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Proposed Rule

Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for FY 2006

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AGENCY:

Centers for Medicare & Medicaid Services (CMS), HHS.

ACTION:

Proposed rule.

SUMMARY:

This proposed rule would 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 in the Federal Register on or before August 1 before each fiscal year, 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 proposing new policies and are proposing to change existing policies regarding the prospective payment system within the authority granted under section 1886(j) of the Act.

DATES:

To be assured consideration, comments must be received at one of the addresses provided below, no later than 5 p.m. on July 18, 2005.

ADDRESSES:

In commenting, please refer to file code CMS-1290-P. Because of staff and resource limitations, we cannot accept comments by facsimile (FAX) transmission.

You may submit comments in one of three ways (no duplicates, please):

1. Electronically. You may submit electronic comments on specific issues in this regulation to http://www.cms.hhs.gov/​regulations/​ecomments. (Attachments should be in Microsoft Word, WordPerfect, or Excel; however, we prefer Microsoft Word.)

2. By mail. You may mail written comments (one original and two copies) to the following address ONLY: Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1290-P, P.O. Box 8010, Baltimore, MD 21244-8010.

Please allow sufficient time for mailed comments to be received before the close of the comment period.

3. By hand or courier. If you prefer, you may deliver (by hand or courier) your written comments (one original and two copies) before the close of the comment period to one of the following addresses. If you intend to deliver your comments to the Baltimore address, please call telephone number (410) 786-7195 in advance to schedule your arrival with one of our staff members. Room 445-G, Hubert H. Humphrey Building, 200 Independence Avenue, SW., Washington, DC 20201; or 7500 Security Boulevard, Baltimore, MD 21244-1850.

(Because access to the interior of the HHH Building is not readily available to persons without Federal Government identification, commenters are encouraged to leave their comments in the CMS drop slots located in the main lobby of the building. A stamp-in clock is available for persons wishing to retain a proof of filing by stamping in and retaining an extra copy of the comments being filed.)

Comments mailed to the addresses indicated as appropriate for hand or courier delivery may be delayed and received after the comment period.

For information on viewing public comments, see the beginning of the SUPPLEMENTARY INFORMATION section.

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FOR FURTHER INFORMATION CONTACT:

Pete Diaz, (410) 786-1235. Susanne Seagrave, (410) 786-0044. Mollie Knight, (410) 786-7984 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).

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SUPPLEMENTARY INFORMATION:

Submitting Comments: We welcome comments from the public on all issues set forth in this rule to assist us in fully considering issues and developing policies. You can assist us by referencing the file code CMS-1290-P and the specific “issue identifier” that precedes the section on which you choose to comment.

Inspection of Public Comments: All comments received before the close of the comment period are available for viewing by the public, including any personally identifiable or confidential business information that is included in a comment. CMS posts all electronic comments received before the close of the comment period on its public Web site as soon as possible after they have been received. Hard copy comments received timely will be available for public inspection as they are received, generally beginning approximately 3 weeks after publication of a document, at the headquarters of the Centers for Medicare & Medicaid Services, 7500 Security Boulevard, Baltimore, Maryland 21244, Monday through Friday of each week from 8:30 a.m. to 4 p.m. To schedule an appointment to view public comments, phone 1-800-743-3951.

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. Quality of Care in IRFs

E. Research to Support Refinements of the Current IRF PPS

F. Proposed Refinements to the IRF PPS for Fiscal Year 2006

II. Proposed Refinements to the Patient Classification System

A. Proposed 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. Proposed Changes to the Existing List of Tier Comorbidities

1. Proposed Changes To Remove Codes That Are Not Positively Related to Treatment Costs

2. Proposed Changes to Move Dialysis to Tier One

3. Proposed Changes to Move Comorbidity Codes Based on Their Marginal Cost

C. Proposed Changes to the CMGs

1. Proposed Changes for Updating the CMGs

2. Proposed Use of a Weighted Motor Score Index and Correction to the Treatment of Unobserved Transfer to Toilet Values

3. Proposed Changes for Updating the Relative Weights

III. Proposed FY 2006 Federal Prospective Payment Rates

A. Proposed Reduction of the Standard Payment Amount to Account for Coding Changes

B. Proposed Adjustments to Determine the Proposed FY 2006 Standard Payment Conversion Factor

1. Proposed Market Basket Used for IRF Market Basket Index

a. Overview of the Proposed RPL Market Basket

b. Proposed Methodology for Operating Portion of the Proposed RPL Market Basket

c. Proposed Methodology for Capital Proportion of the RPL Market Basket

d. Labor-Related Share

2. Proposed Area Wage Adjustment

a. Proposed Revisions of the IRF PPS Geographic Classification

b. Current IRF PPS Labor Market Areas Based on MSAs Start Printed Page 30189

c. Core-Based Statistical Areas (CBSAs)

d. Proposed Revisions of the IRF PPS Labor Market Areas

i. New England MSAs

ii. Metropolitan Divisions

iii. Micropolitan Areas

e. Implementation of the Proposed Changes to Revise the Labor Market Areas

f. Wage Index Data

3. Proposed Teaching Status Adjustment

4. Proposed Adjustment for Rural Location

5. Proposed Adjustment for Disproportionate Share of Low-Income Patients

6. Proposed Update to the Outlier Threshold Amount

7. Proposed Budget Neutrality Factor Methodology for Fiscal Year 2006

8. Description of the Methodology Used to Implement the Proposed Changes in a Budget Neutral Manner

9. Description of the Proposed IRF Standard Payment Conversion Factor for Fiscal Year 2006

10. Example of the Proposed Methodology for Adjusting the Federal Prospective Payment Rates

IV. Provisions of the Proposed Regulations

V. Collection of Information Requirements

VI. Response to Comments

VII. Regulatory Impact Analysis

Acronyms

Because of the many terms to which we refer by acronym in this propose 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-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

[If you choose to comment on issues in this section, please include the caption “Background” at the beginning of your comments.]

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). 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 proposed rule as the standard payment conversion factor.

For each of the tiers within a CMG, the relative weighting factors were Start Printed Page 30190applied 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 provides 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 662/3 percent of the Federal IRF PPS rate and 331/3 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 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 proposed 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 in the Federal Register, on or before August 1 of the preceding FY, 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.

In this proposed rule, we are proposing to update the IRF Federal prospective payment rates from FY 2005 to FY 2006, and we are proposing revisions to the methodology described in § 412.624. The proposed changes to the methodology are described in more detail in this proposed rule. For example, we are proposing to add a new teaching status adjustment, and we are proposing to implement other changes to existing policies in a budget neutral manner, which requires applying additional budget neutrality factors to the standard payment amount to calculate the standard payment conversion factor for FY 2006. See section III of this proposed rule for further discussion of the proposed FY 2006 Federal prospective payment rates. The proposed FY 2006 Federal prospective payment rates would be effective for discharges on or after October 1, 2005 and before October 1, 2006.

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 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 the 5-digit CMG number 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 at 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 Start Printed Page 30191transaction 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 number, 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. 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, we will determine which quality-related items should be listed on the IRF-PAI. The revised IRF-PAI will need to be approved by OMB before it is used in IRFs.

We would like to take this opportunity to discuss our thinking related to broader initiatives in this area related to quality of care. 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, as mentioned above, 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 rehospitalizations 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.

At this time, we do not offer specific proposals related to the preceding discussion. Finally, some of the ideas discussed here 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. We welcome comments on these and other approaches.

E. Research To Support Refinements of the Current IRF PPS

As described in the August 7, 2001 final rule, we contracted with the RAND Corporation (RAND) 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.

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 Start Printed Page 30192data, 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. 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. The refinements we are proposing to make to the IRF PPS 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.

F. Proposed Refinements to the IRF PPS for Fiscal Year 2006

Based on analyses by RAND using calendar year 2002 and FY 2003 data, we are proposing refinements to the IRF PPS case-mix classification system (the CMGs and the corresponding relative weights) and the case-level and facility-level adjustments. Several new developments warrant these proposed 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.

In this proposed rule, we are proposing to make the following revisions:

  • Reduce the standard payment amount by 1.9 percent.

In the August 7, 2001 final rule, we used cost report data from FYs 1998, 1997, and/or 1996 and calendar year 1999 Medicare bill data in calculating the initial PPS payment rates. As discussed in detail in section III.A of this proposed rule, analysis of calendar year 2002 data indicates that the standard payment conversion factor is now at least 1.9 percent higher than it should be to reflect the actual costs of caring for Medicare patients in IRFs. The data demonstrate that this is largely because the implementation of the IRF PPS caused important changes in IRFs' coding practices, including increased accuracy and consistency in coding.

  • Make revisions to the comorbidity tiers and the CMGs.

In the August 7, 2001 final rule, we used FIM and Medicare data from 1998 and 1999 to construct the CMGs and to assign the comorbidity tiers. As discussed in detail in section II of this proposed rule, analysis of calendar year 2002 and FY 2003 data indicates the need to refine the comorbidity tiers and the CMGs to better reflect the costs of Medicare cases in IRFs.

  • Adopt the new geographic labor market area definitions based on the definitions created by the Office of Management and Budget (OMB), known as Core-Based Statistical Areas (CBSAs), for purposes of computing the proposed wage index adjustment to IRF payments.

Historically, Medicare PPSs have used market area definitions developed by OMB. We are proposing to adopt new market area definitions which are based on OMB definitions. As discussed in detail in section III.B.2 of this proposed rule, we believe that these designations more accurately reflect the local economies and wage levels of the areas in which hospitals are located. These are the same labor market area definitions implemented for acute care inpatient hospitals under the hospital inpatient prospective payment system (IPPS) as specified in § 412.64(b)(1)(ii)(A) through (C), which were effective for those hospitals beginning October 1, 2004 as discussed in the August 11, 2004 IPPS final rule (69 FR at 49026 through 49032).

  • Implement a teaching status adjustment to payments for services provided in IRFs that are, or are part of, teaching hospitals.

In previous rules, including the August 7, 2001 final rule, we noted that analyses of the data did not support a teaching adjustment. However, analysis of the more recent calendar year 2002 and fiscal year 2003 data supports a teaching status adjustment. For the first time, as discussed in detail in section III.B.3 of this proposed rule, the data analysis has demonstrated a statistically significant relationship between an IRF's teaching status and the costs of caring for patients in that IRF. We believe this may suggest the need to account for the higher costs associated with major teaching programs. For reasons discussed in detail in section III.B.3 of this proposed rule, we are proposing to implement the new teaching status adjustment in a budget neutral manner. However, we have some concerns about proposing a teaching status adjustment for IRFs at this time (as discussed in detail in section III.B.3 of this proposed rule). Because of these concerns, we are specifically soliciting comments on our consideration of an IRF teaching status adjustment.

  • Update the formulas used to compute the rural and the low-income patient (LIP) adjustments to IRF payments.

In the August 7, 2001 final rule, we implemented an adjustment to account for the higher costs in rural IRFs by multiplying their payments by 1.1914. As discussed in detail in section III.B.4 of this proposed rule, the regression analysis RAND performed on fiscal year 2003 data suggests that this rural adjustment should be updated to 1.241 to account for the differences in costs between rural and urban IRFs.

Similarly, in the August 7, 2001 final rule, we implemented an adjustment to payments to reflect facilities' low-income patient percentage calculated as (1+ the disproportionate share hospital (DSH) patient percentage) raised to the power of 0.4838. As discussed in detail in section III.B.5 of this proposed rule, the regression analysis RAND performed on fiscal year 2003 data indicates that the LIP adjustment should now be calculated as (1 + DSH patient percentage) raised to the power of 0.636. For reasons discussed in detail in section III.B.5 of this proposed rule, we are proposing to implement the changes to these adjustments in a budget neutral manner.

  • Update the outlier threshold amount from $11,211 (FY 2005) to $4,911 (FY 2006) to maintain total estimated outlier payments at 3 percent of total estimated payments. Start Printed Page 30193

In the August 7, 2001 final rule, we describe the process by which we calculate the outlier threshold, which involves simulating payments and then determining a threshold that would result in outlier payments being equal to 3 percent of total payments under the simulation. As discussed in detail in section III.B.6 of this proposed rule, we believe based on RAND's regression analysis that all of the other proposed updates to the IRF PPS, including the structure of the CMGs and the tiers, the relative weights, and the facility-level adjustments (such as the rural adjustment, the LIP adjustment, and the proposed teaching status adjustment) make it necessary to propose to adjust the outlier threshold amount.

II. Proposed Refinements to the Patient Classification System

[If you choose to comment on issues in this section, please include the caption “Proposed Refinements to the Patient Classification System” at the beginning of your comments.]

A. Proposed 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. Currently, we use 95 CMGs as well as 5 special CMGs for scenarios involving short stays or the expiration of the patient.

c. Comorbidities

Under the statutory authority of section 1886(j)(2)(C)(i) of the Act, we are proposing 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 Start Printed Page 30194classification 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 proposed rule, 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 fewer 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. 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.

B. Proposed Changes to the Existing List of Tier Comorbidities

1. Proposed Changes to Remove Codes That Are Not Positively Related to Treatment Costs

While our methodology for this proposed rule for determining the tiers remains unchanged from the August 7, 2001 final rule, 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, we are proposing 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 Start Printed Page 30195additional two codes were also problematic. According to RAND, code 410.91 (AMI, NOS, Initial) was too unspecific to be differentiated from other related codes and code 260, Kwashiorkor, was found to be unrealistically represented in the data according to a 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 not 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.X, 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. Since we are proposing to remove “AMI, NOS, initial” from the tier list because it is not positively related to treatment cost after controlling for the CMG, we believe that “Specific AMI, initial” similarly should be removed from the tier list since it is indistinguishable from “AMI, NOS, initial.”

With respect to the last code in Table One (Kwashiorkor), we are proposing to remove 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 removing these two malnutrition 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, we are proposing their removal.

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.

The remaining proposed changes to our initial list of diagnoses in Table 1 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. Finally, DX V55.0, “attention to tracheostomy” should initially have been 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.

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 proposing to remove these codes from the tier list.

Table 1.—Proposed List of Codes To Be Removed From the Tier List

ICD-9-CM codeAbbreviated code titleCondition
235.1Unc behav neo oral/pharMiscellaneous throat conditions.
933.1Foreign body in larynxMiscellaneous throat conditions.
934.1Foreign body bronchusMiscellaneous throat conditions.
530.0Achalasia & cardiospasmEsophegeal conditions.
530.3Esophageal strictureEsophegeal conditions.
530.6Acquired esophag diverticulumEsophegeal conditions.
V46.1Dependence on respiratorVentilator status.
799.4CachexiaCachexia.
V49.75Status amputation below kneeAmputation of LE.
V49.76Status amputation above kneeAmputation of LE.
V497.7Status amputation hipAmputation of LE.
356.4Idiopathic progressive polyneuropathyMeningitis and encephalitis.
250.90Diabetes II, w unspecified complications, not stated as uncontrolledNon-renal Complications of Diabetes.
250.93Diabetes I, w unspecified complications, uncontrolledNon-renal Complications of Diabetes.
261Nutritional MarasmusMalnutrition.
262Other severe protein calorie deficiencyMalnutrition.
410.91AMI, NOS, initialMajor comorbidities.
410.X1Specific AMI, initialMajor comorbidities.
260KwashiorkorMalnutrition.

2. Proposed Changes To Move Dialysis To Tier One

We are proposing the movement of dialysis to tier one, which is the tier associated with the highest payment. The data from the RAND analysis show that patients on dialysis cost substantially more than current payments for these patients 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 Start Printed Page 30196(because providers were previously permitted to bill for dialysis separately). Dialysis is currently in tier two. However, 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 one short time, 170 percent more patients need dialysis than they did 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 will better and more carefully code comorbidities whose presence will result in higher PPS payments.

Moving dialysis patients to tier one will more adequately compensate hospitals for the extra cost of those patients and thereby maintain or increase access to these services.

3. Proposed Changes To Move Comorbidity Codes Based on Their Marginal Cost

Under statutory authority section 1886(j)(2)(C)(i) of the Act, we are proposing to move comorbidity codes based on their marginal cost. Another limitation with the existing tiers is that costs for several conditions would be more accurately predicted if their tier assignments were changed. After examining RAND's data, we believe that a full 4 percent of FY 2003 cases should be moved down to tiers with lower payment.

We propose that tier assignments be based on the results of statistical analyses RAND has performed under contract with CMS, using as independent variables only the proposed CMGs and conditions that we are proposing for tiers (for example, the CMGs and conditions that remain after the proposed changes have been made). We are proposing that the tier assignments of each of these conditions be decided 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 in the data in which tiers were defined (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 presence of a tier one comorbidity, the highest paid of the tiers, almost quadrupled during this same time period. Although, coding likely improved, the presence of upcoding for a higher payment may play a factor as well.

The 2003 data provide a more accurate explanation of the costs that are associated with each of the comorbidities, largely due to having 100 percent of the Medicare-covered IRF cases in the later data versus slightly more than half of the cases in 1999 data. Therefore, using the 2003 data to propose to assign each diagnosis or condition will considerably improve the matching of payments to their relative costs.

C. Proposed 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 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 are proposing to update the CMGs used to classify IRF patients for purposes of establishing payment amounts. We are also proposing to update the relative weights associated with the payment groups based on FY 2003 Medicare bill and patient assessment data. We are proposing to replace 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 are not proposing 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 have 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 recording instrument was used to collect patient data, the IRF Patient Assessment Instrument (or the IRF PAI). The new instrument contained questions 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. Now, we 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 below, should lead to new CMGs that better predict treatment costs in the IRF PPS.

Using FIM (the inpatient rehabilitation facility assessment instrument before the PPS) 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 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 climbing stairs, and 5 cognitive tasks, such as recall. 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 would be less than the costs of providing care for a particular case so an IRF may try to Start Printed Page 30197limit 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 is better than the 1999 data RAND originally used to construct the IRF PPS because it 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 IRF hospitals. The more recent data contain 523,338 IRF cases (fiscal year 2003), representing all Medicare-covered patients in participating IRF hospitals. The larger file enables RAND to obtain greater precision in the analysis and ensures a more balanced and complete picture of patients under the IRF PPS.

Also, the fiscal year 2003 data are better than the 1999 data used to design the IRF PPS because they 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. Proposed Changes for Updating the CMGs

As described in the August 7, 2001 final rule, RAND developed the original list of CMGs using FIM data from 1998 and 1999 to group patients into RICs. Table 2 below shows the final set of 95 CMGs based on the FIM-FRG methodology, the 5 special CMGs, and their descriptions. Impairment codes from the assessment instrument used by UDSmr and Healthsouth indicated the primary reasons for inpatient rehabilitation admissions. The impairment codes were used to group patients into RICs. Table 3 below shows each RIC and its associated impairment code.

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Given the availability of more recent, post-PPS data, we asked RAND to examine possible refinements to the CMGs to identify potential Start Printed Page 30206improvements 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.

RAND's analysis of the FY 2003 data, along with the support and guidance of the TEP, strongly suggest the need to update the CMGs to better align payments with costs under the IRF PPS. The other option we considered before deciding to propose 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) that updating both the relative weights and the CMGs will allow the classification system to do a much better job of reflecting changes in treatment patterns, technology, case mix, and other factors which may affect the relative use of resources.

We believe it is appropriate to update the CMGs and the relative weights at this time because the 2003 data we now have represent a substantial improvement over the 1999 data. The more recent data include all Medicare-covered IRF cases rather than a subset, allowing us to base the proposed 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 did not allow us to have a complete picture of the types of patients in IRFs. Also, the clinical coding of patient conditions in IRFs is vastly improved 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.

We are currently paying 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 a computer program can examine many more variations of a model in a much shorter time than an individual person. 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 proposed CMGs (the new algorithm would be based on the CART methodology described in detail earlier in this section of the proposed rule). RAND applied the new algorithm to the fiscal year 2003 IRF data. We are proposing to use RAND's new algorithm for refinements to the CMGs. The proposed algorithm would be 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 proposed new CART algorithm would produce all of the possible combinations of CMGs using all available variables. It would then select the variables and the CMG constructions that offer the best predictive ability, as measured by the greatest decrease in the mean-squared error. We propose that the following constraints be placed 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 would be the most substantial differences between the existing CMGs and the proposed new CMGs:

  • Fewer CMGs than before (87 compared with 95 in the current system).
  • 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 currently affects 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 currently affects assignment for CMGs in RICs 1 and 4.

In Table 2 above, we provided the CMGs that are currently being used to pay IRFs. Table 4 below shows the proposed new CMGs.

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Note:

CMG definitions use proposed weighted motor scores, as defined below.

The primary objective in updating the CMGs is to better align IRF payments with the costs of caring for IRF patients, given better, 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 the variance in the system.

2. Proposed Use of a Weighted Motor Score Index and Correction to the Treatment of Unobserved Transfer to Toilet Values

As described in detail below, we are proposing to use a weighted motor score index in assigning patients to CMGs, instead of the current motor score index that treats all components equally. We are also proposing to change the motor score value for the transfer to toilet variable to 2 rather than 1 when it is unobserved. However, we are not proposing changes to the cognitive score index. As described in detail below, we believe that a weighted motor score index, with the correction to the treatment of unobserved transfer to toilet values would improve the classification of patients into CMGs, which in turn would improve the accuracy of payments to IRFs.

In order 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, 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 a patient assessment instrument and scored on a scale of 1 to 7, with a 7 indicating complete independence in this area of functioning, and a 1 indicating that a patient is very impaired in this area of functioning.

As explained in the August 7, 2001 final rule (66 FR at 41349), the Start Printed Page 30211instructions for the IRF-PAI require that providers record an 8 for an item to indicate that the activity did not occur (or was not observed), as opposed to a 1 through 7 indicating that the activity occurred and the estimated level of function connected with that activity.

Please note that when the IRF-PAI form went through the approval process, the code 8 was removed and replaced with the code 0. Therefore, a 0 is now the code facilities use to record when an activity does not occur (or is not observed).

In order to determine the appropriate payment for patients for whom an activity is coded as 0 (that is, either not performed or not observed), 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), we decided to assign a code of 1 (indicating that the patient needed “maximal 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 maximal assistance performing the task. Thus, providers would receive the highest payment available for that item (although it might not be the highest payment overall, depending on the patient's CMG, other functional abilities, and/or comorbidities).

We are proposing 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 for which we are proposing this 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 propose 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 are proposing to make this change in order 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 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 proposing to use a weighted motor score index.

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). 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 are proposing a weighting methodology for the motor score index at this time. We propose 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 5 below shows the proposed optimal weights 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 5 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.

Table 5.—Proposed Optimal Weights, Averaged Across Rehabilitation Impairment Categories (RICs): Motor Items

Item typeFunctional independence itemAverage optimal weight
SelfDressing lower1.4
SelfToilet1.2
SelfBathing0.9
SelfEating0.6
SelfDressing upper0.2
SelfGrooming0.2
SphincterBladder0.5
SphincterBowel0.2
TransferTransfer to bed2.2
TransferTransfer to toilet1.4
TransferTransfer to tubNot included
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LocomotionWalking1.6
LocomotionStairs1.6

Based on RAND's analysis, we considered a number of different candidate indices before proposing a weighted index. We considered proposing to define 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 proposing to sum 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 proposing to assign 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 are proposing a weighting scheme that applies the average optimal weights. To develop the proposed weighting scheme, RAND used regression analysis to estimate the relative contribution of each item to the prediction of costs. Based on this analysis, we are proposing to use the weighting scheme indicated in Table 5 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 are proposing 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.

3. Proposed Changes for Updating the Relative Weights

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 are proposing 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 are not proposing any changes to the methodology we are using for calculating the relative weights, as described in the August 7, 2001 final rule (66 FR 41316, 41351 through 41353); we are only proposing to update the relative weights themselves.

As previously stated, we believe that improved coding of data, the availability of more complete data, proposed changes to the tier comorbidities and CMGs, and changes in IRF cost structures make it very unlikely that the relative weights assigned to the CMGs when the IRF PPS was first implemented still accurately represent the differences in costs across CMGs and across tiers. Therefore, we are proposing to recalculate the relative weights. However, we are not proposing any changes to the methodology for calculating the relative weights. Instead, we are proposing 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 described in detail at the beginning of this section of this proposed rule, applied to FY 2003 Medicare billing data. To summarize, we are proposing 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. Table 6 below shows the proposed relative weights, based on the 2003 data.

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We are proposing to make the tier and the CMG changes in such a way that total estimated aggregate payments to IRFs for FY 2006 are the same with and without the proposed 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 III.A of this proposed 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 resources in the IRF PPS are likely adequate to care for the types of patients IRFs treat. We are open to examining other evidence regarding the amount of aggregate payments in the system and the types of patients IRFs are currently treating.

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 in order to account for relative resource use. Consistent with that broad statutory authority, we are proposing to redistribute aggregate payments to more accurately reflect the IRF case mix.

To ensure that total estimated aggregate payments to IRFs do not change, we propose to apply a factor to the standard payment amount to ensure that estimated aggregate payments under this subsection in the FY are not greater or less than those that would Start Printed Page 30220have been made in the year without such adjustment. In section III.B.7 and section III.B.8 of this proposed rule, we discuss the methodology and factor we are proposing to apply to the standard payment amount.

III. Proposed FY 2006 Federal Prospective Payment Rates

(If you choose to comment on issues in this section, please include the caption “Proposed FY 2006 Federal Prospective Payment Rates” at the beginning of your comments.)

A. Proposed Reduction of the Standard Payment Amount 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 are proposing to reduce the standard payment amount for patients treated in IRFs by 1.9 percent. However, as discussed below, RAND found a range of possible estimates that likely accounts for the amount of case mix change that was due to coding. In light of the range of estimates that may be appropriate, we are continuing to work with RAND to further analyze the data and are considering adoption of an alternative percentage reduction. Accordingly, we solicit comments on whether the proposed 1.9 percent is the percentage reduction that ought to be made, or if another percentage reduction (for example, the 3.4 percent observed case mix change or the 5.8 percent that RAND found in its study, detailed below, to be the maximum amount of change due to coding) should be applied.

We are proposing 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 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). Based on the following discussion, we are proposing lowering the standard payment amount by 1.9 percent to account for coding changes that have increased payments to IRFs. However, we solicit comments regarding other possible percentage reductions within the range RAND identified, as discussed below.

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 this proposed 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. There were also changes to the IRF-PAI instructions given for coding some of the items on the patient assessment instrument, so that the same patient may have been correctly coded differently in 2002 than in 1999.

Furthermore, implementation of the IRF PPS may have 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 and comparing 2002 data (post-PPS) with 1999 data (pre-Start Printed Page 30221PPS), 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 we are confident 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. Although we attempted to identify all of the factors that cause the variation in costs among the IRFs' patient population, this may not have been possible given that the data are from the transitional year of the new PPS. 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 believe 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 proposed 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 Start Printed Page 30222based 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 are proposing 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.

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 potentially 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. However, we are continuing to work with RAND to further analyze the data and are soliciting comments on the following factors which may have an effect on the amount of the reduction. First, whether changes that occurred within the transitional IRF PPS rate year could have impacted coding and patient selection and affected these analyses. Second, since we feel it is crucial to maintain access to IRF care, we are soliciting comments on the effect of the proposed range of reductions on access to IRF care, particularly for patients with greater resource needs. 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 are proposing 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, would adequately adjust for the increased payments to IRFs caused by purely coding changes, but would 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 proposed 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 criteria for IRF classification commonly known as the “75 percent rule.” We are continuing to work with RAND to analyze the data and are soliciting comments on whether the proposed 1.9 percent is the percentage reduction that ought to be made, or if another percentage reduction (for example, the 3.4 percent observed case mix change or the 5.8 percent that RAND found to be maximum amount of change due to coding) should be applied.

To accomplish the proposed reduction of the standard payment conversion factor by 1.9 percent, we first propose to update the FY 2005 standard payment conversion factor by the estimated market basket of 3.1 percent to get the standard payment amount for FY 2006 ($12,958*1.031 = $13,360). Next, we propose to multiply the FY 2006 standard payment amount by 0.981, which reduces the standard payment amount by 1.9 percent ($13,360*0.981 = $13,106). In section III.B.7 of this proposed rule, we propose to further adjust the $13,106 by the proposed budget neutrality factors for the wage index and the other proposed refinements outlined in this proposed rule that would result in the proposed FY 2006 standard payment conversion factor. In section III.B.7 of this proposed rule, we provide a step-by-step calculation that results in the FY 2006 standard payment conversion factor.

B. Proposed Adjustments to Determine the Proposed FY 2006 Standard Payment Conversion Factor

1. Proposed 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, we propose 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. We propose to exclude 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 than 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 Start Printed Page 30223for 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 used to determine the operating and capital portions of the proposed FY 2002-based RPL market basket.

a. Overview of the Proposed RPL Market Basket

The proposed 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, 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 proposing to rebase and revise the market basket used to update the IRF PPS.

b. Proposed Methodology for Operating Portion of the Proposed RPL Market Basket

The operating portion of the proposed FY 2002-based RPL market basket 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 proposed FY 2002-based IPPS market basket (Section IV. Proposed Rebasing and Revision of the Hospital Market Baskets IPPS Hospital Proposed 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 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, we are proposing to limit 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.

We propose to use 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 propose 15 percent because it includes those LTCHs and IRFs whose Medicare LOS is within approximately 5 days of the facility length of stay.

We propose to 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 proposed 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 proposed 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. Proposed cost weights for benefits, contract labor, and blood and blood products were derived using the proposed FY 2002-based IPPS market basket. For example, the ratio of the benefit cost weight to the wages and salaries cost weight in the proposed 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. The remaining proposed 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, using this methodology roughly 59 percent of the proposed 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 7 below sets forth the complete proposed 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. Start Printed Page 30224

Wages and salaries are 52.895 percent of total costs for the proposed 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 proposed 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 proposed 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 proposed 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 proposed 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 used for the operating portion of the proposed market basket. The price proxies for the proposed capital portion are described in more detail in the capital methodology section. (See section III.B.1.c of this proposed rule.)

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Below we provide the proxies that we are proposing to use for the FY 2002-based RPL market basket. With the exception of the Professional Liability proxy, all the proposed price proxies for the operating portion of the proposed 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, Start Printed Page 30228we 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 proposed in this regulation meet these criteria.

We note that the proposed 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 of wages in the proposed FY 2002-based RPL market basket, we propose to use the ECI for wages and salaries for civilian hospital workers as the proxy for wages.

Employee Benefits

The proposed FY 2002-based RPL market basket would use the ECI for employee benefits for civilian hospital workers.

Nonmedical Professional Fees

The ECI for compensation for professional and technical workers in private industry would be 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) would be applied to this component.

Electricity

The percentage change in the price of commercial electric power as measured by the PPI (Commodity Code #0542) would be applied to this component.

Water and Sewage

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) would be applied to this component.

Professional Liability Insurance

The proposed FY 2002-based RPL market basket would use 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 proposed for the proxy in this proposed rule.

Pharmaceuticals

The percentage change in the price of prescription drugs as measured by the PPI (PPI Code # PPI32541DRX) would be 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) would be 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) would be applied to this component.

Chemicals

The percentage change in the price of industrial chemical products as measured by the PPI (Commodity Code #061) would be 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) would be applied to this component

Photographic Supplies

The percentage change in the price of photographic supplies as measured by the PPI (Commodity Code #1542) would be 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) would be 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) would be used.

Apparel

The percentage change in the price of apparel as measured by the PPI (Commodity Code #381) would be 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) would be 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) would be applied to this component. Using this index would remove 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 Start Printed Page 30229category 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. 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, we are proposing to use 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) would be 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) would be applied to this component.

Proposed Changes for All Other Services, Labor Intensive

The percentage change in the ECI for compensation paid to service workers employed in private industry would be 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) would be applied to this component.

c. Proposed Methodology for Capital Portion of the RPL Market Basket

Unlike for the operating costs of the proposed 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, we used 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 aged the data to a FY 2002 base year using relevant price proxies.

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. The resulting proposed 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. 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).

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.

We propose to derive 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, we propose to use 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, we propose to use a 75-25 split to allocate interest expenses to government/nonprofit and for-profit. See the Proposed IPPS Rule for FY 2006, Section IV.D, Capital Input Price Index Section.

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, we are proposing to use the vintage weights to compute vintage-weighted price changes associated with depreciation and interest expense.

Vintage weights are an integral part of the proposed 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 proposed FY 2002-based RPL market basket would reflect the annual price changes associated with capital costs, and would be 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 Start Printed Page 30230for Medicare capital-related costs. The capital component of the proposed FY 2002-based RPL market basket would reflect 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 needed 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 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, we propose to use FY 2001 Medicare cost reports for IPPS hospitals to determine the expected life of building and fixed equipment and movable equipment. 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.

Although we are proposing to use this methodology for deriving the useful life of an asset, we plan to review it between the publication of the proposed and final rules. We plan to review alternate data sources, if available, and analyze in more detail the hospital's capital cost structure reported in the Medicare cost reports.

We also propose to use 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.

For proposed 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 could be 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.

For proposed 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 could be 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 would be 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 would be used to determine the FY 2002 average movable equipment vintage weights.

For proposed 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 could be 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 would be calculated by dividing the nominal total capital purchase Start Printed Page 30231amount for any given year by the total amount of purchases in the 23-year period. This calculation would be 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 would be used to determine the FY 2002 average interest vintage weights. The vintage weights for the index are presented in Table 8 below.

In addition to the proposed price proxies for depreciation and interest costs described above in the vintage weighted capital section, we propose to use 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.

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The proposed FY 2006 update for IRF PPS using the proposed FY 2002-based RPL market basket and Global Insight's 4th quarter 2004 forecast is be 3.1 percent. 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 fourth quarter 2004 Start Printed Page 30233forecast for FY 2006 is also 3.1 percent. Table 4 below compares the proposed 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 proposed 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 proposed 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 proposed FY 2002-based RPL market basket than in the FY 1997-based index, and the slower price changes associated with these costs.

TABLE 9.—Proposed 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)Proposed rebased FY 2002-based RPL market basketFY 1997-based excluded hospital market basket with capital
Historical data:
FY 20003.13.1
FY 20014.04.0
FY 20023.93.6
FY 20033.83.7
FY 20043.63.6
Average FYs 2000-20043.73.6
Forecast:
FY 20053.73.8
FY 20063.13.1
FY 20072.92.8
FY 20082.92.8
Average FYs 2005-20083.23.1
Source: Global Insight, Inc. 4th Qtr 2004, @USMACRO/CNTL1104 @CISSIM/TL1104.SIM

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. We used the proposed FY 2002-based RPL market basket costs to determine the proposed labor-related share for the IRF PPS. The proposed labor-related share for FY 2006 would be the sum of the proposed FY 2006 relative importance of each labor-related cost category, and would reflect the different rates of price change for these cost categories between the base year (FY 2002) and FY 2006. The sum of the proposed relative importance for FY 2006 for operating costs (wages and salaries, employee benefits, professional fees, and labor-intensive services) would be 71.782 percent, as shown in the chart below. The portion of capital that is influenced by local labor markets would estimated to be 46 percent, which is the same percentage currently used in the IRF prospective payment system. Since the relative importance for capital would be 9.079 percent of the proposed FY 2002-based RPL market basket in FY 2006, we are proposing to take 46 percent of 9.079 percent to determine the proposed capital labor-related share for FY 2006. The result would be 4.176 percent, which we propose to add to 71.782 percent for the operating cost amount to determine the total proposed labor-related share for FY 2006. Thus, the labor-related share that we propose to use for IRF PPS in FY 2006 would be 75.958 percent. This proposed labor-related share is determined using the same methodology as employed in calculating all previous IRF labor-related shares (66 FR at 41357).

Table 10 below shows the proposed FY 2006 relative importance labor-related share using the proposed 2002-based RPL market basket and the FY 1997-based excluded hospital with capital market. Start Printed Page 30234

Table 10.—Proposed Total Labor-Related Share

Cost categoryProposed FY 2002-based RPL market basket relative importance (percent) FY 2006FY 1997 excluded hospital with capital market basket relative importance (percent) FY 2006
Wages and salaries52.82348.432
Employee benefits13.86311.415
Professional fees2.9074.540
All other labor intensive services2.1894.496
Subtotal71.78268.883
Labor-related share of capital costs4.1763.307
Total75.95872.190

We are currently continuing an evaluation of our labor-related share methodology used in the IPPS (see 67 FR at 31447 for discussion of our previous analysis). Our evaluation includes regression analysis and reviewing the makeup of cost categories based on our current labor-related definition. A complete discussion of our research is provided in the FY 2006 IPPS proposed rule (See FY 2006 IPPS proposed rule, Section IV, B, 3). The labor-related share used in the IPPS was the first labor-related share used in a prospective payment system. Our methodology for calculating the proposed labor-related share for the IRF PPS is based upon the methodology used in the IPPS.

2. Proposed 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 would have been made in the year without such adjustment.

In our August 1, 2003 final rule, 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 below, we are proposing to use 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).

a. Proposed Revisions of 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 and without applying the “rural floor” under section 4410 of Pub. L. 105-33 (BBA). 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 “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.

Below, we will provide a description of the current 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, although we have published each area's wage index in tables, in the IRF PPS final rules and Start Printed Page 30235update notices, each year and noted the use of the geographic area in applying the wage index adjustment in IRF PPS payment examples in the final regulation implementing the IRF PPS (69 FR at 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) would be used under the IRF PPS. The OMB has adopted new statistical area definitions (as discussed in greater detail below) and we are proposing to adopt new labor market area definitions based on these areas under the IRF PPS (as discussed in greater detail below). Therefore, we believe it is helpful to provide a more detailed description of the current IRF PPS labor market areas, in order to better understand the proposed change to the IRF PPS labor market areas presented below in this proposed rule.

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, which are discussed in greater detail below, are currently used 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 were effective October 1, 2004 for acute care hospitals. These new CBSAs 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 have 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). To this end, we have 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 further discussed in section III.B.2.d.ii of this proposed 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. Proposed 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 nonstatistical 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 nonstatistical 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 Start Printed Page 30236the same general areas. In addition, the most recent CBSAs reflect the most up to date information. The OMB reviews its MA definitions preceding each decennial census to reflect recent population changes and the CBSAs are based on the Census 2000 data. Our analysis and discussion here are focused on issues related to adopting the new CBSA designations to define labor market areas for the purposes of the IRF PPS.

Historically, Medicare PPSs have utilized Metropolitan Area (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 are proposing 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 are proposing to revise the labor market area definitions used under the IRF PPS. Specifically, we are proposing to 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 are proposing to 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 would 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 a conforming change, we are also proposing to revise § 412.602, definitions for rural and urban areas effective for discharges occurring on or after October 1, 2005 would be defined in § 412.64(b)(1)(ii)(A) through (C). To further clarify, 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 designations) implemented under the IPPS at § 412.64(b), which were 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' 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 proposing to revise 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.

Below, we discuss the composition of the proposed 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.

In order to create consistency among all labor market areas and to maintain these areas on the basis of counties, we are proposing 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 would lead to inconsistencies in our designations. Adopting county-based labor market areas for the entire country provides consistency and stability in Medicare program payment because all of the labor market areas throughout the country, including New England, would be defined using the same system (that is, counties) rather than different systems in different areas of the country, and minimizes programmatic complexity.

In addition, 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, in this proposed rule, we are proposing to use the New England MSAs as determined under the proposed new CBSA-based labor market area definitions in defining the proposed revised IRF PPS labor market areas.

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 Start Printed Page 30237Metropolitan 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 would 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, would most accurately maintain our current structuring of the IRF PPS labor market areas. Therefore, as implemented under the IPPS (69 FR at 49029), we are proposing to use the Metropolitan Divisions where applicable (as describe below) under the proposed 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 proposing to use Metropolitan Divisions where applicable (as described below) under the IRF PPS would result in a more accurate adjustment for the variation in local labor market areas for IRFs. Specifically, if we would 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 would be too large and would include the data from too many hospitals to compute a wage index that would 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 would be relevant for all hospitals within the market area designation. Rather, by proposing to recognize Metropolitan Divisions where applicable (as described below) under the proposed 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 would 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.

Below we describe where Metropolitan Divisions would be applicable under the proposed new CBSA-based labor market area definitions under the IRF PPS.

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 proposed CBSA-based labor market area designations, it would be comprised of four Metropolitan Divisions. Los Angeles would go from four PMSAs under the current IRF PPS MSA designations to two Metropolitan Divisions under the proposed CBSA-based labor market area designations. The New York CMSA would go from 15 PMSAs under the current IRF PPS MSA designations to only four Metropolitan Divisions under the proposed CBSA-based labor market area designations. The five PMSAs in Connecticut under the current IRF PPS MSA designations would become separate MSAs under the proposed 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 would 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 proposed CBSA-based labor market area designations. In San Francisco, under the proposed 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 would no longer be designated as CMSAs under the proposed 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 would become separate, independent MSAs under the proposed CBSA-based labor market area designations, leaving only the MSA for the core area under the proposed CBSA-based labor market area designations.

iii. Micropolitan Areas

Under the new OMB's 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 would 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 would 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 III.B.2.b of this proposed 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 area 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.

We currently use MSAs to define urban labor market areas and group all the hospitals in counties within each Start Printed Page 30238State 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 are proposing 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) we are proposing that 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, 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 would 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 proposed 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 would 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 would 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 are proposing not to adopt Micropolitan Areas as independent labor market areas under the IRF PPS. Under the proposed new CBSA-based labor market area definitions, we are proposing that Micropolitan Areas be considered a part of the statewide rural labor market area. Accordingly, we are proposing that the IRF PPS statewide rural wage index be determined 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 proposed rule) from hospitals located in non-MSA areas and that the statewide rural wage index be assigned to IRFs located in those areas.

e. Implementation of the Proposed Changes To Revise the 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.

To facilitate an understanding of the proposed policies related to the proposed change to the IRF PPS labor market areas discussed above, in Table 3 of the Addendum of this proposed rule, we are providing a listing of each IRF's state and county location; existing MSA labor market area designation; and its proposed new CBSA designation based on county information from our online survey, certification, and reporting (OSCAR) database, and an Iowa Foundation for Medical Care (IFMC) report listing providers and their state and county location that submitted IRF-PAIs during the past 18 months (report request made in February 2005). We encourage IRFs to review the county location and both the current and proposed labor market area assignments for accuracy. Any questions or corrections (including additions or deletions) to the information provided in Table 3 of the Addendum should be emailed to the following CMS Web address: IRFPPSInfo@cms.hhs.gov. A link to this address can be found on the following CMS Web page http://www.cms.hhs.gov/​providers/​irfpps/​.

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 boundaries and 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 indices 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.

While we recognize that, just like IPPS hospitals, IRFs may experience decreases in their wage index as a result of the proposed labor market area changes, our data analysis showed that a majority of IRFs either expect no change in wage index or an increase in wage index based on CBSA definitions. Start Printed Page 30239In addition, a very small number of IRFs (3 percent) would 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 proposed labor market areas, we believe it is not necessary to propose a transition to the proposed new CBSA-based labor market area for the purposes of the IRF PPS wage index. The main purpose of a transition is to buffer hospitals that would be significantly impacted by a proposed policy. Since the impact of the proposed labor market areas upon IRFs would be minimal, the need to transition is absent. We recognize that there would be many alternatives to efficiently implement the proposed CBSA-based geographic designations. 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. Thus, we considered three budget neutral alternatives that could implement the adoption of the proposed CBSA-based designations as discussed below. Even though a majority of IRFs would not be significantly impacted by the proposed labor market areas, we wanted to be diligent and at least examine transition policies and the affect on the system. We needed to conduct the analysis to determine how IRFs fare under such a proposed policy.

One alternative we considered institutes a one-year transition with a blended wage index, equal to 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 providers. In this scenario, a blended wage index of 50 percent of the FY 2006 MSA-based wage index and 50 percent of the FY 2006 CBSA-based wage index was used because in the IPPS final rule (69 FR at 49033) a blended wage index employed 50 percent of the FY 2001 hospital wage index data and the old labor market definitions, and 50 percent of the wage index employing FY 2001 wage index data and the new labor market definitions. However, we found that while this would help some IRFs that are adversely affected by the changes to the MSAs, it would also reduce the wage index values (compared to fully adopting the CBSA wage index value) for IRFs that would be positively affected by the changes. Thus, the unadjusted payment rate for all providers would be slightly reduced. Therefore, a majority of the IRFs would not benefit if all providers are given a blended wage index in a budget neutral manner (such that estimated aggregate, overall payments to IRFs would not change under the proposed labor market area definitions).

A second alternative we considered consists of 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 (both based on the FY 2001 hospital wage data), only for providers that would experience a decrease due solely to the changes in the labor market definitions. In this second alternative, a blended wage index of 50 percent of the FY 2006 MSA wage index and 50 percent of the FY 2006 CBSA-based wage index was determined because in the IPPS final rule (69 FR at 49033) a blended wage index employed 50 percent of the FY 2001 hospital wage index data and the old labor market definitions, and 50 percent of the wage index employing FY 2001 wage index data and the new labor market definitions. Therefore, providers that would experience a decrease in their FY 2006 wage index under the CBSA-based definitions compared to the wage index they would have received under the MSA-based definitions (in both cases using FY 2001 hospital wage data) would receive a blended wage index as described above.

When we performed our analysis, we found that the unadjusted payment amounts decreased substantially more under this option than they did either by using the first option discussed above or by fully adopting the CBSA-based designations. As with the first alternative, the positive impact of blending in order decrease the impacts for a relatively small number of IRFs would require reduced payment rates for all providers, including the IRFs receiving a blended wage index.

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 would 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 would be substantially impacted by the significant change in wage index. Although we considered a hold harmless policy for IRFs that would be substantially impacted from the change in wage index due to the CBSA-based designation, we found that an extremely small number of IRFs (4.4 percent) would change designations. In addition, currently urban facilities that become rural under the CBSA-based definitions would receive the rural facility adjustment, which we are proposing to increase from 19.14 percent to 24.1 percent (discussed in further detail in section III.B.4 of this proposed rule). Thus, the impact on urban facilities that become rural would be mitigated by the rural adjustment.

We also found that 91 percent of rural facilities that would be designated as urban under the CBSA-based definitions would experience an increase in the wage index. Furthermore, a majority (74 percent) of rural facilities that become urban would experience at least a 5 percent to 10 percent or more increase in wage index. Thus, we do not believe it is appropriate or necessary to adopt a hold harmless policy for facilities that would experience a change in designation under the CBSA-based definitions.

Finally, we note that section 505 of the MMA established new section 1886(d)(13) of the Act. 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 will not be proposing such an adjustment for the IRF PPS.

We are not proposing a transition, a hold harmless policy, nor an “out-commuting” adjustment under the IRF PPS from the current MSA-based labor market areas designations to the new CBSA-based labor market area designations as discussed below. We are proposing to adopt the new CBSA-based labor market area definitions beginning with the 2006 IRF PPS fiscal year without a transition period, without a hold harmless policy, and without an “out-commuting” adjustment. We believe that this proposed policy is appropriate because despite significant similarities between the IRF PPS and the IPPS, there are clear distinctions between the payment systems, particularly regarding wage index issues.

The most significant distinction upon which we have based this proposed Start Printed Page 30240policy determination is that where acute care hospitals have been paid using full wage index adjusted payments since 1983 and have used the previous IPPS MSA-based labor market area designations for over 10 years, under the IRF PPS we have been using the excluded pre-reclassification and pre-floor MSA-based wage index for cost reporting periods beginning on or after January 1, 2002. Since the implementation of the IRF PPS has only used the MSA-based labor market area designations since 2002 of which the first year was a transition year, many IRFs received a blended payment that consisted of a percentage of TEFRA and a percentage of the IRF PPS rate (as described below). Since many IRFs were initially under the transition period whereby many IRFs received a blend of TEFRA payments and the adjusted Federal prospective payment rates in accordance with section 1886(j)(1) of the Act and as specified in § 412.626, IRFs may still be adjusting to the changes in wage index and thus has not established a long history of an expected wage index from year to year. We may reasonably expect that IRFs would not experience a substantial impact on their respective wage indices because under a relatively new IRF PPS, IRFs are adjusting to the change of being paid a Federal prospective payment rate. Our data analysis also shows that a minimal number of IRFs would experience a decrease of more than 5 percent in the wage index. A 5 percent decrease in the wage index for an IRF would possibly 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. In addition, under the CBSA designation, a small number of IRFs would experience a change from their current urban or rural designation. Therefore, the overall impact of IRFs under the MSA-based designations versus the CBSA-based designations did not result in a dramatic change overall.

Although the wage index has been a stable feature of the acute care hospital IPPS since its 1983 implementation and has utilized the prior MSA-based labor market area designation for over 10 years, this is not the case for the IRF PPS which has only been implemented for cost reporting periods beginning on or after January 1, 2002. Therefore, if the proposed CBSA-based labor market area designations were adopted they would have a negligible impact on IRFs because the adoption of the CBSA-based designations are proposed in a budget neutral manner (as discussed in detail in section IV of this proposed rule).

The impact of adopting the proposed CBSA-based wage index has shown in our impact analysis to have very little impact on the overall payment rates to the extent the proposed refinements to the overall system are also implemented (as discussed below). In addition, unlike other post-acute care payment systems, the IRF PPS payments apply a rural facility adjustment to account for higher costs in rural facilities (as discussed in 66 FR at 41359). We are proposing to increase the current rural adjustment from 19.14 percent to 24.1 percent (as discussed in section III.4 of this proposed rule). Therefore, IRFs that are designated as urban under the MSA-based definitions, but that would be classified as rural under the proposed CBSA-based definitions, will receive a facility add-on of 24.1 percent.

In sum, the IRF PPS has only been implemented for hospital cost reporting periods beginning on or after January 1, 2002 (which means that payment to IRFs have only been governed by the IRF PPS for slightly more than 3 years). In addition, a small number of IRFs would experience a change in rural or urban designations under the CBSA-based designations. To the extent the proposed changes in this rule are adopted, the change in labor market area for an urban facility to a rural facility is expected to be offset by the rural adjustment we are proposing to increase from 19.14 to 24.1 percent as discussed below. We also found that a majority of IRFs would experience no change in wage index or an increase. Thus, we are proposing to fully adopt the CBSA-based designations without a hold harmless policy. We believe that it is not appropriate or necessary to propose a transition to the proposed new CBSA-based labor market area for the purpose of the IRF PPS wage index adjustment as specified under § 412.624 as explained previously in this section. In addition, as explained above, we believe there are not sufficient data to support a transition from MSA-based designations to the proposed CBSA-based designations.

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. We are proposing to use FY 2001 acute care hospital wage data for FY 2006 IRF PPS payment rates because it is the most recent final data available. 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 should accurately capture the relationship of wages and wage-related costs of IRF in an area as comparable to the national average. 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 III.B.2.a of this proposed 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 propose 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 to which the acute care hospital data fully represent IRF wages or if a geographic reclassification adjustment under the IRF PPS is appropriate. However, we 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 propose to 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 Start Printed Page 302411886(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.

In proposing to adopt 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 this occurred in two States—Massachusetts and Puerto Rico—where, using the CBSA-based designations, there were no hospitals located in rural areas. At present, no IRFs are affected by this lack of data, because currently there are no rural IRFs in these two States. If, rural IRFs open in these two States, we propose, for FY 2006, to use the rural FY 2001 MSA-based hospital wage data for that State to determine the wage index of such IRFs. In other words, we would use the same wage data (the FY 2001 hospital wage data) used to calculate the FY 2006 IRF wage index. However, rather than using CBSA-based designations, we would use MSA-based designations to determine the rural wage index of the State. Using such MSA-based designations there would be rural wage indices for both Massachusetts and Puerto Rico. We believe this is the most reasonable approach, as we would be 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, we are proposing to 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 would 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 would 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 we propose to 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. In addition, for FY 2006 and thereafter, we propose to calculate a statewide urban average in the event that there exist urban IRFs in geographic areas with no corresponding hospital wage data. We solicit comments on these approaches to calculate the wage index values for areas without hospital wage data for this and subsequent fiscal years. We note that for fiscal years 2007 and thereafter, we likely will not calculate the MSA-based rural area indices, as the acute care hospital IPPS will no longer publish MSA-based wage tables. Thus, we specifically request comments on the approach to be used for IRFs in rural areas without corresponding hospital wage data for fiscal years 2007 and thereafter.

For the reasons discussed above, we are proposing to 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 III.B.2.a of this proposed rule). We believe that cost reporting period FY 2001 would be used to determine the applicable wage index values under the IRF PPS because these are the best available data. These data are the same FY 2001 acute care hospital inpatient wage data that were used to compute the FY 2005 wage indices. The proposed full wage index values that would be applicable for IRF PPS discharges occurring on or after October 1, 2005 are shown in Addendum 1, Tables 2a (for urban areas) and 2b (for rural areas) in the Addendum of this proposed rule.

In addition, any proposed adjustment or update to the IRF wage index made as specified under section 1886(j)(6) of the Act would 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 would have been made in the year without such adjustment. Therefore, we are proposing to calculate a budget-neutral wage adjustment factor as established in the July 30, 2004 notice and as specified in § 412.624(e)(1). We will continue to use the following steps to ensure that the proposed FY 2006 IRF standard payment conversion factor reflects the update to the proposed CBSA wage indices and to the proposed 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 proposed updated CBSA-based FY 2006 labor-related share and wage indices described above.

Step 3: Divide the amount calculated in step 1 by the amount calculated in step 2, which equals the proposed FY 2006 budget-neutral wage adjustment factor of 0.9996.

Step 4: Apply the proposed 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 proposed FY 2006 standard payment conversion factor.

3. Proposed Teaching Status Adjustment

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 this proposed rule, 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 are proposing to establish a facility level adjustment to the Federal per discharge base rate for IRFs that are, or are part of, Start Printed Page 30242teaching institutions for the reasons discussed below (the “teaching status adjustment”). However, as discussed below, we have some concerns about proposing a teaching status adjustment. The policy implications of implementing a teaching status adjustment on the basis of the results of RAND's recent analysis oblige us to seek assurance that these results do not reflect an aberration based on only a single year's data and that the teaching status adjustment can be implemented in such a way that it would be equitable to all IRFs. Analysis of future data (FY 2004 or later) would give us such assurance because it would allow the effects of the other proposed changes outlined in this proposed rule to be realized and allow us to determine whether the significant coefficient on the teaching variable continues to be present in the future data.

The purpose of the proposed teaching status adjustment would be to account for the higher indirect operating costs experienced by facilities that participate in graduate medical education programs.

We are proposing to implement the proposed teaching status adjustment in a budget neutral manner (that is, keeping aggregate payments for FY 2006 with the proposed teaching adjustment the same as aggregate payments for FY 2006 without the proposed teaching adjustment) for the reasons discussed below. (As a conforming change, we are proposing to revise § 412.624 to add 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 propose to adjust 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 would 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 would 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 proposed 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, 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 would propose 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). Any such 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 would propose 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 Start Printed Page 30243further below) to the IRF's average daily census (ADC). In RAND's most recent cost regressions using data from FY 2003, the logarithm of the teaching variable has a coefficient value of 1.083. We would propose 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—currently 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 is based on regression analysis holding all other components of the payment system constant. Because we are proposing a number of other revisions to the payment system in this proposed rule, the coefficient value is subject to change for the final rule depending on the other revisions included in the final rule. Moreover, we are concerned that IRFs' responses to other proposed changes described in this proposed rule will influence the effects of a teaching variable on IRFs' costs.

In addition, the teaching adjustment we would propose would 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 would propose 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 note 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 are proposing 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 would also propose 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 are proposing 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. We also want 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 would propose not to allow IRFs to aggregate the FTE resident caps used to compute the IRF PPS teaching adjustment through affiliation agreements.

We would propose 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.

The denominator that we would propose to use to calculate the teaching status adjustment under the IPF 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 would propose to implement a 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 Start Printed Page 30244payments during 2002 would have been 17 percent higher than IRFs' costs. We are open to examining other evidence regarding the amount of aggregate payments in the system.

Consideration of 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.

As mentioned above and discussed below, we have some concerns with implementing a teaching status adjustment for IRFs at this time. We are concerned about volatility in the data given the many changes to the IRF PPS that have been made in recent years and may be adopted in this rulemaking process. Other proposed payment policy changes have the potential to change the magnitude or even the effect of a teaching variable on costs once IRFs have fully responded to the other proposed policy changes in this proposed rule. We also believe it is important to ensure that the data accurately counts residents who provide services to IRF patients.

We note that the significant coefficient we found in the analysis of the FY 2003 data contrasts with the statistically insignificant coefficient we found in the analysis of the 1999 data used to construct the initial IRF PPS. Although we currently believe it may be appropriate to propose a teaching status adjustment for IRFs based on analysis of the FY 2003 data, we recognize that we may need to examine new data (that is, FY 2004 or later) to help us to reconcile these contradictory findings. We also believe the analysis of this new data could potentially lead us to conclude that a teaching status adjustment is not needed.

The results of RAND's analysis using FY 2003 data also show that certain refinements to the IRF case mix system (as discussed in section II of this proposed rule) would improve the system by more appropriately accounting for the variation in costs among different types of IRF patients. In this proposed rule, we propose numerous changes to the CMGs and tiers, and to the threshold amount used to determine whether cases qualify for outlier payments, in order to better align IRF payments with the costs of providing care to Medicare beneficiaries in IRFs. In addition, this proposed rule proposes substantial changes to the wage index (the adoption of CBSA market area definitions) and to the rural and the LIP adjustments. We believe that these proposed changes may have an impact on cost differences between teaching and non-teaching IRFs, and that we will be able to assess their impact on teaching and non-teaching IRFs only after the proposed changes have been implemented.

Furthermore, we believe it is important to ensure that the data accurately count residents who participate in managing the rehabilitation of IRF patients. We are particularly interested in ensuring that the FTE resident counts used for the proposed IRF teaching status adjustment do not duplicate resident counts used for purposes of the IPPS IME adjustment, and that hospitals do not have incentives to shift residents from the acute care hospital to the hospital's rehabilitation unit for purposes of computing the proposed IRF teaching adjustment. We are soliciting comments on the most valid and reliable method of counting residents for purposes of a proposed teaching status adjustment. We note that any changes we may make, based on our further investigation of this issue or on comments we receive on this proposed rule, to the methodology for counting residents could affect the magnitude of the proposed teaching adjustment or even whether the data continue to indicate that the proposed teaching status adjustment is appropriate.

In addition, we recognize that the proposed new teaching status adjustment, especially if implemented in a budget-neutral manner, is an important issue for all providers because it involves a redistribution of resources among facilities. That is, under the proposal, IRFs with teaching programs would receive additional payments, while IRFs without teaching programs would have their payments lowered to maintain total estimated payments for FY 2006 at the same level as without the proposed adjustment. For this reason, we believe caution is warranted in this case.

We are specifically soliciting comments on our consideration of the IRF teaching status adjustment.

4. Proposed Adjustment for Rural Location

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, 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 are proposing 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 are proposing this change because RAND's regression analysis, using the best available data we have (FY 2003), indicates that rural facilities now have 24.1 percent higher costs of caring for Medicare patients than urban facilities. We note that we propose 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 both when the PPS was first implemented and for the proposed update described in this proposed 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 note 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 are proposing 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 are proposing to make this proposed update to the rural adjustment in a budget neutral manner because we believe that the results of RAND's analysis of 2002 and 2003 IRF cost data (as discussed previously in this proposed 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. We are open to examining other evidence regarding the amount of estimated aggregate payments in the system.

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 Start Printed Page 30245facilities. To ensure that total estimated aggregate payments to IRFs do not change, we propose to apply a factor to the standard payment conversion factor to assure 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 III.B.7 and III.B.8 of this proposed rule, we discuss the methodology and factor we are proposing to apply to the standard payment amount.

5. Proposed Adjustment for Disproportionate Share of Low-Income Patients

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, 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 are proposing 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 current formula 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 propose to update the LIP adjustment from the power of 0.4838 to the power of 0.636. Therefore, the proposed formula to calculate the LIP adjustment would be as follows: (1 + DSH patient percentage) raised to the power of (.636) Where DSH patient percentage =

We note that we propose 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 propose 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 are proposing to implement the proposed update to the LIP 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 are proposing to make this proposed update to the LIP adjustment in a budget neutral manner because we believe that the results of RAND's analysis of 2002 and 2003 IRF cost data (as discussed previously in this proposed 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. We are open to examining other evidence regarding the amount of estimated aggregate payments in the system.

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 propose to apply a factor to the standard payment conversion factor to assure 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 III.B.7 and III.B.8 of this proposed rule, we discuss the methodology and factor we are proposing to apply to the standard payment amount.

6. Proposed Update to the Outlier Threshold 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, we are proposing 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 discuss our rationale for setting estimated outlier payments at 3 percent of total estimated payments (66 FR at 41362). We continue to propose to use 3 percent for the same reasons outlined in the August 7, 2001 final rule. We believe 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 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 would be redesignated as § 412.624(e)(5)) 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. Start Printed Page 30246

Under § 412.624(e)(4) (which would be redesignated as § 412.624(e)(5)), we 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 (we are proposing to make this $4,911, which is then adjusted for each IRF by the facility's wage adjustment, its LIP adjustment, its rural adjustment, and its teaching status adjustment, if applicable). We 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), we 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 propose 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 would be redesignated 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 are proposing for FY 2006 would be 1.52 based on CBSA-based geographic designations. We are proposing to base this upper threshold CCR on the CBSA-based geographic designations because the CBSAs are the geographic designations we are proposing to adopt for purposes of computing the proposed wage index adjustment to IRF payments for FY 2006. If, instead, we were to use the MSA geographic designations, the upper threshold CCR amount would likely be different than the 1.52 we are proposing above. In addition, this is an estimated threshold and is subject to change in the final rule based on more recent data.

In addition, we are proposing to update the national urban and rural CCRs for IRFs. Under § 412.624(e)(4) (which would be redesignated as § 412.624(e)(5)) and § 412.84(i)(3), we are proposing 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 the fiscal intermediary obtains accurate 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 would be used in each of the three situations cited above. Specifically, for FY 2006, we have estimated a proposed national CCR of 0.631 for rural IRFs and 0.518 for urban IRFs. For new facilities, we are proposing to 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.

In the August 7, 2001 final rule (66 FR at 41362 through 41363), we describe the process by which we calculate the outlier threshold. We continue to use this process for this proposed rule. We begin by simulating aggregate payments with and without an outlier policy, and applying an iterative process to determine a threshold that would result in outlier payments being equal to 3 percent of total simulated payments under the simulation. We note that the simulation analysis used to calculate the proposed $4,911 outlier threshold includes all of the proposed changes to the PPS discussed in this proposed rule, and is therefore subject to change in the final rule depending on the policies contained in the final rule. In addition, 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.

In this proposed rule, we are proposing to update the threshold amount to $4,911 so that outlier payments will 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.

Our analyses of cost report data from FY 1999 through FY 2002 (and projections for FY 2004 though 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, the outlier threshold would need to be lowered from $11,211 to $4,911 for FY 2006 in order that total estimated outlier payments would equal 3 percent of total estimated payments.

In addition, we are proposing 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 this proposed rule 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 this proposed rule, we are proposing to change many of these components of the IRF PPS. For the reasons discussed above, then, we believe it is appropriate to update the outlier loss threshold for FY 2006. 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.

7. Proposed Budget Neutrality Factor Methodology for Fiscal Year 2006

We are proposing to make a one-time revision (for FY 2006) to the methodology found in § 412.624(d) in Start Printed Page 30247order 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 are proposing to revise § 412.624(d) by adding a section § 412.624(d)(4) for fiscal year 2006. Specifically, we are proposing 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 this preamble 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. According to the revised methodology, we propose to apply the market basket increase factor (3.1 percent) to the standard payment conversion factor for FY 2005 ($12,958), which equals $13,360. Then, we propose 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 III.A of this proposed rule), which equals $13,106. We then propose to apply the budget neutral wage adjustment (as discussed in section III.B.2.f of this proposed rule) of 0.9996 to $13,106, which would result in a standard payment amount of $13,101. For FY 2006 only, we propose to change the methodology for computing the standard payment conversion factor by applying budget neutrality factors for the proposed changes to the tiers and CMGs, the rural adjustment, the LIP adjustment, and the proposed teaching status adjustment. The next section contains a detailed explanation of these proposed budget neutrality factors, including the steps for computing these factors and how they affect total estimated aggregate payments and payments to individual IRF providers. The factors we are proposing to apply (as discussed in the next section) are 0.9994 for the proposed tier and CMG changes, 0.9865 for the proposed teaching status adjustment, 0.9963 for the proposed change to the rural adjustment, and 0.9836 for the proposed change to the LIP adjustment. These factors are subject to change as we analyze more current data. We have combined these factors, by multiplying the four factors together, into one budget neutrality factor for all four of these proposed changes (0.9994 * 0.9865 * 0.9963 * 0.9836 = 0.9662). We apply this overall budget neutrality factor to $13,101, resulting in a standard payment conversion factor for FY 2006 of $12,658. Note that the FY 2006 standard payment conversion factor is lower than it was in FY 2005 because it needed to be reduced to ensure that estimated aggregate payments for FY 2006 would remain the same as they otherwise would have been without the proposed changes. If we did not proposed to decrease the standard payment conversion factor, each of the proposed changes 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 payments to a particular type of IRF would likely be affected by the proposed budget-neutral changes, please see Table 13 of this proposed rule.

The FY 2006 standard payment conversion factor would be applied to each CMG relative weight shown in Table 6, Proposed Relative Weights for Case-Mix Groups, to compute the proposed unadjusted IRF prospective payment rates for FY 2006 shown in Table 12. To further clarify, the proposed one-time budget neutrality factors described above will only be applied for FY 2006. In addition, if no further refinements are proposed for subsequent fiscal years, we will use the methodology as described in § 412.624(c)(3)(ii).

8. Description of the Methodology Used To Implement the Proposed 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 are proposing 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.

To ensure that total estimated aggregate payments to IRFs do not change with these proposed changes, we propose to apply a factor to the standard payment amount for each of the proposed changes to ensure that estimated aggregate payments in FY 2006 are not greater or less than those that would have been made in the year without the proposed changes. We propose 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 3.1 percent and reduced by 1.9 percent to account for coding changes (as discussed in section III.A of this proposed 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 (0.9996), as discussed in section III.B.2.f of this proposed 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 proposed new tier and CMG assignments (as discussed in section II) 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 (currently estimated to be 0.9994) that maintains the same total estimated aggregate payments in FY 2006 with and without the proposed 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 proposed change to the rural adjustment (as discussed in section III.B.4 of this proposed 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 (currently estimated to be 0.9963) that keeps total estimated payments in FY 2006 the Start Printed Page 30248same with and without the proposed 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 proposed change to the LIP adjustment (as discussed in section III.B.5 of this proposed 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 (currently estimated to be 0.9836) that maintains the same total estimated aggregate payments in FY 2006 with and without the proposed 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 payment for FY 2006.

Step 13: Apply the proposed teaching status adjustment (as discussed in section III.B.5 of this proposed 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 (currently estimated to be 0.9865) that maintains the same total estimated aggregate payments in FY 2006 with and without the proposed teaching status adjustment.

As discussed in section III.B.9 of this proposed rule, the proposed FY 2006 IRF PPS standard payment conversion factor that accounts for the proposed new tier and CMG assignments, the proposed changes to the rural and the LIP adjustments, and the proposed 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 0.9996, the proposed tier and CMG changes budget neutrality factor of 0.9994, the proposed rural adjustment budget neutrality factor of 0.9963, the proposed LIP adjustment budget neutrality factor of 0.9836, and the proposed teaching status adjustment budget neutrality factor of 0.9865.

Each of these proposed budget neutrality factors lowers the proposed standard payment amount. The budget neutrality factor for the proposed tier and CMG changes lowers the standard payment amount from $13,101 to $13,093. The budget neutrality factor for the proposed change to the rural adjustment lowers the standard payment amount from $13,093 to $13,045. The budget neutrality factor for the proposed change to the LIP adjustment lowers the standard payment amount from $13,045 to $12,831. Finally, the budget neutrality factor for the proposed teaching status adjustment lowers the standard payment amount from $12,831 to $12,658. As indicated previously, the standard payment conversion factor would need to be lowered in order to ensure that total estimated payments for FY 2006 with the proposed changes equal total estimated payments for FY 2006 without the proposed changes. This is because these four proposed changes would 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 proposing to redistribute payments among IRFs. Thus, some redistribution of payments occurs among facilities, while total estimated aggregate payments do not change. To determine how these proposed changes are estimated to affect payments among different types of facilities, please see Table 13 in this proposed rule.

9. Description of the Proposed 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 $11,838.00 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 ($11,838.00) 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).

We propose to use the revised methodology in accordance with § 412.624(c)(3)(ii)and as described in section III.B.7 of this proposed rule. To calculate the standard payment conversion factor for FY 2006, we are proposing to apply the market basket increase factor (3.1 percent) to the standard payment conversion factor for FY 2005 ($12,958), which equals $13,360. Then, we propose 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 $13,106. We then propose to apply the budget neutral wage adjustment of 0.9996 to $13,106, which would result in a standard payment amount of $13,101. Next, we propose to apply a one-time budget neutrality factor (for FY 2006 only) for the proposed 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 0.9662) to $13,101, which would result in a standard payment conversion factor for FY 2006 of $12,658. The FY 2006 standard payment conversion factor would be applied to each CMG weight Start Printed Page 30249shown in Table 6, Proposed Relative Weights for Case-Mix Groups, to compute the unadjusted IRF prospective payment rates for FY 2006 shown in Table 12.

10. Example of the Proposed Methodology for Adjusting the Federal Prospective Payment Rates

To illustrate the methodology that we propose to use to adjust the Federal prospective payments (as described in section III.B.7 and section III.B.8 of this proposed rule), we provide an example in Table 11 below.

One beneficiary is in Facility A, an IRF located in rural Montana, and another beneficiary is in Facility B, an IRF located in the New York City core-based statistical area. Facility A, a non-teaching hospital, has a disproportionate share hospital (DSH) adjustment of 5 percent, with a low-income patient adjustment of (1.0315), a wage index of (0.8701), and an applicable rural area adjustment (24.1 percent). Facility B, a teaching hospital, has a DSH of 15 percent, with a LIP adjustment of (1.0929), a wage index of (1.3311), and an applicable teaching status adjustment of (1.109).

Both Medicare beneficiaries are classified to CMG 0110 (without comorbidities). To calculate each IRF's total proposed adjusted Federal prospective payment, we compute the wage-adjusted Federal prospective payment and multiply the result by the appropriate low-income patient adjustment, the rural adjustment (if applicable), and the teaching hospital adjustment (if applicable). Table 11 illustrates the components of the proposed adjusted payment calculation.

Thus, the proposed adjusted payment for Facility A would be $31,671.57, and the adjusted payment for Facility B would be $41,637.65.

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IV. Provisions of the Proposed Regulations

(If you choose to comment on issues in this section, please include the caption “Provisions of the Proposed Regulations” at the beginning of your comments.)

We are proposing to make revisions to the regulation in order to implement the proposed prospective payment for IRFs for FY 2006 and subsequent fiscal years. Specifically, we are proposing 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 are proposing 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). Start Printed Page 30253

B. Section 412.622 Basis of payment

In this section, we are proposing to correct the cross references in paragraphs (b)(1) and (b)(2)(i). In paragraph (b)(1), we are proposing 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 are proposing 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 paragraph (d)(1), removing the cross reference to “paragraph (e)(4)” and adding in its place “paragraph (e)(5).”
  • Adding a new paragraph (d)(4).
  • Redesignating paragraphs (e)(4) and (e)(5) as paragraphs (e)(5) and (e)(6).
  • Adding a new paragraph (e)(4).
  • Revising newly redesignated paragraph (e)(5).
  • Revising newly redesignated paragraph (e)(6).
  • 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)(1), (e)(2), (e)(3), and (e)(4) of this section.”

D. Additional Changes

  • Reduce the standard payment conversion factor 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.
  • 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 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 proposed CMG and tier changes, the proposed teaching status adjustment, and the proposed updates to the rural and LIP adjustments in a budget neutral manner.

V. 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.

VI. Response to Comments

Because of the large number of public comments we normally receive on Federal Register documents, we are not able to acknowledge or respond to them individually. We will consider all comments we receive by the date and time specified in the DATES section of this preamble, and, when we proceed with a subsequent document, we will respond to the comments in the preamble to that document.

VII. Regulatory Impact Analysis

[If you choose to comment on issues in this section, please include the caption “Regulatory Impact Analysis” at the beginning of your comments.]

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 outlier payments. This proposed rule sets forth updates of the IRF PPS rates contained in the August 7, 2001 final rule and proposes policy changes with regard to the IRF PPS based on analyses conducted by RAND under contract with us on calendar year 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 proposed 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 16, 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 $180 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 proposing to make 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 proposing to improve 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 Start Printed Page 30254payments. To accomplish this redistribution of payments among facilities, we lower the base payment amount, which then gets adjusted upward for each facility according to the facility's characteristics. This proposed redistribution would not, however, affect aggregate payments to facilities. Thus, the proposed changes to the IRF labor-related share and the wage indices, the proposed changes to the CMGs, the tiers, and the motor score index, the proposed teaching status adjustment, the proposed update to the rural adjustment, and the proposed update to the LIP adjustment would have no overall effect on estimated costs to the Medicare program. Therefore, the estimated increased cost to the Medicare program is due to the updated IRF market basket of 3.1 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 proposed 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 2.9 percent increase), we estimate that the total impact of these proposed changes for FY 2006 payments compared to FY 2005 payments would be approximately a $180 million increase. This amount does not reflect changes in IRF admissions or case-mix intensity, which would also affect overall payment changes.

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 proposed regulation would 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 at 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 proposed 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 proposing to adopt, we would 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 proposed rule would 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 proposed rule that may result in an expenditure in any 1 year by State, local, or tribal governments, in the aggregate, or by the private sector, of at least $110 million. This proposed rule would not mandate any requirements for State, local, or tribal governments, nor would it affect private sector costs.

5. Executive Order 13132

Executive Order 13132 establishes certain requirements that an agency must meet when it promulgates a proposed rule that imposes substantial direct requirement costs on State and local governments, preempts State law, or otherwise has Federalism implications. We have reviewed this proposed rule in light of Executive Order 13132 and have determined that it would 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 proposed 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 proposed rule would have 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 Proposed Rule

We discuss below the impacts of this proposed rule on the budget and on IRFs.

1. Basis and Methodology of Estimates

In this proposed rule, we are proposing 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 2.9 percent increase), we estimate the total impact of these proposed changes for FY 2006 payments compared to FY 2005 payments to be approximately a $180 million increase. This amount does not reflect changes in hospital admissions or case-mix intensity, which would also affect overall payment changes.

We have prepared separate impact analyses of each of the proposed 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 proposing in this proposed rule.

The data used in developing the quantitative analyses of 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. Our analysis has several qualifications. First, we do not make adjustments for behavioral changes that hospitals may adopt in response to the proposed 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 proposed change. Start Printed Page 30255

Using cases in the FY 2003 MedPAR file, we simulated payments under the IRF PPS given various combinations of payment parameters.

The proposed changes discussed separately below are the following:

  • The effects of the proposed annual market basket update (using the proposed 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 proposed budget-neutral labor-related share and wage index adjustment, as required under section 1886(j)(6) of the Act.
  • The effects of the proposed decrease to the standard payment conversion factor 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 proposed 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 proposed adoption of new CBSAs based on the new geographic area definitions announced by OMB in June 2003.
  • The effects of the proposed implementation of a budget-neutral teaching status adjustment, as permitted under section 1886(j)(3)(A)(v) of the Act.
  • The effects of the proposed 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 proposed 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 proposed 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 payments based on the proposed FY 2006 policies relative to payments based on FY 2005 policies.

To illustrate the impacts of the proposed FY 2006 changes, our analysis begins with a FY 2005 baseline simulation model using: IRF charges 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 proposed policy change is then added incrementally to this baseline model, finally arriving at a FY 2006 model incorporating all of the proposed 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 proposed 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 payments per discharge from FY 2005 to FY 2006. One factor that affects the proposed changes in IRFs' 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, payments in FY 2006 would increase by an additional 1.8 percent over payments in FY 2005 because of the proposed 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.

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3. Impact of the Proposed Market Basket Update to the IRF PPS Payment Rates (Using the RPL Market Basket) (Column 6, Table 13)

In column 6 of Table 13, we present the effects of the proposed market basket update to the IRF PPS payment rates, as discussed in section III.B.1 of this proposed 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 III.B.1 of this proposed rule, we are proposing to use 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 rehabilitation hospital, psychiatric hospital, and long-term care hospital (RPL) market basket. The proposed FY 2006 update for IRF PPS payments using the proposed FY 2002-based RPL market basket and the Global Insight's 4th quarter 2004 forecast would be 3.1 percent.

In the aggregate, and across all hospital groups, the proposed update would result in a 3.1 percent increase in overall payments to IRFs.

4. Impact of Updating the Budget-Neutral Labor-Related Share and MSA-Based Wage Index Adjustment (Column 4, Table 14)

In column 4 of Table 14, we present the effects of a budget-neutral update to the labor-related share and the wage index adjustment (using the geographic area definitions developed by OMB before June 2003), as discussed in section III.B.2 of this proposed rule. Since we are not proposing to use the MSA labor market definitions, table 14 is for reference purposes only.

Section 1886(j)(6) of the Act requires us annually to adjust the proportion of rehabilitation facilities' costs that are attributable to wages and wage-related costs, of the prospective payment rates under the IRF PPS for area differences in wage levels by a factor reflecting the relative hospital wage level in the geographic area of the rehabilitation facility compared to the national average wage level for such facilities. This section of the Act also requires any such adjustments to be made in a budget-neutral manner.

In accordance with section 1886(j)(6) of the Act, we are proposing to update the labor-related share and adopt the wage index adjustment based on CBSA designations in a budget neutral manner. However, if we do not adopt the CBSA-based designations, this would not change aggregated payments to IRF as indicated in the first row of column 4 in Table 14. If we only update the MSA-based wage index and labor-related share, there would be small distributional effects among different categories of IRFs. For example, rural IRFs would experience a 1.0 percent decrease while urban facilities would experience a 0.1 percent increase in payments based on the RLP labor-related share and MSA-based wage index. Rural IRFs in the East South Central region would experience the largest decrease of 1.8 percent based on the proposed FY 2006 labor-related share and MSA-based wage index. Urban IRFs in the Pacific region would experience the largest increase in payments of 0.8 percent.

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5. Impact of the Proposed 1.9 Percent Decrease in the Standard Payment Amount to Account for Coding Changes (Column 11, Table 13)

In column 11 of Table 13, we present the effects of the proposed 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 III.A of this proposed 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 proposed update would result in a 1.9 percent decrease in overall payments to IRFs. Thus, we estimate that the 1.9 percent reduction in the standard payment amount would result in a cost savings to the Medicare program of approximately $120 million. Start Printed Page 30264

6. Impact of the Proposed Changes to the CMG Reclassifications and Recalibration of Relative Weights (Column 7, Table 13)

In column 7 of Table 13, we present the effects of the proposed changes to the tier comorbidities, the CMGs, the motor score index, and the proposed recalibration of the relative weights, as discussed in section II.A of this proposed 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 II.A.3 of this proposed rule, we are proposing to update the tier comorbidities to remove condition codes from the list that we believe no longer merit additional payments, move dialysis patients to tier one to increase payments for these patients, and to align payments with the comorbidity conditions according to their effects on the relative costliness of patients. We are also proposing to update 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 proposing to replace the current motor score index with a weighted motor score index that better estimates the relative costliness of IRF patients. Finally, we are proposing to change the coding of patients with missing information for the transfer to toilet item in the motor score index from 1 to 2.

To assess the impact of these proposed changes, we compared aggregate payments using the FY 2005 CMG relative weights (GROUPER version 1.22) to aggregate payments using the proposed 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 II.B.4 of this proposed rule, we have applied a budget neutrality factor to ensure that the overall payment impact of the proposed 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 proposed relative weights we would use for calculating the FY 2006 payment rates are slightly higher, on average, than the relative weights we are currently using, and that the effect of this would be to increase aggregate payments, the proposed 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 payments to IRFs. However, the distribution of payments among facilities is affected, with some facilities receiving higher payments and some facilities receiving lower payments as a result of the tier and CMG changes, as shown in column 7 of Table 13.

Although, in the aggregate, these proposed changes would not change overall 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 proposed 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 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.

7. Impact of the Proposed Changes to New Labor Market Areas (Column 4, Table 13)

In accordance with the broad discretion under section 1886(j)(6) of the Act, we currently define 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 III.B.2 of this proposed 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 proposing to adopt the new MSA definitions, consistent with the inpatient prospective payment system, including the 49 new Metropolitan areas designated under the new definitions. We are also proposing to adopt MSA definitions in New England in place of NECMAs. We are proposing not to adopt the newly defined Micropolitan Statistical Areas for use in the payment system, as Micropolitan Statistical Areas would remain part of the statewide rural areas for purposes of the IRF PPS payments, consistent with payments under the inpatient prospective payment system.

The effects of these proposed changes to the new CBSA-based designations 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 payments when going from a model using the current MSA designations to a model using the proposed new CBSA designations (for Metropolitan areas only).

Table 15 below compares the shifts in proposed wage index values for IRFs for FY 2006 relative to FY 2005. A small number of IRFs (1.6 percent) would experience an increase of between 5 and 10 percent and 1.5 percent of IRFs would experience an increase of more than 10 percent. A small number of IRFs (2.5 percent) would experience decreases in their wage index values of at least 5 percent, but less than 10 percent. Furthermore, IRFs that would experience decreases in their wage index values of greater than 10 percent would be 0.7 percent.

The following table shows the projected impact for IRFs.

Table 15.—Proposed Impact of the Proposed FY 2006 CBSA-based Area Wage Index

Percent change in area wage indexPercent of IRFs
Decrease Greater Than 10.00.7
Decrease Between 5.0 and 10.02.5
Decrease Between 2.0 and 5.05.7
Decrease Between 0 and 2.025.6
No Change37.2
Increase Between 0 and 2.022.1
Increase Between 2.0 and 5.03.3
Increase Between 5.0 and 10.01.6
Increase Greater Than 10.01.5
Total 1100.0
1 May not exactly equal 100 percent due to rounding.

8. Impact of the Proposed Adjustment to the Outlier Threshold Amount (Column 5, Table 13)

We estimate total outlier payments in FY 2005 to be approximately 1.2 percent of total estimated payments, so we are proposing to update the threshold from $11,211 in FY 2005 to $4,911 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 proposed 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 would be to increase payments by 3.9 percent, and the effect on payments to urban IRFs would be to increase payments by 1.6 percent. The largest effect would be a 9.5 percent increase in payments to rural IRFs in the Mountain region, and the smallest effect would be Start Printed Page 30265no change in payments for urban IRFs located in the East South Central region.

9. Impact of the Proposed Budget-Neutral Teaching Status Adjustment (Column 10, Table 13)

In column 10 of Table 13, we present the effects of the proposed budget-neutral implementation of a teaching status adjustment to the Federal prospective payment rate for IRFs that have teaching programs, as discussed in section III.B.3 of this proposed 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 proposing to apply a budget neutrality factor to ensure that the overall payment impact of the proposed teaching status adjustment is budget neutral (that is, in order that total estimated aggregate payments for FY 2006 with the proposed adjustment would equal total estimated aggregate payments for FY 2006 without the proposed adjustment). Because IRFs with teaching programs would receive additional payments from the implementation of this proposed new teaching status adjustment, the effect of the proposed budget neutrality factor would be to reduce the standard payment amount, therefore reducing payments to IRFs without teaching programs. By design, however, the increased payments to teaching facilities would balance the decreased payments to non-teaching facilities, and total estimated aggregate payments to all IRFs would remain unchanged. Therefore, the first row of column 10 of Table 13 indicates a zero impact in the aggregate. However, the rest of column 10 gives the distributional effects among different types of providers of this change. Some providers' payments increase and some decrease with this change.

On average, the impacts of this proposed change on any particular group of IRFs are very small, with urban IRFs experiencing a 0.1 percent increase and rural IRFs experiencing a 1.1 percent decrease. The largest impacts are a 2.0 percent increase among urban IRFs in the Middle Atlantic region and 1.2 percent decreases among rural IRFs in the Middle Atlantic, South Atlantic, and West South Central regions.

Overall, non-teaching hospitals would experience a 1.1 percent decrease. The largest impacts are a 24.3 percent 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 would experience an increase of 11 percent. Teaching facilities with resident and intern to ADC ratios less than 10 percent would experience an increase of 2.6 percent.

10. Impact of the Proposed Update to the Rural Adjustment (Column 8, Table 13)

In column 8 of Table 13, we present the effects of the proposed budget-neutral update to the percentage adjustment to the Federal prospective payment rates for IRFs located in rural areas, as discussed in section III.B.4 of this proposed 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 proposing to change the rural adjustment percentage, based on FY 2003 data, from 19.14 percent to 24.1 percent.

Because we are proposing to make this proposed 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, payments to urban facilities would decrease in proportion to the total increase in payments to rural facilities. To accomplish this redistribution of resources between urban and rural facilities, we propose to apply a budget neutrality factor to reduce the standard payment amount. Rural facilities would receive an increase in payments to this amount, and urban facilities would not. Overall, aggregate payments to IRFs would not change, as indicated by the zero impact in the first row of column 8. However, payments would 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 impacts of this proposed change on urban IRFs are relatively small, with all urban IRFs experiencing a 0.3 percent decrease. The impact on rural IRFs is somewhat larger, with rural IRFs experiencing a 3.4 percent increase. The largest impacts are a 3.6 percent increase among rural IRFs in the Middle Atlantic region.

11. Impact of the Proposed Update to the LIP Adjustment (Column 9, Table 13)

In column 9 of Table 13, we present the effects of the proposed 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 III.B.5 of this proposed 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 proposing to change the formula for the LIP adjustment, based on FY 2003 data, to raise the amount of 1 plus the DSH patient percentage to the power of 0.636 instead of the power of 0.4838. Therefore, the formula to calculate the low-income patient or LIP adjustment would be as follows:

(1 + DSH patient percentage) raised to the power of (.636) Where DSH patient percentage =

Because we are proposing to make this proposed update to the LIP adjustment in a budget neutral manner, payments would be redistributed among providers, according to their low-income percentages, but total estimated aggregate payments to facilities would not change. To do this, we propose to apply a budget neutrality factor that lowers the standard payment amount in proportion to the amount of payment increase that is attributable to the increased LIP adjustment payments. This would result in no change to aggregate payments, which is reflected in the zero impact shown in the first row of column 9 of Table 13. The remaining rows of the column show the Start Printed Page 30266impacts on different categories of providers. On average, the impacts of this proposed change on any particular group of IRFs are small, with urban IRFs experiencing no change in aggregate payments and rural IRFs experiencing a 0.1 percent decrease in aggregate payments. The largest impacts are a 1.2 percent increase among IRFs with 10 percent or higher intern and resident to ADC ratios and 0.9 percent decrease among rural IRFs in the Pacific region.

12. All Proposed Changes (Column 12, Table 13)

Column 12 of Table 13 compares our estimates of the proposed payments per discharge, incorporating all proposed changes reflected in this proposed rule for FY 2006, to our estimates of payments per discharge in FY 2005 (without these proposed changes). This column includes all of the proposed policy changes.

Column 12 reflects all FY 2006 proposed changes relative to FY 2005, shown in columns 4 though 11. The average increase for all IRFs is approximately 2.9 percent. This increase includes the effects of the proposed 3.1 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 proposed estimate of the percentage of outlier payments in FY 2006 (3 percent), as described in the introduction to the Addendum to this proposed 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 FY 2006 payments than would otherwise have occurred.

It also includes the impact of the proposed 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 propose to make the remainder of the proposed changes outlined in this proposed rule in a budget-neutral manner, they do not affect total IRF payments in the aggregate. However, as described in more detail in each section, they do affect the distribution of payments among providers.

There might also be interactive effects among the various proposed factors comprising the payment system that we are not able to isolate. For these reasons, the values in column 12 may not equal the sum of the proposed changes described above.

The proposed overall change in payments per discharge for IRFs in FY 2006 would increase by 2.9 percent, as reflected in column 12 of Table 13. IRFs in urban areas would experience a 2.6 percent increase in payments per discharge compared with FY 2005. IRFs in rural areas, meanwhile, would experience a 6.8 percent increase. Rehabilitation units in urban areas would experience a 5 percent increase in payments per discharge, while freestanding rehabilitation hospitals in urban areas would experience a 1.1 percent decrease in payments per discharge. Rehabilitation units in rural areas would experience a 6.5 percent increase in payments per discharge, while freestanding rehabilitation hospitals in rural areas would experience a 8.1 percent increase in payments per discharge.

Overall, the largest payment increase would be 32.1 percent among teaching IRFs with an intern and resident to ADC ratio greater than 19 percent and 15.8 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 proposed teaching status adjustment. Other than for teaching IRFs, the largest payment increase would be 12.3 percent among rural IRFs located in the Middle Atlantic region. This is due largely to the change in the proposed CBSA-based designation from urban to rural, whereby the number of cases in the rural Middle Atlantic Region that would receive the proposed new rural adjustment of 24.1 percent would increase. The only overall decreases in payments would occur among all urban freestanding IRFs and urban IRFs located in the New England, East South Central, and Mountain census regions. The largest of these overall payment decreases would be 1.3 percent among all urban freestanding hospitals. This is due largely to the proposed change in the CBSA-based designation from rural to urban. For non-profit IRFs, we found that rural non-profit facilities would receive the largest payment increase of 8 percent. Conversely, for-profit urban facilities would experience a 1.1 percent overall decrease.

13. Accounting Statement

As required by OMB Circular A-4 (available at http://www.whitehouse.gov/​omb/​circulars/​a004/​a-4.pdf), in Table 16 below, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this proposed rule. This table provides our best estimate of the increase in Medicare payments under the IRF PPS as a result of the proposed changes presented in this proposed 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 16.—Accounting Statement: Classification of Estimated Expenditures, From FY 2005 to FY 2006 (In millions)

CategoryTransfers
Annualized Monetized Transfers$180
From Whom To Whom?Federal Government To IRF Medicare Providers.
Start List of Subjects

List of Subjects in 42 CFR Part 412

End List of Subjects

For the reasons set forth in the preamble, the Centers for Medicare & Medicaid Services proposes to amend 42 CFR chapter IV as follows:

Start Part

PART 412—PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL SERVICES

1. The authority citation for part 412 continues to read as follows:

Start Authority

Authority: Secs. 1102 and 1871 of the Social Security Act (42 U.S.C. 1302 and 1395hh).

End Authority

Subpart P—Prospective Payment for Inpatient Rehabilitation Hospitals and Rehabilitation Units

2. Section 412.602 is amended by revising the definitions of “Rural area” and “Urban area” to read as follows:

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, Start Printed Page 30267urban area means an area as defined in § 412.64(b)(1)(ii)(A) and § 412.64(b)(1)(ii)(B).

[Amended]

3. 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”.

4. 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. Redesignating paragraphs (e)(4) and (e)(5) as paragraphs (e)(5) and (e)(6).

d. Adding a new paragraph (e)(4).

e. Revising newly redesignated paragraph (e)(5).

f. Revising newly redesignated paragraph (e)(6).

g. 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)(1), (e)(2), (e)(3), and (e)(4) of this section”.

The revisions and additions read as follows:

Methodology for calculating the Federal prospective payment rates.
* * * * *

(d) * * *

(4) Payment adjustment for Federal fiscal year 2006 and subsequent Federal fiscal years. CMS adjusts the standard payment conversion factor based on any updates to the adjustments specified in paragraph (e)(2), (e)(3), and (e)(4), of this section, and to any revision specified in § 412.620(c).

(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.

* * * * *

(Catalog of Federal Domestic Assistance Program No. 93.773, Medicare—Hospital Insurance; and Program No. 93.774, Medicare—Supplementary Medical Insurance Program)

Start Signature

Dated: April 14, 2005.

Mark B. McClellan,

Administrator, Centers for Medicare & Medicaid Services.

Approved: May 4, 2005.

Michael O. Leavitt,

Secretary.

End Signature

The following addendum will not appear in the Code of Federal Regulations.

Addendum

This addendum contains the tables referred to throughout the preamble to this proposed rule. The tables presented below are as follows:

Table 1A.—FY 2006 IRF PPS MSA Labor Market Area Designations for Urban Areas for the purposes of comparing Wage Index values with Table 2A.

Table 1B.—FY 2006 IRF PPS MSA Labor Market Area Designations for Rural Areas for the purposes of comparing Wage Index values with Table 2B.

Table 2A.—Proposed Inpatient Rehabilitation Facility (IRF) wage index for urban areas based on proposed CBSA labor market areas for discharges occurring on or after October 1, 2005.

Table 2B.—Proposed Inpatient Rehabilitation Facility (IRF) wage index based on proposed CBSA labor market areas for rural areas for discharges occurring on or after October 1, 2005.

Table 3—Inpatient Rehabilitation Facilities with Corresponding State and County Location; Current Labor Market Area Designation; and Proposed New CBSA-based Labor Market Area Designation.

Table 1A.—FY 2006 IRF PPS MSA Labor Market Area Designations for Urban Areas for the Purposes of Comparing Wage Index Values with Table 2a

MSAUrban area (Constituent Counties or County Equivalents)Wage index
0040Abilene, TX0.8009
Taylor, TX
0060Aguadilla, PR0.4294
Aguada, PR
Aguadilla, PR
Moca, PR
0080Akron, OH0.9055
Portage, OH
Summit, OH
0120Albany, GA1.1266
Dougherty, GA
Lee, GA
0160Albany-Schenectady-Troy, NY0.8570
Albany, NY
Montgomery, NY
Rensselaer, NY
Start Printed Page 30268
Saratoga, NY
Schenectady, NY
Schoharie, NY
0200Albuquerque, NM1.0485
Bernalillo, NM
Sandoval, NM
Valencia, NM
0220Alexandria, LA0.8171
Rapides, LA
0240Allentown-Bethlehem-Easton, PA0.9536
Carbon, PA
Lehigh, PA
Northampton, PA
0280Altoona, PA0.8462
Blair, PA
0320Amarillo, TX0.9178
Potter, TX
Randall, TX
0380Anchorage, AK1.2109
Anchorage, AK
0440Ann Arbor, MI1.0816
Lenawee, MI
Livingston, MI
Washtenaw, MI
0450Anniston,AL0.7881
Calhoun, AL
0460Appleton-Oshkosh-Neenah, WI0.9115
Calumet, WI
Outagamie, WI
Winnebago, WI
0470Arecibo, PR0.3757
Arecibo, PR
Camuy, PR
Hatillo, PR
0480Asheville, NC0.9501
Buncombe, NC
Madison, NC
0500Athens, GA1.0202
Clarke, GA
Madison, GA
Oconee, GA
0520Atlanta, GA0.9971
Barrow, GA
Bartow, GA
Carroll, GA
Cherokee, GA
Clayton, GA
Cobb, GA
Coweta, GA
De Kalb, GA
Douglas, GA
Fayette, GA
Forsyth, GA
Fulton, GA
Gwinnett, GA
Henry, GA
Newton, GA
Paulding, GA
Pickens, GA
Rockdale, GA
Spalding, GA
Walton, GA
0560Atlantic City-Cape May, NJ1.0907
Atlantic City, NJ
Cape May, NJ
0580Auburn-Opelika, AL0.8215
Lee, AL
0600Augusta-Aiken, GA-SC0.9208
Columbia, GA
McDuffie, GA
Start Printed Page 30269
Richmond, GA
Aiken, SC
Edgefield, SC
0640Austin-San Marcos, TX0.9595
Bastrop, TX
Caldwell, TX
Hays, TX
Travis, TX
Williamson, TX
0680Bakersfield, CA1.0036
Kern, CA
0720Baltimore, MD0.9907
Anne Arundel, MD
Baltimore, MD
Baltimore City, MD
Carroll, MD
Harford, MD
Howard, MD
Queen Annes, MD
0733Bangor, ME0.9955
Penobscot, ME
0743Barnstable-Yarmouth, MA1.2335
Barnstable, MA
0760Baton Rouge, LA0.8354
Ascension, LA
East Baton Rouge
Livingston, LA
West Baton Rouge, LA
0840Beaumont-Port Arthur, TX0.8616
Hardin, TX
Jefferson, TX
Orange, TX
0860Bellingham, WA1.1642
Whatcom, WA
0870Benton Harbor, MI0.8847
Berrien, MI
0875Bergen-Passaic, NJ1.1967
Bergen, NJ
Passaic, NJ
0880Billings, MT0.8961
Yellowstone, MT
0920Biloxi-Gulfport-Pascagoula, MS0.8649
Hancock, MS
Harrison, MS
Jackson, MS
0960Binghamton, NY0.8447
Broome, NY
Tioga, NY
1000Birmingham, AL0.9198
Blount, AL
Jefferson, AL
St. Clair, AL
Shelby, AL
1010Bismarck, ND0.7505
Burleigh, ND
Morton, ND
1020Bloomington, IN0.8587
Monroe, IN
1040Bloomington-Normal, IL0.9111
McLean, IL
1080Boise City, ID0.9352
Ada, ID
Canyon, ID
1123Boston-Worcester-Lawrence-Lowell-Brockton, MA-NH1.1290
Bristol, MA
Essex, MA
Middlesex, MA
Norfolk, MA
Plymouth, MA
Suffolk, MA
Start Printed Page 30270
Worcester, MA
Hillsborough, NH
Merrimack, NH
Rockingham, NH
Strafford, NH
1125Boulder-Longmont, CO1.0046
Boulder, CO
1145Brazoria, TX0.8524
Brazoria, TX
1150Bremerton, WA1.0614
Kitsap, WA
1240Brownsville-Harlingen-San Benito, TX1.0125
Cameron, TX
1260Bryan-College Station, TX0.9243
Brazos, TX
1280Buffalo-Niagara Falls, NY0.9339
Erie, NY
Niagara, NY
1303Burlington, VT0.9322
Chittenden, VT
Franklin, VT
Grand Isle, VT
1310Caguas, PR0.4061
Caguas, PR
Cayey, PR
Cidra, PR
Gurabo, PR
San Lorenzo, PR
1320Canton-Massillon, OH0.8895
Carroll, OH
Stark, OH
1350Casper, WY0.9243
Natrona, WY
1360Cedar Rapids, IA0.8975
Linn, IA
1400Champaign-Urbana, IL0.9527
Champaign, IL
1440Charleston-North Charleston, SC0.9420
Berkeley, SC
Charleston, SC
Dorchester, SC
1480Charleston, WV0.8876
Kanawha, WV
Putnam, WV
1520Charlotte-Gastonia-Rock Hill, NC-SC0.9711
Cabarrus, NC
Gaston, NC
Lincoln, NC
Mecklenburg, NC
Rowan, NC
Union, NC
York, SC
1540Charlottesville, VA1.0294
Albemarle, VA
Charlottesville City, VA
Fluvanna, VA
Greene, VA
1560Chattanooga, TN-GA0.9207
Catoosa, GA
Dade, GA
Walker, GA
Hamilton, TN
Marion, TN
1580Cheyenne, WY0.8980
Laramie, WY
1600Chicago, IL1.0851
Cook, IL
De Kalb, IL
Du Page, IL
Grundy, IL
Start Printed Page 30271
Kane, IL
Kendall, IL
Lake, IL
McHenry, IL
Will, IL
1620Chico-Paradise, CA1.0542
Butte, CA
1640Cincinnati, OH-KY-IN0.9595
Dearborn, IN
Ohio, IN
Boone, KY
Campbell, KY
Gallatin, KY
Grant, KY
Kenton, KY
Pendleton, KY
Brown, OH
Clermont, OH
Hamilton, OH
Warren, OH
1660Clarksville-Hopkinsville, TN-KY0.8022
Christian, KY
Montgomery, TN
1680Cleveland-Lorain-Elyria, OH0.9626
Ashtabula, OH
Geauga, OH
Cuyahoga, OH
Lake, OH
Lorain, OH
Medina, OH
1720Colorado Springs, CO0.9792
El Paso, CO
1740Columbia MO0.8396
Boone, MO
1760Columbia, SC0.9450
Lexington, SC
Richland, SC
1800Columbus, GA-AL0.8690
Russell, AL
Chattanoochee, GA
Harris, GA
Muscogee, GA
1840Columbus, OH0.9753
Delaware, OH
Fairfield, OH
Franklin, OH
Licking, OH
Madison, OH
Pickaway, OH
1880Corpus Christi, TX0.8647
Nueces, TX
San Patricio, TX
1890Corvallis, OR1.0545
Benton, OR
1900Cumberland, MD-WV0.8662
Allegany MD
Mineral WV
1920Dallas, TX1.0054
Collin, TX
Dallas, TX
Denton, TX
Ellis, TX
Henderson, TX
Hunt, TX
Kaufman, TX
Rockwall, TX
1950Danville, VA0.8643
Danville City, VA
Pittsylvania, VA
1960Davenport-Moline-Rock Island, IA-IL0.8773
Start Printed Page 30272
Scott, IA
Henry, IL
Rock Island, IL
2000Dayton-Springfield, OH0.9231
Clark, OH
Greene, OH
Miami, OH
Montgomery, OH
2020Daytona Beach, FL0.8900
Flagler, FL
Volusia, FL
2030Decatur, AL0.8894
Lawrence, AL
Morgan, AL
2040Decatur, IL0.8122
Macon, IL
2080Denver, CO1.0904
Adams, CO
Arapahoe, CO
Broomfield, CO
Denver, CO
Douglas, CO
Jefferson, CO
2120Des Moines, IA0.9266
Dallas, IA
Polk, IA
Warren, IA
2160Detroit, MI1.0227
Lapeer, MI
Macomb, MI
Monroe, MI
Oakland, MI
St. Clair, MI
Wayne, MI
2180Dothan, AL0.7596
Dale, AL
Houston, AL
2190Dover, DE0.9825
Kent, DE
2200Dubuque, IA0.8748
Dubuque, IA
2240Duluth-Superior, MN-WI1.0356
St. Louis, MN
Douglas, WI
2281Dutchess County, NY1.1657
Dutchess, NY
2290Eau Claire, WI0.9139
Chippewa, WI
Eau Claire, WI
2320El Paso, TX0.9181
El Paso, TX
2330Elkhart-Goshen, IN0.9278
Elkhart, IN
2335Elmira, NY0.8445
Chemung, NY
2340Enid, OK0.9001
Garfield, OK
2360Erie, PA0.8699
Erie, PA
2400Eugene-Springfield, OR1.0940
Lane, OR
2440Evansville-Henderson, IN-KY0.8395
Posey, IN
Vanderburgh, IN
Warrick, IN
Henderson, KY
2520Fargo-Moorhead, ND-MN0.9114
Clay, MN
Cass, ND
2560Fayetteville, NC0.9363
Start Printed Page 30273
Cumberland, NC
2580Fayetteville-Springdale-Rogers, AR0.8636
Benton, AR
Washington, AR
2620Flagstaff, AZ-UT1.0611
Coconino, AZ
Kane, UT
2640Flint, MI1.1178
Genesee, MI
2650Florence, AL0.7883
Colbert, AL
Lauderdale, AL
2655Florence, SC0.8960
Florence, SC
2670Fort Collins-Loveland, CO1.0218
Larimer, CO
2680Ft. Lauderdale, FL1.0165
Broward, FL
2700Fort Myers-Cape Coral, FL0.9371
Lee, FL
2710Fort Pierce-Port St. Lucie, FL1.0046
Martin, FL
St. Lucie, FL
2720Fort Smith, AR-OK0.8303
Crawford, AR
Sebastian, AR
Sequoyah, OK
2750Fort Walton Beach, FL0.8786
Okaloosa, FL
2760Fort Wayne, IN0.9737
Adams, IN
Allen, IN
De Kalb, IN
Huntington, IN
Wells, IN
Whitley, IN
2800Forth Worth-Arlington, TX0.9520
Hood, TX
Johnson, TX
Parker, TX
Tarrant, TX
2840Fresno, CA1.0407
Fresno, CA
Madera, CA
2880Gadsden, AL0.8049
Etowah, AL
2900Gainesville, FL0.9459
Alachua, FL
2920Galveston-Texas City, TX0.9403
Galveston, TX
2960Gary, IN0.9342
Lake, IN
Porter, IN
2975Glens Falls, NY0.8467
Warren, NY
Washington, NY
2980Goldsboro, NC0.8778
Wayne, NC
2985Grand Forks, ND-MN0.9091
Polk, MN
Grand Forks, ND
2995Grand Junction, CO0.9900
Mesa, CO
3000Grand Rapids-Muskegon-Holland, MI0.9519
Allegan, MI
Kent, MI
Muskegon, MI
Ottawa, MI
3040Great Falls, MT0.8810
Cascade, MT
Start Printed Page 30274
3060Greeley, CO0.9444
Weld, CO
3080Green Bay, WI0.9586
Brown, WI
3120Greensboro-Winston-Salem-High Point, NC0.9312
Alamance, NC
Davidson, NC
Davie, NC
Forsyth, NC
Guilford, NC
Randolph, NC
Stokes, NC
Yadkin, NC
3150Greenville, NC0.9183
Pitt, NC
3160Greenville-Spartanburg-Anderson, SC0.9400
Anderson, SC
Cherokee, SC
Greenville, SC
Pickens, SC
Spartanburg, SC
3180Hagerstown, MD0.9940
Washington, MD
3200Hamilton-Middletown, OH0.9066
Butler, OH
3240Harrisburg-Lebanon-Carlisle, PA0.9286
Cumberland, PA
Dauphin, PA
Lebanon, PA
Perry, PA
3283Hartford, CT1.1054
Hartford, CT
Litchfield, CT
Middlesex, CT
Tolland, CT
3285Hattiesburg, MS0.7362
Forrest, MS
Lamar, MS
3290Hickory-Morganton-Lenoir, NC0.9502
Alexander, NC
Burke, NC
Caldwell, NC
Catawba, NC
3320Honolulu, HI1.1013
Honolulu, HI
3350Houma, LA0.7721
Lafourche, LA
Terrebonne, LA
3360Houston, TX1.0117
Chambers, TX
Fort Bend, TX
Harris, TX
Liberty, TX
Montgomery, TX
Waller, TX
3400Huntington-Ashland, WV-KY-OH0.9564
Boyd, KY
Carter, KY
Greenup, KY
Lawrence, OH
Cabell, WV
Wayne, WV
3440Huntsville, AL0.8851
Limestone, AL
Madison, AL
3480Indianapolis, IN1.0039
Boone, IN
Hamilton, IN
Hancock, IN
Hendricks, IN
Start Printed Page 30275
Johnson, IN
Madison, IN
Marion, IN
Morgan, IN
Shelby, IN
3500Iowa City, IA0.9654
Johnson, IA
3520Jackson, MI0.9146
Jackson, MI
3560Jackson, MS0.8406
Hinds, MS
Madison, MS
Rankin, MS
3580Jackson, TN0.8900
Chester, TN
Madison, TN
3600Jacksonville, FL0.9548
Clay, FL
Duval, FL
Nassau, FL
St. Johns, FL
3605Jacksonville, NC0.8401
Onslow, NC
3610Jamestown, NY0.7589
Chautaqua, NY
3620Janesville-Beloit, WI0.9583
Rock, WI
3640Jersey City, NJ1.0923
Hudson, NJ
3660Johnson City-Kingsport-Bristol, TN-VA0.8202
Carter, TN
Hawkins, TN
Sullivan, TN
Unicoi, TN
Washington, TN
Bristol City, VA
Scott, VA
Washington, VA
3680Johnstown, PA0.7980
Cambria, PA
Somerset, PA
3700Jonesboro, AR0.8144
Craighead, AR
3710Joplin, MO0.8721
Jasper, MO
Newton, MO
3720Kalamazoo-Battlecreek, MI1.0350
Calhoun, MI
Kalamazoo, MI
Van Buren, MI
3740Kankakee, IL1.0603
Kankakee, IL
3760Kansas City, KS-MO0.9641
Johnson, KS
Leavenworth, KS
Miami, KS
Wyandotte, KS
Cass, MO
Clay, MO
Clinton, MO
Jackson, MO
Lafayette, MO
Platte, MO
Ray, MO
3800Kenosha, WI0.9772
Kenosha, WI
3810Killeen-Temple, TX0.9242
Bell, TX
Coryell, TX
3840Knoxville, TN0.8508
Start Printed Page 30276
Anderson, TN
Blount, TN
Knox, TN
Loudon, TN
Sevier, TN
Union, TN
3850Kokomo, IN0.8986
Howard, IN
Tipton, IN
3870La Crosse, WI-MN0.9289
Houston, MN
La Crosse, WI
3880Lafayette, LA0.8105
Acadia, LA
Lafayette, LA
St. Landry, LA
St. Martin, LA
3920Lafayette, IN0.9067
Clinton, IN
Tippecanoe, IN
3960Lake Charles, LA0.7972
Calcasieu, LA
3980Lakeland-Winter Haven, FL0.8930
Polk, FL
4000Lancaster, PA0.9883
Lancaster, PA
4040Lansing-East Lansing, MI0.9658
Clinton, MI
Eaton, MI
Ingham, MI
4080Laredo, TX0.8747
Webb, TX
4100Las Cruces, NM0.8784
Dona Ana, NM
4120Las Vegas, NV-AZ1.1121
Mohave, AZ
Clark, NV
Nye, NV
4150Lawrence, KS0.8644
Douglas, KS
4200Lawton, OK0.8212
Comanche, OK
4243Lewiston-Auburn, ME0.9562
Androscoggin, ME
4280Lexington, KY0.9219
Bourbon, KY
Clark, KY
Fayette, KY
Jessamine, KY
Madison, KY
Scott, KY
Woodford, KY
4320Lima, OH0.9258
Allen, OH
Auglaize, OH
4360Lincoln, NE1.0208
Lancaster, NE
4400Little Rock-North Little, AR0.8826
Faulkner, AR
Lonoke, AR
Pulaski, AR
Saline, AR
4420Longview-Marshall, TX0.8739
Gregg, TX
Harrison, TX
Upshur, TX
4480Los Angeles-Long Beach, CA1.1732
Los Angeles, CA
4520Louisville, KY-IN0.9162
Clark, IN
Start Printed Page 30277
Floyd, IN
Harrison, IN
Scott, IN
Bullitt, KY
Jefferson, KY
Oldham, KY
4600Lubbock, TX0.8777
Lubbock, TX
4640Lynchburg, VA0.9017
Amherst, VA
Bedford City, VA
Bedford, VA
Campbell, VA
Lynchburg City, VA
4680Macon, GA0.9596
Bibb, GA
Houston, GA
Jones, GA
Peach, GA
Twiggs, GA
4720Madison, WI1.0395
Dane, WI
4800Mansfield, OH0.9105
Crawford, OH
Richland, OH
4840Mayaguez, PR0.4769
Anasco, PR
Cabo Rojo, PR
Hormigueros, PR
Mayaguez, PR
Sabana Grande, PR
San German, PR
4880McAllen-Edinburg-Mission, TX0.8602
Hidalgo, TX
4890Medford-Ashland, OR1.0534
Jackson, OR
4900Melbourne-Titusville-Palm Bay, FL0.9633
Brevard, FL
4920Memphis, TN-AR-MS0.9234
Crittenden, AR
De Soto, MS
Fayette, TN
Shelby, TN
Tipton, TN
4940Merced, CA1.0575
Merced, CA
5000Miami, FL0.9870
Dade, FL
5015Middlesex-Somerset-Hunterdon, NJ1.1360
Hunterdon, NJ
Middlesex, NJ
Somerset, NJ
5080Milwaukee-Waukesha, WI1.0076
Milwaukee, WI
Ozaukee, WI
Washington, WI
Waukesha, WI
5120Minneapolis-St. Paul, MN-WI1.1066
Anoka, MN
Carver, MN
Chisago, MN
Dakota, MN
Hennepin, MN
Isanti, MN
Ramsey, MN
Scott, MN
Sherburne, MN
Washington, MN
Wright, MN
Pierce, WI
Start Printed Page 30278
St. Croix, WI
5140Missoula, MT0.9618
Missoula, MT
5160Mobile, AL0.7932
Baldwin, AL
Mobile, AL
5170Modesto, CA1.1966
Stanislaus, CA
5190Monmouth-Ocean, NJ1.0888
Monmouth, NJ
Ocean, NJ
5200Monroe, LA0.7913
Ouachita, LA
5240Montgomery, AL0.8300
Autauga, AL
Elmore, AL
Montgomery, AL
5280Muncie, IN0.8580
Delaware, IN
5330Myrtle Beach, SC0.9022
Horry, SC
5345Naples, FL1.0558
Collier, FL
5360Nashville, TN1.0108
Cheatham, TN
Davidson, TN
Dickson, TN
Robertson, TN
Rutherford, TN
Sumner, TN
Williamson, TN
Wilson, TN
5380Nassau-Suffolk, NY1.2907
Nassau, NY
Suffolk, NY
5483New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT1.2254
Fairfield, CT
New Haven, CT
5523New London-Norwich, CT1.1596
New London, CT
5560New Orleans, LA0.9103
Jefferson, LA
Orleans, LA
Plaquemines, LA
St. Bernard, LA
St. Charles, LA
St. James, LA
St. John The Baptist, LA
St. Tammany, LA
5600New York, NY1.3586
Bronx, NY
Kings, NY
New York, NY
Putnam, NY
Queens, NY
Richmond, NY
Rockland, NY
Westchester, NY
5640Newark, NJ1.1625
Essex, NJ
Morris, NJ
Sussex, NJ
Union, NJ
Warren, NJ
5660Newburgh, NY-PA1.1170
Orange, NY
Pike, PA
5720Norfolk-Virginia Beach-Newport News, VA-NC0.8894
Currituck, NC
Chesapeake City, VA
Start Printed Page 30279
Gloucester, VA
Hampton City, VA
Isle of Wight, VA
James City, VA
Mathews, VA
Newport News City, VA
Norfolk City, VA
Poquoson City,VA
Portsmouth City, VA
Suffolk City, VA
Virginia Beach City, VA
Williamsburg City, VA
York, VA
5775Oakland, CA1.5220
Alameda, CA
Contra Costa, CA
5790Ocala, FL0.9153
Marion, FL
5800Odessa-Midland, TX0.9632
Ector, TX
Midland, TX
5880Oklahoma City, OK0.8966
Canadian, OK
Cleveland, OK
Logan, OK
McClain, OK
Oklahoma, OK
Pottawatomie, OK
5910Olympia, WA1.1006
Thurston, WA
5920Omaha, NE-IA0.9754
Pottawattamie, IA
Cass, NE
Douglas, NE
Sarpy, NE
Washington, NE
5945Orange County, CA1.1611
Orange, CA
5960Orlando, FL0.9742
Lake, FL
Orange, FL
Osceola, FL
Seminole, FL
5990Owensboro, KY0.8434
Daviess, KY
6015Panama City, FL0.8124
Bay, FL
6020Parkersburg-Marietta, WV-OH0.8288
Washington, OH
Wood, WV
6080Pensacola, FL0.8306
Escambia, FL
Santa Rosa, FL
6120Peoria-Pekin, IL0.8886
Peoria, IL
Tazewell, IL
Woodford, IL
6160Philadelphia, PA-NJ1.0824
Burlington, NJ
Camden, NJ
Gloucester, NJ
Salem, NJ
Bucks, PA
Chester, PA
Delaware, PA
Montgomery, PA
Philadelphia, PA
6200Phoenix-Mesa, AZ0.9982
Maricopa, AZ
Pinal, AZ
Start Printed Page 30280
6240Pine Bluff, AR0.8673
Jefferson, AR
6280Pittsburgh, PA0.8756
Allegheny, PA
Beaver, PA
Butler, PA
Fayette, PA
Washington, PA
Westmoreland, PA
6323Pittsfield, MA1.0439
Berkshire, MA
6340Pocatello, ID0.9601
Bannock, ID
6360Ponce, PR0.4954
Guayanilla, PR
Juana Diaz, PR
Penuelas, PR
Ponce, PR
Villalba, PR
Yauco, PR
6403Portland, ME1.0112
Cumberland, ME
Sagadahoc, ME
York, ME
6440Portland-Vancouver, OR-WA1.1403
Clackamas, OR
Columbia, OR
Multnomah, OR
Washington, OR
Yamhill, OR
Clark, WA
6483Providence-Warwick-Pawtucket, RI1.1061
Bristol, RI
Kent, RI
Newport, RI
Providence, RI
Washington, RI
6520Provo-Orem, UT0.9613
Utah, UT
6560Pueblo, CO0.8752
Pueblo, CO
6580Punta Gorda, FL0.9441
Charlotte, FL
6600Racine, WI0.9045
Racine, WI
6640Raleigh-Durham-Chapel Hill, NC1.0258
Chatham, NC
Durham, NC
Franklin, NC
Johnston, NC
Orange, NC
Wake, NC
6660Rapid City, SD0.8912
Pennington, SD
6680Reading, PA0.9215
Berks, PA
6690Redding, CA1.1835
Shasta, CA
6720Reno, NV1.0456
Washoe, NV
6740Richland-Kennewick-Pasco, WA1.0520
Benton, WA
Franklin, WA
6760Richmond-Petersburg, VA0.9397
Charles City County, VA
Chesterfield, VA
Colonial Heights City, VA
Dinwiddie, VA
Goochland, VA
Hanover, VA
Start Printed Page 30281
Henrico, VA
Hopewell City, VA
New Kent, VA
Petersburg City, VA
Powhatan, VA
Prince George, VA
Richmond City, VA
6780Riverside-San Bernardino, CA1.0970
Riverside, CA
San Bernardino, CA
6800Roanoke, VA0.8428
Botetourt, VA
Roanoke, VA
Roanoke City, VA
Salem City, VA
6820Rochester, MN1.1504
Olmsted, MN
6840Rochester, NY0.9196
Genesee, NY
Livingston, NY
Monroe, NY
Ontario, NY
Orleans, NY
Wayne, NY
6880Rockford, IL0.9626
Boone, IL
Ogle, IL
Winnebago, IL
6895Rocky Mount, NC0.8998
Edgecombe, NC
Nash, NC
6920Sacramento, CA1.1848
El Dorado, CA
Placer, CA
Sacramento, CA
6960Saginaw-Bay City-Midland, MI0.9696
Bay, MI
Midland, MI
Saginaw, MI
6980St. Cloud, MN1.0215
Benton, MN
Stearns, MN
7000St. Joseph, MO1.0013
Andrews, MO
Buchanan, MO
7040St. Louis, MO-IL0.9081
Clinton, IL
Jersey, IL
Madison, IL
Monroe, IL
St. Clair, IL
Franklin, MO
Jefferson, MO
Lincoln, MO
St. Charles, MO
St. Louis, MO
St. Louis City, MO
Warren, MO
Sullivan City, MO
7080Salem, OR1.0556
Marion, OR
Polk, OR
7120Salinas, CA1.3823
Monterey, CA
7160Salt Lake City-Ogden, UT0.9487
Davis, UT
Salt Lake, UT
Weber, UT
7200San Angelo, TX0.8167
Tom Green, TX
Start Printed Page 30282
7240San Antonio, TX0.9023
Bexar, TX
Comal, TX
Guadalupe, TX
Wilson, TX
7320San Diego, CA1.1267
San Diego, CA
7360San Francisco, CA1.4712
Marin, CA
San Francisco, CA
San Mateo, CA
7400San Jose, CA1.4744
Santa Clara, CA
7440San Juan-Bayamon, PR0.4802
Aguas Buenas, PR
Barceloneta, PR
Bayamon, PR
Canovanas, PR
Carolina, PR
Catano, PR
Ceiba, PR
Comerio, PR
Corozal, PR
Dorado, PR
Fajardo, PR
Florida, PR
Guaynabo, PR
Humacao, PR
Juncos, PR
Los Piedras, PR
Loiza, PR
Luguillo, PR
Manati, PR
Morovis, PR
Naguabo, PR
Naranjito, PR
Rio Grande, PR
San Juan, PR
Toa Alta, PR
Toa Baja, PR
Trujillo Alto, PR
Vega Alta, PR
Vega Baja, PR
Yabucoa, PR
7460San Luis Obispo-Atascadero-Paso Robles, CA1.1118
San Luis Obispo, CA
7480Santa Barbara-Santa Maria-Lompoc, CA1.0771
Santa Barbara, CA
7485Santa Cruz-Watsonville, CA1.4779
Santa Cruz, CA
7490Santa Fe, NM1.0590
Los Alamos, NM
Santa Fe, NM
7500Santa Rosa, CA1.2961
Sonoma, CA
7510Sarasota-Bradenton, FL0.9629
Manatee, FL
Sarasota, FL
7520Savannah, GA0.9460
Bryan, GA
Chatham, GA
Effingham, GA
7560Scranton—Wilkes-Barre—Hazleton, PA0.8522
Columbia, PA
Lackawanna, PA
Luzerne, PA
Wyoming, PA
7600Seattle-Bellevue-Everett, WA1.1479
Island, WA
King, WA
Start Printed Page 30283
Snohomish, WA
7610Sharon, PA0.7881
Mercer, PA
7620Sheboygan, WI0.8948
Sheboygan, WI
7640Sherman-Denison, TX0.9617
Grayson, TX
7680Shreveport-Bossier City, LA0.9111
Bossier, LA
Caddo, LA
Webster, LA
7720Sioux City, IA-NE0.9094
Woodbury, IA
Dakota, NE
7760Sioux Falls, SD0.9441
Lincoln, SD
Minnehaha, SD
7800South Bend, IN0.9447
St. Joseph, IN
7840Spokane, WA1.0660
Spokane, WA
7880Springfield, IL0.8738
Menard, IL
Sangamon, IL
7920Springfield, MO0.8597
Christian, MO
Greene, MO
Webster, MO
8003Springfield, MA1.0173
Hampden, MA
Hampshire, MA
8050State College, PA0.8461
Centre, PA
8080Steubenville-Weirton, OH-WV0.8280
Jefferson, OH
Brooke, WV
Hancock, WV
8120Stockton-Lodi, CA1.0564
San Joaquin, CA
8140Sumter, SC0.8520
Sumter, SC
8160Syracuse, NY0.9394
Cayuga, NY
Madison, NY
Onondaga, NY
Oswego, NY
8200Tacoma, WA1.1078
Pierce, WA
8240Tallahassee, FL0.8655
Gadsden, FL
Leon, FL
8280Tampa-St. Petersburg-Clearwater, FL0.9024
Hernando, FL
Hillsborough, FL
Pasco, FL
Pinellas, FL
8320Terre Haute, IN0.8582
Clay, IN
Vermillion, IN
Vigo, IN
8360Texarkana, AR-Texarkana, TX0.8413
Miller, AR
Bowie, TX
8400Toledo, OH0.9524
Fulton, OH
Lucas, OH
Wood, OH
8440Topeka, KS0.8904
Shawnee, KS
8480Trenton, NJ1.0276
Start Printed Page 30284
Mercer, NJ
8520Tucson, AZ0.8926
Pima, AZ
8560Tulsa, OK0.8729
Creek, OK
Osage, OK
Rogers, OK
Tulsa, OK
Wagoner, OK
8600Tuscaloosa, AL0.8440
Tuscaloosa, AL
8640Tyler, TX0.9502
Smith, TX
8680Utica-Rome, NY0.8295
Herkimer, NY
Oneida, NY
8720Vallejo-Fairfield-Napa, CA1.3517
Napa, CA
Solano, CA
8735Ventura, CA1.1105
Ventura, CA
8750Victoria, TX0.8469
Victoria, TX
8760Vineland-Millville-Bridgeton, NJ1.0573
Cumberland, NJ
8780Visalia-Tulare-Porterville, CA0.9975
Tulare, CA
8800Waco, TX0.8146
McLennan, TX
8840Washington, DC-MD-VA-WV1.0971
District of Columbia, DC
Calvert, MD
Charles, MD
Frederick, MD
Montgomery, MD
Prince Georges, MD
Alexandria City, VA
Arlington, VA
Clarke, VA
Culpepper, VA
Fairfax, VA
Fairfax City, VA
Falls Church City, VA
Fauquier, VA
Fredericksburg City, VA
King George, VA
Loudoun, VA
Manassas City, VA
Manassas Park City, VA
Prince William, VA
Spotsylvania, VA
Stafford, VA
Warren, VA
Berkeley, WV
Jefferson, WV
8920Waterloo-Cedar Falls, IA0.8633
Black Hawk, IA
8940Wausau, WI0.9570
Marathon, WI
8960West Palm Beach-Boca Raton, FL1.0362
Palm Beach, FL
9000Wheeling, OH-WV0.7449
Belmont, OH
Marshall, WV
Ohio, WV
9040Wichita, KS0.9486
Butler, KS
Harvey, KS
Sedgwick, KS
9080Wichita Falls, TX0.8395
Start Printed Page 30285
Archer, TX
Wichita, TX
9140Williamsport, PA0.8485
Lycoming, PA
9160Wilmington-Newark, DE-MD1.1121
New Castle, DE
Cecil, MD
9200Wilmington, NC0.9237
New Hanover, NC
Brunswick, NC
9260Yakima, WA1.0322
Yakima, WA
9270Yolo, CA0.9378
Yolo, CA
9280York, PA0.9150
York, PA
9320Youngstown-Warren, OH0.9517
Columbiana, OH
Mahoning, OH
Trumbull, OH
9340Yuba City, CA1.0363
Sutter, CA
Yuba, CA
9360Yuma, AZ0.8871
Yuma, AZ

Table 1B.—FY 2006 IRF PPS MSA Labor Market Area Designations for Rural Areas for the Purposes of Comparing Wage Index Values With Table 2B

Nonurban areaWage Index
Alabama0.7637
Alaska1.1637
Arizona0.9140
Arkansas0.7703
California1.0297
Colorado0.9368
Connecticut1.1917
Delaware0.9503
Florida0.8721
Georgia0.8247
Guam0.9611
Hawaii1.0522
Idaho0.8826
Illinois0.8340
Indiana0.8736
Iowa0.8550
Kansas0.8087
Kentucky0.7844
Louisiana0.7290
Maine0.9039
Maryland0.9179
Massachusetts1.0216
Michigan0.8740
Minnesota0.9339
Mississippi0.7583
Missouri0.7829
Montana0.8701
Nebraska0.9035
Nevada0.9832
New Hampshire0.9940
New Jersey 1
New Mexico0.8529
New York0.8403
North Carolina0.8500
North Dakota0.7743
Ohio0.8759
Oklahoma0.7537
Oregon1.0049
Pennsylvania0.8348
Puerto Rico0.4047
Rhode Island 1
South Carolina0.8640
South Dakota0.8393
Tennessee0.7876
Texas0.7910
Utah0.8843
Vermont0.9375
Virginia0.8479
Virgin Islands0.7456
Washington1.0072
West Virginia0.8083
Wisconsin0.9498
Wyoming0.9182
1 All counties within the State are classified urban.

Table 2a.—Proposed Inpatient Rehabilitaion Facility Wage Index for Urban Areas Based on Proposed CBSA Labor Market Areas For Discharges Occurring on or After October 1, 2005

CBSA codeUrban area (Constituent counties)Full wage Index
10180Abilene, TX0.7850
Callahan County, TX
Jones County, TX
Taylor County, TX
10380Aguadilla-Isabela-San Sebastián, PR0.4280
Aguada Municipio, PR
Start Printed Page 30286
Aguadilla Municipio, PR
Aasco Municipio, PR
Isabela Municipio, PR
Lares Municipio, PR
Moca Municipio, PR
Rincín Municipio, PR
San Sebastián Municipio, PR
10420Akron, OH0.9055
Portage County, OH
Summit County, OH
10500Albany, GA1.1266
Baker County, GA
Dougherty County, GA
Lee County, GA
Terrell County, GA
Worth County, GA
10580Albany-Schenectady-Troy, NY0.8650
Albany County, NY
Rensselaer County, NY
Saratoga County, NY
Schenectady County, NY
Schoharie County, NY
10740Albuquerque, NM1.0485
Bernalillo County, NM
Sandoval County, NM
Torrance County, NM
Valencia County, NM
10780Alexandria, LA0.8171
Grant Parish, LA
Rapides Parish, LA
10900Allentown-Bethlehem-Easton, PA-NJ0.9501
Warren County, NJ
Carbon County, PA
Lehigh County, PA
Northampton County, PA
11020Altoona, PA0.8462
Blair County, PA
11100Amarillo, TX0.9178
Armstrong County, TX
Carson County, TX
Potter County, TX
Randall County, TX
11180Ames, IA0.9479
Story County, IA
11260Anchorage, AK1.2165
Anchorage Municipality, AK
Matanuska-Susitna Borough, AK
11300Anderson, IN0.8713
Madison County, IN
11340Anderson, SC0.8670
Anderson County, SC
11460Ann Arbor, MI1.1022
Washtenaw County, MI
11500Anniston-Oxford, AL0.7881
Calhoun County, AL
11540Appleton, WI0.9131
Calumet County, WI
Outagamie County, WI
11700Asheville, NC0.9191
Buncombe County, NC
Haywood County, NC
Henderson County, NC
Madison County, NC
12020Athens-Clarke County, GA1.0202
Clarke County, GA
Madison County, GA
Oconee County, GA
Oglethorpe County, GA
12060Atlanta-Sandy Springs-Marietta, GA0.9971
Barrow County, GA
Start Printed Page 30287
Bartow County, GA
Butts County, GA
Carroll County, GA
Cherokee County, GA
Clayton County, GA
Cobb County, GA
Coweta County, GA
Dawson County, GA
DeKalb County, GA
Douglas County, GA
Fayette County, GA
Forsyth County, GA
Fulton County, GA
Gwinnett County, GA
Haralson County, GA
Heard County, GA
Henry County, GA
Jasper County, GA
Lamar County, GA
Meriwether County, GA
Newton County, GA
Paulding County, GA
Pickens County, GA
Pike County, GA
Rockdale County, GA
Spalding County, GA
Walton County, GA
12100Atlantic City, NJ1.0931
Atlantic County, NJ
12220Auburn-Opelika, AL0.8215
Lee County, AL
12260Augusta-Richmond County, GA-SC0.9154
Burke County, GA
Columbia County, GA
McDuffie County, GA
Richmond County, GA
Aiken County, SC
Edgefield County, SC
12420Austin-Round Rock, TX0.9595
Bastrop County, TX
Caldwell County, TX
Hays County, TX
Travis County, TX
Williamson County, TX
12540Bakersfield, CA1.0036
Kern County, CA
12580Baltimore-Towson, MD0.9907
Anne Arundel County, MD
Baltimore County, MD
Carroll County, MD
Harford County, MD
Howard County, MD
Queen Anne's County, MD
Baltimore City, MD
12620Bangor, ME0.9955
Penobscot County, ME
12700Barnstable Town, MA1.2335
Barnstable County, MA
12940Baton Rouge, LA0.8319
Ascension Parish, LA
East Baton Rouge Parish, LA
East Feliciana Parish, LA
Iberville Parish, LA
Livingston Parish, LA
Pointe Coupee Parish, LA
St. Helena Parish, LA
West Baton Rouge Parish, LA
West Feliciana Parish, LA
12980Battle Creek, MI0.9366
Calhoun County, MI
Start Printed Page 30288
13020Bay City, MI0.9574
Bay County, MI
13140Beaumont-Port Arthur, TX0.8616
Hardin County, TX
Jefferson County, TX
Orange County, TX
13380Bellingham, WA1.1642
Whatcom County, WA
13460Bend, OR1.0603
Deschutes County, OR
13644Bethesda-Frederick-Gaithersburg, MD1.0956
Frederick County, MD
Montgomery County, MD
13740Billings, MT0.8961
Carbon County, MT
Yellowstone County, MT
13780Binghamton, NY0.8447
Broome County, NY
Tioga County, NY
13820Birmingham-Hoover, AL0.9157
Bibb County, AL
Blount County, AL
Chilton County, AL
Jefferson County, AL
St. Clair County, AL
Shelby County, AL
Walker County, AL
13900Bismarck, ND0.7505
Burleigh County, ND
Morton County, ND
13980Blacksburg-Christiansburg-Radford, VA0.7951
Giles County, VA
Montgomery County, VA
Pulaski County, VA
Radford City, VA
14020Bloomington, IN0.8587
Greene County, IN
Monroe County, IN
Owen County, IN
14060Bloomington-Normal, IL0.9111
McLean County, IL
14260Boise City-Nampa, ID0.9352
Ada County, ID
Boise County, ID
Canyon County, ID
Gem County, ID
Owyhee County, ID
14484Boston-Quincy, MA1.1771
Norfolk County, MA
Plymouth County, MA
Suffolk County, MA
14500Boulder, CO1.0046
Boulder County, CO
14540Bowling Green, KY0.8140
Edmonson County, KY
Warren County, KY
14740Bremerton-Silverdale, WA1.0614
Kitsap County, WA
14860Bridgeport-Stamford-Norwalk, CT1.2835
Fairfield County, CT
15180Brownsville-Harlingen, TX1.0125
Cameron County, TX
15260Brunswick, GA1.1933
Brantley County, GA
Glynn County, GA
McIntosh County, GA
15380Buffalo-Niagara Falls, NY0.9339
Erie County, NY
Niagara County, NY
15500Burlington, NC0.8967
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Alamance County, NC
15540Burlington-South Burlington, VT0.9322
Chittenden County, VT
Franklin County, VT
Grand Isle County, VT
15764Cambridge-Newton-Framingham, MA1.1189
Middlesex County, MA
15804Camden, NJ1.0675
Burlington County, NJ
Camden County, NJ
Gloucester County, NJ
15940Canton-Massillon, OH0.8895
Carroll County, OH
Stark County, OH
15980Cape Coral-Fort Myers, FL0.9371
Lee County, FL
16180Carson City, NV1.0352
Carson City, NV
16220Casper, WY0.9243
Natrona County, WY
16300Cedar Rapids, IA0.8975
Benton County, IA
Jones County, IA
Linn County, IA
16580Champaign-Urbana, IL0.9527
Champaign County, IL
Ford County, IL
Piatt County, IL
16620Charleston, WV0.8876
Boone County, WV
Clay County, WV
Kanawha County, WV
Lincoln County, WV
Putnam County, WV
16700Charleston-North Charleston, SC0.9420
Berkeley County, SC
Charleston County, SC
Dorchester County, SC
16740Charlotte-Gastonia-Concord, NC-SC0.9743
Anson County, NC
Cabarrus County, NC
Gaston County, NC
Mecklenburg County, NC
Union County, NC
York County, SC
16820Charlottesville, VA1.0294
Albemarle County, VA
Fluvanna County, VA
Greene County, VA
Nelson County, VA
Charlottesville City, VA
16860Chattanooga, TN-GA0.9207
Catoosa County, GA
Dade County, GA
Walker County, GA
Hamilton County, TN
Marion County, TN
Sequatchie County, TN
16940Cheyenne, WY0.8980
Laramie County, WY
16974Chicago-Naperville-Joliet, IL1.0868
Cook County, IL
DeKalb County, IL
DuPage County, IL
Grundy County, IL
Kane County, IL
Kendall County, IL
McHenry County, IL
Will County, IL
17020Chico, CA1.0542
Start Printed Page 30290
Butte County, CA
17140Cincinnati-Middletown, OH-KY-IN0.9516
Dearborn County, IN
Franklin County, IN
Ohio County, IN
Boone County, KY
Bracken County, KY
Campbell County, KY
Gallatin County, KY
Grant County, KY
Kenton County, KY
Pendleton County, KY
Brown County, OH
Butler County, OH
Clermont County, OH
Hamilton County, OH
Warren County, OH
17300Clarksville, TN-KY0.8022
Christian County, KY
Trigg County, KY
Montgomery County, TN
Stewart County, TN
17420Cleveland, TN0.7844
Bradley County, TN
Polk County, TN
17460Cleveland-Elyria-Mentor, OH0.9650
Cuyahoga County, OH
Geauga County, OH
Lake County, OH
Lorain County, OH
Medina County, OH
17660Coeur d'Alene, ID0.9339
Kootenai County, ID
17780College Station-Bryan, TX0.9243
Brazos County, TX
Burleson County, TX
Robertson County, TX
17820Colorado Springs, CO0.9792
El Paso County, CO
Teller County, CO
17860Columbia, M0.8396
Boone County, MO
Howard County, MO
17900Columbia, SC0.9392
Calhoun County, SC
Fairfield County, SC
Kershaw County, SC
Lexington County, SC
Richland County, SC
Saluda County, SC
17980Columbus, GA-AL0.8690
Russell County, AL
Chattahoochee County, GA
Harris County, GA
Marion County, GA
Muscogee County, GA
18020Columbus, IN0.9388
Bartholomew County, IN
18140Columbus, OH0.9737
Delaware County, OH
Fairfield County, OH
Franklin County, OH
Licking County, OH
Madison County, OH
Morrow County, OH
Pickaway County, OH
Union County, OH
18580Corpus Christi, TX0.8647
Aransas County, TX
Nueces County, TX
Start Printed Page 30291
San Patricio County, TX
18700Corvallis, OR1.0545
Benton County, OR
19060Cumberland, MD-WV0.8662
Allegany County, MD
Mineral County, WV
19124Dallas-Plano-Irving, TX1.0074
Collin County, TX
Dallas County, TX
Delta County, TX
Denton County, TX
Ellis County, TX
Hunt County, TX
Kaufman County, TX
Rockwall County, TX
19140Dalton, GA0.9558
Murray County, GA
Whitfield County, GA
19180Danville, IL0.8392
Vermilion County, IL
19260Danville, VA0.8643
Pittsylvania County, VA
Danville City, VA
19340Davenport-Moline-Rock Island, IA-IL0.8773
Henry County, IL
Mercer County, IL
Rock Island County, IL
Scott County, IA
19380Dayton, OH0.9303
Greene County, OH
Miami County, OH
Montgomery County, OH
Preble County, OH
19460Decatur, AL0.8894
Lawrence County, AL
Morgan County, AL
19500Decatur, IL0.8122
Macon County, IL
19660Deltona-Daytona Beach-Ormond Beach, FL0.8898
Volusia County, FL
19740Denver-Aurora, CO1.0904
Adams County, CO
Arapahoe County, CO
Broomfield County, CO
Clear Creek County, CO
Denver County, CO
Douglas County, CO
Elbert County, CO
Gilpin County, CO
Jefferson County, CO
Park County, CO
19780Des Moines, IA0.9266
Dallas County, IA
Guthrie County, IA
Madison County, IA
Polk County, IA
Warren County, IA
19804Detroit-Livonia-Dearborn, MI1.0349
Wayne County, MI
20020Dothan, AL0.7537
Geneva County, AL
Henry County, AL
Houston County, AL
20100Dover, DE0.9825
Kent County, DE
20220Dubuque, IA0.8748
Dubuque County, IA
20260Duluth, MN-WI1.0340
Carlton County, MN
St. Louis County, MN
Start Printed Page 30292
Douglas County, WI
20500Durham, NC1.0363
Chatham County, NC
Durham County, NC
Orange County, NC
Person County, NC
20740Eau Claire, WI0.9139
Chippewa County, WI
Eau Claire County, WI
20764Edison, NJ1.1136
Middlesex County, NJ
Monmouth County, NJ
Ocean County, NJ
Somerset County, NJ
20940El Centro, CA0.8856
Imperial County, CA
21060Elizabethtown, KY0.8684
Hardin County, KY
Larue County, KY
21140Elkhart-Goshen, IN0.9278
Elkhart County, IN
21300Elmira, NY0.8445
Chemung County, NY
21340El Paso, TX0.9181
El Paso County, TX
21500Erie, PA0.8699
Erie County, PA
21604Essex County, MA1.0662
Essex County, MA
21660Eugene-Springfield, OR1.0940
Lane County, OR
21780Evansville, IN-KY0.8372
Gibson County, IN
Posey County, IN
Vanderburgh County, IN
Warrick County, IN
Henderson County, KY
Webster County, KY
21820Fairbanks, AK1.1146
Fairbanks North Star Borough, AK
21940Fajardo, PR0.3939
Ceiba Municipio, PR
Fajardo Municipio, PR
Luquillo Municipio, PR
22020Fargo, ND-MN0.9114
Cass County, ND
Clay County, MN
22140Farmington, NM0.8049
San Juan County, NM
22180Fayetteville, NC0.9363
Cumberland County, NC
Hoke County, NC
22220Fayetteville-Springdale-Rogers, AR-MO0.8636
Benton County, AR
Madison County, AR
Washington County, AR
McDonald County, MO
22380Flagstaff, AZ1.0787
Coconino County, AZ
22420Flint, MI1.1178
Genesee County, MI
22500Florence, SC0.8833
Darlington County, SC
Florence County, SC
22520Florence-Muscle Shoals, AL0.7883
Colbert County, AL
Lauderdale County, AL
22540Fond du Lac, WI0.9897
Fond du Lac County, WI
22660Fort Collins-Loveland, CO1.0218
Start Printed Page 30293
Larimer County, CO
22744Fort Lauderdale-Pompano Beach-Deerfield Beach, FL1.0165
Broward County, FL
22900Fort Smith, AR-OK0.8283
Crawford County, AR
Franklin County, AR
Sebastian County, AR
Le Flore County, OK
Sequoyah County, OK
23020Fort Walton Beach-Crestview-Destin, FL0.8786
Okaloosa County, FL
23060Fort Wayne, IN0.9807
Allen County, IN
Wells County, IN
Whitley County, IN
23104Fort Worth-Arlington, TX0.9472
Johnson County, TX
Parker County, TX
Tarrant County, TX
Wise County, TX
23420Fresno, CA1.0536
Fresno County, CA
23460Gadsden, AL0.8049
Etowah County, AL
23540Gainesville, FL0.9459
Alachua County, FL
Gilchrist County, FL
23580Gainesville, GA0.9557
Hall County, GA
23844Gary, IN0.9310
Jasper County, IN
Lake County, IN
Newton County, IN
Porter County, IN
24020Glens Falls, NY0.8467
Warren County, NY
Washington County, NY
24140Goldsboro, NC0.8778
Wayne County, NC
24220Grand Forks, ND-MN0.9091
Polk County, MN
Grand Forks County, ND
24300Grand Junction, CO0.9900
Mesa County, CO
24340Grand Rapids-Wyoming, MI0.9420
Barry County, MI
Ionia County, MI
Kent County, MI
Newaygo County, MI
24500Great Falls, MT0.8810
Cascade County, MT
24540Greeley, CO0.9444
Weld County, CO
24580Green Bay, WI0.9590
Brown County, WI
Kewaunee County, WI
Oconto County, WI
24660Greensboro-High Point, NC0.9190
Guilford County, NC
Randolph County, NC
Rockingham County, NC
24780Greenville, NC0.9183
Greene County, NC
Pitt County, NC
24860Greenville, SC0.9557
Greenville County, SC
Laurens County, SC
Pickens County, SC
25020Guayama, PR0.4005
Arroyo Municipio, PR
Start Printed Page 30294
Guayama Municipio, PR
Patillas Municipio, PR
25060Gulfport-Biloxi, MS0.8950
Hancock County, MS
Harrison County, MS
Stone County, MS
25180Hagerstown-Martinsburg, MD-WV0.9715
Washington County, MD
Berkeley County, WV
Morgan County, WV
25260Hanford-Corcoran, CA0.9296
Kings County, CA
25420Harrisburg-Carlisle, PA0.9359
Cumberland County, PA
Dauphin County, PA
Perry County, PA
25500Harrisonburg, VA0.9275
Rockingham County, VA
Harrisonburg City, VA
25540Hartford-West Hartford-East Hartford, CT1.1054
Hartford County, CT
Litchfield County, CT
Middlesex County, CT
Tolland County, CT
25620Hattiesburg, MS0.7362
Forrest County, MS
Lamar County, MS
Perry County, MS
25860Hickory-Lenoir-Morganton, NC0.9502
Alexander County, NC
Burke County, NC
Caldwell County, NC
Catawba County, NC
25980Hinesville-Fort Stewart, GA0.7715
Liberty County, GA
Long County, GA
26100Holland-Grand Haven, MI0.9388
Ottawa County, MI
26180Honolulu, HI1.1013
Honolulu County, HI
26300Hot Springs, AR0.9249
Garland County, AR
26380Houma-Bayou Cane-Thibodaux, LA0.7721
Lafourche Parish, LA
Terrebonne Parish, LA
26420Houston-Baytown-Sugar Land, TX0.9973
Austin County, TX
Brazoria County, TX
Chambers County, TX
Fort Bend County, TX
Galveston County, TX
Harris County, TX
Liberty County, TX
Montgomery County, TX
San Jacinto County, TX
Waller County, TX
26580Huntington-Ashland, WV-KY-OH0.9564
Boyd County, KY
Greenup County, KY
Lawrence County, OH
Cabell County, WV
Wayne County, WV
26620Huntsville, AL0.8851
Limestone County, AL
Madison County, AL
26820Idaho Falls, ID0.9059
Bonneville County, ID
Jefferson County, ID
26900Indianapolis, IN1.0113
Boone County, IN
Start Printed Page 30295
Brown County, IN
Hamilton County, IN
Hancock County, IN
Hendricks County, IN
Johnson County, IN
Marion County, IN
Morgan County, IN
Putnam County, IN
Shelby County, IN
26980Iowa City, IA0.9654
Johnson County, IA
Washington County, IA
27060Ithaca, NY0.9589
Tompkins County, NY
27100Jackson, MI0.9146
Jackson County, MI
27140Jackson, MS0.8291
Copiah County, MS
Hinds County, MS
Madison County, MS
Rankin County, MS
Simpson County, MS
27180Jackson, TN0.8900
Chester County, TN
Madison County, TN
27260Jacksonville, FL0.9537
Baker County, FL
Clay County, FL
Duval County, FL
Nassau County, FL
St. Johns County, FL
27340Jacksonville, NC0.8401
Onslow County, NC
27500Janesville, WI0.9583
Rock County, WI
27620Jefferson City, MO0.8338
Callaway County, MO
Cole County, MO
Moniteau County, MO
Osage County, MO
27740Johnson City, TN0.8146
Carter County, TN
Unicoi County, TN
Washington County, TN
27780Johnstown, PA0.8380
Cambria County, PA
27860Jonesboro, AR0.8144
Craighead County, AR
Poinsett County, AR
27900Joplin, MO0.8721
Jasper County, MO
Newton County, MO
28020Kalamazoo-Portage, MI1.0676
Kalamazoo County, MI
Van Buren County, MI
28100Kankakee-Bradley, IL1.0603
Kankakee County, IL
28140Kansas City, MO-KS0.9629
Franklin County, KS
Johnson County, KS
Leavenworth County, KS
Linn County, KS
Miami County, KS
Wyandotte County, KS
Bates County, MO
Caldwell County, MO
Cass County, MO
Clay County, MO
Clinton County, MO
Jackson County, MO
Start Printed Page 30296
Lafayette County, MO
Platte County, MO
Ray County, MO
28420Kennewick-Richland-Pasco, WA1.0520
Benton County, WA
Franklin County, WA
28660Killeen-Temple-Fort Hood, TX0.9242
Bell County, TX
Coryell County, TX
Lampasas County, TX
28700Kingsport-Bristol-Bristol, TN-VA0.8240
Hawkins County, TN
Sullivan County, TN
Bristol City, VA
Scott County, VA
Washington County, VA
28740Kingston, NY0.9000
Ulster County, NY
28940Knoxville, TN0.8548
Anderson County, TN
Blount County, TN
Knox County, TN
Loudon County, TN
Union County, TN
29020Kokomo, IN0.8986
Howard County, IN
Tipton County, IN
29100La Crosse, WI-MN0.9289
Houston County, MN
La Crosse County, WI
29140Lafayette, IN0.9067
Benton County, IN
Carroll County, IN
Tippecanoe County, IN
29180Lafayette, LA0.8306
Lafayette Parish, LA
St. Martin Parish, LA
29340Lake Charles, LA0.7935
Calcasieu Parish, LA
Cameron Parish, LA
29404Lake County-Kenosha County, IL-WI1.0342
Lake County, IL
Kenosha County, WI
29460Lakeland, FL0.8930
Polk County, FL
29540Lancaster, PA0.9883
Lancaster County, PA
29620Lansing-East Lansing, MI0.9658
Clinton County, MI
Eaton County, MI
Ingham County, MI
29700Laredo, TX0.8747
Webb County, TX
29740Las Cruces, NM0.8784
Dona Ana County, NM
29820Las Vegas-Paradise, NV1.1378
Clark County, NV
29940Lawrence, KS0.8644
Douglas County, KS
30020Lawton, OK0.8212
Comanche County, OK
30140Lebanon, PA0.8570
Lebanon County, PA
30300Lewiston, ID-WA0.9314
Nez Perce County, ID
Asotin County, WA
30340Lewiston-Auburn, ME0.9562
Androscoggin County, ME
30460Lexington-Fayette, KY0.9359
Bourbon County, KY
Start Printed Page 30297
Clark County, KY
Fayette County, KY
Jessamine County, KY
Scott County, KY
Woodford County, KY
30620Lima, OH0.9330
Allen County, OH
30700Lincoln, NE1.0208
Lancaster County, NE
Seward County, NE
30780Little Rock-North Little Rock, AR0.8826
Faulkner County, AR
Grant County, AR
Lonoke County, AR
Perry County, AR
Pulaski County, AR
Saline County, AR
30860Logan, UT-ID0.9094
Franklin County, ID
Cache County, UT
30980Longview, TX0.8801
Gregg County, TX
Rusk County, TX
Upshur County, TX
31020Longview, WA1.0224
Cowlitz County, WA
31084Los Angeles-Long Beach-Glendale, CA1.1732
Los Angeles County, CA
31140Louisville, KY-IN0.9122
Clark County, IN
Floyd County, IN
Harrison County, IN
Washington County, IN
Bullitt County, KY
Henry County, KY
Jefferson County, KY
Meade County, KY
Nelson County, KY
Oldham County, KY
Shelby County, KY
Spencer County, KY
Trimble County, KY
31180Lubbock, TX0.8777
Crosby County, TX
Lubbock County, TX
31340Lynchburg, VA0.9017
Amherst County, VA
Appomattox County, VA
Bedford County, VA
Campbell County, VA
Bedford City, VA
Lynchburg City, VA
31420Macon, GA0.9887
Bibb County, GA
Crawford County, GA
Jones County, GA
Monroe County, GA
Twiggs County, GA
31460Madera, CA0.8521
Madera County, CA
31540Madison, WI1.0306
Columbia County, WI
Dane County, WI
Iowa County, WI
31700Manchester-Nashua, NH1.0642
Hillsborough County, NH
Merrimack County, NH
31900Mansfield, OH0.9189
Richland County, OH
32420Mayaguez, PR0.4493
Start Printed Page 30298
Hormigueros Municipio, PR
Mayaguez Municipio, PR
32580McAllen-Edinburg-Pharr, TX0.8602
Hidalgo County, TX
32780Medford, OR1.0534
Jackson County, OR
32820Memphis, TN-MS-AR0.9217
Crittenden County, AR
DeSoto County, MS
Marshall County, MS
Tate County, MS
Tunica County, MS
Fayette County, TN
Shelby County, TN
Tipton County, TN
32900Merced, CA1.0575
Merced County, CA
33124Miami-Miami Beach-Kendall, FL0.9870
Miami-Dade County, FL
33140Michigan City-La Porte, IN0.9332
LaPorte County, IN
33260Midland, TX0.9384
Midland County, TX
33340Milwaukee-Waukesha-West Allis, WI1.0076
Milwaukee County, WI
Ozaukee County, WI
Washington County, WI
Waukesha County, WI
33460Minneapolis-St. Paul-Bloomington, MN-WI1.1066
Anoka County, MN
Carver County, MN
Chisago County, MN
Dakota County, MN
Hennepin County, MN
Isanti County, MN
Ramsey County, MN
Scott County, MN
Sherburne County, MN
Washington County, MN
Wright County, MN
Pierce County, WI
St. Croix County, WI
33540Missoula, MT0.9618
Missoula County, MT
33660Mobile, AL0.7995
Mobile County, AL
33700Modesto, CA1.1966
Stanislaus County, CA
33740Monroe, LA0.7903
Ouachita Parish, LA
Union Parish, LA
33780Monroe, MI0.9506
Monroe County, MI
33860Montgomery, AL0.8300
Autauga County, AL
Elmore County, AL
Lowndes County, AL
Montgomery County, AL
34060Morgantown, WV0.8730
Monongalia County, WV
Preston County, WV
34100Morristown, TN0.7790
Grainger County, TN
Hamblen County, TN
Jefferson County, TN
34580Mount Vernon-Anacortes, WA1.0576
Skagit County, WA
34620Muncie, IN0.8580
Delaware County, IN
34740Muskegon-Norton Shores, MI0.9741
Start Printed Page 30299
Muskegon County, MI
34820Myrtle Beach-Conway-North Myrtle Beach, SC0.9022
Horry County, SC
34900Napa, CA1.2531
Napa County, CA
34940Naples-Marco Island, FL1.0558
Collier County, FL
34980Nashville-Davidson—Murfreesboro, TN1.0086
Cannon County, TN
Cheatham County, TN
Davidson County, TN
Dickson County, TN
Hickman County, TN
Macon County, TN
Robertson County, TN
Rutherford County, TN
Smith County, TN
Sumner County, TN
Trousdale County, TN
Williamson County, TN
Wilson County, TN
35004Nassau-Suffolk, NY1.2907
Nassau County, NY
Suffolk County, NY
35084Newark-Union, NJ-PA1.1687
Essex County, NJ
Hunterdon County, NJ
Morris County, NJ
Sussex County, NJ
Union County, NJ
Pike County, PA
35300New Haven-Milford, CT1.1807
New Haven County, CT
35380New Orleans-Metairie-Kenner, LA0.9103
Jefferson Parish, LA
Orleans Parish, LA
Plaquemines Parish, LA
St. Bernard Parish, LA
St. Charles Parish, LA
St. John the Baptist Parish, LA
St. Tammany Parish, LA
35644New York-Wayne-White Plains, NY-NJ1.3311
Bergen County, NJ
Hudson County, NJ
Passaic County, NJ
Bronx County, NY
Kings County, NY
New York County, NY
Putnam County, NY
Queens County, NY
Richmond County, NY
Rockland County, NY
Westchester County, NY
35660Niles-Benton Harbor, MI0.8847
Berrien County, MI
35980Norwich-New London, CT1.1596
New London County, CT
36084Oakland-Fremont-Hayward, CA1.5220
Alameda County, CA
Contra Costa County, CA
36100Ocala, FL0.9153
Marion County, FL
36140Ocean City, NJ1.0810
Cape May County, NJ
36220Odessa, TX0.9798
Ector County, TX
36260Ogden-Clearfield, UT0.9216
Davis County, UT
Morgan County, UT
Weber County, UT
Start Printed Page 30300
36420Oklahoma City, OK0.8982
Canadian County, OK
Cleveland County, OK
Grady County, OK
Lincoln County, OK
Logan County, OK
McClain County, OK
Oklahoma County, OK
36500Olympia, WA1.1006
Thurston County, WA
36540Omaha-Council Bluffs, NE-IA0.9754
Harrison County, IA
Mills County, IA
Pottawattamie County, IA
Cass County, NE
Douglas County, NE
Sarpy County, NE
Saunders County, NE
Washington County, NE
36740Orlando, FL0.9742
Lake County, FL
Orange County, FL
Osceola County, FL
Seminole County, FL
36780Oshkosh-Neenah, WI0.9099
Winnebago County, WI
36980Owensboro, KY0.8434
Daviess County, KY
Hancock County, KY
McLean County, KY
37100Oxnard-Thousand Oaks-Ventura, CA1.1105
Ventura County, CA
37340Palm Bay-Melbourne-Titusville, FL0.9633
Brevard County, FL
37460Panama City-Lynn Haven, FL0.8124
Bay County, FL
37620Parkersburg-Marietta, WV-OH0.8288
Washington County, OH
Pleasants County, WV
Wirt County, WV
Wood County, WV
37700Pascagoula, MS0.7974
George County, MS
Jackson County, MS
37860Pensacola-Ferry Pass-Brent, FL0.8306
Escambia County, FL
Santa Rosa County, FL
37900Peoria, IL0.8886
Marshall County, IL
Peoria County, IL
Stark County, IL
Tazewell County, IL
Woodford County, IL
37964Philadelphia, PA1.0865
Bucks County, PA
Chester County, PA
Delaware County, PA
Montgomery County, PA
Philadelphia County, PA
38060Phoenix-Mesa-Scottsdale, AZ0.9982
Maricopa County, AZ
Pinal County, AZ
38220Pine Bluff, AR0.8673
Cleveland County, AR
Jefferson County, AR
Lincoln County, AR
38300Pittsburgh, PA0.8736
Allegheny County, PA
Armstrong County, PA
Beaver County, PA
Start Printed Page 30301
Butler County, PA
Fayette County, PA
Washington County, PA
Westmoreland County, PA
38340Pittsfield, MA1.0439
Berkshire County, MA
38540Pocatello, ID0.9601
Bannock County, ID
Power County, ID
38660Ponce, PR0.5006
Juana Daz Municipio, PR
Ponce Municipio, PR
Villalba Municipio, PR
38860Portland-South Portland-Biddeford, ME1.0112
Cumberland County, ME
Sagadahoc County, ME
York County, ME
38900Portland-Vancouver-Beaverton, OR-WA1.1403
Clackamas County, OR
Columbia County, OR
Multnomah County, OR
Washington County, OR
Yamhill County, OR
Clark County, WA
Skamania County, WA
38940Port St. Lucie-Fort Pierce, FL1.0046
Martin County, FL
St. Lucie County, FL
39100Poughkeepsie-Newburgh-Middletown, NY1.1363
Dutchess County, NY
Orange County, NY
39140Prescott, AZ0.9892
Yavapai County, AZ
39300Providence-New Bedford-Fall River, RI-MA1.0929
Bristol County, MA
Bristol County, RI
Kent County, RI
Newport County, RI
Providence County, RI
Washington County, RI
39340Provo-Orem, UT0.9588
Juab County, UT
Utah County, UT
39380Pueblo, CO0.8752
Pueblo County, CO
39460Punta Gorda, FL0.9441
Charlotte County, FL
39540Racine, WI0.9045
Racine County, WI
39580Raleigh-Cary, NC1.0057
Franklin County, NC
Johnston County, NC
Wake County, NC
39660Rapid City, SD0.8912
Meade County, SD
Pennington County, SD
39740Reading, PA0.9215
Berks County, PA
39820Redding, CA1.1835
Shasta County, CA
39900Reno-Sparks, NV1.0456
Storey County, NV
Washoe County, NV
40060Richmond, VA0.9397
Amelia County, VA
Caroline County, VA
Charles City County, VA
Chesterfield County, VA
Cumberland County, VA
Dinwiddie County, VA
Start Printed Page 30302
Goochland County, VA
Hanover County, VA
Henrico County, VA
King and Queen County, VA
King William County, VA
Louisa County, VA
New Kent County, VA
Powhatan County, VA
Prince George County, VA
Sussex County, VA
Colonial Heights City, VA
Hopewell City, VA
Petersburg City, VA
Richmond City, VA
40140Riverside-San Bernardino-Ontario, CA1.0970
Riverside County, CA
San Bernardino County, CA
40220Roanoke, VA0.8415
Botetourt County, VA
Craig County, VA
Franklin County, VA
Roanoke County, VA
Roanoke City, VA
Salem City, VA
40340Rochester, MN1.1504
Dodge County, MN
Olmsted County, MN
Wabasha County, MN
40380Rochester, NY0.9281
Livingston County, NY
Monroe County, NY
Ontario County, NY
Orleans County, NY
Wayne County, NY
40420Rockford, IL0.9626
Boone County, IL
Winnebago County, IL
40484Rockingham County-Strafford County, NH1.0221
Rockingham County, NH
Strafford County, NH
40580Rocky Mount, NC0.8998
Edgecombe County, NC
Nash County, NC
40660Rome, GA0.8878
Floyd County, GA
40900Sacramento—Arden-Arcade—Roseville, CA1.1700
El Dorado County, CA
Placer County, CA
Sacramento County, CA
Yolo County, CA
40980Saginaw-Saginaw Township North, MI0.9814
Saginaw County, MI
41060St. Cloud, MN1.0215
Benton County, MN
Stearns County, MN
41100St. George, UT0.9458
Washington County, UT
41140St. Joseph, MO-KS1.0013
Doniphan County, KS
Andrew County, MO
Buchanan County, MO
DeKalb County, MO
41180St. Louis, MO-IL0.9076
Bond County, IL
Calhoun County, IL
Clinton County, IL
Jersey County, IL
Macoupin County, IL
Madison County, IL
Monroe County, IL
Start Printed Page 30303
St. Clair County, IL
Crawford County, MO
Franklin County, MO
Jefferson County, MO
Lincoln County, MO
St. Charles County, MO
St. Louis County, MO
Warren County, MO
Washington County, MO
St. Louis City, MO
41420Salem, OR1.0556
Marion County, OR
Polk County, OR
41500Salinas, CA1.3823
Monterey County, CA
41540Salisbury, MD0.9123
Somerset County, MD
Wicomico County, MD
41620Salt Lake City, UT0.9561
Salt Lake County, UT
Summit County, UT
Tooele County, UT
41660San Angelo, TX0.8167
Irion County, TX
Tom Green County, TX
41700San Antonio, TX0.9003
Atascosa County, TX
Bandera County, TX
Bexar County, TX
Comal County, TX
Guadalupe County, TX
Kendall County, TX
Medina County, TX
Wilson County, TX
41740San Diego-Carlsbad-San Marcos, CA1.1267
San Diego County, CA
41780Sandusky, OH0.9017
Erie County, OH
41884San Francisco-San Mateo-Redwood City, CA1.4712
Marin County, CA
San Francisco County, CA
San Mateo County, CA
41900San German-Cabo Rojo, PR0.5240
Cabo Rojo Municipio, PR
Lajas Municipio, PR
Sabana Grande Municipio, PR
San German Municipio, PR
41940San Jose-Sunnyvale-Santa Clara, CA1.4722
San Benito County, CA
Santa Clara County, CA
41980San Juan-Caguas-Guaynabo, PR0.4645
Aguas Buenas Municipio, PR
Aibonito Municipio, PR
Arecibo Municipio, PR
Barceloneta Municipio, PR
Barranquitas Municipio, PR
Bayamón Municipio, PR
Caguas Municipio, PR
Camuy Municipio, PR
Canóvanas Municipio, PR
Carolina Municipio, PR
Cataño Municipio, PR
Cayey Municipio, PR
Ciales Municipio, PR
Cidra Municipio, PR
Comero Municipio, PR
Corozal Municipio, PR
Dorado Municipio, PR
Florida Municipio, PR
Guaynabo Municipio, PR
Start Printed Page 30304
Gurabo Municipio, PR
Hatillo Municipio, PR
Humacao Municipio, PR
Juncos Municipio, PR
Las Piedras Municipio, PR
Loíza Municipio, PR
Manatí Municipio, PR
Maunabo Municipio, PR
Morovis Municipio, PR
Naguabo Municipio, PR
Naranjito Municipio, PR
Orocovis Municipio, PR
Quebradillas Municipio, PR
Río Grande Municipio, PR
San Juan Municipio, PR
San Lorenzo Municipio, PR
Toa Alta Municipio, PR
Toa Baja Municipio, PR
Trujillo Alto Municipio, PR
Vega Alta Municipio, PR
Vega Baja Municipio, PR
Yabucoa Municipio, PR
42020San Luis Obispo-Paso Robles, CA1.1118
San Luis Obispo County, CA
42044Santa Ana-Anaheim-Irvine, CA1.1611
Orange County, CA
42060Santa Barbara-Santa Maria-Goleta, CA1.0771
Santa Barbara County, CA
42100Santa Cruz-Watsonville, CA1.4779
Santa Cruz County, CA
42140Santa Fe, NM1.0909
Santa Fe County, NM
42220Santa Rosa-Petaluma, CA1.2961
Sonoma County, CA
42260Sarasota-Bradenton-Venice, FL0.9629
Manatee County, FL
Sarasota County, FL
42340Savannah, GA0.9460
Bryan County, GA
Chatham County, GA
Effingham County, GA
42540Scranton—Wilkes-Barre, PA0.8543
Lackawanna County, PA
Luzerne County, PA
Wyoming County, PA
42644Seattle-Bellevue-Everett, WA1.1492
King County, WA
Snohomish County, WA
43100Sheboygan, WI0.8948
Sheboygan County, WI
43300Sherman-Denison, TX0.9617
Grayson County, TX
43340Shreveport-Bossier City, LA0.9132
Bossier Parish, LA
Caddo Parish, LA
De Soto Parish, LA
43580Sioux City, IA-NE-SD0.9070
Woodbury County, IA
Dakota County, NE
Dixon County, NE
Union County, SD
43620Sioux Falls, SD0.9441
Lincoln County, SD
McCook County, SD
Minnehaha County, SD
Turner County, SD
43780South Bend-Mishawaka, IN-MI0.9447
St. Joseph County, IN
Cass County, MI
43900Spartanburg, SC0.9519
Start Printed Page 30305
Spartanburg County, SC
44060Spokane, WA1.0660
Spokane County, WA
44100Springfield, IL0.8738
Menard County, IL
Sangamon County, IL
44140Springfield, MA1.0176
Franklin County, MA
Hampden County, MA
Hampshire County, MA
44180Springfield, MO0.8557
Christian County, MO
Dallas County, MO
Greene County, MO
Polk County, MO
Webster County, MO
44220Springfield, OH0.8748
Clark County, OH
44300State College, PA0.8461
Centre County, PA
44700Stockton, CA1.0564
San Joaquin County, CA
44940Sumter, SC0.8520
Sumter County, SC
45060Syracuse, NY0.9468
Madison County, NY
Onondaga County, NY
Oswego County, NY
45104Tacoma, WA1.1078
Pierce County, WA
45220Tallahassee, FL0.8655
Gadsden County, FL
Jefferson County, FL
Leon County, FL
Wakulla County, FL
45300Tampa-St. Petersburg-Clearwater, FL0.9024
Hernando County, FL
Hillsborough County, FL
Pasco County, FL
Pinellas County, FL
45460Terre Haute, IN0.8517
Clay County, IN
Sullivan County, IN
Vermillion County, IN
Vigo County, IN
45500Texarkana, TX-Texarkana, AR0.8413
Miller County, AR
Bowie County, TX
45780Toledo, OH0.9524
Fulton County, OH
Lucas County, OH
Ottawa County, OH
Wood County, OH
45820Topeka, KS0.8904
Jackson County, KS
Jefferson County, KS
Osage County, KS
Shawnee County, KS
Wabaunsee County, KS
45940Trenton-Ewing, NJ1.0276
Mercer County, NJ
46060Tucson, AZ0.8926
Pima County, AZ
46140Tulsa, OK0.8690
Creek County, OK
Okmulgee County, OK
Osage County, OK
Pawnee County, OK
Rogers County, OK
Tulsa County, OK
Start Printed Page 30306
Wagoner County, OK
46220Tuscaloosa, AL0.8336
Greene County, AL
Hale County, AL
Tuscaloosa County, AL
46340Tyler, TX0.9502
Smith County, TX
46540Utica-Rome, NY0.8295
Herkimer County, NY
Oneida County, NY
46660Valdosta, GA0.8341
Brooks County, GA
Echols County, GA
Lanier County, GA
Lowndes County, GA
46700Vallejo-Fairfield, CA1.4279
Solano County, CA
46940Vero Beach, FL0.9477
Indian River County, FL
47020Victoria, TX0.8470
Calhoun County, TX
Goliad County, TX
Victoria County, TX
47220Vineland-Millville-Bridgeton, NJ1.0573
Cumberland County, NJ
47260Virginia Beach-Norfolk-Newport News, VA-NC0.8894
Currituck County, NC
Gloucester County, VA
Isle of Wight County, VA
James City County, VA
Mathews County, VA
Surry County, VA
York County, VA
Chesapeake City, VA
Hampton City, VA
Newport News City, VA
Norfolk City, VA
Poquoson City, VA
Portsmouth City, VA
Suffolk City, VA
Virginia Beach City, VA
Williamsburg City, VA
47300Visalia-Porterville, CA0.9975
Tulare County, CA
47380Waco, TX0.8146
McLennan County, TX
47580Warner Robins, GA0.8489
Houston County, GA
47644Warren-Farmington Hills-Troy, MI1.0112
Lapeer County, MI
Livingston County, MI
Macomb County, MI
Oakland County, MI
St. Clair County, MI
47894Washington-Arlington-Alexandria, DC-VA&-MD-WV1.1023
District of Columbia, DC
Calvert County, MD
Charles County, MD
Prince George's County, MD
Arlington County, VA
Clarke County, VA
Fairfax County, VA
Fauquier County, VA
Loudoun County, VA
Prince William County, VA
Spotsylvania County, VA
Stafford County, VA
Warren County, VA
Alexandria City, VA
Fairfax City, VA
Start Printed Page 30307
Falls Church City, VA
Fredericksburg City, VA
Manassas City, VA
Manassas Park City, VA
Jefferson County, WV
47940Waterloo-Cedar Falls, IA0.8633
Black Hawk County, IA
Bremer County, IA
Grundy County, IA
48140Wausau, WI0.9570
Marathon County, WI
48260Weirton-Steubenville, WV-OH0.8280
Jefferson County, OH
Brooke County, WV
Hancock County, WV
48300Wenatchee, WA0.9427
Chelan County, WA
Douglas County, WA
48424West Palm Beach-Boca Raton-Boynton Beach, FL1.0362
Palm Beach County, FL
48540Wheeling, WV-OH0.7449
Belmont County, OH
Marshall County, WV
Ohio County, WV
48620Wichita, KS0.9457
Butler County, KS
Harvey County, KS
Sedgwick County, KS
Sumner County, KS
48660Wichita Falls, TX0.8332
Archer County, TX
Clay County, TX
Wichita County, TX
48700Williamsport, PA0.8485
Lycoming County, PA
48864Wilmington, DE-MD-NJ1.1049
New Castle County, DE
Cecil County, MD
Salem County, NJ
48900Wilmington, NC0.9237
Brunswick County, NC
New Hanover County, NC
Pender County, NC
49020Winchester, VA-WV1.0496
Frederick County, VA
Winchester City, VA
Hampshire County, WV
49180Winston-Salem, NC0.9401
Davie County, NC
Forsyth County, NC
Stokes County, NC
Yadkin County, NC
49340Worcester, MA1.0996
Worcester County, MA
49420Yakima, WA1.0322
Yakima County, WA
49500Yauco, PR0.4493
Guánica Municipio, PR
Guayanilla Municipio, PR
Peñuelas Municipio, PR
Yauco Municipio, PR
49620York-Hanover, PA0.9150
York County, PA
49660Youngstown-Warren-Boardman, OH-PA0.9237
Mahoning County, OH
Trumbull County, OH
Mercer County, PA
49700Yuba City, CA1.0363
Sutter County, CA
Yuba County, CA
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49740Yuma, AZ0.8871
Yuma County, AZ

Table 2b.—Proposed Inpatient Rehabilitation Facility Wage Index (Based on Proposed CBSA Labor Market Areas) for Rural Areas for Discharges Occurring on or After October 1, 2005

CBSA codeNonurban areaFull wage index
01Alabama0.7628
02Alaska1.1746
03Arizona0.8936
04Arkansas0.7406
05California1.0524
06Colorado0.9368
07Connecticut1.1917
08Delaware0.9503
10Florida0.8574
11Georgia0.7733
12Hawaii1.0522
13Idaho0.8227
14Illinois0.8339
15Indiana0.8653
16Iowa0.8475
17Kansas0.8079
18Kentucky0.7755
19Louisiana0.7345
20Maine0.9039
21Maryland0.9220
22Massachusetts 21.0216
23Michigan0.8786
24Minnesota0.9330
25Mississippi0.7635
26Missouri0.7762
27Montana0.8701
28Nebraska0.9035
29Nevada0.9280
30New Hampshire0.9940
31New Jersey 1
32New Mexico0.8680
33New York0.8151
34North Carolina0.8563
35North Dakota0.7743
36Ohio0.8693
37Oklahoma0.7686
38Oregon0.9914
39Pennsylvania0.8310
40Puerto Rico 20.4047
41Rhode Island 1
42South Carolina0.8683
43South Dakota0.8398
44Tennessee0.7869
45Texas0.7966
46Utah0.8287
47Vermont0.9375
48Virgin Islands0.7456
49Virginia0.8049
50Washington1.0312
51West Virginia0.7865
52Wisconsin0.9492
53Wyoming0.9182
65Guam0.9611
1 All counties within the State are classified urban.
2 Massachusetts and Puerto Rico have areas designated as rural, however, no short-term, acute care hospitals are located in the area(s) for FY 2006 under CBSA-based designations. Therefore, we are proposing to use FY 2001 MSA based hospital wage data.

Table 3.—Inpatient Rehabilitation Facilities With Corresponding State and County Location; Current Labor Market Area Designation; and Proposed New CBSA-Based Labor Market Area Designation

Provider numberProvider nameSSA State and county codeFY 06 MSA codeFY 06 CBSA code
26T1079TH FLOOR REHAB26470376028140
39T231DABINGTON MEMORIAL HOSPITAL39560616037964
193067ACADIA REHABILITATION HOSPITAL19000388019
24T043ACUTE CARE REHABILITATION-ALMC242302424
42T070ACUTE REHAB UNIT AT TUOMEY HEALTHCARE SYSTEM42420814044940
14T182ADVOCATE ILLINOIS MASONIC MEDICAL CENTER14141160016974
14T223ADVOCATE LUTHERAN GENERAL HOSPITAL14141160016974
19T202AHS SUMMIT HOSPITAL LLC19160076012940
05T320ALAMEDA COUNTY MEDICAL CENTER05000577536084
02T017ALASKA REGIONAL HOSPITAL02020038011260
33T013ALBANY MEDICAL CENTER HOSP33000016010580
14T258ALEXIAN BROTHERS MEDICAL CENTER14141160016974
05T281ALHAMBRA HOSPITAL MEDICAL CENTER05200448031084
52T096ALL SAINTS HEALTHCARE, INC.52500660039540
39T074ALLEGHENY GENERAL HOSPITAL SUBURBAN CAMPUS39010628038300
17T116ALLEN COUNTY HOSPITAL170001717
36T131ALLIANCE COMMUNITY HOSPITAL36770132015940
393030ALLIED SERVICES INST OF REHAB SERVICES39420756042540
05T305ALTA BATES MEDICAL CENTER05000577536084
39T073ALTOONA HOSPITAL39120028011020
39T121ALTOONA REGIONAL HEALTH SYSTEM39120028011020
35T019ALTRU REHABILITATION CENTER35170298524220
05T583ALVARADO HOSPITAL MEDICAL CENTER INC.05470732041740
33T010AMSTERDAM MEMORIAL HOSPITAL33380016033
Start Printed Page 30309
01T036ANDALUSIA REGIONAL HOSPITAL011900101
393051ANGELA JANE PAVILION39620616037964
423029ANMED HEALTHSOUTH REHABILITATION HOSPITAL42030316011340
04T039ARKANSAS METHODIST HOSPITAL042700404
39T163ARMSTRONG COUNTY MEMORIAL HOSPITAL390703938300
11T115ATLANTA MEDICAL CENTER11470052012060
15T074AUGUST F. HOOK REHAB CENTER15480348026900
49T018AUGUSTA MEDICAL CENTER498914949
52T193AURORA BAYCARE MEDICAL CENTER52040308024580
52T102AURORA LAKELAND MEDICAL CENTER REHAB UNIT526305252
52T035AURORA SHEBOYGAN MEMORIAL MEDICAL CENTER REHAB UNI52580762043100
52T064AURORA SINAI MEDICAL CENTER52390508033340
43T016AVERA MCKENNAN HOSPITAL43490776043620
43T012AVERA SACRED HEART HOSPITAL436704343
43T014AVERA ST. LUKE'S430604343
45T280BACHARACH INSTITUTE FOR REHABILITATION31000192019124
313030BALL MEMORIAL HOSPITAL-REHAB15170056012100
15T089BAPTIST HEALTH REHABILITATION INSTITUTE04590528034620
043026BAPTIST HEALTH SYSTEM45130440030780
45T058BAPTIST HOSPITAL DAVIS CTR FOR REHABILITATION10120724041700
10T008BAPTIST HOSPITAL DESOTO25160500033124
25T141BAPTIST HOSPITAL EAST18550492032820
18T130BAPTIST HOSPITALS OF SOUTHEAST TEXAS45700452031140
45T346BAPTIST MEMORIAL HOSPITAL NORTH MISSISSIPPI25350084013140
25T034BAPTIST MEMORIAL MED CENTER, NO LITTLE ROCK045902525
04T036BAPTIST REGIONAL MEDICAL CENTER18990440030780
18T080BAPTIST REHAB CENTER441801818
44T133BAPTIST REHABILITATION GERMANTOWN44780536034980
44T147BARBERTON CITIZENS HOSPITAL36780492032820
36T019BARTLETT REGIONAL HOSPITAL02110008010420
02T008BASTROP REHABILITATION HOSPITAL193300202
193058BATON ROUGE GENERAL MEDICAL CENTER191601919
19T065BAXTER REGIONAL MEDICAL CENTER04020076012940
04T027BAY MEDICAL CENTER FOR REHABILITATION230800404
23T041BAYHEALTH MEDICAL CENTER08000696013020
08T004BAYLOR ALL SAINTS MEDICAL CENTER OF FORT WORTH45910219020100
45T137BAYLOR INSTITUTE FOR REHABILITATION AT GASTON45390280023104
453036BAYLOR MEDICAL CENTER45390192019124
45T079BAYLOR MEDICAL CENTER AT GARLAND45390192019124
45T097BAYSHORE MEDICAL CENTER45610336026420
27T0