Friday, 

May 4, 2007 

Part II 

Department of 

Health and Human 

Services 

Centers for Medicare and Medicaid 

Services 

42 CFR Part 484 

Medicare Program; Home Health 

Prospective Payment System Refinement 

and Rate Update for Calendar Year 2008; 

Proposed Rule 


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DEPARTMENT OF HEALTH AND 

HUMAN SERVICES 

Centers for Medicare & Medicaid 

Services 

42 CFR Part 484 

[CMS–1541–P] 

RIN 0938–AO32 

Medicare Program; Home Health 

Prospective Payment System 

Refinement and Rate Update for 

Calendar Year 2008 

AGENCY: Centers for Medicare & 

Medicaid Services (CMS), HHS. 

ACTION: Proposed rule. 

SUMMARY: This proposed rule would set 

forth an update to the 60-day national 

episode rates and the national per-visit 

amounts under the Medicare 

prospective payment system for home 

health services, effective on January 1, 

2008. As part of this proposed rule, we 

are also proposing to rebase and revise 

the home health market basket to ensure 

it continues to adequately reflect the 

price changes of efficiently providing 

home health services. This proposed 

rule also would set forth the refinements 

to the payment system. In addition, this 

proposed rule would establish new 

quality of care data collection 

requirements. 

DATES: To be assured consideration, 

comments must be received at one of 

the addresses provided below, no later 

than 5 p.m. on July 3, 2007. 

ADDRESSES: In commenting, please refer 

to file code CMS–1541–P. Because of 

staff and resource limitations, we cannot 

accept comments by facsimile (FAX) 

transmission. 

You may submit comments in one of 

four ways (no duplicates, please): 

1. Electronically. You may submit 

electronic comments on specific issues 

in this regulation to http:// 

www.cms.hhs.gov/eRulemaking. Click 

on the link ‘‘Submit electronic 

comments on CMS regulations with an 

open comment period.’’ (Attachments 

should be in Microsoft Word, 

WordPerfect, or Excel; however, we 

prefer Microsoft Word.) 

2. By regular 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–1541– 

P, P.O. Box 8012, Baltimore, MD 21244– 

8012. 

Please allow sufficient time for mailed 

comments to be received before the 

close of the comment period. 

3. By express or overnight mail. You 

may send 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–1541–P, Mail Stop C4–26–05, 

7500 Security Boulevard, Baltimore, MD 

21244–1850. 

4. 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. 

Submission of comments on 

paperwork requirements. You may 

submit comments on this document’s 

paperwork requirements by mailing 

your comments to the addresses 

provided at the end of the ‘‘Collection 

of Information Requirements’’ section in 

this document. 

For information on viewing public 

comments, see the beginning of the 

SUPPLEMENTARY INFORMATION section. 

FOR FURTHER INFORMATION CONTACT: 

Randy Throndset, (410) 786–0131. 

General Issues: Sharon Ventura, (410) 

786–1985. 

Clinical (OASIS) Issues: Kathy Walch, 

(410) 786–7970. 

Quality Issues: Doug Brown, (410) 

786–0028. 

Market Basket Update Issues: Mollie 

Knight, (410) 786–7948; and Heidi 

Oumarou, (410) 786–7942. 

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–1541–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. We post all comments 

received before the close of the 

comment period on the following Web 

site as soon as possible after they have 

been received: http://www.cms.hhs.gov/ 

eRulemaking. Click on the link 

‘‘Electronic Comments on CMS 

Regulations’’ on that Web site to view 

public comments. 

Comments received timely will also 

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. Requirements of the Balanced Budget 

Act of 1997 for Updating the Prospective 

Payment System for Home Health 

Services 

B. Deficit Reduction Act of 2005 

C. Updates to the HH PPS 

D. System for Payment of Home Health 

Services 

E. Summary of Home Health Payment 

Research 

II. Provisions of the Proposed Regulation 

A. Refinements to the Home Health 

Prospective Payment System 

1. Current Payment Model 

2. Refinements to the Case-Mix Model 

a. Analysis of Later Episodes 

b. Addition of Variables 

c. Addition of Therapy Thresholds 

d. Determining the Case-Mix Weights 

3. Description & Analysis of Case-Mix 

Coding Change Under the HH PPS 

a. Change in Case-Mix Group Frequencies 

b. Health Characteristics Reported on the 

OASIS 

c. Impact of the Context of OASIS 

Reporting 

4. Partial Episode Payment Adjustment 

(PEP Adjustment) Review 

5. Low-Utilization Payment Adjustment 

(LUPA) Review 

6. Significant Change in Condition (SCIC) 

Adjustment Review 

7. Non-Routine Medical Supply (NRS) 

Amounts Review 

8. Outlier Payment Review 

B. Rebasing and Revising the Home Health 

Market Basket 

1. Background 

2. Rebasing and Revising the Home Health 

Market Basket 


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3. Price Proxies Used To Measure Cost 

Category Growth 

4. Rebasing Results 

5. Labor-Related Share 

C. National Standardized 60-Day Episode 

Payment Rate 

D. Proposed CY 2008 Rate Update by the 

Home Health Market Basket Index (With 

Examples of Standard 60-Day and LUPA 

Episode Payment Calculations) 

E. Hospital Wage Index 

1. Background 

2. Update 

F. Home Health Care Quality Improvement 

III. Collection of Information Requirements 

IV. Response to Comments 

V. Regulatory Impact Analysis 

A. Overall Impact 

B. Anticipated Effects 

C. Accounting Statement 

I. Background 

[If you choose to comment on issues 

in this section, please include the 

caption ‘‘BACKGROUND’’ at the 

beginning of your comments.] 

A. Requirements of the Balanced Budget 

Act of 1997 for Updating the Prospective 

Payment System for Home Health 

Services 

The Balanced Budget Act of 1997 

(BBA) (Pub. L. 105–33) enacted on 

August 5, 1997, significantly changed 

the way Medicare pays for Medicare 

home health services. Until the 

implementation of a home health 

prospective payment system (HH PPS) 

on October 1, 2000, home health 

agencies (HHAs) received payment 

under a cost-based reimbursement 

system. Section 4603 of the BBA 

governed the development of the HH 

PPS. 

Section 4603(a) of the BBA provides 

the authority for the development of a 

PPS for all Medicare-covered home 

health services provided under a plan of 

care that were paid on a reasonable cost 

basis by adding section 1895, entitled 

‘‘Prospective Payment For Home Health 

Services,’’ to the Social Security Act 

(the Act). 

Section 1895(b)(1) of the Act requires 

the Secretary to establish a PPS for all 

costs of home health services paid 

under Medicare. 

Section 1895(b)(3)(A) of the Act 

requires that (1) The computation of a 

standard prospective payment amount 

include all costs for home health 

services covered and paid for on a 

reasonable cost basis and be initially 

based on the most recent audited cost 

report data available to the Secretary, 

and (2) the prospective payment 

amounts be standardized to eliminate 

the effects of case-mix and wage levels 

among HHAs. 

Section 1895(b)(3)(B) of the Act 

addresses the annual update to the 

standard prospective payment amounts 

by the home health applicable increase 

percentage as specified in the statute. 

Section 1895(b)(4) of the Act governs 

the payment computation. Sections 

1895(b)(4)(A)(i) and (b)(4)(A)(ii) of the 

Act require the standard prospective 

payment amount to be adjusted for casemix and geographic differences in wage 

levels. Section 1895(b)(4)(B) of the Act 

requires the establishment of an 

appropriate case-mix adjustment factor 

that explains significant variation in 

costs among different units of services. 

Similarly, section 1895(b)(4)(C) of the 

Act requires the establishment of wage 

adjustment factors that reflect the 

relative level of wages, and wage-related 

costs applicable to home health services 

furnished in a geographic area 

compared to the applicable national 

average level. These wage-adjustment 

factors may be used by the Secretary for 

the different geographic wage levels for 

purposes of section 1886(d)(3)(E) of the 

Act. 

Section 1895(b)(5) of the Act gives the 

Secretary the option to make additions 

or adjustments to the payment amount 

otherwise made in the case of outliers 

because of unusual variations in the 

type or amount of medically necessary 

care. Total outlier payments in a given 

fiscal year (FY) may not exceed 5 

percent of total payments projected or 

estimated. 

In accordance with the statute, we 

published a final rule (65 FR 41128) in 

the Federal Register on July 3, 2000 to 

implement the HH PPS legislation. This 

final rule established requirements for 

the new PPS for home health services as 

required by section 4603 of the BBA, 

and as subsequently amended by 

section 5101 of the Omnibus 

Consolidated and Emergency 

Supplemental Appropriations Act 

(OCESAA) for Fiscal Year 1999, (Pub. L. 

105–277), enacted on October 21, 1998; 

and by sections 302, 305, and 306 of the 

Medicare, Medicaid, and SCHIP 

Balanced Budget Refinement Act 

(BBRA) of 1999, (Pub. L. 106–113), 

enacted on November 29, 1999. The 

requirements include the 

implementation of a PPS for home 

health services, consolidated billing 

requirements, and a number of other 

related changes. The HH PPS described 

in that rule replaced the retrospective 

reasonable-cost-based system that was 

used by Medicare for the payment of 

home health services under Part A and 

Part B. 

For a complete and full description of 

the HH PPS as required by the BBA, see 

the July 2000 HH PPS final rule. 

B. Deficit Reduction Act of 2005 

On February 8, 2006, the Deficit 

Reduction Act (DRA) of 2005 (Pub. L. 

109–171) was enacted. This legislation 

affected updates to HH payment rates 

for CY 2006. The DRA also introduces 

home health care quality data and its 

effects on payments to HHAs beginning 

in CY 2007. 

Specifically, section 5201 of the DRA 

changed the CY 2006 update from the 

applicable home health market basket 

percentage increase minus 0.8 

percentage point to a 0 percent update. 

In addition, section 5201 of the DRA 

amends section 421(a) of the Medicare 

Prescription Drug, Improvement, and 

Modernization Act of 2003 (MMA) (Pub. 

L. 108–173, enacted on December 8, 

2003). The amended section 421(a) of 

the MMA requires that for home health 

services furnished in a rural area (as 

defined in section 1886(d)(2)(D) of the 

Act) on or after January 1, 2006 and 

before January 1, 2007, that the 

Secretary increase the payment amount 

otherwise made under section 1895 of 

the Act for home health services by 5 

percent. The statute waives budget 

neutrality for purposes of this increase 

since it specifically states that the 

Secretary must not reduce the standard 

prospective payment amount (or 

amounts) under section 1895 of the Act 

applicable to home health services 

furnished during a period to offset the 

increase in payments resulting in the 

application of this section of the statute. 

The 0 percent update to the payment 

rates and the rural add-on provisions of 

the DRA were implemented through 

Pub. L. 100–20, One Time Notification, 

Transmittal 211 issued on February 10, 

2006. 

In addition, section 5201 of the DRA 

requires HHAs to submit data for 

purposes of measuring health care 

quality. This requirement is applicable 

for CY 2007 and each subsequent year. 

If an HHA does not submit quality data, 

the home health market basket 

percentage increase will be reduced 2 

percentage points. 

C. Updates to the HH PPS 

As required by section 1895(b)(3)(B) 

of the Act, we have historically updated 

the HH PPS rates annually in a separate 

Federal Register document. In those 

documents, we also incorporated the 

legislative changes to the system 

required by the statute after the BBA, 

specifically the MMA. On November 9, 

2006, we published a final rule titled 

‘‘Medicare Program; Home Health 

Prospective Payment System Rate 

Update for Calendar Year 2007 and 

Deficit Reduction Act of 2005 Changes 


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to Medicare Payment for Oxygen 

Equipment and Capped Rental Durable 

Medical Equipment; Final Rule’’ (CMS–

1304–F) (71 FR 65884) in the Federal 

Register that updated the 60-day 

national episode rates and the national 

per-visit amounts under the Medicare 

PPS for home health services for CY 

2007. In addition, this final rule ended 

the one-year transition period that 

consisted of a blend of 50 percent of the 

new area labor marker designations’

wage index and 50 percent of the 

previous area labor market designations’

wage index. We also revised the fixed 

dollar loss ratio, which is used in the 

calculation of outlier payments. 

According to section 5201(c)(2) of the 

DRA, this final rule also reduced, by 2 

percentage points, the home health 

market basket percentage increase to 

HHAs that did not submit required 

quality data, as determined by the 

Secretary. 

D. System for Payment of Home Health 

Services 

Generally, Medicare makes payment 

under the HH PPS on the basis of a 

national standardized 60-day episode 

payment rate that is adjusted for casemix and wage index. The national 

standardized 60-day episode payment 

rate includes the six home health 

disciplines (skilled nursing, home 

health aide, physical therapy, speechlanguage pathology, occupational 

therapy, and medical social services) 

and medical supplies. Durable medical 

equipment covered under home health 

is paid for outside the HH PPS payment. 

To adjust for case mix, the HH PPS uses 

an 80-category case-mix classification to 

assign patients to a home health 

resource group (HHRG). Clinical, 

functional, and service utilization are 

computed from responses to selected 

data elements in the OASIS assessment 

instrument. 

For episodes with four or fewer visits, 

Medicare pays on the basis of a national 

per-visit amount by discipline, referred 

to as a LUPA. Medicare also adjusts the 

national standardized 60-day episode 

payment rate for certain intervening 

events that are subject to a partial 

episode payment adjustment (PEP 

adjustment) or a significant change in 

condition adjustment (SCIC 

adjustment). For certain cases that 

exceed a specific cost threshold, an 

outlier adjustment may also be 

available. 

E. Summary of Home Health Payment 

Research 

The objective of a prospective 

payment system that is case-mix 

adjusted is to predict resource costs of 

providing care to similar types of 

patients and to align payments to those 

costs. As MEDPAC points out in their 

December 2005 Report to Congress, if 

the case-mix is not aligned 

appropriately to resource costs, then the 

PPS may overpay for some services and 

underpay for others. 

Since the July 3, 2000 final rule, we 

have stated our intention to monitor the 

new PPS and make refinements to the 

system as needed. We believe 

refinements are now needed to improve 

the performance and appropriateness of 

the HH PPS, which has not undergone 

major refinements since its 

implementation in October of 2000. The 

general goal of any refinements would 

be to ensure that the payment system 

continues to produce appropriate 

compensation for providers while 

retaining opportunities to manage home 

health care efficiently. Also important 

in any refinement is maintaining an 

appropriate degree of operational 

simplicity. The analytic goals of our 

refinement research included improving 

the accuracy of the case-mix model, 

understanding the descriptive 

characteristics of the program and the 

use of payment adjusters, understanding 

variations in HHA margins, and the 

simulation of potential changes to 

payment methodology. 

We contracted with Abt Associates, 

Inc., of Cambridge, Massachusetts to 

conduct several analyses in order to 

achieve these objectives. In particular, 

the Abt Associates analyses focused on 

the resource needs of long stay patients; 

alternatives to the current therapy 

threshold; the potential for a more 

extensive set of variables to improve the 

accuracy of the Clinical on Top (COT) 

model used to define the HHRG; 

alternative ways to account for nonroutine medical supplies (NRS); 

utilization and episode characteristics; 

and HHA margins. In order to conduct 

these analyses, Abt Associates primarily 

used data files created from a 20 percent 

sample of claims data collected between 

2001 and 2004, Outcome and 

Assessment Information Set (OASIS) 

data linked to claims, and cost reports. 

For measures of resource use, Abt 

Associates used weighted minutes for 

the case-mix refinements research. For 

research on accounting for nonroutine 

supplies costs, Abt Associates analyzed 

supplies charges reported on claims 

after adjusting them using cost-to-charge 

ratios from selected cost reports. These 

analyses are described in more detail in 

section II.A. 

In addition to these analyses, two 

Technical Expert Panel (TEP) meetings 

were conducted, under contract with 

Abt Associates, on December 15, 2005, 

and March 14, 2006. These TEP 

meetings provided an opportunity for 

experts, industry representatives, and 

practitioners in the field of home health 

care to provide feedback on Abt’s 

research examining the HH PPS and 

exploration of payment policy 

alternatives. Abt considered this 

feedback when developing 

recommendations for refinements to the 

HH PPS. The refinements to the HH PPS 

described in the following sections are 

the culmination of substantial research 

efforts focusing on several areas 

identified for possible improvements. 

II. Provisions of the Proposed 

Regulation 

[If you choose to comment on issues 

in this section, include the caption 

‘‘PROVISIONS OF THE PROPOSED 

REGULATIONS’’ at the beginning of 

your comments.] 

A. Refinements to the Home Health 

Prospective Payment System 

The Medicare HH PPS has been in 

effect since October 1, 2000. As set forth 

in the final rule published July 3, 2000 

in the Federal Register (65 FR 41128), 

the unit of payment under the Medicare 

HH PPS is a national standardized 60- 

day episode payment rate. As set forth 

in 42 CFR 484.220, we adjust the 

national standardized 60-day episode 

payment rate by a case-mix grouping 

and a wage index value based on the 

site of service for the beneficiary. Since 

the July 3, 2000 final rule, we have 

stated our intention to monitor the new 

PPS and make refinements to the system 

as needed. We believe refinements are 

now required to improve the 

performance and appropriateness of 

payment for the HH PPS. After 

implementation of the HH PPS, we 

received a number of public comments 

suggesting ways in which the payment 

system could be improved. We took 

those comments into consideration as 

we proceeded to explore the HH PPS for 

potential areas for refinement. This 

proposed rule sets forth the first major 

refinements to the HH PPS since its 

implementation in October of 2000. 

This proposed rule identifies seven 

major areas of the HH PPS that were 

identified as possible areas for 

refinement. Those areas are: (1) The case 

mix model; (2) changes in case mix 

coding; (3) the PEP adjustment; (4) the 

LUPA; (5) the SCIC adjustment; (6) 

method of accounting for NRS, and (7) 

the outlier adjustment. While this 

proposed rule proposes to implement all 

of refinements discussed in this rule 

effective January 1, 2008, we recognize 

that there may be operational 

considerations, affecting CMS or the 


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industry, which could necessitate an 

implementation schedule that results in 

certain refinements becoming effective 

on different dates (a splitimplementation). We would like to 

solicit suggestions and comments from 

the public on this matter. 

1. Current Payment Model 

On July 3, 2000, we published a final 

rule (65 FR 41128) in the Federal 

Register. In that rule, we described a 

system for home health case-mix 

adjustment developed under a research 

contract with Abt Associates, Inc., of 

Cambridge, Massachusetts. Using 

selected data elements from the OASIS 

and an additional data element 

measuring receipt of at least 10 visits for 

therapy services, the case-mix system 

projects patient resource use based on 

patient characteristics. These data 

elements were selected because they 

were shown to influence home health 

resource utilization upon statistical 

analysis of data from approximately 

30,000 episodes. This model used data 

from first episodes only and a relatively 

small set of clinical, functional, and 

service utilization variables. Clinical 

judgment, the relative predictive value 

of potential case-mix variables, their 

susceptibility to gaming and 

subjectivity, and administrative 

implications were considered in the 

final resolution of the elements retained 

in the final model. 

The data elements are organized into 

three dimensions to capture clinical 

severity factors, functional severity 

factors, and services utilization factors 

influencing case-mix. In the clinical and 

functional dimensions, each data 

element is assigned a score value 

derived from multiple regression 

analysis of the Abt research data. The 

score value measures the impact of the 

data element on total resource use. 

Scores are also assigned to data 

elements in the services utilization 

dimension. To find a patient’s case-mix 

group, the case-mix grouper software 

sums the patient’s scores within each of 

the three dimensions. The resulting sum 

is used to assign the patient to a severity 

level in each dimension. There are four 

clinical severity levels, five functional 

severity levels, and four services 

utilization severity levels. Thus, there 

are 80 possible combinations of severity 

levels across the three dimensions. Each 

combination defines one of the 80 

HHRGs in the case-mix system. For 

example, a patient with high clinical 

severity, moderate functional severity, 

and low services utilization severity is 

placed in the same group with all other 

patients whose summed scores place 

them in the same set of severity levels 

for the three dimensions. 

We summarized the performance of 

the final PPS model for the PPS using 

the R-squared statistic. An initial 

episode was defined as the first home 

health episode of care for a given 

beneficiary in a sequence of adjacent 

episodes. For the purposes of our 

analysis, we defined a sequence of 

adjacent episodes for a beneficiary as a 

series of claims with no more than 60 

days without home care between the 

end of one episode, which is the 60th 

day (except for episodes that have been 

PEP-adjusted), and the beginning of the 

next episode. At the time, based on data 

from the model development sample, 

this model’s R-squared statistic was 

0.34. In other words, the model 

explained 34 percent of the variation in 

resource use. 

2. Refinements to the Case-Mix Model 

Extensive research has been 

conducted to investigate ways to 

improve the performance of the casemix model. We found that the addition 

of separate regression equations to 

account for later episodes and multiple 

therapy thresholds (replacing the 

current threshold of 10 therapy visits) 

significantly improved the fit and 

performance of the case-mix model. 

Further, we expanded the set of 

variables to include new diagnosis 

groups, comorbidities, and interactions, 

yielding models that performed better in 

simulations. We feel that these changes 

would improve the HH PPS by allowing 

more accurate case-mix adjustment 

without providing incentives for 

providers to distort appropriate patterns 

of care. 

As with the original case-mix model, 

the general approach to developing a 

case-mix model was to use patient data 

and other appropriate data to create a 

regression model for resource use over 

the course of a 60-day episode. Case-mix 

refinement analysis focused on 

investigating resource use in episodes 

that occur later in treatment as well as 

the initial episode; testing additional 

clinical, functional, and demographic 

variables; exploring the effect of 

comorbidities; and testing new therapy 

thresholds. 

The basis for selecting these areas of 

analysis will be described in sections 

II.2.a., II.2.b., and II.2.c. 

As with our case-mix studies that 

resulted in the case-mix methodology 

discussed in the July 3, 2000 HH PPS 

final rule, the dependent variable in 

these refinement studies is an estimate 

of cost known as resource cost. To 

derive the resource cost estimate, the 

total minutes reported on the claim for 

each discipline’s visits are converted to 

a resource cost. Resource cost results 

from weighting each minute by the 

national average labor market hourly 

rate for the individual discipline that 

provided the minutes of care. Bureau of 

Labor Statistics data are used to derive 

the hourly rate. The sum of the 

weighted minutes is the total resource 

cost estimate for the claim. This method 

standardizes the resource cost for all 

episodes in the analysis file. 

Based on the findings of our analysis 

of the case-mix adjustment under HH 

PPS, which we describe in section 

II.A.2, we propose that the case-mix 

adjustment be refined to incorporate an 

expanded set of case-mix variables to 

capture the additional clinical 

conditions and comorbidities; four 

separate regression models that 

recognize four different types of 

episodes; and a graduated, threethreshold approach to accounting for 

therapy utilization. We refer to the four 

separate regression models in this 

proposed case-adjustment system as the 

four-equation model. The first 

regression equation is for low-therapy 

episodes (less than 14 therapy visits) 

that occur as the first or second episode 

in a series of adjacent episodes 

(Episodes are considered to be 

‘‘adjacent’’ if they are separated by no 

more than a 60-day period between 

claims). The second regression equation 

is for high-therapy episodes (14 or more 

therapy visits) occurring as the first or 

second episode in a series of adjacent 

episodes. The third equation is for lowtherapy episodes (under 14 therapy 

visits) occurring after the second 

episode in a series of adjacent episodes. 

And the fourth equation is for hightherapy episodes (14 or more therapy 

visits) occurring after the second 

episode in a series of adjacent episodes. 

As described in further detail below, 

these equations incorporate a graduated, 

three-threshold approach to accounting 

for therapy utilization. The 153 case mix 

groups created from the results of the 

four-equation model are also described 

below, as is the method we used to form 

the groups. 

a. Analysis of Later Episodes 

As a starting point for our analysis, 

we examined the performance of our 

original model using data, derived from 

the National Claims History, reflecting 

the period after the HH PPS was 

initiated. These data from the period 

after the commencement of the HH PPS, 

a large random sample of claims from 

CY 2003, indicate the performance of 

the case-mix model differs from the 

original estimate, which reflected data 

from the time of the Abt case-mix study. 


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The more recent data reflect both the 

inclusion of episodes beyond the first 

episode as well as behavioral changes of 

health care providers under the HH PPS. 

The R-squared statistic estimated from 

the more recent data is approximately 

0.21. An appropriate comparison with 

the initial R-square statistic (0.34) is the 

R-squared value estimated from the 

more recent data’s initial episodes, 

which is 0.29. We therefore believe the 

data reflect a more modest reduction in 

model performance of 0.05. However, 

the value of the R-squared statistic 

calculated on all the data, 0.21, is an 

indication that the case-mix model does 

not fit non-initial episodes as well as it 

fits initial episodes. Therefore, one 

focus of our refinement work was to 

investigate resource use in episodes that 

occurred later in treatment as well as 

early episodes. 

Based on exploratory analysis, we 

defined ‘‘early’’ episodes to include, not 

only the initial episode in a sequence of 

adjacent episodes, but also the next 

adjacent episode, if any, that followed 

the initial episode. ‘‘Later’’ episodes 

were defined as all adjacent episodes 

beyond the second episode. When we 

analyzed the performance of the casemix model for later episodes, we 

determined there were two important 

differences for episodes occurring later 

in the home health treatment compared 

to earlier episodes: higher resource use 

per episode and a different relationship 

between clinical conditions and 

resource use. 

Using a large, random sample of 

episodes, we found that the estimated 

resource cost of early episodes is 

approximately 7 percent lower than the 

estimated resource cost of later 

episodes. The current case-mix model 

weights all episodes equally. 

Furthermore, our exploratory 

regression models indicated that the 

relationships between case-mix 

variables and resource use differed 

between earlier and later episodes. This 

suggested that a scoring system that 

differed for earlier and later episodes 

could potentially perform better than a 

single scoring system. The system of 

four separate regression equations 

allows the scores to differ according to 

whether the episode is early or later. We 

recognize that this approach introduces 

more complexity into the case-mix 

adjustment system. However, less 

complex approaches that did not 

depend on separate equations did not 

perform as well in terms of predictive 

accuracy; for example, we explored 

using one equation in which we 

modeled additional lump-sum costs due 

to the timing of an episode in a 

sequence of adjacent episodes. This 

proved to be unsatisfactory because it 

addressed only one of the two important 

differences presented by later episodes, 

that is, their generally higher cost level. 

For the purposes of payment, we 

propose to make changes to the OASIS 

(see section III. Collection of 

Information Requirements), by adding a 

new OASIS item to capture whether an 

episode is an early or later episode. If an 

HHA is uncertain as to whether the 

episode is an early or later episode, we 

propose to base payment as though the 

episode were an early episode. Most 

patients do not have more than one 

episode in a year. Consequently, we 

believe that selecting early as the default 

is the best guess as to the eventual 

outcome of whether an episode is early 

or later. 

b. Addition of Variables 

Since the system for case-mix 

adjustment was first implemented, we 

have received comments suggesting 

ways in which case-mix adjustment may 

be improved. Most of these comments 

requested that we add specific variables 

or conditions to the case-mix model. We 

were also asked to examine the 

appropriateness of including additional 

diagnosis groups, comorbidities in 

general and specific comorbidities, for 

instance, heart conditions, additional 

wound-related indicators, and other 

patient characteristics. We considered 

these comments as we proceeded to 

explore potential case-mix changes. We 

also considered comments received 

during the initial rulemaking process, 

such as comments pertaining to clinical 

issues and social characteristics such as 

caregiver availability. 

We evaluated variables for inclusion 

in a refined case-mix model in much the 

same way that we did for the 2000 final 

rule, in that we analyzed the 

relationship between resource use and 

patient characteristics. Whereas the 

original case-mix study required us to 

collect logs from a sample of episodes 

for the measure of resource use, for this 

analysis, we were able to measure 

resource use directly from the claims 

sample. The measures of patient 

characteristics come from OASIS 

assessments. Under a contract with Fu 

Associates of Arlington, Virginia, 

Standard Analytical Claims Files from 

the National Claims History were 

cleaned, edited, and linked to the 

OASIS assessment associated with the 

beginning of each claim period. Abt 

Associates subsequently used these 

analytic files to draw large samples of 

claims for analysis. 

In the course of refining the current 

case-mix model, we continued to 

monitor the performance of two special 

variables in explaining resource use. 

These variables are dual-eligibility for 

Medicare and Medicaid and caregiver 

support. The two variables are of 

interest to some agencies because of 

their perceived impact on resource use 

and overall profitability. Patients dually 

eligible for Medicare and Medicaid may 

have health care needs that exceed the 

average needs due to the health status 

and utilization differences associated 

with low-income populations. Some 

agencies with caseloads containing large 

numbers of dual eligibles have 

commented that they are penalized 

under the HH PPS system because of 

their willingness to serve a 

disadvantaged population without 

payments explicitly recognizing such 

agencies’ higher costs. We have also 

received comments that episodes 

involving patients without a caregiver 

were underpaid by the HH PPS, and that 

some agencies would be reluctant to 

admit such patients because of financial 

implications. These commenters believe 

that the low admission rate of patients 

without caregivers (about 2 percent of 

all episodes) is evidence of this 

reluctance. 

During our development of the 

original case-mix model implemented in 

the July 2000 final rule, using the Abt 

Associates case-mix study sample, we 

tested the Medicaid variable (which 

indicates whether Medicaid was among 

the patient’s payment sources). At that 

time, we found that it did not contribute 

meaningfully in explaining variation in 

resource use. Similarly, we tested the 

caregiver variable and it did not 

contribute to explaining variation in 

resource cost, either. Regarding the 

caregiver variable, we recognized in the 

July 3, 2000, final rule that adjusting 

payment in response to the presence or 

absence of a caregiver may be seen as 

inequitable. To the extent that 

availability of caregiver services, 

particularly privately paid services, 

reflects socioeconomic status 

differences, we indicated that reducing 

payment for patients who have caregiver 

assistance may be particularly sensitive 

in view of Medicare’s role as an 

insurance program rather than a social 

welfare program. Furthermore, we 

stated that adjusting payment for 

caregiver factors would risk introducing 

new and negative incentives into family 

and patient behavior. In the discussion 

in the July 3, 2000 final rule (65 FR 

41145), we also indicated our belief that 

it is questionable whether Medicare 

should adopt a payment policy that 

could weaken informal familial 

supports currently benefiting patients at 

times when they are most vulnerable. 


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In our analysis for this proposed rule, 

we again tested variables for dual 

eligibility and caregiver support. We 

operationalized the Medicaid variable 

from the OASIS, using the presence of 

a Medicaid number on the assessment 

as the indicator for Medicaid eligibility. 

We found that Medicaid remains a 

marginal predictor at best, with a very 

low score, after accounting for a broad 

range of clinical and functional 

variables that predict resource use. We 

believe adding a Medicaid variable is 

not justified in view of these results, 

especially considering the added 

administrative burdens for both 

agencies and Medicare that using such 

a variable would entail. These include 

costs of ascertaining whether the 

reported Medicaid number is correct 

and whether the eligibility status as 

reported on the assessment is current. 

We also operationalized a variable for 

support from a caregiver from the 

OASIS assessment, item M0350, 

Assisting persons other than home 

health agency staff. This variable 

identified patients without any 

caregiver. While analyzing the payment 

adequacy of the four-equation model (as 

explained further below) for patients 

without a caregiver we found that, on 

average, episodes without caregivers 

would be ‘‘underpaid’’. However, the 

score to be gained by adding the 

variable is not large (5 to 13 points, 

depending on the episode), and the 

overall ability of the four-equation 

model to explain resource costs is 

improved only minimally by adding this 

variable. 

Therefore, we are not proposing that 

this variable be added to the case-mix 

model. We continue to believe that 

including this kind of variable in the 

case-mix system raises significant policy 

concerns. We maintain that a case-mix 

adjustment should not discourage 

assistance from family members of 

home care patients, nor should it make 

patients feel there is some financial 

stake in how they report their familial 

supports during their convalescence. 

We continue to believe that adjusting 

payment in response to the absence of 

a caregiver would introduce negative 

incentives with adverse affects on home 

health Medicare beneficiaries. 

Furthermore, we are doubtful that 

today’s low rate of episodes without a 

caregiver (2 to 3 percent) reflects access 

barriers for these patients and nothing 

more. We believe part of the reason for 

the low rate may be that under a 

bundled payment system agencies are 

more careful about ascertaining whether 

support is available and encourage use 

of caregivers within the beneficiary’s 

home. 

For exploratory modeling of case-mix 

in our refinement work, in addition to 

using existing case-mix variables from 

the OASIS, new variables were created. 

Diagnosis codes reported on both the 

claims and the OASIS were used 

extensively to form new or revised 

diagnosis groups for inclusion in casemix models. As a result, developmental 

models included many new variables, 

including an expanded set of primary 

and secondary diagnoses, as well as 

interaction terms that describe the effect 

of combinations of patient conditions or 

characteristics on resource cost. Using 

these new analytic files, it was possible 

to explore some conditions that were 

too infrequent to study in the original 

case-mix sample. For example, as 

suggested by commenters, Abt’s analysis 

tested the impact on resource use of 

having multiple conditions from M0250, 

which reports on therapies received at 

home, including intravenous infusion, 

and enteral and parenteral nutrition. 

The results showed that a variable 

indicating the simultaneous presence of 

multiple conditions from OASIS item 

M0250 did not improve the accuracy of 

the case-mix model. However, we did 

find that having separate scores for 

parenteral nutrition and IV therapy were 

not necessary. 

Abt’s case-mix analysis focused on 

various issues, such as changes to the 

list of conditions forming our diagnosis 

groups, additions of comorbidities, 

prediction of therapy resources, and 

interactions. The performance of each 

variable was scrutinized based on 

several criteria. First, variables were 

assessed for statistical performance. 

Variables that did not enhance the 

accuracy of the model were marked for 

exclusion. 

Variables were also assessed for 

policy appropriateness. Some 

statistically significant variables were 

excluded if they offered incentives for 

providers to distort patterns of good care 

or posed excessive administrative 

burden on HHAs. In addition, some 

statistically weak variables considered 

important for clinical or policy reasons 

were added back to the model for 

further analysis. 

We note we excluded a variable from 

this proposal, based in part on concerns 

of excessive administrative burden. We 

propose to exclude OASIS item M0175, 

which the case-mix system uses to 

identify the patient’s pre-admission 

location, from the case-mix models. 

Under this proposal, there would be no 

case-mix score for M0175. Operational 

experience with M0175 revealed that 

some agencies have encountered 

difficulties in ascertaining precise 

information about the patient’s preadmission location during the initial 

assessment. These difficulties, 

suggestive of unforeseen administrative 

complexities, contributed to our 

proposal to eliminate M0175 from the 

case-mix model. 

In addition, the M0175 item did not 

perform well in the four-equation 

model. We found that the results 

differed across the equations in ways 

that were difficult to interpret. 

Moreover, the results showed that the 

impact of including information from 

M0175 was small, both in terms of casemix scores and the overall payment 

accuracy of the case-mix model. 

In weighing the indications of 

administrative complexities due to 

M0175 against the limited performance 

of M0175 in our analysis, we do not find 

that the contribution of this item in 

explaining case-mix justifies the 

operational challenge of achieving 

perfectly accurate reporting for 

payment. Thus, as noted above, we are 

proposing to eliminate it from the casemix model. However, we continue to 

believe that it is necessary for the 

conditions of participation and the 

OASIS to require that agencies establish 

the patient’s recent history of health 

care before determining the plan of care. 

This determination must be made with 

sufficient accuracy to allow appropriate 

planning, even if precise dates and 

institutional certifications are not 

exactly known. For example, it will be 

important to know the amount and 

types of rehabilitation treatment the 

patient has received, the type of 

institution that delivered the treatment, 

and how recently it was delivered. 

The final set of proposed clinical 

conditions resulting from our 

exploratory series of analyses covers 

more types of conditions than were used 

in the original case-mix model (Tables 

2a and 2b). We identified conditions 

from diagnosis codes on both claims 

and OASIS in a linked sample of claims 

from FY 2003 (OASIS items M0230 and 

M0240, Diagnoses and Severity Index). 

For example, heart and mental 

conditions are now assigned case-mix 

scores. More wound conditions are 

assigned scores, based on results from 

adding variables to indicate woundrelated diagnosis codes beyond those in 

the current HH PPS case-mix model. 

(See Table 2b for diagnosis codes that 

define each condition in the model.) 

We also propose to assign scores to 

certain secondary diagnoses, used to 

account for cost-increasing effects of 

comorbidities. An example is secondary 

cancer diagnoses, whose cost-increasing 

effects are not as large as those for 

primary cancer diagnoses. However, 

with most diagnosis groups, we did not 


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make a distinction in the final model 

between primary placement and 

secondary placement of a condition in 

the reported list of diagnoses. We made 

case-by-case decisions on this question 

based on differences in the impact on 

resource cost between the primary 

diagnosis and secondary diagnosis. If 

differences were small, we combined 

cases reporting the conditions, 

regardless of whether the listed position 

of the diagnosis was primary or 

secondary. We believe this is an 

important protection against unintended 

and undesirable incentive effects that 

could arise if agencies perceive 

opportunities to change the placement 

of the diagnosis due to nonclinical 

reasons. In a few instances, the reason 

for combining the primary or secondary 

diagnoses was to improve the 

robustness of the scores. 

Finally, we also propose that a small 

number of interactions—combinations 

of conditions in the same episode—be 

assigned scores, to capture the 

synergistic effect on resource use of 

certain conditions that coexist in the 

episode. In some instances, a condition 

appears as an interaction with a 

functional limitation or a treatment 

variable such as parenteral therapy. In 

Table 2a, the interaction scores are 

added to the case-mix score whenever 

the two conditions defining the 

interaction occur together in the 

episode. Interaction scores, therefore, do 

not substitute for scores of other 

variables in Table 2a that involve either 

only one or the other of the two 

conditions. 

As noted earlier, we also found that, 

compared to early episodes, later 

episodes could exhibit a different 

relationship between resource costs and 

a condition. This is reflected in Table 2a 

by the absence of a condition-related 

score from one or more of the four 

equations, or a score that differs from 

one equation to another. 

During the later phases of testing 

alternative formulations of an expanded 

list of clinical conditions, we followed 

two rules in our formation of diagnosis 

groups. These rules would ultimately 

affect the operation of the case-mix 

grouper which would be created 

pursuant to the revisions being 

proposed in this proposed rule. First, if 

an episode record in our sample file 

listed both primary and secondary 

diagnoses from the same diagnosis 

group, the model estimation procedure 

recognized the primary diagnosis 

variable for that case but not the 

secondary diagnosis variable. This 

means that an episode would not be 

eligible to earn more than one score for 

the same diagnosis group. The primary 

reason for this rule is that we are aware 

of diagnosis coding conventions that 

would produce repeated instances of the 

same or similar codes in the diagnosis 

list, and these conventions would build 

redundancy into the modeling process. 

A major goal of the exploratory 

modeling process was to investigate the 

impact of comorbidities by recognizing 

secondary diagnoses, but redundancy 

inhibits our achievement of that goal. 

Consequently, we sought to reduce this 

type of redundancy. A further reason for 

adhering to this rule is to inhibit a 

future decline in model performance, 

which might come about through 

changes in coding behavior. If agencies 

were to perceive that redundant coding 

boosts the episode score, they might 

engage in it more in the future. The 

result would be a degradation in the 

ability of the case-mix model to provide 

for accurate payment. 

The second rule we used affected how 

we define the interactions between 

conditions. The second rule is that, for 

purposes of forming diagnosis groups to 

test interactions between conditions, 

cases with either a primary or secondary 

diagnosis from the same diagnosis group 

are combined into a single group. This 

means that mention of a given diagnosis 

anywhere in the diagnosis list puts 

episodes in a single group for that 

diagnosis, for purposes of analyzing 

interactions between conditions. We 

believe this rule is consistent with our 

goal of isolating effects of comorbidities. 

Specifically, because the reason for 

studying interactions is to identify the 

effects of combinations of conditions, 

we believe it is appropriate to measure 

the combinations, regardless of the 

placement (that is, primary or 

secondary) of a diagnosis on the claim. 

Further, combining the primary and 

secondary diagnoses within groups 

increases the ability of the modeling 

process to uncover meaningful 

interaction effects. The second rule also 

works to keep the model as simple as 

possible. Simplicity helps to limit the 

risk that the model would not fit well 

for later data sets. Simplicity also limits 

the amount of added administrative 

burden that could come from using a 

more-complex model. 

Changes to the OASIS are needed to 

enable agencies to report secondary 

case-mix diagnosis codes. Specifically, 

the addition of secondary diagnoses to 

the case-mix system (see Table 2a, casemix adjustment variables and scores) 

requires that the OASIS allow for 

reporting of instances in which a V-code 

is coded in place of a case-mix diagnosis 

other than the primary diagnosis. A 

case-mix diagnosis is a diagnosis that 

determines the HH PPS case-mix group. 

Currently, the OASIS allows for 

reporting of instances of displacement 

involving primary diagnosis only 

(M0245). Consequently, because of the 

nature and significance of the changes 

needed, we are proposing to delete the 

OASIS item M0245 and replace it with 

a new OASIS item. (see section III. 

Collection of Information 

Requirements). 

c. Addition of Therapy Thresholds 

As set forth in the July 3, 2000 final 

rule (65 FR 1128), patients were 

grouped according to their therapy 

utilization status in order to ensure that 

patients who required therapy would 

maintain access to appropriate services. 

Specifically, we defined a therapy 

threshold of at least 8 hours of 

combined physical, speech, or 

occupational therapy over the 60-day 

episode, to identify ‘‘high’’ therapy 

cases. The 8-hour threshold was 

converted to a threshold of 10 therapy 

visits because the average visit length 

for therapy noted in our data was 

approximately 48 minutes. We 

instituted the threshold based on 

clinical judgment about the level of 

therapy that reflects a clear need for 

rehabilitation services and that would 

reasonably be expected to result in 

meaningful treatment over the course of 

60 days. 

Since the implementation of the 

therapy threshold in the HH PPS, we 

have received comments from the 

public requesting that we study and 

refine this approach to accounting for 

rehabilitation needs in the case-mix 

system. Commenters have suggested 

that a single therapy threshold did not 

fairly reflect the variation in therapy 

utilization and need. Some commenters 

requested that we re-examine the 10- 

visit threshold. Other commenters 

recommended that we work to eliminate 

the therapy threshold, in part due to 

concerns that the therapy threshold 

might introduce incentives to distort 

service delivery patterns for payment 

purposes. 

Our data analysis revealed evidence 

of undesirable incentives from the 10- 

visit therapy threshold. Our analysis 

suggested that the 10-visit therapy 

threshold might have distorted service 

delivery patterns. In our analysis 

sample, of all episodes at or above the 

threshold, half were concentrated in the 

range of 10 to 13 therapy visits. This 

range had the highest concentration of 

therapy episodes among episodes with 

at least one therapy visit. In contrast, a 

large analysis sample from a period 

immediately preceding the HH PPS 

indicated that the highest concentration 

of therapy episodes was in a range 


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below the 10-visit threshold—

approximately 5 to 7 therapy visits. 

Under the HH PPS, there were two 

peaks in the graphic depiction of 

numbers of episodes according to the 

number of therapy visits delivered 

during the episode. One peak was below 

the therapy threshold and the other was 

the 10 to 13 visit peak above the therapy 

threshold. In the pre-PPS sample, there 

was only one peak in the depiction, and 

it was the concentration of episodes at 

5 to 7 therapy visits—below the current 

10-visit therapy threshold. All of these 

results suggested that the 10-visit 

threshold was responsible for a marked 

shift in rehabilitation services delivery 

under the HH PPS, a shift that we 

believe would probably not have 

occurred in the absence of the therapy 

threshold. Commenters have reinforced 

our belief that the impact of the single 

10-visit threshold on therapy provision 

frequently distorted the clinically based 

decision-making that should drive the 

delivery of rehabilitation services. 

In our early efforts to address 

problems inherent in using a therapy 

threshold, we conducted analyses to 

identify new predictors of therapy 

resource use, with the goal of achieving 

large gains in explanatory power that 

would render the therapy threshold 

unnecessary. We used predictor 

variables including pre-admission status 

on activities of daily living (ADL), more 

diagnoses with a focus on conditions 

such as stroke, and more OASIS 

variables. However, models that 

included these particular explanatory 

variables predicted the probability of 

using therapy, but not how much 

therapy would be used. 

Successive studies to account for 

therapy resources followed the goal of 

reducing the impact of a therapy 

threshold on the payment weights. The 

main conclusion from these studies was 

that therapy resources cannot be 

predicted with sufficient accuracy to 

eliminate the need for therapy 

thresholds in the HH PPS case-mix 

system. Although we tried several 

alternative approaches, no approach 

added sufficient predictive power to the 

case-mix model. Therefore, continued 

analysis focused primarily on refining 

the therapy threshold approach to 

reduce undesirable incentives. This 

work involved experimentation with 

alternative sets of thresholds consisting 

of more than one threshold. 

After testing several sets of 

thresholds, and in consideration of the 

comments received, we proceeded to 

construct case-mix models with 

thresholds at 6, 14, and 20 therapy 

visits. We used these thresholds based 

on data analysis and, in part, on policy 

considerations. 

Data analysis suggested it would be 

appropriate to add new thresholds both 

below and above the 10-visit level. One 

reason was that our review of data from 

the HH PPS period showed agencies 

provided large numbers of episodes 

with therapy visits in an interval below 

10 visits. Moreover, data analysis 

suggested that, of all episodes with 

numbers of therapy visits below the 10- 

visit therapy threshold, some subsets 

did not receive an appropriate case-mix 

weight under the HH PPS. Specifically, 

episodes with 6 to 9 therapy visits had 

resource costs that seemingly exceeded 

the payment proxied in our analysis by 

the predicted resource cost under the 

current case mix model. However, we 

now believe that several common 

treatment plans require only about 6 

visits, for example, assessments and 

treatment of certain types of patients at 

high risk for falls. We are therefore 

proposing that one threshold be added 

at 6 therapy visits. 

In considering thresholds above the 

current 10-visit threshold, we observed 

that nearly half of episodes involving 

therapy comprise episodes with 6 to 13 

therapy visits. Therefore, we are 

proposing a second threshold at 14 

therapy visits, which would have two 

advantages. First, this range covers the 

two peaks (that is, the one we observed 

below the 10-visit therapy threshold and 

the one we observed above the 10-visit 

threshold) in the distribution of therapy 

visits under the HH PPS. By avoiding a 

therapy threshold within this range, we 

hope to reduce the influence of payment 

incentives on treatment decisions. 

Second, we believe that the interval of 

6 to 13 therapy visits represents a 

reasonable range of treatment levels for 

most rehabilitation episodes. For 

example, the range of 6 to 13 therapy 

visits encompasses typical treatment 

plans for both knee- and hipreplacement patients. As we describe 

later in this section, we propose to use 

further steps to address payment 

accuracy, by adding payment gradations 

within the intervals bounded by the 

three thresholds we are proposing. 

We further observed that only a 

relatively small fraction of patients use 

14 or more therapy visits. While no 

bright-line tests are available to 

distinguish a 14-visit case, we have 

received comments indicating that 

medical review staff at the fiscal 

intermediaries will have less difficulty 

judging appropriateness of treatment 

plans at this level, because such plans 

are intensive and not the norm. 

Additionally, although few episodes 

require 20 or more therapy visits, we set 

the third therapy threshold at 20 visits. 

Our concern is to ensure access to 

appropriate treatment in the rare cases 

where such intensive treatment is 

necessary. Our analysis suggested that 

these episodes are extremely costly for 

agencies, so a payment adjustment to 

accommodate this service level is 

appropriate. Furthermore, commenters 

indicated that, because only rare cases 

should warrant this high number of 

therapy visits, monitoring of claims to 

prevent abuse of this payment 

provision, using our medical review 

resources, is feasible operationally. 

Adding therapy thresholds in the 

revised case-mix regression model 

improves the ability of the model to 

predict resource use. The R-squared 

values for a three-therapy threshold 

model increased substantially for both 

early and later episodes over the Rsquared values for a single therapy 

threshold model. In other words, using 

additional therapy thresholds clearly 

improved the case-mix system’s ability 

to classify episodes into homogeneous 

cost groups. 

The combined effect of the new 

therapy thresholds and payment 

gradations (to be described below) is 

expected to reduce the undesirable 

emphasis in treatment planning on a 

single therapy visit threshold, and to 

restore the primacy of clinical 

considerations in treatment planning for 

rehabilitation patients. 

During the analysis of the therapy 

threshold, we considered ways to 

provide for payment gradations between 

the therapy thresholds. We sought a way 

to implement a gradual increase in 

payment (see Table 1) between the 

proposed first and third therapy 

thresholds. We believe a case-mix 

model that increases payment with each 

added visit between the proposed first 

and third thresholds would achieve two 

goals. First, a gradual increase better 

matches payments to costs than the 

therapy thresholds alone. Second, a 

gradual increase avoids incentives for 

providers to distort patterns of good care 

created by the increase in payment that 

would occur at each proposed therapy 

threshold. However, as a disincentive 

for agencies to deliver more than the 

appropriate, clinically determined 

number of therapy visits, we are also 

proposing that any per-visit increase 

incorporate a declining, rather than 

constant, amount per added therapy 

visit. We implemented this in the casemix model by decreasing slightly the 

added amount per therapy visit as the 

number of therapy visits grew above the 

proposed 6-visit threshold. Specifically, 

we began with a value determined from 

our sample—the estimated marginal 


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resource cost incurred by adding a 7th 

therapy visit to the treatment plan. This 

is the first additional visit above the 

proposed six-visit therapy threshold. 

The estimated marginal cost of adding a 

7th therapy visit to an episode with six 

therapy visits was $36. Using this value 

as our starting point, we required the 

case-mix model to add a slightly lower 

value to the total episode resource cost 

with each additional therapy visit 

provided, up to the 19th therapy visit. 

This proposed approach imposes a 

deceleration of the growth in payment 

with each additional therapy visit. 

However, this proposed approach does 

not reduce total payments to home 

health providers, because the regression 

analysis still predicts the full resource 

cost of the episode. Table 1 shows the 

values that we imposed in the fourequation model estimation procedure to 

implement a deceleration in the added 

resource cost for individual therapy 

visits between 6 and 20 therapy visits. 

The individual values begin at $36 and 

then decline at a constant rate of one 

resource cost dollar per therapy visit 

between 6 and 20 therapy visits. These 

values represent the score that was 

imposed in the model for adding each 

additional therapy visit. The case-mix 

model that incorporates the imposed 

scores is called a ‘‘restricted regression 

model.’’ The results of the restricted 

regression model of the four-equation 

system, including scores for diagnoses 

and conditions, and R-squared statistics, 

exhibited little change from imposing 

this pattern of deceleration in cost 

growth due to additional therapy visits. 

TABLE 1.—RESOURCE COST VALUES IMPOSING DECELERATION TREND IN FOUR-EQUATION MODEL

Equation and services utilization severity level 

Number of 

therapy visits in 

severity level 

Resource cost 

values imposed 

in regression 

procedure 

1st and 2nd Episodes, 6–13 Therapy Visits 

S3 ............................................................................................................................................................. 7, 8, 9 36, 35, 34 

S4 ............................................................................................................................................................. 10 33 

S5 ............................................................................................................................................................. 11, 12, 13 32, 31, 30 

1st and 2nd Episodes, 14–19 Therapy Visits 

S1* ............................................................................................................................................................ 15 28 

S2 ............................................................................................................................................................. 16, 17 27, 26 

S3 ............................................................................................................................................................. 18, 19 25, 24 

3rd+ Episodes, 6–13 Therapy Visits 

S3 ............................................................................................................................................................. 7, 8, 9 36, 35, 34 

S4 ............................................................................................................................................................. 10 33 

S5 ............................................................................................................................................................. 11, 12, 13 32, 31, 30 

3rd+ Episodes, 14–19 Therapy Visits 

S1* ............................................................................................................................................................ 15 28 

S2 ............................................................................................................................................................. 16, 17 27, 26 

S3 ............................................................................................................................................................. 18, 19 25, 24 

* For the second and fourth equations of the four equation model, S1 includes 14 therapy visits, but no value was imposed in the regression 

procedure for a 14th therapy visit because the regression intercept estimate automatically includes the resource cost impact. 

The case-mix model at this stage was 

very detailed, because it included 

variables incorporating information 

about thresholds and therapy visit 

counts. We were concerned that, 

without streamlining the therapy-related 

information in the case-mix model, the 

ultimate system of case-mix groups 

would contain an excessive number of 

case-mix groups. We recognize an 

extremely large number of case-mix 

groups would make the HH PPS 

complex to administer. Because the 

therapy-related details of the case-mix 

model are based on numbers of therapy 

visits, another issue would be that many 

case-mix groups would be differentiated 

based on visit counts, thereby making 

the system dependent on visits and less 

of a bundled system of services. 

Therefore, in order to form case-mix 

groups from the results of the case-mix 

model, we grouped the individual levels 

of therapy visits into small aggregates (1, 

2, or 3 visits) (see Table 1). By doing so, 

we avoided creating a per-visit schedule 

of payment to account for therapy visits. 

We implemented these aggregations as 

differing severity levels at a subsequent 

stage of payment system development, 

the payment regression, which is 

described later in this section. 

The proposed four equation model, 

with multiple therapy thresholds and 

payment graduation between those 

thresholds, adds a certain amount of 

complexity to the HH PPS. 

Consequently, in order to group 

beneficiaries into case-mix groups in 

this proposed four equation model, we 

propose to make changes to the OASIS 

to capture the projected number of total 

therapy visits for a given episode (see 

section III. Collection of Information 

Requirements), as opposed to indicating 

if there is a projected need for ten or 

more therapy visits (current OASIS item 

M0825). Each severity level of the 

services utilization dimension 

represents a different number of therapy 

visits (see also Table 3: Severity Group 

Definitions: Four-Equation Model). 

An additional aspect of our therapy 

threshold research addressed changing 

the unit of measurement of therapy 

thresholds from visits to minutes. In the 

July 2000 final rule, we indicated our 

intention to continue study of the 

appropriate unit of measurement for 

therapy services. 

An important finding of our initial 

analyses on this question was that the 

length of therapy visits in minutes, on 

average, exhibited little change between 

the period covered by the original Abt 

Associates case-mix study, and the HH 

PPS period, based on data through 2003. 

We also found that the distribution of 

average therapy visit lengths was highly 

similar under HH PPS, regardless of the 

total number of therapy visits in the 

episode. A possible exception was 

episodes with 1 to 4 therapy visits, 

where a relatively high proportion of 

episodes (about 16 percent) had average 

therapy visit lengths of 30 minutes or 

less; no more than 9 percent of 

remaining episodes (more than four 

therapy visits) had averages of 30 

minutes or less. There was also a slight 

tendency for these short average visit 


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lengths to become less frequent as the 

total therapy visit count per episode 

grew. Overall, the data indicated that at 

least 85 percent of episodes with 

therapy visits involved visits averaging 

at least 41 minutes. These results 

suggest that therapy practitioners tend 

to have consistent session lengths across 

many types of episodes. 

We are proposing no change in the 

current way in which we measure 

therapy thresholds, which is based on 

counting therapy visits, in light of our 

analysis indicating that individual 

therapy visits appear to vary little in 

their length, regardless of the frequency 

of visits during the 60-day episode, and 

our analysis indicating that average visit 

lengths have remained stable since the 

time of the Abt case-mix study. 

Additionally, we are concerned 

incentive issues would arise if we 

changed the definition. The low 

variability in visit lengths appears to be 

an indication that under current 

practices, therapy session lengths are 

fairly uniform, regardless of the time 

period or intensity of the rehabilitation 

course of treatment. These practices 

have arisen out of clinical experience in 

the rehabilitation professions. 

Introducing a minutes or time standard 

risks introducing new financial 

incentives that might influence these 

widely held practices. We are concerned 

that changing to a minutes standard 

might result in financially driven 

pressures on clinical decisions 

concerning the number of sessions in a 

patient’s course of treatment, with 

potentially adverse effects on 

beneficiary outcomes. 

One of our original concerns in 

proposing a visit-based threshold was 

that minutes unit reporting on the 

claims, which was a relatively new 

requirement at that time, might be 

unreliable. (Section 1895(c)(2) requires 

the claim to report the length of each 

billed visit as measured in 15-minute 

increments.) Based upon our 

experiences using the claims data in our 

research, we have no reason to believe 

this is a problem. Moreover, we believe 

the dual requirements to report both 

visit dates and minutes of each visit on 

Medicare claims should remain in place 

because they provide important 

information for program integrity 

activities and future research. 

Based upon our analysis of the casemodel described in section II.A.2, we 

propose to use four separate equations 

to derive scores for conditions including 

the proposed therapy thresholds. The 

proposed first equation is for early 

episodes below the 14-visit therapy 

threshold. The proposed second 

equation is for early episodes at or 

above the 14-visit therapy threshold. 

The proposed third equation is for later 

episodes below the 14-visit therapy 

threshold. The proposed fourth equation 

is for later episodes above the 14-visit 

therapy threshold. A threshold at 6 

visits is accounted for by an indicator 

variable in the proposed first and third 

equations, and a threshold at 20 visits 

is accounted for by an indicator variable 

in the proposed second and fourth 

equations. In addition, therapy visit 

count variables are added to the 

equations to model the graduated 

payment with each therapy visit 

between 6 and 20 visits. Finally, as we 

explained above, we imposed specific 

values for the coefficients of the therapy 

visit count variables. The resulting fourequation model has an improved 

statistical performance (an R-squared 

statistic of approximately 0.44) over the 

current model (an R-squared statistic of 

0.21). The primary reason for the 

improvement in the proposed case-mix 

model fit (compared to the R-square 

statistic of 0.21 cited earlier) is the fourequation structure. This structure 

recognizes cost differences between 

early and later episodes, and between 

therapy treatment plans above and 

below the proposed 14-visit therapy 

threshold. Additional improvements 

come from adding other therapy 

variables to the case-mix model, 

specifically, the two additional 

thresholds (6 and 20 visits) and 

graduated payment—and from the new 

case-mix variables discussed in section 

II.A.2.a of this proposed rule. 

We believe that in addition to 

improved statistical performance, the 

proposed model would provide better 

incentives for the provision of highquality home health care without an 

undue increase in administrative 

burden. For a more detailed discussion 

of the technical aspects of the fourequation model go to the CMS Web site 

(http://www.cms.hhs.gov/hha.asp) for a 

link to Abt’s Technical Report. 

Table 2a presents the full set of casemix scores (other than the imposed 

scores for therapy visits) and all clinical 

and functional variables we are 

proposing for the refined case-mix 

model. In Table 2a, the score is the 

value of the regression coefficient for 

the variable; it measures the impact of 

the data element on total resource cost 

of the episode. See Table 2b for an 

inclusive list of ICD–9–CM diagnosis 

codes applicable for each scored 

condition variable in Table 2a. These 

codes define the clinical condition 

variables in our proposed model. We 

intend to continue to evaluate the 

appropriateness of these diagnosis codes 

in Table 2b. We believe the HH PPS 

case-mix system should avoid, to the 

fullest extent possible, nonspecific or 

ambiguous ICD–9–CM codes, codes that 

represent general symptomatic 

complaints in the elderly population, 

and codes that lack consensus for clear 

diagnostic criteria within the medical 

community. We solicit detailed 

suggestions from the public concerning 

codes that threaten to move the system 

away from a foundation of reliable and 

meaningful diagnosis codes. 

Compared to the original four 

diagnosis groups in the case-mix model, 

the code groups in Table 2b incorporate 

additions and new group placements for 

individual ICD–9–CM diagnosis codes. 

Two variables from the original case 

mix system are not proposed: M0175, as 

noted earlier, and M0610, behavioral 

problems, which did not perform well 

in our studies. We believe that several 

additions to our diagnosis groups, 

namely, two groups for psychiatric 

diagnoses, account for the contribution 

of behavioral problems to resource cost 

variation. 

We are aware that some of the 

diagnosis codes listed in Table 2b are 

manifestation codes. The ICD–9–CM 

Official Guidelines for Coding and 

Reporting requires that the underlying 

disease or condition code be sequenced 

first, followed by the manifestation 

code. The underlying disease codes 

associated with the manifestation codes 

are not listed in Table 2b. However, 

appropriate sequencing was accounted 

for in our analysis. When reporting 

certain conditions that have both an 

underlying etiology and a body system 

manifestation due to the underlying 

etiology, the appropriate sequencing 

should be followed according to the 

ICD–9–CM Coding Guidelines. 

For purposes of determining final 

estimates on which to base the data set 

used in the final rule for CY 2008, we 

intend to update the dataset used for the 

four-equation model to CY 2005; as 

noted above, the proposal to use the 

four-equation model is based on linked 

claims and OASIS data from FY 2003. 

We are aware that adding data from a 

later period may result in some 

variations, including some significant 

changes, in the scores presented in 

Table 2a. Some changes may occur 

because, effective October 2003 (FY 

2004), diagnosis coding instructions on 

the OASIS assessment changed to allow 

for the use of ICD–9–CM V-codes. 

V-codes, particularly those applicable to 

home health services, do not in general 

describe disease states; rather, they 

describe reasons for using services. The 

major use of V-codes in the home health 

setting occurs when a person with 

current or resolving disease or injury 


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encounters the health care system for 

specific aftercare of that disease or 

injury. For example, V-code V57.21 is 

reportable when the reason for the visit 

is ‘‘encounter for occupational therapy.’’

As such, V-codes are less specific to the 

clinical condition of the patient than are 

numeric diagnosis codes. A single 

V-code could substitute for various 

numeric codes, each of which describes 

a specific, different clinical condition. 

Medical review activities revealed an 

inappropriate utilization of V-codes 

following the effective date of V-codes 

on OASIS (October, 2003). In response 

to RHHI reports of increased provider 

non-compliance with correct ICD–9–CM 

coding procedures related to V-codes, 

we posted OASIS diagnosis training on 

the CMS Web site and promoted RHHI 

provider educational efforts. 

Nonetheless, medical review activities 

continue to report an excessive 

utilization of the V–57 codes, signaling 

a possible non-compliance with correct 

coding practice related to the V-codes. 

We are concerned that more use of 

V-codes could reduce data adequacy for 

modeling the impacts of clinical 

conditions we are proposing to use to 

predict resource use. One result, for 

example, might be a markedly different 

score for some conditions with lower 

reporting rates under the V-code 

instructions effective October 2003. 

At this time, we do not know whether 

allowing V-codes on the OASIS, along 

with the over-use of V-codes revealed by 

medical review activities, significantly 

lowered the frequencies of non-V-code, 

numeric diagnosis codes for the clinical 

conditions we propose to use in the case 

mix model. Again, this could have 

occurred because of the way V-codes 

can displace a numeric code in the 

diagnosis list. If we find evidence that 

numeric codes’ frequencies were 

reduced to the extent that it strongly 

influenced the scores we present in this 

proposal, we propose to base the refined 

system on the data from FY 2003. 

BILLING CODE 4120–01–P 


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BILLING CODE 4120–01–C 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT VARIABLES

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

Blindness and low vision ..................................... 369.0 ......................... PROFOUND BLIND BOTH EYES 

369.1 ......................... MOD/SEV W PROFND IMPAIR 

369.2 ......................... MOD/SEV IMPAIR-BOTH EYES 

369.3 ......................... BLINDNESS NOS, BOTH EYES 

369.4 ......................... LEGAL BLINDNESS-USA DEF 

950 ......................... INJURY TO OPTIC NERVE AND PATHWAYS 

Blood disorders .................................................... 281 ......................... OTHER DEFICIENCY ANEMIAS 

282 ......................... HEREDITARY HEMOLYTIC ANEMIAS 

283 ......................... ACQUIRED HEMOLYTIC ANEMIAS 

284 ......................... APLASTIC ANEMIA 

285 ......................... OTHER AND UNSPECIFIED ANEMIAS 

286 ......................... COAGULATION DEFECTS 

287 ......................... PURPURA&OTHER HEMORRHAGIC CONDS 

288 ......................... DISEASES OF WHITE BLOOD CELLS 

289 ......................... OTH DISEASES BLD&BLD-FORMING ORGANS 

Cancer and selected benign neoplasms ............. 140 ......................... MALIGNANT NEOPLASM OF LIP 

141 ......................... MALIGNANT NEOPLASM OF TONGUE 

142 ......................... MALIG NEOPLASM MAJOR SALIV GLANDS 

143 ......................... MALIGNANT NEOPLASM OF GUM 

144 ......................... MALIGNANT NEOPLASM FLOOR MOUTH 

145 ......................... MALIG NEOPLSM OTH&UNSPEC PART MOUTH 

146 ......................... MALIGNANT NEOPLASM OF OROPHARYNX 

147 ......................... MALIGNANT NEOPLASM OF NASOPHARYNX 

148 ......................... MALIGNANT NEOPLASM OF HYPOPHARYNX 

149 ......................... OTH MALIG NEO LIP-MOUTH-PHARYNX 

150 ......................... MALIGNANT NEOPLASM OF ESOPHAGUS 

151 ......................... MALIGNANT NEOPLASM OF STOMACH 

152 ......................... MALIG NEOPLSM SM INTEST INCL DUODUM 

153 ......................... MALIGNANT NEOPLASM OF COLON 

154 ......................... MAL NEO RECT RECTOSIGMOID JUNC&ANUS 

155 ......................... MALIG NEOPLASM LIVER&INTRAHEP BDS 

156 ......................... MALIG NEOPLSM GALLBLADD&XTRAHEP BDS 

157 ......................... MALIGNANT NEOPLASM OF PANCREAS 

158 ......................... MALIG NEOPLASM RETROPERITON&PERITON 

159 ......................... MAL NEO DIGES ORGANS&PANCREAS OTH 

160 ......................... MAL NEO NASL CAV/MID EAR&ACSS SINUS 

161 ......................... MALIGNANT NEO LARYNX* 

162 ......................... MALIGNANT NEO TRACHEA/LUNG* 


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TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

163 ......................... MALIGNANT NEOPL PLEURA* 

164 ......................... MAL NEO THYMUS/MEDIASTIN* 

165 ......................... OTH/ILL-DEF MAL NEO RESP* 

170 ......................... MALIG NEOPLASM BONE&ARTICLR CART 

171 ......................... MALIG NEOPLSM CNCTV&OTH SOFT TISSUE 

172 ......................... MALIGNANT MELANOMA OF SKIN 

173 ......................... OTHER MALIGNANT NEOPLASM OF SKIN 

174 ......................... MALIGNANT NEOPLASM OF FEMALE BREAST 

175 ......................... MALIGNANT NEOPLASM OF MALE BREAST 

176 ......................... KAPOSIS SARCOMA 

179 ......................... MALIG NEOPLASM UTERUS PART UNSPEC 

180 ......................... MALIGNANT NEOPLASM OF CERVIX UTERI 

181 ......................... MALIGNANT NEOPLASM OF PLACENTA 

182 ......................... MALIGNANT NEOPLASM BODY UTERUS 

183 ......................... MALIG NEOPLSM OVRY&OTH UTERN ADNEXA 

184 ......................... MALIG NEOPLSM OTH&UNS FE GENIT ORGN 

185 ......................... MALIGNANT NEOPLASM OF PROSTATE 

186 ......................... MALIGNANT NEOPLASM OF TESTIS 

187 ......................... MAL NEOPLSM PENIS&OTH MALE GNT ORGN 

188 ......................... MALIGNANT NEOPLASM OF BLADDER 

189 ......................... MAL NEO KIDNEY&OTH&UNS URIN ORGN 

190 ......................... MALIGNANT NEOPLASM OF EYE 

192.0 ......................... MALIGNANT NEOPLASM, CRANIAL NERVES 

192.8 ......................... MALIGNANT NEOPLASM OTHER NERV SYS 

192.9 ......................... MALIGNANT NEOPLASM, UNS PART NERV SYS 

193 ......................... MALIGNANT NEOPLASM OF THYROID GLAND 

194 ......................... MAL NEO OTH ENDOCRN GLND&REL STRCT 

195 ......................... MALIG NEOPLASM OTH&ILL-DEFIND SITES 

196 ......................... SEC&UNSPEC MALIG NEOPLASM NODES 

197 ......................... SEC MALIG NEOPLASM RESP&DIGESTV SYS 

198 ......................... SEC MALIG NEOPLASM OTHER SPEC SITES 

199 ......................... MALIG NEOPLASM WITHOUT SPEC SITE 

200 ......................... LYMPHOSARCOMA AND RETICULOSARCOMA 

201 ......................... HODGKINS DISEASE 

202 ......................... OTH MAL NEO LYMPHOID&HISTCYT TISS 

203 ......................... MX MYELOMA&IMMUNOPROLIFERAT NEOPLSM 

204 ......................... LYMPHOID LEUKEMIA 

205 ......................... MYELOID LEUKEMIA 

206 ......................... MONOCYTIC LEUKEMIA 

207 ......................... OTHER SPECIFIED LEUKEMIA 

208 ......................... LEUKEMIA OF UNSPECIFIED CELL TYPE 

213 ......................... BEN NEOPLASM BONE&ARTICLR CARTILAGE 

225.1 ......................... BEN NEOPLSM CRANIAL NERVES 

225.8 ......................... BEN NEOPLSM OTH SPEC SITES 

225.9 ......................... BEN NEOPLSM UNSPEC PART NERV SYS 

230 ......................... CA IN SITU—DIGEST 

231 ......................... CA IN SITU—RESP 

232 ......................... CARCINOMA IN SITU OF SKIN 

233 ......................... CA IN SITU—BREAST AND GU 

234 ......................... CA IN SITU—OTH 

Diabetes ............................................................... 250 ......................... DIABETES MELLITUS 

357.2 M ..................... POLYNEUROPATHY IN DIABETES 

362.01 M ..................... BACKGROUND DIABETIC RETINOPATHY 

362.02 M ..................... PROLIFERATIVE DIABETIC RETINOPATHY 

366.41 M ..................... DIABETIC CATARACT 

Dysphagia ............................................................ 787.2 ......................... DYSPHAGIA 

Gait Abnormality .................................................. 781.2 ......................... ABNORM GAIT 

Gastrointestinal disorders .................................... 002 ......................... TYPHOID AND PARATYPHOID FEVERS 

003 ......................... OTHER SALMONELLA INFECTIONS 

004 ......................... SHIGELLOSIS 

005 ......................... OTHER FOOD POISONING 

006 ......................... AMEBIASIS 

007 ......................... OTHER PROTOZOAL INTESTINAL DISEASES 

008 ......................... INTESTINAL INFS DUE OTH ORGANISMS 

009 ......................... ILL-DEFINED INTESTINAL INFECTIONS 

530 ......................... DISEASES OF ESOPHAGUS 

531 ......................... GASTRIC ULCER 

532 ......................... DUODENAL ULCER 

533 ......................... PEPTIC ULCER, SITE UNSPECIFIED 

534 ......................... GASTROJEJUNAL ULCER 


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TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

535 ......................... GASTRITIS AND DUODENITIS 

536 ......................... DISORDERS OF FUNCTION OF STOMACH 

537 ......................... OTHER DISORDERS OF STOMACH&DUODENUM 

540 ......................... ACUTE APPENDICITIS 

541 ......................... APPENDICITIS, UNQUALIFIED 

542 ......................... OTHER APPENDICITIS 

543 ......................... OTHER DISEASES OF APPENDIX 

555 ......................... REGIONAL ENTERITIS 

556 ......................... ULCERATIVE COLITIS 

557 ......................... VASCULAR INSUFFICIENCY OF INTESTINE 

558 ......................... OTH NONINF GASTROENTERITIS&COLITIS 

560 ......................... INTEST OBST W/O MENTION HERN 

562 ......................... DIVERTICULA OF INTESTINE 

564 ......................... FUNCTIONAL DIGESTIVE DISORDERS NEC 

567 M ..................... PERITONITIS 

568 ......................... OTHER DISORDERS OF PERITONEUM 

569 ......................... OTHER DISORDERS OF INTESTINE 

570 ......................... ACUTE&SUBACUTE NECROSIS OF LIVER 

571 ......................... CHRONIC LIVER DISEASE AND CIRRHOSIS 

572 ......................... LIVER ABSC&SEQUELAE CHRON LIVR DZ 

573 M ..................... OTHER DISORDERS OF LIVER 

574 ......................... CHOLELITHIASIS 

575 ......................... OTHER DISORDERS OF GALLBLADDER 

576 ......................... OTHER DISORDERS OF BILIARY TRACT 

577 ......................... DISEASES OF PANCREAS 

578 ......................... GASTROINTESTINAL HEMORRHAGE 

579 ......................... INTESTINAL MALABSORPTION 

783.2 ......................... ABNORMAL LOSS OF WEIGHT 

Heart Disease ...................................................... 410 ......................... ACUTE MYOCARDIAL INFARCTION 

411 ......................... OTH AC&SUBAC FORMS ISCHEMIC HRT DZ 

428 ......................... HEART FAILURE 

Hypertension ........................................................ 401 ......................... ESSENTIAL HYPERTENSION 

402 ......................... HYPERTENSIVE HEART DISEASE 

403 ......................... HYPERTENSIVE RENAL DISEASE 

404 ......................... HYPERTENSIVE HEART&RENAL DISEASE 

405 ......................... SECONDARY HYPERTENSION 

Neuro 1—Brain disorders and paralysis .............. 013 ......................... TB MENINGES&CNTRL NERV SYS 

047 ......................... MENINGITIS DUE TO ENTEROVIRUS 

046 ......................... SLOW VIRUS INFECTION CNTRL NERV SYS 

048 ......................... OTH ENTEROVIRUS DZ CNTRL NERV SYS 

049 ......................... OTH NON-ARTHROPOD BORNE VIRL DX-CNS 

191 ......................... MALIGNANT NEOPLASM OF BRAIN 

192.2 ......................... MALIG NEOPLSM SPINAL CORD 

192.3 ......................... MALIG NEOPLSM SPINAL MENINGES 

225.0 ......................... BEN NEOPLSM BRAIN 

225.2 ......................... BEN NEOPLSM BRAIN MENINGES 

225.3 ......................... BEN NEOPLSM SPINAL CORD 

225.4 ......................... BEN NEOPLSM SPINAL CORD MENINGES 

320.0 ......................... HEMOPHILUS MENINGITIS 

320.1 ......................... PNEUMOCOCCAL MENINGITIS 

320.2 ......................... STREPTOCOCCAL MENINGITIS 

320.3 ......................... STAPHYLOCOCCAL MENINGITIS 

320.7 M ..................... MENINGITIS OTH BACT DZ CLASS ELSW 

320.81 ......................... ANAEROBIC MENINGITIS 

320.82 ......................... MENINGITIS DUE GM-NEG BACTER NEC 

320.89 ......................... MENINGITIS DUE OTHER SPEC BACTERIA 

320.9 ......................... MENINGITIS DUE UNSPEC BACTERIUM 

321.0 M ..................... CRYPTOCOCCAL MENINGITIS 

321.1 M ..................... MENINGITIS IN OTHER FUNGAL DISEASES 

321.2 M ..................... MENINGITIS DUE TO VIRUSES NEC 

321.3 M ..................... MENINGITIS DUE TO TRYPANOSOMIASIS 

321.4 M ..................... MENINGITIS IN SARCOIDOSIS 

321.8 M ..................... MENINGITIS-OTH NONBCTRL ORGNISMS CE 

322 ......................... MENINGITIS OF UNSPECIFIED CAUSE 

323.0 M ..................... ENCEPHALITIS VIRAL DZ CLASS ELSW 

323.1 M ..................... ENCEPHALIT RICKETTS DZ CLASS ELSW 

323.2 M ..................... ENCEPHALIT PROTOZOAL DZ CLASS ELSW 

323.4 M ..................... OTH ENCEPHALIT DUE INF CLASS ELSW 

323.5 ......................... ENCEPHALIT FOLLOW IMMUNIZATION PROC 

323.6 M ..................... POSTINFECTIOUS ENCEPHALITIS 


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TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

323.7 M ..................... TOXIC ENCEPHALITIS 

323.8 ......................... OTHER CAUSES OF ENCEPHALITIS 

323.9 ......................... ENCEPHALITUS NOS 

324 ......................... INTRACRANIAL&INTRASPINAL ABSCESS 

325 ......................... PHLEBIT&THRMBOPHLB INTRACRAN VENUS 

326 ......................... LATE EFF INTRACRAN ABSC/PYOGEN INF 

330.0 ......................... LEUKODYSTROPHY 

330.1 ......................... CEREBRAL LIPIDOSES 

330.2 M ..................... CEREB DEGEN IN LIPIDOSIS 

330.3 M ..................... CERB DEG CHLD IN OTH DIS 

330.8 ......................... CEREB DEGEN IN CHILD NEC 

330.9 ......................... CEREB DEGEN IN CHILD NOS 

334.1 ......................... HERED SPASTIC PARAPLEGIA 

335 ......................... ANTERIOR HORN CELL DISEASE 

336.1 ......................... VASCULAR MYELOPATHIES 

336.2 M ..................... SUBACUTE COMB DEGEN SPINL CRD DZ CE 

336.3 M ..................... MYELOPATHY OTH DISEASES CLASS ELSW 

336.8 ......................... OTHER MYELOPATHY 

336.9 ......................... UNSPECIFIED DISEASE OF SPINAL CORD 

337.3 ......................... AUTONOMIC DYSREFLEXIA 

344.1 ......................... PARAPLEGIA 

344.8 ......................... LOCKED-IN STATE 

344.9 ......................... PARALYSIS UNSPECIFIED 

348 ......................... OTHER CONDITIONS OF BRAIN 

349.82 ......................... OTH&UNSPEC DISORDERS NERVOUS SYSTEM 

336.0 ......................... SYRINGOMYELIA AND SYRINGOBULBIA 

344.0 ......................... QUADRAPLEGIA 

741 ......................... SPINA BIFIDA 

780.01 ......................... COMA 

780.03 ......................... PERSISTENT VEGETATIVE STATE 

806 ......................... FX VERT COLUMN W/SPINAL CORD INJURY 

851 ......................... CEREBRAL LACERATION AND CONTUSION 

852 ......................... SUBARACH SUB&XTRADURL HEMOR FLW INJ 

853 ......................... OTH&UNS INTRACRAN HEMOR FLW INJURY 

854 ......................... INTRACRAN INJURY OTH&UNSPEC NATURE 

907.0 ......................... LATE EFF INTRACRANIAL INJURY 

907.1 ......................... LATE EFFECT OF INJURY TO CRANIAL NERVE 

907.2 ......................... LATE EFFECT OF SPINAL CORD INJURY 

907.3 ......................... LATE EFFECT OF INJURY TO NERVE ROOT(S), SPINAL 

PLEXUS(ES), AND OTHER NERVES OF TRUNK 

907.4 ......................... LATE EFFECT OF INJURY TO PERIPHERAL NERVE OF 

SHOULDER GIRDLE AND UPPER LIMB 

907.5 ......................... LATE EFFECT OF INJURY TO PERIPHERAL NERVE OF 

PELVIC GIRDLE AND LOWER LIMB 

907.9 ......................... LATE EFFECT OF INJURY TO OTHER AND UNSPECIFIED 

NERVE 

952 ......................... SP CRD INJR W/O EVIDENCE SP BN INJR 

Neuro 2—Peripheral neurological disorders ........ 045 ......................... ACUTE POLIOMYELITIS 

332 ......................... PARKINSONS DISEASE 

333 ......................... OTH XTRAPYRAMIDAL DZ&ABN MOVMNT D/O 

334.0 ......................... FRIEDREICH’S ATAXIA 

334.2 ......................... PRIMARY CEREBELLAR DEGEN 

334.3 ......................... CEREBELLAR ATAXIA NEC 

334.4 M ..................... CEREBEL ATAX IN OTH DIS 

334.8 ......................... SPINOCEREBELLAR DIS NEC 

334.9 ......................... SPINOCEREBELLAR DIS NOS 

337.0 ......................... IDIOPATH PERIPH AUTONOM NEUROPATHY 

337.1 M ..................... PRIPHERL AUTONOMIC NEUROPTHY D/O CE 

337.20 ......................... UNSPEC REFLEX SYMPATHETIC DYSTROPHY 

337.21 ......................... REFLX SYMPATHET DYSTROPHY UP LIMB 

337.22 ......................... REFLX SYMPATHET DYSTROPHY LOW LIMB 

337.29 ......................... REFLX SYMPATHET DYSTROPHY OTH SITE 

337.9 ......................... UNSPEC DISORDER AUTONOM NERV SYSTEM 

343 ......................... INFANTILE CEREBRAL PALSY 

344.2 ......................... DIPLEGIA OF BOTH UPPER LIMBS 

352 ......................... DISORDERS OF OTHER CRANIAL NERVES 

353.0 ......................... BRACHIAL PLEXUS LESION 

353.1 ......................... LUMBOSACRAL PLEXUS LESION 

353.5 ......................... NEURALGIC AMYLOTROPHY 

354.5 ......................... MONONEURITIS MULTIPLEX 


pwalker on PROD1PC71 with PROPOSALS2
25376 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

355.2 ......................... OTHER LESION OF FEMORAL NERVE 

355.9 ......................... LESION OF SCIATIC NERVE 

356 ......................... HEREDIT&IDIOPATH PERIPH NEUROPATHY 

357.0 ......................... ACUTE INFECTIVE POLYNEURITIS 

357.1 M ..................... POLYNEUROPATHY COLL VASC DISEASE 

357.3 M ..................... POLYNEUROPATHY IN MALIGNANT DISEASE 

357.4 M ..................... POLYNEUROPATHY OTH DZ CLASS ELSW 

357.5 ......................... ALCOHOLIC POLYNEUROPATHY 

357.6 ......................... POLYNEUROPATHY DUE TO DRUGS 

357.7 ......................... POLYNEUROPATHY DUE OTH TOXIC AGENTS 

357.82 ......................... CRIT ILLNESS NEUROPATHY 

357.89 ......................... INFLAM/TOX NEUROPATHY 

357.9 ......................... UNSPEC INFLAM&TOXIC NEUROPATHY 

358.00 ......................... MYASTHENIA GRAVIS W/O ACUTE 

358.01 ......................... MYASTHENIA GRAVIS W/ACUTE 

358.1 M ..................... MYASTHENIC SYNDROMES DZ CLASS ELSW 

358.2 ......................... TOXIC MYONEURAL DISORDERS 

358.9 ......................... UNSPECIFIED MYONEURAL DISORDERS 

359.0 ......................... CONGEN HEREDIT MUSCULAR DYSTROPHY 

359.1 ......................... HEREDITARY PROGRESSIVE MUSC DYSTROPH 

359.3 ......................... FAMILIAL PERIODIC PARALYSIS 

359.4 ......................... TOXIC MYOPATHY 

359.5 M ..................... MYOPATHY ENDOCRINE DZ CLASS ELSW 

359.6 M ..................... SX INFLAM MYOPATHY DZ CLASS ELSW 

359.8 ......................... OTHER MYOPATHIES 

359.9 ......................... UNSPECIFIED MYOPATHY 

386.0 ......................... MENIERE’S DISEASE 

386.2 ......................... VERTIGO OF CENTRAL ORIGIN 

386.3 ......................... LABYRINTHITIS 

392 ......................... RHEUMATIC CHOREA 

953 ......................... INJURY TO NERVE ROOTS&SPINAL PLEXUS 

954 ......................... INJR OTH NRV TRNK NO SHLDR&PLV GIRD 

955.8 ......................... INJR PERIPH NRV SHLDR GIRDL&UP LIMB 

956.0 ......................... INJR TO SCIATIC NERVE 

956.1 ......................... INJ TO FEMORAL NERVE 

956.8 ......................... INJR TO MULTIPLE PELVIC AND LE NERVES 

Neuro 3—Stroke .................................................. 342 ......................... HEMIPLEGIA AND HEMIPARESIS 

344.3 ......................... MONOPLEGIA OF LOWER LIMB 

344.4 ......................... MONOPLEGIA OF UPPER LIMB 

344.6 ......................... UNSPECIFIED MONOPLEGIA 

430 ......................... SUBARACHNOID HEMORRHAGE 

431 ......................... INTRACEREBRAL HEMORRHAGE 

432 ......................... OTH&UNSPEC INTRACRANIAL HEMORRHAGE 

433.01 ......................... OCCLUSION&STENOSIS BASILAR ART W INFARC 

433.11 ......................... OCCLUSION&STENOSIS CAROTID ART W INFARC 

433.21 ......................... OCCLUSION&STENOSIS VERTEBRAL ART W INFARC 

433.31 ......................... OCCLUSION&STENOSIS MULT BILAT ART W INFARC 

433.81 ......................... OCCLUSION&STENOSIS OTH PRECER ART W INFARC 

434.01 ......................... CEREBRAL THROMBOSIS W INFARCTION 

434.11 ......................... CEREBRAL EMBOLISM W INFARCTION 

781.8 ......................... NEURO NEGLECT SYNDROME 

436 ......................... ACUT BUT ILL-DEFINED CEREBRVASC DZ 

438 ......................... LATE EFF CEREBROVASCULAR DZ 

435 ......................... TRANSIENT CEREBRAL ISCHEMIA 

Neuro 4—Multiple Sclerosis ................................ 340 ......................... MULTIPLE SCLEROSIS 

341 M ..................... OTH DEMYELINATING DZ CNTRL NERV SYS 

Ortho 1—Leg Disorders ....................................... 711.05 ......................... PYOGEN ARTHRITIS-PELVIS 

711.06 ......................... PYOGEN ARTHRITIS-L/LEG 

711.07 ......................... PYOGEN ARTHRITIS-ANKLE 

711.15 M ..................... REITER ARTHRITIS-PELVIS 

711.16 M ..................... REITER ARTHRITIS-L/LEG 

711.17 M ..................... REITER ARTHRITIS-ANKLE 

711.25 M ..................... BEHCET ARTHRITIS-PELVIS 

711.26 M ..................... BEHCET ARTHRITIS-L/LEG 

711.27 M ..................... BEHCET ARTHRITIS-ANKLE 

711.35 M ..................... DYSENTER ARTHRIT-PELVIS 

711.36 M ..................... DYSENTER ARTHRIT-L/LEG 

711.37 M ..................... DYSENTER ARTHRIT-ANKLE 

711.45 M ..................... BACT ARTHRITIS-PELVIS 

711.46 M ..................... BACT ARTHRITIS-L/LEG 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25377 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

711.47 M ..................... BACT ARTHRITIS-ANKLE 

711.55 M ..................... VIRAL ARTHRITIS-PELVIS 

711.56 M ..................... VIRAL ARTHRITIS-L/LEG 

711.57 M ..................... VIRAL ARTHRITIS-ANKLE 

711.65 M ..................... MYCOTIC ARTHRITIS-PELVI 

711.66 M ..................... MYCOTIC ARTHRITIS-L/LEG 

711.67 M ..................... MYCOTIC ARTHRITIS-ANKLE 

711.75 M ..................... HELMINTH ARTHRIT-PELVIS 

711.76 M ..................... HELMINTH ARTHRIT-L/LEG 

711.77 M ..................... HELMINTH ARTHRIT-ANKLE 

711.85 M ..................... INF ARTHRITIS NEC-PELVI 

711.86 M ..................... INF ARTHRITIS NEC-L/LEG 

711.87 M ..................... INF ARTHRITIS NEC-ANKLE 

711.95 ......................... INF ARTHRIT NOS-PELVIS 

711.96 ......................... INF ARTHRIT NOS-L/LEG 

711.97 ......................... INF ARTHRIT NOS-ANKLE 

712.15 M ..................... DICALC PHOS CRYST-PELVI 

712.16 M ..................... DICALC PHOS CRYST-L/LEG 

712.17 M ..................... DICALC PHOS CRYST-ANKLE 

712.25 M ..................... PYROPHOSPH CRYST-PELVIS 

712.26 M ..................... PYROPHOSPH CRYST-L/LEG 

712.27 M ..................... PYROPHOSPH CRYST-ANKLE 

712.35 M ..................... CHONDROCALCIN NOS-PELVI 

712.36 M ..................... CHONDROCALCIN NOS-L/LEG 

712.37 M ..................... CHONDROCALCIN NOS-ANKLE 

712.85 ......................... CRYST ARTHROP NEC-PELVI 

712.86 ......................... CRYST ARTHROP NEC-L/LEG 

712.87 ......................... CRYST ARTHROP NEC-ANKLE 

712.95 ......................... CRYST ARTHROP NOS-PELVI 

712.96 ......................... CRYST ARTHROP NOS-L/LEG 

712.97 ......................... CRYST ARTHROP NOS-ANKLE 

716.05 ......................... KASCHIN-BECK DIS-PELVIS 

716.06 ......................... KASCHIN-BECK DIS-L/LEG 

716.07 ......................... KASCHIN-BECK DIS-ANKLE 

716.15 ......................... TRAUM ARTHROPATHY-PELVIS 

716.16 ......................... TRAUM ARTHROPATHY-L/LEG 

716.17 ......................... TRAUM ARTHROPATHY-ANKLE 

716.25 ......................... ALLERG ARTHRITIS-PELVIS 

716.26 ......................... ALLERG ARTHRITIS-L/LEG 

716.27 ......................... ALLERG ARTHRITIS-ANKLE 

716.35 ......................... CLIMACT ARTHRITIS-PELVIS 

716.36 ......................... CLIMACT ARTHRITIS-L/LEG 

716.37 ......................... CLIMACT ARTHRITIS-ANKLE 

716.45 ......................... TRANS ARTHROPATHY-PELVIS 

716.46 ......................... TRANS ARTHROPATHY-L/LEG 

716.47 ......................... TRANS ARTHROPATHY-ANKLE 

716.55 ......................... POLYARTHRITIS NOS-PELVIS 

716.56 ......................... POLYARTHRITIS NOS-L/LEG 

716.57 ......................... POLYARTHRITIS NOS-ANKLE 

716.67 ......................... MONOARTHRITIS NOS-ANKLE 

716.85 ......................... ARTHROPATHY NEC-PELVIS 

716.86 ......................... ARTHROPATHY NEC-L/LEG 

716.87 ......................... ARTHROPATHY NEC-ANKLE 

716.95 ......................... ARTHROPATHY NOS-PELVIS 

716.96 ......................... ARTHROPATHY NOS-L/LEG 

716.97 ......................... ARTHROPATHY NOS-ANKLE 

717 ......................... INTERNAL DERANGEMENT OF KNEE 

718.05 ......................... ART CARTIL DISORDER PELVIS AND THIGH 

718.06 ......................... ART CARTIL DISORDER LOWER LEG 

718.07 ......................... ART CARTIL DIS ANKLE FOOT 

718.25 ......................... PATHOLOGIC DISLOCATION PELVIS AND THIGH 

718.26 ......................... PATHOLOGIC DISLOCATION LOWER LEG 

718.27 ......................... PATHOLOGIC DISLOCATION ANKLE FOOT 

718.35 ......................... RECURRENT DISLOCATION PELVIS AND THIGH 

718.36 ......................... RECURRENT DISLOCATION LOW LEG 

718.37 ......................... RECURRENT DISLOCATION ANKLE FOOT 

718.45 ......................... CONTRACTURE PELVIS AND THIGH 

718.46 ......................... CONTRACTURE LOWER LEG 

718.47 ......................... CONTRACTURE OF JOINT ANKLE FOOT 

718.55 ......................... ANKYLOSIS OF PELVIS AND THIGH 


pwalker on PROD1PC71 with PROPOSALS2
25378 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

718.56 ......................... ANKYLOSIS OF LOWER LEG 

718.57 ......................... ANKYLOSIS OF JOINT ANKLE FOOT 

718.85 ......................... OTHER DERANGEMENT OF PELVIS AND THIGH 

718.86 ......................... OTHER DERANGEMENT OF JOINT OF LOWER LEG 

718.87 ......................... OTH DERANGMENT JT NEC ANKLE FOOT 

719.15 ......................... HEMARTHROSIS PELVIS AND THIGH 

719.16 ......................... HEMARTHROSIS LOWER LEG 

719.17 ......................... HEMARTHROSIS ANKLE AND FOOT 

719.25 ......................... VILLONODULAR SYNOVITIS PELVIS AND THIGH 

719.26 ......................... VILLONODULAR SYNOVITIS LOWER LEG 

719.27 ......................... VILLONODULAR SYNOVITIS ANKLE AND FOOT 

719.35 ......................... PALANDROMIC RHEUMATISM PELVIS AND THIGH 

719.36 ......................... PALANDROMIC RHEUMATISM LOWER LEG 

719.37 ......................... PALANDROMIC RHEUMATISM ANKLE AND FOOT 

727.65 ......................... RUPTURE OF TENDON QUADRACEPS 

727.66 ......................... RUPTURE OF TENDON PATELLAR 

727.67 ......................... RUPTURE OF TENDON ACHILLES 

727.68 ......................... RUPTURE OTHER TENDONS FOOT AND ANKLE 

730.05 ......................... AC OSTEOMYELITIS-PELVIS 

730.06 ......................... AC OSTEOMYELITIS-L/LEG 

730.07 ......................... AC OSTEOMYELITIS-ANKLE 

730.15 ......................... CHR OSTEOMYELIT-PELVIS 

730.16 ......................... CHR OSTEOMYELIT-L/LEG 

730.17 ......................... CHR OSTEOMYELIT-ANKLE 

730.25 ......................... OSTEOMYELITIS NOS-PELVI 

730.26 ......................... OSTEOMYELITIS NOS-L/LEG 

730.27 ......................... OSTEOMYELITIS NOS-ANKLE 

730.35 ......................... PERIOSTITIS-PELVIS 

730.36 ......................... PERIOSTITIS-L/LEG 

730.37 ......................... PERIOSTITIS-ANKLE 

730.75 M ..................... POLIO OSTEOPATHY-PELVIS 

730.76 M ..................... POLIO OSTEOPATHY-L/LEG 

730.77 M ..................... POLIO OSTEOPATHY-ANKLE 

730.85 M ..................... BONE INFECT NEC-PELVIS 

730.86 M ..................... BONE INFECT NEC-L/LEG 

730.87 M ..................... BONE INFECT NEC-ANKLE 

730.95 ......................... BONE INFECT NOS-PELVIS 

730.96 ......................... BONE INFECT NOS-L/LEG 

730.97 ......................... BONE INFECT NOS-ANKLE 

733.14 ......................... PATHOLOGIC FRACTURE OF NECK OF FEMUR 

733.15 ......................... PATHOLOGIC FRACTURE OF FEMUR 

733.16 ......................... PATHOLOGIC FRACTURE OF TIBIA OR FIBULA 

733.42 ......................... ASEPTIC NECROSIS OF HEAD AND NECK OF FEMUR 

733.43 ......................... ASEPTIC NECROSIS OF MEDIAL FEMORAL CONDYLE 

808 ......................... FRACTURE OF PELVIS 

820 ......................... FRACTURE OF NECK OF FEMUR 

821 ......................... FRACTURE OTHER&UNSPEC PARTS FEMUR 

822 ......................... FRACTURE OF PATELLA 

823 ......................... FRACTURE OF TIBIA AND FIBULA 

824 ......................... FRACTURE OF ANKLE 

825 ......................... FRACTURE 1/MORE TARSAL&MT BNS 

827 ......................... OTH MX&ILL-DEFINED FX LOWER LIMB 

828 ......................... MX FX LEGS-LEG W/ARM-LEGS W/RIBS 

835 ......................... DISLOCATION OF HIP 

836 ......................... DISLOCATION OF KNEE 

897 ......................... TRAUMATIC AMPUTATION OF LEG 

928 ......................... CRUSHING INJURY OF LOWER LIMB 

Ortho 2—Other Orthopedic disorders .................. 711.01 ......................... PYOGEN ARTHRITIS-SHLDER 

711.02 ......................... PYOGEN ARTHRITIS-UP/ARM 

711.03 ......................... PYOGEN ARTHRITIS-FOREAR 

711.04 ......................... PYOGEN ARTHRITIS-HAND 

711.08 ......................... PYOGEN ARTHRITIS NEC 

711.09 ......................... PYOGEN ARTHRITIS-MULT 

711.10 M ..................... REITER ARTHRITIS-UNSPEC 

711.11 M ..................... REITER ARTHRITIS-SHLDER 

711.12 M ..................... REITER ARTHRITIS-UP/ARM 

711.13 M ..................... REITER ARTHRITIS-FOREAR 

711.14 M ..................... REITER ARTHRITIS-HAND 

711.18 M ..................... REITER ARTHRITIS NEC 

711.19 M ..................... REITER ARTHRITIS-MULT 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25379 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

711.20 M ..................... BEHCET ARTHRITIS-UNSPEC 

711.21 M ..................... BEHCET ARTHRITIS-SHLDER 

711.22 M ..................... BEHCET ARTHRITIS-UP/ARM 

711.23 M ..................... BEHCET ARTHRITIS-FOREAR 

711.24 M ..................... BEHCET ARTHRITIS-HAND 

711.28 M ..................... BEHCET ARTHRITIS NEC 

711.29 M ..................... BEHCET ARTHRITIS-MULT 

711.30 M ..................... DYSENTER ARTHRIT-UNSPEC 

711.31 M ..................... DYSENTER ARTHRIT-SHLDER 

711.32 M ..................... DYSENTER ARTHRIT-UP/ARM 

711.33 M ..................... DYSENTER ARTHRIT-FOREAR 

711.34 M ..................... DYSENTER ARTHRIT-HAND 

711.38 M ..................... DYSENTER ARTHRIT NEC 

711.39 M ..................... DYSENTER ARTHRIT-MULT 

711.40 M ..................... BACT ARTHRITIS-UNSPEC 

711.41 M ..................... BACT ARTHRITIS-SHLDER 

711.42 M ..................... BACT ARTHRITIS-UP/ARM 

711.43 M ..................... BACT ARTHRITIS-FOREARM 

711.44 M ..................... BACT ARTHRITIS-HAND 

711.48 M ..................... BACT ARTHRITIS NEC 

711.49 M ..................... BACT ARTHRITIS-MULT 

711.50 M ..................... VIRAL ARTHRITIS-UNSPEC 

711.51 M ..................... VIRAL ARTHRITIS-SHLDER 

711.52 M ..................... VIRAL ARTHRITIS-UP/ARM 

711.53 M ..................... VIRAL ARTHRITIS-FOREARM 

711.54 M ..................... VIRAL ARTHRITIS-HAND 

711.58 M ..................... VIRAL ARTHRITIS NEC 

711.59 M ..................... VIRAL ARTHRITIS-MULT 

711.60 M ..................... MYCOTIC ARTHRITIS-UNSPE 

711.61 M ..................... MYCOTIC ARTHRITIS-SHLDE 

711.62 M ..................... MYCOTIC ARTHRITIS-UP/AR 

711.63 M ..................... MYCOTIC ARTHRIT-FOREARM 

711.64 M ..................... MYCOTIC ARTHRITIS-HAND 

711.68 M ..................... MYCOTIC ARTHRITIS NEC 

711.69 M ..................... MYCOTIC ARTHRITIS-MULT 

711.70 M ..................... HELMINTH ARTHRIT-UNSPEC 

711.71 M ..................... HELMINTH ARTHRIT-SHLDER 

711.72 M ..................... HELMINTH ARTHRIT-UP/ARM 

711.73 M ..................... HELMINTH ARTHRIT-FOREAR 

711.74 M ..................... HELMINTH ARTHRIT-HAND 

711.78 M ..................... HELMINTH ARTHRIT NEC 

711.79 M ..................... HELMINTH ARTHRIT-MULT 

711.80 M ..................... INF ARTHRITIS NEC-UNSPE 

711.81 M ..................... INF ARTHRITIS NEC-SHLDE 

711.82 M ..................... INF ARTHRITIS NEC-UP/AR 

711.83 M ..................... INF ARTHRIT NEC-FOREARM 

711.84 M ..................... INF ARTHRITIS NEC-HAND 

711.88 M ..................... INF ARTHRIT NEC-OTH SIT 

711.89 M ..................... INF ARTHRITIS NEC-MULT 

711.90 ......................... INF ARTHRITIS NOS-UNSPE 

711.91 ......................... INF ARTHRITIS NOS-SHLDE 

711.92 ......................... INF ARTHRITIS NOS-UP/AR 

711.93 ......................... INF ARTHRIT NOS-FOREARM 

711.94 ......................... INF ARTHRIT NOS-HAND 

711.98 ......................... INF ARTHRIT NOS-OTH SIT 

711.99 ......................... INF ARTHRITIS NOS-MULT 

712.10 M ..................... DICALC PHOS CRYST-UNSPE 

712.11 M ..................... DICALC PHOS CRYST-SHLDE 

712.12 M ..................... DICALC PHOS CRYST-UP/AR 

712.13 M ..................... DICALC PHOS CRYS-FOREAR 

712.14 M ..................... DICALC PHOS CRYST-HAND 

712.18 M ..................... DICALC PHOS CRY-SITE NE 

712.19 M ..................... DICALC PHOS CRYST-MULT 

712.20 M ..................... PYROPHOSPH CRYST-UNSPEC 

712.21 M ..................... PYROPHOSPH CRYST-SHLDER 

712.22 M ..................... PYROPHOSPH CRYST-UP/ARM 

712.23 M ..................... PYROPHOSPH CRYST-FOREAR 

712.24 M ..................... PYROPHOSPH CRYST-HAND 

712.28 M ..................... PYROPHOS CRYST-SITE NEC 

712.29 M ..................... PYROPHOS CRYST-MULT 


pwalker on PROD1PC71 with PROPOSALS2
25380 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

712.30 M ..................... CHONDROCALCIN NOS-UNSPE 

712.31 M ..................... CHONDROCALCIN NOS-SHLDE 

712.32 M ..................... CHONDROCALCIN NOS-UP/AR 

712.33 M ..................... CHONDROCALC NOS-FOREARM 

712.34 M ..................... CHONDROCALCIN NOS-HAND 

712.38 M ..................... CHONDROCALC NOS-OTH SIT 

712.39 M ..................... CHONDROCALCIN NOS-MULT 

712.80 ......................... CRYST ARTHROP NEC-UNSPE 

712.81 ......................... CRYST ARTHROP NEC-SHLDE 

712.82 ......................... CRYST ARTHROP NEC-UP/AR 

712.83 ......................... CRYS ARTHROP NEC-FOREAR 

712.84 ......................... CRYST ARTHROP NEC-HAND 

712.88 ......................... CRY ARTHROP NEC-OTH SIT 

712.89 ......................... CRYST ARTHROP NEC-MULT 

712.90 ......................... CRYST ARTHROP NOS-UNSPE 

712.91 ......................... CRYST ARTHROP NOS-SHLDR 

712.92 ......................... CRYST ARTHROP NOS-UP/AR 

712.93 ......................... CRYS ARTHROP NOS-FOREAR 

712.94 ......................... CRYST ARTHROP NOS-HAND 

712.98 ......................... CRY ARTHROP NOS-OTH SIT 

712.99 ......................... CRYST ARTHROP NOS-MULT 

713.0 M ..................... ARTHROP W ENDOCR/MET DI 

713.1 M ..................... ARTHROP W NONINF GI DIS 

713.2 M ..................... ARTHROPATH W HEMATOL DI 

713.3 M ..................... ARTHROPATHY W SKIN DIS 

713.4 M ..................... ARTHROPATHY W RESP DIS 

713.5 M ..................... ARTHROPATHY W NERVE DIS 

713.6 M ..................... ARTHROP W HYPERSEN REAC 

713.7 M ..................... ARTHROP W SYSTEM DIS NE 

713.8 M ..................... ARTHROP W OTH DIS NEC 

714 ......................... RA&OTH INFLAM POLYARTHROPATHIES 

715.15 ......................... OSTEOARTHROSIS, LOCALIZED, PRIMARY, PELVIS AND 

THIGH 

715.16 ......................... OSTEOARTHROSIS, LOCALIZED, PRIMARY, LOWER LEG 

715.25 ......................... OSTEOARTHROSIS, LOCALIZED, SECONDARY, PELVIS 

AND THIGH 

715.26 ......................... OSTEOARTHROSIS, LOCALIZED, SECONDARY, LOWER 

LEG 

715.35 ......................... OSTEOARTHROSIS, LOCALIZED, NOT SPEC PRIMARY OR 

SECONDARY, PELVIS AND THIGH 

715.36 ......................... OSTEOARTHROSIS, LOCALIZED, NOT SPEC PRIMARY OR 

SECONDARY, LOWER LEG 

715.95 ......................... OSTEOARTHROSIS, UNSPECIFIED, PELVIS AND THIGH 

715.96 ......................... OSTEOARTHROSIS, UNSPECIFIED, LOWER LEG 

716.00 ......................... KASCHIN-BECK DIS-UNSPEC 

716.01 ......................... KASCHIN-BECK DIS-SHLDER 

716.02 ......................... KASCHIN-BECK DIS-UP/ARM 

716.03 ......................... KASCHIN-BECK DIS-FOREARM 

716.04 ......................... KASCHIN-BECK DIS-HAND 

716.08 ......................... KASCHIN-BECK DIS NEC 

716.09 ......................... KASCHIN-BECK DIS-MULT 

716.10 ......................... TRAUM ARTHROPATHY-UNSPEC 

716.11 ......................... TRAUM ARTHROPATHY-SHLDER 

716.12 ......................... TRAUM ARTHROPATHY-UP/ARM 

716.13 ......................... TRAUM ARTHROPATH-FOREARM 

716.14 ......................... TRAUM ARTHROPATHY-HAND 

716.18 ......................... TRAUM ARTHROPATHY NEC 

716.19 ......................... TRAUM ARTHROPATHY-MULT 

716.20 ......................... ALLERG ARTHRITIS-UNSPEC 

716.21 ......................... ALLERG ARTHRITIS-SHLDER 

716.22 ......................... ALLERG ARTHRITIS-UP/ARM 

716.23 ......................... ALLERG ARTHRITIS-FOREARM 

716.24 ......................... ALLERG ARTHRITIS-HAND 

716.28 ......................... ALLERG ARTHRITIS NEC 

716.29 ......................... ALLERG ARTHRITIS-MULT 

716.30 ......................... CLIMACT ARTHRITIS-UNSPEC 

716.31 ......................... CLIMACT ARTHRITIS-SHLDER 

716.32 ......................... CLIMACT ARTHRITIS-UP/ARM 

716.33 ......................... CLIMACT ARTHRIT-FOREARM 

716.34 ......................... CLIMACT ARTHRITIS-HAND 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25381 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

716.38 ......................... CLIMACT ARTHRITIS NEC 

716.39 ......................... CLIMACT ARTHRITIS-MULT 

716.40 ......................... TRANS ARTHROPATHY-UNSPEC 

716.41 ......................... TRANS ARTHROPATHY-SHLDER 

716.42 ......................... TRANS ARTHROPATHY-UP/ARM 

716.43 ......................... TRANS ARTHROPATH-FOREARM 

716.44 ......................... TRANS ARTHROPATHY-HAND 

716.48 ......................... TRANS ARTHROPATHY NEC 

716.49 ......................... TRANS ARTHROPATHY-MULT 

716.50 ......................... POLYARTHRITIS NOS-UNSPEC 

716.51 ......................... POLYARTHRITIS NOS-SHLDER 

716.52 ......................... POLYARTHRITIS NOS-UP/ARM 

716.53 ......................... POLYARTHRIT NOS-FOREARM 

716.54 ......................... POLYARTHRITIS NOS-HAND 

716.58 ......................... POLYARTHRIT NOS-OTH SITE 

716.59 ......................... POLYARTHRITIS NOS-MULT 

716.60 ......................... MONOARTHRITIS NOS-UNSPEC 

716.61 ......................... MONOARTHRITIS NOS-SHLDER 

716.62 ......................... MONOARTHRITIS NOS-UP/ARM 

716.63 ......................... MONOARTHRIT NOS-FOREARM 

716.64 ......................... MONOARTHRITIS NOS-HAND 

716.65 ......................... UNSPECIFIED MONOARTHRITIS, PELVIS AND THIGH 

716.66 ......................... UNSPECIFIED MONOARTHRITIS, LOWER LEG 

716.68 ......................... MONOARTHRIT NOS-OTH SITE 

716.80 ......................... ARTHROPATHY NEC-UNSPEC 

716.81 ......................... ARTHROPATHY NEC-SHLDER 

716.82 ......................... ARTHROPATHY NEC-UP/ARM 

716.83 ......................... ARTHROPATHY NEC-FOREARM 

716.84 ......................... ARTHROPATHY NEC-HAND 

716.88 ......................... ARTHROPATHY NEC-OTH SITE 

716.89 ......................... ARTHROPATHY NEC-MULT 

716.90 ......................... ARTHROPATHY NOS-UNSPEC 

716.91 ......................... ARTHROPATHY NOS-SHLDER 

716.92 ......................... ARTHROPATHY NOS-UP/ARM 

716.93 ......................... ARTHROPATHY NOS-FOREARM 

716.94 ......................... ARTHROPATHY NOS-HAND 

716.98 ......................... ARTHROPATHY NOS-OTH SITE 

716.99 ......................... ARTHROPATHY NOS-MULT 

718.01 ......................... ART CARTIL DISORDER SHOULDER 

718.02 ......................... ART CARTIL DIS UPPER ARM 

718.03 ......................... ART CARTIL DIS FOREARM 

718.04 ......................... ART CARTIL DIS HAND 

718.08 ......................... ART CART DIS OTH SITES 

718.09 ......................... ART CART DIS MULT 

718.1 ......................... LOOSE BODY IN JT 

718.20 ......................... PATHOLOGIC DISLOCATION UNSPEC SITE 

718.21 ......................... PATHOLOGIC DISLOCATION SHOULDER 

718.22 ......................... PATHOLOGIC DISLOCATION UPPER ARM 

718.23 ......................... PATHOLOGIC DISLOCATION FOREARM 

718.24 ......................... PATHOLOGIC DISLOCATION HAND 

718.28 ......................... PATHOLOGIC DISLOCATION OTH LOC 

718.29 ......................... PATHOLOGIC DISLOCATION MULT LOC 

718.30 ......................... RECURRENT DISLOCATION UNSPEC SITE 

718.31 ......................... RECURRENT DISLOCATION SHOULDER 

718.32 ......................... RECURRENT DISLOCATION UPPER ARM 

718.33 ......................... RECURRENT DISLOCATION FOREARM 

718.34 ......................... RECURRENT DISLOCATION HAND 

718.38 ......................... RECURRENT DISLOCATION OTH LOC 

718.39 ......................... RECURRENT DISLOCATION MULT LOC 

718.40 ......................... CONTRACTURE OF JOINT UNSPEC SITE 

718.41 ......................... CONTRACTURE SHOULDER 

718.42 ......................... CONTRACTURE OF JOINT UPPER ARM 

718.43 ......................... CONTRACTURE OF JOINT FOREARM 

718.44 ......................... CONTRACTURE OF JOINT HAND 

718.48 ......................... CONTRACTURE OF JOINT OTH LOC 

718.49 ......................... CONTRACTURE OF JOINT MULT LOC 

718.50 ......................... ANKYLOSIS OF JOINT UNSPEC SITE 

718.51 ......................... ANKYLOSIS OF SHOULDER 

718.52 ......................... ANKYLOSIS OF JOINT UPPER ARM 

718.53 ......................... ANKYLOSIS OF JOINT FOREARM 


pwalker on PROD1PC71 with PROPOSALS2
25382 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

718.54 ......................... ANKYLOSIS OF JOINT HAND 

718.58 ......................... ANKYLOSIS OF JOINT OTH LOC 

718.59 ......................... ANKYLOSIS OF JOINT MULT LOC 

718.60 ......................... UNSPED ’INTRAPELVIC PROTRUSION ACETAB 

718.7 ......................... DEV DISLOC JOINT 

718.80 ......................... OTH DERANGMENT JT NEC UNSPEC SITE 

718.81 ......................... OTHER DERANGEMENT OF SHOULDER 

718.82 ......................... OTH DERANGMENT JT NEC UPPER ARM 

718.83 ......................... OTH DERANGMENT JT NEC FOREARM 

718.84 ......................... OTH DERANGMENT JT NEC HAND 

718.88 ......................... OTH DERANGMENT JT NEC OTH LOC 

718.89 ......................... OTH DERANGMENT JT NEC MULT LOC 

718.9 ......................... UNSPEC DERANGMENT JT 

719.1 ......................... HEMARTHROSIS UNSPECIFIED SITE 

719.11 ......................... HEMARTHROSIS SHOULDER 

719.12 ......................... HEMARTHROSIS UPPER ARM 

719.13 ......................... HEMARTHROSIS FOREARM 

719.14 ......................... HEMARTHROSIS HAND 

719.18 ......................... HEMARTHROSIS OTHER SPECIFIED 

719.19 ......................... HEMARTHROSIS MULTIPLE SITES 

719.2 ......................... VILLONODULAR SYNOVITIS UNSPECIFIED SITE 

719.21 ......................... VILLONODULAR SYNOVITIS SHOULDER 

719.22 ......................... VILLONODULAR SYNOVITIS UPPER ARM 

719.23 ......................... VILLONODULAR SYNOVITIS FOREARM 

719.24 ......................... VILLONODULAR SYNOVITIS HAND 

719.28 ......................... VILLONODULAR SYNOVITIS OTHER SITES 

719.29 ......................... VILLONODULAR SYNOVITIS MULTIPLE SITES 

719.3 ......................... PALANDROMIC RHEUMATISM UNSPECIFIED SITE 

719.31 ......................... PALANDROMIC RHEUMATISM SHOULDER 

719.32 ......................... PALANDROMIC RHEUMATISM UPPER ARM 

719.33 ......................... PALANDROMIC RHEUMATISM FOREARM 

719.34 ......................... PALANDROMIC RHEUMATISM HAND 

719.38 ......................... PALANDROMIC RHEUMATISM OTHER SITES 

719.39 ......................... PALANDROMIC RHEUMATISM MULTIPLE SITES 

720.0 ......................... ANKYLOSING SPONDYLITIS 

720.1 ......................... SPINAL ENTHESOPATHY 

720.2 ......................... SACROILIITIS NEC 

720.8 M ..................... OTHER INFLAMMATORY SPONDYLOPATHIES 

720.81 M ..................... SPONDYLOPATHY IN OTH DI 

720.89 ......................... OTHER INFLAMMATORY SPONDYLOPATHIES 

720.9 ......................... UNSPEC INFLAMMATORY SPONDYLOPATHY 

721 ......................... SPONDYLOSIS AND ALLIED DISORDERS 

722.0 ......................... DISPLACEMENT OF CERVICAL INTERVERTEBRAL DISC 

WITHOUT MYELOPATHY 

722.1 ......................... DISPLACEMENT OF THORACIC OR LUMBAR 

INTERVERTEBRAL DISC WITHOUT MYELOPATHY 

722.2 ......................... DISPLACEMENT OF INTERVERTEBRAL DISC, SITE UNSPECIFIED, WITHOUT MYELOPATHY 

722.4 ......................... DEGENERATION OF CERVICAL INTERVERTEBRAL DISC 

722.5 ......................... DEGENERATION OF THORACIC OR LUMBAR 

INTERVERTEBRAL DISC 

722.6 ......................... DEGENERATION OF INTERVERTEBRAL DISC, SITE UNSPECIFIED 

722.7 ......................... INTERVERTEBRAL DISC DISORDER WITH MYELOPATHY 

722.8 ......................... POSTLAMINECTOMY SYNDROME 

722.9 ......................... OTHER AND UNSPECIFIED DISC DISORDER 

723.0 ......................... SPINAL STENOSIS OF CERVICAL REGION 

723.1 ......................... CERVICALGIA 

723.2 ......................... CERVICOCRANIAL SYNDROME 

723.3 ......................... CERVICOBRACHIAL SYNDROME 

723.4 ......................... BRACHIA NEURITIS OR RADICULITIS 

723.5 ......................... TORTICOLLIS, UNSPECIFIED 

723.6 ......................... PANNICULITIS SPECIFIED AS AFFECTING NECK 

723.7 ......................... OSSIFICATION OF POSTERIOR LONGITUDINAL LIGAMENT IN CERVICAL REGION 

723.8 ......................... OTHER SYNDROMES AFFECTING CERVICAL REGION 

723.9 ......................... UNSPEC MUSCULOSKEL SX OF NECK 

724 ......................... OTHER&UNSPECIFIED DISORDERS OF BACK 

725 ......................... POLYMYALGIA RHEUMATICA 

726.0 ......................... ADHESIVE CAPSULITIS 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25383 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

726.10 ......................... DISORDERS OF BURSAE AND TENDONS 

726.11 ......................... CALCIFYING TENDINITIS 

726.12 ......................... BICIPITAL TENOSYNOVITIS 

726.19 ......................... ROTATOR CUFF SYNDROME OTHER 

727.61 ......................... COMPLETE RUPTURE OF ROTATOR CUFF 

728.0 ......................... INFECTIVE MYOSITIS 

728.10 ......................... CALCIFICATION AND OSSIFICATION, UNSPECIFIED 

728.11 ......................... PROGRESSIVE MYOSITIS OSSIFICANS 

728.12 ......................... TRAUMATIC MYOSITIS OSSIFICATIONS 

728.13 ......................... POST OP HETEROTOPIC CALCIFICATION 

728.19 ......................... OTHER MUSCULAR CALCIFICATION AND OSSIFICATION 

728.2 ......................... MUSCULAR WASTING AND DISUSE ATROPHY 

728.3 ......................... OTHER SPECIFIC MUSCLE DISORDERS 

728.4 ......................... LAXITY OF LIGAMENT 

728.5 ......................... HYPERMOBILITY SYNDROME 

728.6 ......................... CONTRACTURE OF PALMAR FASCIA 

730.00 ......................... AC OSTEOMYELITIS-UNSPEC 

730.01 ......................... AC OSTEOMYELITIS-SHLDER 

730.02 ......................... AC OSTEOMYELITIS-UP/ARM 

730.03 ......................... AC OSTEOMYELITIS-FOREAR 

730.04 ......................... AC OSTEOMYELITIS-HAND 

730.08 ......................... AC OSTEOMYELITIS NEC 

730.09 ......................... AC OSTEOMYELITIS-MULT 

730.10 ......................... CHR OSTEOMYELITIS-UNSP 

730.11 ......................... CHR OSTEOMYELIT-SHLDER 

730.12 ......................... CHR OSTEOMYELIT-UP/ARM 

730.13 ......................... CHR OSTEOMYELIT-FOREARM 

730.14 ......................... CHR OSTEOMYELIT-HAND 

730.18 ......................... CHR OSTEOMYELIT NEC 

730.19 ......................... CHR OSTEOMYELIT-MULT 

730.20 ......................... OSTEOMYELITIS NOS-UNSPE 

730.21 ......................... OSTEOMYELITIS NOS-SHLDE 

730.22 ......................... OSTEOMYELITIS NOS-UP/AR 

730.23 ......................... OSTEOMYELIT NOS-FOREARM 

730.24 ......................... OSTEOMYELITIS NOS-HAND 

730.28 ......................... OSTEOMYELIT NOS-OTH SIT 

730.29 ......................... OSTEOMYELITIS NOS-MULT 

730.30 ......................... PERIOSTITIS-UNSPEC 

730.31 ......................... PERIOSTITIS-SHLDER 

730.32 ......................... PERIOSTITIS-UP/ARM 

730.33 ......................... PERIOSTITIS-FOREARM 

730.34 ......................... PERIOSTITIS-HAND 

730.38 ......................... PERIOSTITIS NEC 

730.39 ......................... PERIOSTITIS-MULT 

730.70 M ..................... POLIO OSTEOPATHY-UNSPEC 

730.71 M ..................... POLIO OSTEOPATHY-SHLDER 

730.72 M ..................... POLIO OSTEOPATHY-UP/ARM 

730.73 M ..................... POLIO OSTEOPATHY-FOREAR 

730.74 M ..................... POLIO OSTEOPATHY-HAND 

730.78 M ..................... POLIO OSTEOPATHY NEC 

730.79 M ..................... POLIO OSTEOPATHY-MULT 

730.80 M ..................... BONE INFECT NEC-UNSPEC 

730.81 M ..................... BONE INFECT NEC-SHLDER 

730.82 M ..................... BONE INFECT NEC-UP/ARM 

730.83 M ..................... BONE INFECT NEC-FOREARM 

730.84 M ..................... BONE INFECT NEC-HAND 

730.88 M ..................... BONE INFECT NEC-OTH SIT 

730.89 M ..................... BONE INFECT NEC-MULT 

730.90 ......................... BONE INFEC NOS-UNSP SIT 

730.91 ......................... BONE INFECT NOS-SHLDER 

730.92 ......................... BONE INFECT NOS-UP/ARM 

730.93 ......................... BONE INFECT NOS-FOREARM 

730.94 ......................... BONE INFECT NOS-HAND 

730.98 ......................... BONE INFECT NOS-OTH SIT 

730.99 ......................... BONE INFECT NOS-MULT 

731.0 ......................... OSTEITIS DEFORMANS W/O BN TUMR 

731.1 M ..................... OSTEITIS DEFORMANS DZ CLASS ELSW 

731.2 ......................... HYPERTROPH PULM OSTEOARTHROPATHY 

731.8 M ..................... OTH BONE INVOLVEMENT DZ CLASS EL 

732 ......................... OSTEOCHONDROPATHIES 


pwalker on PROD1PC71 with PROPOSALS2
25384 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

733.10 ......................... PATHOLOGIC FRACTURE UNSPEC 

733.11 ......................... PATHOLOGIC FRACTURE HUMERUS 

733.12 ......................... PATHOLOGIC FRACTURE DISTAL RADIUS ULNA 

733.13 ......................... PATHOLOGIC FRACTURE OF VERTEBRAE 

733.19 ......................... PATHOLOGIC FRACTURE OTH SPEC SITE 

800 ......................... FRACTURE OF VAULT OF SKULL 

801 ......................... FRACTURE OF BASE OF SKULL 

802 ......................... FRACTURE OF FACE BONES 

803 ......................... OTHER&UNQUALIFIED SKULL FRACTURES 

804 ......................... MX FX INVLV SKULL/FACE W/OTH BNS 

805 ......................... FX VERT COLUMN W/O SP CRD INJR 

807 ......................... FRACTURE RIB STERNUM LARYNX&TRACHEA 

809 ......................... ILL-DEFINED FRACTURES BONES TRUNK 

810 ......................... FRACTURE OF CLAVICLE 

811 ......................... FRACTURE OF SCAPULA 

812 ......................... FRACTURE OF HUMERUS 

813 ......................... FRACTURE OF RADIUS AND ULNA 

814 ......................... FRACTURE OF CARPAL BONE 

815 ......................... FRACTURE OF METACARPAL BONE 

816 ......................... FRACTURE ONE OR MORE PHALANGES HAND 

817 ......................... MULTIPLE FRACTURES OF HAND BONES 

818 ......................... ILL-DEFINED FRACTURES OF UPPER LIMB 

819 ......................... MX FX UP LIMBS&LIMBS W/RIB&STERNUM 

831 ......................... DISLOCATION OF SHOULDER 

832 ......................... DISLOCATION OF ELBOW 

833 ......................... DISLOCATION OF WRIST 

837 ......................... DISLOCATION OF ANKLE 

838 ......................... DISLOCATION OF FOOT 

846 ......................... SPRAINS&STRAINS SACROILIAC REGION 

847 ......................... SPRAINS&STRAINS OTH&UNS PART BACK 

Psych 1—Affective and other psychoses, depression.295 ......................... SCHIZOPHRENIA 

296 ......................... AFFECTIVE PSYCHOSES 

297 ......................... DELUSIONAL DIS 

298 ......................... OTH PSYCHOSES 

311 ......................... DEPRESSIVE DISORDER NEC 

Psych 2—Degenerative and other organic psychiatric disorders.331.0 ......................... ALZHEIMER’S DISEASE 

331.11 ......................... PICK’S DISEASE 

331.19 ......................... OTH FRONTO-TEMPORAL DEMENTIA 

331.2 ......................... SENILE DEGENERAT BRAIN 

331.3 ......................... COMMUNICAT HYDROCEPHALUS 

331.4 ......................... OBSTRUCTIV HYDROCEPHALUS 

331.7 M ..................... CEREB DEGEN IN OTH DIS 

331.81 ......................... REYE’S SYNDROME 

331.82 ......................... DEMENTIA WITH LEWY BODIES 

331.89 ......................... CEREB DEGENERATION NEC 

331.9 ......................... CEREB DEGENERATION NOS 

290.0 M ..................... SENILE DEMENTIA, UNCOMPLICATED 

290.10 M ..................... PRESENILE DEMENTIA UNCOMP 

290.11 M ..................... PRESENILE DEMENTIA WITH DELIRIUM 

290.12 M ..................... PRESENILE DEMENTIA WITH DELUSIONAL FEATURES 

290.13 M ..................... PRESENILE DEMENTIA WITH DEPRESSIVE FEATURES 

290.20 M ..................... SENILE DEMENTIA WITH DELUSIONAL FEATURES 

290.21 M ..................... SENILE DEMENTIA WITH DEPRESSIVE FEATURES 

290.3 M ..................... SENILE DEMENTIA WITH DELIRIUM 

290.40 M ..................... VASCULAR DEMENTIA, UNCOMPLICATED 

290.41 M ..................... VASCULAR DEMENTIA, WITH DELIRIUM 

290.42 M ..................... VASCULAR DEMENTIA, WITH DELUSIONS 

290.43 M ..................... VASCULAR DEMENTIA, WITH DEPRESSED MOOD 

291.1 ......................... ALCOHOL PSYCHOSIS 

291.2 ......................... ALCOHOL DEMENTIA 

292.8 ......................... DRUG PSYCHOSES 

294.0 M ..................... AMNESTIC DISORD OTH DIS 

294.1 M ..................... DEMENTIA 

294.8 ......................... MENTAL DISOR NEC OTH DIS 

294.9 ......................... MENTAL DISOR NOS OTH DIS 

Pulmonary disorders ............................................ 491 ......................... CHRONIC BRONCHIT 

492 ......................... EMPHYSEMA 

493.2 ......................... ASTHMA 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25385 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

496 ......................... CHRONIC AIRWAY OBSTRUCTION NEC 

Skin 1—Traumatic wounds, burns and post-operative complications.870 ......................... OPEN WOUND OF OCULAR ADNEXA 

872 ......................... OPEN WOUND OF EAR 

873 ......................... OTHER OPEN WOUND OF HEAD 

874 ......................... OPEN WOUND OF NECK 

875 ......................... OPEN WOUND OF CHEST 

876 ......................... OPEN WOUND OF BACK 

877 ......................... OPEN WOUND OF BUTTOCK 

878 ......................... OPEN WND GNT ORGN INCL TRAUMAT AMP 

879 ......................... OPEN WOUND OTH&UNSPEC SITE NO LIMBS 

880 ......................... OPEN WOUND OF SHOULDER&UPPER ARM 

881 ......................... OPEN WOUND OF ELBOW FOREARM&WRIST 

882 ......................... OPEN WOUND HAND EXCEPT FINGER ALONE 

883 ......................... OPEN WOUND OF FINGER 

884 ......................... MX&UNSPEC OPEN WOUND UPPER LIMB 

885 ......................... TRAUMATIC AMPUTATION OF THUMB 

886 ......................... TRAUMATIC AMPUTATION OTHER FINGER 

887 ......................... TRAUMATIC AMPUTATION OF ARM&HAND 

890 ......................... OPEN WOUND OF HIP AND THIGH 

891 ......................... OPEN WOUND OF KNEE, LEG , AND ANKLE 

892 ......................... OPEN WOUND OF FOOT EXCEPT TOE ALONE 

893 ......................... OPEN WOUND OF TOE 

894 ......................... MX&UNSPEC OPEN WOUND LOWER LIMB 

895 ......................... TRAUMATIC AMPUTATION OF TOE 

896 ......................... TRAUMATIC AMPUTATION OF FOOT 

941 ......................... BURN OF FACE, HEAD, AND NECK 

942 ......................... BURN OF TRUNK 

943 ......................... BURN UPPER LIMB EXCEPT WRIST&HAND 

944 ......................... BURN OF WRIST AND HAND 

945 ......................... BURN OF LOWER LIMB 

946 ......................... BURNS OF MULTIPLE SPECIFIED SITES 

948 ......................... BURN CLASS ACCORD-BODY SURF INVOLVD 

949 ......................... BURN, UNSPECIFIED SITE 

927 ......................... CRUSHING INJURY OF UPPER LIMB 

951 ......................... INJURY TO OTHER CRANIAL NERVE 

955.0 ......................... INJURY TO AXILLARY NERVE 

955.1 ......................... INJURY TO MEDIAN NERVE 

955.2 ......................... INJURY TO ULNAR NERVE 

955.3 ......................... INJURY TO RADIAL NERVE 

955.4 ......................... INJURY TO MUSCULOCUTANEOUS NERVE 

955.5 ......................... INJURY TO CUTANEOUS SENSORY NERVE, UPPER LIMB 

955.6 ......................... INJURY TO DIGITAL NERVE 

955.7 ......................... INJURY TO OTHER SPECIFIED NERVE(S) SHOULDER 

GIRDLE AND UPPER LIMB 

955.9 ......................... INJURY TO UNSPEC NERVE(S) SHOULDER GIRDLE AND 

UPPER LIMB 

956.2 ......................... INJURY TO POSTERIOR TIBIAL NERVE 

956.3 ......................... INJURY TO PERONEAL NERVE 

956.4 ......................... INJURY TO CUTANEOUS SENSORY NERVE, LOWER LIMB 

956.5 ......................... INJURY TO OTHER SPECIFIED NERVE(S) OF PELVIC GIRDLE AND LOWER LIMB 

956.9 ......................... INJURY TO UNSPECIFIED NERVE OF PELVIC GIRDLE 

AND LOWER LIMB 

998.1 ......................... HEMORR/HEMAT/SEROMA COMP PROC NEC 

998.2 ......................... ACC PUNCT/LACRATION DURING PROC NEC 

998.3 ......................... DISRUPTION OF OPERATION WOUND NEC 

998.4 ......................... FB ACC LEFT DURING PROC NEC 

998.5 ......................... POSTOPERATIVE INFECTION NEC 

998.6 ......................... PERSISTENT POSTOPERATIVE FIST NEC 

998.83 ......................... NON-HEALING SURGICAL WOUND NEC 

Skin 2—Ulcers and other skin conditions ............ 440.23 ......................... ATHEROSCLER-ART EXTREM W/ULCERATION 

707.1 ......................... ULCER LOWER LIMBS EXCEPT DECUBITUS 

707.8 ......................... CHRONIC ULCER OTHER SPECIFIED SITE 

707.9 ......................... CHRONIC ULCER OF UNSPECIFIED SITE 

681 ......................... CELLULITIS&ABSCESS OF FINGER&TOE 

683 ......................... ACUTE LYMPHADENITIS 

684 ......................... IMPETIGO 

685 ......................... PILONIDAL CYST 

686 ......................... OTH LOCAL INF SKIN&SUBCUT TISSUE 


pwalker on PROD1PC71 with PROPOSALS2
25386 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

TABLE 2B.—ICD–9–CM DIAGNOSES INCLUDED IN THE DIAGNOSTIC CATEGORIES FOR CASE-MIX ADJUSTMENT

VARIABLES—Continued 

Diagnostic category ICD–9–CM 

code** Manifestation* Short description of ICD–9–CM code 

440.24 ......................... ATHERSCLER-ART EXTREM W/GANGRENE 

785.4 M ..................... GANGRENE 

565 ......................... ANAL FISSURE AND FISTULA 

566 ......................... ABSCESS OF ANAL AND RECTAL REGIONS 

682 ......................... OTHER CELLULITIS AND ABSCESS 

680 ......................... CARBUNCLE AND FURUNCLE 

*We are aware that some of these codes or code categories involve manifestation codes. The ICD–9–CM Official Guidelines for Coding and 

Reporting requires that the underlying disease or condition code be sequenced first followed by the manifestation code. The underlying disease 

codes associated with the manifestation codes are not listed in Table 2b, and these underlying codes were not specified in the analysis process. 

However, when reporting certain conditions that have both an underlying etiology and body system manifestations due to the underlying etiology, 

the appropriate sequencing must be followed according to the ICD–9–CM Coding Guidelines. Equally important, the reported etiology must be 

valid for the manifestation specified. 

**Note: ‘‘ICD–9–CM Official Guidelines for Coding and Reporting’’ dictate that a three-digit code is to be used only if it is not further subdivided. Where fourth-digit subcategories and/or fifth-digit subclassifications are provided, they must be assigned. A code is invalid if it has not 

been coded to the full number of digits required for that code. Codes with three digits are included in ICD–9–CM as the heading of a category of 

codes that may be further subdivided by the use of fourth and/or fifth digits, which provide greater detail. The category codes listed in Table 2b 

include all the related 4- and 5-digit codes. 

d. Determining the Case-Mix Weights 

In the case-mix model adopted in July 

2000, we examined the sum of scores for 

the clinical dimension of the system, 

and the sum of scores for the functional 

dimension, and determined ranges of 

scores to assign a severity level. For 

example, in the original case-mix model 

adopted in July 2000, severity levels 

ranged from minimum to high for the 

clinical dimension. Severity levels were 

used to derive regression coefficients for 

calculating case-mix relative weights. 

The calculated coefficients from this 

regression, which we call the payment 

regression, were displayed in the July 3, 

2000 Federal Register (65 FR 41201) 

(‘‘Regression Coefficients for Calculating 

Case-Mix Relative Weights’’). 

Now using the proposed fourequation case-mix model, we again 

derived severity levels for the clinical, 

functional, and services utilization 

dimensions. We classified activities of 

daily living variables as functional 

variables, diagnostic, interaction, and 

other OASIS variables as clinical 

variables, and therapy-related variables 

(threshold variables and visit count 

variables) as services utilization 

variables. For each episode in the 

sample, we summed the variables’

scores by dimension. Then, we 

examined the range of summed scores 

within each equation and threshold 

group of the sample, in order to 

determine severity level intervals. We 

determined how many severity levels to 

define for each of the equation/ 

threshold groups based on the relative 

number of episodes in a potential 

severity level, and on the clustering of 

summed scores. In addition, for the 

services utilization dimension, which is 

based only on therapy visit utilization, 

we defined severity intervals based on 

relatively small aggregates (ones, twos, 

and threes) of therapy visits above the 

six-visit threshold up to 13 visits 

(equations 1 and 3) and above the 14- 

visit therapy threshold, up to 19 therapy 

visits (equations 2 and 4). Our goal was 

to ensure payment graduation due to 

added numbers of therapy visits 

between thresholds, without creating 

too many severity levels. 

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BILLING CODE 4120–01–C 

We derived the relative payment 

weights for the proposed four-equation 

model using the same kind of payment 

regression we employed in July 2000. 

The sample episodes were classified 

into severity levels as just described. We 

defined indicator variables for the 

payment regression based on these 

severity classifications. The major 

difference between the July 2000 

payment regression and the one in this 


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proposal is that additional indicator 

variables were defined to identify the 

episodes classified into each equation of 

the four-equation model, as well as 

certain thresholds and therapy visit 

intervals. Including the indicator 

variables allows us to combine 

information derived from the fourequation model into a single payment 

regression equation. For example, an 

indicator variable was created for the 

group of later episodes below 14 therapy 

visits and, within this group, indicator 

variables were created for the six-visit 

therapy threshold and successive 

therapy-visit aggregates. See the table of 

regression coefficients (Table 4) for the 

remaining indicator variables; the 

indicator variables for the underlying 

four equations are denoted by the terms 

‘‘constant’’ and ‘‘intercept.’’ An 

additional indicator variable denoted by 

a constant was used for all episodes 

with at least 20 therapy visits; it is 

explained further below. 

As with the original HH PSS rule, 

regression coefficients in Table 4 

represent the average addition to 

resource cost due to each severity level. 

(To show the coefficients in actual, as 

opposed to resource cost, dollars, the 

coefficients were scaled by a multiplier 

representing the ratio of the HH PPS 

average payment level to the Abt 

Associates average resource cost level.) 

However, the severity level coefficients 

in Table 4 are specific to the 

classification of the episode in the fourequation model; for example, only for 

early episodes below 14 therapy visits 

are the severity level coefficients 

$861.74 for the third clinical severity 

level, and $219.44 for the second 

functional severity level. 

The lowest-severity case-mix group is 

the base group for the payment 

regression, whose predicted cost is the 

regression intercept value of $1,265.18. 

This group consists of the lowest 

clinical, functional, and services 

utilization severity levels for episodes 

classified as early episodes below the 

14-visit therapy threshold (Equation 1 of 

the four-equation model). The service 

severity level for this group is severity 

level 1 (S1), which comprises episodes 

of 0 to 5 therapy visits. 

To use the results of the payment 

regression for determining payments, 

find the severity level coefficients for 

the applicable equation and add those 

amounts to the regression intercept and 

to the constant for the applicable 

equation. There is no constant for the 

first equation/group, the early episodes 

below the 14-visit therapy threshold; for 

this group, the constant is the regression 

intercept. For example, later episodes 

below the 14-visit therapy threshold 

with clinical severity level 2, functional 

severity level 1, and service severity 

level 2 have the following scaled 

coefficients summed to represent the 

resource cost: $1,265.18 for the 

regression intercept; $139.26 for the 

second clinical severity level; $645.90 

for the second service severity level (6 

therapy visits); and $210.94, a constant 

amount for all later episodes below 14 

therapy visits. The constant 

incorporates the predicted average 

resource cost for the lowest functional 

severity group. The predicted average 

resource cost, $2,261.28, is the sum of 

these four coefficients from the 

regression. Table 5 shows the results of 

the computational procedure for all 

combinations of severity levels within 

each equation/threshold group. 

TABLE 4.—REGRESSION COEFFICIENTS

FOR CALCULATING CASE-MIX RELATIVE WEIGHTS

Intercept (constant for all case 

mix groups) ........................... $1,265.18 

1st and 2nd Episodes, 0 to 13 

Therapy Visits 

C2 ............................................. 380.66 

C3 ............................................. 861.74 

F2 .............................................. 219.44 

F3 .............................................. 379.06 

S2 (6 therapy visits) ................. 499.96 

S3 (7–9 therapy visits) ............. 935.02 

S4 (10 therapy visits) ............... 1,375.38 

S5 (11–13 therapy visits) ......... 1,755.92 

1st and 2nd Episodes, 14 to 19 

Therapy Visits 

Constant ................................... 2,171.56 

C2 ............................................. 534.70 

C3 ............................................. 1,246.47 

F2 .............................................. 268.36 

F3 .............................................. 425.68 

S2 (16–17 therapy visits) ......... 425.49 

S3 (18–19 therapy visits) ......... 698.92 

3rd+ Episodes, 0 to 13 Therapy Visits 

Constant ................................... 210.94 

C2 ............................................. 139.26 

C3 ............................................. 613.76 

F2 .............................................. 414.74 

F3 .............................................. 818.25 

S2 (6 therapy visits) ................. 645.90 

S3 (7–9 therapy visits) ............. 1,083.30 

S4 (10 therapy visits) ............... 1,507.60 

S5 (11–13 therapy visits) ......... 1,890.78 

3rd+ Episodes, 14 to 19 Therapy Visits 

Constant ................................... 2,178.93 

C2 ............................................. 672.65 

C3 ............................................. 1,392.59 

F2 .............................................. 390.72 

F3 .............................................. 687.07 

S2 (16–17 therapy visits) ......... 292.06 

S3 (18–19 therapy visits) ......... 712.62 

TABLE 4.—REGRESSION COEFFICIENTS

FOR CALCULATING CASE-MIX RELATIVE WEIGHTS—Continued 

All Episodes, 20+ Therapy Visits 

Constant ................................... 3,996.82 

C2 ............................................. 578.49 

C3 ............................................. 1,383.67 

F2 .............................................. 485.73 

F3 .............................................. 1,043.13 

Note: Regression coefficients were scaled 

by multiplier representing the ratio of the HH 

PS average payment level to the Abt Associates average resource cost level. 

The payment regression in Table 4 

reflects a decision to group together 

early and later episodes for purposes of 

deriving the payment regression 

coefficients for episodes at or above the 

20-visit therapy threshold. This has the 

advantage of producing a lower number 

of case-mix groups than we would have 

had without grouping. Earlier analysis 

had revealed that the coefficients, 

predicted average resource cost, and 

relative weights of the case-mix groups 

for episodes of 20 or more therapy visits 

in Equations 2 (early episodes) and 4 

(later episodes) had very similar values. 

Specifically, of the 9 case groups 

defined for these noted episodes in each 

equation (a total of 18 groups), the 

relative weights did not differ by more 

than 3.5 percent for 7 pairs of groups; 

in the remaining two pairs of groups, 

the difference was slightly more than 7 

percent. Because of the virtually 

identical values, we specified our 

payment regression procedure to 

produce a single set of case-mix groups 

for all episodes in the 20-visit threshold 

group, with the result that the relative 

case-mix weights do not differ according 

to whether the episode is early or later. 

This final step produced a total of 153 

case-mix groups. 

The predicted average resource cost 

for each case-mix group is shown in 

Table 5. As with the coefficients in 

Table 4, these values are scaled up from 

the resource cost values used to model 

the case-mix, using a single multiplier. 

The multiplier allows us to report the 

coefficients and the predicted average 

resource cost using dollars of the same 

magnitude as the payments we would 

make. It does not change the 

relationships among the predicted 

average resource costs, which are the 

values that determine the relative case 

mix weights. 

We used the predicted average 

resource costs for the 153 case-mix 

groups to calculate the relative case-mix 

weights. The relative case-mix weight 

for a case-mix group is simply the 

predicted average resource cost for the 

group divided by the sample’s overall 


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average resource cost. Table 5 shows the 

final relative case-mix weights, after we 

applied two further adjustments, the 

budget neutrality adjustment and the 

adjustment for nominal changes in casemix coding, which are explained further 

in this section II.A.2.c. 

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BILLING CODE 4120–01–C 

*Note: Case-mix weight is after applying 

budget neutrality adjustment factor (see text 

for description of adjustment of the weights). 

Predicted average cost is calculated from the 

regression coefficients in Table 4. 

The budget neutrality adjustment to 

the relative case-mix weights is required 

to achieve no change in outlays when 

moving from the original case-mix 

system to the proposed new case-mix 

system. The process of revising the casemix system results in relative weights 

with an average value of 1.0 over all 

1,656,551 sample episodes we used to 

represent the totality of reimbursable 

episodes in the first year of the new 

case-mix system. The budget neutrality 

adjustment restores the average casemix weight that results from the 

revision process to the average level 

observed before implementing the 

proposed new case-mix system. To 

implement the budget neutrality 

adjustment, we used the constant 

budget neutrality factor to increase the 

weights for all 153 case-mix groups to 

the prior average level. The resulting 

adjusted case-mix weights prevent total 

payments under the proposed revised 

HH PPS system from dropping below a 

budget-neutral level. The budget 

neutrality adjustment factor is 

1.194227193. 

Based upon our review of trends in 

the national average case-mix index 

(CMI), we are proposing an additional 

adjustment to the HH PPS national 

standardized rate to account for casemix upcoding that is not due to change 

in the underlying health status of home 

health users. Section 1895(b)(3)(B)(iv) of 

the Act specifically provides the 

Secretary with the authority to adjust 

the standard payment amount (or 

amounts) if the Secretary determines 

that the case-mix adjustments resulted 

(or would likely result in) a change in 

aggregate payments that are the result of 

changes in the coding or classification 

of different units of services that do not 

reflect real changes in case-mix. The 

Secretary may then adjust the payment 

amount to eliminate the effect of the 

coding or classification changes that do 

not reflect real changes in case-mix. To 

identify whether such an adjustment 

factor was needed, we first determined 

the current average case-mix weight per 

paid episode. 

The most recent available data from 

which to compute an average case-mix 

weight, or case mix index, under the HH 

PPS is from 2003. Using the most 

current available data from 2003, the 

average case-mix weight per episode for 

initial episodes is 1.233. To proceed 

with this analysis, next we determined 

the baseline year needed to evaluate the 

trend in the average case-mix per 

episode. 

There are two different baseline years 

that could be used to measure the 

increase in case-mix: 

1. A Cohort Admitted to Home Care 

From October 1997 to April 1998 (the 

Abt Case-Mix Study Sample Which Was 

Used To Develop the Current Case-Mix 

Model) 

There are several advantages to using 

data from this period of time as the 

baseline from which we measure the 

increase in case-mix. This time period is 

free from any anticipatory response to 

the HH PPS, and data from this time 

period were used to develop the original 


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HH PPS model. Also, this is the only 

nationally representative dataset from 

the 1997–1998 time period that 

measures patient characteristics using 

an OASIS assessment form comparable 

to the one adopted for the HH PPS. 

Because the Abt case-mix dataset was 

used to determine the current set of 

case-mix weights, the average case-mix 

weight in the sample equals 1.0. The 

sample’s value of 1.0 provides a starting 

point from which to measure the 

increase in case-mix. The increase in the 

average case-mix using this time period 

as the baseline results in a 23.3 percent 

increase (from 1.0 to 1.233). 

However, agencies included in the 

sample were volunteers for the study 

and cannot be considered a perfectly 

representative, unbiased sample. 

Furthermore, the response to Balanced 

Budget Act of 1997 provisions such as 

the home health interim payment 

system (HH IPS) during this period 

might produce data from this sample 

that reflect a case-mix in flux; for 

example, venipuncture patients were 

suddenly no longer eligible, and longterm-care patients were less likely to be 

admitted. Therefore, we are not 

confident the trend in the CMI between 

the time of the Abt Associates study and 

2003 reflects only changes in nominal 

coding practices, as will be explained in 

more detail further below in this 

section. Therefore, we are not proposing 

to use this baseline year to determine 

the baseline. 

2. 12 Months Ending September 30, 

2000 (HH IPS Baseline) 

Analysis of a 1 percent sample of 

initial episodes from the 1999–2000 

data under the HH IPS revealed an 

average case-mix weight of 1.125. 

Standardized to the distribution of 

agency type (freestanding proprietary, 

freestanding not-for-profit, hospitalbased, government, and SNF-based) that 

existed in 2003 under the HH PPS, the 

average weight was 1.134. We note this 

time period is likely not free from 

anticipatory response to the HH PPS, 

because we published our initial HH 

PPS proposal on October 28, 1999. The 

increase in the average case-mix using 

this time period as the baseline results 

in an 8.7 percent increase (from 1.134 to 

1.233; 1.233–1.134=0.099; 0.099/ 

1.134=0.087; 0.087*100=8.7%). 

Since the HH IPS, reported severity 

has increased as episodes have shifted 

from low severity groups to high 

severity groups. Concurrently, there has 

been a reduction in resource utilization. 

For example, the number of visits per 

episode has significantly declined under 

the HH PPS since 1999. This decline is 

illustrated in Table 6. 

TABLE 6.—AVERAGE NUMBER OF

HOME HEALTH VISITS PER EPISODE

Year 

Total home 

health hisits 

(excluding 

LUPAs) 

1997 .......................................... 36.04 

1998 .......................................... 31.56 

IPS ............................................ 25.51 

2001 .......................................... 21.78 

2002 .......................................... 21.44 

2003 .......................................... 20.98 

We believe that change in case-mix 

between the time of the Abt Associates 

case-mix study and the end of the HH 

IPS period reflected substantial change 

in real case-mix. First, throughout most 

of this period, HHAs had no incentive 

to bring about nominal changes in casemix because case-mix was not a part of 

the payment system at that time. 

Dramatic changes in the home health 

benefit also became evident under the 

HH IPS as a result of provisions of the 

Balanced Budget Act of 1997. 

Venipuncture patients were suddenly 

no longer eligible; members of this 

group often had multiple comorbidities 

and commonly used substantial 

amounts of personal care. In addition, 

according to a study in the literature, 

beneficiaries age 85 and older, as well 

as beneficiaries dually eligible for 

Medicare and Medicaid, were slightly 

less likely to be admitted to home care 

(McCall et al., 2003). Both of these 

groups are associated with high needs 

for personal care services, suggesting 

that long-term care patients were less 

likely to be admitted under the HH IPS. 

The agency closure rates in States 

associated with high utilization (for 

example, Louisiana, Oklahoma, and 

Texas) also suggests that admissions 

among long-term care patients 

experienced decline. The OASIS data 

comparing the case-mix sample and the 

HH IPS period exhibit some consistency 

with these ideas, in that they indicate 

substantial decline in admission of the 

kinds of patients likely to be long-term 

homebound beneficiaries with chronic 

medical care needs—patients with 

diabetes, impaired vision, parenteral 

nutrition, bowel and urinary 

incontinence, behavioral problems, 

toileting dependency, and more-severe 

transferring dependency. 

Various studies are consistent with 

the incentives created by the HH IPS 

per-beneficiary cost cap—particularly 

an incentive to admit many different 

patients with low care needs and/or for 

short periods to keep per-beneficiary 

costs low (MedPac, 1999; GAO, 1998; 

GAO, 1999; Smith et al., 1999). 

An important implication of these 

studies and our comparative OASIS data 

is that patients with intensive or lengthy 

needs for nursing and personal care 

services as opposed to short-term or 

rehabilitative needs were less likely to 

be found in the national home care 

caseload as a result of the HH IPS. This 

would mean that a larger share of 

patients in the caseload would have 

acute, post-acute, and rehabilitative 

needs. Practice patterns began to change 

concomitantly with the share of visits 

shifting towards rehabilitation services 

and, to a lesser extent skilled nursing. 

In 1997 through 1998, the average 

number of therapy visits per 60-day 

period was about 3, whereas by the last 

year of the HH IPS, it rose to 4.4, with 

growth moderating thereafter. Skilled 

nursing visits declined from more than 

12 at the beginning of the HH IPS, and 

stabilized at slightly more than 9 under 

the HH PPS. Aide visits declined by 44 

percent from 1997 to 2000, the last year 

of the HH IPS, and continued to decline 

at a slower rate under the HH PPS. An 

issue in interpreting these trends in the 

utilization data is the uncertainty about 

how much of the startling change in 

therapy provision was driven by patient 

case-mix, and how much was driven by 

an anticipatory response of the practice 

pattern itself to our proposals for the 

original HH PPS case-mix system. By 

using a 10-visit therapy threshold, the 

proposal installed a substantial payment 

increase for high-therapy episodes. If 

providers started responding to the 

incentives in the anticipated HH PPS 

even before it became effective, then our 

measure of case-mix change between the 

time of the Abt Associates case-mix 

study sample and the HH IPS baseline 

is affected by provider behavioral 

change that is not strictly reflective of 

the case-mix of the treated population. 

In contrast to the 13.4 percent 

increase that we consider a real casemix change, we believe that the 8.7 

percent increase in the national casemix index between the HH IPS baseline 

and CY 2003 cannot be considered a 

real increase in case-mix. The trend data 

on visits (Table 6), resource data 

(presented below), and our analysis of 

changes in rates of health characteristics 

on OASIS assessments and changes in 

reporting practices (presented in section 

II.A.3.c of this proposed rule) all lead to 

the conclusion that the underlying casemix of the population of home health 

users actually was essentially stable 

between the IPS baseline and CY 2003. 

Our research shows that HHAs have 

reduced services (see Tables 6 and 7) 

while the CMI continued to rise (see 

Table 7). We would normally expect 


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growth in the CMI to be accompanied by 

more consumption of services; but, to 

the contrary, we measure slightly lower 

resource consumption. This is indicated 

by the data in Table 7 that illustrates, by 

quarter, the average resource cost per 

episode as well as the average CMI for 

initial (admissions) episodes and all 

episodes. (Note: In Table 7, the CMI data 

for the HH IPS quarters are not adjusted 

for distribution of agency types; that is, 

they do not reflect the adjustment to the 

HH IPS baseline that we cited earlier, 

which caused the HH IPS baseline to 

increase to 1.134 from 1.125). In 

addition, in Table 7, the average 

resource cost is not adjusted for wage 

inflation. If the average resource cost 

had been adjusted for wage inflation, 

there would be an even larger reduction 

in resource cost between the HH IPS 

and HH PPS.) 

TABLE 7.—AVERAGE RESOURCE COST AND CMI 

Period Average resources 

CMI admissions CMI all 

HH IPS: 

1999Q4 ................................................................................................................................. $477.06 1.1278 1.0823 

2000Q1 ................................................................................................................................. 467.70 1.1074 1.0815 

2000Q2 ................................................................................................................................. 466.59 1.1223 1.0982 

2000Q3 ................................................................................................................................. 469.52 1.1453 1.1138 

HH PPS: 

2000Q4 ................................................................................................................................. N/A N/A N/A 

2001Q1 ................................................................................................................................. 432.84 1.1841 1.1622 

2001Q2 ................................................................................................................................. 440.73 1.1910 1.1774 

2001Q3 ................................................................................................................................. 445.59 1.1965 1.1724 

2001Q4 ................................................................................................................................. 446.93 1.2003 1.1818 

2002Q1 ................................................................................................................................. 452.48 1.2052 1.1800 

2002Q2 ................................................................................................................................. 453.89 1.1999 1.1835 

2002Q3 ................................................................................................................................. 456.69 1.2099 1.1832 

2002Q4 ................................................................................................................................. 460.10 1.2213 1.1957 

2003Q1 ................................................................................................................................. 453.74 1.2152 1.1889 

2003Q2 ................................................................................................................................. 459.97 1.2295 1.2018 

2003Q3 ................................................................................................................................. 458.86 1.2302 1.2002 

2003Q4 ................................................................................................................................. 462.59 1.2465 1.2159 

According to the data in Table 7, in 

Year 2 (2002) of HH PPS, home health 

resources per episode for new 

admissions were approximately 2 

percent lower than they were in the year 

immediately before implementation of 

HH PPS. At the same time, the national 

case-mix index for new admissions rose 

by approximately 0.02 per year. (The 

national case-mix index for all episodes, 

new and continuing, rose by 

approximately 0.01 per year.) By Year 3 

(2003) of the HH PPS, home health 

resources per admission episode rose 

slightly above the Year 2 level, and then 

stabilized at levels similar to the HH 

IPS. The national CMI for new 

admissions continued to rise by about 

0.02 per year (with the CMI for all 

episodes rising by about 0.01 per year). 

Therefore, based upon our trend 

analysis described above, we believe the 

change in the case-mix index between 

the Abt case-mix sample (a cohort 

admitted between October 1997 and 

April 1998) and the HH IPS period (the 

12 months ending September 30, 2000) 

is due to real case-mix change. We take 

this view, even though we understand 

that there may be some issue as to 

whether this period was affected by 

nominal case-mix change due to 

providers’ anticipating, in the last year 

of HH IPS, the forthcoming case-mix 

system, with its incentives to intensify 

rehabilitation services. This change 

from these two periods is from 1.00 to 

1.134, an increase of 13.4 percent. 

However, we are not proposing to adjust 

for case-mix change based on this 

change in values. However, we are 

proposing that the 8.7 percent of casemix change that occurred between the 

12 months ending September 30, 2000 

(HH IPS baseline, CMI=1.134), and the 

most recent available data from 2003 

(CMI=1.233), be considered a nominal 

change in the CMI that does not reflect 

a ‘‘real’’ change in case-mix. 

In addition to the trend analysis 

above, we conducted several additional 

kinds of analyses of data and 

documentary materials related to home 

health case mix coding change. These 

analyses are described in detail in 

section II.A.3.e. The results support our 

view that the change in the CMI since 

the HH IPS baseline mostly reflects 

provider responses to the changes that 

accompanied the HH PPS, including 

particulars of the payment system itself 

and changes to OASIS reporting 

requirements. Our analyses indicated 

generally modest changes in overall 

OASIS health characteristics between 

the two periods noted above, a specific 

pattern of changes in scaled OASIS 

responses that is not indicative of 

material worsening of presenting health 

status, various changes in the OASIS 

reporting instructions that help account 

for numerous coding changes we 

observe, and a large increase in postsurgical patients with their traditionally 

lower case-mix index. 

Our past experience establishing other 

prospective payment systems also led us 

to believe a proposal to make this 

adjustment for nominal change in casemix is warranted. In other systems, 

Medicare payments were almost 

invariably found to be affected by 

nominal case-mix change. We are 

considering several options for 

implementing this case-mix adjustment. 

These options include incorporating the 

entire ¥8.7 percent adjustment in CY 

2008, incorporating an adjustment of 

¥5.0 percent in CY 2008 and an 

adjustment of ¥2.7 percent in CY 2009, 

and incorporating an adjustment of 

¥4.35 percent in CY 2008 and an 

adjustment of ¥4.35 percent in CY 

2009. However, because of the potential 

impact our proposed adjustment may 

have on providers, we are proposing 

and requesting comment on whether to 

adjust for the nominal increase in 

national average CMI by gradually 

reducing the national standardized 60- 

day episode payment rate over 3 years. 

During that period we would continue 

to update our estimate of nominal casemix change and adjust the national 

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rate accordingly for any nominal change 

in case-mix that might occur. We 

propose to implement a 3-year phase-in 

of the total downward adjustment for 

nominal changes in case-mix by 

reducing the national standardized 60- 

day episode payment rate by 2.75 

percent each year up to and including 

CY 2010. This annual reduction percent 

is based on our current estimate of the 

nominal change in case-mix that has 

occurred between the HH IPS baseline 

(+0.099) and 2003. However, if, at the 

time of publication of the final CY 2008 

HH PPS rule, updates of the national 

claims data to 2005 indicate that the 

nominal change in case-mix between 

the HH IPS baseline and 2005 is not 

+0.099, we would revise the percentage 

reduction in the next year’s update. The 

revision would be determined by the 

ratio of the updated 3-year annual 

reduction factor to the previous year’s 

annual reduction factor. For example, 

the scheduled annual reduction factor is 

now estimated to be 0.9725 (equivalent 

to a 2.75 percent reduction); for CY 2008 

we would multiply this reduction factor 

by the ratio of the updated reduction 

factor to 0.9725. For the CY 2010 rule, 

which governs the third and final year 

of the case-mix adjustment transition 

period, we would obtain the CY 2007 

national average CMI to compute the 

updated value for nominal case-mix 

adjustment. Again, we would form the 

ratio of the updated adjustment factor to 

the previous year’s effective adjustment 

factor. The annual updating procedure 

avoids a large reduction for the final 

year of the phase-in, in the event that 

the CY 2007 national average case-mix 

index reflects continued growth since 

CY 2005. The calculation of the adjusted 

national prospective 60-day episode 

payment rate for case-mix and area wage 

levels is set forth in § 484.220. We are 

proposing to revise § 484.220 to address 

changes to case-mix that are not a real 

change in case-mix. 

CMS proposes to adjust the national 

prospective 60-day episode payment 

rate to account for the following: 

• HHA case-mix using a case-mix 

index to explain the relative resource 

utilization of different patients. To 

address changes to the case-mix that 

were a result of changes in the coding 

or classification of different units of 

service that did not reflect real changes 

in case-mix, the national prospective 60- 

day episode payment rate will be 

adjusted downward as follows: 

—For CY 2008 the adjustment is 2.75 

percent. 

—For CY 2009 and CY 2010, the 

adjustment is 2.75 percent in each 

year. 

• Geographic differences in wage 

levels using an appropriate wage index 

based on the site of service of the 

beneficiary. 

We plan to continue to monitor 

changes in the national average CMI to 

determine if any adjustment for nominal 

change in case-mix is warranted in the 

future. 

Accordingly, based upon our analysis 

and conclusions, we are proposing a 

new set of case-mix weights that reflect 

the four-equation model and a payment 

adjustment for the nominal change in 

the case-mix index described above. We 

arrived at these weights, listed in Table 

5, by first determining relative weights 

for each of the 153 groups using the 

four-equation model and the payment 

regression. The definition for each of 

these groups based on clinical, 

functional, and service severity levels is 

described in Table 5. Each of these 

relative weights was adjusted by 

multiplying it by an adjustment factor to 

make the proposed payments budgetneutral to current estimated payments 

for CY 2008. This budget neutrality 

factor raised the proposed average casemix weight to the case-mix index 

reflected by the most recent data 

available from 2003. The proposed 

budget-neutrality factor for 2008 is 

1.194227193. Each budget neutral, 

adjusted, weight in Table 5 was 

calculated in the following manner: 

Relative Weight × 1.194227193. 

References to literature cited in this 

section: 

N. McCall et al., ‘‘Utilization of Home Health 

Services before and after the Balanced 

Budget Act of 1997: What Were the 

Initial Effects?’’ Health Services 

Research, Feb. 2003: 85–106. 

MedPac, Report to the Congress: Selected 

Medicare Issues, June 1999: 105–115. 

General Accounting Office (GAO), ‘‘Medicare 

Home Health Benefit: Impact of Interim 

Payment System and Agency Closures on 

Access to Services,’’ GAO/HEHS–98–

238, Sept. 1998. 

General Accounting Office (GAO), ‘‘Medicare 

Home Health Agencies: Closures 

Continue, with Little Evidence 

Beneficiary Access Is Impaired,’’ GAO/ 

HEHS–99–120, May 1999. 

B.M. Smith et al., ‘‘An Examination of 

Medicare Home Health Services: A 

Descriptive Study of the Effects of the 

Balanced Budget Act Interim Payment 

System on Access to and Quality of 

Care,’’ Center for Health Services 

Research and Policy, George Washington 

University, Sept. 1999. 

3. Description and Analysis of Case-Mix 

Coding Change under the HH PPS 

As stated in section II.A.2.c of this 

proposed rule, under section 

1895(b)(3)(B)(iv) of the Act, we are 

proposing a reduction in HH PPS 

national standardized 60-Day episode 

payment rate to offset a change in 

coding practice that has resulted in 

significant growth in the national casemix index (CMI) since the inception of 

the HH PPS that is not related to ‘‘real’’

change in case mix. The factor was 

determined by calculating the change in 

the national CMI between the HH IPS 

and the HH PPS. 

In this section II.A.3, for purposes of 

illuminating the sources of CMI increase 

in terms of the case-mix system itself, 

we identify the severity levels with the 

largest growth between the two periods. 

We will provide, in Table 8, the 

percentage change in volume for each of 

the 80 case-mix groups, and summary 

statistics of the changes. Table 9 shows 

the rates of all OASIS assessment items 

in the two time periods. We will explain 

below our inferences from Table 9 about 

the comparative health status of the 

populations treated in the two time 

periods. Subsequent to that, we will 

explain our analysis of the changes to 

OASIS reporting instructions that were 

likely to have affected reported case 

mix. We also describe analyses we 

performed to quantify the effect on the 

CMI of increases in post-surgical 

episodes in the national caseload, and 

our interpretation of the analyses. We 

conclude with a summary and 

interpretation of our key findings from 

the descriptive analysis of OASIS 

assessment data, analysis of OASIS 

reporting instructions, and analysis of 

changes in post-surgical volume. 

In making these analyses, we 

reviewed data from two samples. The 

first, the HH IPS sample, is the same 

sample used in section II.A.2.c of this 

proposed rule for determining the IPS 

baseline that we used to determine the 

proposed adjustment for nominal 

change in case-mix. The HH IPS sample 

is a 1 percent random sample of claims 

(total number of 18,480) with its 

matched start of care OASIS 

assessments from the 12 months 

immediately preceding HH PPS. We 

matched the assessments to determine 

what the patient’s case-mix group 

would have been had HH PPS been in 

effect. To simulate 60-day episodes from 

actual claims we used the same method 

that was used to create the initial 

development sample for the HH PPS 

case-mix system. In performing the 

simulation, we took into account the 

timing of the start of care in relation to 

previous service periods, and used only 

60-day periods that would have 

corresponded to initial episodes in a 

sequence of adjacent episodes that 

consisted of one or more simulated 

episodes. We considered initial 

episodes as the first episodes that follow 


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periods of at least 60 days without 

receiving home health service. 

The second sample is a 20 percent 

sample of FY 2003 claims for initial 

episodes again matched to start of care 

OASIS assessments. In both samples, we 

corrected any initial errors in 

determining the beneficiary’s preadmission location that affected the 

HHRG before determining the HHRG. 

We made the correction by consulting 

the sample member’s claims history for 

information about previous inpatient 

stays. 

a. Change in Case-Mix Group 

Frequencies 

Table 8 presents the share of the 

population assigned to each severity 

level of the case-mix system’s three 

dimensions (clinical, functional, and 

service). The table indicates there was a 

strong shift away from the lowestseverity case-mix groups towards higher 

severity level between the two sample 

periods. Growth of the two highest 

severity levels of the clinical domain 

was approximately 23 percent; for every 

100 beneficiaries, 8 additional 

beneficiaries were classified to the 

highest two clinical dimensions in 2003 

compared to the HH IPS period. 

Growth of the functional severity 

levels F2 and F3 totaled 12 percent. The 

12 percent growth in share was 

concentrated in F2. Share growth for F2 

and F3 was offset by a decline for the 

two lowest functional severity levels 

and, potentially, a tiny decline in share 

for the severest functional level, F4. 

Notwithstanding the small decrease in 

the share assigned to F4, for every 

hundred beneficiaries, about 7 

additional beneficiaries were classified 

to the higher severity levels F2 and F3. 

The data also indicate that the 

proportion of patients with a prior SNF 

or rehabilitation facility discharge in the 

14 days before admission, but no 

hospital discharge in that period, grew 

by 25 percent for episodes below the 10- 

visit therapy threshold, and 64 percent 

for episodes above the 10-visit therapy 

threshold. These patients receive a 

higher case-mix score than patients from 

all other pre-admission locations on the 

OASIS (including inpatient discharge). 

In addition, the table indicates growth 

in the high-therapy groups (levels S2 

and S3) of 30 percent. This means that 

for every hundred beneficiaries, 8 

additional beneficiaries were assigned 

to receive at least 10 therapy visits in 

2003 compared to the HH IPS period. 

Under the HH PPS, approximately 35 

percent of patients in their initial 

episode received at least 10 therapy 

visits. 

TABLE 8.—COMPARISON OF SEVERITY LEVEL PREVALENCE, HH IPS SAMPLE AND 2003 HH PPS SAMPLE

HH IPS 

(percent) 

HH PPS 2003 

(percent) Difference 

All C0 .............................................................. Min .................................................................. 29.69 22.07 -7.62 

All C1 .............................................................. Low ................................................................. 36.49 36.19 -0.31 

All C2 .............................................................. Mod ................................................................ 28.91 35.50 6.58 

All C3 .............................................................. High ................................................................ 4.91 6.25 1.34 

All F0 ............................................................... Min .................................................................. 9.27 6.15 -3.12 

All F1 ............................................................... Low ................................................................. 28.57 25.40 -3.17 

All F2 ............................................................... Mod ................................................................ 45.18 51.30 6.12 

All F3 ............................................................... High ................................................................ 10.39 10.83 0.44 

All F4 ............................................................... Max ................................................................. 6.60 6.33 -0.27 

All S0 ............................................................... Min .................................................................. 65.74 55.87 -9.87 

All S1 ............................................................... Low ................................................................. 7.40 9.22 1.83 

All S2 ............................................................... Mod ................................................................ 19.94 23.59 3.64 

All S3 ............................................................... High ................................................................ 6.92 11.32 4.40 

Table 9 shows the shares of total 

episodes for the complete set of 80 

original case-mix groups, during both 

the HH IPS and the HH PPS FY 2003. 

Table 9 also displays each group’s casemix weight. Ten groups had no change 

in their share of episodes between the 

HH IPS period and the HH PPS period 

in the table. Of the remaining 70 groups, 

38 groups, slightly more than half, had 

a larger share of total episodes under 

HH PPS than the HH IPS. However, 

decline in share of total episodes was 

associated with minimal or low clinical 

severity (C0 and C1). Only 8 of 40 

groups with moderate (C2) or high (C3) 

clinical severity had decrease in their 

share of episodes under HH PPS, with 

most of the remaining moderate or high 

clinical severity groups having a share 

increase. As noted above, growth in 

functional severity level F2 almost 

entirely offset the loss of population 

from groups F0 and F1. Only three of 16 

groups in the functional severity level 

F2 experienced a decline in episode 

shares, and this was concentrated 

entirely in the two lowest clinical 

severity groups. 

We summarized the association 

between case-mix group severity and 

change in episode share by calculating 

the rate ratio for growth in episode 

shares. We sorted the groups by casemix weight and divided the groups into 

the top 40 weights of the 80-group casemix system and the remaining 40 

weights. The rate ratio was determined 

by dividing the growth in total share of 

the top 40 weights by the growth in total 

share for the remaining 40 weights. The 

groups with the 40 smallest weights 

have mostly reductions in episode 

shares (24 of 40 have reductions), and 

the groups with the largest 40 weights 

have mostly increases in episode shares 

(24 of 40 groups). The rate ratio for 

positive changes was 1.71, which means 

that as a group the top 40 case-mix 

weights were about 70 percent more 

likely than the bottom 40 to have an 

increase in share of total episodes. 

BILLING CODE 4120–01–P 


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b. Health Characteristics Reported on 

the OASIS 

To further our understanding of the 

relative roles of case-mix change and 

coding changes that might be 

responsible for the .0991 increase of the 

national HHRG CMI, we analyzed the 

HH IPS and HH PPS samples’ health 

characteristics, based on the start-of-care 

OASIS assessment. We compared the 

proportion of start-of-care assessments 

that had each OASIS characteristic, 

using data from our HH IPS and HH PPS 

2003 samples. We used the woundrelated OASIS data to compute statistics 

on changes in numbers of wounds. The 

results are shown in Table 10 and 

discussed below. (Items scored in the 

HH PPS 80 group case-mix system are 

shown in bold.) Table 10: Comparison 

of rates of response categories on OASIS 

Start of Care Assessments, HH IPS 

Sample and 2003 HH PPS Sample 


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In general, the results showed that 

health characteristics as measured by 

the OASIS items were stable or changed 

little. Exceptions to the general findings 

were indications that the HH PPS 

population included: 

• More post-acute and more postsurgical patients; 

• More patients that had a recent 

history of post-acute institutional care; 

• More patients with a recent change 

in medical or treatment regimen; 

• More patients in the orthopedic 

diagnosis group defined under the PPS 

system’s clinical dimension; and 

• More patients assessed with 

dependencies in Activities of Daily 

Living (ADLs) and Instrumental 

Activities of Daily Living (IADLs) as of 

14 days before the assessment. The 

proportion of patients using at least 10 

therapy visits also rose noticeably. 

Otherwise, the rate comparisons of 

OASIS items are generally 

unremarkable. Several measures usually 

reflective of a more compromised health 

status, including ADL limitations, 

incontinence, pain, short life 

expectancy, and diagnosis severity had 

a somewhat higher rate in the HH PPS 

sample than the HH IPS sample. 


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However, various physiologic measures 

and risk factors showed little or no 

change, including urinary tract 

infection, visual and aural functioning, 

dyspnea, bowel ostomy, bowel 

incontinence, obesity, alcoholism, drug 

dependence, depressive symptoms, 

behavioral problem frequency, use of 

home oxygen, infusion therapy, and 

nutritional therapies. In addition, the 

probability that a patient used 

psychiatric nursing was reduced, from 2 

percent to 1 percent. 

The current HH PPS case-mix system 

recognizes four types of diagnoses for 

purposes of assigning patients to casemix groups: diabetes, orthopedic 

conditions, neurological conditions, and 

burns and trauma. These diagnoses were 

found to be associated with higher-thanaverage resource costs in the original 

case-mix research. The data in Table 10 

indicate that the share of patients 

assigned to the four case-mix diagnosis 

groups grew by 23 percent. This change 

was due to an additional 7 per hundred 

patients assigned to the orthopedic 

diagnosis group, and an additional 2 per 

hundred assigned to the diabetes 

diagnosis group. The share of patients 

assigned to the neurological diagnosis 

group remained unchanged (at 8 per 

hundred), and the share of patients 

assigned to the burns/trauma diagnosis 

group declined by 2 per hundred. 

There are two important reasons why 

we believe these changes reflect mostly 

nominal, as opposed to real, underlying 

case-mix change. First, the notable 

increase in the proportion of orthopedic 

diagnoses is due at least in part to the 

listing of the diagnosis code for 

abnormality of gait in this diagnosis 

group. The diagnosis code for 

abnormality of gait (781.2) is commonly 

used to indicate that the primary reason 

for the home health treatment is 

rehabilitation services (for example, 

physical therapy). Detailed analysis 

shows that this use of this code grew by 

50 percent between the HH IPS period 

and the early years of the HH PPS. We 

believe agencies had an incentive to use 

this code on Medicare claims to support 

treatment plans that included large 

amounts of rehabilitation services. This 

code could be used even if the 

underlying condition was not 

orthopedic. Second, the decline in 

burns/trauma assignment may be due in 

part to agencies’ early confusion about 

how to use the ICD–9–CM coding 

system when a patient has an open 

wound not due to an injury. We believe 

traumatic open wounds were thus 

overreported early in HH PPS. However, 

with educational efforts initiated by 

CMS and the home health industry after 

HH PPS began, understanding and 

application of the coding instructions 

for traumatic wound diagnoses 

improved, resulting in a lower, and 

more accurate, rate of reported burns/ 

trauma cases, which we believe is now 

more representative and not an actual 

change in case-mix. 

Other wound-related items varied in 

the types of change they experienced. 

The basic wound-related item 

measuring the presence of a skin 

disturbance or lesion (M0440) increased 

by 15 percentage points; however, this 

measure is general and covers a broad 

range of both clinically significant and 

insignificant problems. We note the 

three detailed series of OASIS items 

following M0440, that is, surgical 

wounds, pressure ulcers, and stasis 

ulcers, had varying results. The 

proportion of patients with pressure 

ulcers increased from 5.4 percent to 6.6 

percent with more than half of the 

pressure ulcers at Stage 2. (Pressure 

ulcers are staged using four levels, 1 to 

4, in order of increasing severity.) The 

average number of pressure ulcers per 

hundred patients increased from 9.2 to 

11.1. Pressure ulcers per 100 persons 

with any pressure ulcers were 1.70 in 

the HH IPS sample and 1.68 in HH PPS 

sample. Excluding the approximately 5 

percent of pressure ulcers that were 

unobservable, the average number of 

stage 1 and stage 2 pressure ulcers per 

patient with pressure ulcers did not 

change; the number of stage 3 and stage 

4 pressure ulcers per patient with 

pressure ulcers declined by 13 percent 

and 27 percent, respectively. In terms of 

the overall population, stage 1 and stage 

2 pressure ulcers per beneficiary 

increased by about 23 percent between 

the HH IPS and HH PPS; stage 3 

pressure ulcers per beneficiary 

increased 7 percent; and stage 4 

pressure ulcers decreased by 11 percent. 

There was no change in the item 

measuring the healing status of the most 

problematic pressure ulcer. 

Review of these data suggest to us that 

the population of home health 

beneficiaries was more likely to include 

pressure ulcer patients under HH PPS, 

that such patients had about the same 

number of pressure ulcers per person in 

both periods, and that the pressure ulcer 

stage tended to be of lower severity, on 

average, under HH PPS compared to the 

HH IPS. We note that under OASIS 

coding policy, there is ‘‘no reverse 

staging’’ of pressure ulcers, which 

means that a healed pressure ulcer 

could be recorded and contribute to the 

statistics. Therefore, because of such 

policy, from these statistics it is difficult 

to draw conclusions about change in the 

burden of care related to pressure ulcers 

under the HH PPS. 

We also found little change in 

numbers of stasis ulcers reported or 

their overall seriousness. The 

proportion of patients with any stasis 

ulcers was 3 percent under the HH IPS 

and 2 percent under HH PPS. 

Furthermore, while some patients have 

more than one stasis ulcer, the number 

of stasis ulcers per 100 patients 

decreased from approximately 5.0 to 

4.5. The status of the most problematic 

stasis ulcer (if any) did not change. The 

stasis ulcer decline may be attributable 

in part to improved knowledge among 

agency clinical staff in distinguishing 

among different types of ulcers. 

Based on the HH IPS and the HH PPS 

samples, the case-mix of the population 

of home health beneficiaries clearly 

shifted towards more post-surgical 

patients, with a possible indication that 

the average patient’s healing status 

worsened. The proportion of patients 

with any surgical wounds increased 

from 22.7 percent to 30.0 percent. The 

number of surgical wounds per hundred 

patients increased from 37.4 to 49.2, due 

entirely to the increased numbers of 

post-surgical patients; there was no 

change in the estimated average number 

of surgical wounds per person with any 

surgical wound (our estimate assumed 

patients recorded as having at least one 

unobservable surgical wound had only 

one such wound). There was a 6 

percentage point increase in the 

probability that the most problematic 

surgical wound’s healing status would 

be in an early stage of healing (indicated 

on the OASIS by the response category 

‘‘early/partial granulation,’’ which refers 

to the type of newly forming tissue 

which may be visible in a healing 

wound), and a 1 percentage point 

increase in the probability that the 

wound’s healing status would be ‘‘not 

healing’’. This amounts to a 13 percent 

increase in the share of mostproblematic surgical wounds assigned 

to the two less-favorable healing 

categories, early and partial granulation 

or not healing. 

Our review of current functional 

measures also showed mixed results, 

with some (grooming, upper body 

dressing, meal preparation, laundry, 

telephone use, independence with 

inhalant, and injective medications) 

exhibiting minor or little change. Other 

measures experienced negative and 

sometimes substantial change 

(transferring, ambulation, feeding, and 

housekeeping). In both the HH IPS and 

the HH PPS sample periods, prior 

functional measures were almost 

invariably reflective of a better average 

prior status (as of the 14 days before the 

assessment) compared to the current 

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the overall difference between prior and 

current status is less than in the HH IPS 

sample. In other words, average current 

status is reported as generally more 

functionally impaired under HH PPS 

than under the HH IPS, and accordingly, 

average prior status reflects a different 

relationship to current status in the two 

sample periods. We believe this pattern 

may reflect better understanding of the 

definition and interpretation of the prior 

status items as agencies became more 

familiar with the assessment. 

We also found that quite a few items 

with scaled responses indicated a 

decline in the numbers of patients at the 

best end of the scale (for example, 

independent in bathing), as well as a 

decline or stability in the numbers 

(usually very small numbers) at the 

worst end of the scale (for example, 

totally dependent in bathing). Often, the 

decline in numbers of patients at the 

best end was offset by increased 

numbers rated just below the best end 

of the scale. This pattern was evident 

with measures of primary and 

secondary diagnosis symptom severity, 

cognitive functioning, confusion, 

hearing, speech, current upper and 

lower body dressing, current bathing, 

current toileting, current transferring, 

current ambulation, and several of the 

prior function-related items. 

Table 10 results indicated a pattern of 

change in functional severity away from 

the two lowest severity groups and 

towards the middle severity group. The 

shift towards the middle severity group 

could be explainable by seemingly 

minimal changes in a person’s ADL 

ratings. The examples below show how 

an incremental change in reported 

dependency on a single functional item 

in the HHRG system could change the 

case-mix group functional severity to F2 

from F1. For a hypothetical individual 

in the second-lowest functional severity 

group (F1), a single added limitation 

(that is, going from independence to a 

minimal limitation) could result in the 

individual moving from severity 

category F1 into severity category F2. 

Similarly, in the case of transferring or 

locomotion, a score change that is due 

only to going from one level of 

limitation to the next worst level could 

possibly result in the individual moving 

from severity category F1 into severity 

category F2. 

The three prognosis-related items also 

showed mixed results, with the overall 

and rehabilitative prognosis items 

changing minimally and the life 

expectancy item indicating a more than 

two-fold increase in the proportion of 

the population of home health 

beneficiaries with a life expectancy 

below 6 months. We believe that as 

agencies increasingly recognized that 

the life expectancy item was used in 

measuring adverse events under the 

Outcome-based Quality Improvement 

(OBQM) system, which commenced in 

the early years of HH PPS, agencies 

became more careful to record the 

prognosis accurately. 

We discuss below some of the 

influences on the reporting of the 

OASIS health characteristics since the 

HH PPS began. Our conclusion from 

review of the changes in rates of OASIS 

characteristics, however, is that it is far 

from certain that the essential health 

status and service needs of the 

population of home health beneficiaries 

changed dramatically under the HH 

PPS. A very substantial majority of the 

OASIS characteristics rates noted for 

2003 in Table 10 were within 2 

percentage points of their initial value at 

the HH IPS baseline. Also, few OASIS 

items experienced more than moderate 

adverse change. Included within our 

analysis of adverse changes were several 

items unrelated to the HHRG system, 

including diagnosis symptom severity, 

recent regimen or treatment change, 

feeding, housekeeping, laundry, life 

expectancy, and various prior functional 

status items. Items with adverse change 

that are related to the HHRG system 

include use of post-acute institutional 

care, orthopedic cases, incontinence, 

pain, surgical wound healing status, and 

transferring. 

c. Impact of the Context of OASIS 

Reporting 

As noted above, some items with 

adverse changes are related to the HHRG 

system. We believe that some of these 

changes are a likely result of more care 

being taken in conducting the 

assessment. Agencies were exposed to 

OASIS training and educational 

initiatives in the early HH PPS period 

and, beginning with the HH PPS, 

agencies had an incentive to ensure they 

did not overlook items that could affect 

the HHRG. The new emphasis on proper 

application of OASIS guidelines was 

later reinforced when CMS began to 

implement outcome-based quality 

reporting (OBQI) in early 2002. 

We further believe that, to some 

extent, incentives brought by the 

payment and quality program changes 

interacted with the subjective aspects of 

the assessment process to cause nominal 

coding change. The process of coding, 

especially diagnosis coding and 

determining certain rating scales, entails 

some discretion by the agency. With 

diagnosis coding, patients may have 

more than one diagnosis that can 

reasonably be called the primary 

diagnosis. The significant growth in 

orthopedic diagnosis codes partly 

reflects the ambiguity in the diagnosis 

assignment process itself, particularly in 

the context of a system where financial 

incentives to choose one diagnosis over 

another may be operating. Furthermore, 

scales of ADL functioning can be 

difficult to apply with some patients 

because of daily variability in their 

status and the multiple dimensions of 

the functional item. This difficulty may 

also result in a bias towards selecting a 

more-severe rating in the context of the 

financial incentives of the HH PPS. We 

believe that such bias was likely 

reinforced by the financial incentive 

created by the 10-visit therapy 

threshold. As a result of that incentive, 

high-therapy treatment plans became 

more common under HH PPS. OASIS 

coding practices regarding ‘‘functional 

status’’ could have changed in ways to 

make coding more harmonious with the 

new emphasis on therapy in treatment 

plans. 

Not only is the process of coding 

likely subject to discretion, several 

issuances providing official guidance on 

specific OASIS items released early in 

the HH PPS could have caused some 

clinicians to downgrade patients in their 

assessment of the specific item. 

Instructions regarding the dressing, 

bathing, toileting, transferring, and 

locomotion items, assessment items all 

used in the HH PPS case-mix system, 

were amended in August 2000 in such 

a way that the concept of performing the 

function safely was highlighted 

prominently in the item-by-item 

instructions. (See M0650 to M0700 in 

Chapter 8 at http://www.cms.hhs.gov/ 

apps/hha/usermanu.asp). 

This change alone arguably 

emphasized the concept that ‘‘safety’’ is 

a consideration in assessing the 

patient’s ability to perform the activity 

and in determining the functional item 

on the OASIS. Thus, it seems a likely 

contributing factor in explaining why 

the OASIS data in Table 10 show a 

strong tendency for several ADL 

statistics to shift away from the 

completely independent level. In terms 

of impact on the patient’s case-mix 

group, it should be noted that the casemix score for most of these items 

becomes a positive value if the assessing 

clinician selects any response category 

other than the one indicating that the 

patient is able to function 

independently. (Note: Selecting 

‘‘unknown’’ does not add to the casemix score.) 

Another change in OASIS instructions 

affected the pain item, M0420, in 

August 2000. The section on 

Assessment Strategies offered additional 

strategies for assessing pain in a 


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nonverbal patient, such as facial 

expression and physiological indicators 

(for example, perspiration, pallor). If 

many clinicians were not using these 

strategies during the HH IPS period, it 

is likely that fewer patients would have 

been assessed to have pain. The 

strategies section also introduced the 

term ‘‘well controlled’’ in referring to 

pain assessment, by adding the 

following sentence: ‘‘Pain that is well 

controlled with treatment may not 

interfere with activity or movement at 

all.’’ If, as a result of this guidance, 

clinicians began taking into account 

patient adherence to pain medication, 

one result could have been more 

patients were assessed with pain. 

Adherence to pain medication is an 

important issue in medicine, because 

many patients experience side effects 

that may cause them to trade off pain 

control for diminution of side effects. 

The assessment instructions for 

incontinence were also amended in 

August 2000. The Assessment Strategies 

section for M0520 included a new 

statement: ‘‘Urinary incontinence may 

result from multiple causes, including 

physiologic reasons, cognitive 

impairments, or mobility problems.’’

This clarification could have potentially 

sensitized clinicians to the idea that the 

definition of incontinence is not simply 

about physiologic status (that is, bladder 

control), but instead involves 

considerations such as mobility and 

cognition that can intervene to produce 

wetting on clothing. Because more 

patients were assessed as incontinent in 

the HH PPS period according to M0520 

(which is not used in the case-mix 

system), the OASIS skip pattern drew 

more responses for M0530, the case-mix 

item used to assess the type of 

incontinence. A similar change in the 

Assessment Strategies section was made 

for M0540, bowel incontinence, with 

the potentially similar impact of 

increasing the reported rate. 

Finally, two changes to the OASIS 

manual in August 2000 could have 

expanded the number of patients 

reported to have surgical wounds. The 

first change affecting surgical wounds 

was to expand the definition to read: 

‘‘Medi-port sites and other implanted 

infusion devices or venous access 

devices are considered surgical 

wounds.’’ The possible impact on the 

national case-mix index of broadening 

this instruction is that more openings in 

the skin would be considered surgical 

wounds, requiring more assessments to 

respond to OASIS item M0488, a casemix variable, provided that the site is 

the most problematic surgical wound 

under the expanded definition. It is 

possible for the healing status of these 

types of openings to be ‘‘fully 

granulating’’ (with no case-mix score 

available), at a stage of ‘‘early or partial 

granulation’’ (a score of 7), or even ‘‘not 

healing’’ (a score of 15). For example, a 

central line site being held open by the 

line itself may not reach a fully 

granulating state, or a site that has 

become infected may be assessable as 

‘‘not healing.’’ Before these 

clarifications, it may not have occurred 

to many assessing clinicians to classify 

these device-related sites as surgical 

wounds, so it seems reasonable to 

assume that more surgical wounds 

would be reported after the manual 

change, and to assume that some of 

these would add to the higher rates of 

wounds reported to be not healing or in 

early healing stages. 

The second manual change was a new 

bulleted item in the OASIS responsespecific instructions: ‘‘A muscle flap 

performed to surgically replace a 

pressure ulcer is a surgical wound and 

is no longer a pressure ulcer.’’ We note 

it is not uncommon for home health 

patients to be admitted after 

hospitalization for pressure ulcer 

procedures, such as debridements or 

grafts. While the OASIS manual change 

noted that debridements do not change 

the classification of the pressure ulcer to 

a surgical wound, the muscle flap does 

change the classification. Again, we 

would expect this technical clarification 

to have added to the reported number of 

surgical wounds. 

Another OASIS manual change added 

the statement that ‘‘A PICC line is not 

a surgical wound, as it is peripherally 

inserted, although it is considered a skin 

lesion (see M0440).’’ The PICC line is a 

common method of delivering antibiotic 

treatment intravenously at home. 

However, using the same reasoning 

about the perception of device-related 

openings before the issuance of the 

August 2000 manual, we believe it is 

unlikely that the peripherally inserted 

central catheters (PICC) line clarification 

caused reduction in reported surgical 

wounds as it would not have originally 

occurred to many assessing clinicians to 

have classified it as such in the first 

place. 

The changes to the OASIS manual 

instructions noted in this section 

present concrete potential causes of 

increased OASIS reporting rates for 

case-mix items measuring ADL 

dependencies, pain, incontinence, and 

surgical wounds. While it is difficult to 

know with data available how much of 

the reported increase is traceable to 

these clarifications, we believe that in 

the environment at the time the HH PPS 

was initiated, which included strong 

efforts in the public and private sectors 

to educate home health agencies on the 

proper application of OASIS, the 

changes must have had some impact. To 

the extent that the result was a new 

approach to classifying patients for 

purposes of the OASIS items involved, 

we note the increased item reporting 

rates may not represent an actual 

material change in the health status of 

the population under treatment in home 

care. Given the potential impact of 

OASIS reporting instructions on casemix, we will continue to monitor 

appropriate requirements in an effort to 

promote effectiveness in the HH PPS 

payment methodology. Clarifications to 

the ‘‘OASIS Implementation Manual’’

are issued administratively through 

normal operating procedures. 

• Impact of more post-surgical 

patients 

We also reviewed the increase in rates 

of post-surgical patients that occurred 

under the HH PPS to improve our 

understanding of how this increase 

contributed to the growth in the casemix index between the IPS baseline and 

the 2003 HH PPS period. Being a patient 

with a surgical wound does not in and 

of itself increase the case-mix score. 

However, if the surgical wound is not 

assigned to the best healing status on 

the OASIS assessment, the score will 

increase. Therefore, an increase in the 

proportion of post-surgical patients 

makes more episodes eligible for an 

addition to the score based on the 

healing status. Furthermore, data shown 

in Table 10 indicate that under the HH 

PPS, post-surgical patients were more 

likely to be assessed with a healing 

status that impacts upon a case-mix 

score. Because surgical patients have 

historically had other characteristics 

associated with relatively low resource 

use, we hypothesized that a higher 

occurrence of surgical wound patients 

would not necessarily lead to a rise in 

the overall CMI. 

We analyzed the extent to which the 

severity of HHRG-related OASIS items 

is due to the increased presence of postsurgical patients, of whom many would 

have mobility restrictions, pain, and an 

evolving surgical wound status in the 

early post-acute phase. First, we 

analyzed the relationship between 

having a surgical wound and having a 

characteristic indicative of increased 

severity. Second, we recalculated the 

average case-mix change under two 

alternative assumptions: (1) The higher 

share of post-surgical cases is entirely 

responsible for the changed CMI; (2) 

growth in the CMI for post-surgical 

patients was the same as growth in the 

CMI for non-surgical patients. The 

second assumption would reveal the 

potential effect of a faster worsening of 


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presenting health status through time 

among post-surgical patients compared 

to non-surgical patients. 

As expected, post-surgical patients 

exhibited certain characteristics at 

different rates. Specifically, compared to 

non-surgical patients, they were slightly 

less likely to have no home therapies 

(M0250), about 40 percent more likely 

to have frequent pain (M0420), nearly 

three times as likely to have a bowel 

ostomy, nearly twice as likely to have 

come from an inpatient rehabilitation 

facility and to have intractable pain, and 

15 percent less likely to be independent 

in lower body dressing. Many other 

characteristics were less prevalent 

among post-surgical patients, such as 

having any pressure or stasis ulcers; 

dyspnea; urinary and bowel 

incontinence; behavioral problems 

(M0610); upper body dressing, toileting, 

and ambulation functional limitations. 

If we make the first assumption, that 

the only cause of change in the national 

CMI under the HH PPS was the 

increased share of post-surgical patients 

in the population of home health users, 

then the national case-mix under the 

HH PPS sample should have been 

slightly below the CMI of the HH IPS 

sample. This is because the CMI for 

post-surgical patients is smaller than the 

CMI for non-surgical patients, and 

because even under the HH PPS the 

share of post-surgical patients is a 

minority of all patients. However, in 

actuality, as stated in section II.A.2.b of 

this proposed rule, the national CMI 

increased by 0.099 between the HH IPS 

sample and the 2003 HH PPS sample. 

Post-surgical patients’ CMI grew 

slightly faster than non-surgical 

patients’ CMI over this period. This may 

represent a change in the mix of postsurgical patients, or it may represent 

stronger effects of changed coding 

practices on post-surgical patients than 

on non-surgical patients. If we make the 

second assumption—that the growth 

rate of post-surgical patients’ case mix 

was the same as the growth rate of nonsurgical patients’ case mix—then the 

increase in the national CMI should 

have been marginally smaller than 0.099 

(smaller by about one-half of 1 percent). 

Because our second assumption caused 

a very small reduction in the CMI 

increase, we conclude that only a very 

small portion of the substantial growth 

in CMI might be attributable to having 

more severe surgical patients under HH 

PPS compared to HH IPS. 

We believe one possible contributing 

factor in the slightly faster growth in the 

CMI for surgical patients was 

uncertainty about how to assess the 

healing status of a surgical wound. As 

noted above, twice as many surgical 

wounds judged ‘‘most problematic’’

were assigned a status of ‘‘not healing’’

under the HH PPS than under the HH 

IPS. Fifty percent more surgical wounds 

were assigned a status of ‘‘early and 

partial granulation,’’ under the HH PPS. 

A recent clarification in the guidance for 

assessing healing status is significant, 

we believe, in understanding this 

change. In July 2006 the Wound Ostomy 

and Continence Nurses Society 

(WOCN), a national source of expertise 

in wound assessment, and one that CMS 

encouraged agencies to consult, issued a 

change in guidance on surgical wound 

assessment. Before that time, criteria for 

a status of ‘‘non-healing’’ in a wound 

closed by primary intention were the 

following: ‘‘incisional separation OR 

incisional necrosis OR signs or 

symptoms of infection OR no palpable 

healing ridge’’ (WOCN Society OASIS 

Guidance Document—Spring 2001). 

Criteria for a status of ‘‘fully 

granulating/healing’’ were: ‘‘incision 

well-approximated with complete 

epithelialization of incision; no signs or 

symptoms of infection; healing ridge 

well-defined.’’ The July 2006 revision 

removed all references to a ‘‘healing 

ridge’’ due to the lack of scientific 

evidence supporting its use as a sign of 

wound healing. Many surgical wounds 

will not exhibit a healing ridge, though 

the wound is actually healing. To the 

extent that assessing clinicians paid 

heightened attention to the nowoutdated WOCN guidance in adapting to 

the HH PPS, it is likely that they applied 

the pre-2006 criteria, with the result that 

the national OASIS rate for the healing 

status of surgical wounds indicated 

more wounds ‘‘not healing’’ or at a stage 

of ‘‘early and partial granulation.’’

In summary, based upon our above 

discussion of review of the data on 

OASIS items and our discussion of 

reasons for coding change, we conclude 

that growth in the national average CMI 

reflects, to a very large extent, coding 

practice changes against a background 

of new financial incentives. The impact 

of these forces is evidenced by mostly 

incremental changes in home health 

population rates of case-mix relevant 

items and not to actual changes in 

health status. Other than the increase in 

reported numbers of surgical wound 

patients, changes in numbers and 

characteristics of wound care patients 

documented on the OASIS were modest. 

While there was substantially more use 

of aggressive treatment plans involving 

at least 10 therapy visits, the pattern of 

decline in many ADL, IADL and other 

scale ratings is suggestive of added 

numbers of marginally limited patients, 

not severely limited patients. Moreover, 

scale ratings for ADL measures, an 

important part of the case-mix system, 

were likely affected by the manual 

changes noted above emphasizing that 

safety is a consideration in determining 

the rating. Lastly, we found that the 

higher rate of reported post-surgical 

patients does not contribute to CMI 

change. Accordingly, as noted 

previously, we are proposing to adjust 

the national standardized 60-day 

episode payment amount to reflect the 

nominal change in the CMI. 

4. Partial Episode Payment Adjustment 

(PEP Adjustment) Review 

In our July 3, 2000 final rule (65 FR 

41128), we described a PEP adjustment 

under the PPS. The PEP adjustment 

provides a simplified approach to the 

episode definition and accounts for key 

intervening events in a patient’s care 

defined as a beneficiary elected transfer, 

or a discharge and return to the same 

HHA that warrants a new start of care 

for payment purposes, OASIS, and 

physician certification of the new plan 

of care. When a new 60-day episode 

begins, the original national 

standardized 60-day episode payment 

rate is proportionally adjusted to reflect 

the length of time the beneficiary 

remained under the agency’s care before 

the intervening event. The proportional 

payment is the PEP adjustment. 

The PEP-adjusted episode is paid 

based on the span of days including 

start of care date or first billable service 

date through and including the last 

billable service date under the original 

plan of care before the intervening 

event. The PEP-adjusted payment is 

calculated by using the span of days 

(first billable service date through the 

last billable service date) under the 

original plan of care as a proportion of 

60. The proportion is then multiplied by 

the original case-mix and wage-adjusted 

national standardized 60-day episode 

payment rate. This method of proration 

in relation to the span of days between 

the first and last billable service date 

assumes that the rate of visits through 

time is constant during the episode 

period. 

Since the July 2000 final rule, we 

have received comments and 

correspondence pertaining to the PEP 

adjustment. These have guided our 

research efforts since the HH PPS has 

been in place. Through a contract with 

Abt Associates, descriptive analysis has 

been conducted on a large sample of 

claims linked to OASIS assessments 

from the first 3 years of the HH PPS in 

an effort to better understand the patient 

characteristics associated with PEPadjusted episodes and the 

circumstances under which PEPVerDate Aug<31>2005 21:12 May 03, 2007 Jkt 211001 PO 00000 Frm 00068 Fmt 4701 Sfmt 4702 E:\FR\FM\04MYP2.SGM 04MYP2

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adjusted episodes occur. Analysis of 

patient characteristics revealed no 

appreciable differences between 

patients in normal episodes and patients 

in PEP episodes with regard to 

conditions or clinical characteristics. 

(Normal episodes are defined as home 

health episodes of care that are not 

subject to any of the payment systems 

adjustments (for instance, LUPAs, PEPs, 

SCICs).) The mix of visits for PEP 

episodes is similar to that of normal 

episodes. 

Additionally, analysis of a 20 percent 

sample of 2003 episodes showed that 

approximately 3 percent of all episodes 

were PEP-adjusted. Of those, three types 

of PEP-adjusted episodes were 

identified: approximately 55 percent of 

PEP-adjusted episodes involved a 

discharge and return to the same HHA; 

about 42 percent involved transfers to 

other agencies; and approximately 3 

percent involved a move to managed 

care. Regarding the circumstances under 

which PEP-adjusted episodes occur, 

analysis showed the incidence of 

inpatient utilization during the 60 days 

following the first day of a PEP-adjusted 

episode was 14.5 percent which is lower 

than the incidence during normal 

episodes (21.4 percent). The lower 

incidence of hospitalizations for 

patients with PEP-adjusted episodes 

may indicate that these patients are in 

better health than the average home 

health patient. Along with the patient 

characteristics we examined, this seems 

to suggest that patients experiencing 

PEP episodes are not necessarily very 

different from the overall population of 

home health beneficiaries. 

As part of our research efforts, we also 

examined the different components that 

make up PEP episodes. Our analysis 

showed that PEP-adjusted episodes have 

significantly shorter service periods on 

average (approximately 23.4 days) than 

all episodes other than LUPAs and SCIC 

episodes (42.0 days). The average of 

23.4 days was calculated by dividing 

PEP episodes into their four 

components. The number of days 

between the start of the episode and the 

first billable visit averaged 0.2 days, or 

0.4 percent of a full 60-day episode. The 

paid days, or the days between the first 

billable and last billable visit days, 

averaged 23.4 days or 38.9 percent of a 

full 60-day episode. The number of days 

between last billable visit to the new 

episode from-date averaged 17.9 days, 

or 29.9 percent of a full 60-day episode. 

Finally, the number of days between the 

from-date of the new episode from-date 

to the first episode’s original day 60 

averaged 18.5 days or 30.8 percent of a 

full 60-day episode. Under the current 

system, payment for a PEP episode is 

adjusted to reflect the paid days only 

(23.4 days on average). 

We further examined the number of 

visits that occurred during PEP 

episodes. We found that an average of 

13.8 visits occur during PEP episodes. 

We recognize that this average 

represents 75 percent of the average 

number of visits for normal episodes, 

while the number of paid days 

represents less than 40 percent of the 

normal 60-day episode. Thus, the 

average proration fraction is about 40 

percent of the normal episode payment 

while the number of visits is 

approximately 75 percent of the number 

delivered during the average normal 

episode. Additionally, the average 

number of minutes per visit during a 

PEP episode is slightly longer than that 

of a normal episode for most types of 

visits. Both results provide evidence 

that there is some front-loading of visits 

compared to normal episodes, causing 

PEP episodes to have a faster average 

rate of visits during the span of days 

used to prorate the episode payment. 

Because the PEP adjustment proration 

methodology does not take visit 

occurrence into account, commenters 

have argued that, PEP episodes appear 

to be systematically ‘‘underpaid’’. 

As we described in the July 3, 2000 

final rule, the decision to use the span 

of billable visit dates was made because 

of the HHA’s involvement in decisions 

influencing the intervening events for a 

beneficiary who elected transfer or 

discharge and returned to the same 

HHA during the same 60-day episode 

period. Agencies have some flexibility 

in discharge decisions that affect the 

likelihood of incurring a partial episode, 

whether or not a hospital stay 

intervenes. They also have indirect 

influence on a beneficiary’s decision to 

transfer to another home care provider 

through the quality of care they provide. 

Current data suggest that PEP episodes 

are rare and, therefore, the current PEP 

policy may be serving as a deterrent to 

premature discharge. We believe that 

the PEP adjustment is provided in a 

manner that maintains the opportunity 

for Medicare patients to choose the 

provider with which they feel most 

comfortable. Therefore, we are 

proposing that the current system of 

proportional payments based on billable 

visit dates continue to be the payment 

methodology for PEP episodes. It should 

also be noted that in many cases, an 

HHA receives payment for an additional 

full episode which it might not have 

received had the first episode not been 

subject to a PEP adjustment. We will 

continue to research the nature of HHA 

resource use during and following PEP 

episodes, as well as explore alternative 

methodologies for payment adjustment. 

At this time, our analysis of PEP 

episodes does not suggest a more 

appropriate alternative payment policy. 

We believe that many alternative 

proration rules that we could devise 

would likely introduce adverse 

incentives into the HH PPS. For 

example, a proposal to pay PEP 

episodes amounts proportional to the 

average visit accrual rate we observe for 

PEP episodes would provide agencies 

with a financial incentive to reduce 

visits in the first few weeks of the 

episode and/or to time the date 

discharge in relation to the new, 

prorated schedule of payments. For 

many types of patients, such a delivery 

pattern would likely worsen patient 

outcomes. We would like to solicit 

suggestions and comments from the 

public on this matter to guide our 

continued efforts to improve the PEP 

adjustment policy. 

5. Low-Utilization Payment Adjustment 

(LUPA) Review 

In our July 3, 2000 final rule (65 FR 

4117), we described a low-utilization 

payment to be implemented under the 

HH PPS. The LUPA was established to 

reduce the national standardized 60-day 

episode payment rate regardless if the 

episode is adjusted as a PEP adjustment 

or SCIC adjustment when minimal 

services are provided during a 60-day 

episode. LUPAs are episodes with four 

or fewer visits. Payments under a LUPA 

episode are made on a per-visit basis by 

discipline. For the July 2000 final rule, 

the per-visit rates were determined from 

the audited cost report sample we used 

to design the HH PPS. (The same rates 

were used in calculating the standard 

episode amount.) 

The per-visit amounts include 

payment for (1) Non-routine medical 

supplies (NRS) paid under a home 

health plan of care, (2) NRS possibly 

unbundled to Part B, and (3) a per-visit 

ongoing OASIS reporting adjustment as 

discussed in the July 3, 2000 final rule 

(65 FR 41180). The LUPA payment rates 

are not case-mix adjusted. As discussed 

in the July 3, 2000 HH PPS final rule, 

a standardization factor used to adjust 

the LUPAs was calculated using 

national claims data for episodes 

containing four or fewer visits. This 

standardization factor includes 

adjustments only for the wage index. 

The per-visit rates originally listed in 

the July 2000 rule have been updated in 

the same manner as the standard 

episode amount. Additionally, the 

payments are adjusted by the wage 

index in the same manner as the 

standard episode amount. 


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As part of our ongoing research of the 

HH PPS and to analyze the general 

appropriateness of an adjustment for 

low-utilization episodes, Abt Associates 

analyzed a 20 percent sample of home 

health episodes covering more than 

three years of experience under the HH 

PPS. The analysis file was the Fu 

Associates analytical file linking OASIS 

with home health claims. This allowed 

the grouping of LUPAs into categories 

for analysis of patient characteristics. 

There were approximately 179,845 

LUPA episodes in this file, accounting 

for approximately 13 percent of 

episodes. 

The analysis revealed minor 

differences between patients in LUPA 

episodes and patients in normal 

episodes. Although, overall, patients in 

LUPA episodes on average had 

somewhat lower clinical and functional 

severity, a substantial number of 

patients were in high severity groups. 

LUPA episodes were also just as likely 

as normal episodes to include a hospital 

stay during the 60-day episode. We 

believe that some LUPAs result from the 

hospitalization of the patient before a 

significant number of visits have been 

delivered. 

One indication from these data is that 

LUPAs are serving as a low-end outlier 

payment for certain episodes that incur 

unexpectedly low costs. Other LUPAs 

result from expected care patterns for 

patients with conditions such as 

neurogenic bladder and pernicious 

anemia. The incidence of LUPAs has 

changed little since the HH PPS began, 

which suggests that LUPA episodes are 

not excessively vulnerable to incentives 

to manipulate care plans for payment 

purposes. However, we continue to 

believe that the distinction between 

LUPAs and full episodes requires 

sustained monitoring through medical 

review and other activities. Further, we 

are aware of the potential for 

inappropriate admissions into LUPA 

episodes among patients with 

questionable medical necessity for home 

health care. 

Since the HH PPS went into effect, we 

have received comments and 

correspondence pertaining to the LUPA 

policy. In particular, these have focused 

on the suggestion that LUPA payment 

rates do not adequately account for the 

front-loading of costs in an episode. 

Further, commenters suggested that 

because of the small number of visits in 

a LUPA episode, HHAs have little 

opportunity to spread the costs of 

lengthy initial visits over a full episode. 

CMS has also received comments 

regarding the appropriateness of the 4- 

visit threshold for LUPAs. CMS is not 

proposing to modify the 4-visit 

threshold for LUPA episodes in this 

proposed rule. We did look at, and 

consider, the 4-visit threshold and 

possible alternatives to that threshold in 

our analysis of LUPA episodes. 

Increasing the 4-visit threshold to some 

number greater than 4 would result in 

a HH PPS in which we have an even 

greater percentage of LUPA, which are 

per-visit reimbursed episodes and could 

be interpreted as a move closer toward 

a per-visit payment system. This is not 

the direction we want to go with a 

bundled prospective payment system as 

is the HH PPS. Conversely, decreasing 

the 4-visit threshold to some number 

less than 4 would result in an 

overpayment of episodes, in that 

episodes with 4 visits would then 

receive a full episode payment. As a 

result, we have concentrated our efforts 

to address the payment of certain types 

of LUPA episodes, in particular, LUPA 

episodes occurring as the only episode 

and circumstances where a LUPA 

episode is the initial episode in a 

sequence of adjacent episodes. 

To examine this assertion, Abt 

Associates conducted a descriptive 

analysis of LUPA episodes. Of particular 

interest are the findings pertaining to 

the average visit length of LUPAs 

occurring in the initial episode of a 

sequence of adjacent episodes or 

occurring as the only episode 

(constituting approximately 59 percent 

of all LUPA episodes). An examination 

of visit log data predating the HH PPS, 

used for the original Abt case-mix study 

(July 2000 Final Rule), revealed that the 

average visit length for nursing for an 

initial assessment is, on average, twice 

as long as the length for other nursing 

visits. Likewise, an initial assessment 

visit made by a physical therapist 

averaged 25 percent more than other 

physical therapy visits. These estimates 

paralleled findings from a 2001 

Government Accountability Office 

(GAO) study that reported that the 

OASIS added an average of 40 minutes 

to a typical start of care visit. We found 

that the average visit lengths in initial 

and only episode LUPAs are 16 to 18 

percent higher than the average visit 

length in initial non-LUPA episodes. In 

comparison, the average visit length for 

LUPA episodes that occurred between 

initial and ending episodes in a 

sequence of adjacent episodes 

(approximately 24 percent of all LUPAs) 

or at the end of a sequence of adjacent 

episodes (approximately 17 percent of 

all LUPAs) is less than or about equal 

to average visit lengths for 

corresponding non-LUPA episodes. 

The results of this data analysis 

suggest that initial and only episode 

LUPAs require longer visits, on average, 

than non-LUPA episodes, and that the 

longer average visit length is due to the 

start of care visit, when the case is 

opened and the initial assessment takes 

place. We agree with commenters to the 

extent that these analyses of initial and 

only episode LUPA episodes indicate 

that payments for such episodes may 

not offset the full cost of initial visits. 

This is likely due to the fact that the 

LUPA per-visit payment rates were 

originally set based on the costs of an 

average visit, not the costs of the subset 

of visits incurred by patients receiving 

four or fewer visits during an initial or 

only episode LUPA; for these patients, 

a large share of total visits comprises 

initial visits. However, the comparisons 

of average minutes per visit for LUPA 

episodes occurring within or at the end 

of a sequence of episodes do not support 

a proposal for payment increases for 

those types of LUPAs. 

Based upon our initial review that 

initial or only episode LUPAs may not 

reflect the full costs incurred for the 

visits delivered, we then conducted 

further analysis to determine an 

appropriate payment increase for initial 

or only episode LUPAs. Analyzing a 10 

percent sample of 2003 episodes, we 

found that 75 percent of LUPA episodes 

involved nursing without physical 

therapy while 15 percent of LUPAs 

involved physical therapy without 

skilled nursing. Almost all of the 

remaining 10 percent of episodes 

involved a mix of physical therapy and 

skilled nursing. Although the discipline 

that delivered the initial visit may not 

be identified in the sample file, for 

deriving payment rates based upon our 

analysis noted above, we have assumed 

the share of initial assessment visits 

from skilled nursing is 80 percent and 

the share of initial assessment visits 

from physical therapy is 20 percent. We 

then used these percentages to calculate 

the estimated value of 40 minutes added 

to the initial visit for start of care 

episodes. We relied upon the GAO 

report noted above, as the basis for the 

estimate of 40 minutes. For this 

calculation, we multiplied the current 

per-visit rate by the percentage increase 

in the average visit length. The average 

visit length was calculated from all nonLUPA episodes in the Abt sample file. 

Specifically, we multiplied, for the 

value of extra skilled nursing visits, the 

LUPA base rate of $105.07 for skilled 

nursing (trended forward from the 

original rate of $98.85) by the 

percentage over average skilled nursing 

visit length (0.860215) and by the share 

of initial assessment visits from skilled 

nursing (0.80). The product was $72.31. 

Next, we multiplied, for the value of 


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extra physical therapy minutes, the 

LUPA base rate of $114.89 for physical 

therapy (trended forward to CY 2008 

from the original rate of $108.08) by the 

percentage over average physical 

therapy visit length (0.858369) and by 

the share of initial assessment visits 

from physical therapy (0.20). The 

product was $19.72. Finally, we 

summed these weighted values to 

calculate a total average value of $92.03 

($72.31 + $19.72 = $92.03). 

In the July 3, 2000, HH PPS final rule 

(65 FR 41187), we adjusted the per-visit 

rate by 1.05 to account for outlier 

payments. Therefore, we are proposing 

to multiply the $92.03 by 1.05 and then 

reduce this amount to account for the 

estimated percentage of outlier 

payments as a result of the current FDL 

ratio of 0.67 (see section II.A.8. of this 

proposed regulation), resulting in an 

amount of $92.63. 

Given the findings from the 

descriptive analysis of LUPA episodes 

and total average value of excess visit 

length for initial visits in certain LUPA 

episodes, we propose an increase of 

$92.63 for LUPA episodes that occur as 

the only episode or the initial episode 

during a sequence of adjacent episodes. 

Again, as defined in section II.A.2 of 

this proposed rule, a sequence of 

adjacent episodes is defined as a series 

of claims with no more than 60 days 

between the end of one episode and the 

beginning of the next episode (except 

for episodes that have been PEPadjusted). In § 484.230, we are 

proposing to add a third, fourth, and 

fifth sentence after the second sentence 

to define the term ‘‘sequence of adjacent 

episodes’’ for the purpose of identifying 

situations where the LUPA is the 

beneficiary’s only episode or the initial 

episode in a sequence of adjacent 

episodes. We propose to pay an 

additional low-utilization payment 

adjustment LUPA episodes which are 

either the only episode or the initial 

episode in a sequence of adjacent 

episodes, and note the additional 

payment for such LUPA episodes will 

be updated annually by the home health 

market basket percentage increase. As 

with the other components of the LUPA 

methodology, this increase for situations 

where a LUPA is the only episode or the 

initial episode in a sequence of adjacent 

episodes will be wage-adjusted. We 

believe this increase allows HHAs fair 

compensation for the cost of lengthier 

start of care visits in LUPA episodes. To 

maintain budget neutrality, we further 

propose that the national standardized 

60-day episode payment rate be 

reduced. We determined the budget 

neutral national standardized 60-day 

episode payment rate that compensates 

for the extra payment of $92.63, as well 

as for other proposed changes in this 

proposed rule, from simulating the new 

payment system on our 2003 claims 

sample. The results are shown in the 

section II. D. 

We are soliciting comments on our 

methodology for arriving at an 

adjustment to achieve fair compensation 

for the cost of lengthier start of care 

visits in LUPA episodes. An alternative 

methodology is basing the estimated 

additional time on claims-based reports 

of lengths of the first visit in initial and 

only episode LUPAs. We expect to test 

the adequacy of such an alternative 

methodology using a large, 

representative CY 2005 claims sample 

that would be available before the final 

rule. We are specifically soliciting 

comments on alternative methodologies. 

6. Significant Change in Condition 

(SCIC) Review 

The SCIC adjustment occurs when a 

beneficiary experiences a SCIC during 

the 60-day episode that was not 

envisioned in the original plan of care. 

In our final rule published July 3, 2000 

in the Federal Register (65 FR 41128), 

we established the SCIC adjustment to 

be the proportional payment adjustment 

reflecting the time both before and after 

the patient experienced a SCIC during 

the 60-day episode. In order to receive 

a new case-mix assignment for purposes 

of SCIC payment during the 60-day 

episode, the HHA must complete an 

OASIS and obtain the necessary 

physician orders reflecting the 

significant change in treatment in the 

patient’s plan of care. 

Currently, the SCIC adjustment is 

calculated in two parts. The first part of 

the SCIC adjustment reflects the 

adjustment to the level of payment 

before the significant change in the 

patient’s condition during the 60-day 

episode. The second part of the SCIC 

adjustment reflects the adjustment to 

the level of payment after the significant 

change in the patient’s condition occurs 

during the 60-day episode. 

The first part of the SCIC adjustment 

is determined by taking the span of days 

(first billable service date through the 

last billable service date) before the 

patient’s SCIC as a proportion of 60 

multiplied by the original episode 

payment amount. The original episode 

payment level is proportionally adjusted 

using the span of time the patient was 

under the care of the HHA before the 

SCIC that required an OASIS, physician 

orders indicating the need for a change 

in the treatment plan, and the new casemix assignment for the remainder of the 

60-day episode. 

The second part of the SCIC 

adjustment reflects the time the patient 

is under the care of the HHA after the 

patient experienced a SCIC during the 

60-day episode that required the new 

case-mix assignment. The second part of 

the SCIC adjustment is a proportional 

payment adjustment reflecting the time 

the patient will be under the care of the 

HHA after the SCIC and continuing 

until another significant change or until 

the end of the 60-day episode. Once the 

HHA completes the OASIS, determines 

the new case-mix assignment, and 

obtains the necessary physician change 

orders reflecting the need for a new 

course of treatment, the second part of 

the SCIC adjustment begins. The second 

part of the SCIC adjustment is 

determined by taking the span of days 

(first billable service date through the 

last billable service date) after the 

patient experiences the SCIC through 

the balance of the 60-day episode (or 

until the next significant change, if any) 

as a proportion of 60 multiplied by the 

new episode payment level resulting 

from the significant change. 

Since we proposed the SCIC 

adjustment in October 1999 (64 FR 

58134), we have received comments and 

correspondence regarding the 

appropriateness and the complexity of 

the SCIC adjustment methodology. 

These suggestions expressed concerns 

that SCIC adjustments may be difficult 

to apply appropriately. Additionally, 

analysis of HHA margins using a sample 

of approximately 2,500 cost reports 

suggested that SCIC episodes did not 

necessarily account for the cost 

associated with a patient in a SCIC 

episode. These concerns guided our 

descriptive analysis of SCIC episodes 

and our investigation of possible 

alternatives to SCIC adjustment. 

The SCIC policy was designed and 

implemented primarily to protect HHAs 

from receiving a lower, inadequate 

payment for a patient that unexpectedly 

got worse and became more expensive 

to the agency during the course of a 60- 

day episode. While it is also possible 

that a patient could become 

unexpectedly better, resulting in a 

patient needing far fewer resources and 

costing the agency less, such instances 

were expected to be few. For patients 

who experienced an unexpected adverse 

significant change in condition, but the 

agency would actually receive lower 

payments when applying the 

computation for deriving a SCIC 

payment, agencies were instructed that 

they did not have to report a SCIC. 

Abt Associates, under contract to 

CMS to conduct analysis and simulation 

of refinements to HH PPS, first 

conducted several descriptive analyses 


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examining the payment accuracy for 

SCIC-adjusted episodes. As with the 

LUPA, they used the Fu Associates’

large analytic file consisting of home 

health claims linked to OASIS. Analyses 

included examination of trends in rates 

and other utilization statistics relating to 

SCIC episodes, OASIS characteristics for 

SCIC episodes, and estimation of 

margins for SCIC episodes. 

Results of the analyses indicated that 

SCIC episodes have been declining 

since HH PPS began. Approximately 3.7 

percent of episodes were reported as 

SCIC episodes in the first quarter of the 

HH PPS (October 1, 2000, to December 

31, 2000); they decreased to 2.1 percent 

of episodes by the first quarter of CY 

2004. SCIC episodes tended to be longer 

than the average episode (excluding 

LUPAs), and were more likely to occur 

in facility-based agencies and rural 

agencies. There was some evidence that 

the percentage of episodes in the highest 

category of the services utilization 

dimension of the case-mix system 

increased for SCIC episodes over time. 

SCIC episodes had a higher likelihood 

of using at least 10 therapy visits, and 

this excess grew over time. Overall, 

patients experiencing SCIC episodes 

differed little in terms of case-mix 

characteristics from the average home 

health patient, except for a higher 

incidence of dyspnea, ADL limitations, 

and those recently discharged from 

acute care. 

The margin analysis suggested that, 

on average, SCIC episodes had negative 

margins, even though the SCIC payment 

policy allows agencies to avoid 

declaring a SCIC if an episode that 

experiences an adverse significant 

change in condition would be paid less 

than the original case-mix adjusted 

payment. One reason for the negative 

margin estimate appears to be that in 

some cases agencies inappropriately 

applied the SCIC adjustment for patients 

experiencing a significant adverse 

change, when in doing so the agency 

actually received lower payments for 

those patients. Also, the proportional 

payment policy, which reduces 

payment in proportion to the number of 

days between the last visit before the 

significant change in condition and the 

first visit following the significant 

change, results in increasingly lower 

payments as the number of days 

between the last and next visit 

increases. In contrast, a normal episode 

payment is not affected by periods when 

visits do not occur. 

As noted above, we believe that HHAs 

have had difficulty in interpreting when 

to apply the SCIC adjustment policy. 

Agencies also reported additional 

administrative burdens from adhering to 

the policy. Furthermore, there has been 

a 2 percent decline in use of the SCIC 

adjustments since the implementation 

of the HH PPS. We have received 

comments that stated eliminating the 

SCIC policy altogether might be better 

than having a SCIC policy that is 

difficult to understand and adhere to. 

Given these concerns, we decided to 

focus our analysis on simulating the 

impact of eliminating the SCIC 

adjustment policy. We performed this 

simulation by repricing SCIC claims to 

use the first HHRG during the episode 

for determining the payment, and 

eliminating any proration. We then 

compared the total expenditures before 

and after making this change. 

The results of eliminating the SCIC 

policy suggested little impact on 

outlays—an increase of 0.5 percent of 

total payments. The difference in total 

payments was less than one-half of one 

percent for all categories of agencies 

(urban versus rural, by size, and 

ownership). 

Based on these findings, we are 

proposing to eliminate the SCIC 

adjustment from the HH PPS. 

Specifically, we are proposing in 

§ 484.205 to remove paragraph (e) 

concerning the SCIC adjustment policy 

from the HHA PPS. We are also 

proposing to redesignate paragraph (f) as 

paragraph (e). In addition, we are 

proposing to amend our regulations at 

§ 484.205 by removing paragraph (a)(3) 

and redesignating paragraph (a)(4) as 

paragraph (a)(3). Furthermore, we 

proposing to revise paragraph (b) 

introductory text to read as follows: ‘‘(b) 

Episode payment. The national 

prospective 60-day episode payment 

represents payment in full for all costs 

associated with furnishing home health 

services previously paid on a reasonable 

cost basis (except the osteoporosis drug 

listed in section 1861(m) of the Act as 

defined in section 1861(kk) of the Act) 

as of August 5, 1997 unless the national 

60-day episode payment is subject to a 

low-utilization payment adjustment set 

forth in § 484.230, a partial episode 

payment adjustment set forth at 

§ 484.235, or an additional outlier 

payment set forth in § 484.240. All 

payments under this system may be 

subject to a medical review adjustment 

reflecting beneficiary eligibility, medical 

necessity determinations, and HHRG 

assignment. DME provided as a home 

health service as defined in section 

1861(m) of the Act continues to be paid 

the fee schedule amount.’’ We are also 

proposing to remove § 484.237 relating 

to the methodology used for the 

calculation of the significant change in 

condition payment adjustment. 

Episodes that are currently SCIC 

adjusted would be treated as normal 

episodes and will receive payment for 

the entire 60-day period based on the 

initial, and only, HHRG code. The 

national standardized 60-day episode 

payment rate in section II.A.2.c of the 

proposed rule takes into account this 

proposed change in SCIC policy and is, 

therefore, slightly lower than it would 

have been without proposing this 

change. We believe the elimination of 

the SCIC adjustment policy would have 

a minor impact on home health agency 

operations and revenues, because SCIC 

episodes are very infrequent. Our 

estimate of the cost of eliminating the 

SCIC policy, implemented in a budget 

neutral manner as a reduction to the 

national standardized 60-day payment 

rate, is presented in section II.D and 

reported in the accompanying table 

(Table 23b). The estimated reduction is 

$15.71. We discussed this proposal at a 

meeting with the contractor’s TEP in 

March 2006. We received favorable 

feedback noting that our proposal would 

be an appropriate simplification of the 

HH PPS. 

7. Non-Routine Medical Supply (NRS) 

Amounts Review 

As described in the HH PPS final rule 

published in the Federal Register (65 

FR 41180) and modified in the June 1, 

2001, correction notice (66 FR 32777), 

the NRS amounts included in the perepisode payment and initially paid on a 

reasonable cost basis under a home 

health plan of care, were calculated by 

summing the NRS costs using audited 

cost reports from 1997. The NRS costs 

for all the providers in that audited cost 

report sample were then weighted to 

represent the national population and 

updated to FY 2001. That weighted total 

was divided by the number of episodes 

for the providers in the audited cost 

report sample, to obtain the average cost 

per episode of NRS reported as costs on 

the cost report. This amount was $43.54. 

The possible unbundled NRS, billed 

under Medicare Part B and not reflected 

in on the home health cost report, were 

also included in the HH PPS national 

standardized 60-day episode payment 

rate by summing the allowed charges for 

176 Healthcare Common Procedure 

Coding System (HCPCS) codes, 

reflecting NRS codes, in CY 1998 for 

beneficiaries under a home health plan 

of care. That total was divided by the 

total number of episodes in CY 1998 

from the episode database, to obtain the 

average cost of unbundled NRS per 

episode. This amount was $6.08. 

The total of the two amounts $43.54 

and $6.08, or $49.62, was added to the 

national total prospective payment 


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amount per 60-day episode for CY 2001 

(before standardization). The 

standardized amount has been 

subsequently updated annually. 

Since the proposal and adoption of 

this methodology for payment of NRS, 

we have received comments expressing 

concern about the cost of supplies for 

certain patients with ‘‘high’’ supply 

costs. In particular, commenters were 

concerned about the adequacy of 

payment for some patients with 

pressure ulcers, stasis ulcers, other 

ulcers, wounds, burns or trauma, 

cellulitis, and skin cancers. 

In general, NRS use is unevenly 

distributed across episodes of care in 

home health. While most patients do 

not use NRS, many use a small amount, 

and a small number of patients use a 

large amount of NRS. The payment for 

NRS included in the HH PPS 

standardized payment rate does not 

reflect this distributional variation. 

Furthermore, the current case-mix 

adjustment of the standardized amount, 

which effectively adjusts the NRS 

payment we originally included, may 

not be the most appropriate way to 

account for NRS costs. 

In order to investigate the 

performance of the payment 

methodology for NRS and to explore an 

approach to case-mix adjustment of the 

NRS component of the payment, our 

contractor, Abt Associates, performed 

several analyses of the current system. 

The analysis file was constructed by Abt 

Associates from a sample of 2001 cost 

reports, which were needed to 

determine cost-to-charge ratios. The cost 

reports were then linked to claims. The 

claims came from an analytic file 

constructed by Fu Associates that links 

home health claims and OASIS. 

The cost report sample was analyzed 

to detect or correct extremely 

implausible cost data (that is, if cost 

report erroneously inverted ratio of 

costs to charges, this was corrected). 

Many cost reports were dropped after 

this initial analysis because the cost-tocharge ratio for nonroutine medical 

supplies was zero. Then, we retrieved 

Medicare claims for patients admitted to 

the agencies with remaining cost 

reports, in order to ensure that the cost 

report totals for non-routine supplies 

were consistent with total charges for 

non-routine supplies that we obtained 

from the provider’s claims. Additional 

cost reports were dropped from the 

sample at this step. At the end of this 

process, from an initial sample of 2,864 

cost reports, 1,207 cost reports were 

considered usable. 

The cost report data were then merged 

with a random sample of data from 

496,237 ‘‘normal’’ home health episodes 

from the same set of agencies used in 

the sample data. Normal episodes were 

defined as episodes that did not include 

additional adjustments such as LUPAs 

or PEP adjustments. ‘‘Cost-to-charge’’

ratios generated from the cost reports 

were used to estimate NRS costs for the 

episodes in the sample. 

The exploration of case-mix 

adjustment for NRS costs was 

conducted in a manner similar to the 

way Abt Associates developed the 

initial case-mix model. We created 

regression equations that used OASIS 

measures to predict episode-level NRS 

costs. One equation used the current 

case-mix variables. This equation 

explained approximately 10 percent of 

the variation in NRS costs in this data 

sample. This provided a baseline against 

which to judge the performance of set 

variables that differ from the set used in 

the current HH PPS case-mix system. 

Models were developed after creating 

additional variables from OASIS items 

and targeting certain conditions 

expected to be predictors of NRS use 

based on clinical considerations. Many 

of these conditions were skin-related. 

The end result of the model 

exploration process was two versions of 

the ‘‘best-fitting’’ variable set. This best 

fitting variable set consisted of more 

than two dozen indicators for diagnoses, 

wound conditions, and certain 

prosthetics captured on the OASIS. The 

variables could be used as the basis for 

improved prediction of NRS costs. 

These variables represent measurable 

conditions that have been the subject of 

extensive education by CMS in its 

administration of the OASIS system, 

and by others such as the ICD–9–CM 

coding committee with its interest in 

coding accuracy. Therefore, we believe 

this variable set would be the basis for 

a methodology to account for NRS costs 

that is feasible to administer and does 

not create significant new payment 

concerns. 

The first alternative model using the 

best-fitting variables divided episodes 

into two episode groups, with one group 

containing first and second episodes 

(early), and the second containing third 

and later episodes (later). The second 

alternative model does not distinguish 

between early and later episodes. These 

‘‘best fit’’ models were then used to 

construct a scoring system. Each 

condition in the best-fit models was 

assigned one point for each $5 

increment in NRS cost as determined 

from the model results. For example, if 

a variable representing a clinical 

condition predicted a $50 increase in 

cost, an episode with that variable 

would be given 10 points. We summed 

the condition-specific scores for each 

episode. We then placed those sums 

into five severity groups. For the model 

that separated early from later episodes 

we defined 10 severity groups, five for 

early episodes and 5 for later episodes. 

This system explained about 13.7 

percent of NRS cost variation in the 

sample. The model that pooled all 

episodes had 5 severity groups and 

explained 13.0 percent of the variation 

in NRS costs. 

We note, because there is a limited 

performance advantage of the twoepisode group model over the single 

model, we are proposing to use the 

simpler model that pays all episodes, 

whether early or later episodes, using 

the same set of severity groups. Table 11 

shows the relative weights and payment 

weights for the five severity levels in the 

proposed NRS model, and Table 12a 

sets forth the NRS scores for the fivegroup model. We will continue to 

evaluate the ICD–9–CM codes listed for 

each group (Table 12b) to ensure as 

much as possible that condition-related 

scores are based on ICD–9–CM codes 

that are specific, unambiguous, and use 

diagnostic criteria widely accepted 

within the medical community. In 

addition to refining the list of 

conditions contained within each 

diagnostic group (Table 12b), we intend 

to continue to study ways of improving 

the statistical performance of all the 

variables represented in Table 12a. We 

solicit public comment to help inform 

our efforts. We also intend to update the 

data base upon which our payment 

proposal for NRS is based. Our ability 

to update the data files will depend on 

the quality of data available in claims 

and cost reports for succeeding years. If 

the data are not found to be sufficiently 

complete and accurate, we would use 

the existing data for any final revisions 

that result from further analysis and 

public comments. 

In addition to computing the R-square 

statistic as a summary of the system’s 

performance, we examined the 

improvements in payment accuracy for 

NRS costs per episode, according to 

selected characteristics of the episode. 

The magnitude of change is difficult to 

report with a high degree of certainty 

because of the limited data resources 

available for these analyses. 

We found that under our proposal 

NRS payments for episodes reporting no 

NRS charges on the episode claim 

would better reflect the absence of NRS 

costs incurred in such an episode, by 

having their payment for NRS reduced. 

For the remaining claims—those 

reporting any amount of NRS costs—on 

average we estimate that NRS payments 

would come significantly closer to their 

estimated NRS costs under the proposed 


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new system of accounting for NRS. For 

the subgroups of episodes with the 

OASIS conditions listed in Table 11, 

under our proposal, the difference 

between the estimate of average NRS 

costs incurred and the proposed amount 

to account for those NRS costs would 

decrease in a similar manner, with some 

differences becoming even smaller. 

However, our ability to predict NRS 

costs remains limited. We have not yet 

developed a statistical model that has 

performed with a high degree of 

predictive accuracy. Some of the 

reasons for this result include the 

limited data available to model NRS 

costs, and the likelihood that OASIS 

does not have any measures available 

for some kinds of NRS. Nevertheless, we 

are proposing to change the payment 

system because the majority of episodes 

do not incur any NRS costs, and the 

current payment system 

overcompensates these episodes. 

Further, we believe the proposed 

approach is appropriate to the extent 

that we have developed a way to 

account for NRS costs that is based on 

measurable conditions, is feasible to 

administer, and offers HHAs some 

protection against episodes with 

extremely high NRS costs. As we noted 

earlier in this section, we will continue 

to look into ways to improve the 

predictive model we are proposing to 

account for NRS costs. We solicit 

suggestions and comments from the 

public on this matter. 

In the course of conducting the NRS 

analysis, we discovered a possible 

source of error in reporting on claims. 

Data analysis suggested that enteral 

nutrition patients were incurring higher 

NRS costs than average and, in our 

model, could be assigned a moderate 

score for NRS cost. However, we did not 

find evidence from our analyses that 

any category of NRS other than enteral 

supplies would systematically account 

for the NRS finding in the model for 

enteral nutrition patients. These 

patients often have a very compromised 

health status, including skin and other 

conditions that are already accounted 

for in our model. Further, we explored 

other possibilities to determine if 

information was missing from the 

model. If available, such information 

could be added to the model to explain 

the scores we found for the enteral 

nutrition variable. However, we did not 

gather any information that produced 

any additional hypotheses. An 

important remaining hypothesis is that 

some providers are reporting enteral 

supplies charges for these patients in 

error; in fact, at least one large provider 

has indicated this was the case. We are 

proposing to exclude the enteral 

nutrition variable from the model to 

ensure compliance with the statute and 

regulations governing enteral nutrition, 

as noted below; but, we welcome 

comments on this issue. 

As we stated in the final HH PPS rule 

dated July 3, 2000 (65 FR 41139), ‘‘Part 

B services such as parenteral or enteral 

nutrition are neither currently covered 

as home health services nor defined as 

non-routine medical supplies. 

Parenteral or enteral nutrition would 

therefore not be subject to the 

requirements governing home health 

consolidated billing.’’

If the patient requires medical 

supplies that are currently covered and 

paid for under the Medicare home 

health benefit during a certified episode 

under HH PPS, the billing for those 

medical supplies falls under the 

auspices of the HHA due to the 

consolidated billing requirements. As 

parenteral and enteral nutrition are not 

covered or paid for under the Medicare 

home health benefit, they should be 

billed separately by the supplier or 

provider. Because we assumed that 

some providers are reporting these 

supplies in error, we believe it is 

important to again note the Medicare 

coverage requirements for parenteral 

and enteral nutrition to prevent any 

potential future reporting errors. 

Medicare’s coverage guidelines for 

enteral nutrition state: ‘‘Coverage of 

nutritional therapy as a Part B benefit is 

provided under the prosthetic device 

benefit provision which requires that 

the patient must have a permanently 

inoperative internal body organ or 

function thereof. Therefore, enteral and 

parenteral nutritional therapy is not 

covered under Part B in situations 

involving temporary impairments.’’ The 

National Coverage Decision (NCD) 

provides guidance in applying the 

definition of temporary impairment: 

‘‘Coverage of such therapy, however, 

does not require a medical judgment 

that the impairment giving rise to the 

therapy will persist throughout the 

patient’s remaining years. If the medical 

record, including the judgment of the 

attending physician, indicates that the 

impairment will be of long and 

indefinite duration, the test of 

permanence is considered met.’’ (See 

Medicare National Coverage 

Determinations [NCD] Manual, Pub. 

100–03, Section 180.2, Chapter 1 (Part 

3). Section 1842(s) of the Act 

implements the fee schedule for 

parenteral and enteral nutrition (PEN) 

nutrients, equipment and supplies. The 

general payment rules for PEN effective 

on or after January 1, 2002, are 

stipulated in § 414.102 and § 414.104. 

The following is the list of HCPCS 

codes which may be used to claim 

reimbursement for enteral nutrition. 

Providers may claim reimbursement for 

it on the UB–92 claim form if they 

report the appropriate HCPCS code and 

revenue center code. Payment is made 

by the RHHI under the Medicare Fee 

Schedule. 

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Notwithstanding our proposal to 

exclude enteral nutrition from the list of 

conditions included as NRS, we now 

describe our proposed revision to the 

payment methodology to account for 

NRS costs. We propose to account for 


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NRS costs based on five severity groups 

and a national conversion factor. Table 

12a shows the condition-specific scores 

derived from the NRS model. Table 12b 

shows the ICD–9–CM diagnosis codes 

used to define conditions that are based 

on diagnosis codes. The sum of scores 

for each episode is then used to group 

episodes into one of five severity 

groups, as follows: Group 0 if the sum 

is zero; group 1 for 1 to 16; group 2 for 

17 to 34; group 3 for 35 to 59; and group 

4 for 60 or more. We defined these five 

scoring levels from examining the 

distribution of scores in our analysis 

sample. Most of the episodes (64 

percent, see Table 11) fell into the group 

with a score of zero (that is, no 

conditions listed in Table 12b were 

reported on the OASIS assessment). For 

purposes of payment, relative weights 

were calculated for each severity group 

based on the estimated average NRS 

cost, divided by the overall average in 

the sample. The relative weights are 

listed below in Table 11. 

To derive payment, each relative 

weight is multiplied by the conversion 

factor. We calculated the conversion 

factor by inflating the original allowance 

included in the episode base rate 

($49.62) by the total percentage increase 

since October 2000 using the statutory 

market basket updates. We take the 

inflated conversion factor of $53.91 and 

multiply it by 1.05 to account for the 

initial outlier payment noted in the July 

3, 2000 final rule (65 FR 41187). We 

then take that product and multiply it 

by 0.958614805 to account for the 

estimated percentage of outlier 

payments as a result of the current FDL 

ratio of 0.67. To further adjust for the 

nominal change in case-mix, we 

multiply the $54.26 by 0.9725 for a 

proposed NRS conversion factor of 

$52.77. Because the market for most 

NRS is national, we do not propose to 

have a geographic adjustment to the 

conversion factor. We plan to continue 

to monitor NRS costs to determine if 

any adjustment for the NRS weights is 

warranted in the future. 

We determined the budget-neutral 

national standardized 60-day episode 

payment rate that compensates for the 

payments for NRS under the proposed 

new case-mix-adjusted HH PPS as part 

of the simulation of all proposed 

changes on our 2003 claims sample. The 

results are shown in section II.D. 

For an example of calculating an HH 

PPS payment using the NRS proposed 

payment methodology see section II.D. 

We do not propose to apply the fivelevel NRS payment approach to LUPA 

episodes. In the original design of the 

HH PPS, $1.94 was built into the pervisit rates used to pay for visits in a 

LUPA episode. This amount was the 

sum of $1.71, the average cost per visit 

for NRS reported as costs on the cost 

report, and $.23, the average cost per 

visit for NRS possibly unbundled and 

billed separately to Part B and 

reimbursed on the fee schedule. Recent 

analysis shows that NRS charges for 

non-LUPA episodes are almost 3 times 

higher than that for LUPA episodes. In 

general, approximately 1 in 5 LUPAs 

report NRS while 1 in 3 non-LUPA 

episodes report NRS. Our proposal is to 

redistribute the $53.96 currently paid to 

all non-LUPA episodes. Given that 

LUPA episodes, by nature, are of 

extremely low visit volume, we do not 

propose to redistribute that $1.94 now 

paid to LUPA episodes. We believe an 

attempt to develop a model for 

redistributing the small amount of NRS 

payments ($1.94) paid to LUPA 

episodes would be unproductive. 

Furthermore, we are also concerned 

that additional payment for LUPAs to 

account for NRS costs could promote 

increases in medically unnecessary 

home health episodes. In proposing 

refinements for LUPA payments, as 

discussed in section II.A.5 of this 

proposed rule, we are aware of the 

potential for increases in medically 

unnecessary LUPA episodes that could 

result from our proposal for increased 

LUPA payment for only or initial LUPA 

episodes. Providing for additional NRS 

payments for such LUPAs could only 

adversely add to this potential. 

Consequently, we are not proposing any 

additional payments for NRS costs for 

LUPA episodes. However, we are 

specifically soliciting comment on 

alternative approaches for NRS payment 

in LUPAs. 

We also considered proposing an 

outlier policy for NRS costs, but we 

believe one is not administratively 

feasible at this time. An outlier policy 

for NRS costs would depend on having 

an infrastructure, including a reporting 

system for the extensive range of 

nonroutine supplies used in home 

health care, and a basis for assigning 

allowable costs for those supply items. 

At this time, this kind of infrastructure 

is not sufficiently developed. Many 

types of NRS cannot be coded under the 

existing reporting system, the HCPCS 

system, and reliable cost data are 

limited. Therefore, at this time, we also 

believe an outlier policy for NRS cost 

would be premature. We also recognize 

the additional administrative burdens 

on agencies that would exist under such 

an outlier policy. 

While we are not proposing an outlier 

policy for NRS costs, we nonetheless 

urge agencies to provide cost data on 

cost reports and charge data on all 

claims (including LUPA claims) with 

the utmost precision for possible future 

use in developing payment proposals 

for NRS under the HH PPS. 

TABLE 11.—PROPOSED RELATIVE WEIGHTS FOR NON-ROUTINE MEDICAL SUPPLIES

Severity level Percentage of 

episodes 

Points 

(scoring) 

Relative 

weight 

Payment 

amount 

0 ....................................................................................................................... 63 0 0.2456 $12.96 

1 ....................................................................................................................... 17 1–16 1.0356 54.65 

2 ....................................................................................................................... 12 17–34 2.0746 109.48 

3 ....................................................................................................................... 5 35–59 4.0776 215.17 

4 ....................................................................................................................... 3 60+ 6.9612 367.34 

Note: Proposed conversion factor = $52.77. 

TABLE 12a.—NRS CASE-MIX ADJUSTMENT VARIABLES AND SCORES

Description Score 

SELECTED SKIN CONDITIONS: 

1 .......... Primary diagnosis = Anal fissure, fistula and abscess .............................................................................................................. 19 

2 .......... Primary diagnosis = Cellulitis and abscess ............................................................................................................................... 13 

3 .......... Primary diagnosis = Gangrene .................................................................................................................................................. 11 

4 .......... Primary diagnosis = Malignant neoplasms of skin .................................................................................................................... 16 


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TABLE 12a.—NRS CASE-MIX ADJUSTMENT VARIABLES AND SCORES—Continued 

Description Score 

5 .......... Primary diagnosis = Non-pressure and non-stasis ulcers ......................................................................................................... 9 

6 .......... Primary diagnosis = Other infections of skin and subcutaneous tissue .................................................................................... 19 

7 .......... Primary diagnosis = Post-operative Complications 1 ................................................................................................................ 32 

8 .......... Primary diagnosis = Post-operative Complications 2 ................................................................................................................ 22 

9 .......... Primary diagnosis = Traumatic Wounds and Burns .................................................................................................................. 16 

10 ........ Other diagnosis = Anal fissure, fistula and abscess ................................................................................................................. 9 

11 ........ Other diagnosis = Cellulitis and abscess ................................................................................................................................... 6 

12 ........ Other diagnosis = Gangrene ...................................................................................................................................................... 11 

13 ........ Other diagnosis = Non-pressure and non-stasis ulcers ............................................................................................................ 8 

14 ........ Other diagnosis = Other infections of skin and subcutaneous tissue ....................................................................................... 7 

15 ........ Other diagnosis = Post-operative Complications 1 ................................................................................................................... 15 

16 ........ Other diagnosis = Post-operative Complications 2 ................................................................................................................... 15 

17 ........ Other diagnosis = Traumatic Wounds and Burns ..................................................................................................................... 7 

18 ........ M0450 = 1 pressure ulcer, stage 1 or 2 .................................................................................................................................... 12 

19 ........ M0450 = 2 or 3 pressure ulcers, stage 1 or 2 ........................................................................................................................... 20 

20 ........ M0450 = 4+ pressure ulcers, stage 1 or 2 ................................................................................................................................ 31 

21 ........ M0450 = 1 or 2 pressure ulcers, stage 3 or 4 ........................................................................................................................... 41 

22 ........ M0450 = 3 pressure ulcers, stage 3 or 4 .................................................................................................................................. 75 

23 ........ M0450 = 4+ pressure ulcers, stage 3 or 4 ................................................................................................................................ 80 

24 ........ M0450 = 5+ pressure ulcers, stage 3 or 4 ................................................................................................................................ 143 

25 ........ M0450e = 1(unobserved pressure ulcer(s)) .............................................................................................................................. 18 

26 ........ M0476 = 2 (status of most problematic stasis ulcer: early/partial granulation) ......................................................................... 18 

27 ........ M0476 = 3 (status of most problematic stasis ulcer: not healing) ............................................................................................ 28 

28 ........ M0488 = 3 (status of most problematic surgical wound: not healing) ...................................................................................... 18 

29 ........ M0488 = 2 (status of most problematic surgical wound: early/partial granulation) ................................................................... 5 

OTHER CLINICAL FACTORS: 

30 ........ M0550 = 1 (ostomy not related to inpt stay/no regimen change) ............................................................................................. 21 

31 ........ M0550 = 2 (ostomy related to inpt stay/regimen change) ......................................................................................................... 35 

32 ........ Any ‘‘Selected Skin Conditions’’ (see rows 1 to 29 above) AND M0550=1(ostomy not related to inpt stay/no regimen 

change).

24 

33 ........ Any ‘‘Selected Skin Conditions’’ (see rows 1 to 29 above) AND M0550=2 (ostomy related to inpt stay/regimen change) .... 8 

34 ........ M0250 (Therapy at home) =1 (IV/Infusion) ............................................................................................................................... 11 

35 ........ M0470 = 2 or 3 (2 or 3 stasis ulcers) ........................................................................................................................................ 17 

36 ........ M0470 = 4 (4 stasis ulcers) ....................................................................................................................................................... 34 

37 ........ M0520 = 2 (patient requires urinary catheter) ........................................................................................................................... 17 

BILLING CODE 4120–01–P 


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*Note: ‘‘ICD–9–CM Official Guidelines for 

Coding and Reporting’’ dictate that a threedigit code is to be used only if it is not 

further subdivided. Where fourth-digit 

subcategories and/or fifth-digit 

subclassifications are provided, they must be 

assigned. A code is invalid if it has not been 

coded to the full number of digits required 

for that code. Codes with three digits are 

included in ICD–9–CM as the heading of a 

category of codes that may be further 

subdivided by the use of fourth and/or fifth 

digits, which provide greater detail. The 

category codes listed in Table 12b include all 

the related 4- and 5-digit codes. 

8. Outlier Payment Review 

Section 1895(b)(5) of the Act allows 

for the provision of an addition or 

adjustment to the regular 60-day casemix and wage-adjusted episode 

payment amount in the case of episodes 

that incur unusually large costs due to 

patient home health care needs. This 

section further stipulates that total 

outlier payments in a given CY may not 

exceed 5 percent of total projected 

estimated HH PPS payments. 

In the July 2000 final rule, we 

described a method for determining 

outlier payments. Under this system, 

outlier payments are made for episodes 

whose estimated cost exceeds a 

threshold amount. The episode’s 

estimated cost is the sum of the national 

wage-adjusted per-visit payment 

amounts for all visits delivered during 

the episode. The outlier threshold for 

each case-mix group, PEP adjustment, or 

total SCIC adjustment is defined as the 

national standardized 60-day episode 

payment rate, PEP adjustment, or total 

SCIC adjustment for that group plus a 

fixed dollar loss (FDL) amount. Both 

components of the outlier threshold are 

wage-adjusted. 

The wage-adjusted FDL amount 

represents the amount of loss that an 

agency must experience before an 

episode becomes eligible for outlier 

payments. The FDL is computed by 

multiplying the wage-adjusted national 

standardized 60-day episode payment 

amount by the FDL ratio, which is a 

proportion expressed in terms of the 

national standardized episode payment 

amount. The outlier payment is defined 

to be a proportion of the wage-adjusted 

estimated costs beyond the wageadjusted threshold. The proportion of 

additional costs paid as outlier 

payments is referred to as the losssharing ratio. The FDL ratio and the 

loss-sharing ratio were selected so that 

the estimated total outlier payments 

would not exceed the 5 percent level. 

For a given level of outlier payments, 

there is a trade-off between the values 

selected for the FDL ratio and the losssharing ratio. A high FDL ratio reduces 

the number of episodes that may receive 

outlier payments, but makes it possible 

to select a higher loss-sharing ratio and, 

therefore, increase outlier payments for 

outlier episodes. Alternatively, a lower 

FDL ratio means that more episodes 

may qualify for outlier payments, but 

outlier payments per episode must be 

lower. As a result of public comments 

on the October 28, 1999 proposed rule, 

and in our July 2000 final rule, we made 

the decision to attempt to cover a 

relatively high proportion of the costs of 

outlier cases for the most expensive 

episodes that would qualify for outlier 

payments within the 5 percent 

constraint. 

We chose a value of 0.80 for the losssharing ratio, which is relatively high, 

but preserves incentives for agencies to 

attempt to provide care efficiently for 

outlier cases. It was also consistent with 

the loss-sharing ratios used in other 

Medicare PPS outlier policies. Having 

made this decision, we estimated the 

value of the FDL ratio that would yield 

estimated total outlier payments that 

were projected to be no more than 5 

percent of total HH PPS payments. The 

resulting value for the FDL ratio was 

1.13. 

When the data became available, we 

performed an analysis of CY 2001 home 

health claims data. This analysis 

revealed that outlier episodes 

represented approximately 3 percent of 

total episodes and 3 percent of total HH 

PPS payments. Additionally, we 

performed the same analysis on CY 

2002 and CY 2003 home health claims 

data and found the number of outlier 

episodes and payments held at 

approximately 3 percent of total 

episodes and total HH PPS payments, 

respectively. Based on these analyses 

and comments we received, we decided 

that an update to the FDL ratio would 

be appropriate. 

To that end, for the October 2004 final 

rule, we performed data analysis on CY 

2003 HH PPS analytic data. The results 

of this analysis indicated that a FDL 

ratio of 0.70 is consistent with the 

existing loss-sharing ratio of 0.80 and a 

projected target percentage of estimated 

outlier payments of no more than 5 

percent. Consequently, we updated the 

FDL ratio from the initial ratio of 1.13 

to the FDL ratio of 0.70. Our analysis 

showed that reducing the FDL ratio 

from 1.13 to 0.70 would increase the 

percentage of episodes that qualified for 

outlier episodes from 3.0 percent to 

approximately 5.9 percent. A FDL ratio 

of 0.70 also better met the estimated 5 

percent target of outlier payments to 

total HH PPS payments. We believed 

that this updated FDL ratio of 0.70 

preserved a reasonable degree of cost 

sharing, while allowing a greater 

number of episodes to qualify for outlier 

payments. 

Our CY 2006 update to the HH PPS 

rates (70 FR 68132) changed the FDL 

ratio from 0.70 to 0.65 to allow even 

more home health episodes to qualify 

for outlier payments and to better meet 

the estimated 5 percent target of outlier 

payments to total HH PPS payments. 

For the CY 2006 update, we used CY 

2004 home health claims data. 

In our CY 2007 update to the HH PPS 

rates (71 FR 65884) we again changed 

the FDL ratio from 0.65 to 0.67 to better 

meet the estimated 5 percent target of 

outlier payments to total HH PPS 

payments. For the CY 2007 update, we 

used CY 2005 home health claims data. 

Under the HH PPS, outlier payments 

have thus far not exceeded 5 percent of 

total HH PPS payments. However, 

preliminary analysis shows that outlier 

payments, as a percentage of total HH 

PPS payments, have increased on a 

yearly basis. With outlier payments 

having increased in recent years, and 

given the unknown effects that the 

proposed refinements of this rule may 

have on outliers, we are proposing to 

maintain the FDL ratio of 0.67. By 

maintaining the FDL ratio of 0.67, we 

believe we will continue to meet the 

statutory requirement of having an 

outlier payment outlay that does not 

exceed 5 percent of total HH PPS 

payments, while still providing for an 

adequate number of episodes to qualify 

for outlier payments. Some preliminary 

analysis shows the FDL ratio could be 

as low as 0.42 in a refined HH PPS. We 

believe that analysis of more recent data 

could indicate that a change in the FDL 

ratio is appropriate. Consequently for 

the final rule, we will rely on the latest 


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data and best analysis available at the 

time to estimate outlier payments and 

update the FDL ratio if appropriate. 

Because payment for NRS was 

included in the base rate of the national 

standardized 60-day episode payment 

rate, under the refined system proposed 

in this proposed rule, both the proposed 

national standardized 60-day episode 

payment rate and the proposed 

computed NRS amount contribute 

towards reaching the outlier threshold 

in the outlier payment calculation. 

B. Rebasing and Revising of the Home 

Health Market Basket 

1. Background 

Section 1895(b)(3)(B) of the Act, as 

amended by section 701(b)(3) of the 

MMA, requires the standard prospective 

payment amounts to be adjusted by a 

factor equal to the applicable home 

health market basket increase for CY 

2008. 

Effective for cost reporting periods 

beginning on or after July 1, 1980, we 

developed and adopted an HHA input 

price index (that is, the home health 

‘‘market basket’’). Although ‘‘market 

basket’’ technically describes the mix of 

goods and services used to produce 

home health care, this term is also 

commonly used to denote the input 

price index derived from that market 

basket. Accordingly, the term ‘‘home 

health market basket’’ used in this 

document refers to the HHA input price 

index. 

The percentage change in the home 

health market basket reflects the average 

change in the price of goods and 

services purchased by HHAs in 

providing an efficient level of home 

health care services. We first used the 

home health market basket to adjust 

HHA cost limits by an amount that 

reflected the average increase in the 

prices of the goods and services used to 

furnish reasonable cost home health 

care. This approach linked the increase 

in the cost limits to the efficient 

utilization of resources. For a greater 

discussion on the home health market 

basket, see the notice with comment 

period published in the Federal 

Register on February 15, 1980 (45 FR 

10450, 10451), the notice with comment 

period published in the Federal 

Register on February 14, 1995 (60 FR 

8389, 8392), and the notice with 

comment period published in Federal 

Register on July 1, 1996 (61 FR 34344, 

34347). Beginning with the FY 2002 HH 

PPS payments, we used the home health 

market basket to update payments under 

the HH PPS. We last rebased the home 

health market basket effective with the 

CY 2005 update. For more information 

on the HH PPS home health market 

basket, see our proposed rule published 

in the Federal Register on June 2, 2004 

(69 FR 31251, 31255). 

The home health market basket is a 

fixed-weight Laspeyres-type price 

index; its weights reflect the cost 

distribution for the base year while 

current period price changes are 

measured. The home health market 

basket is constructed in three steps. 

First, a base period is selected and total 

base period expenditures are estimated 

for mutually exclusive and exhaustive 

spending categories based upon the type 

of expenditure. Then the proportion of 

total costs that each spending category 

represents is determined. These 

proportions are called cost or 

expenditure weights. 

The second step essential for 

developing an input price index is to 

match each expenditure category to an 

appropriate price/wage variable, called 

a price proxy. These proxy variables are 

drawn from publicly available statistical 

series published on a consistent 

schedule, preferably at least quarterly. 

In the third and final step, the price 

level for each spending category is 

multiplied by the expenditure weight 

for that category. The sum of these 

products for all cost categories yields 

the composite index level in the market 

basket in a given year. Repeating the 

third step for other years will produce 

a time series of market basket index 

levels. Dividing one index level by an 

earlier index level will produce rates of 

growth in the input price index. 

We described the market basket as a 

fixed-weight index because it answers 

the question of how much more or less 

it would cost, at a later time, to 

purchase the same mix of goods and 

services that was purchased in the base 

period. As such, it measures ‘‘pure’’

price changes only. The effects on total 

expenditures resulting from changes in 

the quantity or mix of goods and 

services purchased subsequent to the 

base period are, by design, not 

considered. 

2. Rebasing and Revising the Home 

Health Market Basket 

We believe that it is desirable to 

rebase the home health market basket 

periodically so the cost category weights 

reflect changes in the mix of goods and 

services that HHAs purchase in 

furnishing home health care. We based 

the cost category weights in the current 

home health market basket on FY 2000 

data. We are proposing to rebase and 

revise the home health market basket to 

reflect FY 2003 Medicare cost report 

data, the latest available and most 

complete data on the structure of HHA 

costs. 

The terms ‘‘rebasing’’ and ‘‘revising,’’

while often used interchangeably, 

actually denote different activities. The 

term ‘‘rebasing’’ means moving the base 

year for the structure of costs of an input 

price index (that is, in this exercise, we 

are proposing to move the base year cost 

structure from FY 2000 to FY 2003). The 

term ‘‘revising’’ means changing data 

sources, cost categories, and/or price 

proxies used in the input price index. 

For this proposed revising and 

rebasing, we modified the wages and 

salaries and benefits cost categories in 

order to reflect a new data source on the 

occupational mix of HHAs. We mainly 

relied on this alternative proposed data 

source to construct the cost weights for 

the blended wage and benefit index. We 

are not proposing any changes to the 

price proxies used in the HH market 

basket or the HH blended wage and 

benefit proxies. 

The weights for this proposed revised 

and rebased home health market basket 

are based off of the cost report data for 

freestanding HHAs, whose cost 

reporting period began on or after 

October 1, 2002 and before October 1, 

2003. Using this methodology allowed 

our sample to include HHA facilities 

with varying cost report years including, 

but not limited to, the federal fiscal or 

calendar year. We refer to the market 

basket as a fiscal year market basket 

because the base period for all price 

proxies and weights are set to FY 2003. 

For this proposed rebased and revised 

market basket, we reviewed HHA 

expenditure data for the market basket 

cost categories. 

We proposed to maintain our policy 

of using data from freestanding HHAs 

because they better reflect HHAs actual 

cost structure. Expense data for a 

hospital-based HHA are affected by the 

allocation of overhead costs over the 

entire institution (including but not 

limited to hospital, hospital-based 

skilled nursing facility, and hospitalbased HHA). Due to the method of 

allocation, total expenses will be 

correct, but the individual components’

expenses may be skewed. Therefore, if 

data from hospital-based HHAs were 

included, the resultant cost structure 

could be unrepresentative of the average 

HHA costs. 

Data on HHA expenditures for nine 

major expense categories (wages and 

salaries, employee benefits, 

transportation, operation and 

maintenance, administrative and 

general, insurance, fixed capital, 

movable capital, and a residual ‘‘all 

other’’) were tabulated from the FY 2003 

Medicare HHA cost reports. As 


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prescription drugs and DME are not 

payable under the HH PPS, we excluded 

those items from the home health 

market basket and from the 

expenditures. Expenditures for contract 

services were also tabulated from these 

FY 2003 Medicare HHA cost reports and 

allocated to wages and salaries, 

employee benefits, administrative and 

general, and other expenses. After totals 

for these cost categories were edited to 

remove reports where the data were 

deemed unreasonable (for example, 

when total costs were not greater than 

zero), we then determined the 

proportion of total costs that each 

category represents. The proportions 

represent the major rebased home health 

market basket weights. 

We determined the weights for 

subcategories (telephone, postage, 

professional fees, other products, and 

other services) within the combined 

administrative and general and other 

expenses using the latest available (1997 

Benchmark) U.S. Department of 

Commerce, Bureau of Economic 

Analysis (BEA) Input-Output (I–O) 

Table, from which we extracted data for 

HHAs. The BEA I–O data, which are 

updated at 5-year intervals, were most 

recently described in the Survey of 

Current Business article, ‘‘Benchmark 

Input-Output Accounts of the U.S., 

1997’’ (December 2002). These data 

were aged from 1997 to 2003 using 

relevant price changes. 

The methodology we used to age the 

data applied the annual price changes 

from the price proxies to the appropriate 

cost categories. We repeated this 

practice for each year. 

This work resulted in the 

identification of 12 separate cost 

categories, the same number found in 

the FY 2000-based home health market 

basket. The differences between the 

major categories for the proposed FY 

2003-based index and those used for the 

current FY 2000-based index are 

summarized in Table 13. We have 

allocated the contracted services weight 

to the wages and salaries, employee 

benefits, and administrative and general 

and other expenses cost categories in 

the proposed FY 2003-based index as 

we did in the FY 2000-based index. 

TABLE 13.—COMPARISON OF 2000-BASED AND PROPOSED 2003-BASED HOME HEALTH MARKET BASKETS MAJOR COST

CATEGORIES AND WEIGHTS

Cost categories 

2000-Based 

home health 

market basket 

Proposed 

2003-based 

home health 

market basket 

Wages and Salaries, including allocated contract services’ labor .......................................................................... 65.766 64.484 

Employee Benefits, including allocated contract services’ labor ............................................................................ 11.009 12.598 

All Other Expenses including allocated contract services’ labor ............................................................................ 23.225 22.918 

Total .................................................................................................................................................................. 100.000 100.000 

The complete proposed 2003-based 

cost categories and weights are listed in 

Table 14. 

TABLE 14.—COST CATEGORIES, WEIGHTS, AND PRICE PROXIES IN PROPOSED 2003-BASED HOME HEALTH MARKET

BASKET

Cost categories Weight Price proxy 

Compensation, including allocated contract services’ labor ......... 77.082 

Wages and Salaries, including allocated contract services’ labor 64.484 Proposed Home Health Occupational Wage Index. 

Employee Benefits, including allocated contract services’ labor .. 12.598 Proposed Home Health Occupational Benefits Index. 

Operations & Maintenance ........................................................... 0.694 CPI–U Fuel & Other Utilities. 

Administrative & General & Other Expenses including allocated 

contract services’ labor.

16.712 

Telephone ..................................................................................... 0.785 CPI–U Telephone Services. 

Postage ......................................................................................... 0.605 CPI–U Postage. 

Professional Fees ......................................................................... 1.471 ECI for Compensation for Professional and Technical Workers. 

Other Products .............................................................................. 7.228 CPI–U All Items Less Food and Energy. 

Other Services .............................................................................. 6.622 ECI for Compensation for Service Workers. 

Transportation ............................................................................... 2.494 CPI–U Private Transportation. 

Capital-Related ............................................................................. 3.018 

Insurance ...................................................................................... 0.510 CPI–U Household Insurance. 

Fixed Capital ................................................................................. 1.618 CPI–U Owner’s Equivalent Rent. 

Movable Capital ............................................................................ 0.890 PPI Machinery & Equipment. 

Total ....................................................................................... 100.000 ** 

** Figures may not sum to total due to rounding. 

After we computed the FY 2003 cost 

category weights for the proposed 

rebased home health market basket, we 

selected the most appropriate wage and 

price indexes to proxy the rate of change 

for each expenditure category. These 

price proxies are based on Bureau of 

Labor Statistics (BLS) data and are 

grouped into one of the following BLS 

categories: 

• 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. 


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These indexes are fixed-weight indexes 

and strictly measure the change in wage 

rates and employee benefits per hour. 

They are not affected by shifts in skill 

mix. ECIs are superior to average hourly 

earnings as price proxies for input price 

indexes for two reasons: (a) They 

measure pure price change; and (b) they 

are available by occupational groups, 

not just by industry. 

• Consumer Price Indexes—

Consumer Price Indexes (CPIs) measure 

change in the prices of final goods and 

services bought by the typical 

consumer. Consumer price indexes are 

used when the expenditure is more 

similar to that of a purchase at the retail 

level rather than at the wholesale level, 

or if no appropriate Producer Price 

Indexes (PPIs) were available. 

• Producer Price Indexes—PPIs are 

used to measure price changes for goods 

sold in other than retail markets. For 

example, a PPI for movable equipment 

is used rather than a CPI for equipment. 

PPIs in some cases are preferable price 

proxies for goods that HHAs purchase at 

wholesale levels. These fixed-weight 

indexes are a measure of price change 

at the producer or at the intermediate 

stage of production. 

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. Widely accepted 

statistical methods ensure that the data 

were collected and aggregated in way 

that can be replicated. Low sampling 

variability is desirable because it 

indicates that sample reflects the typical 

members of the population. (Sampling 

variability is variation that occurs by 

chance because a sample was surveyed 

rather than the entire population.) 

Timeliness implies that the proxy is 

published regularly, preferably at least 

once a quarter. The market baskets are 

updated quarterly and therefore it is 

important the underlying price proxies 

be up-to-date, reflecting the most recent 

data available. We believe that using 

proxies that are published regularly (at 

least quarterly, whenever possible) 

helps ensure that we are using the most 

recent data available to update the 

market basket. We strive to use 

publications that are disseminated 

frequently because we believe that this 

is an optimal way to stay abreast of the 

most current data available. Availability 

means that the proxy is publicly 

available. We prefer that our proxies are 

publicly available because this will help 

ensure that our market basket updates 

are as transparent to the public as 

possible. In addition, this enables the 

public to be able to obtain the price 

proxy data on a regular basis. 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. Therefore, we believe that 

they continue to be the best measure of 

price changes for the cost categories to 

which they would be applied. 

As part of the revising and rebasing of 

the home health market basket, we are 

proposing to revise and rebase the home 

health blended wage and salary index 

and the home health blended benefits 

index. 

We would use these blended indexes 

as price proxies for the wages and 

salaries and the employee benefits 

portions of the proposed FY 2003-based 

home health market basket, as we did in 

the FY 2000-based home health market 

basket. The price proxies for these two 

cost categories are the same as those 

used in the FY 2000-based home health 

market basket but with occupational 

weights reflecting the FY 2003 

occupational mix in HHAs. These 

proxies are a combination of health 

industry specific and economy-wide 

proxies. 

3. Price Proxies Used To Measure Cost 

Category Growth 

• Wages and salaries, including an 

allocation for contract services’ labor: 

For measuring price growth in the FY 

2003-based home health market basket, 

as we did in the FY 2000-based index, 

five price proxies would be applied to 

the four occupational subcategories 

within the wages and salaries 

component, and would be weighted to 

reflect the HHA occupational mix. This 

approach was used because there is not 

a wage proxy for home health care 

workers that reflects only wage changes 

and not both wage and skill mix 

changes. The professional and technical 

occupational subcategory is represented 

by a 50–50 blend of hospital industry 

and economy-wide price proxies. 

Therefore, there are five price proxies 

used for the four occupational 

subcategories. The percentage change in 

the blended wages and salaries price is 

applied to the wages and salaries 

component of the home health market 

basket, which is described in Table 15. 

TABLE 15.—PROPOSED HOME HEALTH OCCUPATIONAL WAGES AND SALARIES INDEX

[Wages and salaries component of the proposed FY 2003-based home health market basket] 

Cost category 2000 

weight 

2003 

weight Price proxy 

Skilled Nursing & Therapists & Other Professional/Technical, including an allocation for contract services’ labor.53.816 50.812 • 50 percent ECI for Wages & Salaries in Private Industry 

for Professional, Specialty & Technical Workers. 

• 50 percent ECI for Wages & Salaries for Civilian Hospital Workers. 

Managerial/Supervisory, including an allocation for contract 

services’ labor.

7.431 9.007 ECI for Wages & Salaries in Private Industry for Executive, 

Administrative & Managerial Workers. 

Clerical, including an allocation for contract services’ labor 6.822 7.596 ECI for Wages & Salaries in Private Industry for Administrative Support, Including Clerical Workers. 

Service, including an allocation for contract services’ labor 31.931 32.584 ECI for Wages & Salaries in Private Industry Service Occupations. 

Total ............................................................................... 100.000 100.000 

Beginning with the FY 2001 Medicare 

cost report, the occupational specific 

wage and benefit expenditure data was 

no longer collected in the cost report. 

Previously, we used these data to 

estimate weights for the home health 

blended wage and salary index and the 

home health blended benefits index. We 

believed the options to obtain these data 

were: 

• To obtain the home health 

occupational specific expenditure data 

from an alternative source, or 

• To propose a change to the home 

health wages and salaries and the home 


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health benefits proxy used in the market 

basket. 

However, there is no publicly 

available data source that tracks wage 

and salary price growth for the home 

health industry while holding skill mix 

constant. There is also no publicly 

available data source that tracks benefit 

price growth for the home health 

industry while holding skill mix 

constant. Therefore, option 2 was not an 

viable solution. Next, we investigated if 

there was home health occupational 

specific expenditure data from an 

alternative source other than the 

Medicare cost reports. We believe an 

alternative source exists in the form of 

data from the November 2003 National 

industry-specific occupational 

employment and wage estimates 

published by the BLS Office of 

Occupational Employment Statistics 

(OES). Accordingly, we propose to use 

that data to determine weights for the 

home health specific blended wage and 

benefits proxy. Detailed information on 

the methodology for the national 

industry-specific occupational 

employment and wage estimates survey 

can be found at http://www.bls.gov/oes/ 

current/oes_tec.htm. 

Therefore, the needed data on HHA 

expenditures for the four occupational 

subcategories (managerial, professional 

and technical, service, and clerical) for 

the wages and salaries component were 

tabulated from the November 2003 OES 

data for North American Industrial 

Classification System (NAICS) 621600, 

Home Health Care Services. We 

assigned the occupations to the groups 

in a manner consistent with the 

occupational groupings used in the 

Medicare cost report. Table 16 shows 

the specific occupational assignments to 

the four CMS designated subcategories. 

BILLING CODE 4120–01–P 


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Total expenditures by occupation 

were calculated by taking the OES 

number of employees multiplied by the 

OES annual average salary. The wage 

and salary expenditures were aggregated 

based on the groupings in table 14. 

Next, contract labor expenditures were 

obtained from the 1997 I-O for the home 

health industry, NAICS 621600 and 

aged forward to FY 2003 using the PPI 

for employment services. We then 

proportionally allocated the contract 

labor to each of the four subcategories. 

We determined the proportion of total 

wage costs (contract wages plus 


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industry wages) that each subcategory 

represents. These proportions represent 

the major rebased and revised home 

health blended wage and salary index 

weights. 

We did not propose a change from our 

current blended measure because we 

believe it reflects the competition 

between HHAs and hospitals for 

registered nurses, while still capturing 

the overall wage trends for professional 

and technical workers. 

• Employee benefits, including an 

allocation for contract services’ labor: 

For measuring employee benefits price 

growth in the FY 2003-based home 

health market basket, price proxies are 

applied to the four occupational 

subcategories within the employee 

benefits component, weighted to reflect 

the home health occupational mix. The 

professional and technical occupational 

subcategory is represented by a blend of 

hospital industry and economy-wide 

price proxies. Therefore, there are five 

price proxies for four occupational 

subcategories. The percentage change in 

the blended price of home health 

employee benefits is applied to this 

component, which is described in Table 

17. 

TABLE 17.—PROPOSED HOME HEALTH OCCUPATIONAL BENEFITS INDEX

[Employee benefits component of the proposed 2003-based home health market basket] 

Cost category 2000 

weight 

2003 

weight Price proxy 

Skilled Nursing & Therapists & Other Professional/Technical, including an allocation for contract services’ labor.53.492 50.506 • 50 percent ECI for Benefits in Private Industry for Professional, Specialty &Technical Workers. 

• 50 percent ECI for Benefits for Civilian Hospital Workers. 

Managerial/Supervisory, including an allocation for contract 

services’ labor.

7.232 8.766 ECI for Benefits in Private Industry for Executive, Administrative & Managerial Workers. 

Clerical, including an allocation for contract services’ labor 6.941 7.698 ECI for Benefits in Private Industry for Administrative Support, Including Clerical Workers. 

Service, including an allocation for contract services’ labor 32.362 33.024 ECI for Benefits in Private Industry Service Occupations. 

Total ............................................................................... 100.000 100.000 

After conducting research we could 

find no data source that exists for 

benefit expenditures by occupation for 

the home health industry. Thus, to 

construct weights for the home health 

occupational benefits index we 

calculated the ratio of benefits to wages 

and salaries from the 2000 Home health 

occupational wages and occupational 

benefits indices for the four 

occupational subcategories. We then 

applied the benefit-to-wage ratios to 

each of the four occupational 

subcategories from the 2003 OES wage 

and salary weights. For example, the 

ratio of benefits to wages from the 2000 

home health occupational wage and 

benefit indexes for home health 

managers is 0.973. We apply this ratio 

to the 2003 OES weight for wages and 

salaries for home health managers, 

9.007, to obtain a benefit weight in the 

home health occupational benefit index 

for home health managers of 8.766 

percent. 

We are proposing to continue to use 

the same 50-50 split for benefits for 

professional and technical workers (50 

percent hospital workers and 50 percent 

professional and technical workers) as 

we did in the FY 2000-based market 

basket. 

• Operations and Maintenance: The 

percentage change in the price of fuel 

and other utilities as measured by the 

Consumer Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

• Telephone: The percentage change 

in the price of telephone service as 

measured by the Consumer Price Index 

is applied to this component. The same 

proxy was used for the FY 2000-based 

market basket. 

• Postage: The percentage change in 

the price of postage as measured by the 

Consumer Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

• Professional Fees: The percentage 

change in the price of professional fees 

as measured by the ECI for 

compensation for professional and 

technical workers is applied to this 

component. The same proxy was used 

for the 2000-based market basket. 

• Other Products: The percentage 

change in the price for all items less 

food and energy as measured by the 

Consumer Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

• Other Services: The percentage 

change in the employment cost index 

for compensation for service workers is 

applied to this component. The same 

proxy was used for the FY 2000-based 

market basket. 

• Transportation: The percentage 

change in the price of private 

transportation as measured by the 

Consumer Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

• Insurance: The percentage change 

in the price of household insurance as 

measured by the Consumer Price Index 

is applied to this component. The same 

proxy was used for the FY 2000-based 

market basket. 

• Fixed capital: The percentage 

change in the price of an owner’s 

equivalent rent as measured by the 

Consumer Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

• Movable Capital: The percentage 

change in the price of machinery and 

equipment as measured by the Producer 

Price Index is applied to this 

component. The same proxy was used 

for the FY 2000-based market basket. 

As we did in the FY 2000-based home 

health market basket, we allocated the 

Contract Services’ share of home health 

agency expenditures among wages and 

salaries, employee benefits, 

administrative and general and other 

expenses. 

Table 18 summarizes the proposed FY 

2003-based proxies and compares them 

to the FY 2000-based proxies. 


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TABLE 18.—COMPARISON OF PRICE PROXIES USED IN THE 2000–BASED AND THE PROPOSED 2003–BASED HOME

HEALTH MARKET BASKETS

Cost category 2000-Based 

price proxy 2003-Based proposed price proxy 

Compensation, including allocated contract services’ labor 

Wages and Salaries, including allocated contract services’

labor 

Same ................ Home Health Agency Occupational Wage Index. 

Employee Benefits, including allocated contract services’

labor 

Same ................ Home Health Agency Occupational Benefits Index. 

Operations and Maintenance .................................................... Same ................ CPI–Fuel and Other Utilities. 

Administrative & General & Other Expenses, including allocated contract services’ labor 

Telephone .................................................................................. Same ................ CPI–U Telephone. 

Postage ...................................................................................... Same ................ CPI–U Postage. 

Professional Fees ...................................................................... Same ................ ECI for Compensation for Professional and Technical Workers. 

Other Products .......................................................................... Same ................ CPI–U for All Items Less Food and Energy. 

Other Services ........................................................................... Same ................ ECI for Compensation for Service Workers. 

Transportation ............................................................................ Same ................ CPI–U Private Transportation. 

Capital-Related 

Insurance ................................................................................... Same ................ CPI–U Household Insurance. 

Fixed Capital .............................................................................. Same ................ CPI–U Owner’s Equivalent Rent. 

Movable Capital ......................................................................... Same ................ PPI Machinery and Equipment. 

Contract Services ...................................................................... Same ................ Contained within Wages & Salaries, Employee Benefits, Administrative & General & Other Expenses; see those price 

proxies. 

4. Rebasing Results 

A comparison of the yearly changes 

from CY 2005 to CY 2008 for the FY 

2000-based home health market basket 

and the proposed FY 2003-based home 

health market basket is shown in Table 

19. The average annual increase in the 

two market baskets is similar, and in no 

year is the difference greater than 0.1 

percentage point. 

TABLE 19.—COMPARISON OF THE 2000–BASED HOME HEALTH MARKET BASKET AND THE PROPOSED 2003–BASED

HOME HEALTH MARKET BASKET, PERCENT CHANGE, 2005–2008 

Fiscal years beginning October 1 

Home health 

market basket, 

2000-based 

Proposed 

home health 

market basket, 

2003-based 

Difference 

(proposed 

2003-based 

less 2000- 

based) 

Historical: 

CY 2005 ....................................................................................................................................... 3.1 3.1 0.0 

CY 2006 ....................................................................................................................................... 3.2 3.1 ¥0.1 

CY 2007 ....................................................................................................................................... 3.1 3.1 0.0 

CY 2008 ....................................................................................................................................... 2.9 2.9 0.0 

Average Change: 2005–2008 ...................................................................................................... 3.1 3.1 0.0 

Source: Global Insights, Inc, 4th Qtr, 2006. 

Table 20 shows that the forecasted 

rate of growth for CY 2008, beginning 

January 1, 2008, for the proposed 

rebased and revised home health market 

basket is 2.9 percent, while the 

forecasted rate of growth for the current 

2000-based home health market basket 

is also 2.9 percent. As previously 

mentioned, we rebase the home health 

market basket periodically so the cost 

category weights continue to reflect 

changes in the mix of goods and 

services that HHAs purchase in 

furnishing home health care. 

TABLE 20.—FORECASTED ANNUAL PERCENT CHANGE IN THE CURRENT AND PROPOSED REVISED AND REBASED HOME

HEALTH MARKET BASKETS

Calendar year beginning January 1 

Home health 

market basket, 

2000-based 

Proposed 

home health 

market basket, 

2003-based 

Difference 

(proposed 

2003-based 

Less 2000- 

based) 

January 2008, CY 2008 ............................................................................................................... 2.9 2.9 0.0 

Source: Global Insights, Inc, 4th Qtr, 2006. 


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Table 21 shows the percent changes 

for CY 2008 for each cost category in the 

home health market basket. 

TABLE 21.—CY 2008 FORECASTED ANNUAL PERCENT CHANGE FOR ALL COST CATEGORIES IN THE PROPOSED 2003–

BASED HOME HEALTH MARKET BASKET

Cost categories Weight Price proxy 

Forecasted 

annual percent 

change for CY 

2008 

Total .............................................................................. 100.00 .................................................................................. 2.9 

Compensation ............................................................... 77.082 .................................................................................. 3.1 

Wages and Salaries ...................................................... 64.484 Proposed Home Health Occupational Wage Index ..... 2.9 

Employee Benefits ........................................................ 12.598 Proposed Home Health Occupational Benefits Index 3.8 

Operations & Maintenance ........................................... 0.694 CPI–U Fuel & Other Utilities ........................................ 3.2 

Administrative & General & Other Expenses ............... 16.712 .................................................................................. 2.6 

Telephone ..................................................................... 0.785 CPI–U Telephone Services .......................................... 0.8 

Postage ......................................................................... 0.605 CPI–U Postage ............................................................ 4.8 

Professional Fees ......................................................... 1.471 ECI for Compensation for Professional and Technical 

Workers.

3.0 

Other Products .............................................................. 6.622 CPI–U All Items Less Food and Energy ...................... 2.0 

Other Services .............................................................. 7.228 ECI for Compensation for Service Workers ................ 3.1 

Transportation ............................................................... 2.494 CPI–U Private Transportation ...................................... 0.5 

Capital-Related ............................................................. 3.018 .................................................................................. 1.8 

Insurance ...................................................................... 0.510 CPI–U Household Insurance ....................................... 2.6 

Fixed Capital ................................................................. 1.618 CPI–U Owner’s Equivalent Rent .................................. 2.6 

Movable Capital ............................................................ 0.890 PPI Machinery & Equipment ........................................ ¥0.3 

Source: Global Insights, Inc, 4th Qtr, 2006. 

5. Labor-Related Share 

In the 2000-based home health market 

basket the labor-related share was 

76.775 percent while the remaining 

non-labor-related share was 23.225 

percent. In the proposed revised and 

rebased home health market basket, the 

labor-related share would be 77.082 

percent. The labor-related share 

includes wages and salaries and 

employee benefits. The proposed nonlabor-related share would be 22.918 

percent. The increase in the laborrelated share using the FY 2003-based 

HH market basket is primarily due to 

the increase in the benefit cost weight. 

Our preliminary analysis of Medicare 

cost report data for skilled nursing 

facilities and acute care hospitals also 

shows a similar upward trend for the 

SNF and hospital benefit cost weights 

from FY 2000 to FY 2003. 

Table 22 details the components of 

the labor-related share for the FY 2000- 

based and proposed FY 2003-based 

home health market baskets. 

TABLE 22.—LABOR-RELATED SHARE OF CURRENT AND PROPOSED HOME HEALTH MARKET BASKETS

Cost category 

2000-based 

market basket 

weight 

Proposed 

2003-based 

market basket 

weight 

Wages and Salaries ................................................................................................................................................ 65.766 64.484 

Employee Benefits ................................................................................................................................................... 11.009 12.598 

Total Labor Related .......................................................................................................................................... 76.775 77.082 

Total Non-Labor Related .................................................................................................................................. 23.225 22.918 

C. National Standardized 60-Day 

Episode Payment Rate 

The Medicare HH PPS has been 

effective since October 1, 2000. As set 

forth in the final rule published July 3, 

2000 in the Federal Register (65 FR 

41128), the unit of payment under the 

Medicare HH PPS is a national 

standardized 60-day episode payment 

rate. As set forth in § 484.220, we adjust 

the national standardized 60-day 

episode payment rate by a case-mix 

grouping and a wage index value based 

on the site of service for the beneficiary. 

The proposed CY 2008 HH PPS rates 

use the case-mix methodology proposed 

in section II.A.2 of this proposed rule 

and application of the wage index 

adjustment to the labor portion of the 

HH PPS rates as set forth in the July 3, 

2000 final rule. As stated above, we are 

proposing to rebase and revise the home 

health market basket, resulting in a 

revised and rebased labor related share 

of 77.082 percent and a non-labor 

portion of 22.918 percent. We multiply 

the national standardized 60-day 

episode payment rate by the patient’s 

applicable case-mix weight. We divide 

the case-mix adjusted amount into a 

labor and non-labor portion. We 

multiply the labor portion by the 

applicable wage index based on the site 

of service of the beneficiary. 

For CY 2008, we are proposing to base 

the wage index adjustment to the labor 

portion of the HH PPS rates on the most 

recent pre-floor and pre-reclassified 

hospital wage index as discussed in 

section II.B of this proposed rule (not 

including any reclassifications under 

section 1886(d)(8)(B)) of the Act. 

As discussed in the July 3, 2000 HH 

PPS final rule, for episodes with four or 


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fewer visits, Medicare pays the national 

per-visit amount by discipline, referred 

to as a LUPA. We update the national 

per-visit amounts by discipline annually 

by the applicable home health market 

basket percentage. We adjust the 

national per-visit amount by the 

appropriate wage index based on the 

site of service for the beneficiary as set 

forth in § 484.230. We propose to adjust 

the labor portion of the updated 

national per-visit amounts by discipline 

used to calculate the LUPA by the most 

recent pre-floor and pre-reclassified 

hospital wage index, as discussed in 

section II.D of this proposed rule. 

Medicare pays the 60-day case-mix 

and wage-adjusted episode payment on 

a split percentage payment approach. 

The split percentage payment approach 

includes an initial percentage payment 

and a final percentage payment as set 

forth in § 484.205(b)(1) and (b)(2). We 

may base the initial percentage payment 

on the submission of a request for 

anticipated payment and the final 

percentage payment on the submission 

of the claim for the episode, as 

discussed in § 409.43. The claim for the 

episode that the HHA submits for the 

final percentage payment determines 

the total payment amount for the 

episode and whether we make an 

applicable adjustment to the 60-day 

case-mix and wage-adjusted episode 

payment. The end date of the 60-day 

episode as reported on the claim 

determines which CY rates Medicare 

will use to pay the claim. 

We may also adjust the 60-day casemix and wage-adjusted episode 

payment based on the information 

submitted on the claim to reflect the 

following: 

• A LUPA provided on a per-visit 

basis as set forth in § 484.205(c) and 

§ 484.230. 

• A PEP adjustment as set forth in 

§ 484.205(d) and § 484.235. 

• An outlier payment as set forth in 

§ 484.205(f) and § 484.240. 

Currently, we may also adjust the 

episode payment by a SCIC adjustment 

as set forth in § 484.202, but as noted in 

section II.A.6 of this proposed rule, we 

are now proposing to remove the SCIC 

adjustment from HH PPS. 

This proposed rule reflects the 

proposed updated CY 2008 rates that 

would be effective January 1, 2008. 

D. Proposed CY 2008 Rate Update by 

the Home Health Market Basket Index 

(With Examples of Standard 60-Day and 

LUPA Episode Payment Calculations) 

Section 1895(b)(3)(B) of the Act, as 

amended by section 5201 of the DRA, 

requires for CY 2008 that the standard 

prospective payment amounts be 

increased by a factor equal to the 

applicable home health market basket 

update for those HHAs that submit 

quality data as required by the 

Secretary. The applicable home health 

market basket update will be reduced by 

2 percentage points for those HHAs that 

fail to submit the required quality data. 

• Proposed CY 2008 Adjustments 

In calculating the annual update for 

the CY 2008 national standardized 60- 

day episode payment rates, we are 

proposing to first look at the CY 2007 

rates as a starting point. The CY 2007 

national standardized 60-day episode 

payment rate is $2,339.00. 

In order to calculate the CY 2008 

national standardized 60-day episode 

payment rate, we are proposing to first 

increase the CY 2007 national 

standardized 60-day episode payment 

rate ($2,339.00) by the proposed 

estimated rebased and revised home 

health market basket update of 2.9 

percent for CY 2008. 

Given this updated rate, we would 

then take a reduction of 2.75 percent to 

account for nominal change in case-mix. 

We would multiply the resulting value 

by 1.05 and 0.958614805 to account for 

the estimated percentage of outlier 

payments as a result of the current FDL 

ratio of 0.67 (that is, $2,339.00 * 1.029 

* .9725 * 1.05 * 0.958614805), to yield 

an updated CY 2008 national 

standardized 60-day episode payment 

rate of $2,355.96 for episodes that begin 

in CY 2007 and end in CY 2008 (see 

Table 23a). For episodes that begin in 

CY 2007 and end in CY 2008, the new 

proposed 153 HHRG case-mix model 

(and associated Grouper) would not yet 

be in effect. For that reason, we propose 

that episodes that begin in CY 2007 and 

end in CY 2008 be paid at the rate of 

$2,355.96, and be further adjusted for 

wage differences and for case-mix, 

based on the current 80 HHRG case-mix 

model. We recognize that the annual 

update for CY 2008 is for all episodes 

that end on or after January 1, 2008 and 

before January 1, 2009. By paying this 

rate ($2,355.96) for episodes that begin 

in CY 2007 and end in CY 2008, we will 

have appropriately recognized that these 

episodes are entitled to receive the CY 

2008 home health market, even though 

the new case-mix model will not yet be 

in effect. 

TABLE 23A.—PROPOSED NATIONAL 60-DAY EPISODE AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET

BASKET UPDATE FOR CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF

SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT FOR EPISODES BEGINNING IN CY 2007 AND

ENDING IN CY 2008 

Total CY 2007 national standardized 60-day episode payment rate 

Multiply by the 

proposed estimated home 

health market 

basket update 

(2.9 percent) 1

Reduce by 

2.75 percent 

for nominal 

change in 

case-mix 

Adjusted to 

account for the 

5 percent 

outlier policy 

Proposed national standardized 60-day 

episode payment rate for 

episodes beginning in CY 

2007 and ending in CY 2008 

$2,339.00 ......................................................................................................... × 1.029 × 0.9725 × 1.05 

× 0.958614805 

$2,355.96 

1The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

Next, in order to establish new rates 

based on a proposed new case-mix 

system, we again start with the CY 2007 

national standardized 60-day episode 

payment rate and increase that rate by 

the proposed estimated rebased and 

revised home health market basket 

update (2.9 percent) ($2,339.00 * 1.029 

= $2,406.83). We next have to put 

dollars associated with the outlier 

targeted estimates back into the base 

rate. In the 2000 HH PPS final rule (65 

FR 41184), we divided the base rate by 

1.05 to account for the outlier target 

policy. Therefore, we are proposing to 


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multiply the $2,406.83 by 1.05, resulting 

in $2,527.17. Next we need to reduce 

this amount to pay for each of our 

proposed policies. As noted previously, 

based upon our proposed change to the 

LUPA payment, the NRS redistribution, 

the elimination of the SCIC policy, the 

amounts needed to account for outlier 

payments, and the reduction accounting 

for nominal change in case-mix, we 

would reduce the national standardized 

60-day episode payment rate by $6.46, 

$40.88, $15.71, $94.02, and $69.50, 

respectively. This results in a proposed 

CY 2008 updated national standardized 

60-day episode payment rate, for 

episodes beginning and ending in CY 

2008, of $2,300.60 (see Table 23b). 

These episodes would be further 

adjusted for case-mix based on the 

proposed 153 HHRG case-mix model for 

episodes beginning and ending in CY 

2008. As we noted in section II.A.2.d., 

we increased the case-mix weights by a 

budget neutrality factor of 1.194227193. 

TABLE 23b.—PROPOSED NATIONAL 60-DAY EPISODE AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET

BASKET UPDATE FOR CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF

SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT FOR EPISODES BEGINNING AND ENDING IN

CY 2008 

Total CY 2007 national standardized 

60-day episode payment rate 

Multiply by the 

proposed estimated home 

health market basket update 

(2.9 percent) 1

Adjusted to return 

the outlier funds to 

the national standardized 60-day 

episode payment 

rate 

Updated and 

outlier adjusted 

national standardized 60-day episode payment 

Changes to account for LUPA 

adjustment 

($6.46), NRS payment ($40.88), 

elimination of 

SCIC policy 

($15.71), maintaining a 0.67 FDL 

ratio ($94.02), and 

2.75 percent reduction for nominal change in 

case-mix ($69.50) 

for episodes beginning and ending in CY 2008 

Proposed CY 

2008 national 

standardized 60- 

day episode payment rate for episodes beginning 

and ending in CY 

2008 

$2,339.00 ............................................... × 1.029 × 1.05 $2,527.17 ¥$226.57 $2,300.60 

1The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

Under the HH PPS, NRS payment, 

which was $49.62 at the onset of the HH 

PPS, has been updated yearly as part of 

the national standardized 60-day 

episode payment rate. As discussed 

previously in section II.A.7., we propose 

to remove the current NRS payment 

amount portion from the national 

standardized 60-day episode payment 

rate and add a severity adjusted NRS 

payment amount subject to case-mix 

and wage adjustment to the national 

standardized 60-day episode payment 

rate. Therefore, to calculate an episode’s 

prospective payment amount, the NRS 

adjusted payment amount must first be 

calculated by multiplying the episode’s 

NRS weight (taken from Table 11 of this 

proposed rule) by the NRS conversion 

factor. This NRS adjusted payment 

amount is then added to, and, becomes 

a part of, the non-adjusted HH PPS 

standardized prospective payment rate 

for CY 2008. Then, for any HHRG group, 

to compute a case-mix adjusted 

payment, the sum of the non-adjusted 

national standardized 60-day episode 

payment rate and the NRS adjusted 

payment amount are multiplied by the 

appropriate case-mix weight taken from 

Table 5. Finally, to compute a wage 

adjusted national standardized 60-day 

episode payment rate, that labor-related 

portion of the national standardized 60- 

day episode payment rate for CY 2008 

is multiplied by the appropriate wage 

index factor listed in Addendum A. The 

product of that calculation is added to 

the corresponding non-labor-related 

amount. The resulting amount is the 

national case-mix and wage adjusted 

national standardized 60-day episode 

payment rate for that particular episode. 

The following example illustrates the 

computation described above: 

Example 1. An HHA is providing services 

to a Medicare beneficiary in Grand Forks, 

ND. The national standardized payment rate 

is $2,300.60 (see Table 23). The HHA 

determines that the beneficiary is in his or 

her 3rd episode and thus falls under the 

C1F3S3 HHRG for 3rd+ episodes with 0 to 13 

therapy visits (Case Mix Weight = 1.4815). It 

is also determined that the beneficiary falls 

under NRS severity level #4. The NRS 

Severity Level #4 weight = 6.9612 and the 

NRS Conversion Factor = $52.77 (see Table 

11). 

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• National Per-visit Amounts Used to 

Pay LUPAs and Compute Imputed Costs 

Used in Outlier Calculations 

As discussed previously in this 

proposed rule, the policies governing 

LUPAs and the outlier calculations set 

forth in the July 3, 2000 HH PPS final 

rule will continue (65 FR 41128) with 

an increase of $92.63 for initial and only 

episode LUPAs during CY 2008. In 

calculating the proposed CY 2008 

national per-visit amounts used to 

calculate payments for LUPA episodes 

and to compute the imputed costs in 

outlier calculations, we are proposing to 

start with the CY 2007 per-visit 

amounts. We propose to increase the CY 

2007 per-visit amounts for each home 

health discipline for CY 2008 by the 

proposed estimated rebased and revised 

home health market basket update (2.9 

percent), then multiply by 1.05 and 

0.958614805 to account for the 

estimated percentage of outlier 

payments as a result of the current FDL 

ratio of 0.67 (see Table 24). 

TABLE 24.—PROPOSED NATIONAL PER-VISIT AMOUNTS FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A

BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, BEFORE WAGE

INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY

Home health discipline type 

Final CY 2007 

per-visit 

amounts per 

60-day episode 

for LUPAs 

Multiply by the 

proposed estimated home 

health market 

basket 

(2.9 percent) 1

Adjusted to account for the 5 

percent outlier 

policy 

Proposed CY 

2008 per-visit 

payment 

amount per 

discipline 

Home Health Aide ................................................................................... $46.24 × 1.029 × 1.05 

× 0.958614805 

$47.91. 

Medical Social Services ........................................................................... 163.68 × 1.029 × 1.05 

× 0.958614805 

169.53. 

Occupational Therapy .............................................................................. 112.40 × 1.029 × 1.05 

× 0.958614805 

116.42. 

Physical Therapy ..................................................................................... 111.65 × 1.029 × 1.05 

× 0.958614805 

115.63. 

Skilled Nursing ......................................................................................... 102.11 × 1.029 × 1.05 

× 0.958614805 

105.76. 

Speech-Language Pathology .................................................................. 121.22 × 1.029 × 1.05 

× 0.958614805 

125.55. 

1The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

Payment for LUPA episodes is 

changed in that for LUPAs that occur as 

initial episodes in a sequence of 

adjacent episodes or as the only 

episode, we are proposing an increased 

payment amount (see section II.A.5. of 

this proposed regulation) to the LUPA 

payment. Table 24 rates are before that 

adjustment and are the rates paid to all 

other LUPA episodes. LUPA episodes 

that occur as the only episode or initial 

episode in a sequence of adjacent 

episodes are adjusted by including the 

proposed amount of $92.63 to the LUPA 

payment before adjusting for wage 

index. 

Example 2. An HHA is providing services 

to a Medicare beneficiary in rural New 

Hampshire. During the 60-day episode the 

beneficiary receives only 3 visits. It is the 

initial episode during a sequence of adjacent 

episodes for this beneficiary. 

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Outlier payments are determined and 

calculated using the same methodology 

that has been used since the 

implementation of the HH PPS. 

E. Hospital Wage Index 

Sections 1895(b)(4)(A)(ii) and (b)(4)(C) 

of the Act require the Secretary to 


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establish area wage adjustment factors 

that reflect the relative level of wages 

and wage-related costs applicable to the 

furnishing of home health services and 

to provide appropriate adjustments to 

the episode payment amounts under the 

HH PPS to account for area wage 

differences. We apply the appropriate 

wage index value to the proposed labor 

portion (77.082 percent; see Table 22) of 

the HH PPS rates based on the 

geographic area where the beneficiary 

received the home health services. As 

implemented under the HH PPS in the 

July 3, 2000 HH PPS final rule, each 

HHA’s labor market area is based on 

definitions of Metropolitan Statistical 

Areas (MSAs) issued by the OMB. 

In the August 11, 2004 IPPS final rule 

[69 FR 49206], revised labor market area 

definitions were adopted at § 412.64(b), 

which were effective October 1, 2004 for 

acute care hospitals. The new standards, 

Core Based Statistical Areas (CBSAs), 

were announced by OMB in late 2000 

and were also discussed in greater detail 

in the July 14, 2005 HH PPS proposed 

rule. For the purposes of the HH PPS, 

the term ‘‘MSA-based’’ refers to wage 

index values and designations based on 

the previous MSA designations. 

Conversely, the term ‘‘CBSA-based’’

refers to wage index values and 

designations based on the new OMB 

revised MSA designations which now 

include CBSAs. In the November 9, 

2005 HH PPS final rule (70 FR 68132), 

we implemented a 1-year transition 

policy using a 50/50 blend of the CBSAbased wage index values and the MSAbased wage index values for CY 2006. 

The one-year transition policy ended in 

CY 2006. For CY 2008, we propose to 

use a wage index based solely on the 

CBSA designations. 

1. Background 

As implemented under the HH PPS in 

the July 3, 2000 HH PPS final rule, each 

HHA’s labor market is determined based 

on definitions of MSAs issued by OMB. 

In general, an urban area is defined as 

an MSA or New England County 

Metropolitan Area (NECMA) as defined 

by OMB. Under § 412.64(b)(1)(ii)(C), a 

rural area is defined as any area outside 

of the urban area. The urban and rural 

area geographic classifications are 

defined in § 412.64(b)(1)(ii)(A) and 

§ 412.64.(b)(1)(II)(C) respectively, and 

have been used under the HH PPS since 

implementation. 

Under the HH PPS, the wage index 

value used is based upon the location of 

the beneficiary’s home. As has been our 

longstanding practice, any area not 

included in an MSA (urban area) is 

considered to be non-urban 

§ 412.64(b)(1)(ii)(C) and receives the 

statewide rural wage index value (see, 

for example, 65 FR 41173). 

As discussed previously and set forth 

in the July 3, 2000 final rule, the statute 

provides that the wage adjustment 

factors may be the factors used by the 

Secretary for purposes of section 

1886(d)(3)(E) of the Act for hospital 

wage adjustment factors. As discussed 

in the July 3, 2000 final rule, we are 

proposing again to use the pre-floor and 

pre-reclassified hospital wage index 

data to adjust the labor portion of the 

HH PPS rates based on the geographic 

area where the beneficiary receives 

home health services. We believe the 

use of the pre-floor and pre-reclassified 

hospital wage index data results in the 

appropriate adjustment to the labor 

portion of the costs as required by 

statute. For the CY 2008 update to home 

health payment rates, we would 

continue to use the most recent pre-floor 

and pre-reclassified hospital wage index 

available at the time of publication. 

In adopting the CBSA designations, 

we identified some geographic areas 

where there are no hospitals, and thus 

no hospital wage data on which to base 

the calculation of the home health wage 

index. Beginning in CY 2006, we 

adopted a policy that, for urban labor 

markets without an urban hospital from 

which a hospital wage index can be 

derived, all of the urban CBSA wage 

index values within the State would be 

used to calculate a statewide urban 

average wage index to use as a 

reasonable proxy for these areas. 

Currently, the only CBSA that would be 

affected by this policy is CBSA 25980, 

Hinesville, Georgia. We propose to 

continue this policy for CY 2008. 

2. Update 

Currently, the only rural areas where 

there are no hospitals from which to 

calculate a hospital wage index are 

Massachusetts and Puerto Rico. For CY 

2006, we adopted a policy in the HH 

PPS November 9, 2005 final rule (70 FR 

68138) of using the CY 2005 pre-floor, 

pre-reclassified hospital wage index 

value. In the August 3, 2006 proposed 

rule, we again proposed to apply the CY 

2005 pre-floor/pre-reclassified hospital 

wage index to rural areas where no 

hospital wage data is available. In 

response to commenters’ concerns and 

in recognition that, in the future, there 

may be additional rural areas impacted 

by a lack of hospital wage data from 

which to derive a wage index, we 

adopted, in the November 9, 2006 final 

rule (71 FR 65905), the following 

methodology for imputing a rural wage 

index for areas where no hospital wage 

data are available as an acceptable 

proxy. The methodology that we 

implemented for CY 2007 imputed an 

average wage index value by averaging 

the wage index values from contiguous 

CBSAs as a reasonable proxy for rural 

areas with no hospital wage data from 

which to calculate a wage index. We 

believe this methodology best meets our 

criteria for imputing a rural wage index 

as well as representing an appropriate 

wage index proxy for rural areas 

without hospital wage data. 

Specifically, such a methodology uses 

pre-floor, pre-reclassified hospital wage 

data, is easy to evaluate, is updateable 

from year to year, and uses the most 

local data available. In determining an 

imputed rural wage index, we define 

‘‘contiguous’’ as sharing a border. For 

Massachusetts, rural Massachusetts 

currently consists of Dukes and 

Nantucket Counties. We determined 

that the borders of Dukes and Nantucket 

counties are ‘‘contiguous’’ with 

Barnstable and Bristol counties. We are 

again proposing to apply this 

methodology for imputing a rural wage 

index for those rural areas without rural 

hospital wage data. While we continue 

to believe that this policy could be 

readily applied to other rural areas that 

lack hospital wage data (possibly due to 

hospitals converting to a different 

provider type (such as a CAH) that does 

not submit the appropriate wage data), 

we specifically solicit comments on this 

issue. 

However, as we noted in the HH PPS 

final rule for CY 2007, we did not 

believe that this policy was appropriate 

for Puerto Rico. As noted in the August 

3, 2006 proposed rule, there are 

sufficient economic differences between 

the hospitals in the United States and 

those in Puerto Rico, including the fact 

that hospitals in Puerto Rico are paid on 

blended Federal/Commonwealthspecific rates, that a separate distinct 

policy for Puerto Rico is necessary. 

Consequently, any alternative 

methodology for imputing a wage index 

for rural Puerto Rico would need to take 

into account those differences. Our 

policy of imputing a rural wage index 

by using an averaged wage index of 

CBSAs contiguous to that rural area 

does not recognize the unique 

circumstances of Puerto Rico. For CY 

2008, we again propose to continue to 

use the most recent wage index 

previously available for Puerto Rico 

which is 0.4047. 

The rural and urban hospital wage 

indexes can be found in Addenda A and 

B of this proposed rule. For HH PPS 

rates addressed in this proposed rule, 

we are using the 2007 pre-floor and prereclassified hospital wage index data, as 

2008 pre-floor and pre-reclassified 

hospital wage index data are not yet 


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available. We propose to use the 2008 

pre-floor and pre-reclassified hospital 

wage index (not including any 

reclassification under section 

1886(d)(8)(B) of the Act) to adjust rates 

for CY 2008 and will publish those wage 

index values in the final rule. 

F. Home Health Care Quality 

Improvement 

Section 5201(c)(2) of the DRA added 

section 1895(b)(3)(B)(v)(II) to the Act, 

requiring that ‘‘each home health agency 

shall submit to the Secretary such data 

that the Secretary determines are 

appropriate for the measurement of 

health care quality. Such data shall be 

submitted in a form and manner, and at 

a time, specified by the Secretary for 

purposes of this clause.’’ In addition, 

section 1895(b)(3)(B)(v)(I) of the Act, as 

also added by section 5201(c)(2) of the 

DRA, dictates that ‘‘for 2007 and each 

subsequent year, in the case of a home 

health agency that does not submit data 

to the Secretary in accordance with 

subclause (II) with respect to such a 

year, the home health market basket 

percentage increase applicable under 

such clause for such year shall be 

reduced by 2 percentage points.’’

The OASIS data currently provide 

consumers and HHAs with 10 publiclyreported home health quality measures 

which have been endorsed by the 

National Quality Forum (NQF). 

Reporting these quality data have also 

required the development of several 

supporting mechanisms such as the 

HAVEN software used to encode and 

transmit data using a CMS standard 

electronic record layout, edit 

specifications, and data dictionary. The 

HAVEN software includes the required 

OASIS data set that has become a 

standard part of HHA operations. These 

early investments in data infrastructure 

and supporting software that CMS and 

HHAs have made over the past several 

years in order to create this quality 

reporting structure have been successful 

in making quality reporting and 

measurement an integral component of 

the HHA industry. The 10 measures 

are—

• Improvement in ambulation/ 

locomotion; 

• Improvement in bathing; 

• Improvement in transferring; 

• Improvement in management of 

oral medications; 

• Improvement in pain interfering 

with activity; 

• Acute care hospitalization; 

• Emergent care; 

• Improvement in dyspnea; 

• Improvement in urinary 

incontinence; and 

• Discharge to community. 

We are proposing to continue to use 

OASIS data and the current 10 quality 

measures, and to add two additional 

quality measures based on those data for 

the CY 2008 HH PPS quality data 

reporting requirement. Continuing to 

use the OASIS instrument ensures that 

providers will not have an additional 

burden of reporting through a separate 

mechanism and that the costs associated 

with the development and testing of a 

new reporting mechanism can be 

avoided. Accordingly, for CY 2008, we 

propose to continue to use submission 

of OASIS data to meet the requirement 

that the HHA submit data appropriate 

for the measurement of health care 

quality. 

We specifically propose to add the 

following two additional quality 

measures as data appropriate for 

measuring health care quality. Adding 

new measures to the currently available 

outcome measures could broaden the 

patient population we can assess, 

expand the types of quality care we can 

measure, and capture an aspect of care 

directly under providers’ control. These 

two wound measures focus on a 

prevalent condition among home health 

beneficiaries. We believe that by adding 

these two measures, we can address 

agencies’ ability to maintain patients in 

their homes. These additional NQF 

endorsed measures that will provide a 

more complete picture of the level of 

quality care delivered by HHAs are the 

following: 

• Emergent Care for Wound 

Infections, Deteriorating Wound Status; 

and 

• Improvement in Status of Surgical 

Wound. 

The data elements used to calculate 

these measures are already captured by 

the OASIS instrument and do not 

require additional reporting or burden 

to HHAs. 

Additionally, section 

1895(b)(3)(B)(v)(II) of the Act provides 

the Secretary with the discretion to 

submit the required data in a form, 

manner, and time specified by him. We 

are proposing for CY 2008 to consider 

OASIS data submitted by HHAs to CMS 

for episodes beginning on or after July 

1, 2006 and before July 1, 2007 as 

meeting the reporting requirement for 

CY 2008. This reporting time period 

would allow 12 full months of data and 

would provide us the time necessary to 

analyze and make any necessary 

payment adjustments to the CY 2008 

payment rates. HHAs that meet the 

reporting requirement would be eligible 

for the full home health market basket 

percentage increase. 

We recognize, however, that the home 

health conditions of participations 

(CoPs) in (42 CFR part 484) that require 

OASIS submission also provide for 

exclusions from the CoP submission 

requirement. Generally, agencies 

excluded from the CoP OASIS 

submission requirement do not receive 

Medicare payments as they either do not 

provide services to Medicare 

beneficiaries or the patients are not 

receiving Medicare-covered home 

health services. Under the CoP, agencies 

are excluded from the OASIS reporting 

requirement on individual patients if—

• Those patients are receiving only 

non-skilled services; 

• Neither Medicare nor Medicaid is 

paying for home health care (patients 

receiving care under a Medicare or 

Medicaid Managed Care Plan are not 

excluded from the OASIS reporting 

requirement); 

• Those patients are receiving pre- or 

post-partum services; and 

• Those patients are under the age of 

18 years. 

We believe that the rationale behind 

the exclusion of these agencies from 

submission of OASIS on patients which 

are excluded from OASIS CoP 

submission is equally applicable to 

HHAs for quality purposes. If an agency 

is not submitting OASIS for patients 

excluded from OASIS submission for 

purposes of a CoP, we believe that the 

submission of OASIS for quality 

measures for Medicare purposes is 

likewise not necessary. Therefore, we 

propose that those agencies do not need 

to submit quality measures for reporting 

purposes for those patients who are 

excluded from the OASIS CoP 

submission. 

Additionally, we propose that 

agencies newly certified (on or after 

May 31, 2007 for payments to be made 

in CY 2008) be excluded from the 

quality reporting requirement as data 

submission and analysis would not be 

possible for an agency certified this late 

in the reporting time period. We again 

propose that in future years, agencies 

that certify on or after May 31 of the 

preceding year involved be excluded 

from any payment penalty for quality 

reporting purposes for the following CY. 

We note these exclusions only affect 

quality reporting requirements and do 

not affect the agency’s OASIS reporting 

responsibilities under the CoP. 

We propose to require that all HHAs, 

unless covered by these specific 

exclusions, meet the reporting 

requirement, or be subject to a 2 percent 

reduction in the home health market 

basket percentage increase in 

accordance with section 

895(b)(3)(B)(v)(I) of the Act. The 2 

percent reduction would apply to all 

episode payments beginning on or after 


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January 1, 2008. We provide the 

proposed reduced payment rates in 

tables 25 and 26. We would reconcile 

the OASIS submissions with claims data 

in order to verify full compliance with 

the quality reporting requirements. 

For episodes that begin in CY 2007 

and end in CY 2008, the new proposed 

153 HHRG case-mix model (and 

associated Grouper) would not yet be in 

effect. For that reason, we propose, for 

HHAs that do not submit required 

quality data (for episodes that begin in 

CY 2007 and end in CY 2008), the 

following: First, we update the CY 2007 

rate of $2,339.00 by the home health 

market basket percentage update (2.9 

percent) minus 2 percent, reduced by 

2.75 percent to account for nominal 

change in case-mix, and multiplied by 

1.05 and 0.958614805 to account for the 

estimated percentage of outlier 

payments as a result of the current FDL 

ratio of 0.67 ($2,339.00 * 1.009 * .9725 

* 1.05 * 0.958614805), to yield an 

updated CY 2008 national standardized 

60-day episode payment rate of 

$2,310.17 for episodes that begin in CY 

2007 and end in CY 2008 for HHAs that 

do not submit required quality data (see 

Table 25a). 

These episodes would be further 

adjusted for case-mix based on the 80 

HHRG case-mix model for episodes 

beginning in CY 2007 and ending in CY 

2008. 

TABLE 25A.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL 60-DAY EPISODE

AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, FOR EPISODES THAT BEGIN IN CY 2007 AND END IN CY 2008 BEFORE CASE-MIX ADJUSTMENT, WAGE

INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT

Total CY 2007 national standardized 60-Day episode payment rate 

Multiply by the 

proposed estimated home 

health market 

basket update 

(2.9 percent)1

Minus 2 percent 

Reduce by 

2.75 percent 

for nominal 

change in 

case-mix 

Adjusted to 

account for the 

5 percent 

outlier policy 

Proposed national standardized 60-day 

episode payment rate for 

episodes beginning in CY 

2007 and ending in CY 2008 

for HHAs that 

do not submit 

required quality data 

$2,339.00 ......................................................................................................... × 1.009 × 0.9725 × 1.05 

× 0.958614805 

$2,310.17 

1The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

Next, in order to establish new rates 

based on a proposed new case-mix 

system, we again start with the CY 2007 

national standardized 60-day episode 

payment rate and increase that rate by 

the proposed estimated rebased and 

revised home health market basket 

update (2.9 percent) minus 2 percent 

($2,339.00 * 1.009 = $2,360.05). We next 

have to put dollars associated with the 

outlier target estimate back into the base 

rate. In the 2000 HH PPS final rule (65 

FR 41184), we divided the base rate by 

1.05 to account for outlier payments. 

Therefore, we are proposing to multiply 

the $2,360.05 by 1.05, resulting in 

$2,478.05. Next we need to reduce this 

amount to pay for each of our proposed 

policies. To do this, we take the 

payment adjustment amount to pay for 

our proposed policies of this rule, 

determined in Table 23a of $226.57, 

multiply it by (1/1.029) to take away the 

2.9 percent increase, and multiply that 

number by 1.009 to impose the 0.9 

percent update for episodes where 

HHAs have not submitted the required 

quality data. This results in a payment 

adjustment amount of $222.17. Finally, 

subtract the payment adjustment 

amount of $222.17 from $2,478.05, for a 

final rate of $2,255.88 for HHAs that do 

not submit quality data, for episodes 

that begin and end in CY 2008. 

These episodes would be further 

adjusted for case-mix based on the 153 

HHRG case-mix model for episodes 

beginning and ending in CY 2008. As 

we noted in section II.A.2.d., we 

increased the case-mix weights by a 

budget neutrality factor of 1.194227193. 


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TABLE 25B.—FOR HHAS THAT DO NOT SUBMIT THE REQURIED QUALITY DATA-PROPOSED NATIONAL 60-DAY EPISODE

AMOUNTS UPDATED BY THE ESTIMATED HOME HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, FOR EPISODES THAT BEGIN AND END IN CY 2008, BEFORE CASE-MIX ADJUSTMENT, WAGE INDEX ADJUSTMENT BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY OR APPLICABLE PAYMENT ADJUSTMENT

Total CY 2007 national standardized 60-day episode payment rate 

Multiply by the 

proposed estimated home 

health market 

basket update 

(2.9 percent) 1

Adjusted to return the outlier 

funds to the 

national standardized 60-day 

episode payment rate 

Updated and 

outlier adjusted national 

standardized 

60-day episode payment 

Changes to 

account for 

LUPA adjustment ($6.46), 

NRS payment 

($40.88), elimination of SCIC 

policy 

($15.71), 

outlier target 

($94.02), and 

2.75 percent 

reduction for 

nominal 

change in 

case-mix 

($69.50) = 

$226.57; 

minus 2 percentage points 

off of the 

home health 

market basket 

update (2.9 

Percent) 1 for 

episodes beginning and 

ending in CY 

2008 

Proposed CY 

2008 national 

standardized 

60-day episode payment 

rate for episodes beginning and ending in CY 2008 

$2,339.00 ............................................................................. × 1.009 × 1.05 $2,478.05 ¥$222.17 $2,255.88 

1The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

In calculating the proposed CY 2008 

national per-visit amounts used to 

calculate payments for LUPA episodes 

for HHAs that do not submit required 

quality data and to compute the 

imputed costs in outlier calculations for 

those episodes, we are proposing to start 

with the CY 2007 per-visit rates. We 

propose to multiply those amounts by 

the proposed estimated home health 

market basket update (2.9 percent) 

minus 2 percentage points, then 

multiply by 1.05 and 0.958614805 to 

account for the estimated percentage of 

outlier payments as a result of the 

current FDL ratio of 0.67, to yield the 

updated per-visit amounts for each 

home health discipline for CY 2008 for 

HHAs that do not submit required 

quality data. 

TABLE 26.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL PER-VISIT AMOUNTS

FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME

HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, BEFORE WAGE INDEX ADJUSTMENT

BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY

Home health discipline type 

Final CY 2007 

per-visit 

amounts per 

60-day episode 

for LUPAs 

Multiply by the 

proposed estimated home 

health market 

basket 

(2.9 percent) 1

Adjusted to 

account for the 

5 percent outlier 

policy 

Proposed CY 

2008 per-visit 

payment 

amount per 

discipline for a 

beneficiary who 

resides in a 

non-MSA for 

HHAs that do 

not submit 

required quality 

data 

Home Health Aide ................................................................................... $46.24 ×1.009 ×1.05 $46.96 

×0.958614805 . 

Medical Social Services ........................................................................... 163.68 ×1.009 ×1.05 166.23 

× 0.958614805 

Occupational Therapy .............................................................................. 112.40 ×1.009 ×10.5 114.15 

×0.958614805 

Physical Therapy ..................................................................................... 111.65 ×1.009 × 1.05 113.39 

×0.958614805 

Skilled Nursing ......................................................................................... 102.11 ×1.009 ×1.05 103.70 

.......................... .......................... ×0.958614805 


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TABLE 26.—FOR HHAS THAT DO NOT SUBMIT THE REQUIRED QUALITY DATA-PROPOSED NATIONAL PER-VISIT AMOUNTS

FOR LUPAS (NOT INCLUDING THE INCREASE IN PAYMENT FOR A BENEFICIARY’S ONLY EPISODE OR THE INITIAL EPISODE IN A SEQUENCE OF ADJACENT EPISODES) AND OUTLIER CALCULATIONS UPDATED BY THE ESTIMATED HOME

HEALTH MARKET BASKET UPDATE FOR CY 2008, MINUS 2 PERCENTAGE POINTS, BEFORE WAGE INDEX ADJUSTMENT

BASED ON THE SITE OF SERVICE FOR THE BENEFICIARY—Continued 

Home health discipline type 

Final CY 2007 

per-visit 

amounts per 

60-day episode 

for LUPAs 

Multiply by the 

proposed estimated home 

health market 

basket 

(2.9 percent) 1

Adjusted to 

account for the 

5 percent outlier 

policy 

Proposed CY 

2008 per-visit 

payment 

amount per 

discipline for a 

beneficiary who 

resides in a 

non-MSA for 

HHAs that do 

not submit 

required quality 

data 

Speech-Language Pathology .................................................................. 121.22 ×1.009 ×1.05 123.11 

×0.958614805 

The estimated home health market basket update of 2.9 percent for CY 2008 is based on Global Insight, Inc, 4th Qtr, 2006 forecast with historical data through 3rd Qtr, 2006. 

Section 1895(b)(3)(B)(v)(III) of the Act 

further requires that the ‘‘Secretary shall 

establish procedures for making data 

submitted under subclause (II) available 

to the public.’’ Additionally, the statute 

requires that ‘‘such procedures shall 

ensure that a home health agency has 

the opportunity to review the data that 

is to be made public with respect to the 

agency before such data being made 

public.’’ To meet the requirement for 

making such data public, we are 

proposing to continue to use the Home 

Health Compare Web site whereby 

HHAs are listed geographically. 

Currently, the 10 existing quality 

measures are posted on the Home 

Health Compare Web site. The Home 

Health Compare Web site will also 

include the two proposed additional 

measures discussed earlier. Consumers 

can search for all Medicare-approved 

home health providers that serve their 

city or zip code and then find the 

agencies offering the types of services 

they need as well as the proposed 

quality measures. See http:// 

www.medicare.gov/HHCompare/ 

Home.asp. HHAs currently have access 

(through the Home Health Compare 

contractor) to their own agency’s quality 

data (updated periodically) and we 

propose to continue this process thus 

enabling each agency to know how it is 

performing before public posting of data 

on the Home Health Compare Web site. 

Over the next year, we will be testing 

patient level process measures for 

HHAs, as well as continuing to refine 

the current OASIS tool in response to 

recommendations from a TEP 

conducted to review the data elements 

that make up the OASIS tool. We expect 

to introduce these complementary 

additional measures during CY 2008 to 

determine if they should be 

incorporated into the statutory quality 

measure reporting requirements. We 

hope to apply these measures to the CY 

2010 reporting period. Before usage in 

the HH PPS, we will test and refine 

these measures to determine if they can 

more accurately reflect the level of 

quality care being provided at HHAs 

without being overly burdensome with 

the data collection instrument. To the 

extent that evidence-based data are 

available on which to determine the 

appropriate measure specifications, and 

adequate risk-adjustments are made, we 

anticipate collecting and reporting these 

measures as part of each agency’s home 

health quality plan. We believe that 

future modifications to the current 

OASIS tool, refinements to the possible 

responses as well as adding new process 

measures will be made. In all cases, we 

anticipate that any future quality 

measures should be evidence-based, 

clearly linked to improved outcomes, 

and able to be reliably captured with the 

least burden to the provider. We are also 

working on developing measures of 

patient experience in the home health 

setting through the development of the 

Home Health Consumer Assessment of 

Healthcare Providers and Systems 

(CAHPS) Survey. We will be working 

with the Agency for Healthcare 

Research and Quality (AHRQ) to field 

test this instrument in summer/fall 

2007. We anticipate implementing the 

Home Health CAHPS Survey in late 

2008 for potential application to the CY 

2010 pay for reporting requirements. 

III. Collection of Information 

Requirements 

Under the Paperwork Reduction Act 

(PRA) of 1995, we are required to 

provide 60-day notice in the Federal 

Register and solicit public comment 

before a collection of information 

requirement is submitted to the Office of 

Management and Budget (OMB) for 

review and approval. In order to fairly 

evaluate whether an information 

collection should be approved by OMB, 

section 3506(c)(2)(A) of the PRA of 1995 

requires that we solicit comment on the 

following issues: 

• The need for the information 

collection and its usefulness in carrying 

out the proper functions of our agency. 

• The accuracy of our estimate of the 

information collection burden. 

• The quality, utility, and clarity of 

the information to be collected. 

• Recommendations to minimize the 

information collection burden on the 

affected public, including automated 

collection techniques. 

Therefore, we are soliciting public 

comments on each of these issues for 

the information collection requirements 

discussed below. 

To implement the OASIS changes 

discussed in sections II.A.(2)(a), 

II.A.(2)(b), and II.A.(2)(c) of this 

proposed rule, which are currently 

approved in § 484.55, § 484.205, and 

§ 484.250, a few items in the OASIS will 

need to be modified, deleted, or added. 

The requirements and burden associated 

with the OASIS are currently approved 

under OMB control number 0938–0760 

with an expiration date of August 31, 

2007. We are soliciting public comment 

on each of the proposed changes for the 

information collection requirements 

(ICRs) as summarized and discussed 

below. For the purposes of soliciting 

public review and comment, we have 

placed a current draft of the proposed 

changes to the OASIS on the CMS Web 

site at: http://www.cms.hhs.gov/ 


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PaperworkReductionActof1995/PRAL/ 

list.asp#TopOfPage. 

As discussed in section II.A.(2)(a) of 

this proposed rule, in order for the 

OASIS to have the information 

necessary to allow the grouper to priceout the claim, we propose to make the 

following changes to the OASIS to 

capture whether an episode is an early 

or later episode: 

The creation of a new OASIS item to 

capture whether a particular 

assessment, is for an episode considered 

to be an early episode or a later episode 

in the patient’s current sequence of 

adjacent Medicare home health payment 

episodes. As defined in section II.A.1. of 

this proposed rule, we defined a 

sequence of adjacent episodes for a 

beneficiary as a series of claims with no 

more than 60-days without home care 

between the end of one episode, which 

is the 60th day (except for episode that 

have been PEP-adjusted), and the 

beginning of the next episode. This 

definition holds true regardless of 

whether or not the same HHA provided 

care for the entire sequence of adjacent 

episodes. The HHA will chose from the 

options: ‘‘Early’’ for single episodes or 

the first or second episode in a sequence 

of adjacent episodes, ‘‘Later’’ for third or 

later episodes, ‘‘UK’’ for unknown if the 

HHA is uncertain as to whether the 

episode is an early or later episode (the 

payment grouper software will default 

to the definition of an ‘‘early’’ episode), 

and ‘‘NA’’ for not applicable (no 

Medicare case-mix group to be defined 

by this assessment). 

As discussed in section II.A.(2)(b) of 

this proposed rule, we propose to make 

changes to the OASIS in order to enable 

agencies to report secondary case-mix 

diagnosis codes. The proposed changes 

clarify how to appropriately fill out 

OASIS items M0230 and M0240, using 

ICD–9–CM sequencing requirements if 

multiple coding is indicated for any 

diagnosis. Additionally, if a V-code is 

reported in place of a case-mix 

diagnosis for OASIS item M0230 or 

M0240, then the new optional OASIS 

item (which is replacing existing OASIS 

item M0245) may then be completed. A 

case-mix diagnosis is a diagnosis that 

determines the HH PPS case-mix group. 

As discussed in section II.A.(2)(c) of 

this proposed rule, we propose to make 

changes to the OASIS to capture the 

projected total number of therapy visits 

for a given episode. With the projected 

total number of therapy visits, the 

payment grouper would be able to group 

that episode into the appropriate casemix group for payment. The existing 

OASIS item M0825 asks an HHA if the 

projected number of therapy visits 

would meet the therapy threshold or 

not. As noted previously, we propose to 

delete OASIS item M0825 and replace it 

with a new OASIS item. The OASIS 

item would ask the following: ‘‘In the 

plan of care for the Medicare payment 

episode for which this assessment will 

define a case-mix group, what is the 

indicated need for therapy visits (total 

of reasonable and necessary physical, 

occupational, and speech-pathology 

visits combined)?’’ The HHA would 

provide the total number of projected 

therapy visits for that Medicare payment 

episode, unless not applicable (that is, 

no case-mix group defined by this 

assessment). The HHA would enter 

‘‘000’’ if no therapy visits were 

projected for that particular episode. 

The burden associated with the 

proposed changes discussed in sections 

II.A.(2)(a), II.A.(2)(b), and II.A.(2)(c) of 

this rule includes possible training of 

staff, the time and effort associated with 

downloading a new form and replacing 

previously pre-printed versions of the 

OASIS, and utilizing updated vendor 

software. However, as stated above, 

CMS would be removing or modifying 

existing questions in the OASIS data set 

to accommodate the proposed 

requirements referenced above. In 

addition, as a result of the proposed 

changes of this rule, we expect that the 

claims processing system is expected to 

automatically adjust the therapy visits, 

upward and downward on the final 

claim, according to the information on 

the final claim. 

Consequently, the HHA would no 

longer have to withdraw and resubmit a 

revised claim when the number of 

therapy visits delivered to the patient is 

higher than the level report on the RAP. 

Therefore, CMS believes the burden 

increase associated with these changes 

is negated by the removal or 

modification of several current data 

items. 

We have submitted a copy of this 

proposed rule to OMB for its review of 

the information collection requirements 

described above. These requirements are 

not effective until OMB has approved 

them. 

If you comment on any of these 

information collection and record 

keeping requirements, please mail 

copies directly to the following: 

Centers for Medicare & Medicaid 

Services, Office of Strategic 

Operations and Regulatory Affairs, 

Regulations Development Group, 

Attn.: Melissa Musotto, CMS–1541–P, 

Room C4–26–05, 7500 Security 

Boulevard, Baltimore, MD 21244–

1850; and Office of Information and 

Regulatory Affairs, Office of 

Management and Budget, Room 

10235, New Executive Office 

Building, Washington, DC 20503, 

Attn: Carolyn Lovett, CMS Desk 

Officer, (CMS–1541–P), 

carolyn_lovett@omb.eop.gov. Fax 

(202) 395–6974. 

IV. Response to Comments 

Because of the large number of public 

comments 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 proposed rule, and, when we 

proceed with subsequent document, we 

will respond to the comments in the 

preamble to that document. 

V. 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. Overall Impact 

We have examined the impacts of this 

rule as required by Executive Order 

12866 (September 1993, Regulatory 

Planning and Review), the Regulatory 

Flexibility Act (RFA) (September 19, 

1980, Pub. L. 96–354), section 1102(b) of 

the Social Security Act, the Unfunded 

Mandates Reform Act of 1995 (Pub. L. 

104–4), and Executive Order 13132. 

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). 

This proposed rule would be a major 

rule, as defined in Title 5, United States 

Code, section 804(2), because we 

estimate the impact to the Medicare 

program, and the annual effects to the 

overall economy, would be more than 

$100 million. The update set forth in 

this proposed rule would apply to 

Medicare payments under the HH PPS 

in CY 2008. 

Accordingly, the following analysis 

describes the impact in CY 2008 only. 

We estimate that the net impact of the 

proposals in this rule, including a 2.75 

percent reduction to the case-mix 

weights to account for nominal increase 

in case-mix, is estimated to be 


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approximately $140 million in CY 2008 

expenditures. That estimate 

incorporates the 2.9 percent home 

health market basket increase (an 

estimated additional $410 million in CY 

2008 expenditures attributable only to 

the CY 2008 proposed estimated home 

health market basket update), an 

estimated additional $130 million due 

to the increase in the HH PPS rates as 

a result of maintaining a FDL ratio of 

0.67, and the 2.75 percent decrease 

(¥$400 million for the first year of a 3- 

year phase-in) to the HH PPS national 

standardized 60-day episode rate to 

account for the nominal increase in 

case-mix under the HH PPS. Given that 

we allowed for a FDL ratio of 0.67, all 

HH PPS rates were adjusted slightly 

upward by a factor of 0.008614805. 

Column 6 of Table 27 displays a 0.95 

percent increase in expenditures when 

comparing the CY 2007 current system 

to the proposed revised CY 2008 system. 

This equates to approximately $140 

million and is driven primarily by the 

adjustment made to maintain the FDL 

ratio at 0.67 and partially by the 

difference between the 2.9 percent 

update and the 2.75 percent reduction 

to the HH PPS rates. 

The RFA requires agencies to analyze 

options for regulatory relief of small 

businesses. For purposes of the RFA, 

small entities include small businesses, 

nonprofit organizations, and small 

governmental jurisdictions. Most 

hospitals and most other providers and 

suppliers are small entities, either by 

nonprofit status or by having revenues 

of $6 million to $29 million in any 1 

year. For purposes of the RFA, 

approximately 75 percent of HHAs are 

considered small businesses according 

to the Small Business Administration’s 

size standards with total revenues of 

$11.5 million or less in any 1 year. 

Individuals and States are not included 

in the definition of a small entity. As 

stated above, this proposed rule would 

have an estimated positive effect upon 

small entities that are HHAs. 

In addition, section 1102(b) of the Act 

requires us to prepare a regulatory 

impact analysis if a rule 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. For purposes of section 1102(b) of 

the Act, we define a small rural hospital 

as a hospital that is located outside of 

a Metropolitan Statistical Area and has 

fewer than 100 beds. We have 

determined that this proposed rule 

would not have a significant economic 

impact on the operations of a substantial 

number of small rural hospitals. 

Section 202 of the Unfunded 

Mandates Reform Act of 1995 also 

requires that agencies assess anticipated 

costs and benefits before issuing any 

rule that may result in expenditure in 

any 1 year by State, local, or tribal 

governments, in the aggregate, or by the 

private sector, of $110 million. We 

believe this proposed rule would not 

mandate expenditures in that amount. 

Executive Order 13132 establishes 

certain requirements that an agency 

must meet when it promulgates a 

proposed rule (and subsequent final 

rule) that imposes substantial direct 

requirement costs on State and local 

governments, preempts State law, or 

otherwise has Federalism implications. 

We have determined that this proposed 

rule would not have substantial direct 

effects on the rights, roles, and 

responsibilities of States. 

B. Anticipated Effects 

This proposed rule would update the 

HH PPS rates contained in the CY 2007 

final rule (71 FR 65884, November 9, 

2006). The impact analysis of this 

proposed rule presents the refinement 

related policy changes proposed in this 

rule. We use the best data available, but 

we do not attempt to predict behavioral 

responses to these changes, and we do 

not make adjustments for future changes 

in such variables as days or case-mix. 

This analysis incorporates the latest 

estimates of growth in service use and 

payments under the Medicare home 

health benefit, based on the latest 

available Medicare claims from 2003. 

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 made by the Congress, or 

changes specifically related to HHAs. In 

addition, changes to the Medicare 

program may continue to be made as a 

result of the BBA, the BBRA, the 

Medicare, Medicaid, and SCHIP 

Benefits Improvement and Protection 

Act of 2000, the MMA, the DRA, or new 

statutory provisions. Although these 

changes may not be specific to the HH 

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 HHAs. 

Table 27 represents how home health 

agencies are likely to be affected by the 

policy changes described in this rule. 

For each agency type listed below, Table 

27 displays the average case-mix index, 

both under the current HH PPS case-mix 

system and the proposed CY 2008 HH 

PPS case-mix system. For this analysis, 

we used the most recent data available 

that linked home health claims and 

OASIS assessments, a 10 percent sample 

of episodes occurring in FY 2003. In 

Table 27, the average case-mix is the 

same, in the aggregate, between the 

current HH PPS system and the 

proposed revised HH PPS system, due 

to our application of a budget neutrality 

factor for the case-mix weights. Column 

one of this table classifies HHAs 

according to a number of characteristics 

including provider type, geographic 

region, and urban versus rural location. 

Column two displays the average casemix weight for each type of agency 

under the current payment system. 

Column three displays the average casemix weight for each type of agency 

incorporating all of the changes/ 

refinements discussed above. The 

average case-mix weight for proprietary 

(for profit) agencies is estimated to 

decrease from 1.2601 to 1.2227. 

Comparatively, the average case-mix 

weight for voluntary non-profit agencies 

is estimated to increase from 1.1404 to 

1.1716. Rural agencies are estimated to 

experience a decrease in their average 

case-mix from 1.1583 to 1.1417. It is 

estimated that urban agencies would see 

a slight increase in their average casemix weight from 1.2032 to 1.2074. In 

particular, the New England, MidAtlantic, East North Central, Mountain, 

and West North Central areas of the 

country are estimated to see their 

average case-mix increase under the 

proposed refinements of this rule. 

Conversely, the West South Central, 

East South Central, Pacific, and South 

Atlantic areas of the country are 

estimated to see their average case-mix 

decrease as a result of proposed 

refinements of this rule. Both small and 

large agencies are estimated to see 

decreases in their average case-mix 

under the new proposed case-mix 

system, the only exception being much 

larger agencies (200+ first episodes), 

which are estimated to see an increase 

of their average case-mix from 1.1769 to 

1.1920. 

For the purposes of analyzing impacts 

on payments, we performed three 

simulations and compared them to each 

other. The first simulation estimated 

2007 payments under the current 

system. The second simulation 

estimated 2008 payments as though 

there would be no changes to the 

payment system other than the rebased 

and revised home health market basket 

increase of 2.9 percent. The second 


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simulation produces an estimate of what 

total payments using the sample data 

would be in 2008 without making any 

of the proposed changes described in 

this proposed rule. 

The third simulation estimates what 

total payments would be in 2008, using 

the proposed case-mix model, the 

proposed additional payment for initial 

and only episode LUPA episodes, the 

proposed removal of SCIC adjustments, 

and the proposed revised approach to 

making NRS payments. The third 

simulation also assumed payments 

would incorporate the rebased and 

revised home health market basket 

increase of 2.9 percent, the current 

outlier threshold determined by a FDL 

ratio of 0.67, and the 2.75 percent 

reduction in the national standardized 

60-day episode payment rate to account 

for the proposed nominal change in 

case-mix. All three simulations used the 

same CBSA wage index (we used a 

crosswalk from the MSA reported on the 

2003 claims to the CBSA to determine 

the appropriate wage index). The results 

of comparing these simulations are 

displayed in columns four, five, and six 

of Table 27. 

Column four shows the percentage 

change in estimated total payments in 

moving from CY 2007 to a CY 2008 

system incorporating none of the 

proposed refinements to the HH PPS 

except for the rebased and revised home 

health market basket increase of 2.9 

percent. Column five shows the 

percentage change in estimated total 

payments in moving from a CY 2008 

system that incorporates none of the 

proposed changes to the HH PPS except 

for the rebased and revised home health 

market basket increase of 2.9 percent to 

the proposed revised CY 2008 system of 

this rule. Finally, column six shows the 

percentage change in estimated total 

payments in moving from CY 2007 to 

the proposed revised CY 2008 system of 

this rule. 

In general terms, the percentage 

change in estimated total payments from 

CY 2007 to a CY 2008 system that 

incorporates none of the proposed 

refinements to the HH PPS except for 

the rebased and revised home health 

market basket update of 2.9 percent is 

approximately the home health market 

basket increase of 2.9 percent. Some of 

the classifications of HHAs show a 

slightly less than 2.9 percent increase in 

this comparison, which is due to the CY 

2007 system incorporating the current 

labor share, which is slightly less than 

the labor share being proposed for the 

CY 2008 system. 

When comparing a CY 2008 system 

that incorporates none of the 

refinements to the HH PPS except for 

the rebased and revised home health 

market basket increase of 2.9 percent 

with the proposed revised CY 2008 

system of this rule, it is estimated that 

under the proposed revised CY 2008 

system of this rule, total estimated 

payments would decrease by 

approximately 1.88 percent. 

Comparatively, the percentage change in 

estimated total payments from CY 2007 

to the proposed revised CY 2008 system 

of this rule is an increase of just under 

1 percent (0.95 percent). All three 

simulations incorporate a FDL ratio of 

0.67. By maintaining the FDL ratio of 

0.67, we believe we will continue to 

meet the statutory requirement of 

having an outlier payment outlay that 

does not exceed 5 percent of total HH 

PPS payments. In maintaining a 0.67 

FDL ratio for CY 2008, in order to 

maintain budget neutrality (other than 

the 2.75 percent reduction to the HH 

PPS rates to account for nominal casemix change), HH PPS rates are increased 

slightly, as stated earlier in this section. 

In general, voluntary non-profit HHAs 

(3.56 percent), facility-based HHAs (3.50 

percent), government owned HHAs 

(3.04 percent) and free-standing HHAs 

(0.10 percent) are estimated to see an 

increase in the percentage change in 

estimated total payments from CY 2007 

to the proposed revised CY 2008 system. 

Proprietary HHAs, on the other hand are 

estimated to see a decrease of 1.90 

percent in estimated total payments 

from CY 2007 to the proposed revised 

CY 2008 system. The major contributor 

to this decrease of 1.90 percent is the 

free-standing proprietary HHAs, which 

are estimated to see a decrease of 

slightly more than 2 percent in the 

percentage change in estimated total 

payment from CY 2007 to the proposed 

revised CY 2008 system. 

We note that some of these impacts 

are partly explained by practice patterns 

associated with certain types of 

agencies. For example, LUPA episodes 

are relatively common among nonprofit 

agencies and freestanding governmentowned agencies. Our proposal for an 

additional payment for certain LUPA 

episodes would tend to increase 

payments for such classes of agencies 

with higher-than-average LUPA rates, 

while tending to decrease payments for 

agencies with comparatively low LUPA 

rates. Similarly, the proposed 

elimination of the SCIC policy would 

tend to favorably affect total payments 

for agencies with relatively high rates of 

SCIC episodes, such as facility-based 

proprietary agencies and facility-based 

government agencies. The percentage 

change in estimated total payments from 

CY 2007 to a CY 2008 system that 

incorporates all of the refinements to the 

HH PPS for rural HHAs is a slight 

decrease of 0.50 percent, while for 

urban HHAs an increase of 1.26 percent 

is expected. Urban agencies have 

somewhat higher LUPA rates than rural 

agencies, so urban agencies would be 

expected to benefit, relative to rural 

agencies, from the proposal to make an 

additional payment for certain LUPA 

episodes. Urban agencies are also more 

likely to benefit from elimination of the 

SCIC policy. Urban agencies are less 

likely to bill a SCIC episode than rural 

agencies. However, when urban 

agencies do bill a SCIC episode the 

payment is reduced more, on average, 

than when rural agencies bill a SCIC. 

The net effect of these two components 

(relative frequency and payment impact 

per SCIC episode) is a larger expected 

reduction for urban agencies under the 

SCIC adjustment policy. Therefore, 

while both urban and rural agencies 

benefit from eliminating the SCIC 

policy, urban agencies benefit more. 

HHAs in the North are expected to 

experience a percentage change increase 

of 4.33 percent in estimated total 

payments from CY 2007 to the proposed 

revised CY 2008 system. The only 

region estimated to experience a 

decrease in the percentage change in 

estimated total payments from CY 2007 

to the proposed revised CY 2008 system 

is the South. That percentage change is 

an estimated decrease of 1.84 percent. It 

is estimated that New England and Mid 

Atlantic area HHAs will experience 

percentage change increases of slightly 

more than 4 percent (New England, 4.10 

percent and the Mid-Atlantic, 4.45 

percent) in estimated total payments 

from CY 2007 to the proposed revised 

CY 2008 system. Conversely, West 

South Central HHAs are expected to 

experience a decrease (¥3.80 percent) 

in the percentage change in estimated 

total payments from CY 2007 to the 

proposed CY 2008 system. In general, 

smaller HHAs are expected to 

experience a decrease (ranging from 

¥0.63 percent to ¥2.76 percent) for 

their percentage change in estimated 

total payments from CY 2007 to the 

proposed revised CY 2008 system. 

Conversely, larger HHAs are estimated 

to experience an increase (ranging from 

0.59 percent to 2.16 percent) in the 

percent change in estimated total 

payments from CY 2007 to the proposed 

CY 2008 system. 

BILLING CODE 4120–01–P 


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pwalker on PROD1PC71 with PROPOSALS2EP04MY07.041</GPH>
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25457 

C. Accounting Statement 

As Required by OMB Circular A–4 

(available at http:// 

www.whitehouse.gov/omb/circulars/ 

a004/a-4.pdf), in Table 28 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 HH PPS as a result of the changes 

presented in this proposed rule based 

on the data for 8,164 HHAs in our 

database. All expenditures are classified 

as transfers to Medicare providers (that 

is, HHAs). 

TABLE 28.—ACCOUNTING STATEMENT: CLASSIFICATION OF ESTIMATED EXPENDITURES, FROM CY 2007 TO CY 2008 

[In millions] 

Category Transfers 

Annualized Monetized Transfers .................................................................................................... $140. 

From Whom to Whom? ................................................................................................................... Federal Government to HHAs. 

In accordance with the provisions of 

Executive Order 12866, this regulation 

was reviewed by the Office of 

Management and Budget. 

List of Subjects in 42 CFR Part 484 

Health facilities, Health professions, 

Medicare, and Reporting and 

recordkeeping requirements. 

For the reasons set forth in the 

preamble, the Centers for Medicare & 

Medicaid Services would amend 42 CFR 

chapter IV as set forth below: 

PART 484—HOME HEALTH SERVICES 

1. The authority citation for part 484 

continues to read as follows: 

Authority: Secs. 1102 and 1871 of the 

Social Security Act (42 U.S.C.1302 and 

1395(hh)). 

Subpart E—Prospective Payment 

System for Home Health Agencies 

§ 484.205 [Amended] 

2. Amend § 484.205 by—

A. Removing paragraph (a)(3). 

B. Redesignating paragraph (a)(4) as 

paragraph (a)(3). 

C. Revising paragraph (b) introductory 

text. 

D. Removing paragraph (e). 

E. Redesignating paragraph (f) as 

paragraph (e). 

The revisions read as follows: 

§ 484.205 Basis of payment. 

* * * * * 

(b) Episode payment. The national 

prospective 60-day episode payment 

represents payment in full for all costs 

associated with furnishing home health 

services previously paid on a reasonable 

cost basis (except the osteoporosis drug 

listed in section 1861(m) of the Act as 

defined in section 1861(kk) of the Act) 

as of August 5, 1997 unless the national 

60-day episode payment is subject to a 

low-utilization payment adjustment set 

forth in § 484.230, a partial episode 

payment adjustment set forth at 

§ 484.235, or an additional outlier 

payment set forth in § 484.240. All 

payments under this system may be 


pwalker on PROD1PC71 with PROPOSALS2EP04MY07.042</GPH>
25458 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

subject to a medical review adjustment 

reflecting beneficiary eligibility, medical 

necessity determinations, and HHRG 

assignment. DME provided as a home 

health service as defined in section 

1861(m) of the Act continues to be paid 

the fee schedule amount. 

* * * * * 

3. Revise § 484.220 to read as follows: 

§ 484.220 Calculation of the adjusted 

national prospective 60-day episode 

payment rate for case-mix and area wage 

levels. 

CMS adjusts the national prospective 

60-day episode payment rate to account 

for the following: 

(a) HHA case-mix using a case-mix 

index to explain the relative resource 

utilization of different patients. To 

address changes to the case-mix that are 

a result of changes in the coding or 

classification of different units of 

service that do not reflect real changes 

in case-mix, the national prospective 60- 

day episode payment rate will be 

adjusted downward as follows: 

(1) For CY 2008 the adjustment is 2.75 

percent. 

(2) For CY 2009 and CY 2010, the 

adjustment is 2.75 percent in each year. 

(b) Geographic differences in wage 

levels using an appropriate wage index 

based on the site of service of the 

beneficiary. 

4. Amend § 484.230 by adding a third, 

fourth, and fifth sentence after the 

second sentence to read as follows: 

§ 484.230 Methodology used for the 

calculation of the low-utilization payment 

adjustment. 

* * * For 2008 and subsequent 

calendar years, an amount will be added 

to low-utilization payment adjustments 

for low-utilization episodes that occur 

as the beneficiary’s only episode or 

initial episode in a sequence of adjacent 

episodes. For purposes of the home 

health PPS, a sequence of adjacent 

episodes for a beneficiary is a series of 

claims with no more than 60 days 

without home care between the end of 

one episode, which is the 60th day 

(except for episodes that have been PEPadjusted), and the beginning of the next 

episode. This additional amount will be 

updated annually after 2008 by a factor 

equal to the applicable home health 

market basket percentage. 

§ 484.237 [Removed] 

5. Remove § 484.237. 

(Catalog of Federal Domestic Assistance 

Program No. 93.773, Medicare—Hospital 

Insurance; and Program No. 93.774, 

Medicare—Supplementary Medical 

Insurance Program) 

Dated: February 15, 2007. 

Leslie V. Norwalk, 

Acting Administrator, Centers for Medicare 

& Medicaid Services. 

Approved: April 2, 2007. 

Michael O. Leavitt, 

Secretary. 


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Note: The following addenda will not be 

published in the Code of Federal Regulations. 

ADDENDUM A.—CY 2007 WAGE INDEX

FOR RURAL AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED HOSPITAL WAGE INDEX

CBSA 

code Nonurban area Wage index 

01 ............. Alabama .................. 0.7592 

02 ............. Alaska ..................... 1.0661 

03 ............. Arizona .................... 0.8909 

04 ............. Arkansas ................. 0.7307 

05 ............. California ................. 1.1454 

06 ............. Colorado ................. 0.9325 

07 ............. Connecticut ............. 1.1709 

08 ............. Delaware ................. 0.9706 

10 ............. Florida ..................... 0.8594 

11 ............. Georgia ................... 0.7593 

12 ............. Hawaii ..................... 1.0449 

13 ............. Idaho ....................... 0.8120 

14 ............. Illinois ...................... 0.8320 

15 ............. Indiana .................... 0.8539 

16 ............. Iowa ........................ 0.8682 

17 ............. Kansas .................... 0.7999 

18 ............. Kentucky ................. 0.7769 

19 ............. Louisiana ................ 0.7438 

20 ............. Maine ...................... 0.8443 

21 ............. Maryland ................. 0.8927 

22 ............. Massachusetts 1 ...... 1.0661 

ADDENDUM A.—CY 2007 WAGE INDEX

FOR RURAL AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED HOSPITAL WAGE

INDEX—Continued 

CBSA 

code Nonurban area Wage index 

23 ............. Michigan ................. 0.9063 

24 ............. Minnesota ............... 0.9153 

25 ............. Mississippi .............. 0.7738 

26 ............. Missouri .................. 0.7927 

27 ............. Montana .................. 0.8590 

28 ............. Nebraska ................ 0.8678 

29 ............. Nevada ................... 0.8944 

30 ............. New Hampshire ...... 1.0853 

31 ............. New Jersey 1,2 ........ ................

32 ............. New Mexico ............ 0.8333 

33 ............. New York ................ 0.8232 

34 ............. North Carolina ........ 0.8589 

35 ............. North Dakota .......... 0.7216 

36 ............. Ohio ........................ 0.8659 

37 ............. Oklahoma ............... 0.7629 

38 ............. Oregon .................... 0.9753 

39 ............. Pennsylvania .......... 0.8321 

40 ............. Puerto Rico 3 ........... 0.4047 

41 ............. Rhode Island 2 ........ ................

42 ............. South Carolina ........ 0.8566 

43 ............. South Dakota .......... 0.8480 

44 ............. Tennessee .............. 0.7827 

45 ............. Texas ...................... 0.7965 

ADDENDUM A.—CY 2007 WAGE INDEX

FOR RURAL AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED HOSPITAL WAGE

INDEX—Continued 

CBSA 

code Nonurban area Wage index 

46 ............. Utah ........................ 0.8141 

47 ............. Vermont .................. 0.9744 

48 ............. Virgin Islands .......... 0.8467 

49 ............. Virginia .................... 0.7941 

50 ............. Washington ............. 1.0263 

51 ............. West Virginia .......... 0.7607 

52 ............. Wisconsin ............... 0.9553 

53 ............. Wyoming ................. 0.9295 

65 ............. Guam ...................... 0.9611 

1All counties within the State are classified 

as rural. No short-term, acute care hospitals 

are located in the area(s). The rural wage 

index for Massachusetts is imputed using the 

methodology discussed in section II.E.2 of this 

rule. 2All counties within the State are classified 

as urban. 3All counties within the State are classified 

as rural. No short-term, acute care hospitals 

are located in the area(s). We will continue to 

use the wage index from CY 2005, which was 

the last year in which we had ‘‘rural’’ hospital 

wage data for Puerto Rico. 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX

CBSA 

code Urban area (constituent counties) Wage index 

10180 ....... Abilene, TX ............................................................................................................................................................................ 0.8001 

Callahan County, TX.

Jones County, TX.

Taylor County, TX.

10380 ....... Aguadilla-Isabela-San Sebastia ́n, PR ................................................................................................................................... 0.3915 

Aguada Municipio, PR.

Aguadilla Municipio, PR.

An ̃asco Municipio, PR.

Isabela Municipio, PR.

Lares Municipio, PR.

Moca Municipio, PR.

Rinco ́n Municipio, PR.

San Sebastia ́n Municipio, PR.

10420 ....... Akron, OH .............................................................................................................................................................................. 0.8654 

Portage County, OH.

Summit County, OH.

10500 ....... Albany, GA ............................................................................................................................................................................. 0.8991 

Baker County, GA.

Dougherty County, GA.

Lee County, GA.

Terrell County, GA.

Worth County, GA.

10580 ....... Albany-Schenectady-Troy, NY .............................................................................................................................................. 0.8720 

Albany County, NY.

Rensselaer County, NY.

Saratoga County, NY.

Schenectady County, NY.

Schoharie County, NY.

10740 ....... Albuquerque, NM ................................................................................................................................................................... 0.9458 

Bernalillo County, NM.

Sandoval County, NM.

Torrance County, NM.

Valencia County, NM.

10780 ....... Alexandria, LA ....................................................................................................................................................................... 0.8006 

Grant Parish, LA.

Rapides Parish, LA.


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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

10900 ....... Allentown-Bethlehem-Easton, PA-NJ .................................................................................................................................... 0.9947 

Warren County, NJ.

Carbon County, PA.

Lehigh County, PA.

Northampton County, PA.

11020 ....... Altoona, PA ............................................................................................................................................................................ 0.8812 

Blair County, PA.

11100 ....... Amarillo, TX ........................................................................................................................................................................... 0.9161 

Armstrong County, TX.

Carson County, TX.

Potter County, TX.

Randall County, TX.

11180 ....... Ames, IA ................................................................................................................................................................................ 0.9760 

Story County, IA.

11260 ....... Anchorage, AK ....................................................................................................................................................................... 1.2024 

Anchorage Municipality, AK.

Matanuska-Susitna Borough, AK.

11300 ....... Anderson, IN .......................................................................................................................................................................... 0.8681 

Madison County, IN.

11340 ....... Anderson, SC ........................................................................................................................................................................ 0.9017 

Anderson County, SC.

11460 ....... Ann Arbor, MI ........................................................................................................................................................................ 1.0826 

Washtenaw County, MI.

11500 ....... Anniston-Oxford, AL .............................................................................................................................................................. 0.7770 

Calhoun County, AL.

11540 ....... Appleton, WI .......................................................................................................................................................................... 0.9455 

Calumet County, WI.

Outagamie County, WI.

11700 ....... Asheville, NC ......................................................................................................................................................................... 0.9077 

Buncombe County, NC.

Haywood County, NC.

Henderson County, NC.

Madison County, NC.

12020 ....... Athens-Clarke County, GA .................................................................................................................................................... 0.9856 

Clarke County, GA.

Madison County, GA.

Oconee County, GA.

Oglethorpe County, GA.

12060 ....... Atlanta-Sandy Springs-Marietta, GA ..................................................................................................................................... 0.9762 

Barrow County, GA.

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 ..................................................................................................................................................................... 1.1831 

Atlantic County, NJ.

12220 ....... Auburn-Opelika, AL ............................................................................................................................................................... 0.8096 


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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Lee County, AL.

12260 ....... Augusta-Richmond County, GA-SC ...................................................................................................................................... 0.9667 

Burke County, GA.

Columbia County, GA.

McDuffie County, GA.

Richmond County, GA.

Aiken County, SC.

Edgefield County, SC.

12420 ....... Austin-Round Rock, TX ......................................................................................................................................................... 0.9344 

Bastrop County, TX.

Caldwell County, TX.

Hays County, TX.

Travis County, TX.

Williamson County, TX.

12540 ....... Bakersfield, CA ...................................................................................................................................................................... 1.0726 

Kern County, CA.

12580 ....... Baltimore-Towson, MD .......................................................................................................................................................... 1.0088 

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 ............................................................................................................................................................................ 0.9712 

Penobscot County, ME.

12700 ....... Barnstable Town, MA ............................................................................................................................................................ 1.2540 

Barnstable County, MA.

12940 ....... Baton Rouge, LA ................................................................................................................................................................... 0.8085 

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 ..................................................................................................................................................................... 0.9763 

Calhoun County, MI.

13020 ....... Bay City, MI ........................................................................................................................................................................... 0.9252 

Bay County, MI.

13140 ....... Beaumont-Port Arthur, TX ..................................................................................................................................................... 0.8595 

Hardin County, TX.

Jefferson County, TX.

Orange County, TX.

13380 ....... Bellingham, WA ..................................................................................................................................................................... 1.1105 

Whatcom County, WA.

13460 ....... Bend, OR ............................................................................................................................................................................... 1.0743 

Deschutes County, OR.

13644 ....... Bethesda-Frederick-Gaithersburg, MD .................................................................................................................................. 1.0904 

Frederick County, MD.

Montgomery County, MD.

13740 ....... Billings, MT ............................................................................................................................................................................ 0.8713 

Carbon County, MT.

Yellowstone County, MT.

13780 ....... Binghamton, NY ..................................................................................................................................................................... 0.8786 

Broome County, NY.

Tioga County, NY.

13820 ....... Birmingham-Hoover, AL ........................................................................................................................................................ 0.8994 

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 ......................................................................................................................................................................... 0.7240 

Burleigh County, ND.

Morton County, ND.


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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

13980 ....... Blacksburg-Christiansburg-Radford, VA ................................................................................................................................ 0.8213 

Giles County, VA.

Montgomery County, VA.

Pulaski County, VA.

Radford City, VA.

14020 ....... Bloomington, IN ..................................................................................................................................................................... 0.8533 

Greene County, IN.

Monroe County, IN.

Owen County, IN.

14060 ....... Bloomington-Normal, IL ......................................................................................................................................................... 0.8945 

McLean County, IL.

14260 ....... Boise City-Nampa, ID ............................................................................................................................................................ 0.9401 

Ada County, ID.

Boise County, ID.

Canyon County, ID.

Gem County, ID.

Owyhee County, ID.

14484 ....... Boston-Quincy, MA ................................................................................................................................................................ 1.1679 

Norfolk County, MA.

Plymouth County, MA.

Suffolk County, MA.

14500 ....... Boulder, CO ........................................................................................................................................................................... 1.0350 

Boulder County, CO.

14540 ....... Bowling Green, KY ................................................................................................................................................................ 0.8148 

Edmonson County, KY.

Warren County, KY.

14740 ....... Bremerton-Silverdale, WA ..................................................................................................................................................... 1.0914 

Kitsap County, WA.

14860 ....... Bridgeport-Stamford-Norwalk, CT ......................................................................................................................................... 1.2659 

Fairfield County, CT.

15180 ....... Brownsville-Harlingen, TX ..................................................................................................................................................... 0.9430 

Cameron County, TX.

15260 ....... Brunswick, GA ....................................................................................................................................................................... 1.0165 

Brantley County, GA.

Glynn County, GA.

McIntosh County, GA.

15380 ....... Buffalo-Niagara Falls, NY ...................................................................................................................................................... 0.9424 

Erie County, NY.

Niagara County, NY.

15500 ....... Burlington, NC ....................................................................................................................................................................... 0.8674 

Alamance County, NC.

15540 ....... Burlington-South Burlington, VT ............................................................................................................................................ 0.9475 

Chittenden County, VT.

Franklin County, VT.

Grand Isle County, VT.

15764 ....... Cambridge-Newton-Framingham, MA ................................................................................................................................... 1.0970 

Middlesex County, MA.

15804 ....... Camden, NJ ........................................................................................................................................................................... 1.0393 

Burlington County, NJ.

Camden County, NJ.

Gloucester County, NJ.

15940 ....... Canton-Massillon, OH ............................................................................................................................................................ 0.9032 

Carroll County, OH.

Stark County, OH.

15980 ....... Cape Coral-Fort Myers, FL .................................................................................................................................................... 0.9343 

Lee County, FL.

16180 ....... Carson City, NV ..................................................................................................................................................................... 1.0026 

Carson City, NV.

16220 ....... Casper, WY ........................................................................................................................................................................... 0.9145 

Natrona County, WY.

16300 ....... Cedar Rapids, IA ................................................................................................................................................................... 0.8888 

Benton County, IA.

Jones County, IA.

Linn County, IA.

16580 ....... Champaign-Urbana, IL .......................................................................................................................................................... 0.9645 

Champaign County, IL.

Ford County, IL.

Piatt County, IL.

16620 ....... Charleston, WV ...................................................................................................................................................................... 0.8543 

Boone County, WV.


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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Clay County, WV.

Kanawha County, WV.

Lincoln County, WV.

Putnam County, WV.

16700 ....... Charleston-North Charleston, SC .......................................................................................................................................... 0.9145 

Berkeley County, SC.

Charleston County, SC.

Dorchester County, SC.

16740 ....... Charlotte-Gastonia-Concord, NC-SC .................................................................................................................................... 0.9555 

Anson County, NC.

Cabarrus County, NC.

Gaston County, NC.

Mecklenburg County, NC.

Union County, NC.

York County, SC.

16820 ....... Charlottesville, VA ................................................................................................................................................................. 1.0125 

Albemarle County, VA.

Fluvanna County, VA.

Greene County, VA.

Nelson County, VA.

Charlottesville City, VA.

16860 ....... Chattanooga, TN-GA ............................................................................................................................................................. 0.8948 

Catoosa County, GA.

Dade County, GA.

Walker County, GA.

Hamilton County, TN.

Marion County, TN.

Sequatchie County, TN.

16940 ....... Cheyenne, WY ....................................................................................................................................................................... 0.9060 

Laramie County, WY.

16974 ....... Chicago-Naperville-Joliet, IL .................................................................................................................................................. 1.0752 

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 ............................................................................................................................................................................... 1.1054 

Butte County, CA.

17140 ....... Cincinnati-Middletown, OH-KY-IN ......................................................................................................................................... 0.9601 

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 .................................................................................................................................................................. 0.8436 

Christian County, KY.

Trigg County, KY.

Montgomery County, TN.

Stewart County, TN.

17420 ....... Cleveland, TN ........................................................................................................................................................................ 0.8110 

Bradley County, TN.

Polk County, TN.

17460 ....... Cleveland-Elyria-Mentor, OH ................................................................................................................................................. 0.9400 

Cuyahoga County, OH.

Geauga County, OH.

Lake County, OH.

Lorain County, OH.


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25464 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Medina County, OH.

17660 ....... Coeur d’Alene, ID .................................................................................................................................................................. 0.9344 

Kootenai County, ID.

17780 ....... College Station-Bryan, TX ..................................................................................................................................................... 0.9046 

Brazos County, TX.

Burleson County, TX.

Robertson County, TX.

17820 ....... Colorado Springs, CO ........................................................................................................................................................... 0.9701 

El Paso County, CO.

Teller County, CO.

17860 ....... Columbia, MO ........................................................................................................................................................................ 0.8543 

Boone County, MO.

Howard County, MO.

17900 ....... Columbia, SC ......................................................................................................................................................................... 0.8934 

Calhoun County, SC.

Fairfield County, SC.

Kershaw County, SC.

Lexington County, SC.

Richland County, SC.

Saluda County, SC.

17980 ....... Columbus, GA-AL .................................................................................................................................................................. 0.8239 

Russell County, AL.

Chattahoochee County, GA.

Harris County, GA.

Marion County, GA.

Muscogee County, GA.

18020 ....... Columbus, IN ......................................................................................................................................................................... 0.9318 

Bartholomew County, IN.

18140 ....... Columbus, OH ....................................................................................................................................................................... 1.0107 

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 ................................................................................................................................................................. 0.8564 

Aransas County, TX.

Nueces County, TX.

San Patricio County, TX.

18700 ....... Corvallis, OR .......................................................................................................................................................................... 1.1546 

Benton County, OR.

19060 ....... Cumberland, MD-WV ............................................................................................................................................................. 0.8447 

Allegany County, MD.

Mineral County, WV.

19124 ....... Dallas-Plano-Irving, TX .......................................................................................................................................................... 1.0076 

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 ............................................................................................................................................................................. 0.9093 

Murray County, GA.

Whitfield County, GA.

19180 ....... Danville, IL ............................................................................................................................................................................. 0.9267 

Vermilion County, IL.

19260 ....... Danville, VA ........................................................................................................................................................................... 0.8451 

Pittsylvania County, VA.

Danville City, VA.

19340 ....... Davenport-Moline-Rock Island, IA-IL ..................................................................................................................................... 0.8847 

Henry County, IL.

Mercer County, IL.

Rock Island County, IL.

Scott County, IA.

19380 ....... Dayton, OH ............................................................................................................................................................................ 0.9037 

Greene County, OH.


pwalker on PROD1PC71 with PROPOSALS2
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ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Miami County, OH.

Montgomery County, OH.

Preble County, OH.

19460 ....... Decatur, AL ............................................................................................................................................................................ 0.8160 

Lawrence County, AL.

Morgan County, AL.

19500 ....... Decatur, IL ............................................................................................................................................................................. 0.8173 

Macon County, IL.

19660 ....... Deltona-Daytona Beach-Ormond Beach, FL ......................................................................................................................... 0.9264 

Volusia County, FL.

19740 ....... Denver-Aurora, CO ................................................................................................................................................................ 1.0931 

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 ...................................................................................................................................................................... 0.9214 

Dallas County, IA.

Guthrie County, IA.

Madison County, IA.

Polk County, IA.

Warren County, IA.

19804 ....... Detroit-Livonia-Dearborn, MI ................................................................................................................................................. 1.0282 

Wayne County, MI.

20020 ....... Dothan, AL ............................................................................................................................................................................. 0.7381 

Geneva County, AL.

Henry County, AL.

Houston County, AL.

20100 ....... Dover, DE .............................................................................................................................................................................. 0.9848 

Kent County, DE.

20220 ....... Dubuque, IA ........................................................................................................................................................................... 0.9134 

Dubuque County, IA.

20260 ....... Duluth, MN-WI ....................................................................................................................................................................... 1.0042 

Carlton County, MN.

St. Louis County, MN.

Douglas County, WI.

20500 ....... Durham, NC ........................................................................................................................................................................... 0.9826 

Chatham County, NC.

Durham County, NC.

Orange County, NC.

Person County, NC.

20740 ....... Eau Claire, WI ....................................................................................................................................................................... 0.9630 

Chippewa County, WI.

Eau Claire County, WI.

20764 ....... Edison, NJ ............................................................................................................................................................................. 1.1190 

Middlesex County, NJ.

Monmouth County, NJ.

Ocean County, NJ.

Somerset County, NJ.

20940 ....... El Centro, CA ......................................................................................................................................................................... 0.9076 

Imperial County, CA.

21060 ....... Elizabethtown, KY .................................................................................................................................................................. 0.8698 

Hardin County, KY.

Larue County, KY.

21140 ....... Elkhart-Goshen, IN ................................................................................................................................................................ 0.9426 

Elkhart County, IN.

21300 ....... Elmira, NY .............................................................................................................................................................................. 0.8240 

Chemung County, NY.

21340 ....... El Paso, TX ............................................................................................................................................................................ 0.9053 

El Paso County, TX.

21500 ....... Erie, PA .................................................................................................................................................................................. 0.8828 

Erie County, PA.

21604 ....... Essex County, MA ................................................................................................................................................................. 1.0419 

Essex County, MA.

21660 ....... Eugene-Springfield, OR ......................................................................................................................................................... 1.0877 


pwalker on PROD1PC71 with PROPOSALS2
25466 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Lane County, OR.

21780 ....... Evansville, IN-KY ................................................................................................................................................................... 0.9071 

Gibson County, IN.

Posey County, IN.

Vanderburgh County, IN.

Warrick County, IN.

Henderson County, KY.

Webster County, KY.

21820 ....... Fairbanks, AK ........................................................................................................................................................................ 1.1060 

Fairbanks North Star Borough, AK.

21940 ....... Fajardo, PR ............................................................................................................................................................................ 0.4037 

Ceiba Municipio, PR.

Fajardo Municipio, PR.

Luquillo Municipio, PR.

22020 ....... Fargo, ND-MN ....................................................................................................................................................................... 0.8251 

Cass County, ND.

Clay County, MN.

22140 ....... Farmington, NM ..................................................................................................................................................................... 0.8589 

San Juan County, NM.

22180 ....... Fayetteville, NC ..................................................................................................................................................................... 0.8946 

Cumberland County, NC.

Hoke County, NC.

22220 ....... Fayetteville-Springdale-Rogers, AR-MO ............................................................................................................................... 0.8865 

Benton County, AR.

Madison County, AR.

Washington County, AR.

McDonald County, MO.

22380 ....... Flagstaff, AZ .......................................................................................................................................................................... 1.1601 

Coconino County, AZ.

22420 ....... Flint, MI .................................................................................................................................................................................. 1.0969 

Genesee County, MI.

22500 ....... Florence, SC .......................................................................................................................................................................... 0.8388 

Darlington County, SC.

Florence County, SC.

22520 ....... Florence-Muscle Shoals, AL .................................................................................................................................................. 0.7844 

Colbert County, AL.

Lauderdale County, AL.

22540 ....... Fond du Lac, WI .................................................................................................................................................................... 1.0064 

Fond du Lac County, WI.

22660 ....... Fort Collins-Loveland, CO ..................................................................................................................................................... 0.9545 

Larimer County, CO.

22744 ....... Fort Lauderdale-Pompano Beach-Deerfield Beach, FL ........................................................................................................ 1.0134 

Broward County, FL.

22900 ....... Fort Smith, AR-OK ................................................................................................................................................................. 0.7732 

Crawford County, AR.

Franklin County, AR.

Sebastian County, AR.

Le Flore County, OK.

Sequoyah County, OK.

23020 ....... Fort Walton Beach-Crestview-Destin, FL .............................................................................................................................. 0.8643 

Okaloosa County, FL.

23060 ....... Fort Wayne, IN ...................................................................................................................................................................... 0.9517 

Allen County, IN.

Wells County, IN.

Whitley County, IN.

23104 ....... Fort Worth-Arlington, TX ........................................................................................................................................................ 0.9570 

Johnson County, TX.

Parker County, TX.

Tarrant County, TX.

Wise County, TX.

23420 ....... Fresno, CA ............................................................................................................................................................................. 1.0943 

Fresno County, CA.

23460 ....... Gadsden, AL .......................................................................................................................................................................... 0.8066 

Etowah County, AL.

23540 ....... Gainesville, FL ....................................................................................................................................................................... 0.9277 

Alachua County, FL.

Gilchrist County, FL.

23580 ....... Gainesville, GA ...................................................................................................................................................................... 0.8959 

Hall County, GA.

23844 ....... Gary, IN ................................................................................................................................................................................. 0.9334 


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25467 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Jasper County, IN.

Lake County, IN.

Newton County, IN.

Porter County, IN.

24020 ....... Glens Falls, NY ...................................................................................................................................................................... 0.8325 

Warren County, NY.

Washington County, NY.

24140 ....... Goldsboro, NC ....................................................................................................................................................................... 0.9171 

Wayne County, NC.

24220 ....... Grand Forks, ND-MN ............................................................................................................................................................. 0.7949 

Polk County, MN.

Grand Forks County, ND.

24300 ....... Grand Junction, CO ............................................................................................................................................................... 0.9669 

Mesa County, CO.

24340 ....... Grand Rapids-Wyoming, MI .................................................................................................................................................. 0.9455 

Barry County, MI.

Ionia County, MI.

Kent County, MI.

Newaygo County, MI.

24500 ....... Great Falls, MT ...................................................................................................................................................................... 0.8598 

Cascade County, MT.

24540 ....... Greeley, CO ........................................................................................................................................................................... 0.9602 

Weld County, CO.

24580 ....... Green Bay, WI ....................................................................................................................................................................... 0.9787 

Brown County, WI.

Kewaunee County, WI.

Oconto County, WI.

24660 ....... Greensboro-High Point, NC ................................................................................................................................................... 0.8866 

Guilford County, NC.

Randolph County, NC.

Rockingham County, NC.

24780 ....... Greenville, NC ....................................................................................................................................................................... 0.9432 

Greene County, NC.

Pitt County, NC.

24860 ....... Greenville, SC ........................................................................................................................................................................ 0.9804 

Greenville County, SC.

Laurens County, SC.

Pickens County, SC.

25020 ....... Guayama, PR ........................................................................................................................................................................ 0.3235 

Arroyo Municipio, PR.

Guayama Municipio, PR.

Patillas Municipio, PR.

25060 ....... Gulfport-Biloxi, MS ................................................................................................................................................................. 0.8915 

Hancock County, MS.

Harrison County, MS.

Stone County, MS.

25180 ....... Hagerstown-Martinsburg, MD-WV ......................................................................................................................................... 0.9039 

Washington County, MD.

Berkeley County, WV.

Morgan County, WV.

25260 ....... Hanford-Corcoran, CA ........................................................................................................................................................... 1.0282 

Kings County, CA.

25420 ....... Harrisburg-Carlisle, PA .......................................................................................................................................................... 0.9402 

Cumberland County, PA.

Dauphin County, PA.

Perry County, PA.

25500 ....... Harrisonburg, VA ................................................................................................................................................................... 0.9074 

Rockingham County, VA.

Harrisonburg City, VA.

25540 ....... Hartford-West Hartford-East Hartford, CT ............................................................................................................................. 1.0894 

Hartford County, CT.

Litchfield County, CT.

Middlesex County, CT.

Tolland County, CT.

25620 ....... Hattiesburg, MS ..................................................................................................................................................................... 0.7430 

Forrest County, MS.

Lamar County, MS.

Perry County, MS.

25860 ....... Hickory-Lenoir-Morganton, NC .............................................................................................................................................. 0.9010 

Alexander County, NC.


pwalker on PROD1PC71 with PROPOSALS2
25468 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Burke County, NC.

Caldwell County, NC.

Catawba County, NC.

259801 ..... Hinesville-Fort Stewart, GA ................................................................................................................................................... 0.9178 

Liberty County, GA.

Long County, GA.

26100 ....... Holland-Grand Haven, MI ...................................................................................................................................................... 0.9163 

Ottawa County, MI.

26180 ....... Honolulu, HI ........................................................................................................................................................................... 1.1096 

Honolulu County, HI.

26300 ....... Hot Springs, AR ..................................................................................................................................................................... 0.8782 

Garland County, AR.

26380 ....... Houma-Bayou Cane-Thibodaux, LA ...................................................................................................................................... 0.8082 

Lafourche Parish, LA.

Terrebonne Parish, LA.

26420 ....... Houston-Baytown-Sugar Land, TX ........................................................................................................................................ 1.0009 

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 ........................................................................................................................................... 0.8998 

Boyd County, KY.

Greenup County, KY.

Lawrence County, OH.

Cabell County, WV.

Wayne County, WV.

26620 ....... Huntsville, AL ......................................................................................................................................................................... 0.9007 

Limestone County, AL.

Madison County, AL.

26820 ....... Idaho Falls, ID ....................................................................................................................................................................... 0.9088 

Bonneville County, ID.

Jefferson County, ID.

26900 ....... Indianapolis, IN ...................................................................................................................................................................... 0.9896 

Boone County, IN.

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 .......................................................................................................................................................................... 0.9714 

Johnson County, IA.

Washington County, IA.

27060 ....... Ithaca, NY .............................................................................................................................................................................. 0.9928 

Tompkins County, NY.

27100 ....... Jackson, MI ............................................................................................................................................................................ 0.9560 

Jackson County, MI.

27140 ....... Jackson, MS .......................................................................................................................................................................... 0.8271 

Copiah County, MS.

Hinds County, MS.

Madison County, MS.

Rankin County, MS.

Simpson County, MS.

27180 ....... Jackson, TN ........................................................................................................................................................................... 0.8853 

Chester County, TN.

Madison County, TN.

27260 ....... Jacksonville, FL ..................................................................................................................................................................... 0.9166 

Baker County, FL.

Clay County, FL.

Duval County, FL.

Nassau County, FL.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25469 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

St. Johns County, FL.

27340 ....... Jacksonville, NC .................................................................................................................................................................... 0.8231 

Onslow County, NC.

27500 ....... Janesville, WI ......................................................................................................................................................................... 0.9655 

Rock County, WI.

27620 ....... Jefferson City, MO ................................................................................................................................................................. 0.8333 

Callaway County, MO.

Cole County, MO.

Moniteau County, MO.

Osage County, MO.

27740 ....... Johnson City, TN ................................................................................................................................................................... 0.8043 

Carter County, TN.

Unicoi County, TN.

Washington County, TN.

27780 ....... Johnstown, PA ....................................................................................................................................................................... 0.8620 

Cambria County, PA.

27860 ....... Jonesboro, AR ....................................................................................................................................................................... 0.7662 

Craighead County, AR.

Poinsett County, AR.

27900 ....... Joplin, MO .............................................................................................................................................................................. 0.8606 

Jasper County, MO.

Newton County, MO.

28020 ....... Kalamazoo-Portage, MI ......................................................................................................................................................... 1.0705 

Kalamazoo County, MI.

Van Buren County, MI.

28100 ....... Kankakee-Bradley, IL ............................................................................................................................................................ 1.0083 

Kankakee County, IL.

28140 ....... Kansas City, MO-KS .............................................................................................................................................................. 0.9495 

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.

Lafayette County, MO.

Platte County, MO.

Ray County, MO.

28420 ....... Kennewick-Richland-Pasco, WA ........................................................................................................................................... 1.0343 

Benton County, WA.

Franklin County, WA.

28660 ....... Killeen-Temple-Fort Hood, TX ............................................................................................................................................... 0.8902 

Bell County, TX.

Coryell County, TX.

Lampasas County, TX.

28700 ....... Kingsport-Bristol-Bristol, TN-VA ............................................................................................................................................ 0.7985 

Hawkins County, TN.

Sullivan County, TN.

Bristol City, VA.

Scott County, VA.

Washington County, VA.

28740 ....... Kingston, NY .......................................................................................................................................................................... 0.9367 

Ulster County, NY.

28940 ....... Knoxville, TN .......................................................................................................................................................................... 0.8249 

Anderson County, TN.

Blount County, TN.

Knox County, TN.

Loudon County, TN.

Union County, TN.

29020 ....... Kokomo, IN ............................................................................................................................................................................ 0.9669 

Howard County, IN.

Tipton County, IN.

29100 ....... La Crosse, WI-MN ................................................................................................................................................................. 0.9426 

Houston County, MN.

La Crosse County, WI.


pwalker on PROD1PC71 with PROPOSALS2
25470 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

29140 ....... Lafayette, IN .......................................................................................................................................................................... 0.8932 

Benton County, IN.

Carroll County, IN.

Tippecanoe County, IN.

29180 ....... Lafayette, LA .......................................................................................................................................................................... 0.8289 

Lafayette Parish, LA.

St. Martin Parish, LA.

29340 ....... Lake Charles, LA ................................................................................................................................................................... 0.7914 

Calcasieu Parish, LA.

Cameron Parish, LA.

29404 ....... Lake County-Kenosha County, IL-WI .................................................................................................................................... 1.0571 

Lake County, IL.

Kenosha County, WI.

29460 ....... Lakeland, FL .......................................................................................................................................................................... 0.8879 

Polk County, FL.

29540 ....... Lancaster, PA ........................................................................................................................................................................ 0.9589 

Lancaster County, PA.

29620 ....... Lansing-East Lansing, MI ...................................................................................................................................................... 1.0088 

Clinton County, MI.

Eaton County, MI.

Ingham County, MI.

29700 ....... Laredo, TX ............................................................................................................................................................................. 0.7812 

Webb County, TX.

29740 ....... Las Cruces, NM ..................................................................................................................................................................... 0.9273 

Dona Ana County, NM.

29820 ....... Las Vegas-Paradise, NV ....................................................................................................................................................... 1.1430 

Clark County, NV.

29940 ....... Lawrence, KS ........................................................................................................................................................................ 0.8366 

Douglas County, KS.

30020 ....... Lawton, OK ............................................................................................................................................................................ 0.8066 

Comanche County, OK.

30140 ....... Lebanon, PA .......................................................................................................................................................................... 0.8680 

Lebanon County, PA.

30300 ....... Lewiston, ID-WA .................................................................................................................................................................... 0.9854 

Nez Perce County, ID.

Asotin County, WA.

30340 ....... Lewiston-Auburn, ME ............................................................................................................................................................ 0.9126 

Androscoggin County, ME.

30460 ....... Lexington-Fayette, KY ........................................................................................................................................................... 0.9181 

Bourbon County, KY.

Clark County, KY.

Fayette County, KY.

Jessamine County, KY.

Scott County, KY.

Woodford County, KY.

30620 ....... Lima, OH ................................................................................................................................................................................ 0.9042 

Allen County, OH.

30700 ....... Lincoln, NE ............................................................................................................................................................................ 1.0092 

Lancaster County, NE.

Seward County, NE.

30780 ....... Little Rock-North Little Rock, AR ........................................................................................................................................... 0.8890 

Faulkner County, AR.

Grant County, AR.

Lonoke County, AR.

Perry County, AR.

Pulaski County, AR.

Saline County, AR.

30860 ....... Logan, UT-ID ......................................................................................................................................................................... 0.9022 

Franklin County, ID.

Cache County, UT.

30980 ....... Longview, TX ......................................................................................................................................................................... 0.8788 

Gregg County, TX.

Rusk County, TX.

Upshur County, TX.

31020 ....... Longview, WA ........................................................................................................................................................................ 1.0011 

Cowlitz County, WA.

31084 ....... Los Angeles-Long Beach-Glendale, CA ................................................................................................................................ 1.1760 

Los Angeles County, CA.

31140 ....... Louisville, KY-IN .................................................................................................................................................................... 0.9119 

Clark County, IN.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25471 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

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 ........................................................................................................................................................................... 0.8613 

Crosby County, TX.

Lubbock County, TX.

31340 ....... Lynchburg, VA ....................................................................................................................................................................... 0.8694 

Amherst County, VA.

Appomattox County, VA.

Bedford County, VA.

Campbell County, VA.

Bedford City, VA.

Lynchburg City, VA.

31420 ....... Macon, GA ............................................................................................................................................................................. 0.9520 

Bibb County, GA.

Crawford County, GA.

Jones County, GA.

Monroe County, GA.

Twiggs County, GA.

31460 ....... Madera, CA ............................................................................................................................................................................ 0.8155 

Madera County, CA.

31540 ....... Madison, WI ........................................................................................................................................................................... 1.0840 

Columbia County, WI.

Dane County, WI.

Iowa County, WI.

31700 ....... Manchester-Nashua, NH ....................................................................................................................................................... 1.0243 

Hillsborough County, NH.

Merrimack County, NH.

31900 ....... Mansfield, OH ........................................................................................................................................................................ 0.9271 

Richland County, OH.

32420 ....... Mayagu ̈ez, PR ....................................................................................................................................................................... 0.3848 

Hormigueros Municipio, PR.

Mayagu ̈ez Municipio, PR.

32580 ....... McAllen-Edinburg-Pharr, TX .................................................................................................................................................. 0.8773 

Hidalgo County, TX.

32780 ....... Medford, OR .......................................................................................................................................................................... 1.0818 

Jackson County, OR.

32820 ....... Memphis, TN-MS-AR ............................................................................................................................................................. 0.9373 

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 ............................................................................................................................................................................ 1.1471 

Merced County, CA.

33124 ....... Miami-Miami Beach-Kendall, FL ............................................................................................................................................ 0.9813 

Miami-Dade County, FL.

33140 ....... Michigan City-La Porte, IN .................................................................................................................................................... 0.9118 

LaPorte County, IN.

33260 ....... Midland, TX ............................................................................................................................................................................ 0.9786 

Midland County, TX.

33340 ....... Milwaukee-Waukesha-West Allis, WI .................................................................................................................................... 1.0218 

Milwaukee County, WI.

Ozaukee County, WI.

Washington County, WI.

Waukesha County, WI.

33460 ....... Minneapolis-St. Paul-Bloomington, MN-WI ........................................................................................................................... 1.0946 

Anoka County, MN.


pwalker on PROD1PC71 with PROPOSALS2
25472 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

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 ......................................................................................................................................................................... 0.8929 

Missoula County, MT.

33660 ....... Mobile, AL .............................................................................................................................................................................. 0.7914 

Mobile County, AL.

33700 ....... Modesto, CA .......................................................................................................................................................................... 1.1730 

Stanislaus County, CA.

33740 ....... Monroe, LA ............................................................................................................................................................................ 0.7997 

Ouachita Parish, LA.

Union Parish, LA.

33780 ....... Monroe, MI ............................................................................................................................................................................. 0.9708 

Monroe County, MI.

33860 ....... Montgomery, AL .................................................................................................................................................................... 0.8009 

Autauga County, AL.

Elmore County, AL.

Lowndes County, AL.

Montgomery County, AL.

34060 ....... Morgantown, WV ................................................................................................................................................................... 0.8423 

Monongalia County, WV.

Preston County, WV.

34100 ....... Morristown, TN ...................................................................................................................................................................... 0.7933 

Grainger County, TN.

Hamblen County, TN.

Jefferson County, TN.

34580 ....... Mount Vernon-Anacortes, WA ............................................................................................................................................... 1.0518 

Skagit County, WA.

34620 ....... Muncie, IN .............................................................................................................................................................................. 0.8562 

Delaware County, IN.

34740 ....... Muskegon-Norton Shores, MI ................................................................................................................................................ 0.9941 

Muskegon County, MI.

34820 ....... Myrtle Beach-Conway-North Myrtle Beach, SC .................................................................................................................... 0.8811 

Horry County, SC.

34900 ....... Napa, CA ............................................................................................................................................................................... 1.3375 

Napa County, CA.

34940 ....... Naples-Marco Island, FL ....................................................................................................................................................... 0.9941 

Collier County, FL.

34980 ....... Nashville-Davidson-Murfreesboro, TN ................................................................................................................................... 0.9847 

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 ............................................................................................................................................................... 1.2663 

Nassau County, NY.

Suffolk County, NY.

35084 ....... Newark-Union, NJ-PA ............................................................................................................................................................ 1.1892 

Essex County, NJ.

Hunterdon County, NJ.

Morris County, NJ.

Sussex County, NJ.

Union County, NJ.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25473 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Pike County, PA.

35300 ....... New Haven-Milford, CT ......................................................................................................................................................... 1.1953 

New Haven County, CT.

35380 ....... New Orleans-Metairie-Kenner, LA ......................................................................................................................................... 0.8832 

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-NJ ................................................................................................................................. 1.3177 

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 ........................................................................................................................................................ 0.8915 

Berrien County, MI.

35980 ....... Norwich-New London, CT ..................................................................................................................................................... 1.1932 

New London County, CT.

36084 ....... Oakland-Fremont-Hayward, CA ............................................................................................................................................ 1.5819 

Alameda County, CA.

Contra Costa County, CA.

36100 ....... Ocala, FL ............................................................................................................................................................................... 0.8867 

Marion County, FL.

36140 ....... Ocean City, NJ ...................................................................................................................................................................... 1.0472 

Cape May County, NJ.

36220 ....... Odessa, TX ............................................................................................................................................................................ 1.0102 

Ector County, TX.

36260 ....... Ogden-Clearfield, UT ............................................................................................................................................................. 0.8995 

Davis County, UT.

Morgan County, UT.

Weber County, UT.

36420 ....... Oklahoma City, OK ................................................................................................................................................................ 0.8843 

Canadian County, OK.

Cleveland County, OK.

Grady County, OK.

Lincoln County, OK.

Logan County, OK.

McClain County, OK.

Oklahoma County, OK.

36500 ....... Olympia, WA .......................................................................................................................................................................... 1.1081 

Thurston County, WA.

36540 ....... Omaha-Council Bluffs, NE-IA ................................................................................................................................................ 0.9450 

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 ............................................................................................................................................................................ 0.9452 

Lake County, FL.

Orange County, FL.

Osceola County, FL.

Seminole County, FL.

36780 ....... Oshkosh-Neenah, WI ............................................................................................................................................................ 0.9315 

Winnebago County, WI.

36980 ....... Owensboro, KY ...................................................................................................................................................................... 0.8748 

Daviess County, KY.

Hancock County, KY.

McLean County, KY.


pwalker on PROD1PC71 with PROPOSALS2
25474 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

37100 ....... Oxnard-Thousand Oaks-Ventura, CA ................................................................................................................................... 1.1546 

Ventura County, CA.

37340 ....... Palm Bay-Melbourne-Titusville, FL ........................................................................................................................................ 0.9443 

Brevard County, FL.

37460 ....... Panama City-Lynn Haven, FL ............................................................................................................................................... 0.8027 

Bay County, FL.

37620 ....... Parkersburg-Marietta, WV-OH ............................................................................................................................................... 0.7978 

Washington County, OH.

Pleasants County, WV.

Wirt County, WV.

Wood County, WV.

37700 ....... Pascagoula, MS ..................................................................................................................................................................... 0.8215 

George County, MS.

Jackson County, MS.

37860 ....... Pensacola-Ferry Pass-Brent, FL ........................................................................................................................................... 0.8000 

Escambia County, FL.

Santa Rosa County, FL.

37900 ....... Peoria, IL ............................................................................................................................................................................... 0.8982 

Marshall County, IL.

Peoria County, IL.

Stark County, IL.

Tazewell County, IL.

Woodford County, IL.

37964 ....... Philadelphia, PA .................................................................................................................................................................... 1.0997 

Bucks County, PA.

Chester County, PA.

Delaware County, PA.

Montgomery County, PA.

Philadelphia County, PA.

38060 ....... Phoenix-Mesa-Scottsdale, AZ ............................................................................................................................................... 1.0288 

Maricopa County, AZ.

Pinal County, AZ.

38220 ....... Pine Bluff, AR ........................................................................................................................................................................ 0.8383 

Cleveland County, AR.

Jefferson County, AR.

Lincoln County, AR.

38300 ....... Pittsburgh, PA ........................................................................................................................................................................ 0.8674 

Allegheny County, PA.

Armstrong County, PA.

Beaver County, PA.

Butler County, PA.

Fayette County, PA.

Washington County, PA.

Westmoreland County, PA.

38340 ....... Pittsfield, MA .......................................................................................................................................................................... 1.0266 

Berkshire County, MA.

38540 ....... Pocatello, ID .......................................................................................................................................................................... 0.9401 

Bannock County, ID.

Power County, ID.

38660 ....... Ponce, PR .............................................................................................................................................................................. 0.4843 

Juana Dı ́az Municipio, PR.

Ponce Municipio, PR.

Villalba Municipio, PR.

38860 ....... Portland-South Portland-Biddeford, ME ................................................................................................................................ 0.9909 

Cumberland County, ME.

Sagadahoc County, ME.

York County, ME.

38900 ....... Portland-Vancouver-Beaverton, OR-WA ............................................................................................................................... 1.1416 

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 ................................................................................................................................................ 0.9834 

Martin County, FL.

St. Lucie County, FL.

39100 ....... Poughkeepsie-Newburgh-Middletown, NY ............................................................................................................................ 1.0911 

Dutchess County, NY.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25475 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Orange County, NY.

39140 ....... Prescott, AZ ........................................................................................................................................................................... 0.9836 

Yavapai County, AZ.

39300 ....... Providence-New Bedford-Fall River, RI-MA .......................................................................................................................... 1.0783 

Bristol County, MA.

Bristol County, RI.

Kent County, RI.

Newport County, RI.

Providence County, RI.

Washington County, RI.

39340 ....... Provo-Orem, UT .................................................................................................................................................................... 0.9538 

Juab County, UT.

Utah County, UT.

39380 ....... Pueblo, CO ............................................................................................................................................................................ 0.8754 

Pueblo County, CO.

39460 ....... Punta Gorda, FL .................................................................................................................................................................... 0.9405 

Charlotte County, FL.

39540 ....... Racine, WI ............................................................................................................................................................................. 0.9356 

Racine County, WI.

39580 ....... Raleigh-Cary, NC ................................................................................................................................................................... 0.9864 

Franklin County, NC.

Johnston County, NC.

Wake County, NC.

39660 ....... Rapid City, SD ....................................................................................................................................................................... 0.8833 

Meade County, SD.

Pennington County, SD.

39740 ....... Reading, PA ........................................................................................................................................................................... 0.9623 

Berks County, PA.

39820 ....... Redding, CA .......................................................................................................................................................................... 1.3198 

Shasta County, CA.

39900 ....... Reno-Sparks, NV ................................................................................................................................................................... 1.1964 

Storey County, NV.

Washoe County, NV.

40060 ....... Richmond, VA ........................................................................................................................................................................ 0.9177 

Amelia County, VA.

Caroline County, VA.

Charles City County, VA.

Chesterfield County, VA.

Cumberland County, VA.

Dinwiddie County, VA.

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, CA ................................................................................................................................. 1.0904 

Riverside County, CA.

San Bernardino County, CA.

40220 ....... Roanoke, VA .......................................................................................................................................................................... 0.8647 

Botetourt County, VA.

Craig County, VA.

Franklin County, VA.

Roanoke County, VA.

Roanoke City, VA.

Salem City, VA.

40340 ....... Rochester, MN ....................................................................................................................................................................... 1.1408 

Dodge County, MN.

Olmsted County, MN.

Wabasha County, MN.

40380 ....... Rochester, NY ....................................................................................................................................................................... 0.8994 

Livingston County, NY.


pwalker on PROD1PC71 with PROPOSALS2
25476 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Monroe County, NY.

Ontario County, NY.

Orleans County, NY.

Wayne County, NY.

40420 ....... Rockford, IL ........................................................................................................................................................................... 0.9990 

Boone County, IL.

Winnebago County, IL.

40484 ....... Rockingham County-Strafford County, NH ........................................................................................................................... 1.0159 

Rockingham County, NH.

Strafford County, NH.

40580 ....... Rocky Mount, NC .................................................................................................................................................................. 0.8854 

Edgecombe County, NC.

Nash County, NC.

40660 ....... Rome, GA .............................................................................................................................................................................. 0.9194 

Floyd County, GA.

40900 ....... SacramentoArden-ArcadeRoseville, CA ................................................................................................................................ 1.3373 

El Dorado County, CA.

Placer County, CA.

Sacramento County, CA.

Yolo County, CA.

40980 ....... Saginaw-Saginaw Township North, MI ................................................................................................................................. 0.8874 

Saginaw County, MI.

41060 ....... St. Cloud, MN ........................................................................................................................................................................ 1.0362 

Benton County, MN.

Stearns County, MN.

41100 ....... St. George, UT ...................................................................................................................................................................... 0.9265 

Washington County, UT.

41140 ....... St. Joseph, MO-KS ................................................................................................................................................................ 1.0118 

Doniphan County, KS.

Andrew County, MO.

Buchanan County, MO.

DeKalb County, MO.

41180 ....... St. Louis, MO-IL ..................................................................................................................................................................... 0.9006 

Bond County, IL.

Calhoun County, IL.

Clinton County, IL.

Jersey County, IL.

Macoupin County, IL.

Madison County, IL.

Monroe County, IL.

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 ............................................................................................................................................................................. 1.0439 

Marion County, OR.

Polk County, OR.

41500 ....... Salinas, CA ............................................................................................................................................................................ 1.4338 

Monterey County, CA.

41540 ....... Salisbury, MD ........................................................................................................................................................................ 0.8953 

Somerset County, MD.

Wicomico County, MD.

41620 ....... Salt Lake City, UT ................................................................................................................................................................. 0.9402 

Salt Lake County, UT.

Summit County, UT.

Tooele County, UT.

41660 ....... San Angelo, TX ..................................................................................................................................................................... 0.8362 

Irion County, TX.

Tom Green County, TX.

41700 ....... San Antonio, TX .................................................................................................................................................................... 0.8845 

Atascosa County, TX.

Bandera County, TX.

Bexar County, TX.

Comal County, TX.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25477 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Guadalupe County, TX.

Kendall County, TX.

Medina County, TX.

Wilson County, TX.

41740 ....... San Diego-Carlsbad-San Marcos, CA ................................................................................................................................... 1.1354 

San Diego County, CA.

41780 ....... Sandusky, OH ........................................................................................................................................................................ 0.9302 

Erie County, OH.

41884 ....... San Francisco-San Mateo-Redwood City, CA ...................................................................................................................... 1.5166 

Marin County, CA.

San Francisco County, CA.

San Mateo County, CA.

41900 ....... San Germa ́n-Cabo Rojo, PR ................................................................................................................................................. 0.4885 

Cabo Rojo Municipio, PR.

Lajas Municipio, PR.

Sabana Grande Municipio, PR.

San Germa ́n Municipio, PR.

41940 ....... San Jose-Sunnyvale-Santa Clara, CA .................................................................................................................................. 1.5543 

San Benito County, CA.

Santa Clara County, CA.

41980 ....... San Juan-Caguas-Guaynabo, PR ......................................................................................................................................... 0.4452 

Aguas Buenas Municipio, PR.

Aibonito Municipio, PR.

Arecibo Municipio, PR.

Barceloneta Municipio, PR.

Barranquitas Municipio, PR.

Bayamo ́n Municipio, PR.

Caguas Municipio, PR.

Camuy Municipio, PR.

Cano ́vanas Municipio, PR.

Carolina Municipio, PR.

Catan ̃o Municipio, PR.

Cayey Municipio, PR.

Ciales Municipio, PR.

Cidra Municipio, PR.

Comer ́ıo Municipio, PR.

Corozal Municipio, PR.

Dorado Municipio, PR.

Florida Municipio, PR.

Guaynabo Municipio, PR.

Gurabo Municipio, PR.

Hatillo Municipio, PR.

Humacao Municipio, PR.

Juncos Municipio, PR.

Las Piedras Municipio, PR.

Lo ́ıza Municipio, PR.

Manat ́ı Municipio, PR.

Maunabo Municipio, PR.

Morovis Municipio, PR.

Naguabo Municipio, PR.

Naranjito Municipio, PR.

Orocovis Municipio, PR.

Quebradillas Municipio, PR.

R ́ıo 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 ....................................................................................................................................... 1.1599 

San Luis Obispo County, CA.

42044 ....... Santa Ana-Anaheim-Irvine, CA ............................................................................................................................................. 1.1473 

Orange County, CA.

42060 ....... Santa Barbara-Santa Maria-Goleta, CA ................................................................................................................................ 1.1092 

Santa Barbara County, CA.

42100 ....... Santa Cruz-Watsonville, CA .................................................................................................................................................. 1.5458 


pwalker on PROD1PC71 with PROPOSALS2
25478 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

Santa Cruz County, CA.

42140 ....... Santa Fe, NM ........................................................................................................................................................................ 1.0825 

Santa Fe County, NM.

42220 ....... Santa Rosa-Petaluma, CA .................................................................................................................................................... 1.4464 

Sonoma County, CA.

42260 ....... Sarasota-Bradenton-Venice, FL ............................................................................................................................................ 0.9868 

Manatee County, FL.

Sarasota County, FL.

42340 ....... Savannah, GA ....................................................................................................................................................................... 0.9351 

Bryan County, GA.

Chatham County, GA.

Effingham County, GA.

42540 ....... ScrantonWilkes-Barre, PA ..................................................................................................................................................... 0.8348 

Lackawanna County, PA.

Luzerne County, PA.

Wyoming County, PA.

42644 ....... Seattle-Bellevue-Everett, WA ................................................................................................................................................ 1.1434 

King County, WA.

Snohomish County, WA.

42680 ....... Sebastian-Vero Beach, FL .................................................................................................................................................... 0.9573 

43100 ....... Sheboygan, WI ...................................................................................................................................................................... 0.9027 

Sheboygan County, WI.

43300 ....... Sherman-Denison, TX ........................................................................................................................................................... 0.8503 

Grayson County, TX.

43340 ....... Shreveport-Bossier City, LA .................................................................................................................................................. 0.8865 

Bossier Parish, LA.

Caddo Parish, LA.

De Soto Parish, LA.

43580 ....... Sioux City, IA-NE-SD ............................................................................................................................................................. 0.9201 

Woodbury County, IA.

Dakota County, NE.

Dixon County, NE.

Union County, SD.

43620 ....... Sioux Falls, SD ...................................................................................................................................................................... 0.9559 

Lincoln County, SD.

McCook County, SD.

Minnehaha County, SD.

Turner County, SD.

43780 ....... South Bend-Mishawaka, IN-MI .............................................................................................................................................. 0.9842 

St. Joseph County, IN.

Cass County, MI.

43900 ....... Spartanburg, SC .................................................................................................................................................................... 0.9174 

Spartanburg County, SC.

44060 ....... Spokane, WA ......................................................................................................................................................................... 1.0447 

Spokane County, WA.

44100 ....... Springfield, IL ......................................................................................................................................................................... 0.8890 

Menard County, IL.

Sangamon County, IL.

44140 ....... Springfield, MA ...................................................................................................................................................................... 1.0079 

Franklin County, MA.

Hampden County, MA.

Hampshire County, MA.

44180 ....... Springfield, MO ...................................................................................................................................................................... 0.8469 

Christian County, MO.

Dallas County, MO.

Greene County, MO.

Polk County, MO.

Webster County, MO.

44220 ....... Springfield, OH ...................................................................................................................................................................... 0.8593 

Clark County, OH.

44300 ....... State College, PA .................................................................................................................................................................. 0.8784 

Centre County, PA.

44700 ....... Stockton, CA .......................................................................................................................................................................... 1.1443 

San Joaquin County, CA.

44940 ....... Sumter, SC ............................................................................................................................................................................ 0.8084 

Sumter County, SC.

45060 ....... Syracuse, NY ......................................................................................................................................................................... 0.9692 

Madison County, NY.

Onondaga County, NY.

Oswego County, NY.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25479 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

45104 ....... Tacoma, WA .......................................................................................................................................................................... 1.0789 

Pierce County, WA.

45220 ....... Tallahassee, FL ..................................................................................................................................................................... 0.8942 

Gadsden County, FL.

Jefferson County, FL.

Leon County, FL.

Wakulla County, FL.

45300 ....... Tampa-St. Petersburg-Clearwater, FL .................................................................................................................................. 0.9144 

Hernando County, FL.

Hillsborough County, FL.

Pasco County, FL.

Pinellas County, FL.

45460 ....... Terre Haute, IN ...................................................................................................................................................................... 0.8765 

Clay County, IN.

Sullivan County, IN.

Vermillion County, IN.

Vigo County, IN.

45500 ....... Texarkana, TX-Texarkana, AR .............................................................................................................................................. 0.8104 

Miller County, AR.

Bowie County, TX.

45780 ....... Toledo, OH ............................................................................................................................................................................ 0.9586 

Fulton County, OH.

Lucas County, OH.

Ottawa County, OH.

Wood County, OH.

45820 ....... Topeka, KS ............................................................................................................................................................................ 0.8730 

Jackson County, KS.

Jefferson County, KS.

Osage County, KS.

Shawnee County, KS.

Wabaunsee County, KS.

45940 ....... Trenton-Ewing, NJ ................................................................................................................................................................. 1.0836 

Mercer County, NJ.

46060 ....... Tucson, AZ ............................................................................................................................................................................ 0.9203 

Pima County, AZ.

46140 ....... Tulsa, OK ............................................................................................................................................................................... 0.8103 

Creek County, OK.

Okmulgee County, OK.

Osage County, OK.

Pawnee County, OK.

Rogers County, OK.

Tulsa County, OK.

Wagoner County, OK.

46220 ....... Tuscaloosa, AL ...................................................................................................................................................................... 0.8542 

Greene County, AL.

Hale County, AL.

Tuscaloosa County, AL.

46340 ....... Tyler, TX ................................................................................................................................................................................ 0.8812 

Smith County, TX.

46540 ....... Utica-Rome, NY ..................................................................................................................................................................... 0.8397 

Herkimer County, NY.

Oneida County, NY.

46660 ....... Valdosta, GA .......................................................................................................................................................................... 0.8369 

Brooks County, GA.

Echols County, GA.

Lanier County, GA.

Lowndes County, GA.

46700 ....... Vallejo-Fairfield, CA ............................................................................................................................................................... 1.5138 

Solano County, CA.

47020 ....... Victoria, TX ............................................................................................................................................................................ 0.8560 

Calhoun County, TX.

Goliad County, TX.

Victoria County, TX.

47220 ....... Vineland-Millville-Bridgeton, NJ ............................................................................................................................................. 0.9832 

Cumberland County, NJ.

47260 ....... Virginia Beach-Norfolk-Newport News, VA-NC ..................................................................................................................... 0.8790 

Currituck County, NC.

Gloucester County, VA.

Isle of Wight County, VA.

James City County, VA.


pwalker on PROD1PC71 with PROPOSALS2
25480 Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

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 ............................................................................................................................................................ 0.9968 

Tulare County, CA.

47380 ....... Waco, TX ............................................................................................................................................................................... 0.8633 

McLennan County, TX.

47580 ....... Warner Robins, GA ............................................................................................................................................................... 0.8380 

Houston County, GA.

47644 ....... Warren-Farmington Hills-Troy, MI ......................................................................................................................................... 1.0054 

Lapeer County, MI.

Livingston County, MI.

Macomb County, MI.

Oakland County, MI.

St. Clair County, MI.

47894 ....... Washington-Arlington-Alexandria, DC-VA-MD-WV ............................................................................................................... 1.1054 

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.

Falls Church City, VA.

Fredericksburg City, VA.

Manassas City, VA.

Manassas Park City, VA.

Jefferson County, WV.

.

47940 ....... Waterloo-Cedar Falls, IA ....................................................................................................................................................... 0.8408 

Black Hawk County, IA.

Bremer County, IA.

Grundy County, IA.

48140 ....... Wausau, WI ........................................................................................................................................................................... 0.9723 

Marathon County, WI.

48260 ....... Weirton-Steubenville, WV-OH ............................................................................................................................................... 0.8064 

Jefferson County, OH.

Brooke County, WV.

Hancock County, WV.

48300 ....... Wenatchee, WA ..................................................................................................................................................................... 1.0347 

Chelan County, WA.

Douglas County, WA.

48424 ....... West Palm Beach-Boca Raton-Boynton Beach, FL .............................................................................................................. 0.9649 

Palm Beach County, FL.

48540 ....... Wheeling, WV-OH ................................................................................................................................................................. 0.7010 

Belmont County, OH.

Marshall County, WV.

Ohio County, WV.

48620 ....... Wichita, KS ............................................................................................................................................................................ 0.9063 

Butler County, KS.

Harvey County, KS.

Sedgwick County, KS.

Sumner County, KS.


pwalker on PROD1PC71 with PROPOSALS2
Federal Register / Vol. 72, No. 86 / Friday, May 4, 2007 / Proposed Rules 25481 

ADDENDUM B.—CY 2007 WAGE INDEX FOR URBAN AREAS BY CBSA; APPLICABLE PRE-FLOOR AND PRE-RECLASSIFIED

HOSPITAL WAGE INDEX—Continued 

CBSA 

code Urban area (constituent counties) Wage index 

48660 ....... Wichita Falls, TX .................................................................................................................................................................... 0.8311 

Archer County, TX.

Clay County, TX.

Wichita County, TX.

48700 ....... Williamsport, PA .................................................................................................................................................................... 0.8139 

Lycoming County, PA.

48864 ....... Wilmington, DE-MD-NJ .......................................................................................................................................................... 1.0684 

New Castle County, DE.

Cecil County, MD.

Salem County, NJ.

48900 ....... Wilmington, NC ...................................................................................................................................................................... 0.9836 

Brunswick County, NC.

New Hanover County, NC.

Pender County, NC.

49020 ....... Winchester, VA-WV ............................................................................................................................................................... 1.0091 

Frederick County, VA.

Winchester City, VA.

Hampshire County, WV.

49180 ....... Winston-Salem, NC ............................................................................................................................................................... 0.9276 

Davie County, NC.

Forsyth County, NC.

Stokes County, NC.

Yadkin County, NC.

49340 ....... Worcester, MA ....................................................................................................................................................................... 1.0690 

Worcester County, MA.

49420 ....... Yakima, WA ........................................................................................................................................................................... 0.9848 

Yakima County, WA.

49500 ....... Yauco, PR .............................................................................................................................................................................. 0.3854 

Gua ́nica Municipio, PR.

Guayanilla Municipio, PR.

Pen ̃uelas Municipio, PR.

Yauco Municipio, PR.

49620 ....... York-Hanover, PA .................................................................................................................................................................. 0.9398 

York County, PA.

49660 ....... Youngstown-Warren-Boardman, OH-PA ............................................................................................................................... 0.8802 

Mahoning County, OH.

Trumbull County, OH.

Mercer County, PA.

49700 ....... Yuba City, CA ........................................................................................................................................................................ 1.0731 

Sutter County, CA.

Yuba County, CA.

49740 ....... Yuma, AZ ............................................................................................................................................................................... 0.9109 

Yuma County, AZ.

1At this time, there are no hospitals in these urban areas on which to base a wage index. Therefore, the urban wage index value is based on 

the average wage index of all urban areas within the State. 

[FR Doc. 07–2167 Filed 4–27–07; 4:45 am] 

BILLING CODE 4120–01–P 


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