2010-9870. Medicare Program; Inpatient Psychiatric Facilities Prospective Payment System Payment-Update for Rate Year Beginning July 1, 2010 (RY 2011)

AGENCY:

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

ACTION:

Notice

SUMMARY:

This notice updates the payment rates for the Medicare prospective payment system (PPS) for inpatient psychiatric hospital services provided by inpatient psychiatric facilities (IPFs). These changes are applicable to IPF discharges occurring during the rate year beginning July 1, 2010 through June 30, 2011. We are also responding to comments on the IPF PPS teaching adjustment and the market basket, which we received in response to our May 2009 IPF PPS notice with request for comments.

DATES:

Effective Date: The updated IPF prospective payment rates are effective for discharges occurring on or after July 1, 2010 through June 30, 2011.

FOR FURTHER INFORMATION CONTACT:

Dorothy Myrick or Jana Lindquist, [phone] (for general information).

Mary Carol Barron, [phone] (for information regarding the market basket and labor-related share).

Theresa Bean, [phone] (for information regarding the regulatory impact analysis).

SUPPLEMENTARY INFORMATION:

Table of Contents

To assist readers in referencing sections contained in this document, we are providing the following table of contents.

I. Background

A. Annual Requirements for Updating the IPF PPS

B. Overview of the Legislative Requirements of the IPF PPS

C. IPF PPS—General Overview

II. Transition Period for Implementation of the IPF PPS

III. Updates to the IPF PPS for RY Beginning July 1, 2010

A. Determining the Standardized Budget-Neutral Federal Per Diem Base Rate

1. Standardization of the Federal Per Diem Base Rate and

Electroconvulsive Therapy (ECT) Rate

2. Calculation of the Budget Neutrality Adjustment

a. Outlier Adjustment

b. Stop-Loss Provision Adjustment

c. Behavioral Offset

B. Update of the Federal Per Diem Base Rate and Electroconvulsive Therapy Rate

1. Market Basket for IPFs Reimbursed under the IPF PPS

a. Market Basket Index for the IPF PPS

b. Overview of the RPL Market Basket

2. Labor-Related Share

3. Comments on Creating a Stand-Alone IPF Market Basket

IV. Update of the IPF PPS Adjustment Factors

A. Overview of the IPF PPS Adjustment Factors

B. Patient-Level Adjustments

1. Adjustment for MS-DRG Assignment

2. Payment for Comorbid Conditions

3. Patient Age Adjustments

4. Variable Per Diem Adjustments

C. Facility-Level Adjustments

1. Wage Index Adjustment

a. Background

b. Wage Index for RY 2011

c. OMB Bulletins

2. Adjustment for Rural Location

3. Teaching Adjustment

4. Cost of Living Adjustment for IPFs Located in Alaska and Hawaii

5. Adjustment for IPFs With a Qualifying Emergency Department (ED)

D. Other Payment Adjustments and Policies

1. Outlier Payments

a. Update to the Outlier Fixed Dollar Loss Threshold Amount

b. Statistical Accuracy of Cost-to-Charge Ratios

2. Expiration of the Stop-Loss Provision

V. Comments Beyond the Scope of the May 2009 IPF PPS Notice With Request for Comments

VI. Waiver of Proposed Rulemaking

VII. Collection of Information Requirements

VIII. Regulatory Impact Analysis

Addenda

Acronyms

Because of the many terms to which we refer by acronym in this notice, we are listing the acronyms used and their corresponding terms in alphabetical order below:

BBRA  Medicare, Medicaid and SCHIP [State Children's Health Insurance Program] Balanced Budget Refinement Act of 1999, (Pub. L. 106-113).

CBSA  Core-Based Statistical Area.

CCR  Cost-to-charge ratio.

CAH  Critical access hospital.

DSM-IV-TR  Diagnostic and Statistical Manual of Mental Disorders Fourth Edition—Text Revision.

DRGs  Diagnosis-related groups.

FY  Federal fiscal year.

ICD-9-CM  International Classification of Diseases, 9th Revision, Clinical Modification.

IPFs  Inpatient psychiatric facilities.

IRFs  Inpatient rehabilitation facilities.

LTCHs  Long-term care hospitals.

MedPAR  Medicare provider analysis and review file.

RY  Rate Year.

TEFRA  Tax Equity and Fiscal Responsibility Act of 1982, (Pub. L. 97-248).

I. Background

A. Annual Requirements for Updating the IPF PPS

In November 2004, we implemented the inpatient psychiatric facilities (IPF) prospective payment system (PPS) in a final rule that appeared in the November 15, 2004 Federal Register (69 FR 66922). In developing the IPF PPS, in order to ensure that the IPF PPS is able to account adequately for each IPF's case-mix, we performed an extensive regression analysis of the relationship between the per diem costs and certain patient and facility characteristics to determine those characteristics associated with statistically significant cost differences on a per diem basis. For characteristics with statistically significant cost differences, we used the regression coefficients of those variables to determine the size of the corresponding payment adjustments.

In that final rule, we explained that we believe it is important to delay updating the adjustment factors derived from the regression analysis until we have IPF PPS data that includes as much information as possible regarding the patient-level characteristics of the population that each IPF serves. Therefore, we indicated that we did not intend to update the regression analysis and recalculate the Federal per diem base rate and the patient- and facility-level adjustments until we complete that analysis. Until that analysis is complete, we stated our intention to publish a notice in the Federal Register each spring to update the IPF PPS (71 FR 27041).

Updates to the IPF PPS as specified in 42 CFR § 412.428 include the following:

• A description of the methodology and data used to calculate the updated Federal per diem base payment amount.
• The rate of increase factor as described in § 412.424(a)(2)(iii), which is based on the excluded hospital with capital market basket under the update methodology of section 1886(b)(3)(B)(ii) of the Social Security Act (the Act) for each year (effective from the implementation period until June 30, 2006).
• For discharges occurring on or after July 1, 2006, the rate of increase factor for the Federal portion of the IPF's payment, which is based on the rehabilitation, psychiatric, and long-term care (RPL) market basket.
• The best available hospital wage index and information regarding whether an adjustment to the Federal per diem base rate is needed to maintain budget neutrality.
• Updates to the fixed dollar loss threshold amount in order to maintain the appropriate outlier percentage.
• Description of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) coding and diagnosis-related groups (DRGs) classification changes discussed in the annual update to the hospital inpatient prospective payment system (IPPS) regulations.
• Update to the electroconvulsive therapy (ECT) payment by a factor specified by CMS.
• Update to the national urban and rural cost-to-charge ratio medians and ceilings.
• Update to the cost of living adjustment factors for IPFs located in Alaska and Hawaii, if appropriate.

Our most recent annual update occurred in the May 2009 IPF PPS notice with request for comments (74 FR 20362) (hereinafter referred to as the May 2009 IPF PPS notice) that set forth updates to the IPF PPS payment rates for RY 2010. This notice updates the IPF per diem payment rates that were published in the May 2009 IPF PPS notice in accordance with our established policies.

B. Overview of the Legislative Requirements of the IPF PPS

Section 124 of the Medicare, Medicaid, and SCHIP (State Children's Health Insurance Program) Balanced Budget Refinement Act of 1999, (Pub. L. 106-113) (BBRA) required implementation of the IPF PPS. Specifically, section 124 of the BBRA mandated that the Secretary develop a per diem PPS for inpatient hospital services furnished in psychiatric hospitals and psychiatric units that includes an adequate patient classification system that reflects the differences in patient resource use and costs among psychiatric hospitals and psychiatric units.

Section 405(g)(2) of the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA) (Pub. L. 108-173) extended the IPF PPS to distinct part psychiatric units of critical access hospitals (CAHs).

To implement these provisions, we published various proposed and final rules in the Federal Register. For more information regarding these rules, see the CMS Web sites http://www.cms.hhs.gov/​InpatientPsychFacilPPS/​ and http://www.cms.hhs.gov/​InpatientpsychfacilPPS/​02_​regulations.asp.

Section 1886(s)(3)(A) of the Act, which was added by Section 3401(f) of the Patient Protection and Affordable Care Act (Pub. L. 111-148) as amended by Section 10319(e) of that Act and by Section 1105 of the Health Care and Education Reconciliation Act of 2010 (Pub. L. 111-152), requires the application of an “Other Adjustment” that reduces any update to the IPF PPS base rate by 0.25 percentage point for the rate year beginning in 2010. We are implementing that provision for RY 2011 in this notice.

C. IPF PPS—General Overview

The November 2004 IPF PPS final rule (69 FR 66922) established the IPF PPS, as authorized under section 124 of the BBRA and codified at subpart N of part 412 of the Medicare regulations. The November 2004 IPF PPS final rule set forth the per diem Federal rates for the implementation year (the 18-month period from January 1, 2005 through June 30, 2006), and it provided payment for the inpatient operating and capital costs to IPFs for covered psychiatric services they furnish (that is, routine, ancillary, and capital costs, but not costs of approved educational activities, bad debts, and other services or items that are outside the scope of the IPF PPS). Covered psychiatric services include services for which benefits are provided under the fee-for-service Part A (Hospital Insurance Program) Medicare program.

The IPF PPS established the Federal per diem base rate for each patient day in an IPF derived from the national average daily routine operating, ancillary, and capital costs in IPFs in FY 2002. The average per diem cost was updated to the midpoint of the first year under the IPF PPS, standardized to account for the overall positive effects of the IPF PPS payment adjustments, and adjusted for budget neutrality.

The Federal per diem payment under the IPF PPS is comprised of the Federal per diem base rate described above and certain patient- and facility-level payment adjustments that were found in the regression analysis to be associated with statistically significant per diem cost differences.

The patient-level adjustments include age, DRG assignment, comorbidities, and variable per diem adjustments to reflect higher per diem costs in the early days of an IPF stay. Facility-level adjustments include adjustments for the IPF's wage index, rural location, teaching status, a cost of living adjustment for IPFs located in Alaska and Hawaii, and presence of a qualifying emergency department (ED).

The IPF PPS provides additional payment policies for: outlier cases; stop-loss protection (which was applicable only during the IPF PPS transition period); interrupted stays; and a per treatment adjustment for patients who undergo ECT.

A complete discussion of the regression analysis appears in the November 2004 IPF PPS final rule (69 FR 66933 through 66936).

Section 124 of BBRA does not specify an annual update rate strategy for the IPF PPS and is broadly written to give the Secretary discretion in establishing an update methodology. Therefore, in the November 2004 IPF PPS final rule, we implemented the IPF PPS using the following update strategy:

• Calculate the final Federal per diem base rate to be budget neutral for the 18-month period of January 1, 2005 through June 30, 2006.
• Use a July 1 through June 30 annual update cycle.
• Allow the IPF PPS first update to be effective for discharges on or after July 1, 2006 through June 30, 2007.

II. Transition Period for Implementation of the IPF PPS

In the November 2004 IPF PPS final rule, we provided for a 3-year transition period. During this 3-year transition period, an IPF's total payment under the PPS was based on an increasing percentage of the Federal rate with a corresponding decreasing percentage of the IPF PPS payment that is based on reasonable cost concepts. However, effective for cost reporting periods beginning on or after January 1, 2008, IPF PPS payments are based on 100 percent of the Federal rate.

III. Updates to the IPF PPS for RY Beginning July 1, 2010

The IPF PPS is based on a standardized Federal per diem base rate calculated from IPF average per diem costs and adjusted for budget-neutrality in the implementation year. The Federal per diem base rate is used as the standard payment per day under the IPF PPS and is adjusted by the patient- and facility-level adjustments that are applicable to the IPF stay. A detailed explanation of how we calculated the average per diem cost appears in the November 2004 IPF PPS final rule (69 FR 66926).

A. Determining the Standardized Budget-Neutral Federal Per Diem Base Rate

Section 124(a)(1) of the BBRA requires that we implement the IPF PPS in a budget neutral manner. In other words, the amount of total payments under the IPF PPS, including any payment adjustments, must be projected to be equal to the amount of total payments that would have been made if the IPF PPS were not implemented. Therefore, we calculated the budget-neutrality factor by setting the total estimated IPF PPS payments to be equal to the total estimated payments that would have been made under the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) (Pub. L. 97-248) methodology had the IPF PPS not been implemented.

Under the IPF PPS methodology, we calculated the final Federal per diem base rate to be budget neutral during the IPF PPS implementation period (that is, the 18-month period from January 1, 2005 through June 30, 2006) using a July 1 update cycle. We updated the average cost per day to the midpoint of the IPF PPS implementation period (that is, October 1, 2005), and this amount was used in the payment model to establish the budget-neutrality adjustment.

A step-by-step description of the methodology used to estimate payments under the TEFRA payment system appears in the November 2004 IPF PPS final rule (69 FR 66926).

1. Standardization of the Federal Per Diem Base Rate and Electroconvulsive Therapy (ECT) Rate

In the November 2004 IPF PPS final rule, we describe how we standardized the IPF PPS Federal per diem base rate in order to account for the overall positive effects of the IPF PPS payment adjustment factors. To standardize the IPF PPS payments, we compared the IPF PPS payment amounts calculated from the FY 2002 Medicare Provider Analysis and Review (MedPAR) file to the projected TEFRA payments from the FY 2002 cost report file updated to the midpoint of the IPF PPS implementation period (that is, October 2005). The standardization factor was calculated by dividing total estimated payments under the TEFRA payment system by estimated payments under the IPF PPS. The standardization factor was calculated to be 0.8367.

As described in detail in the May 2006 IPF PPS final rule (71 FR 27045), in reviewing the methodology used to simulate the IPF PPS payments used for the November 2004 IPF PPS final rule, we discovered that due to a computer code error, total IPF PPS payments were underestimated by about 1.36 percent. Since the IPF PPS payment total should have been larger than the estimated figure, the standardization factor should have been smaller (0.8254 vs. 0.8367). In turn, the Federal per diem base rate and the ECT rate should have been reduced by 0.8254 instead of 0.8367.

To resolve this issue, in RY 2007, we amended the Federal per diem base rate and the ECT payment rate prospectively. Using the standardization factor of 0.8254, the average cost per day was effectively reduced by 17.46 percent (100 percent minus 82.54 percent = 17.46 percent).

2. Calculation of the Budget Neutrality Adjustment

To compute the budget neutrality adjustment for the IPF PPS, we separately identified each component of the adjustment, that is, the outlier adjustment, stop-loss adjustment, and behavioral offset.

A complete discussion of how we calculate each component of the budget neutrality adjustment appears in the November 2004 IPF PPS final rule (69 FR 66932 through 66933) and in the May 2006 IPF PPS final rule (71 FR 27044 through 27046).

a. Outlier Adjustment

Since the IPF PPS payment amount for each IPF includes applicable outlier amounts, we reduced the standardized Federal per diem base rate to account for aggregate IPF PPS payments estimated to be made as outlier payments. The outlier adjustment was calculated to be 2 percent. As a result, the standardized Federal per diem base rate was reduced by 2 percent to account for projected outlier payments.

b. Stop-Loss Provision Adjustment

As explained in the November 2004 IPF PPS final rule, we provided a stop-loss payment during the transition from cost-based reimbursement to the per diem payment system to ensure that an IPF's total PPS payments were no less than a minimum percentage of their TEFRA payment, had the IPF PPS not been implemented. We reduced the standardized Federal per diem base rate by the percentage of aggregate IPF PPS payments estimated to be made for stop-loss payments. As a result, the standardized Federal per diem base rate was reduced by 0.39 percent to account for stop-loss payments. Since the transition was completed in RY 2009, the stop-loss provision is no longer applicable, and for cost reporting periods beginning on or after January 1, 2008, IPFs were paid 100 percent PPS.

c. Behavioral Offset

As explained in the November 2004 IPF PPS final rule, implementation of the IPF PPS may result in certain changes in IPF practices, especially with respect to coding for comorbid medical conditions. As a result, Medicare may make higher payments than assumed in our calculations. Accounting for these effects through an adjustment is commonly known as a behavioral offset.

Based on accepted actuarial practices and consistent with the assumptions made in other PPSs, we assumed in determining the behavioral offset that IPFs would regain 15 percent of potential “losses” and augment payment increases by 5 percent. We applied this actuarial assumption, which is based on our historical experience with new payment systems, to the estimated “losses” and “gains” among the IPFs. The behavioral offset for the IPF PPS was calculated to be 2.66 percent. As a result, we reduced the standardized Federal per diem base rate by 2.66 percent to account for behavioral changes. As indicated in the November 2004 IPF PPS final rule, we do not plan to change adjustment factors or projections until we analyze IPF PPS data.

If we find that an adjustment is warranted, the percent difference may be applied prospectively to the established PPS rates to ensure the rates accurately reflect the payment level intended by the statute. In conducting this analysis, we will be interested in the extent to which improved coding of patients' principal and other diagnoses, which may not reflect real increases in underlying resource demands, has occurred under the PPS.

B. Update of the Federal Per Diem Base Rate and Electroconvulsive Therapy Rate

1. Market Basket for IPFs Reimbursed under the IPF PPS

As described in the November 2004 IPF PPS final rule (69 FR 66931), the average per diem cost was updated to the midpoint of the implementation year. This updated average per diem cost of $724.43 was reduced by 17.46 percent to account for standardization to projected TEFRA payments for the implementation period, by 2 percent to account for outlier payments, by 0.39 percent to account for stop-loss payments, and by 2.66 percent to account for the behavioral offset. The Federal per diem base rate in the implementation year was $575.95. The increase in the per diem base rate for RY 2009 included the 0.39 percent increase due to the removal of the stop-loss provision. We indicated in the November 2004 IPF PPS final rule (69 FR 66932) that we would remove this 0.39 percent reduction to the Federal per diem base rate after the transition. For RY 2009 and beyond, the stop-loss provision has ended and is therefore no longer a part of budget neutrality.

Due to new section 1886(s)(3)(A) of the Act, which requires the application of an “Other Adjustment” that reduces the update to the IPF PPS base rate for the rate year beginning in CY 2010, we reduced the update to the IPF PPS base rate by 0.25 percent for rate year 2011. Applying the market basket increase of 2.4 percent, with the “Other Adjustment” of −0.25%, and the wage index budget neutrality factor of 0.9999 to the RY 2010 Federal per diem base rate of $651.76 yields a Federal per diem base rate of $665.71 for RY 2011. Similarly, applying the market basket increase with the “Other Adjustment”, and the wage index budget neutrality factor to the RY 2010 ECT rate yields an ECT rate of $286.60 for RY 2011.

a. Market Basket Index for the IPF PPS

The market basket index that was used to develop the IPF PPS was the excluded hospital with capital market basket. This market basket was based on 1997 Medicare cost report data and included data for Medicare-participating IPFs, inpatient rehabilitation facilities (IRFs), long-term care hospitals (LTCHs), cancer, and children's hospitals.

Beginning with the May 2006 IPF PPS final rule (71 FR 27046 through 27054), IPF PPS payments were updated using a 2002-based market basket reflecting the operating and capital cost structures for IRFs, IPFs, and LTCHs (hereafter referred to as the rehabilitation, psychiatric, long-term care (RPL) market basket).

We excluded cancer and children's hospitals from the RPL market basket because their payments are based entirely on reasonable costs subject to rate-of-increase limits established under the authority of section 1886(b) of the Act, which are implemented in regulations at § 413.40. They are not reimbursed through a PPS. Also, the FY 2002 cost structures for cancer and children's hospitals are noticeably different than the cost structures of the IRFs, IPFs, and LTCHs. A complete discussion of the RPL market basket appears in the May 2006 IPF PPS final rule (71 FR 27046 through 27054).

In the May 2009 IPF PPS notice (74 FR 20362), we requested public comment on the possibility of creating a stand-alone IPF market basket. In this notice, we are responding to those comments in the “Comments on Creating a Stand-Alone IPF Market Basket” section.

b. Overview of the RPL Market Basket

The RPL market basket is a fixed weight, Laspeyres-type price index. A market basket is described as a fixed-weight index because it answers the question of how much it would cost, at another time, to purchase the same mix (quantity and intensity) of goods and services needed to provide services in a base period. The effects on total expenditures resulting from changes in the mix of goods and services purchased subsequent to the base period are not measured. In this manner, the market basket measures pure price change only. Only when the index is rebased would changes in the quantity and intensity be captured in the cost weights. Therefore, we rebase the market basket periodically so that cost weights reflect recent changes in the mix of goods and services that hospitals purchase to furnish patient care between base periods.

The terms “rebasing” and “revising,” while often used interchangeably, actually denote different activities. Rebasing means moving the base year for the structure of costs of an input price index (for example, shifting the base year cost structure from FY 1997 to FY 2002). Revising means changing data sources, methodology, or price proxies used in the input price index. In 2006, we rebased and revised the market basket used to update the IPF PPS. Table 1 below sets forth the completed FY 2002-based RPL market basket including the cost categories, weights, and price proxies.

Table 1—FY 2002-Based RPL Market Basket Cost Categories, Weights, and Price Proxies

Cost categories	FY 2002-based RPL market basket cost weight	FY 2002-based RPL market basket price proxies
TOTAL	100.000
Compensation	65.877
Wages and Salaries*	52.895	ECI—Wages and Salaries, Civilian Hospital Workers.
Employee Benefits*	12.982	ECI—Benefits, Civilian Hospital Workers.
Professional Fees, Non-Medical*	2.892	ECI—Compensation for Professional & Related occupations.
Utilities	0.656
Electricity	0.351	PPI—Commercial Electric Power.
Fuel Oil, Coal, etc	0.108	PPI—Commercial Natural Gas.
Water and Sewage	0.197	CPI—U—Water & Sewage Maintenance.
Professional Liability Insurance	1.161	CMS Professional Liability Premium Index.
All Other Products and Services	19.265
All Other Products	13.323
Pharmaceuticals	5.103	PPI Prescription Drugs.
Food: Direct Purchase	0.873	PPI Processed Foods & Feeds.
Food: Contract Service	0.620	CPI—U Food Away From Home.
Chemicals	1.100	PPI Industrial Chemicals.
Medical Instruments	1.014	PPI Medical Instruments & Equipment.
Photographic Supplies	0.096	PPI Photographic Supplies.
Rubber and Plastics	1.052	PPI Rubber & Plastic Products.
Paper Products	1.000	PPI Converted Paper & Paperboard Products.
Apparel	0.207	PPI Apparel.
Machinery and Equipment	0.297	PPI Machinery & Equipment.
Miscellaneous Products**	1.963	PPI Finished Goods less Food & Energy.
All Other Services	5.942
Telephone	0.240	CPI—U Telephone Services.
Postage	0.682	CPI—U Postage.
All Other: Labor Intensive*	2.219	ECI—Compensation for Private Service Occupations.
All Other: Non-labor Intensive	2.800	CPI—U All Items.
Capital-Related Costs***	10.149
Depreciation	6.186
Fixed Assets	4.250	Boeckh Institutional Construction 23-year useful life.
Movable Equipment	1.937	PPI Machinery & Equipment 11-year useful life.
Interest Costs	2.775
Nonprofit	2.081	Average yield on domestic municipal bonds (Bond Buyer 20 bonds) vintage-weighted (23 years).
For Profit	0.694	Average yield on Moody's Aaa bond vintage-weighted (23 years).
Other Capital-Related Costs	1.187	CPI—U Residential Rent.
* Labor-related.
** Blood and blood-related products is included in miscellaneous products.
*** A portion of capital costs (0.46) are labor-related.

Note:

Due to rounding, weights may not sum to total.

We evaluated the price proxies using the criteria of reliability, timeliness, availability, and relevance. Reliability indicates that the index is based on valid statistical methods and has low sampling variability. Timeliness implies that the proxy is published regularly (preferably at least once a quarter). Availability means that the proxy is publicly available. Finally, relevance means that the proxy is applicable and representative of the cost category weight to which it is applied. The Consumer Price Indexes (CPIs), Producer Price Indexes (PPIs), and Employment Cost Indexes (ECIs) used as proxies in this market basket meet these criteria.

We note that the proxies are the same as those used for the FY 1997-based excluded hospital with capital market basket. Because these proxies meet our criteria of reliability, timeliness, availability, and relevance, we believe they continue to be the best measure of price changes for the cost categories. For further discussion on the FY 1997-based excluded hospital with capital market basket, see the August 1, 2002 hospital inpatient prospective payment system (IPPS) final rule (67 FR at 50042).

The RY 2011 (that is, beginning July 1, 2010) update for the IPF PPS using the FY 2002-based RPL market basket and Information Handling Services (IHS) Global Insight's 1st quarter 2010 forecast for the market basket components is 2.4 percent. This includes increases in both the operating section and the capital section for the 12-month RY period (that is, July 1, 2010 through June 30, 2011). IHS Global Insight, Inc. is a nationally recognized economic and financial forecasting firm that contracts with CMS to forecast the components of the market baskets.

2. Labor-Related Share

Due to the variations in costs and geographic wage levels, we believe that payment rates under the IPF PPS should continue to be adjusted by a geographic wage index. This wage index applies to the labor-related portion of the Federal per diem base rate, hereafter referred to as the labor-related share.

The labor-related share is determined by identifying the national average proportion of operating costs that are related to, influenced by, or vary with the local labor market. Using our current definition of labor-related, the labor-related share is the sum of the relative importance of wages and salaries, fringe benefits, professional fees, labor-intensive services, and a portion of the capital share from an appropriate market basket. We used the FY 2002-based RPL market basket cost weights relative importance to determine the labor-related share for the IPF PPS.

The labor-related share for RY 2011 is the sum of the RY 2011 relative importance of each labor-related cost category, and reflects the different rates of price change for these cost categories between the base year (FY 2002) and RY 2011. The sum of the relative importance for the RY 2011 operating costs (wages and salaries, employee benefits, professional fees, and labor-intensive services) is 71.506 percent, as shown in Table 2 below. The portion of capital that is influenced by the local labor market is estimated to be 46 percent, which is the same percentage used in the FY 1997-based IRF and IPF payment systems.

Since the relative importance for capital is 8.466 percent of the FY 2002-based RPL market basket in RY 2011, we are taking 46 percent of 8.466 percent to determine the labor-related share of capital for RY 2011. The result is 3.894 percent, which we added to 71.506 percent for the operating cost amount to determine the total labor-related share for RY 2011. Thus, the labor-related share that we are using for IPF PPS in RY 2011 is 75.400 percent. Table 2 below shows the RY 2011 labor-related share using the FY 2002-based RPL market basket. We note that this labor-related share is determined by using the same methodology as employed in calculating all previous IPF labor-related shares.

A complete discussion of the IPF labor-related share methodology appears in the November 2004 IPF PPS final rule (69 FR 66952 through 66954).

Table 2—Total Labor-Related Share—Relative Importance for RY 2011

Cost category	FY 2002-based RPL market basket labor-related share relative importance (percent) RY 2010 *	FY 2002-based RPL market basket labor-related share relative importance (percent) RY 2011 **
Wages and salaries	53.062	52.600
Employee benefits	13.852	13.935
Professional fees	2.895	2.853
All other labor-intensive services	2.126	2.118
Subtotal	71.935	71.506
Labor-related share of capital costs (0.46)	3.954	3.894
Total	75.889	75.400
* Based on 2009 1st Quarter forecast.
** Based on 2010 1st Quarter forecast.

3. Comments on Creating a Stand-Alone IPF Market Basket

In the May 2009 IPF PPS notice (74 FR 20362), we expressed our interest in exploring the possibility of creating a stand-alone IPF market basket that reflects the cost structures of only IPF providers. Of the available options, one would be to join the Medicare cost report data from freestanding IPF providers (presently incorporated into the RPL market basket) with data from hospital-based IPF providers. An examination of the Medicare cost report data comparing freestanding and hospital-based IPFs reveals considerable differences between the two with respect to cost levels and cost structures.

In order to better understand the observed cost differences between freestanding and hospital-based IPFs, we reviewed, in detail, several explanatory variables such as geographic variation, case mix (including DRG, comorbidity, and age), urban or rural status, length of stay, teaching status, and the presence of a qualifying emergency department. Despite this analysis, we were unable to sufficiently explain the differences in costs between these two types of IPF providers. As a result, we felt that further research was required and solicited public comment on additional information that would help us to better understand the reasons for the variations in costs and cost structures, as reported by cost report data, between freestanding and hospital-based IPFs (74 FR 20376).

We received several timely comments from the public on this issue. A summary of the comments and our responses to those comments are below.

Comment: Several commenters recommended that CMS consider creating an IPF-specific market basket. These commenters stated that including hospital-based IPF data in the market basket and pursuing a greater understanding of the differences between freestanding and hospital-based IPFs are both worthy undertakings. The commenters cited that from 2005 through 2007, the number of hospital-based IPFs has decreased by 1.4 percent while the number of freestanding IPFs has increased by 1.0 percent. The commenters expressed concern that these trends will continue, and likely accelerate. Furthermore, the commenters stated that in 2007, hospital-based IPFs experienced negative margins while freestanding IPF margins were positive. Given that more than 60 percent of IPF discharges are from hospital-based units, the commenters believe that preserving access to care for these patients (especially those who have coexisting physical conditions or experience a crisis and enter the emergency department for treatment) is vital. One commenter stated that including hospital-based IPF data in the market basket would increase transparency and highlight the differences between freestanding and hospital-based providers.

Response: We are actively examining the technical merits of creating a stand-alone IPF market basket. Since publication of the May 2009 IPF PPS notice, we have been reviewing the Medicare cost report and claims data for both hospital-based and freestanding IPFs to better understand the differences in total Medicare costs per day. Parts of our analysis were based on comments received by the public, which we address in more detail below. Based on our research to date, which has not adequately explained the cost-per-day differences between freestanding and hospital-based providers, we do not believe it is technically appropriate to move from the RPL market basket to update IPF payments at this time.

Comment: Several commenters supported the ongoing application of the RPL market basket to update inpatient psychiatric facility payment rates. One commenter recommended we continue this method in order to maintain a reasonable population size of facilities to ensure stability in the calculation of the market basket. The commenter asserted that if the RPL market basket was split into separate market baskets for IRFs, IPFs, and LTCHs, there would be much more volatility in the year-to-year changes, especially for LTCHs.

Response: We appreciate the comments regarding the continued support for using the RPL market basket to update inpatient psychiatric facility payment rates. Likewise, we appreciate the comment regarding sample size considerations with respect to splitting the RPL market basket into its respective pieces. Indeed, sample size and its impact on the volatility of the estimates will be extensively scrutinized before we would propose to change the mechanism used to update payments to inpatient psychiatric facilities, inpatient rehabilitation facilities, and long-term care hospitals.

Comment: One commenter supported the investigation of the differences in cost structures between hospital-based and freestanding IPFs. Besides determining the source of these differences, the commenter also stated it is important for CMS to determine whether the differences should be recognized (for example, are higher costs in IPF hospital-based facilities due to allocation of overhead to the unit or to differences in case mix or patient severity that is not measurable using available administrative data). This commenter also acknowledged that seeking outside input regarding differences in cost structures between hospital-based and freestanding IPFs is appropriate. However, the commenter recommended that CMS proceed with caution as it may be difficult for CMS to confirm that the methods used to collect outside data are sound and that the data are representative of the industry as a whole. The commenter also stated that CMS should ultimately determine whether the market basket should in fact be based on the cost structure of hospital-based and freestanding IPFs (instead of just one type of facility) if the higher costs cannot be explained by differences in case mix and other patient characteristics.

Response: Although we asked for outside information to help us better understand these differences, we agree with the commenter that any outside information should be carefully examined.

As we have stated, we currently do not feel it is appropriate to incorporate data from hospital-based IPFs with that of freestanding IPFs to create a stand-alone IPF market basket given the observed and unexplained differences in cost structures.

Comment: Several commenters stated that creating a stand-alone IPF market basket could be a more accurate index for the costs of delivering care incurred by IPFs. However, the commenters stated that they did not have any independent data to help CMS in developing a stand-alone market basket at this time. The commenters suggested that the issue of a stand-alone IPF market basket continue to be analyzed by CMS.

Response: We agree with the commenters and plan to continue to analyze costs and Medicare claims data for hospital-based and freestanding providers.

Comment: One commenter supports the development of a stand-alone IPF market basket. However, the commenter encourages CMS to avoid mixing data from hospital-based and freestanding IPFs. The commenter claims that hospital-based IPFs incur higher costs than freestanding IPFs in treating Medicare patients for the following reasons:

• The acuity levels and medical needs of psychiatric patients that present in a hospital's qualified emergency room will result in higher treatment costs and lengths of stay.
• Hospitals provide a greater range of ancillary services.
• Some hospitals operate approved psychiatric residency teaching programs.

Therefore, the commenter is reluctant to support a combined hospital-based, freestanding IPF market basket at this time. The commenter also offered to assist CMS with any information he or she can provide.

Response: We appreciate the commenter's input on possible reasons why hospital-based IPFs have higher costs than freestanding IPFs. As stated above, we compared the medical needs of the patients, as measured by the adjustments for DRG, comorbidities, and age. Our analysis did show that hospital-based providers, on average, treat more complex patients; however, the differences in the complexity of the patients, as well as other facility-based adjustments, did not adequately explain the differences in total Medicare costs per day between hospital-based and freestanding providers. In addition, using both Medicare cost report and claims data, we found that hospital-based providers, on average, had shorter lengths of stay than freestanding providers.

Per the commenter's suggestion, and using MCR data, we also compared the Medicare ancillary costs per day of hospital-based and freestanding providers. We found that hospital-based facilities, on average, tend to have higher Medicare ancillary costs per day than freestanding facilities. The differences were mostly attributable to higher emergency room and laboratory costs. These higher ancillary costs accounted for about ten percent of the overall difference between hospital-based and freestanding providers' total Medicare costs per day.

In addition, we compared the average approved teaching costs for hospital-based and freestanding providers. We found that hospital-based providers have higher teaching-related costs associated with Medicare approved programs relative to free standing providers; however, the difference accounted for only three percent of the total difference in Medicare costs per day for hospital-based and freestanding providers.

Comment: One commenter simply agreed with CMS that before implementation of a new market basket method, the method should be fully evaluated and the projected impact known.

Response: We agree with the commenter's suggestion. Before any implementation, CMS will fully evaluate our methodology to ensure that any proposed market basket most accurately reflects the cost structures associated with providing psychiatric care to Medicare patients.

Comment: One commenter does not support the adoption of a stand-alone IPF market basket at this time, pending further study, as the commenter is not convinced that CMS has the appropriate level of psychiatric cost data available to compile an accurate market basket for IPFs alone. These conclusions were based on the following reasons:

• There are a small number of facilities and often limited data (for example, only 4 percent of IPFs reported contract labor costs for FY 2002).
• Benefits, contract labor, and blood cost weights were developed using the FY 2002-based IPPS market basket.
• Other detailed cost categories were derived from the FY 2002-based IPPS market basket.
• No cost data specific to psychiatry (that is, Wages and Salaries—based on Civilian Hospital Workers).

The commenter stated that without release of both relevant internal data available only to CMS on the previously mentioned IPF market basket issues, as well as specific data on the types of cost differences between the various cost categories of IRF, IPF, and LTCH facilities, they are unable to comment on an independent IPF market basket at this time. The commenter believes that more detailed analysis needs to be conducted and released before they could consider supporting any change to the current RPL-based market basket update process.

Response: We are in the process of evaluating multiple years of data in order to determine whether a stand-alone IPF market basket would be a more appropriate index for updating IPF PPS payments. We agree with the commenter that there is a lack of IPF-specific benefit and contract labor cost data. Currently, benefit and contract labor cost data are collected on Worksheet S-3, part II of the Medicare cost report (MCR), but are only required of IPPS hospitals. We proposed under separate cover to modify the present-day hospital MCR in order to collect benefit and contract labor data on a separate worksheet (proposed Worksheet S-3, part V) which would be completed by all hospitals (http://www.cms.hhs.gov/​PaperworkReductionActof1995/​PRAL/​itemdetail.asp?​filterType=​none&​filterByDID=​-99&​sortByDID=​2&​sortOrder=​descending&​itemID=​CMS1224069&​intNumPerPage). We disagree with the commenter that we are not capturing IPF-specific data for wages and salaries since all hospitals are required to report this data on the MCRs, which provides the sources of our wages and salaries cost weight. We believe the commenter may be referencing the Employment Cost Index (ECI) for wages and salaries for hospital civilian workers which we use to proxy price changes associated with the wages and salary cost weight. This proxy is used because the Bureau of Labor Statistics does not publish a wages and salaries price index specific to IPFs only. However, the ECI for wages and salaries for hospital civilian workers does include the price changes of IPFs, as well as other hospital-types (including general surgical hospitals).

IV. Update of the IPF PPS Adjustment Factors

A. Overview of the IPF PPS Adjustment Factors

The IPF PPS payment adjustments were derived from a regression analysis of 100 percent of the FY 2002 MedPAR data file, which contained 483,038 cases. For this notice, we used the same results of the regression analysis used to implement the November 2004 IPF PPS final rule. For a more detailed description of the data file used for the regression analysis, see the November 2004 IPF PPS final rule (69 FR 66935 through 66936). While we have since used more recent claims data to set the fixed dollar loss threshold amount, we use the same results of this regression analysis to update the IPF PPS for RY 2010 as well as RY 2011.

As previously stated, we do not plan to update the regression analysis until we are able to analyze IPF PPS claims and cost report data. However, we continue to monitor claims and payment data independently from cost report data to assess issues, to determine whether changes in case-mix or payment shifts have occurred among freestanding governmental, non-profit and private psychiatric hospitals, and psychiatric units of general hospitals, and CAHs and other issues of importance to IPFs.

B. Patient-Level Adjustments

In the May 2008 IPF PPS notice (73 FR 25709) and in the May 2009 IPF PPS notice (74 FR 20362), we provided payment adjustments for the following patient-level characteristics: Medicare Severity diagnosis related groups (MS-DRGs) assignment of the patient's principal diagnosis, selected comorbidities, patient age, and the variable per diem adjustments.

1. Adjustment for MS-DRG Assignment

The IPF PPS includes payment adjustments for the psychiatric DRG assigned to the claim based on each patient's principal diagnosis. The IPF PPS recognizes the MS-DRGs. The DRG adjustment factors were expressed relative to the most frequently reported psychiatric DRG in FY 2002, that is, DRG 430 (psychoses). The coefficient values and adjustment factors were derived from the regression analysis.

In accordance with § 412.27(a), payment under the IPF PPS is conditioned on IPFs admitting “only patients whose admission to the unit is required for active treatment, of an intensity that can be provided appropriately only in an inpatient hospital setting, of a psychiatric principal diagnosis that is listed in Chapter Five (“Mental Disorders”) of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)” or in the Fourth Edition, Text Revision of the American Psychiatric Association's Diagnostic and Statistical Manual, (DSM-IV-TR). IPF claims with a principal diagnosis included in Chapter Five of the ICD-9-CM or the DSM-IV-TR are paid the Federal per diem base rate under the IPF PPS and all other applicable adjustments, including any applicable DRG adjustment. Psychiatric principal diagnoses that do not group to one of the designated DRGs still receive the Federal per diem base rate and all other applicable adjustments, but the payment would not include a DRG adjustment.

The Standards for Electronic Transaction final rule published in the Federal Register on August 17, 2000 (65 FR 50312), adopted the ICD-9-CM as the designated code set for reporting diseases, injuries, impairments, other health related problems, their manifestations, and causes of injury, disease, impairment, or other health related problems. Therefore, we use the ICD-9-CM as the designated code set for the IPF PPS.

We believe that it is important to maintain the same diagnostic coding and DRG classification for IPFs that are used under the IPPS for providing the psychiatric care. Therefore, when the IPF PPS was implemented for cost reporting periods beginning on or after January 1, 2005, we adopted the same diagnostic code set and DRG patient classification system (that is, the CMS DRGs) that was utilized at the time under the hospital inpatient prospective payment system (IPPS). Since the inception of the IPF PPS, the DRGs used as the patient classification system under the IPF PPS have corresponded exactly with the CMS DRGs applicable under the IPPS for acute care hospitals.

Every year, changes to the ICD-9-CM coding system are addressed in the IPPS proposed and final rules. The changes to the codes are effective October 1 of each year and must be used by acute care hospitals as well as other providers to report diagnostic and procedure information. The IPF PPS has always incorporated ICD-9-CM coding changes made in the annual IPPS update. We publish coding changes in a Transmittal/Change Request, similar to how coding changes are announced by the IPPS and LTCH PPS. Those ICD-9-CM coding changes are also published in the following IPF PPS RY update, in either the IPF PPS proposed and final rules, or in an IPF PPS update notice.

In the May 2008 IPF PPS notice (73 FR 25714), we discussed CMS' effort to better recognize resource use and the severity of illness among patients. CMS adopted the new MS-DRGs for the IPPS in the FY 2008 IPPS final rule with comment period (72 FR 47130). We believe by better accounting for patients' severity of illness in Medicare payment rates, the MS-DRGs encourage hospitals to improve their coding and documentation of patient diagnoses. The MS-DRGs, which are based on the CMS DRGs, represent a significant increase in the number of DRGs (from 538 to 745, an increase of 207). For a full description of the development and implementation of the MS-DRGs, see the FY 2008 IPPS final rule with comment period (72 FR 47141 through 47175).

All of the ICD-9-CM coding changes are reflected in the FY 2010 GROUPER, Version 27.0, effective for IPPS discharges occurring on or after October 1, 2009 through September 30, 2010. The GROUPER Version 27.0 software package assigns each case to an MS-DRG on the basis of the diagnosis and procedure codes and demographic information (that is, age, sex, and discharge status). The Medicare Code Editor (MCE) 26.0 uses the new ICD-9-CM codes to validate coding for IPPS discharges on or after October 1, 2009. For additional information on the GROUPER Version 27.0 and MCE 26.0, see Transmittal 1816 (Change Request 6634), dated October 1, 2009. The IPF PPS has always used the same GROUPER and Code Editor as the IPPS. Therefore, the ICD-9-CM changes, which were reflected in the GROUPER Version 27.0 and MCE 26.0 on October 1, 2009, also became effective for the IPF PPS for discharges occurring on or after October 1, 2009.

The impact of the new MS-DRGs on the IPF PPS was negligible. Mapping to the MS-DRGs resulted in the current 17 MS-DRGs, instead of the original 15 DRGs, for which the IPF PPS provides an adjustment. Although the code set is updated, the same associated adjustment factors apply now that have been in place since implementation of the IPF PPS, with one exception that is unrelated to the update to the codes. When DRGs 521 and 522 were consolidated into MS-DRG 895, we carried over the adjustment factor of 1.02 from DRG 521 to the newly consolidated MS-DRG. This was done to reflect the higher claims volume under DRG 521, with more than eight times the number of claims than billed under DRG 522. The updates are reflected in Table 5. For a detailed description of the mapping changes from the original DRG adjustment categories to the current MS-DRG adjustment categories, we refer readers to the May 2008 IPF PPS notice (73 FR 25714).

The official version of the ICD-9-CM is available on CD-ROM from the U.S. Government Printing Office. The FY 2009 version can be ordered by contacting the Superintendent of Documents, U.S. Government Printing Office, Department 50, Washington, DC 20402-9329, telephone number [phone]. Questions concerning the ICD-9-CM should be directed to Patricia E. Brooks, Co-Chairperson, ICD-9-CM Coordination and Maintenance Committee, CMS, Center for Medicare Management, Hospital and Ambulatory Policy Group, Division of Acute Care, Mailstop C4-08-06, 7500 Security Boulevard, Baltimore, Maryland 21244-1850.

Further information concerning the official version of the ICD-9-CM can be found in the IPPS final rule with comment period, “Changes to Hospital Inpatient Prospective Payment System and Fiscal Year 2010 Rates” in the August 27, 2009 Federal Register (74 FR 43754) and at http://www.cms.hhs.gov/​AcuteInpatientPPS/​IPPS/​list.asp#TopOfPage.

Tables 3 and 4 below list the FY 2010 new and invalid ICD-9-CM diagnosis codes that group to one of the 17 MS-DRGs for which the IPF PPS provides an adjustment. These tables are only a listing of FY 2010 changes and do not reflect all of the currently valid and applicable ICD-9-CM codes classified in the MS-DRGs. When coded as a principal code or diagnosis, these codes receive the correlating MS-DRG adjustment.

Table 3—FY 2010 New Diagnosis Codes

Diagnosis code	Description	MS-DRG
438.13	Late effects of cerebrovascular disease, dysarthria	056, 057
438.14	Late effects of cerebrovascular disease, fluency disorder	056, 057
799.21	Nervousness	880
799.22	Irritability	880
799.23	Impulsiveness	882
799.24	Emotional lability	883
799.25	Demoralization and apathy	880
799.29	Other signs and symptoms involving emotional state	880

Table 4—FY 2010 Invalid Diagnosis Codes

Diagnosis code	Description	MS-DRG
799.2	Nervousness	880

We do not plan to update the regression analysis until we are able to analyze IPF PPS data. The MS-DRG adjustment factors (as shown in Table 5 below) will continue to be paid for discharges occurring in RY 2011.

Table 5—RY 2011 Current MS-DRGs Applicable for the Principal Diagnosis Adjustment

MS-DRG	MS-DRG descriptions	Adjustment factor
056	Degenerative nervous system disorders w MCC	1.05
057	Degenerative nervous system disorders w/o MCC	1.05
080	Nontraumatic stupor & coma w MCC	1.07
081	Nontraumatic stupor & coma w/o MCC	1.07
876	O.R. procedure w principal diagnoses of mental illness	1.22
880	Acute adjustment reaction & psychosocial dysfunction	1.05
881	Depressive neuroses	0.99
882	Neuroses except depressive	1.02
883	Disorders of personality & impulse control	1.02
884	Organic disturbances & mental retardation	1.03
885	Psychoses	1.00
886	Behavioral & developmental disorders	0.99
887	Other mental disorder diagnoses	0.92
894	Alcohol/drug abuse or dependence, left AMA	0.97
895	Alcohol/drug abuse or dependence w rehabilitation therapy	1.02
896	Alcohol/drug abuse or dependence w/o rehabilitation therapy w MCC	0.88
897	Alcohol/drug abuse or dependence w/o rehabilitation therapy w/o MCC	0.88

2. Payment for Comorbid Conditions

The intent of the comorbidity adjustments is to recognize the increased costs associated with comorbid conditions by providing additional payments for certain concurrent medical or psychiatric conditions that are expensive to treat. In the May 2009 IPF PPS notice (74 FR 20362), we explained that the IPF PPS includes 17 comorbidity categories and identified the new, revised, and deleted ICD-9-CM diagnosis codes that generate a comorbid condition payment adjustment under the IPF PPS for RY 2010 (77 FR 20372).

Comorbidities are specific patient conditions that are secondary to the patient's principal diagnosis and that require treatment during the stay. Diagnoses that relate to an earlier episode of care and have no bearing on the current hospital stay are excluded and must not be reported on IPF claims. Comorbid conditions must exist at the time of admission or develop subsequently, and affect the treatment received, length of stay (LOS), or both treatment and LOS.

For each claim, an IPF may receive only one comorbidity adjustment per comorbidity category, but it may receive an adjustment for more than one comorbidity category. Billing instructions require that IPFs must enter the full ICD-9-CM codes for up to 8 additional diagnoses if they co-exist at the time of admission or develop subsequently and impact the treatment provided.

The comorbidity adjustments were determined based on the regression analysis using the diagnoses reported by IPFs in FY 2002. The principal diagnoses were used to establish the DRG adjustments and were not accounted for in establishing the comorbidity category adjustments, except where ICD-9-CM “code first” instructions apply. As we explained in the May 2008 IPF PPS notice (73 FR 25716), the code first rule applies when a condition has both an underlying etiology and a manifestation due to the underlying etiology. For these conditions, the ICD-9-CM has a coding convention that requires the underlying conditions to be sequenced first followed by the manifestation. Whenever a combination exists, there is a “use additional code” note at the etiology code and a code first note at the manifestation code.

As discussed in the MS-DRG section, it is our policy to maintain the same diagnostic coding set for IPFs that is used under the IPPS for providing the same psychiatric care. Although the ICD-9-CM code set has been updated, the same adjustment factors have been in place since the implementation of the IPF PPS. Table 6 below lists the FY 2010 new ICD diagnosis codes that impact the comorbidity adjustments under the IPF PPS. Table 6 is not a list of all currently valid ICD codes applicable for the IPF PPS comorbidity adjustments.

Table 6—FY 2010 New ICD Codes Applicable for the Comorbidity Adjustment

Diagnosis code	Description	Comorbidity category
209.31	Merkel cell carcinoma of the face	Oncology Treatment.
209.32	Merkel cell carcinoma of the scalp and neck	Oncology Treatment.
209.33	Merkel cell carcinoma of the upper limb	Oncology Treatment.
209.34	Merkel cell carcinoma of the lower limb	Oncology Treatment.
209.35	Merkel cell carcinoma of the trunk	Oncology Treatment.
209.36	Merkel cell carcinoma of other sites	Oncology Treatment.
209.70	Secondary neuroendocrine tumor, unspecified site	Oncology Treatment.
209.71	Secondary neuroendocrine tumor of distant lymph nodes	Oncology Treatment.
209.72	Secondary neuroendocrine tumor of liver	Oncology Treatment.
209.73	Secondary neuroendocrine tumor of bone	Oncology Treatment.
209.74	Secondary neuroendocrine tumor of peritoneum	Oncology Treatment.
209.75	Secondary Merkel cell carcinoma	Oncology Treatment.
209.79	Secondary neuroendocrine tumor of other sites	Oncology Treatment.
239.81	Neoplasms of unspecified nature, retina and choroid	Oncology Treatment.
239.89	Neoplasms of unspecified nature, other specified sites	Oncology Treatment.
969.00	Poisoning by antidepressant, unspecified	Poisoning.
969.01	Poisoning by monoamine oxidase inhibitors	Poisoning.
969.02	Poisoning by selective serotonin and norepinephrine reuptake inhibitors	Poisoning.
969.03	Poisoning by selective serotonin reuptake inhibitors	Poisoning.
969.04	Poisoning by tetracyclic antidepressants	Poisoning.
969.05	Poisoning by tricyclic antidepressants	Poisoning.
969.09	Poisoning by other antidepressants	Poisoning.
969.70	Poisoning by psychostimulant, unspecified	Poisoning.
969.71	Poisoning by caffeine	Poisoning.
969.72	Poisoning by amphetamines	Poisoning.
969.73	Poisoning by methylphenidate	Poisoning.
969.79	Poisoning by other psychostimulants	Poisoning.

Table 7 below lists the FY 2010 revised ICD diagnosis codes that are applicable for the comorbidity adjustment.

Table 7—FY 2010 Revised ICD Codes Applicable for the Comorbidity Adjustment

Diagnosis code	Description	Comorbidity category
584.5	Acute kidney failure with lesion of tubular necrosis	Renal Failure, Acute.
584.6	Acute kidney failure with lesion of renal cortical necrosis	Renal Failure, Acute.
584.7	Acute kidney failure with lesion of renal medullary [papillary] necrosis	Renal Failure, Acute.
584.8	Acute kidney failure with other specified pathological lesion in kidney	Renal Failure, Acute.
584.9	Acute kidney failure, unspecified	Renal Failure, Acute.
639.3	Kidney failure following abortion and ectopic and molar pregnancies	Renal Failure, Acute.
669.32	Acute kidney failure following labor and delivery, delivered, with mention of postpartum complication	Renal Failure, Acute.
669.34	Acute kidney failure following labor and delivery, postpartum condition or complication	Renal Failure, Acute.

Table 8 below lists the invalid FY 2010 ICD-9-CM codes no longer applicable for the comorbidity adjustment.

Table 8—FY 2010 Invalid ICD Codes No Longer Applicable for the Comorbidity Adjustment

Diagnosis code	Description	Comorbidity category
239.8	Neoplasm of unspecified nature of other specified sites	Oncology Treatment.
969.0	Poisoning by antidepressants	Poisoning.
969.7	Poisoning by psychostimulants	Poisoning.

For RY 2011, we are applying the seventeen comorbidity categories for which we are providing an adjustment, their respective codes, including the new FY 2010 ICD-9-CM codes, and their respective adjustment factors in Table 9 below.

Table 9—RY 2011 Diagnosis Codes and Adjustment Factors for Comorbidity Categories

Description of comorbidity	ICD-9CM code	Adjustment factor
Developmental Disabilities	317, 3180, 3181, 3182, and 319	1.04
Coagulation Factor Deficits	2860 through 2864	1.13
Tracheostomy	51900 through 51909 and V440	1.06
Renal Failure, Acute	5845 through 5849, 63630, 63631, 63632, 63730, 63731, 63732, 6383, 6393, 66932, 66934, 9585	1.11
Renal Failure, Chronic	40301, 40311, 40391, 40402, 40412, 40413, 40492, 40493, 5853, 5854, 5855, 5856, 5859, 586, V4511, V4512, V560, V561, and V562	1.11
Oncology Treatment	1400 through 2399 with a radiation therapy code 92.21-92.29 or chemotherapy code 99.25	1.07
Uncontrolled Diabetes-Mellitus with or without complications	25002, 25003, 25012, 25013, 25022, 25023, 25032, 25033, 25042, 25043, 25052, 25053, 25062, 25063, 25072, 25073, 25082, 25083, 25092, and 25093	1.05
Severe Protein Calorie Malnutrition	260 through 262	1.13
Eating and Conduct Disorders	3071, 30750, 31203, 31233, and 31234	1.12
Infectious Disease	01000 through 04110, 042, 04500 through 05319, 05440 through 05449, 0550 through 0770, 0782 through 07889, and 07950 through 07959	1.07
Drug and/or Alcohol Induced Mental Disorders	2910, 2920, 29212, 2922, 30300, and 30400	1.03
Cardiac Conditions	3910, 3911, 3912, 40201, 40403, 4160, 4210, 4211, and 4219	1.11
Gangrene	44024 and 7854	1.10
Chronic Obstructive Pulmonary Disease	49121, 4941, 5100, 51883, 51884, V4611, V4612, V4613 and V4614	1.12
Artificial Openings—Digestive and Urinary	56960 through 56969, 9975, and V441 through V446	1.08
Severe Musculoskeletal and Connective Tissue Diseases	6960, 7100, 73000 through 73009, 73010 through 73019, and 73020 through 73029	1.09
Poisoning	96500 through 96509, 9654, 9670 through 9699, 9770, 9800 through 9809, 9830 through 9839, 986, 9890 through 9897	1.11

3. Patient Age Adjustments

As explained in the November 2004 IPF PPS final rule (69 FR 66922), we analyzed the impact of age on per diem cost by examining the age variable (that is, the range of ages) for payment adjustments.

In general, we found that the cost per day increases with age. The older age groups are more costly than the under 45 age group, the differences in per diem cost increase for each successive age group, and the differences are statistically significant.

For RY 2011, we are continuing to use the patient age adjustments currently in effect as shown in Table 10 below.

Table 10—Age Groupings and Adjustment Factors

Age	Adjustment factor
Under 45	1.00
45 and under 50	1.01
50 and under 55	1.02
55 and under 60	1.04
60 and under 65	1.07
65 and under 70	1.10
70 and under 75	1.13
75 and under 80	1.15
80 and over	1.17

4. Variable Per Diem Adjustments

We explained in the November 2004 IPF PPS final rule (69 FR 66946) that the regression analysis indicated that per diem cost declines as the LOS increases. The variable per diem adjustments to the Federal per diem base rate account for ancillary and administrative costs that occur disproportionately in the first days after admission to an IPF.

We used a regression analysis to estimate the average differences in per diem cost among stays of different lengths. As a result of this analysis, we established variable per diem adjustments that begin on day 1 and decline gradually until day 21 of a patient's stay. For day 22 and thereafter, the variable per diem adjustment remains the same each day for the remainder of the stay. However, the adjustment applied to day 1 depends upon whether the IPF has a qualifying ED. If an IPF has a qualifying ED, it receives a 1.31 adjustment factor for day 1 of each stay. If an IPF does not have a qualifying ED, it receives a 1.19 adjustment factor for day 1 of the stay. The ED adjustment is explained in more detail in section IV.C.5 of this notice.

For RY 2011, we are continuing to use the variable per diem adjustment factors currently in effect as shown in Table 11 below. A complete discussion of the variable per diem adjustments appears in the November 2004 IPF PPS final rule (69 FR 66946).

Table 11—Variable Per Diem Adjustments

Day-of-stay	Adjustment factor
Day 1—IPF Without a Qualifying ED	1.19
Day 1—IPF With a Qualifying ED	1.31
Day 2	1.12
Day 3	1.08
Day 4	1.05
Day 5	1.04
Day 6	1.02
Day 7	1.01
Day 8	1.01
Day 9	1.00
Day 10	1.00
Day 11	0.99
Day 12	0.99
Day 13	0.99
Day 14	0.99
Day 15	0.98
Day 16	0.97
Day 17	0.97
Day 18	0.96
Day 19	0.95
Day 20	0.95
Day 21	0.95
After Day 21	0.92

C. Facility-Level Adjustments

The IPF PPS includes facility-level adjustments for the wage index, IPFs located in rural areas, teaching IPFs, cost of living adjustments for IPFs located in Alaska and Hawaii, and IPFs with a qualifying ED.

1. Wage Index Adjustment

a. Background

As discussed in the May 2006 IPF PPS final rule and in the May 2008 and May 2009 update notices, in providing an adjustment for geographic wage levels, the labor-related portion of an IPF's payment is adjusted using an appropriate wage index. Currently, an IPF's geographic wage index value is determined based on the actual location of the IPF in an urban or rural area as defined in § 412.64(b)(1)(ii)(A) through § 412.64(C).

b. Wage Index for RY 2011

Since the inception of the IPF PPS, we have used hospital wage data in developing a wage index to be applied to IPFs. We are continuing that practice for RY 2011. We apply the wage index adjustment to the labor-related portion of the Federal rate, which is 75.400 percent. This percentage reflects the labor-related relative importance of the RPL market basket for RY 2011 (see section III.B.2 of this notice). The IPF PPS uses the pre-floor, pre-reclassified hospital wage index. Changes to the wage index are made in a budget neutral manner so that updates do not increase expenditures.

For RY 2011, we are applying the most recent hospital wage index (that is, the FY 2010 pre-floor, pre-reclassified hospital wage index because this is the most appropriate index as it best reflects the variation in local labor costs of IPFs in the various geographic areas) using the most recent hospital wage data (that is, data from hospital cost reports for the cost reporting period beginning during FY 2006), and applying an adjustment in accordance with our budget neutrality policy. This policy requires us to estimate the total amount of IPF PPS payments in RY 2010 using the applicable wage index value divided by the total estimated IPF PPS payments in RY 2011 using the most recent wage index. The estimated payments are based on FY 2008 IPF claims, inflated to the appropriate RY. This quotient is the wage index budget neutrality factor, and it is applied in the update of the Federal per diem base rate for RY 2011 in addition to the market basket described in section III.B.1 of this notice. The wage index budget neutrality factor for RY 2011 is 0.9999.

The wage index applicable for RY 2011 appears in Table 1 and Table 2 in Addendum B of this notice. As explained in the May 2006 IPF PPS final rule for RY 2007 (71 FR 27061), the IPF PPS applies the hospital wage index without a hold-harmless policy, and without an out-commuting adjustment or out-migration adjustment because the statutory authority for these policies applies only to the IPPS.

Also in the May 2006 IPF PPS final rule for RY 2007 (71 FR 27061), we adopted the changes discussed in the Office of Management and Budget (OMB) Bulletin No. 03-04 (June 6, 2003), which announced revised definitions for Metropolitan Statistical Areas (MSAs), and the creation of Micropolitan Statistical Areas and Combined Statistical Areas. In adopting the OMB Core-Based Statistical Area (CBSA) geographic designations, since the IPF PPS was already in a transition period from TEFRA payments to PPS payments, we did not provide a separate transition for the CBSA-based wage index.

As was the case in RY 2010, for RY 2011 we will continue to use the CBSA-based wage index values as presented in Tables 1 and 2 in Addendum B of this notice. A complete discussion of the CBSA labor market definitions appears in the May 2006 IPF PPS final rule (71 FR 27061 through 27067).

In summary, for RY 2011 we will use the FY 2010 wage index data (collected from cost reports submitted by hospitals for cost reporting periods beginning during FY 2006) to adjust IPF PPS payments beginning July 1,2010.

c. OMB Bulletins

The Office of Management and Budget (OMB) publishes bulletins regarding CBSA changes, including changes to CBSA numbers and titles. In the May 2008 IPF PPS notice, we incorporated the CBSA nomenclature changes published in the most recent OMB bulletin that applies to the hospital wage data used to determine the current IPF PPS wage index (73 FR 25721). We will continue to do the same for all such OMB CBSA nomenclature changes in future IPF PPS rules and notices, as necessary. The OMB bulletins may be accessed online at http://www.whitehouse.gov/​omb/​bulletins/​index.html.

2. Adjustment for Rural Location

In the November 2004 IPF PPS final rule, we provided a 17 percent payment adjustment for IPFs located in a rural area. This adjustment was based on the regression analysis, which indicated that the per diem cost of rural facilities was 17 percent higher than that of urban facilities after accounting for the influence of the other variables included in the regression. For RY 2011, we are applying a 17 percent payment adjustment for IPFs located in a rural area as defined at § 412.64(b)(1)(ii)(C). As stated in the November 2004 IPF PPS final rule, we do not intend to update the adjustment factors derived from the regression analysis until we are able to analyze IPF PPS data. A complete discussion of the adjustment for rural locations appears in the November 2004 IPF PPS final rule (69 FR 66954).

3. Teaching Adjustment

In the November 2004 IPF PPS final rule, we implemented regulations at § 412.424(d)(1)(iii) to establish a facility-level adjustment for IPFs that are, or are part of, teaching hospitals. The teaching adjustment accounts for the higher indirect operating costs experienced by hospitals that participate in graduate medical education (GME) programs. The payment adjustments are made based on the number of full-time equivalent (FTE) interns and residents training in the IPF and the IPF's average daily census.

Medicare makes direct GME payments (for direct costs such as resident and teaching physician salaries, and other direct teaching costs) to all teaching hospitals including those paid under the IPPS, and those that were once paid under the TEFRA rate-of-increase limits but are now paid under other PPSs. These direct GME payments are made separately from payments for hospital operating costs and are not part of the PPSs. The direct GME payments do not address the estimated higher indirect operating costs teaching hospitals may face.

For teaching hospitals paid under the TEFRA rate-of-increase limits, Medicare did not make separate payments for indirect medical education costs because payments to these hospitals were based on the hospitals' reasonable costs which already included these higher indirect costs that may be associated with teaching programs.

The results of the regression analysis of FY 2002 IPF data established the basis for the payment adjustments included in the November 2004 IPF PPS final rule. The results showed that the indirect teaching cost variable is significant in explaining the higher costs of IPFs that have teaching programs. We calculated the teaching adjustment based on the IPF's “teaching variable,” which is one plus the ratio of the number of FTE residents training in the IPF (subject to limitations described below) to the IPF's average daily census (ADC).

We established the teaching adjustment in a manner that limited the incentives for IPFs to add FTE residents for the purpose of increasing their teaching adjustment. We imposed a cap on the number of FTE residents that may be counted for purposes of calculating the teaching adjustment. We emphasize that the cap limits the number of FTE residents that teaching IPFs may count for the purposes of calculating the IPF PPS teaching adjustment, not the number of residents teaching institutions can hire or train. We calculated the number of FTE residents that trained in the IPF during a “base year” and used that FTE resident number as the cap. An IPF's FTE resident cap is ultimately determined based on the final settlement of the IPF's most recent cost report filed before November 15, 2004 (that is, the publication date of the IPF PPS final rule).

In the regression analysis, the logarithm of the teaching variable had a coefficient value of 0.5150. We converted this cost effect to a teaching payment adjustment by treating the regression coefficient as an exponent and raising the teaching variable to a power equal to the coefficient value. We note that the coefficient value of 0.5150 was based on the regression analysis holding all other components of the payment system constant.

As with other adjustment factors derived through the regression analysis, we do not plan to rerun the regression analysis until we analyze IPF PPS data. Therefore, for RY 2011, we are retaining the coefficient value of 0.5150 for the teaching adjustment to the Federal per diem base rate.

A complete discussion of how the teaching adjustment was calculated appears in the November 2004 IPF PPS final rule (69 FR 66954 through 66957) and the May 2008 IPF PPS notice (73 FR 25721).

FTE Intern and Resident Cap Adjustment

CMS has been asked to reconsider the current policy on the FTE intern and resident cap adjustment and to permit an increase in the FTE resident cap when the IPF increases the number of FTE residents it trains due to the acceptance of relocated residents when another IPF closes or closes its psychiatry residency program. To help us assess how many IPFs have been, or expect to be adversely affected by their inability to adjust their caps under § 412.424(d)(1) and under these situations, we specifically requested public comment from IPFs in the May 2009 IPF PPS notice (74 FR 20362). A summary of the comments and our response to those comments are below.

Comment: We received several comments on the FTE Intern and Resident Cap Adjustment. All of the commenters recommended that CMS modify the IPF PPS resident cap policy, supporting a policy change that would permit the IPF PPS residency cap to be increased when residents in a psychiatry residency program must be relocated from one IPF to another due to closure of an IPF or an IPF's psychiatry residency training program. Many commenters expressed concern that a cap on the number of FTE residents used to calculate the teaching adjustment is based on a snapshot of activity essentially “freezing” the status of residency education at a random point in time, CY 2004. Commenters stated that it is time to substantially modify the resident cap policy for the IPF PPS. Several commenters stated that this change in residency policy could help address the psychiatrist shortage, and help ensure access to care for beneficiaries who suffer from mental health and substance use disorders. Other commenters pointed out that the demand for health care services will continue to rise with the growing needs of the 78 million “baby boomers” who will retire in 2010 and with the recent passage of Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equality Act of 2008. The commenters further stated that the U.S. already faces a shortage of psychiatrists, and these factors could potentially elevate what is now a problem to what could be a crisis.

Several commenters stated that in FY 2000, CMS instituted a temporary adjustment to the IPPS FTE cap policy when a hospital increases the number of FTE residents it trains due to the acceptance of relocated residents when another hospital closes (64 FR 41552). The commenters further stated that in FY 2002, CMS also implemented a similar policy for acute care hospitals that accept relocated residents from a closed program (66 FR 39899). The commenters indicated that the same need exists for IPFs that accept displaced residents when an IPF closes or when an IPF or acute care hospital closes its psychiatric residency program. The commenters recommended that CMS implement a temporary resident cap increase policy to the current FTE resident cap when an IPF increases the number of FTE residents it trains due to the acceptance of relocated residents. The commenters believe this change is necessary in order to promote consistency among payment systems and to ensure that residents training in psychiatry can continue their training when their original residency training program closes.

Several commenters suggested that although the extent of the problem of displaced psychiatry residents is not clear at this time, the number of inpatient psychiatric units is declining. Therefore, they agreed that a temporary increase in the resident cap, similar to that allowed for acute care hospitals, would provide an incentive for IPFs to accept those psychiatry residents who are displaced by the closure of residency training programs. Some commenters expressed concern that inpatient psychiatric programs are closing in different parts of the country and believe the cap issue could become more of a problem in the future.

One association surveyed IPFs and concluded that the cap does impact IPF training of psychiatric residents. Specifically, they stated that certain IPFs reported that they trained additional residents from a closed residency program and have exceeded their caps because of those residents. Other IPFs in the survey reported that they had been asked to train additional residents but had not agreed because these additional residents would have caused them to exceed their cap.

Another commenter believes the cap limits the flexibility of health systems to become more efficient by consolidating programs and residency training. This commenter indicated that while they have not heard of many facilities that have experienced a problem exceeding the cap, they were aware of specific cases where it has created problems and prevented some changes in the training of residents from one IPF to another. The example cited was a facility in the northwest that is part of a large health system, wanted to close down their training program in their outpatient department and shift the residents to an IPF owned by the health system. However, they indicated that the cap prevented the system from moving the residents from the outpatient program to the IPF.

Another commenter believes this change is necessary and has personally encountered this situation when a local IPF was closed and their residents had to be relocated, some of which came to the commenter's facility. The commenter stated that a change in this policy would help keep needed residency slots in the local communities.

One commenter indicated that they trained 24.56 FTE(s), which included 1.60 FTE(s) from a closed IPF. The commenter's cap is 18.18. The commenter indicated training of the closed IPF's residents did not give them relief from the cap.

Response: We appreciate all comments received on the FTE intern and resident cap adjustment. We will take all comments into consideration as we assess the IPF PPS regulations with respect to developing the policy for the teaching cap adjustment in the future.

4. Cost of Living Adjustment for IPFs Located in Alaska and Hawaii

The IPF PPS includes a payment adjustment for IPFs located in Alaska and Hawaii based upon the county in which the IPF is located. As we explained in the November 2004 IPF PPS final rule, the FY 2002 data demonstrated that IPFs in Alaska and Hawaii had per diem costs that were disproportionately higher than other IPFs. Other Medicare PPSs (for example, the IPPS and LTCH PPS) have adopted a cost of living adjustment (COLA) to account for the cost differential of care furnished in Alaska and Hawaii.

We analyzed the effect of applying a COLA to payments for IPFs located in Alaska and Hawaii. The results of our analysis demonstrated that a COLA for IPFs located in Alaska and Hawaii would improve payment equity for these facilities. As a result of this analysis, we provided a COLA in the November 2004 IPF PPS final rule.

A COLA adjustment for IPFs located in Alaska and Hawaii is made by multiplying the non-labor share of the Federal per diem base rate by the applicable COLA factor based on the COLA area in which the IPF is located.

As previously stated in the November 2004 IPF PPS final rule, we will update the COLA factors according to updates established by the U.S. Office of Personnel Management (OPM), which issued a final rule, May 28, 2008 to change COLA rates.

The COLA factors are published on the OPM Web site at (http://www.opm.gov/​oca/​cola/​rates.asp).

We note that the COLA areas for Alaska are not defined by county as are the COLA areas for Hawaii. In 5 CFR 591.207, the OPM established the following COLA areas:

(a) City of Anchorage, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse;

(b) City of Fairbanks, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse;

(c) City of Juneau, and 80-kilometer (50-mile) radius by road, as measured from the Federal courthouse;

(d) Rest of the State of Alaska.

For RY 2011, IPFs located in Alaska and Hawaii will continue to receive the updated COLA factors based on the COLA area in which the IPF is located as shown in Table 12 below.

Table 12—COLA Factors for Alaska and Hawaii IPFs

Location	COLA
Alaska:
Anchorage	1.23
Fairbanks	1.23
Juneau	1.23
Rest of Alaska	1.25
Hawaii:
Honolulu County	1.25
Hawaii County	1.18
Kauai County	1.25
Maui County	1.25
Kalawao County	1.25

5. Adjustment for IPFs With a Qualifying Emergency Department (ED)

Currently, the IPF PPS includes a facility-level adjustment for IPFs with qualifying EDs. We provide an adjustment to the Federal per diem base rate to account for the costs associated with maintaining a full-service ED. The adjustment is intended to account for ED costs incurred by a freestanding psychiatric hospital with a qualifying ED or a distinct part psychiatric unit of an acute hospital or a CAH for preadmission services otherwise payable under the Medicare Outpatient Prospective Payment System (OPPS) furnished to a beneficiary during the day immediately preceding the date of admission to the IPF (see § 413.40(c)(2)) and the overhead cost of maintaining the ED. This payment is a facility-level adjustment that applies to all IPF admissions (with one exception described below), regardless of whether a particular patient receives preadmission services in the hospital's ED.

The ED adjustment is incorporated into the variable per diem adjustment for the first day of each stay for IPFs with a qualifying ED. That is, IPFs with a qualifying ED receive an adjustment factor of 1.31 as the variable per diem adjustment for day 1 of each stay. If an IPF does not have a qualifying ED, it receives an adjustment factor of 1.19 as the variable per diem adjustment for day 1 of each patient stay.

The ED adjustment is made on every qualifying claim except as described below. As specified in § 412.424(d)(1)(v)(B), the ED adjustment is not made where a patient is discharged from an acute care hospital or critical access hospital (CAH) and admitted to the same hospital's or CAH's psychiatric unit. An ED adjustment is not made in this case because the costs associated with ED services are reflected in the DRG payment to the acute care hospital or through the reasonable cost payment made to the CAH. If we provided the ED adjustment in these cases, the hospital would be paid twice for the overhead costs of the ED, as stated in the November 2004 IPF PPS final rule (69 FR 66960).

Therefore, when patients are discharged from an acute care hospital or CAH and admitted to the same hospital's or CAH's psychiatric unit, the IPF receives the 1.19 adjustment factor as the variable per diem adjustment for the first day of the patient's stay in the IPF.

For RY 2011, we are retaining the 1.31 adjustment factor for IPFs with qualifying EDs. A complete discussion of the steps involved in the calculation of the ED adjustment factor appears in the November 2004 IPF PPS final rule (69 FR 66959 through 66960) and the May 2006 IPF PPS final rule (71 FR 27070 through 27072).

D. Other Payment Adjustments and Policies

For RY 2011, the IPF PPS includes: An outlier adjustment to promote access to IPF care for those patients who require expensive care and to limit the financial risk of IPFs treating unusually costly patients. In this section, we also explain the reason for ending the stop-loss provision that was applicable during the transition period.

1. Outlier Payments

In the November 2004 IPF PPS final rule, we implemented regulations at § 412.424(d)(3)(i) to provide a per-case payment for IPF stays that are extraordinarily costly. Providing additional payments to IPFs for extremely costly cases strongly improves the accuracy of the IPF PPS in determining resource costs at the patient and facility level. These additional payments reduce the financial losses that would otherwise be incurred in treating patients who require more costly care and, therefore, reduce the incentives for IPFs to under-serve these patients.

We make outlier payments for discharges in which an IPF's estimated total cost for a case exceeds a fixed dollar loss threshold amount (multiplied by the IPF's facility-level adjustments) plus the Federal per diem payment amount for the case.

In instances when the case qualifies for an outlier payment, we pay 80 percent of the difference between the estimated cost for the case and the adjusted threshold amount for days 1 through 9 of the stay (consistent with the median LOS for IPFs in FY 2002), and 60 percent of the difference for day 10 and thereafter. We established the 80 percent and 60 percent loss sharing ratios because we were concerned that a single ratio established at 80 percent (like other Medicare PPSs) might provide an incentive under the IPF per diem payment system to increase LOS in order to receive additional payments. After establishing the loss sharing ratios, we determined the current fixed dollar loss threshold amount of $6,565 through payment simulations designed to compute a dollar loss beyond which payments are estimated to meet the 2 percent outlier spending target.

a. Update to the Outlier Fixed Dollar Loss Threshold Amount

In accordance with the update methodology described in § 412.428(d), we are updating the fixed dollar loss threshold amount used under the IPF PPS outlier policy. Based on the regression analysis and payment simulations used to develop the IPF PPS, we established a 2 percent outlier policy which strikes an appropriate balance between protecting IPFs from extraordinarily costly cases while ensuring the adequacy of the Federal per diem base rate for all other cases that are not outlier cases.

We believe it is necessary to update the fixed dollar loss threshold amount because analysis of the latest available data (that is, FY 2008 IPF claims) and rate increases indicates adjusting the fixed dollar loss amount is necessary in order to maintain an outlier percentage that equals 2 percent of total estimated IPF PPS payments.

In the May 2006 IPF PPS final rule (71 FR 27072), we describe the process by which we calculate the outlier fixed dollar loss threshold amount. We continue to use this process for RY 2011. We begin by simulating aggregate payments with and without an outlier policy, and applying an iterative process to determine an outlier fixed dollar loss threshold amount that will result in outlier payments being equal to 2 percent of total estimated payments under the simulation. Based on this process, we are updating the outlier fixed dollar loss threshold amount to $6,372 to maintain estimated outlier payments at 2 percent of total estimated IPF payments for RY 2011.

b. Statistical Accuracy of Cost-to-Charge Ratios

As previously stated, under the IPF PPS, an outlier payment is made if an IPF's cost for a stay exceeds a fixed dollar loss threshold amount. In order to establish an IPF's cost for a particular case, we multiply the IPF's reported charges on the discharge bill by its overall cost-to-charge ratio (CCR). This approach to determining an IPF's cost is consistent with the approach used under the IPPS and other PPSs. In FY 2004, we implemented changes to the IPPS outlier policy used to determine CCRs for acute care hospitals because we became aware that payment vulnerabilities resulted in inappropriate outlier payments. Under the IPPS, we established a statistical measure of accuracy for CCRs in order to ensure that aberrant CCR data did not result in inappropriate outlier payments.

As we indicated in the November 2004 IPF PPS final rule, because we believe that the IPF outlier policy is susceptible to the same payment vulnerabilities as the IPPS, we adopted an approach to ensure the statistical accuracy of CCRs under the IPF PPS (69 FR 66961). Therefore, we adopted the following procedure in the November 2004 IPF PPS final rule:

• We calculated two national ceilings, one for IPFs located in rural areas and one for IPFs located in urban areas. We computed the ceilings by first calculating the national average and the standard deviation of the CCR for both urban and rural IPFs.

To determine the rural and urban ceilings, we multiplied each of the standard deviations by 3 and added the result to the appropriate national CCR average (either rural or urban). The upper threshold CCR for IPFs in RY 2011 is 1.7383 for rural IPFs, and 1.7377 for urban IPFs, based on CBSA-based geographic designations. If an IPF's CCR is above the applicable ceiling, the ratio is considered statistically inaccurate and we assign the appropriate national (either rural or urban) median CCR to the IPF.

We are applying the national CCRs to the following situations:

++ New IPFs that have not yet submitted their first Medicare cost report.

++ IPFs whose overall CCR is in excess of 3 standard deviations above the corresponding national geometric mean (that is, above the ceiling).

++ Other IPFs for which the Medicare contractor obtains inaccurate or incomplete data with which to calculate a CCR.

For new IPFs, we are using these national CCRs until the facility's actual CCR can be computed using the first tentatively or final settled cost report.

We are not making any changes to the procedures for ensuring the statistical accuracy of CCRs in RY 2011. However, we are updating the national urban and rural CCRs (ceilings and medians) for IPFs for RY 2011 based on the CCRs entered in the latest available IPF PPS Provider Specific File.

The national CCRs for RY 2011 are 0.6480 for rural IPFs and 0.5170 for urban IPFs and will be used in each of the three situations listed above. These calculations are based on the IPF's location (either urban or rural) using the CBSA-based geographic designations.

A complete discussion regarding the national median CCRs appears in the November 2004 IPF PPS final rule (69 FR 66961 through 66964).

2. Expiration of the Stop-Loss Provision

In the November 2004 IPF PPS final rule, we implemented a stop-loss policy that reduced financial risk to IPFs projected to experience substantial reductions in Medicare payments during the period of transition to the IPF PPS. This stop-loss policy guaranteed that each facility received total IPF PPS payments that were no less than 70 percent of its TEFRA payments had the IPF PPS not been implemented. This policy was applied to the IPF PPS portion of Medicare payments during the 3-year transition.

In the implementation year, the 70 percent of TEFRA payment stop-loss policy required a reduction in the standardized Federal per diem and ECT base rates of 0.39 percent in order to make the stop-loss payments budget neutral. As described in the May 2008 IPF PPS notice for RY 2009, we increased the Federal per diem base rate and ECT rate by 0.39 percent because these rates were reduced by 0.39 percent in the implementation year to ensure stop-loss payments were budget neutral.

The stop-loss provision ended during RY 2009 (that is for discharges occurring on or after July 1, 2008 through June 30, 2009). The stop-loss policy is no longer applicable under the IPF PPS.

V. Comments Beyond the Scope of the May 2009 IPF PPS Notice With Request for Comments

In the May 2009 IPF PPS notice, which specifically solicited comments on the IPF PPS teaching adjustment and the market basket, we received several public comments which were outside the scope of that notice. Below, we are providing a summary of the comments and our response.

Comment: Two commenters recommended that CMS continue its study of the wage index in favor of future changes that create a more equitable system and adequately reimburse hospitals for providing quality care to beneficiaries. The commenters recommend that the Bureau of Labor Statistics (BLS) data approach be used to construct a hospital compensation index. They support the elimination of the separate Occupational Mix Survey documents and the large additional reporting burden it creates for hospitals.

One commenter expressed concern that a large increase in the fixed dollar threshold amount will significantly reduce the number of inpatient cases eligible for outlier payments and consequently, further reduce the ability of psychiatric facilities to provide necessary psychiatric care to Medicare beneficiaries. The commenter recommends that CMS continue examining its data to determine more specifically the causes for the increase and if further analysis suggests that the threshold increase is still valid, CMS should publish these reasons as part of the final rule.

One commenter recommended that CMS revisit the Variable Per Diem Adjustments that have been established in the November 2004 IPF PPS final rule (69 FR 66946) and to validate these adjustments based on current claim information. The commenter believes the current system does not reflect all factors affecting cost. The example cited was that inpatient prospective payment system facilities receive a special payment treatment for servicing a disproportionate share of low-income patients, which is intended to reimburse a facility for additional cost incurred for handling such patients. The commenter stated that the current IPF PPS payment system does not consider this type of patient in its payment mechanism.

Response: We are not addressing these comments in this notice because they are beyond the scope of the May 2009 notice. However, we will consider the comments and decide whether to take actions based on the information or recommendations of the commenters in future rulemaking.

VI. Waiver of Proposed Rulemaking

We ordinarily publish a notice of proposed rulemaking in the Federal Register to provide a period for public comment before the provisions of a rule take effect. We can waive this procedure, however, if we find good cause that notice and comment procedures are impracticable, unnecessary, or contrary to the public interest and we incorporate a statement of finding and its reasons in the notice. We find it is unnecessary to undertake notice and comment rulemaking for the update in this notice because the update does not make any substantive changes in policy, but merely reflects the application of previously established methodologies. In addition, new section 1886(s)(3)(A) of the Act requires the application of an “Other Adjustment” to the update to the IPF PPS base rate in RY 2011. We applied the statutorily-required adjustment in this notice. We find that notice and comment rulemaking is unnecessary to implement that statutory provision because it is a self-implementing provision of law, not requiring the exercise of any discretion on the part of CMS. Therefore, under 5 U.S.C. 553(b)(3)(B), for good cause, we waive notice and comment procedures.

VII. Collection of Information Requirements

This document does not impose any information collection and recordkeeping requirements. Consequently, it need not be reviewed by the Office of Management and Budget under the authority of the Paperwork Reduction Act of 1995 (44 U.S.C. 35).

VIII. Regulatory Impact Analysis

A. Overall Impact

We have examined the impacts of this notice as required by Executive Order 12866 (September 1993, Regulatory Planning and Review), the September 19, 1980 Regulatory Flexibility Act (RFA) (Pub. L. 96-354), section 1102(b) of the Act, the Unfunded Mandates Reform Act of 1995 (Pub. L. 104-4), Executive Order 13132 on Federalism, and the Congressional Review Act (5 U.S.C. 804(2)).

Executive Order 12866 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). Although this notice does not meet the $100 million threshold established by Executive Order 12866, we are considering this notice to be “economically significant” because the redistributive effects are estimated to be close to constituting a shift of $100 million. For purposes of Title 5, United States Code, section 804(2), we estimate that this rulemaking is “economically significant”, and is also a major rule under the Congressional Review Act. Accordingly, we have prepared a Regulatory Impact Analysis that to the best of our ability presents the costs and benefits of the rulemaking on the 1,679 IPFs.

The updates to the IPF labor-related share and wage indices are made in a budget neutral manner and thus have no effect on estimated costs to the Medicare program. Therefore, the estimated increased cost to the Medicare program is due to the update to the IPF payment rates, which results in an approximate $91 million increase in payments (due to the 2.4% market basket increase with the 0.25% “Other Adjustment” reduction, as required by new section 1886(a)(3)(A) of the Act, and the update to the outlier fixed dollar loss threshold amount, which results in about a $4 million increase in payments). The distribution of these impacts is summarized in Table 13. The net effect of the updates described in this notice results in an overall estimated $95 million increase in payments from RY 2010 to RY 2011.

The RFA requires agencies to analyze options for regulatory relief of small businesses, if a rule has a significant impact on a substantial number of small entities. For purposes of the RFA, we estimate that the great majority of IPFs are small entities as that term is used in the RFA (include small businesses, nonprofit organizations, and small governmental jurisdictions). The majority of hospitals and most other health care providers and suppliers are small entities, either by being nonprofit organizations or by meeting the SBA definition of a small business (having revenues of $7 million to $34.5 million in any 1 year). (For details, see the Small Business Administration's Interim final rule that set forth size standards at 70 FR 72577, December 6, 2005.) Because we lack data on individual hospital receipts, we cannot determine the number of small proprietary IPFs or the proportion of IPFs' revenue that is derived from Medicare payments. Therefore, we assume that all IPFs are considered small entities. The Department of Health and Human Services (HHS) generally uses a revenue impact of 3 to 5 percent as a significance threshold under the RFA. As shown in Table 13, we estimate that the net revenue impact of this notice on all IPFs is to increase estimated payments by about 2.26 percent. Since the estimated impact of this notice is a net increase in revenue across all categories of IPFs, we believe that this notice would not impose a significant burden on small entities. Medicare fiscal intermediaries and carriers are not considered to be small entities. Individuals and States are not included in the definition of a small entity.

Although section 1102(b) of the Act applies to regulations for which a proposed rule is published, the HHS policy is to prepare an analysis of the impact on small rural hospitals for any regulation published. As a result, we are voluntarily determining whether this notice will have a significant impact on the operations of a substantial number of small rural hospitals. For purposes of section 1102(b) of the Act, we define a small rural hospital as a hospital with fewer than 100 beds that is located outside of an MSA. As discussed in detail below, the rates and policies set forth in this notice will not have an adverse impact on the rural hospitals based on the data of the 312 rural units and 64 rural hospitals in our database of 1,679 IPFs for which data were available.

Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) also requires that agencies assess anticipated costs and benefits before issuing any rule whose mandates require spending in any 1 year of $100 million in 1995 dollars, updated annually for inflation. In 2010, that threshold is approximately $135 million. This notice will not impose spending costs on State, local, or Tribal governments in the aggregate, or by the private sector, of $135 million.

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 reviewed this notice under the criteria set forth in Executive Order 13132 and have determined that the notice will not have any substantial direct impact on State or local governments, preempt State law, or otherwise have a Federalism implication.

B. Anticipated Effects

We discuss below the historical background of the IPF PPS and the impact of this notice on the Federal Medicare budget and on IPFs.

1. Budgetary Impact

As discussed in the November 2004 and May 2006 IPF PPS final rules, we applied a budget neutrality factor to the Federal per diem and ECT base rates to ensure that total estimated payments under the IPF PPS in the implementation period would equal the amount that would have been paid if the IPF PPS had not been implemented. The budget neutrality factor includes the following components: Outlier adjustment, stop-loss adjustment, and the behavioral offset. As discussed in the May 2008 IPF PPS notice (73 FR 25711), the stop-loss adjustment is no longer applicable under the IPF PPS.

In accordance with § 412.424(c)(3)(ii), we indicated that we would evaluate the accuracy of the budget neutrality adjustment within the first 5 years after implementation of the payment system. We may make a one-time prospective adjustment to the Federal per diem and ECT base rates to account for differences between the historical data on cost-based TEFRA payments (the basis of the budget neutrality adjustment) and estimates of TEFRA payments based on actual data from the first year of the IPF PPS. As part of that process, we will reassess the accuracy of all of the factors impacting budget neutrality.

In addition, as discussed in section III.B.2 of this notice, we are using the wage index and labor market share in a budget neutral manner by applying a wage index budget neutrality factor to the Federal per diem and ECT base rates. Therefore, the budgetary impact to the Medicare program by this update to the IPF PPS will be due to the market basket update (see section III.B.2.a of this notice) with the “Other Adjustment,” as required by new section 1886(s)(3)(A) of the Act, and the update to the outlier fixed dollar loss threshold amount.

2. Impacts on Providers

To understand the impact of the changes to the IPF PPS on providers, discussed in this notice, it is necessary to compare estimated payments under the IPF PPS rates and factors for RY 2011 versus those under RY 2010. The estimated payments for RY 2010 and RY 2011 will be 100 percent of the IPF PPS payment, since the transition period has ended and stop-loss payments are no longer paid. We determined the percent change of estimated RY 2011 IPF PPS payments to estimated RY 2010 IPF PPS payments for each category of IPFs. In addition, for each category of IPFs, we have included the estimated percent change in payments resulting from the update to the outlier fixed dollar loss threshold amount, the wage index changes for the RY 2011 IPF PPS, and the market basket update, as adjusted by the “Other Adjustment”.

To illustrate the impacts of the final RY 2011 changes in this notice, our analysis begins with an RY 2010 baseline simulation model based on FY 2008 IPF payments inflated to the midpoint of RY 2010 using IHS Global Insight's most recent forecast of the market basket update (see section III.2.b of this notice); the estimated outlier payments in RY 2010; the CBSA designations for IPFs based on OMB's MSA definitions after June 2003; the FY 2009 pre-floor, pre-reclassified hospital wage index; the RY 2010 labor-market share; and the RY 2010 percentage amount of the rural adjustment. During the simulation, the total estimated outlier payments are maintained at 2 percent of total estimated IPF PPS payments.

Each of the following changes is added incrementally to this baseline model in order for us to isolate the effects of each change:

• The update to the outlier fixed dollar loss threshold amount.
• The FY 2010 pre-floor, pre-reclassified hospital wage index and RY 2011 final labor-related share.
• Our final comparison illustrates the percent change in payments from RY 2010 (that is, July 1, 2009 to June 30, 2010) to RY 2011 (that is, July 1, 2010 to June 30, 2011) and includes a 2.4 percent market basket update to the IPF PPS base rates with a −0.25% “Other Adjustment” to the IPF PPS base rates, as required by new section 1886(s)(3)(A) of the Act.

Table 13—Projected Impacts

Projected impacts (% Change)
Facility by type	Number of facilities	Outlier	CBSA wage index & labor share	Total with market basket & other adjustment 1
(1)	(2)	(3)	(4)	(5)
All Facilities	1,679	0.11	0.00	2.26
Total Urban	1,303	0.11	0.02	2.28
Total Rural	376	0.09	−0.10	2.14
Urban DPU	899	0.15	−0.01	2.29
Urban CAH unit	14	0.35	−0.30	2.20
Urban hospital	390	0.03	0.07	2.26
Rural DPU	259	0.11	−0.13	2.13
Rural CAH unit	53	0.06	0.17	2.39
Rural hospital	64	0.03	−0.13	2.05
Freestanding IPF By Type of Ownership:
Urban Psychiatric Hospitals:
Government	170	0.03	0.03	2.22
Non-Profit	115	0.03	0.16	2.35
For-Profit	105	0.03	0.02	2.20
Rural Psychiatric Hospitals:
Government	41	0.03	−0.51	1.66
Non-Profit	10	0.04	0.20	2.40
For-Profit	13	0.01	0.88	3.06
IPF Units By Type of Ownership:
Urban DPU:
Government	156	0.23	0.30	2.69
Non-Profit	616	0.14	−0.13	2.17
For-Profit	127	0.10	0.12	2.37
Urban CAH:
Government	5	0.53	−1.61	1.03
Non-Profit	8	0.28	0.13	2.56
For-Profit	1	0.03	3.18	5.43
Rural DPU:
Government	61	0.12	0.08	2.35
Non-Profit	150	0.11	−0.26	2.00
For-Profit	48	0.11	−0.03	2.24
Rural CAH:
Government	21	0.05	0.43	2.64
Non-Profit	28	0.07	−0.01	2.21
For-Profit	4	0.07	0.09	2.32
By Teaching Status:
Non-teaching	1,442	0.10	−0.03	2.22
Less than 10% interns and residents to beds	131	0.11	0.15	2.42
10% to 30% interns and residents to beds	73	0.19	0.07	2.41
More than 30% interns and residents to beds	33	0.27	−0.11	2.31
By Region:
New England	118	0.15	0.52	2.83
Mid-Atlantic	285	0.09	−0.04	2.20
South Atlantic	234	0.09	−0.03	2.21
East North Central	284	0.14	−0.40	1.88
East South Central	167	0.08	0.01	2.24
West North Central	149	0.11	0.07	2.33
West South Central	228	0.09	−0.08	2.16
Mountain	85	0.11	0.67	2.95
Pacific	129	0.15	0.02	2.32
By Bed Size:
Psychiatric Hospitals:
Under 12 beds	3	0.01	−0.31	1.84
Beds: 12-24	64	0.08	0.60	2.85
Beds: 25-49	69	0.08	0.09	2.32
Beds: 50-75	74	0.04	0.58	2.78
Over 75 beds	244	0.02	−0.13	2.03
Psychiatric Units:
Under 12 beds	191	0.18	−0.09	2.24
Beds: 12-24	529	0.16	−0.16	2.14
Beds: 25-49	335	0.14	0.00	2.30
Beds: 50-75	106	0.13	−0.15	2.13
Over 75 beds	64	0.13	0.36	2.65
1 This column shows changes in payments from RY 2010 to RY 2011. It reflects the impact of the RY 2011 market basket update with the “Other Adjustment” for the rate year beginning in 2010, as required by new section 1886(s)(3)(A) of the Act. The RY 2011 market basket update is 2.4% and the “Other Adjustment” for the rate year beginning in 2010 is −0.25%. It incorporates all of the changes displayed in Columns 3 and 4. The product of these impacts may be different from the percentage changes shown here due to rounding effects.

3. Results

Table 13 above displays the results of our analysis. The table groups IPFs into the categories listed below based on characteristics provided in the Provider of Services (POS) file, the IPF provider specific file, and cost report data from HCRIS:

• Facility Type.
• Location.
• Teaching Status Adjustment.
• Census Region.
• Size.

The top row of the table shows the overall impact on the 1,679 IPFs included in the analysis.

In column 3, we present the effects of the update to the outlier fixed dollar loss threshold amount. We estimate total outlier payments in RY 2010 to be approximately 1.9 percent of total estimated payments. Therefore, we are updating the threshold from $6,565 in RY 2010 to $6,372 in RY 2011 in order to maintain total estimated outlier payments equal to 2 percent of total estimated payments for RY 2011. The overall aggregate effect of this change (as shown in column 3 of table 13), across all hospital groups, is to increase total estimated payments to IPFs by about 0.11 percent. All categories of IPFs are projected to receive either an increase or no change in payments. There are distributional effects of this change among different categories of IPFs. Urban and rural, freestanding psychiatric hospitals; urban, for-profit IPF units located in CAHs; and psychiatric hospitals with under 12 beds and 50 or more will experience approximately a zero percent change in their payments. Alternatively, urban, government IPF units located in CAHs will receive the largest increase of 0.53 percent.

In column 4, we present the effects of the budget-neutral update to the labor-related share and the wage index adjustment under the CBSA geographic area definitions announced by OMB in June 2003. This is a comparison of the simulated RY 2011 payments under the FY 2010 hospital wage index under CBSA classification and associated labor-related share to the simulated RY 2010 payments under the FY 2009 hospital wage index under CBSA classifications and associated labor-related share. We note that there is no projected change in aggregate payments to IPFs, as indicated in the first row of column 4. However, there would be distributional effects among different categories of IPFs. For example, urban, government IPF units located in CAHs will experience a 1.61 percent decrease in payments. An urban, for-profit IPF CAH unit will receive the largest increase of 3.18 percent.

Column 5 compares our estimates of the changes reflected in this notice for RY 2011, to our estimates of payments for RY 2010 (without these changes). This column reflects all RY 2011 changes relative to RY 2010 (as shown in columns 3 and 4 and including the market basket update with the −.25% “Other Adjustment”). The average increase for all IPFs is approximately 2.26 percent. This increase includes the effects of the market basket update (2.4%) with the “Other Adjustment” (−0.25%) resulting in a 2.15 percent increase in total RY 2011 payments, and an approximate 0.11 percent increase in RY 2011 payments due to the update to the outlier fixed dollar loss threshold.

Overall, the largest payment increases ranging from 3.06 percent to 5.43 percent are projected to be among rural, for-profit freestanding IPFs and urban, for-profit IPF units located in CAHs. Urban, government IPF units located in CAHs will receive the smallest increase of 1.03 percent.

4. Effect on the Medicare Program

Based on actuarial projections resulting from our experience with other PPSs, we estimate that Medicare spending (total Medicare program payments) for IPF services over the next 5 years would be as shown in Table 14 below.

Table 14—Estimated Payments

Rate year	Dollars in millions
July 1, 2010 to June 30, 2011	$4,438
July 1, 2011 to June 30, 2012	4,685
July 1, 2012 to June 30, 2013	4,930
July 1, 2013 to June 30, 2014	5,178
July 1, 2014 to June 30, 2015	5,450

These estimates are based on the current forecast of the increases in the RPL market basket, including an adjustment for productivity, for which we are using a preliminary estimate, for the rate year beginning in 2012 and each subsequent rate year, as required by new section 1886(s)(3)(A) of the Act, as follows:

• 2.4 percent for rate years beginning in 2010 (RY 2011).
• 2.9 percent for rate years beginning in 2011 (RY 2012).
• 1.7 percent for rate years beginning in 2012 (RY 2013).
• 1.9 percent for rate years beginning in 2013 (RY 2014).
• 2.1 percent for rate years beginning in 2014 (RY 2015).

The estimates in Table 14 also include the application of the “Other Adjustment,” as required by section 1886(s)(A)(3) of the Act, as follows:

• −0.25 percent for rate years beginning in 2010.
• −0.25 percent for rate years beginning in 2011.
• −0.1 percent for rate years beginning in 2012.
• −0.1 percent for rate years beginning in 2013.
• −0.3 percent for rate years beginning in 2014.

We estimate that there would be a change in fee-for-service Medicare beneficiary enrollment as follows:

• 2.5 percent in RY 2011.
• 3.2 percent in RY 2012.
• 3.1 percent in RY 2013.
• 3.1 percent in RY 2014.
• 2.8 percent in RY 2015.

5. Effect on Beneficiaries

Under the IPF PPS, IPFs will receive payment based on the average resources consumed by patients for each day. We do not expect changes in the quality of care or access to services for Medicare beneficiaries under the RY 2011 IPF PPS. In fact, we believe that access to IPF services will be enhanced due to the patient- and facility-level adjustment factors, all of which are intended to adequately reimburse IPFs for expensive cases. Finally, the outlier policy is intended to assist IPFs that experience high-cost cases.

C. Alternatives Considered

The statute does not specify an update strategy for the IPF PPS and is broadly written to give the Secretary discretion in establishing an update methodology. Therefore, we are updating the IPF PPS using the methodology published in the November 2004 IPF PPS final rule.

We note that this notice does not initiate any policy changes with regard to the IPF PPS; rather, it simply provides an update to the rates for RY 2011. Therefore, no options were considered.

D. Accounting Statement

As required by OMB Circular A-4 (available at http://www.whitehouse.gov/​omb/​circulars/​a004/​a-4.pdf), in Table 15 below, we have prepared an accounting statement showing the classification of the expenditures associated with the provisions of this notice. This table provides our best estimate of the increase in Medicare payments under the IPF PPS notice, as a result of the changes presented in this notice, and based on the data for 1,679 IPFs in our database. All expenditures are classified as transfers to Medicare providers (that is, IPFs).

Table 15—Accounting Statement: Classification of Estimated Expenditures, From the 2010 IPF PPS RY to the 2011 IPF PPS RY

[In millions]

Category	Transfers
Annualized Monetized Transfers	$95.
From Whom To Whom?	Federal Government To IPF Medicare Providers.

In accordance with the provisions of Executive Order 12866, this notice was reviewed by OMB.

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

Dated: March 4, 2010.

Charlene Frizzera,

Acting Administrator, Centers for Medicare & Medicaid Services.

Approved: April 20, 2010.

Kathleen Sebelius,

Secretary.

Addendum A—Rate and Adjustment Factors

Per Diem Rate

Federal Per Diem Base Rate	$665.71
Labor Share (0.75400)	501.95
Non-Labor Share (0.24600)	163.76

Fixed Dollar Loss Threshold Amount: $6,372.

Wage Index Budget Neutrality Factor: 0.9999.

Facility Adjustments

Rural Adjustment Factor	1.17.
Teaching Adjustment Factor	0.5150.
Wage Index	Pre-reclass Hospital Wage Index (FY 2010).

Cost of Living Adjustments (COLAs)

Alaska
Anchorage	1.23
Fairbanks	1.23
Juneau	1.23
Rest of Alaska	1.25
Hawaii
Honolulu County	1.25
Hawaii County	1.18
Kauai County	1.25
Maui County	1.25
Kalawao County	1.25

Patient Adjustments

ECT—Per Treatment	$286.60

Variable Per Diem Adjustments

	Adjustment factor
Day 1—Facility Without a Qualifying Emergency Department	1.19
Day 1—Facility With a Qualifying Emergency Department	1.31
Day 2	1.12
Day 3	1.08
Day 4	1.05
Day 5	1.04
Day 6	1.02
Day 7	1.01
Day 8	1.01
Day 9	1.00
Day 10	1.00
Day 11	0.99
Day 12	0.99
Day 13	0.99
Day 14	0.99
Day 15	0.98
Day 16	0.97
Day 17	0.97
Day 18	0.96
Day 19	0.95
Day 20	0.95
Day 21	0.95
After Day 21	0.92

Age Adjustments

Age (in years)	Adjustment factor
Under 45	1.00
45 and under 50	1.01
50 and under 55	1.02
55 and under 60	1.04
60 and under 65	1.07
65 and under 70	1.10
70 and under 75	1.13
75 and under 80	1.15
80 and over	1.17

DRG Adjustments

MS-DRG	MS-DRG descriptions	Adjustment factor
056	Degenerative nervous system disorders w MCC	1.05
057	Degenerative nervous system disorders w/o MCC
080	Nontraumatic stupor & coma w MCC	1.07
081	Nontraumatic stupor & coma w/o MCC
876	O.R. procedure w principal diagnoses of mental illness	1.22
880	Acute adjustment reaction & psychosocial dysfunction	1.05
881	Depressive neuroses	0.99
882	Neuroses except depressive	1.02
883	Disorders of personality & impulse control	1.02
884	Organic disturbances & mental retardation	1.03
885	Psychoses	1.00
886	Behavioral & developmental disorders	0.99
887	Other mental disorder diagnoses	0.92
894	Alcohol/drug abuse or dependence, left AMA	0.97
895	Alcohol/drug abuse or dependence w rehabilitation therapy	1.02
896	Alcohol/drug abuse or dependence w/o rehabilitation therapy w MCC	0.88
897	Alcohol/drug abuse or dependence w/o rehabilitation therapy w/o MCC

Comorbidity Adjustments

Comorbidity	Adjustment factor
Developmental Disabilities	1.04
Coagulation Factor Deficit	1.13
Tracheostomy	1.06
Eating and Conduct Disorders	1.12
Infectious Diseases	1.07
Renal Failure, Acute	1.11
Renal Failure, Chronic	1.11
Oncology Treatment	1.07
Uncontrolled Diabetes Mellitus	1.05
Severe Protein Malnutrition	1.13
Drug/Alcohol Induced Mental Disorders	1.03
Cardiac Conditions	1.11
Gangrene	1.10
Chronic Obstructive Pulmonary Disease	1.12
Artificial Openings—Digestive & Urinary	1.08
Severe Musculoskeletal & Connective Tissue Diseases	1.09
Poisoning	1.11

Addendum B—RY 2011 CBSA Wage Index Tables

In this addendum, we provide Tables 1 and 2 which indicate the CBSA-based wage index values for urban and rural providers.

Table 1—RY 2011 Wage Index For Urban Areas Based on CBSA Labor Market Areas

CBSA code	Urban area (constituent counties)	Wage index
10180	Abilene, TX	0.7946
	Callahan County, TX
	Jones County, TX
	Taylor County, TX
10380	Aguadilla-Isabela-San Sebastián, PR	0.3462
	Aguada Municipio, PR
	Aguadilla Municipio, PR
	Añasco Municipio, PR
	Isabela Municipio, PR
	Lares Municipio, PR
	Moca Municipio, PR
	Rincón Municipio, PR
	San Sebastián Municipio, PR
10420	Akron, OH	0.8850
	Portage County, OH
	Summit County, OH
10500	Albany, GA	0.8899
	Baker County, GA
	Dougherty County, GA
	Lee County, GA
	Terrell County, GA
	Worth County, GA
10580	Albany-Schenectady-Troy, NY	0.8777
	Albany County, NY
	Rensselaer County, NY
	Saratoga County, NY
	Schenectady County, NY
	Schoharie County, NY
10740	Albuquerque, NM	0.9399
	Bernalillo County, NM
	Sandoval County, NM
	Torrance County, NM
	Valencia County, NM
10780	Alexandria, LA	0.8012
	Grant Parish, LA
	Rapides Parish, LA
10900	Allentown-Bethlehem-Easton, PA-NJ	0.9611
	Warren County, NJ
	Carbon County, PA
	Lehigh County, PA
	Northampton County, PA
11020	Altoona, PA	0.8863
	Blair County, PA
11100	Amarillo, TX	0.8689
	Armstrong County, TX
	Carson County, TX
	Potter County, TX
	Randall County, TX
11180	Ames, IA	0.9493
	Story County, IA
11260	Anchorage, AK	1.2013
	Anchorage Municipality, AK
	Matanuska-Susitna Borough, AK
11300	Anderson, IN	0.9052
	Madison County, IN
11340	Anderson, SC	0.9023
	Anderson County, SC
11460	Ann Arbor, MI	1.0293
	Washtenaw County, MI
11500	Anniston-Oxford, AL	0.7643
	Calhoun County, AL
11540	Appleton, WI	0.9289
	Calumet County, WI
	Outagamie County, WI
11700	Asheville, NC	0.9057
	Buncombe County, NC
	Haywood County, NC
	Henderson County, NC
	Madison County, NC
12020	Athens-Clarke County, GA	0.9492
	Clarke County, GA
	Madison County, GA
	Oconee County, GA
	Oglethorpe County, GA
12060	Atlanta-Sandy Springs-Marietta, GA	0.9591
	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-Hammonton, NJ	1.1554
	Atlantic County, NJ
12220	Auburn-Opelika, AL	0.8138
	Lee County, AL
12260	Augusta-Richmond County, GA-SC	0.9409
	Burke County, GA
	Columbia County, GA
	McDuffie County, GA
	Richmond County, GA
	Aiken County, SC
	Edgefield County, SC
12420	Austin-Round Rock, TX	0.9518
	Bastrop County, TX
	Caldwell County, TX
	Hays County, TX
	Travis County, TX
	Williamson County, TX
12540	Bakersfield, CA	1.1232
	Kern County, CA
12580	Baltimore-Towson, MD	1.0214
	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	1.0154
	Penobscot County, ME
12700	Barnstable Town, MA	1.2618
	Barnstable County, MA
12940	Baton Rouge, LA	0.8180
	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	1.0000
	Calhoun County, MI
13020	Bay City, MI	0.9267
	Bay County, MI
13140	Beaumont-Port Arthur, TX	0.8383
	Hardin County, TX
	Jefferson County, TX
	Orange County, TX
13380	Bellingham, WA	1.1395
	Whatcom County, WA
13460	Bend, OR	1.1446
	Deschutes County, OR
13644	Bethesda-Frederick-Gaithersburg, MD	1.0298
	Frederick County, MD
	Montgomery County, MD
13740	Billings, MT	0.8781
	Carbon County, MT
	Yellowstone County, MT
13780	Binghamton, NY	0.8780
	Broome County, NY
	Tioga County, NY
13820	Birmingham-Hoover, AL	0.8554
	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	0.7637
	Burleigh County, ND
	Morton County, ND
13980	Blacksburg-Christiansburg-Radford, VA	0.8394
	Giles County, VA
	Montgomery County, VA
	Pulaski County, VA
	Radford City, VA
14020	Bloomington, IN	0.9043
	Greene County, IN
	Monroe County, IN
	Owen County, IN
14060	Bloomington-Normal, IL	0.9378
	McLean County, IL
14260	Boise City-Nampa, ID	0.9318
	Ada County, ID
	Boise County, ID
	Canyon County, ID
	Gem County, ID
	Owyhee County, ID
14484	Boston-Quincy, MA	1.2186
	Norfolk County, MA
	Plymouth County, MA
	Suffolk County, MA
14500	Boulder, CO	1.0266
	Boulder County, CO
14540	Bowling Green, KY	0.8469
	Edmonson County, KY
	Warren County, KY
14600	Bradenton-Sarasota-Venice, FL	0.9735
	Manatee County, FL
	Sarasota County, FL
14740	Bremerton-Silverdale, WA	1.0755
	Kitsap County, WA
14860	Bridgeport-Stamford-Norwalk, CT	1.2792
	Fairfield County, CT
15180	Brownsville-Harlingen, TX	0.9020
	Cameron County, TX
15260	Brunswick, GA	0.9178
	Brantley County, GA
	Glynn County, GA
	McIntosh County, GA
15380	Buffalo-Niagara Falls, NY	0.9740
	Erie County, NY
	Niagara County, NY
15500	Burlington, NC	0.8749
	Alamance County, NC
15540	Burlington-South Burlington, VT	1.0106
	Chittenden County, VT
	Franklin County, VT
	Grand Isle County, VT
15764	Cambridge-Newton-Framingham, MA	1.1278
	Middlesex County, MA
15804	Camden, NJ	1.0374
	Burlington County, NJ
	Camden County, NJ
	Gloucester County, NJ
15940	Canton-Massillon, OH	0.8813
	Carroll County, OH
	Stark County, OH
15980	Cape Coral-Fort Myers, FL	0.9076
	Lee County, FL
16020	Cape Girardeau-Jackson, MO-IL	0.9047
	Alexander County, IL
	Bollinger County, MO
	Cape Girardeau County, MO
16180	Carson City, NV	1.0531
	Carson City, NV
16220	Casper, WY	0.9520
	Natrona County, WY
16300	Cedar Rapids, IA	0.8984
	Benton County, IA
	Jones County, IA
	Linn County, IA
16580	Champaign-Urbana, IL	1.0108
	Champaign County, IL
	Ford County, IL
	Piatt County, IL
16620	Charleston, WV	0.8141
	Boone County, WV
	Clay County, WV
	Kanawha County, WV
	Lincoln County, WV
	Putnam County, WV
16700	Charleston-North Charleston-Summerville, SC	0.9279
	Berkeley County, SC
	Charleston County, SC
	Dorchester County, SC
16740	Charlotte-Gastonia-Concord, NC-SC	0.9474
	Anson County, NC
	Cabarrus County, NC
	Gaston County, NC
	Mecklenburg County, NC
	Union County, NC
	York County, SC
16820	Charlottesville, VA	0.9372
	Albemarle County, VA
	Fluvanna County, VA
	Greene County, VA
	Nelson County, VA
	Charlottesville City, VA
16860	Chattanooga, TN-GA	0.8831
	Catoosa County, GA
	Dade County, GA
	Walker County, GA
	Hamilton County, TN
	Marion County, TN
	Sequatchie County, TN
16940	Cheyenne, WY	0.9344
	Laramie County, WY
16974	Chicago-Naperville-Joliet, IL	1.0471
	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	1.1198
	Butte County, CA
17140	Cincinnati-Middletown, OH-KY-IN	0.9483
	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	0.7980
	Christian County, KY
	Trigg County, KY
	Montgomery County, TN
	Stewart County, TN
17420	Cleveland, TN	0.7564
	Bradley County, TN
	Polk County, TN
17460	Cleveland-Elyria-Mentor, OH	0.8914
	Cuyahoga County, OH
	Geauga County, OH
	Lake County, OH
	Lorain County, OH
	Medina County, OH
17660	Coeur d'Alene, ID	0.9235
	Kootenai County, ID
17780	College Station-Bryan, TX	0.9498
	Brazos County, TX
	Burleson County, TX
	Robertson County, TX
17820	Colorado Springs, CO	0.9821
	El Paso County, CO
	Teller County, CO
17860	Columbia, MO	0.8618
	Boone County, MO
	Howard County, MO
17900	Columbia, SC	0.8789
	Calhoun County, SC
	Fairfield County, SC
	Kershaw County, SC
	Lexington County, SC
	Richland County, SC
	Saluda County, SC
17980	Columbus, GA-AL	0.8724
	Russell County, AL
	Chattahoochee County, GA
	Harris County, GA
	Marion County, GA
	Muscogee County, GA
18020	Columbus, IN	0.9536
	Bartholomew County, IN
18140	Columbus, OH	1.0101
	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	0.8693
	Aransas County, TX
	Nueces County, TX
	San Patricio County, TX
18700	Corvallis, OR	1.1002
	Benton County, OR
19060	Cumberland, MD-WV	0.8045
	Allegany County, MD
	Mineral County, WV
19124	Dallas-Plano-Irving, TX	0.9853
	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	0.8666
	Murray County, GA
	Whitfield County, GA
19180	Danville, IL	0.8738
	Vermilion County, IL
19260	Danville, VA	0.8323
	Pittsylvania County, VA
	Danville City, VA
19340	Davenport-Moline-Rock Island, IA-IL	0.8284
	Henry County, IL
	Mercer County, IL
	Rock Island County, IL
	Scott County, IA
19380	Dayton, OH	0.9211
	Greene County, OH
	Miami County, OH
	Montgomery County, OH
	Preble County, OH
19460	Decatur, AL	0.7799
	Lawrence County, AL
	Morgan County, AL
19500	Decatur, IL	0.7995
	Macon County, IL
19660	Deltona-Daytona Beach-Ormond Beach, FL	0.8865
	Volusia County, FL
19740	Denver-Aurora-Broomfield, CO	1.0731
	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-West Des Moines, IA	0.9649
	Dallas County, IA
	Guthrie County, IA
	Madison County, IA
	Polk County, IA
	Warren County, IA
19804	Detroit-Livonia-Dearborn, MI	0.9729
	Wayne County, MI
20020	Dothan, AL	0.7406
	Geneva County, AL
	Henry County, AL
	Houston County, AL
20100	Dover, DE	0.9931
	Kent County, DE
20220	Dubuque, IA	0.8869
	Dubuque County, IA
20260	Duluth, MN-WI	1.0448
	Carlton County, MN
	St. Louis County, MN
	Douglas County, WI
20500	Durham-Chapel Hill, NC	0.9618
	Chatham County, NC
	Durham County, NC
	Orange County, NC
	Person County, NC
20740	Eau Claire, WI	0.9567
	Chippewa County, WI
	Eau Claire County, WI
20764	Edison-New Brunswick, NJ	1.1061
	Middlesex County, NJ
	Monmouth County, NJ
	Ocean County, NJ
	Somerset County, NJ
20940	El Centro, CA	0.8766
	Imperial County, CA
21060	Elizabethtown, KY	0.8388
	Hardin County, KY
	Larue County, KY
21140	Elkhart-Goshen, IN	0.9489
	Elkhart County, IN
21300	Elmira, NY	0.8341
	Chemung County, NY
21340	El Paso, TX	0.8541
	El Paso County, TX
21500	Erie, PA	0.8779
	Erie County, PA
21660	Eugene-Springfield, OR	1.1034
	Lane County, OR
21780	Evansville, IN-KY	0.8522
	Gibson County, IN
	Posey County, IN
	Vanderburgh County, IN
	Warrick County, IN
	Henderson County, KY
	Webster County, KY
21820	Fairbanks, AK	1.1114
	Fairbanks North Star Borough, AK
21940	Fajardo, PR	0.3790
	Ceiba Municipio, PR
	Fajardo Municipio, PR
	Luquillo Municipio, PR
22020	Fargo, ND-MN	0.8172
	Cass County, ND
	Clay County, MN
22140	Farmington, NM	0.7889
	San Juan County, NM
22180	Fayetteville, NC	0.9358
	Cumberland County, NC
	Hoke County, NC
22220	Fayetteville-Springdale-Rogers, AR-MO	0.8775
	Benton County, AR
	Madison County, AR
	Washington County, AR
	McDonald County, MO
22380	Flagstaff, AZ	1.2475
	Coconino County, AZ
22420	Flint, MI	1.1234
	Genesee County, MI
22500	Florence, SC	0.8114
	Darlington County, SC
	Florence County, SC
22520	Florence-Muscle Shoals, AL	0.7998
	Colbert County, AL
	Lauderdale County, AL
22540	Fond du Lac, WI	0.9660
	Fond du Lac County, WI
22660	Fort Collins-Loveland, CO	1.0175
	Larimer County, CO
22744	Fort Lauderdale-Pompano Beach-Deerfield Beach, FL	1.0383
	Broward County, FL
22900	Fort Smith, AR-OK	0.7861
	Crawford County, AR
	Franklin County, AR
	Sebastian County, AR
	Le Flore County, OK
	Sequoyah County, OK
23020	Fort Walton Beach-Crestview-Destin, FL	0.8758
	Okaloosa County, FL
23060	Fort Wayne, IN	0.9012
	Allen County, IN
	Wells County, IN
	Whitley County, IN
23104	Fort Worth-Arlington, TX	0.9499
	Johnson County, TX
	Parker County, TX
	Tarrant County, TX
	Wise County, TX
23420	Fresno, CA	1.1267
	Fresno County, CA
23460	Gadsden, AL	0.8266
	Etowah County, AL
23540	Gainesville, FL	0.8978
	Alachua County, FL
	Gilchrist County, FL
23580	Gainesville, GA	0.9123
	Hall County, GA
23844	Gary, IN	0.9288
	Jasper County, IN
	Lake County, IN
	Newton County, IN
	Porter County, IN
24020	Glens Falls, NY	0.8456
	Warren County, NY
	Washington County, NY
24140	Goldsboro, NC	0.9056
	Wayne County, NC
24220	Grand Forks, ND-MN	0.7775
	Polk County, MN
	Grand Forks County, ND
24300	Grand Junction, CO	0.9721
	Mesa County, CO
24340	Grand Rapids-Wyoming, MI	0.9178
	Barry County, MI
	Ionia County, MI
	Kent County, MI
	Newaygo County, MI
24500	Great Falls, MT	0.8354
	Cascade County, MT
24540	Greeley, CO	0.9578
	Weld County, CO
24580	Green Bay, WI	0.9621
	Brown County, WI
	Kewaunee County, WI
	Oconto County, WI
24660	Greensboro-High Point, NC	0.9062
	Guilford County, NC
	Randolph County, NC
	Rockingham County, NC
24780	Greenville, NC	0.9401
	Greene County, NC
	Pitt County, NC
24860	Greenville-Mauldin-Easley, SC	0.9980
	Greenville County, SC
	Laurens County, SC
	Pickens County, SC
25020	Guayama, PR	0.3537
	Arroyo Municipio, PR
	Guayama Municipio, PR
	Patillas Municipio, PR
25060	Gulfport-Biloxi, MS	0.8783
	Hancock County, MS
	Harrison County, MS
	Stone County, MS
25180	Hagerstown-Martinsburg, MD-WV	0.8965
	Washington County, MD
	Berkeley County, WV
	Morgan County, WV
25260	Hanford-Corcoran, CA	1.1010
	Kings County, CA
25420	Harrisburg-Carlisle, PA	0.9286
	Cumberland County, PA
	Dauphin County, PA
	Perry County, PA
25500	Harrisonburg, VA	0.9025
	Rockingham County, VA
	Harrisonburg City, VA
25540	Hartford-West Hartford-East Hartford, CT	1.1194
	Hartford County, CT
	Middlesex County, CT
	Tolland County, CT
25620	Hattiesburg, MS	0.7664
	Forrest County, MS
	Lamar County, MS
	Perry County, MS
25860	Hickory-Lenoir-Morganton, NC	0.9000
	Alexander County, NC
	Burke County, NC
	Caldwell County, NC
	Catawba County, NC
25980	Hinesville-Fort Stewart, GA 1	0.9028
	Liberty County, GA
	Long County, GA
26100	Holland-Grand Haven, MI	0.8696
	Ottawa County, MI
26180	Honolulu, HI	1.1662
	Honolulu County, HI
26300	Hot Springs, AR	0.9004
	Garland County, AR
26380	Houma-Bayou Cane-Thibodaux, LA	0.7875
	Lafourche Parish, LA
	Terrebonne Parish, LA
26420	Houston-Sugar Land-Baytown, TX	0.9841
	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	0.9097
	Boyd County, KY
	Greenup County, KY
	Lawrence County, OH
	Cabell County, WV
	Wayne County, WV
26620	Huntsville, AL	0.9064
	Limestone County, AL
	Madison County, AL
26820	Idaho Falls, ID	0.9436
	Bonneville County, ID
	Jefferson County, ID
26900	Indianapolis-Carmel, IN	0.9742
	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	0.9548
	Johnson County, IA
	Washington County, IA
27060	Ithaca, NY	1.0112
	Tompkins County, NY
27100	Jackson, MI	0.8720
	Jackson County, MI
27140	Jackson, MS	0.8186
	Copiah County, MS
	Hinds County, MS
	Madison County, MS
	Rankin County, MS
	Simpson County, MS
27180	Jackson, TN	0.8581
	Chester County, TN
	Madison County, TN
27260	Jacksonville, FL	0.9105
	Baker County, FL
	Clay County, FL
	Duval County, FL
	Nassau County, FL
	St. Johns County, FL
27340	Jacksonville, NC	0.8026
	Onslow County, NC
27500	Janesville, WI	0.9201
	Rock County, WI
27620	Jefferson City, MO	0.8709
	Callaway County, MO
	Cole County, MO
	Moniteau County, MO
	Osage County, MO
27740	Johnson City, TN	0.7722
	Carter County, TN
	Unicoi County, TN
	Washington County, TN
27780	Johnstown, PA	0.8233
	Cambria County, PA
27860	Jonesboro, AR	0.7722
	Craighead County, AR
	Poinsett County, AR
27900	Joplin, MO	0.8285
	Jasper County, MO
	Newton County, MO
28020	Kalamazoo-Portage, MI	1.0264
	Kalamazoo County, MI
	Van Buren County, MI
28100	Kankakee-Bradley, IL	1.0174
	Kankakee County, IL
28140	Kansas City, MO-KS	0.9679
	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-Pasco-Richland, WA	1.0448
	Benton County, WA
	Franklin County, WA
28660	Killeen-Temple-Fort Hood, TX	0.8702
	Bell County, TX
	Coryell County, TX
	Lampasas County, TX
28700	Kingsport-Bristol-Bristol, TN-VA	0.7999
	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	0.7881
	Anderson County, TN
	Blount County, TN
	Knox County, TN
	Loudon County, TN
	Union County, TN
29020	Kokomo, IN	0.9862
	Howard County, IN
	Tipton County, IN
29100	La Crosse, WI-MN	0.9915
	Houston County, MN
	La Crosse County, WI
29140	Lafayette, IN	0.9181
	Benton County, IN
	Carroll County, IN
	Tippecanoe County, IN
29180	Lafayette, LA	0.8516
	Lafayette Parish, LA
	St. Martin Parish, LA
29340	Lake Charles, LA	0.7985
	Calcasieu Parish, LA
	Cameron Parish, LA
29404	Lake County-Kenosha County, IL-WI	1.0475
	Lake County, IL
	Kenosha County, WI
29420	Lake Havasu City-Kingman, AZ	1.0567
	Mohave County, AZ
29460	Lakeland-Winter Haven, FL	0.8390
	Polk County, FL
29540	Lancaster, PA	0.9204
	Lancaster County, PA
29620	Lansing-East Lansing, MI	0.9770
	Clinton County, MI
	Eaton County, MI
	Ingham County, MI
29700	Laredo, TX	0.8078
	Webb County, TX
29740	Las Cruces, NM	0.8939
	Dona Ana County, NM
29820	Las Vegas-Paradise, NV	1.2130
	Clark County, NV
29940	Lawrence, KS	0.8580
	Douglas County, KS
30020	Lawton, OK	0.7847
	Comanche County, OK
30140	Lebanon, PA	0.8119
	Lebanon County, PA
30300	Lewiston, ID-WA	0.9570
	Nez Perce County, ID
	Asotin County, WA
30340	Lewiston-Auburn, ME	0.9085
	Androscoggin County, ME
30460	Lexington-Fayette, KY	0.8889
	Bourbon County, KY
	Clark County, KY
	Fayette County, KY
	Jessamine County, KY
	Scott County, KY
	Woodford County, KY
30620	Lima, OH	0.9379
	Allen County, OH
30700	Lincoln, NE	0.9563
	Lancaster County, NE
	Seward County, NE
30780	Little Rock-North Little Rock-Conway, AR	0.8559
	Faulkner County, AR
	Grant County, AR
	Lonoke County, AR
	Perry County, AR
	Pulaski County, AR
	Saline County, AR
30860	Logan, UT-ID	0.8993
	Franklin County, ID
	Cache County, UT
30980	Longview, TX	0.8049
	Gregg County, TX
	Rusk County, TX
	Upshur County, TX
31020	Longview, WA	1.0707
	Cowlitz County, WA
31084	Los Angeles-Long Beach-Santa Ana, CA	1.2039
	Los Angeles County, CA
31140	Louisville-Jefferson County, KY-IN	0.8964
	Clark County, IN
	Floyd County, IN
	Harrison County, IN
	Washington County, IN
	Bullitt County, KY
	Henry County, KY
	Meade County, KY
	Nelson County, KY
	Oldham County, KY
	Shelby County, KY
	Spencer County, KY
	Trimble County, KY
31180	Lubbock, TX	0.8751
	Crosby County, TX
	Lubbock County, TX
31340	Lynchburg, VA	0.8521
	Amherst County, VA
	Appomattox County, VA
	Bedford County, VA
	Campbell County, VA
	Bedford City, VA
	Lynchburg City, VA
31420	Macon, GA	0.9826
	Bibb County, GA
	Crawford County, GA
	Jones County, GA
	Monroe County, GA
	Twiggs County, GA
31460	Madera-Chowchilla, CA	0.7958
	Madera County, CA
31540	Madison, WI	1.1234
	Columbia County, WI
	Dane County, WI
	Iowa County, WI
31700	Manchester-Nashua, NH	1.0171
	Hillsborough County, NH
31740	Manhattan, KS	0.7878
	Geary County, KS
	Pottawatomie County, KS
	Riley County, KS
31860	Mankato-North Mankato, MN	0.9177
	Blue Earth County, MN
	Nicollet County, MN
31900	Mansfield, OH	0.9100
	Richland County, OH
32420	Mayagüez, PR	0.3704
	Hormigueros Municipio, PR
	Mayagüez Municipio, PR
32580	McAllen-Edinburg-Mission, TX	0.8852
	Hidalgo County, TX
32780	Medford, OR	1.0070
	Jackson County, OR
32820	Memphis, TN-MS-AR	0.9268
	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	1.2123
	Merced County, CA
33124	Miami-Miami Beach-Kendall, FL	0.9954
	Miami-Dade County, FL
33140	Michigan City-La Porte, IN	0.9311
	LaPorte County, IN
33260	Midland, TX	0.9546
	Midland County, TX
33340	Milwaukee-Waukesha-West Allis, WI	1.0151
	Milwaukee County, WI
	Ozaukee County, WI
	Washington County, WI
	Waukesha County, WI
33460	Minneapolis-St. Paul-Bloomington, MN-WI	1.1095
	Anoka County, MN
	Carver County, MN
	Chisago County, MN
	Dakota County, MN
	Hennepin County, MN
	Isanti County, MN
	Ramsey County, MN
	Scott County, MN
	Sherburne County, MN
	Washington County, MN
	Wright County, MN
	Pierce County, WI
	St. Croix County, WI
33540	Missoula, MT	0.9206
	Missoula County, MT
33660	Mobile, AL	0.7785
	Mobile County, AL
33700	Modesto, CA	1.2502
	Stanislaus County, CA
33740	Monroe, LA	0.7752
	Ouachita Parish, LA
	Union Parish, LA
33780	Monroe, MI	0.8885
	Monroe County, MI
33860	Montgomery, AL	0.8304
	Autauga County, AL
	Elmore County, AL
	Lowndes County, AL
	Montgomery County, AL
34060	Morgantown, WV	0.8459
	Monongalia County, WV
	Preston County, WV
34100	Morristown, TN	0.7201
	Grainger County, TN
	Hamblen County, TN
	Jefferson County, TN
34580	Mount Vernon-Anacortes, WA	1.0452
	Skagit County, WA
34620	Muncie, IN	0.8386
	Delaware County, IN
34740	Muskegon-Norton Shores, MI	0.9823
	Muskegon County, MI
34820	Myrtle Beach-North Myrtle Beach-Conway, SC	0.8730
	Horry County, SC
34900	Napa, CA	1.4453
	Napa County, CA
34940	Naples-Marco Island, FL	0.9662
	Collier County, FL
34980	Nashville-Davidson—Murfreesboro—Franklin, TN	0.9689
	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	1.2477
	Nassau County, NY
	Suffolk County, NY
35084	Newark-Union, NJ-PA	1.1419
	Essex County, NJ
	Hunterdon County, NJ
	Morris County, NJ
	Sussex County, NJ
	Union County, NJ
	Pike County, PA
35300	New Haven-Milford, CT	1.1545
	New Haven County, CT
35380	New Orleans-Metairie-Kenner, LA	0.9092
	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-White Plains-Wayne, NY-NJ	1.3005
	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	0.8903
	Berrien County, MI
35980	Norwich-New London, CT	1.1399
	New London County, CT
36084	Oakland-Fremont-Hayward, CA	1.6404
	Alameda County, CA
	Contra Costa County, CA
36100	Ocala, FL	0.8556
	Marion County, FL
36140	Ocean City, NJ	1.0160
	Cape May County, NJ
36220	Odessa, TX	0.9862
	Ector County, TX
36260	Ogden-Clearfield, UT	0.9361
	Davis County, UT
	Morgan County, UT
	Weber County, UT
36420	Oklahoma City, OK	0.8900
	Canadian County, OK
	Cleveland County, OK
	Grady County, OK
	Lincoln County, OK
	Logan County, OK
	McClain County, OK
	Oklahoma County, OK
36500	Olympia, WA	1.1531
	Thurston County, WA
36540	Omaha-Council Bluffs, NE-IA	0.9608
	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-Kissimmee, FL	0.8951
	Lake County, FL
	Orange County, FL
	Osceola County, FL
	Seminole County, FL
36780	Oshkosh-Neenah, WI	0.9152
	Winnebago County, WI
36980	Owensboro, KY	0.8357
	Daviess County, KY
	Hancock County, KY
	McLean County, KY
37100	Oxnard-Thousand Oaks-Ventura, CA	1.2301
	Ventura County, CA
37340	Palm Bay-Melbourne-Titusville, FL	0.9060
	Brevard County, FL
37380	Palm Coast, FL	0.9603
	Flagler County, FL
37460	Panama City-Lynn Haven-Panama City Beach, FL	0.8324
	Bay County, FL
37620	Parkersburg-Marietta-Vienna, WV-OH	0.7716
	Washington County, OH
	Pleasants County, WV
	Wirt County, WV
	Wood County, WV
37700	Pascagoula, MS	0.8433
	George County, MS
	Jackson County, MS
37764	Peabody, MA	1.0871
	Essex County, MA
37860	Pensacola-Ferry Pass-Brent, FL	0.8312
	Escambia County, FL
	Santa Rosa County, FL
37900	Peoria, IL	0.9155
	Marshall County, IL
	Peoria County, IL
	Stark County, IL
	Tazewell County, IL
	Woodford County, IL
37964	Philadelphia, PA	1.0739
	Bucks County, PA
	Chester County, PA
	Delaware County, PA
	Montgomery County, PA
	Philadelphia County, PA
38060	Phoenix-Mesa-Scottsdale, AZ	1.0630
	Maricopa County, AZ
	Pinal County, AZ
38220	Pine Bluff, AR	0.7281
	Cleveland County, AR
	Jefferson County, AR
	Lincoln County, AR
38300	Pittsburgh, PA	0.8625
	Allegheny County, PA
	Armstrong County, PA
	Beaver County, PA
	Butler County, PA
	Fayette County, PA
	Washington County, PA
	Westmoreland County, PA
38340	Pittsfield, MA	1.0658
	Berkshire County, MA
38540	Pocatello, ID	0.9239
	Bannock County, ID
	Power County, ID
38660	Ponce, PR	0.4220
	Juana Díaz Municipio, PR
	Ponce Municipio, PR
	Villalba Municipio, PR
38860	Portland-South Portland-Biddeford, ME	1.0187
	Cumberland County, ME
	Sagadahoc County, ME
	York County, ME
38900	Portland-Vancouver-Beaverton, OR-WA	1.1498
	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, FL	0.9896
	Martin County, FL
	St. Lucie County, FL
39100	Poughkeepsie-Newburgh-Middletown, NY	1.1216
	Dutchess County, NY
	Orange County, NY
39140	Prescott, AZ	1.0121
	Yavapai County, AZ
39300	Providence-New Bedford-Fall River, RI-MA	1.0782
	Bristol County, MA
	Bristol County, RI
	Kent County, RI
	Newport County, RI
	Providence County, RI
	Washington County, RI
39340	Provo-Orem, UT	0.9548
	Juab County, UT
	Utah County, UT
39380	Pueblo, CO	0.8570
	Pueblo County, CO
39460	Punta Gorda, FL	0.8774
	Charlotte County, FL
39540	Racine, WI	0.9373
	Racine County, WI
39580	Raleigh-Cary, NC	0.9663
	Franklin County, NC
	Johnston County, NC
	Wake County, NC
39660	Rapid City, SD	1.0046
	Meade County, SD
	Pennington County, SD
39740	Reading, PA	0.9263
	Berks County, PA
39820	Redding, CA	1.4039
	Shasta County, CA
39900	Reno-Sparks, NV	1.0285
	Storey County, NV
	Washoe County, NV
40060	Richmond, VA	0.9521
	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	1.1285
	Riverside County, CA
	San Bernardino County, CA
40220	Roanoke, VA	0.8671
	Botetourt County, VA
	Craig County, VA
	Franklin County, VA
	Roanoke County, VA
	Roanoke City, VA
	Salem City, VA
40340	Rochester, MN	1.1136
	Dodge County, MN
	Olmsted County, MN
	Wabasha County, MN
40380	Rochester, NY	0.8724
	Livingston County, NY
	Monroe County, NY
	Ontario County, NY
	Orleans County, NY
	Wayne County, NY
40420	Rockford, IL	1.0152
	Boone County, IL
	Winnebago County, IL
40484	Rockingham County, NH	1.0125
	Rockingham County, NH
	Strafford County, NH
40580	Rocky Mount, NC	0.8845
	Edgecombe County, NC
	Nash County, NC
40660	Rome, GA	0.8915
	Floyd County, GA
40900	Sacramento—Arden-Arcade—Roseville, CA	1.4073
	El Dorado County, CA
	Placer County, CA
	Sacramento County, CA
	Yolo County, CA
40980	Saginaw-Saginaw Township North, MI	0.9122
	Saginaw County, MI
41060	St. Cloud, MN	1.1107
	Benton County, MN
	Stearns County, MN
41100	St. George, UT	0.9236
	Washington County, UT
41140	St. Joseph, MO-KS	1.0189
	Doniphan County, KS
	Andrew County, MO
	Buchanan County, MO
	DeKalb County, MO
41180	St. Louis, MO-IL	0.9102
	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	1.0974
	Marion County, OR
	Polk County, OR
41500	Salinas, CA	1.5207
	Monterey County, CA
41540	Salisbury, MD	0.9110
	Somerset County, MD
	Wicomico County, MD
41620	Salt Lake City, UT	0.9378
	Salt Lake County, UT
	Summit County, UT
	Tooele County, UT
41660	San Angelo, TX	0.7914
	Irion County, TX
	Tom Green County, TX
41700	San Antonio, TX	0.8857
	Atascosa County, TX
	Bandera County, TX
	Bexar County, TX
	Comal County, TX
	Guadalupe County, TX
	Kendall County, TX
	Medina County, TX
	Wilson County, TX
41740	San Diego-Carlsbad-San Marcos, CA	1.1752
	San Diego County, CA
41780	Sandusky, OH	0.8888
	Erie County, OH
41884	San Francisco-San Mateo-Redwood City, CA	1.5874
	Marin County, CA
	San Francisco County, CA
	San Mateo County, CA
41900	San Germán-Cabo Rojo, PR	0.4740
	Cabo Rojo Municipio, PR
	Lajas Municipio, PR
	Sabana Grande Municipio, PR
	San Germán Municipio, PR
41940	San Jose-Sunnyvale-Santa Clara, CA	1.6404
	San Benito County, CA
	Santa Clara County, CA
41980	San Juan-Caguas-Guaynabo, PR	0.4363
	Aguas Buenas Municipio, PR
	Aibonito Municipio, PR
	Arecibo Municipio, PR
	Barceloneta Municipio, PR
	Barranquitas Municipio, PR
	Bayamón Municipio, PR
	Caguas Municipio, PR
	Camuy Municipio, PR
	Canóvanas Municipio, PR
	Carolina Municipio, PR
	Cataño Municipio, PR
	Cayey Municipio, PR
	Ciales Municipio, PR
	Cidra Municipio, PR
	Comerío 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
	Loíza Municipio, PR
	Manatí Municipio, PR
	Maunabo Municipio, PR
	Morovis Municipio, PR
	Naguabo Municipio, PR
	Naranjito Municipio, PR
	Orocovis Municipio, PR
	Quebradillas Municipio, PR
	Río Grande Municipio, PR
	San Juan Municipio, PR
	San Lorenzo Municipio, PR
	Toa Alta Municipio, PR
	Toa Baja Municipio, PR
	Trujillo Alto Municipio, PR
	Vega Alta Municipio, PR
	Vega Baja Municipio, PR
	Yabucoa Municipio, PR
42020	San Luis Obispo-Paso Robles, CA	1.2550
	San Luis Obispo County, CA
42044	Santa Ana-Anaheim-Irvine, CA	1.1972
	Orange County, CA
42060	Santa Barbara-Santa Maria-Goleta, CA	1.2213
	Santa Barbara County, CA
42100	Santa Cruz-Watsonville, CA	1.6735
	Santa Cruz County, CA
42140	Santa Fe, NM	1.0694
	Santa Fe County, NM
42220	Santa Rosa-Petaluma, CA	1.5891
	Sonoma County, CA
42340	Savannah, GA	0.9043
	Bryan County, GA
	Chatham County, GA
	Effingham County, GA
42540	Scranton—Wilkes-Barre, PA	0.8375
	Lackawanna County, PA
	Luzerne County, PA
	Wyoming County, PA
42644	Seattle-Bellevue-Everett, WA	1.1577
	King County, WA
	Snohomish County, WA
42680	Sebastian-Vero Beach, FL	0.9362
	Indian River County, FL
43100	Sheboygan, WI	0.9166
	Sheboygan County, WI
43300	Sherman-Denison, TX	0.8064
	Grayson County, TX
43340	Shreveport-Bossier City, LA	0.8383
	Bossier Parish, LA
	Caddo Parish, LA
	De Soto Parish, LA
43580	Sioux City, IA-NE-SD	0.9094
	Woodbury County, IA
	Dakota County, NE
	Dixon County, NE
	Union County, SD
43620	Sioux Falls, SD	0.8983
	Lincoln County, SD
	McCook County, SD
	Minnehaha County, SD
	Turner County, SD
43780	South Bend-Mishawaka, IN-MI	0.9690
	St. Joseph County, IN
	Cass County, MI
43900	Spartanburg, SC	0.9341
	Spartanburg County, SC
44060	Spokane, WA	1.0444
	Spokane County, WA
44100	Springfield, IL	0.9545
	Menard County, IL
	Sangamon County, IL
44140	Springfield, MA	1.0373
	Franklin County, MA
	Hampden County, MA
	Hampshire County, MA
44180	Springfield, MO	0.8453
	Christian County, MO
	Dallas County, MO
	Greene County, MO
	Polk County, MO
	Webster County, MO
44220	Springfield, OH	0.9195
	Clark County, OH
44300	State College, PA	0.9096
	Centre County, PA
44700	Stockton, CA	1.2331
	San Joaquin County, CA
44940	Sumter, SC	0.8152
	Sumter County, SC
45060	Syracuse, NY	0.9785
	Madison County, NY
	Onondaga County, NY
	Oswego County, NY
45104	Tacoma, WA	1.1195
	Pierce County, WA
45220	Tallahassee, FL	0.8406
	Gadsden County, FL
	Jefferson County, FL
	Leon County, FL
	Wakulla County, FL
45300	Tampa-St. Petersburg-Clearwater, FL	0.8982
	Hernando County, FL
	Hillsborough County, FL
	Pasco County, FL
	Pinellas County, FL
45460	Terre Haute, IN	0.9061
	Clay County, IN
	Sullivan County, IN
	Vermillion County, IN
	Vigo County, IN
45500	Texarkana, TX—Texarkana, AR	0.8113
	Miller County, AR
	Bowie County, TX
45780	Toledo, OH	0.9541
	Fulton County, OH
	Lucas County, OH
	Ottawa County, OH
	Wood County, OH
45820	Topeka, KS	0.9026
	Jackson County, KS
	Jefferson County, KS
	Osage County, KS
	Shawnee County, KS
	Wabaunsee County, KS
45940	Trenton-Ewing, NJ	1.0552
	Mercer County, NJ
46060	Tucson, AZ	0.9505
	Pima County, AZ
46140	Tulsa, OK	0.8662
	Creek County, OK
	Okmulgee County, OK
	Osage County, OK
	Pawnee County, OK
	Rogers County, OK
	Tulsa County, OK
	Wagoner County, OK
46220	Tuscaloosa, AL	0.8698
	Greene County, AL
	Hale County, AL
	Tuscaloosa County, AL
46340	Tyler, TX	0.8312
	Smith County, TX
46540	Utica-Rome, NY	0.8460
	Herkimer County, NY
	Oneida County, NY
46660	Valdosta, GA	0.7944
	Brooks County, GA
	Echols County, GA
	Lanier County, GA
	Lowndes County, GA
46700	Vallejo-Fairfield, CA	1.4934
	Solano County, CA
47020	Victoria, TX	0.8054
	Calhoun County, TX
	Goliad County, TX
	Victoria County, TX
47220	Vineland-Millville-Bridgeton, NJ	1.0207
	Cumberland County, NJ
47260	Virginia Beach-Norfolk-Newport News, VA-NC	0.8960
	Currituck County, NC
	Gloucester County, VA
	Isle of Wight County, VA
	James City County, VA
	Mathews County, VA
	Surry County, VA
	York County, VA
	Chesapeake City, VA
	Hampton City, VA
	Newport News City, VA
	Norfolk City, VA
	Poquoson City, VA
	Portsmouth City, VA
	Suffolk City, VA
	Virginia Beach City, VA
	Williamsburg City, VA
47300	Visalia-Porterville, CA	1.0221
	Tulare County, CA
47380	Waco, TX	0.8377
	McLennan County, TX
47580	Warner Robins, GA	0.8754
	Houston County, GA
47644	Warren-Troy-Farmington Hills, MI	0.9806
	Lapeer County, MI
	Livingston County, MI
	Macomb County, MI
	Oakland County, MI
	St. Clair County, MI
47894	Washington-Arlington-Alexandria, DC-VA-MD-WV	1.0882
	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	0.8518
	Black Hawk County, IA
	Bremer County, IA
	Grundy County, IA
48140	Wausau, WI	0.9440
	Marathon County, WI
48260	Weirton-Steubenville, WV-OH	0.7368
	Jefferson County, OH
	Brooke County, WV
	Hancock County, WV
48300	Wenatchee-East Wenatchee, WA	0.9719
	Chelan County, WA
	Douglas County, WA
48424	West Palm Beach-Boca Raton-Boynton Beach, FL	0.9879
	Palm Beach County, FL
48540	Wheeling, WV-OH	0.6869
	Belmont County, OH
	Marshall County, WV
	Ohio County, WV
48620	Wichita, KS	0.9018
	Butler County, KS
	Harvey County, KS
	Sedgwick County, KS
	Sumner County, KS
48660	Wichita Falls, TX	0.9197
	Archer County, TX
	Clay County, TX
	Wichita County, TX
48700	Williamsport, PA	0.7877
	Lycoming County, PA
48864	Wilmington, DE-MD-NJ	1.0555
	New Castle County, DE
	Cecil County, MD
	Salem County, NJ
48900	Wilmington, NC	0.8986
	Brunswick County, NC
	New Hanover County, NC
	Pender County, NC
49020	Winchester, VA-WV	0.9777
	Frederick County, VA
	Winchester City, VA
	Hampshire County, WV
49180	Winston-Salem, NC	0.8953
	Davie County, NC
	Forsyth County, NC
	Stokes County, NC
	Yadkin County, NC
49340	Worcester, MA	1.1089
	Worcester County, MA
49420	Yakima, WA	0.9949
	Yakima County, WA
49500	Yauco, PR	0.3348
	Guánica Municipio, PR
	Guayanilla Municipio, PR
	Peñuelas Municipio, PR
	Yauco Municipio, PR
49620	York-Hanover, PA	0.9299
	York County, PA
49660	Youngstown-Warren-Boardman, OH-PA	0.8679
	Mahoning County, OH
	Trumbull County, OH
	Mercer County, PA
49700	Yuba City, CA	1.1265
	Sutter County, CA
	Yuba County, CA
49740	Yuma, AZ	0.9143
	Yuma County, AZ
1 At this time, there are no hospitals located in this urban area on which to base a wage index.

Table 2—RY 2011 Wage Index Based on CBSA Labor Market Areas for Rural Areas

State code	Nonurban area	Wage index
1	Alabama	0.7327
2	Alaska	1.1669
3	Arizona	0.8790
4	Arkansas	0.7332
5	California	1.2051
6	Colorado	0.9929
7	Connecticut	1.1093
8	Delaware	0.9910
10	Florida	0.8566
11	Georgia	0.7623
12	Hawaii	1.1113
13	Idaho	0.7733
14	Illinois	0.8312
15	Indiana	0.8529
16	Iowa	0.8624
17	Kansas	0.8167
18	Kentucky	0.7813
19	Louisiana	0.7611
20	Maine	0.8579
21	Maryland	0.9131
22	Massachusetts 1	1.1700
23	Michigan	0.8778
24	Minnesota	0.9160
25	Mississippi	0.7638
26	Missouri	0.7671
27	Montana	0.8399
28	Nebraska	0.8705
29	Nevada	0.9674
30	New Hampshire	0.9957
31	New Jersey 1
32	New Mexico	0.8938
33	New York	0.8269
34	North Carolina	0.8535
35	North Dakota	0.7813
36	Ohio	0.8506
37	Oklahoma	0.7654
38	Oregon	1.0236
39	Pennsylvania	0.8306
40	Puerto Rico 1	0.4047
41	Rhode Island 1
42	South Carolina	0.8394
43	South Dakota	0.8510
44	Tennessee	0.7808
45	Texas	0.7759
46	Utah	0.8363
47	Vermont	0.9763
48	Virgin Islands	0.7416
49	Virginia	0.7869
50	Washington	1.0224
51	West Virginia	0.7396
52	Wisconsin	0.9206
53	Wyoming	0.9535
65	Guam	0.9611
1 All counties within the State are classified as urban, with the exception of Massachusetts and Puerto Rico. Massachusetts and Puerto Rico have areas designated as rural; however, no short-term, acute care hospitals are located in the area(s) for FY 2010. The rural Massachusetts wage index is calculated as the average of all contiguous CBSAs. The Puerto Rico wage index is the same as FY 2009.