[Federal Register: May 25, 2005 (Volume 70, Number 100)]
[Proposed Rules]
[Page 30187-30327]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
[DOCID:fr25my05-49]
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Part II
Department of Health and Human Services
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Centers for Medicare & Medicaid Services
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42 CFR Part 412
Medicare Program; Inpatient Rehabilitation Facility Prospective Payment
System for FY 2006; Proposed Rule
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 412
[CMS-1290-P]
RIN 0938-AN43
Medicare Program; Inpatient Rehabilitation Facility Prospective
Payment System for FY 2006
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Proposed rule.
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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.
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).
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
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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 Sec.
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
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applied to the standard payment conversion factor to compute the
unadjusted Federal prospective payment rates. Under the current system,
adjustments that accounted for geographic variations in wages (wage
index), the percentage of low-income patients, and location in a rural
area were applied to the IRF's unadjusted Federal prospective payment
rates. In addition, adjustments were made to account for the early
transfer of a patient, interrupted stays, and high cost outliers.
Lastly, the IRF's final prospective payment amount was determined
under the transition methodology prescribed in section 1886(j) of the
Act. Specifically, for cost reporting periods that began on or after
January 1, 2002 and before October 1, 2002, section 1886(j)(1) of the
Act and as specified in Sec. 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 66\2/3\ percent of the
Federal IRF PPS rate and 33\1/3\ percent of the payment that the IRF
would have been paid had the IRF PPS not been implemented. However,
during the transition period, an IRF with a cost reporting period
beginning on or after January 1, 2002 and before October 1, 2002 could
have elected to bypass this blended payment and be paid 100 percent of
the Federal IRF PPS rate. For cost reporting periods beginning on or
after October 1, 2002 (FY 2003), the transition methodology expired,
and payments for all IRFs consist of 100 percent of the Federal IRF PPS
rate.
We established a CMS Web site that contains useful information
regarding the IRF PPS. The Web site URL is http://www.cms.hhs.gov/providers/irfpps/default.asp
and may be accessed to download or view
publications, software, and other information pertinent to the IRF PPS.
B. Requirements for Updating the Prospective Payment Rates for IRFs
On August 7, 2001, we published a final rule entitled ``Medicare
Program; Prospective Payment System for Inpatient Rehabilitation
Facilities'' in the Federal Register (66 FR at 41316), that established
a PPS for IRFs as authorized under section 1886(j) of the Act and
codified at subpart P of part 412 of the Medicare regulations. In the
August 7, 2001 final rule, we set forth the per discharge Federal
prospective payment rates for fiscal year (FY) 2002 that provided
payment for inpatient operating and capital costs of furnishing covered
rehabilitation services (that is, routine, ancillary, and capital
costs) but not costs of approved educational activities, bad debts, and
other services or items that are outside the scope of the IRF PPS. The
provisions of the August 7, 2001 final rule were effective for cost
reporting periods beginning on or after January 1, 2002. On July 1,
2002, we published a correcting amendment to the August 7, 2001 final
rule in the Federal Register (67 FR at 44073). Any references to the
August 7, 2001 final rule in this proposed rule include the provisions
effective in the correcting amendment.
Section 1886(j)(5) of the Act and Sec. 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 Sec. 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 Sec. 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
http://www.cms.hhs.gov/providers/irfpps/default.asp).
Once the patient is discharged, the IRF completes the Medicare
claim (UB-92 or its equivalent) using 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
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transaction standards. See the program claim memoranda issued and
published by CMS at www.cms.hhs.gov/providers/edi/default.asp, http://www.cms.hhs.gov/provider/edi/default.asp
and listed in the addenda to
the Medicare Intermediary Manual, Part 3, section 3600. Instructions
for the limited number of claims submitted to Medicare on paper are
located in section 3604 of Part 3 of the Medicare Intermediary Manual.)
The Medicare Fiscal Intermediary (FI) processes the claim through
its software system. This software system includes pricing programming
called the PRICER software. The PRICER software uses the CMG 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
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data, were released in September 1997 and are contained in two reports
available through the National Technical Information Service (NTIS).
The reports are: Classification System for Inpatient Rehabilitation
Patients--A Review and Proposed Revisions to the Function Independence
Measure-Function Related Groups, NTIS order number PB98-105992INZ, and
Prospective Payment System for Inpatient Rehabilitation, NTIS order
number PB98-106024INZ.
In July 1999, we contracted with RAND to update its earlier
research. The update included an analysis of Functional Independence
Measure (FIM) data, the Function Related Groups (FRGs), and the model
rehabilitation PPS using 1996 and 1997 data. The purpose of updating
the earlier research was to develop the underlying data necessary to
support the Medicare IRF PPS based on CMGs for the November 3, 2000
proposed rule (65 FR at 66313). RAND expanded the scope of its earlier
research to include the examination of several payment elements, such
as comorbidities, facility-level adjustments, and implementation
issues, including evaluation and monitoring. Then, to develop the
provisions of the August 7, 2001 final rule (66 FR 41316, 41323), RAND
did similar analysis on calendar year 1998 and 1999 Medicare Provider
Analysis and Review (MedPAR) files and patient assessment data.
We have continued to contract with RAND to help us identify
potential refinements to the IRF PPS. 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
Sec. 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.
[[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 Sec. 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
[[Page 30194]]
classification system are a result of real changes and are not a result
of changes in coding that are unrelated to real changes in case mix.
A comorbidity is a specific patient condition that is secondary to
the patient's principal diagnosis or impairment that is used to place a
patient into a RIC. A patient could have one or more comorbidities
present during the inpatient rehabilitation stay. Our analysis for the
August 7, 2001 final rule found that the presence of a comorbidity
could have a major effect on the cost of furnishing inpatient
rehabilitation care. We also stated that the effect of comorbidities
varied across RICs, significantly increasing the costs of patients in
some RICs, while having no effect in others. Therefore, for the August
7, 2001 final rule, we linked frequently occurring comorbidities to
impairment categories in order to ensure that all of the chosen
comorbidities were not an inherent part of the diagnosis that assigns
the patient to the RIC.
Furthermore, in the August 7, 2001 final rule, we indicated that
comorbidities can affect cost per case for some of the CMGs, but not
all. When comorbidities substantially increased the average cost of the
CMG and were determined to be clinically relevant (not inherent in the
diagnosis in the RIC), we developed CMG relative weights adjusted for
comorbidities (Sec. 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 Sec.
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
[[Page 30195]]
additional 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 code Abbreviated code title Condition
----------------------------------------------------------------------------------------------------------------
235.1.................... Unc behav neo oral/phar...... Miscellaneous throat conditions.
933.1.................... Foreign body in larynx....... Miscellaneous throat conditions.
934.1.................... Foreign body bronchus........ Miscellaneous throat conditions.
530.0.................... Achalasia & cardiospasm...... Esophegeal conditions.
530.3.................... Esophageal stricture......... Esophegeal conditions.
530.6.................... Acquired esophag diverticulum Esophegeal conditions.
V46.1.................... Dependence on respirator..... Ventilator status.
799.4.................... Cachexia..................... Cachexia.
V49.75................... Status amputation below knee. Amputation of LE.
V49.76................... Status amputation above knee. Amputation of LE.
V497.7................... Status amputation hip........ Amputation of LE.
356.4.................... Idiopathic progressive Meningitis and encephalitis.
polyneuropathy.
250.90................... Diabetes II, w unspecified Non-renal Complications of Diabetes.
complications, not stated as
uncontrolled.
250.93................... Diabetes I, w unspecified Non-renal Complications of Diabetes.
complications, uncontrolled.
261...................... Nutritional Marasmus......... Malnutrition.
262...................... Other severe protein calorie Malnutrition.
deficiency.
410.91................... AMI, NOS, initial............ Major comorbidities.
410.X1................... Specific AMI, initial........ Major comorbidities.
260...................... Kwashiorkor.................. Malnutrition.
----------------------------------------------------------------------------------------------------------------
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
[[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 x (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 Sec. 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
[[Page 30197]]
limit its financial ``losses''; conversely, if the PPS overpays,
resources are wasted because IRFs' payments exceed the costs of
providing care for a particular case.
The fiscal year 2003 data file currently available for refining the
CMGs 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.
BILLING CODE 4120-01-P
[[Page 30198]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.000
[[Page 30199]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.001
[[Page 30200]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.002
[[Page 30201]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.003
[[Page 30202]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.004
[[Page 30203]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.005
[[Page 30204]]
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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
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improvements in the alignment between Medicare payments and actual IRF
costs. In addition to analyzing fiscal year 2003 data, RAND also
convened a TEP, made up of researchers from industry, provider
organizations, government, and academia, to provide support and
guidance through the process of developing possible refinements to the
PPS. Members of the TEP reviewed drafts of RAND's reports, offered
suggestions for additional analyses, and provided clinicians' views of
the importance and significance of various findings.
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
[[Page 30211]]
instructions 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
------------------------------------------------------------------------
Average
Item type Functional independence item optimal
weight
------------------------------------------------------------------------
Self.......................... Dressing lower............... 1.4
Self.......................... Toilet....................... 1.2
Self.......................... Bathing...................... 0.9
Self.......................... Eating....................... 0.6
Self.......................... Dressing upper............... 0.2
Self.......................... Grooming..................... 0.2
Sphincter..................... Bladder...................... 0.5
Sphincter..................... Bowel........................ 0.2
Transfer...................... Transfer to bed.............. 2.2
Transfer...................... Transfer to toilet........... 1.4
Transfer...................... Transfer to tub.............. Not
included
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Locomotion.................... Walking...................... 1.6
Locomotion.................... Stairs....................... 1.6
------------------------------------------------------------------------
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
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have 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 x $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-
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PPS), RAND found that the observed case mix--the expected costliness of
patients--in IRFs increased by 3.4 percent between the two time
periods. Thus, we paid 3.4 percent, or about $140 million, more than
expected during 2002 because of changes in the classification of cases
in IRFs. However, RAND found little evidence that the patients admitted
to IRFs in 2002 had higher resource needs (that is, more impairments,
lower functioning, or more comorbidities) than the patients admitted in
1999. In fact, most of the changes in case mix that RAND documented
from the acute care hospital records implied that IRF patients should
have been less costly to treat in 2002 than in 1999. For example, RAND
found a 16 percent decrease in the proportion of patients treated in
IRFs following acute hospitalizations for stroke, when it compared the
results of the 2002 data with the 1999 data. Stroke patients tend to be
relatively more costly than other types of patients for IRFs because
they tend to require more intensive services than other types of
patients. A decrease in the proportion of stroke patients relative to
other types of patients, therefore, would likely contribute to a
decrease in the overall expected costliness of IRF patients. RAND also
found a 22 percent increase in the proportion of cases treated in IRFs
following a lower extremity joint replacement. Lower extremity joint
replacement patients tend to be relatively less costly for IRFs than
other types of patients because their care needs tend to be less
intensive than other types of patients. For this reason, the increase
in the proportion of these patients treated in IRFs would suggest a
decrease in the overall expected costliness of IRF patients.
We asked RAND to quantify the amount of the case mix change that
was due to real case mix change (that is, the extent to which IRF
patients had more impairments, lower functioning, or more
comorbidities) and the amount that was due to coding. However, while
the data permit RAND to observe the total change in expected costliness
of patients over time with some precision, estimating the amount of
this total change that is real and the amount that is due to coding
generally cannot be done with the same level of precision. Therefore,
in order to quantify the amounts that were due to real case mix change
and the amounts that were due to coding, RAND used two approaches to
give a range of estimates within which the correct estimates would
logically fall--(1) one that potentially underestimates the amount of
real case mix change and overestimates the amount of case mix change
due to coding; and (2) one that potentially overestimates real change
and underestimates change due to coding. These two approaches give us a
range of estimates, which 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
[[Page 30222]]
based on patient characteristics. In part, this second approach
involves analyzing some specific examples of coding that we know have
changed over time, such as direct indications of improvements in
impairment coding, changes in coding instruction for bladder and bowel
functioning, and dramatic increases in coding of certain conditions
that affect patients' placement into tiers (resulting in higher
payments).
Using the two approaches, RAND found that real case mix changes in
IRFs over this period ranged from a decrease of 2.4 percent (using the
first approach) to an increase of 1.5 percent (using the second
approach). This suggests that coding changes accounted for between 1.9
percent (if real case mix increased by 1.5 percent (that is, 3.4
percent minus 1.5 percent)) and 5.8 percent (if real case mix decreased
by 2.4 percent (that is, 3.4 percent plus 2.4 percent)) of the increase
in aggregate payments for 2002 compared with 1999. Thus, RAND
recommended decreasing the standard per discharge payment amount by
between 1.9 and 5.8 percent to adjust for the coding changes. We 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 Sec. 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
[[Page 30223]]
for IRFs, IPFs, and LTCHs are noticeably smaller than those for
children's and cancer hospitals.
In the following discussion, we provide a background on market
baskets and describe the methodologies 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.
[[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.)
BILLING CODE 4120-01-P
[[Page 30225]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.019
[[Page 30226]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.020
[[Page 30227]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.021
BILLING CODE 4120-01-C
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,
[[Page 30228]]
we used CPIs only if an appropriate PPI was not available, or if the
expenditures were more similar to those of retail consumers in general
rather than purchases at the wholesale level. For example, the CPI for
food purchased away from home is used as a proxy for contracted food
services.
Employment Cost Indexes--Employment Cost Indexes (ECIs)
measure the rate of change in employee wage rates and employer costs
for employee benefits per hour worked. These indexes are fixed-weight
indexes and strictly measure the change in wage rates and employee
benefits per hour. Appropriately, they are not affected by shifts in
employment mix.
We evaluated the price proxies using the criteria of reliability,
timeliness, availability, and relevance. Reliability indicates that the
index is based on valid statistical methods and has low sampling
variability. Timeliness implies that the proxy is published regularly,
at least once a quarter. Availability means that the proxy is publicly
available. Finally, relevance means that the proxy is applicable and
representative of the cost category weight to which it is applied. The
CPIs, PPIs, and ECIs selected by us to be 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
[[Page 30229]]
category for blood and blood products, using the BLS Producer Price
Index for blood and derivatives as a price proxy. A review of recent
trends in the PPI for blood and derivatives suggests that its movements
may not be consistent with the trends in blood costs faced by
hospitals. While this proxy did not match exactly with the product
hospitals are buying, its trend over time appears to be reflective of
the historical price changes of blood purchased by hospitals. However,
an apparent divergence in trends in the PPI for blood and derivatives
and trends in blood costs faced by hospitals over recent years led us
to reevaluate whether the PPI for blood and derivatives was an
appropriate measure of the changing price of blood. 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
[[Page 30230]]
for 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
[[Page 30231]]
amount 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.
BILLING CODE 4120-01-P
[[Page 30232]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.022
BILLING CODE 4120-01-C
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
[[Page 30233]]
forecast 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
----------------------------------------------------------------------------------------------------------------
FY 1997-based
Proposed rebased excluded hospital
Fiscal year (FY) FY 2002-based RPL market basket with
market basket capital
----------------------------------------------------------------------------------------------------------------
Historical data:
FY 2000............................................................. 3.1 3.1
FY 2001............................................................. 4.0 4.0
FY 2002............................................................. 3.9 3.6
FY 2003............................................................. 3.8 3.7
FY 2004............................................................. 3.6 3.6
Average FYs 2000-2004............................................... 3.7 3.6
Forecast:
FY 2005............................................................. 3.7 3.8
FY 2006............................................................. 3.1 3.1
FY 2007............................................................. 2.9 2.8
FY 2008............................................................. 2.9 2.8
Average FYs 2005-2008............................................... 3.2 3.1
----------------------------------------------------------------------------------------------------------------
d. Labor-Related Share
Section 1886(j)(6) of the Act specifies that the Secretary shall
adjust the proportion (as estimated by the Secretary from time to time)
of rehabilitation facilities' costs which are attributable to wages and
wage-related costs, of the prospective payment rates computed under
paragraph (3) for area differences in wage levels by a factor
(established by the Secretary) reflecting the relative hospital wage
level in the geographic area of the rehabilitation facility compared to
the national average wage level for such facilities. Not later than
October 1, 2001 (and at least every 36 months thereafter), the
Secretary shall update the factor under the preceding sentence on the
basis of information available to the Secretary (and updated as
appropriate) of the wages and wage-related costs incurred in furnishing
rehabilitation services. Any adjustments or updates made under this
paragraph for a fiscal year shall be made in a manner that assures that
the aggregated payments under this subsection in the fiscal year shall
be made in a manner that assures that the aggregated payments under
this subsection in the fiscal year are not greater or less than those
that would have been made in the year without such adjustment.
The labor-related share is determined by identifying the national
average proportion of operating costs that are related to, influenced
by, or vary with the local labor market. Using our current definition
of labor-related, the labor-related share is the sum of the relative
importance of wages and salaries, fringe benefits, professional fees,
labor-intensive services, and a portion of the capital share from an
appropriate market basket. 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.
[[Page 30234]]
Table 10.--Proposed Total Labor-Related Share
----------------------------------------------------------------------------------------------------------------
FY 1997 excluded
Proposed FY 2002- hospital with
based RPL market capital market
Cost category basket relative basket relative
importance importance
(percent) FY 2006 (percent) FY 2006
----------------------------------------------------------------------------------------------------------------
Wages and salaries...................................................... 52.823 48.432
Employee benefits....................................................... 13.863 11.415
Professional fees....................................................... 2.907 4.540
All other labor intensive services...................................... 2.189 4.496
---------------------
Subtotal............................................................ 71.782 68.883
Labor-related share of capital costs.................................... 4.176 3.307
---------------------
Total............................................................... 75.958 72.190
----------------------------------------------------------------------------------------------------------------
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 Sec. 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 Sec. 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 Sec. 412.602 and further defined in Sec.
412.62(f)(1)(ii) and Sec. 412.62(f)(1)(iii) as urban and rural areas,
respectively. An urban area, under the IRF PPS, is defined in Sec.
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 Sec. 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 Sec. 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 Sec. 412.62(f)(1)(ii) and
(f)(1)(iii), respectively, were used under the IPPS from FYs 1985
through 2004 (as specified in Sec. 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
[[Page 30235]]
update 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 (Sec.
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 Sec.
412.602 and further defined in Sec. 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
[[Page 30236]]
the 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 Sec. 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 Sec. 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 Sec.
412.64(b)(1)(ii)(A) through (C). (As a conforming change, we are also
proposing to revise Sec. 412.602, definitions for rural and urban
areas effective for discharges occurring on or after October 1, 2005
would be defined in Sec. 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
Sec. 412.62(f)(1)(ii) and Sec. 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 Sec. 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
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Metropolitan Areas with a population of 1 million or more and comprised
of two or more PMSAs. Under the IRF PPS, we currently use the PMSAs
rather than CMSAs to define labor market areas because they comprise a
smaller geographic area with potentially varying labor costs due to
different local economies. We believe that CMSAs may be too large of an
area with a relatively large number of hospitals, to accurately reflect
the local labor costs of all the individual hospitals included in that
relatively ``large'' area. A large market area designation increased
the likelihood of including many hospitals located in areas with very
different labor market conditions within the same market area
designation. This variation could increase the difficulty in
calculating a single wage index that 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
[[Page 30238]]
State that are not assigned to an MSA into a statewide rural labor
market area. Therefore, we used the terms ``urban'' and ``rural'' wage
indices in the past for ease of reference. However, the introduction of
Micropolitan Areas by the OMB potentially complicates this terminology
because these areas include many hospitals that are currently included
in the statewide rural labor market areas.
We 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.
[[Page 30239]]
In 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
[[Page 30240]]
policy 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 Sec. 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 Sec. 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
[[Page 30241]]
1886(d)(10) of the Act which permits reclassification is applicable
only to inpatient acute care hospitals at this time. The wage
adjustment established under the IRF PPS is based on an IRF's actual
location without regard to the urban or rural designation of any
related or affiliated provider.
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 Sec. 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,
[[Page 30242]]
teaching 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 Sec. 412.624 to add a new section (e)(4) as
the teaching status adjustment. Specifically, Sec. 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 Sec. 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
[[Page 30243]]
further 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 Sec. 413.75 through Sec. 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 Sec. 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 Sec. 413.79(l))
may be subsequently adjusted in accordance with the policies that are
being applied in the IPF PPS (as described in Sec.
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 Sec. 412.105(f)(1)(vi), Sec. 413.75(b), and Sec. 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 Sec. 412.322) and
the IPF PPS (as specified at Sec. 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
[[Page 30244]]
payments 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 Sec. 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
[[Page 30245]]
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.
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 Sec. 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 =
[GRAPHIC] [TIFF OMITTED] TP25MY05.023
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 Sec. 412.624(e)(4) of the
regulations (which would be redesignated as Sec. 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.
[[Page 30246]]
Under Sec. 412.624(e)(4) (which would be redesignated as Sec.
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 Sec.
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 Sec. 412.624(e)(4) (which would be
redesignated as Sec. 412.624(e)(5)) and Sec. 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 Sec. 412.624(e)(4) (which would be
redesignated as Sec. 412.624(e)(5)) and Sec. 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 Sec. 412.624(d) in
[[Page 30247]]
order to make the proposed changes to the tiers and CMGs, the rural
adjustment, the LIP adjustment, and the proposed teaching status
adjustment in a budget neutral manner. Accordingly, we are proposing to
revise Sec. 412.624(d) by adding a section Sec. 412.624(d)(4) for
fiscal year 2006. Specifically, we are proposing to revise the
methodology found in Sec. 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 Sec. 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
[[Page 30248]]
same 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 Sec.
412.624(c). In accordance with the methodology described in Sec.
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 Sec.
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 Sec. 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 Sec. 412.624(c)(3)(i), the standard payment conversion
factor for FY 2002 of $11,838.00 reflected the budget neutrality
adjustment described in Sec. 412.624(d)(2). Under the then existing
Sec. 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 Sec.
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
[[Page 30249]]
shown 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.
BILLING CODE 4120-01-P
[GRAPHIC] [TIFF OMITTED] TP25MY05.024
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.
[[Page 30250]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.025
[[Page 30251]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.026
[[Page 30252]]
[GRAPHIC] [TIFF OMITTED] TP25MY05.027
BILLING CODE 4120-01-C
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 Sec. 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 Sec. 412.62(f)(1)(iii). For discharges occurring on or
after October 1, 2005, rural area means an area as defined in Sec.
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 Sec. 412.62(f)(1)(ii). For discharges occurring on or after
October 1, 2005, urban area means an area as defined in Sec.
412.64(b)(1)(ii)(A) and Sec. 412.64(b)(1)(ii)(B).
[[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 ``Sec. Sec. 413.85 and 413.86
of this chapter'' and add in their place ``Sec. 413.75 and Sec.
413.85 of this chapter.'' In paragraph (b)(2)(i), we are proposing to
remove the cross reference ``Sec. 413.80 of this chapter'' and add in
its place ``Sec. 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 Sec. 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
[[Page 30254]]
payments. 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.
[[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.
[[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
Percent change in area wage index of IRFs
------------------------------------------------------------------------
Decrease Greater Than 10.0.................................... 0.7
Decrease Between 5.0 and 10.0................................. 2.5
Decrease Between 2.0 and 5.0.................................. 5.7
Decrease Between 0 and 2.0.................................... 25.6
No Change..................................................... 37.2
Increase Between 0 and 2.0.................................... 22.1
Increase Between 2.0 and 5.0.................................. 3.3
Increase Between 5.0 and 10.0................................. 1.6
Increase Greater Than 10.0.................................... 1.5
---------
Total \1\................................................... 100.0
------------------------------------------------------------------------
\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
[[Page 30265]]
no 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 =
[GRAPHIC] [TIFF OMITTED] TP25MY05.035
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
[[Page 30266]]
impacts 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)
------------------------------------------------------------------------
Category Transfers
------------------------------------------------------------------------
Annualized Monetized Transfers............ $180
From Whom To Whom? Federal Government To IRF
Medicare Providers.
------------------------------------------------------------------------
List of Subjects in 42 CFR Part 412
Administrative practice and procedure, Health facilities, Medicare,
Puerto Rico, Reporting and recordkeeping requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services proposes to amend 42 CFR chapter IV as follows:
PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
1. The authority citation for part 412 continues to read as
follows:
Authority: Secs. 1102 and 1871 of the Social Security Act (42
U.S.C. 1302 and 1395hh).
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:
Sec. 412.602 Definitions.
* * * * *
Rural area means: For cost-reporting periods beginning on or after
January 1, 2002, with respect to discharges occurring during the period
covered by such cost reports but before October 1, 2005, an area as
defined in Sec. 412.62(f)(1)(iii). For discharges occurring on or
after October 1, 2005, rural area means an area as defined in Sec.
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 Sec. 412.62(f)(1)(ii). For discharges occurring on or after
October 1, 2005,
[[Page 30267]]
urban area means an area as defined in Sec. 412.64(b)(1)(ii)(A) and
Sec. 412.64(b)(1)(ii)(B).
Sec. 412.622 [Amended]
3. Section 412.622 is amended by--
A. In paragraph (b)(1), removing the cross references ``Sec. Sec.
413.85 and 413.86 of this chapter'' and adding in their place ``Sec.
413.75 and Sec. 413.85 of this chapter''.
B. In paragraph (b)(2)(i), removing the cross reference to ``Sec.
413.80 of this chapter'' and adding in its place ``Sec. 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:
Sec. 412.624 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 Sec. 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 Sec. 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 Sec.
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 Sec. 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)
Dated: April 14, 2005.
Mark B. McClellan,
Administrator, Centers for Medicare & Medicaid Services.
Approved: May 4, 2005.
Michael O. Leavitt,
Secretary.
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
------------------------------------------------------------------------
Urban area (Constituent Counties Wage
MSA or County Equivalents) index
------------------------------------------------------------------------
0040...................... Abilene, TX...................... 0.8009
Taylor, TX
0060...................... Aguadilla, PR.................... 0.4294
Aguada, PR
Aguadilla, PR
Moca, PR
0080...................... Akron, OH........................ 0.9055
Portage, OH
Summit, OH
0120...................... Albany, GA....................... 1.1266
Dougherty, GA
Lee, GA
0160...................... Albany-Schenectady-Troy, NY...... 0.8570
Albany, NY
Montgomery, NY
Rensselaer, NY
[[Page 30268]]
Saratoga, NY
Schenectady, NY
Schoharie, NY
0200...................... Albuquerque, NM.................. 1.0485
Bernalillo, NM
Sandoval, NM
Valencia, NM
0220...................... Alexandria, LA................... 0.8171
Rapides, LA
0240...................... Allentown-Bethlehem-Easton, PA... 0.9536
Carbon, PA
Lehigh, PA
Northampton, PA
0280...................... Altoona, PA...................... 0.8462
Blair, PA
0320...................... Amarillo, TX..................... 0.9178
Potter, TX
Randall, TX
0380...................... Anchorage, AK.................... 1.2109
Anchorage, AK
0440...................... Ann Arbor, MI.................... 1.0816
Lenawee, MI
Livingston, MI
Washtenaw, MI
0450...................... Anniston,AL...................... 0.7881
Calhoun, AL
0460...................... Appleton-Oshkosh-Neenah, WI...... 0.9115
Calumet, WI
Outagamie, WI
Winnebago, WI
0470...................... Arecibo, PR...................... 0.3757
Arecibo, PR
Camuy, PR
Hatillo, PR
0480...................... Asheville, NC.................... 0.9501
Buncombe, NC
Madison, NC
0500...................... Athens, GA....................... 1.0202
Clarke, GA
Madison, GA
Oconee, GA
0520...................... Atlanta, GA...................... 0.9971
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
0560...................... Atlantic City-Cape May, NJ....... 1.0907
Atlantic City, NJ
Cape May, NJ
0580...................... Auburn-Opelika, AL............... 0.8215
Lee, AL
0600...................... Augusta-Aiken, GA-SC............. 0.9208
Columbia, GA
McDuffie, GA
[[Page 30269]]
Richmond, GA
Aiken, SC
Edgefield, SC
0640...................... Austin-San Marcos, TX............ 0.9595
Bastrop, TX
Caldwell, TX
Hays, TX
Travis, TX
Williamson, TX
0680...................... Bakersfield, CA.................. 1.0036
Kern, CA
0720...................... Baltimore, MD.................... 0.9907
Anne Arundel, MD
Baltimore, MD
Baltimore City, MD
Carroll, MD
Harford, MD
Howard, MD
Queen Annes, MD
0733...................... Bangor, ME....................... 0.9955
Penobscot, ME
0743...................... Barnstable-Yarmouth, MA.......... 1.2335
Barnstable, MA
0760...................... Baton Rouge, LA.................. 0.8354
Ascension, LA
East Baton Rouge
Livingston, LA
West Baton Rouge, LA
0840...................... Beaumont-Port Arthur, TX......... 0.8616
Hardin, TX
Jefferson, TX
Orange, TX
0860...................... Bellingham, WA................... 1.1642
Whatcom, WA
0870...................... Benton Harbor, MI................ 0.8847
Berrien, MI
0875...................... Bergen-Passaic, NJ............... 1.1967
Bergen, NJ
Passaic, NJ
0880...................... Billings, MT..................... 0.8961
Yellowstone, MT
0920...................... Biloxi-Gulfport-Pascagoula, MS... 0.8649
Hancock, MS
Harrison, MS
Jackson, MS
0960...................... Binghamton, NY................... 0.8447
Broome, NY
Tioga, NY
1000...................... Birmingham, AL................... 0.9198
Blount, AL
Jefferson, AL
St. Clair, AL
Shelby, AL
1010...................... Bismarck, ND..................... 0.7505
Burleigh, ND
Morton, ND
1020...................... Bloomington, IN.................. 0.8587
Monroe, IN
1040...................... Bloomington-Normal, IL........... 0.9111
McLean, IL
1080...................... Boise City, ID................... 0.9352
Ada, ID
Canyon, ID
1123...................... Boston-Worcester-Lawrence-Lowell- 1.1290
Brockton, MA-NH.
Bristol, MA
Essex, MA
Middlesex, MA
Norfolk, MA
Plymouth, MA
Suffolk, MA
[[Page 30270]]
Worcester, MA
Hillsborough, NH
Merrimack, NH
Rockingham, NH
Strafford, NH
1125...................... Boulder-Longmont, CO............. 1.0046
Boulder, CO
1145...................... Brazoria, TX..................... 0.8524
Brazoria, TX
1150...................... Bremerton, WA.................... 1.0614
Kitsap, WA
1240...................... Brownsville-Harlingen-San Benito, 1.0125
TX.
Cameron, TX
1260...................... Bryan-College Station, TX........ 0.9243
Brazos, TX
1280...................... Buffalo-Niagara Falls, NY........ 0.9339
Erie, NY
Niagara, NY
1303...................... Burlington, VT................... 0.9322
Chittenden, VT
Franklin, VT
Grand Isle, VT
1310...................... Caguas, PR....................... 0.4061
Caguas, PR
Cayey, PR
Cidra, PR
Gurabo, PR
San Lorenzo, PR
1320...................... Canton-Massillon, OH............. 0.8895
Carroll, OH
Stark, OH
1350...................... Casper, WY....................... 0.9243
Natrona, WY
1360...................... Cedar Rapids, IA................. 0.8975
Linn, IA
1400...................... Champaign-Urbana, IL............. 0.9527
Champaign, IL
1440...................... Charleston-North Charleston, SC.. 0.9420
Berkeley, SC
Charleston, SC
Dorchester, SC
1480...................... Charleston, WV................... 0.8876
Kanawha, WV
Putnam, WV
1520...................... Charlotte-Gastonia-Rock Hill, NC- 0.9711
SC.
Cabarrus, NC
Gaston, NC
Lincoln, NC
Mecklenburg, NC
Rowan, NC
Union, NC
York, SC
1540...................... Charlottesville, VA.............. 1.0294
Albemarle, VA
Charlottesville City, VA
Fluvanna, VA
Greene, VA
1560...................... Chattanooga, TN-GA............... 0.9207
Catoosa, GA
Dade, GA
Walker, GA
Hamilton, TN
Marion, TN
1580...................... Cheyenne, WY..................... 0.8980
Laramie, WY
1600...................... Chicago, IL...................... 1.0851
Cook, IL
De Kalb, IL
Du Page, IL
Grundy, IL
[[Page 30271]]
Kane, IL
Kendall, IL
Lake, IL
McHenry, IL
Will, IL
1620...................... Chico-Paradise, CA............... 1.0542
Butte, CA
1640...................... Cincinnati, OH-KY-IN............. 0.9595
Dearborn, IN
Ohio, IN
Boone, KY
Campbell, KY
Gallatin, KY
Grant, KY
Kenton, KY
Pendleton, KY
Brown, OH
Clermont, OH
Hamilton, OH
Warren, OH
1660...................... Clarksville-Hopkinsville, TN-KY.. 0.8022
Christian, KY
Montgomery, TN
1680...................... Cleveland-Lorain-Elyria, OH...... 0.9626
Ashtabula, OH
Geauga, OH
Cuyahoga, OH
Lake, OH
Lorain, OH
Medina, OH
1720...................... Colorado Springs, CO............. 0.9792
El Paso, CO
1740...................... Columbia MO...................... 0.8396
Boone, MO
1760...................... Columbia, SC..................... 0.9450
Lexington, SC
Richland, SC
1800...................... Columbus, GA-AL.................. 0.8690
Russell, AL
Chattanoochee, GA
Harris, GA
Muscogee, GA
1840...................... Columbus, OH..................... 0.9753
Delaware, OH
Fairfield, OH
Franklin, OH
Licking, OH
Madison, OH
Pickaway, OH
1880...................... Corpus Christi, TX............... 0.8647
Nueces, TX
San Patricio, TX
1890...................... Corvallis, OR.................... 1.0545
Benton, OR
1900...................... Cumberland, MD-WV................ 0.8662
Allegany MD
Mineral WV
1920...................... Dallas, TX....................... 1.0054
Collin, TX
Dallas, TX
Denton, TX
Ellis, TX
Henderson, TX
Hunt, TX
Kaufman, TX
Rockwall, TX
1950...................... Danville, VA..................... 0.8643
Danville City, VA
Pittsylvania, VA
1960...................... Davenport-Moline-Rock Island, IA- 0.8773
IL.
[[Page 30272]]
Scott, IA
Henry, IL
Rock Island, IL
2000...................... Dayton-Springfield, OH........... 0.9231
Clark, OH
Greene, OH
Miami, OH
Montgomery, OH
2020...................... Daytona Beach, FL................ 0.8900
Flagler, FL
Volusia, FL
2030...................... Decatur, AL...................... 0.8894
Lawrence, AL
Morgan, AL
2040...................... Decatur, IL...................... 0.8122
Macon, IL
2080...................... Denver, CO....................... 1.0904
Adams, CO
Arapahoe, CO
Broomfield, CO
Denver, CO
Douglas, CO
Jefferson, CO
2120...................... Des Moines, IA................... 0.9266
Dallas, IA
Polk, IA
Warren, IA
2160...................... Detroit, MI...................... 1.0227
Lapeer, MI
Macomb, MI
Monroe, MI
Oakland, MI
St. Clair, MI
Wayne, MI
2180...................... Dothan, AL....................... 0.7596
Dale, AL
Houston, AL
2190...................... Dover, DE........................ 0.9825
Kent, DE
2200...................... Dubuque, IA...................... 0.8748
Dubuque, IA
2240...................... Duluth-Superior, MN-WI........... 1.0356
St. Louis, MN
Douglas, WI
2281...................... Dutchess County, NY.............. 1.1657
Dutchess, NY
2290...................... Eau Claire, WI................... 0.9139
Chippewa, WI
Eau Claire, WI
2320...................... El Paso, TX...................... 0.9181
El Paso, TX
2330...................... Elkhart-Goshen, IN............... 0.9278
Elkhart, IN
2335...................... Elmira, NY....................... 0.8445
Chemung, NY
2340...................... Enid, OK......................... 0.9001
Garfield, OK
2360...................... Erie, PA......................... 0.8699
Erie, PA
2400...................... Eugene-Springfield, OR........... 1.0940
Lane, OR
2440...................... Evansville-Henderson, IN-KY...... 0.8395
Posey, IN
Vanderburgh, IN
Warrick, IN
Henderson, KY
2520...................... Fargo-Moorhead, ND-MN............ 0.9114
Clay, MN
Cass, ND
2560...................... Fayetteville, NC................. 0.9363
[[Page 30273]]
Cumberland, NC
2580...................... Fayetteville-Springdale-Rogers, 0.8636
AR.
Benton, AR
Washington, AR
2620...................... Flagstaff, AZ-UT................. 1.0611
Coconino, AZ
Kane, UT
2640...................... Flint, MI........................ 1.1178
Genesee, MI
2650...................... Florence, AL..................... 0.7883
Colbert, AL
Lauderdale, AL
2655...................... Florence, SC..................... 0.8960
Florence, SC
2670...................... Fort Collins-Loveland, CO........ 1.0218
Larimer, CO
2680...................... Ft. Lauderdale, FL............... 1.0165
Broward, FL
2700...................... Fort Myers-Cape Coral, FL........ 0.9371
Lee, FL
2710...................... Fort Pierce-Port St. Lucie, FL... 1.0046
Martin, FL
St. Lucie, FL
2720...................... Fort Smith, AR-OK................ 0.8303
Crawford, AR
Sebastian, AR
Sequoyah, OK
2750...................... Fort Walton Beach, FL............ 0.8786
Okaloosa, FL
2760...................... Fort Wayne, IN................... 0.9737
Adams, IN
Allen, IN
De Kalb, IN
Huntington, IN
Wells, IN
Whitley, IN
2800...................... Forth Worth-Arlington, TX........ 0.9520
Hood, TX
Johnson, TX
Parker, TX
Tarrant, TX
2840...................... Fresno, CA....................... 1.0407
Fresno, CA
Madera, CA
2880...................... Gadsden, AL...................... 0.8049
Etowah, AL
2900...................... Gainesville, FL.................. 0.9459
Alachua, FL
2920...................... Galveston-Texas City, TX......... 0.9403
Galveston, TX
2960...................... Gary, IN......................... 0.9342
Lake, IN
Porter, IN
2975...................... Glens Falls, NY.................. 0.8467
Warren, NY
Washington, NY
2980...................... Goldsboro, NC.................... 0.8778
Wayne, NC
2985...................... Grand Forks, ND-MN............... 0.9091
Polk, MN
Grand Forks, ND
2995...................... Grand Junction, CO............... 0.9900
Mesa, CO
3000...................... Grand Rapids-Muskegon-Holland, MI 0.9519
Allegan, MI
Kent, MI
Muskegon, MI
Ottawa, MI
3040...................... Great Falls, MT.................. 0.8810
Cascade, MT
[[Page 30274]]
3060...................... Greeley, CO...................... 0.9444
Weld, CO
3080...................... Green Bay, WI.................... 0.9586
Brown, WI
3120...................... Greensboro-Winston-Salem-High 0.9312
Point, NC.
Alamance, NC
Davidson, NC
Davie, NC
Forsyth, NC
Guilford, NC
Randolph, NC
Stokes, NC
Yadkin, NC
3150...................... Greenville, NC................... 0.9183
Pitt, NC
3160...................... Greenville-Spartanburg-Anderson, 0.9400
SC.
Anderson, SC
Cherokee, SC
Greenville, SC
Pickens, SC
Spartanburg, SC
3180...................... Hagerstown, MD................... 0.9940
Washington, MD
3200...................... Hamilton-Middletown, OH.......... 0.9066
Butler, OH
3240...................... Harrisburg-Lebanon-Carlisle, PA.. 0.9286
Cumberland, PA
Dauphin, PA
Lebanon, PA
Perry, PA
3283...................... Hartford, CT..................... 1.1054
Hartford, CT
Litchfield, CT
Middlesex, CT
Tolland, CT
3285...................... Hattiesburg, MS.................. 0.7362
Forrest, MS
Lamar, MS
3290...................... Hickory-Morganton-Lenoir, NC..... 0.9502
Alexander, NC
Burke, NC
Caldwell, NC
Catawba, NC
3320...................... Honolulu, HI..................... 1.1013
Honolulu, HI
3350...................... Houma, LA........................ 0.7721
Lafourche, LA
Terrebonne, LA
3360...................... Houston, TX...................... 1.0117
Chambers, TX
Fort Bend, TX
Harris, TX
Liberty, TX
Montgomery, TX
Waller, TX
3400...................... Huntington-Ashland, WV-KY-OH..... 0.9564
Boyd, KY
Carter, KY
Greenup, KY
Lawrence, OH
Cabell, WV
Wayne, WV
3440...................... Huntsville, AL................... 0.8851
Limestone, AL
Madison, AL
3480...................... Indianapolis, IN................. 1.0039
Boone, IN
Hamilton, IN
Hancock, IN
Hendricks, IN
[[Page 30275]]
Johnson, IN
Madison, IN
Marion, IN
Morgan, IN
Shelby, IN
3500...................... Iowa City, IA.................... 0.9654
Johnson, IA
3520...................... Jackson, MI...................... 0.9146
Jackson, MI
3560...................... Jackson, MS...................... 0.8406
Hinds, MS
Madison, MS
Rankin, MS
3580...................... Jackson, TN...................... 0.8900
Chester, TN
Madison, TN
3600...................... Jacksonville, FL................. 0.9548
Clay, FL
Duval, FL
Nassau, FL
St. Johns, FL
3605...................... Jacksonville, NC................. 0.8401
Onslow, NC
3610...................... Jamestown, NY.................... 0.7589
Chautaqua, NY
3620...................... Janesville-Beloit, WI............ 0.9583
Rock, WI
3640...................... Jersey City, NJ.................. 1.0923
Hudson, NJ
3660...................... Johnson City-Kingsport-Bristol, 0.8202
TN-VA.
Carter, TN
Hawkins, TN
Sullivan, TN
Unicoi, TN
Washington, TN
Bristol City, VA
Scott, VA
Washington, VA
3680...................... Johnstown, PA.................... 0.7980
Cambria, PA
Somerset, PA
3700...................... Jonesboro, AR.................... 0.8144
Craighead, AR
3710...................... Joplin, MO....................... 0.8721
Jasper, MO
Newton, MO
3720...................... Kalamazoo-Battlecreek, MI........ 1.0350
Calhoun, MI
Kalamazoo, MI
Van Buren, MI
3740...................... Kankakee, IL..................... 1.0603
Kankakee, IL
3760...................... Kansas City, KS-MO............... 0.9641
Johnson, KS
Leavenworth, KS
Miami, KS
Wyandotte, KS
Cass, MO
Clay, MO
Clinton, MO
Jackson, MO
Lafayette, MO
Platte, MO
Ray, MO
3800...................... Kenosha, WI...................... 0.9772
Kenosha, WI
3810...................... Killeen-Temple, TX............... 0.9242
Bell, TX
Coryell, TX
3840...................... Knoxville, TN.................... 0.8508
[[Page 30276]]
Anderson, TN
Blount, TN
Knox, TN
Loudon, TN
Sevier, TN
Union, TN
3850...................... Kokomo, IN....................... 0.8986
Howard, IN
Tipton, IN
3870...................... La Crosse, WI-MN................. 0.9289
Houston, MN
La Crosse, WI
3880...................... Lafayette, LA.................... 0.8105
Acadia, LA
Lafayette, LA
St. Landry, LA
St. Martin, LA
3920...................... Lafayette, IN.................... 0.9067
Clinton, IN
Tippecanoe, IN
3960...................... Lake Charles, LA................. 0.7972
Calcasieu, LA
3980...................... Lakeland-Winter Haven, FL........ 0.8930
Polk, FL
4000...................... Lancaster, PA.................... 0.9883
Lancaster, PA
4040...................... Lansing-East Lansing, MI......... 0.9658
Clinton, MI
Eaton, MI
Ingham, MI
4080...................... Laredo, TX....................... 0.8747
Webb, TX
4100...................... Las Cruces, NM................... 0.8784
Dona Ana, NM
4120...................... Las Vegas, NV-AZ................. 1.1121
Mohave, AZ
Clark, NV
Nye, NV
4150...................... Lawrence, KS..................... 0.8644
Douglas, KS
4200...................... Lawton, OK....................... 0.8212
Comanche, OK
4243...................... Lewiston-Auburn, ME.............. 0.9562
Androscoggin, ME
4280...................... Lexington, KY.................... 0.9219
Bourbon, KY
Clark, KY
Fayette, KY
Jessamine, KY
Madison, KY
Scott, KY
Woodford, KY
4320...................... Lima, OH......................... 0.9258
Allen, OH
Auglaize, OH
4360...................... Lincoln, NE...................... 1.0208
Lancaster, NE
4400...................... Little Rock-North Little, AR..... 0.8826
Faulkner, AR
Lonoke, AR
Pulaski, AR
Saline, AR
4420...................... Longview-Marshall, TX............ 0.8739
Gregg, TX
Harrison, TX
Upshur, TX
4480...................... Los Angeles-Long Beach, CA....... 1.1732
Los Angeles, CA
4520...................... Louisville, KY-IN................ 0.9162
Clark, IN
[[Page 30277]]
Floyd, IN
Harrison, IN
Scott, IN
Bullitt, KY
Jefferson, KY
Oldham, KY
4600...................... Lubbock, TX...................... 0.8777
Lubbock, TX
4640...................... Lynchburg, VA.................... 0.9017
Amherst, VA
Bedford City, VA
Bedford, VA
Campbell, VA
Lynchburg City, VA
4680...................... Macon, GA........................ 0.9596
Bibb, GA
Houston, GA
Jones, GA
Peach, GA
Twiggs, GA
4720...................... Madison, WI...................... 1.0395
Dane, WI
4800...................... Mansfield, OH.................... 0.9105
Crawford, OH
Richland, OH
4840...................... Mayaguez, PR..................... 0.4769
Anasco, PR
Cabo Rojo, PR
Hormigueros, PR
Mayaguez, PR
Sabana Grande, PR
San German, PR
4880...................... McAllen-Edinburg-Mission, TX..... 0.8602
Hidalgo, TX
4890...................... Medford-Ashland, OR.............. 1.0534
Jackson, OR
4900...................... Melbourne-Titusville-Palm Bay, FL 0.9633
Brevard, FL
4920...................... Memphis, TN-AR-MS................ 0.9234
Crittenden, AR
De Soto, MS
Fayette, TN
Shelby, TN
Tipton, TN
4940...................... Merced, CA....................... 1.0575
Merced, CA
5000...................... Miami, FL........................ 0.9870
Dade, FL
5015...................... Middlesex-Somerset-Hunterdon, NJ. 1.1360
Hunterdon, NJ
Middlesex, NJ
Somerset, NJ
5080...................... Milwaukee-Waukesha, WI........... 1.0076
Milwaukee, WI
Ozaukee, WI
Washington, WI
Waukesha, WI
5120...................... Minneapolis-St. Paul, MN-WI...... 1.1066
Anoka, MN
Carver, MN
Chisago, MN
Dakota, MN
Hennepin, MN
Isanti, MN
Ramsey, MN
Scott, MN
Sherburne, MN
Washington, MN
Wright, MN
Pierce, WI
[[Page 30278]]
St. Croix, WI
5140...................... Missoula, MT..................... 0.9618
Missoula, MT
5160...................... Mobile, AL....................... 0.7932
Baldwin, AL
Mobile, AL
5170...................... Modesto, CA...................... 1.1966
Stanislaus, CA
5190...................... Monmouth-Ocean, NJ............... 1.0888
Monmouth, NJ
Ocean, NJ
5200...................... Monroe, LA....................... 0.7913
Ouachita, LA
5240...................... Montgomery, AL................... 0.8300
Autauga, AL
Elmore, AL
Montgomery, AL
5280...................... Muncie, IN....................... 0.8580
Delaware, IN
5330...................... Myrtle Beach, SC................. 0.9022
Horry, SC
5345...................... Naples, FL....................... 1.0558
Collier, FL
5360...................... Nashville, TN.................... 1.0108
Cheatham, TN
Davidson, TN
Dickson, TN
Robertson, TN
Rutherford, TN
Sumner, TN
Williamson, TN
Wilson, TN
5380...................... Nassau-Suffolk, NY............... 1.2907
Nassau, NY
Suffolk, NY
5483...................... New Haven-Bridgeport-Stamford- 1.2254
Waterbury-Danbury, CT.
Fairfield, CT
New Haven, CT
5523...................... New London-Norwich, CT........... 1.1596
New London, CT
5560...................... New Orleans, LA.................. 0.9103
Jefferson, LA
Orleans, LA
Plaquemines, LA
St. Bernard, LA
St. Charles, LA
St. James, LA
St. John The Baptist, LA
St. Tammany, LA
5600...................... New York, NY..................... 1.3586
Bronx, NY
Kings, NY
New York, NY
Putnam, NY
Queens, NY
Richmond, NY
Rockland, NY
Westchester, NY
5640...................... Newark, NJ....................... 1.1625
Essex, NJ
Morris, NJ
Sussex, NJ
Union, NJ
Warren, NJ
5660...................... Newburgh, NY-PA.................. 1.1170
Orange, NY
Pike, PA
5720...................... Norfolk-Virginia Beach-Newport 0.8894
News, VA-NC.
Currituck, NC
Chesapeake City, VA
[[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
5775...................... Oakland, CA...................... 1.5220
Alameda, CA
Contra Costa, CA
5790...................... Ocala, FL........................ 0.9153
Marion, FL
5800...................... Odessa-Midland, TX............... 0.9632
Ector, TX
Midland, TX
5880...................... Oklahoma City, OK................ 0.8966
Canadian, OK
Cleveland, OK
Logan, OK
McClain, OK
Oklahoma, OK
Pottawatomie, OK
5910...................... Olympia, WA...................... 1.1006
Thurston, WA
5920...................... Omaha, NE-IA..................... 0.9754
Pottawattamie, IA
Cass, NE
Douglas, NE
Sarpy, NE
Washington, NE
5945...................... Orange County, CA................ 1.1611
Orange, CA
5960...................... Orlando, FL...................... 0.9742
Lake, FL
Orange, FL
Osceola, FL
Seminole, FL
5990...................... Owensboro, KY.................... 0.8434
Daviess, KY
6015...................... Panama City, FL.................. 0.8124
Bay, FL
6020...................... Parkersburg-Marietta, WV-OH...... 0.8288
Washington, OH
Wood, WV
6080...................... Pensacola, FL.................... 0.8306
Escambia, FL
Santa Rosa, FL
6120...................... Peoria-Pekin, IL................. 0.8886
Peoria, IL
Tazewell, IL
Woodford, IL
6160...................... Philadelphia, PA-NJ.............. 1.0824
Burlington, NJ
Camden, NJ
Gloucester, NJ
Salem, NJ
Bucks, PA
Chester, PA
Delaware, PA
Montgomery, PA
Philadelphia, PA
6200...................... Phoenix-Mesa, AZ................. 0.9982
Maricopa, AZ
Pinal, AZ
[[Page 30280]]
6240...................... Pine Bluff, AR................... 0.8673
Jefferson, AR
6280...................... Pittsburgh, PA................... 0.8756
Allegheny, PA
Beaver, PA
Butler, PA
Fayette, PA
Washington, PA
Westmoreland, PA
6323...................... Pittsfield, MA................... 1.0439
Berkshire, MA
6340...................... Pocatello, ID.................... 0.9601
Bannock, ID
6360...................... Ponce, PR........................ 0.4954
Guayanilla, PR
Juana Diaz, PR
Penuelas, PR
Ponce, PR
Villalba, PR
Yauco, PR
6403...................... Portland, ME..................... 1.0112
Cumberland, ME
Sagadahoc, ME
York, ME
6440...................... Portland-Vancouver, OR-WA........ 1.1403
Clackamas, OR
Columbia, OR
Multnomah, OR
Washington, OR
Yamhill, OR
Clark, WA
6483...................... Providence-Warwick-Pawtucket, RI. 1.1061
Bristol, RI
Kent, RI
Newport, RI
Providence, RI
Washington, RI
6520...................... Provo-Orem, UT................... 0.9613
Utah, UT
6560...................... Pueblo, CO....................... 0.8752
Pueblo, CO
6580...................... Punta Gorda, FL.................. 0.9441
Charlotte, FL
6600...................... Racine, WI....................... 0.9045
Racine, WI
6640...................... Raleigh-Durham-Chapel Hill, NC... 1.0258
Chatham, NC
Durham, NC
Franklin, NC
Johnston, NC
Orange, NC
Wake, NC
6660...................... Rapid City, SD................... 0.8912
Pennington, SD
6680...................... Reading, PA...................... 0.9215
Berks, PA
6690...................... Redding, CA...................... 1.1835
Shasta, CA
6720...................... Reno, NV......................... 1.0456
Washoe, NV
6740...................... Richland-Kennewick-Pasco, WA..... 1.0520
Benton, WA
Franklin, WA
6760...................... Richmond-Petersburg, VA.......... 0.9397
Charles City County, VA
Chesterfield, VA
Colonial Heights City, VA
Dinwiddie, VA
Goochland, VA
Hanover, VA
[[Page 30281]]
Henrico, VA
Hopewell City, VA
New Kent, VA
Petersburg City, VA
Powhatan, VA
Prince George, VA
Richmond City, VA
6780...................... Riverside-San Bernardino, CA..... 1.0970
Riverside, CA
San Bernardino, CA
6800...................... Roanoke, VA...................... 0.8428
Botetourt, VA
Roanoke, VA
Roanoke City, VA
Salem City, VA
6820...................... Rochester, MN.................... 1.1504
Olmsted, MN
6840...................... Rochester, NY.................... 0.9196
Genesee, NY
Livingston, NY
Monroe, NY
Ontario, NY
Orleans, NY
Wayne, NY
6880...................... Rockford, IL..................... 0.9626
Boone, IL
Ogle, IL
Winnebago, IL
6895...................... Rocky Mount, NC.................. 0.8998
Edgecombe, NC
Nash, NC
6920...................... Sacramento, CA................... 1.1848
El Dorado, CA
Placer, CA
Sacramento, CA
6960...................... Saginaw-Bay City-Midland, MI..... 0.9696
Bay, MI
Midland, MI
Saginaw, MI
6980...................... St. Cloud, MN.................... 1.0215
Benton, MN
Stearns, MN
7000...................... St. Joseph, MO................... 1.0013
Andrews, MO
Buchanan, MO
7040...................... St. Louis, MO-IL................. 0.9081
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
7080...................... Salem, OR........................ 1.0556
Marion, OR
Polk, OR
7120...................... Salinas, CA...................... 1.3823
Monterey, CA
7160...................... Salt Lake City-Ogden, UT......... 0.9487
Davis, UT
Salt Lake, UT
Weber, UT
7200...................... San Angelo, TX................... 0.8167
Tom Green, TX
[[Page 30282]]
7240...................... San Antonio, TX.................. 0.9023
Bexar, TX
Comal, TX
Guadalupe, TX
Wilson, TX
7320...................... San Diego, CA.................... 1.1267
San Diego, CA
7360...................... San Francisco, CA................ 1.4712
Marin, CA
San Francisco, CA
San Mateo, CA
7400...................... San Jose, CA..................... 1.4744
Santa Clara, CA
7440...................... San Juan-Bayamon, PR............. 0.4802
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
7460...................... San Luis Obispo-Atascadero-Paso 1.1118
Robles, CA.
San Luis Obispo, CA
7480...................... Santa Barbara-Santa Maria-Lompoc, 1.0771
CA.
Santa Barbara, CA
7485...................... Santa Cruz-Watsonville, CA....... 1.4779
Santa Cruz, CA
7490...................... Santa Fe, NM..................... 1.0590
Los Alamos, NM
Santa Fe, NM
7500...................... Santa Rosa, CA................... 1.2961
Sonoma, CA
7510...................... Sarasota-Bradenton, FL........... 0.9629
Manatee, FL
Sarasota, FL
7520...................... Savannah, GA..................... 0.9460
Bryan, GA
Chatham, GA
Effingham, GA
7560...................... Scranton--Wilkes-Barre--Hazleton, 0.8522
PA.
Columbia, PA
Lackawanna, PA
Luzerne, PA
Wyoming, PA
7600...................... Seattle-Bellevue-Everett, WA..... 1.1479
Island, WA
King, WA
[[Page 30283]]
Snohomish, WA
7610...................... Sharon, PA....................... 0.7881
Mercer, PA
7620...................... Sheboygan, WI.................... 0.8948
Sheboygan, WI
7640...................... Sherman-Denison, TX.............. 0.9617
Grayson, TX
7680...................... Shreveport-Bossier City, LA...... 0.9111
Bossier, LA
Caddo, LA
Webster, LA
7720...................... Sioux City, IA-NE................ 0.9094
Woodbury, IA
Dakota, NE
7760...................... Sioux Falls, SD.................. 0.9441
Lincoln, SD
Minnehaha, SD
7800...................... South Bend, IN................... 0.9447
St. Joseph, IN
7840...................... Spokane, WA...................... 1.0660
Spokane, WA
7880...................... Springfield, IL.................. 0.8738
Menard, IL
Sangamon, IL
7920...................... Springfield, MO.................. 0.8597
Christian, MO
Greene, MO
Webster, MO
8003...................... Springfield, MA.................. 1.0173
Hampden, MA
Hampshire, MA
8050...................... State College, PA................ 0.8461
Centre, PA
8080...................... Steubenville-Weirton, OH-WV...... 0.8280
Jefferson, OH
Brooke, WV
Hancock, WV
8120...................... Stockton-Lodi, CA................ 1.0564
San Joaquin, CA
8140...................... Sumter, SC....................... 0.8520
Sumter, SC
8160...................... Syracuse, NY..................... 0.9394
Cayuga, NY
Madison, NY
Onondaga, NY
Oswego, NY
8200...................... Tacoma, WA....................... 1.1078
Pierce, WA
8240...................... Tallahassee, FL.................. 0.8655
Gadsden, FL
Leon, FL
8280...................... Tampa-St. Petersburg-Clearwater, 0.9024
FL.
Hernando, FL
Hillsborough, FL
Pasco, FL
Pinellas, FL
8320...................... Terre Haute, IN.................. 0.8582
Clay, IN
Vermillion, IN
Vigo, IN
8360...................... Texarkana, AR-Texarkana, TX...... 0.8413
Miller, AR
Bowie, TX
8400...................... Toledo, OH....................... 0.9524
Fulton, OH
Lucas, OH
Wood, OH
8440...................... Topeka, KS....................... 0.8904
Shawnee, KS
8480...................... Trenton, NJ...................... 1.0276
[[Page 30284]]
Mercer, NJ
8520...................... Tucson, AZ....................... 0.8926
Pima, AZ
8560...................... Tulsa, OK........................ 0.8729
Creek, OK
Osage, OK
Rogers, OK
Tulsa, OK
Wagoner, OK
8600...................... Tuscaloosa, AL................... 0.8440
Tuscaloosa, AL
8640...................... Tyler, TX........................ 0.9502
Smith, TX
8680...................... Utica-Rome, NY................... 0.8295
Herkimer, NY
Oneida, NY
8720...................... Vallejo-Fairfield-Napa, CA....... 1.3517
Napa, CA
Solano, CA
8735...................... Ventura, CA...................... 1.1105
Ventura, CA
8750...................... Victoria, TX..................... 0.8469
Victoria, TX
8760...................... Vineland-Millville-Bridgeton, NJ. 1.0573
Cumberland, NJ
8780...................... Visalia-Tulare-Porterville, CA... 0.9975
Tulare, CA
8800...................... Waco, TX......................... 0.8146
McLennan, TX
8840...................... Washington, DC-MD-VA-WV.......... 1.0971
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
8920...................... Waterloo-Cedar Falls, IA......... 0.8633
Black Hawk, IA
8940...................... Wausau, WI....................... 0.9570
Marathon, WI
8960...................... West Palm Beach-Boca Raton, FL... 1.0362
Palm Beach, FL
9000...................... Wheeling, OH-WV.................. 0.7449
Belmont, OH
Marshall, WV
Ohio, WV
9040...................... Wichita, KS...................... 0.9486
Butler, KS
Harvey, KS
Sedgwick, KS
9080...................... Wichita Falls, TX................ 0.8395
[[Page 30285]]
Archer, TX
Wichita, TX
9140...................... Williamsport, PA................. 0.8485
Lycoming, PA
9160...................... Wilmington-Newark, DE-MD......... 1.1121
New Castle, DE
Cecil, MD
9200...................... Wilmington, NC................... 0.9237
New Hanover, NC
Brunswick, NC
9260...................... Yakima, WA....................... 1.0322
Yakima, WA
9270...................... Yolo, CA......................... 0.9378
Yolo, CA
9280...................... York, PA......................... 0.9150
York, PA
9320...................... Youngstown-Warren, OH............ 0.9517
Columbiana, OH
Mahoning, OH
Trumbull, OH
9340...................... Yuba City, CA.................... 1.0363
Sutter, CA
Yuba, CA
9360...................... Yuma, AZ......................... 0.8871
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
------------------------------------------------------------------------
Wage
Nonurban area Index
------------------------------------------------------------------------
Alabama...................................................... 0.7637
Alaska....................................................... 1.1637
Arizona...................................................... 0.9140
Arkansas..................................................... 0.7703
California................................................... 1.0297
Colorado..................................................... 0.9368
Connecticut.................................................. 1.1917
Delaware..................................................... 0.9503
Florida...................................................... 0.8721
Georgia...................................................... 0.8247
Guam......................................................... 0.9611
Hawaii....................................................... 1.0522
Idaho........................................................ 0.8826
Illinois..................................................... 0.8340
Indiana...................................................... 0.8736
Iowa......................................................... 0.8550
Kansas....................................................... 0.8087
Kentucky..................................................... 0.7844
Louisiana.................................................... 0.7290
Maine........................................................ 0.9039
Maryland..................................................... 0.9179
Massachusetts................................................ 1.0216
Michigan..................................................... 0.8740
Minnesota.................................................... 0.9339
Mississippi.................................................. 0.7583
Missouri..................................................... 0.7829
Montana...................................................... 0.8701
Nebraska..................................................... 0.9035
Nevada....................................................... 0.9832
New Hampshire................................................ 0.9940
New Jersey \1\............................................... .........
New Mexico................................................... 0.8529
New York..................................................... 0.8403
North Carolina............................................... 0.8500
North Dakota................................................. 0.7743
Ohio......................................................... 0.8759
Oklahoma..................................................... 0.7537
Oregon....................................................... 1.0049
Pennsylvania................................................. 0.8348
Puerto Rico.................................................. 0.4047
Rhode Island \1\............................................. .........
South Carolina............................................... 0.8640
South Dakota................................................. 0.8393
Tennessee.................................................... 0.7876
Texas........................................................ 0.7910
Utah......................................................... 0.8843
Vermont...................................................... 0.9375
Virginia..................................................... 0.8479
Virgin Islands............................................... 0.7456
Washington................................................... 1.0072
West Virginia................................................ 0.8083
Wisconsin.................................................... 0.9498
Wyoming...................................................... 0.9182
------------------------------------------------------------------------
\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
------------------------------------------------------------------------
Urban area (Constituent Full wage
CBSA code counties) Index
------------------------------------------------------------------------
10180..................... Abilene, TX...................... 0.7850
Callahan County, TX
Jones County, TX
Taylor County, TX
10380..................... Aguadilla-Isabela-San 0.4280
Sebasti[aacute]n, PR.
Aguada Municipio, PR
[[Page 30286]]
Aguadilla Municipio, PR
Aasco Municipio, PR
Isabela Municipio, PR
Lares Municipio, PR
Moca Municipio, PR
Rinc[iacute]n Municipio, PR
San Sebasti[aacute]n Municipio,
PR
10420..................... Akron, OH........................ 0.9055
Portage County, OH
Summit County, OH
10500..................... Albany, GA....................... 1.1266
Baker County, GA
Dougherty County, GA
Lee County, GA
Terrell County, GA
Worth County, GA
10580..................... Albany-Schenectady-Troy, NY...... 0.8650
Albany County, NY
Rensselaer County, NY
Saratoga County, NY
Schenectady County, NY
Schoharie County, NY
10740..................... Albuquerque, NM.................. 1.0485
Bernalillo County, NM
Sandoval County, NM
Torrance County, NM
Valencia County, NM
10780..................... Alexandria, LA................... 0.8171
Grant Parish, LA
Rapides Parish, LA
10900..................... Allentown-Bethlehem-Easton, PA-NJ 0.9501
Warren County, NJ
Carbon County, PA
Lehigh County, PA
Northampton County, PA
11020..................... Altoona, PA...................... 0.8462
Blair County, PA
11100..................... Amarillo, TX..................... 0.9178
Armstrong County, TX
Carson County, TX
Potter County, TX
Randall County, TX
11180..................... Ames, IA......................... 0.9479
Story County, IA
11260..................... Anchorage, AK.................... 1.2165
Anchorage Municipality, AK
Matanuska-Susitna Borough, AK
11300..................... Anderson, IN..................... 0.8713
Madison County, IN
11340..................... Anderson, SC..................... 0.8670
Anderson County, SC
11460..................... Ann Arbor, MI.................... 1.1022
Washtenaw County, MI
11500..................... Anniston-Oxford, AL.............. 0.7881
Calhoun County, AL
11540..................... Appleton, WI..................... 0.9131
Calumet County, WI
Outagamie County, WI
11700..................... Asheville, NC.................... 0.9191
Buncombe County, NC
Haywood County, NC
Henderson County, NC
Madison County, NC
12020..................... Athens-Clarke County, GA......... 1.0202
Clarke County, GA
Madison County, GA
Oconee County, GA
Oglethorpe County, GA
12060..................... Atlanta-Sandy Springs-Marietta, 0.9971
GA.
Barrow County, GA
[[Continued on page 30287]]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
]
[[pp. 30287-30327]] Medicare Program; Inpatient Rehabilitation Facility Prospective
Payment System for FY 2006
[[Continued from page 30286]]
[[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
12100..................... Atlantic City, NJ................ 1.0931
Atlantic County, NJ
12220..................... Auburn-Opelika, AL............... 0.8215
Lee County, AL
12260..................... Augusta-Richmond County, GA-SC... 0.9154
Burke County, GA
Columbia County, GA
McDuffie County, GA
Richmond County, GA
Aiken County, SC
Edgefield County, SC
12420..................... Austin-Round Rock, TX............ 0.9595
Bastrop County, TX
Caldwell County, TX
Hays County, TX
Travis County, TX
Williamson County, TX
12540..................... Bakersfield, CA.................. 1.0036
Kern County, CA
12580..................... Baltimore-Towson, MD............. 0.9907
Anne Arundel County, MD
Baltimore County, MD
Carroll County, MD
Harford County, MD
Howard County, MD
Queen Anne's County, MD
Baltimore City, MD
12620..................... Bangor, ME....................... 0.9955
Penobscot County, ME
12700..................... Barnstable Town, MA.............. 1.2335
Barnstable County, MA
12940..................... Baton Rouge, LA.................. 0.8319
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
12980..................... Battle Creek, MI................. 0.9366
Calhoun County, MI
[[Page 30288]]
13020..................... Bay City, MI..................... 0.9574
Bay County, MI
13140..................... Beaumont-Port Arthur, TX......... 0.8616
Hardin County, TX
Jefferson County, TX
Orange County, TX
13380..................... Bellingham, WA................... 1.1642
Whatcom County, WA
13460..................... Bend, OR......................... 1.0603
Deschutes County, OR
13644..................... Bethesda-Frederick-Gaithersburg, 1.0956
MD.
Frederick County, MD
Montgomery County, MD
13740..................... Billings, MT..................... 0.8961
Carbon County, MT
Yellowstone County, MT
13780..................... Binghamton, NY................... 0.8447
Broome County, NY
Tioga County, NY
13820..................... Birmingham-Hoover, AL............ 0.9157
Bibb County, AL
Blount County, AL
Chilton County, AL
Jefferson County, AL
St. Clair County, AL
Shelby County, AL
Walker County, AL
13900..................... Bismarck, ND..................... 0.7505
Burleigh County, ND
Morton County, ND
13980..................... Blacksburg-Christiansburg- 0.7951
Radford, VA.
Giles County, VA
Montgomery County, VA
Pulaski County, VA
Radford City, VA
14020..................... Bloomington, IN.................. 0.8587
Greene County, IN
Monroe County, IN
Owen County, IN
14060..................... Bloomington-Normal, IL........... 0.9111
McLean County, IL
14260..................... Boise City-Nampa, ID............. 0.9352
Ada County, ID
Boise County, ID
Canyon County, ID
Gem County, ID
Owyhee County, ID
14484..................... Boston-Quincy, MA................ 1.1771
Norfolk County, MA
Plymouth County, MA
Suffolk County, MA
14500..................... Boulder, CO...................... 1.0046
Boulder County, CO
14540..................... Bowling Green, KY................ 0.8140
Edmonson County, KY
Warren County, KY
14740..................... Bremerton-Silverdale, WA......... 1.0614
Kitsap County, WA
14860..................... Bridgeport-Stamford-Norwalk, CT.. 1.2835
Fairfield County, CT
15180..................... Brownsville-Harlingen, TX........ 1.0125
Cameron County, TX
15260..................... Brunswick, GA.................... 1.1933
Brantley County, GA
Glynn County, GA
McIntosh County, GA
15380..................... Buffalo-Niagara Falls, NY........ 0.9339
Erie County, NY
Niagara County, NY
15500..................... Burlington, NC................... 0.8967
[[Page 30289]]
Alamance County, NC
15540..................... Burlington-South Burlington, VT.. 0.9322
Chittenden County, VT
Franklin County, VT
Grand Isle County, VT
15764..................... Cambridge-Newton-Framingham, MA.. 1.1189
Middlesex County, MA
15804..................... Camden, NJ....................... 1.0675
Burlington County, NJ
Camden County, NJ
Gloucester County, NJ
15940..................... Canton-Massillon, OH............. 0.8895
Carroll County, OH
Stark County, OH
15980..................... Cape Coral-Fort Myers, FL........ 0.9371
Lee County, FL
16180..................... Carson City, NV.................. 1.0352
Carson City, NV
16220..................... Casper, WY....................... 0.9243
Natrona County, WY
16300..................... Cedar Rapids, IA................. 0.8975
Benton County, IA
Jones County, IA
Linn County, IA
16580..................... Champaign-Urbana, IL............. 0.9527
Champaign County, IL
Ford County, IL
Piatt County, IL
16620..................... Charleston, WV................... 0.8876
Boone County, WV
Clay County, WV
Kanawha County, WV
Lincoln County, WV
Putnam County, WV
16700..................... Charleston-North Charleston, SC.. 0.9420
Berkeley County, SC
Charleston County, SC
Dorchester County, SC
16740..................... Charlotte-Gastonia-Concord, NC-SC 0.9743
Anson County, NC
Cabarrus County, NC
Gaston County, NC
Mecklenburg County, NC
Union County, NC
York County, SC
16820..................... Charlottesville, VA.............. 1.0294
Albemarle County, VA
Fluvanna County, VA
Greene County, VA
Nelson County, VA
Charlottesville City, VA
16860..................... Chattanooga, TN-GA............... 0.9207
Catoosa County, GA
Dade County, GA
Walker County, GA
Hamilton County, TN
Marion County, TN
Sequatchie County, TN
16940..................... Cheyenne, WY..................... 0.8980
Laramie County, WY
16974..................... Chicago-Naperville-Joliet, IL.... 1.0868
Cook County, IL
DeKalb County, IL
DuPage County, IL
Grundy County, IL
Kane County, IL
Kendall County, IL
McHenry County, IL
Will County, IL
17020..................... Chico, CA........................ 1.0542
[[Page 30290]]
Butte County, CA
17140..................... Cincinnati-Middletown, OH-KY-IN.. 0.9516
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
17300..................... Clarksville, TN-KY............... 0.8022
Christian County, KY
Trigg County, KY
Montgomery County, TN
Stewart County, TN
17420..................... Cleveland, TN.................... 0.7844
Bradley County, TN
Polk County, TN
17460..................... Cleveland-Elyria-Mentor, OH...... 0.9650
Cuyahoga County, OH
Geauga County, OH
Lake County, OH
Lorain County, OH
Medina County, OH
17660..................... Coeur d'Alene, ID................ 0.9339
Kootenai County, ID
17780..................... College Station-Bryan, TX........ 0.9243
Brazos County, TX
Burleson County, TX
Robertson County, TX
17820..................... Colorado Springs, CO............. 0.9792
El Paso County, CO
Teller County, CO
17860..................... Columbia, M...................... 0.8396
Boone County, MO
Howard County, MO
17900..................... Columbia, SC..................... 0.9392
Calhoun County, SC
Fairfield County, SC
Kershaw County, SC
Lexington County, SC
Richland County, SC
Saluda County, SC
17980..................... Columbus, GA-AL.................. 0.8690
Russell County, AL
Chattahoochee County, GA
Harris County, GA
Marion County, GA
Muscogee County, GA
18020..................... Columbus, IN..................... 0.9388
Bartholomew County, IN
18140..................... Columbus, OH..................... 0.9737
Delaware County, OH
Fairfield County, OH
Franklin County, OH
Licking County, OH
Madison County, OH
Morrow County, OH
Pickaway County, OH
Union County, OH
18580..................... Corpus Christi, TX............... 0.8647
Aransas County, TX
Nueces County, TX
[[Page 30291]]
San Patricio County, TX
18700..................... Corvallis, OR.................... 1.0545
Benton County, OR
19060..................... Cumberland, MD-WV................ 0.8662
Allegany County, MD
Mineral County, WV
19124..................... Dallas-Plano-Irving, TX.......... 1.0074
Collin County, TX
Dallas County, TX
Delta County, TX
Denton County, TX
Ellis County, TX
Hunt County, TX
Kaufman County, TX
Rockwall County, TX
19140..................... Dalton, GA....................... 0.9558
Murray County, GA
Whitfield County, GA
19180..................... Danville, IL..................... 0.8392
Vermilion County, IL
19260..................... Danville, VA..................... 0.8643
Pittsylvania County, VA
Danville City, VA
19340..................... Davenport-Moline-Rock Island, IA- 0.8773
IL.
Henry County, IL
Mercer County, IL
Rock Island County, IL
Scott County, IA
19380..................... Dayton, OH....................... 0.9303
Greene County, OH
Miami County, OH
Montgomery County, OH
Preble County, OH
19460..................... Decatur, AL...................... 0.8894
Lawrence County, AL
Morgan County, AL
19500..................... Decatur, IL...................... 0.8122
Macon County, IL
19660..................... Deltona-Daytona Beach-Ormond 0.8898
Beach, FL.
Volusia County, FL
19740..................... Denver-Aurora, CO................ 1.0904
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
19780..................... Des Moines, IA................... 0.9266
Dallas County, IA
Guthrie County, IA
Madison County, IA
Polk County, IA
Warren County, IA
19804..................... Detroit-Livonia-Dearborn, MI..... 1.0349
Wayne County, MI
20020..................... Dothan, AL....................... 0.7537
Geneva County, AL
Henry County, AL
Houston County, AL
20100..................... Dover, DE........................ 0.9825
Kent County, DE
20220..................... Dubuque, IA...................... 0.8748
Dubuque County, IA
20260..................... Duluth, MN-WI.................... 1.0340
Carlton County, MN
St. Louis County, MN
[[Page 30292]]
Douglas County, WI
20500..................... Durham, NC....................... 1.0363
Chatham County, NC
Durham County, NC
Orange County, NC
Person County, NC
20740..................... Eau Claire, WI................... 0.9139
Chippewa County, WI
Eau Claire County, WI
20764..................... Edison, NJ....................... 1.1136
Middlesex County, NJ
Monmouth County, NJ
Ocean County, NJ
Somerset County, NJ
20940..................... El Centro, CA.................... 0.8856
Imperial County, CA
21060..................... Elizabethtown, KY................ 0.8684
Hardin County, KY
Larue County, KY
21140..................... Elkhart-Goshen, IN............... 0.9278
Elkhart County, IN
21300..................... Elmira, NY....................... 0.8445
Chemung County, NY
21340..................... El Paso, TX...................... 0.9181
El Paso County, TX
21500..................... Erie, PA......................... 0.8699
Erie County, PA
21604..................... Essex County, MA................. 1.0662
Essex County, MA
21660..................... Eugene-Springfield, OR........... 1.0940
Lane County, OR
21780..................... Evansville, IN-KY................ 0.8372
Gibson County, IN
Posey County, IN
Vanderburgh County, IN
Warrick County, IN
Henderson County, KY
Webster County, KY
21820..................... Fairbanks, AK.................... 1.1146
Fairbanks North Star Borough,
AK
21940..................... Fajardo, PR...................... 0.3939
Ceiba Municipio, PR
Fajardo Municipio, PR
Luquillo Municipio, PR
22020..................... Fargo, ND-MN..................... 0.9114
Cass County, ND
Clay County, MN
22140..................... Farmington, NM................... 0.8049
San Juan County, NM
22180..................... Fayetteville, NC................. 0.9363
Cumberland County, NC
Hoke County, NC
22220..................... Fayetteville-Springdale-Rogers, 0.8636
AR-MO.
Benton County, AR
Madison County, AR
Washington County, AR
McDonald County, MO
22380..................... Flagstaff, AZ.................... 1.0787
Coconino County, AZ
22420..................... Flint, MI........................ 1.1178
Genesee County, MI
22500..................... Florence, SC..................... 0.8833
Darlington County, SC
Florence County, SC
22520..................... Florence-Muscle Shoals, AL....... 0.7883
Colbert County, AL
Lauderdale County, AL
22540..................... Fond du Lac, WI.................. 0.9897
Fond du Lac County, WI
22660..................... Fort Collins-Loveland, CO........ 1.0218
[[Page 30293]]
Larimer County, CO
22744..................... Fort Lauderdale-Pompano Beach- 1.0165
Deerfield Beach, FL.
Broward County, FL
22900..................... Fort Smith, AR-OK................ 0.8283
Crawford County, AR
Franklin County, AR
Sebastian County, AR
Le Flore County, OK
Sequoyah County, OK
23020..................... Fort Walton Beach-Crestview- 0.8786
Destin, FL.
Okaloosa County, FL
23060..................... Fort Wayne, IN................... 0.9807
Allen County, IN
Wells County, IN
Whitley County, IN
23104..................... Fort Worth-Arlington, TX......... 0.9472
Johnson County, TX
Parker County, TX
Tarrant County, TX
Wise County, TX
23420..................... Fresno, CA....................... 1.0536
Fresno County, CA
23460..................... Gadsden, AL...................... 0.8049
Etowah County, AL
23540..................... Gainesville, FL.................. 0.9459
Alachua County, FL
Gilchrist County, FL
23580..................... Gainesville, GA.................. 0.9557
Hall County, GA
23844..................... Gary, IN......................... 0.9310
Jasper County, IN
Lake County, IN
Newton County, IN
Porter County, IN
24020..................... Glens Falls, NY.................. 0.8467
Warren County, NY
Washington County, NY
24140..................... Goldsboro, NC.................... 0.8778
Wayne County, NC
24220..................... Grand Forks, ND-MN............... 0.9091
Polk County, MN
Grand Forks County, ND
24300..................... Grand Junction, CO............... 0.9900
Mesa County, CO
24340..................... Grand Rapids-Wyoming, MI......... 0.9420
Barry County, MI
Ionia County, MI
Kent County, MI
Newaygo County, MI
24500..................... Great Falls, MT.................. 0.8810
Cascade County, MT
24540..................... Greeley, CO...................... 0.9444
Weld County, CO
24580..................... Green Bay, WI.................... 0.9590
Brown County, WI
Kewaunee County, WI
Oconto County, WI
24660..................... Greensboro-High Point, NC........ 0.9190
Guilford County, NC
Randolph County, NC
Rockingham County, NC
24780..................... Greenville, NC................... 0.9183
Greene County, NC
Pitt County, NC
24860..................... Greenville, SC................... 0.9557
Greenville County, SC
Laurens County, SC
Pickens County, SC
25020..................... Guayama, PR...................... 0.4005
Arroyo Municipio, PR
[[Page 30294]]
Guayama Municipio, PR
Patillas Municipio, PR
25060..................... Gulfport-Biloxi, MS.............. 0.8950
Hancock County, MS
Harrison County, MS
Stone County, MS
25180..................... Hagerstown-Martinsburg, MD-WV.... 0.9715
Washington County, MD
Berkeley County, WV
Morgan County, WV
25260..................... Hanford-Corcoran, CA............. 0.9296
Kings County, CA
25420..................... Harrisburg-Carlisle, PA.......... 0.9359
Cumberland County, PA
Dauphin County, PA
Perry County, PA
25500..................... Harrisonburg, VA................. 0.9275
Rockingham County, VA
Harrisonburg City, VA
25540..................... Hartford-West Hartford-East 1.1054
Hartford, CT.
Hartford County, CT
Litchfield County, CT
Middlesex County, CT
Tolland County, CT
25620..................... Hattiesburg, MS.................. 0.7362
Forrest County, MS
Lamar County, MS
Perry County, MS
25860..................... Hickory-Lenoir-Morganton, NC..... 0.9502
Alexander County, NC
Burke County, NC
Caldwell County, NC
Catawba County, NC
25980..................... Hinesville-Fort Stewart, GA...... 0.7715
Liberty County, GA
Long County, GA
26100..................... Holland-Grand Haven, MI.......... 0.9388
Ottawa County, MI
26180..................... Honolulu, HI..................... 1.1013
Honolulu County, HI
26300..................... Hot Springs, AR.................. 0.9249
Garland County, AR
26380..................... Houma-Bayou Cane-Thibodaux, LA... 0.7721
Lafourche Parish, LA
Terrebonne Parish, LA
26420..................... Houston-Baytown-Sugar Land, TX... 0.9973
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
26580..................... Huntington-Ashland, WV-KY-OH..... 0.9564
Boyd County, KY
Greenup County, KY
Lawrence County, OH
Cabell County, WV
Wayne County, WV
26620..................... Huntsville, AL................... 0.8851
Limestone County, AL
Madison County, AL
26820..................... Idaho Falls, ID.................. 0.9059
Bonneville County, ID
Jefferson County, ID
26900..................... Indianapolis, IN................. 1.0113
Boone County, IN
[[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
26980..................... Iowa City, IA.................... 0.9654
Johnson County, IA
Washington County, IA
27060..................... Ithaca, NY....................... 0.9589
Tompkins County, NY
27100..................... Jackson, MI...................... 0.9146
Jackson County, MI
27140..................... Jackson, MS...................... 0.8291
Copiah County, MS
Hinds County, MS
Madison County, MS
Rankin County, MS
Simpson County, MS
27180..................... Jackson, TN...................... 0.8900
Chester County, TN
Madison County, TN
27260..................... Jacksonville, FL................. 0.9537
Baker County, FL
Clay County, FL
Duval County, FL
Nassau County, FL
St. Johns County, FL
27340..................... Jacksonville, NC................. 0.8401
Onslow County, NC
27500..................... Janesville, WI................... 0.9583
Rock County, WI
27620..................... Jefferson City, MO............... 0.8338
Callaway County, MO
Cole County, MO
Moniteau County, MO
Osage County, MO
27740..................... Johnson City, TN................. 0.8146
Carter County, TN
Unicoi County, TN
Washington County, TN
27780..................... Johnstown, PA.................... 0.8380
Cambria County, PA
27860..................... Jonesboro, AR.................... 0.8144
Craighead County, AR
Poinsett County, AR
27900..................... Joplin, MO....................... 0.8721
Jasper County, MO
Newton County, MO
28020..................... Kalamazoo-Portage, MI............ 1.0676
Kalamazoo County, MI
Van Buren County, MI
28100..................... Kankakee-Bradley, IL............. 1.0603
Kankakee County, IL
28140..................... Kansas City, MO-KS............... 0.9629
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
[[Page 30296]]
Lafayette County, MO
Platte County, MO
Ray County, MO
28420..................... Kennewick-Richland-Pasco, WA..... 1.0520
Benton County, WA
Franklin County, WA
28660..................... Killeen-Temple-Fort Hood, TX..... 0.9242
Bell County, TX
Coryell County, TX
Lampasas County, TX
28700..................... Kingsport-Bristol-Bristol, TN-VA. 0.8240
Hawkins County, TN
Sullivan County, TN
Bristol City, VA
Scott County, VA
Washington County, VA
28740..................... Kingston, NY..................... 0.9000
Ulster County, NY
28940..................... Knoxville, TN.................... 0.8548
Anderson County, TN
Blount County, TN
Knox County, TN
Loudon County, TN
Union County, TN
29020..................... Kokomo, IN....................... 0.8986
Howard County, IN
Tipton County, IN
29100..................... La Crosse, WI-MN................. 0.9289
Houston County, MN
La Crosse County, WI
29140..................... Lafayette, IN.................... 0.9067
Benton County, IN
Carroll County, IN
Tippecanoe County, IN
29180..................... Lafayette, LA.................... 0.8306
Lafayette Parish, LA
St. Martin Parish, LA
29340..................... Lake Charles, LA................. 0.7935
Calcasieu Parish, LA
Cameron Parish, LA
29404..................... Lake County-Kenosha County, IL-WI 1.0342
Lake County, IL
Kenosha County, WI
29460..................... Lakeland, FL..................... 0.8930
Polk County, FL
29540..................... Lancaster, PA.................... 0.9883
Lancaster County, PA
29620..................... Lansing-East Lansing, MI......... 0.9658
Clinton County, MI
Eaton County, MI
Ingham County, MI
29700..................... Laredo, TX....................... 0.8747
Webb County, TX
29740..................... Las Cruces, NM................... 0.8784
Dona Ana County, NM
29820..................... Las Vegas-Paradise, NV........... 1.1378
Clark County, NV
29940..................... Lawrence, KS..................... 0.8644
Douglas County, KS
30020..................... Lawton, OK....................... 0.8212
Comanche County, OK
30140..................... Lebanon, PA...................... 0.8570
Lebanon County, PA
30300..................... Lewiston, ID-WA.................. 0.9314
Nez Perce County, ID
Asotin County, WA
30340..................... Lewiston-Auburn, ME.............. 0.9562
Androscoggin County, ME
30460..................... Lexington-Fayette, KY............ 0.9359
Bourbon County, KY
[[Page 30297]]
Clark County, KY
Fayette County, KY
Jessamine County, KY
Scott County, KY
Woodford County, KY
30620..................... Lima, OH......................... 0.9330
Allen County, OH
30700..................... Lincoln, NE...................... 1.0208
Lancaster County, NE
Seward County, NE
30780..................... Little Rock-North Little Rock, AR 0.8826
Faulkner County, AR
Grant County, AR
Lonoke County, AR
Perry County, AR
Pulaski County, AR
Saline County, AR
30860..................... Logan, UT-ID..................... 0.9094
Franklin County, ID
Cache County, UT
30980..................... Longview, TX..................... 0.8801
Gregg County, TX
Rusk County, TX
Upshur County, TX
31020..................... Longview, WA..................... 1.0224
Cowlitz County, WA
31084..................... Los Angeles-Long Beach-Glendale, 1.1732
CA.
Los Angeles County, CA
31140..................... Louisville, KY-IN................ 0.9122
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
31180..................... Lubbock, TX...................... 0.8777
Crosby County, TX
Lubbock County, TX
31340..................... Lynchburg, VA.................... 0.9017
Amherst County, VA
Appomattox County, VA
Bedford County, VA
Campbell County, VA
Bedford City, VA
Lynchburg City, VA
31420..................... Macon, GA........................ 0.9887
Bibb County, GA
Crawford County, GA
Jones County, GA
Monroe County, GA
Twiggs County, GA
31460..................... Madera, CA....................... 0.8521
Madera County, CA
31540..................... Madison, WI...................... 1.0306
Columbia County, WI
Dane County, WI
Iowa County, WI
31700..................... Manchester-Nashua, NH............ 1.0642
Hillsborough County, NH
Merrimack County, NH
31900..................... Mansfield, OH.................... 0.9189
Richland County, OH
32420..................... Mayaguez, PR..................... 0.4493
[[Page 30298]]
Hormigueros Municipio, PR
Mayaguez Municipio, PR
32580..................... McAllen-Edinburg-Pharr, TX....... 0.8602
Hidalgo County, TX
32780..................... Medford, OR...................... 1.0534
Jackson County, OR
32820..................... Memphis, TN-MS-AR................ 0.9217
Crittenden County, AR
DeSoto County, MS
Marshall County, MS
Tate County, MS
Tunica County, MS
Fayette County, TN
Shelby County, TN
Tipton County, TN
32900..................... Merced, CA....................... 1.0575
Merced County, CA
33124..................... Miami-Miami Beach-Kendall, FL.... 0.9870
Miami-Dade County, FL
33140..................... Michigan City-La Porte, IN....... 0.9332
LaPorte County, IN
33260..................... Midland, TX...................... 0.9384
Midland County, TX
33340..................... Milwaukee-Waukesha-West Allis, WI 1.0076
Milwaukee County, WI
Ozaukee County, WI
Washington County, WI
Waukesha County, WI
33460..................... Minneapolis-St. Paul-Bloomington, 1.1066
MN-WI.
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
33540..................... Missoula, MT..................... 0.9618
Missoula County, MT
33660..................... Mobile, AL....................... 0.7995
Mobile County, AL
33700..................... Modesto, CA...................... 1.1966
Stanislaus County, CA
33740..................... Monroe, LA....................... 0.7903
Ouachita Parish, LA
Union Parish, LA
33780..................... Monroe, MI....................... 0.9506
Monroe County, MI
33860..................... Montgomery, AL................... 0.8300
Autauga County, AL
Elmore County, AL
Lowndes County, AL
Montgomery County, AL
34060..................... Morgantown, WV................... 0.8730
Monongalia County, WV
Preston County, WV
34100..................... Morristown, TN................... 0.7790
Grainger County, TN
Hamblen County, TN
Jefferson County, TN
34580..................... Mount Vernon-Anacortes, WA....... 1.0576
Skagit County, WA
34620..................... Muncie, IN....................... 0.8580
Delaware County, IN
34740..................... Muskegon-Norton Shores, MI....... 0.9741
[[Page 30299]]
Muskegon County, MI
34820..................... Myrtle Beach-Conway-North Myrtle 0.9022
Beach, SC.
Horry County, SC
34900..................... Napa, CA......................... 1.2531
Napa County, CA
34940..................... Naples-Marco Island, FL.......... 1.0558
Collier County, FL
34980..................... Nashville-Davidson--Murfreesboro, 1.0086
TN.
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
35004..................... Nassau-Suffolk, NY............... 1.2907
Nassau County, NY
Suffolk County, NY
35084..................... Newark-Union, NJ-PA.............. 1.1687
Essex County, NJ
Hunterdon County, NJ
Morris County, NJ
Sussex County, NJ
Union County, NJ
Pike County, PA
35300..................... New Haven-Milford, CT............ 1.1807
New Haven County, CT
35380..................... New Orleans-Metairie-Kenner, LA.. 0.9103
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
35644..................... New York-Wayne-White Plains, NY- 1.3311
NJ.
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
35660..................... Niles-Benton Harbor, MI.......... 0.8847
Berrien County, MI
35980..................... Norwich-New London, CT........... 1.1596
New London County, CT
36084..................... Oakland-Fremont-Hayward, CA...... 1.5220
Alameda County, CA
Contra Costa County, CA
36100..................... Ocala, FL........................ 0.9153
Marion County, FL
36140..................... Ocean City, NJ................... 1.0810
Cape May County, NJ
36220..................... Odessa, TX....................... 0.9798
Ector County, TX
36260..................... Ogden-Clearfield, UT............. 0.9216
Davis County, UT
Morgan County, UT
Weber County, UT
[[Page 30300]]
36420..................... Oklahoma City, OK................ 0.8982
Canadian County, OK
Cleveland County, OK
Grady County, OK
Lincoln County, OK
Logan County, OK
McClain County, OK
Oklahoma County, OK
36500..................... Olympia, WA...................... 1.1006
Thurston County, WA
36540..................... Omaha-Council Bluffs, NE-IA...... 0.9754
Harrison County, IA
Mills County, IA
Pottawattamie County, IA
Cass County, NE
Douglas County, NE
Sarpy County, NE
Saunders County, NE
Washington County, NE
36740..................... Orlando, FL...................... 0.9742
Lake County, FL
Orange County, FL
Osceola County, FL
Seminole County, FL
36780..................... Oshkosh-Neenah, WI............... 0.9099
Winnebago County, WI
36980..................... Owensboro, KY.................... 0.8434
Daviess County, KY
Hancock County, KY
McLean County, KY
37100..................... Oxnard-Thousand Oaks-Ventura, CA. 1.1105
Ventura County, CA
37340..................... Palm Bay-Melbourne-Titusville, FL 0.9633
Brevard County, FL
37460..................... Panama City-Lynn Haven, FL....... 0.8124
Bay County, FL
37620..................... Parkersburg-Marietta, WV-OH...... 0.8288
Washington County, OH
Pleasants County, WV
Wirt County, WV
Wood County, WV
37700..................... Pascagoula, MS................... 0.7974
George County, MS
Jackson County, MS
37860..................... Pensacola-Ferry Pass-Brent, FL... 0.8306
Escambia County, FL
Santa Rosa County, FL
37900..................... Peoria, IL....................... 0.8886
Marshall County, IL
Peoria County, IL
Stark County, IL
Tazewell County, IL
Woodford County, IL
37964..................... Philadelphia, PA................. 1.0865
Bucks County, PA
Chester County, PA
Delaware County, PA
Montgomery County, PA
Philadelphia County, PA
38060..................... Phoenix-Mesa-Scottsdale, AZ...... 0.9982
Maricopa County, AZ
Pinal County, AZ
38220..................... Pine Bluff, AR................... 0.8673
Cleveland County, AR
Jefferson County, AR
Lincoln County, AR
38300..................... Pittsburgh, PA................... 0.8736
Allegheny County, PA
Armstrong County, PA
Beaver County, PA
[[Page 30301]]
Butler County, PA
Fayette County, PA
Washington County, PA
Westmoreland County, PA
38340..................... Pittsfield, MA................... 1.0439
Berkshire County, MA
38540..................... Pocatello, ID.................... 0.9601
Bannock County, ID
Power County, ID
38660..................... Ponce, PR........................ 0.5006
Juana Daz Municipio, PR
Ponce Municipio, PR
Villalba Municipio, PR
38860..................... Portland-South Portland- 1.0112
Biddeford, ME.
Cumberland County, ME
Sagadahoc County, ME
York County, ME
38900..................... Portland-Vancouver-Beaverton, OR- 1.1403
WA.
Clackamas County, OR
Columbia County, OR
Multnomah County, OR
Washington County, OR
Yamhill County, OR
Clark County, WA
Skamania County, WA
38940..................... Port St. Lucie-Fort Pierce, FL... 1.0046
Martin County, FL
St. Lucie County, FL
39100..................... Poughkeepsie-Newburgh-Middletown, 1.1363
NY.
Dutchess County, NY
Orange County, NY
39140..................... Prescott, AZ..................... 0.9892
Yavapai County, AZ
39300..................... Providence-New Bedford-Fall 1.0929
River, RI-MA.
Bristol County, MA
Bristol County, RI
Kent County, RI
Newport County, RI
Providence County, RI
Washington County, RI
39340..................... Provo-Orem, UT................... 0.9588
Juab County, UT
Utah County, UT
39380..................... Pueblo, CO....................... 0.8752
Pueblo County, CO
39460..................... Punta Gorda, FL.................. 0.9441
Charlotte County, FL
39540..................... Racine, WI....................... 0.9045
Racine County, WI
39580..................... Raleigh-Cary, NC................. 1.0057
Franklin County, NC
Johnston County, NC
Wake County, NC
39660..................... Rapid City, SD................... 0.8912
Meade County, SD
Pennington County, SD
39740..................... Reading, PA...................... 0.9215
Berks County, PA
39820..................... Redding, CA...................... 1.1835
Shasta County, CA
39900..................... Reno-Sparks, NV.................. 1.0456
Storey County, NV
Washoe County, NV
40060..................... Richmond, VA..................... 0.9397
Amelia County, VA
Caroline County, VA
Charles City County, VA
Chesterfield County, VA
Cumberland County, VA
Dinwiddie County, VA
[[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
40140..................... Riverside-San Bernardino-Ontario, 1.0970
CA.
Riverside County, CA
San Bernardino County, CA
40220..................... Roanoke, VA...................... 0.8415
Botetourt County, VA
Craig County, VA
Franklin County, VA
Roanoke County, VA
Roanoke City, VA
Salem City, VA
40340..................... Rochester, MN.................... 1.1504
Dodge County, MN
Olmsted County, MN
Wabasha County, MN
40380..................... Rochester, NY.................... 0.9281
Livingston County, NY
Monroe County, NY
Ontario County, NY
Orleans County, NY
Wayne County, NY
40420..................... Rockford, IL..................... 0.9626
Boone County, IL
Winnebago County, IL
40484..................... Rockingham County-Strafford 1.0221
County, NH.
Rockingham County, NH
Strafford County, NH
40580..................... Rocky Mount, NC.................. 0.8998
Edgecombe County, NC
Nash County, NC
40660..................... Rome, GA......................... 0.8878
Floyd County, GA
40900..................... Sacramento--Arden-Arcade-- 1.1700
Roseville, CA.
El Dorado County, CA
Placer County, CA
Sacramento County, CA
Yolo County, CA
40980..................... Saginaw-Saginaw Township North, 0.9814
MI.
Saginaw County, MI
41060..................... St. Cloud, MN.................... 1.0215
Benton County, MN
Stearns County, MN
41100..................... St. George, UT................... 0.9458
Washington County, UT
41140..................... St. Joseph, MO-KS................ 1.0013
Doniphan County, KS
Andrew County, MO
Buchanan County, MO
DeKalb County, MO
41180..................... St. Louis, MO-IL................. 0.9076
Bond County, IL
Calhoun County, IL
Clinton County, IL
Jersey County, IL
Macoupin County, IL
Madison County, IL
Monroe County, IL
[[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
41420..................... Salem, OR........................ 1.0556
Marion County, OR
Polk County, OR
41500..................... Salinas, CA...................... 1.3823
Monterey County, CA
41540..................... Salisbury, MD.................... 0.9123
Somerset County, MD
Wicomico County, MD
41620..................... Salt Lake City, UT............... 0.9561
Salt Lake County, UT
Summit County, UT
Tooele County, UT
41660..................... San Angelo, TX................... 0.8167
Irion County, TX
Tom Green County, TX
41700..................... San Antonio, TX.................. 0.9003
Atascosa County, TX
Bandera County, TX
Bexar County, TX
Comal County, TX
Guadalupe County, TX
Kendall County, TX
Medina County, TX
Wilson County, TX
41740..................... San Diego-Carlsbad-San Marcos, CA 1.1267
San Diego County, CA
41780..................... Sandusky, OH..................... 0.9017
Erie County, OH
41884..................... San Francisco-San Mateo-Redwood 1.4712
City, CA.
Marin County, CA
San Francisco County, CA
San Mateo County, CA
41900..................... San German-Cabo Rojo, PR......... 0.5240
Cabo Rojo Municipio, PR
Lajas Municipio, PR
Sabana Grande Municipio, PR
San German Municipio, PR
41940..................... San Jose-Sunnyvale-Santa Clara, 1.4722
CA.
San Benito County, CA
Santa Clara County, CA
41980..................... San Juan-Caguas-Guaynabo, PR..... 0.4645
Aguas Buenas Municipio, PR
Aibonito Municipio, PR
Arecibo Municipio, PR
Barceloneta Municipio, PR
Barranquitas Municipio, PR
Bayam[oacute]n Municipio, PR
Caguas Municipio, PR
Camuy Municipio, PR
Can[oacute]vanas Municipio, PR
Carolina Municipio, PR
Cata[ntilde]o 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
[[Page 30304]]
Gurabo Municipio, PR
Hatillo Municipio, PR
Humacao Municipio, PR
Juncos Municipio, PR
Las Piedras Municipio, PR
Lo[iacute]za Municipio, PR
Manat[iacute] Municipio, PR
Maunabo Municipio, PR
Morovis Municipio, PR
Naguabo Municipio, PR
Naranjito Municipio, PR
Orocovis Municipio, PR
Quebradillas Municipio, PR
R[iacute]o 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
42020..................... San Luis Obispo-Paso Robles, CA.. 1.1118
San Luis Obispo County, CA
42044..................... Santa Ana-Anaheim-Irvine, CA..... 1.1611
Orange County, CA
42060..................... Santa Barbara-Santa Maria-Goleta, 1.0771
CA.
Santa Barbara County, CA
42100..................... Santa Cruz-Watsonville, CA....... 1.4779
Santa Cruz County, CA
42140..................... Santa Fe, NM..................... 1.0909
Santa Fe County, NM
42220..................... Santa Rosa-Petaluma, CA.......... 1.2961
Sonoma County, CA
42260..................... Sarasota-Bradenton-Venice, FL.... 0.9629
Manatee County, FL
Sarasota County, FL
42340..................... Savannah, GA..................... 0.9460
Bryan County, GA
Chatham County, GA
Effingham County, GA
42540..................... Scranton--Wilkes-Barre, PA....... 0.8543
Lackawanna County, PA
Luzerne County, PA
Wyoming County, PA
42644..................... Seattle-Bellevue-Everett, WA..... 1.1492
King County, WA
Snohomish County, WA
43100..................... Sheboygan, WI.................... 0.8948
Sheboygan County, WI
43300..................... Sherman-Denison, TX.............. 0.9617
Grayson County, TX
43340..................... Shreveport-Bossier City, LA...... 0.9132
Bossier Parish, LA
Caddo Parish, LA
De Soto Parish, LA
43580..................... Sioux City, IA-NE-SD............. 0.9070
Woodbury County, IA
Dakota County, NE
Dixon County, NE
Union County, SD
43620..................... Sioux Falls, SD.................. 0.9441
Lincoln County, SD
McCook County, SD
Minnehaha County, SD
Turner County, SD
43780..................... South Bend-Mishawaka, IN-MI...... 0.9447
St. Joseph County, IN
Cass County, MI
43900..................... Spartanburg, SC.................. 0.9519
[[Page 30305]]
Spartanburg County, SC
44060..................... Spokane, WA...................... 1.0660
Spokane County, WA
44100..................... Springfield, IL.................. 0.8738
Menard County, IL
Sangamon County, IL
44140..................... Springfield, MA.................. 1.0176
Franklin County, MA
Hampden County, MA
Hampshire County, MA
44180..................... Springfield, MO.................. 0.8557
Christian County, MO
Dallas County, MO
Greene County, MO
Polk County, MO
Webster County, MO
44220..................... Springfield, OH.................. 0.8748
Clark County, OH
44300..................... State College, PA................ 0.8461
Centre County, PA
44700..................... Stockton, CA..................... 1.0564
San Joaquin County, CA
44940..................... Sumter, SC....................... 0.8520
Sumter County, SC
45060..................... Syracuse, NY..................... 0.9468
Madison County, NY
Onondaga County, NY
Oswego County, NY
45104..................... Tacoma, WA....................... 1.1078
Pierce County, WA
45220..................... Tallahassee, FL.................. 0.8655
Gadsden County, FL
Jefferson County, FL
Leon County, FL
Wakulla County, FL
45300..................... Tampa-St. Petersburg-Clearwater, 0.9024
FL.
Hernando County, FL
Hillsborough County, FL
Pasco County, FL
Pinellas County, FL
45460..................... Terre Haute, IN.................. 0.8517
Clay County, IN
Sullivan County, IN
Vermillion County, IN
Vigo County, IN
45500..................... Texarkana, TX-Texarkana, AR...... 0.8413
Miller County, AR
Bowie County, TX
45780..................... Toledo, OH....................... 0.9524
Fulton County, OH
Lucas County, OH
Ottawa County, OH
Wood County, OH
45820..................... Topeka, KS....................... 0.8904
Jackson County, KS
Jefferson County, KS
Osage County, KS
Shawnee County, KS
Wabaunsee County, KS
45940..................... Trenton-Ewing, NJ................ 1.0276
Mercer County, NJ
46060..................... Tucson, AZ....................... 0.8926
Pima County, AZ
46140..................... Tulsa, OK........................ 0.8690
Creek County, OK
Okmulgee County, OK
Osage County, OK
Pawnee County, OK
Rogers County, OK
Tulsa County, OK
[[Page 30306]]
Wagoner County, OK
46220..................... Tuscaloosa, AL................... 0.8336
Greene County, AL
Hale County, AL
Tuscaloosa County, AL
46340..................... Tyler, TX........................ 0.9502
Smith County, TX
46540..................... Utica-Rome, NY................... 0.8295
Herkimer County, NY
Oneida County, NY
46660..................... Valdosta, GA..................... 0.8341
Brooks County, GA
Echols County, GA
Lanier County, GA
Lowndes County, GA
46700..................... Vallejo-Fairfield, CA............ 1.4279
Solano County, CA
46940..................... Vero Beach, FL................... 0.9477
Indian River County, FL
47020..................... Victoria, TX..................... 0.8470
Calhoun County, TX
Goliad County, TX
Victoria County, TX
47220..................... Vineland-Millville-Bridgeton, NJ. 1.0573
Cumberland County, NJ
47260..................... Virginia Beach-Norfolk-Newport 0.8894
News, VA-NC.
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
47300..................... Visalia-Porterville, CA.......... 0.9975
Tulare County, CA
47380..................... Waco, TX......................... 0.8146
McLennan County, TX
47580..................... Warner Robins, GA................ 0.8489
Houston County, GA
47644..................... Warren-Farmington Hills-Troy, MI. 1.0112
Lapeer County, MI
Livingston County, MI
Macomb County, MI
Oakland County, MI
St. Clair County, MI
47894..................... Washington-Arlington-Alexandria, 1.1023
DC-VA&-MD-WV.
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
[[Page 30307]]
Falls Church City, VA
Fredericksburg City, VA
Manassas City, VA
Manassas Park City, VA
Jefferson County, WV
47940..................... Waterloo-Cedar Falls, IA......... 0.8633
Black Hawk County, IA
Bremer County, IA
Grundy County, IA
48140..................... Wausau, WI....................... 0.9570
Marathon County, WI
48260..................... Weirton-Steubenville, WV-OH...... 0.8280
Jefferson County, OH
Brooke County, WV
Hancock County, WV
48300..................... Wenatchee, WA.................... 0.9427
Chelan County, WA
Douglas County, WA
48424..................... West Palm Beach-Boca Raton- 1.0362
Boynton Beach, FL.
Palm Beach County, FL
48540..................... Wheeling, WV-OH.................. 0.7449
Belmont County, OH
Marshall County, WV
Ohio County, WV
48620..................... Wichita, KS...................... 0.9457
Butler County, KS
Harvey County, KS
Sedgwick County, KS
Sumner County, KS
48660..................... Wichita Falls, TX................ 0.8332
Archer County, TX
Clay County, TX
Wichita County, TX
48700..................... Williamsport, PA................. 0.8485
Lycoming County, PA
48864..................... Wilmington, DE-MD-NJ............. 1.1049
New Castle County, DE
Cecil County, MD
Salem County, NJ
48900..................... Wilmington, NC................... 0.9237
Brunswick County, NC
New Hanover County, NC
Pender County, NC
49020..................... Winchester, VA-WV................ 1.0496
Frederick County, VA
Winchester City, VA
Hampshire County, WV
49180..................... Winston-Salem, NC................ 0.9401
Davie County, NC
Forsyth County, NC
Stokes County, NC
Yadkin County, NC
49340..................... Worcester, MA.................... 1.0996
Worcester County, MA
49420..................... Yakima, WA....................... 1.0322
Yakima County, WA
49500..................... Yauco, PR........................ 0.4493
Gu[aacute]nica Municipio, PR
Guayanilla Municipio, PR
Pe[ntilde]uelas Municipio, PR
Yauco Municipio, PR
49620..................... York-Hanover, PA................. 0.9150
York County, PA
49660..................... Youngstown-Warren-Boardman, OH-PA 0.9237
Mahoning County, OH
Trumbull County, OH
Mercer County, PA
49700..................... Yuba City, CA.................... 1.0363
Sutter County, CA
Yuba County, CA
[[Page 30308]]
49740..................... Yuma, AZ......................... 0.8871
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
------------------------------------------------------------------------
Full wage
CBSA code Nonurban area index
------------------------------------------------------------------------
01........................... Alabama....................... 0.7628
02........................... Alaska........................ 1.1746
03........................... Arizona....................... 0.8936
04........................... Arkansas...................... 0.7406
05........................... California.................... 1.0524
06........................... Colorado...................... 0.9368
07........................... Connecticut................... 1.1917
08........................... Delaware...................... 0.9503
10........................... Florida....................... 0.8574
11........................... Georgia....................... 0.7733
12........................... Hawaii........................ 1.0522
13........................... Idaho......................... 0.8227
14........................... Illinois...................... 0.8339
15........................... Indiana....................... 0.8653
16........................... Iowa.......................... 0.8475
17........................... Kansas........................ 0.8079
18........................... Kentucky...................... 0.7755
19........................... Louisiana..................... 0.7345
20........................... Maine......................... 0.9039
21........................... Maryland...................... 0.9220
22........................... Massachusetts \2\............. 1.0216
23........................... Michigan...................... 0.8786
24........................... Minnesota..................... 0.9330
25........................... Mississippi................... 0.7635
26........................... Missouri...................... 0.7762
27........................... Montana....................... 0.8701
28........................... Nebraska...................... 0.9035
29........................... Nevada........................ 0.9280
30........................... New Hampshire................. 0.9940
31........................... New Jersey \1\................ .........
32........................... New Mexico.................... 0.8680
33........................... New York...................... 0.8151
34........................... North Carolina................ 0.8563
35........................... North Dakota.................. 0.7743
36........................... Ohio.......................... 0.8693
37........................... Oklahoma...................... 0.7686
38........................... Oregon........................ 0.9914
39........................... Pennsylvania.................. 0.8310
40........................... Puerto Rico \2\............... 0.4047
41........................... Rhode Island \1\.............. .........
42........................... South Carolina................ 0.8683
43........................... South Dakota.................. 0.8398
44........................... Tennessee..................... 0.7869
45........................... Texas......................... 0.7966
46........................... Utah.......................... 0.8287
47........................... Vermont....................... 0.9375
48........................... Virgin Islands................ 0.7456
49........................... Virginia...................... 0.8049
50........................... Washington.................... 1.0312
51........................... West Virginia................. 0.7865
52........................... Wisconsin..................... 0.9492
53........................... Wyoming....................... 0.9182
65........................... Guam.......................... 0.9611
------------------------------------------------------------------------
\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
------------------------------------------------------------------------
SSA State
and FY 06 MSA FY 06
Provider number Provider name county code CBSA code
code
------------------------------------------------------------------------
26T107......... 9TH FLOOR REHAB....... 26470 3760 28140
39T231......... DABINGTON MEMORIAL 39560 6160 37964
HOSPITAL.
193067......... ACADIA REHABILITATION 19000 3880 19
HOSPITAL.
24T043......... ACUTE CARE 24230 24 24
REHABILITATION-ALMC.
42T070......... ACUTE REHAB UNIT AT 42420 8140 44940
TUOMEY HEALTHCARE
SYSTEM.
14T182......... ADVOCATE ILLINOIS 14141 1600 16974
MASONIC MEDICAL
CENTER.
14T223......... ADVOCATE LUTHERAN 14141 1600 16974
GENERAL HOSPITAL.
19T202......... AHS SUMMIT HOSPITAL 19160 0760 12940
LLC.
05T320......... ALAMEDA COUNTY MEDICAL 05000 5775 36084
CENTER.
02T017......... ALASKA REGIONAL 02020 0380 11260
HOSPITAL.
33T013......... ALBANY MEDICAL CENTER 33000 0160 10580
HOSP.
14T258......... ALEXIAN BROTHERS 14141 1600 16974
MEDICAL CENTER.
05T281......... ALHAMBRA HOSPITAL 05200 4480 31084
MEDICAL CENTER.
52T096......... ALL SAINTS HEALTHCARE, 52500 6600 39540
INC..
39T074......... ALLEGHENY GENERAL 39010 6280 38300
HOSPITAL SUBURBAN
CAMPUS.
17T116......... ALLEN COUNTY HOSPITAL. 17000 17 17
36T131......... ALLIANCE COMMUNITY 36770 1320 15940
HOSPITAL.
393030......... ALLIED SERVICES INST 39420 7560 42540
OF REHAB SERVICES.
05T305......... ALTA BATES MEDICAL 05000 5775 36084
CENTER.
39T073......... ALTOONA HOSPITAL...... 39120 0280 11020
39T121......... ALTOONA REGIONAL 39120 0280 11020
HEALTH SYSTEM.
35T019......... ALTRU REHABILITATION 35170 2985 24220
CENTER.
05T583......... ALVARADO HOSPITAL 05470 7320 41740
MEDICAL CENTER INC..
33T010......... AMSTERDAM MEMORIAL 33380 0160 33
HOSPITAL.
[[Page 30309]]
01T036......... ANDALUSIA REGIONAL 01190 01 01
HOSPITAL.
393051......... ANGELA JANE PAVILION.. 39620 6160 37964
423029......... ANMED HEALTHSOUTH 42030 3160 11340
REHABILITATION
HOSPITAL.
04T039......... ARKANSAS METHODIST 04270 04 04
HOSPITAL.
39T163......... ARMSTRONG COUNTY 39070 39 38300
MEMORIAL HOSPITAL.
11T115......... ATLANTA MEDICAL CENTER 11470 0520 12060
15T074......... AUGUST F. HOOK REHAB 15480 3480 26900
CENTER.
49T018......... AUGUSTA MEDICAL CENTER 49891 49 49
52T193......... AURORA BAYCARE MEDICAL 52040 3080 24580
CENTER.
52T102......... AURORA LAKELAND 52630 52 52
MEDICAL CENTER REHAB
UNIT.
52T035......... AURORA SHEBOYGAN 52580 7620 43100
MEMORIAL MEDICAL
CENTER REHAB UNI.
52T064......... AURORA SINAI MEDICAL 52390 5080 33340
CENTER.
43T016......... AVERA MCKENNAN 43490 7760 43620
HOSPITAL.
43T012......... AVERA SACRED HEART 43670 43 43
HOSPITAL.
43T014......... AVERA ST. LUKE'S...... 43060 43 43
45T280......... BACHARACH INSTITUTE 31000 1920 19124
FOR REHABILITATION.
313030......... BALL MEMORIAL HOSPITAL- 15170 0560 12100
REHAB.
15T089......... BAPTIST HEALTH 04590 5280 34620
REHABILITATION
INSTITUTE.
043026......... BAPTIST HEALTH SYSTEM. 45130 4400 30780
45T058......... BAPTIST HOSPITAL DAVIS 10120 7240 41700
CTR FOR
REHABILITATION.
10T008......... BAPTIST HOSPITAL 25160 5000 33124
DESOTO.
25T141......... BAPTIST HOSPITAL EAST. 18550 4920 32820
18T130......... BAPTIST HOSPITALS OF 45700 4520 31140
SOUTHEAST TEXAS.
45T346......... BAPTIST MEMORIAL 25350 0840 13140
HOSPITAL NORTH
MISSISSIPPI.
25T034......... BAPTIST MEMORIAL MED 04590 25 25
CENTER, NO LITTLE
ROCK.
04T036......... BAPTIST REGIONAL 18990 4400 30780
MEDICAL CENTER.
18T080......... BAPTIST REHAB CENTER.. 44180 18 18
44T133......... BAPTIST REHABILITATION 44780 5360 34980
GERMANTOWN.
44T147......... BARBERTON CITIZENS 36780 4920 32820
HOSPITAL.
36T019......... BARTLETT REGIONAL 02110 0080 10420
HOSPITAL.
02T008......... BASTROP REHABILITATION 19330 02 02
HOSPITAL.
193058......... BATON ROUGE GENERAL 19160 19 19
MEDICAL CENTER.
19T065......... BAXTER REGIONAL 04020 0760 12940
MEDICAL CENTER.
04T027......... BAY MEDICAL CENTER FOR 23080 04 04
REHABILITATION.
23T041......... BAYHEALTH MEDICAL 08000 6960 13020
CENTER.
08T004......... BAYLOR ALL SAINTS 45910 2190 20100
MEDICAL CENTER OF
FORT WORTH.
45T137......... BAYLOR INSTITUTE FOR 45390 2800 23104
REHABILITATION AT
GASTON.
453036......... BAYLOR MEDICAL CENTER. 45390 1920 19124
45T079......... BAYLOR MEDICAL CENTER 45390 1920 19124
AT GARLAND.
45T097......... BAYSHORE MEDICAL 45610 3360 26420
CENTER.
27T012......... BELLEVUE HOSPITAL 33420 3040 24500
CENTRE.
33T204......... BELMONT COMMUNITY 36060 5600 35644
HOSPITAL.
36T153......... BELOIT MEMORIAL 52520 9000 48540
HOSPITAL.
52T100......... BENEDICTINE HOSPITAL.. 33740 3620 27500
33T224......... BENEFIS HEALTHCARE.... 27060 33 28740
15T088......... BENNETT REHAB CENTER 15470 3480 11300
SAINT JOHN'S HEALTH
SYSTEM.
193070......... BENTON REHABILITATION 19160 0760 12940
HOSPITAL.
36T170......... BERGER HEALTH SYSTEM.. 36660 1840 18140
22T046......... BERKSHIRE MEDICAL 22010 6323 38340
CENTER.
33T169......... BETH ISRAEL MEDICAL 33420 5600 35644
CENTER.
36T179......... BETHESDA NORTH 36310 1640 17140
HOSPITAL.
01T104......... BIRMINGHAM BAPT MED 01360 1000 13820
CNTR MONTCLAIR SNU.
10T213......... BLAKE MEDICAL CENTER.. 10400 7510 42260
14T015......... BLESSING HOSPITAL..... 14000 14 14
23T135......... BOGALUSA COMMUNITY 19580 2160 19804
REHABILITAION
HOSPITAL.
193052......... BON SECOUR ST. FRANCIS 42220 19 19
INPATIENT REHAB
CENTER.
42T023......... BONE AND JOINT 37540 3160 24860
HOSPITAL REHAB CENTER.
37T105......... BOONE HOSPITAL CENTER. 26090 5880 36420
26T068......... BORGESS-PIPP HEALTH 23380 1740 17860
CENTER.
23T117......... BOSTON MED CTR CORP/ 22160 3720 28020
UNIVE HOSP CAMPUS.
22T031......... BOTHWELL REGIONAL 26790 1123 14484
HEALTH CENTER.
26T009......... BOTSFORD GENERAL 23620 26 26
HOSPITAL.
23T151......... BOULDER COMMUNITY 06060 2160 47644
HOSPITAL.
06T027......... BRANDYWINE HOSPITAL... 39210 1125 14500
39T076......... BRAZOSPORT MEMORIAL 45180 6160 37964
HOSPITAL.
45T072......... BRIDGEPORT HOSPITAL... 07010 1145 26420
07T010......... BROADWAY METHODIST 15440 3283 25540
REHAB.
[[Page 30310]]
15T132......... BROKEN ARROW 37710 2960 23844
REHABILITATION.
37T176......... BROMENN REGIONAL 14650 8560 46140
MEDICAL CENTER.
14T127......... BRONSON VICKSBURG 23380 1040 14060
HOSPITAL.
23T190......... BROOKS REHABILITATION 10150 3720 28020
HOSPITAL.
103039......... BROOKWOOD MEDICAL 01360 3600 27260
CENTER.
01T139......... BROTMAN MEDICAL CENTER 05200 1000 13820
05T144......... BROWNSVILLE GENERAL 39330 4480 31084
HOSPITAL.
39T166......... BROWNWOOD REGIONAL 45220 6280 38300
MEDICAL CENTER.
45T587......... BRUNSWICK HOSPITAL.... 33700 45 45
33T314......... BRYANLGH MEDICAL 28540 5380 35004
CENTER WEST.
28T003......... BRYANT T. ALDRIDGE 34630 4360 30700
REHABILITATION CENTER.
34T147......... BRYN MAWR 39210 6895 40580
REHABILITATION
HOSPITAL.
393025......... BSA HEALTH SYSTEM..... 45860 6160 37964
45T231......... BUFFALO MERCY 33240 0320 11100
REHABILITATION UNIT.
33T279......... BURBANK REHABILITATION 22170 1280 15380
CENTER.
22T001......... BURKE REHABILIATION 33800 1123 49340
HOSPITAL.
333028......... CABRINI MEDICAL CENTER 33420 5600 35644
39T160......... CALDWELL MEMORIAL 19100 6280 38300
HOSPITAL.
33T133......... CAMERON REGIONAL 26240 5600 35644
MEDICAL CTR.
19T190......... CANONSBURG GENERAL 39750 19 19
HOSPITAL.
26T057......... CAPITAL REGION MEDICAL 26250 3760 28140
CENTER.
26T047......... CARDINAL HILL 18330 26 27620
REHABILITATION
HOSPITAL.
183026......... CARILION HEALTH SYSTEM 49801 4280 30460
49T024......... CARLE FOUNDATION 14090 6800 40220
HOSPITAL.
14T091......... CARLISLE REGIONAL 39270 1400 16580
MEDICAL CENTER.
39T058......... CARLSBAD MEDICAL 32070 3240 25420
CENTER.
32T063......... CAROLINAS HOSPITAL 42200 32 32
SYSTEM.
42T091......... CARONDELET ST JOSEPHS 03090 2655 22500
HOSPITAL.
03T011......... CARONDELET ST MARYS 03090 8520 46060
HOSPITAL.
03T010......... CARSON REHABILITATION 29120 8520 46060
CENTER.
293029......... CARTHAGE AREA HOSPITAL 33330 29 16180
33T263......... CASA COLINA HOSP FOR 05200 33 33
REHAB MEDICINE.
053027......... CATAWBA VALLEY MEDICAL 34170 4480 31084
CENTER.
34T143......... CATHOLIC MEDICAL 30050 3290 25860
CENTER.
30T034......... CATSKILL REGIONAL 33710 1123 31700
MEDICAL CENTER.
33T386......... CAYUGA MEDICAL CENTER. 33730 33 33
33T307......... CCMH INPATIENT REHAB.. 39640 33 27060
39T246......... CEDARS-SINAI MEDICAL 05200 39 39
CENTER.
44T161......... CENTENNIAL MEDICAL 44180 5360 34980
CENTER.
05T625......... CENTINELA HOSPITAL 05200 4480 31084
MEDICAL CENTER.
05T240......... CENTRAL ARKANSAS 04720 4480 31084
HOSPITAL.
04T014......... CENTRAL KANSAS MEDICAL 17040 04 04
CENTER.
17T033......... CENTRAL MAINE 20000 17 17
REHABILITATION CENTER.
20T024......... CENTRAL MONTGOMERY 39560 4243 30340
MEDICAL CENTER.
39T012......... CENTURA HEALTH-ST. 06150 6160 37964
ANTHONY CENTRAL
HOSPITAL.
06T015......... CGRMC ACUTE 03100 2080 19740
REHABILITATION UNIT.
03T016......... CHALMETTE MEDICAL 19430 6200 38060
CENTER.
45T035......... CHAMBERSBURG HOSPITAL. 39350 3360 26420
45T237......... CHARLESTON AREA MED 51190 7240 41700
CNTR.
19T185......... CHARLOTTE INSTITUTE OF 34590 5560 35380
REHABILITATION.
39T151......... CHATTANOOGA........... 44320 39 39
51T022......... CHELSEA COMMUNITY 23800 1480 16620
HOSPITAL.
343026......... CHESHIRE MEDICAL 30020 1520 16740
CENTER.
44T162......... CHESTNUT HILL 39620 1560 16860
REHABILITATION
HOSPITAL.
23T259......... CHNE REHAB............ 26940 0440 11460
30T019......... CHRISTUS JASPER 45690 30 30
MEMORIAL HOSPITAL.
393032......... CHRISTUS SANTA ROSA 45130 6160 37964
HOSPITAL.
26T180......... CHRISTUS SCHUMPERT 19080 7040 41180
HEALTH SYSTEM.
45T573......... CHRISTUS SPOHN 45830 45 45
HOSPITAL SHORELINE.
19T041......... CHRISTUS ST MICHAEL 45170 7680 43340
REHAB HOSPITAL.
45T046......... CHRISTUS ST. FRANCES 19390 1880 18580
CABRINI HOSPITAL.
453065......... CHRISTUS ST. JOHN..... 45610 8360 45500
19T019......... CHRISTUS ST. JOSEPH 45610 0220 10780
HOSPITAL.
45T709......... CHRISTUS ST. PATRICK 19090 3360 26420
HOSPITAL.
19T027......... CHS,INC DBA ST CHARLES 38080 3960 29340
MEDICAL CTR.
38T047......... CITRUS VALLEY MEDICAL 05200 38 13460
CENTER-VQ CAMPUS.
05T369......... CJW INPATIENT REHAB... 49791 4480 31084
[[Page 30311]]
49T112......... CL.................... 45610 6760 40060
45T617......... CLAXTON-HEPBURN 33630 3360 26420
MEDICAL CENTER.
33T211......... CLINCH VALLEY MEDICAL 49920 33 33
CENTER.
49T060......... CLINTON MEMORIAL 36130 49 49
HOSPITAL.
36T175......... COASTAL REHABILITATION 34240 36 36
CTR.
36T172......... COLISEUM 11090 1680 17460
REHABILITATION CENTER.
34T131......... COLLEGE STATION 45190 34 34
MEDICAL CENTER.
11T164......... COLLETON MEDICAL 42140 4680 31420
CENTER.
45T299......... COLORADO PLAINS 06430 1260 17780
MEDICAL CTR.
42T030......... COLORADO RIVER MEDICAL 05460 42 42
CENTER.
06T044......... COLUMBIA HOSPITAL..... 52390 06 06
05T469......... COLUMBIA REGIONAL 26090 6780 40140
HOSPITAL.
52T140......... COLUMBUS REGIONAL 15020 5080 33340
HOSPITAL.
26T178......... COMANCHE COUNTY 37150 1740 17860
MEMORIAL HOSPITAL.
15T112......... COMMUNITY GENERAL 33520 15 18020
HOSPITAL PM&R.
37T056......... COMMUNITY HEALTH 36480 4200 30020
PARTNERS OF OH-WEST.
33T159......... COMMUNITY HOSPITAL LOS 05530 8160 45060
GATOS.
05T188......... COMMUNITY HOSPITAL OF 36110 7400 41940
SPRINGFIELD.
36T187......... COMMUNITY HOSPITAL/ 36870 2000 44220
WELLNESS CTRS
MONTPELI.
36R327......... COMMUNITY HOSPITALS OF 36870 36 36
WILLIAMS COUNTY.
36T121......... COMMUNITY HOSPTIAL.... 15440 36 36
15T125......... COMMUNITY MEDICAL 27310 2960 23844
CENTER.
27T023......... COMMUNITY MEMORIAL 52660 5140 33540
HOSPITAL.
52T103......... COMMUNITY 23100 5080 33340
REHABILITATION CENTER.
23T078......... COMMUNITY 19400 0870 35660
REHABILITATION
HOSPITAL OF COUSHATTA.
193080......... CONEY ISLAND HOSPITAL. 33331 19 19
33T196......... CORNERSTONE 45650 5600 35644
REHABILITATION
HOSPITAL.
453085......... CORONA REGINAL MEDICAL 05430 4880 32580
CENTER.
05T329......... CORPUS CHRISTI WARM 45830 6780 40140
SPGS REHAB HOSP.
453055......... COTTAGE HOSPITAL...... 23810 1880 18580
45T040......... COVENANT HEALTH SYSTEM 45770 4600 31180
23T070......... COVENANT HEALTHCARE... 23720 6960 40980
16T067......... COVENANT MEDICAL 16060 8920 47940
CENTER.
26T040......... COX HEALTH SYSTEMS.... 26380 7920 44180
05T008......... CPMC REGIONAL 05480 7360 41884
REHABILITATION CENTER.
39T110......... CRICHTON 39160 3680 27780
REHABILITATION CENTER.
04T042......... CRITTENDEN MEMORIAL 04170 4920 32820
HOSPITAL.
23T254......... CRITTENTON REHABCENTRE 23730 2160 47644
44T175......... CROCKETT HOSPITAL 44490 44 44
REHAB.
26T198......... CROSSROADS REGIONAL 26910 7040 41180
MEDICAL CENTER.
193088......... CROWLEY REHAB HOSP, 19000 3880 19
LLC.
39T180......... CROZER CHESTER MEDICAL 39290 6160 37964
CENTER.
34T008......... CTR FOR REHAB SCOTLAND 34820 34 34
MEMORIAL HOSPIT.
39T233......... CTR. FOR ACUTE 39800 9280 49620
REHABILITATIVE
MEDICINE AT HANOVER.
07T033......... DANBURY HOSPITAL...... 07000 5483 14860
05T729......... DANIEL FREEMAN........ 05200 4480 31084
49T075......... DANVILLE REGIONAL 49241 1950 19260
MEDICAL CENTER.
19T003......... DAUTERIVE HOSPITAL.... 19220 19 19
15T061......... DAVIESS COMMUNITY 15130 15 15
HOSPITAL.
46T041......... DAVIS HOSPITAL AND 46050 7160 36260
MEDICAL CENTER.
36T038.........