, already accounts for the costs associated with the treatment of inpatients with an amputation and no additional payment is needed beyond the CMG amount to adequately reimburse for such a case. Therefore we are removing these codes from the list of comorbidities that increase the CMG payment. *Comment:* Several commenters mentioned a concern with the code V497.7 in the table of codes to be removed. They believed it to be a typographical error where the actual code to be removed is V49.77. *Response:* We agree with the commenters and have made the correction to the typographical error. The corrected code to be removed is V49.77. *Comment:* Several commenters noted that there is a discrepancy with code 428.3 (vocal cord paralysis, not otherwise specified) in CMS' list of codes being reassigned based on their marginal cost in the *Comorbidity Tier Reassignment Changes* File found at *http://www.cms.hhs.gov/providers/irfpps/fy06nprm.asp* . They stated that it should actually be code 478.30 (vocal cord paralysis, not otherwise specified). *Response:* We agree with the commenters and shall make the appropriate corrections to the typographical error within the file. *Comment:* Several commenters noted an error with the description of meningitis and encephalitis for code 356.4 in the *Comorbidity Tier Reassignment Changes* File found at *http://www.cms.hhs.gov/providers/irfpps/fy06nprm.asp* . *Response:* We agree with the commenters and the description will be amended to read idiopathic progressive polyneuropathy for code 356.4. *Comment:* Commenters expressed concern for the removal of codes 530.0 (achalasia and cardiospasm), 530.3 (stricture and stenosis of esophagus) and 530.6 (diverticulum of esophagus) that are used to record esophageal conditions because of costs associated with these conditions and requested that they not be removed from the tier list which increases payment for these comorbidities. *Response:* After controlling for the CMG, RAND found that these comorbidities do not positively impact costs, meaning that the CMG encompasses sufficient payment to compensate for these comorbidities. Therefore, we are removing codes 530.0, 530.3 and 530.6 from the list of comorbidities that increase CMG payment. *Comment:* Several commenters agreed with CMS' proposed policy to remove malnutrition codes 261 (nutritional marasmus) and 262 (other severe protein-calorie malnutrition), while others opposed the proposed policy to remove these codes. In addition, several commenters suggested that CMS examine the impact of malnutrition on increasing the length of stay within an IRF. *Response:* We acknowledge both opinions as expressed by the different commenters. The RAND TEP, and our Medical Officers, believes these codes are drastically overstated and inpatients with these levels of malnutrition would not be candidates for three hours of intensive therapy. In addition, after controlling for the CMG, both of these codes do not positively affect payment. Therefore we believe it is appropriate to remove malnutrition codes 261 and 262 from the list of comorbidity codes that are used to increase the CMG payment rate. Additionally, we will continue to examine the impact of comorbidities, including malnutrition, upon IRF Medicare-covered inpatients. *Comment:* One commenter suggested adding codes 250.91 and 250.92 to the list of comorbidities to be removed from the list of codes used to increase payment because they believe those codes to be similar in description to codes 250.90 and 250.93. *Response:* Only the first 17 codes within Table 1 were found to have no positive effect on cost after controlling for the CMG. The data analysis performed by RAND does not indicate that at this time 250.91 and 250.93 should be removed from the list of codes used to increase the CMG payment rate because they continue to positively affect costs. Therefore we believe it is inappropriate to remove them from the list of comorbidities that impact cost. Consequently, we are not removing any other codes from the list of codes used to increase the CMG payment rate. *Comment:* One commenter recommended that several codes be added to our comorbidity tier system based upon suggestions from the RAND TEP, namely codes 428.0 (congestive heart failure), V43.3 (heart valve replacement), 250.1 (insulin dependent diabetes without mention of complications, not stated as controlled) and 438.2X (hemi-paresis due to an old stroke). *Response:* After examining the RAND recommendations, our Medical Officers felt that codes V43.3 and 438.2X were too vague and non-descript to capture the necessary information needed for these codes to be added to the list of codes used to increase the CMG payment rate. However, in response to the comments our Medical Officers re-evaluated the effect on cost by the comorbid condition represented by code 250.1 (insulin dependent diabetes without mention of complications, not stated as controlled). They determined that code 250.1 should be added to the list of codes used to increase the CMG payment rate. They also determined that the code should be a tier 3 code because the other 250 series of codes related to diabetes are in tier 3. Therefore, this code will be added as a tier 3 code to the list of codes used to increase the CMG payment rate. There will be no excluded RICs with code 250.1. After examining the comments, our Medical Officers continue to believe that 428.9 (heart failure, unspecified), was too non-descript and should not be added to the list of codes that can increase payment. However, our Medical Officers agree with the commenter regarding other numerous congestive heart failure codes including Code 428.1—Left Heart Failure, Code 428.20—Systolic Heart Failure Unspecified, Code 428.21—Systolic Heart Failure Acute, Code 428.22—Systolic Heart Failure Chronic, Code 428.23—Systolic Hear Failure Acute on Chronic, Code 428.30—Diastolic Heart Failure Unspecified, Code 428.31—Diastolic Heart Failure Acute, Code 428.32—Diastolic Heart Failure Chronic, Code 428.33—Diastolic Heart Failure Acute on Chronic, Code 428.40—Combined Systolic and Diastolic Heart Failure Unspecified, Code 428.41—Combined Systolic and Diastolic Heart Failure Acute, Code 428.42—Combined Systolic and Diastolic Heart Failure Chronic, and Code 428.43—Combined Systolic and Diastolic Heart Failure Acute on Chronic, largely due to the increased costs associated with these codes. Therefore, these 428 cardiac codes will be added to the list of codes used to increase the CMG payment rate as tier 3 codes because of their similarity to certain cardiac codes with respect to resource utilization. However, these codes will not be used to increase the CMG payment rate if the CMG code is one of the CMG codes derived from RIC 14 (the cardiac RIC) because these cardiac codes costs have been accounted for in the CMGs associated with RIC 14. *Comment:* A commenter believes that the CMG payment rate should include an adjustment for mental health problems, such as a depression. The commenter believes that a patient's mental health status has an effect on the patient treatment costs an IRF incurs. *Response:* The significance and appropriateness of a patient's state of mental health in response to an impairment that requires a patient to undergo intensive inpatient rehabilitation is a subject that we believe requires further study. Additional study will help to determine the effect of the patient's state of mental health on treatment costs. An ICD-9-CM code may be used to show that a patient is exhibiting signs that a rehabilitation clinician believes indicate a mental disorder. However, quantifying by use of ICD-9-CM codes the association between a patient's state of mental health and how it affects a patient's response to rehabilitation treatment is at best limited. For example, we believe that in response to a stroke or hip fracture, or some other impairment, a situational depression may be a rational response. However, that does not mean that the IRF will incur additional costs that were not already taken into account when the CMG payment rates were developed. In addition, mental disorders vary greatly in severity as does how a patient's functioning is affected by a mental disorder. There would have to be multiple factors taken into consideration before any type of mental disorder could be added to the list of comorbidities that would increase payment of the CMG. The data for a complete psychiatric evaluation must be made available to correctly code for these comorbidities. In addition, this is a budget neutral system, and no additional funding will be added to the system. Under our final rule, funds will not be added but simply be redistributed among the comorbidities among the tiers that increase payment. This is because the changes associated with the comorbidity tiers and CMGs are done in a budget neutral manner. On the assumption that there is an even distribution of these psychiatric patients among IRFs, and these patients may receive the redistributed payment, the addition of these codes may not contribute to an increased payment for inpatients with these comorbid conditions and may affectively lower payments for CMG's with other comorbid conditions because the same amount of funding is distributed across more comorbid conditions. Also, few IRFs have psychiatric personnel and rehabilitation doctors rarely have the time required to observe the patient to make a complete psychiatric evaluation and thus some codes may be assigned (or not assigned) in error. In addition, RAND's TEP believed that it would be inappropriate to use ICD-9-CM diagnoses to identify patients with affective disorders. Therefore, in this final rule, we are not adding codes for depression and mental disorders to the list of codes used to increase payment. *Comment:* We received comments to both challenge and support the removal of certain comorbidity codes from the tier list including code 799.4 Cachexia, and code 933.1 (foreign body in larynx). Commenters stated that these conditions required more resources, and thus increased treatment costs. The other commenter stated that the CMG already covered these costs. *Response:* The data analysis did not show that the comorbid conditions indicated by these codes increased the costs of treating an inpatient with these comobidities after controlling for the CMG because their CMG payment rate covers costs associated with their corresponding treatment. The more recent RAND analysis found that after controlling for the CMG, these comobidities do not impact cost. Therefore, we are removing them from the comorbidity tiers that would increase payment. *Comment:* One commenter made a general statement stating that the list of comorbidities that comprise the tiers do not reflect the challenges that contribute to higher costs in the rehabilitation setting. *Response:* We disagree with the commenter because the RAND regression analyses show that the comorbid conditions that comprise the tiers positively impact cost and provide additional payments for services not included in the payment associated with the CMG. *Final Decision:* In this final rule, we are adopting the proposal to remove the comorbidity tier codes set forth in Table 1 of the FY 2006 proposed rule (70 FR 30188). We are also removing codes V46.11 and V46.12 because they are subcategories of code V46.1, which has been found to have no impact on cost after controlling for the CMG. We are adding several codes that the RAND analyses found to positively impact costs. We chose to add codes 250.1 (insulin dependent diabetes without mention of complications, not stated as controlled), as well as numerous congestive heart failure codes including Code 428.1—Left Heart Failure, Code 428.20—Systolic Heart Failure Unspecified, Code 428.21—Systolic Heart Failure Acute, Code 428.22—Systolic Heart Failure Chronic, Code 428.23—Systolic Heart Failure Acute on Chronic, Code 428.30—Diastolic Heart Failure Unspecified, Code 428.31—Diastolic Heart Failure Acute, Code 428.32—Diastolic Heart Failure Chronic, Code 428.33—Diastolic Heart Failure Acute on Chronic, Code 428.40—Combined Systolic and Diastolic Heart Failure Unspecified, Code 428.41—Combined Systolic and Diastolic Heart Failure Acute, Code 428.42—Combined Systolic and Diastolic Heart Failure Chronic, and Code 428.43—Combined Systolic and Diastolic Heart Failure Acute on Chronic, which our Medical Officers believe were specific enough to be used in our list of codes that are used to increase the CMG payment amount. 2. Changes To Move Dialysis to Tier One As we proposed in the FY 2006 proposed rule (70 FR 30188), we are finalizing the movement of dialysis from comorbidity tier two to comorbidity tier one, which is the tier associated with the highest payment. The data from the RAND analysis show that patients on dialysis cost more than the tier payment to which dialysis is currently assigned, and should be moved into the highest paid tier because this tier would more closely align payment with the cost of a case. Based on RAND's analysis using 2003 data, a patient with dialysis costs 31 percent more than a non-dialysis patient in the same CMG and with the same other accompanying comorbidities. Overall, the largest increase in the cost of a condition occurs among patients on dialysis, where the coefficient in the cost regression increases by 93 percent, from 0.1400 to 0.2697. Part of the explanation for the increased coefficient could be that some IRFs had not borne all dialysis costs for their patients in the pre-PPS period, which was the previous data analysis time period(because providers were previously permitted to bill for dialysis separately). It is likely that, in the 1999 data, some IRFs had not borne all dialysis costs for their patients. Because the fraction of cases coded with dialysis increased by 170 percent, it is also likely that improved coding was part of the explanation for the increased coefficient. We believe a 170 percent increase is such a dramatic increase that it would be highly unlikely that in the time periods used for the data analysis, 170 percent more patients needed dialysis when compared to the time period before the implementation of the IRF PPS. We also believe that the improved coding is likely due to the fact that higher costs are associated with dialysis patients, and therefore IRFs, in an effort to ensure that their payments cover these higher expenses better and more carefully coded comorbidities whose presence resulted in higher PPS payments. Therefore we are moving dialysis patients to comorbidity tier one will more adequately compensate IRFs for the extra cost of those patients and thereby maintain or increase access to these services. *Comment:* A number of commenters supported our decision to move dialysis patients to tier one due to the increase cost of dialysis patients. *Response:* We agree with these commenters. The data analyses performed by RAND found evidence that suggested that a dialysis patient cost 31 percent more than a non-dialysis patient in the same CMG. Therefore, as proposed in the FY 2006 proposed rule (70 FR 30188), we are moving dialysis to tier 1 because the additional payment associated with tier 1 more closely approximate the additional costs associated with the treatment of an inpatient with this condition. *Final Decision:* As proposed in the FY 2006 proposed rule (70 FR 30188), we are adopting the decision to move dialysis patients to comorbidity tier one. 3. Changes To Move Comorbidity Codes Based on Their Marginal Cost Under section 1886(j)(2)(C)(i) of the Act, as was proposed in the FY 2006 proposed rule (70 FR 30188), we are refining how we pay for a comorbidity based on marginal cost. A commonly understood definition of marginal cost is the increase or decrease in costs as a result of one higher or lower unit of a good or service. In this situation, we are reassigning comorbidities to tiers based on their marginal costs, and by this we mean the increase or decrease in costs as a result of one higher or lower comorbidity tier. Payment for several comorbidities would be more accurate if their tier assignments were changed, and after examining RAND's data, we believe that of the FY 2003 cases, a full 4 percent of cases should be associated with comorbidity tiers that have a lower payment than the comorbidity tiers to which they were assigned. Therefore, comorbidities would be more accurate if their tier assignments were more appropriately based on their marginal costs. As we proposed in the FY 2006 proposed rule (70 FR 30188), comorbidity tier assignments in this final rule are based on the results of statistical analyses RAND has performed under contract with CMS, using as independent variables only the CMGs and conditions for tiers. As we proposed in the FY 2006 proposed rule (70 FR 30188), tier assignments of each of these conditions for the final rule are determined based on the magnitude of their coefficients in RAND's statistical analysis. We believe the IRF PPS led to substantial changes in coding of comorbidities between 1999 (pre-implementation of the IRF PPS) and 2003 (post-implementation of the IRF PPS). The percentage of cases with one or more comorbidities increased from 16.79 percent according to the data used to define the comorbidity tiers (1998 through 1999) to 25.51 percent in FY 2003. This is an increase of 52 percent in tier incidence (52 = 100 × (25.51-16.79)/16.79). The recording of a tier one comorbidity, the highest paid of the tiers, almost quadrupled during this same time period. Although, improved coding likely increased the recording of comorbidities, those coding the comorbidities may have been motivated by the objective to use coding changes as a means to increase the CMG payment. The 2003 data provides an excellent comprehensive picture of the costs that are associated with each of the comorbidities. We believe this because CMS has data for 100 percent of the Medicare-covered IRF cases. Therefore, as we indicated in the FY 2006 proposed rule, we believe that using the 2003 data to assign the comorbidities to a payment tier ensures heightened accuracy with respect to the matching of payments to relative costs of a case. We received several comments on the proposed changes to the existing list identifying which tier is associated with a particular comorbidity. The public comments are summarized below. *Comment:* One commenter suggested that we postpone reassigning comorbidity tiers based on their marginal costs, and again instead perform the data analysis used to reassign the comorbidity codes based on marginal costs using more current data. *Response:* This final rule reflects the most recent analysis of data. In the future, we will continue to perform data analyses and, as necessary, adjust the payment rates to achieve the most accurate payment. In this final rule, we are adopting the policy we proposed in the FY 2006 proposed rule (70 FR 30188), and reassigning comorbidities to tiers based on their marginal cost because we believe that this reassignment is based on the best comprehensive post-PPS implementation data that are available at this time. *Comment:* One commenter recommended that we not reassign any comorbidity codes based on their marginal costs under the premise that there is no concrete evidence of upcoding. *Response:* Taking into consideration that we believe that there has been improved coding due to prospective payment based system, the recommendations of RAND's technical expert panel, and the guidance of our Medical Officers, we believe that the comorbidity codes should be assigned based on their marginal costs in order to increase the association between costs and payment. *Final Decision:* In summary, we are adopting all of the proposals set forth in the FY 2006 proposed rule (70 FR 30188), with regard to the removal of the list of codes from comorbidity tiers that increase payment, the movement of dialysis patients to tier one, the code V55.0 will no longer be excluded from RIC 15, and comorbidity codes will now be reassigned based on their marginal costs. C. Changes to the CMGs Section 1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to adjust the classifications and weighting factors of patients under the IRF PPS to reflect changes in treatment patterns, technology, case mix, number of payment units for which payment is made, and other factors that may affect the relative use of resources. These adjustments shall be made in a manner so that changes in aggregate payments under the classification system are the result of real changes and not the result of changes in coding that are unrelated to real changes in case mix. In the FY 2006 proposed rule (70 FR 30188, 30196), in accordance with section 1886(j)(2)(C)(i) of the Act and as specified in § 412.620(c) and based on the research conducted by RAND, we proposed to update the CMGs used to classify IRF patients for purposes of establishing payment amounts. We also proposed to update the relative weights associated with the payment groups based on FY 2003 Medicare bill and patient assessment data. We proposed replacing the current unweighted motor score index used to assign patients to CMGs with a weighted motor score index that would improve our ability to accurately predict the costs of caring for IRF patients, as described in detail below. However, we proposed not to change the methodology for computing the cognitive score index. As described in the August 7, 2001 final rule, we contracted with RAND to analyze IRF data to support our efforts in developing our patient classification system and the IRF PPS. We continued our contract with RAND to support us in developing potential refinements to the classification system and the PPS. As part of this research, we asked RAND to examine possible refinements to the CMGs to identify potential improvements in the alignment between Medicare payments and actual IRF costs. In conducting its research, RAND used a technical expert panel
(TEP)made up of experts from industry groups, other government entities, academia, and other interested parties. The technical expert panel reviewed RAND's methodologies and advised RAND on many technical issues. Several recent developments make significant improvements in the alignment between Medicare payments and actual IRF costs possible. First, when the IRF PPS was implemented in 2002, a new assessment instrument was used to collect patient data, the IRF Patient Assessment Instrument (IRF-PAI). The new instrument contained items that improved the quality of the patient-level information available to researchers. Second, more recent data are available on a larger patient population. Until now, the design of the IRF PPS was based entirely on 1999 data on Medicare rehabilitation patients from just a sample of hospitals (the best available data at the time). Now, we have post-PPS data from 2002 and 2003 that describe the entire universe of Medicare-covered rehabilitation patients. Finally, we believe that improvements in the algorithms that produced the initial CMGs, as described below, should lead to new CMGs that better predict treatment costs in the IRF PPS. Using the inpatient rehabilitation facility assessment instrument before the PPS, which is commonly referred to as the FIM, and Medicare data from 1998 and 1999, RAND helped us develop the original structure of the IRF PPS. IRFs became subject to the PPS beginning with cost reporting periods starting on or after January 1, 2002. The PPS is based on assigning patients to particular CMGs that are designed to predict the costs of treating particular Medicare patients according to how well they function in four general categories: Transfers, sphincter control, self-care (for example, grooming, eating), and locomotion. Patient functioning is measured according to 18 categories of activity: 13 motor tasks, such as putting on clothing, and 5 cognitive tasks, such as memory. The PPS is intended to align payments to IRFs as closely as possible with the actual costs of treating patients. If the PPS “underpays” for some kinds of care, IRFs have incentives to limit access for patients requiring that kind of care because payments for a particular case would be less than the costs of providing care, so an IRF may try to limit its financial “losses”; conversely, if the PPS overpays, resources are wasted because IRFs' payments exceed the costs of providing care for a particular case. The fiscal year 2003 data file currently available for refining the CMGs contains many more IRF cases and represents the universe of Medicare-covered IRF cases, rather than a sample. The best available data that CMS and RAND had for analysis in 1999 contained 390,048 IRF cases, representing 64 percent of all Medicare-covered patients in participating IRFs. The more recent data contain 523,338 IRF cases (fiscal year 2003), representing all Medicare-covered patients in participating IRFs. The larger file enables RAND to obtain greater precision in the analysis and portrays a more recent and complete picture of patients under the IRF PPS. Also, the fiscal year 2003 data include more detailed information about patients' level of functioning. For example, new variables are included in the more recent data that provide further details on patient functioning. Standard bowel and bladder scores on the FIM instrument (used to assess patients before the IRF PPS), for example, measured some combination of the level of assistance required and the frequency of accidents (that is, soiling of clothes and surroundings). New variables on the IRF-PAI instrument measure the level and the frequency separately. Since measures of the level of assistance required and the frequency of accidents contain slightly different information about the expected costliness of an IRF patient, having measures for these two variables separately provides additional information to researchers. Furthermore, additional optional information is recorded on the health status of patients in the more recent data (for example, shortness of breath, presence of ulcers, inability to balance). 1. Changes for Updating the CMGs In the FY 2006 proposed rule (70 FR 30188), we proposed to revise the definitions of the CMGs based on regression analysis by RAND of the FY 2003 data. As described in the August 7, 2001 final rule, RAND developed the original list of CMGs using FIM data from 1998 and 1999 (see the FY 2006 proposed rule (70 FR 30188, 30198 through 30202) for a table of the original CMG listing). Given the availability of more recent, post-PPS data, we asked RAND to examine possible refinements to the CMGs to identify potential improvements in the alignment between Medicare payments and actual IRF costs. In addition to analyzing fiscal year 2003 data, RAND also convened a TEP, made up of researchers from industry, provider organizations, government, and academia, to provide support and guidance through the process of developing possible refinements to the PPS. Members of the TEP reviewed drafts of RAND's reports, offered suggestions for additional analyses, and provided clinicians' views of the importance and significance of various findings. As we explained in the FY 2006 proposed rule (70 FR 30188), RAND's analysis of the FY 2003 data, along with the support and guidance of the TEP, strongly suggested the need to update the CMGs to better align payments with costs under the IRF PPS. The other option we considered before proposing to update the CMGs with the fiscal year 2003 data was to maintain the same CMG structure but recalculate the relative weights for the current CMGs using the 2003 data. After carefully reviewing the results of RAND's regression analysis, which compared the predictive ability of the CMGs under 3 scenarios (not updating the CMGs or the relative weights, updating only the relative weights and not the CMGs, and updating both the relative weights and the CMGs), as we stated in the FY 2006 proposed rule (70 FR 30188), we believed and continue to believe (based on RAND's analysis) that updating both the relative weights and the CMGs will allow the classification system to do a better job of reflecting changes in treatment patterns, technology, case mix, and other factors which may affect the relative use of resources. We continue to believe it is appropriate to update both the CMGs and the relative weights at this time because the 2003 data we now have represent a more recent and broader set of data elements. The more recent data include all Medicare-covered IRF cases rather than a subset, allowing us to base the CMG changes on a complete picture of the types of patients in IRFs. In designing the IRF PPS, we used the best available data, but those data may not have contained a complete picture of the types of patients in IRFs. Also, the improved clinical coding of patient conditions in IRFs is better reflected in the more recent data than it was in the best available data we had to design the IRF PPS. In addition, changes in treatment patterns, technology, case mix, and other factors affecting the relative use of resources in IRFs since the IRF PPS was implemented likely require an update to the classification system. Prior to the finalization of the proposed changes contained in this final rule, we paid IRFs based on 95 CMGs and 5 special CMGs developed using the CART algorithm applied to 1999 data. The CART algorithm that was used in designing the IRF PPS assigned patients to RICs according to their age and their motor and cognitive FIM scores. CART produced the partitions so that the reported wage-adjusted rehabilitation cost of the patients was relatively constant within partitions. Then, a subjective decision-making process was used to decrease the number of CMGs (to ensure that the payment system did not become unduly complicated), to enforce certain constraints on the CMGs (to ensure that, for instance, IRFs were not paid more for patients who had fewer comorbidities than for patients with more comorbidities), and to fit the comorbidity tiers. Although the use of a subjective decision-making process (rather than a computer algorithm) was very useful, there were limitations. For example, it made it difficult to explore the implications of variations to the CART models because an individual person is not able to examine as many variations of a model in as short a period of time as a computer program. Furthermore, the computer is more efficient at accounting for all of the possible combinations and interactions between important variables that affect patient costs. In analyzing potential refinements to the IRF PPS, RAND created a new algorithm that would be very useful in constructing the CMGs (the new algorithm would be based on the CART methodology described in detail in section V.A.2.b of this final rule). RAND applied the new algorithm to the fiscal year 2003 IRF data. In the FY 2006 proposed rule (70 FR 30188), we proposed to use RAND's new algorithm for refinements to the CMGs. The algorithm is based entirely on an iterative computerized process to decrease the number of CMGs, enforce constraints on the CMGs, and assign the comorbidity tiers. At each step in the process, the new CART algorithm produces all of the possible combinations of CMGs using all available variables. It then selects the variables and the CMG constructions that offer the best predictive ability, as measured by the greatest decrease in the mean-squared error. We proposed to place the following constraints on the algorithm, based on RAND's analysis:
(1)Neighboring CMGs would have to differ by at least $1,500, unless eliminating the CMG would change the estimated costs of patients in that CMG by more than $1,000;
(2)estimated costs for patients with lower motor or cognitive index scores (more functionally dependent) would always have to be higher than estimated costs for patients with higher motor or cognitive index scores (less functionally dependent). We believe that the PPS should not pay more for a patient who is less functionally dependent than for one who is more functionally dependent; and
(3)each CMG must contain at least 50 observations (for statistical validity). RAND's technical expert panel, which included representatives from industry groups, other government entities, academia, and other researchers, reviewed and commented on these constraints and the rest of RAND's proposed methodology (developed based on RAND's analysis of the data) for updating the CMGs as RAND developed the improvements to the CART methodology. The following are the most substantial differences between the CMGs used prior to October 1, 2005 and the proposed new CMGs for FY 2006: • Fewer CMGs than before (87 now compared with 95 in the prior system). The 5 special CMGs for very short stay cases and cases in which the patient expires would remain unchanged. • The number of CMGs under the RIC for stroke patients (RIC 1) would decrease from 14 to 10. • The cognitive index score would affect patient classification in two of the RICs (RICs 1 and 2), whereas it previously affected RICs 1, 2, 5, 8, 12, and 18. • A patient's age would now affect assignment for CMGs in RICs 1, 4, and 8, whereas it previously affected assignment for CMGs in RICs 1 and 4. The primary objective in updating the CMGs is to better align IRF payments with the costs of caring for IRF patients, given more recent information. This requires that we improve the ability of the system to predict patient costs. RAND's analysis suggests that the proposed new CMGs clearly improve the ability of the payment system to predict patient costs. The proposed new CMGs would greatly improve the explanation of variance in the system. Public comments and our responses on the proposed changes for updating the CMGs are summarized below. *Comment:* Several commenters raised concerns that the FY 2003 data used to update the CMGs did not reflect the full enforcement of the 75 percent rule and that CMS should, therefore, wait until the data reflect full enforcement before making any changes to the CMGs. *Response:* We agree that additional changes to the CMGs may potentially be necessary in the future if enforcement of the 75 percent rule results in substantial changes to IRFs' patient populations. However, we believe it is now appropriate to begin refining the system because several recent developments make significant improvements in the alignment between Medicare payments and actual IRF costs possible. First, when the IRF PPS was implemented for cost reporting periods beginning on or after January 1, 2002, a new recording instrument called the IRF-PAI was used to collect patient data. The new instrument contained questions that improved the quality of the patient-level information available to researchers. The 2003 data used in the proposed refinements reflects this data. Second, more recent data are available on a larger patient population. Until now, the design of the IRF PPS was based entirely on 1999 data on Medicare rehabilitation patients from just a sample of hospitals. Even though this was the best available data at the time, we now have post-PPS data from 2002 and 2003 that describe the entire universe of Medicare-covered rehabilitation patients. Finally, we believe that proposed improvements in the algorithms that produced the initial CMGs, as described above, lead to new CMGs that better predict treatment costs in the IRF PPS. We further note that making refinements to the IRF patient classification system now, based on post-PPS data, does not preclude us from making future refinements to the system if IRFs' case mix and care practices change over time. We will continue to monitor the IRF PPS, and make refinements as needed, to ensure that IRF payments are aligned as closely as possible with the costs of providing care. *Comment:* One commenter believed that the proposed changes to the CMGs would make IRF quality measurement more difficult over time because the proposed changes to the CMG definitions would mean that a case classified into a particular CMG (such as CMG 0107) before October 1, 2005 (when the proposed changes would be implemented) would not necessarily be classified into CMG 0107 after October 1, 2005. Thus, people attempting to create a one-for-one crosswalk between the CMGs before October 1, 2005 and the proposed CMGs after October 1, 2005 would be unable to do so. The commenter noted that many quality measurement tools currently being used by IRFs require such a one-for-one crosswalk. *Response:* We recognize the importance of monitoring IRF quality of care over time. However, we do not believe that the proposed changes to the CMGs inhibit the ability to monitor quality in IRFs over time. Quality of care is not measured by a payment rate, but by data reflecting various indicators of the treatment patients receive. In the FY 2006 proposed rule (70 FR 30188), we did not propose changes to the patient assessment form itself or changes to the coding of the underlying data that is used to classify patients into CMGs. Therefore, comparisons of the underlying patient classification data could still be used to monitor quality in these facilities over time. *Comment:* One commenter expressed concerns that the cognitive scores are not used as often in the definitions of the proposed revisions to the CMGs as they were in the original CMGs defined in the August 7, 2001 final rule. This commenter stated that the cognitive scores are important predictors of how costly patients are likely to be in the IRF setting. The commenter also stated that, if cognitive scores are not used as often as motor scores for assigning patients to CMGs, the reason may be that measures of patients' cognitive abilities may not currently be as well developed as measures of patients' motor abilities. Therefore, this commenter recommended that we develop more sensitive measures that have better predictive qualities. *Response:* As we noted previously, the cognitive score used to classify IRF patients into CMGs is made up of cognitive items from the IRF-PAI. These cognitive items are generally indications of the patient's mental functioning level, and are related to the patient's ability to process and respond to empirical factual information, use judgment, and accurately perceive what is happening. Patients' cognitive functioning clearly affects their expected costliness in an IRF. However, RAND's regression analysis, in which they explored the relationship of the FIM motor and cognitive scores to cost, showed that patients' cognitive scores generally did not predict patients' expected costliness above and beyond what patients' motor scores already were able to predict. Thus, we see no reason to use cognitive scores in CMG definitions for which they do not add predictive ability. When the cognitive scores add information that increases the predictive ability of the classification system, we make use of this information in the CMG assignment. We agree with one of the commenter's points that the cognitive score may not predict costs as well as the motor score because the cognitive items may not be as sensitive to patients' cognitive status as the motor items are to patients' physical functioning. We further agree with the commenter that more work could be done to better identify measures of cognitive functioning. Along these lines, CMS has awarded a contract to the Research Triangle Institute
(RTI)to perform research and data analysis to support possible changes to the IRF-PAI instrument that would better capture physical and cognitive functioning information on IRF patients. CMS remains open to examining well-constructed peer-reviewed studies by other types of providers, researchers, and other interested parties in order to improve upon the cognitive assessment functioning measures for the Medicare population. Until then, we will use the best cognitive functioning information available for IRF patients to classify patients into the most appropriate CMGs so IRF payments align as closely as possible with the costs of care in IRFs. *Final Decision:* After carefully considering all the comments we received on the proposed changes to the CMG definitions, we are finalizing our decision to adopt the CMG definitions presented below in Table 2. Based on RAND's regression analysis of FY 2003 data, the best data available for analysis, we believe these changes will increase the accuracy of IRF PPS payments. Table 2.—Case Mix Groups (CMGs), With the Associated Rehabilitation Impairment Categories
(RICs)[Beginning with discharges on or after October 1, 2005] RIC CMG No. CMG description 01 Stroke (Stroke) 0101 Motor >51.05. 0102 Motor >44.45 & Motor <51.05 & Cognitive >18.5. 0103 Motor >44.45 & Motor <51.05 & Cognitive <18.5. 01 Stroke (Stroke) 0104 Motor >38.85 & Motor <44.45. 0105 Motor >34.25 & Motor <38.85. 0106 Motor >30.05 & Motor <34.25. 0107 Motor >26.15 & Motor <30.05. 0108 Motor <26.15 & Age >84.5. 0109 Motor >22.35 & Motor <26.15 & Age <84.5. 0110 Motor <22.35 & Age <84.5. 02 Traumatic brain injury
(TBI)0201 Motor >53.35 & Cognitive >23.5. 0202 Motor >44.25 & Motor <53.35 & Cognitive >23.5. 0203 Motor >44.25 & Cognitive <23.5. 0204 Motor >40.65 & Motor <44.25. 0205 Motor >28.75 & Motor <40.65. 0206 Motor >22.05 & Motor <28.75. 0207 Motor <22.05. 03 Nontraumatic brain injury
(NTBI)0301 Motor >41.05. 0302 Motor >35.05 & Motor <41.05. 0303 Motor >26.15 & Motor <35.05. 0304 Motor <26.15. 04 Traumatic spinal cord injury
(TSCI)0401 Motor >48.45. 0402 Motor >30.35 & Motor <48.45. 0403 Motor >16.05 & Motor <30.35. 0404 Motor <16.05 & Age >63.5. 0405 Motor <16.05 & Age <63.5. 05 Nontraumatic spinal cord injury (NTSCI) 0501 Motor >51.35. 05 Nontraumatic spinal cord injury (NTSCI) 0502 Motor >40.15 & Motor <51.35. 0503 Motor >31.25 & Motor <40.15. 0504 Motor >29.25 & Motor <31.25. 0505 Motor >23.75 & Motor <29.25. 0506 Motor <23.75. 06 Neurological (Neuro) 0601 Motor >47.75. 0602 Motor >37.35 & Motor <47.75. 0603 Motor >25.85 & Motor <37.35. 0604 Motor <25.85. 07 Fracture of LE (FracLE) 0701 Motor >42.15. 0702 Motor >34.15 & Motor <42.15. 0703 Motor >28.15 & Motor <34.15. 0704 Motor <28.15. 08 Replacement of LE joint (RepLE) 0801 Motor >49.55. 0802 Motor >37.05 & Motor <49.55. 0803 Motor >28.65 & Motor <37.05 & Age >83.5. 0804 Motor >28.65 & Motor <37.05 & Age <83.5. 0805 Motor >22.05 & Motor <28.65. 0806 Motor <22.05. 09 Other orthopedic(Ortho) 0901 Motor >44.75. 0902 Motor >34.35 & Motor <44.75. 0903 Motor >24.15 & Motor <34.35. 0904 Motor <24.15. 10 Amputation, lower extremity (AMPLE) 1001 Motor >47.65. 1002 Motor >36.25 & Motor <47.65. 1003 Motor <36.25. 11 Amputation, other (AMP-NLE) 1101 Motor >36.35. 11 Amputation, other (AMP-NLE) 1102 Motor <36.35. 12 Osteoarthritis (OsteoA) 1201 Motor >37.65. 1202 Motor >30.75 & Motor <37.65. 1203 Motor <30.75. 13 Rheumatoid, other arthritis (RheumA) 1301 Motor >36.35. 1302 Motor >26.15 & Motor <36.35. 1303 Motor <26.15. 14 Cardiac (Cardiac) 1401 Motor >48.85. 1402 Motor >38.55 & Motor <48.85. 1403 Motor >31.15 & Motor <38.55. 1404 Motor <31.15. 15 Pulmonary (Pulmonary) 1501 Motor >49.25. 1502 Motor >39.05 & Motor <49.25. 1503 Motor >29.15 & Motor <39.05. 1504 Motor <29.15. 16 Pain Syndrome
(Pain)1601 Motor >37.15. 1602 Motor >26.75 & Motor <37.15. 1603 Motor <26.75. 17 Major multiple trauma, no brain injury or spinal cord injury (MMT-NBSCI) 1701 Motor >39.25. 1702 Motor >31.05 & Motor <39.25. 1703 Motor >25.55 & Motor <31.05. 1704 Motor <25.55. 18 Major multiple trauma, with brain or spinal cord injury (MMT-BSCI) 1801 Motor >40.85. 1802 Motor >23.05 & Motor <40.85. 1803 Motor <23.05. 19 Guillian Barre
(GB)1901 Motor >35.95. 19 Guillian Barre (GB 1902 Motor >18.05 & Motor <35.95 1903 Motor <18.05. 20 Miscellaneous
(Misc)2001 Motor >49.15. 2002 Motor >38.75 & Motor <49.15. 2003 Motor >27.85 & Motor <38.75. 2004 Motor <27.85. 21 Burns (Burns) 2101 Motor >0. Special CMGs 5001 Short-stay cases, length of stay is 3 days or fewer. 5101 Expired, orthopedic, length of stay is 13 days or fewer. 5102 Expired, orthopedic, length of stay is 14 days or more. 5103 Expired, not orthopedic, length of stay is 15 days or fewer. 5104 Expired, not orthopedic, length of stay is 16 days or more. Note: CMG definitions use weighted motor scores, as defined below. 2. Use of a Weighted Motor Score Index and Change to the Treatment of Unobserved Transfer to Toilet Values In the FY 2006 proposed rule (70 FR 30188, 30210), we proposed to use a weighted motor score index in assigning patients to CMGs, instead of the motor score index previously used that treated all components equally. We also proposed to change how the IRF PPS GROUPER software would assign a value for the transfer-to-toilet item when it is coded by the provider with a 0. We proposed that the software would assign this item a value of 2 instead of a 1 when the activity is coded by the provider with a 0. However, we proposed not to change the cognitive score index. As described in detail below, we continue to believe that a weighted motor score index, with the change to the scoring of the transfer to toilet item when the provider records a 0 value for the activity on the IRF-PAI, will improve the classification of patients into CMGs, which in turn will improve the accuracy of payments to IRFs. To classify a patient into a CMG, IRFs use the admission assessment data from the IRF-PAI to score a patient's functional independence measures. The functional independence measures consist of what are termed “motor” items and “cognitive” items. In addition to the functional independence measures, the patient's age may also influence the patient's CMG classification. The motor items are generally indications of the patient's physical functioning level. The cognitive items are generally indications of the patient's mental functioning level, and are related to the patient's ability to process and respond to empirical factual information, use judgment, and accurately perceive what is happening. The motor items are eating, grooming, bathing, dressing upper body, dressing lower body, toileting, bladder management, bowel management, transfer to bed/chair/wheelchair, transfer to toilet, transfer to tub or shower, walking or wheelchair use, and stair climbing. The cognitive items are comprehension, expression, social interaction, problem solving, and memory. (The CMS IRF-PAI manual includes more information on these items.) Each item is generally recorded on the IRF-PAI and scored on a scale of 0 to 7, with a 7 indicating complete independence in this area of functioning, a 1 indicating that a patient is very impaired in this area of functioning, and a 0 indicating that the activity did not occur. As explained in the August 7, 2001 final rule (66 FR 41349), the instructions for the IRF-PAI required that providers record an 8 for an item to indicate that the activity did not occur, as opposed to a 1 through 7 indicating that the activity occurred and the estimated level of function connected with that activity. However, when the IRF-PAI form was finalized, the code 8 had been removed and was replaced with the code 0. Therefore, facilities now record a 0 when an activity does not occur. To determine the appropriate payment for patients for whom an activity is coded as 0 (that is, the activity did not occur), we needed to decide an appropriate way of changing the 0 to another code for which payment could be assigned. As discussed in the August 7, 2001 final rule (66 FR at 41349), for purposes of classifying patients into CMGs, we decided to assign a code of 1 (indicating that the patient needed “total assistance”) whenever a code of 0 appeared for one of the items on the IRF-PAI used to determine payment. This was the most conservative approach we could have taken based on the best available data at the time because a value of 1 indicates that the patient needed total assistance performing the task. The result of recoding a 0 as a 1 and using that value to classify a patient into a CMG is that the provider might receive a higher payment for that item (although it might not be the highest payment overall, depending on the patient's other functional abilities and/or comorbidities). In the FY 2006 proposed rule (70 FR 30188), we proposed to change the way we treat a code of 0 on the IRF-PAI for the transfer to toilet item. This is the only item that we proposed to change at this time because RAND's regression analysis demonstrated that, of all the motor score values, the evidence supporting a change in the motor score values was the strongest with respect to this item. We proposed to assign a code of 2, instead of a code of 1, to patients for whom a 0 is recorded on the IRF-PAI for the transfer to toilet item (as discussed below) because RAND's analysis of calendar year 2002 and FY 2003 data indicates that patients for whom a 0 is recorded are more similar in terms of their characteristics and costliness to patients with a recorded score of 2 than to patients with a recorded score of 1. We proposed to make this change to provide the most accurate payment for each patient. Using regression analysis on the calendar year 2002 and FY 2003 data, which is more complete and provides more detailed information on patients' functional abilities than the FY 1999 data used to construct the IRF PPS (even though the 1999 data were the best available data at the time), RAND analyzed whether the assignment of 1 to items for which a 0 is recorded on the IRF-PAI continues to correctly assign payments based on patients' expected costliness. RAND examined all of the items in the motor score index, focusing on how often a code of 0 appears for the item, how similar patients with a code of 0 are to other patients with the same characteristics that have a score of 1 though 7, and how much a change in the item's score affects the prediction of a patient's expected costliness. Based on RAND's regression analysis, we believed and continue to believe it is appropriate to change the assignment of 0 on the transfer to toilet item from a 1 to a 2 for the purposes of determining IRF payments. Until now, the IRF PPS has used standard motor and cognitive scores, the sum of either 12 or 13 motor items and the sum of 5 cognitive items, to assign patients to CMGs. This summing equally weights the components of the indices. These indices have been accepted and used for many years. Although the weighted motor score is an option that has been considered before, most experts believed that the data were not complete and accurate enough before the IRF PPS (although they were the most complete and accurate data available at the time). Now, it is believed that the data are complete and accurate enough to support using a weighted motor score index. In developing candidate indices that would weight the items in the score, RAND had the following competing goals: developing indices that would increase the predictive power of the system while at the same time maintaining simplicity and transparency in the payment system. For example, RAND found that an “optimal” weighting methodology from the standpoint of predictive power would require computing 378 different weights (18 different weights for the motor and cognitive indices that could all differ across 21 RICs). Rather than introduce this level of complexity to the system, RAND decided to explore simpler weighting methodologies that would still increase the predictive power of the system. RAND used regression analysis to explore the relationship of the FIM motor and cognitive scores to cost. The idea of these models was to determine the impact of each of the FIM items on cost and then weight each item in the index according to its relative impact on cost. Based on the regression analysis, RAND was able to design a weighting methodology for the motor score that could potentially be applied uniformly across all RICs. RAND assessed different weighting methodologies for both the motor score index and the cognitive score index. They discovered that weighting the motor score index improved the predictive ability of the system, whereas weighting the cognitive score index did not. Furthermore, the cognitive score index has never had much of an effect (in some RICs, it has no effect) on the assignment of patients to CMGs because the motor score tends to be much stronger at predicting a patient's expected costs in an IRF than the cognitive score. For these reasons, we proposed a weighting methodology for the motor score index. We proposed to continue using the same methodology we have been using since the IRF PPS was first implemented to compute the cognitive score index (that is, summing the components of the index) because, among other things, a change in methodology for calculating this component of the system failed to improve the accuracy of the IRF PPS payments. Therefore, it would be futile to expend resources on changing this method when it would not benefit the program. Table 3 below shows the optimal weights from the regression analysis for the components of the motor score, averaged across all RICs and normalized to sum to 100.0, obtained through the regression analysis. The weights relate to the FIM items' relative ability to predict treatment costs. Table 3 indicates that dressing lower, toilet, bathing, and eating are the most effective self-care items for predicting costs; bowel and bladder control may not be effective at predicting costs; and that the items grouped in the transfer and locomotion categories might be somewhat more effective at predicting costs than the other categories. We are making no changes to Table 3, which was Table 5 in the FY 2006 proposed rule (70 FR 30188, 30211). Table 3.-Optimal Weights, Averaged Across Rehabilitation Impairment Categories
(RICs)[Motor Items] Item type Functional independence item Average optimal weight Self Dressing lower 1.4 Self Toilet 1.2 Self Bathing 0.9 Self Eating 0.6 Self Dressing upper 0.2 Self Grooming 0.2 Sphincter Bladder 0.5 Sphincter Bowel 0.2 Transfer Transfer to bed 2.2 Transfer Transfer to toilet 1.4 Transfer Transfer to tub ( 1 ) Locomotion Walking 1.6 Locomotion Stairs 1.6 1 Not included. Based on RAND's analysis, we considered a number of different candidate indices before we proposed using a weighted index. We considered defining some simple combinations of the four item types that make up the motor score index and assigning weights to the groups of items instead of to the individual items. For example, we considered summing the three transfer items together to form a group with a weight of two, since they contributed about twice as much in the cost regression as the self-care items. We also considered assigning the self-care items a weight of one and the bladder and bowel items as a group a weight close to zero, since they contributed little to predicting cost in the regression analysis. We tried a number of variations and combinations of this, but RAND's TEP generally rejected these weighting schemes. They believed that introducing elements of subjectivity into the development of the weighting scheme may invite controversy, and that it is better to use an objective algorithm to derive the appropriate weights. We agree that an objective weighting scheme is best because it is based on regression analysis of the amount that various components of the motor score index contribute to predicting patient costs, using the best available data we have. Therefore, we proposed to use a weighting scheme that applies the average optimal weights. To develop the weighting scheme, RAND used regression analysis to estimate the relative contribution of each item to the prediction of costs. Based on this analysis, we proposed the weighting scheme indicated in Table 3 above and in the following simple equation: Motor score index = 1.4*dressing lower + 1.2*toilet + 0.9*bathing + 0.6*eating + 0.2*dressing upper + 0.2*grooming + 0.5*bladder + 0.2*bowel + 2.2*transfer to bed + 1.4*transfer to toilet + 1.6*walking + 1.6*stairs. Another reason we proposed to use a weighted motor score index to assign patients to CMGs is that RAND's regression analysis showed that it predicts costs better than the current unweighted motor score index. Across all 21 RICs, the proposed weighted motor score index improves the explanation of variance within each RIC by 9.5 percent, on average. Public comments and our responses on the proposal to use a weighted motor score index and to change the treatment of unobserved transfer to toilet values are summarized below. *Comment:* One commenter suggested that the optimal weights for the bladder and bowel items may be too low because incontinence is the most cited reason patients receive inpatient post-acute care. *Response:* We believe that the weights for the bladder and bowel items are appropriate since they were determined based on regression analysis of the effects of these items on the prediction of IRF costs. The purpose of the optimal weights for the proposed weighted motor score index is not to indicate the reasons patients receive inpatient post-acute care but rather to estimate the influence of various motor score items on the expected costs of treating patients in the IRF setting. While we do not disagree that incontinence may be a significant reason that many patients receive post-acute care in an inpatient setting, the optimal weights described above were obtained from RAND's regression analysis of the functional items on patient costs using FY 2003 data. *Comment:* Several commenters were concerned that the proposed weighted motor score is complex, creates added costs for providers, will require retraining of staff, is not sensitive to differences among RICs, and that RAND's technical expert panel did not support the weighting methodology. *Response:* We proposed a weighted motor score index because RAND's analysis indicates that a weighted motor score index will improve the classification of patients into CMGs, which in turn will improve the accuracy of payments to IRFs. As we stated earlier, in developing candidate indices that would weight the items in the score, RAND had competing goals: To develop indices that would increase the predictive power of the system while at the same time maintaining simplicity and transparency in the payment system. For example, they found that an “optimal” weighting methodology from the standpoint of predictive power would require computing 378 different weights (18 different weights for the motor and cognitive indices that could all differ across 21 RICs). Although this would have made the score more sensitive to differences among RICs, as the commenter requested, it would have made the score substantially more complex and less transparent. Thus, we proposed a weighting methodology that balances these two competing goals. With regard to the commenter's statement regarding the lack of support for the weighting methodology, RAND's technical expert panel generally endorsed the particular weighting methodology we proposed to implement. Furthermore, in the technical expert panel's discussions, participants told RAND that the weighting methodology would not be difficult for providers to implement. They stated that providers typically have software that computes the motor score, and that software would only require slight modifications to accommodate the new weighting methodology. Staff members in IRFs that complete the patient assessments would continue to input the same information they currently do into the software and therefore, in general, staff should not need to be retrained. We are not proposing any changes to how providers code items on the IRF-PAI, only how the information is used to classify patients into CMGs for determining the payment rate. We wish to point out that the weighted motor score for classifying patients into CMGs will be computed automatically by the GROUPER software, not by a clinician. CMS will issue the new GROUPER software at no cost to providers, and the new GROUPER software can be used in the same manner as the old GROUPER software. Thus, the proposed change to the weighted motor score index would not be expected to add to providers' costs. However, CMS will assist providers in any training efforts that may be required to implement the proposed new weighting methodology. *Comment:* Two commenters raised concerns regarding the proposed change in assignment of the transfer-to-toilet item. They indicated that this change could artificially elevate the motor score, reduce payments, and have a negative impact on severely ill patients, specifically spinal cord injury patients. *Response:* We proposed to assign the transfer-to-toilet item on the IRF-PAI a value of 2, instead of 1, when the provider has recorded a value of 0 (meaning the activity did not occur) because RAND's regression analysis of calendar year 2002 and FY 2003 data indicates that patients for whom a 0 is recorded are more similar in terms of their characteristics and costliness to patients with a recorded score of 2 than to patients with a recorded score of 1. We proposed to make this change in order to provide the most accurate payment for each patient. We do not believe this proposed change will have a significant effect on payment or on access to care for patients for the following reasons:
(1)The transfer-to-toilet item is only 1 of 12 items that make up the motor score index,
(2)we are only proposing to change the score on this item by 1 point (which results in a 1.4 increase to the weighted motor score index), and
(3)this change will only affect those patients for whom a 0 is recorded for this item (only about 2.8 percent of all IRF cases RAND examined). Furthermore, the payment for a particular patient with a 0 value for this item would only change if the proposed 1.4 point increase in the motor score index changes the patient's CMG classification. For this to happen, the patient's motor score would have to be within 1.4 points of a CMG boundary. In particular, as the commenter noted the example of spinal cord injury patients, we will use RIC 04 (traumatic spinal cord injury) as an example. The difference in motor scores values that would qualify a patient for CMG 0402 versus CMG 0401 is 18.1 points, and the difference in motor scores values that would qualify a patient for CMG 0403 versus CMG 0402 is 14.3 points. Because these ranges are relatively large, we believe patients will rarely change CMGs as a result of a 1.4 point increase in the motor score index. We proposed this change in coding of the transfer-to-toilet item because, based on RAND's analysis, we believe this proposed change will improve the accuracy of payments in the IRF PPS. As always, we are concerned that all patients have appropriate access to IRF services. Accordingly, we will monitor the impact of this proposed change and the other proposed changes to the IRF classification system finalized in this final rule to ensure that patients continue to have adequate access to IRF care. *Comment:* One commenter was concerned that the weighted motor score might disproportionately affect IRF payments for certain types of patients with certain conditions, such as cognitively impaired patients with significant lower body impairments or with significant dysfunctions in upper body and bladder/bowel problems. *Response:* We do not believe the weighted motor score methodology will have a disproportionate affect on any particular groups of patients. RAND's data analysis and RAND's technical expert panel did not raise any concerns regarding any particular groups of patients that would be unduly affected by these changes. We believe that the types of patients the commenter mentioned were included in the data RAND used to determine the optimal weights for the weighted motor score and to calibrate the appropriate payments. The purpose of the proposed weighted motor score, as with all of the proposed changes discussed in this final rule, is to align payments more appropriately with the costs of caring for all types of patients in IRFs. CMS will continue to closely monitor the data to ensure that no groups of patients are disproportionately affected by the change to a weighted motor score index. *Comment:* One commenter indicated that CMS, in proposing to implement the weighted motor score, did not seek enough review from experts who developed and researched the FIM items. *Response:* As discussed in this final rule under section IV, we contracted with RAND to examine potential refinements to the IRF PPS. RAND sought advice from a technical expert panel, which reviewed their methodology and findings regarding the proposed weighted motor score methodology and generally endorsed the methodology we proposed in the FY 2006 proposed rule (70 FR 30188). RAND's technical expert panel included representatives from industry groups, other government entities, academia, and other researchers, including members with expertise in the FIM items. Thus, we believe RAND sought sufficient review from experts in the field in developing the proposed weighted motor score methodology. *Comment:* One commenter requested that CMS remove the transfer to tub item from the IRF-PAI, to reduce the length of the form, because the transfer-to-tub item is not used in classifying patients into CMGs for payment purposes. *Response:* We did not propose any changes to the IRF-PAI. However, we will take this comment into consideration in future reviews of the IRF-PAI. We would need to more fully consider the benefits and costs of removing this item from the IRF-PAI form to determine if this change is appropriate. Final Decision: After carefully considering all of the comments we received on the proposed weighted motor score methodology, we are finalizing our decision to adopt the methodology as described above. Specifically, the weighted motor score index will be computed using the following equation: Motor score index = 1.4*dressing lower + 1.2*toilet + 0.9*bathing + 0.6*eating + 0.2*dressing upper + 0.2*grooming + 0.5*bladder + 0.2*bowel + 2.2*transfer to bed + 1.4*transfer to toilet + 1.6*walking + 1.6*stairs. In addition, we are finalizing our decision to reassign a value of 2 instead of 1 when providers code a 0 for the transfer-to-toilet item on a patient's IRF-PAI. Based on RAND's regression analysis of FY 2003 data, the best data available for analysis, we believe these changes will increase the accuracy of IRF PPS payments. 3. Changes to the Relative Weights In the FY 2006 proposed rule (70 FR 30188), we proposed to update the relative weights assigned to each CMG. Section 1886(j)(2)(B) of the Act requires that an appropriate relative weight be assigned to each CMG. Relative weights that account for the variance in cost per discharge and resource utilization among payment groups are a primary element of a case-mix adjusted prospective payment system. The accuracy of the relative weights helps to ensure that payments reflect as much as possible the relative costs of IRF patients and, therefore, that beneficiaries have access to care and receive the appropriate services. Section 1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to adjust the classifications and weighting factors to reflect changes in treatment patterns, technology, case mix, number of payment units for which payment to IRFs is made, and other factors which may affect the relative use of resources. In accordance with this section of the Act, we proposed to recalculate a relative weight for each CMG that is proportional to the resources needed by an average inpatient rehabilitation case in that CMG. For example, cases in a CMG with a relative weight of 2, on average, would cost twice as much as cases in a CMG with a relative weight of 1. We did not propose to change the methodology for calculating the relative weights, as described in the August 7, 2001 final rule (66 FR 41316, 41351 through 41353) and consequently, we only proposed to update the relative weights themselves. As previously stated, we believe that improved coding of data, the availability of more complete data, and changes to the tier comorbidities and CMGs helped us decide to propose to update the relative weights assigned to the CMGs so that they could continue to accurately represent the differences in costs across CMGs and across tiers. Therefore, we proposed to recalculate the relative weights. However, we proposed no change to the methodology for calculating the relative weights. Instead, we proposed to update the relative weights (the relative weights that are multiplied by the standard payment conversion factor to assign relative payments for each CMG and tier) using the same methodology as described in the August 7, 2001 final rule (66 FR 41316, 41351 through 41353) and as noted previously in section V.C.3 of this final rule, using FY 2003 Medicare billing data. To summarize, we proposed to use the following basic steps to update the relative weights: The first step in calculating the CMG weights is to estimate the effects that comorbidities have on costs. The second step is to adjust the cost of each Medicare discharge
(case)to reflect the effects found in the first step. In the third step, the adjusted costs from the second step are used to calculate “relative adjusted weights” in each CMG using the hospital-specific relative value method. The final steps are to calculate the CMG relative weights by modifying the “relative adjusted weight” with the effects of the existence of the comorbidity tiers (explained below) and normalize the weights to 1. We proposed to make the tier and the CMG changes in such a way that total estimated aggregate payments to IRFs for FY 2006 would be the same with or without the changes (that is, in a budget neutral manner) for the following reasons. First, we believe that the results of RAND's analysis of 2002 and 2003 IRF cost data suggest that additional money does not need to be added to the IRF PPS. RAND's analysis found, for example, that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. Furthermore, RAND did not find evidence that the overall costliness of patients (average case mix) in IRFs increased substantially in 2002 compared with 1999. As discussed in detail in section VI.A of this final rule, RAND found that real case mix increased by at most 1.5 percent, and may have decreased by as much as 2.4 percent. The available evidence, therefore, suggests that IRF PPS payments, in aggregate, are likely adequate to pay for the types of patients IRFs treat. The purpose of the CMG and tier changes is to ensure that the existing resources already in the IRF PPS are distributed better among IRFs according to the relative costliness of the types of patient they treat. Section 1886(j)(2)(C)(i) of the Act confers broad statutory authority upon the Secretary to adjust the classification and weighting factors to account for relative resource use. Consistent with that broad statutory authority, we proposed to update the relative weights to more accurately reflect the IRF case mix. To ensure that total estimated aggregate payments to IRFs do not change, we proposed to apply a factor to the standard payment amount to ensure that estimated aggregate payments due to the proposed changes to the tier comorbidities, the CMGs, the weighted motor score, and the relative weights for FY 2006 are not greater or less than those that would have been made in FY 2006 without the proposed changes. In section VI.B.7 and section VI.B.8 of this final rule, we discuss the methodology and factor we proposed to apply to the standard payment amount. Public comments and our responses on the proposed changes for updating the relative weights are summarized below. *Comment:* Several commenters noted that, in many of the CMGs, the average length of stay has decreased. One commenter suggested that there might have been inconsistencies between the relative weights and the average length of stay values reported in the proposed Table 6 in the FY 2006 proposed rule (70 FR 30188, 30213 through 30219). *Response:* RAND's analysis found that the average length of stay in IRFs has decreased substantially in recent years. This decrease is reflected in the average length of stay values for most of the CMGs in the proposed Table 6 in the FY 2006 proposed rule (70 FR 30188, 30213 through 30219). However, with the exception of determining IRF payments in certain transfer cases, the average length of stay does not affect IRF payments. CMS does not require IRFs to treat these average length of stay values as goals or targets for particular cases. IRFs are generally free to treat particular patients for as few or as many days as they deem medically appropriate. We encourage IRFs to admit patients for the length of time that results in the best quality of care for the patient. The length of stay portion of the proposed Table 6 in the FY 2006 proposed rule (70 FR 30188, 30213 through 30219) is provided for informational purposes only. The relative weights for each of the CMGs and tiers represent the relative costliness of patients in those CMGs and tiers compared with patients in other CMGs and tiers. The average length of stay for each CMG and tier represents the average number of days patients in that CMG and tier were treated in IRFs, based on the FY 2003 data. IRF PPS payments are determined on a per-discharge basis, meaning that providers are paid a pre-determined payment amount according to that patient's CMG and tier classification, regardless of the number of days the patient is treated in the IRF. The only exceptions to this general policy are for very short-stay cases and for certain transfer cases. Because payments are made on a per-discharge basis, there is not necessarily any correlation between the number of days a patient is treated in the IRF and the payment amount for that patient. If, for example, the relative weight for a particular CMG in tier 1 is higher than the relative weight for that same CMG in the no-comorbidity tier, this means that cases in that CMG in tier 1 are expected to be more costly for the IRF to treat than cases in that CMG in the no-comorbidity tier. The average length of stay for patients in that CMG in tier 1, however, could be lower than the average length of stay of patients in that CMG in the no-comorbidity tier because the treatment for patients in that CMG in tier 1 could be much more intensive for a shorter period of time than the treatment for patients in the no-comorbidity tier, who could require less-intensive treatment over a longer period of time. Thus, the relative weights may not bear a relationship to the length of stay, and the two need not be consistent with each other. *Comment:* Several commenters expressed concerns about decreases in the relative weights for certain CMGs, particularly for the stroke and traumatic brain injury CMGs. These commenters stated that, if the relative weights and, consequently, the payment rates for certain CMGs were to decrease, it could potentially lead to reduced access to IRF care for patients in the affected CMGs. *Response:* The commenters were not clear as to which CMG weights they were using as a comparison with the proposed FY 2006 relative weights in Table 6 of the FY 2006 proposed rule (70 FR 30188, 30213 through 30219). We believe that the commenter was comparing the proposed FY 2006 relative weights published in the FY 2006 proposed rule (70 FR 30188, 30213 through 30219) to the FY 2005 relative weights published in the July 30, 2004 notice updating the payment rates (69 FR 45721). Because we proposed revised definitions of the CMGs, as described in section V.C.1 of this final rule, the proposed new relative weights for the proposed new CMGs cannot be compared with the FY 2005 relative weights based on the FY 2005 CMG definitions. The types of patients included in each CMG, as defined in Table 4 and Table 6 of the FY 2006 proposed rule (70 FR 30188, 30207 through 30210, 30213 through 30219) are likely not the same patients included in the CMGs under the FY 2005 CMG definitions. Furthermore, as previously stated, the improved coding of data, the availability of more complete data, proposed changes to the tier comorbidities and CMGs, and changes in IRF cost structures contributed to our decision to propose to update the relative weights assigned to the CMGs so that the weights continue to represent the differences in costs across CMGs and across tiers. For these reasons, we have proposed to recalculate the relative weights to ensure that IRF payments remain aligned as closely as possible with the costs of care. We will continue to monitor beneficiaries' access to IRF care to ensure that the changes to the IRF classification system noted in this final rule do not impede access to IRF care for Medicare beneficiaries in general or for beneficiaries with any particular conditions. In particular, we believe it is important to ensure that stroke patients have appropriate access to rehabilitation services, as this population benefits considerably from receiving prompt rehabilitation care. Nevertheless, we asked RAND to review the average relative weights for the stroke and traumatic brain injury RICs both under the FY 2005 CMG definitions and under the proposed new CMG definitions. The average relative weights were essentially identical within these two RICs, meaning that providers would use essentially the same relative weight to calculate payments for an “average” stroke patient and an “average” traumatic brain injury patient in FY 2006 as they used to calculate payments for the “average” stroke patient and the “average” traumatic brain injury patient in FY 2005. We believe, based on RAND's regression analysis of FY 2003 data, that the proposed changes to the classification system will improve the alignment of IRF payments with the costs of care and, thereby, improve access to care for IRF patients. *Comment:* One commenter stated that if the proposed recalculation of the relative weights were to result in lower payments for some patients and, therefore, were to lead to payments that did not adequately cover treatment costs for those patients, then patients' access to IRF care might suffer. A couple of commenters requested that CMS phase in the proposed changes to the classification system. *Response:* We considered proposing a phase in of the proposed changes to the classification system, but we believe a phase in of the changes would have introduced undue complication to the classification system because it would have required individual providers, fiscal intermediaries, and CMS to compute two different sets of CMGs to determine payments. The intent of the proposed changes to the IRF classification system, including the proposed recalculation of the relative weights, was to ensure that IRF payments are aligned as closely as possible with the costs of care. We believe these proposed revisions will help us to ensure that IRF payments and costs continue to be aligned as appropriately as possible. We will continue to monitor beneficiaries' access to IRF care to ensure that the payment system continues to provide such access to IRF care. To assist providers in adopting the changes to the classification system we are finalizing in this final rule, we will make the new GROUPER and PRICER software available for download on the CMS Web site as soon as possible and before implementation of the final changes. Furthermore, our analysis of the impacts, detailed in section XII of this final rule, indicate that aggregate effects on provider payments of the proposed changes are expected to be small. *Comment:* One commenter noted that the proposed relative weights for the burn CMG (CMG 2101) for tier 1 and tier 2 are the same. The commenter asked whether this could be an error. *Response:* This was not an error. The FY 2003 data do not contain enough patients in CMG 2101 in tiers 1 and 2 to estimate precise relative weights for each tier. Accordingly, RAND combined patients in these two tiers to estimate the proposed and final relative weights for both tiers. *Comment:* Several commenters requested that CMS make available to the public the patient-level data on CMG assignments, the IRF-PAI data, the MedPAR files, and the cost report data RAND used for their analysis to enable the public to replicate RAND's analysis. *Response:* The data files the commenters requested are generally available (and were generally available during the comment period for the FY 2006 proposed rule) through CMS's standard data distribution systems. Please refer to CMS's Web site at *http://www.cms.hhs.gov/researchers/statsdata.asp* for more information about obtaining data from CMS. *Comment:* One commenter asked if CMS could provide the standard deviation information for the average length of stay information listed for each CMG and tier. *Response:* We will consider posting this type of information on our Web site. *Comment:* One commenter noted the operational challenges, such as the large number of revisions that need to be made to the GROUPER software, of implementing the changes to the IRF classification system that CMS has proposed and further requested that CMS make available the new CMG GROUPER to the public. *Response:* We agree with the commenter that the operational issues of implementing the proposed changes to the classification system may be challenging, but we will provide the necessary assistance to ensure a smooth transition to the new tiers and CMGs, the new weighted motor score methodology, and the new relative weights. As is our practice, we will make the new GROUPER and PRICER software available for download on the CMS Web site as soon as possible and prior to implementation of the finalized changes. In addition, we will evaluate whether provider, fiscal intermediary, or regional office training may be required to promote understanding of any final changes and assist in the implementation of such changes. Our foremost goal will be to ensure a smooth implementation of changes because we believe that any final changes to the classification system will improve the accuracy of payments in the IRF PPS. *Comment:* Several commenters requested that CMS evaluate the effects of the proposed changes to the IRF classification system after the changes are implemented and propose additional refinements to the classification system in future years, if necessary. *Response:* We agree with the commenter that it will be important to evaluate the effects of any changes to the classification system to ensure that IRF payments continue to be aligned as closely as possible with the costs of care. CMS intends to monitor the data carefully to ensure that patients who require inpatient rehabilitation services have adequate access to these services. We will propose refinements if, in the future, we later identify the need to make modifications to the classification system to ensure that IRF payments remain aligned with the costs of care. *Final Decision:* After carefully considering all the comments we received on the proposed re-calculation of the relative weights, we are finalizing our proposal to adopt the relative weights presented in Table 4, without change. However, we note that, after reviewing the average length of stay values in response to the comments we received, we have made a slight revision to the methodology for computing the average length of stay values reported in Table 4 to be consistent with the way we presented average length of stay values in the August 7, 2001 final rule (66 FR 41316). Table 4.—Relative Weights for Case-Mix Groups
(CMGs)CMG CMG description (M = motor, C = cognitive, A = age) Relative weights Tier 1 Tier 2 Tier 3 None Average length of stay Tier 1 Tier 2 Tier 3 None 0101 Stroke M > 51.05 0.7691 0.7299 0.6484 0.6350 8 11 9 9 0102 Stroke M > 44.45 and M < 51.05 and C > 18.5 0.9471 0.8989 0.7985 0.7820 11 15 11 10 0103 Stroke M > 44.45 and M < 51.05 and C < 18.5 1.1162 1.0594 0.9411 0.9217 14 13 12 12 0104 Stroke M > 38.85 and M < 44.45 1.1859 1.1255 0.9999 0.9792 13 14 13 13 0105 Stroke M > 34.25 and M < 38.85 1.4233 1.3509 1.2001 1.1753 16 17 15 15 0106 Stroke M > 30.05 and M < 34.25 1.6567 1.5724 1.3969 1.3680 18 20 18 18 0107 Stroke M > 26.15 and M < 30.05 1.9121 1.8148 1.6122 1.5790 21 23 20 21 0108 Stroke M < 26.15 and A > 84.5 2.2106 2.0981 1.8639 1.8254 27 29 24 24 0109 Stroke M > 22.35 and M < 26.15 and A < 84.5 2.1976 2.0858 1.8529 1.8147 23 26 24 23 0110 Stroke M < 22.35 and A < 84.5 2.6262 2.4926 2.2143 2.1686 30 33 28 28 0201 Traumatic brain injury M > 53.35 and C > 23.5 0.8140 0.6826 0.6021 0.5648 10 9 9 8 0202 Traumatic brain injury M > 44.25 and M < 53.35 and C > 23.5 1.0437 0.8753 0.7720 0.7241 12 10 11 9 0203 Traumatic brain injury M > 44.25 and C < 23.5 1.2487 1.0472 0.9236 0.8664 15 15 12 12 0204 Traumatic brain injury M > 40.65 and M < 44.25 1.3356 1.1201 0.9879 0.9267 15 16 13 13 0205 Traumatic brain injury M > 28.75 and M < 40.65 1.6381 1.3738 1.2116 1.1365 17 18 16 15 0206 Traumatic brain injury M > 22.05 and M < 28.75 2.1379 1.7930 1.5814 1.4833 23 22 21 20 0207 Traumatic brain injury M < 22.05 2.7657 2.3194 2.0457 1.9188 35 29 26 25 0301 Non-traumatic brain injury M > 41.05 1.1293 0.9536 0.8440 0.7764 12 12 11 10 0302 Non-traumatic brain injury M > 35.05 and M < 41.05 1.4729 1.2438 1.1008 1.0126 14 16 14 13 0303 Non-traumatic brain injury M > 26.15 and M < 35.05 1.7575 1.4841 1.3136 1.2083 20 19 17 16 0304 Non-traumatic brain injury M < 26.15 2.4221 2.0453 1.8103 1.6651 31 25 23 21 0401 Traumatic spinal cord injury M > 48.45 0.9891 0.8517 0.7656 0.6837 12 12 10 10 0402 Traumatic spinal cord injury M > 30.35 and M < 48.45 1.3640 1.1746 1.0558 0.9428 19 16 14 12 0403 Traumatic spinal cord injury M > 16.05 and M < 30.35 2.3743 2.0446 1.8379 1.6412 22 24 23 22 0404 Traumatic spinal cord injury M < 16.05 and A > 63.5 4.2567 3.6656 3.2950 2.9424 51 46 39 37 0405 Traumatic spinal cord injury M < 16.05 and A < 63.5 3.2477 2.7967 2.5139 2.2449 32 38 33 28 0501 Non-traumatic spinal cord injury M > 51.35 0.7705 0.6449 0.5641 0.5059 9 8 8 7 0502 Non-traumatic spinal cord injury M > 40.15 and M < 51.35 1.0316 0.8634 0.7553 0.6774 13 12 10 9 0503 Non-traumatic spinal cord injury M > 31.25 and M < 40.15 1.3676 1.1446 1.0013 0.8979 15 15 13 12 0504 Non-traumatic spinal cord injury M > 29.25 and M < 31.25 1.7120 1.4328 1.2534 1.1240 20 19 16 15 0505 Non-traumatic spinal cord injury M > 23.75 and M < 29.25 2.0289 1.6981 1.4855 1.3321 23 22 19 18 0506 Non-traumatic spinal cord injury M < 23.75 2.7607 2.3106 2.0212 1.8126 29 28 25 23 0601 Neurological M > 47.75 0.8965 0.7331 0.6966 0.6493 11 10 9 9 0602 Neurological M > 37.35 and M < 47.75 1.1925 0.9752 0.9267 0.8636 13 13 12 12 0603 Neurological M > 25.85 and M < 37.35 1.5266 1.2484 1.1863 1.1056 16 17 15 15 0604 Neurological M < 25.85 1.9539 1.5979 1.5183 1.4151 22 20 20 19 0701 Fracture of lower extremity M > 42.15 0.9055 0.7736 0.7265 0.6585 12 11 10 9 0702 Fracture of lower extremity M > 34.15 and M < 42.15 1.1757 1.0044 0.9432 0.8549 13 14 13 12 0703 Fracture of lower extremity M > 28.15 and M < 34.15 1.4636 1.2504 1.1742 1.0643 16 17 15 14 0704 Fracture of lower extremity M < 28.15 1.7962 1.5345 1.4410 1.3062 20 20 19 18 0801 Replacement of lower extremity joint M > 49.55 0.6561 0.5511 0.5109 0.4596 7 7 7 6 0802 Replacement of lower extremity joint M > 37.05 and M < 49.55 0.8570 0.7198 0.6673 0.6004 10 10 9 8 0803 Replacement of lower extremity joint M > 28.65 and M < 37.05 and A > 83.5 1.2707 1.0672 0.9894 0.8901 15 15 13 12 0804 Replacement of lower extremity joint M > 28.65 and M < 37.05 and A < 83.5 1.1069 0.9296 0.8618 0.7754 13 12 11 10 0805 Replacement of lower extremity joint M > 22.05 and M < 28.65 1.3937 1.1705 1.0852 0.9763 17 16 14 13 0806 Replacement of lower extremity joint M < 22.05 1.6726 1.4047 1.3023 1.1716 18 19 17 15 0901 Other orthopedic M > 44.75 0.8412 0.7658 0.6805 0.6090 10 11 10 9 0902 Other orthopedic M > 34.35 and M < 44.75 1.1054 1.0063 0.8942 0.8002 13 13 12 11 0903 Other orthopedic M > 24.15 and M < 34.35 1.4583 1.3276 1.1797 1.0557 18 19 16 15 0904 Other orthopedic M < 24.15 1.8281 1.6643 1.4788 1.3234 25 23 20 19 1001 Amputation, lower extremity M > 47.65 0.9638 0.8888 0.7931 0.7312 11 11 11 10 1002 Amputation, lower extremity M > 36.25 and M < 47.65 1.2709 1.1719 1.0457 0.9641 14 15 14 13 1003 Amputation, lower extremity M < 36.25 1.7876 1.6483 1.4709 1.3561 19 22 19 18 1101 Amputation, non-lower extremity M > 36.35 1.2544 1.0496 0.9189 0.8462 14 15 12 11 1102 Amputation, non-lower extremity M < 36.35 1.8780 1.5713 1.3756 1.2668 19 19 18 17 1201 Osteoarthritis M > 37.65 1.0184 0.8794 0.8106 0.7317 11 12 11 10 1202 Osteoarthritis M > 30.75 and M < 37.65 1.3181 1.1383 1.0492 0.9470 15 16 14 13 1203 Osteoarthritis M < 30.75 1.6238 1.4022 1.2925 1.1666 21 19 17 16 1301 Rheumatoid, other arthritis M > 36.35 1.0338 0.9617 0.8325 0.7358 12 13 11 10 1302 Rheumatoid, other arthritis M > 26.15 and M < 36.35 1.4324 1.3325 1.1534 1.0195 15 18 15 14 1303 Rheumatoid, other arthritis M < 26.15 1.8308 1.7032 1.4743 1.3032 22 21 20 18 1401 Cardiac M > 48.85 0.8172 0.7352 0.6396 0.5806 10 9 9 8 1402 Cardiac M > 38.55 and M < 48.85 1.1034 0.9926 0.8636 0.7839 12 13 12 11 1403 Cardiac M > 31.15 and M < 38.55 1.3735 1.2356 1.0750 0.9759 16 16 14 13 1404 Cardiac M < 31.15 1.7419 1.5671 1.3633 1.2376 21 20 18 16 1501 Pulmonary M > 49.25 0.9222 0.8995 0.7687 0.7397 11 12 10 10 1502 Pulmonary M > 39.05 and M < 49.25 1.1659 1.1371 0.9718 0.9352 12 15 12 12 1503 Pulmonary M > 29.15 and M < 39.05 1.4269 1.3917 1.1894 1.1445 12 17 15 15 1504 Pulmonary M < 29.15 1.8812 1.8348 1.5681 1.5089 21 22 20 18 1601 Pain syndrome M > 37.15 1.0065 0.8544 0.7731 0.6904 12 11 10 9 1602 Pain syndrome M > 26.75 and M < 37.15 1.3810 1.1724 1.0607 0.9473 15 17 14 13 1603 Pain syndrome M < 26.75 1.6988 1.4421 1.3048 1.1653 19 19 17 16 1701 Major multiple trauma without brain or spinal cord injury M > 39.25 1.0102 0.9634 0.8323 0.7321 12 12 11 10 1702 Major multiple trauma without brain or spinal cord injury M > 31.05 and M < 39.25 1.3305 1.2688 1.0962 0.9643 14 16 15 13 1703 Major multiple trauma without brain or spinal cord injury M > 25.55 and M < 31.05 1.5832 1.5098 1.3043 1.1474 17 20 17 16 1704 Major multiple trauma without brain or spinal cord injury M < 25.55 1.9808 1.8889 1.6319 1.4355 26 26 21 20 1801 Major multiple trauma with brain or spinal cord injury M > 40.85 1.2118 0.9832 0.8245 0.7282 15 13 12 10 1802 Major multiple trauma with brain or spinal cord injury M > 23.05 and M < 40.85 1.9385 1.5728 1.3190 1.1649 20 21 18 16 1803 Major multiple trauma with brain or spinal cord injury M < 23.05 3.4784 2.8222 2.3668 2.0903 43 33 30 27 1901 Guillian Barre M > 35.95 1.2362 1.0981 1.0677 0.9349 14 13 14 12 1902 Guillian Barre M > 18.05 and M < 35.95 2.3162 2.0574 2.0004 1.7515 27 25 24 23 1903 Guillian Barre M < 18.05 3.3439 2.9703 2.8881 2.5287 37 39 31 33 2001 Miscellaneous M > 49.15 0.8743 0.7387 0.6623 0.6047 10 10 9 8 2002 Miscellaneous M > 38.75 and M < 49.15 1.1448 0.9672 0.8671 0.7917 12 13 11 11 2003 Miscellaneous M > 27.85 and M < 38.75 1.4789 1.2495 1.1202 1.0227 16 16 15 14 2004 Miscellaneous M < 27.85 1.9756 1.6692 1.4964 1.3663 25 22 20 18 2101 Burns M > 0 2.1858 2.1858 1.5910 1.4762 29 24 19 17 5001 Short-stay cases, length of stay is 3 days or fewer 0.2201 2 5101 Expired, orthopedic, length of stay is 13 days or fewer 0.6351 8 5102 Expired, orthopedic, length of stay is 14 days or more 1.6002 22 5103 Expired, not orthopedic, length of stay is 15 days or fewer 0.7204 8 5104 Expired, not orthopedic, length of stay is 16 days or more 1.8771 24 Based on RAND's regression analysis of FY 2003 data, the best data available for analysis, we believe these changes will increase the accuracy of IRF PPS payments. VI. FY 2006 Federal Prospective Payment Rates A. Reduction of the Standard Payment Amount To Account for Coding Changes In the FY 2006 proposed rule (70 FR 30188), we proposed to reduce the standard payment amount by 1.9 percent to account for coding changes. Section 1886(j)(2)(C)(ii) of the Act requires the Secretary to adjust the per payment unit payment rate for IRF services to eliminate the effect of coding or classification changes that do not reflect real changes in case mix if the Secretary determines that changes in coding or classification of patients have resulted or will result in changes in aggregate payments under the classification system. As described below, in accordance with this section of the Act and based on research conducted by RAND under contract with us, we proposed to reduce the standard payment amount for patients treated in IRFs by 1.9 percent. We proposed to reduce the standard payment amount by 1.9 percent because RAND's regression analysis of calendar year 2002 data found that payments to IRFs were about $140 million more than expected during 2002 because of changes in the classification of patients in IRFs, and that a portion of this increase in payments was due to coding changes that do not reflect real changes in case mix. If IRF patients have more costly impairments, lower functional status, or more comorbidities, and thus require more resources in the IRF in 2002 than in 1999, we would consider this a real change in case mix. Conversely, if IRF patients have the same impairments, functional status, and comorbidities in 2002 as they did in 1999 but are coded differently resulting in higher payment, we consider this a case mix increase due to coding. We believe that changes in payment amounts should accurately reflect changes in IRFs' patient case mix (that is, the true cost of treating patients), and should not be influenced by changes in coding practices. Under the IRF PPS, payments for each Medicare rehabilitation patient are determined using a multi-step process. First, a patient is assigned to a particular CMG and a tier based on as many as four patient characteristics at admission: impairment, functional independence, comorbidities, and age. The amount of the payment for each patient is then calculated by taking the standard payment conversion factor ($12,958 in FY 2005) and adjusting it by multiplying by a relative weight, which depends on each patient's CMG and tier assignment. For example, an 80-year old hip replacement patient with a motor score between 47 and 54 and no comorbidities would be assigned to a particular CMG and tier based on these characteristics. The CMG and tier to which he is assigned would have an associated relative weight, in this case 0.5511 in FY 2005 (69 FR at 45725). This relative weight would be multiplied by the standard payment conversion factor of $12,958 to equal the payment of $7,141 in FY 2005 (0.5511 × $12,958 = $7,141). However, based on the following discussion, we are lowering the standard payment amount by 1.9 percent to account for coding changes, as opposed to real case mix changes, that have increased payments to IRFs. As described in the August 7, 2001 final rule, we contracted with RAND to analyze IRF data to support our efforts in developing the classification system and the IRF PPS. We have continued our contract with RAND to support us in developing potential refinements to the classification system and the PPS for the FY 2006 proposed rule (70 FR 30188) and this final rule. As part of this research, we asked RAND to examine changes in case mix and coding since the IRF PPS. To examine these changes, RAND compared 2002 data from the first year of implementation of the PPS with the 1999 (pre-PPS) data used to construct the IRF PPS. RAND's analysis of the 2002 data, as described in more detail below, demonstrates that changes in the types of patients going to IRFs and changes in coding both caused increases in payments to IRFs between 1999 and 2002. The 2002 data are more complete than the 1999 data that were first used to design the IRF PPS because they include all Medicare-covered IRF cases. Although the 1999 data we used in designing the original standard payment rate for the IRF PPS were the best available data we had at the time, they were based on a sample (64 percent) of IRF cases. In addition, such review was necessary because, as explained below, we believe that the implementation of the IRF PPS caused important changes in coding. The IRF PPS likely improved the accuracy and consistency of coding across IRFs, because of the educational programs that were implemented in 2001 and 2002 and because items that previously did not affect payments (such as comorbidities) became important factors for determining the PPS payments. Since these items now affect payments, there is greater incentive to code for them. In addition, the IRF PPS changed the instructions for coding some of the FIM items on the IRF-PAI, so that the same patient may have been correctly coded differently in 2002 than in 1999. Although we believe implementation of the IRF PPS resulted in changes to how the patient assessment data have been coded, implementation of the IRF PPS may have also caused changes in case mix because it increased incentives for IRFs to take patients with greater impairment, lower function, or comorbidities. Under the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) (Pub. L. 97-248), IRFs were paid on the basis of Medicare reasonable costs limited by a facility-specific target amount per discharge. IRFs were paid on a per discharge basis without per discharge adjustments being made for the impairments, functional status, or comorbidities of patients. Thus, IRFs had a strong incentive to admit less costly patients to ensure that the costs of treating patients did not exceed their TEFRA payments. Under the IRF PPS, however, IRFs' PPS payments are tied directly to the principle diagnosis and accompanying comorbidities of the patient. Thus, based on the characteristics of the patients (that is, impairments, functional status, and comorbidities), the more costly the patient is expected to be, the higher the PPS payment. Therefore, IRFs may have greater incentives than they had under TEFRA to admit more costly patients. Thus, in light of these concerns, RAND performed an analysis using IRF Medicare claims data matched with FIM and IRF-PAI data. Comparing 2002 data (post-PPS) with 1999 data (pre-PPS), RAND found that the observed case mix the expected costliness of patients-in IRFs increased by 3.4 percent between the two time periods. Thus, we paid 3.4 percent, or about $140 million, more than expected during 2002 because of changes in the classification of cases in IRFs. However, RAND found little evidence that the patients admitted to IRFs in 2002 had higher resource needs (that is, more impairments, lower functioning, or more comorbidities) than the patients admitted in 1999. In fact, most of the changes in case mix that RAND documented from the acute care hospital records implied that IRF patients should have been less costly to treat in 2002 than in 1999. For example, RAND found a 16 percent decrease in the proportion of patients treated in IRFs following acute hospitalizations for stroke, when it compared the results of the 2002 data with the 1999 data. Stroke patients tend to be relatively more costly than other types of patients for IRFs because they tend to require more intensive services than other types of patients. A decrease in the proportion of stroke patients relative to other types of patients, therefore, would likely contribute to a decrease in the overall expected costliness of IRF patients. RAND also found a 22 percent increase in the proportion of cases treated in IRFs following a lower extremity joint replacement. Lower extremity joint replacement patients tend to be relatively less costly for IRFs than other types of patients because their care needs tend to be less intensive than other types of patients. For this reason, the increase in the proportion of these patients treated in IRFs would suggest a decrease in the overall expected costliness of IRF patients. We asked RAND to quantify the amount of the case mix change that was due to real case mix change (that is, the extent to which IRF patients had more impairments, lower functioning, or more comorbidities) and the amount that was due to coding. However, while the data permit RAND to observe the total change in expected costliness of patients over time with some precision, estimating the amount of this total change that is real and the amount that is due to coding generally cannot be done with the same level of precision. Therefore, in order to quantify the amounts that were due to real case mix change and the amounts that were due to coding, RAND used two approaches to give a range of estimates within which the correct estimates would logically fall—(1) one that potentially underestimates the amount of real case mix change and overestimates the amount of case mix change due to coding; and
(2)one that potentially overestimates real change and underestimates change due to coding. These two approaches give us a range of estimates, which should logically border the actual amount of real case mix and coding change. The first approach uses the following assumptions: • Changes over time in characteristics recorded during the acute hospitalizations preceding the inpatient rehabilitation facility stay were real case mix changes (as acute care hospitals had little incentive to change their coding of patients in response to the IRF PPS); and • Changes over time in IRF coding that did not correspond with changes in the characteristics recorded during the acute hospitalizations were attributable to changes in IRF coding practices. To illustrate this point, suppose, for example, that the IRF records showed that there were a greater number of patients with a pulmonary condition in IRFs in 2002 than in 1999. Patients with a pulmonary condition tend to be relatively more costly for IRFs to treat than other types of patients, so an increase in the number of these patients would indicate an increase in the costliness of IRF patients (that is, an increase in IRFs' case mix). However, in 2002 IRFs had a much greater incentive to record if patients had a pulmonary condition than they did in 1999 because they got paid more for this condition in 2002, whereas they did not in 1999. Therefore, it is reasonable to expect that some of the increase in the number of patients with a pulmonary condition was due to the fact that IRFs were recording that condition for patients more frequently, not that there were really more patients of that type (although there may also have been some more patients of that type). To determine the extent to which IRFs may have just been coding that condition more often versus the extent to which there actually may have been more patients with a pulmonary condition going to IRFs than before, RAND looked at the one source of information that we believe was least likely to be influenced by the incentive to code patients with this condition more frequently in the IRF: the acute care hospital record from the stay preceding the IRF stay. We believe that the acute care hospitals are not likely to be influenced by IRF PPS policies that only affect IRF payments (that is, changes in IRF payment policies would not likely result in monetary benefits to the acute care hospitals). Thus, if RAND found a substantial increase in the number of IRF patients with a pulmonary condition in the acute care hospital before going to the IRF, it would be reasonable to assume that more patients with a pulmonary condition were going to IRFs (a real increase in case mix). However, if there was little change in the number of IRF patients with a pulmonary condition in the acute care hospital before going to the IRF, then we believe it is reasonable to assume that a portion of the increase in patients with a pulmonary condition in IRFs was due to the incentives to code more of these patients in the IRFs. We believe that this first approach shows that both factors, real case mix change and coding change, contributed to the amount of observed change in 2002, the first IRF PPS rate year. However, these estimates (based on the best available data) do not fully address all of the variables that may have contributed to the change in case mix. For example, the model does not account for the possibility that patients could develop impairments, functional problems, or comorbidities after they leave the acute care hospital (prior to the IRF admission) that would make them more costly when they are in the IRF. We note that the introduction of a new payment system may have interrelated effects on providers as they adapt to new (or perceived) program incentives. Thus, an analysis of first year experience may not be fully representative of providers' behavior under a fully implemented system. In addition, hospital coding practices may change at a different rate in facilities where the IRF is a unit of an acute care hospital compared with freestanding IRF hospitals. Finally, we want to ensure that the rate reduction will not have an adverse effect on beneficiaries' access to IRF care. For the reasons described above, we believed and continue to believe that we should provide some flexibility to account for the possibility that some of the observed changes may be attributable to other than coding changes. Thus, in determining the amount of the reduction in the standard payment amount, we examined RAND's second approach that recognizes the difficulty of precise measurement of real case mix and coding changes. Using this second approach, RAND developed an analytical procedure that allowed them to distinguish more fully between real case mix change and coding change based on patient characteristics. In part, this second approach involves analyzing some specific examples of coding that we know have changed over time, such as direct indications of improvements in impairment coding, changes in coding instruction for bladder and bowel functioning, and dramatic increases in coding of certain conditions that affect patients' placement into tiers (resulting in higher payments). Using the two approaches, RAND found that real case mix changes in IRFs over this period ranged from a decrease of 2.4 percent (using the first approach) to an increase of 1.5 percent (using the second approach). This suggests that coding changes accounted for between 1.9 percent (if real case mix increased by 1.5 percent (that is, 3.4 percent minus 1.5 percent)) and 5.8 percent (if real case mix decreased by 2.4 percent (that is, 3.4 percent plus 2.4 percent)) of the increase in aggregate payments for 2002 compared with 1999. Thus, RAND recommended decreasing the standard per discharge payment amount by between 1.9 and 5.8 percent to adjust for the coding changes. We proposed to reduce the standard payment amount by the lower of these two numbers, 1.9 percent, because we believe it is a reasonable estimate for the amount of coding change, based on RAND's analysis of direct indications of coding change. That is, RAND analyzed specific examples of coding that we know have changed over time, such as direct indications of improvements in impairment coding, changes in coding instructions for bladder and bowel functioning, and dramatic increases in coding of certain conditions that affect patients' placement into tiers (resulting in higher payments) in deriving the 1.9 percent estimate. We considered proposing a reduction to the standard payment amount by an amount up to 5.8 percent because RAND's first approach suggested that coding changes could possibly have been responsible for up to 5.8 percent of the observed increase in IRFs' case mix. Furthermore, a separate analysis by RAND found that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. This suggests that we could have proposed a reduction greater than 1.9 and up to 5.8 percent. We decided to propose a reduction of 1.9 percent, the lowest possible amount of change attributable to coding change. The analyses described here are only the first of an ongoing series of studies to evaluate the existence and extent of payment increases due to coding changes. We will continue to review the need for any further reduction in the standard payment amount in subsequent years as part of our overall monitoring and evaluation of the IRF PPS. Therefore, for FY 2006, we proposed to reduce the standard payment amount by the lowest amount (1.9 percent) attributable to coding changes. We believe this approach, which is supported by RAND's analysis of the data, will adequately adjust for the increased payments to IRFs caused by purely coding changes, but will still provide the flexibility to account for the possibility that some of the observed changes in case mix may be attributed to other than coding changes. Furthermore, we chose to propose a 1.9 percent reduction in the standard payment amount to recognize that IRFs' current cost structures may be changing as they strive to comply with other recent Medicare policy changes, such as the criteria for IRF classification commonly known as the “75 percent rule.” Public comments and our responses on the proposed reduction of the standard payment amount to account for coding changes are summarized below. *Comment:* Several commenters objected to CMS implementing an across the board reduction to payment rates to account for coding changes until the full impact of CMS's recent decision to enforce the 75 percent rule is known. These commenters generally also noted that RAND's analysis was based on 2002 data, which was the year facilities were transitioning to the IRF PPS. *Response:* We believe a 1.9 percent reduction to the standard payment amount to account for coding changes is appropriate at this time for the following reasons. First, CMS is required by statute (section 1886(j)(2)(C)(ii) of the Act) to adjust payment rates for IRF services if we find evidence that changes in coding (that do not reflect real changes in case mix) have resulted or will result in changes in aggregate payments under the IRF classification system. As discussed in the proposed rule and above, CMS contracted with RAND to examine changes in case mix and coding since the IRF PPS, using the most current available data. Using regression analysis of calendar year 2002 data, RAND found that payments to IRFs were about $140 million more than expected during 2002 because of changes in the classification of patients in IRFs, and that a portion of this increase in payments was due to coding changes that do not reflect real changes in case mix. Specifically, RAND found that IRF payments were at least 1.9 percent higher because of changes in coding, based on direct indications of coding changes. Thus, we believe we have a responsibility to conform to the requirements of the statute and accordingly adjust payment rates for IRFs. Second, analyses by RAND and by CMS's Office of the Actuary have both shown high Medicare margins among IRFs since implementation of the IRF PPS. RAND's analysis found that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. An analysis by CMS's Office of the Actuary supports these results. Given the evidence of high Medicare margins among IRFs, we believe that a 1.9 percent decrease in rates to account for coding changes will not affect beneficiary access to IRF services because IRFs will continue to be paid adequately to reflect the cost of resources needed to treat Medicare beneficiaries. Furthermore, we continue to find evidence that enforcement of the 75 percent rule between July 2004 and July 2005 at the 50 percent compliance threshold did not have as large an impact on patients' access to IRF care as some industry analysts contend. At this time, CMS is finding no significant problems regarding access to care in IRFs; to the contrary, the trend is toward increasing utilization in all settings. For example, when we compared calendar years 2003 to 2004, we found that the number of IRF cases increased about 1.2 percent. We do not believe that beneficiary access to rehabilitation care will be unduly affected when IRFs have to meet a compliance threshold of 60 percent for cost reporting periods starting between July 1, 2005 and June 30, 2006. Based on the current available evidence, we do not believe that simultaneously reducing the standard payment amount by 1.9 percent to adjust for coding changes and phasing in enforcement of the 75 percent rule will have an undue effect on beneficiary access to IRF services. However, we will closely monitor the available data to ensure that beneficiaries' access to rehabilitation care is maintained. Finally, we believe that the fact that 2002 was the year IRFs were transitioning to the IRF PPS further supports the finding that at least 1.9 percent of the payments in that year were due to coding changes and not to real changes in case mix. IRFs had not fully transitioned to the full Federal payment rates in 2002. Therefore, they were likely only beginning to adjust to the new incentives of the IRF PPS and had only begun changing their coding practices. Had the full Federal payment rates for 2002 been fully implemented in 2002, then providers might have changed their coding practices even more than they did in 2002. Accordingly, RAND was likely only observing the initial provider responses to the new IRF PPS. Because RAND's estimate of the 1.9 percent is based on direct indication of coding changes that occurred in 2002, we believe that the 1.9 percent proposed reduction to the standard payment amount is appropriate at this time. In the future, we will examine later years of data in which providers were fully subject to the IRF PPS and make any necessary adjustments to the standard payment amount as we are required to do by statute to eliminate the effect on payments of coding or classification changes that do not reflect real changes in case mix. *Comment:* A few commenters questioned RAND's assumption that characteristics of the patients recorded during the acute hospitalizations preceding the IRF stays are relevant for the condition of those same patients in the IRF stays. *Response:* RAND's methodology in which they assumed that patient characteristics recorded during the acute hospitalizations preceding the IRF stays were relevant for the case mix of patients in the IRF stays produced a much higher estimate of the amount of coding change than we proposed to adopt in the FY 2006 proposed rule (70 FR 30188, 30221 though 30222). This methodology suggested a 5.8 percent reduction to the standard payment amount to account for coding change, as discussed above. As explained in the FY 2006 proposed rule (70 FR 30188, 30222), we used the estimate of the amount of coding change from RAND's second approach, which involved analyzing specific examples of coding that we know have changed over time, such as direct indications of improvements in impairment coding, changes in coding instructions for bladder and bowel functioning, and dramatic increases in coding of certain conditions that affect patients' placement into tiers (resulting in higher payments). This second approach produced the 1.9 percent estimate we proposed to use to adjust the standard payment amount. *Comment:* One commenter requested that CMS conduct educational efforts for providers that instruct providers on how to code patients appropriately, rather than reducing the standard payment amount by 1.9 percent. *Response:* As we discussed earlier in detail in this final rule under section VI.A, we proposed to reduce the standard payment amount by 1.9 percent to account for the effects of coding changes that occurred between 1999 and 2002 that resulted in higher than expected payments to IRFs, beginning in 2002. Section 1886(j)(2)(C)(ii) of the Act requires the Secretary to make such an adjustment to eliminate the effects of coding or classification changes that do not reflect real changes in case mix if the Secretary determines that changes in coding or classification of patients have resulted or will result in changes in aggregate payments under the classification system. RAND's regression analysis of calendar year 2002 data found that payments to IRFs were about $140 million more than expected during 2002 because of changes in the classification of patients in IRFs, and that a portion of this increase was due to coding changes that do not reflect real changes in case mix. Any provider education and training that CMS would conduct now would not revise RAND's finding that, based upon calendar year 2002 data, coding changes occurred that did not reflect real changes in case mix. However, we agree with the commenter that provider education and training is important so that providers correctly code patients in IRFs. For this reason, CMS conducted extensive provider training in 2002 when the IRF PPS was first implemented, and we will continue to educate providers as to how to code the IRF-PAI items through our IRF-PAI coding help desk. We are open to considering other methods of provider education to encourage accurate provider coding. The primary resource providers should refer to is the IRF-PAI manual when they have questions regarding the correct way to code patients in IRFs. This manual is available on CMS's Web site at *http://www.cms.hhs.gov/providers/IRFPPS/IRFPAI-MANUAL040104.asp* and is updated regularly. The 1.9 percent reduction adjustment to the standard payment amount is not intended to penalize providers for coding changes, but to reflect the statutory mandate to adjust IRF PPS payments when the Secretary determines that changes in coding or classification of patients have resulted or will result in changes in aggregate payments under the classification system. *Comment:* One commenter questioned whether, in doing the analysis described above, RAND accounted for the 1.16 percent behavioral offset adjustment that CMS applied to the initial IRF PPS payment rates in the August 7, 2001 final rule (66 FR 41316). *Response:* As explained in detail in RAND's report entitled “Preliminary Analyses of Changes in Coding and Case Mix Under the Inpatient Rehabilitation Facility Prospective Payment System” (available on RAND's Web site at *http://www.rand.org/publications/TR/TR213/* ), RAND accounted for the 1.16 percent behavioral offset adjustment when they estimated the amount of observed case mix change that was due to real case mix change and the amount that was due to coding change. The range of estimates for the amount of case mix and coding change that RAND developed and that is reported above in this final rule contains an adjustment to account for this behavioral offset. If RAND had not taken account of the behavioral offset, their estimates of the amount of observed case mix change that was due to coding change would have been larger than noted in both the FY 2006 proposed rule (70 FR 30188) and in this final rule. *Comment:* One commenter suggested that the proposed 1.9 percent reduction of the standard payment amount could be implemented without undue hardship for facilities. *Response:* We agree with the commenter. RAND estimates that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. This suggests that IRF payments are likely more than adequate to support this type of adjustment for coding changes. Final Decision: After carefully considering all the comments we received on the proposed 1.9 percent reduction to the standard payment amount to adjust for coding changes between 1999 and 2002 that did not reflect real changes in case mix and resulted in increases in aggregate payments under the IRF classification system, we are finalizing our proposal to adopt the adjustment described above. In accordance with section 1886(j)(2)(C)(ii) of the Act, and based on RAND's analysis of 2002 data compared with 1999 data, we believe this change is necessary to allow payment amounts to accurately reflect changes in IRFs' patient case mix (that is, the true cost of treating patients), and to ensure that they are not influenced by changes in coding practices. We are finalizing our methodology for reducing the standard payment amount by 1.9 percent. First, we update the FY 2005 standard payment conversion factor by the estimated FY 2006 market basket of 3.6 percent (estimated for this final rule) to get the standard payment amount for FY 2006 ($12,958*1.036 = $13,425). Next, we multiply the FY 2006 standard payment amount by 0.981, which reduces the standard payment amount by 1.9 percent ($13,425*0.981 = $13,169). In section VI.B.7 of this final rule, we will further adjust the $13,169 by the budget neutrality factors for the wage index and the other final changes outlined in this final rule that will result in the FY 2006 standard payment conversion factor. In section VI.B.7 of this final rule, we provide a step-by-step calculation that results in the FY 2006 standard payment conversion factor. B. Adjustments To Determine the FY 2006 Standard Payment Conversion Factor 1. Market Basket Used for IRF Market Basket Index Under the broad authority of section 1886(j)(3)(C) of the Act, the Secretary establishes an increase factor that reflects changes over time in the prices of an appropriate mix of goods and services included in covered IRF services, which is referred to as a market basket index. The market basket needs to include both operating and capital. Thus, although the Secretary is required to develop an increase factor under section 1886(j)(3)(C) of the Act, this provision gives the Secretary discretion in the design of such factor. The index currently used to update payments for rehabilitation facilities is the excluded hospital including capital market basket. This market basket is based on 1997 Medicare cost report data and includes Medicare-participating rehabilitation (IRF), LTCH, psychiatric (IPF), cancer, and children's hospitals. We are unable to create a separate market basket specifically for rehabilitation hospitals due to the small number of facilities and the limited data that are provided (for instance, only about 25 percent of rehabilitation facility cost reports reported contract labor cost data for 2002). Since all IRFs are paid under the IRF PPS, nearly all LTCHs are paid under the LTCH PPS, and IPFs for cost reporting periods beginning on or after January 1, 2005 will be paid under the IPF PPS, in the FY 2006 proposed rule (70 FR 30188), we proposed and are finalizing to update payments for rehabilitation facilities using a market basket reflecting the operating and capital cost structures for IRFs, IPFs, and LTCHs, hereafter referred to as the RPL (rehabilitation, psychiatric, long-term care) market basket. As proposed and for this final rule, we are excluding children's and cancer hospitals from the RPL market basket because their payments are based entirely on reasonable costs subject to rate-of-increase limits established under the authority of section 1886(b) of the Act, which is implemented in § 413.40 of the regulations. They are not reimbursed under a prospective payment system. Also, the FY 2002 cost structures for children's and cancer hospitals are noticeably different than the cost structures of the IRFs, IPFs, and LTCHs. The services offered in IRFs, IPFs, and LTCHs are typically more labor-intensive then those offered in cancer and children's hospitals. Therefore, the compensation cost weights for IRFs, IPFs, and LTCHs are larger than those in cancer and children's hospitals. In addition, the depreciation cost weights for IRFs, IPFs, and LTCHs are noticeably smaller than those for children's and cancer hospitals. In the following discussion, we provide a background on market baskets and describe the methodologies we proposed and are finalizing for purposes of determining the operating and capital portions of the FY 2002-based RPL market basket. a. Overview of the RPL Market Basket The RPL market basket is a fixed weight, Laspeyres-type price index that is constructed in three steps. First, a base period is selected (in this case, FY 2002), and total base period expenditures are estimated for a set of mutually exclusive and exhaustive spending categories based upon type of expenditure. Then the proportion of total operating costs that each category represents is determined. These proportions are called cost or expenditure weights. Second, each expenditure category is matched to an appropriate price or wage variable, referred to as a price proxy. In nearly every instance, these price proxies are price levels derived from publicly available statistical series that are published on a consistent schedule, preferably at least on a quarterly basis. Finally, the expenditure weight for each cost category is multiplied by the level of its respective price proxy for a given period. The sum of these products (that is, the expenditure weights multiplied by their price levels) for all cost categories yields the composite index level of the market basket in a given period. Repeating this step for other periods produces a series of market basket levels over time. Dividing an index level for a given period by an index level for an earlier period produces a rate of growth in the input price index over that time period. A market basket is described as a fixed-weight index because it answers the question of how much it would cost, at another time, to purchase the same mix of goods and services purchased to provide hospital services in a base period. The effects on total expenditures resulting from changes in the quantity or mix of goods and services (intensity) purchased subsequent to the base period are not measured. In this manner, the market basket measures only the pure price change. Only when the index is rebased would the quantity and intensity effects be captured in the cost weights. Therefore, we rebase the market basket periodically so the cost weights reflect changes in the mix of goods and services that hospitals purchase (hospital inputs) to furnish patient care between base periods. The terms rebasing and revising, while often used interchangeably, actually denote different activities. Rebasing means moving the base year for the structure of costs of an input price index (for example, we are shifting the base year cost structure from FY 1997 to FY 2002). Revising means changing data sources, methodology, or price proxies used in the input price index. We are rebasing and revising the market basket used to update the IRF PPS. b. Methodology for Operating Portion of the RPL Market Basket As proposed, the operating portion of the FY 2002-based RPL market basket, which is being adopted in this final rule, consists of several major cost categories derived from the FY 2002 Medicare cost reports for IRFs, IPFs, and LTCHs: Wages, drugs, professional liability insurance and a residual. We choose FY 2002 as the base year because we believe this is the most recent, relatively complete year of Medicare cost report data. Due to insufficient Medicare cost report data for IRFs, IPFs, and LTCHs, cost weights for benefits, contract labor, and blood and blood products were developed using the FY 2002-based IPPS market basket (Section IV. Rebasing and Revision of the Hospital Market Baskets IPPS Hospital Rule for FY 2006), which we explain in more detail later in this section. For example, less than 30 percent of IRFs, IPFs, and LTCHs reported benefit cost data in FY 2002. We have noticed an increase in cost data for these expense categories over the last 4 years. The next time we propose to rebase the RPL market basket, there may be sufficient IRFs, IPFs, and LTCHs cost report data to develop the weights for these expenditure categories. Since the cost weights for the RPL market basket are based on facility costs, as proposed and for this final rule, we are limiting our sample to hospitals with a Medicare average length of stay within a comparable range of the total facility average length of stay. We believe this provides a more accurate reflection of the structure of costs for Medicare treatments. Our goal is to measure cost shares that are reflective of case mix and practice patterns associated with providing services to Medicare beneficiaries. As proposed, for this final rule, we are using those cost reports for IRFs and LTCHs whose Medicare average length of stay is within 15 percent (that is, 15 percent higher or lower) of the total facility average length of stay for the hospital. This is the same edit applied to the FY 1992 and FY 1997 excluded hospital with capital market baskets. We are using 15 percent because it includes those LTCHs and IRFs whose Medicare LOS is within approximately 5 days of the facility length of stay. As proposed, for this final rule, we use a less stringent measure of Medicare length of stay for IPFs whose average length of stay is within 30 or 50 percent (depending on the total facility average length of stay) of the total facility length of stay. This less stringent edit allows us to increase our sample size by over 150 reports and produce a cost weight more consistent with the overall facility. The edit we applied to IPFs when developing the FY-1997 based excluded hospital with capital market basket was based on the best available data at the time. The detailed cost categories under the residual (that is, the remaining portion of the market basket after excluding wages and salaries, drugs, and professional liability cost weights) are derived from the FY 2002-based IPPS market basket and the 1997 Benchmark Input-Output Tables published by the Bureau of Economic Analysis, U.S. Department of Commerce. The FY 2002-based IPPS market basket is developed using FY 2002 Medicare hospital cost reports with the most recent and detailed cost data. The 1997 Benchmark I-O is the most recent, comprehensive source of cost data for all hospitals. Consistent with the proposed rule, cost weights for benefits, contract labor, and blood and blood products for this final rule were derived using the FY 2002-based IPPS market basket. For example, the ratio of the benefit cost weight to the wages and salaries cost weight in the FY 2002-based IPPS market basket was applied to the RPL wages and salaries cost weight to derive a benefit cost weight for the RPL market basket. As proposed and for this final rule, the remaining operating cost categories were derived using the 1997 Benchmark Input-Output Tables aged to 2002 using relative price changes. (The methodology we used to age the data involves applying the annual price changes from the price proxies to the appropriate cost categories. We repeat this practice for each year.) Therefore, this methodology results in roughly 59 percent of the RPL market basket is accounted for by wages, drugs and professional liability insurance data from FY 2002 Medicare cost report data for IRFs, LTCHs, and IPFs. Table 5 below sets forth the complete FY 2002-based RPL market basket including cost categories, weights, and price proxies. For comparison purposes, the corresponding FY 1997-based excluded hospital with capital market basket is listed as well. As proposed and for this final rule, wages and salaries are 52.895 percent of total costs for the FY 2002-based RPL market basket compared to 47.335 percent for FY 1997-based excluded hospital with capital market basket. Employee benefits are 12.982 percent for the FY 2002-based RPL market basket compared to 10.244 percent for FY 1997-based excluded hospital with capital market basket. As a result, compensation costs (wages and salaries plus employee benefits) for the FY 2002-based RPL market basket are 65.877 percent of costs compared to 57.579 percent for the FY 1997-based excluded hospital with capital market basket. Of the 8 percentage point difference between the compensation shares, approximately 3 percentage points are due to the new base year (FY 2002 instead of FY 1997), 3 percentage points are due to the revised length of stay edit and the remaining 2 percentage points are due to the exclusion of other hospitals (that is, only including IRFs, IPFs, and LTCHs in the market basket). Following the table is a summary outlining the choice of the proxies that we proposed and we are finalizing for the operating portion of the RPL market basket. The price proxies for the capital portion are described in more detail in the capital methodology section. (See section III.B.1.c of this rule.) Table 5.—FY 2002-Based RPL Market Basket Cost Categories, Weights and Proxies With FY 1997-Based Excluded Hospital With Capital Market Basket Used for Comparison Expense categories FY 1997-based excluded hospital with capital market basket FY 2002-based RPL market basket FY 2002 RPL market basket price proxies Total 100.000 100.000 Compensation 57.579 65.877 Wages and Salaries * 47.335 52.895 ECI—Wages and Salaries, Civilian Hospital Workers. Employee Benefits * 10.244 12.982 ECI—Benefits, Civilian Hospital Workers. Professional fees Non-Medical * 4.423 2.892 ECI—Compensation for Professional, Specialty & Technical Workers. Utilities 1.180 0.656 Electricity 0.726 0.351 PPI—Commercial Electric Power. Fuel Oil, Coal, etc. 0.248 0.108 PPI Refined Petroleum Products. Water and Sewage 0.206 0.197 CPI-U—Water & Sewage Maintenance. Professional Liability Insurance 0.733 1.161 CMS—Professional Liability Premium Index. All Other Products and Services 27.117 19.265 All Other Prod. Products 17.914 13.323 Pharmaceuticals 6.318 5.103 PPI Prescription Drugs. Food: Direct Purchase 1.122 0.873 PPI Processed Foods & Feeds. Food: Contract Service 1.043 0.620 CPI-U Food Away From Home. Chemicals 2.133 1.100 PPI Industrial Chemicals. Blood and Blood Products ** 0.748 Medical Instruments 1.795 1.014 PPI Medical Instruments & Equipment. Photographic Supplies 0.167 0.096 PPI Photographic Supplies. Rubber and Plastics 1.366 1.052 PPI Rubber & Plastic Products. Paper Products 1.110 1.000 PPI Converted Paper & Paperboard Products. Apparel 0.478 0.207 PPI Apparel. Machinery and Equipment 0.852 0.297 PPI Machinery & Equipment. Miscellaneous Products 0.783 1.963 PPI Finished Goods less Food and Energy. All Other Services 9.203 5.942 Telephone 0.348 0.240 CPI-U—Telephone Services. Postage 0.702 0.682 CPI-U—Postage. All Other: Labor Intensive* 4.453 2.219 ECI—Compensation for Private Service Occupations. All Other: Non-Labor Intensive 3.700 2.800 CPI-U All Items. Capital-Related Costs 8.968 10.149 Depreciation 5.586 6.186 Fixed Assets 3.503 4.250 Boeckh Institutional Construction: 23 year useful life. Movable Equipment 2.083 1.937 WPI—Machinery & Equipment: 11 year useful life. Interest Costs 2.682 2.775 Non-profit 2.280 2.081 Average yield on domestic municipal bonds (Bond Buyer 20 bonds)—vintage weighted (23 years). For-profit 0.402 0.694 Average yield on Moody's Aaa bonds—vintage weighted (23 years). Other Capital-Related Costs 0.699 1.187 CPI-U—Residential Rent. * Labor-related. ** Blood and blood related products is included in miscellaneous products. Note: Due to rounding, weights may not sum to total. Below we provide the proxies that we are using for the FY 2002-based RPL market basket in this final rule. We made no changes to the proposed price proxies in this final rule. With the exception of the Professional Liability proxy, all the price proxies for the operating portion of the RPL market basket are based on Bureau of Labor Statistics
(BLS)data and are grouped into one of the following BLS categories: • Producer Price Indexes—Producer Price Indexes
(PPIs)measure price changes for goods sold in other than retail markets. PPIs are preferable price proxies for goods that hospitals purchase as inputs in producing their outputs because the PPIs would better reflect the prices faced by hospitals. For example, we use a special PPI for prescription drugs, rather than the Consumer Price Index
(CPI)for prescription drugs because hospitals generally purchase drugs directly from the wholesaler. The PPIs that we use measure price change at the final stage of production. • Consumer Price Indexes—Consumer Price Indexes
(CPIs)measure change in the prices of final goods and services bought by the typical consumer. Because they may not represent the price faced by a producer, we used CPIs only if an appropriate PPI was not available, or if the expenditures were more similar to those of retail consumers in general rather than purchases at the wholesale level. For example, the CPI for food purchased away from home is used as a proxy for contracted food services. • Employment Cost Indexes—Employment Cost Indexes
(ECIs)measure the rate of change in employee wage rates and employer costs for employee benefits per hour worked. These indexes are fixed-weight indexes and strictly measure the change in wage rates and employee benefits per hour. Appropriately, they are not affected by shifts in employment mix. We evaluated the price proxies using the criteria of reliability, timeliness, availability, and relevance. Reliability indicates that the index is based on valid statistical methods and has low sampling variability. Timeliness implies that the proxy is published regularly, at least once a quarter. Availability means that the proxy is publicly available. Finally, relevance means that the proxy is applicable and representative of the cost category weight to which it is applied. The CPIs, PPIs, and ECIs selected by us to be used in this regulation meet these criteria. We note that the proxies are the same as those used for the FY 1997-based excluded hospital with capital market basket. Because these proxies meet our criteria of reliability, timeliness, availability, and relevance, we believe they continue to be the best measure of price changes for the cost categories. For further discussion on the FY 1997-based excluded hospital with capital market basket, see the IPPS final rule (67 FR at 50042), published in the **Federal Register** on August 1, 2002. Wages and Salaries For measuring the price growth in the FY 2002-based RPL market basket, we use the ECI for wages and salaries for civilian hospital workers as the proxy for wages for measuring the price growth of wages in the FY 2002-based RPL market basket. Employee Benefits The FY 2002-based RPL market basket uses the ECI for employee benefits for civilian hospital workers. Nonmedical Professional Fees The ECI for compensation for professional and technical workers in private industry is applied to this category since it includes occupations such as management and consulting, legal, accounting and engineering services. Fuel, Oil, and Gasoline The percentage change in the price of gas fuels as measured by the PPI (Commodity Code #0552) is applied to this component. Electricity The percentage change in the price of commercial electric power as measured by the PPI (Commodity Code #0542) is applied to this component. Water and Sewerage The percentage change in the price of water and sewage maintenance as measured by the Consumer Price Index
(CPI)for all urban consumers (CPI Code # CUUR0000SEHG01) is applied to this component. Professional Liability Insurance The FY 2002-based RPL market basket uses the percentage change in the hospital professional liability insurance
(PLI)premiums as estimated by the CMS Hospital professional liability index for the proxy of this category. In the FY 1997-based excluded hospital with capital market basket, the same price proxy was used. We continue to research options for improving our proxy for professional liability insurance. This research includes exploring various options for expanding our current survey, including the identification of another entity that would be willing to work with us to collect more complete and comprehensive data. We are also exploring other options such as third party or industry data that might assist us in creating a more precise measure of PLI premiums. At this time we have not identified a preferred option, therefore, no change is implemented in the proxy in this final rule. Pharmaceuticals The percentage change in the price of prescription drugs as measured by the PPI (PPI Code #PPI32541DRX) is used as a proxy for this category. This is a special index produced by BLS and is the same proxy used in the 1997-based excluded hospital with capital market basket. Food, Direct Purchases The percentage change in the price of processed foods and feeds as measured by the PPI (Commodity Code #02) is applied to this component. Food, Contract Services The percentage change in the price of food purchased away from home as measured by the CPI for all urban consumers (CPI Code #CUUR0000SEFV) is applied to this component. Chemicals The percentage change in the price of industrial chemical products as measured by the PPI (Commodity Code #061) is applied to this component. While the chemicals hospital's purchase include industrial as well as other types of chemicals, the industrial chemicals component constitutes the largest proportion by far. Thus, we believe that commodity Code #061 is the appropriate proxy. Medical Instruments The percentage change in the price of medical and surgical instruments as measured by the PPI (Commodity Code #1562) is applied to this component. Photographic Supplies The percentage change in the price of photographic supplies as measured by the PPI (Commodity Code #1542) is applied to this component. Rubber and Plastics The percentage change in the price of rubber and plastic products as measured by the PPI (Commodity Code #07) is applied to this component. Paper Products The percentage change in the price of converted paper and paperboard products as measured by the PPI (Commodity Code #0915) is used. Apparel The percentage change in the price of apparel as measured by the PPI (Commodity Code #381) is applied to this component. Machinery and Equipment The percentage change in the price of machinery and equipment as measured by the PPI (Commodity Code #11) is applied to this component. Miscellaneous Products The percentage change in the price of all finished goods less food and energy as measured by the PPI (Commodity Code #SOP3500) is applied to this component. Using this index removes the double-counting of food and energy prices, which are captured elsewhere in the market basket. The weight for this cost category is higher than in the 1997-based index because the weight for blood and blood products (1.322) is added to it. In the 1997-based excluded hospital with capital market basket we included a separate cost category for blood and blood products, using the BLS Producer Price Index for blood and derivatives as a price proxy. A review of recent trends in the PPI for blood and derivatives suggests that its movements may not be consistent with the trends in blood costs faced by hospitals. While this proxy did not match exactly with the product hospitals are buying, its trend over time appears to be reflective of the historical price changes of blood purchased by hospitals. However, an apparent divergence in trends in the PPI for blood and derivatives and trends in blood costs faced by hospitals over recent years led us to reevaluate whether the PPI for blood and derivatives was an appropriate measure of the changing price of blood. As discussed in the FY 2006 proposed rule (70 FR 30188), we ran test market baskets classifying blood in 3 separate cost categories: Blood and blood products, contained within chemicals as was done for the 1992-based excluded hospital with capital market basket, and within miscellaneous products. These categories use as proxies the following PPIs: the PPI for blood and blood products, the PPI for chemicals, and the PPI for finished goods less food and energy, respectively. Of these three proxies, the PPI for finished goods less food and energy moved most like the recent blood cost and price trends. In addition, the impact on the overall market basket by using different proxies for blood was negligible, mostly due to the relatively small weight for blood in the market basket. Therefore, as proposed, for this final rule, we are using the PPI for finished goods less food and energy for the blood proxy because we believe it would best be able to proxy only price changes rather than nonprice factors such as changes in quantities or required tests associated with blood purchased by hospitals. We will continue to evaluate this proxy for its appropriateness and will explore the development of alternative price indexes to proxy the price changes associated with this cost. Telephone The percentage change in the price of telephone services as measured by the CPI for all urban consumers (CPI Code #CUUR0000SEED) is applied to this component. Postage The percentage change in the price of postage as measured by the CPI for all urban consumers (CPI Code #CUUR0000SEEC01) is applied to this component. All Other Services, Labor Intensive The percentage change in the ECI for compensation paid to service workers employed in private industry is applied to this component. All Other Services, Nonlabor Intensive The percentage change in the all-items component of the CPI for all urban consumers (CPI Code #CUUR0000SA0) is applied to this component. c. Methodology for Capital Portion of the RPL Market Basket Unlike for the operating costs of the FY 2002-based RPL market basket, we did not have IRFs, IPFs, and LTCHs FY 2002 Medicare cost report data for the capital cost weights, due to a change in the FY 2002 cost reporting requirements. Rather, as was proposed, for this final rule we are using these hospitals' expenditure data for the capital cost categories of depreciation, interest, and other capital expenses for the most recent year available (FY 2001), and aging the data to a FY 2002 base year using relevant price proxies. As proposed, for this final rule we calculated weights for the RPL market basket capital costs using the same set of Medicare cost reports used to develop the operating share for IRFs, IPFs, and LTCHs. As proposed, for this final rule the resulting capital weight for the FY 2002 base year is 10.149 percent. This is based on FY 2001 Medicare cost report data for IRFs, IPFs, and LTCHs, aged to FY 2002 using relevant price proxies. Lease expenses are not a separate cost category in the market basket, but are distributed among the cost categories of depreciation, interest, and other, reflecting the assumption that the underlying cost structure of leases is similar to capital costs in general. We assumed 10 percent of lease expenses are overhead and assigned them to the other capital expenses cost category as overhead. We base this assignment of 10 percent of lease expenses to overhead on the common assumption that overhead is 10 percent of costs. The remaining lease expenses were distributed to the three cost categories based on the weights of depreciation, interest, and other capital expenses not including lease expenses. Depreciation contains two subcategories: Building and fixed equipment and movable equipment. As proposed, for this final rule the split between building and fixed equipment and movable equipment was determined using the FY 2001 Medicare cost reports for IRFs, IPFs, and LTCHs. This methodology was also used to compute the 1997-based index (67 FR at 50044). As proposed, for this final rule total interest expense cost category is split between the government/nonprofit and for-profit hospitals. The 1997-based excluded hospital with capital market basket allocated 85 percent of the total interest cost weight to the government/nonprofit interest, proxied by average yield on domestic municipal bonds, and 15 percent to for-profit interest, proxied by average yield on Moody's Aaa bonds. As proposed, for this final rule we derived the split using the relative FY 2001 Medicare cost report data for IPPS hospitals on interest expenses for the government/nonprofit and for-profit hospitals. Due to insufficient Medicare cost report data for IRFs, IPFs and LTCHs, as proposed and for this final rule, we used the same split used in the IPPS capital input price index, which is 75-25. We believe it is important that this split reflects the latest relative cost structure of interest expenses for hospitals. Therefore, as proposed in the FY 2006 proposed rule (70 FR 30188) we are using a 75-25 split to allocate interest expenses to government/nonprofit and for-profit. See the IPPS Rule for FY 2006, Section IV.D, Capital Input Price Index Section (70 FR 23406). Since capital is acquired and paid for over time, capital expenses in any given year are determined by both past and present purchases of physical and financial capital. The vintage-weighted capital index is intended to capture the long-term consumption of capital, using vintage weights for depreciation (physical capital) and interest (financial capital). These vintage weights reflect the purchase patterns of building and fixed equipment and movable equipment over time. Depreciation and interest expenses are determined by the amount of past and current capital purchases. Therefore, as proposed, for this final rule we are using the vintage weights to compute vintage-weighted price changes associated with depreciation and interest expense. Vintage weights are an integral part of the FY 2002-based RPL market basket. Capital costs are inherently complicated and are determined by complex capital purchasing decisions, over time, based on such factors as interest rates and debt financing. In addition, capital is depreciated over time instead of being consumed in the same period it is purchased. The capital portion of the FY 2002-based RPL market basket reflects the annual price changes associated with capital costs, and is a useful simplification of the actual capital investment process. By accounting for the vintage nature of capital, we are able to provide an accurate, stable annual measure of price changes. Annual non-vintage price changes for capital are unstable due to the volatility of interest rate changes and, therefore, do not reflect the actual annual price changes for Medicare capital-related costs. The capital component of the FY 2002-based RPL market basket reflects the underlying stability of the capital acquisition process and provide hospitals with the ability to plan for changes in capital payments. To calculate the vintage weights for depreciation and interest expenses, we need a time series of capital purchases for building and fixed equipment and movable equipment. We found no single source that provides the best time series of capital purchases by hospitals for all of the above components of capital purchases. The early Medicare Cost Reports did not have sufficient capital data to meet this need because these data were not required. While the AHA Panel Survey provided a consistent database back to 1963, it did not provide annual capital purchases. The AHA Panel Survey provided a time series of depreciation expenses through 1997 which could be used to infer capital purchases over time. From 1998 to 2001, total hospital depreciation expenses were calculated by multiplying the AHA Annual Survey total hospital expenses by the ratio of depreciation to total hospital expenses from the Medicare cost reports. Beginning in 2001, the AHA Annual survey began collecting depreciation expenses. We hope to be able to use this data in any future rebasings. In order to estimate capital purchases from AHA data on depreciation and interest expenses, the expected life for each cost category (building and fixed equipment, movable equipment, and debt instruments) is needed. Due to insufficient Medicare cost report data for IRFs, IPFs and LTCHs, as proposed, for this final rule, we are using FY 2001 Medicare cost reports for IPPS hospitals to determine the expected life of building and fixed equipment and movable equipment. We believe this data source reflects the latest relative cost structure of depreciation expenses for hospitals. The expected life of any piece of equipment can be determined by dividing the value of the asset (excluding fully depreciated assets) by its current year depreciation amount. This calculation yields the estimated useful life of an asset if depreciation were to continue at current year levels, assuming straight-line depreciation. From the FY 2001 Medicare cost reports for IPPS hospitals the expected life of building and fixed equipment was determined to be 23 years, and the expected life of movable equipment was determined to be 11 years. Between the publication of the June 24, 2005 proposed rule and this final rule, we conducted a further review of the methodology used to derive the useful life of an asset. Based on this brief analysis into the capital cost structures of hospitals, we are not changing the expected life of fixed and moveable assets for the final rule. As proposed, for this final rule, we are using the fixed and movable weights derived from FY 2001 Medicare cost reports for IRFs, IPFs and LTCHs to separate the depreciation expenses into annual amounts of building and fixed equipment depreciation and movable equipment depreciation. By multiplying the annual depreciation amounts by the expected life calculations from the FY 2001 Medicare cost reports, year-end asset costs for building and fixed equipment and movable equipment could be determined. We then calculated a time series back to 1963 of annual capital purchases by subtracting the previous year asset costs from the current year asset costs. From this capital purchase time series we were able to calculate the vintage weights for building and fixed equipment, movable equipment, and debt instruments. Each of these sets of vintage weights are explained in detail below. As proposed, for this final rule, for building and fixed equipment vintage weights, the real annual capital purchase amounts for building and fixed equipment derived from the AHA Panel Survey were used. The real annual purchase amount was used to capture the actual amount of the physical acquisition, net of the effect of price inflation. This real annual purchase amount for building and fixed equipment was produced by deflating the nominal annual purchase amount by the building and fixed equipment price proxy, the Boeckh Institutional Construction Index. This is the same proxy used for the FY 1997-based excluded hospital with capital market basket. We believe this proxy continues to meet our criteria of reliability, timeliness, availability, and relevance. Since building and fixed equipment has an expected life of 23 years, the vintage weights for building and fixed equipment are deemed to represent the average purchase pattern of building and fixed equipment over 23-year periods. With real building and fixed equipment purchase estimates available back to 1963, sixteen 23-year periods are averaged to determine the average vintage weights for building and fixed equipment that are representative of average building and fixed equipment purchase patterns over time. Vintage weights for each 23-year period are calculated by dividing the real building and fixed capital purchase amount in any given year by the total amount of purchases in the 23-year period. This calculation is done for each year in the 23-year period, and for each of the sixteen 23-year periods. The average of each year across the sixteen 23-year periods is used to determine the 2002 average building and fixed equipment vintage weights. As proposed, for this final rule, for movable equipment vintage weights, the real annual capital purchase amounts for movable equipment derived from the AHA Panel Survey were used to capture the actual amount of the physical acquisition, net of price inflation. This real annual purchase amount for movable equipment was calculated by deflating the nominal annual purchase amount by the movable equipment price proxy, the Producer Price Index for Machinery and Equipment. This is the same proxy used for the FY 1997-based excluded hospital with capital market basket. We believe this proxy, which meets our criteria, is the best measure of price changes for this cost category. Since movable equipment has an expected life of 11 years, the vintage weights for movable equipment are deemed to represent the average purchase pattern of movable equipment over 11-year periods. With real movable equipment purchase estimates available back to 1963, twenty-eight 11-year periods are averaged to determine the average vintage weights for movable equipment that are representative of average movable equipment purchase patterns over time. Vintage weights for each 11-year period are calculated by dividing the real movable capital purchase amount for any given year by the total amount of purchases in the 11-year period. This calculation is done for each year in the 11-year period, and for each of the twenty-eight 11-year periods. The average of each year across the twenty-eight 11-year periods is used to determine the FY 2002 average movable equipment vintage weights. As proposed, for this final rule, for interest vintage weights, the nominal annual capital purchase amounts for total equipment (building and fixed, and movable) derived from the AHA Panel and Annual Surveys were used. Nominal annual purchase amounts were used to capture the value of the debt instrument. Since hospital debt instruments have an expected life of 23 years, the vintage weights for interest are deemed to represent the average purchase pattern of total equipment over 23-year periods. With nominal total equipment purchase estimates available back to 1963, sixteen 23-year periods are averaged to determine the average vintage weights for interest that are representative of average capital purchase patterns over time. Vintage weights for each 23-year period are calculated by dividing the nominal total capital purchase amount for any given year by the total amount of purchases in the 23-year period. This calculation is done for each year in the 23-year period and for each of the sixteen 23-year periods. The average of the sixteen 23-year periods is used to determine the FY 2002 average interest vintage weights. The vintage weights for the index are presented in Table 6 below. In addition to the price proxies for depreciation and interest costs described above in the vintage weighted capital section, as proposed, for this final rule, we used the CPI-U for Residential Rent as a price proxy for other capital-related costs. The price proxies for each of the capital cost categories are the same as those used for the IPPS final rule (67 FR at 50044) capital input price index. Table 6.—CMS FY 2002-Based RPL Market Basket Capital Vintage Weights Year Fixed assets (23 year weights) Movable assets (11 year weights) Interest: capital-related (23 year weights) 1 0.021 0.065 0.010 2 0.022 0.071 0.012 3 0.025 0.077 0.014 4 0.027 0.082 0.016 5 0.029 0.086 0.019 6 0.031 0.091 0.023 7 0.033 0.095 0.026 8 0.035 0.100 0.029 9 0.038 0.106 0.033 10 0.040 0.112 0.036 11 0.042 0.117 0.039 12 0.045 0.043 13 0.047 0.048 14 0.049 0.053 15 0.051 0.056 16 0.053 0.059 17 0.056 0.062 18 0.057 0.064 19 0.058 0.066 20 0.060 0.070 21 0.060 0.071 22 0.061 0.074 23 0.061 0.076 Total 1.0000 1.0000 1.0000 The final FY 2006 update for IRF PPS using the FY 2002-based RPL market basket is 3.6 percent. This is based on Global Insight's 2nd quarter 2005 forecast, incorporating two more quarters of historical data than published in the FY 2006 IRF proposed rule. This includes increases in both the operating section and the capital section. Global Insight, Inc. is a nationally recognized economic and financial forecasting firm that contracts with CMS to forecast the components of the market baskets. Using the current FY 1997-based excluded hospital with capital market basket (66 FR at 41427), Global Insight's second quarter 2005 forecast for FY 2006 is also 3.6 percent. Table 7 below compares the FY 2002-based RPL market basket and the FY 1997-based excluded hospital with capital market basket percent changes. For both the historical and forecasted periods between FY 2000 and FY 2008, the difference between the two market baskets is minor with the exception of FY 2002 where the FY 2002-based RPL market basket increased three tenths of a percentage point higher than the FY 1997-based excluded hospital with capital market basket. This is primarily due to the FY 2002-based RPL market basket having a larger compensation (that is, the sum of wages and salaries and benefits) cost weight than the FY 1997-based index and the price changes associated with compensation costs increasing much faster than the prices of other market basket components. Also contributing is the all other nonlabor intensive cost weight, which is smaller in the FY 2002-based RPL market basket than in the FY 1997-based index, and the slower price changes associated with these costs. Table 7.—FY 2002-Based RPL Market Basket and FY 1997-Based Excluded Hospital With Capital Market Basket Percent Changes, FY 2000-FY 2008 Fiscal year
(FY)Rebased FY 2002-based RPL market basket FY 1997-based excluded hospital market basket with capital Historical data: FY 2000 3.1 3.1 FY 2001 4.0 4.0 FY 2002 3.9 3.6 FY 2003 3.8 3.7 FY 2004 3.6 3.7 Average FYs 2000-2004 3.7 3.6 Forecast: FY 2005 3.8 3.9 FY 2006 3.6 3.6 FY 2007 3.2 3.1 FY 2008 3.1 2.9 Average FYs 2005-2008 3.4 3.4 Source: Global Insight, Inc. 2nd Qtr 2005, @USMACRO/CNTL0605 @CISSIM/TL0505.SIM. *Comment:* One commenter recommended that the current update be increased to reflect the differences between the updates given in FY 2004 and FY 2005 and the final market basket increases. Another commenter recommended that CMS adopt a forecast error adjustment. *Response:* There is currently no mechanism for adjusting for forecast error in the IRF PPS. Also, the FY 2005 updates is not based on historical data. The forecast error for FY 2005 will not be available until we publish the 2005q4 forecast (with historical data through 2005q3) version of the market basket. We have been actively working with our contractor to minimize forecast error. The specific details of our analysis are provided in the response to following comment. *Comment:* Several commenters requested that CMS review and revise the methodology used to forecast the FY 2006 market basket. They are concerned that the proposed FY 2006 update of 3.1 percent is a dramatic underestimation. One commenter requested that CMS make the calculation of the projected FY 2006 available to the public. *Response:* Before we published the FY 2006 proposed rule, we had been actively working with our forecasting firm, Global Insight, Inc. (GII), to improve the forecasting accuracy of the market baskets. GII is a nationally recognized economic and financial forecasting firm that contracts with CMS to forecast the components of the market baskets. Among other services GII provides to CMS, GII calculates projected inflation factors for price proxies using models that take into account sectoral, national, and global economic trends. Over the last several years, dramatic fluctuations in the price of certain costs have made it difficult to forecast price proxy inflation. The driving force behind a significant portion of this uncertainty has been the instability of energy costs. With our input and consultation, however, GII recently re-evaluated and modified their forecasting models to help improve their forecasting accuracy. Using these improved forecasting models, GII calculated updated inflation factors for the major cost categories in Table 8. Table 8.—Comparison of the 4 Quarter Moving Average Percent Changes for Several Cost Category Weights Between the FY 2006 Proposed and Final Rules Expense category FY 2002-based cost weights GII 2004q4 forecast of FY 2006 (Proposed Rule) GII 2005q2 forecast of FY 2006 (Final Rule) Total—RPL02 100.00 3.1 3.6 Compensation 65.877 3.5 3.9 Utilities 0.656 0.8 3.6 Professional Fees 2.892 3.6 3.8 Professional Liability Insurance 1.161 8.4 5.2 All Other 19.265 2.5 3.2 All Other Products 13.323 2.6 3.5 All Other Services 5.942 2.4 2.6 Capital 10.149 0.9 1.1 d. Labor-Related Share Section 1886(j)(6) of the Act specifies that the Secretary shall adjust the proportion (as estimated by the Secretary from time to time) of rehabilitation facilities' costs which are attributable to wages and wage-related costs, of the prospective payment rates computed under paragraph
(3)for area differences in wage levels by a factor (established by the Secretary) reflecting the relative hospital wage level in the geographic area of the rehabilitation facility compared to the national average wage level for such facilities. Not later than October 1, 2001 (and at least every 36 months thereafter), the Secretary shall update the factor under the preceding sentence on the basis of information available to the Secretary (and updated as appropriate) of the wages and wage-related costs incurred in furnishing rehabilitation services. Any adjustments or updates made under this paragraph for a fiscal year shall be made in a manner that assures that the aggregated payments under this subsection in the fiscal year shall be made in a manner that assures that the aggregated payments under this subsection in the fiscal year are not greater or less than those that would have been made in the year without such adjustment. The labor-related share is determined by identifying the national average proportion of operating costs that are related to, influenced by, or vary with the local labor market. Using our current definition of labor-related, the labor-related share is the sum of the relative importance of wages and salaries, fringe benefits, professional fees, labor-intensive services, and a portion of the capital share from an appropriate market basket. As proposed, for this final rule, we are using the FY 2002-based RPL market basket costs to determine the labor-related share for the IRF PPS. The labor-related share for FY 2006 is the sum of the FY 2006 relative importance of each labor-related cost category, and reflects the different rates of price change for these cost categories between the base year (FY 2002) and FY 2006. For this final rule, we are revising the labor-related share to reflect Global Insight's second quarter 2005 forecast, incorporating two more quarters of historical data than published in the FY 2006 IRF proposed rule. Thus, for this final rule, the sum of the relative importance for FY 2006 for operating costs (wages and salaries, employee benefits, professional fees, and labor-intensive services) is 71.708 percent, as shown in the chart below. The portion of capital that is influenced by local labor markets is estimated to be 46 percent, which is the same percentage currently used in the IRF prospective payment system. Since the relative importance for capital is 9.037 percent of the FY 2002-based RPL market basket in FY 2006, we took 46 percent of 9.037 percent to determine the capital labor-related share for FY 2006. The result is 4.157 percent, which we add to 71.708 percent for the operating cost amount to determine the total labor-related share for FY 2006. Thus, the labor-related share that we are using for IRF PPS in FY 2006 is 75.865 percent. This labor-related share is determined using the same methodology as employed in calculating all previous IRF labor-related shares (66 FR at 41357). Table 9 below shows the final FY 2006 relative importance labor-related share using the 2002-based RPL market basket and the labor-related share using the FY 1997-based excluded hospital with capital market. Table 9.—Total Labor-Related Share Cost category FY 2002-based RPL market basket relative importance (percent) FY 2006 FY 1997 excluded hospital with capital market basket relative importance (percent) FY 2006 Wages and salaries 52.592 48.185 Employee benefits 14.028 11.542 Professional fees 2.921 4.558 All other labor intensive services 2.167 4.450 Subtotal 71.708 68.735 Labor-related share of capital costs 4.157 3.289 Total 75.865 72.024 Public comments that we received are summarized below. *Comment:* Several commenters objected to our proposal to change the labor-related share to 75.958 percent. One commenter suggested CMS maintain the FY 2005 labor-related share of 72.359 percent until CMS can develop an IRF-specific wage index. Another commenter stated there is no precedent to change the labor-related share. Another commenter requested that if CMS implemented a change in the LRS, they request a transition where the transitional labor-related share would be composed of 80 percent of the current labor-related share and 20 percent of the proposed labor-related share. *Response:* Identical to previous updates, the labor-related share is calculated as the sum of the relative importance of those costs that are related to, influenced by, or vary with the local labor market. Specifically, the FY 2006 labor related share is equal to the relative importance of wages and salaries, fringe benefits, professional fees, labor-intensive services, and a portion of the capital share from the RPL market basket. We calculate the labor-related relative importance for FY 2006 in four steps. First, we compute the FY 2006 price index level for the total market basket and each cost category of the market basket. Second, we calculate a ratio for each cost category by dividing the FY 2006 price index level for that cost category by the total market basket price index level. Third, we determine the FY 2006 relative importance for each cost category by multiplying this ratio by the base year (FY 2002) weight. Finally, we sum the FY 2006 relative importance for each of the labor-related cost categories (wages and salaries, employee benefits, nonmedical professional fees, labor-intensive services, and capital-related expenses) to produce the FY 2006 labor-related relative importance. The price proxies that move the different cost categories in the market basket do not necessarily change at the same rate, and the relative importance captures these changes. Accordingly, the relative importance figure more closely reflects the cost share weights for FY 2006 when compared to the base year weights from the RPL market basket. Thus, the LRS has been and should be revised with each fiscal year update. CMS disagrees with the commenter's suggestion to transition from the FY 2005 to the FY 2006 labor-related share. We note the FY 2006 labor-related share is based on the same methodology used to calculate the FY 2005 labor-related share (that is, it is composed of the costs that are related to, influenced by, or vary with the local labor market). Furthermore, the FY 2006 labor-related share is based on the 2002-based RPL market basket, which we believe adequately reflects the current cost structures of Medicare-participating IRFs. Therefore, we do not believe a transition is necessary. *Comment:* Several commenters suggested that we include professional liability insurance
(PLI)in the labor-related share since these costs are included in the wage index. The commenters also claim that professional liability insurance costs are wage-related. *Response:* The wage index includes, as a fringe benefit cost, PLI for those policies that list actual names or specific titles of covered employees (59 FR 45358). The benefit cost weight in the market basket, included in the labor-related share, is also based on the same wage index benefit data. Therefore, the labor-related share includes these PLI costs. General PLI coverage maintained by hospitals is not recognized as a wage-related cost for purposes of the wage index or labor-related share. Although general PLI costs do vary by geographic region, this variance is primarily influenced by state legislation and risk level, not by local wage rates. In fact, areas with high wage indices may have low relative PLI costs. For example, the malpractice geographic price indices, used in the Medicare physician payment system, for San Francisco, Los Angeles, and Boston regions are below 1, while their hospital wage indices for comparable areas are much greater than 1. *Comment:* Several commenters recommended CMS delay the implementation of the RPL market basket until CMS has reviewed the accuracy of the cost report data. Specifically, they requested CMS investigate HealthSouth's claim to have omitted home office and some depreciation costs from their 2002 and 2003 Medicare cost reports. *Response:* The FY 2006 market basket update is based on the RPL market basket using FY 2002 Medicare cost report data. CMS has determined that, in the absence of FY 2002 HealthSouth home office cost report data, we will not incorporate preliminary FY 2004 HealthSouth home office costs into the 2002-based RPL market basket. (Due to a change in Medicare cost report requirements beginning with FY 2002, we used FY 2001 capital costs aged to FY 2002 in the 2002-based RPL market basket. Therefore, HealthSouth's depreciation costs were included in the RPL market basket and reflected in the FY 2006 market basket update.) Home office costs represent only one of many cost categories (including but not limited to salaries, benefits, professional liability insurance, and pharmaceuticals) that are used to develop the cost category weights. We believe the absence of HealthSouth home office costs in this market basket has a minor impact on the distribution of these weights and, by extension, the final market basket update itself. When CMS receives full FY 2004 Medicare cost report data from HealthSouth, we plan to re-evaluate this decision. *Final Decision:* We are finalizing our decision to update payments for rehabilitation facilities using the RPL market basket reflecting the operating and capital cost structures for IRFs, IPFs, and LTCHs. 2. Area Wage Adjustment Section 1886(j)(6) of the Act requires the Secretary to adjust the proportion (as estimated by the Secretary from time to time) of rehabilitation facilities' costs that are attributable to wages and wage-related costs by a factor (established by the Secretary) reflecting the relative hospital wage level in the geographic area of the rehabilitation facility compared to the national average wage level for those facilities. Not later than October 1, 2001 and at least every 36 months thereafter, the Secretary is required to update the factor under the preceding sentence on the basis of information available to the Secretary (and updated as appropriate) of the wages and wage-related costs incurred in furnishing rehabilitation services. Any adjustments or updates made under section 1886(j)(6) of the Act for a FY shall be made in a manner that assures the aggregated payments under section 1886(j)(6) of the Act are not greater or less than those that will have been made in the year without such adjustment. In our August 1, 2003 final rule (68 FR 45674), we acknowledged that on June 6, 2003, the Office of Management and Budget
(OMB)issued “OMB Bulletin No. 03-04,” announcing revised definitions of Metropolitan Statistical Areas, and new definitions of Micropolitan Statistical Areas and Combined Statistical Areas. A copy of the Bulletin may be obtained at the following Internet address: *http://www.whitehouse.gov/omb/bulletins/b03-04.html* . At that time, we did not propose to apply these new definitions known as the Core-Based Statistical Areas (CBSAs). After further analysis and discussed in detail in section VI.B.2.d, we proposed to revised labor market area definitions as a result of the OMB revised definitions to adjust the FY 2006 IRF PPS payment rate. In addition, the IPPS is applying these revised definitions as discussed in the August 11, 2004 final rule (69 FR at 49207). We will adopt the CBSA-based geographic classifications as proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188) and described below in section VI.B.2.d and section VI.B.2.e. a. Revisions to the IRF PPS Geographic Classification As discussed in the August 7, 2001 final rule, which implemented the IRF PPS (66 FR at 41316), in establishing an adjustment for area wage levels under § 412.624(e)(1), the labor-related portion of an IRF's Federal prospective payment is adjusted by using an appropriate wage index. As set forth in § 412.624(e)(1), an IRF's wage index is determined based on the location of the IRF in an urban or rural area as defined in §412.602 and further defined in § 412.62(f)(1)(ii) and § 412.62(f)(1)(iii) as urban and rural areas, respectively. An urban area, under the IRF PPS, is defined in § 412.62(f)(1)(ii) as a Metropolitan Statistical Area
(MSA)or New England County Metropolitan Area (NECMA) as defined by the Office of Management and Budget (OMB). Under § 412.62(f)(1)(iii), a rural area is defined as any area outside of an urban area. In general, an urban area is defined as a Metropolitan Statistical Area
(MSA)or New England County Metropolitan Area (NECMA) as defined by the Office of Management and Budget. Under § 412.62(f)(1)(iii), a rural area is defined as any area outside of an urban area. The urban and rural area geographic classifications defined in § 412.62(f)(1)(ii) and (f)(1)(iii), respectively, were used under the IPPS from FYs 1985 through 2004 (as specified in § 412.63(b)), and have been used under the IRF PPS since it was implemented for cost reporting periods beginning on or after January 1, 2002. The wage index used for the IRF PPS is calculated by using the acute care IPPS wage index data on the basis of the labor market area in which the acute care hospital is located, but without taking into account geographic reclassification under sections 1886(d)(8) and (d)(10) of the Act commonly referred to as “pre-reclassification”. In addition, Section 4410 of Pub. L. 105-33
(BBA)provides that for the purposes of section 1886(d)(3)(E) of the Act, that the area wage index applicable to hospitals located in an urban area of a State may not be less than the area wage index applicable to hospitals located in rural areas in the State. Consistent with past IRF policy, we treat this provision, commonly referred to as the “rural floor”, as applicable to the acute inpatient hospitals and not IRFs. Therefore, the hospital wage index used for IRFs is commonly referred to as “pre-floor” indicating that the “rural floor” provision is not applied. As a result, the applicable IRF wage index value is assigned to the IRF on the basis of the labor market area in which the IRF is geographically located. In the FY 2006 IRF PPS proposed rule (70 FR 30188, 30235), we described the labor markets that have been used for area wage adjustments under the IRF PPS since its implementation of cost reporting periods beginning on or after January 1, 2002. Previously, we have not described the labor market areas used under the IRF PPS in detail. However, we published each area's wage index in the IRF PPS final rules and update notices, each year and noted the use of the geographic area in applying the wage index adjustment in the IRF PPS payment examples in the final regulation implementing the IRF PPS (69 FR 41316, 41367 through 41368). The IRF industry has also understood that the same labor market areas in use under the IPPS (from the time the IRF PPS was implemented, for cost reporting periods beginning on or after January 1, 2002) are used under the IRF PPS. The OMB adopted new statistical area definitions (70 FR 30188, 30235-30238) and we proposed to adopt the new labor market area definitions based on these areas under the IRF PPS. Therefore, we are providing a more detailed description of the current IRF PPS labor market areas in this final rule, in order for the public to better understand the change to the IRF PPS labor market areas. The current IRF PPS labor market areas are defined based on the definitions of MSAs, Primary MSAs (PMSAs), and NECMAs issued by the OMB (commonly referred to collectively as “MSAs”). These MSA definitions are used before October 1, 2005, under the IRF PPS and other prospective payment systems, such as LTCH, IPF, Home Health Agency (HHA), and SNF (Skilled Nursing Facility) PPSs. In the IPPS final rule (67 FR at 49026 through 49034), revised labor market area definitions were adopted under the hospital IPPS (§ 412.64(b)), which are effective October 1, 2004 for acute care hospitals. These new CBSA standards were announced by the OMB late in 2000. b. Current IRF PPS Labor Market Areas Based on MSAs As mentioned earlier, since the implementation of the IRF PPS in the August 7, 2001 IRF PPS final rule, we used labor market areas to further characterize urban and rural areas as determined under § 412.602 and further defined in § 412.62(f)(1)(ii) and (f)(1)(iii) for discharges before October 1, 2005. We defined labor market areas under the IRF PPS based on the definitions of MSAs, PMSAs, and NECMAs issued by the OMB, which is consistent with the IPPS approach. The OMB also designates Consolidated MSAs (CMSAs). A CMSA is a metropolitan area with a population of 1 million or more, comprising two or more PMSAs (identified by their separate economic and social character). For purposes of the wage index, we use the PMSAs rather than CMSAs because they allow a more precise breakdown of labor costs (as described in section VI.B.2.d.ii of this final rule). If a metropolitan area is not designated as part of a PMSA, we use the applicable MSA. These different designations use counties as the building blocks upon which they are based. Therefore, IRFs are assigned to either an MSA, PMSA, or NECMA based on whether the county in which the IRF is located is part of that area. All of the counties in a State outside a designated MSA, PMSA, or NECMA are designated as rural. For the purposes of calculating the wage index, we combine all of the counties in a State outside a designated MSA, PMSA, or NECMA together to calculate the statewide rural wage index for each State. c. Core-Based Statistical Areas (CBSAs) OMB reviews its Metropolitan Area definitions preceding each decennial census. As discussed in the IPPS final rule (69 FR at 49027), in the fall of 1998, OMB chartered the Metropolitan Area Standards Review Committee to examine the Metropolitan Area standards and develop recommendations for possible changes to those standards. Three notices related to the review of the standards, providing an opportunity for public comment on the recommendations of the Committee, were published in the **Federal Register** on the following dates: December 21, 1998 (63 FR at 70526); October 20, 1999 (64 FR at 56628); and August 22, 2000 (65 FR at 51060). In the December 27, 2000 **Federal Register** (65 FR at 82228 through 82238), OMB announced its new standards. In that notice, OMB defines CBSA, beginning in 2003, as “a geographic entity associated with at least one core of 10,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.” The standards designate and define two categories of CBSAs: MSAs and Micropolitan Statistical Areas (65 FR at 82235 through 82238). According to OMB, MSAs are based on urbanized areas of 50,000 or more population, and Micropolitan Statistical Areas (referred to in this discussion as Micropolitan Areas) are based on urban clusters of at least 10,000 population, but less than 50,000 population. Counties that do not fall within CBSAs (either MSAs or Micropolitan Areas) are deemed “Outside CBSAs.” In the past, OMB defined MSAs around areas with a minimum core population of 50,000, and smaller areas were “Outside MSAs.” On June 6, 2003, OMB announced the new CBSAs, comprised of MSAs and the new Micropolitan Areas based on Census 2000 data. (A copy of the announcement may be obtained at the following Internet address: *http://www.whitehouse.gov/omb/bulletins/fy04/b04-03.html.* ) The new CBSA designations recognize 49 new MSAs and 565 new Micropolitan Areas, and revise the composition of many of the existing MSAs. There are 1,090 counties in MSAs under the new CBSA designations (previously, there were 848 counties in MSAs). Of these 1,090 counties, 737 are in the same MSA as they were prior to the change in designations, 65 are in a different MSA, and 288 were not previously designated to any MSA. There are 674 counties in Micropolitan Areas. Of these, 41 were previously in an MSA, while 633 were not previously designated to an MSA. There are five counties that previously were designated to an MSA but are no longer designated to either an MSA or a new Micropolitan Area: Carter County, KY; St. James Parish, LA; Kane County, UT; Culpepper County, VA; and King George County, VA. For a more detailed discussion of the conceptual basis of the new CBSAs, refer to the IPPS final rule (67 FR at 49026 through 49034). d. Revisions to the IRF PPS Labor Market Areas In its June 6, 2003 announcement, OMB cautioned that these new definitions “should not be used to develop and implement Federal, State, and local non-statistical programs and policies without full consideration of the effects of using these definitions for such purposes. These areas should not serve as a general-purpose geographic framework for non-statistical activities, and they may or may not be suitable for use in program funding formulas.” We currently use MSAs to define labor market areas for purposes of the wage index. In fact, MSAs are also used to define labor market areas for purposes of the wage index for many of the other Medicare prospective payment systems (for example, LTCH, SNF, HHA, IPF, and Outpatient). While we recognize MSAs are not designed specifically to define labor market areas, we believe they represent a reasonable and appropriate proxy for this purpose, because they are based upon characteristics we believe also generally reflect the characteristics of unified labor market areas. For example, CBSAs reflect a core population plus an adjacent territory that reflects a high degree of social and economic integration. This integration is measured by commuting ties, thus demonstrating that these areas may draw workers from the same general areas. In addition, the most recent CBSAs reflect the most up-to-date information. The OMB reviews its Metropolitan Area
(MA)definitions preceding each decennial census to reflect recent population changes and the CBSAs are based on the Census 2000 data. Thus, we proposed to adopt the new CBSA designations to define labor market areas for the purposes of the IRF PPS. Historically, Medicare PPSs have utilized MA definitions developed by OMB. The labor market areas currently used under the IRF PPS are based on the MA definitions issued by OMB. OMB reviews its MA definitions preceding each decennial census to reflect more recent population changes. Thus, the CBSAs are OMB's latest MA definitions based on the Census 2000 data. Because we believe that the OMB's latest MA designations more accurately reflect the local economies and wage levels of the areas in which hospitals are currently located, we proposed to adopt the revised labor market area designations based on the OMB's CBSA designations. As specified in § 412.624(e)(1), we explained in the August 7, 2001 final rule that the IRF PPS wage index adjustment was intended to reflect the relative hospital wage levels in the geographic area of the hospital as compared to the national average hospital wage level. Since OMB's CBSA designations are based on Census 2000 data and reflect the most recent available geographic classifications, we will adopt the labor market area definitions used under the IRF PPS as proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188). Specifically, we will revise the IRF PPS labor market definitions based on the OMB's new CBSA designations effective for IRF PPS discharges occurring on or after October 1, 2005. Accordingly, we will revise § 412.602 to specify that for discharges occurring on or after October 1, 2005, the application of the wage index under the IRF PPS will be made on the basis of the location of the facility in an urban or rural area as defined in § 412.64(b)(1)(ii)(A) through
(C)as proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188). As a conforming change, we will revise § 412.602, definitions for rural and urban areas effective for discharges occurring on or after October 1, 2005 will be defined in § 412.64(b)(1)(ii)(A) through
(C)as proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188) and adopted in this final rule. In addition (as proposed in the FY 2006 IRF PPS proposed rule at 70 FR 30188), we will revise the regulation text to explicitly reference urban and rural definitions for a cost-reporting period beginning on or after January 1, 2002, with respect to discharges occurring during the period covered by such cost reports but before October 1, 2005 under § 412.62(f)(1)(ii) and § 412.62(f)(1)(iii). We note that these are the same labor market area definitions (based on the OMB's new CBSA-based designations) implemented under the IPPS at § 412.64(b), which are effective for those hospitals beginning October 1, 2004 as discussed in the IPPS final rule (69 FR at 49026 through 49034). The similarity between the IPPS and the IRF PPS includes the adoption in the initial implementation of the IRF PPS of the same labor market area definitions under the IRF PPS that existed under the IPPS at that time, as well as the use of acute care hospitals' pre-reclassification and pre-floor wage data in calculating the IRF PPS wage index. In addition, the OMB's CBSA-based designations reflect the most recent available geographic classifications and more accurately reflects current labor markets. Therefore, we believe that revising the IRF PPS labor market area definitions based on OMB's CBSA-based designations are consistent with our historical practice of modeling IRF PPS policy after IPPS policy. In sections VI.B.2.d.i. through VI.B.2.d.iii of this final rule and as described in the FY 2006 IRF PPS proposed rule (70 FR 30188), we describe the composition of the IRF PPS labor market areas based on the OMB's new CBSA designations. i. New England MSAs As stated above, in the August 7, 2001 final rule, we currently use NECMAs to define labor market areas in New England, because these are county-based designations rather than the 1990 MSA definitions for New England, which used minor civil divisions such as cities and towns. Under the current MSA definitions, NECMAs provided more consistency in labor market definitions for New England compared with the rest of the country, where MSAs are county-based. Under the new CBSAs, OMB has now defined the MSAs and Micropolitan Areas in New England on the basis of counties. The OMB also established New England City and Town Areas, which are similar to the previous New England MSAs. To create consistency among all labor market areas and to maintain these areas on the basis of counties, we proposed to and are adopting in this final rule to use the county-based areas for all MSAs in the nation, including those in New England. Census has now defined the New England area based on counties, creating a city- and town-based system as an alternative. We believe that adopting county-based labor market areas for the entire country except those in New England will lead to inconsistencies in our designations. Adopting county-based labor market areas for the entire country provides consistency and stability in the Medicare payment program because all the labor market areas throughout the country, including New England, will be defined using the same system (that is, counties) rather than different systems in different areas of the country, and minimizes programmatic complexity. We have consistently employed a county-based system for New England for precisely that reason: To maintain consistency with the labor market area definitions used throughout the country. Because we have never used cities and towns for defining IRF labor market areas, employing a county-based system in New England maintains that consistent practice. We note that this is consistent with the implementation of the CBSA-based designations under the IPPS for New England (see 69 FR at 49028). Accordingly, as specified in the FY 2006 proposed rule (70 FR 30188), we are using the New England MSAs as determined under the new CBSA-based labor market area definitions in defining the revised IRF PPS labor market areas in this final rule. ii. Metropolitan Divisions Under OMB's new CBSA designations, a Metropolitan Division is a county or group of counties within a CBSA that contains a core population of at least 2.5 million, representing an employment center, plus adjacent counties associated with the main county or counties through commuting ties. A county qualifies as a main county if 65 percent or more of its employed residents work within the county and the ratio of the number of jobs located in the county to the number of employed residents is at least 0.75. A county qualifies as a secondary county if 50 percent or more, but less than 65 percent, of its employed residents work within the county and the ratio of the number of jobs located in the county to the number of employed residents is at least 0.75. After all the main and secondary counties are identified and grouped, each additional county that already has qualified for inclusion in the MSA falls within the Metropolitan Division associated with the main/secondary county or counties with which the county at issue has the highest employment interchange measure. Counties in a Metropolitan Division must be contiguous (65 FR at 82236). The construct of relatively large MSAs being comprised of Metropolitan Divisions is similar to the current construct of the CMSAs comprised of PMSAs. As noted above, in the past, OMB designated CMSAs as Metropolitan Areas with a population of 1 million or more and comprised of two or more PMSAs. Under the IRF PPS, we currently use the PMSAs rather than CMSAs to define labor market areas because they comprise a smaller geographic area with potentially varying labor costs due to different local economies. We believe that CMSAs may be too large of an area with a relatively large number of hospitals, to accurately reflect the local labor costs of all the individual hospitals included in that relatively “large” area. A large market area designation increased the likelihood of including many hospitals located in areas with very different labor market conditions within the same market area designation. This variation could increase the difficulty in calculating a single wage index that will be relevant for all hospitals within the market area designation. Similarly, we believe that MSAs with a population of 2.5 million or greater may be too large of an area to accurately reflect the local labor costs of all the individual hospitals included in that relatively “large” area. Furthermore, as indicated above, Metropolitan Divisions represent the closest approximation to PMSAs, the building block of the current IRF PPS labor market area definitions, and therefore, will most accurately maintain our current structuring of the IRF PPS labor market areas. As implemented under the IPPS (69 FR at 49029), we proposed and for this final rule, we are using the Metropolitan Divisions where applicable (as describe below) under the new CBSA-based labor market area definitions. In addition to being comparable to the organization of the labor market areas under the current MSA designations (that is, the use of PMSAs rather than CMSAs), we believe that using Metropolitan Divisions where applicable (as described below) under the IRF PPS will result in a more accurate adjustment for the variation in local labor market areas for IRFs. Specifically, if we were to recognize the relatively “larger” CBSA that comprises two or more Metropolitan Divisions as an independent labor market area for purposes of the wage index, it will be too large and will include the data from too many hospitals to compute a wage index that will accurately reflect the various local labor costs of all the individual hospitals included in that relatively “large” CBSA. As mentioned earlier, a large market area designation increases the likelihood of including many hospitals located in areas with very different labor market conditions within the same market area designation. This variation could increase the difficulty in calculating a single wage index that will be relevant for all hospitals within the market area designation. Rather, by recognizing Metropolitan Divisions where applicable (as described below) under the new CBSA-based labor market area definitions under the IRF PPS, we believe that in addition to more accurately maintaining the current structuring of the IRF PPS labor market areas, the local labor costs will be more accurately reflected, thereby resulting in a wage index adjustment that better reflects the variation in the local labor costs of the local economies of the IRFs located in these relatively “smaller” areas. In section VI.B.2.d.ii.of this final rule, we describe where Metropolitan Divisions will be applicable under the new CBSA-based labor market area definitions under the IRF PPS final rule. Under the OMB's CBSA-based designations, there are 11 MSAs containing Metropolitan Divisions: Boston; Chicago; Dallas; Detroit; Los Angeles; Miami; New York; Philadelphia; San Francisco; Seattle; and Washington, DC. Although these MSAs were also CMSAs under the prior definitions, in some cases their areas have been altered. Under the current IRF PPS MSA designations, Boston is a single NECMA. Under the CBSA-based labor market area designations, it is comprised of four Metropolitan Divisions. Los Angeles will go from four PMSAs under the current IRF PPS MSA designations to two Metropolitan Divisions under the CBSA-based labor market area designations. The New York CMSA will go from 15 PMSAs under the current IRF PPS MSA designations to four Metropolitan Divisions under the CBSA-based labor market area designations. The five PMSAs in Connecticut under the current IRF PPS MSA designations will become separate MSAs under the CBSA-based labor market area designations because two MSAs became separate MSAs. The number of PMSAs in New Jersey, under the current IRF PPS MSA designations will go from five to two, with the consolidation of two New Jersey PMSAs (Bergen-Passaic and Jersey City) into the New York-Wayne-White Plains, NY-NJ Division, under the CBSA-based labor market area designations. In San Francisco, under the CBSA-based labor market area designations there are only two Metropolitan Divisions. Currently, there are six PMSAs, some of which are now separate MSAs under the current IRF PPS labor market area designations. Under the current IRF PPS labor market area designations, Cincinnati, Cleveland, Denver, Houston, Milwaukee, Portland, Sacramento, and San Juan are all designated as CMSAs, but will no longer be designated as CMSAs under the CBSA-based labor market area designations. As noted previously, the population threshold to be designated a CMSA under the current IRF PPS labor market area designations is 1 million. In most of these cases, counties currently in a PMSA will become separate, independent MSAs under the CBSA-based labor market area designations, leaving only the MSA for the core area under the CBSA-based labor market area designations. We note that subsequent to the publication of the FY 2006 IRF PPS proposed rule (70 FR 30188), titles to certain CBSAs were changed based on OMB Bulletin No. 05-02 (November 2004). The title changes listed below are nomenclatures that do not result in substantive changes to the CBSA-based designations. Thus, these changes are listed below and will be incorporated into the FY 2007 CBSA-based urban wage index tables. • CBSA 36740: Orlando-Kissimmee, FL • CBSA 37620: Parkersburg-Marietta-Vienna, WV-OH • CBSA 42060: Santa Barbara-Santa Monica, CA • CBSA 13644: Bethesda-Gaithersburg-Frederick, MD • CBSA 32580: McAllen-Edinburg-Mission, TX • CBSA 26420: Huston-Sugar Land-Baytown, TX • CBSA 35644: New York-White Plains-Wayne, NY-NJ ii. Micropolitan Areas Under the New OMB CBSA-Based Designations, Micropolitan Areas are essentially a third area definition consisting primarily of areas that are currently rural, but also include some or all of areas that are currently designated as urban MSA. As discussed in greater detail in the IPPS final rule (69 FR at 49029 through 49032), how these areas are treated will have significant impacts on the calculation and application of the wage index. Specifically, whether or not Micropolitan Areas are included as part of the respective statewide rural wage indices will impact the value of the statewide rural wage index of any State that contains a Micropolitan Area because a hospital's classification as urban or rural affects which hospitals' wage data are included in the statewide rural wage index. As discussed above in section VI.B.2.b of this final rule, we combine all of the counties in a State outside a designated urban area to calculate the statewide rural wage index for each State. Including Micropolitan Areas as part of the statewide rural labor market would result in an increase to the statewide rural wage index because hospitals located in those Micropolitan Areas typically have higher labor costs than other rural hospitals in the State. Alternatively, if Micropolitan Areas were to be recognized as independent labor market areas, because there would be so few hospitals in those areas to complete a wage index, the wage indices for IRFs in those areas could become relatively unstable as they might change considerably from year to year. Since the implementation of the IRF PPS, we used MSAs to define urban labor market areas and group all the hospitals in counties within each State that are not assigned to an MSA into a statewide rural labor market area. Therefore, we used the terms “urban” and “rural” wage indices in the past for ease of reference. However, the introduction of Micropolitan Areas by the OMB potentially complicates this terminology because these areas include many hospitals that are currently included in the statewide rural labor market areas. We proposed to treat Micropolitan Areas as rural labor market areas under the IRF PPS for the reasons outlined below. That is, counties that are assigned to a Micropolitan Area under the CBSA-based designations would be treated the same as other “rural” counties that are not assigned to either an MSA or a Micropolitan Area. Therefore, in determining an IRF's applicable wage index (based on IPPS hospital wage index data) an IRF in a Micropolitan Area under OMB's CBSA designations would be classified as “rural” and would be assigned the statewide rural wage index for the State in which it resides. In the IPPS final rule (69 FR at 49029 through 49032), we discuss our evaluation of the impact of treating Micropolitan areas as part of the statewide rural labor market area instead of treating Micropolitan Areas as independent labor market areas for hospitals paid under the IPPS. As an alternative to treating Micropolitan Areas as part of the statewide rural labor market area for purposes of the IRF PPS, in the FY 2006 proposed rule (70 FR 30188), we examined treating Micropolitan Areas as separate (urban) labor market areas, just as we did when implementing the revised labor market areas under the IPPS. As discussed in greater detail in that same final rule, the designation of Micropolitan Areas as separate urban areas for wage index purposes will have a dramatic impact on the calculation of the wage index. This is because Micropolitan areas encompass smaller populations than MSAs, and tend to include fewer hospitals per Micropolitan area. Currently, there are only 25 MSAs with one hospital in the MSA. However, under the new CBSA-based definitions, there are 373 Micropolitan Areas with one hospital, and 49 MSAs with only one hospital. Since Micropolitan Areas encompass smaller populations than MSAs, they tend to include fewer hospitals per Micropolitan Area, recognizing Micropolitan Areas as independent labor market areas will generally increase the potential for dramatic shifts in those areas' wage indices from one year to the next because a single hospital (or group of hospitals) could have a disproportionate effect on the wage index of the area. The large number of labor market areas with only one hospital and the increased potential for dramatic shifts in the wage indexes from one year to the next is a problem for several reasons. First, it creates instability in the wage index from year to year for a large number of hospitals. Second, it reduces the averaging effect (this averaging effect allows for more data points to be used to calculate the representative standard of measured labor costs within a market area) lessening some of the incentive for hospitals to operate efficiently. This incentive is inherent in a system based on the average hourly wages for a large number of hospitals, as hospitals could profit more by operating below that average. In labor market areas with a single hospital, high wage costs are passed directly into the wage index with no counterbalancing averaging with lower wages paid at nearby competing hospitals. Third, it creates an arguably inequitable system when so many hospitals have wage indexes based solely on their own wages, while other hospitals' wage indexes are based on an average hourly wage across many hospitals. Therefore, in order to minimize the potential instability in payment levels from year to year, we believe it will be appropriate to treat Micropolitan Areas as part of the statewide rural labor market area under the IRF PPS. For the reasons noted above, and consistent with the treatment of these areas under the IPPS, we proposed and are adopting Micropolitan Areas as independent labor market areas under the IRF PPS. Under the new CBSA-based labor market area definitions, Micropolitan Areas are considered a part of the statewide rural labor market area. Accordingly, we will determine an IRF PPS statewide rural wage index using the acute-care IPPS hospital wage data (the rational for using IPPS hospital wage data is discussed in section III.B.2.f of this final rule) from hospitals located in non-MSA areas assign the statewide rural wage index to IRFs located in those areas. e. Implementation of the CBSA-Based Labor Market Areas Under section 1886(j) of the Act, as added by section 4421 of the Balanced Budget Act of 1997
(BBA)(Pub. L. 105-33) and as amended by section 125 of the Medicare, Medicaid, and State Children's Health Insurance Program (SCHIP) Balanced Budget Refinement Act of 1999
(BBRA)(Pub. L. 106-113) and section 305 of the Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000
(BIPA)(Pub. L. 106-554), which requires the implementation of such prospective payment system, the Secretary generally has broad authority in developing the IRF PPS, including whether and how to make adjustments to the IRF PPS. In the FY 2006 IRF PPS proposed rule (70 FR 30188), Table 3 listed IRFs that submitted an IRF-PAI in the past 18-months. The data in Table 3 was obtained from a report we requested in February 2005 from the Iowa Foundation for Medical Care (IFMC). IFMC is the CMS contractor where the IRF-PAI database is located. Table 3 listed each IRF's provider number; provider name; and State and county location; existing MSA-based labor market area designation; and its CBSA-based designation. The purpose of Table 3 was to only facilitate an understanding of the policies related to the proposed change to the IRF PPS labor market areas discussed above by illustrating an IRF's change from the MSA-based designation to the proposed CBSA-based designation. Thus, FIs will not be instructed to use Table 3 in the FY 2006 IRF PPS proposed rule (70 FR 30188) to alter the information regarding an IRF's State and county location or to make changes to the provider specific file based on Table 3 of the FY 2006 IRF PPS proposed rule. Table 1 of the addendum of this final rule is a crosswalk file of all counties/areas in the United States, Guam, Puerto Rico, and the Virgin Islands with the corresponding State and county code, county and State name, FY 2006 MSA number, FY 2006 MSA-based urban or rural designation, FY 2006 MSA-based wage index, FY 2006 CBSA-based wage index, FY 2006 CBSA number, FY 2006 CBSA-based urban or rural designation, and FY 2006 blended one-year transition wage index as discussed below in Section VI.B.2.e. Table 1 of the addendum to this final rule will be used by FIs to determine the FY 2006 one-year transition wage index for IRFs located in areas as documented in the FI's provider specific file. When the revised labor market areas based on OMB's new CBSA-based designations were adopted under the IPPS beginning on October 1, 2004, a transition to the new designations was established due to the scope and substantial implications of these new CBSA-based designations in order to buffer the subsequent substantial impacts on numerous hospitals. As discussed in the IPPS final rule (69 FR at 49032), during FY 2005, a blend of wage indices is calculated for those acute care IPPS hospitals experiencing a drop in their wage index because of the adoption of the new labor market areas. The most substantial decrease in wage index impacts urban acute-care hospitals that were designated as rural under the CBSA-based designations. In the FY 2006 IRF PPS proposed rule (70 FR 30188), we recognize that, just like IPPS hospitals, IRFs may experience decreases in their wage index as a result of the labor market area changes. Our data analysis for the FY 2006 IRF PPS proposed rule (70 FR 30188) indicated that a majority of IRFs either expect no change in wage index or an increase in wage index based on CBSA definitions. Based on this analysis for the FY 2006 IRF PPS proposed rule (70 FR 30188), we found a very small number of IRFs (3 percent) will experience a decline of 5 percent or more in the wage index based on CBSA designations. A 5 percent decrease in the wage index for an IRF may result in a noticeable decrease in their wage index compared to what their wage index would have been for FY 2006 under the MSA-based designations. We also found that a very small number of IRFs (4 percent) would experience a change in either rural or urban designation under the CBSA-based definitions. Since a majority of IRFs would not be significantly impacted by the labor market areas, we did not propose a transition to the new CBSA-based labor market area, nor did we propose to adopt a hold harmless policy, nor an “out-commuting” policy for the purposes of the IRF PPS wage index. Public comments and our responses on the proposed changes for implementing the area wage adjustments are summarized below: *Comment:* A large number of commenters urged CMS to develop a transition policy or implement a similar transition policy as was implemented under the IPPS to minimize the fiscal impact of the change in wage index. Many advocated for a one-year transition with a blended wage index, equal to 50 percent of the FY 2006 MSA wage index and 50 percent of the FY 2006 CBSA-based wage index. We also received a few comments recommending a multi-year transition and possibly a permanent blended wage index. Overall, commenters expressed concerns for IRFs that would experience a significant decrease in the wage index. In general, commenters request that we mitigate the impact of the change from the MSA-based designation to the CBSA-based designations over time with a transition policy. *Response:* We recognize that some IRFs will experience decreases in their applicable wage index as a result of the conversion from the MSA-based designations to the CBSA-based designations. After further analysis of various transition options suggested by commenters as well as our further data analysis to support the policies in this final rule, we considered various transition options to determine a transition policy that would mitigate the impact on IRFs that would experience a decrease in the wage index, and buffer the overall impact on the unadjusted payment rate. Based on the commenters' recommendations, we carefully reviewed various budget neutral transition policies such as a blended wage index as well as a floor and ceiling approach as discussed in detail below. We reviewed a floor and ceiling transition policy option. Although this option seemed to minimize the impact on IRFs, we found that this approach would provide relief to IRFs that experience a decrease in the wage index, but with respect to IRFs that would get a significant increase in the wage index, it would also limit the amount they could expect their wage index to increase. The difficulty of developing a floor and ceiling transition policy is determining an appropriate floor and a ceiling that would best mitigate IRFs that experience a decrease in the wage index while lessening the overall impact on the unadjusted base payment kept us from choosing this option. Although a few commenters recommended a permanent blended wage index (comprised of the MSA-based wage index and the CBSA-based wage index), we do not believe this is appropriate. Beginning in FY 2006, acute care hospital will receive 100 percent of the IPPS wage index based on the new CBSA wage index. From FY 2006 and forward, CMS will no longer maintain the geographic classifications based on MSAs. Therefore, MSA-based wage indexes will not be able to reflect the same amount of accuracy as they currently represent by having the geographical classification updated annually. By developing a permanent blended wage index, CMS would only be geographically updating the CBSA-based areas and not the MSA-based areas. Consequently, we believe that implementation of a permanent blended wage index would result in a wage index that is not as accurate as a wage index based on the CBSA methodology, as thoroughly discussed in section VI.B.2.d. Several commenters suggested that IRFs be afforded the same transition as adopted by IPPS (69 FR 48916, 49032-49034). Therefore, another budget neutral one-year transition policy we considered would blend the wage index for IRFs that would experience a reduction in the wage index. The blended wage index would consist of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index (both based on the FY 2001 hospital wage data), only for IRFs that experience a decrease due solely to the changes in the labor market definitions. Although some commenters recommended this transition policy, we believe that this would not allow all IRFs the ability to transition from the MSA-based wage index to the CBSA-based wage index because this transition policy only focuses on the blending the wage index for IRFs that experience a decrease in the wage index. In addition, we found that this would change the budget neutrality factor applied to the base rates from 0.9996 if there was no transition to 0.9977 under this transition policy. Therefore, the budget neutrality factor under the transition policy for only those IRFs that experience a decrease in the wage index would reduce the unadjusted base rate by approximately more than 20 dollars. The overall impact based on the reduction of the unadjusted base rate would result in all IRFs experiencing a reduction in payments. Under this approach, we found that IRFs would experience a significant reduction in the unadjusted payment amount, which would not mitigate the change in estimated payments for IRFs. The last one-year budget neutral blended transition policy we analyzed would allow all IRFs to transition from an MSA-based wage index to a CBSA-based wage index. This transition policy would be comprised of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index (both based on the FY 2001 hospital wage data) for all IRFs. As discussed in the FY 2006 IRF PPS proposed rule (70 FR 30188), the one-year blended wage index for all IRFs would result in a slight decrease of budget neutrality factor applied to the base rates from 0.9996 if there was no transition to 0.9995 under this transition policy. As a result, the budget neutrality factor applied to the unadjusted payment amount would reduce the unadjusted payment amount by approximately 1 dollar as compared to fully adopting the CBSA-based designations. This slight decrease to the unadjusted payment amount will lessen the overall payment reduction impact on all providers—regardless of urban or rural designations. Although a blended wage index for all IRFs would also help IRFs that are adversely affected by the changes from MSAs to CBSAs, it would reduce the expected higher CBSA wage index values for IRFs that are positively affected by the changes (compared to fully adopting the CBSA-based wage index). To clarify, a blended wage index for IRFs that experience any increase due to the change from an MSA-based wage index to a CBSA-based wage index would be lessened. Thus, this would allow all IRFs one year to financially prepare for a change in wage index due to the change from FY 2005 MSA-based to FY 2006 CBSA-based designations—regardless of an increase or decrease in wage index. In addition, although the blended wage index would limit the wage index increase for IRFs that experience an increase due to the change from an MSA-based wage index to a CBSA-based wage index during FY 2006, these IRFs will continue to see an increase in their wage index. However, the dampening effect of the blended wage index for IRFs that experience an increase in their wage index does not significantly impact these IRFs based solely on the wage index. The increase in the wage index these IRFs would experience would still take effect because the blended wage index would be an average of the MSA-based wage index and a CBSA-based wage index and the CBSA-based wage index would be greater than the MSA-based wage index. Therefore, IRFs in this scenario would not be significantly impacted by a blended wage index. In other words, IRFs that have higher CBSA wage index values and are subject to the blend will continue to have a benefit of having their payment derived, in part, from the higher CBSA wage index. We believe this option helps create an equitable situation for all IRFs. Many commenters urged and supported a transition to adopting the CBSA-based designations. Thus, this blended wage index (50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index and both based on the FY 2001 hospital wage data) would provide IRFs a one-year transition from the MSA-based designations to the CBSA-based designations. In addition, the one year transition of a blended wage for all IRFs would result in 93 percent of all IRFs experiencing a wage index change between a decrease by up to 2 percent or an increase by up to 2 percent. In any given year, even under the MSA-based wage index, many IRFs experience a 2 percent change in wage index and this 2 percent change would most likely be a wage index change that would not significantly impact IRF payments based solely on the wage index. Thus, from year to year, almost all IRFs are expected to experience a minimal change in wage index values. In comparison, if we fully adopted the CBSA-based wage index without a transition as proposed, 85 percent of the IRFs would experience a change between a decrease by up to 2 percent or an increase by up to 2 percent. By providing a one year transition for all IRFs to receive a blended wage index (50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index and both based on the FY 2001 hospital wage data), a larger majority of IRFs will experience a minimal change in wage index from FY 2005 to FY 2006. We decided not to provide for a longer transition, as recommended by a few commenters, because we have already, in effect, provided one year at a higher wage index level for all IRFs by retaining the previous labor market definitions for two years after the new labor market definitions became available. For example, we did not implement the new labor market area definitions as quickly as was done for facilities paid under the IPPS. Furthermore, since most IRFs benefit from a one year blended wage index, there will be minimal affect on IRFs. Thus, a one year transition is sufficient to minimize the impact of adopting the CBSA-based designations because we believe that the transition period allows IRFs sufficient time to adjust their necessary business practices. In addition to the one year blended wage index, we are implementing a longer, 3-year hold harmless transition (as discussed in this section below of this final rule (section VI.B.2.e)) for a group of IRFs that during FY 2005 are as designated as rural, and for FY 2006 will be designated as urban under the new CBSA-based geographic designation method. We are implementing a longer hold harmless transition for these IRFs because, as a group they experience a reduction in payments due to the labor market revisions and the loss of the rural adjustment. The statute confers broad authority to the Secretary under 1886(j)(6) of the Act to establish factor for area wage differences by a factor such that budget neutral wage index options may be considered. After consideration of the recommendations presented by the commenters and based on our further analysis, we will implement a budget neutral one-year transition policy such that a blended wage index (50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index that are both based on the FY 2001 hospital wage data) will apply to all IRFs. This transition policy will be effective for discharges occurring on or after October 1, 2005 and on or before September 30, 2006. This transition will mitigate the large negative impacts for IRFs that experience a decrease in the wage index and allow all IRFs to transition from the MSA-based wage index to the CBSA-based wage index for one-year. Therefore, for FY 2007 and subsequent years, we will adopt the full CBSA-based wage index for all IRFs. *Comment:* Several commenters requested CMS to consider a multi-year hold harmless policy as was implemented by IPPS. *Response:* As discussed in the August 11, 2004 IPPS final rule (69 FR at 49032), during FY 2005, a hold harmless policy was implemented to minimize the overall impact of hospitals that were in FY 2004 designated as urban under the MSA designations, but will become rural under the CBSA designations. In the same final rule, hospitals were afforded a three-year hold harmless policy because the IPPS determined that acute-care hospitals that changed designations from urban to rural will be substantially impacted by the significant change in wage index. Although we considered a hold harmless policy in our FY 2006 proposed rule, we did not propose a hold harmless policy because we believed that rural IRFs (under the MSA-based designations) that change to an urban designation (based on the CBSA-based geographic classification) would experience a significant increase to the wage index under the CBSA-based designations that would mitigate a significant decrease in payments. However, many commenters urged CMS to reconsider a hold harmless policy because the commenters demonstrated that some rural facilities would experience undue hardship with the loss of the rural adjustment under § 412.624(e)(3). In our analysis (discussed in the FY 2006 IRF PPS proposed rule (70 FR 30188)), we found that 91 percent of rural facilities that would be designated as urban under the CBSA-based definitions will experience an increase in the wage index. A majority (74 percent) of rural facilities that become urban will experience at least a 5 percent to 10 percent or more increase in wage index. Although these rural IRFs experience wage index increases, several commenters emphasized that a majority of rural providers that change designations may experience a wage index increase of at least 5 percent or more, the loss of the rural adjustment would be such a large negative impact on the rural IRFs that it may potentially cause undue hardship for these rural facilities. In response to the commenters concerns, we considered different hold harmless policies such as a multi-year hold harmless policy as well as a phase-out of the rural adjustment for rural IRFs under the MSA-based designations that received a rural adjustment of 19.14 percent in FY 2005. A commenter recommended a phase-out of the FY 2005 rural adjustment of 19.14 percent because this option allows IRFs that change designations, from rural to urban, time to adjust to the loss of the 19.14 percent rural adjustment which would result in loss of payments. Other commenters concurred that the loss of the FY 2005 rural adjustment far exceeds the urban CBSA-based increase in wage index. Thus, commenters believed that this would have significant payment implications, particularly large negative impacts for rural IRFs that change designations because they will experience significant payment losses. After further consideration of hold harmless policies as recommended by commenters, we have decided to implement a hold harmless policy to mitigate significant payment implications, particularly large negative impacts. We will implement a 3 year budget neutral hold harmless policy for those IRFs that meet the definition in § 412.602 as rural in FY 2005 and will become urban under the FY 2006 CBSA-based designations. We will afford existing IRFs designated in FY 2005 as rural IRFs (pursuant to § 412.602) and redesignated as an urban facility in FY 2006 (pursuant to § 412.602) in FY 2006, whose payment is lower because of such redesignation, a 3 year time span to adjust to the loss of the FY 2005 rural adjustment of 19.14 percent because the loss of the 19.14 percent rural adjustment would result in a significant loss of payments. This adjustment will be in addition to the one-year blended wage index (comprised of FY 2006 MSA-based wage index and FY 2006 CBSA-based wage index both based on FY 2001 hospital data) for all IRFs. Although our intent under our hold harmless policy is to mitigate the negative payment effect upon a rural facility that is redesignated as an urban facility (effective FY 2006), the hold harmless policy should not result in an IRF that comes under the hold harmless policy to realize greater payments than the IRF would have if instead the IRF would have been paid under its rural designation in FY 2006 including the FY 2005 rural adjustment of 19.14 percent. Therefore, we will make the appropriate payment modification to the additional adjustment made under our hold harmless policy so that an existing FY 2005 rural IRF that is redesignated from rural to urban in FY 2006 will in FY 2006 or FY 2007 not realize payments that are greater than what the payments would have been if the facility would have instead been paid under its rural designation in FY 2006 including the FY 2005 rural adjustment of 19.14 percent. In other words, if an existing FY 2005 IRF is redesignated from rural to urban in FY 2006, and it will realize an increase in payments during the one year transition due to the hold harmless policy, it will not receive the full two-thirds of the 19.14 percent rural adjustment. However, if this same IRF realizes a decrease in payment in FY 2007 solely because of such redesignation in FY 2006, it will receive one-third of the 19.14 percent rural adjustment in such case. As stated above, the hold harmless policy is specifically for FY 2005 rural IRFs that become urban in FY 2006 and that experience a loss in payment because of this redesignation. Thus, we are not implementing a hold harmless policy for urban facilities (under the MSA-based designation) that become rural (under the CBSA-based designation) because these IRFs will receive the updated FY 2006 rural adjustment of 21.3 percent that they did not receive in FY 2005 as an urban facility. The gain of this payment adjustment should more than mitigate the loss of the wage index decreases associated with the rural designations. For FY 2005, rural facilities that remain rural under the FY 2006 CBSA-based designation, we are not extending the hold harmless policy for these IRFs because these rural IRFs will receive the updated FY 2006 rural adjustment of 21.3 percent, which is higher than the FY 2005 rural adjustment of 19.14 percent. We are also not extending the hold harmless policy for facilities that remain in their urban geographic designations from the MSA-based designation to the CBSA-based designation because we have mitigated the impact of the change in wage index value by implementing a one year transition wage index (comprised of 50 percent FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index) for all IRFs as discussed in detail above. As was previously stated, the purpose of the hold harmless policy is to mitigate the significant payment implications for existing rural IRFs that may need time to adjust to the loss of their FY 2005 rural payment adjustment that experience a reduction in payments solely because of such redesignation. Our decision to implement the hold harmless policy only for existing FY 2005 rural IRFs that will be adversely impacted, is supported by comments received primarily requesting implementation of a method that mitigates the adverse payment impacts because of the loss of the rural adjustment. Due to our review and analysis, we determined that a 3 year budget neutral hold harmless policy would best accomplish the goals of mitigating the loss of the rural adjustment for existing FY 2005 rural IRFs. The incremental steps needed to reduce the impact of the loss of the FY 2005 rural adjustment of 19.14 percent will be phased out for years FY 2006, FY 2007, and FY 2008. Thus, the budget neutral 3 year hold harmless policy will apply to the existing FY 2005 rural IRFs (under the MSA-based designation) that will change designations and experience a reduction in payments due to the loss of the FY 2005 rural adjustment of 19.14 percent and meets the intent of this policy. The hold harmless policy will allow existing FY 2005 rural IRFs adversely affected by the change in designation to receive two-thirds of the FY 2005 rural adjustment of 19.14 percent (specifically 12.76 percent hold harmless adjustment) for FY 2006 as well as the blended wage index (comprised of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index both based on FY 2001 hospital data). For FY 2007, existing FY 2005 rural IRFs that are a part of the FY 2006 hold harmless policy will receive the full FY 2007 CBSA wage index and one-third of the FY 2005 rural adjustment of 19.14 percent (specifically, a 6.38 percent hold harmless adjustment). For FY 2008, existing FY 2005 rural IRFs that are a part of the FY 2006 hold harmless policy will receive the full FY 2008 CBSA-based wage index without a rural adjustment as long as the IRF is designated as urban under the FY 2008 CBSA-based designation (illustrated in Table 10 below). Table 10.—IRF 3-Year Hold Harmless Policy for IRFs Designated as Rural Under the MSA-Based Designation Wage Index FY 2006 50% of MSA Wage Index and 50% of CBSA Wage Index FY 2007 Full FY 2007 CBSA Wage Index FY 2008 Full FY 2008 CBSA Wage Index Rural Adjustment (Phase out)* 12.76 6.38 N/A *Based on the FY 2005 Rural Adjustment of 19.14 percent. As is shown by the table, making incremental reductions to the 19.14 percent rural adjustment that certain rural IRFs received during FY 2005 results in these IRFs still being paid a portion of that rural adjustment in FY 2006 and FY 2007. We believe that an incremental reduction of the FY 2005 rural adjustment of 19.14 percent is appropriate because of our analysis to implement a one third compared to a two thirds hold harmless adjustment of the 19.14 percent rural adjustment in FY 2006. We analyzed the 34 IRFs (in our analysis file) that would be impacted by the hold harmless policy to determine the effect on their IRF PPS payments if we did not implement a hold harmless policy. We also reviewed the payment impacts on these IRFs if the hold harmless policy implemented one third of the FY 2005 rural adjustment of 19.14 percent versus two thirds of the FY 2005 rural adjustment of 19.14 percent in FY 2006 (as described in the section XII). We found that if we did not adopt a hold harmless policy, the 34 rural IRFs that change designations from a rural facility (under the MSA-based designations) to an urban facility (under the CBSA-based designations) would experience a significant reduction in per case payment. We also considered a one year hold harmless policy that would allow the 34 IRFs in our analysis to receive a blended wage index as well as only a one third of the FY 2005 rural adjustment of 19.14 percent. Based on our analysis, a one year hold harmless policy would slightly mitigate the payment reductions for rural IRFs in our analysis file. Our analysis of whether a multi-year hold harmless policy would provide a sufficient buffer to the loss of payments, found that a 3 year hold harmless policy of two thirds of the 19.14 percent rural adjustment in the FY 2006 and one third in FY 2007 would be the most appropriate. Based on a 3 year hold harmless policy, we found these IRFs would be mitigated from significant payment reductions. We determined that a 3 year hold harmless policy that provides two thirds of the 19.14 percent adjustment in FY 2006 and one third in FY 2007 would appropriately mitigate the adverse payment impacts for existing FY 2005 rural IRFs that are designated as urban IRFs in FY 2006. To determine whether an existing FY 2005 rural IRF would meet part of the criteria for the hold harmless policy, we have developed Table 2 in the addendum. Table 2 of this addendum is a crosswalk file of counties/areas in the United States and Puerto Rico that would change from a rural MSA-based designation to an urban area under the CBSA-based designation. These areas are listed in Table 2 of the addendum to identify areas affected by the budget neutral 3 year hold harmless policy as described in this section. Table 2 of the addendum provides the State and county code, State and county name, MSA number, MSA rural designations, FY 2006 MSA-based wage index, FY 2006 CBSA-based wage index, CBSA number, CBSA urban designations, and the applicable FY 2006 transition wage index as described in section VI.2.B.e. The FIs will also be instructed to use Table 2 of the addendum to identify IRFs in these areas that will be impacted by the budget neutral 3 year hold harmless policy (as discussed in detail in this section) based on the FI's existing data in the provider specific file. As a conforming change to §412.624(e), we are finalizing the hold harmless policy by adding new paragraph (e)(7). Paragraph (e)(7) of §412.624(e) will read as follows: Adjustments for certain facilities geographically redesignated in FY 2006.
(i)*General.* For a facility defined as an urban facility under §412.602 in FY 2006 that was previously defined as a rural facility in FY 2005 as the term rural was defined in FY 2005 under §412.602 and whose payment, after applying the adjustment under this paragraph, will be lower only because of being defined as an urban facility in FY 2006 and it no longer qualified for the rural adjustment under §412.624(e)(3) in FY 2006, CMS will adjust the facility's payment using the following method:
(A)For discharges occurring on or after October 1, 2005, and on or before September 30, 2006, the facility's payment will be increased by an adjustment of two thirds of its prior FY 2005 19.14 percent rural adjustment.
(B)For discharges occurring on or after October 1, 2006, and on or before September 30, 2007, the facility's payment will be increased by an adjustment of one third of its FY 2005 19.14 percent rural adjustment.
(ii)*Exception.* For discharges occurring on or after October 1, 2005 and on or before September 30, 2007, facilities whose payments, after applying the adjustment under this paragraph (e)(7)(i) of this section, will be higher because of being defined as an urban facility in FY 2006 and no longer being qualified for the rural adjustment under 412.624(e)(3) in FY 2006, CMS will adjust the facility's payment by a portion of the applicable additional adjustment described in paragraph (e)(7)(i)(A) and (e)(7)(i)(B) of this section as determined by us. In addition, we did not receive comments regarding section 505 of the MMA that established a new section 1886(d)(13) of the Act. As discussed in the FY 2006 IRF PPS proposed rule (70 FR 30188), the new section 1886(d)(13) requires that the Secretary establish a process to make adjustments to the hospital wage index based on commuting patterns of hospital employees. We believe that this requirement for an “out-commuting” or “out-migration” adjustment applies specifically to the IPPS. Therefore, we are not implementing such an adjustment for the IRF PPS in this final rule. *Comment:* A number of commenters advised us that Table 3 of the FY 2006 IRF PPS proposed rule contained a formatting problem that resulted in provider numbers, provider names, state and county location, MSA-based designation, and CBSA-based designations to be misaligned. *Response:* Once this error was brought to our attention, we immediately published a public use file on our webpage to show the provider level table as developed in Microsoft Excel. The web address for the FY 2006 IRF PPS proposed rule's public use files may be found at *http://www.cms.hhs.gov/providers/irfpps/fy06nprm.asp.* Table 3, as published in the FY 2006 IRF PPS proposed rule (70 FR 30188), was produced for informational purposes only. Therefore, the information an IRF's FI has on file for each IRF will not be altered based on Table 3. We will not be reproducing a provider level table that crosswalks the MSA-based and CBSA-based designations for this final rule as it was only published in the proposed rule to help facilitate the public's understanding of the proposed policy. For the purposes of determining a wage index for FY 2006 IRF PPS rate year, we will publish a crosswalk table (Table 1 of this addendum) listing the State and county code, State and county name, the MSA-based designations, CBSA-based designations and the blended wage index (comprised of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-base wage index both based on the FY 2001 hospital wage data) for discharges occurring on or after October 1, 2005 and on or before September 30, 2006. In the FY 2006 IRF PPS proposed rule (70 FR 30188), we published a FY 2006 CBSA urban and rural wage index table to illustrate the proposed policy to fully adopt the FY 2006 CBSA wage index. Since we are no longer fully adopting the FY 2006 CBSA wage index, we will publish a table for FIs to determine an IRFs blended wage index values for FY 2006 (specifically a blend of 50 percent FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index). Thus, Table 1 of this addendum will be used by FIs to determine the FY 2006 one-year blended transitional wage index (comprised of FY 2006 MSA-based and FY 2006 CBSA-based wage index) as finalized in this rule. *Final Decision:* In summary (as discussed in detail above in the comments and responses, and based on further analysis of various policy options to implement the CBSA-based designations), we will implement a budget neutral one-year transition policy that blends the FY 2006 MSA-based wage index and FY 2006 CBSA-based wage index (both based on FY 2001 hospital wage data) for discharges occurring on or after October 1, 2005 and on or before September 30, 2006 for all IRFs. In addition to the blended wage index for FY 2006, we will implement a budget neutral 3 year hold harmless policy for existing FY 2005 rural IRFs that will lose the FY 2005 rural adjustment of 19.14 percent, experience a loss in payments due to the change from an MSA-based rural designation to a CBSA-based urban designation, and meets the intent of the hold harmless policy (as discussed in detail above). f. Wage Index Data In the August 7, 2001 final rule, we established an IRF wage index based on FY 1997 acute care hospital wage data to adjust the FY 2002 IRF payment rates. For the FY 2003 IRF PPS payment rates, we applied the same wage adjustment as used for FY 2002 IRF PPS rates because we determined that the application of the wage index and labor-related share used in FY 2002 provided an appropriate adjustment to account for geographic variation in wage levels that was consistent with the statute. For the FY 2004 IRF PPS payment rates, we used the hospital wage index based on FY 1999 acute care hospital wage data. For the FY 2005 IRF PPS payment rates, we used the hospital wage index based on FY 2000 acute care hospital wage data. As was proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188) and for this final rule, we will use FY 2001 acute care hospital wage data for FY 2006 IRF PPS payment rates because it is the most recent final data available. As was proposed in the FY 2006 IRF PPS proposed rule (70 FR 30188), and for this final rule, we will adopt the methodology discussed in the proposed rule (70 FR at 30188, 30241) to calculate a wage index in the event that there is no hospital data for an area (urban or rural) under the CBSA-based designations (70 FR 30188, 30241). A summary of public comments and our responses on the wage index data are discussed below: *Comment:* Many commenters argue that a majority of IRFs are hospital units and should be treated the same as hospitals whereby IRFs should be allowed to be reclassified to the same geographic area as the hospital. One commenter urged CMS to develop instructions and begin collecting IRF-specific wage index data in order to allow IRFs to establish a geographic reclassification criteria for IRFs. Commenters also urged CMS to use FY 2002 hospital wage data for the FY 2006 IRF PPS rate year because it is more current than the finalized data available. One commenter request that CMS develop a “rural floor” like that of IPPS. *Response:* In the August 1, 2001 final rule (66 FR at 41358) we established FY 2002 IRF PPS wage index values for the 2002 IRF PPS fiscal year calculated from the same data used to compute the FY 2001 acute care hospital inpatient wage index data without taking into account geographic reclassification under sections 1886(d)(8) and (d)(10) of the Act and without applying the “rural floor” under section 4410 of Pub. L. 105-33
(BBA)(as discussed in section VI.B.2.a of this final rule). Acute care hospital inpatient wage index data is also used to establish the wage index adjustment used in other PPSs (for example, LTCH, IPF, HHA, and SNF). As we discussed in the August 7, 2001 final rule (66 FR at 41316, 41358), since hospitals that are excluded from the IPPS are not required to provide wage-related information on the Medicare cost report and because we would need to establish instructions for the collection of this IRF data it is not appropriate at this time to implement a wage index specific to IRF facilities. Because we do not have an IRF specific wage index that we can compare to the hospital wage index, we are unable to determine at this time the degree, if any, to which the acute care hospital data fully represent IRF wages or if a geographic reclassification adjustment under the IRF PPS is appropriate. Although commenters request CMS to develop a “rural floor” like the IPPS, we believe the “rural floor” is applicable only to the acute care hospital payment system. Furthermore, as stated in section VI.B.2, section 4410 of the Balanced Budget Act of 1997 (Pub. L. 105-33) applies specifically to acute care hospitals and not excluded hospitals and excluded units. Thus, we believe that the acute care hospital “pre-reclassification and pre-floor” wage data is the best proxy and most appropriate wage index. In addition and as discussed above in section VI.B.2.e we will implement a blended wage index to mitigate the impacts an IRF may experience as a result of the change from MSA-based designations to CBSA-based designations. Furthermore, under the IRF PPS, IRFs are paid a rural adjustment under § 412.624(e)(3) as discussed in detail in section VI.B.4 to account for higher costs among rural facilities versus urban facilities. Although commenters request instructions to be developed in order to collect IRF specific wage data, we did not propose to develop instructions at this time. At this time, we are unable to develop a separate wage index for rehabilitation facilities. Further, in order to accumulate the data needed, we would need to make modifications to the cost report. In the future, we will continue to research wage data specific to IRF facilities. Because we do not have an IRF specific wage index that we can compare to the hospital wage index, we are unable to determine at this time the degree to which the acute care hospital data fully represents IRF wages. However, we continue to believe it is an appropriate proxy because the hospital wage data is currently the most appropriate data for adjusting payments made to IRFs. Several comments request the ability to allow IRFs to reclassify like that of acute care hospitals. To emphasize and as discussed in section VI.B.2, we believe that actual location of an IRF as opposed to the location of affiliated providers is most appropriate for determining the wage adjustment because the data support the premise that the prevailing wages in the area in which a facility is located influences the cost of a case. As demonstrated by the update rural adjustment and research conducted by RAND. The research and findings that update the rural adjustment is discussed in detail in section VI.B.4. We continue to review the facility adjustment to account for higher costs in different types of IRFs by updating our facility adjustments. *Final Decision:* We believe that a wage index based on acute care hospital wage data is the best proxy and most appropriate wage index to use in adjusting payments to IRFs, since both acute care hospitals and IRFs compete in the same labor markets. Since acute care hospitals compete in the same labor market areas as IRFs, the wage data of acute care hospitals would accurately capture the relationship of wages and wage-related costs of IRF in an area as comparable to the national average. Therefore, as we proposed in the FY 2006 proposed rule (70 FR 30188) and for this final rule, we continue to believe that a wage index based on acute care hospital data is the best and most appropriate wage index to use in adjusting payments to IRFs, since both acute care hospitals and IRFs compete in the same labor markets. Also, we will continue to use the same method for calculating wage indices as was indicated in the August 7, 2001 final rule (69 FR at 41357 through 41358). In addition, 1886(d)(8) and 1886(d)(10) of the Act which permits reclassification is applicable only to inpatient acute care hospitals at this time. The wage adjustment established under the IRF PPS is based on an IRF's actual location without regard to the urban or rural designation of any related or affiliated provider. Therefore, we continue to believe reclassification of IRFs is inappropriate at this time. In adopting the CBSA-based designations, we recognize that there may be geographic areas where there are no hospitals, and thus no hospital wage data on which to base the calculation of the IRF PPS wage index. We found that for FY 2006, this occurred in two States—Massachusetts and Puerto Rico—where, using the CBSA-based designations, there were no hospitals located in rural areas. If rural IRFs open in Massachusetts or Puerto Rico for FY 2006, we proposed and for this final rule, we are using the rural FY 2001 MSA-based hospital wage data for Massachusetts and Puerto Rico to determine the wage index of such IRFs. In other words, we proposed and as finalized in this final rule, we will use the same wage data (the FY 2001 hospital wage data) used to calculate the FY 2006 IRF wage index. However, as we proposed in the FY 2006 proposed rule (70 FR 30188), for this final rule, rather than using CBSA-based designations, we will use MSA-based designations to determine the rural wage index of any States where there is no wage data available under the CBSA-based designations. By using such MSA-based designations there will be rural wage indices for both Massachusetts and Puerto Rico. We believe this is the most reasonable approach, as we are using the same hospital wage data used to calculate the CBSA-based wage indices. In the event this occurs in urban areas where IRFs are located, as we proposed in the FY 2006 proposed rule (70 FR 30188), for this final rule, we will use the average of the urban hospital wage data throughout the State as a reasonable proxy for the urban areas without hospital wage data. Therefore, urban IRFs located in geographic areas without any hospital wage data will receive a wage index based on the average wage index for all urban areas within the State. This does not presently affect any urban IRFs for FY 2006 because there are no IRFs located in urban areas without hospital wage data. However, the policy will apply to future years when there may be urban IRFs located in geographic areas with no corresponding hospital wage data. We believe this policy is reasonable because it maintains a CBSA-based wage index system, while creating an urban proxy for IRFs located in urban areas without corresponding hospital wage data. We note that we could not apply a similar averaging in rural areas, because in the rural areas there is no State rural hospital wage data available for averaging on a State-wide basis. For example, in Massachusetts and Puerto Rico, using a CBSA-based designation system, there are simply no rural hospitals in the State upon which we could base an average. In addition, we note that the Secretary has broad authority under 1886(j)(6) to update the wage index on the basis of information available to the Secretary (and updated as appropriate) of the wages and wage-related costs incurred in furnishing rehabilitation services. Therefore, for FY 2006, as we proposed in the FY 2006 proposed rule (70 FR 30188), for this final rule, we will use FY 2001 MSA-based hospital wage data for rural Massachusetts and rural Puerto Rico in the event there are rural IRFs in such States. To clarify for rural areas without hospital wage data, we will use the most recent final years wage index available. In addition, for FY 2006 and thereafter, we are finalizing our proposed policy to calculate a statewide urban average in the event that there exist urban IRFs in geographic areas with no corresponding hospital wage data. Although we solicited comments on these approaches to calculate the wage index values for areas without hospital wage data for this and subsequent fiscal years, we did not receive any comments regarding our proposed methodology as discussed in our FY 2006 IRF PPS proposed rule. As a result, for any urban areas where there is no urban hospital wage data, we will calculate an average of the urban hospital wage data throughout the State as a reasonable proxy. For the reasons discussed above, as we proposed in the FY 2006 proposed rule (70 FR 30188), for this final rule, we will continue the use of the acute care hospital inpatient wage index data generated from cost reporting periods beginning during FY 2001 without taking into account geographic reclassification as specified under sections 1886(d)(8) and (d)(10) of the Act and without applying the “rural floor” under section 4410 of Pub. L. 105-33
(BBA)(as discussed in section VI.B.2.a of this final rule). We believe that data from FY 2001 cost reporting periods to determine the applicable wage index values under the IRF PPS in this final rule are appropriate because these are the most recent final available data. These data are the same FY 2001 acute care hospital inpatient wage data that were used to compute the IPPS FY 2005 wage indices. The final IRF wage indices are computed as follows: • Compute an average hourly wage for each urban and rural area. • Compute a national average hourly wage. • Divide the average hourly wage for each urban and rural area by the national average hourly wage—the result is a wage index for each urban and rural area. The one-year blended wage index values that are applicable for IRF PPS discharges occurring on or after October 1, 2005 and on or before September 30, 2006 are shown in Table 1 of the addendum of this final rule. In addition, for this final rule as we proposed in the FY 2006 proposed rule (70 FR 30188), any adjustment or update to the IRF wage index made as specified under section 1886(j)(6) of the Act will be made in a budget neutral manner that assures that the estimated aggregated payments under this subsection in the FY year are not greater or less than those that will have been made in the year without such adjustment. Therefore, as we proposed in the FY 2006 proposed rule (70 FR 30188), for this final rule, we will calculate a budget-neutral wage adjustment factor as specified in § 412.624(e)(1). We will continue to use the following steps to ensure that the FY 2006 IRF standard payment conversion factor reflects the one-year blended FY 2006 MSA and CBSA wage indices (both based on FY 2001 hospital wage data) and to the labor-related share in a budget neutral manner: *Step 1* Determine the total amount of the estimated FY 2005 IRF PPS rates using the FY 2005 standard payment conversion factor and the labor-related share and the wage indices from FY 2005 (as published in the July 30, 2004 final notice). *Step 2* Calculate the total amount of estimated IRF PPS payments using the FY 2005 standard payment conversion factor and the updated CBSA-based FY 2006 labor-related share and FY 2006 blended wage indices described above. *Step 3* Divide the amount calculated in step 1 by the amount calculated in step 2, which equals the FY 2006 budget-neutral wage adjustment factor of 0.9995 (as discussed in section VI.B.7 and VI.B.8). *Step 4* Apply the FY 2006 budget-neutral wage adjustment factor from step 3 to the FY 2005 IRF PPS standard payment conversion factor after the application of the market basket update, described above, to determine the FY 2006 standard payment conversion factor. 3. Teaching Status Adjustment In the FY 2006 proposed rule (70 FR 30188), we proposed to implement a teaching status adjustment for IRFs that are, or are part of, teaching institutions. Section 1886(j)(3)(A)(v) of the Act requires the Secretary to adjust the prospective payment rates for the IRF PPS by such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. Under this authority, in the August 7, 2001 final rule (66 FR 41316, 41359), we considered implementing an adjustment for IRFs that are, or are part of, teaching institutions. However, because the results of our regression analysis, using FY 1999 data, showed that the indirect teaching cost variable was not significant, we did not implement a payment adjustment for indirect teaching costs in that final rule. The regression analysis conducted by RAND for the FY 2006 proposed rule (70 FR 30188), using FY 2003 data, shows that the indirect teaching cost variable is significant in explaining the higher costs of IRFs that have teaching programs. Therefore, we proposed to establish a facility level adjustment to the Federal per discharge base rate for IRFs that are, or are part of, teaching institutions for the reasons discussed below (the “teaching status adjustment”). The purpose of the proposed teaching status adjustment is to account for the higher indirect operating costs experienced by facilities that participate in graduate medical education programs. We proposed to implement the proposed teaching status adjustment in a budget neutral manner (that is, keeping estimated aggregate payments for FY 2006 with the proposed teaching adjustment the same as estimated aggregate payments for FY 2006 without the proposed teaching adjustment) for the reasons discussed below. (As a conforming change, we proposed to revise § 412.624 by adding a new section (e)(4) as the teaching status adjustment. Specifically, § 412.624(e)(4) would be for discharges on or after October 1, 2005. We proposed to adjust the Federal prospective payment on a facility basis by a factor that we specified for facilities that are teaching institutions or units of teaching institutions. We proposed that this adjustment be made on a claim basis as an interim payment and the final payment in full for the claim would be made during the final settlement of the cost report. Thus, we proposed to redesignate the current (e)(4) and (e)(5) as (e)(5) and (e)(6)). Medicare makes direct graduate medical education
(GME)payments (for direct costs such as resident and teaching physician salaries, and other direct teaching costs) to all teaching hospitals including those paid under the IPPS, and those that were once paid under the TEFRA rate of increase limits but are now paid under other PPSs. These direct GME payments are made separately from payments for hospital operating costs and are not part of the PPSs. However, the direct GME payments may not address the higher indirect operating costs which may often be experienced by teaching hospitals. For teaching hospitals paid under the TEFRA rate-of-increase limits, Medicare did not make separate medical education payments because payments to these hospitals were based on the hospitals' reasonable costs. Because payments under TEFRA were based on hospitals' reasonable costs, the higher indirect costs that might be associated with teaching programs would automatically have been factored into the TEFRA payments. When the IRF PPS was implemented, we did not adjust payments to IRFs for indirect medical education costs because we did not find that adjustments for such costs were supported by the regression analyses or by the impact analyses. As discussed in the August 7, 2001 final rule (69 FR 41316, 41359), the indirect teaching variable was not significant for either the fully specified regression or the payment regression in RAND's analysis. Furthermore, the impacts among the various classes of facilities reflecting the fully phased-in IRF PPS illustrated that IRFs with the highest measure of indirect teaching would lose approximately 2 percent of estimated payments under the IRF PPS when compared with payments under TEFRA rate-of-increase limits. These impacts did not account for changes in behavior that facilities were likely to adopt in response to the inherent incentives of the IRF PPS, and we believed that IRFs could change their behavior to mitigate any potential reduction in payments. The earlier research conducted by RAND was based on 1999 data and on a sample of IRFs. RAND recently conducted research to support us in developing potential refinements to the IRF classification system and the PPS. The regression analysis conducted by RAND for this final rule, using FY 2003 data, showed that the indirect teaching cost variable is significant in explaining the higher costs of IRFs that have teaching programs. In conducting the analysis on the FY 2003 data, RAND used the resident counts that were reported on the hospital cost reports (worksheet S-3, Part 1, line 25, column 9 for freestanding IRF hospitals and worksheet S-3, Part 1, line 14 (or line 14.01 for subprovider 2), column 9 for rehabilitation units of acute care hospitals). That is, for the freestanding rehabilitation hospitals, RAND used the number of residents and interns reported for the entire hospital. For the rehabilitation units of acute care hospitals, RAND used the number of residents and interns reported for the rehabilitation unit (reported separately on the cost report from the number reported for the rest of the hospital). RAND did not distinguish between different types of resident specialties, nor did they distinguish among the different types of services residents provide, because this information is not reported on the cost reports. RAND used regression analysis (with the logarithm of costs as the dependent variable) to re-examine the effect of IRFs' teaching status on the costs of care. With FY 2003 data that include all Medicare-covered IRF discharges, RAND found a statistically significant difference in costs between IRFs with teaching programs and those without teaching programs in the regression analysis. The different results obtained using the FY 2003 data (compared with the 1999 data) may be due to improvements in IRF coding after implementation of the IRF PPS. More accurately coded data may have allowed RAND to determine better the differences in case mix among hospitals with and without teaching programs, which would then have allowed the effect of whether or not an IRF has a teaching program to become significant in the regression analysis. There are two main reasons that indirect operating costs may be higher in teaching hospitals:
(1)Because the teaching activities themselves result in inefficiencies that increase costs, and
(2)because patients needing more costly services tend to be treated more often in teaching hospitals than in non-teaching hospitals, that is, the case mix that is drawn to teaching hospitals. Quantifying more precisely the amount of cost increase that is due to teaching hospitals' case mix allows RAND to more precisely quantify the amount of increase due to the inefficiencies associated with a teaching program. We proposed to treat the teaching status adjustment as an additional payment to the Federal prospective payment rate, similar to the IME payments made under the IPPS (see § 412.105). In addition, we proposed that the teaching status adjustments for the IRF PPS facilities would be made on a claim basis as interim payments, but the final payment in full for the cost reporting period would be made through the cost report. The difference between those interim payments and the actual teaching status adjustment amount computed in the cost report would be adjusted through lump sum payments/recoupments when the cost report is filed and later settled. As in the IPF PPS, we proposed to calculate a teaching adjustment based on the IRF's “teaching variable,” which would be one plus the ratio of the number of FTE residents training in the IRF (subject to limitations described further below) to the IRF's average daily census (ADC). In RAND's cost regressions for the FY 2006 proposed rule (70 FR 30188), using data from FY 2003, the logarithm of the teaching variable had a coefficient value of 1.083. We proposed to convert this cost effect to a teaching status payment adjustment by treating the regression coefficient as an exponent and raising the teaching variable to a power equal to the coefficient value, then estimated at 1.083 (that is, the teaching status adjustment would be calculated by raising the teaching variable (1 + FTE residents/ADC) to the 1.083 power). For a facility with a teaching variable of 0.10, and using a coefficient based upon the coefficient value (1.083) from the FY 2003 data, this method would yield a 10.9 percent increase in the per discharge payment; for a facility with a teaching variable of 0.05, the payment would increase by 5.4 percent. We note that the coefficient value of 1.083 was based on regression analysis holding all other components of the payment system constant. In the FY 2006 proposed rule (70 FR 30188) we noted that, because we were proposing a number of other revisions to the payment system, the coefficient value was subject to change for the final rule depending on the other revisions included in the final rule. Moreover, we noted that we had concerns that IRFs' responses to other proposed changes described in the FY 2006 proposed rule (70 FR 30188) would influence the effects of a teaching variable on IRFs' costs. In addition, we proposed that the teaching adjustment limit the incentives for IRFs to add FTE residents for the purpose of increasing their teaching adjustment, as has been done in the payment systems for psychiatric facilities and acute inpatient hospitals. Thus, we proposed to impose a cap on the number of FTE residents that may be counted for purposes of calculating the teaching adjustment, similar to that established by sections 4621 (IME FTE cap for IPPS hospitals) and 4623 (direct GME FTE cap for all hospitals) of the BBA. We noted that the FTE resident cap already applies to teaching hospitals, including IRFs, for purposes of direct GME payments as specified in § 413.75 through § 413.83. The proposed cap would limit the number of residents that teaching hospitals may count for the purposes of calculating the IRF PPS teaching status adjustment, not the number of residents teaching institutions can hire or train. The proposed FTE resident cap would be identical in freestanding teaching rehabilitation hospitals and in distinct part rehabilitation units with GME programs. Similar to the regulations for counting FTE residents under the IPPS as described in § 412.105(f), we proposed to calculate a number of FTE residents that trained in the IRF during a “base year” and use that FTE resident number as the cap. An IRF's FTE resident cap would ultimately be determined based on the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2003. We also proposed that, similar to new IPPS teaching hospitals, IRFs that first begin training residents after November 15, 2003 would initially receive an FTE cap of “0”. The FTE caps for new IRFs (as well as existing IRFs) that start training residents in a new GME program (as defined in § 413.79(l)) may be subsequently adjusted in accordance with the policies that are being applied in the IPF PPS (as described in § 412.424(d)(1)(iii)(B)(2)), which in turn are made in accordance with the policies described in 42 CFR 413.79(e) for IPPS hospitals. However, contrary to the policy for IME FTE resident caps under the IPPS, we would not allow IRFs to aggregate the FTE resident caps used to compute the IRF PPS teaching status adjustment through affiliation agreements. We proposed these policies because we believe it is important to limit the total pool of resident FTE cap positions within the IRF community and avoid incentives for IRFs to add FTE residents in order to increase their payments. In proposing not to allow affiliation agreements, we also wanted to avoid the possibility of hospitals transferring residents between IPPS and IRF training settings in order to increase Medicare payments. We recognize that under the regulations applicable to the IPPS IME adjustment, a new teaching hospital that trains residents from an existing program (not a new program as defined in 42 CFR 413.79(l)) can receive an adjustment to its IME FTE cap by entering into a Medicare GME affiliation agreement (see § 412.105(f)(1)(vi), § 413.75(b), and § 413.79(f)) with other hospitals. However, this option would not be available to new teaching IRFs because, as noted above, we proposed not to allow IRFs to aggregate the FTE resident caps used to compute the IRF PPS teaching adjustment through affiliation agreements. We also proposed that residents with less than full-time status and residents rotating through the rehabilitation hospital or unit for less than a full year be counted in proportion to the time they spend in their assignment with the IRF (for example, a resident on a full-time, 3-month rotation to the IRF would be counted as 0.25 FTEs for purposes of counting residents to calculate the ratio). No FTE resident time counted for purposes of the IPPS IME adjustment would be allowed to be counted for purposes of the teaching status adjustment for the IRF PPS. We proposed that the denominator used to calculate the teaching status adjustment under the IRF PPS would be the IRF's average daily census
(ADC)from the current cost reporting period because it is closely related to the IRF's patient load, which determines the number of interns and residents the IRF can train. We also believe the ADC is a measure that can be defined precisely and is difficult to manipulate. Although the IPPS IME adjustment uses the hospital's number of beds as the denominator, the capital PPS (as specified at § 412.322) and the IPF PPS (as specified at § 412.424) both use the ADC as the denominator for the indirect graduate medical education adjustments. If a rehabilitation hospital or unit has more FTE residents in a given year than in the base year (the base year being used to establish the cap), we would base payments in that year on the lower number (the cap amount). This approach would be consistent with the IME adjustment under the IPPS and the IPF PPS. The IRF would be free to add FTE residents above the cap amount, but it would not be allowed to count the number of FTE residents above the cap for purposes of calculating the teaching adjustment. This means that the cap would be an upper limit on the number of FTE residents that may be counted for purposes of calculating the teaching status adjustment. IRFs could adjust their number of FTE residents counted for purposes of calculating the teaching adjustment as long as they remained under the cap. On the other hand, if a rehabilitation hospital or unit were to have fewer FTE residents in a given year than in the base year (that is, fewer residents than its FTE resident cap), an adjustment in payments in that year would be based on the lower number (the actual number of FTE residents the facility hires and trains). We proposed to implement the teaching status adjustment in such a way that total estimated aggregate payments to IRFs for FY 2006 would be the same with and without the proposed adjustment (that is, in a budget neutral manner). This is because we believe that the results of RAND's analysis of 2002 and 2003 IRF cost data suggest that additional money does not need to be added to the IRF PPS. RAND's analysis found, for example, that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. We noted that we were open to examining other evidence regarding the amount of aggregate payments in the system. An adjustment to payments based on an IRF's teaching status is consistent with section 1886 (j)(3)(A)(v) of the Act, which confers broad statutory authority upon the Secretary to adjust the per payment unit payment rate by such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. In the FY 2006 proposed rule, we discussed some concerns we had with implementing a teaching status adjustment at this time, including concerns about the volatility of the data, concerns about the effect that other proposed changes could have on the magnitude of the teaching status adjustment, and concerns about the best way to count residents who provide services to IRF patients. These concerns are described in more detail in the FY 2006 proposed rule (70 FR 30188). As a result of these concerns, we specifically solicited comments on our consideration of a teaching status adjustment. Public comments and our responses on the proposed teaching status adjustment are summarized below. *Comment:* Several commenters questioned CMS's rationale for not allowing affiliation agreements, if CMS is only concerned about not increasing the pool of residents in IRFs. One commenter suggested that allowing affiliation agreements among IRFs would not necessarily increase the total pool of residents in IRFs. *Response:* In the FY 2006 proposed rule (70 FR 30188), we stated that we are not allowing IRFs to enter into affiliation agreements with IPPS hospitals for the purposes of aggregating the FTE resident caps because we want to avoid the possibility that hospitals will transfer residents between IPPS and IRF training settings in order to increase Medicare payments. In deciding on our proposal not to allow affiliation agreements under the IRF PPS, we considered several factors. First, in general, we considered that IPPS hospitals provide training to residents in a wide range of specialties. Because of the wide variety of training provided, IPPS hospitals often need to send residents to train at other hospitals, since the case mix of one hospital might not be sufficiently broad to provide residents with an acceptable range of training opportunities in a particular specialty. The broad nature of the training offered at IPPS hospitals, and hence, the need to cross-train residents, is a primary reason for permitting IPPS hospitals under the Balanced Budget Act of 1997 to enter into GME affiliation agreements with other IPPS hospitals. However, because IRFs are a highly specialized type of provider, we do not believe that a significant amount of cross-training is required among IRFs. Although we imagine that there could be instances in which residents training in one IRF could receive a different type of training experience in another IRF, we believe these situations are likely to be limited and do not warrant having an affiliation agreement policy to allow IRFs to aggregate their FTE resident caps for the teaching status adjustment. Furthermore, we note that even without a specific affiliations policy, IRFs are not precluded from cross-training residents amongst themselves or with IPPS hospitals. If cross-training is necessary, it can be done in such a way that the overall number of FTE residents training in each facility remains unchanged. Accordingly, we are finalizing our proposed policy to not create a specific GME affiliation provision for the IRF teaching status adjustment. In the future, if we find there is in fact a need to allow affiliation agreements among IRFs, we may consider revising this policy in a future rulemaking process. *Comment:* Several commenters noted possible inaccuracies in the teaching status information for a few of the facilities in the rate setting file we posted on the CMS website in conjunction with the FY 2006 proposed rule (70 FR 30188). *Response:* To clarify, the rate setting file posted on the CMS website will not be used to determine payments for providers. The fiscal intermediaries use their own data files to determine whether the IRFs under their responsibility qualify for teaching status adjustment payments and the amounts of any such payments. Therefore, if providers have concerns about their particular teaching status data, they should contact their fiscal intermediaries to ensure that the fiscal intermediaries have the correct information. With regard to the information in the rate setting file posted on the CMS website, this information was used to compute the value of the coefficient used as the exponent in the formula for the proposed teaching status adjustment. Consequently, we asked RAND to investigate the accuracy of the information. RAND has made the appropriate corrections to the information and, using the revised information, has recomputed the coefficient used as the exponent. Based on this and the incorporation of the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule), we have revised the exponent from 1.083, which is what we had proposed in the FY 2006 proposed rule (70 FR 30188), to 0.9012 for this final rule. *Comment:* Several commenters objected to our proposal to implement the proposed teaching adjustment based on analysis of one year of data. However, several other commenters suggested that such concerns were unfounded and did not warrant overriding RAND's statistically valid findings. *Response:* Since publication of the FY 2006 proposed rule (70 FR 30188), RAND has further analyzed FY 2002 and FY 2003 data, and has found that the teaching status variable is significantly related to costs in both sets of data. Furthermore, we believe that IRFs with teaching programs may have been underrepresented in the 1998 and 1999 data used to construct the IRF PPS, and that this may have contributed to the lack of a statistically significant finding using the pre-PPS data. In addition, the statistically significant difference in costs between teaching and non-teaching facilities has been validated in other inpatient settings, including IPPS hospitals and IPFs. Therefore, we are reassured that this result does not represent an aberration based on only a single year's data, but instead represents a result of using more recent, more complete data. However, we will continue to evaluate the need for this adjustment in the future. If we later find that the other refinements described in this final rule constitute enough of an improvement to the system by more appropriately accounting for the variation in costs among different types of IRF patients that the teaching status adjustment becomes unnecessary, we will consider eliminating the adjustment in the future. However, we believe there is enough evidence at this time that IRFs with teaching programs have higher costs to implement the adjustment. *Comment:* One commenter requested that CMS change the data that will be used to establish the FTE resident cap for IRFs from our proposal to use IRFs' most recent cost reporting periods ending on or before November 15, 2003, to use IRFs' most recent cost reporting periods ending on or before November 15, 2004 to ensure that the FTE resident caps will be based on the most accurate historical resident count data possible. *Response:* We agree with this commenter and are revising our methodology for setting the FTE resident cap accordingly. Since we published the FY 2006 proposed rule (70 FR 30188), the FTE resident cap used for the teaching status adjustment for IPFs has been set similarly based on cost reporting periods ending on or before November 15, 2004. We believe this change is appropriate and maintains consistency within the Medicare program. *Comment:* One commenter requested that CMS have a process in place for re-examining the teaching status data, especially the data used to set the FTE resident cap, so that facilities would have the opportunity to rectify any problems with the data that might affect payments. *Response:* We agree with this commenter. We recognize that there may be problems with some of the resident count data on the historical cost reports, since this data has not previously been used for payment adjustments in the IRF PPS. For this reason, we proposed in the FY 2006 proposed rule (70 FR 30188) that an IRF's FTE resident cap would ultimately be determined based on the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2003 and, based on this and the previous comment (refer to the response above), we are changing this to the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2004. We believe this will allow facilities the opportunity to ensure the accuracy of the FTE resident count data before the final settlement of the cost report data. In case this does not occur, we will authorize the fiscal intermediaries to resolve any disputes that may occur regarding the data used to set an IRF's FTE resident cap and correct any inaccuracies. With regard to the FTE resident count data or the average daily census data used to compute an IRF's teaching status adjustment, we specifically note in this final rule that any teaching status adjustments for the IRF PPS facilities will be made on a claim basis as interim payments, but the final payments in full for the cost reporting periods will be made through the final settlement of the cost report. The difference between the interim payments and the actual teaching status adjustment amounts computed in the cost reports will be adjusted through lump sum payments/recoupments when the cost report is filed and later settled. We believe this process gives providers and fiscal intermediaries ample opportunity to ensure that the data used to compute the teaching status adjustment payments is as complete and accurate as possible. As the proposed teaching status adjustment is implemented, we will monitor the situation and issue further guidance to the fiscal intermediaries as necessary to ensure fair and accurate payments for this adjustment. *Comment:* The majority of commenters expressed support for CMS eventually implementing an IRF teaching status adjustment, especially since teaching IRFs were likely underrepresented in the 1998 and 1999 data used in the August 7, 2001 final rule to design the IRF PPS. However, while supporting the adjustment, several commenters suggested that CMS wait to implement a teaching status adjustment for at least a year, until data from FY 2004 (or later) can be analyzed. *Response:* CMS considered carefully the suggestion to wait an additional year or more before implementing the proposed teaching status adjustment. However, RAND's regression analyses of calendar year 2002 and FY 2003 data both support the need for a teaching status adjustment for IRFs because they both indicate that IRFs with teaching programs have significantly higher costs than IRFs without teaching programs. Given RAND's findings, we believe it is important to adjust IRF payments accordingly in order to better align IRF payments with the costs of care. In addition, we believe it is important to maintain consistency with other parts of the Medicare program, such as the IPF PPS that recently instituted a teaching status adjustment for IPFs based on regression analysis that shows that IPFs with teaching programs have significantly higher costs than IPFs without teaching programs. *Comment:* Several commenters strongly disagreed with the proposed implementation of a teaching status adjustment for IRFs. Among the reasons cited were that it was based on analysis of a single year of data, that it would support inefficiencies in teaching hospitals (when the purpose of a PPS is to encourage providers to operate efficiently), that the data do not adequately support the need for a teaching status adjustment, that it would reduce payments to non-teaching hospitals, and that teaching hospitals would likely continue to operate even if they do not receive the adjustment. *Response:* We carefully considered these comments. However, we continue to believe that an IRF teaching status adjustment is warranted at this time because RAND's regression analysis, based on calendar year 2002 and FY 2003 data shows that IRFs with teaching programs have significantly higher costs than non-teaching IRFs. Although we do not believe it is appropriate to encourage or perpetuate inefficiencies, we believe that IRFs with teaching programs provide a valuable service to beneficiaries and to the Medicare program. To the extent that the residency training services, therefore, lead to higher indirect costs of providing care, we believe it is important to recognize these differences and encourage access to care in these facilities. While, as one commenter notes, teaching IRFs more than likely would continue to operate even without the IRF teaching status adjustment, the intent of the adjustment is to better align payments in these facilities with the costs of care. Furthermore, we believe that IRFs with teaching programs may have been underrepresented in the 1998 and 1999 data used to construct the IRF PPS, and that this may have contributed to the lack of a statistically significant finding using the pre-PPS data. In addition, the statistically significant difference in costs between teaching and non-teaching facilities has been validated in other inpatient settings, including IPPS hospitals and IPFs. We proposed, and are finalizing in this final rule, to implement the IRF teaching status adjustment in a budget neutral manner in order to ensure that estimated aggregate payments to IRFs for FY 2006 will be the same with or without the teaching status adjustment. Given that the impact on IRFs without teaching programs of this provision is not large (see Table 13 of this final rule), we do not believe that implementing the teaching status adjustment in a budget neutral manner will unduly affect non-teaching IRFs. However, the teaching status adjustment will help to better align payments with the costs of care in teaching IRFs. Furthermore, we believe that a teaching status adjustment for IRFs is consistent with the teaching status adjustment recently implemented in the IPF PPS. *Comment:* One commenter suggested that CMS track the percentage of time residents spend in the rehabilitation unit of the hospital to compute the teaching adjustment, instead of using the resident and intern to ADC ratio we proposed in the proposed rule. *Response:* This information is not currently captured in the cost report data, which would make this suggestion substantially more difficult to implement than the teaching status variable we proposed in the FY 2006 proposed rule (70 FR 30188). We also believe that collecting this type of information would impose additional costs on acute care hospitals that have IRF units, because they would be required to record the amount of time residents spend on rehabilitation units. We also believe that it would be difficult if not impossible to audit this type of information. *Comment:* One commenter suggested that CMS focus the teaching adjustment on rehabilitation education programs, to the exclusion of other resident training programs. *Response:* Information on resident specialties is not currently reported in the cost report data. We believe that collecting and reporting this new type of data would impose undue additional costs on IRFs and on hospitals that have IRF units. Furthermore, we believe that this policy would contradict the way that residency programs traditionally operate because they require residents from different specialties to rotate in different areas of the hospital to gain experience in various areas of medicine. *Comment:* One commenter recommended that an exception process be allowed to enable IRF teaching programs to apply for an increase in their cap should a compelling reason arise, such as an expansion of the teaching hospital or unit or the addition of a new program. *Response:* Similar to the GME resident cap policy for IPPS hospitals, we will not allow exceptions to the FTE resident caps for IRFs due to expansions of existing facilities or additions of new teaching programs. As we indicated previously, we believe it is important to limit the total pool of FTE resident cap positions within the IRF community. *Final Decision:* After carefully considering all of the comments we received on the proposed IRF teaching status adjustment, we are finalizing our decision to adopt the proposed policy in this final rule, with the following revisions. In RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule), the logarithm of the teaching variable has a coefficient value of 0.9012 (as opposed to the coefficient value of 1.083 we proposed in the FY 2006 proposed rule (70 FR 30188)). In the final policy, we are converting this cost effect to a teaching status payment adjustment by treating the regression coefficient as an exponent and raising the teaching variable to a power equal to the coefficient value of 0.9012 (that is, the teaching status adjustment would be calculated by raising the teaching variable (1 + FTE residents/ADC) to the 0.9012 power). Secondly, based on a commenter's suggestion, we are changing the base period for determining an IRF's FTE resident cap from the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2003, which was what we had proposed in the FY 2006 proposed rule (70 FR 30188), to the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2004. Thus, the policy in the IRF PPS would be consistent with the FTE resident cap policy in the IPF PPS. 4. Adjustment for Rural Location In the FY 2006 proposed rule (70 FR 30188), we proposed to update the adjustment to the Federal prospective payment amount for IRFs located in rural areas from 19.14 percent to 24.1 percent, based on analysis of FY 2003 data. Consistent with the broad statutory authority conferred upon the Secretary in section 1886(j)(3)(A)(v) of the Act, we adjust the Federal prospective payment amount associated with a CMG to account for an IRF's geographic wage variation, low-income patients and, if applicable, teaching status and location in a rural area, as described in § 412.624(e). Under the broad statutory authority conferred upon the Secretary in section 1886(j)(3)(A)(v) of the Act, we proposed to increase the adjustment to the Federal prospective payment amount for IRFs located in rural areas from 19.14 percent to 24.1 percent. We proposed this change because RAND's regression analysis, using the best available data we had (FY 2003), indicated that rural facilities had 24.1 percent higher costs of caring for Medicare patients than urban facilities. We noted that we proposed to use the same statistical approach, as described in the November 3, 2000 proposed rule (65 FR 66304, 66356 through 66357) and adopted in the August 7, 2001 final rule (66 FR at 41359) to estimate the proposed update to the rural adjustment. The statistical approach RAND used when the PPS was first implemented, for the FY 2006 proposed rule (70 FR 30188), and for this final rule relies on the coefficient determined from the regression analysis. The 19.14 percent rural adjustment has been applied to payments for IRFs located in rural areas since the implementation of the IRF PPS. We noted that the FY 2003 data are the best available data we have, just as the 1998 and 1999 data used in the initial development of the IRF PPS were the best available data at that time. We proposed to implement the proposed update to the rural adjustment so that total estimated aggregate payments for FY 2006 are the same with the proposed update to the adjustment as they would have been without the proposed update to the adjustment (that is, in a budget neutral manner). We proposed to make this update to the rural adjustment in a budget neutral manner because we believed and continue to believe that the results of RAND's analysis of 2002 and 2003 IRF cost data (as discussed previously in section IV of this final rule) suggest that additional money does not need to be added to the IRF PPS. RAND's analysis found, for example, that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. This is consistent with section 1886(j)(3)(A)(v) of the Act which confers broad statutory authority upon the Secretary to adjust the per payment unit payment rate by such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. To ensure that total estimated aggregate payments to IRFs do not change, we proposed to apply a factor to the standard payment amount to ensure that the estimated aggregate payments under this subsection in the FY are not greater or less than those that would have been made in the year without the proposed update to the adjustment. In sections VI.B.7 and VI.B.8 of this final rule, we discuss the methodology and factor we proposed to apply to the standard payment amount. Public comments and our responses on the proposed update to the rural adjustment are summarized below. *Comment:* Overall, commenters generally supported this proposal. Some said that CMS should delay implementing the proposal until the full effects of the 75 percent rule can be analyzed. *Response:* For the reasons discussed in section IV of this final rule, we do not believe we should wait until the full effects of the 75 percent rule can be analyzed before implementing any of the proposed changes in this final rule. Making the changes now does not preclude us from making additional revisions in the future if we find any potential effects of the 75 percent rule on IRFs' case mix or cost structures that would warrant such refinements. *Comment:* One commenter expressed concerns that the proposed increases to the facility-level adjustments would encourage inefficiencies in the provision of care. *Response:* While we agree with the commenter that one of the purposes of a PPS is to encourage the efficient provision of services, we also believe it is important to recognize that certain providers, such as those operating in rural areas, may incur higher costs than other providers, for reasons largely beyond their control. To encourage the efficient provision of care in rural areas, so that Medicare beneficiaries have adequate access to IRF services in these areas, we believe it is important to recognize the differential in costs between urban and rural providers. *Final Decision:* After carefully considering all of the comments we received on this proposed change to the rural adjustment, we are finalizing our decision to adopt the update to the rural adjustment in this final rule, with the following change. In RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule), rural facilities were found to have 21.3 percent higher costs of caring for Medicare patients than urban facilities (rather than the 24.1 percent we proposed in the FY 2006 proposed rule (70 FR 30188)). Thus, we are implementing a rural adjustment of 21.3 percent. 5. Adjustment for Disproportionate Share of Low-Income Patients In the FY 2006 proposed rule (70 FR 30188), we proposed to update the low-income patient
(LIP)adjustment to the Federal prospective payment rate, based on analysis of FY 2003 data. Consistent with the broad statutory authority conferred upon the Secretary in section 1886(j)(3)(A)(v) of the Act, we adjust the Federal prospective payment amount associated with a CMG to account for an IRF's geographic wage variation, low-income patients and, if applicable, teaching status and location in a rural area, as described in § 412.624(e). Under the broad statutory authority conferred upon the Secretary in section 1886(j)(3)(A)(v) of the Act, we proposed to update the low-income patient
(LIP)adjustment to the Federal prospective payment rate to account for differences in costs among IRFs associated with differences in the proportion of low-income patients they treat. RAND's regression analysis of 2003 data indicates that the LIP formula could be updated to better distribute current payments among facilities according to the proportion of low-income patients they treat. Although the formula used prior to October 1, 2005 appropriately distributed LIP-adjusted payments among facilities when the IRF PPS was first implemented, we believe the formula should be updated from time to time to reflect changes in the costs of caring for low-income patients. The proposed LIP adjustment is based on the formula used to account for the costs of furnishing care to low-income patients as discussed in the August 7, 2001 final rule (67 FR at 41360). We proposed to update the LIP adjustment from the power of 0.4838 to the power of 0.636. Therefore, the formula we proposed to use to calculate the LIP adjustment was as follows: (1 + DSH patient percentage) raised to the power of (0.636) ER15AU05.000 We note that we proposed to use the same statistical approach, as described in the August 7, 2001 final rule (66 FR at 41359 through 41360), that was used to develop the original LIP adjustment. We note that the FY 2003 data we proposed to use in calculating this adjustment are the best available data, just as the 1998 and 1999 data used in the initial development of the IRF PPS were the best available data at that time. We proposed to implement this update to the LIP adjustment so that total estimated aggregate payments for FY 2006 would be the same with the proposed update to the adjustment as they would have been without the update to the adjustment (that is, in a budget neutral manner). We proposed to make this proposed update to the LIP adjustment in a budget neutral manner because we believed and continue to believe that the results of RAND's analysis of 2002 and 2003 IRF cost data (as discussed previously in this final rule) suggest that additional money does not need to be added to the IRF PPS. RAND's analysis found, for example, that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. This is consistent with section 1886 (j)(3)(A)(v) of the Act which confers broad statutory authority upon the Secretary to adjust the per payment unit payment rate by such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. To ensure that total estimated aggregate payments to IRFs do not change, we proposed to apply a factor to the standard payment amount to ensure that the estimated aggregate payments under this subsection in the FY are not greater or less than those that would have been made in the year without the proposed update to the adjustment. In sections VI.B.7 and VI.B.8 of this final rule, we discuss the methodology and factor we proposed to apply to the standard payment amount. Public comments and our responses on the proposed update to the LIP adjustment are summarized below. *Comment:* Overall, commenters generally supported this proposal. Some said that CMS should delay implementing the proposal until the full effects of the 75 percent rule can be analyzed. *Response:* For the reasons discussed in section IV of this final rule, we do not believe we should wait until the full effects of the 75 percent rule can be analyzed before implementing any of the proposed changes in this final rule. Making the changes now does not preclude us from making additional revisions in the future if we find any potential effects of the 75 percent rule on IRFs' case mix or cost structures that would warrant such refinements. *Comment:* One commenter expressed concerns that the proposed increases to the facility-level adjustments would encourage inefficiencies in the provision of care. *Response:* While we agree with the commenter that one of the purposes of a PPS is to encourage the efficient provision of services, we also believe it is important to recognize that certain providers, such as those providers that treat a higher proportion of low-income patients, may incur higher costs than other providers, for reasons largely beyond their control. To encourage the efficient provision of care among providers that treat a large number of low-income patients, so that low-income Medicare beneficiaries have adequate access to IRF services, we believe it is important to recognize the higher costs these providers incur. *Final Decision:* After carefully considering all of the comments we received on this proposed change to the LIP adjustment, we are finalizing our decision to adopt the proposed policy in this final rule, with the following change. Based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule), we are updating the LIP adjustment to the power of 0.6229 (rather than the value of 0.636 we proposed in the FY 2006 proposed rule (70 FR 30188)). Therefore, the formula for calculating the LIP adjustment will be as follows: (1 + DSH patient percentage) raised to the power of (0.6229) where the DSH patient percentage = ER15AU05.001 6. Update to the Outlier Threshold Amount In the FY 2006 proposed rule (70 FR 30188), we proposed to update the outlier threshold amount, based on analysis of FY 2003 data. Consistent with the broad statutory authority conferred upon the Secretary in sections 1886(j)(4)(A)(i) and 1886(j)(4)(A)(ii) of the Act, we proposed to update the outlier threshold amount from the $11,211 threshold amount for FY 2005 to $4,911 in FY 2006 to maintain total estimated outlier payments at 3 percent of total estimated payments. In the August 7, 2001 final rule, we discussed our rationale for setting estimated outlier payments at 3 percent of total estimated payments (66 FR at 41362). In the FY 2006 proposed rule (70 FR 30188), we proposed to continue using 3 percent for the same reasons outlined in the August 7, 2001 final rule. We believed and continue to believe that it is necessary to update the outlier threshold amount because RAND's analysis of the calendar year 2002 and FY 2003 data indicates that total estimated outlier payments will not equal 3 percent of total estimated payments in FY 2006 unless we update the outlier loss threshold. We will continue to analyze the estimated outlier payments for subsequent years and adjust as appropriate in order to maintain estimated outlier payments at 3 percent of total estimated payments. The reasons for estimated outlier payments not equaling 3 percent of total estimated payments are discussed in more detail below. Section 1886(j)(4) of the Act provides the Secretary with the authority to make payments in addition to the basic IRF prospective payments for cases incurring extraordinarily high costs. In the August 7, 2001 final rule, we codified at § 412.624(e)(4) of the regulations (which we proposed to redesignate as § 412.624(e)(5) in the FY 2006 proposed rule (70 FR 30188)) the provision to make an adjustment for additional payments for outlier cases that have extraordinarily high costs relative to the costs of most discharges. Providing additional payments for outliers strongly improves the accuracy of the IRF PPS in determining resource costs at the patient and facility level because facilities receive additional compensation over and above the adjusted Federal prospective payment amount for uniquely high-cost cases. These additional payments reduce the financial losses that would otherwise be caused by treating patients who require more costly care and, therefore, reduce the incentives to underserve these patients. Under § 412.624(e)(4) (which we proposed to redesignate as § 412.624(e)(5) in the FY 2006 proposed rule (70 FR 30188)), we would make outlier payments for any discharges if the estimated cost of a case exceeds the adjusted IRF PPS payment for the CMG plus the adjusted threshold amount. In the FY 2006 proposed rule (70 FR 30188), we proposed to make this $4,911, which would then be adjusted for each IRF by the facility's wage adjustment, its LIP adjustment, its rural adjustment, and its teaching status adjustment, if applicable. In the FY 2006 proposed rule (70 FR 30188), we stated that we would calculate the estimated cost of a case by multiplying the IRF's overall cost-to-charge ratio by the Medicare allowable covered charge. In accordance with § 412.624(e)(4) (which we proposed in the FY 2006 proposed rule (70 FR 30188) to redesignate as § 412.624(e)(5)), we also stated that we would pay outlier cases 80 percent of the difference between the estimated cost of the case and the outlier threshold (the sum of the adjusted IRF PPS payment for the CMG and the adjusted fixed threshold dollar amount). Consistent with the broad statutory authority conferred upon the Secretary in sections 1886(j)(4)(A)(i) and 1886(j)(4)(A)(ii) of the Act, and in accordance with the methodology stated in the August 1, 2003 final rule (68 FR at 45692 through 45693), we proposed in the FY 2006 proposed rule (70 FR 30188) to continue to apply a ceiling to an IRF's cost-to-charge ratios (CCR). Also, in the August 1, 2003 final rule (68 FR at 45693 through 45694), we stated the methodology we use to adjust IRF outlier payments and the methodology we use to make these adjustments. We indicated that the methodology is codified in § 412.624(e)(4) (which we proposed in the FY 2006 proposed rule (70 FR 30188) to redesignate as § 412.624(e)(5)) and § 412.84(i)(3). On February 6, 2004, we issued manual instructions in Change Request 2998 stating that we would set forth the upper threshold (ceiling) and the national CCRs applicable to IRFs in each year's annual notice of prospective payment rates published in the **Federal Register** . The upper threshold CCR for IRFs that we proposed in the FY 2006 proposed rule (70 FR 30188) for FY 2006 would be 1.52 based on CBSA-based geographic designations. We proposed to base this upper threshold CCR on the CBSA-based geographic designations because the CBSAs are the geographic designations we proposed in the FY 2006 proposed rule (70 FR 30188) to adopt for purposes of computing the proposed wage index adjustment to IRF payments for FY 2006. In addition, in the FY 2006 proposed rule (70 FR 30188), we proposed to update the national urban and rural CCRs for IRFs. Under § 412.624(e)(4) (which we proposed in the FY 2006 proposed rule (70 FR 30188) to redesignate as § 412.624(e)(5)) and § 412.84(i)(3), we proposed to apply the national CCRs to the following situations: • New IRFs that have not yet submitted their first Medicare cost report. • IRFs whose operating or capital CCR is in excess of 3 standard deviations above the corresponding national geometric mean. • Other IRFs for whom accurate data with which to calculate either an operating or capital CCR (or both) are not available. In the FY 2006 proposed rule (70 FR 30188), we proposed to use the national CCR based on the facility location of either urban or rural in each of the three situations cited above. Specifically, for FY 2006, we estimated a proposed national CCR of 0.631 for rural IRFs and 0.518 for urban IRFs. For new facilities, we proposed to use these national ratios until the facility's actual CCR could be computed using the first tentative settled or final settled cost report data, which would then be used for the subsequent cost report period. In the August 7, 2001 final rule (66 FR at 41362 through 41363), we describe the process by which we calculate the outlier threshold. In the FY 2006 proposed rule (70 FR 30188), we proposed to use this same process for the FY 2006 IRF PPS. We proposed to simulate aggregate payments with and without an outlier policy, and then apply an iterative process to determine a threshold that would result in the simulated outlier payments being equal to 3 percent of total simulated payments under the simulation. In the FY 2006 proposed rule (70 FR 30188), we noted that the simulation analysis used to calculate the proposed outlier threshold amount included all of the other proposed changes to the PPS discussed in the FY 2006 proposed rule (70 FR 30188). As stated in the FY 2006 proposed rule (70 FR 30188), we proposed to continue to analyze the estimated outlier payments for subsequent years and adjust as appropriate in order to maintain estimated outlier payments at 3 percent of total estimated payments. In the FY 2006 proposed rule (70 FR 30188), we proposed to update the threshold amount so that estimated outlier payments would continue to equal 3 percent of total estimated payments under the IRF PPS. RAND found that 2002 outlier payments were equal to 3.1 percent of total payments in 2002. Nevertheless, the outlier loss threshold is affected by cost-to-charge ratios because the cost-to-charge ratios are used to compute the estimated cost of a case, which in turn is used to determine if a particular case qualifies for an outlier payment or not. For example, if the cost-to-charge ratio decreases, then the estimated costs of a case with the same reported charges would decrease. Thus, the chances that the case would exceed the outlier loss threshold and qualify for an outlier payment would decrease, decreasing the likelihood that the case would qualify for an outlier payment. If fewer cases were to qualify for outlier payments, then total estimated outlier payments could fall below 3 percent of total estimated payments. As we discussed in the FY 2006 proposed rule (70 FR 30188), our analyses of cost report data from FY 1999 through FY 2002 (and projections for FY 2004 through FY 2006) indicate that the overall cost-to-charge ratios in IRFs have been falling since the IRF PPS was implemented. We are still analyzing possible reasons for this finding. However, because cost-to-charge ratios are used to determine whether a particular case qualifies for an outlier payment, this drop in the cost-to-charge ratios is likely responsible for much of the drop in total estimated outlier payments below 3 percent of total estimated payments. Thus, as we discussed in the FY 2006 proposed rule (70 FR 30188), the outlier threshold would need to be lowered for FY 2006 in order that total estimated outlier payments would equal 3 percent of total estimated payments. In addition, we proposed in the FY 2006 proposed rule (70 FR 30188) to adjust the outlier threshold for FY 2006 because RAND's analysis of calendar year 2002 and FY 2003 data indicates that many of the other proposed changes discussed in the FY 2006 proposed rule (70 FR 30188) would affect what the outlier threshold would need to be in order for total estimated outlier payments to equal 3 percent of total estimated payments. The outlier loss threshold is affected by the definitions of all other elements of the IRF PPS, including the structure of the CMGs and the tiers, the relative weights, the policies for very short-stay cases and for cases in which the patient expires in the facility (that is, cases that qualify for the special CMG assignments), and the facility-level adjustments (such as the rural adjustment, the LIP adjustment, and the proposed teaching status adjustment). In the FY 2006 proposed rule (70 FR 30188), we proposed to change many of these components of the IRF PPS. For the reasons discussed above and in the FY 2006 proposed rule (70 FR 30188), then, we believed and continue to believe that it is appropriate to update the outlier loss threshold for FY 2006. We also stated in the FY 2006 proposed rule (70 FR 30188) that we expect to continue to adjust the outlier threshold in the future when the data indicate that total estimated outlier payments would deviate from equaling 3 percent of total estimated payments. Public comments and our responses on the proposed update to the outlier threshold amount are summarized below. *Comment:* One commenter suggested that CMS notify fiscal intermediaries that, as a result of the lowering of the outlier threshold amount, more cases would likely qualify for outlier payments. Such notification would enable the fiscal intermediaries to adjust their systems accordingly. *Response:* We agree with the commenter's suggestion and will notify the fiscal intermediaries about the change to the outlier threshold amount and the implications of this for the number of cases that qualify for outlier payments. *Comment:* Several commenters requested that CMS incorporate any unused outlier payments from years in which aggregate outlier payments are below the 3 percent target back into the base payments. *Response:* We have responded to similar comments a number of times in the context of other prospective payment systems, including in rules at 70 FR 24168, 24196-24197, 57 FR 39784, 58 FR 46347, 59 FR 45408, 60 FR 45856, 61 FR 27496, and 56 FR 43227, 61 FR 46229-46230. As we have explained before and as explained below, we do not make adjustments to PPS payment rates to account for differences between projected and actual outlier payments in a previous year. We believe our outlier policies are consistent with the statute and the goals of the prospective payment system and are equitable. In accordance with section 1886(j)(4) of the Act, we implemented the IRF PPS outlier policy at 42 CFR 412.624(d)(1). These regulations provide that CMS determines a reduction factor equal to the estimated proportion of additional outlier payments described in paragraph (e)(4) of this section (which is redesignated as (e)(5) in this final rule). We set outlier criteria before the beginning of each fiscal year so that outlier payments are projected to equal 3 percent of estimated total IRF PPS payments. In doing so, we use the best available data at the time to make our estimates. We do not believe that Congress intended that the standardized amounts for a given fiscal year should be adjusted (upward or downward) to reflect any difference between projected and actual outlier payments for a past year. Payments for a given discharge in a given fiscal year are generally intended to reflect or address the average costs of that discharge in that year; that goal would be undermined if we adjusted PPS payments to account for “underpayments” or “overpayments” in other years. Outlier payments are “funded” through a prospective adjustment to the base rates. We do not set money aside into a discrete “pool” dedicated solely for outlier payments. Outlier payments are based on estimates. If outlier payments for a given year turn out to be greater than projected, we do not recoup money from hospitals; if outlier payments for a given year are lower than projected, we do not make an adjustment to account for the difference. If estimates turn out to be inaccurate, we believe the more appropriate action is to continue to examine the outlier policy and to try to refine the methodology for setting outlier thresholds. Thus, consistent with this approach, for this final rule we are finalizing our decision to update the outlier threshold amount to $5,132 for FY 2006 to make estimated outlier payments equal to 3 percent of total estimated IRF PPS payments in FY 2006. *Comment:* One commenter indicated a concern about the methodology used by CMS to estimate cost and charge growth for the purposes of calculating the outlier threshold amount. This commenter recommended an alternative methodology for the IPPS and encouraged CMS to apply that same methodology to the IRF PPS to ensure that the full 3 percent of outlier funds is used. *Response:* We have reviewed the comments submitted for consideration in the IPPS, and we appreciate the alternative methodologies suggested by the commenters and have considered them carefully. The cost-to-charge ratio applied to charges provides Medicare the most accurate measure of a provider's per-case cost for the purpose of paying for high-cost outlier cases at the point that we process the initial claim. The cost-to-charge ratio is based on the providers' own cost and charge information as reported by the providers. For the purposes of this final rule, we have used the same methodology for projecting cost and charge growth that is used in the IPPS and in other Medicare payment systems, and we believe this methodology is appropriate for IRFs for the same reasons it is appropriate for IPPS hospitals. This methodology ensures that we pay the appropriate amounts over and above the standard PPS payment amount for unusually high-cost cases. *Comment:* Overall, commenters generally supported the proposal to decrease the outlier threshold. Some said that CMS should delay implementing the proposal until the full effects of the 75 percent rule can be analyzed. *Response:* For the reasons discussed in section IV of this final rule, we do not believe we should wait until the full effects of the 75 percent rule can be analyzed before implementing any of the proposed changes in this final rule. Making the changes now does not preclude us from making additional revisions in the future if we find any potential effects of the 75 percent rule on IRFs' case mix or cost structures that would warrant such refinements. *Final Decision:* After carefully considering all of the comments we received on this proposed change to the outlier threshold amount, we are finalizing our decision to adopt the proposed policy in this final rule (including the redesignation of § 412.624(e)(4) as § 412.624(e)(5)), with the following change. Using data from FY 2003, and including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule), RAND has calculated the outlier threshold amount of $5,132 (instead of the $4,911 outlier threshold amount we proposed in the FY 2006 proposed rule (70 FR 30188)) that would maintain estimated outlier payments at 3 percent of total estimated IRF payments for FY 2006. Therefore, we are finalizing our decision to set the FY 2006 outlier loss threshold at $5,132. In addition, we are finalizing our decision to adopt the proposed upper threshold CCR for IRFs for FY 2006 of 1.52 based on CBSA-based geographic designations. We are basing this upper threshold CCR on the CBSA-based geographic designations because the CBSAs are the geographic designations we are adopting (with a one-year transition policy as described in section VI.B.2.e of this final rule) for the purposes of computing the wage index adjustment to IRF payments for FY 2006. We are also finalizing our decision to update the national urban and rural CCRs for IRFs. Under § 412.624(e)(4) (which we are redesignating as § 412.624(e)(5) in this final rule), we will apply the national CCRs to the following situations: • New IRFs that have not yet submitted their first Medicare cost report. • IRFs whose operating or capital CCR is in excess of 3 standard deviations above the corresponding national geometric mean. • Other IRFs for whom data with which to calculate either an operating or capital CCR (or both) are not available. The national CCR based on the facility location of either urban or rural will be used in each of the three situations cited above. Specifically, for FY 2006, we are adopting a national CCR of 0.631 for rural IRFs and 0.518 for urban IRFs. For new facilities, we will use these national ratios until the facility's actual CCR can be computed using the first tentative settled or final settled cost report data, which will then be used for the subsequent cost report period. 7. Budget Neutrality Factor Methodology for Fiscal Year 2006 In the FY 2006 proposed rule (70 FR 30188), we proposed to make a revision (for FY 2006) to the methodology found in § 412.624(d) in order to make the proposed changes to the tiers and CMGs, the rural adjustment, the LIP adjustment, and the proposed teaching status adjustment in a budget neutral manner. Accordingly, we proposed to revise § 412.624(d) by adding a section § 412.624(d)(4) for fiscal year 2006 and, as applicable, for fiscal years thereafter to the extent the adjustments are updated in the future. Specifically, we proposed to revise the methodology found in § 412.624(d) by adding a new paragraph (d)(4). The addition of this paragraph would provide for the application of a factor, as specified by the Secretary, which would be applied to the standard payment amount in order to make the proposed changes described in the preamble of the FY 2006 proposed rule (70 FR 30188) in a budget neutral manner for FY 2006. In addition, this paragraph would be used in future years if we propose refinements to the above-cited adjustments. *Final Decision:* We did not specifically receive any comments on the proposed budget neutrality factor methodology for FY 2006. Therefore, we are finalizing our decision to adopt this budget neutrality factor methodology for FY 2006, with the change that we are incorporating HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) into the data we used previously to compute the budget neutrality factors. Based on RAND's analysis of FY 2003 data, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) and using the methodology described in section VI.B.8 of this final rule, we will apply the market basket increase factor (estimated for this final rule to be 3.6 percent) to the standard payment conversion factor for FY 2005 ($12,958), which equals $13,425. Then, we will apply a one-time reduction to the standard payment amount of 1.9 percent to adjust for coding changes that increased payment to IRFs (as discussed in section VI.A of this final rule), which equals $13,169. We will then apply the budget neutral wage adjustment (as discussed in section VI.B.2.f of this final rule) of 0.9995 to $13,169, which will result in a standard payment amount of $13,163. For FY 2006 and any applicable FYs thereafter, to the extent any of the adjustments are updated, we will apply budget neutrality factors to the standard payment amount using § 412.624(c)(3)(ii), which incorporates by reference § 412.624(d)(4), for the applicable changes to the tiers and CMGs, the rural adjustment, the LIP adjustment, and the teaching status adjustment we are finalizing in this final rule. We note that even if we do not update any of the adjustments (and therefore utilize § 412.624(d)(4)), we will use § 412.624(c)(3) to update the payment rates for FY 2006 and thereafter. The next section contains a detailed explanation of these budget neutrality factors we are finalizing in this final rule, including the steps for computing these factors and how they will affect total estimated aggregate payments and estimated payments to individual IRF providers. The factors we will apply (as discussed in the next section) are 0.9995 for the tier and CMG changes, 0.9889 for the teaching status adjustment, 0.9961 for the change to the rural adjustment, and 0.9851 for the change to the LIP adjustment. We have combined these factors, by multiplying the four factors together, into one budget neutrality factor for all four of these changes (0.9995 * 0.9889 * 0.9961 * 0.9851 = 0.9699). We will apply this overall budget neutrality factor to $13,163, resulting in a standard payment conversion factor for FY 2006 of $12,767. Note that the FY 2006 standard payment conversion factor will be lower than it was in FY 2005 because it needs to be reduced to ensure that estimated aggregate payments for FY 2006 will remain the same as they otherwise would have been without the proposed changes. If we do not decrease the standard payment conversion factor, each of the changes we are finalizing in this final rule would increase total estimated aggregate payments by increasing payments to rural and teaching facilities, and to facilities with a higher average case mix of patients and facilities that treat a higher proportion of low-income patients. To assess how overall estimated payments to a particular type of IRF will likely be affected by any of the changes we are finalizing in this final rule, please see Table 13 of this final rule. The FY 2006 standard payment conversion factor would be applied to each CMG relative weight shown in Table 4, Relative Weights for Case-Mix Groups, to compute the unadjusted IRF prospective payment rates for FY 2006 shown in Table 12. To further clarify, the budget neutrality factors described above will only be applied for FY 2006 and in applicable years thereafter to the extent the adjustments are updated. Therefore, for fiscal years 2006 and thereafter, we will generally use the methodology as described in § 412.624(c)(3)(ii). 8. Description of the Methodology Used To Implement the Changes in a Budget Neutral Manner Section 1886(j)(2)(C)(i) of the Act confers broad statutory authority upon the Secretary to adjust the classification and weighting factors in order to account for relative resource use. In addition, section 1886(j)(2)(C)(ii) provides that insofar as the Secretary determines that such adjustments for a previous fiscal year (or estimates of such adjustments for a future fiscal year) did (or are likely to) result in a change in aggregated payments under the classification system during the fiscal year that are a result of changes in the coding or classification of patients that do not reflect real changes in case mix, the Secretary shall adjust the per payment unit payment rate for subsequent years to eliminate the effect of such coding or classification changes. Similarly, section 1886(j)(3)(A)(v) of the Act confers broad statutory authority upon the Secretary to adjust the per discharge payment rate by such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among IRFs. Consistent with this broad statutory authority, we proposed in the FY 2006 proposed rule (70 FR 30188) to better distribute aggregate payments among IRFs to more accurately reflect their case mix and the increased costs associated with IRFs that have teaching programs, are located in rural areas, or treat a high proportion of low-income patients. Furthermore, to ensure that total estimated aggregate payments to IRFs would not change with these proposed changes, we also proposed in the FY 2006 proposed rule (70 FR 30188) to apply a factor to the standard payment amount for each of the proposed changes to ensure that estimated aggregate payments in FY 2006 would not be greater or less than those that would have been made in the year without the proposed changes. *Final Decision:* We did not specifically receive any comments on the description of the methodology used to implement the changes in a budget neutral manner. Therefore, we are finalizing our decision to adopt this budget neutrality factor methodology for FY 2006, with the change that we are incorporating HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) into the data we used previously to compute the budget neutrality factors. Based on RAND's analysis of FY 2003 data, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) and using the methodology described below, we will apply the budget neutrality factors to the standard payment amount for each of the changes described below to ensure that estimated aggregate payments in FY 2006 will be the same with or without the changes. We are finalizing our decision in this final rule to calculate these four factors using the following steps: *Step 1* Determine the FY 2006 IRF PPS standard payment amount using the FY 2005 standard payment conversion factor increased by the estimated market basket of 3.6 percent (estimated for this final rule) and reduced by 1.9 percent to account for coding changes (as discussed in section VI.A of this final rule). *Step 2* Multiply the CBSA-based budget neutrality factor discussed in this preamble by the standard payment amount computed in step 1 to account for the wage index and labor-related share (0.9995), as discussed in section VI.B.2.f of this final rule. *Step 3* Calculate the estimated total amount of IRF PPS payments for FY 2006 (with no change to the tiers and CMGs, no teaching status adjustment, and no changes to the rural and LIP adjustments). *Step 4* Apply the new tier and CMG assignments (as discussed in section V of this final rule) to calculate the estimated total amount of IRF PPS payments for FY 2006. *Step 5* Divide the amount calculated in step 3 by the amount calculated in step 4 to determine the factor (0.9995) that maintains the same total estimated aggregate payments in FY 2006 with and without the changes to the tier and CMG assignments. *Step 6* Apply the factor computed in step 5 to the standard payment amount from step 2, and calculate estimated total IRF PPS payment for FY 2006. *Step 7* Apply the change to the rural adjustment (as discussed in section VI.B.4 of this final rule) to calculate the estimated total amount of IRF PPS payments for FY 2006. *Step 8* Divide the amount calculated in step 6 by the amount calculated in step 7 to determine the factor (0.9961) that keeps total estimated payments in FY 2006 the same with and without the change to the rural adjustment. *Step 9* Apply the factor computed in step 8 to the standard payment amount from step 6, and calculate estimated total IRF PPS payment for FY 2006. *Step 10* Apply the change to the LIP adjustment (as discussed in section VI.B.5 of this final rule) to calculate the estimated total amount of IRF PPS payments for FY 2006. *Step 11* Divide the amount calculated in step 9 by the amount calculated in step 10 to determine the factor (0.9851) that maintains the same total estimated aggregate payments in FY 2006 with and without the change to the LIP adjustment. *Step 12* Apply the factor computed in step 11 to the standard payment amount from step 9, and calculate estimated total IRF PPS payments for FY 2006. *Step 13* Apply the teaching status adjustment (as discussed in section VI.B.3 of this final rule) to calculate the estimated total amount of IRF PPS payments for FY 2006. *Step 14* Divide the amount calculated in step 12 by the amount calculated in step 13 to determine the factor (0.9889) that maintains the same total estimated aggregate payments in FY 2006 with and without the teaching status adjustment. As discussed in section VI.B.9 of this final rule, the FY 2006 IRF PPS standard payment conversion factor that accounts for the new tier and CMG assignments, the changes to the rural and the LIP adjustments, and the teaching status adjustment applies the following factors: the market basket update, the reduction of 1.9 percent to account for coding changes, the budget-neutral CBSA-based wage index and labor-related share budget neutrality factor of 0.9995, the tier and CMG changes budget neutrality factor of 0.9995, the rural adjustment budget neutrality factor of 0.9961, the LIP adjustment budget neutrality factor of 0.9851, and the teaching status adjustment budget neutrality factor of 0.9889. Each of these budget neutrality factors lowers the standard payment amount. The budget neutrality factor for the tier and CMG changes lowers the standard payment amount from $13,163 to $13,156. The budget neutrality factor for the change to the rural adjustment lowers the standard payment amount from $13,156 to $13,105. The budget neutrality factor for the change to the LIP adjustment lowers the standard payment amount from $13,105 to $12,910. Finally, the budget neutrality factor for the teaching status adjustment lowers the standard payment amount from $12,910 to $12,767. As indicated previously, the standard payment conversion factor will be lowered in order to ensure that total estimated payments for FY 2006 with the changes equal total estimated payments for FY 2006 without the changes. This is because these four changes would otherwise result in an increase, on average, to total estimated aggregate payments to IRFs, because IRFs with teaching programs, IRFs located in rural areas, IRFs with higher case mix, and IRFs with higher proportions of low-income patients would receive higher payments. To maintain the same total estimated aggregate payments to all IRFs, then, we are redistributing payments among IRFs. Thus, some redistribution of payments occurs among facilities, while total estimated aggregate payments do not change. To determine how the changes we are finalizing in this final rule are estimated to affect payments among different types of facilities, please see Table 13 in this final rule. 9. Description of the IRF Standard Payment Conversion Factor for Fiscal Year 2006 In the August 7, 2001 final rule, we established a standard payment amount referred to as the budget neutral conversion factor under § 412.624(c). In accordance with the methodology described in § 412.624(c)(3)(i), the budget neutral conversion factor for FY 2002, as published in the August 7,2001 final rule, was $11,838.00. Under § 412.624(c)(3)(i), this amount reflects, as appropriate, any adjustments for outlier payments, budget neutrality, and coding and classification changes as described in § 412.624(d). The budget neutral conversion factor is a standardized payment amount and the amount reflects the budget neutrality adjustment for FY 2002. The statute required a budget neutrality adjustment only for FYs 2001 and 2002. Accordingly, we believed it was more consistent with the statute to refer to the standard payment as a standard payment conversion factor, rather than refer to it as a budget neutral conversion factor. Consequently, we changed all references to budget neutral conversion factor to “standard payment conversion factor.” Under § 412.624(c)(3)(i), the standard payment conversion factor for FY 2002 of $11,838 reflected the budget neutrality adjustment described in § 412.624(d)(2). Under the then existing § 412.624(c)(3)(ii), we updated the FY 2002 standard payment conversion factor ($11,838) to FY 2003 by applying an increase factor (the market basket) of 3.0 percent, as described in the update notice published in the August 1, 2002 **Federal Register** (67 FR at 49931). This yielded the FY 2003 standard payment conversion factor of $12,193.00 that was published in the August 1, 2002 update notice (67 FR at 49931). The FY 2003 standard payment conversion factor ($12,193) was used to update the FY 2004 standard payment conversion factor by applying an increase factor (the market basket) of 3.2 percent and budget neutrality factor of 0.9954, as described in the August 1, 2003 **Federal Register** (68 FR at 45689). This yielded the FY 2004 standard payment conversion factor of $12,525 that was published in the August 1, 2003 **Federal Register** (68 FR at 45689). The FY 2004 standard payment conversion factor ($12,525) was used to update the FY 2005 standard payment conversion factor by applying an increase factor (the market basket) of 3.1 percent and budget neutrality factor of 1.0035, as described in the July 30, 2004 **Federal Register** (69 FR at 45766). This yielded the FY 2005 standard payment conversion factor of $12,958 as published in the July 30, 2004 **Federal Register** (69 FR at 45766). In the FY 2006 proposed rule (70 FR 30188), we proposed to use the revised methodology in accordance with § 412.624(c)(3)(ii) and as described in section VI.B.7 of the FY 2006 proposed rule (70 FR 30188) to propose an update to the standard payment conversion factor for FY 2006. *Final Decision:* We did not specifically receive any comments on the proposed standard payment conversion factor for FY 2006. Therefore, we are finalizing our decision to adopt the proposed methodology for computing the standard payment conversion factor, with the change that we are incorporating HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) into the FY 2003 data we used previously to compute the final standard payment conversion factor for FY 2006. Based on RAND's analysis of FY 2003 data, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule) and using the methodology we are finalizing in section VI.B.7 and section VI.B.8 of this final rule, we will calculate the standard payment conversion factor for FY 2006 by applying the market basket increase factor (estimated for this final rule to be 3.6 percent) to the standard payment conversion factor for FY 2005 ($12,958), which equals $13,425. Then, we will apply a one-time reduction to the standard payment amount of 1.9 percent to adjust for coding changes that increased payment to IRFs, which equals $13,169. We will then apply the budget neutral wage adjustment of 0.9995 to $13,169, which will result in a standard payment amount of $13,163. Next, we will apply a budget neutrality factor for FY 2006 for the budget-neutral refinements to the tiers and CMGs, the teaching status adjustment, the rural adjustment, and the adjustment for the proportion of low-income patients (of 0.9699) to $13,163, which will result in a standard payment conversion factor for FY 2006 of $12,767. The FY 2006 standard payment conversion factor will be applied to each CMG weight shown in Table 4 of this final rule, Relative Weights for Case-Mix Groups, to compute the unadjusted IRF prospective payment rates for FY 2006 shown in Table 12 of this final rule. 10. Example of the Methodology for Adjusting the Federal Prospective Payment Rates To illustrate the methodology that we will use to adjust the Federal prospective payments (as described in section VI.B.7 and section VI.B.8 of this final rule), we provide an example in Table 11 below. Note that the methodology we are finalizing in this final rule has changed somewhat from the methodology we proposed in the FY 2006 proposed rule (70 FR 30188) because, upon further analysis, CMS discovered that the example used to illustrate the proposed adjustments to the Federal prospective payments in the FY 2006 proposed rule (70 FR 30188) did not calculate payments as accurately as the one we are finalizing in this final rule. Therefore, we have made a slight adjustment to the methodology we are finalizing in this final rule to ensure that payments are calculated as accurately as possible. Accordingly, we will multiply the teaching status adjustment, if applicable, by the wage adjusted Federal payment amount, rather than by the rural and LIP adjusted Federal payment amount as we proposed in the FY 2006 proposed rule (70 FR 30188), and add the resulting amount to the FY 2006 adjusted Federal prospective payment to compute the total FY 2006 adjusted Federal prospective payment (as illustrated in the following example). We summarize 3 examples for computing total FY 2006 adjusted Federal prospective payment rates in Table 11 below. These examples are based on 3 beneficiaries classified into CMG 0110 (without comorbidities) receiving care in 3 different hypothetical IRFs. IRFs A, B, and C have the following characteristics: • Facility A is a non-teaching IRF located in rural Duke County, Massachusetts with a disproportionate share hospital
(DSH)adjustment of 5 percent (1.031) and the FY 2006 blended wage index of 1.0216; • Facility B is a teaching IRF located in urban Queens County, New York with a disproportionate share hospital
(DSH)adjustment of 10 percent (1.0612) and a FY 2006 blended wage index of 1.3449. The teaching status adjustment of 1.0910 will also be applied; and, • Facility C is a non-teaching IRF located in Kings County, California with a disproportionate share hospital
(DSH)adjustment of 20 percent (1.1203) and a FY 2006 blended wage index of 0.9797. The Kings County, California IRF was designated as a rural facility in FY 2005 (based on the MSA designation), but is classified as urban in FY 2006 (based on the CBSA designation). Therefore, this IRF will receive a hold harmless adjustment of 12.76 percent. The hold harmless adjustment applies to IRFs that are defined as rural under § 412.602 during FY 2005 and are classified as urban under § 412.602 in FY 2006 (as discussed in detail in section VI.B.2.e). To calculate each IRF's total adjusted Federal prospective payment, we compute the wage-adjusted Federal prospective payment and multiply the result by the appropriate low-income patient adjustment, and the rural adjustment (if applicable). In order to calculate the teaching hospital adjustment (if applicable), we multiply the teaching adjustment by the Wage Adjusted Federal payment. Then, we apply the amount to the Adjusted Rural and LIP Federal Prospective Payment Rate. Table 11 illustrates the components of the adjusted payment calculation. Table 11.—Example of Computing an IRF's Federal Prospective Payment Facility A Dukes County, MA Facility B Queens County, NY Facility C Kings County, CA Federal Prospective Payment $27,686.52 $27,686.52 $27,686.52 Labor Share × 0.75865 × 0.75865 × 0.75865 Labor Portion of Federal Payment = $21,004.38 = $21,004.38 = $21,004.38 FY 2006 Transition Wage Index (shown in Table 1 in the addendum) × 1.0216 × 1.3449 × 0.9797 Wage-Adjusted Amount = $21,458.07 = $28,248.79 = $20,577.99 Nonlabor Amount $6,682.14 $6,682.14 $6,682.14 Wage-Adjusted Federal Payment $28,140.21 $34,930.93 $27,260.13 Rural Adjustment × 1.2130 × 1.0000 × 1.1276 Subtotal = $34,134.08 = $34,930.93 = $30,738.52 LIP Adjustment 1.0310 1.0612 1.1203 FY 2006 Adjusted Rural and LIP Federal Prospective Payment Rate $35,192.24 $37,068.70 $34,436.37 Wage-Adjusted Federal Payment $28,140.21 $34,930.93 $27,260.13 Teaching status adjustment × 1.0000 × 1.0900 × 1.0000 = $28,140.21 = $38,074.71 = $27,260.13 Teaching Status addition to FY 2006 Adjusted Rural and LIP Federal Prospective Payment Rate $0.00 $3,143.78 $0.00 Total FY 2006 Adjusted Federal Prospective Payment $35,192.24 $40,212.49 $34,436.37 Thus, the adjusted payment for Facility A will be $35,192.24, the adjusted payment for Facility B will be $40,212.49, and the adjusted payment for Facility C will be $34,436.37. Table 12.—FY 2006 Payment Rate Table Based on All Refinements CMG Payment Rate Tier 1 Payment Rate Tier 2 Payment Rate Tier 3 Payment Rate No Comorbidity 0101 $9,819.10 $9,318.63 $8,278.12 $8,107.05 0102 12,091.63 11,476.26 10,194.45 9,983.79 0103 14,250.53 13,525.36 12,015.02 11,767.34 0104 15,140.39 14,369.26 12,765.72 12,501.45 0105 18,171.27 17,246.94 15,321.68 15,005.06 0106 21,151.09 20,074.83 17,834.22 17,465.26 0107 24,411.78 23,169.55 20,582.96 20,159.09 0108 28,222.73 26,786.44 23,796.41 23,304.88 0109 28,056.76 26,629.41 23,655.97 23,168.27 0110 33,528.70 31,823.02 28,269.97 27,686.52 0201 10,392.34 8,714.75 7,687.01 7,210.80 0202 13,324.92 11,174.96 9,856.12 9,244.58 0203 15,942.15 13,369.60 11,791.60 11,061.33 0204 17,051.61 14,300.32 12,612.52 11,831.18 0205 20,913.62 17,539.30 15,468.50 14,509.70 0206 27,294.57 22,891.23 20,189.73 18,937.29 0207 35,309.69 29,611.78 26,117.45 24,497.32 0301 14,417.77 12,174.61 10,775.35 9,912.30 0302 18,804.51 15,879.59 14,053.91 12,927.86 0303 22,438.00 18,947.50 16,770.73 15,426.37 0304 30,922.95 26,112.35 23,112.10 21,258.33 0401 12,627.84 10,873.65 9,774.42 8,728.80 0402 17,414.19 14,996.12 13,479.40 12,036.73 0403 30,312.69 26,103.41 23,464.47 20,953.20 0404 54,345.29 46,798.72 42,067.27 37,565.62 0405 41,463.39 35,705.47 32,094.96 28,660.64 0501 9,836.97 8,233.44 7,201.86 6,458.83 0502 13,170.44 11,023.03 9,642.92 8,648.37 0503 17,460.15 14,613.11 12,783.60 11,463.49 0504 21,857.10 18,292.56 16,002.16 14,350.11 0505 25,902.97 21,679.64 18,965.38 17,006.92 0506 35,245.86 29,499.43 25,804.66 23,141.46 0601 11,445.62 9,359.49 8,893.49 8,289.61 0602 15,224.65 12,450.38 11,831.18 11,025.58 0603 19,490.10 15,938.32 15,145.49 14,115.20 0604 24,945.44 20,400.39 19,384.14 18,066.58 0701 11,560.52 9,876.55 9,275.23 8,407.07 0702 15,010.16 12,823.17 12,041.83 10,914.51 0703 18,685.78 15,963.86 14,991.01 13,587.92 0704 22,932.09 19,590.96 18,397.25 16,676.26 0801 8,376.43 7,035.89 6,522.66 5,867.71 0802 10,941.32 9,189.69 8,519.42 7,665.31 0803 16,223.03 13,624.94 12,631.67 11,363.91 0804 14,131.79 11,868.20 11,002.60 9,899.53 0805 17,793.37 14,943.77 13,854.75 12,464.42 0806 21,354.08 17,933.80 16,626.46 14,957.82 0901 10,739.60 9,776.97 8,687.94 7,775.10 0902 14,112.64 12,847.43 11,416.25 10,216.15 0903 18,618.12 16,949.47 15,061.23 13,478.12 0904 23,339.35 21,248.12 18,879.84 16,895.85 1001 12,304.83 11,347.31 10,125.51 9,335.23 1002 16,225.58 14,961.65 13,350.45 12,308.66 1003 22,822.29 21,043.85 18,778.98 17,313.33 1101 16,014.92 13,400.24 11,731.60 10,803.44 1102 23,976.43 20,060.79 17,562.29 16,173.24 1201 13,001.91 11,227.30 10,348.93 9,341.61 1202 16,828.18 14,532.68 13,395.14 12,090.35 1203 20,731.05 17,901.89 16,501.35 14,893.98 1301 13,198.52 12,278.02 10,628.53 9,393.96 1302 18,287.45 17,012.03 14,725.46 13,015.96 1303 23,373.82 21,744.75 18,822.39 16,637.95 1401 10,433.19 9,386.30 8,165.77 7,412.52 1402 14,087.11 12,672.52 11,025.58 10,008.05 1403 17,535.47 15,774.91 13,724.53 12,459.32 1404 22,238.84 20,007.17 17,405.25 15,800.44 1501 11,773.73 11,483.92 9,813.99 9,443.75 1502 14,885.05 14,517.36 12,406.97 11,939.70 1503 18,217.23 17,767.83 15,185.07 14,611.83 1504 24,017.28 23,424.89 20,019.93 19,264.13 1601 12,849.99 10,908.12 9,870.17 8,814.34 1602 17,631.23 14,968.03 13,541.96 12,094.18 1603 21,688.58 18,411.29 16,658.38 14,877.39 1701 12,897.22 12,299.73 10,625.97 9,346.72 1702 16,986.49 16,198.77 13,995.19 12,311.22 1703 20,212.71 19,275.62 16,652.00 14,648.86 1704 25,288.87 24,115.59 20,834.47 18,327.03 1801 15,471.05 12,552.51 10,526.39 9,296.93 1802 24,748.83 20,079.94 16,839.67 14,872.28 1803 44,408.73 36,031.03 30,216.94 26,686.86 1901 15,782.57 14,019.44 13,631.33 11,935.87 1902 29,570.93 26,266.83 25,539.11 22,361.40 1903 42,691.57 37,921.82 36,872.37 32,283.91 2001 11,162.19 9,430.98 8,455.58 7,720.20 2002 14,615.66 12,348.24 11,070.27 10,107.63 2003 18,881.12 15,952.37 14,301.59 13,056.81 2004 25,222.49 21,310.68 19,104.54 17,443.55 2101 27,906.11 27,906.11 20,312.30 18,846.65 5001 0.00 0.00 0.00 2810.02 5101 0.00 0.00 0.00 18,108.32 5102 0.00 0.00 0.00 20,429.75 5103 0.00 0.00 0.00 9,197.35 5104 0.00 0.00 0.00 23,964.94 VII. Quality of Care in IRFs The IRF-PAI is the patient data collection instrument for IRFs. Currently, the IRF-PAI contains a blend of the functional independence measures items and quality and medical needs questions. The quality and medical needs questions (which are currently collected on a voluntary basis) may need to be modified to encapsulate those data necessary for calculation of quality indicators in the future. We awarded a contract to the Research Triangle Institute
(RTI)with the primary tasks of identifying quality indicators pertinent to the inpatient rehabilitation setting and determining what information is necessary to calculate those quality indicators. These tasks included reviewing literature and other sources for existing rehabilitation quality indicators. It also involved identifying organizations involved in measuring or monitoring quality of care in the inpatient rehabilitation setting. In addition, RTI was tasked with performing independent testing of the quality indicators identified in their research. Once RTI has issued a final report, taking into account and responding to public comments in the **Federal Register** as part of the Paperwork Reduction Act process, we will publish our rationale for revising the IRF-PAI. Then in accordance with the Paperwork Reduction Act, we will publish our proposed revisions to the IRF-PAI and solicit public comments. The revised IRF-PAI will need to be approved by OMB before it is used in IRFs. We have supported the development of valid quality measures and have been engaged in a variety of quality improvement efforts focused in other post-acute care settings such as nursing homes. However, any new quality-related data collected from the IRF-PAI would have to be analyzed to determine the feasibility of developing a payment method that accounts for the performance of the IRF in providing the necessary rehabilitative care. Medicare beneficiaries are the primary users of IRF services. Any quality measures must be carefully constructed to address the unique characteristics of this population. Similarly, we need to consider how to design effective incentives; that is, superior performance measured against pre-established benchmarks and/or performance improvements. In addition, while our efforts to develop the various post-acute care PPSs, including the IRF PPS, have generated substantial improvements over the preexisting cost-based systems, each of these individual systems was developed independently. As a result, we have focused on phases of a patient's illness as defined by a specific site of service, rather than on the entire post-acute episode. As the differentiation among provider types (such as SNFs and IRFs) becomes less pronounced, we need to investigate a more coordinated approach to payment and delivery of post-acute services that focuses on the overall post-acute episode. This could entail a strategy of developing payment policy that is as neutral as possible regarding provider and patient decisions about the use of particular post-acute services. That is, Medicare should provide payments sufficient to ensure that beneficiaries receive high quality care in the most appropriate setting, so that admissions and any transfers between settings occur only when consistent with good care, rather than to generate additional revenues. In order to accomplish this objective, we need to collect and compare clinical data across different sites of service. In fact, in the long run, our ability to compare clinical data across care settings is one of the benefits that will be realized as a basic component of the Department's interest in the use of a standardized electronic health record
(EHR)across all settings including IRFs. It is also important to recognize the complexity of the effort, not only in developing an integrated assessment tool that is designed using health information standards, but in examining the various provider-centric prospective payment methodologies and considering payment approaches that are based on patient characteristics and outcomes. MedPAC has recently taken a preliminary look at the challenges in improving the coordination of our post-acute care payment methods, and suggested that it may be appropriate to explore additional options for paying for post-acute services. We agree that CMS, in conjunction with MedPAC and other stakeholders, should consider a full range of options in analyzing our post-acute care payment methods, including the IRF PPS. We also want to encourage incremental changes that will help us build towards these longer term objectives. For example, medical records tools are now available that could allow better coordinated discharge planning procedures. These tools can be used to ensure communication of a standardized data set that then can be used to establish a comprehensive IRF care plan. Improved communications may reduce the incidence of potentially avoidable re-hospitalizations and other negative impacts on quality of care that occur when patients are transferred to IRFs without a full explanation of their care needs. We are looking at ways that Medicare providers can use these tools to generate timely data across settings. It is important to note that some of the ideas discussed above may exceed our current statutory authority. However, we believe that it is useful to encourage discussion of a broad range of ideas for debate of the relative advantages and disadvantages of the various policies affecting this important component of the health care sector. Thus, we solicited comments on these and other approaches. *Comment:* Most commenters were supportive of the concept of providing incentives for high quality and improved patient outcomes within the structure of Medicare's payment systems. Commenters were also generally supportive of advancing approaches that resulted in more consistent payments for similar services across the various post acute care settings and a more seamless system of care, though several noted important distinctions between the type of care provided in IRF compared to other settings. For example, one commenter objected to the implication that the differentiation among provider types (such as SNFs and IRFs) could become less pronounced. This commenter stated that there is a big difference in care and rehabilitation between these two types of facilities and suggested that we ask patients about this difference. Many Commenters noted that, in advancing these policy goals, CMS should facilitate stakeholder input to ensure that the knowledge and experience of providers, beneficiaries, and others with critical knowledge is factored into the development process. *Response:* CMS appreciates the thoughtful comments provided on these important issues. By advancing a more seamless system of payments and benefits in post acute care, Medicare can ensure that patients receive high quality care in the most appropriate setting, and that decisions about where patients receive care are guided by decisions of patients and their families working with physicians, rather than in response to financial incentives or barriers created by administrative guidelines. In addition, pay for performance has the potential to promote real improvements in quality and outcomes as demonstrated by the work CMS has advanced already; for example, the Premier Hospital Demonstration. We agree with commenters that CMS should involve stakeholders and work collaboratively with providers, patients and practitioners in the field to advance these objectives. In developing additional IRF-PAI quality items and related quality measures through our research with RTI, as described in section VII above, RTI has already begun to do that by convening meetings of a Technical Expert Panel to consider the critical methodological and clinical issues. The research we are conducting through the RTI contract will provide data that will promote and advance efforts to develop and consider pay for performance approaches in IRFs, as well as approaches to measuring and rewarding quality improvement more broadly in post acute care. We also agree that, in developing a more integrated strategy for payment and care delivery within Medicare's post acute benefits, it will be important to consider not only how various provider types are similar but also how they are different. VIII. Miscellaneous Public Comments Within the Scope of the Proposed Rule *Comment:* We received a comment regarding a change made to § 412.25(a) when the inpatient psychiatric facility
(IPF)PPS was published on November 15, 2004 (69 FR 66922). The commenter requested that we add the reference to a rehabilitation unit that was removed by the IPF PPS final rule. *Response:* We agree with making the change requested by the commenter. Section 412.1 specifies the scope of part 412. In order to expand the existing scope of part 412 the IPF PPS final rule revised § 412.1 by redesignating paragraphs (a)(2) and (a)(3) as paragraphs (a)(3) and (a)(4) and adding a new paragraph (a)(2). The added paragraph (a)(2) specified that in accordance with section 124 of Pub. L. 106-113 we were establishing a per diem prospective payment system for the inpatient operating and capital costs of hospital inpatient services furnished to Medicare beneficiaries by a psychiatric facility that meets the conditions of subpart N of part 412. Redesignated as paragraph (a)(3) is the paragraph that specifies the statutory basis for the establishment of the IRF PPS. In order to conform § 412.25(a) to the revision we made as stipulated above to § 412.1 the IPF PPS final rule revised § 412.25(a), which specifies the basis for exclusion from being paid under the IPPS. Prior to publishing the IPF PPS final rule, § 412.25(a) read as follows:
(a)Basis for exclusion. In order to be excluded from the prospective payment systems specified in § 412.1(a)(1), a psychiatric or rehabilitation unit must meet the following requirements. When the IPF PPS final rule revised § 412.25(a) the intended purpose of the revision was to include a reference to new paragraph (a)(2) that, as stipulated above, we had added to § 412.1. However, when we revised § 412.25(a), we inadvertently removed the words “or rehabilitation” from the existing § 412.25(a). Therefore, in order to correct the inadvertent removal of the words “or rehabilitation” from § 412.25(a), we are making a technical correction so that § 412.25(a) will read as follows:
(a)Basis for exclusion. In order to be excluded from the prospective payment systems as specified in § 412.1(a)(1) and be paid under the inpatient psychiatric facility prospective payment system as specified in § 412.1(a)(2) or the inpatient rehabilitation facility prospective payment system as specified in § 412.1(a)(3), a psychiatric or rehabilitation unit must meet the following requirements. IX. Miscellaneous Public Comments Outside the Scope of the Proposed Rule *Comment:* We received a number of comments expressing concerns about various aspects of CMS's enforcement of the 75 percent rule. Several commenters stated that enforcement of the 75 percent rule would lead many IRFs to close, would arbitrarily exclude patients in certain RICs from receiving treatment in IRFs, and would create access to care problems for patients. *Response:* These comments are not specifically related to the proposed changes to the IRF PPS that were discussed in the FY 2006 proposed rule (70 FR 30188). We responded to similar comments in the May 7, 2004 final rule (69 FR 25752) that established the changes to the criteria for being classified as an IRF. Because the responses to these comments in the May 7, 2004 final rule are very lengthy, we refer the reader to that final rule for the detailed responses to these and other comments regarding the 75 percent rule. *Comment:* One commenter asked that we provide the algorithm (that is, the computer software) that the fiscal intermediaries use in their presumptive determinations of IRF compliance with the 75 percent rule. *Response:* We will take this into consideration, and may make the computer software available to all interested parties at a future date. *Comment:* One commenter suggested that CMS consider implementing a cost-of-living adjustment for IRFs located in Alaska, to offset higher non-labor costs in Alaska. *Response:* In the August 7, 2001 final rule (66 FR 41316, 41361), we referred to Section 1886(j)(4)(B), which authorizes, but does not require, the Secretary to take into account the unique circumstances of IRFs located in Alaska and Hawaii. In the data used to prepare the August 7, 2001 final rule, there was only one IRF in Hawaii and one in Alaska. In the August 7, 2001 final rule, we explained that, due to the small number of IRFs in Alaska and Hawaii in the data, analyses were inconclusive regarding whether a cost-of-living adjustment would improve payment equity for these facilities. Therefore, we did not implement an adjustment for facilities located in Alaska and Hawaii in the August 7, 2001 final rule. In the FY 2003 data used for the FY 2006 proposed rule (70 FR 30188) and for this final rule, there were 3 IRFs in Alaska and 1 IRF in Hawaii. We continue to believe that this may be too small a number of facilities for us to determine, based on analysis of the data, whether a cost-of-living adjustment would improve payment equity for these facilities. However, we will consider conducting such an analysis in the future. *Comment:* Some commenters suggested changes to the items on the IRF-PAI, such as deleting the transfer to tub item and revising the instructions for the items that describe preventable conditions that occur on admission to the IRF and preventable conditions that occur while the patient is in an IRF. *Response:* We have contracted with the Research Triangle Institute
(RTI)to analyze and recommend changes to the IRF-PAI that would improve our ability to assess quality of care in IRFs. Any changes to the IRF-PAI that CMS might decide to propose in the future, based on RTI's recommendations, would require clearance by the Office of Management and Budget. However, we will take the commenters suggestions into consideration. *Comment:* Several commenters suggested that CMS allow general hospitals to increase physiatrist training if they also decrease training in one or more specialties reimbursed under the inpatient PPS. *Response:* This comment does not relate to the IRF PPS and is outside the scope of this rule. We will forward it to the component of the Agency that works on the IPPS for their consideration. IX. Provisions of the Final Regulations The provisions of this final rule restate the provisions of the FY 2006 proposed rule (70 FR 30188), except as noted elsewhere in the preamble. Following is a highlight of the changes we made from the proposed rule: • We are adding 2 codes that were not on the proposed list of ICD-9-CM codes to be removed from the comorbidity tiers (V46.11 and V46.12). We are adding these codes to the list to be removed because these codes are derived from code V46.1, which was determined by RAND to have no positive impact on payment when controlling for the CMG. • We are adding the following codes to the list of comorbidities we proposed in the proposed rule: 250.1 (insulin dependent diabetes without mention of complications, not stated as controlled), code 428.1-Left Heart Failure, code 428.20-Systolic Heart Failure Unspecified, code 428.21-Systolic Heart Failure Acute, code 428.22-Systolic Heart Failure Chronic, code 428.23-Systolic Hear Failure Acute on Chronic, code 428.30-Diastolic Heart Failure Unspecified, code 428.31-Diastolic Heart Failure Acute, code 428.32-Diastolic Heart Failure Chronic, code 428.33-Diastolic Heart Failure Acute on Chronic, code 428.40-Combined Systolic and Diastolic Heart Failure Unspecified, code 428.41-Combined Systolic and Diastolic Heart Failure Acute, code 428.42-Combined Systolic and Diastolic Heart Failure Chronic, and code 428.43-Combined Systolic and Diastolic Heart Failure Acute on Chronic. For this final rule, we decided to add these codes to the list of comorbidities we proposed in the proposed rule because of the increased costs associated with these codes. After receiving the comments to add additional codes to the list of comorbidity codes used to increase the CMG payment rate, our Medical Officers, similar to RAND's TEP, believe that several of the codes suggested should be added to these tiers that increase payment for the CMG. • We are updating the market basket estimate, based on the FY 2002-based RPL market basket and the Global Insight's 2nd quarter 2005 forecast, to 3.6 percent (from 3.1 percent in the proposed rule). • We are changing our proposed policy to adopt the CBSA-based wage index without a transition to implementing the CBSA-based wage index with a budget neutral one-year blended wage index. Thus, the FY 2006 wage index is comprised of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index (both based on FY 2001 hospital wage data) for all IRFs. • We are changing our proposed policy to not adopt a hold harmless policy to adopting a budget neutral 3 year hold harmless policy for FY 2005 rural IRFs that will be classified as urban under the FY 2006 CBSA-based designations. The 3 year hold harmless policy will only apply to existing rural FY 2005 IRFs that will experience a decrease in payments due solely to the loss of the FY 2005 rural adjustment of 19.14 percent because of the adoption of the CBSA-based designations. • We are changing the exponent for the teaching status adjustment formula to 0.9012 (from 1.083 in the proposed rule), based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule). • We are changing the rural adjustment to 21.3 percent (from 24.1 percent in the proposed rule), based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule). • We are changing the exponent for the LIP adjustment formula to 0.6229 (from 0.636 in the proposed rule), based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule). • We are changing the outlier threshold amount to $5,132 (from $4,911 in the proposed rule), based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule). • We are changing the base period for determining an IRF's FTE resident cap from the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2003, which was what we had proposed in the FY 2006 proposed rule, to the final settlement of the IRF's most recent cost reporting period ending on or before November 15, 2004. • We are changing the budget neutrality factors applied to the standard payment amount in the methodology used to implement the changes in a budget neutral manner (section VI.B.8 of this final rule) to 0.9995 for the changes to the tier comorbidities and the CMGs, 0.9961 for the change to the rural adjustment, 0.9851 for the change to the LIP adjustment, and 0.9889 for the implementation of the new teaching status adjustment. These changes are necessary to ensure that the tier and CMG changes, the rural adjustment change, the LIP adjustment change, and the implementation of the new teaching status adjustment will be done in a budget neutral manner for FY 2006 (that is, such that estimated aggregate IRF payments for FY 2006 with the changes will equal estimated aggregate IRF payment in FY 2006 without the changes). • We are changing the budget neutrality factor for the wage index changes for FY 2006 to 0.9995, to ensure that the wage index changes described in section VI.B.2 of this final rule will be made in a budget neutral manner. • We are changing the standard payment conversion factor for FY 2006 to $12,767 (from $12,658 in the proposed rule), based on RAND's most recent cost regressions using data from FY 2003, including the HealthSouth home office cost data from FY 2004 (as described in detail in section IV of this final rule). X. Collection of Information Requirements This document does not impose information collection and recordkeeping requirements. Consequently, it need not be reviewed by the Office of Management and Budget under the authority of the Paperwork Reduction Act of 1995. XI. Regulatory Impact Analysis A. Introduction The August 7, 2001 final rule established the IRF PPS for the payment of Medicare services for cost reporting periods beginning on or after January 1, 2002. We incorporated a number of elements into the IRF PPS, such as case-level adjustments, a wage adjustment, an adjustment for the percentage of low-income patients, a rural adjustment, and an outlier payment policy. This final rule updates the FY 2005 IRF PPS payment rates specified in the July 30, 2004 notice (69 FR 45721) and implements policy changes with regard to the IRF PPS based on analyses conducted by RAND under contract with us on CY 2002 and FY 2003 data (updated from the 1999 data used to design the IRF PPS). In constructing these impacts, we do not attempt to predict behavioral responses, nor do we make adjustments for future changes in such variables as discharges or case-mix. We note that certain events may combine to limit the scope or accuracy of our impact analysis, because such an analysis is future-oriented and, thus, susceptible to forecasting errors due to other changes in the forecasted impact time period. Some examples of such possible events are newly legislated general Medicare program funding changes by the Congress, or changes specifically related to IRFs. In addition, changes to the Medicare program may continue to be made as a result of the BBA, the BBRA, the BIPA, or new statutory provisions. Although these changes may not be specific to the IRF PPS, the nature of the Medicare program is such that the changes may interact, and the complexity of the interaction of these changes could make it difficult to predict accurately the full scope of the impact upon IRFs. We have examined the impacts of this final rule as required by Executive Order 12866 (September 1993, Regulatory Planning and Review) and the Regulatory Flexibility Act
(RFA)and Impact on Small Hospitals (September 19, 1980, Pub. L. 96-354), section 1102(b) of the Social Security Act, the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-4), and Executive Order 13132. 1. Executive Order 12866 Executive Order 12866 (as amended by Executive Order 13258, which merely reassigns responsibility of duties) directs agencies to assess all costs and benefits of available regulatory alternatives and, if regulation is necessary, to select regulatory approaches that maximize net benefits (including potential economic, environmental, public health and safety effects, distributive impacts, and equity). A regulatory impact analysis
(RIA)must be prepared for major rules with economically significant effects ($100 million or more in any 1 year). We estimate that the cost to the Medicare program for IRF services in FY 2006 will increase by $210 million over FY 2005 levels. The updates to the IRF labor-related share and wage indices are made in a budget neutral manner. We are making changes to the CMGs and the tiers, the teaching status adjustment, and the rural and LIP adjustments in a budget neutral manner (that is, in order that total estimated aggregate payments with the changes equal total estimated aggregate payments without the changes). This means that we are improving the distribution of payments among facilities depending on the mix of patients they treat, their teaching status, their geographic location (rural vs. urban), and the percentage of low-income patients they treat, without changing total estimated aggregate payments. To redistribute payments among facilities, we lowered the base payment amount, which then gets adjusted upward for each facility according to the facility's characteristics. This redistribution will not, however, affect estimated aggregate payments to facilities. Thus, the changes to the IRF labor-related share and the wage indices, the changes to the CMGs, the tiers, and the motor score index, the teaching status adjustment, the update to the rural adjustment, and the update to the LIP adjustment have no overall effect on estimated costs to the Medicare program. Therefore, the estimated increased cost to the Medicare program is due to the combined effect of the updated IRF market basket of 3.6 percent, the 1.9 percent reduction to the standard payment conversion factor to account for changes in coding that affect total aggregate payments, and the update to the outlier threshold amount. We have determined that this final rule is a major rule as defined in 5 U.S.C. 804(2). Based on the overall percentage change in payments per case estimated using our payment simulation model (a 3.4 percent increase), we estimate that the total impact of these changes for estimated FY 2006 payments compared to estimated FY 2005 payments will be approximately a $210 million increase. This amount does not reflect changes in IRF admissions or case-mix intensity, which also may affect the overall estimated change in payments from FY 2005 to FY 2006. 2. Regulatory Flexibility Act
(RFA)The RFA requires agencies to analyze the economic impact of our regulations on small entities. If we determine that the regulation will impose a significant burden on a substantial number of small entities, we must examine options for reducing the burden. For purposes of the RFA, small entities include small businesses, nonprofit organizations, and government agencies. Most IRFs and most other providers and suppliers are considered small entities, either by nonprofit status or by having revenues of $6 million to $29 million in any 1 year. (For details, see the Small Business Administration's regulation that set forth size standards for health care industries at 65 FR 69432.) Because we lack data on individual hospital receipts, we cannot determine the number of small proprietary IRFs. Therefore, we assume that all IRFs (approximate total of 1,200 IRFs, of which approximately 60 percent are nonprofit facilities) are considered small entities for the purpose of the analysis that follows. Medicare fiscal intermediaries and carriers are not considered to be small entities. Individuals and States are not included in the definition of a small entity. 3. Impact on Rural Hospitals Section 1102(b) of the Act requires us to prepare a regulatory impact analysis for any final rule that may have a significant impact on the operations of a substantial number of small rural hospitals. This analysis must conform to the provisions of section 603 of the RFA. With the exception of hospitals located in certain New England counties, for purposes of section 1102(b) of the Act, we previously defined a small rural hospital as a hospital with fewer than 100 beds that is located outside of a Metropolitan Statistical Area
(MSA)or New England County Metropolitan Area (NECMA). However, under the new labor market definitions that we are adopting, we will no longer employ NECMAs to define urban areas in New England. Therefore, for purposes of this analysis, we now define a small rural hospital as a hospital with fewer than 100 beds that is located outside of a Metropolitan Statistical Area (MSA). As discussed in detail below, the rates and policies set forth in this final rule will not have an adverse impact on rural hospitals based on the data of the 169 rural units and 21 rural hospitals in our database of 1,188 IRFs for which data were available. 4. Unfunded Mandates Reform Act Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-4) also requires that agencies assess anticipated costs and benefits before issuing any final rule that may result in expenditures in any 1 year by State, local, or tribal governments, in the aggregate, or by the private sector, of at least $110 million. This final rule will not mandate any requirements for State, local, or tribal governments, nor will it affect private sector costs. 5. Executive Order 13132 Executive Order 13132 establishes certain requirements that an agency must meet when it promulgates a final rule that imposes substantial direct requirement costs on State and local governments, preempts State law, or otherwise has Federalism implications. We have reviewed this final rule in light of Executive Order 13132 and have determined that it will not have any negative impact on the rights, roles, or responsibilities of State, local, or tribal governments. 6. Overall Impact The following analysis, in conjunction with the remainder of this document, demonstrates that this final rule is consistent with the regulatory philosophy and principles identified in Executive Order 12866, the RFA, and section 1102(b) of the Act. We have determined that the final rule has a significant economic impact on a substantial number of small entities or a significant impact on the operations of a substantial number of small rural hospitals. B. Anticipated Effects of the Final Rule We discuss below the impacts of this final rule on the budget and on IRFs. 1. Basis and Methodology of Estimates In this final rule, we are implementing policy changes and payment rate updates for the IRF PPS. Based on the overall percentage change in payments per discharge estimated using a payment simulation model developed by RAND under contract with CMS (a 3.4 percent increase), we estimate the total impact of these changes for estimated FY 2006 payments compared to estimated FY 2005 payments to be approximately a $210 million increase. This amount does not reflect changes in hospital admissions or case-mix intensity, which also may affect the overall change in payments from FY 2005 to FY 2006. We have prepared separate impact analyses of each of the changes to the IRF PPS. RAND's payment simulation model relies on the most recent available data (FY 2003) to enable us to estimate the impacts on payments per discharge of certain changes we are implementing in this final rule. The data used in developing the quantitative analyses of estimated changes in payments per discharge presented below are taken from the FY 2003 MedPAR file and the most current Provider-Specific File that is used for payment purposes. Data from the most recently available IRF cost reports were used to estimate costs and to categorize hospitals. The data also include the FY 2004 home office costs for HealthSouth facilities, as described in section IV of the preamble to this final rule. Our analysis has several qualifications. First, we do not make adjustments for behavioral changes that hospitals may adopt in response to the policy changes, and we do not adjust for future changes in such variables as admissions, lengths of stay, or case-mix. Second, due to the interdependent nature of the IRF PPS payment components, it is very difficult to precisely quantify the impact associated with each change. Using cases in the FY 2003 MedPAR file, we simulated payments under the IRF PPS given various combinations of payment parameters. The changes discussed separately below are the following: • The effects of the annual market basket update (using the rehabilitation hospital, psychiatric hospital, and long-term care hospital
(RPL)market basket) to IRF PPS payment rates required by sections 1886(j)(3)(A)(i) and 1886(j)(3)(C) of the Act. • The effects of applying the budget-neutral labor-related share and wage index adjustment, as required under section 1886(j)(6) of the Act. • The effects of the decrease to the standard payment amount to account for the increase in estimated aggregate payments due to changes in coding, as required under section 1886(j)(2)(C)(ii) of the Act. • The effects of the budget-neutral changes to the tier comorbidities, CMGs, motor score index, and relative weights, under the authority of section 1886(j)(2)(C)(i) of the Act. • The effects of the one year budget-neutral transition policy for adopting the new CBSA-based geographic area definitions announced by OMB in June 2003. • The effects of the 3 year budget-neutral hold-harmless policy for IRFs that are rural under § 412.602 during FY 2005, but are urban under § 412.602 during FY 2006 and lose the rural adjustment resulting in a loss of estimated IRF PPS payments and meets the intent of the hold harmless policy. • The effects of the implementation of a budget-neutral teaching status adjustment, as permitted under section 1886(j)(3)(A)(v) of the Act. • The effects of the budget-neutral update to the percentage amount by which payments are adjusted for IRFs located in rural areas, as permitted under section 1886(j)(3)(A)(v) of the Act. • The effects of the budget-neutral update to the formula used to calculate the payment adjustment for IRFs based on the percentage of low-income patients they treat, as permitted under section 1886(j)(3)(A)(v) of the Act. • The effects of the change to the outlier loss threshold amount to maintain total estimated outlier payments at 3 percent of total estimated payments to IRFs in FY 2006, consistent with section 1886(j)(4) of the Act. • The total change in estimated payments based on the FY 2006 policies relative to estimated payments based on FY 2005 policies. To illustrate the impacts of the FY 2006 estimated changes, our analysis begins with a FY 2005 baseline simulation model using: IRF charges from FY 2003 inflated to FY 2005 using the market basket; the FY 2005 PRICER; the estimated percent of outlier payments in FY 2005; the FY 2005 CMG GROUPER (version 1.22); the MSA designations for IRFs based on OMB's MSA definitions prior to June 2003; the FY 2005 wage index; the FY 2005 labor-market share; the FY 2005 formula for the LIP adjustment; and the FY 2005 percentage amount of the rural adjustment. Each policy change is then added incrementally to this baseline model, finally arriving at an FY 2006 model incorporating all of the changes to the IRF PPS. This allows us to isolate the effects of each change. Note that, in computing estimated payments per discharge for each of the policy changes, the outlier loss threshold has been adjusted so that estimated outlier payments are 3 percent of total estimated payments. Our final comparison illustrates the percent change in estimated payments per discharge from FY 2005 to FY 2006. One factor that affects the changes in IRFs' estimated payments from FY 2005 to FY 2006 is that we currently estimate total outlier payments during FY 2005 to be 1.2 percent of total estimated payments. As discussed in the August 7, 2001 final rule (66 FR at 41362), our policy is to set total estimated outlier payments at 3 percent of total estimated payments. Because estimated outlier payments during FY 2005 were below 3 percent of total payments, estimated outlier payments in FY 2006 are projected to increase by an additional 1.8 percent over estimated payments in FY 2005 because of the change in the outlier loss threshold to achieve the 3 percent target. 2. Analysis of Table 13 Table 13 displays the results of our analysis. The table categorizes IRFs by geographic location, including urban or rural location and location with respect to CMS' nine regions of the country. In addition, the table divides IRFs into those that are separate rehabilitation hospitals (otherwise called freestanding hospitals in this section), those that are rehabilitation units of a hospital (otherwise called hospital units in this section), rural or urban facilities by ownership (otherwise called for-profit, non-profit, and government), and by teaching status. The top row of the table shows the overall impact on the 1,188 IRFs included in the analysis. The next twelve rows of Table 13 contain IRFs categorized according to their geographic location, designation as either a freestanding hospital or a unit of a hospital, and by type of ownership: All urban, which is further divided into urban units of a hospital, urban freestanding hospitals, by type of ownership, and rural, which is further divided into rural units of a hospital, rural freestanding hospitals, and by type of ownership. There are 998 IRFs located in urban areas included in our analysis. Among these, there are 802 IRF units of hospitals located in urban areas and 196 freestanding IRF hospitals located in urban areas. There are 190 IRFs located in rural areas included in our analysis. Among these, there are 169 IRF units of hospitals located in rural areas and 21 freestanding IRF hospitals located in rural areas. There are 354 for-profit IRFs. Among these, there are 295 IRFs in urban areas and 59 IRFs in rural areas. There are 708 non-profit IRFs. Among these, there are 603 urban IRFs and 105 rural IRFs. There are 126 government-owned IRFs. Among these, there are 100 urban IRFs and 26 rural IRFs. The following three parts of Table 13 show IRFs grouped by their geographic location within a region, and the last part groups IRFs by teaching status. First, IRFs located in urban areas are categorized with respect to their location within a particular one of nine geographic regions. Second, IRFs located in rural areas are categorized with respect to their location within a particular one of the nine CMS regions. In some cases, especially for rural IRFs located in the New England, Mountain, and Pacific regions, the number of IRFs represented is small. Finally, IRFs are grouped by teaching status, including non-teaching IRFs, IRFs with an intern and resident to ADC ratio less than 10 percent, IRFs with an intern and resident to ADC ratio greater than or equal to 10 percent and less than or equal to 19 percent, and IRFs with an intern and resident to ADC ratio greater than 19 percent. Table 13.—Projected Impact of FY 2006 Refinements to the IRF PPS Facility classification
(1)Number of IRFs
(2)Number of cases
(3)FY06 Wage Index and Labor-share
(4)Outlier
(5)Market Basket
(6)New CMG, new tiers, and motor score
(7)Rural adjust.
(8)New LIP adjust.
(9)Teach. Status adjust.
(10)1.9% reduct.
(11)Total change %
(12)Total 1,188 461,738 0.0% 1.8% 3.6% 0.0% 0.0% 0.0% 0.0% −1.9% 3.4 Urban unit 802 261,229 0.1 2.3 3.6 0.9 −0.2 0.1 0.5 −1.9 5.3 Rural unit 169 34,664 −1.3 3.1 3.6 1.7 1.3 −0.1 −0.9 −1.9 5.5 Urban hospital 196 158,968 0.2 0.5 3.6 −1.7 0.0 −0.1 −0.5 −1.9 0.0 Rural hospital 21 6,877 −1.6 7.0 3.6 −0.7 1.3 0.0 −1.0 −1.9 6.5 Urban For-Profit 295 154,526 0.4 0.7 3.6 −1.8 0.0 0.0 −0.8 −1.9 0.0 Rural For-Profit 59 11,952 −1.9 3.8 3.6 0.2 1.3 0.2 −1.0 −1.9 4.2 Urban Non-Profit 603 237,384 0.0 2.1 3.6 1.0 −0.2 0.0 0.5 −1.9 5.0 Rural Non-Profit 105 23,793 −1.0 4.1 3.6 1.7 1.3 −0.3 −0.8 −1.9 6.7 Urban Government 100 28,287 −0.2 2.5 3.6 0.5 0.0 0.5 1.7 −1.9 6.7 Rural Government 26 5,796 −1.5 2.6 3.6 1.4 1.3 0.3 −1.0 −1.9 4.8 Urban 998 420,197 0.1 1.6 3.6 −0.1 −0.1 0.0 0.1 −1.9 3.2 Rural 190 41,541 −1.4 3.8 3.6 1.2 1.3 −0.1 −0.9 −1.9 5.7 Urban by region: New England 35 20,612 −0.3 1.7 3.6 −0.7 −0.3 −0.3 −0.6 −1.9 1.1 Middle Atlantic 156 76,962 −0.4 2.0 3.6 1.1 −0.2 0.0 1.6 −1.9 5.8 South Atlantic 124 73,677 0.4 0.6 3.6 −0.5 0.1 0.0 −0.3 −1.9 1.9 East North Central 189 69,315 0.1 2.3 3.6 1.2 −0.2 −0.2 0.1 −1.9 4.9 East South Central 54 30,473 0.2 0.0 3.6 −1.4 0.4 0.1 −0.5 −1.9 0.6 West North Central 71 22,217 −0.1 2.1 3.6 0.6 −0.2 −0.1 0.1 −1.9 4.2 West South Central 184 76,088 0.5 1.8 3.6 −0.7 −0.3 −0.1 −0.5 −1.9 2.3 Mountain 69 24,287 −0.2 1.2 3.6 −2.2 −0.1 −0.1 −0.5 −1.9 −0.2 Pacific 116 26,566 0.8 2.2 3.6 −0.8 −0.3 1.1 0.0 −1.9 4.7 Rural by region: New England 4 924 0.4 2.1 3.6 1.7 1.2 −0.4 −0.9 −1.9 5.9 Middle Atlantic 19 5,377 −1.1 8.2 3.6 1.5 1.4 −0.4 −1.0 −1.9 10.3 South Atlantic 22 5,440 −1.0 2.5 3.6 1.2 1.3 0.1 −1.0 −1.9 4.8 East North Central 28 5,618 −1.0 3.0 3.6 1.9 1.2 −0.4 −0.9 −1.9 5.5 East South Central 20 5,362 −1.9 2.2 3.6 1.1 1.3 0.3 −0.7 −1.9 3.9 West North Central 30 5,351 −1.3 2.3 3.6 2.7 1.2 −0.2 −0.6 −1.9 5.8 West South Central 54 12,016 −1.7 4.3 3.6 0.3 1.3 0.1 −1.0 −1.9 4.9 Mountain 9 902 −3.2 9.4 3.6 2.6 1.2 −0.4 −0.9 −1.9 10.2 Pacific 4 551 0.9 2.8 3.6 −2.7 1.1 −0.8 −0.8 −1.9 2.0 Teaching status: Non-teaching 1,053 400,072 0.0 1.6 3.6 −0.1 0.0 −0.1 −0.9 −1.9 2.2 Resident to ADC less than 10% 71 39,888 0.3 2.5 3.6 0.3 −0.3 0.2 2.2 −1.9 7.0 Resident to ADC 10%-19% 42 17,793 −0.9 2.8 3.6 0.4 −0.3 1.1 9.1 −1.9 14.3 Resident to ADC greater than 19% 22 3,985 −0.1 4.1 3.6 0.0 −0.3 1.1 19.5 −1.9 27.4 3. Impact of the Market Basket Update to the IRF PPS Payment Rates (Using the RPL Market Basket) (Column 6) In column 6 of Table 13, we present the estimated effects of the market basket update to the IRF PPS payment rates, as discussed in section VI.B.1 of this final rule. Section 1886(j)(3)(A)(i) of the Act requires us annually to update the per discharge prospective payment rate for IRFs by an increase factor specified by the Secretary and based on an appropriate percentage increase in a market basket of goods and services comprising services for which payment is made to IRFs, as specified in section 1886(j)(3)(C) of the Act. As discussed in detail in section VI.B.1 of this final rule, we are using a new market basket that reflects the operating and capital cost structures of inpatient rehabilitation facilities, inpatient psychiatric facilities, and long-term care hospitals, referred to as the RPL market basket. The FY 2006 update for IRF PPS payments using the FY 2002-based RPL market basket and the Global Insight's 2nd quarter 2005 forecast will be 3.6 percent. In the aggregate, and across all hospital groups, the update will result in a 3.6 percent increase in overall estimated payments to IRFs. 4. Impact of the 1.9 Percent Decrease in the Standard Payment Amount To Account for Coding Changes (Column 11) In column 11 of Table 13, we present the estimated effects of the decrease in the standard payment amount to account for the increase in aggregate payments due to changes in coding that do not reflect real changes in case mix, as discussed in section VI.A of this final rule. Section 1886(j)(2)(C)(ii) of the Act requires us to adjust the per discharge PPS payment rate to eliminate the effect of coding or classification changes that do not reflect real changes in case mix if we determine that such changes result in a change in aggregate payments under the classification system. In the aggregate, and across all hospital groups, the update will result in a 1.9 percent decrease in overall estimated payments to IRFs. Thus, we estimate that the 1.9 percent reduction in the standard payment amount will result in a cost savings to the Medicare program of approximately $120 million. 5. Impact of the Changes to the CMGs and Tiers and Recalibration of Relative Weights (Column 7) In column 7 of Table 13, we present the estimated effects of the changes to the tier comorbidities, the CMGs, the motor score index, and the recalibration of the relative weights, as discussed in section V of this final rule. Section 1886(j)(2)(C)(i) of the Act requires us to adjust from time to time the classifications and weighting factors as appropriate to reflect changes in treatment patterns, technology, case mix, number of payment units for which payment under the IRF PPS is made, and any other factors which may affect the relative use of resources. As described in section V.A.3 of this final rule, we are updating the tier comorbidities to remove certain comorbid condition codes from the list of comorbid conditions used to increase payment that we believe no longer merit additional payments, moving dialysis patients to tier one to increase payments for these patients, and aligning payments with the comorbidity conditions according to their effects on the relative costliness of patients. We are also updating the CMGs and the relative weights for the CMGs so that they better reflect the relative costliness of different types of IRF patients. We are also replacing the previous, unweighted motor score index with a weighted motor score index that better estimates the relative costliness of IRF patients. Finally, we are changing the GROUPER software so that, in cases where the provider has coded a 0 for the transfer to toilet item on the IRF-PAI, the GROUPER will change this raw score of 0 to a 2 instead of a 1. To assess the impact of these changes, we compared estimated aggregate payments using the FY 2005 CMG relative weights (GROUPER version 1.22) to estimated aggregate payments using the FY 2006 CMG relative weights (GROUPER version 1.30). We note that, under the authority in section 1886(j)(2)(C)(i) of the Act and consistent with our rationale as described in section VI.B.8 of this final rule, we have applied a budget neutrality factor to ensure that the overall estimated payment impact of the tier and CMG changes is budget neutral (that is, in order that total estimated aggregate payments for FY 2006 with the change are equal to total estimated aggregate payment for FY 2006 without the change). Because we found that the relative weights we will use for calculating the FY 2006 payment rates are slightly higher, on average, than the relative weights we used in FY 2005, and that the effect of this would have been to increase estimated aggregate payments in FY 2006, the budget neutrality factor for the CMG and tier changes lowers the standard payment amount somewhat. Because the lower standard payment amount is balanced by the higher average weights, the effect is no change in overall estimated payments to IRFs. However, the distribution of estimated payments among facilities is affected, with some facilities receiving higher estimated payments and some facilities receiving lower estimated payments as a result of the tier and CMG changes, as shown in column 7 of Table 13. Although, in the aggregate, these changes will not change overall estimated payments to IRFs, as shown in the zero impact in the first row of column 7, there are distributional effects of these changes. On average, the impacts of these changes on any particular group of IRFs are very small, with urban IRFs experiencing a 0.1 percent decrease and rural IRFs experiencing a 1.2 percent increase in estimated aggregate payments. The largest impacts are a 2.7 percent increase among rural IRFs in the West North Central region and a 2.7 percent decrease among rural IRFs in the Pacific region. 6. Impact of the Adoption (With a Blended One-Year Transition) of the New CBSA Labor Market Areas and the Changes to the Labor Share (Column 4) In accordance with the broad discretion under section 1886(j)(6) of the Act, we previously defined hospital labor market areas based on the definitions of Metropolitan Statistical Areas (MSAs), Primary MSAs (PMSAs), and New England County Metropolitan Areas (NECMAs) issued by OMB as discussed in section VI.B.2 of this final rule. On June 6, 2003, OMB announced new Core-Based Statistical Areas (CBSAs), comprised of MSAs and the new Micropolitan Statistical Areas based on Census 2000 data. We are adopting the new CBSA definitions with a one-year blended transition as described in section VI.B.2 of this final rule, consistent with the inpatient prospective payment system, including the 49 new Metropolitan areas designated under the new definitions. We are also adopting CBSA definitions in New England in place of NECMAs. We are not adopting the newly defined Micropolitan Statistical Areas for use in the payment system, as Micropolitan Statistical Areas will remain part of the statewide rural areas for purposes of the IRF PPS payments, consistent with payments under the inpatient prospective payment system. The estimated effects of these changes to the new CBSA-based designations with a one year blended transition, combined with the new labor share, are isolated in column 4 of Table 13 by holding all other payment parameters constant in this simulation. That is, column 4 shows the percentage changes in estimated payments when going from a model using the FY 2005 MSA designations to a model using the FY 2006 CBSA designations blended with the FY 2006 MSA designations and using the new labor share. As described in section VI.B.2 of this final rule, we are implementing a blended wage index for FY 2006 equal to 50 percent of the FY 2006 CBSA wage index value and 50 percent of the FY 2006 MSA wage index value for all IRFs for one year. The estimated effects of this policy are shown in column 4 of table 13. Table 14 below compares the shifts in wage index values for IRFs for FY 2006 relative to FY 2005. A small number of IRFs (0.9 percent) will experience an increase of between 5 and 10 percent and 0.6 percent of IRFs will experience an increase of more than 10 percent. A small number of IRFs (0.6 percent) will experience decreases in their wage index values of at least 5 percent, but less than 10 percent. Furthermore, IRFs that will experience decreases in their wage index values of greater than 10 percent will be 0.1 percent. The following table shows the projected impact for IRFs. Table 14.—Impact of the FY 2006 Blended Transition Wage Index Percent change in area wage index Percent of IRFs Decrease Greater Than 10.0 0.1 Decrease Between 5.0 and 10.0 0.6 Decrease Between 2.0 and 5.0 2.7 Decrease Between 0 and 2.0 31.0 No Change 37.2 Increase Between 0 and 2.0 24.5 Increase Between 2.0 and 5.0 2.4 Increase Between 5.0 and 10.0 0.9 Increase Greater Than 10.0 0.6 Total 1 100.0 1 May not exactly equal 100 percent due to rounding. In addition, our analysis file consisted of 34 rural IRFs that change designations from a rural facility (under the MSA-based designations) to an urban facility (under the CBSA-based designations) and would experience estimated payment reductions due to the loss of the 19.14 percent rural adjustment. Based on our analysis, these IRFs would experience a reduction in estimated payments of between approximately $207 to up to approximately $3,070 (average amount of approximately $1,472) without a hold harmless policy. Based on our estimates, the hold harmless policy would mitigate the estimated payment reductions of those rural IRFs in our analysis file. Although, we found that 5 IRFs would experience estimated payment increases under the hold harmless policy of between approximately $9 to approximately $380, these IRFs will not receive additional payments under the hold harmless policy. The remaining 29 rural IRFs under our hold harmless policy can expect estimated payment reductions of between approximately $32 to approximately $1,167 (average amount of approximately $426) in FY 2006 compared to our estimates above. 7. Impact of the Change to the Outlier Threshold Amount (Column 5) We estimate total outlier payments in FY 2005 to be approximately 1.2 percent of total estimated payments, so we are updating the threshold from $11,211 in FY 2005 to $5,132 in FY 2006 in order to set total estimated outlier payments in FY 2006 equal to 3 percent of total estimated payments in FY 2006. The impact of this change (as shown in column 5 of table 13) is to increase total estimated payments to IRFs by about 1.8 percent. The effect on payments to rural IRFs will be to increase estimated payments by 3.8 percent, and the effect on payments to urban IRFs will be to increase estimated payments by 1.6 percent. The largest effect will be a 9.4 percent increase in estimated payments to rural IRFs in the Mountain region, and the smallest effect will be no change in estimated payments for urban IRFs located in the East South Central region. 8. Impact of the Budget-Neutral Teaching Status Adjustment (Column 10) In column 10 of Table 13, we present the estimated effects of the budget-neutral implementation of a teaching status adjustment to the Federal prospective payment rate for IRFs that have teaching programs, as discussed in section VI.B.3 of this final rule. Section 1886(j)(3)(A)(v) of the Act requires the Secretary to adjust the Federal prospective payment rates for IRFs under the IRF PPS for such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. Under the authority of section 1886(j)(3)(A)(v) of the Act, we are applying a budget neutrality factor to ensure that the overall estimated payment impact of the teaching status adjustment is budget neutral (that is, in order that total estimated aggregate payments for FY 2006 with the adjustment will equal total estimated aggregate payments for FY 2006 without the adjustment). Because IRFs with teaching programs will receive additional payments from the implementation of this new teaching status adjustment, the effect of the budget neutrality factor will be to reduce the standard payment amount, therefore reducing estimated payments to IRFs without teaching programs. By design, however, the estimated increases in payments to teaching facilities will balance the estimated decreases in payments to non-teaching facilities, and total estimated aggregate payments to all IRFs will remain unchanged. Therefore, the first row of column 10 of Table 13 contains our projection of a zero impact in the aggregate. However, the rest of column 10 gives the estimated distributional effects among different types of providers of this change. Some providers' estimated payments increase and some decrease with this change. On average, the estimated impacts of this change on any particular group of IRFs are very small, with urban IRFs experiencing a 0.1 percent estimated increase and rural IRFs experiencing a 0.9 percent estimated decrease. The largest decrease in estimated payments is a 1.0 percent decrease among freestanding rural IRFs, rural for-profit facilities, rural government-owned facilities, and rural facilities in the Middle Atlantic, South Atlantic, and West South Central regions. Overall, non-teaching hospitals will experience a 0.9 percent estimated decrease. The largest impacts are a 19.5 percent estimated increase among teaching facilities with intern and resident to ADC ratios greater than 19 percent. Teaching facilities that have intern and resident to ADC ratios greater than or equal to 10 percent and less than or equal to 19 percent will experience an estimated increase of 9.1 percent. Teaching facilities with resident and intern to ADC ratios less than 10 percent will experience an estimated increase of 2.2 percent. 9. Impact of the Update to the Rural Adjustment (Column 8) In column 8 of Table 13, we present the estimated effects of the budget-neutral update to the percentage adjustment to the Federal prospective payment rates for IRFs located in rural areas, as discussed in section VI.B.4 of this final rule. Section 1886(j)(3)(A)(v) of the Act requires the Secretary to adjust the Federal prospective payment rates for IRFs under the IRF PPS for such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. In accordance with section 1886(j)(3)(A)(v) of the Act, we are changing the rural adjustment percentage, based on FY 2003 data with an adjustment to account for the absence of HealthSouth home office costs in that year (see the discussion in section IV of the preamble to this final rule), from 19.14 percent to 21.3 percent. Because we are making this update to the rural adjustment in a budget neutral manner under the broad authority conferred by section 1886(j)(3)(A)(v) of the Act, estimated payments to urban facilities will decrease in proportion to the total increase in estimated payments to rural facilities. To accomplish this estimated redistribution of resources between urban and rural facilities, we applied a budget neutrality factor to reduce the standard payment amount. Rural facilities will receive an increase to the standard payment amount, and urban facilities will not. Overall, estimated aggregate payments to IRFs will not change, as indicated by the zero impact we project in the first row of column 8. However, estimated payments will be redistributed among rural and urban IRFs, as indicated by the rest of the column. On average, because there are a relatively small number of rural facilities, the estimated impacts of this change on urban IRFs are relatively small, with all urban IRFs experiencing a 0.1 percent estimated decrease. The estimated impact on rural IRFs is somewhat larger, with rural IRFs experiencing a 1.3 percent estimated increase. The largest estimated impacts are a 1.4 percent estimated increase among rural IRFs in the Middle Atlantic region and a 0.3 percent estimated decrease among urban facilities in the New England, West South Central, and Pacific regions, and among all categories of teaching facilities. 10. Impact of the Update to the LIP Adjustment (Column 9) In column 9 of Table 13, we present the estimated effects of the budget-neutral update to the adjustment to the Federal prospective payment rates for IRFs according to the percentage of low-income patients they treat, as discussed in section VI.B.5 of this final rule. Section 1886(j)(3)(A)(v) of the Act requires the Secretary to adjust the Federal prospective payment rates for IRFs under the IRF PPS for such factors as the Secretary determines are necessary to properly reflect variations in necessary costs of treatment among rehabilitation facilities. In accordance with section 1886(j)(3)(A)(v) of the Act, we are changing the formula for the LIP adjustment, based on FY 2003 data with an adjustment to account for the absence of HealthSouth home office costs in that year (see the discussion in section IV of the preamble to this final rule), to raise the amount of 1 plus the DSH patient percentage to the power of 0.6229 instead of the power of 0.4838. Therefore, the formula to calculate the low-income patient or LIP adjustment will be as follows: (1 + DSH patient percentage) raised to the power of (.6229) Where DSH patient percentage = ER15AU05.002 Because we are making this update to the LIP adjustment in a budget neutral manner, estimated payments will be redistributed among providers, according to their low-income percentages, but total estimated aggregate payments to facilities will not change. To do this, we applied a budget neutrality factor that lowered the standard payment amount in proportion to the amount of estimated payment increase that is attributable to the increased LIP adjustment payments. This will result in no change to estimated aggregate payments, which is reflected in the projected zero impact shown in the first row of column 9 of Table 13. The remaining rows of the column show the estimated impacts on different categories of providers. On average, the estimated impacts of this change on any particular group of IRFs are small, with urban IRFs experiencing no change in estimated aggregate payments and rural IRFs experiencing a 0.1 percent decrease in estimated aggregate payments. The largest estimated impacts are a 1.1 percent estimated increase among IRFs with 10 percent or higher intern and resident to ADC ratios and a 0.8 percent estimated decrease among rural IRFs in the Pacific region. 11. All Changes (Column 12) Column 12 of Table 13 compares our estimates of the payments per discharge, incorporating all changes reflected in this final rule for FY 2006, to our estimates of payments per discharge in FY 2005 (without these changes). This column includes all of the policy changes. Column 12 reflects all estimated FY 2006 changes relative to FY 2005, shown in columns 4 though 11. The average estimated increase for all IRFs is approximately 3.4 percent. This estimated increase includes the effects of the 3.6 percent market basket update. It also reflects the 1.8 percentage point difference between the estimated outlier payments in FY 2005 (1.2 percent of total estimated payments) and the estimate of the percentage of outlier payments in FY 2006 (3 percent), as described in section VI.B.6 of this final rule. As a result, payments per discharge are estimated to be 1.8 percent lower in FY 2005 than they would have been had the 3 percent target outlier payment percentage been met, resulting in a 1.8 percent greater increase in total estimated FY 2006 payments than would otherwise have occurred. It also includes the estimated impact of the one-time 1.9 percent reduction in the standard payment conversion factor to account for changes in coding that increased payments to IRFs. Because we are making the remainder of the changes outlined in this final rule in a budget-neutral manner, they do not affect total estimated IRF payments in the aggregate. However, as described in more detail in each section, they do affect the estimated distribution of payments among providers. There might also be interactive effects among the various factors comprising the payment system that we are not able to isolate. For these reasons, the estimated values in column 12 may not equal the sum of the estimated changes described above. 12. Accounting Statement As required by OMB Circular A-4 (available at *http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf* ), in Table 15 below, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this final rule. This table provides our best estimate of the increase in Medicare payments under the IRF PPS as a result of the changes presented in this final rule based on the data for 1,188 IRFs in our database. All expenditures are classified as transfers to Medicare providers (that is, IRFs). Table 15.—Accounting Statement: Classification of Estimated Expenditures, From FY 2005 to FY 2006 [In millions] Category Transfers Annualized Monetized Transfers $210. From Whom to Whom? Federal Government to IRF Medicare Providers. 13. Alternatives Considered Because we have determined that this final rule will have a significant economic impact on IRFs, we will discuss the alternative changes to the IRF PPS that we considered. We reviewed the options considered in the proposed rule and took into consideration comments received during the public comment period as discussed in the preamble of this final rule. The other option we considered before deciding to update the CMGs with the fiscal year 2003 data was to maintain the same CMG structure but recalculate the relative weights for the current CMGs using the 2003 data. After carefully reviewing the results of RAND's regression analysis, which compared the predictive ability of the CMGs under 3 scenarios (not updating the CMGs or the relative weights, updating only the relative weights and not the CMGs, and updating both the relative weights and the CMGs), we believe (based on RAND's analysis and a careful review of the comments we received on the FY 2006 proposed rule (70 FR 30188)) that updating both the relative weights and the CMGs will allow the classification system to do a better job of reflecting changes in treatment patterns, technology, case mix, and other factors which may affect the relative use of resources. For these reasons, we believe these changes will improve the accuracy of payments in the IRF PPS. We considered alternative options before deciding to implement an objective weighted motor score methodology for classifying patients into CMGs. The first of these options was to keep the non-weighted motor score methodology used previously. However, we considered weighted motor score methodologies because RAND's regression analysis indicated that the weighted methodologies would substantially improve the predictive ability of the system. We had not previously proposed weighted motor score methodologies for the IRF PPS because most experts previously believed that the data were not complete and accurate enough before the IRF PPS (although they were the most complete and accurate data available at the time). However, the technical expert panel that reviewed RAND's analyses and advised RAND regarding the methodology generally indicated that the data are now sufficient to support a weight motor score. RAND assessed different weighting methodologies for both the motor score index and the cognitive score index. They discovered that weighting the motor score index improved the predictive ability of the system, whereas weighting the cognitive score index did not. Furthermore, the cognitive score index has never had much of an effect (in some RICs, it has no effect) on the assignment of patients to CMGs because the motor score tends to be much stronger at predicting a patient's expected costs in an IRF than the cognitive score. For these reasons, we proposed a weighting methodology for the motor score index, but proposed to use the same cognitive score index used previously for the IRF classification system. We believe that it would be futile to expend resources on changing the cognitive score methodology at this time when it would not benefit the Medicare program. We considered various weighted motor score methodologies, including one which would require computing 378 different weights (18 different weights for the motor and cognitive indices that could all differ across 21 RICs). Rather than introduce this level of complexity to the system, RAND decided to explore simpler weighting methodologies that would still increase the predictive power of the system. We also considered defining some simple combinations of the items that make up the motor score index and assigning weights to the groups of items instead of to the individual items. For example, we considered summing the three transfer items together to form a group with a weight of two, since they contributed about twice as much in the cost regression as the self-care items. We also considered assigning the self-care items a weight of one and the bladder and bowel items as a group a weight close to zero, since they contributed little to predicting cost in the regression analysis. We tried a number of variations and combinations of this, but RAND's TEP generally rejected these weighting schemes. They believed that introducing elements of subjectivity into the development of the weighting scheme may invite controversy, and that it is better to use an objective algorithm to derive the appropriate weights. We agree that an objective weighting scheme is best because it is based on regression analysis of the amount that various components of the motor score index contribute to predicting patient costs, using the best available data we have. For this reason, we decided to adopt the weighting scheme that applies the average optimal weights. We considered a reduction to the standard payment amount by an amount up to 5.8 percent because one of RAND's methodologies for determining the amount of real change in case mix and the amount of coding change that occurred between 1999 and 2002 suggested that coding change could possibly have been responsible for up to 5.8 percent of the observed increase in IRFs' case mix. Furthermore, a separate analysis by RAND found that if all IRFs had been paid based on 100 percent of the IRF PPS payment rates throughout all of 2002 (some IRFs were still transitioning to PPS payments during 2002), PPS payments during 2002 would have been 17 percent higher than IRFs' costs. This suggests that we could potentially have implemented a reduction greater than 1.9 and up to 5.8 percent. We decided to implement a 1.9 percent reduction to the standard payment amount, the lowest possible amount of change attributable to coding change for the following reasons. First, the analyses described in this final rule are only the first of an ongoing series of studies to evaluate the existence and extent of payment increases due to coding changes. We will continue to review the need for any further reduction in the standard payment amount in subsequent years as part of our overall monitoring and evaluation of the IRF PPS. Second, we believe this approach, which is supported by RAND's analysis of the data, will adequately adjust for the increased payments to IRFs caused purely by coding changes, but will still provide the flexibility to account for the possibility that some of the observed changes in case mix may be attributed to other than coding changes. Furthermore, we chose the amount of the reduction in the standard payment amount in order to recognize that IRFs' current cost structures may be changing as they strive to comply with other recent Medicare policy changes, such as the criterion for IRF classification commonly known as the “75 percent rule.” We considered the public comments we received on this issue and believe that 1.9 percent is the appropriate reduction to the standard payment amount at this time. We considered no transition to implement the CBSA-based geographic classifications. However, based on further analysis (and in response to comments), we considered various transition options. One option we considered was a 1-year budget neutral transition with a blended wage index (comprised of the FY 2006 MSA-based wage index and FY 2006 CBSA-based wage index) for IRFs that would experience a decrease in the wage index. We also considered floor and ceiling options as requested by commenters. However, the options did not reflect the policy goals to mitigate the overall impact of IRFs transitioning from the MSA-based wage index to the CBSA-based wage index while lessening the overall impact on the unadjusted base payment that would be equitable to all IRFs. We also considered not adopting a hold harmless policy. However, based on additional review we determined that it was appropriate to implement a budget neutral 3 year hold harmless policy that would better reflect policy and maintain fiscal integrity of existing FY 2005 rural IRFs that will be redesignated as urban facilities under the CBSA-based designation. We considered not proposing to add a teaching status adjustment to the IRF PPS because we had some concerns about proposing a teaching status adjustment for IRFs. The policy implications of implementing a teaching status adjustment on the basis of the results of RAND's recent analysis caused us to seek assurance that these results did not reflect an aberration based on only a single year's data and that the teaching status adjustment could be implemented in such a way that it would be equitable to all IRFs. However, the regression analysis conducted by RAND for CY 2002 and FY 2003 showed a statistically significant difference in costs between IRFs with teaching programs and those without teaching programs. After reviewing RAND's analysis and the comments we received on the teaching status adjustment we proposed in the FY 2006 proposed rule (70 FR 30188), which were generally favorable, we determined that a teaching status adjustment for IRFs is appropriate at this time. We will continue to analyze the need for this adjustment in future data. We believe that the analysis conducted by RAND using calendar year 2002 and FY 2003 data (the best available data we have and the first available data since implementation of the IRF PPS) left us little option other than to update the rural and LIP adjustments and the outlier loss threshold amount. The regression analysis indicated that facility-level adjustments (the rural and the LIP adjustments) should be updated to better reflect the costs of care among different types of IRF facilities. Similarly the regression analysis indicated that the outlier threshold amount needed to be updated so that estimated outlier payments for FY 2006 would equal 3 percent of total estimated IRF payments for FY 2006. 14. Conclusion Overall, estimated payments per discharge for IRFs in FY 2006 are projected to increase by 3.4 percent, as reflected in column 12 of Table 13. IRFs in urban areas will experience a 3.2 percent increase in estimated payments per discharge compared with FY 2005. IRFs in rural areas, meanwhile, will experience a 5.7 percent estimated increase. Rehabilitation units in urban areas will experience a 5.3 percent increase in estimated payments per discharge, while freestanding rehabilitation hospitals in urban areas will experience no change in estimated payments per discharge. Rehabilitation units in rural areas will experience a 5.5 percent increase in estimated payments per discharge, while freestanding rehabilitation hospitals in rural areas will experience a 6.5 percent increase in estimated payments per discharge. Overall, the largest estimated payment increase will be 27.4 percent among teaching IRFs with an intern and resident to ADC ratio greater than 19 percent and 14.3 percent among teaching IRFs with an intern and resident to ADC ratio greater than or equal to 10 percent and less than or equal to 19 percent. This is largely due to the teaching status adjustment. Other than for teaching IRFs, the largest estimated payment increase will be 10.3 percent among rural IRFs located in the Middle Atlantic region. This is due largely to the change in the CBSA-based designation from urban to rural, whereby the number of cases in the rural Middle Atlantic Region that will receive the new rural adjustment of 21.3 percent is projected to increase. The only overall decrease in estimated payments will occur among urban IRFs located in the Mountain census region, a decrease in estimated payments of 0.2 percent. This is due largely to the change in the CBSA-based designation from rural to urban. For non-profit IRFs, we found that rural non-profit facilities will receive the largest estimated payment increase of 6.7 percent. Conversely, for-profit urban facilities are projected to experience no change in payments for FY 2006. In accordance with the provisions of Executive Order 12866, this regulation was reviewed by the Office of Management and Budget. List of Subjects in 42 CFR Part 412 Administrative practice and procedure, Health facilities, Medicare, Puerto Rico, Reporting and recordkeeping requirements. For the reasons set forth in the preamble, CMS amends 42 CFR chapter IV part 412 as set forth below: PART 412—PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL SERVICES 1. The authority citation for part 412 continues to read as follows: Authority: Secs. 1102 and 1871 of the Social Security Act (42 U.S.C. 1302 and 1395hh). 2. Section 412.25 is amended by revising paragraph (a), introductory text, to read as follows: § 412.25 Excluded hospital units: Common requirements.
(a)Basis for exclusion. In order to be excluded from the prospective payment systems as specified in § 412.1(a)(1) and be paid under the inpatient psychiatric facility prospective payment system as specified in § 412.1(a)(2) or the inpatient rehabilitation facility prospective payment system as specified in § 412.1(a)(3), a psychiatric or rehabilitation unit must meet the following requirements. 3. Section 412.602 is amended by revising the definitions of “Rural area” and “Urban area” to read as follows: § 412.602 Definitions. Rural area means: For cost-reporting periods beginning on or after January 1, 2002, with respect to discharges occurring during the period covered by such cost reports but before October 1, 2005, an area as defined in § 412.62(f)(1)(iii). For discharges occurring on or after October 1, 2005, rural area means an area as defined in § 412.64(b)(1)(ii)(C). Urban area means: For cost-reporting periods beginning on or after January 1, 2002, with respect to discharges occurring during the period covered by such cost reports but before October 1, 2005, an area as defined in § 412.62(f)(1)(ii). For discharges occurring on or after October 1, 2005, urban area means an area as defined in § 412.64(b)(1)(ii)(A) and § 412.64(b)(1)(ii)(B). § 412.622 [Amended] 4. Section 412.622 is amended by— A. In paragraph (b)(1), removing the cross references “§§ 413.85 and 413.86 of this chapter” and adding in their place “§ 413.75 and § 413.85 of this chapter”. B. In paragraph (b)(2)(i), removing the cross reference to “§ 413.80 of this chapter” and adding in its place “§ 413.89 of this chapter”. 5. Section 412.624 is amended by— A. In paragraph (d)(1), removing the cross reference to “paragraph (e)(4)” and adding in its place “paragraph (e)(5)”. B. Adding a new paragraph (d)(4). C. Revising paragraphs (e)(4) and (e)(5). D. Adding new paragraphs (e)(6) and (e)(7). E. In paragraph (f)(2)(v), removing the cross references to “paragraphs (e)(1), (e)(2), and (e)(3) of this section” and adding in their place “paragraphs (e)(2), (e)(3), (e)(4), and (e)(7) of this section”. The revisions and additions read as follows: § 412.624 Methodology for calculating the Federal prospective payment rates.
(d)* * *
(4)*Payment adjustment for Federal fiscal year 2006 and applicable Federal fiscal years.* CMS adjusts the standard payment conversion factor based on any updates to the adjustments specified in paragraph (e)(2), (e)(3), (e)(4) and (e)(7), of this section, and to any revision specified in § 412.620(c) by a factor as specified by the Secretary.
(e)* * *
(4)*Adjustments for teaching hospitals.* For discharges on or after October 1, 2005, CMS adjusts the Federal prospective payment on a facility basis by a factor as specified by CMS for facilities that are teaching institutions or units of teaching institutions. This adjustment is made on a claim basis as an interim payment and the final payment in full for the claim is made during the final settlement of the cost report.
(5)*Adjustment for high-cost outliers.* CMS provides for an additional payment to an inpatient rehabilitation facility if its estimated costs for a patient exceed a fixed dollar amount (adjusted for area wage levels and factors to account for treating low-income patients, for rural location, and for teaching programs) as specified by CMS. The additional payment equals 80 percent of the difference between the estimated cost of the patient and the sum of the adjusted Federal prospective payment computed under this section and the adjusted fixed dollar amount. Effective for discharges occurring on or after October 1, 2003, additional payments made under this section will be subject to the adjustments at § 412.84(i), except that national averages will be used instead of statewide averages. Effective for discharges occurring on or after October 1, 2003, additional payments made under this section will also be subject to adjustments at § 412.84(m).
(6)*Adjustments related to the patient assessment instrument.* An adjustment to a facility's Federal prospective payment amount for a given discharge will be made, as specified under § 412.614(d), if the transmission of data from a patient assessment instrument is late.
(7)Adjustments for certain facilities geographically redesignated in FY 2006.
(i)*General.* For a facility defined as an urban facility under § 412.602 in FY 2006 that was previously defined as a rural facility in FY 2005 as the term rural was defined in FY 2005 under § 412.602 and whose payment, after applying the adjustment under this paragraph, will be lower only because of being defined as an urban facility in FY 2006 and it no longer qualified for the rural adjustment under § 412.624(e)(3) in FY 2006, CMS will adjust the facility's payment using the following method:
(A)For discharges occurring on or after October 1, 2005, and on or before September 30, 2006, the facility's payment will be increased by an adjustment of two thirds of its prior FY 2005 19.14 percent rural adjustment.
(B)For discharges occurring on or after October 1, 2006, and on or before September 30, 2007, the facility's payment will be increased by an adjustment of one third of its FY 2005 19.14 percent rural adjustment.
(ii)*Exception.* For discharges occurring on or after October 1, 2005 and on or before September 30, 2007, facilities whose payments, after applying the adjustment under this paragraph (e)(7)(i) of this section, will be higher because of being defined as an urban facility in FY 2006 and no longer being qualified for the rural adjustment under § 412.624(e)(3) in FY 2006, CMS will adjust the facility's payment by a portion of the applicable additional adjustment described in paragraph (e)(7)(i)(A) and (e)(7)(i)(B) of this section as determined by us. (Catalog of Federal Domestic Assistance Program No. 93.773, Medicare—Hospital Insurance; and Program No. 93.774, Medicare—Supplementary Medical Insurance Program) Dated: July 26, 2005. Mark B. McClellan, Administrator, Centers for Medicare & Medicaid Services. Approved: July 27, 2005. Michael O. Leavitt, Secretary. The following addendum will not appear in the Code of Federal Regulations. Table 1.—FY 2006 IRF PPS Transition Wage Index Table [For discharges occurring on or after October 1, 2005 and on or before September 30, 2006] SSA state/ county code County name MSA No. MSA urban/rural 2006 MSA-based WI 2006 CBSA-based WI CBSA No. CBSA urban/rural Transition wage index * 01000 Autauga County, Alabama 5240 Urban 0.8300 0.8300 33860 Urban 0.8300 01010 Baldwin County, Alabama 5160 Urban 0.7932 0.7628 99901 Rural 0.7780 01020 Barbour County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01030 Bibb County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 01040 Blount County, Alabama 1000 Urban 0.9198 0.9157 13820 Urban 0.9178 01050 Bullock County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01060 Butler County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01070 Calhoun County, Alabama 0450 Urban 0.7881 0.7881 11500 Urban 0.7881 01080 Chambers County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01090 Cherokee County, Alabama 01 Rural 0.7637 .7628 99901 Rural 0.7633 01100 Chilton County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 01110 Choctaw County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01120 Clarke County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01130 Clay County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01140 Cleburne County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01150 Coffee County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01160 Colbert County, Alabama 2650 Urban 0.7883 0.7883 22520 Urban 0.7883 01170 Conecuh County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01180 Coosa County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01190 Covington County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01200 Crenshaw County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01210 Cullman County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01220 Dale County, Alabama 2180 Urban 0.7596 0.7628 99901 Rural 0.7612 01230 Dallas County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01240 De Kalb County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01250 Elmore County, Alabama 5240 Urban 0.8300 0.8300 33860 Urban 0.8300 01260 Escambia County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01270 Etowah County, Alabama 2880 Urban 0.8049 0.8049 23460 Urban 0.8049 01280 Fayette County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01290 Franklin County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01300 Geneva County, Alabama 01 Rural 0.7637 0.7537 20020 Urban 0.7587 01310 Greene County, Alabama 01 Rural 0.7637 0.8336 46220 Urban 0.7987 01320 Hale County, Alabama 01 Rural 0.7637 0.8336 46220 Urban 0.7987 01330 Henry County, Alabama 01 Rural 0.7637 0.7537 20020 Urban 0.7587 01340 Houston County, Alabama 2180 Urban 0.7596 0.7537 20020 Urban 0.7567 01350 Jackson County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01360 Jefferson County, Alabama 1000 Urban 0.9198 0.9157 13820 Urban 0.9178 01370 Lamar County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01380 Lauderdale County, Alabama 2650 Urban 0.7883 0.7883 22520 Urban 0.7883 01390 Lawrence County, Alabama 21030 Urban 0.8894 0.8894 19460 Urban 0.8894 01400 Lee County, Alabama 0580 Urban 0.8215 0.8215 12220 Urban 0.8215 01410 Limestone County, Alabama 3440 Urban 0.8851 0.8851 26620 Urban 0.8851 01420 Lowndes County, Alabama 01 Rural 0.7637 0.8300 33860 Urban 0.7969 01430 Macon County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01440 Madison County, Alabama 3440 Urban 0.8851 0.8851 26620 Urban 0.8851 01450 Marengo County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01460 Marion County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01470 Marshall County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01480 Mobile County, Alabama 5160 Urban 0.7932 0.7995 33660 Urban 0.7964 01490 Monroe County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01500 Montgomery County, Alabama 5240 Urban 0.8300 0.8300 33860 Urban 0.8300 01510 Morgan County, Alabama 2030 Urban 0.8894 0.8894 19460 Urban 0.8894 01520 Perry County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01530 Pickens County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01540 Pike County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01550 Randolph County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01560 Russell County, Alabama 1800 Urban 0.8690 0.8690 17980 Urban 0.8690 01570 St Clair County, Alabama 1000 Urban 0.9198 0.9157 13820 Urban 0.9178 01580 Shelby County, Alabama 1000 Urban 0.9198 0.9157 13820 Urban 0.9178 01590 Sumter County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01600 Talladega County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01610 Tallapoosa County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01620 Tuscaloosa County, Alabama 8600 Urban 0.8440 0.8336 46220 Urban 0.8388 01630 Walker County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 01640 Washington County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01650 Wilcox County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 01660 Winston County, Alabama 01 Rural 0.7637 0.7628 99901 Rural 0.7633 02013 Aleutians County East, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02016 Aleutians County West, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02020 Anchorage County, Alaska 0380 Urban 1.2109 1.2165 11260 Urban 1.2137 02030 Angoon County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02040 Barrow-North Slope County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02050 Bethel County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02060 Bristol Bay Borough County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02068 Denali County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02070 Bristol Bay County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02080 Cordova-Mc Carthy County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02090 Fairbanks County, Alaska 02 Rural 1.1637 1.1146 21820 Urban 1.1392 02100 Haines County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02110 Juneau County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02120 Kenai-Cook Inlet County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02122 Kenai Peninsula Borough, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02130 Ketchikan County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02140 Kobuk County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02150 Kodiak County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02160 Kuskokwin County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02164 Lake and Peninsula Borough, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02170 Matanuska County, Alaska 02 Rural 1.1637 1.2165 11260 Urban 1.1901 02180 Nome County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02185 North Slope Borough, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02188 Northwest Arctic Borough, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02190 Outer Ketchikan County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02200 Prince Of Wales County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02201 Prince of Wales-Outer Ketchikan Census Area, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02210 Seward County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02220 Sitka County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02230 Skagway-Yakutat County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02231 Skagway-Yakutat-Angoon Census Area, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02232 Skagway-Hoonah-Angoon Census Area, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02240 Southeast Fairbanks County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02250 Upper Yukon County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02260 Valdz-Chitna-Whitier County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02261 Valdex-Cordove Census Area, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02270 Wade Hampton County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02280 Wrangell-Petersburg County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02282 Yakutat Borough, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 02290 Yukon-Koyukuk County, Alaska 02 Rural 1.1637 1.1746 99902 Rural 1.1692 03000 Apache County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03010 Cochise County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03020 Coconino County, Arizona 2620 Urban 1.0611 1.0787 22380 Urban 1.0699 03030 Gila County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03040 Graham County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03050 Greenlee County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03055 La Paz County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03060 Maricopa County, Arizona 6200 Urban 0.9982 0.9982 38060 Urban 0.9982 03070 Mohave County, Arizona 4120 Urban 1.1121 0.8936 99903 Rural 1.0029 03080 Navajo County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03090 Pima County, Arizona 8520 Urban 0.8926 0.8926 46060 Urban 0.8926 03100 Pinal County, Arizona 6200 Urban 0.9982 0.9982 38060 Urban 0.9982 03110 Santa Cruz County, Arizona 03 Rural 0.9140 0.8936 99903 Rural 0.9038 03120 Yavapai County, Arizona 03 Rural 0.9140 0.9892 39140 Urban 0.9516 03130 Yuma County, Arizona 9360 Urban 0.8871 0.8871 49740 Urban 0.8871 04000 Arkansas County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04010 Ashley County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04020 Baxter County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04030 Benton County, Arkansas 2580 Urban 0.8636 0.8636 22220 Urban 0.8636 04040 Boone County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04050 Bradley County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04060 Calhoun County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04070 Carroll County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04080 Chicot County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04090 Clark County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04100 Clay County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04110 Cleburne County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04120 Cleveland County, Arkansas 04 Rural 0.7703 0.8673 38220 Urban 0.8188 04130 Columbia County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04140 Conway County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04150 Craighead County, Arkansas 3700 Urban 0.8144 0.8144 27860 Urban 0.8144 04160 Crawford County, Arkansas 2720 Urban 0.8303 0.8283 22900 Urban 0.8293 04170 Crittenden County, Arkansas 4920 Urban 0.9234 0.9217 32820 Urban 0.9226 04180 Cross County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04190 Dallas County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04200 Desha County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04210 Drew County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04220 Faulkner County, Arkansas 4400 Urban 0.8826 0.8826 30780 Urban 0.8826 04230 Franklin County, Arkansas 04 Rural 0.7703 0.8283 22900 Urban 0.7993 04240 Fulton County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04250 Garland County, Arkansas 04 Rural 0.7703 0.9249 26300 Urban 0.8476 04260 Grant County, Arkansas 04 Rural 0.7703 0.8826 30780 Urban 0.8265 04270 Greene County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04280 Hempstead County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04290 Hot Spring County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04300 Howard County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04310 Independence County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04320 Izard County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04330 Jackson County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04340 Jefferson County, Arkansas 6240 Urban 0.8673 0.8673 38220 Urban 0.8673 04350 Johnson County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04360 Lafayette County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04370 Lawrence County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04380 Lee County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04390 Lincoln County, Arkansas 04 Rural 0.7703 0.8673 38220 Urban 0.8188 04400 Little River County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04410 Logan County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04420 Lonoke County, Arkansas 4400 Urban 0.8826 0.8826 30780 Urban 0.8826 04430 Madison County, Arkansas 04 Rural 0.7703 0.8636 22220 Urban 0.8170 04440 Marion County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04450 Miller County, Arkansas 8360 Urban 0.8413 0.8413 45500 Urban 0.8413 04460 Mississippi County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04470 Monroe County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04480 Montgomery County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04490 Nevada County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04500 Newton County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04510 Ouachita County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04520 Perry County, Arkansas 04 Rural 0.7703 0.8826 30780 Urban 0.8265 04530 Phillips County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04540 Pike County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04550 Poinsett County, Arkansas 04 Rural 0.7703 0.8144 27860 Urban 0.7924 04560 Polk County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04570 Pope County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04580 Prairie County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04590 Pulaski County, Arkansas 4400 Urban 0.8826 0.8826 30780 Urban 0.8826 04600 Randolph County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04610 St Francis County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04620 Saline County, Arkansas 4400 Urban 0.8826 0.8826 30780 Urban 0.8826 04630 Scott County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04640 Searcy County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04650 Sebastian County, Arkansas 2720 Urban 0.8303 0.8283 22900 Urban 0.8293 04660 Sevier County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04670 Sharp County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04680 Stone County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04690 Union County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04700 Van Buren County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04710 Washington County, Arkansas 2580 Urban 0.8636 0.8636 22220 Urban 0.8636 04720 White County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04730 Woodruff County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 04740 Yell County, Arkansas 04 Rural 0.7703 0.7406 99904 Rural 0.7555 05000 Alameda County, California 5775 Urban 1.5220 1.5220 36084 Urban 1.5220 05010 Alpine County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05020 Amador County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05030 Butte County, California 1620 Urban 1.0542 1.0542 17020 Urban 1.0542 05040 Calaveras County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05050 Colusa County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05060 Contra Costa County, California 5775 Urban 1.5220 1.5220 36084 Urban 1.5220 05070 Del Norte County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05080 Eldorado County, California 6920 Urban 1.1848 1.1700 40900 Urban 1.1774 05090 Fresno County, California 2840 Urban 1.0407 1.0536 23420 Urban 1.0472 05100 Glenn County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05110 Humboldt County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05120 Imperial County, California 05 Rural 1.0297 0.8856 20940 Urban 0.9577 05130 Inyo County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05140 Kern County, California 0680 Urban 1.0036 1.0036 12540 Urban 1.0036 05150 Kings County, California 05 Rural 1.0297 0.9296 25260 Urban 0.9797 05160 Lake County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05170 Lassen County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05200 Los Angeles County, California 4480 Urban 1.1732 1.1732 31084 Urban 1.1732 05210 Los Angeles County, California 4480 Urban 1.1732 1.1732 31084 Urban 1.1732 05300 Madera County, California 2840 Urban 1.0407 0.8521 31460 Urban 0.9464 05310 Marin County, California 7360 Urban 1.4712 1.4712 41884 Urban 1.4712 05320 Mariposa County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05330 Mendocino County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05340 Merced County, California 4940 Urban 1.0575 1.0575 32900 Urban 1.0575 05350 Modoc County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05360 Mono County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05370 Monterey County, California 7120 Urban 1.3823 1.3823 41500 Urban 1.3823 05380 Napa County, California 8720 Urban 1.3517 1.2531 34900 Urban 1.3024 05390 Nevada County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05400 Orange County, California 5945 Urban 1.1611 1.1611 42044 Urban 1.1611 05410 Placer County, California 6920 Urban 1.1848 1.1700 40900 Urban 1.1774 05420 Plumas County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05430 Riverside County, California 6780 Urban 1.0970 1.0970 40140 Urban 1.0970 05440 Sacramento County, California 6920 Urban 1.1848 1.1700 40900 Urban 1.1774 05450 San Benito County, California 05 Rural 1.0297 1.4722 41940 Urban 1.2510 05460 San Bernardino County, California 6780 Urban 1.0970 1.0970 40140 Urban 1.0970 05470 San Diego County, California 7320 Urban 1.1267 1.1267 41740 Urban 1.1267 05480 San Francisco County, California 7360 Urban 1.4712 1.4712 41884 Urban 1.4712 05490 San Joaquin County, California 8120 Urban 1.0564 1.0564 44700 Urban 1.0564 05500 San Luis Obispo County, California 7460 Urban 1.1118 1.1118 42020 Urban 1.1118 05510 San Mateo County, California 7360 Urban 1.4712 1.4712 41884 Urban 1.4712 05520 Santa Barbara County, California 7480 Urban 1.0771 1.0771 42060 Urban 1.0771 05530 Santa Clara County, California 7400 Urban 1.4744 1.4722 41940 Urban 1.4733 05540 Santa Cruz County, California 7485 Urban 1.4779 1.4779 42100 Urban 1.4779 05550 Shasta County, California 6690 Urban 1.1835 1.1835 39820 Urban 1.1835 05560 Sierra County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05570 Siskiyou County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05580 Solano County, California 8720 Urban 1.3517 1.4279 46700 Urban 1.3898 05590 Sonoma County, California 7500 Urban 1.2961 1.2961 42220 Urban 1.2961 05600 Stanislaus County, California 5170 Urban 1.1966 1.1966 33700 Urban 1.1966 05610 Sutter County, California 9340 Urban 1.0363 1.0363 49700 Urban 1.0363 05620 Tehama County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05630 Trinity County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05640 Tulare County, California 8780 Urban 0.9975 0.9975 47300 Urban 0.9975 05650 Tuolumne County, California 05 Rural 1.0297 1.0524 99905 Rural 1.0411 05660 Ventura County, California 8735 Urban 1.1105 1.1105 37100 Urban 1.1105 05670 Yolo County, California 9270 Urban 0.9378 1.1700 40900 Urban 1.0539 05680 Yuba County, California 9340 Urban 1.0363 1.0363 49700 Urban 1.0363 06000 Adams County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 06010 Alamosa County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06020 Arapahoe County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 06030 Archuleta County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06040 Baca County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06050 Bent County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06060 Boulder County, Colorado 1125 Urban 1.0046 1.0046 14500 Urban 1.0046 06070 Chaffee County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06080 Cheyenne County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06090 Clear Creek County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06100 Conejos County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06110 Costilla County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06120 Crowley County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06130 Custer County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06140 Delta County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06150 Denver County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 06160 Dolores County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06170 Douglas County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 06180 Eagle County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06190 Elbert County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06200 El Paso County, Colorado 1720 Urban 0.9792 0.9792 17820 Urban 0.9792 06210 Fremont County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06220 Garfield County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06230 Gilpin County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06240 Grand County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06250 Gunnison County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06260 Hinsdale County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06270 Huerfano County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06280 Jackson County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06290 Jefferson County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 06300 Kiowa County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06310 Kit Carson County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06320 Lake County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06330 La Plata County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06340 Larimer County, Colorado 2670 Urban 1.0218 1.0218 22660 Urban 1.0218 06350 Las Animas County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06360 Lincoln County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06370 Logan County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06380 Mesa County, Colorado 2995 Urban 0.9900 0.9900 24300 Urban 0.9900 06390 Mineral County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06400 Moffat County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06410 Montezuma County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06420 Montrose County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06430 Morgan County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06440 Otero County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06450 Ouray County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06460 Park County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06470 Phillips County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06480 Pitkin County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06490 Prowers County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06500 Pueblo County, Colorado 6560 Urban 0.8752 0.8752 39380 Urban 0.8752 06510 Rio Blanco County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06520 Rio Grande County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06530 Routt County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06540 Saguache County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06550 San Juan County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06560 San Miguel County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06570 Sedgwick County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06580 Summit County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06590 Teller County, Colorado 06 Rural 0.9368 0.9792 17820 Urban 0.9580 06600 Washington County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06610 Weld County, Colorado 3060 Urban 0.9444 0.9444 24540 Urban 0.9444 06620 Yuma County, Colorado 06 Rural 0.9368 0.9368 99906 Rural 0.9368 06630 Broomfield County, Colorado 2080 Urban 1.0904 1.0904 19740 Urban 1.0904 07000 Fairfield County, Connecticut 5483 Urban 1.2254 1.2835 14860 Urban 1.2545 07010 Hartford County, Connecticut 3283 Urban 1.1054 1.1054 25540 Urban 1.1054 07020 Litchfield County, Connecticut 3283 Urban 1.1054 1.1054 25540 Urban 1.1054 07030 Middlesex County, Connecticut 3283 Urban 1.1054 1.1054 25540 Urban 1.1054 07040 New Haven County, Connecticut 5483 Urban 1.2254 1.1807 35300 Urban 1.2031 07050 New London County, Connecticut 5523 Urban 1.1596 1.1596 35980 Urban 1.1596 07060 Tolland County, Connecticut 3283 Urban 1.1054 1.1054 25540 Urban 1.1054 07070 Windham County, Connecticut 07 Rural 1.1917 1.1917 99907 Rural 1.1917 08000 Kent County, Delaware 2190 Urban 0.9825 0.9825 20100 Urban 0.9825 08010 New Castle County, Delaware 9160 Urban 1.1121 1.1049 48864 Urban 1.1085 08020 Sussex County, Delaware 08 Rural 0.9503 0.9503 99908 Rural 0.9503 09000 Washington Dc County, Dist Of Col 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 10000 Alachua County, Florida 2900 Urban 0.9459 0.9459 23540 Urban 0.9459 01010 Baker County, Florida 10 Rural 0.8721 0.9537 27260 Urban 0.9129 10020 Bay County, Florida 6015 Urban 0.8124 0.8124 37460 Urban 0.8124 10030 Bradford County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10040 Brevard County, Florida 4900 Urban 0.9633 0.9633 37340 Urban 0.9633 10050 Broward County, Florida 2680 Urban 1.0165 1.0165 22744 Urban 1.0165 10060 Calhoun County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10070 Charlotte County, Florida 6580 Urban 0.9441 0.9441 39460 Urban 0.9441 10080 Citrus County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10090 Clay County, Florida 3600 Urban 0.9548 0.9537 27260 Urban 0.9543 10100 Collier County, Florida 5345 Urban 1.0558 1.0558 34940 Urban 1.0558 10110 Columbia County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10120 Dade County, Florida 5000 Urban 0.9870 0.9870 33124 Urban 0.9870 10130 De Soto County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10140 Dixie County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10150 Duval County, Florida 3600 Urban 0.9548 0.9537 27260 Urban 0.9543 10160 Escambia County, Florida 6080 Urban 0.8306 0.8306 37860 Urban 0.8306 10170 Flagler County, Florida 2020 Urban 0.8900 0.8574 99910 Rural 0.8737 10180 Franklin County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10190 Gadsden County, Florida 8240 Urban 0.8655 0.8655 45220 Urban 0.8655 10200 Gilchrist County, Florida 10 Rural 0.8721 0.9459 23540 Urban 0.9090 10210 Glades County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10220 Gulf County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10230 Hamilton County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10240 Hardee County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10250 Hendry County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10260 Hernando County, Florida 8280 Urban 0.9024 0.9024 45300 Urban 0.9024 10270 Highlands County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10280 Hillsborough County, Florida 8280 Urban 0.9024 0.9024 45300 Urban 0.9024 10290 Holmes County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10300 Indian River County, Florida 10 Rural 0.8721 0.9477 46940 Urban 0.9099 10310 Jackson County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10320 Jefferson County, Florida 10 Rural 0.8721 0.8655 45220 Urban 0.8688 10330 Lafayette County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10340 Lake County, Florida 5960 Urban 0.9742 0.9742 36740 Urban 0.9742 10350 Lee County, Florida 2700 Urban 0.9371 0.9371 15980 Urban 0.9371 10360 Leon County, Florida 8240 Urban 0.8655 0.8655 45220 Urban 0.8655 10370 Levy County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10380 Liberty County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10390 Madison County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10400 Manatee County, Florida 7510 Urban 0.9629 0.9629 42260 Urban 0.9629 10410 Marion County, Florida 5790 Urban 0.9153 0.9153 36100 Urban 0.9153 10420 Martin County, Florida 2710 Urban 1.0046 1.0046 38940 Urban 1.0046 10430 Monroe County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10440 Nassau County, Florida 3600 Urban 0.9548 0.9537 27260 Urban 0.9543 10450 Okaloosa County, Florida 2750 Urban 0.8786 0.8786 23020 Urban 0.8786 10460 Okeechobee County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10470 Orange County, Florida 5960 Urban 0.9742 0.9742 36740 Urban 0.9742 10480 Osceola County, Florida 5960 Urban 0.9742 0.9742 36740 Urban 0.9742 10490 Palm Beach County, Florida 8960 Urban 1.0362 1.0362 48424 Urban 1.0362 10500 Pasco County, Florida 8280 Urban 0.9024 0.9024 45300 Urban 0.9024 10510 Pinellas County, Florida 8280 Urban 0.9024 0.9024 45300 Urban 0.9024 10520 Polk County, Florida 3980 Urban 0.8930 0.8930 29460 Urban 0.8930 10530 Putnam County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10540 Johns County, Florida 3600 Urban 0.9548 0.9537 27260 Urban 0.9543 10550 St Lucie County, Florida 2710 Urban 1.0046 1.0046 38940 Urban 1.0046 10560 Santa Rosa County, Florida 6080 Urban 0.8306 0.8306 37860 Urban 0.8306 10570 Sarasota County, Florida 7510 Urban 0.9629 0.9629 42260 Urban 0.9629 10580 Seminole County, Florida 5960 Urban 0.9742 0.9742 36740 Urban 0.9742 10590 Sumter County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10600 Suwannee County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10610 Taylor County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10620 Union County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10630 Volusia County, Florida 2020 Urban 0.8900 0.8898 19660 Urban 0.8899 10640 Wakulla County, Florida 10 Rural 0.8721 0.8655 45220 Urban 0.8688 10650 Walton County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 10660 Washington County, Florida 10 Rural 0.8721 0.8574 99910 Rural 0.8648 11000 Appling County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11010 Atkinson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11011 Bacon County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11020 Baker County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 11030 Baldwin County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11040 Banks County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11050 Barrow County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11060 Bartow County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11070 Ben Hill County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11080 Berrien County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11090 Bibb County, Georgia 4680 Urban 0.9596 0.9887 31420 Urban 0.9742 11100 Bleckley County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11110 Brantley County, Georgia 11 Rural 0.8247 1.1933 5260 Urban 1.0090 11120 Brooks County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11130 Bryan County, Georgia 7520 Urban 0.9460 0.9460 42340 Urban 0.9460 11140 Bulloch County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11150 Burke County, Georgia 11 Rural 0.8247 0.9154 12260 Urban 0.8701 11160 Butts County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11161 Calhoun County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11170 Camden County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11180 Candler County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11190 Carroll County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11200 Catoosa County, Georgia 1560 Urban 0.9207 0.9207 16860 Urban 0.9207 11210 Charlton County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11220 Chatham County, Georgia 7520 Urban 0.9460 0.9460 42340 Urban 0.9460 11230 Chattahoochee County, Georgia 1800 Urban 0.8690 0.8690 17980 Urban 0.8690 11240 Chattooga County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11250 Cherokee County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11260 Clarke County, Georgia 0500 Urban 1.0202 1.0202 12020 Urban 1.0202 11270 Clay County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11280 Clayton County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11281 Clinch County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11290 Cobb County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11291 Coffee County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11300 Colquitt County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11310 Columbia County, Georgia 0600 Urban 0.9208 0.9154 12260 Urban 0.9181 11311 Cook County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11320 Coweta County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11330 Crawford County, Georgia 11 Rural 0.8247 0.9887 31420 Urban 0.9067 11340 Crisp County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11341 Dade County, Georgia 1560 Urban 0.9207 0.9207 16860 Urban 0.9207 11350 Dawson County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11360 Decatur County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11370 De Kalb County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11380 Dodge County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11381 Dooly County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11390 Dougherty County, Georgia 0120 Urban 1.1266 1.1266 10500 Urban 1.1266 11400 Douglas County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11410 Early County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11420 Echols County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11421 Effingham County, Georgia 7520 Urban 0.9460 0.9460 42340 Urban 0.9460 11430 Elbert County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11440 Emanuel County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11441 Evans County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11450 Fannin County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11451 Fayette County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11460 Floyd County, Georgia 11 Rural 0.8247 0.8878 40660 Urban 0.8563 11461 Forsyth County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11462 Franklin County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11470 Fulton County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11471 Gilmer County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11480 Glascock County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11490 Glynn County, Georgia 11 Rural 0.8247 1.1933 15260 Urban 1.0090 11500 Gordon County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11510 Grady County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11520 Greene County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11530 Gwinnett County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11540 Habersham County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11550 Hall County, Georgia 11 Rural 0.8247 0.9557 23580 Urban 0.8902 11560 Hancock County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11570 Haralson County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11580 Harris County, Georgia 1800 Urban 0.8690 0.8690 17980 Urban 0.8690 11581 Hart County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11590 Heard County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11591 Henry County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11600 Houston County, Georgia 4680 Urban 0.9596 0.8489 47580 Urban 0.9043 11601 Irwin County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11610 Jackson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11611 Jasper County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11612 Jeff Davis County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11620 Jefferson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11630 Jenkins County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11640 Johnson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11650 Jones County, Georgia 4680 Urban 0.9596 0.9887 31420 Urban 0.9742 11651 Lamar County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11652 Lanier County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11660 Laurens County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11670 Lee County, Georgia 0120 Urban 1.1266 1.1266 10500 Urban 1.1266 11680 Liberty County, Georgia 11 Rural 0.8247 0.7715 25980 Urban 0.7981 11690 Lincoln County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11691 Long County, Georgia 11 Rural 0.8247 0.7715 25980 Urban 0.7981 11700 Lowndes County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11701 Lumpkin County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11702 Mc Duffie County, Georgia 0600 Urban 0.9208 0.9154 12260 Urban 0.9181 11703 Mc Intosh County, Georgia 11 Rural 0.8247 1.1933 5260 Urban 1.0090 11710 Macon County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11720 Madison County, Georgia 0500 Urban 1.0202 1.0202 12020 Urban 1.0202 11730 Marion County, Georgia 11 Rural 0.8247 0.8690 17980 Urban 0.8469 11740 Meriwether County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11741 Miller County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11750 Mitchell County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11760 Monroe County, Georgia 11 Rural 0.8247 0.9887 31420 Urban 0.9067 11770 Montgomery County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11771 Morgan County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11772 Murray County, Georgia 11 Rural 0.8247 0.9558 19140 Urban 0.8903 11780 Muscogee County, Georgia 1800 Urban 0.8690 0.8690 17980 Urban 0.8690 11790 Newton County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11800 Oconee County, Georgia 0500 Urban 1.0202 1.0202 12020 Urban 1.0202 11801 Oglethorpe County, Georgia 11 Rural 0.8247 1.0202 12020 Urban 0.9225 11810 Paulding County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11811 Peach County, Georgia 4680 Urban 0.9596 0.7733 99911 Rural 0.8665 11812 Pickens County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11820 Pierce County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11821 Pike County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11830 Polk County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11831 Pulaski County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11832 Putnam County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11833 Quitman County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11834 Rabun County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11835 Randolph County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11840 Richmond County, Georgia 0600 Urban 0.9208 0.9154 12260 Urban 0.9181 11841 Rockdale County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11842 Schley County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11850 Screven County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11851 Seminole County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11860 Spalding County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11861 Stephens County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11862 Stewart County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11870 Sumter County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11880 Talbot County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11881 Taliaferro County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11882 Tattnall County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11883 Taylor County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11884 Telfair County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11885 Terrell County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 11890 Thomas County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11900 Tift County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11901 Toombs County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11902 Towns County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11903 Treutlen County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11910 Troup County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11911 Turner County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11912 Twiggs County, Georgia 4680 Urban 0.9596 0.9887 31420 Urban 0.9742 11913 Union County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11920 Upson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11921 Walker County, Georgia 1560 Urban 0.9207 0.9207 16860 Urban 0.9207 11930 Walton County, Georgia 0520 Urban 0.9971 0.9971 12060 Urban 0.9971 11940 Ware County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11941 Warren County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11950 Washington County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11960 Wayne County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11961 Webster County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11962 Wheeler County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11963 White County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11970 Whitfield County, Georgia 11 Rural 0.8247 0.9558 19140 Urban 0.8903 11971 Wilcox County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11972 Wilkes County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11973 Wilkinson County, Georgia 11 Rural 0.8247 0.7733 99911 Rural 0.7990 11980 Worth County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 12005 Kalawao County, Hawaii 12 Rural 1.0522 1.0522 99912 Rural 1.0522 12010 Hawaii County, Hawaii 12 Rural 1.0522 1.0522 99912 Rural 1.0522 12020 Honolulu County, Hawaii 3320 Urban 1.1013 1.1013 26180 Urban 1.1013 12040 Kauai County, Hawaii 12 Rural 1.0522 1.0522 99912 Rural 1.0522 12050 Maui County, Hawaii 12 Rural 1.0522 1.0522 99912 Rural 1.0522 13000 Ada County, Idaho 1080 Urban 0.9352 0.9352 14260 Urban 0.9352 13010 Adams County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13020 Bannock County, Idaho 6340 Urban 0.9601 0.9601 38540 Urban 0.9601 13030 Bear Lake County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13040 Benewah County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13050 Bingham County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13060 Blaine County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13070 Boise County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13080 Bonner County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13090 Bonneville County, Idaho 13 Rural 0.8826 0.9059 26820 Urban 0.8943 13100 Boundary County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13110 Butte County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13120 Camas County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13130 Canyon County, Idaho 1080 Urban 0.9352 0.9352 14260 Urban 0.9352 13140 Caribou County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13150 Cassia County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13160 Clark County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13170 Clearwater County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13180 Custer County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13190 Elmore County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13200 Franklin County, Idaho 13 Rural 0.8826 0.9094 30860 Urban 0.8960 13210 Fremont County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13220 Gem County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13230 Gooding County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13240 Idaho County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13250 Jefferson County, Idaho 13 Rural 0.8826 0.9059 26820 Urban 0.8943 13260 Jerome County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13270 Kootenai County, Idaho 13 Rural 0.8826 0.9339 17660 Urban 0.9083 13280 Latah County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13290 Lemhi County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13300 Lewis County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13310 Lincoln County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13320 Madison County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13330 Minidoka County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13340 Nez Perce County, Idaho 13 Rural 0.8826 0.9314 30300 Urban 0.9070 13350 Oneida County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13360 Owyhee County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13370 Payette County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13380 Power County, Idaho 13 Rural 0.8826 0.9601 38540 Urban 0.9214 13390 Shoshone County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13400 Teton County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13410 Twin Falls County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13420 Valley County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 13430 Washington County, Idaho 13 Rural 0.8826 0.8227 99913 Rural 0.8527 14000 Adams County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14010 Alexander County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14020 Bond County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14030 Boone County, Illinois 6880 Urban 0.9626 0.9626 40420 Urban 0.9626 14040 Brown County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14050 Bureau County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14060 Calhoun County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14070 Carroll County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14080 Cass County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14090 Champaign County, Illinois 1400 Urban 0.9527 0.9527 16580 Urban 0.9527 14100 Christian County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14110 Clark County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14120 Clay County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14130 Clinton County, Illinois 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 14140 Coles County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14141 Cook County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14150 Crawford County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14160 Cumberland County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14170 De Kalb County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14180 De Witt County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14190 Douglas County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14250 Du Page County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14310 Edgar County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14320 Edwards County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14330 Effingham County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14340 Fayette County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14350 Ford County, Illinois 14 Rural 0.8340 0.9527 16580 Urban 0.8934 14360 Franklin County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14370 Fulton County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14380 Gallatin County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14390 Greene County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14400 Grundy County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14410 Hamilton County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14420 Hancock County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14421 Hardin County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14440 Henderson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14450 Henry County, Illinois 1960 Urban 0.8773 0.8773 19340 Urban 0.8773 14460 Iroquois County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14470 Jackson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14480 Jasper County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14490 Jefferson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14500 Jersey County, Illinois 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 14510 Jo Daviess County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14520 Johnson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14530 Kane County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14540 Kankakee County, Illinois 3740 Urban 1.0603 1.0603 28100 Urban 1.0603 14550 Kendall County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14560 Knox County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14570 Lake County, Illinois 1600 Urban 1.0851 1.0342 29404 Urban 1.0597 14580 La Salle County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14590 Lawrence County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14600 Lee County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14610 Livingston County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14620 Logan County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14630 Mc Donough County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14640 Mc Henry County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14650 Mclean County, Illinois 1040 Urban 0.9111 0.9111 14060 Urban 0.9111 14660 Macon County, Illinois 2040 Urban 0.8122 0.8122 19500 Urban 0.8122 14670 Macoupin County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14680 Madison County, Illinois 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 14690 Marion County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14700 Marshall County, Illinois 14 Rural 0.8340 0.8886 37900 Urban 0.8613 14710 Mason County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14720 Massac County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14730 Menard County, Illinois 7880 Urban 0.8738 0.8738 44100 Urban 0.8738 14740 Mercer County, Illinois 14 Rural 0.8340 0.8773 19340 Urban 0.8557 14750 Monroe County, Illinois 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 14760 Montgomery County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14770 Morgan County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14780 Moultrie County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14790 Ogle County, Illinois 6880 Urban 0.9626 0.8339 99914 Rural 0.8983 14800 Peoria County, Illinois 6120 Urban 0.8886 0.8886 37900 Urban 0.8886 14810 Perry County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14820 Piatt County, Illinois 14 Rural 0.8340 0.9527 16580 Urban 0.8934 14830 Pike County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14831 Pope County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14850 Pulaski County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14860 Putnam County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14870 Randolph County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14880 Richland County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14890 Rock Island County, Illinois 1960 Urban 0.8773 0.8773 19340 Urban 0.8773 14900 St Clair County, Illinois 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 14910 Saline County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14920 Sangamon County, Illinois 7880 Urban 0.8738 0.8738 44100 Urban 0.8738 14921 Schuyler County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14940 Scott County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14950 Shelby County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14960 Stark County, Illinois 14 Rural 0.8340 0.8886 37900 Urban 0.8613 14970 Stephenson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14980 Tazewell County, Illinois 6120 Urban 0.8886 0.8886 37900 Urban 0.8886 14981 Union County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14982 Vermilion County, Illinois 14 Rural 0.8340 0.8392 19180 Urban 0.8366 14983 Wabash County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14984 Warren County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14985 Washington County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14986 Wayne County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14987 White County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14988 Whiteside County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14989 Will County, Illinois 1600 Urban 1.0851 1.0868 16974 Urban 1.0860 14990 Williamson County, Illinois 14 Rural 0.8340 0.8339 99914 Rural 0.8340 14991 Winnebago County, Illinois 6880 Urban 0.9626 0.9626 40420 Urban 0.9626 14992 Woodford County, Illinois 6120 Urban 0.8886 0.8886 37900 Urban 0.8886 15000 Adams County, Indiana 2760 Urban 0.9737 0.8653 99915 Rural 0.9195 15010 Allen County, Indiana 2760 Urban 0.9737 0.9807 23060 Urban 0.9772 15020 Bartholomew County, Indiana 15 Rural 0.8736 0.9388 18020 Urban 0.9062 15030 Benton County, Indiana 15 Rural 0.8736 0.9067 29140 Urban 0.8902 15040 Blackford County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15050 Boone County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15060 Brown County, Indiana 15 Rural 0.8736 1.0113 26900 Urban 0.9425 15070 Carroll County, Indiana 15 Rural 0.8736 0.9067 29140 Urban 0.8902 15080 Cass County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15090 Clark County, Indiana 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 15100 Clay County, Indiana 8320 Urban 0.8582 0.8517 45460 Urban 0.8550 15110 Clinton County, Indiana 3920 Urban 0.9067 0.8653 99915 Rural 0.8860 15120 Crawford County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15130 Daviess County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15140 Dearborn County, Indiana 11640 Urban 0.9595 0.9516 17140 Urban 0.9556 15150 Decatur County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15160 De Kalb County, Indiana 2760 Urban 0.9737 0.8653 99915 Rural 0.9195 15170 Delaware County, Indiana 5280 Urban 0.8580 0.8580 34620 Urban 0.8580 15180 Dubois County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15190 Elkhart County, Indiana 2330 Urban 0.9278 0.9278 21140 Urban 0.9278 15200 Fayette County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15210 Floyd County, Indiana 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 15220 Fountain County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15230 Franklin County, Indiana 15 Rural 0.8736 0.9516 17140 Urban 0.9126 15240 Fulton County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15250 Gibson County, Indiana 15 Rural 0.8736 0.8372 21780 Urban 0.8554 15260 Grant County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15270 Greene County, Indiana 15 Rural 0.8736 0.8587 14020 Urban 0.8662 15280 Hamilton County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15290 Hancock County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15300 Harrison County, Indiana 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 15310 Hendricks County, Indiana 3480 Urban 1.0039 1.0113 0126900 Urban 1.0076 15320 Henry County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15330 Howard County, Indiana 3850 Urban 0.8986 0.8986 29020 Urban 0.8986 15340 Huntington County, Indiana 2760 Urban 0.9737 0.8653 99915 Rural 0.9195 15350 Jackson County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15360 Jasper County, Indiana 15 Rural 0.8736 0.9310 23844 Urban 0.9023 15370 Jay County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15380 Jefferson County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15390 Jennings County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15400 Johnson County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15410 Knox County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15420 Kosciusko County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15430 Lagrange County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15440 Lake County, Indiana 2960 Urban 0.9342 0.9310 23844 Urban 0.9326 15450 La Porte County, Indiana 15 Rural 0.8736 0.9332 33140 Urban 0.9034 15460 Lawrence County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15470 Madison County, Indiana 3480 Urban 1.0039 0.8713 11300 Urban 0.9376 15480 Marion County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15490 Marshall County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15500 Martin County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15510 Miami County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15520 Monroe County, Indiana 1020 Urban 0.8587 0.8587 14020 Urban 0.8587 15530 Montgomery County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15540 Morgan County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15550 Newton County, Indiana 15 Rural 0.8736 0.9310 23844 Urban 0.9023 15560 Noble County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15570 Ohio County, Indiana 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 15580 Orange County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15590 Owen County, Indiana 15 Rural 0.8736 0.8587 14020 Urban 0.8662 15600 Parke County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15610 Perry County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15620 Pike County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15630 Porter County, Indiana 2960 Urban 0.9342 0.9310 23844 Urban 0.9326 15640 Posey County, Indiana 2440 Urban 0.8395 0.8372 21780 Urban 0.8384 15650 Pulaski County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15660 Putnam County, Indiana 15 Rural 0.8736 1.0113 26900 Urban 0.9425 15670 Randolph County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15680 Ripley County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15690 Rush County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15700 St Joseph County, Indiana 7800 Urban 0.9447 0.9447 43780 Urban 0.9447 15710 Scott County, Indiana 4520 Urban 0.9162 0.8653 99915 Rural 0.8908 15720 Shelby County, Indiana 3480 Urban 1.0039 1.0113 26900 Urban 1.0076 15730 Spencer County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15740 Starke County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15750 Steuben County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15760 Sullivan County, Indiana 15 Rural 0.8736 0.8517 45460 Urban 0.8627 15770 Switzerland County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15780 Tippecanoe County, Indiana 3920 Urban 0.9067 0.9067 29140 Urban 0.9067 15790 Tipton County, Indiana 3850 Urban 0.8986 0.8986 29020 Urban 0.8986 15800 Union County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15810 Vanderburgh County, Indiana 2440 Urban 0.8395 0.8372 21780 Urban 0.8384 15820 Vermillion County, Indiana 8320 Urban 0.8582 0.8517 45460 Urban 0.8550 15830 Vigo County, Indiana 8320 Urban 0.8582 0.8517 45460 Urban 0.8550 15840 Wabash County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15850 Warren County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15860 Warrick County, Indiana 2440 Urban 0.8395 0.8372 21780 Urban 0.8384 15870 Washington County, Indiana 15 Rural 0.8736 0.9122 31140 Urban 0.8929 15880 Wayne County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15890 Wells County, Indiana 2760 Urban 0.9737 0.9807 23060 Urban 0.9772 15900 White County, Indiana 15 Rural 0.8736 0.8653 99915 Rural 0.8695 15910 Whitley County, Indiana 2760 Urban 0.9737 0.9807 23060 Urban 0.9772 16000 Adair County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16010 Adams County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16020 Allamakee County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16030 Appanoose County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16040 Audubon County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16050 Benton County, Iowa 16 Rural 0.8550 0.8975 16300 Urban 0.8763 16060 Black Hawk County, Iowa 8920 Urban 0.8633 0.8633 47940 Urban 0.8633 16070 Boone County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16080 Bremer County, Iowa 16 Rural 0.8550 0.8633 47940 Urban 0.8592 16090 Buchanan County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16100 Buena Vista County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16110 Butler County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16120 Calhoun County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16130 Carroll County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16140 Cass County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16150 Cedar County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16160 Cerro Gordo County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16170 Cherokee County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16180 Chickasaw County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16190 Clarke County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16200 Clay County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16210 Clayton County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16220 Clinton County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16230 Crawford County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16240 Dallas County, Iowa 2120 Urban 0.9266 0.9266 19780 Urban 0.9266 16250 Davis County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16260 Decatur County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16270 Delaware County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16280 Des Moines County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16290 Dickinson County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16300 Dubuque County, Iowa 2200 Urban 0.8748 0.8748 20220 Urban 0.8748 16310 Emmet County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16320 Fayette County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16330 Floyd County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16340 Franklin County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16350 Fremont County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16360 Greene County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16370 Grundy County, Iowa 16 Rural 0.8550 0.8633 47940 Urban 0.8592 16380 Guthrie County, Iowa 16 Rural 0.8550 0.9266 19780 Urban 0.8908 16390 Hamilton County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16400 Hancock County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16410 Hardin County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16420 Harrison County, Iowa 16 Rural 0.8550 0.9754 36540 Urban 0.9152 16430 Henry County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16440 Howard County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16450 Humboldt County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16460 Ida County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16470 Iowa County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16480 Jackson County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16490 Jasper County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16500 Jefferson County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16510 Johnson County, Iowa 3500 Urban 0.9654 0.9654 26980 Urban 0.9654 16520 Jones County, Iowa 16 Rural 0.8550 0.8975 16300 Urban 0.8763 16530 Keokuk County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16540 Kossuth County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16550 Lee County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16560 Linn County, Iowa 1360 Urban 0.8975 0.8975 16300 Urban 0.8975 16570 Louisa County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16580 Lucas County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16590 Lyon County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16600 Madison County, Iowa 16 Rural 0.8550 0.9266 19780 Urban 0.8908 16610 Mahaska County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16620 Marion County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16630 Marshall County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16640 Mills County, Iowa 16 Rural 0.8550 0.9754 36540 Urban 0.9152 16650 Mitchell County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16660 Monona County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16670 Monroe County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16680 Montgomery County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16690 Muscatine County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16700 OBrien County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16710 Osceola County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16720 Page County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16730 Palo Alto County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16740 Plymouth County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16750 Pocahontas County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16760 Polk County, Iowa 2120 Urban 0.9266 0.9266 19780 Urban 0.9266 16770 Pottawattamie County, Iowa 5920 Urban 0.9754 0.9754 36540 Urban 0.9754 16780 Poweshiek County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16790 Ringgold County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16800 Sac County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16810 Scott County, Iowa 1960 Urban 0.8773 0.8773 19340 Urban 0.8773 16820 Shelby County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16830 Sioux County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16840 Story County, Iowa 16 Rural 0.8550 0.9479 11180 Urban 0.9015 16850 Tama County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16860 Taylor County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16870 Union County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16880 Van Buren County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16890 Wapello County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16900 Warren County, Iowa 2120 Urban 0.9266 0.9266 19780 Urban 0.9266 16910 Washington County, Iowa 16 Rural 0.8550 0.9654 26980 Urban 0.9102 16920 Wayne County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16930 Webster County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16940 Winnebago County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16950 Winneshiek County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16960 Woodbury County, Iowa 7720 Urban 0.9094 0.9070 43580 Urban 0.9082 16970 Worth County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 16980 Wright County, Iowa 16 Rural 0.8550 0.8475 99916 Rural 0.8513 17000 Allen County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17010 Anderson County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17020 Atchison County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17030 Barber County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17040 Barton County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17050 Bourbon County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17060 Brown County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17070 Butler County, Kansas 9040 Urban 0.9486 0.9457 48620 Urban 0.9472 17080 Chase County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17090 Chautauqua County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17100 Cherokee County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17110 Cheyenne County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17120 Clark County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17130 Clay County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17140 Cloud County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17150 Coffey County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17160 Comanche County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17170 Cowley County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17180 Crawford County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17190 Decatur County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17200 Dickinson County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17210 Doniphan County, Kansas 17 Rural 0.8087 1.0013 41140 Urban 0.9050 17220 Douglas County, Kansas 4150 Urban 0.8644 0.8644 29940 Urban 0.8644 17230 Edwards County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17240 Elk County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17250 Ellis County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17260 Ellsworth County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17270 Finney County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17280 Ford County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17290 Franklin County, Kansas 17 Rural 0.8087 0.9629 28140 Urban 0.8858 17300 Geary County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17310 Gove County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17320 Graham County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17330 Grant County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17340 Gray County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17350 Greeley County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17360 Greenwood County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17370 Hamilton County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17380 Harper County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17390 Harvey County, Kansas 9040 Urban 0.9486 0.9457 48620 Urban 0.9472 17391 Haskell County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17410 Hodgeman County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17420 Jackson County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17430 Jefferson County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17440 Jewell County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17450 Johnson County, Kansas 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 17451 Kearny County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17470 Kingman County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17480 Kiowa County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17490 Labette County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17500 Lane County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17510 Leavenworth County, Kansas 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 17520 Lincoln County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17530 Linn County, Kansas 17 Rural 0.8087 0.9629 28140 Urban 0.8858 17540 Logan County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17550 Lyon County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17560 Mc Pherson County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17570 Marion County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17580 Marshall County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17590 Meade County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17600 Miami County, Kansas 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 17610 Mitchell County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17620 Montgomery County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17630 Morris County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17640 Morton County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17650 Nemaha County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17660 Neosho County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17670 Ness County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17680 Norton County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17690 Osage County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17700 Osborne County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17710 Ottawa County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17720 Pawnee County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17730 Phillips County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17740 Pottawatomie County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17750 Pratt County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17760 Rawlins County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17770 Reno County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17780 Republic County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17790 Rice County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17800 Riley County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17810 Rooks County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17820 Rush County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17830 Russell County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17840 Saline County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17841 Scott County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17860 Sedgwick County, Kansas 9040 Urban 0.9486 0.9457 48620 Urban 0.9472 17870 Seward County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17880 Shawnee County, Kansas 8440 Urban 0.8904 0.8904 45820 Urban 0.8904 17890 Sheridan County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17900 Sherman County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17910 Smith County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17920 Stafford County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17921 Stanton County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17940 Stevens County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17950 Sumner County, Kansas 17 Rural 0.8087 0.9457 48620 Urban 0.8772 17960 Thomas County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17970 Trego County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17980 Wabaunsee County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17981 Wallace County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17982 Washington County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17983 Wichita County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17984 Wilson County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17985 Woodson County, Kansas 17 Rural 0.8087 0.8079 99917 Rural 0.8083 17986 Wyandotte County, Kansas 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 18000 Adair County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18010 Allen County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18020 Anderson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18030 Ballard County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18040 Barren County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18050 Bath County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18060 Bell County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18070 Boone County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18080 Bourbon County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 18090 Boyd County, Kentucky 13400 Urban 0.9564 0.9564 26580 Urban 0.9564 18100 Boyle County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18110 Bracken County, Kentucky 18 Rural 0.7844 0.9516 17140 Urban 0.8680 18120 Breathitt County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18130 Breckinridge County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18140 Bullitt County, Kentucky 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 18150 Butler County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18160 Caldwell County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18170 Calloway County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18180 Campbell County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18190 Carlisle County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18191 Carroll County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18210 Carter County, Kentucky 3400 Urban 0.9564 0.7755 99918 Rural 0.8660 18220 Casey County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18230 Christian County, Kentucky 1660 Urban 0.8022 0.8022 17300 Urban 0.8022 18240 Clark County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 18250 Clay County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18260 Clinton County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18270 Crittenden County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18271 Cumberland County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18290 Daviess County, Kentucky 5990 Urban 0.8434 0.8434 36980 Urban 0.8434 18291 Edmonson County, Kentucky 18 Rural 0.7844 0.8140 14540 Urban 0.7992 18310 Elliott County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18320 Estill County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18330 Fayette County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 18340 Fleming County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18350 Floyd County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18360 Franklin County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18361 Fulton County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18362 Gallatin County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18390 Garrard County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18400 Grant County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18410 Graves County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18420 Grayson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18421 Green County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18440 Greenup County, Kentucky 3400 Urban 0.9564 0.9564 26580 Urban 0.9564 18450 Hancock County, Kentucky 18 Rural 0.7844 0.8434 36980 Urban 0.8139 18460 Hardin County, Kentucky 18 Rural 0.7844 0.8684 21060 Urban 0.8264 18470 Harlan County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18480 Harrison County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18490 Hart County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18500 Henderson County, Kentucky 2440 Urban 0.8395 0.8372 21780 Urban 0.8384 18510 Henry County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18511 Hickman County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18530 Hopkins County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18540 Jackson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18550 Jefferson County, Kentucky 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 18560 Jessamine County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 18570 Johnson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18580 Kenton County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18590 Knott County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18600 Knox County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18610 Larue County, Kentucky 18 Rural 0.7844 0.8684 21060 Urban 0.8264 18620 Laurel County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18630 Lawrence County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18640 Lee County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18650 Leslie County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18660 Letcher County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18670 Lewis County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18680 Lincoln County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18690 Livingston County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18700 Logan County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18710 Lyon County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18720 Mc Cracken County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18730 Mc Creary County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18740 Mc Lean County, Kentucky 18 Rural 0.7844 0.8434 36980 Urban 0.8139 18750 Madison County, Kentucky 4280 Urban 0.9219 0.7755 99918 Rural 0.8487 18760 Magoffin County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18770 Marion County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18780 Marshall County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18790 Martin County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18800 Mason County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18801 Meade County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18802 Menifee County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18830 Mercer County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18831 Metcalfe County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18850 Monroe County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18860 Montgomery County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18861 Morgan County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18880 Muhlenberg County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18890 Nelson County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18900 Nicholas County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18910 Ohio County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18920 Oldham County, Kentucky 4520 Urban 0.9162 0.9122 31140 Urban 0.9142 18930 Owen County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18931 Owsley County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18932 Pendleton County, Kentucky 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 18960 Perry County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18970 Pike County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18971 Powell County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18972 Pulaski County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18973 Robertson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18974 Rockcastle County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18975 Rowan County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18976 Russell County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18977 Scott County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 18978 Shelby County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18979 Simpson County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18980 Spencer County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18981 Taylor County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18982 Todd County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18983 Trigg County, Kentucky 18 Rural 0.7844 0.8022 17300 Urban 0.7933 18984 Trimble County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18985 Union County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18986 Warren County, Kentucky 18 Rural 0.7844 0.8140 14540 Urban 0.7992 18987 Washington County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18988 Wayne County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18989 Webster County, Kentucky 18 Rural 0.7844 0.8372 21780 Urban 0.8108 18990 Whitley County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18991 Wolfe County, Kentucky 18 Rural 0.7844 0.7755 99918 Rural 0.7800 18992 Woodford County, Kentucky 4280 Urban 0.9219 0.9359 30460 Urban 0.9289 19000 Acadia County, Louisiana 3880 Urban 0.8105 0.7345 99919 Rural 0.7725 19010 Allen County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19020 Ascension County, Louisiana 0760 Urban 0.8354 0.8319 12940 Urban 0.8337 19030 Assumption County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19040 Avoyelles County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19050 Beauregard County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19060 Bienville County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19070 Bossier County, Louisiana 7680 Urban 0.9111 0.9132 43340 Urban 0.9122 19080 Caddo County, Louisiana 7680 Urban 0.9111 0.9132 43340 Urban 0.9122 19090 Calcasieu County, Louisiana 3960 Urban 0.7972 0.7935 29340 Urban 0.7954 19100 Caldwell County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19110 Cameron County, Louisiana 19 Rural 0.7290 0.7935 29340 Urban 0.7613 19120 Catahoula County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19130 Claiborne County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19140 Concordia County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19150 De Soto County, Louisiana 19 Rural 0.7290 0.9132 43340 Urban 0.8211 19160 East Baton Rouge County, Louisiana 0760 Urban 0.8354 0.8319 12940 Urban 0.8337 19170 East Carroll County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19180 East Feliciana County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19190 Evangeline County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19200 Franklin County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19210 Grant County, Louisiana 19 Rural 0.7290 0.8171 10780 Urban 0.7731 19220 Iberia County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19230 Iberville County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19240 Jackson County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19250 Jefferson County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19260 Jefferson Davis County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19270 Lafayette County, Louisiana 3880 Urban 0.8105 0.8306 29180 Urban 0.8206 19280 Lafourche County, Louisiana 3350 Urban 0.7721 0.7721 26380 Urban 0.7721 19290 La Salle County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19300 Lincoln County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19310 Livingston County, Louisiana 0760 Urban 0.8354 0.8319 12940 Urban 0.8337 19320 Madison County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19330 Morehouse County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19340 Natchitoches County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19350 Orleans County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19360 Ouachita County, Louisiana 5200 Urban 0.7913 0.7903 33740 Urban 0.7908 19370 Plaquemines County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19380 Pointe Coupee County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19390 Rapides County, Louisiana 0220 Urban 0.8171 0.8171 10780 Urban 0.8171 19400 Red River County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19410 Richland County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19420 Sabine County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19430 St Bernard County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19440 St Charles County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19450 St Helena County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19460 St James County, Louisiana 5560 Urban 0.9103 0.7345 99919 Rural 0.8224 19470 St John Baptist County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19480 St Landry County, Louisiana 3880 Urban 0.8105 0.7345 99919 Rural 0.7725 19490 St Martin County, Louisiana 3880 Urban 0.8105 0.8306 29180 Urban 0.8206 19500 St Mary County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19510 St Tammany County, Louisiana 5560 Urban 0.9103 0.9103 35380 Urban 0.9103 19520 Tangipahoa County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19530 Tensas County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19540 Terrebonne County, Louisiana 3350 Urban 0.7721 0.7721 26380 Urban 0.7721 19550 Union County, Louisiana 19 Rural 0.7290 0.7903 33740 Urban 0.7597 19560 Vermilion County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19570 Vernon County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19580 Washington County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19590 Webster County, Louisiana 7680 Urban 0.9111 0.7345 99919 Rural 0.8228 19600 West Baton Rouge County, Louisiana 0760 Urban 0.8354 0.8319 12940 Urban 0.8337 19610 West Carroll County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 19620 West Feliciana County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19630 Winn County, Louisiana 19 Rural 0.7290 0.7345 99919 Rural 0.7318 20000 Androscoggin County, Maine 4243 Urban 0.9562 0.9562 30340 Urban 0.9562 20010 Aroostook County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20020 Cumberland County, Maine 6403 Urban 1.0112 1.0112 38860 Urban 1.0112 20030 Franklin County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20040 Hancock County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20050 Kennebec County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20060 Knox County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20070 Lincoln County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20080 Oxford County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20090 Penobscot County, Maine 0733 Urban 0.9955 0.9955 12620 Urban 0.9955 20100 Piscataquis County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20110 Sagadahoc County, Maine 6403 Urban 1.0112 1.0112 38860 Urban 1.0112 20120 Somerset County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20130 Waldo County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20140 Washington County, Maine 20 Rural 0.9039 0.9039 99920 Rural 0.9039 20150 York County, Maine 6403 Urban 1.0112 1.0112 38860 Urban 1.0112 21000 Allegany County, Maryland 1900 Urban 0.8662 0.8662 19060 Urban 0.8662 21010 Anne Arundel County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21020 Baltimore County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21030 Baltimore City County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21040 Calvert County, Maryland 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 21050 Caroline County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21060 Carroll County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21070 Cecil County, Maryland 9160 Urban 1.1121 1.1049 48864 Urban 1.1085 21080 Charles County, Maryland 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 21090 Dorchester County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21100 Frederick County, Maryland 8840 Urban 1.0971 1.0956 13644 Urban 1.0964 21110 Garrett County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21120 Harford County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21130 Howard County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21140 Kent County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21150 Montgomery County, Maryland 8840 Urban 1.0971 1.0956 13644 Urban 1.0964 21160 Prince Georges County, Maryland 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 21170 Queen Annes County, Maryland 0720 Urban 0.9907 0.9907 12580 Urban 0.9907 21180 St Marys County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21190 Somerset County, Maryland 21 Rural 0.9179 0.9123 41540 Urban 0.9151 21200 Talbot County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 21210 Washington County, Maryland 3180 Urban 0.9940 0.9715 25180 Urban 0.9828 21220 Wicomico County, Maryland 21 Rural 0.9179 0.9123 41540 Urban 0.9151 21230 Worcester County, Maryland 21 Rural 0.9179 0.9220 99921 Rural 0.9200 22000 Barnstable County, Massachusetts 0743 Urban 1.2335 1.2335 12700 Urban 1.2335 22010 Berkshire County, Massachusetts 6323 Urban 1.0439 1.0439 38340 Urban 1.0439 22020 Bristol County, Massachusetts 1123 Urban 1.1290 1.0929 39300 Urban 1.1110 22030 Dukes County, Massachusetts 22 Rural 1.0216 1.0216 99922 Rural 1.0216 22040 Essex County, Massachusetts 1123 Urban 1.1290 1.0662 21604 Urban 1.0976 22060 Franklin County, Massachusetts 22 Rural 1.0216 1.0176 44140 Urban 1.0196 22070 Hampden County, Massachusetts 8003 Urban 1.0173 1.0176 44140 Urban 1.0175 22080 Hampshire County, Massachusetts 8003 Urban 1.0173 1.0176 44140 Urban 1.0175 22090 Middlesex County, Massachusetts 1123 Urban 1.1290 1.1189 15764 Urban 1.1240 22120 Nantucket County, Massachusetts 22 Rural 1.0216 1.0216 99922 Rural 1.0216 22130 Norfolk County, Massachusetts 1123 Urban 1.1290 1.1771 14484 Urban 1.1531 22150 Plymouth County, Massachusetts 1123 Urban 1.1290 1.1771 14484 Urban 1.1531 22160 Suffolk County, Massachusetts 1123 Urban 1.1290 1.1771 14484 Urban 1.1531 22170 Worcester County, Massachusetts 1123 Urban 1.1290 1.0996 49340 Urban 1.1143 23000 Alcona County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23010 Alger County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23020 Allegan County, Michigan 3000 Urban 0.9519 0.8786 99923 Rural 0.9153 23030 Alpena County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23040 Antrim County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23050 Arenac County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23060 Baraga County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23070 Barry County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 23080 Bay County, Michigan 6960 Urban 0.9696 0.9574 13020 Urban 0.9635 23090 Benzie County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23100 Berrien County, Michigan 0870 Urban 0.8847 0.8847 35660 Urban 0.8847 23110 Branch County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23120 Calhoun County, Michigan 3720 Urban 1.0350 0.9366 12980 Urban 0.9858 23130 Cass County, Michigan 23 Rural 0.8740 0.9447 43780 Urban 0.9094 23140 Charlevoix County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23150 Cheboygan County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23160 Chippewa County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23170 Clare County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23180 Clinton County, Michigan 4040 Urban 0.9658 0.9658 29620 Urban 0.9658 23190 Crawford County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23200 Delta County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23210 Dickinson County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23220 Eaton County, Michigan 4040 Urban 0.9658 0.9658 29620 Urban 0.9658 23230 Emmet County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23240 Genesee County, Michigan 2640 Urban 1.1178 1.1178 22420 Urban 1.1178 23250 Gladwin County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23260 Gogebic County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23270 Grand Traverse County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23280 Gratiot County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23290 Hillsdale County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23300 Houghton County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23310 Huron County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23320 Ingham County, Michigan 4040 Urban 0.9658 0.9658 29620 Urban 0.9658 23330 Ionia County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 23340 Iosco County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23350 Iron County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23360 Isabella County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23370 Jackson County, Michigan 3520 Urban 0.9146 0.9146 27100 Urban 0.9146 23380 Kalamazoo County, Michigan 3720 Urban 1.0350 1.0676 2820 Urban 1.0513 23390 Kalkaska County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23400 Kent County, Michigan 3000 Urban 0.9519 0.9420 24340 Urban 0.9470 23410 Keweenaw County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23420 Lake County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23430 Lapeer County, Michigan 2160 Urban 1.0227 1.0112 47644 Urban 1.0170 23440 Leelanau County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23450 Lenawee County, Michigan 0440 Urban 1.0816 0.8786 99923 Rural 0.9801 23460 Livingston County, Michigan 0440 Urban 1.0816 1.0112 47644 Urban 1.0464 23470 Luce County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23480 Mackinac County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23490 Macomb County, Michigan 2160 Urban 1.0227 1.0112 47644 Urban 1.0170 23500 Manistee County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23510 Marquette County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23520 Mason County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23530 Mecosta County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23540 Menominee County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23550 Midland County, Michigan 6960 Urban 0.9696 0.8786 99923 Rural 0.9241 23560 Missaukee County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23570 Monroe County, Michigan 2160 Urban 1.0227 0.9506 33780 Urban 0.9867 23580 Montcalm County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23590 Montmorency County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23600 Muskegon County, Michigan 3000 Urban 0.9519 0.9741 34740 Urban 0.9630 23610 Newaygo County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 23620 Oakland County, Michigan 2160 Urban 1.0227 1.0112 47644 Urban 1.0170 23630 Oceana County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23640 Ogemaw County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23650 Ontonagon County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23660 Osceola County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23670 Oscoda County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23680 Otsego County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23690 Ottawa County, Michigan 3000 Urban 0.9519 0.9388 26100 Urban 0.9454 23700 Presque Isle County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23710 Roscommon County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23720 Saginaw County, Michigan 6960 Urban 0.9696 0.9814 40980 Urban 0.9755 23730 St Clair County, Michigan 2160 Urban 1.0227 1.0112 47644 Urban 1.0170 23740 St Joseph County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23750 Sanilac County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23760 Schoolcraft County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23770 Shiawassee County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23780 Tuscola County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 23790 Van Buren County, Michigan 3720 Urban 1.0350 1.0676 28020 Urban 1.0513 23800 Washtenaw County, Michigan 0440 Urban 1.0816 1.1022 11460 Urban 1.0919 23810 Wayne County, Michigan 2160 Urban 1.0227 1.0349 19804 Urban 1.0288 23830 Wexford County, Michigan 23 Rural 0.8740 0.8786 99923 Rural 0.8763 24000 Aitkin County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24010 Anoka County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24020 Becker County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24030 Beltrami County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24040 Benton County, Minnesota 6980 Urban 1.0215 1.0215 41060 Urban 1.0215 24050 Big Stone County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24060 Blue Earth County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24070 Brown County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24080 Carlton County, Minnesota 24 Rural 0.9339 1.0340 20260 Urban 0.9840 24090 Carver County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24100 Cass County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24110 Chippewa County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24120 Chisago County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24130 Clay County, Minnesota 2520 Urban 0.9114 0.9114 22020 Urban 0.9114 24140 Clearwater County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24150 Cook County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24160 Cottonwood County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24170 Crow Wing County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24180 Dakota County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24190 Dodge County, Minnesota 24 Rural 0.9339 1.1504 40340 Urban 1.0422 24200 Douglas County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24210 Faribault County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24220 Fillmore County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24230 Freeborn County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24240 Goodhue County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24250 Grant County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24260 Hennepin County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24270 Houston County, Minnesota 3870 Urban 0.9289 0.9289 29100 Urban 0.9289 24280 Hubbard County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24290 Isanti County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24300 Itasca County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24310 Jackson County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24320 Kanabec County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24330 Kandiyohi County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24340 Kittson County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24350 Koochiching County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24360 Lac Qui Parle County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24370 Lake County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24380 Lake Of Woods County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24390 Le Sueur County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24400 Lincoln County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24410 Lyon County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24420 Mc Leod County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24430 Mahnomen County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24440 Marshall County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24450 Martin County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24460 Meeker County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24470 Mille Lacs County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24480 Morrison County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24490 Mower County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24500 Murray County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24510 Nicollet County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24520 Nobles County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24530 Norman County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24540 Olmsted County, Minnesota 6820 Urban 1.1504 1.1504 40340 Urban 1.1504 24550 Otter Tail County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24560 Pennington County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24570 Pine County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24580 Pipestone County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24590 Polk County, Minnesota 2985 Urban 0.9091 0.9091 24220 Urban 0.9091 24600 Pope County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24610 Ramsey County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24620 Red Lake County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24630 Redwood County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24640 Renville County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24650 Rice County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24660 Rock County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24670 Roseau County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24680 St Louis County, Minnesota 2240 Urban 1.0356 1.0340 20260 Urban 1.0348 24690 Scott County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24700 Sherburne County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24710 Sibley County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24720 Stearns County, Minnesota 6980 Urban 1.0215 1.0215 41060 Urban 1.0215 24730 Steele County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24740 Stevens County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24750 Swift County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24760 Todd County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24770 Traverse County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24780 Wabasha County, Minnesota 24 Rural 0.9339 1.1504 40340 Urban 1.0422 24790 Wadena County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24800 Waseca County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24810 Washington County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24820 Watonwan County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24830 Wilkin County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24840 Winona County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 24850 Wright County, Minnesota 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 24860 Yellow Medicine County, Minnesota 24 Rural 0.9339 0.9330 99924 Rural 0.9335 25000 Adams County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25010 Alcorn County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25020 Amite County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25030 Attala County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25040 Benton County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25050 Bolivar County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25060 Calhoun County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25070 Carroll County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25080 Chickasaw County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25090 Choctaw County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25100 Claiborne County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25110 Clarke County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25120 Clay County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25130 Coahoma County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25140 Copiah County, Mississippi 25 Rural 0.7583 0.8291 27140 Urban 0.7937 25150 Covington County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25160 Desoto County, Mississippi 4920 Urban 0.9234 0.9217 32820 Urban 0.9226 25170 Forrest County, Mississippi 3285 Urban 0.7362 0.7362 25620 Urban 0.7362 25180 Franklin County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25190 George County, Mississippi 25 Rural 0.7583 0.7974 37700 Urban 0.7779 25200 Greene County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25210 Grenada County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25220 Hancock County, Mississippi 0920 Urban 0.8649 0.8950 25060 Urban 0.8800 25230 Harrison County, Mississippi 0920 Urban 0.8649 0.8950 25060 Urban 0.8800 25240 Hinds County, Mississippi 3560 Urban 0.8406 0.8291 27140 Urban 0.8349 25250 Holmes County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25260 Humphreys County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25270 Issaquena County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25280 Itawamba County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25290 Jackson County, Mississippi 0920 Urban 0.8649 0.7974 37700 Urban 0.8312 25300 Jasper County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25310 Jefferson County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25320 Jefferson Davis County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25330 Jones County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25340 Kemper County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25350 Lafayette County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25360 Lamar County, Mississippi 3285 Urban 0.7362 0.7362 25620 Urban 0.7362 25370 Lauderdale County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25380 Lawrence County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25390 Leake County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25400 Lee County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25410 Leflore County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25420 Lincoln County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25430 Lowndes County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25440 Madison County, Mississippi 3560 Urban 0.8406 0.8291 27140 Urban 0.8349 25450 Marion County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25460 Marshall County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 25470 Monroe County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25480 Montgomery County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25490 Neshoba County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25500 Newton County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25510 Noxubee County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25520 Oktibbeha County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25530 Panola County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25540 Pearl River County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25550 Perry County, Mississippi 25 Rural 0.7583 0.7362 25620 Urban 0.7473 25560 Pike County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25570 Pontotoc County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25580 Prentiss County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25590 Quitman County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25600 Rankin County, Mississippi 3560 Urban 0.8406 0.8291 27140 Urban 0.8349 25610 Scott County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25620 Sharkey County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25630 Simpson County, Mississippi 25 Rural 0.7583 0.8291 27140 Urban 0.7937 25640 Smith County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25650 Stone County, Mississippi 25 Rural 0.7583 0.8950 25060 Urban 0.8267 25660 Sunflower County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25670 Tallahatchie County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25680 Tate County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 25690 Tippah County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25700 Tishomingo County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25710 Tunica County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 25720 Union County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25730 Walthall County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25740 Warren County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25750 Washington County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25760 Wayne County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25770 Webster County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25780 Wilkinson County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25790 Winston County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25800 Yalobusha County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 25810 Yazoo County, Mississippi 25 Rural 0.7583 0.7635 99925 Rural 0.7609 26000 Adair County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26010 Andrew County, Missouri 7000 Urban 1.0013 1.0013 41140 Urban 1.0013 26020 Atchison County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26030 Audrain County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26040 Barry County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26050 Barton County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26060 Bates County, Missouri 26 Rural 0.7829 0.9629 28140 Urban 0.8729 26070 Benton County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26080 Bollinger County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26090 Boone County, Missouri 1740 Urban 0.8396 0.8396 17860 Urban 0.8396 26100 Buchanan County, Missouri 7000 Urban 1.0013 1.0013 41140 Urban 1.0013 26110 Butler County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26120 Caldwell County, Missouri 26 Rural 0.7829 0.9629 28140 Urban 0.8729 26130 Callaway County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26140 Camden County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26150 Cape Girardeau County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26160 Carroll County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26170 Carter County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26180 Cass County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26190 Cedar County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26200 Chariton County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26210 Christian County, Missouri 7920 Urban 0.8597 0.8557 44180 Urban 0.8577 26220 Clark County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26230 Clay County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26240 Clinton County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26250 Cole County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26260 Cooper County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26270 Crawford County, Missouri 26 Rural 0.7829 0.9076 41180 Urban 0.8453 26280 Dade County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26290 Dallas County, Missouri 26 Rural 0.7829 0.8557 44180 Urban 0.8193 26300 Daviess County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26310 De Kalb County, Missouri 26 Rural 0.7829 1.0013 41140 Urban 0.8921 26320 Dent County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26330 Douglas County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26340 Dunklin County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26350 Franklin County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26360 Gasconade County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26370 Gentry County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26380 Greene County, Missouri 7920 Urban 0.8597 0.8557 44180 Urban 0.8577 26390 Grundy County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26400 Harrison County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26410 Henry County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26411 Hickory County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26412 Holt County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26440 Howard County, Missouri 26 Rural 0.7829 0.8396 17860 Urban 0.8113 26450 Howell County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26460 Iron County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26470 Jackson County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26480 Jasper County, Missouri 3710 Urban 0.8721 0.8721 27900 Urban 0.8721 26490 Jefferson County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26500 Johnson County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26510 Knox County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26520 Laclede County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26530 Lafayette County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26540 Lawrence County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26541 Lewis County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26560 Lincoln County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26570 Linn County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26580 Livingston County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26590 Mc Donald County, Missouri 26 Rural 0.7829 0.8636 22220 Urban 0.8233 26600 Macon County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26601 Madison County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26620 Maries County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26630 Marion County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26631 Mercer County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26650 Miller County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26660 Mississippi County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26670 Moniteau County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26680 Monroe County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26690 Montgomery County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26700 Morgan County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26710 New Madrid County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26720 Newton County, Missouri 3710 Urban 0.8721 0.8721 27900 Urban 0.8721 26730 Nodaway County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26740 Oregon County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26750 Osage County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26751 Ozark County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26770 Pemiscot County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26780 Perry County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26790 Pettis County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26800 Phelps County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26810 Pike County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26820 Platte County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26821 Polk County, Missouri 26 Rural 0.7829 0.8557 44180 Urban 0.8193 26840 Pulaski County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26850 Putnam County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26860 Ralls County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26870 Randolph County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26880 Ray County, Missouri 3760 Urban 0.9641 0.9629 28140 Urban 0.9635 26881 Reynolds County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26900 Ripley County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26910 St Charles County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26911 St Clair County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26930 St Francois County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26940 St Louis County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26950 St Louis City County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26960 Ste Genevieve County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26970 Saline County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26980 Schuyler County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26981 Scotland County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26982 Scott County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26983 Shannon County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26984 Shelby County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26985 Stoddard County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26986 Stone County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26987 Sullivan County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26988 Taney County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26989 Texas County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26990 Vernon County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26991 Warren County, Missouri 7040 Urban 0.9081 0.9076 41180 Urban 0.9079 26992 Washington County, Missouri 26 Rural 0.7829 0.9076 41180 Urban 0.8453 26993 Wayne County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26994 Webster County, Missouri 7920 Urban 0.8597 0.8557 44180 Urban 0.8577 26995 Worth County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 26996 Wright County, Missouri 26 Rural 0.7829 0.7762 99926 Rural 0.7796 27000 Beaverhead County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27010 Big Horn County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27020 Blaine County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27030 Broadwater County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27040 Carbon County, Montana 27 Rural 0.8701 0.8961 13740 Urban 0.8831 27050 Carter County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27060 Cascade County, Montana 3040 Urban 0.8810 0.8810 24500 Urban 0.8810 27070 Chouteau County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27080 Custer County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27090 Daniels County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27100 Dawson County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27110 Deer Lodge County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27113 Yellowstone National Park, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27120 Fallon County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27130 Fergus County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27140 Flathead County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27150 Gallatin County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27160 Garfield County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27170 Glacier County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27180 Golden Valley County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27190 Granite County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27200 Hill County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27210 Jefferson County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27220 Judith Basin County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27230 Lake County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27240 Lewis And Clark County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27250 Liberty County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27260 Lincoln County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27270 Mc Cone County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27280 Madison County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27290 Meagher County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27300 Mineral County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27310 Missoula County, Montana 5140 Urban 0.9618 0.9618 33540 Urban 0.9618 27320 Musselshell County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27330 Park County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27340 Petroleum County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27350 Phillips County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27360 Pondera County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27370 Powder River County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27380 Powell County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27390 Prairie County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27400 Ravalli County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27410 Richland County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27420 Roosevelt County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27430 Rosebud County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27440 Sanders County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27450 Sheridan County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27460 Silver Bow County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27470 Stillwater County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27480 Sweet Grass County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27490 Teton County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27500 Toole County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27510 Treasure County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27520 Valley County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27530 Wheatland County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27540 Wibaux County, Montana 27 Rural 0.8701 0.8701 99927 Rural 0.8701 27550 Yellowstone County, Montana 0880 Urban 0.8961 0.8961 13740 Urban 0.8961 28000 Adams County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28010 Antelope County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28020 Arthur County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28030 Banner County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28040 Blaine County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28050 Boone County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28060 Box Butte County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28070 Boyd County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28080 Brown County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28090 Buffalo County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28100 Burt County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28110 Butler County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28120 Cass County, Nebraska 5920 Urban 0.9754 0.9754 36540 Urban 0.9754 28130 Cedar County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28140 Chase County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28150 Cherry County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28160 Cheyenne County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28170 Clay County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28180 Colfax County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28190 Cuming County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28200 Custer County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28210 Dakota County, Nebraska 7720 Urban 0.9094 0.9070 43580 Urban 0.9082 28220 Dawes County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28230 Dawson County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28240 Deuel County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28250 Dixon County, Nebraska 28 Rural 0.9035 0.9070 43580 Urban 0.9053 28260 Dodge County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28270 Douglas County, Nebraska 5920 Urban 0.9754 0.9754 36540 Urban 0.9754 28280 Dundy County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28290 Fillmore County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28300 Franklin County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28310 Frontier County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28320 Furnas County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28330 Gage County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28340 Garden County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28350 Garfield County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28360 Gosper County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28370 Grant County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28380 Greeley County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28390 Hall County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28400 Hamilton County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28410 Harlan County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28420 Hayes County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28430 Hitchcock County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28440 Holt County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28450 Hooker County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28460 Howard County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28470 Jefferson County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28480 Johnson County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28490 Kearney County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28500 Keith County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28510 Keya Paha County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28520 Kimball County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28530 Knox County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28540 Lancaster County, Nebraska 4360 Urban 1.0208 1.0208 30700 Urban 1.0208 28550 Lincoln County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28560 Logan County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28570 Loup County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28580 Mc Pherson County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28590 Madison County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28600 Merrick County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28610 Morrill County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28620 Nance County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28630 Nemaha County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28640 Nuckolls County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28650 Otoe County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28660 Pawnee County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28670 Perkins County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28680 Phelps County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28690 Pierce County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28700 Platte County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28710 Polk County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28720 Redwillow County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28730 Richardson County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28740 Rock County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28750 Saline County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28760 Sarpy County, Nebraska 5920 Urban 0.9754 0.9754 36540 Urban 0.9754 28770 Saunders County, Nebraska 28 Rural 0.9035 0.9754 36540 Urban 0.9395 28780 Scotts Bluff County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28790 Seward County, Nebraska 28 Rural 0.9035 1.0208 30700 Urban 0.9622 28800 Sheridan County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28810 Sherman County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28820 Sioux County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28830 Stanton County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28840 Thayer County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28850 Thomas County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28860 Thurston County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28870 Valley County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28880 Washington County, Nebraska 5920 Urban 0.9754 0.9754 36540 Urban 0.9754 28890 Wayne County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28900 Webster County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28910 Wheeler County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 28920 York County, Nebraska 28 Rural 0.9035 0.9035 99928 Rural 0.9035 29000 Churchill County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29010 Clark County, Nevada 4120 Urban 1.1121 1.1378 29820 Urban 1.1250 29020 Douglas County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29030 Elko County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29040 Esmeralda County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29050 Eureka County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29060 Humboldt County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29070 Lander County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29080 Lincoln County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29090 Lyon County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29100 Mineral County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29110 Nye County, Nevada 4120 Urban 1.1121 0.9280 99929 Rural 1.0201 29120 Carson City County, Nevada 29 Rural 0.9832 1.0352 16180 Urban 1.0092 29130 Pershing County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 29140 Storey County, Nevada 29 Rural 0.9832 1.0456 39900 Urban 1.0144 29150 Washoe County, Nevada 6720 Urban 1.0456 1.0456 39900 Urban 1.0456 29160 White Pine County, Nevada 29 Rural 0.9832 0.9280 99929 Rural 0.9556 30000 Belknap County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 30010 Carroll County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 30020 Cheshire County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 30030 Coos County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 30040 Grafton County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 30050 Hillsboro County, New Hampshire 1123 Urban 1.1290 1.0642 31700 Urban 1.0966 30060 Merrimack County, New Hampshire 1123 Urban 1.1290 1.0642 31700 Urban 1.0966 30070 Rockingham County, New Hampshire 1123 Urban 1.1290 1.0221 40484 Urban 1.0756 30080 Strafford County, New Hampshire 1123 Urban 1.1290 1.0221 40484 Urban 1.0756 30090 Sullivan County, New Hampshire 30 Rural 0.9940 0.9940 99930 Rural 0.9940 31000 Atlantic County, New Jersey 0560 Urban 1.0907 1.0931 12100 Urban 1.0919 31100 Bergen County, New Jersey 0875 Urban 1.1967 1.3311 35644 Urban 1.2639 31150 Burlington County, New Jersey 6160 Urban 1.0824 1.0675 15804 Urban 1.0750 31160 Camden County, New Jersey 6160 Urban 1.0824 1.0675 15804 Urban 1.0750 31180 Cape May County, New Jersey 0560 Urban 1.0907 1.0810 36140 Urban 1.0859 31190 Cumberland County, New Jersey 8760 Urban 1.0573 1.0573 47220 Urban 1.0573 31200 Essex County, New Jersey 5640 Urban 1.1625 1.1687 35084 Urban 1.1656 31220 Gloucester County, New Jersey 6160 Urban 1.0824 1.0675 15804 Urban 1.0750 31230 Hudson County, New Jersey 3640 Urban 1.0923 1.3311 35644 Urban 1.2117 31250 Hunterdon County, New Jersey 5015 Urban 1.1360 1.1687 35084 Urban 1.1524 31260 Mercer County, New Jersey 8480 Urban 1.0276 1.0276 45940 Urban 1.0276 31270 Middlesex County, New Jersey 5015 Urban 1.1360 1.1136 20764 Urban 1.1248 31290 Monmouth County, New Jersey 5190 Urban 1.0888 1.1136 20764 Urban 1.1012 31300 Morris County, New Jersey 5640 Urban 1.1625 1.1687 35084 Urban 1.1656 31310 Ocean County, New Jersey 5190 Urban 1.0888 1.1136 20764 Urban 1.1012 31320 Passaic County, New Jersey 0875 Urban 1.1967 1.3311 35644 Urban 1.2639 31340 Salem County, New Jersey 6160 Urban 1.0824 1.1049 48864 Urban 1.0937 31350 Somerset County, New Jersey 5015 Urban 1.1360 1.1136 20764 Urban 1.1248 31360 Sussex County, New Jersey 5640 Urban 1.1625 1.1687 35084 Urban 1.1656 31370 Union County, New Jersey 5640 Urban 1.1625 1.1687 35084 Urban 1.1656 31390 Warren County, New Jersey 5640 Urban 1.1625 0.9501 10900 Urban 1.0563 32000 Bernalillo County, New Mexico 0200 Urban 1.0485 1.0485 10740 Urban 1.0485 32010 Catron County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32020 Chaves County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32025 Cibola County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32030 Colfax County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32040 Curry County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32050 De Baca County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32060 Dona Ana County, New Mexico 4100 Urban 0.8784 0.8784 29740 Urban 0.8784 32070 Eddy County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32080 Grant County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32090 Guadalupe County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32100 Harding County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32110 Hidalgo County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32120 Lea County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32130 Lincoln County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32131 Los Alamos County, New Mexico 7490 Urban 1.0590 0.8680 99932 Rural 0.9635 32140 Luna County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32150 Mc Kinley County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32160 Mora County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32170 Otero County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32180 Quay County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32190 Rio Arriba County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32200 Roosevelt County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32210 Sandoval County, New Mexico 0200 Urban 1.0485 1.0485 10740 Urban 1.0485 32220 San Juan County, New Mexico 32 Rural 0.8529 0.8049 22140 Urban 0.8289 32230 San Miguel County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32240 Santa Fe County, New Mexico 7490 Urban 1.0590 1.0909 42140 Urban 1.0750 32250 Sierra County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32260 Socorro County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32270 Taos County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32280 Torrance County, New Mexico 32 Rural 0.8529 1.0485 10740 Urban 0.9507 32290 Union County, New Mexico 32 Rural 0.8529 0.8680 99932 Rural 0.8605 32300 Valencia County, New Mexico 0200 Urban 1.0485 1.0485 10740 Urban 1.0485 33000 Albany County, New York 0160 Urban 0.8570 0.8650 10580 Urban 0.8610 33010 Allegany County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33020 Bronx County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33030 Broome County, New York 0960 Urban 0.8447 0.8447 13780 Urban 0.8447 33040 Cattaraugus County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33050 Cayuga County, New York 8160 Urban 0.9394 0.8151 99933 Rural 0.8773 33060 Chautauqua County, New York 3610 Urban 0.7589 0.8151 99933 Rural 0.7870 33070 Chemung County, New York 2335 Urban 0.8445 0.8445 21300 Urban 0.8445 33080 Chenango County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33090 Clinton County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33200 Columbia County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33210 Cortland County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33220 Delaware County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33230 Dutchess County, New York 2281 Urban 1.1657 1.1363 39100 Urban 1.1510 33240 Erie County, New York 1280 Urban 0.9339 0.9339 15380 Urban 0.9339 33260 Essex County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33270 Franklin County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33280 Fulton County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33290 Genesee County, New York 6840 Urban 0.9196 0.8151 99933 Rural 0.8674 33300 Greene County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33310 Hamilton County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33320 Herkimer County, New York 8680 Urban 0.8295 0.8295 46540 Urban 0.8295 33330 Jefferson County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33331 Kings County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33340 Lewis County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33350 Livingston County, New York 6840 Urban 0.9196 0.9281 40380 Urban 0.9239 33360 Madison County, New York 8160 Urban 0.9394 0.9468 45060 Urban 0.9431 33370 Monroe County, New York 6840 Urban 0.9196 0.9281 40380 Urban 0.9239 33380 Montgomery County, New York 0160 Urban 0.8570 0.8151 99933 Rural 0.8361 33400 Nassau County, New York 5380 Urban 1.2907 1.2907 35004 Urban 1.2907 33420 New York County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33500 Niagara County, New York 1280 Urban 0.9339 0.9339 15380 Urban 0.9339 33510 Oneida County, New York 8680 Urban 0.8295 0.8295 46540 Urban 0.8295 33520 Onondaga County, New York 8160 Urban 0.9394 0.9468 45060 Urban 0.9431 33530 Ontario County, New York 6840 Urban 0.9196 0.9281 40380 Urban 0.9239 33540 Orange County, New York 5660 Urban 1.1170 1.1363 39100 Urban 1.1267 33550 Orleans County, New York 6840 Urban 0.9196 0.9281 40380 Urban 0.9239 33560 Oswego County, New York 8160 Urban 0.9394 0.9468 45060 Urban 0.9431 33570 Otsego County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33580 Putnam County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33590 Queens County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33600 Rensselaer County, New York 0160 Urban 0.8570 0.8650 10580 Urban 0.8610 33610 Richmond County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33620 Rockland County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33630 St Lawrence County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33640 Saratoga County, New York 0160 Urban 0.8570 0.8650 10580 Urban 0.8610 33650 Schenectady County, New York 0160 Urban 0.8570 0.8650 10580 Urban 0.8610 33660 Schoharie County, New York 0160 Urban 0.8570 0.8650 10580 Urban 0.8610 33670 Schuyler County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33680 Seneca County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33690 Steuben County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33700 Suffolk County, New York 5380 Urban 1.2907 1.2907 35004 Urban 1.2907 33710 Sullivan County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33720 Tioga County, New York 0960 Urban 0.8447 0.8447 13780 Urban 0.8447 33730 Tompkins County, New York 33 Rural 0.8403 0.9589 27060 Urban 0.8996 33740 Ulster County, New York 33 Rural 0.8403 0.9000 28740 Urban 0.8702 33750 Warren County, New York 2975 Urban 0.8467 0.8467 24020 Urban 0.8467 33760 Washington County, New York 2975 Urban 0.8467 0.8467 24020 Urban 0.8467 33770 Wayne County, New York 6840 Urban 0.9196 0.9281 40380 Urban 0.9239 33800 Westchester County, New York 5600 Urban 1.3586 1.3311 35644 Urban 1.3449 33900 Wyoming County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 33910 Yates County, New York 33 Rural 0.8403 0.8151 99933 Rural 0.8277 34000 Alamance County, N Carolina 3120 Urban 0.9312 0.8967 15500 Urban 0.9140 34010 Alexander County, N Carolina 3290 Urban 0.9502 0.9502 25860 Urban 0.9502 34020 Alleghany County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34030 Anson County, N Carolina 34 Rural 0.8500 0.9743 16740 Urban 0.9122 34040 Ashe County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34050 Avery County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34060 Beaufort County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34070 Bertie County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34080 Bladen County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34090 Brunswick County, N Carolina 9200 Urban 0.9237 0.9237 48900 Urban 0.9237 34100 Buncombe County, N Carolina 0480 Urban 0.9501 0.9191 11700 Urban 0.9346 34110 Burke County, N Carolina 3290 Urban 0.9502 0.9502 25860 Urban 0.9502 34120 Cabarrus County, N Carolina 1520 Urban 0.9711 0.9743 16740 Urban 0.9727 34130 Caldwell County, N Carolina 3290 Urban 0.9502 0.9502 25860 Urban 0.9502 34140 Camden County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34150 Carteret County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34160 Caswell County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34170 Catawba County, N Carolina 3290 Urban 0.9502 0.9502 25860 Urban 0.9502 34180 Chatham County, N Carolina 6640 Urban 1.0258 1.0363 20500 Urban 1.0311 34190 Cherokee County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34200 Chowan County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34210 Clay County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34220 Cleveland County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34230 Columbus County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34240 Craven County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34250 Cumberland County, N Carolina 2560 Urban 0.9363 0.9363 22180 Urban 0.9363 34251 Currituck County, N Carolina 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 34270 Dare County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34280 Davidson County, N Carolina 3120 Urban 0.9312 0.8563 99934 Rural 0.8938 34290 Davie County, N Carolina 3120 Urban 0.9312 0.9401 49180 Urban 0.9357 34300 Duplin County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34310 Durham County, N Carolina 6640 Urban 1.0258 1.0363 20500 Urban 1.0311 34320 Edgecombe County, N Carolina 6895 Urban 0.8998 0.8998 40580 Urban 0.8998 34330 Forsyth County, N Carolina 3120 Urban 0.9312 0.9401 49180 Urban 0.9357 34340 Franklin County, N Carolina 6640 Urban 1.0258 1.0057 39580 Urban 1.0158 34350 Gaston County, N Carolina 1520 Urban 0.9711 0.9743 16740 Urban 0.9727 34360 Gates County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34370 Graham County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34380 Granville County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34390 Greene County, N Carolina 34 Rural 0.8500 0.9183 24780 Urban 0.8842 34400 Guilford County, N Carolina 13120 Urban 0.9312 0.9190 24660 Urban 0.9251 34410 Halifax County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34420 Harnett County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34430 Haywood County, N Carolina 34 Rural 0.8500 0.9191 11700 Urban 0.8846 34440 Henderson County, N Carolina 34 Rural 0.8500 0.9191 11700 Urban 0.8846 34450 Hertford County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34460 Hoke County, N Carolina 34 Rural 0.8500 0.9363 22180 Urban 0.8932 34470 Hyde County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34480 Iredell County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34490 Jackson County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34500 Johnston County, N Carolina 6640 Urban 1.0258 1.0057 39580 Urban 1.0158 34510 Jones County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34520 Lee County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34530 Lenoir County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34540 Lincoln County, N Carolina 1520 Urban 0.9711 0.8563 99934 Rural 0.9137 34550 Mc Dowell County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34560 Macon County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34570 Madison County, N Carolina 0480 Urban 0.9501 0.9191 11700 Urban 0.9346 34580 Martin County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34590 Mecklenburg County, N Carolina 1520 Urban 0.9711 0.9743 16740 Urban 0.9727 34600 Mitchell County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34610 Montgomery County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34620 Moore County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34630 Nash County, N Carolina 6895 Urban 0.8998 0.8998 40580 Urban 0.8998 34640 New Hanover County, N Carolina 9200 Urban 0.9237 0.9237 48900 Urban 0.9237 34650 Northampton County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34660 Onslow County, N Carolina 3605 Urban 0.8401 0.8401 27340 Urban 0.8401 34670 Orange County, N Carolina 6640 Urban 1.0258 1.0363 20500 Urban 1.0311 34680 Pamlico County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34690 Pasquotank County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34700 Pender County, N Carolina 34 Rural 0.8500 0.9237 48900 Urban 0.8869 34710 Perquimans County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34720 Person County, N Carolina 34 Rural 0.8500 1.0363 20500 Urban 0.9432 34730 Pitt County, N Carolina 3150 Urban 0.9183 0.9183 24780 Urban 0.9183 34740 Polk County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34750 Randolph County, N Carolina 3120 Urban 0.9312 0.9190 24660 Urban 0.9251 34760 Richmond County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34770 Robeson County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34780 Rockingham County, N Carolina 34 Rural 0.8500 0.9190 24660 Urban 0.8845 34790 Rowan County, N Carolina 1520 Urban 0.9711 0.8563 99934 Rural 0.9137 34800 Rutherford County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34810 Sampson County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34820 Scotland County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34830 Stanly County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34840 Stokes County, N Carolina 3120 Urban 0.9312 0.9401 49180 Urban 0.9357 34850 Surry County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34860 Swain County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34870 Transylvania County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34880 Tyrrell County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34890 Union County, N Carolina 1520 Urban 0.9711 0.9743 16740 Urban 0.9727 34900 Vance County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34910 Wake County, N Carolina 6640 Urban 1.0258 1.0057 39580 Urban 1.0158 34920 Warren County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34930 Washington County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34940 Watauga County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34950 Wayne County, N Carolina 2980 Urban 0.8778 0.8778 24140 Urban 0.8778 34960 Wilkes County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34970 Wilson County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 34980 Yadkin County, N Carolina 3120 Urban 0.9312 0.9401 49180 Urban 0.9357 34981 Yancey County, N Carolina 34 Rural 0.8500 0.8563 99934 Rural 0.8532 35000 Adams County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35010 Barnes County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35020 Benson County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35030 Billings County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35040 Bottineau County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35050 Bowman County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35060 Burke County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35070 Burleigh County, N Dakota 1010 Urban 0.7505 0.7505 13900 Urban 0.7505 35080 Cass County, N Dakota 2520 Urban 0.9114 0.9114 22020 Urban 0.9114 35090 Cavalier County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35100 Dickey County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35110 Divide County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35120 Dunn County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35130 Eddy County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35140 Emmons County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35150 Foster County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35160 Golden Valley County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35170 Grand Forks County, N Dakota 2985 Urban 0.9091 0.9091 24220 Urban 0.9091 35180 Grant County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35190 Griggs County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35200 Hettinger County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35210 Kidder County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35220 La Moure County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35230 Logan County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35240 Mc Henry County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35250 Mc Intosh County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35260 Mc Kenzie County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35270 Mc Lean County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35280 Mercer County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35290 Morton County, N Dakota 1010 Urban 0.7505 0.7505 13900 Urban 0.7505 35300 Mountrail County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35310 Nelson County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35320 Oliver County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35330 Pembina County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35340 Pierce County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35350 Ramsey County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35360 Ransom County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35370 Renville County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35380 Richland County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35390 Rolette County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35400 Sargent County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35410 Sheridan County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35420 Sioux County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35430 Slope County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35440 Stark County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35450 Steele County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35460 Stutsman County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35470 Towner County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35480 Traill County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35490 Walsh County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35500 Ward County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35510 Wells County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 35520 Williams County, N Dakota 35 Rural 0.7743 0.7743 99935 Rural 0.7743 36000 Adams County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36010 Allen County, Ohio 4320 Urban 0.9258 0.9330 30620 Urban 0.9294 36020 Ashland County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36030 Ashtabula County, Ohio 1680 Urban 0.9626 0.8693 99936 Rural 0.9160 36040 Athens County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36050 Auglaize County, Ohio 4320 Urban 0.9258 0.8693 99936 Rural 0.8976 36060 Belmont County, Ohio 9000 Urban 0.7449 0.7449 48540 Urban 0.7449 36070 Brown County, Ohio 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 36080 Butler County, Ohio 3200 Urban 0.9066 0.9516 17140 Urban 0.9291 36090 Carroll County, Ohio 1320 Urban 0.8895 0.8895 15940 Urban 0.8895 36100 Champaign County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36110 Clark County, Ohio 2000 Urban 0.9231 0.8748 44220 Urban 0.8990 36120 Clermont County, Ohio 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 36130 Clinton County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36140 Columbiana County, Ohio 9320 Urban 0.9517 0.8693 99936 Rural 0.9105 36150 Coshocton County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36160 Crawford County, Ohio 4800 Urban 0.9105 0.8693 99936 Rural 0.8899 36170 Cuyahoga County, Ohio 1680 Urban 0.9626 0.9650 17460 Urban 0.9638 36190 Darke County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36200 Defiance County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36210 Delaware County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36220 Erie County, Ohio 36 Rural 0.8759 0.9017 41780 Urban 0.8888 36230 Fairfield County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36240 Fayette County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36250 Franklin County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36260 Fulton County, Ohio 8400 Urban 0.9524 0.9524 45780 Urban 0.9524 36270 Gallia County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36280 Geauga County, Ohio 1680 Urban 0.9626 0.9650 17460 Urban 0.9638 36290 Greene County, Ohio 2000 Urban 0.9231 0.9303 19380 Urban 0.9267 36300 Guernsey County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36310 Hamilton County, Ohio 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 36330 Hancock County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36340 Hardin County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36350 Harrison County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36360 Henry County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36370 Highland County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36380 Hocking County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36390 Holmes County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36400 Huron County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36410 Jackson County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36420 Jefferson County, Ohio 8080 Urban 0.8280 0.8280 48260 Urban 0.8280 36430 Knox County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36440 Lake County, Ohio 1680 Urban 0.9626 0.9650 17460 Urban 0.9638 36450 Lawrence County, Ohio 3400 Urban 0.9564 0.9564 26580 Urban 0.9564 36460 Licking County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36470 Logan County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36480 Lorain County, Ohio 1680 Urban 0.9626 0.9650 17460 Urban 0.9638 36490 Lucas County, Ohio 8400 Urban 0.9524 0.9524 45780 Urban 0.9524 36500 Madison County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36510 Mahoning County, Ohio 9320 Urban 0.9517 0.9237 49660 Urban 0.9377 36520 Marion County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36530 Medina County, Ohio 1680 Urban 0.9626 0.9650 17460 Urban 0.9638 36540 Meigs County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36550 Mercer County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36560 Miami County, Ohio 2000 Urban 0.9231 0.9303 19380 Urban 0.9267 36570 Monroe County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36580 Montgomery County, Ohio 2000 Urban 0.9231 0.9303 19380 Urban 0.9267 36590 Morgan County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36600 Morrow County, Ohio 36 Rural 0.8759 0.9737 18140 Urban 0.9248 36610 Muskingum County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36620 Noble County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36630 Ottawa County, Ohio 36 Rural 0.8759 0.9524 45780 Urban 0.9142 36640 Paulding County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36650 Perry County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36660 Pickaway County, Ohio 1840 Urban 0.9753 0.9737 18140 Urban 0.9745 36670 Pike County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36680 Portage County, Ohio 0080 Urban 0.9055 0.9055 10420 Urban 0.9055 36690 Preble County, Ohio 36 Rural 0.8759 0.9303 19380 Urban 0.9031 36700 Putnam County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36710 Richland County, Ohio 4800 Urban 0.9105 0.9189 31900 Urban 0.9147 36720 Ross County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36730 Sandusky County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36740 Scioto County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36750 Seneca County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36760 Shelby County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36770 Stark County, Ohio 1320 Urban 0.8895 0.8895 15940 Urban 0.8895 36780 Summit County, Ohio 0080 Urban 0.9055 0.9055 10420 Urban 0.9055 36790 Trumbull County, Ohio 9320 Urban 0.9517 0.9237 49660 Urban 0.9377 36800 Tuscarawas County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36810 Union County, Ohio 36 Rural 0.8759 0.9737 18140 Urban 0.9248 36820 Van Wert County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36830 Vinton County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36840 Warren County, Ohio 1640 Urban 0.9595 0.9516 17140 Urban 0.9556 36850 Washington County, Ohio 6020 Urban 0.8288 0.8288 37620 Urban 0.8288 36860 Wayne County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36870 Williams County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 36880 Wood County, Ohio 8400 Urban 0.9524 0.9524 45780 Urban 0.9524 36890 Wyandot County, Ohio 36 Rural 0.8759 0.8693 99936 Rural 0.8726 37000 Adair County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37101 Alfalfa County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37020 Atoka County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37030 Beaver County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37040 Beckham County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37050 Blaine County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37060 Bryan County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37070 Caddo County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37080 Canadian County, Oklahoma 5880 Urban 0.8966 0.8982 36420 Urban 0.8974 37090 Carter County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37100 Cherokee County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37110 Choctaw County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37120 Cimarron County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37130 Cleveland County, Oklahoma 5880 Urban 0.8966 0.8982 36420 Urban 0.8974 37140 Coal County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37150 Comanche County, Oklahoma 4200 Urban 0.8212 0.8212 30020 Urban 0.8212 37160 Cotton County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37170 Craig County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37180 Creek County, Oklahoma 8560 Urban 0.8729 0.8690 46140 Urban 0.8710 37190 Custer County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37200 Delaware County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37210 Dewey County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37220 Ellis County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37230 Garfield County, Oklahoma 2340 Urban 0.9001 0.7686 99937 Rural 0.8344 37240 Garvin County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37250 Grady County, Oklahoma 37 Rural 0.7537 0.8982 36420 Urban 0.8260 37260 Grant County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37270 Greer County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37280 Harmon County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37290 Harper County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37300 Haskell County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37310 Hughes County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37320 Jackson County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37330 Jefferson County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37340 Johnston County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37350 Kay County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37360 Kingfisher County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37370 Kiowa County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37380 Latimer County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37390 Le Flore County, Oklahoma 37 Rural 0.7537 0.8283 22900 Urban 0.7910 37400 Lincoln County, Oklahoma 37 Rural 0.7537 0.8982 36420 Urban 0.8260 37410 Logan County, Oklahoma 5880 Urban 0.8966 0.8982 36420 Urban 0.8974 37420 Love County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37430 Mc Clain County, Oklahoma 5880 Urban 0.8966 0.8982 36420 Urban 0.8974 37440 Mc Curtain County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37450 Mc Intosh County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37460 Major County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37470 Marshall County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37480 Mayes County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37490 Murray County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37500 Muskogee County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37510 Noble County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37520 Nowata County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37530 Okfuskee County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37540 Oklahoma County, Oklahoma 5880 Urban 0.8966 0.8982 36420 Urban 0.8974 37550 Okmulgee County, Oklahoma 37 Rural 0.7537 0.8690 46140 Urban 0.8114 37560 Osage County, Oklahoma 8560 Urban 0.8729 0.8690 46140 Urban 0.8710 37570 Ottawa County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37580 Pawnee County, Oklahoma 37 Rural 0.7537 0.8690 46140 Urban 0.8114 37590 Payne County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37600 Pittsburg County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37610 Pontotoc County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37620 Pottawatomie County, Oklahoma 5880 Urban 0.8966 0.7686 99937 Rural 0.8326 37630 Pushmataha County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37640 Roger Mills County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37650 Rogers County, Oklahoma 8560 Urban 0.8729 0.8690 46140 Urban 0.8710 37660 Seminole County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37670 Sequoyah County, Oklahoma 2720 Urban 0.8303 0.8283 22900 Urban 0.8293 37680 Stephens County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37690 Texas County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37700 Tillman County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37710 Tulsa County, Oklahoma 8560 Urban 0.8729 0.8690 46140 Urban 0.8710 37720 Wagoner County, Oklahoma 8560 Urban 0.8729 0.8690 46140 Urban 0.8710 37730 Washington County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37740 Washita County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37750 Woods County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 37760 Woodward County, Oklahoma 37 Rural 0.7537 0.7686 99937 Rural 0.7612 38000 Baker County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38010 Benton County, Oregon 1890 Urban 1.0545 1.0545 18700 Urban 1.0545 38020 Clackamas County, Oregon 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 38030 Clatsop County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38040 Columbia County, Oregon 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 38050 Coos County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38060 Crook County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38070 Curry County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38080 Deschutes County, Oregon 38 Rural 1.0049 1.0603 13460 Urban 1.0326 38090 Douglas County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38100 Gilliam County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38110 Grant County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38120 Harney County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38130 Hood River County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38140 Jackson County, Oregon 4890 Urban 1.0534 1.0534 32780 Urban 1.0534 38150 Jefferson County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38160 Josephine County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38170 Klamath County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38180 Lake County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38190 Lane County, Oregon 2400 Urban 1.0940 1.0940 21660 Urban 1.0940 38200 Lincoln County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38210 Linn County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38220 Malheur County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38230 Marion County, Oregon 7080 Urban 1.0556 1.0556 41420 Urban 1.0556 38240 Morrow County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38250 Multnomah County, Oregon 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 38260 Polk County, Oregon 7080 Urban 1.0556 1.0556 41420 Urban 1.0556 38270 Sherman County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38280 Tillamook County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38290 Umatilla County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38300 Union County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38310 Wallowa County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38320 Wasco County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38330 Washington County, Oregon 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 38340 Wheeler County, Oregon 38 Rural 1.0049 0.9914 99938 Rural 0.9982 38350 Yamhill County, Oregon 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 39000 Adams County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39010 Allegheny County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39070 Armstrong County, Pennsylvania 39 Rural 0.8348 0.8736 38300 Urban 0.8542 39080 Beaver County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39100 Bedford County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39110 Berks County, Pennsylvania 6680 Urban 0.9215 0.9215 39740 Urban 0.9215 39120 Blair County, Pennsylvania 0280 Urban 0.8462 0.8462 11020 Urban 0.8462 39130 Bradford County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39140 Bucks County, Pennsylvania 6160 Urban 1.0824 1.0865 37964 Urban 1.0845 39150 Butler County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39160 Cambria County, Pennsylvania 3680 Urban 0.7980 0.8380 27780 Urban 0.8180 39180 Cameron County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39190 Carbon County, Pennsylvania 0240 Urban 0.9536 0.9501 10900 Urban 0.9519 39200 Centre County, Pennsylvania 8050 Urban 0.8461 0.8461 44300 Urban 0.8461 39210 Chester County, Pennsylvania 6160 Urban 1.0824 1.0865 37964 Urban 1.0845 39220 Clarion County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39230 Clearfield County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39240 Clinton County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39250 Columbia County, Pennsylvania 7560 Urban 0.8522 0.8310 99939 Rural 0.8416 39260 Crawford County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39270 Cumberland County, Pennsylvania 3240 Urban 0.9286 0.9359 25420 Urban 0.9323 39280 Dauphin County, Pennsylvania 3240 Urban 0.9286 0.9359 25420 Urban 0.9323 39290 Delaware County, Pennsylvania 6160 Urban 1.0824 1.0865 37964 Urban 1.0845 39310 Elk County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39320 Erie County, Pennsylvania 2360 Urban 0.8699 0.8699 21500 Urban 0.8699 39330 Fayette County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39340 Forest County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39350 Franklin County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39360 Fulton County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39370 Greene County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39380 Huntingdon County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39390 Indiana County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39400 Jefferson County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39410 Juniata County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39420 Lackawanna County, Pennsylvania 7560 Urban 0.8522 0.8543 42540 Urban 0.8533 39440 Lancaster County, Pennsylvania 4000 Urban 0.9883 0.9883 29540 Urban 0.9883 39450 Lawrence County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39460 Lebanon County, Pennsylvania 3240 Urban 0.9286 0.8570 30140 Urban 0.8928 39470 Lehigh County, Pennsylvania 0240 Urban 0.9536 0.9501 10900 Urban 0.9519 39480 Luzerne County, Pennsylvania 7560 Urban 0.8522 0.8543 42540 Urban 0.8533 39510 Lycoming County, Pennsylvania 9140 Urban 0.8485 0.8485 48700 Urban 0.8485 39520 Mc Kean County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39530 Mercer County, Pennsylvania 7610 Urban 0.7881 0.9237 49660 Urban 0.8559 39540 Mifflin County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39550 Monroe County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39560 Montgomery County, Pennsylvania 6160 Urban 1.0824 1.0865 37964 Urban 1.0845 39580 Montour County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39590 Northampton County, Pennsylvania 0240 Urban 0.9536 0.9501 10900 Urban 0.9519 39600 Northumberland County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39610 Perry County, Pennsylvania 3240 Urban 0.9286 0.9359 25420 Urban 0.9323 39620 Philadelphia County, Pennsylvania 6160 Urban 1.0824 1.0865 37964 Urban 1.0845 39630 Pike County, Pennsylvania 5660 Urban 1.1170 1.1687 35084 Urban 1.1429 39640 Potter County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39650 Schuylkill County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39670 Snyder County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39680 Somerset County, Pennsylvania 3680 Urban 0.7980 0.8310 99939 Rural 0.8145 39690 Sullivan County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39700 Susquehanna County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39710 Tioga County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39720 Union County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39730 Venango County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39740 Warren County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39750 Washington County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39760 Wayne County, Pennsylvania 39 Rural 0.8348 0.8310 99939 Rural 0.8329 39770 Westmoreland County, Pennsylvania 6280 Urban 0.8756 0.8736 38300 Urban 0.8746 39790 Wyoming County, Pennsylvania 7560 Urban 0.8522 0.8543 42540 Urban 0.8533 39800 York County, Pennsylvania 9280 Urban 0.9150 0.9150 49620 Urban 0.9150 40010 Adjuntas County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40020 Aguada County, Puerto Rico 0060 Urban 0.4294 0.4280 10380 Urban 0.4287 40030 Aguadilla County, Puerto Rico 0060 Urban 0.4294 0.4280 10380 Urban 0.4287 40040 Aguas Buenas County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40050 Aibonito County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40060 Anasco County, Puerto Rico 4840 Urban 0.4769 0.4280 10380 Urban 0.4525 40070 Arecibo County, Puerto Rico 0470 Urban 0.3757 0.4645 41980 Urban 0.4201 40080 Arroyo County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40090 Barceloneta County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40100 Barranquitas County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40110 Bayamon County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40120 Cabo Rojo County, Puerto Rico 4840 Urban 0.4769 0.5240 41900 Urban 0.5005 40130 Caguas County, Puerto Rico 1310 Urban 0.4061 0.4645 41980 Urban 0.4353 40140 Camuy County, Puerto Rico 0470 Urban 0.3757 0.4645 41980 Urban 0.4201 40145 Canovanas County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40150 Carolina County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40160 Catano County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40170 Cayey County, Puerto Rico 1310 Urban 0.4061 0.4645 41980 Urban 0.4353 40180 Ceiba County, Puerto Rico 7440 Urban 0.4802 0.3939 21940 Urban 0.4371 40190 Ciales County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40200 Cidra County, Puerto Rico 1310 Urban 0.4061 0.4645 41980 Urban 0.4353 40210 Coamo County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40220 Comerio County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40230 Corozal County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40240 Culebra County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40250 Dorado County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40260 Fajardo County, Puerto Rico 7440 Urban 0.4802 0.3939 21940 Urban 0.4371 40265 Florida County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40270 Guanica County, Puerto Rico 40 Rural 0.4047 0.4493 49500 Urban 0.4270 40280 Guayama County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40290 Guayanilla County, Puerto Rico 6360 Urban 0.4954 0.4493 49500 Urban 0.4724 40300 Guaynabo County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40310 Gurabo County, Puerto Rico 1310 Urban 0.4061 0.4645 41980 Urban 0.4353 40320 Hatillo County, Puerto Rico 0470 Urban 0.3757 0.4645 41980 Urban 0.4201 40330 Hormigueros County, Puerto Rico 4840 Urban 0.4769 0.4493 32420 Urban 0.4631 40340 Humacao County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40350 Isabela County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40360 Jayuya County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40370 Juana Diaz County, Puerto Rico 6360 Urban 0.4954 0.5006 38660 Urban 0.4980 40380 Juncos County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40390 Lajas County, Puerto Rico 40 Rural 0.4047 0.5240 41900 Urban 0.4644 40400 Lares County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40410 Las Marias County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40420 Las Piedras County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40430 Loiza County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40440 Luquillo County, Puerto Rico 7440 Urban 0.4802 0.3939 21940 Urban 0.4371 40450 Manati County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40460 Maricao County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40470 Maunabo County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40480 Mayaguez County, Puerto Rico 4840 Urban 0.4769 0.4493 32420 Urban 0.4631 40490 Moca County, Puerto Rico 0060 Urban 0.4294 0.4280 10380 Urban 0.4287 40500 Morovis County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40510 Naguabo County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40520 Naranjito County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40530 Orocovis County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40540 Patillas County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40550 Penuelas County, Puerto Rico 6360 Urban 0.4954 0.4493 49500 Urban 0.4724 40560 Ponce County, Puerto Rico 6360 Urban 0.4954 0.5006 38660 Urban 0.4980 40570 Quebradillas County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40580 Rincon County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40590 Rio Grande County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40610 Sabana Grande County, Puerto Rico 4840 Urban 0.4769 0.5240 41900 Urban 0.5005 40620 Salinas County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40630 San German County, Puerto Rico 4840 Urban 0.4769 0.5240 41900 Urban 0.5005 40640 San Juan County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40650 San Lorenzo County, Puerto Rico 310 Urban 0.4061 0.4645 41980 Urban 0.4353 40660 San Sebastian County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40670 Santa Isabel County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40680 Toa Alta County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40690 Toa Baja County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40700 Trujillo Alto County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40710 Utuado County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40720 Vega Alta County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40730 Vega Baja County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40740 Vieques County, Puerto Rico 40 Rural 0.4047 0.4047 99940 Rural 0.4047 40750 Villalba County, Puerto Rico 6360 Urban 0.4954 0.5006 38660 Urban 0.4980 40760 Yabucoa County, Puerto Rico 7440 Urban 0.4802 0.4645 41980 Urban 0.4724 40770 Yauco County, Puerto Rico 6360 Urban 0.4954 0.4493 49500 Urban 0.4724 41000 Bristol County, Rhode Island 6483 Urban 1.1061 1.0929 39300 Urban 1.0995 41010 Kent County, Rhode Island 6483 Urban 1.1061 1.0929 39300 Urban 1.0995 41020 Newport County, Rhode Island 6483 Urban 1.1061 1.0929 39300 Urban 1.0995 41030 Providence County, Rhode Island 6483 Urban 1.1061 1.0929 39300 Urban 1.0995 41050 Washington County, Rhode Island 6483 Urban 1.1061 1.0929 39300 Urban 1.0995 42000 Abbeville County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42010 Aiken County, S Carolina 0600 Urban 0.9208 0.9154 12260 Urban 0.9181 42020 Allendale County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42030 Anderson County, S Carolina 3160 Urban 0.9400 0.8670 11340 Urban 0.9035 42040 Bamberg County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42050 Barnwell County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42060 Beaufort County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42070 Berkeley County, S Carolina 1440 Urban 0.9420 0.9420 16700 Urban 0.9420 42080 Calhoun County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42090 Charleston County, S Carolina 1440 Urban 0.9420 0.9420 16700 Urban 0.9420 42100 Cherokee County, S Carolina 3160 Urban 0.9400 0.8683 99942 Rural 0.9042 42110 Chester County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42120 Chesterfield County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42130 Clarendon County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42140 Colleton County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42150 Darlington County, S Carolina 42 Rural 0.8640 0.8833 22500 Urban 0.8737 42160 Dillon County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42170 Dorchester County, S Carolina 1440 Urban 0.9420 0.9420 16700 Urban 0.9420 42180 Edgefield County, S Carolina 0600 Urban 0.9208 0.9154 12260 Urban 0.9181 42190 Fairfield County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42200 Florence County, S Carolina 2655 Urban 0.8960 0.8833 22500 Urban 0.8897 42210 Georgetown County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42220 Greenville County, S Carolina 3160 Urban 0.9400 0.9557 24860 Urban 0.9479 42230 Greenwood County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42240 Hampton County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42250 Horry County, S Carolina 5330 Urban 0.9022 0.9022 34820 Urban 0.9022 42260 Jasper County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42270 Kershaw County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42280 Lancaster County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42290 Laurens County, S Carolina 42 Rural 0.8640 0.9557 24860 Urban 0.9099 42300 Lee County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42310 Lexington County, S Carolina 1760 Urban 0.9450 0.9392 17900 Urban 0.9421 42320 Mc Cormick County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42330 Marion County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42340 Marlboro County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42350 Newberry County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42360 Oconee County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42370 Orangeburg County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42380 Pickens County, S Carolina 3160 Urban 0.9400 0.9557 24860 Urban 0.9479 42390 Richland County, S Carolina 1760 Urban 0.9450 0.9392 17900 Urban 0.9421 42400 Saluda County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42410 Spartanburg County, S Carolina 3160 Urban 0.9400 0.9519 43900 Urban 0.9460 42420 Sumter County, S Carolina 8140 Urban 0.8520 0.8520 44940 Urban 0.8520 42430 Union County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42440 Williamsburg County, S Carolina 42 Rural 0.8640 0.8683 99942 Rural 0.8662 42450 York County, S Carolina 1520 Urban 0.9711 0.9743 16740 Urban 0.9727 43010 Aurora County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43020 Beadle County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43030 Bennett County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43040 Bon Homme County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43050 Brookings County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43060 Brown County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43070 Brule County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43080 Buffalo County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43090 Butte County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43100 Campbell County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43110 Charles Mix County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43120 Clark County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43130 Clay County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43140 Codington County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43150 Corson County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43160 Custer County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43170 Davison County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43180 Day County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43190 Deuel County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43200 Dewey County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43210 Douglas County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43220 Edmunds County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43230 Fall River County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43240 Faulk County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43250 Grant County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43260 Gregory County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43270 Haakon County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43280 Hamlin County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43290 Hand County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43300 Hanson County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43310 Harding County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43320 Hughes County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43330 Hutchinson County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43340 Hyde County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43350 Jackson County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43360 Jerauld County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43370 Jones County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43380 Kingsbury County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43390 Lake County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43400 Lawrence County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43410 Lincoln County, S Dakota 7760 Urban 0.9441 0.9441 43620 Urban 0.9441 43420 Lyman County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43430 Mc Cook County, S Dakota 43 Rural 0.8393 0.9441 43620 Urban 0.8917 43440 Mc Pherson County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43450 Marshall County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43460 Meade County, S Dakota 43 Rural 0.8393 0.8912 39660 Urban 0.8653 43470 Mellette County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43480 Miner County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43490 Minnehaha County, S Dakota 7760 Urban 0.9441 0.9441 43620 Urban 0.9441 43500 Moody County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43510 Pennington County, S Dakota 6660 Urban 0.8912 0.8912 39660 Urban 0.8912 43520 Perkins County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43530 Potter County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43540 Roberts County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43550 Sanborn County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43560 Shannon County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43570 Spink County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43580 Stanley County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43590 Sully County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43600 Todd County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43610 Tripp County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43620 Turner County, S Dakota 43 Rural 0.8393 0.9441 43620 Urban 0.8917 43630 Union County, S Dakota 43 Rural 0.8393 0.9070 43580 Urban 0.8732 43640 Walworth County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43650 Washabaugh County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43670 Yankton County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 43680 Ziebach County, S Dakota 43 Rural 0.8393 0.8398 99943 Rural 0.8396 44000 Anderson County, Tennessee 3840 Urban 0.8508 0.8548 28940 Urban 0.8528 44010 Bedford County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44020 Benton County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44030 Bledsoe County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44040 Blount County, Tennessee 3840 Urban 0.8508 0.8548 28940 Urban 0.8528 44050 Bradley County, Tennessee 44 Rural 0.7876 0.7844 17420 Urban 0.7860 44060 Campbell County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44070 Cannon County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44080 Carroll County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44090 Carter County, Tennessee 3660 Urban 0.8202 0.8146 27740 Urban 0.8174 44100 Cheatham County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44110 Chester County, Tennessee 3580 Urban 0.8900 0.8900 27180 Urban 0.8900 44120 Claiborne County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44130 Clay County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44140 Cocke County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44150 Coffee County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44160 Crockett County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44170 Cumberland County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44180 Davidson County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44190 Decatur County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44200 De Kalb County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44210 Dickson County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44220 Dyer County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44230 Fayette County, Tennessee 4920 Urban 0.9234 0.9217 32820 Urban 0.9226 44240 Fentress County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44250 Franklin County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44260 Gibson County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44270 Giles County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44280 Grainger County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44290 Greene County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44300 Grundy County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44310 Hamblen County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44320 Hamilton County, Tennessee 1560 Urban 0.9207 0.9207 16860 Urban 0.9207 44330 Hancock County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44340 Hardeman County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44350 Hardin County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44360 Hawkins County, Tennessee 3660 Urban 0.8202 0.8240 28700 Urban 0.8221 44370 Haywood County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44380 Henderson County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44390 Henry County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44400 Hickman County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44410 Houston County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44420 Humphreys County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44430 Jackson County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44440 Jefferson County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44450 Johnson County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44460 Knox County, Tennessee 3840 Urban 0.8508 0.8548 28940 Urban 0.8528 44470 Lake County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44480 Lauderdale County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44490 Lawrence County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44500 Lewis County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44510 Lincoln County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44520 Loudon County, Tennessee 3840 Urban 0.8508 0.8548 28940 Urban 0.8528 44530 Mc Minn County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44540 Mc Nairy County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44550 Macon County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44560 Madison County, Tennessee 3580 Urban 0.8900 0.8900 27180 Urban 0.8900 44570 Marion County, Tennessee 1560 Urban 0.9207 0.9207 16860 Urban 0.9207 44580 Marshall County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44590 Maury County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44600 Meigs County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44610 Monroe County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44620 Montgomery County, Tennessee 1660 Urban 0.8022 0.8022 17300 Urban 0.8022 44630 Moore County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44640 Morgan County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44650 Obion County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44660 Overton County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44670 Perry County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44680 Pickett County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44690 Polk County, Tennessee 44 Rural 0.7876 0.7844 17420 Urban 0.7860 44700 Putnam County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44710 Rhea County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44720 Roane County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44730 Robertson County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44740 Rutherford County, Tennessee 5360 Urban 1.0108 1.0086 4980 Urban 1.0097 44750 Scott County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44760 Sequatchie County, Tennessee 44 Rural 0.7876 0.9207 16860 Urban 0.8542 44770 Sevier County, Tennessee 3840 Urban 0.8508 0.7869 99944 Rural 0.8189 44780 Shelby County, Tennessee 4920 Urban 0.9234 0.9217 32820 Urban 0.9226 44790 Smith County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44800 Stewart County, Tennessee 44 Rural 0.7876 0.8022 17300 Urban 0.7949 44810 Sullivan County, Tennessee 3660 Urban 0.8202 0.8240 28700 Urban 0.8221 44820 Sumner County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44830 Tipton County, Tennessee 4920 Urban 0.9234 0.9217 32820 Urban 0.9226 44840 Trousdale County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44850 Unicoi County, Tennessee 3660 Urban 0.8202 0.8146 27740 Urban 0.8174 44860 Union County, Tennessee 3840 Urban 0.8508 0.8548 28940 Urban 0.8528 44870 Van Buren County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44880 Warren County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44890 Washington County, Tennessee 3660 Urban 0.8202 0.8146 27740 Urban 0.8174 44900 Wayne County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44910 Weakley County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44920 White County, Tennessee 44 Rural 0.7876 0.7869 99944 Rural 0.7873 44930 Williamson County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 44940 Wilson County, Tennessee 5360 Urban 1.0108 1.0086 34980 Urban 1.0097 45000 Anderson County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45010 Andrews County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45020 Angelina County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45030 Aransas County, Texas 45 Rural 0.7910 0.8647 18580 Urban 0.8279 45040 Archer County, Texas 9080 Urban 0.8395 0.8332 48660 Urban 0.8364 45050 Armstrong County, Texas 45 Rural 0.7910 0.9178 11100 Urban 0.8544 45060 Atascosa County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45070 Austin County, Texas 45 Rural 0.7910 0.9973 26420 Urban 0.8942 45080 Bailey County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45090 Bandera County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45100 Bastrop County, Texas 0640 Urban 0.9595 0.9595 12420 Urban 0.9595 45110 Baylor County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45113 Bee County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45120 Bell County, Texas 3810 Urban 0.9242 0.9242 28660 Urban 0.9242 45130 Bexar County, Texas 7240 Urban 0.9023 0.9003 41700 Urban 0.9013 45140 Blanco County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45150 Borden County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45160 Bosque County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45170 Bowie County, Texas 8360 Urban 0.8413 0.8413 45500 Urban 0.8413 45180 Brazoria County, Texas 1145 Urban 0.8524 0.9973 26420 Urban 0.9249 45190 Brazos County, Texas 1260 Urban 0.9243 0.9243 17780 Urban 0.9243 45200 Brewster County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45201 Briscoe County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45210 Brooks County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45220 Brown County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45221 Burleson County, Texas 45 Rural 0.7910 0.9243 7780 Urban 0.8577 45222 Burnet County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45223 Caldwell County, Texas 0640 Urban 0.9595 0.9595 12420 Urban 0.9595 45224 Calhoun County, Texas 45 Rural 0.7910 0.8470 47020 Urban 0.8190 45230 Callahan County, Texas 45 Rural 0.7910 0.7850 10180 Urban 0.7880 45240 Cameron County, Texas 1240 Urban 1.0125 1.0125 15180 Urban 1.0125 45250 Camp County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45251 Carson County, Texas 45 Rural 0.7910 0.9178 11100 Urban 0.8544 45260 Cass County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45270 Castro County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45280 Chambers County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45281 Cherokee County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45290 Childress County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45291 Clay County, Texas 45 Rural 0.7910 0.8332 48660 Urban 0.8121 45292 Cochran County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45300 Coke County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45301 Coleman County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45310 Collin County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45311 Collingsworth County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45312 Colorado County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45320 Comal County, Texas 7240 Urban 0.9023 0.9003 41700 Urban 0.9013 45321 Comanche County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45330 Concho County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45340 Cooke County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45341 Coryell County, Texas 3810 Urban 0.9242 0.9242 28660 Urban 0.9242 45350 Cottle County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45360 Crane County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45361 Crockett County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45362 Crosby County, Texas 45 Rural 0.7910 0.8777 31180 Urban 0.8344 45370 Culberson County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45380 Dallam County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45390 Dallas County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45391 Dawson County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45392 Deaf Smith County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45400 Delta County, Texas 45 Rural 0.7910 1.0074 19124 Urban 0.8992 45410 Denton County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45420 De Witt County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45421 Dickens County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45430 Dimmit County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45431 Donley County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45440 Duval County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45450 Eastland County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45451 Ector County, Texas 5800 Urban 0.9632 0.9798 36220 Urban 0.9715 45460 Edwards County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45470 Ellis County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45480 El Paso County, Texas 2320 Urban 0.9181 0.9181 21340 Urban 0.9181 45490 Erath County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45500 Falls County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45510 Fannin County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45511 Fayette County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45520 Fisher County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45521 Floyd County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45522 Foard County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45530 Fort Bend County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45531 Franklin County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45540 Freestone County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45541 Frio County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45542 Gaines County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45550 Galveston County, Texas 2920 Urban 0.9403 0.9973 26420 Urban 0.9688 45551 Garza County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45552 Gillespie County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45560 Glasscock County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45561 Goliad County, Texas 45 Rural 0.7910 0.8470 47020 Urban 0.8190 45562 Gonzales County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45563 Gray County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45564 Grayson County, Texas 7640 Urban 0.9617 0.9617 43300 Urban 0.9617 45570 Gregg County, Texas 4420 Urban 0.8739 0.8801 30980 Urban 0.8770 45580 Grimes County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45581 Guadaloupe County, Texas 7240 Urban 0.9023 0.9003 41700 Urban 0.9013 45582 Hale County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45583 Hall County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45590 Hamilton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45591 Hansford County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45592 Hardeman County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45600 Hardin County, Texas 0840 Urban 0.8616 0.8616 13140 Urban 0.8616 45610 Harris County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45620 Harrison County, Texas 4420 Urban 0.8739 0.7966 99945 Rural 0.8353 45621 Hartley County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45630 Haskell County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45631 Hays County, Texas 0640 Urban 0.9595 0.9595 12420 Urban 0.9595 45632 Hemphill County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45640 Henderson County, Texas 1920 Urban 1.0054 0.7966 99945 Rural 0.9010 45650 Hidalgo County, Texas 4880 Urban 0.8602 0.8602 32580 Urban 0.8602 45651 Hill County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45652 Hockley County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45653 Hood County, Texas 2800 Urban 0.9520 0.7966 99945 Rural 0.8743 45654 Hopkins County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45660 Houston County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45661 Howard County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45662 Hudspeth County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45670 Hunt County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45671 Hutchinson County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45672 Irion County, Texas 45 Rural 0.7910 0.8167 41660 Urban 0.8039 45680 Jack County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45681 Jackson County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45690 Jasper County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45691 Jeff Davis County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45700 Jefferson County, Texas 0840 Urban 0.8616 0.8616 13140 Urban 0.8616 45710 Jim Hogg County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45711 Jim Wells County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45720 Johnson County, Texas 2800 Urban 0.9520 0.9472 23104 Urban 0.9496 45721 Jones County, Texas 45 Rural 0.7910 0.7850 10180 Urban 0.7880 45722 Karnes County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45730 Kaufman County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45731 Kendall County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45732 Kenedy County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45733 Kent County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45734 Kerr County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45740 Kimble County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45741 King County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45742 Kinney County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45743 Kleberg County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45744 Knox County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45750 Lamar County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45751 Lamb County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45752 Lampasas County, Texas 45 Rural 0.7910 0.9242 28660 Urban 0.8576 45753 La Salle County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45754 Lavaca County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45755 Lee County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45756 Leon County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45757 Liberty County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45758 Limestone County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45759 Lipscomb County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45760 Live Oak County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45761 Llano County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45762 Loving County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45770 Lubbock County, Texas 4600 Urban 0.8777 0.8777 31180 Urban 0.8777 45771 Lynn County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45772 Mc Culloch County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45780 Mc Lennan County, Texas 8800 Urban 0.8146 0.8146 47380 Urban 0.8146 45781 Mc Mullen County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45782 Madison County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45783 Marion County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45784 Martin County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45785 Mason County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45790 Matagorda County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45791 Maverick County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45792 Medina County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45793 Menard County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45794 Midland County, Texas 5800 Urban 0.9632 0.9384 33260 Urban 0.9508 45795 Milam County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45796 Mills County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45797 Mitchell County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45800 Montague County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45801 Montgomery County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45802 Moore County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45803 Morris County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45804 Motley County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45810 Nacogdoches County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45820 Navarro County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45821 Newton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45822 Nolan County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45830 Nueces County, Texas 1880 Urban 0.8647 0.8647 18580 Urban 0.8647 45831 Ochiltree County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45832 Oldham County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45840 Orange County, Texas 0840 Urban 0.8616 0.8616 13140 Urban 0.8616 45841 Palo Pinto County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45842 Panola County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45843 Parker County, Texas 2800 Urban 0.9520 0.9472 23104 Urban 0.9496 45844 Parmer County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45845 Pecos County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45850 Polk County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45860 Potter County, Texas 0320 Urban 0.9178 0.9178 11100 Urban 0.9178 45861 Presidio County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45870 Rains County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45871 Randall County, Texas 0320 Urban 0.9178 0.9178 11100 Urban 0.9178 45872 Reagan County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45873 Real County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45874 Red River County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45875 Reeves County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45876 Refugio County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45877 Roberts County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45878 Robertson County, Texas 45 Rural 0.7910 0.9243 17780 Urban 0.8577 45879 Rockwall County, Texas 1920 Urban 1.0054 1.0074 19124 Urban 1.0064 45880 Runnels County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45881 Rusk County, Texas 45 Rural 0.7910 0.8801 30980 Urban 0.8356 45882 Sabine County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45883 San Augustine County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45884 San Jacinto County, Texas 45 Rural 0.7910 0.9973 26420 Urban 0.8942 45885 San Patricio County, Texas 1880 Urban 0.8647 0.8647 18580 Urban 0.8647 45886 San Saba County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45887 Schleicher County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45888 Scurry County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45889 Shackelford County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45890 Shelby County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45891 Sherman County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45892 Smith County, Texas 8640 Urban 0.9502 0.9502 46340 Urban 0.9502 45893 Somervell County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45900 Starr County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45901 Stephens County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45902 Sterling County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45903 Stonewall County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45904 Sutton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45905 Swisher County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45910 Tarrant County, Texas 2800 Urban 0.9520 0.9472 23104 Urban 0.9496 45911 Taylor County, Texas 0040 Urban 0.8009 0.7850 10180 Urban 0.7930 45912 Terrell County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45913 Terry County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45920 Throckmorton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45921 Titus County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45930 Tom Green County, Texas 7200 Urban 0.8167 0.8167 41660 Urban 0.8167 45940 Travis County, Texas 0640 Urban 0.9595 0.9595 12420 Urban 0.9595 45941 Trinity County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45942 Tyler County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45943 Upshur County, Texas 4420 Urban 0.8739 0.8801 30980 Urban 0.8770 45944 Upton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45945 Uvalde County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45946 Val Verde County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45947 Van Zandt County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45948 Victoria County, Texas 8750 Urban 0.8469 0.8470 47020 Urban 0.8470 45949 Walker County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45950 Waller County, Texas 3360 Urban 1.0117 0.9973 26420 Urban 1.0045 45951 Ward County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45952 Washington County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45953 Webb County, Texas 4080 Urban 0.8747 0.8747 29700 Urban 0.8747 45954 Wharton County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45955 Wheeler County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45960 Wichita County, Texas 9080 Urban 0.8395 0.8332 48660 Urban 0.8364 45961 Wilbarger County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45962 Willacy County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45970 Williamson County, Texas 0640 Urban 0.9595 0.9595 12420 Urban 0.9595 45971 Wilson County, Texas 7240 Urban 0.9023 0.9003 41700 Urban 0.9013 45972 Winkler County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45973 Wise County, Texas 45 Rural 0.7910 0.9472 23104 Urban 0.8691 45974 Wood County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45980 Yoakum County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45981 Young County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45982 Zapata County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 45983 Zavala County, Texas 45 Rural 0.7910 0.7966 99945 Rural 0.7938 46000 Beaver County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46010 Box Elder County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46020 Cache County, Utah 46 Rural 0.8843 0.9094 30860 Urban 0.8969 46030 Carbon County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46040 Daggett County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46050 Davis County, Utah 7160 Urban 0.9487 0.9216 36260 Urban 0.9352 46060 Duchesne County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46070 Emery County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46080 Garfield County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46090 Grand County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46100 Iron County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46110 Juab County, Utah 46 Rural 0.8843 0.9588 39340 Urban 0.9216 46120 Kane County, Utah 2620 Urban 1.0611 0.8287 99946 Rural 0.9449 46130 Millard County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46140 Morgan County, Utah 46 Rural 0.8843 0.9216 36260 Urban 0.9030 46150 Piute County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46160 Rich County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46170 Salt Lake County, Utah 7160 Urban 0.9487 0.9561 41620 Urban 0.9524 46180 San Juan County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46190 Sanpete County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46200 Sevier County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46210 Summit County, Utah 46 Rural 0.8843 0.9561 41620 Urban 0.9202 46220 Tooele County, Utah 46 Rural 0.8843 0.9561 41620 Urban 0.9202 46230 Uintah County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46240 Utah County, Utah 6520 Urban 0.9613 0.9588 39340 Urban 0.9601 46250 Wasatch County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46260 Washington County, Utah 46 Rural 0.8843 0.9458 41100 Urban 0.9151 46270 Wayne County, Utah 46 Rural 0.8843 0.8287 99946 Rural 0.8565 46280 Weber County, Utah 7160 Urban 0.9487 0.9216 36260 Urban 0.9352 47000 Addison County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47010 Bennington County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47020 Caledonia County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47030 Chittenden County, Vermont 1303 Urban 0.9322 0.9322 15540 Urban 0.9322 47040 Essex County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47050 Franklin County, Vermont 1303 Urban 0.9322 0.9322 15540 Urban 0.9322 47060 Grand Isle County, Vermont 1303 Urban 0.9322 0.9322 15540 Urban 0.9322 47070 Lamoille County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47080 Orange County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47090 Orleans County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47100 Rutland County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47110 Washington County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47120 Windham County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 47130 Windsor County, Vermont 47 Rural 0.9375 0.9375 99947 Rural 0.9375 48010 St Croix County, Virgin Islands 48 Rural 0.7456 0.7456 99948 Rural 0.7456 48020 St Thomas-John County, Virgin Islands 48 Rural 0.7456 0.7456 99948 Rural 0.7456 49000 Accomack County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49010 Albemarle County, Virginia 1540 Urban 1.0294 1.0294 16820 Urban 1.0294 49011 Alexandria City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49020 Alleghany County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49030 Amelia County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49040 Amherst County, Virginia 4640 Urban 0.9017 0.9017 31340 Urban 0.9017 49050 Appomattox County, Virginia 49 Rural 0.8479 0.9017 31340 Urban 0.8748 49060 Arlington County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49070 Augusta County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49080 Bath County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49088 Bedford City County, Virginia 4640 Urban 0.9017 0.9017 31340 Urban 0.9017 49090 Bedford County, Virginia 4640 Urban 0.9017 0.9017 31340 Urban 0.9017 49100 Bland County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49110 Botetourt County, Virginia 6800 Urban 0.8428 0.8415 40220 Urban 0.8422 49111 Bristol City County, Virginia 3660 Urban 0.8202 0.8240 28700 Urban 0.8221 49120 Brunswick County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49130 Buchanan County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49140 Buckingham County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49141 Buena Vista City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49150 Campbell County, Virginia 4640 Urban 0.9017 0.9017 31340 Urban 0.9017 49160 Caroline County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49170 Carroll County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49180 Charles City County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49190 Charlotte County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49191 Charlottesville City County, Virginia 1540 Urban 1.0294 1.0294 16820 Urban 1.0294 49194 Chesapeake County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49200 Chesterfield County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49210 Clarke County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49211 Clifton Forge City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49212 Colonial Heights County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49213 Covington City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49220 Craig County, Virginia 49 Rural 0.8479 0.8415 40220 Urban 0.8447 49230 Culpeper County, Virginia 8840 Urban 1.0971 0.8049 99949 Rural 0.9510 49240 Cumberland County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49241 Danville City County, Virginia 1950 Urban 0.8643 0.8643 19260 Urban 0.8643 49250 Dickenson County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49260 Dinniddie County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49270 Emporia County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49280 Essex County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49288 Fairfax City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49290 Fairfax County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49291 Falls Church City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49300 Fauquier County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49310 Floyd County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49320 Fluvanna County, Virginia 1540 Urban 1.0294 1.0294 16820 Urban 1.0294 49328 Franklin City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49330 Franklin County, Virginia 49 Rural 0.8479 0.8415 40220 Urban 0.8447 49340 Frederick County, Virginia 49 Rural 0.8479 1.0496 49020 Urban 0.9488 49342 Fredericksburg City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49343 Galax City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49350 Giles County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49360 Gloucester County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49370 Goochland County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49380 Grayson County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49390 Greene County, Virginia 1540 Urban 1.0294 1.0294 16820 Urban 1.0294 49400 Greensville County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49410 Halifax County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49411 Hampton City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49420 Hanover County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49421 Harrisonburg City County, Virginia 49 Rural 0.8479 0.9275 25500 Urban 0.8877 49430 Henrico County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49440 Henry County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49450 Highland County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49451 Hopewell City County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49460 Isle Of Wight County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49470 James City Co County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49480 King And Queen County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49490 King George County, Virginia 8840 Urban 1.0971 0.8049 99949 Rural 0.9510 49500 King William County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49510 Lancaster County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49520 Lee County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49522 Lexington County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49530 Loudoun County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49540 Louisa County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49550 Lunenburg County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49551 Lynchburg City County, Virginia 4640 Urban 0.9017 0.9017 31340 Urban 0.9017 49560 Madison County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49561 Martinsville City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49563 Manassas City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49565 Manassas Park City County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49570 Mathews County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49580 Mecklenburg County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49590 Middlesex County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49600 Montgomery County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49610 Nansemond, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49620 Nelson County, Virginia 49 Rural 0.8479 1.0294 16820 Urban 0.9387 49621 New Kent County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49622 Newport News City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49641 Norfolk City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49650 Northampton County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49660 Northumberland County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49661 Norton City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49670 Nottoway County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49680 Orange County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49690 Page County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49700 Patrick County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49701 Petersburg City County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49710 Pittsylvania County, Virginia 1950 Urban 0.8643 0.8643 19260 Urban 0.8643 49711 Portsmouth City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49712 Poquoson City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49720 Powhatan County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49730 Prince Edward County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49740 Prince George County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49750 Prince William County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49770 Pulaski County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49771 Radford City County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49780 Rappahannock County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49790 Richmond County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49791 Richmond City County, Virginia 6760 Urban 0.9397 0.9397 40060 Urban 0.9397 49800 Roanoke County, Virginia 6800 Urban 0.8428 0.8415 40220 Urban 0.8422 49801 Roanoke City County, Virginia 6800 Urban 0.8428 0.8415 40220 Urban 0.8422 49810 Rockbridge County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49820 Rockingham County, Virginia 49 Rural 0.8479 0.9275 25500 Urban 0.8877 49830 Russell County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49838 Salem County, Virginia 6800 Urban 0.8428 0.8415 40220 Urban 0.8422 49840 Scott County, Virginia 3660 Urban 0.8202 0.8240 28700 Urban 0.8221 49850 Shenandoah County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49860 Smyth County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49867 South Boston City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49870 Southampton County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49880 Spotsylvania County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49890 Stafford County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49891 Staunton City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49892 Suffolk City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49900 Surry County, Virginia 49 Rural 0.8479 0.8894 47260 Urban 0.8687 49910 Sussex County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49920 Tazewell County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49921 Virginia Beach City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49930 Warren County, Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 49950 Washington County, Virginia 3660 Urban 0.8202 0.8240 28700 Urban 0.8221 49951 Waynesboro City County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49960 Westmoreland County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49961 Williamsburg City County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 49962 Winchester City County, Virginia 49 Rural 0.8479 1.0496 49020 Urban 0.9488 49970 Wise County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49980 Wythe County, Virginia 49 Rural 0.8479 0.8049 99949 Rural 0.8264 49981 York County, Virginia 5720 Urban 0.8894 0.8894 47260 Urban 0.8894 50000 Adams County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50010 Asotin County, Washington 50 Rural 1.0072 0.9314 30300 Urban 0.9693 50020 Benton County, Washington 6740 Urban 1.0520 1.0520 28420 Urban 1.0520 50030 Chelan County, Washington 50 Rural 1.0072 0.9427 48300 Urban 0.9750 50040 Clallam County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50050 Clark County, Washington 6440 Urban 1.1403 1.1403 38900 Urban 1.1403 50060 Columbia County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50070 Cowlitz County, Washington 50 Rural 1.0072 1.0224 31020 Urban 1.0148 50080 Douglas County, Washington 50 Rural 1.0072 0.9427 48300 Urban 0.9750 50090 Ferry County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50100 Franklin County, Washington 6740 Urban 1.0520 1.0520 28420 Urban 1.0520 50110 Garfield County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50120 Grant County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50130 Grays Harbor County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50140 Island County, Washington 7600 Urban 1.1479 1.0312 99950 Rural 1.0896 50150 Jefferson County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50160 King County, Washington 7600 Urban 1.1479 1.1492 42644 Urban 1.1486 50170 Kitsap County, Washington 1150 Urban 1.0614 1.0614 14740 Urban 1.0614 50180 Kittitas County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50190 Klickitat County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50200 Lewis County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50210 Lincoln County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50220 Mason County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50230 Okanogan County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50240 Pacific County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50250 Pend Oreille County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50260 Pierce County, Washington 8200 Urban 1.1078 1.1078 45104 Urban 1.1078 50270 San Juan County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50280 Skagit County, Washington 50 Rural 1.0072 1.0576 34580 Urban 1.0324 50290 Skamania County, Washington 50 Rural 1.0072 1.1403 38900 Urban 1.0738 50300 Snohomish County, Washington 7600 Urban 1.1479 1.1492 42644 Urban 1.1486 50310 Spokane County, Washington 7840 Urban 1.0660 1.0660 44060 Urban 1.0660 50320 Stevens County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50330 Thurston County, Washington 5910 Urban 1.1006 1.1006 36500 Urban 1.1006 50340 Wahkiakum County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50350 Walla Walla County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50360 Whatcom County, Washington 0860 Urban 1.1642 1.1642 13380 Urban 1.1642 50370 Whitman County, Washington 50 Rural 1.0072 1.0312 99950 Rural 1.0192 50380 Yakima County, Washington 9260 Urban 1.0322 1.0322 49420 Urban 1.0322 51000 Barbour County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51010 Berkeley County, W Virginia 8840 Urban 1.0971 0.9715 25180 Urban 1.0343 51020 Boone County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51030 Braxton County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51040 Brooke County, W Virginia 8080 Urban 0.8280 0.8280 48260 Urban 0.8280 51050 Cabell County, W Virginia 3400 Urban 0.9564 0.9564 26580 Urban 0.9564 51060 Calhoun County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51070 Clay County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51080 Doddridge County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51090 Fayette County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51100 Gilmer County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51110 Grant County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51120 Greenbrier County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51130 Hampshire County, W Virginia 51 Rural 0.8083 1.0496 49020 Urban 0.9290 51140 Hancock County, W Virginia 8080 Urban 0.8280 0.8280 48260 Urban 0.8280 51150 Hardy County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51160 Harrison County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51170 Jackson County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51180 Jefferson County, W Virginia 8840 Urban 1.0971 1.1023 47894 Urban 1.0997 51190 Kanawha County, W Virginia 1480 Urban 0.8876 0.8876 16620 Urban 0.8876 51200 Lewis County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51210 Lincoln County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51220 Logan County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51230 Mc Dowell County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51240 Marion County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51250 Marshall County, W Virginia 9000 Urban 0.7449 0.7449 48540 Urban 0.7449 51260 Mason County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51270 Mercer County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51280 Mineral County, W Virginia 1900 Urban 0.8662 0.8662 19060 Urban 0.8662 51290 Mingo County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51300 Monongalia County, W Virginia 51 Rural 0.8083 0.8730 34060 Urban 0.8407 51310 Monroe County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51320 Morgan County, W Virginia 51 Rural 0.8083 0.9715 25180 Urban 0.8899 51330 Nicholas County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51340 Ohio County, W Virginia 9000 Urban 0.7449 0.7449 48540 Urban 0.7449 51350 Pendleton County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51360 Pleasants County, W Virginia 51 Rural 0.8083 0.8288 37620 Urban 0.8186 51370 Pocahontas County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51380 Preston County, W Virginia 51 Rural 0.8083 0.8730 34060 Urban 0.8407 51390 Putnam County, W Virginia 1480 Urban 0.8876 0.8876 16620 Urban 0.8876 51400 Raleigh County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51410 Randolph County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51420 Ritchie County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51430 Roane County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51440 Summers County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51450 Taylor County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51460 Tucker County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51470 Tyler County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51480 Upshur County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51490 Wayne County, W Virginia 3400 Urban 0.9564 0.9564 26580 Urban 0.9564 51500 Webster County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51510 Wetzel County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 51520 Wirt County, W Virginia 51 Rural 0.8083 0.8288 37620 Urban 0.8186 51530 Wood County, W Virginia 6020 Urban 0.8288 0.8288 37620 Urban 0.8288 51540 Wyoming County, W Virginia 51 Rural 0.8083 0.7865 99951 Rural 0.7974 52000 Adams County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52010 Ashland County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52020 Barron County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52030 Bayfield County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52040 Brown County, Wisconsin 3080 Urban 0.9586 0.9590 24580 Urban 0.9588 52050 Buffalo County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52060 Burnett County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52070 Calumet County, Wisconsin 0460 Urban 0.9115 0.9131 11540 Urban 0.9123 52080 Chippewa County, Wisconsin 2290 Urban 0.9139 0.9139 20740 Urban 0.9139 52090 Clark County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52100 Columbia County, Wisconsin 52 Rural 0.9498 1.0306 31540 Urban 0.9902 52110 Crawford County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52120 Dane County, Wisconsin 4720 Urban 1.0395 1.0306 31540 Urban 1.0351 52130 Dodge County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52140 Door County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52150 Douglas County, Wisconsin 2240 Urban 1.0356 1.0340 20260 Urban 1.0348 52160 Dunn County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52170 Eau Claire County, Wisconsin 2290 Urban 0.9139 0.9139 20740 Urban 0.9139 52180 Florence County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52190 Fond Du Lac County, Wisconsin 52 Rural 0.9498 0.9897 22540 Urban 0.9698 52200 Forest County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52210 Grant County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52220 Green County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52230 Green Lake County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52240 Iowa County, Wisconsin 52 Rural 0.9498 1.0306 31540 Urban 0.9902 52250 Iron County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52260 Jackson County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52270 Jefferson County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52280 Juneau County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52290 Kenosha County, Wisconsin 3800 Urban 0.9772 1.0342 29404 Urban 1.0057 52300 Kewaunee County, Wisconsin 52 Rural 0.9498 0.9590 24580 Urban 0.9544 52310 La Crosse County, Wisconsin 3870 Urban 0.9289 0.9289 29100 Urban 0.9289 52320 Lafayette County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52330 Langlade County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52340 Lincoln County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52350 Manitowoc County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52360 Marathon County, Wisconsin 8940 Urban 0.9570 0.9570 48140 Urban 0.9570 52370 Marinette County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52380 Marquette County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52381 Menominee County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52390 Milwaukee County, Wisconsin 5080 Urban 1.0076 1.0076 33340 Urban 1.0076 52400 Monroe County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52410 Oconto County, Wisconsin 52 Rural 0.9498 0.9590 24580 Urban 0.9544 52420 Oneida County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52430 Outagamie County, Wisconsin 0460 Urban 0.9115 0.9131 11540 Urban 0.9123 52440 Ozaukee County, Wisconsin 5080 Urban 1.0076 1.0076 33340 Urban 1.0076 52450 Pepin County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52460 Pierce County, Wisconsin 5120 Urban 1.1066 1.1066 133460 Urban 1.1066 52470 Polk County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52480 Portage County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52490 Price County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52500 Racine County, Wisconsin 6600 Urban 0.9045 0.9045 39540 Urban 0.9045 52510 Richland County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52520 Rock County, Wisconsin 3620 Urban 0.9583 0.9583 27500 Urban 0.9583 52530 Rusk County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52540 St Croix County, Wisconsin 5120 Urban 1.1066 1.1066 33460 Urban 1.1066 52550 Sauk County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52560 Sawyer County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52570 Shawano County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52580 Sheboygan County, Wisconsin 7620 Urban 0.8948 0.8948 43100 Urban 0.8948 52590 Taylor County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52600 Trempealeau County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52610 Vernon County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52620 Vilas County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52630 Walworth County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52640 Washburn County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52650 Washington County, Wisconsin 5080 Urban 1.0076 1.0076 33340 Urban 1.0076 52660 Waukesha County, Wisconsin 5080 Urban 1.0076 1.0076 33340 Urban 1.0076 52670 Waupaca County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52680 Waushara County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 52690 Winnebago County, Wisconsin 0460 Urban 0.9115 0.9099 36780 Urban 0.9107 52700 Wood County, Wisconsin 52 Rural 0.9498 0.9492 99952 Rural 0.9495 53000 Albany County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53010 Big Horn County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53020 Campbell County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53030 Carbon County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53040 Converse County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53050 Crook County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53060 Fremont County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53070 Goshen County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53080 Hot Springs County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53090 Johnson County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53100 Laramie County, Wyoming 1580 Urban 0.8980 0.8980 16940 Urban 0.8980 53110 Lincoln County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53120 Natrona County, Wyoming 1350 Urban 0.9243 0.9243 16220 Urban 0.9243 53130 Niobrara County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53140 Park County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53150 Platte County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53160 Sheridan County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53170 Sublette County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53180 Sweetwater County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53190 Teton County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53200 Uinta County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53210 Washakie County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 53220 Weston County, Wyoming 53 Rural 0.9182 0.9182 99953 Rural 0.9182 65010 Agana County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65020 Agana Heights County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65030 Agat County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65040 Asan County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65050 Barrigada County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65060 Chalan Pago County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65070 Dededo County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65080 Inarajan County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65090 Maite County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65100 Mangilao County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65110 Merizo County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65120 Mongmong County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65130 Ordot County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65140 Piti County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65150 Santa Rita County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65160 Sinajana County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65170 Talofofo County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65180 Tamuning County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65190 Toto County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65200 Umatac County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65210 Yigo County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 65220 Yona County, Guam 65 Rural 0.9611 0.9611 99965 Rural 0.9611 * Transition Wage Index is comprised of 50 percent of FY 2006 MSA-based wage index and 50 percent of FY 2006 CBSA based wage index (both based on FY 2001 hospital wage data). Table 2.—FY 2006 IRF PPS Hold Harmless Areas [For Federal Fiscal Years 2006 and 2007] SSA state/county code County name MSA No. MSA urban/rural 2006 MSA-based WI 2006 CBSA-based WI CBSA No. CBSA urban/rural Transition wage index * 01030 Bibb County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 01100 Chilton County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 01300 Geneva County, Alabama 01 Rural 0.7637 0.7537 20020 Urban 0.7587 01310 Greene County, Alabama 01 Rural 0.7637 0.8336 46220 Urban 0.7987 01320 Hale County, Alabama 01 Rural 0.7637 0.8336 46220 Urban 0.7987 01330 Henry County, Alabama 01 Rural 0.7637 0.7537 20020 Urban 0.7587 01420 Lowndes County, Alabama 01 Rural 0.7637 0.8300 33860 Urban 0.7969 01630 Walker County, Alabama 01 Rural 0.7637 0.9157 13820 Urban 0.8397 02090 Fairbanks County, Alaska 02 Rural 1.1637 1.1146 21820 Urban 1.1392 02170 Matanuska County, Alaska 02 Rural 1.1637 1.2165 11260 Urban 1.1901 03120 Yavapai County, Arizona 03 Rural 0.9140 0.9892 39140 Urban 0.9516 04120 Cleveland County, Arkansas 04 Rural 0.7703 0.8673 38220 Urban 0.8188 04230 Franklin County, Arkansas 04 Rural 0.7703 0.8283 22900 Urban 0.7993 04250 Garland County, Arkansas 04 Rural 0.7703 0.9249 26300 Urban 0.8476 04260 Grant County, Arkansas 04 Rural 0.7703 0.8826 30780 Urban 0.8265 04390 Lincoln County, Arkansas 04 Rural 0.7703 0.8673 38220 Urban 0.8188 04430 Madison County, Arkansas 04 Rural 0.7703 0.8636 22220 Urban 0.8170 04520 Perry County, Arkansas 04 Rural 0.7703 0.8826 30780 Urban 0.8265 04550 Poinsett County, Arkansas 04 Rural 0.7703 0.8144 27860 Urban 0.7924 05120 Imperial County, California 05 Rural 1.0297 0.8856 20940 Urban 0.9577 05150 Kings County, California 05 Rural 1.0297 0.9296 25260 Urban 0.9797 05450 San Benito County, California 05 Rural 1.0297 1.4722 41940 Urban 1.2510 06090 Clear Creek County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06190 Elbert County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06230 Gilpin County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06460 Park County, Colorado 06 Rural 0.9368 1.0904 19740 Urban 1.0136 06590 Teller County, Colorado 06 Rural 0.9368 0.9792 17820 Urban 0.9580 10010 Baker County, Florida 10 Rural 0.8721 0.9537 27260 Urban 0.9129 10200 Gilchrist County, Florida 10 Rural 0.8721 0.9459 23540 Urban 0.9090 10300 Indian River County, Florida 10 Rural 0.8721 0.9477 46940 Urban 0.9099 10320 Jefferson County, Florida 10 Rural 0.8721 0.8655 45220 Urban 0.8688 10640 Wakulla County, Florida 10 Rural 0.8721 0.8655 45220 Urban 0.8688 11020 Baker County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 11110 Brantley County, Georgia 11 Rural 0.8247 1.1933 15260 Urban 1.0090 11120 Brooks County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11150 Burke County, Georgia 11 Rural 0.8247 0.9154 12260 Urban 0.8701 11160 Butts County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11330 Crawford County, Georgia 11 Rural 0.8247 0.9887 31420 Urban 0.9067 11350 Dawson County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11420 Echols County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11460 Floyd County, Georgia 11 Rural 0.8247 0.8878 40660 Urban 0.8563 11490 Glynn County, Georgia 11 Rural 0.8247 1.1933 15260 Urban 1.0090 11550 Hall County, Georgia 11 Rural 0.8247 0.9557 23580 Urban 0.8902 11570 Haralson County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11590 Heard County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11611 Jasper County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11651 Lamar County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11652 Lanier County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11680 Liberty County, Georgia 11 Rural 0.8247 0.7715 25980 Urban 0.7981 11691 Long County, Georgia 11 Rural 0.8247 0.7715 25980 Urban 0.7981 11700 Lowndes County, Georgia 11 Rural 0.8247 0.8341 46660 Urban 0.8294 11703 Mc Intosh County, Georgia 11 Rural 0.8247 1.1933 15260 Urban 1.0090 11730 Marion County, Georgia 11 Rural 0.8247 0.8690 17980 Urban 0.8469 11740 Meriwether County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11760 Monroe County, Georgia 11 Rural 0.8247 0.9887 31420 Urban 0.9067 11772 Murray County, Georgia 11 Rural 0.8247 0.9558 19140 Urban 0.8903 11801 Oglethorpe County, Georgia 11 Rural 0.8247 1.0202 12020 Urban 0.9225 11821 Pike County, Georgia 11 Rural 0.8247 0.9971 12060 Urban 0.9109 11885 Terrell County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 11970 Whitfield County, Georgia 11 Rural 0.8247 0.9558 19140 Urban 0.8903 11980 Worth County, Georgia 11 Rural 0.8247 1.1266 10500 Urban 0.9757 13070 Boise County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13090 Bonneville County, Idaho 13 Rural 0.8826 0.9059 26820 Urban 0.8943 13200 Franklin County, Idaho 13 Rural 0.8826 0.9094 30860 Urban 0.8960 13220 Gem County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13250 Jefferson County, Idaho 13 Rural 0.8826 0.9059 26820 Urban 0.8943 13270 Kootenai County, Idaho 13 Rural 0.8826 0.9339 17660 Urban 0.9083 13340 Nez Perce County, Idaho 13 Rural 0.8826 0.9314 30300 Urban 0.9070 13360 Owyhee County, Idaho 13 Rural 0.8826 0.9352 14260 Urban 0.9089 13380 Power County, Idaho 13 Rural 0.8826 0.9601 38540 Urban 0.9214 14020 Bond County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14060 Calhoun County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14350 Ford County, Illinois 14 Rural 0.8340 0.9527 16580 Urban 0.8934 14670 Macoupin County, Illinois 14 Rural 0.8340 0.9076 41180 Urban 0.8708 14700 Marshall County, Illinois 14 Rural 0.8340 0.8886 37900 Urban 0.8613 14740 Mercer County, Illinois 14 Rural 0.8340 0.8773 19340 Urban 0.8557 14820 Piatt County, Illinois 14 Rural 0.8340 0.9527 16580 Urban 0.8934 14960 Stark County, Illinois 14 Rural 0.8340 0.8886 37900 Urban 0.8613 14982 Vermilion County, Illinois 14 Rural 0.8340 0.8392 19180 Urban 0.8366 15020 Bartholomew County, Indiana 15 Rural 0.8736 0.9388 18020 Urban 0.9062 15030 Benton County, Indiana 15 Rural 0.8736 0.9067 29140 Urban 0.8902 15060 Brown County, Indiana 15 Rural 0.8736 1.0113 26900 Urban 0.9425 15070 Carroll County, Indiana 15 Rural 0.8736 0.9067 29140 Urban 0.8902 15230 Franklin County, Indiana 15 Rural 0.8736 0.9516 17140 Urban 0.9126 15250 Gibson County, Indiana 15 Rural 0.8736 0.8372 21780 Urban 0.8554 15270 Greene County, Indiana 15 Rural 0.8736 0.8587 14020 Urban 0.8662 15360 Jasper County, Indiana 15 Rural 0.8736 0.9310 23844 Urban 0.9023 15450 La Porte County, Indiana 15 Rural 0.8736 0.9332 33140 Urban 0.9034 15550 Newton County, Indiana 15 Rural 0.8736 0.9310 23844 Urban 0.9023 15590 Owen County, Indiana 15 Rural 0.8736 0.8587 14020 Urban 0.8662 15660 Putnam County, Indiana 15 Rural 0.8736 1.0113 26900 Urban 0.9425 15760 Sullivan County, Indiana 15 Rural 0.8736 0.8517 45460 Urban 0.8627 15870 Washington County, Indiana 15 Rural 0.8736 0.9122 31140 Urban 0.8929 16050 Benton County, Iowa 16 Rural 0.8550 0.8975 16300 Urban 0.8763 16080 Bremer County, Iowa 16 Rural 0.8550 0.8633 47940 Urban 0.8592 16370 Grundy County, Iowa 16 Rural 0.8550 0.8633 47940 Urban 0.8592 16380 Guthrie County, Iowa 16 Rural 0.8550 0.9266 19780 Urban 0.8908 16420 Harrison County, Iowa 16 Rural 0.8550 0.9754 36540 Urban 0.9152 16520 Jones County, Iowa 16 Rural 0.8550 0.8975 16300 Urban 0.8763 16600 Madison County, Iowa 16 Rural 0.8550 0.9266 19780 Urban 0.8908 16640 Mills County, Iowa 16 Rural 0.8550 0.9754 36540 Urban 0.9152 16840 Story County, Iowa 16 Rural 0.8550 0.9479 11180 Urban 0.9015 16910 Washington County, Iowa 16 Rural 0.8550 0.9654 26980 Urban 0.9102 17210 Doniphan County, Kansas 17 Rural 0.8087 1.0013 41140 Urban 0.9050 17290 Franklin County, Kansas 17 Rural 0.8087 0.9629 28140 Urban 0.8858 17420 Jackson County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17430 Jefferson County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17530 Linn County, Kansas 17 Rural 0.8087 0.9629 28140 Urban 0.8858 17690 Osage County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 17950 Sumner County, Kansas 17 Rural 0.8087 0.9457 48620 Urban 0.8772 17980 Wabaunsee County, Kansas 17 Rural 0.8087 0.8904 45820 Urban 0.8496 18110 Bracken County, Kentucky 18 Rural 0.7844 0.9516 17140 Urban 0.8680 18291 Edmonson County, Kentucky 18 Rural 0.7844 0.8140 14540 Urban 0.7992 18450 Hancock County, Kentucky 18 Rural 0.7844 0.8434 36980 Urban 0.8139 18460 Hardin County, Kentucky 18 Rural 0.7844 0.8684 21060 Urban 0.8264 18510 Henry County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18610 Larue County, Kentucky 18 Rural 0.7844 0.8684 21060 Urban 0.8264 18740 Mc Lean County, Kentucky 18 Rural 0.7844 0.8434 36980 Urban 0.8139 18801 Meade County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18890 Nelson County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18978 Shelby County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18980 Spencer County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18983 Trigg County, Kentucky 18 Rural 0.7844 0.8022 17300 Urban 0.7933 18984 Trimble County, Kentucky 18 Rural 0.7844 0.9122 31140 Urban 0.8483 18986 Warren County, Kentucky 18 Rural 0.7844 0.8140 14540 Urban 0.7992 18989 Webster County, Kentucky 18 Rural 0.7844 0.8372 21780 Urban 0.8108 19110 Cameron County, Louisiana 19 Rural 0.7290 0.7935 29340 Urban 0.7613 19150 De Soto County, Louisiana 19 Rural 0.7290 0.9132 43340 Urban 0.8211 19180 East Feliciana County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19210 Grant County, Louisiana 19 Rural 0.7290 0.8171 10780 Urban 0.7731 19230 Iberville County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19380 Pointe Coupee County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19450 St Helena County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 19550 Union County, Louisiana 19 Rural 0.7290 0.7903 33740 Urban 0.7597 19620 West Feliciana County, Louisiana 19 Rural 0.7290 0.8319 12940 Urban 0.7805 21190 Somerset County, Maryland 21 Rural 0.9179 0.9123 41540 Urban 0.9151 21220 Wicomico County, Maryland 21 Rural 0.9179 0.9123 41540 Urban 0.9151 22060 Franklin County, Massachusetts 22 Rural 1.0216 1.0176 44140 Urban 1.0196 23070 Barry County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 23130 Cass County, Michigan 23 Rural 0.8740 0.9447 43780 Urban 0.9094 23330 Ionia County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 23610 Newaygo County, Michigan 23 Rural 0.8740 0.9420 24340 Urban 0.9080 24080 Carlton County, Minnesota 24 Rural 0.9339 1.0340 20260 Urban 0.9840 24190 Dodge County, Minnesota 24 Rural 0.9339 1.1504 40340 Urban 1.0422 24780 Wabasha County, Minnesota 24 Rural 0.9339 1.1504 40340 Urban 1.0422 25140 Copiah County, Mississippi 25 Rural 0.7583 0.8291 27140 Urban 0.7937 25190 George County, Mississippi 25 Rural 0.7583 0.7974 37700 Urban 0.7779 25460 Marshall County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 25550 Perry County, Mississippi 25 Rural 0.7583 0.7362 25620 Urban 0.7473 25630 Simpson County, Mississippi 25 Rural 0.7583 0.8291 27140 Urban 0.7937 25650 Stone County, Mississippi 25 Rural 0.7583 0.8950 25060 Urban 0.8267 25680 Tate County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 25710 Tunica County, Mississippi 25 Rural 0.7583 0.9217 32820 Urban 0.8400 26060 Bates County, Missouri 26 Rural 0.7829 0.9629 28140 Urban 0.8729 26120 Caldwell County, Missouri 26 Rural 0.7829 0.9629 28140 Urban 0.8729 26130 Callaway County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26250 Cole County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26270 Crawford County, Missouri 26 Rural 0.7829 0.9076 41180 Urban 0.8453 26290 Dallas County, Missouri 26 Rural 0.7829 0.8557 44180 Urban 0.8193 26310 De Kalb County, Missouri 26 Rural 0.7829 1.0013 41140 Urban 0.8921 26440 Howard County, Missouri 26 Rural 0.7829 0.8396 17860 Urban 0.8113 26590 Mc Donald County, Missouri 26 Rural 0.7829 0.8636 22220 Urban 0.8233 26670 Moniteau County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26750 Osage County, Missouri 26 Rural 0.7829 0.8338 27620 Urban 0.8084 26821 Polk County, Missouri 26 Rural 0.7829 0.8557 44180 Urban 0.8193 26992 Washington County, Missouri 26 Rural 0.7829 0.9076 41180 Urban 0.8453 27040 Carbon County, Montana 27 Rural 0.8701 0.8961 13740 Urban 0.8831 28250 Dixon County, Nebraska 28 Rural 0.9035 0.9070 43580 Urban 0.9053 28770 Saunders County, Nebraska 28 Rural 0.9035 0.9754 36540 Urban 0.9395 28790 Seward County, Nebraska 28 Rural 0.9035 1.0208 30700 Urban 0.9622 29120 Carson City County, Nevada 29 Rural 0.9832 1.0352 16180 Urban 1.0092 29140 Storey County, Nevada 29 Rural 0.9832 1.0456 39900 Urban 1.0144 32220 San Juan County, New Mexico 32 Rural 0.8529 0.8049 22140 Urban 0.8289 32280 Torrance County, New Mexico 32 Rural 0.8529 1.0485 10740 Urban 0.9507 33730 Tompkins County, New York 33 Rural 0.8403 0.9589 27060 Urban 0.8996 33740 Ulster County, New York 33 Rural 0.8403 0.9000 28740 Urban 0.8702 34030 Anson County, N Carolina 34 Rural 0.8500 0.9743 16740 Urban 0.9122 34390 Greene County, N Carolina 34 Rural 0.8500 0.9183 24780 Urban 0.8842 34430 Haywood County, N Carolina 34 Rural 0.8500 0.9191 11700 Urban 0.8846 34440 Henderson County, N Carolina 34 Rural 0.8500 0.9191 11700 Urban 0.8846 34460 Hoke County, N Carolina 34 Rural 0.8500 0.9363 22180 Urban 0.8932 34700 Pender County, N Carolina 34 Rural 0.8500 0.9237 48900 Urban 0.8869 34720 Person County, N Carolina 34 Rural 0.8500 1.0363 20500 Urban 0.9432 34780 Rockingham County, N Carolina 34 Rural 0.8500 0.9190 24660 Urban 0.8845 36220 Erie County, Ohio 36 Rural 0.8759 0.9017 41780 Urban 0.8888 36600 Morrow County, Ohio 36 Rural 0.8759 0.9737 18140 Urban 0.9248 36630 Ottawa County, Ohio 36 Rural 0.8759 0.9524 45780 Urban 0.9142 36690 Preble County, Ohio 36 Rural 0.8759 0.9303 19380 Urban 0.9031 36810 Union County, Ohio 36 Rural 0.8759 0.9737 18140 Urban 0.9248 37250 Grady County, Oklahoma 37 Rural 0.7537 0.8982 36420 Urban 0.8260 37390 Le Flore County, Oklahoma 37 Rural 0.7537 0.8283 22900 Urban 0.7910 37400 Lincoln County, Oklahoma 37 Rural 0.7537 0.8982 36420 Urban 0.8260 37550 Okmulgee County, Oklahoma 37 Rural 0.7537 0.8690 46140 Urban 0.8114 37580 Pawnee County, Oklahoma 37 Rural 0.7537 0.8690 46140 Urban 0.8114 38080 Deschutes County, Oregon 38 Rural 1.0049 1.0603 13460 Urban 1.0326 39070 Armstrong County, Pennsylvania 39 Rural 0.8348 0.8736 38300 Urban 0.8542 40050 Aibonito County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40080 Arroyo County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40100 Barranquitas County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40190 Ciales County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40270 Guanica County, Puerto Rico 40 Rural 0.4047 0.4493 49500 Urban 0.4270 40280 Guayama County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40350 Isabela County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40390 Lajas County, Puerto Rico 40 Rural 0.4047 0.5240 41900 Urban 0.4644 40400 Lares County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40470 Maunabo County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40530 Orocovis County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40540 Patillas County, Puerto Rico 40 Rural 0.4047 0.4005 25020 Urban 0.4026 40570 Quebradillas County, Puerto Rico 40 Rural 0.4047 0.4645 41980 Urban 0.4346 40580 Rincon County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 40660 San Sebastian County, Puerto Rico 40 Rural 0.4047 0.4280 10380 Urban 0.4164 42080 Calhoun County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42150 Darlington County, S Carolina 42 Rural 0.8640 0.8833 22500 Urban 0.8737 42190 Fairfield County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42270 Kershaw County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 42290 Laurens County, S Carolina 42 Rural 0.8640 0.9557 24860 Urban 0.9099 42400 Saluda County, S Carolina 42 Rural 0.8640 0.9392 17900 Urban 0.9016 43430 Mc Cook County, S Dakota 43 Rural 0.8393 0.9441 43620 Urban 0.8917 43460 Meade County, S Dakota 43 Rural 0.8393 0.8912 39660 Urban 0.8653 43620 Turner County, S Dakota 43 Rural 0.8393 0.9441 43620 Urban 0.8917 43630 Union County, S Dakota 43 Rural 0.8393 0.9070 43580 Urban 0.8732 44050 Bradley County, Tennessee 44 Rural 0.7876 0.7844 17420 Urban 0.7860 44070 Cannon County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44280 Grainger County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44310 Hamblen County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44400 Hickman County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44440 Jefferson County, Tennessee 44 Rural 0.7876 0.7790 34100 Urban 0.7833 44550 Macon County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44690 Polk County, Tennessee 44 Rural 0.7876 0.7844 17420 Urban 0.7860 44760 Sequatchie County, Tennessee 44 Rural 0.7876 0.9207 16860 Urban 0.8542 44790 Smith County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 44800 Stewart County, Tennessee 44 Rural 0.7876 0.8022 17300 Urban 0.7949 44840 Trousdale County, Tennessee 44 Rural 0.7876 1.0086 34980 Urban 0.8981 45030 Aransas County, Texas 45 Rural 0.7910 0.8647 18580 Urban 0.8279 45050 Armstrong County, Texas 45 Rural 0.7910 0.9178 11100 Urban 0.8544 45060 Atascosa County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45070 Austin County, Texas 45 Rural 0.7910 0.9973 26420 Urban 0.8942 45090 Bandera County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45221 Burleson County, Texas 45 Rural 0.7910 0.9243 17780 Urban 0.8577 45224 Calhoun County, Texas 45 Rural 0.7910 0.8470 47020 Urban 0.8190 45230 Callahan County, Texas 45 Rural 0.7910 0.7850 10180 Urban 0.7880 45251 Carson County, Texas 45 Rural 0.7910 0.9178 11100 Urban 0.8544 45291 Clay County, Texas 45 Rural 0.7910 0.8332 48660 Urban 0.8121 45362 Crosby County, Texas 45 Rural 0.7910 0.8777 31180 Urban 0.8344 45400 Delta County, Texas 45 Rural 0.7910 1.0074 19124 Urban 0.8992 45561 Goliad County, Texas 45 Rural 0.7910 0.8470 47020 Urban 0.8190 45672 Irion County, Texas 45 Rural 0.7910 0.8167 41660 Urban 0.8039 45721 Jones County, Texas 45 Rural 0.7910 0.7850 10180 Urban 0.7880 45731 Kendall County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45752 Lampasas County, Texas 45 Rural 0.7910 0.9242 28660 Urban 0.8576 45792 Medina County, Texas 45 Rural 0.7910 0.9003 41700 Urban 0.8457 45878 Robertson County, Texas 45 Rural 0.7910 0.9243 17780 Urban 0.8577 45881 Rusk County, Texas 45 Rural 0.7910 0.8801 30980 Urban 0.8356 45884 San Jacinto County, Texas 45 Rural 0.7910 0.9973 26420 Urban 0.8942 45973 Wise County, Texas 45 Rural 0.7910 0.9472 23104 Urban 0.8691 46020 Cache County, Utah 46 Rural 0.8843 0.9094 30860 Urban 0.8969 46110 Juab County, Utah 46 Rural 0.8843 0.9588 39340 Urban 0.9216 46140 Morgan County, Utah 46 Rural 0.8843 0.9216 36260 Urban 0.9030 46210 Summit County, Utah 46 Rural 0.8843 0.9561 41620 Urban 0.9202 46220 Tooele County, Utah 46 Rural 0.8843 0.9561 41620 Urban 0.9202 46260 Washington County, Utah 46 Rural 0.8843 0.9458 41100 Urban 0.9151 49030 Amelia County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49050 Appomattox County, Virginia 49 Rural 0.8479 0.9017 31340 Urban 0.8748 49160 Caroline County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49220 Craig County, Virginia 49 Rural 0.8479 0.8415 40220 Urban 0.8447 49240 Cumberland County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49330 Franklin County, Virginia 49 Rural 0.8479 0.8415 40220 Urban 0.8447 49340 Frederick County, Virginia 49 Rural 0.8479 1.0496 49020 Urban 0.9488 49350 Giles County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49421 Harrisonburg City County, Virginia 49 Rural 0.8479 0.9275 25500 Urban 0.8877 49480 King And Queen County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49500 King William County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49540 Louisa County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49600 Montgomery County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49620 Nelson County, Virginia 49 Rural 0.8479 1.0294 16820 Urban 0.9387 49770 Pulaski County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49771 Radford City County, Virginia 49 Rural 0.8479 0.7951 13980 Urban 0.8215 49820 Rockingham County, Virginia 49 Rural 0.8479 0.9275 25500 Urban 0.8877 49900 Surry County, Virginia 49 Rural 0.8479 0.8894 47260 Urban 0.8687 49910 Sussex County, Virginia 49 Rural 0.8479 0.9397 40060 Urban 0.8938 49962 Winchester City County, Virginia 49 Rural 0.8479 1.0496 49020 Urban 0.9488 50010 Asotin County, Washington 50 Rural 1.0072 0.9314 30300 Urban 0.9693 50030 Chelan County, Washington 50 Rural 1.0072 0.9427 48300 Urban 0.9750 50070 Cowlitz County, Washington 50 Rural 1.0072 1.0224 31020 Urban 1.0148 50080 Douglas County, Washington 50 Rural 1.0072 0.9427 48300 Urban 0.9750 50280 Skagit County, Washington 50 Rural 1.0072 1.0576 34580 Urban 1.0324 50290 Skamania County, Washington 50 Rural 1.0072 1.1403 38900 Urban 1.0738 51020 Boone County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51070 Clay County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51130 Hampshire County, W Virginia 51 Rural 0.8083 1.0496 49020 Urban 0.9290 51210 Lincoln County, W Virginia 51 Rural 0.8083 0.8876 16620 Urban 0.8480 51300 Monongalia County, W Virginia 51 Rural 0.8083 0.8730 34060 Urban 0.8407 51320 Morgan County, W Virginia 51 Rural 0.8083 0.9715 25180 Urban 0.8899 51360 Pleasants County, W Virginia 51 Rural 0.8083 0.8288 37620 Urban 0.8186 51380 Preston County, W Virginia 51 Rural 0.8083 0.8730 34060 Urban 0.8407 51520 Wirt County, W Virginia 51 Rural 0.8083 0.8288 37620 Urban 0.8186 52100 Columbia County, Wisconsin 52 Rural 0.9498 1.0306 31540 Urban 0.9902 52190 Fond Du Lac County, Wisconsin 52 Rural 0.9498 0.9897 22540 Urban 0.9698 52240 Iowa County, Wisconsin 52 Rural 0.9498 1.0306 31540 Urban 0.9902 52300 Kewaunee County, Wisconsin 52 Rural 0.9498 0.9590 24580 Urban 0.9544 52410 Oconto County, Wisconsin 52 Rural 0.9498 0.9590 24580 Urban 0.9544 * Transition Wage Index is comprised of 50 percent of FY 2006 MSA-based wage index and 50 percent of FY 2006 CBSA based wage index (both based on FY 2001 hospital wage data). [FR Doc. 05-15419 Filed 8-1-05; 4:16 pm]
Connectionstraces to 5
13 references not yet in our index
  • 42 CFR 412
  • Pub. L. 105-33
  • Pub. L. 106-113
  • Pub. L. 106-554
  • Pub. L. 107-105
  • Pub. L. 104-191
  • 45 CFR 160
  • Pub. L. 97-248
  • 42 CFR 413.79(e)
  • 42 CFR 413.79(l)
  • 42 CFR 412.624(d)(1)
  • Pub. L. 96-354
  • Pub. L. 104-4
Citation graph
cites case law
Notices
Final rule
Fed. Reg.Notices
Fed. Reg.Proposed Rules
Fed. Reg.Rules and Regulations
U.S.C.§ 804
U.S.C.§ 1302
Cite42 CFR 412
Pub. L.Pub. L. 105-33
Pub. L.Pub. L. 106-113
Pub. L.Pub. L. 106-554
Pub. L.Pub. L. 107-105
Cites 18 · showing 10Cited by 0 across 0 sources
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