Medicare: Geographic Areas Used to Adjust Physician Payments for 
Variation in Practice Costs Should Be Revised (29-JUN-07,	 
GAO-07-466).							 
                                                                 
The Centers for Medicare & Medicaid Services (CMS) adjusts	 
Medicare physician fees for geographic differences in the costs  
of operating a medical practice. CMS uses 89 physician payment	 
localities among which fees are adjusted. Concerns have been	 
raised that the boundaries of some payment localities do not	 
accurately address variations in physicians' costs. GAO was asked
to examine how CMS has revised the localities; the extent to	 
which they accurately reflect variations in physicians' costs;	 
and alternative approaches to constructing the localities. To do 
so, GAO reviewed selected Federal Register documents; compared	 
data on the costs physicians incur in different areas with the	 
Medicare geographic adjustment; and used the physician cost data 
to construct and evaluate alternative approaches.		 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-07-466 					        
    ACCNO:   A71711						        
  TITLE:     Medicare: Geographic Areas Used to Adjust Physician      
Payments for Variation in Practice Costs Should Be Revised	 
     DATE:   06/29/2007 
  SUBJECT:   Administrative costs				 
	     Cost analysis					 
	     Local governments					 
	     Medical fees					 
	     Medicare						 
	     Overpayments					 
	     Payments						 
	     Physicians 					 

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GAO-07-466

   

     * [1]Results in Brief
     * [2]Background
     * [3]Physician Payment Localities Are Primarily Consolidations of
     * [4]More Than Half of the Physician Payment Localities Had Count
     * [5]Several Alternative Approaches to the Physician Payment Loca

          * [6]Alternative Approaches Could Be Used to Modify the Current P
          * [7]Several Alternative Approaches to the Payment Localities Wou
          * [8]Several Alternative Approaches to the Payment Localities Wou

               * [9]Statewide Payment Localities That Would Remain Statewide
                 und
               * [10]Statewide Payment Localities That Would Become
                 Multiple-Loca
               * [11]Statewide Payment Localities That Would Become
                 Multiple-Loca
               * [12]States That Currently Have, and Would Generally Retain,
                 Mult

          * [13]Several Alternative Approaches to the Payment Localities Wou

     * [14]Conclusions
     * [15]Recommendations for Executive Action
     * [16]Agency Comments and Our Evaluation
     * [17]Appendix I: Scope and Methodology
     * [18]Appendix II: Information on Configuration of the Current Med
     * [19]Appendix III: Comments from the Centers for Medicare & Medic
     * [20]Appendix IV: GAO Contact and Staff Acknowledgments

          * [21]GAO Contact
          * [22]Acknowledgments

               * [23]Order by Mail or Phone

Report to the Chairman, Subcommittee on Health, Committee on Ways and
Means, House of Representatives

United States Government Accountability Office

GAO

June 2007

MEDICARE

Geographic Areas Used to Adjust Physician Payments for Variation in
Practice Costs Should Be Revised

GAO-07-466

Contents

Letter 1

Results in Brief 4
Background 7
Physician Payment Localities Are Primarily Consolidations of the
Carrier-Defined Localities That Were Established in 1966, Which CMS Has
Since Revised Using Three Approaches That Were Not Uniformly Applied 12
More Than Half of the Physician Payment Localities Had Counties within
Them with Large Payment Differences 18
Several Alternative Approaches to the Physician Payment Localities Could
Improve Payment Accuracy While Generally Imposing Minimal Additional
Administrative Burden 23
Conclusions 39
Recommendations for Executive Action 40
Agency Comments and Our Evaluation 41
Appendix I Scope and Methodology 45
Appendix II Information on Configuration of the Current Medicare Physician
Payment Localities and the Alternative Approaches 50
Appendix III Comments from the Centers for Medicare & Medicaid Services 73
Appendix IV GAO Contact and Staff Acknowledgments 78

Tables

Table 1: Selected Alternative Approaches to Current Medicare Physician
Payment Localities 24
Table 2: Medicare Physician Payment Localities, by State 50
Table 3: Physician Payment Localities Created Using the County-Based
Iterative Alternative Approach, by State 54
Table 4: Physician Payment Localities Created Using the County-Based GAF
Ranges Alternative Approach, by State 61
Table 5: Physician Payment Localities Created Using the Metropolitan
Statistical Area (MSA)-Based Iterative Alternative Approach, by State 67

Figures

Figure 1: Calculation of the Medicare Payment for a Mid-level Office Visit
in the South Carolina and District of Columbia Medicare Physician Payment
Localities, 2007 9
Figure 2: Calculation of the GAF for the South Carolina and District of
Columbia Medicare Physician Payment Localities, 2007 11
Figure 3: Approaches Used to Establish and Revise Geographic Boundaries of
Medicare Physician Payment Localities as of May 2007 13
Figure 4: Counties in Which Physicians Had a Payment Difference of Less
Than 5 Percent, or 5 Percent or More, between Medicare's Locality GAF and
Their County-Specific GAF 19
Figure 5: Percentage of Counties in Which Physicians Were Overpaid or
Underpaid by 5 Percent or More, Relative to Their County-Specific GAF, by
Urban and Rural 21
Figure 6: Average Payment Difference for the Current Medicare Physician
Payment Localities and Selected Alternative Approaches 26
Figure 7: Percentage of Medicare Payments to Physicians Who Were Overpaid
or Underpaid by 5 Percent or More Relative to Their County-Specific GAF,
for the Current Medicare Physician Payment Localities and Selected
Alternative Approaches 27
Figure 8: Average Adjacent-Locality GAF Difference, for the Current
Medicare Physician Payment Localities and Selected Alternative Approaches
29
Figure 9: Number of Statewide Physician Payment Localities for the Current
Medicare Physician Payment Localities and Selected Alternative Approaches
31
Figure 10: Configuration of Minnesota's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected Alternative
Approaches 33
Figure 11: Configuration of Ohio's Physician Payment Localities under the
Current Medicare Physician Payment Localities and Selected Alternative
Approaches 34
Figure 12: Configuration of Florida's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected Alternative
Approaches 35
Figure 13: Number of Physician Payment Localities for the Current Medicare
Physician Payment Localities and Selected Alternative Approaches 36
Figure 14: Percentage of Medicare Physician Payments for Which the
Locality GAF Would Change by 5 Percent or More, Relative to the Current
Locality GAF, under the Selected Alternative Approaches 39

Abbreviations

CMS Centers for Medicare & Medicaid Services
CPT current procedural terminology
GAF geographic adjustment factor
GPCI geographic practice cost index
HUD Department of Housing and Urban Development
MMA Medicare Prescription Drug, Improvement, and Modernization Act of 2003
MSA metropolitan statistical area
OBRA Omnibus Budget Reconciliation Act of 1989
RVU relative value unit

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United States Government Accountability Office
Washington, DC 20548

June 29, 2007

The Honorable Pete Stark
Chairman
Subcommittee on Health Committee on Ways and Means
House of Representatives

Dear Mr. Chairman:

In 2005, Medicare spending for physician services totaled about $59
billion and in April 2005, just over 467,000 physicians billed Medicare
for services provided to Medicare beneficiaries. Since 1966, Medicare has
adjusted physicians' fees for the costs of operating a private medical
practice in different geographic areas. The purpose of this adjustment is
to help ensure that Medicare's payment is appropriate and adequate in all
areas. Medicare has set 89 distinct geographic areas, referred to as
physician payment localities, among which payments are adjusted.
Thirty-four of these payment localities are statewide, meaning that all
physician fees in the state are adjusted by a uniform amount. The
remaining payment localities are composed of one or more counties within a
state and differ in size, population density, and the extent to which they
are urban or rural. For example, large metropolitan areas such as
Manhattan, New York; smaller metropolitan areas such as Galveston, Texas;
and less populated areas such as rural Missouri, are each considered
payment localities. As part of its responsibility to set and adjust
Medicare payments, the Centers for Medicare & Medicaid Services (CMS) sets
the boundaries of the payment localities and has expressed a goal of
balancing the extent to which the localities accurately address variations
in physicians' costs with the administrative burden associated with making
geographic adjustments to physician payments in a large number of
localities.^1 The agency has stated that it generally prefers statewide
payment localities to states with multiple localities because they
simplify program administration by reducing the number of payment
localities and encourage physicians to practice in rural areas by reducing
payment differences between urban and rural areas.^2

1See 61 Fed. Reg. 34,616-17 (1996).

Medicare's geographic adjustment for a particular physician payment
locality is determined using three geographic practice cost indices (GPCI)
that correspond to the three components of a Medicare fee--physician work,
practice expense, and malpractice expense. These GPCIs adjust physician
fees for variations in physicians' costs of providing care in different
payment localities. Specifically, they raise or lower Medicare fees
depending on whether a payment locality's average cost of operating a
physician practice is above or below the national average. CMS is required
to review the GPCIs at least every 3 years and, at that time, may update
them using more recent data. The major data source used in calculating the
GPCIs, the decennial census, provides new data once every 10 years. The
GPCIs were last updated in 2005 and CMS is scheduled to review and, if
necessary, update them again in 2008.

Concerns have been raised in Congress and among stakeholders, including
state medical associations, that the geographic boundaries of some payment
localities do not accurately address variations in the costs of operating
a private medical practice. If they do not, beneficiaries could
potentially experience problems accessing physician services. You asked us
to evaluate the Medicare physician payment localities. In this report, we
(1) determine how CMS has revised the physician payment localities since
they were established in 1966 and the approaches the agency used, (2)
determine the extent to which the current payment localities accurately
reflect variations in physicians' costs of providing care in different
geographic areas, and (3) evaluate whether alternative approaches to the
physician payment localities could improve payment accuracy without
imposing a substantial amount of additional administrative burden.

To determine how CMS has revised the physician payment localities since
they were established and the approaches the agency used, we reviewed
selected documents published in the Federal Register to examine when and
how the boundaries of the payment localities have changed and a
CMS-contracted report on the payment localities that was used as the basis
for the agency's 1997 modifications.^3 We also interviewed officials at
CMS; five Medicare Part B^4 carriers, the CMS contractors responsible for
processing physician claims; four county medical associations; 11 state
medical associations; and one national medical association. In addition,
we interviewed physicians referred to us by the state medical
associations.

^2See 61 Fed. Reg. 34,616 (1996).

To determine the extent to which the current physician payment localities
accurately reflect variations in physicians' costs of providing care, we
compared data on the costs physicians incur for providing services in
different areas with the geographic adjustment that Medicare applies to
those areas. We calculated a proxy measure of physicians' costs of
operating a practice in a particular geographic area using a summary
measure of the three GPCIs--physician work, practice expense, and
malpractice expense. This geographic adjustment factor (GAF) broadly
measures differences in costs across geographic areas. To the extent that
county-specific data were available, we calculated a "county-specific GAF"
as a proxy for physicians' costs in a county. We compared this measure to
a "locality GAF," which represents Medicare's 2005 geographic adjustment
to the payment locality to which that county is assigned and is a proxy
for physicians' costs in a locality. To compare the two measures, we
calculated the difference between them, which we refer to as the "payment
difference."^5 For purposes of this report, we defined counties with a
payment difference of 5 percent or more as having a large payment
difference. These large payment differences consisted of both
underpayments (the locality GAF was lower than the county-specific GAF)
and overpayments (the locality GAF was higher than the county-specific
GAF).

We used 2000 Census Bureau data, fiscal year 2006 Department of Housing
and Urban Development (HUD) data, and 2005 CMS data to calculate
county-specific GAFs using the same methodology CMS used for its most
recent update to the GPCIs, in 2005. These data were the most recent
available at the time of our analysis. Although we refer to these GAFs as
"county-specific," we were not able to compute unique county GAFs for each
county in the United States because Census Bureau data are not available
at that level. Instead, we obtained data that allowed us to calculate
unique county GAFs for those counties that belong to a metropolitan
statistical area (MSA) and one composite GAF for each non-MSA area per
state. We assessed the reliability of these data and found them suitable
for our purposes. In addition, we limited our analysis to the 87 payment
localities within the 50 states and the District of Columbia.^6

^3Health Economics Research, Inc., Assessment and Redesign of Medicare Fee
Schedule Areas (Localities) (Waltham, Mass., 1995).

^4Medicare Part B provides coverage for certain physician, outpatient
hospital, laboratory, and other services to beneficiaries who pay monthly
premiums.

^5Specifically, we calculated payment difference as the absolute value of
the county's locality GAF minus its county-specific GAF, divided by its
county-specific GAF.

To evaluate whether alternative approaches to the Medicare physician
payment localities could improve payment accuracy without imposing a
substantial amount of additional administrative burden, we used the
county-specific GAFs to illustrate five possible alternative approaches to
constructing payment localities. We evaluated the payment accuracy of each
approach, the extent to which each approach accurately measures variations
in physicians' costs of providing care, based on its payment difference;
we evaluated the administrative burden of each approach based on the
number of payment localities that it would generate, as well as interviews
with CMS officials, Medicare carrier representatives, and physicians.
Three of our approaches are designed to balance payment accuracy with
administrative burden. The two additional approaches are useful for
comparison purposes because they illustrate the tradeoffs between payment
accuracy and administrative burden. Appendix I contains a more complete
description of our methodology. We conducted our work from June 2006
through May 2007 in accordance with generally accepted government auditing
standards.

Results in Brief

The current 89 physician payment localities are primarily consolidations
of the localities that Medicare carriers established in 1966. CMS has
since revised them using three different approaches that were not
uniformly applied. Specifically, in 1966, Medicare carriers set 240
payment localities, 16 of which were statewide, using their knowledge of
local medical practice and economic patterns at the time. According to
CMS, their boundaries remained relatively stable for the next 26 years.
From 1992 through 1995, CMS continued to use the 240 carrier-defined
payment localities, but permitted state medical associations in
multiple-locality states to petition to consolidate into a statewide
payment locality by demonstrating that the change had the "overwhelming
support" of the state's physicians. Six states successfully demonstrated
overwhelming support for a statewide payment locality; their consolidation
reduced the number of localities to 210, including 22 statewide localities
and 28 multiple-locality states. In 1997, CMS revised the 28
multiple-locality states using two different approaches. In 25 of these
states, CMS used a methodology designed to consolidate the carrier-defined
payment localities. In the remaining 3 multiple-locality states, CMS
stated that this consolidation methodology would have yielded inaccurate
payment localities and therefore created entirely new payment localities.
These revisions yielded the current 89 payment localities, including 34
statewide payment localities.

^6Of the 2 additional payment localities, one encompasses Puerto Rico and
one encompasses the U.S. Virgin Islands. The District of Columbia payment
locality currently consists of the District, five Virginia counties, and
two Maryland counties. These Virginia and Maryland counties are excluded
from the Virginia and Rest-of-Maryland payment localities.

More than half of the current physician payment localities had at least
one county within them with a large payment difference--that is, there was
a payment difference of 5 percent or more between physicians' costs and
Medicare's geographic adjustment for an area. Overall, there were 447
counties with large payment differences--representing 14 percent of all
counties. These counties were located across the United States, but a
disproportionate number were located in five states. Specifically, 60
percent of counties with large payment differences were located in
California, Georgia, Minnesota, Ohio, and Virginia. Large payment
differences occur because many payment localities combine counties with
very different costs, which may be attributed to several factors. For
example, although substantial population growth has occurred in certain
geographic areas, potentially leading to increased costs, CMS has not
revised the payment localities to reflect these changes.

Many alternative approaches could be used to revise the geographic
boundaries of the current payment localities. We examined five possible
approaches and found that three would improve payment accuracy while
generally imposing a minimal amount of additional administrative burden on
CMS, Medicare carriers, and physicians. Compared to the current payment
localities, four of the five approaches we examined would improve payment
accuracy, the extent to which each approach accurately measures variations
in physicians' costs of providing care. For example, one approach improved
payment accuracy by 52 percent. In addition, while all approaches would
impose upfront administrative costs on CMS and Medicare carriers
regardless of the number of payment localities generated, four of the
approaches we examined would impose a minimal amount of additional ongoing
administrative burden on CMS, Medicare carriers, and physicians. The
ongoing costs would be minimal largely because these four approaches would
generally create three or fewer additional payment localities in each
state. One approach, however, would create a substantial number of
additional payment localities--1,054 more than currently exist.

To help ensure that Medicare's payments to physicians more accurately
represent geographic differences in physicians' costs of operating a
private medical practice, we recommend that the Administrator of CMS
examine and revise the physician payment localities using an approach that
is uniformly applied to all states and based on the most current data. We
also recommend that the Administrator examine and, if necessary, update
the physician payment localities on a periodic basis, with no more than 10
years between updates.

In comments on a draft of this report, CMS stated that it would consider
our first recommendation--to examine and revise the physician payment
localities using an approach that is uniformly applied to all states and
based on the most current data. The agency also stated that, in doing so,
it would give full consideration to the redistributive effects and
administrative burdens of any change to the payment locality structure. We
agree that redistributive effects and administrative burden should be
considered when making the necessary changes to the physician payment
localities. Regarding our second recommendation--that CMS examine and, if
necessary, update the payment localities on a periodic basis--the agency
stated that it considers payment locality issues when concerns are raised
by interested parties and based on its own initiative, an approach that it
believes is more flexible and efficient than examining the payment
localities every 10 years. Reviewing payment localities in response to
concerns raised by interested parties, however, could result in CMS
examining only selected physician payment localities, rather than
examining all payment localities using a uniform approach. Updating the
payment localities at least every 10 years when new decennial census data
become available would ensure that Medicare appropriately accounts for
changes in the geographic distribution of physicians' costs of operating a
private medical practice. In addition, CMS raised concerns about our use
of the word "inaccurate" in the draft report to describe counties with a
payment difference of 5 percent or more between physicians' costs and
Medicare's geographic adjustment. The agency stated that our
characterization of payments as inaccurate could be construed to mean that
there has been an overpayment for which recoupment of the overpayment, as
well as other actions, should be pursued. As a result, we have deleted the
term and instead define counties with a payment difference of 5 percent or
more as having a "large payment difference." As we did in the draft
report, however, we use the term "payment accuracy" to refer to the extent
to which the payment localities reflect variations in physicians' costs of
providing care in different geographic areas.

Background

From 1966 through 1991, Medicare paid physicians based on what they
charged for services. The Omnibus Budget Reconciliation Act of 1989 (OBRA)
required the establishment of a national Medicare physician fee
schedule,^7 which was implemented in 1992, replacing the charge-based
system. Currently, the Medicare physician fee schedule includes more than
7,000 services together with their corresponding payment rates.^8 In
addition, each service on the fee schedule has three relative value units
(RVU) that correspond to the three components of physician payment:

           o Physician work--the financial value of physicians' time, skill,
           and effort that are associated with providing the service.

           o Practice expense--the costs incurred by physicians in employing
           office staff, renting office space, and buying supplies and
           equipment.

           o Malpractice expense--the premiums paid by physicians for
           professional liability insurance.

           Each RVU measures the relative costliness of providing a
           particular service. For example, in 2007, for a mid-level office
           visit for an established patient, the three RVUs sum to 1.66.^9 In
           contrast, total RVUs for a chemotherapy infusion procedure are
           4.73, indicating that this procedure is almost three times as
           costly as a mid-level office visit.^10
			  
^7See Pub. L. No. 101-239, S 6102(a), 103 Stat. 2106, 2169-84 (adding
section 1848 of the Social Security Act) (codified at 42 U.S.C. S 1395w-4
(2000)).
			  
^8By law, these payment rates were updated by 1.5 percent in 2004 and
2005, and by 0 percent in 2006 and 2007. See Pub. L. No. 108-173, S
601(a)(1), 117 Stat. 2066, 2300-01, Pub. L. No. 109-171, S 5104, 120 Stat.
4, 40-41, Pub. L. No. 109-432, Div. B, Tit. I, S 101, 120 Stat. 2922,
2975.

^9A more complete description is "office or other outpatient visit for the
evaluation and management of an established patient." In the American
Medical Association coding system, the current procedural terminology
(CPT) code for this service is 99213.

^10The full description for this procedure, CPT code 96425, is "infusion
technique, initiation of prolonged infusion (more than 8 hours) requiring
the use of a portable or implantable pump."

           Medicare's geographic adjustment for a particular physician
           payment locality is determined using three GPCIs that also
           correspond to the three components of a Medicare
           payment--physician work, practice expense, and malpractice
           expense. These GPCIs adjust physician fees for variations in
           physicians' costs of providing care in different geographic
           areas.^11 Other Medicare adjustments to physician fees address
           issues other than geographic variation in costs. For example,
           physicians practicing in designated health professional shortage
           areas receive a 10 percent bonus payment for Medicare services
           they provide, and physicians practicing in designated physician
           scarcity areas receive a 5 percent bonus payment for Medicare
           services they provide.

           The GPCIs are numerical factors expressed as the ratio of an
           area's cost to the national average cost. For example, in 2007,
           the practice expense GPCI for Orlando, Florida, is 0.936, which
           means that the practice expense component of the fee for a service
           is 6.4 percent below the national average. Because the GPCIs
           measure physician costs relative to the national average costs, an
           increase in the GPCIs of one area will result in a decrease in the
           GPCIs of other areas. In general, GPCIs are higher in urban areas
           than in rural areas.

           To calculate the Medicare payment amount for a service in a
           particular payment locality, each of the three RVUs for a service
           is adjusted for geographic differences in resource costs and
           converted into dollars. This process has several steps. First, to
           adjust for differences in costs, each of the three RVUs is
           multiplied by the appropriate GPCI. Second, these adjusted RVUs
           are added together. Third, that sum is converted into dollars
           using a conversion factor--a dollar amount CMS calculates that
           translates each service's RVUs into a payment amount. The result
           equals the Medicare payment for that service in that payment
           locality. For example, to determine the Medicare payment for a
           mid-level office visit in South Carolina in 2007, first, the three
           RVUs--work, practice expense, and malpractice expense--are
           multiplied by the appropriate GPCI (see fig. 1). Second, these
           adjusted RVUs are summed together to total 1.57. Third, this sum
           is multiplied by the conversion factor ($37.8975), resulting in a
           Medicare payment of $59.50 for this service. In the District of
           Columbia, total adjusted RVUs for a mid-level office visit sum to
           1.88, which the conversion factor translates into a payment of
           $71.25. Physicians practicing in the District of Columbia payment
           locality receive a higher overall payment for the same service
           because of the higher costs of operating a private medical
           practice compared with physicians practicing in the South Carolina
           payment locality. Since the work, practice expense, and
           malpractice expense RVUs for a single service are the same in
           every payment locality, the geographic variation in the Medicare
           payment for a service mirrors the variation in the GPCIs across
           payment localities.
			  
^11In 2005, we found that because Medicare revenue constitutes only
one-quarter of physicians' income, on average, the effect of the GPCIs on
physicians' income is limited. Income is also only one of several factors
that affect physicians' location decisions and employers' efforts to
recruit and retain physicians. See GAO, Medicare Physician Fees:
Geographic Adjustment Indices Are Valid in Design, but Data and Methods
Need Refinement, [24]GAO-05-119 (Washington, D.C.: Mar. 11, 2005).		  

Figure 1: Calculation of the Medicare Payment for a Mid-level Office Visit
in the South Carolina and District of Columbia Medicare Physician Payment
Localities, 2007

Note: The South Carolina payment locality is statewide. The District of
Columbia payment locality consists of the District, five Virginia
counties, and two Maryland counties. These Virginia and Maryland counties
are excluded from the Virginia and Rest-of-Maryland payment localities.

CMS is required to review the GPCIs at least every 3 years and, based on
that review, may revise them using the most recent data available.^12 The
agency last updated the GPCIs in 2005 and is scheduled to review and, if
necessary, update them again in 2008. The data used for the different
GPCIs are updated on different intervals. Specifically, the decennial
census, which is the major data source used in calculating the GPCIs,
provides new data once every 10 years. These data are used in calculating
the work^13 and practice expense GPCI. HUD data, which are also used in
calculating the practice expense GPCI, are updated annually. CMS collects
state insurance department and private insurer data, which are used in
calculating the malpractice expense GPCI, when the GPCIs are reviewed
every 3 years.^14 In addition, CMS is required to review the RVUs at least
every 5 years and last updated them in 2007.

GPCIs can be summarized by the GAF, which broadly illustrates differences
in costs across physician payment localities.^15 The GAF is an average of
the GPCIs, with each of the three GPCIs weighted by the percentage of
costs accounted for by its corresponding RVU. Specifically, on average,
across all services, work represents 52.5 percent of costs, practice
expense represents 43.7 percent, and malpractice expense represents 3.9
percent.^16 For example, to calculate the GAF for the statewide South
Carolina payment locality in 2007, the work, practice expense, and
malpractice expense GPCIs for South Carolina are weighted and then summed
to equal a GAF of 0.931 (see fig. 2). For the District of Columbia payment
locality in 2007, the GPCIs are weighted and summed to equal a GAF of
1.133.

^12In 2005, we reported on CMS's methods for calculating the GPCIs. See
[25]GAO-05-119 .

^13By law, the work GPCI incorporates only one-quarter of the relative
cost of physicians' work, compared to the national average, meaning that a
20 percent difference in costs results in a 5 percent difference in the
work GPCI. In addition, from 2004 through 2006, the Medicare Prescription
Drug, Improvement, and Modernization Act of 2003 (MMA) established a floor
of 1.0 for any locality where the work GPCI would otherwise fall below
1.0. Pub. L. No. 108-173, S 412, 117 Stat. at 2274 (codified at 42 U.S.C.
S 1395w-4(e) (1)(E)). This provision was extended through 2007 by the Tax
Relief and Health Care Act of 2006, Pub. L. No. 109-432, Div. B, Tit. I, S
102, 120 Stat. 2922, 2981.

^14From 2004 through 2005, MMA set the work, practice expense, and
malpractice expense GPCIs for the state of Alaska at 1.67 if any GPCI
would otherwise be less than 1.67. Pub. L. No. 108-173, S 602, 117 Stat.
at 2301 (codified at 42 U.S.C. S 1395w-4(e)(1)(G)).

^15Across the United States, Medicare's 2007 locality GAFs vary, ranging
from a minimum of 0.905 for the Arkansas payment locality, to a maximum of
1.265 for the Santa Clara, California, payment locality. The GAF is not
used to compute fees for specific physician services.

^16These percentages do not total to 100 percent due to rounding. The
percentages correspond to shares of the average cost of running a
physician practice.

Figure 2: Calculation of the GAF for the South Carolina and District of
Columbia Medicare Physician Payment Localities, 2007

Note: The South Carolina payment locality is statewide. The District of
Columbia payment locality consists of the District, five Virginia
counties, and two Maryland counties. These Virginia and Maryland counties
are excluded from the Virginia and Rest-of-Maryland payment localities.

Physician Payment Localities Are Primarily Consolidations of the Carrier-Defined
Localities That Were Established in 1966, Which CMS Has Since Revised Using
Three Approaches That Were Not Uniformly Applied

The current 89 physician payment localities are primarily consolidations
of the payment localities that Medicare carriers first defined in 1966.
CMS has since revised them over two different time periods using three
approaches that were not uniformly applied (see fig. 3). In 1966, Medicare
carriers established 240 payment localities, including 16 statewide
localities, using their knowledge of local medical practice and economic
patterns at the time. These payment localities varied in size, ranging
from a single zip code to statewide. For example, California had 28
payment localities, including 8 zip-code-based localities within the
county of Los Angeles, whereas New Mexico was a statewide payment
locality. According to CMS, the payment locality boundaries were
relatively stable for the next 26 years.

Figure 3: Approaches Used to Establish and Revise Geographic Boundaries of
Medicare Physician Payment Localities as of May 2007

Note: Includes the 87 payment localities within the 50 states and District
of Columbia. Where no other payment localities are present within a state,
the state is a statewide locality.

In 1989, OBRA required the establishment of a national Medicare physician
fee schedule, replacing the charge-based payment system.^17 Under the law,
the new fee schedule was phased in over a 4-year period, from 1992 through
1995. To facilitate this transition, CMS continued to use the 240
carrier-defined payment localities, but permitted state medical
associations to petition to consolidate their state into one statewide
payment locality. Under this approach, from 1992 through 1995, CMS
consolidated six states into statewide localities,^18 reducing the number
of payment localities to 210, including 22 statewide localities and 28
multiple-locality states.

Consolidation into a statewide payment locality would have generally
resulted in urban physicians experiencing a decrease in payment and rural
physicians experiencing an increase in payment. Citing this fact, CMS
stated it would consider a petition for consolidation from a state medical
association that could demonstrate that it had the "overwhelming support"
of both groups of physicians. The agency declined to set a numerical level
of support that it would consider "overwhelming," but did enumerate
several elements it would require, at a minimum, for state medical
associations to demonstrate overwhelming support.^19 CMS assessed the
level of physician support by reviewing both the petition from the state
medical association and the comments regarding the change that the agency
received directly from physicians. For example, in 1995, CMS consolidated
Iowa to a statewide payment locality when the state medical association,
which represented 75 percent of Iowa physicians, submitted a resolution in
favor of consolidation, and 98 percent of the comments CMS received,
including 94 percent of comments from physicians who would experience a
payment decrease, also supported the transition. CMS has not required
medical associations in the states that it consolidated to continue to
demonstrate that there is overwhelming support from the physician
community for a statewide payment locality.

^17See Pub. L. No. 101-239, S 6102(a), 103 Stat. 2106, 2169-84 (adding
section 1848 of the Social Security Act) (codified at 42 U.S.C. S 1395w-4
(2000)).

^18These six states were: Iowa (1995), Minnesota (1992), Nebraska (1992),
North Carolina (1994), Ohio (1994), and Oklahoma (1992).

^19CMS stated that it did not set an absolute numerical level of support
because of the uniqueness of the locality structure in each state; it said
that setting a numerical level of support would limit the discretion
required for it to properly evaluate each request. It did, however,
identify four elements that it would require, at a minimum, for
overwhelming support to be demonstrated: (1) a formal request for the
change from the state medical association, including a copy of a recently
adopted resolution requesting the change; (2) the number of licensed
actively practicing physicians in the state and the number that were
society members; (3) the number of society members in each local (county)
society; and (4) letters from the local societies representing physicians
in areas experiencing a payment decrease indicating the level of support
for the change. 59 Fed. Reg. 63,416 (1994).

In 1996, CMS cited a lack of consistency among the carrier-defined payment
localities^20 and, in 1997, revised the 28 multiple-locality states. As a
result of these revisions, the total number of payment localities was
reduced from 210 to the current total of 89. Thirty-four states have
statewide payment localities and 16 states have multiple payment
localities.^21

In revising the payment localities in 1997, CMS used two different
approaches. Specifically, in 25 of the multiple-locality states, CMS
revised the carrier-defined payment localities using a methodology
designed to consolidate them. As a result, the agency converted 12 states
to statewide payment localities, while it retained multiple payment
localities in 13 states. In the remaining 3 multiple-locality states, CMS
concluded that its consolidation methodology would have yielded inaccurate
localities and therefore created entirely new payment localities. When
making these revisions, the agency did not examine any of the 22
then-existing statewide payment localities that had been set using carrier
definitions and the overwhelming support policy; therefore, these payment
localities have not been examined since they were created, which in most
cases was over 40 years ago.

In 25 of the 28 multiple-locality states, CMS applied a methodology that
was designed to consolidate the carrier-defined payment localities: new
localities could not be created. The agency did not examine the geographic
boundaries of the carrier-defined payment localities before consolidating
them, even though in 1993, it had stated that the existing payment
localities had not been established on "any consistent basis."^22
Specifically, within the 25 states, CMS ranked the carrier-defined payment
localities from highest to lowest cost, as measured by the locality GAF.
The agency compared the GAF of the highest-cost payment locality to the
weighted average GAF of all lower-cost payment localities in the state.^23
If the percentage difference between the two GAFs exceeded 5 percent, CMS
retained the highest-cost payment locality as distinct. It then repeated
(or iterated) the process with the second highest-cost payment locality,
the third highest-cost payment locality, and so on, until a locality's GAF
no longer exceeded the weighted average GAF of lower-cost payment
localities by more than 5 percent. At this point, CMS did not make further
comparisons and grouped the remaining payment localities into one
Rest-of-State locality. Where the highest-cost payment locality in a state
did not exceed the weighted average GAF of all lower-cost payment
localities by more than 5 percent, CMS converted the state to a statewide
locality.

^20Specifically, CMS stated that payment localities had not been
established on a consistent geographic basis. 61 Fed. Reg. 34,615 (1996).
Some were based on zip codes or MSAs, while others were based on political
boundaries, such as cities, counties, or states. 56 Fed. Reg. 25,832
(1991).

^21In addition, the District of Columbia locality currently consists of
the District, five Virginia counties, and two Maryland counties.

^22See 58 Fed. Reg. 38,003 (1993).

To illustrate, before the 1997 consolidation, Illinois had 16
carrier-defined payment localities. When CMS applied the consolidation
methodology, it found that the GAFs of the 3 highest-cost payment
localities (Chicago, Suburban Chicago, and East St. Louis) each exceeded
the weighted average GAF of all lower-cost payment localities in Illinois
by more than 5 percent, and therefore retained each as a distinct
locality. The agency found that the fourth highest-cost payment locality,
Springfield, did not exceed the weighted average GAF of all lower-cost
payment localities by more than 5 percent; therefore, it consolidated
Springfield and the remaining 12 localities into a single Rest-of-Illinois
payment locality. In Alabama, CMS found that the GAF of Birmingham, the
highest-cost payment locality, did not exceed the weighted average GAF of
all lower-cost payment localities by more than 5 percent; therefore, it
converted Alabama to a statewide locality.

As part of the 1997 revision, CMS also eliminated all subcounty payment
localities, such as those based on zip codes and city boundaries. The
agency stated that, in most cases, the 1997 consolidation methodology
appropriately consolidated any subcounty payment localities; for example,
all payment localities in Arizona, including each of the city-based
localities of Flagstaff, Phoenix, Prescott, Tucson, and Yuma, were
consolidated into a statewide payment locality. However, in three
states--Massachusetts, Missouri, and Pennsylvania--CMS concluded that
consolidation of the subcounty payment localities under its methodology
would have yielded significant payment inaccuracies.^24 Therefore, in
these states, the agency did not apply the consolidation methodology and
instead, discarded the carrier-defined payment localities, creating
entirely new payment localities based on groupings of counties.^25

^23The average GAF was weighted by locality RVUs.

Although CMS cited the payment inaccuracy that would have resulted from
the consolidation methodology as the reason for creating new payment
localities in these three states, other states had comparably high payment
inaccuracy when the methodology was applied. Specifically, CMS determined
that the methodology would have yielded the average payment inaccuracies
of 3.16, 3.86, and 3.90 percent in Massachusetts, Missouri, and
Pennsylvania, respectively. However, it yielded comparable payment
inaccuracies when CMS applied it to Kansas and Virginia (3.85 and 3.06
percent, respectively). Despite these comparable payment inaccuracies, CMS
did not create entirely new payment localities in Kansas and Virginia
because their carrier-defined localities had been county-based and not
subcounty-based.

CMS has not revised the geographic boundaries of the physician payment
localities since the 1997 revision. Also since that year, CMS has
indicated that the only mechanism the agency has set forth to modify the
payment localities is for state medical associations to petition for a
change by demonstrating that the change has the overwhelming support of
the physician community.^26

^24CMS's contractor calculated "payment inaccuracy" in a different manner
than we calculate "payment difference" in this report. CMS's contractor
calculated payment inaccuracy as the absolute value of the county's
locality GAF minus its county-specific GAF. See Health Economics Research,
Inc., Assessment and Redesign of Medicare Fee Schedule Areas (Localities).
We calculated payment difference as the absolute value of the county's
locality GAF minus its county-specific GAF, divided by its county-specific
GAF. CMS stated that in Missouri, the methodology would have resulted in
significant payment inaccuracies because it failed to separate the Kansas
City and St. Louis areas from the rest of the state. In Massachusetts, the
agency stated that the methodology would have failed to separate the
high-cost Boston area from lower-cost central and western Massachusetts.
In Pennsylvania, it stated the methodology would have continued to
inappropriately group Pittsburgh with more expensive Philadelphia. 61 Fed.
Reg. 34,620 (1996).

^25CMS generally created separate localities for the central counties of
the highest-cost metropolitan areas in each state and grouped all other
counties into a Rest-of-State locality.

^26Since 1997, CMS has indicated that only one state medical association
has petitioned for a change to the payment localities. In 2004,
California's state medical association petitioned for a change. CMS denied
its petition, stating that CMS did not have the statutory authority to
make the specific change the association had requested.

More Than Half of the Physician Payment Localities Had Counties within Them with
Large Payment Differences

More than half of the physician payment localities we analyzed--47 of
87--had at least one county within them with a large payment
difference--that is, there was a payment difference of 5 percent or more
between physicians' costs and Medicare's geographic adjustment for an
area.^27 In total, there were 447 counties with large payment differences,
representing 14 percent of all counties. We determined counties with large
payment differences by calculating the payment difference between the
costs that physicians incur for providing services in a particular county
that we calculated (the "county-specific" GAF) compared with Medicare's
geographic adjustment for the locality in which that county is assigned
(the "locality" GAF).

Counties with large payment differences were located across the United
States and varied in size, whether they were urban or rural, and whether
they made up a large or small portion of their locality (see fig. 4).
However, a disproportionate number were located in five states.
Specifically, 60 percent of counties with large payment differences were
located in California, Georgia, Minnesota, Ohio, and Virginia. Of these
five states, Minnesota, Ohio, and Virginia are statewide localities for
Medicare physician payments.

^27Our analysis excluded 2 of the 89 physician payment localities: Puerto
Rico and the U.S. Virgin Islands.

Figure 4: Counties in Which Physicians Had a Payment Difference of Less
Than 5 Percent, or 5 Percent or More, between Medicare's Locality GAF and
Their County-Specific GAF

Note: We calculated county-specific GAFs as a measure of the costs
physicians incur for providing services in a particular county. For
purposes of this report, we defined counties with a payment difference of
5 percent or more as counties with large payment differences. Payment
difference is the absolute value of the locality GAF minus the
county-specific GAF, divided by the county-specific GAF.

Large payment differences consisted of both overpayments and
underpayments, relative to the county-specific GAFs we calculated.
Physicians in 12 percent of counties were overpaid by 5 percent or more,
relative to the county-specific GAF. These physicians accounted for 3
percent of Medicare payments to physicians in 2005. In contrast,
physicians in 2 percent of counties were underpaid by 5 percent or more,
relative to their county-specific GAF, and these physicians accounted for
almost 5 percent of Medicare payments to physicians in 2005. This occurs
because the volume and costliness of Medicare services delivered by
physicians in relatively underpaid counties is much higher than the volume
and costliness of services delivered by physicians in relatively overpaid
counties. Relative underpayments to physicians may have important
consequences for beneficiary access. Officials from several state medical
associations told us that geographic areas that are relatively underpaid
have difficulty attracting and retaining physicians, which may lead to
beneficiary access problems.

Physicians in urban counties, and specifically urban counties within the
largest MSAs, had the highest relative underpayment differences, whereas
physicians in rural counties generally had the highest relative
overpayment differences. Specifically, all counties in which physicians
were underpaid by 5 percent or more, relative to their county-specific
GAF, were urban (see fig. 5). About three-quarters of these urban counties
were part of MSAs with populations of at least 1 million. In contrast,
about 60 percent of counties in which physicians were overpaid by 5
percent or more, relative to their county-specific GAF, were rural. More
than half of these rural counties had populations of less than 25,000.

Figure 5: Percentage of Counties in Which Physicians Were Overpaid or
Underpaid by 5 Percent or More, Relative to Their County-Specific GAF, by
Urban and Rural

Note: We calculated county-specific GAFs as a measure of the costs
physicians incur for providing services in a particular county. There were
390 counties in which physicians were overpaid by 5 percent or more and 57
counties in which physicians were underpaid by 5 percent or more, relative
to their county-specific GAF.

Large payment differences occur because many payment localities combine
counties with very different costs. Specifically, within 39 of the 87
payment localities we analyzed, county-specific GAFs varied by at least 10
percent. For example, county-specific GAFs in the Poughkeepsie/Northern
New York City Suburbs locality ranged from 0.948 to 1.105--a variation of
17 percent.

The fact that many payment localities combine counties with different
costs may be due to several factors. First, the current payment localities
are primarily consolidations of the localities Medicare carriers
established in 1966, and the carriers may have established locality
boundaries in 1966 that combined counties with different costs. However,
we could not assess the accuracy of the payment localities at the time the
carriers established them because no data are available that would allow
us to do such an analysis.

Second, a majority of states are statewide payment localities; because
such localities contain many counties, they are more likely than
nonstatewide localities to combine counties with very different costs. Of
the 39 payment localities with county-specific GAFs that varied by at
least 10 percent, 23 were statewide. However, several state medical
associations we spoke with favor having a statewide payment locality. For
example, in Iowa's statewide payment locality, the highest and lowest
county-specific GAFs varied by 11 percent; as a result, 19 percent of
payments to physicians in Iowa had a large payment difference. However, an
official from Iowa's state medical association told us that it supports
maintaining Iowa's current statewide payment locality because many
physicians in the state maintain urban and rural offices and are not
reimbursed for their travel between these offices; having a uniform
reimbursement across the state helps mitigate these travel costs.

Large payment differences may also be due to the fact that although large
demographic changes have occurred in certain geographic areas, CMS has not
revised the payment localities in accordance with these changes. Certain
payment localities contain counties that have experienced large population
growth relative to the rest of their locality, which may be associated
with increasing costs relative to the rest of their locality. For example,
physicians in Loudoun County, Virginia, which is part of the Virginia
statewide payment locality, were underpaid by 12 percent relative to their
county-specific GAF. From 1980 through 2000, the population of Loudoun
County increased by 195 percent, while the population of the rest of the
Virginia payment locality increased by only 27 percent. Officials from
Virginia's state medical association reported that, because Loudoun County
has experienced higher population growth relative to the rest of the
state, the area has also become more costly relative to the rest of the
state. Accordingly, they stated that physicians from Loudoun County have
expressed discontent with Virginia's statewide payment locality and wish
to be reimbursed by Medicare at a rate more representative of their local
costs.

Several Alternative Approaches to the Physician Payment Localities Could Improve
Payment Accuracy While Generally Imposing Minimal Additional Administrative
Burden

Many alternative approaches could be used to revise the geographic
boundaries of the current payment localities. We examined five possible
approaches and found that three would improve payment accuracy while
generally imposing a minimal amount of additional administrative burden on
CMS, Medicare carriers, and physicians. Compared to the current payment
localities, four of the five alternative approaches would improve payment
accuracy, the extent to which each approach accurately measures variations
in physicians' costs of providing care. In addition, while all approaches
would impose upfront administrative costs on CMS and Medicare carriers,
four of the approaches we examined would impose a minimal amount of
additional ongoing administrative burden on CMS, Medicare carriers, and
physicians.

Alternative Approaches Could Be Used to Modify the Current Payment Localities

Although many alternative approaches could be used to modify the current
physician payment localities, in this report, we present five possible
approaches. The approaches and methodologies that we examined are detailed
in table 1. Three of our approaches are designed to balance payment
accuracy, the extent to which each approach accurately measures variations
in physicians' costs of providing care, with administrative burden. The
first of these, the county-based iterative approach, creates a
single-county payment locality for each of the highest-cost counties in a
state. It then groups that state's moderate- and low-cost counties
together into one "Rest-of-State" locality. In contrast, the second
approach, the county-based GAF ranges approach, groups high-, moderate-,
and low-cost counties in each state into separate, multiple-county
localities. The third approach, the MSA-based iterative approach, creates
a single-MSA payment locality for each of the highest-cost MSAs in the
nation. It then groups all other counties into a single "Rest-of-Nation"
locality. Appendix II contains detailed information on the configuration
of the payment localities under each of these approaches, as well as under
the current payment localities.

Table 1: Selected Alternative Approaches to Current Medicare Physician
Payment Localities

Alternative approach    Methodology used to construct localities           
County-based iterative  Using counties as a starting point, this           
                           methodology creates a single-county payment        
                           locality for any county whose GAF exceeds the      
                           weighted average GAF of all counties in the state  
                           with lower GAFs by 5 percent or more. This         
                           comparison begins with the highest-cost county and 
                           continues until a county's GAF does not exceed the 
                           weighted average GAF of all lower-cost counties by 
                           5 percent or more. At this point, that county and  
                           all lower-cost counties are grouped into a         
                           Rest-of-State payment locality.^a                  
County-based GAF ranges Using counties as a starting point, this           
                           methodology groups counties with similar GAFs into 
                           one locality. County-specific GAFs within a state  
                           are ranked from lowest to highest. The lowest      
                           county-specific GAF in each state becomes the      
                           lower boundary of the first GAF range. This lower  
                           boundary is increased by 5 percent to create the   
                           upper boundary of the first range. All counties    
                           with a GAF in that GAF range are grouped into      
                           locality 1. The first GAF that exceeds the upper   
                           boundary of the first GAF range becomes the lower  
                           boundary of a second GAF range and is increased by 
                           5 percent to create the upper boundary of this     
                           range for each state. The process is repeated      
                           until all counties in the state are assigned to a  
                           locality.^b If a county in an MSA has a GAF lower  
                           than that of the non-MSA counties in the state,    
                           the MSA county is grouped into the first GAF range 
                           containing non-MSA counties.^c                     
MSA-based iterative     Using MSAs as a starting point, this methodology   
                           creates a single-MSA payment locality for any MSA  
                           whose GAF exceeds the weighted average GAF of all  
                           counties in the nation with lower GAFs by 5        
                           percent or more. This comparison begins with the   
                           highest-cost MSA and continues until an MSA's      
                           weighted average GAF does not exceed the weighted  
                           average GAF of all lower-cost counties by 5        
                           percent or more. At this point, that MSA and all   
                           lower-cost counties are grouped into a             
                           Rest-of-Nation payment locality.                   
Statewide               All states have one statewide payment locality.    
County-based unique GAF Each group of counties in a state with a unique    
                           GAF is a distinct payment locality.                

Source: GAO.

Notes: In our calculations, we weighted average GAFs by county RVUs--a
measure of the volume and costliness of Medicare services in a county. We
used 5-percent thresholds because that is what CMS used for its 1997
consolidation methodology. For each new payment locality, we calculated
the locality's GAF as the average county-specific GAF of all counties in
the payment locality, weighted by county RVUs.

aFor example, King County, Washington's, county-specific GAF is 1.045. The
weighted average county-specific GAF of all counties in the state with
lower GAFs is 0.982. Therefore, because 1.045 exceeds 0.982 by 5 percent
or more, King County becomes a single-county payment locality.

bFor example, the lowest county-specific GAF in Arizona is 0.943, and this
becomes the lower boundary of the first GAF range. This boundary is
increased by 5 percent to yield 0.990, which becomes the upper boundary of
the first GAF range. All Arizona counties that fall into the first range
of 0.943 to 0.990 are grouped into locality 1. The first GAF that exceeds
this upper boundary is 1.003; therefore, 1.003 becomes the lower boundary
of a second GAF range for Arizona, and the process is repeated.

cFor example, the non-MSA counties in North Carolina have county-specific
GAFs of 0.911. However, Greene County, North Carolina, which is in the
Greenville MSA, has a county-specific GAF of 0.838, and is in a lower
range than the non-MSA counties. Under this methodology, Greene County is
grouped with the non-MSA range.

We also present two approaches that are useful for comparison because they
illustrate the tradeoffs between payment accuracy and administrative
burden. Under the statewide approach, each state has one statewide payment
locality. This approach minimizes administrative burden, but maximizes
large payment differences. In contrast, under the county-based unique GAF
approach, each group of counties in a state with a unique county-specific
GAF is a distinct payment locality. This approach minimizes large payment
differences, but maximizes administrative burden.

While we limited our analysis to five possible approaches, CMS could
examine additional approaches by modifying the ones we selected. For
example, three of our approaches use a 5-percent threshold to determine
new payment locality boundaries. We used a 5-percent threshold because
that is what CMS used for its 1997 consolidation methodology; however, a
different percentage threshold may also be feasible. In general, lower
thresholds generate more payment localities and further improve payment
accuracy. The first time an approach is applied, it is likely to have a
large redistributive effect on the payment localities, especially given
that many of the localities, particularly the statewide localities, have
not been reexamined recently, and in some cases since they were created in
1966. Subsequent changes to the payment localities, if made periodically,
would likely be smaller.

Several Alternative Approaches to the Payment Localities Would Improve Payment
Accuracy

Compared to the current Medicare physician payment localities, we found
that four of our five alternative approaches would improve payment
accuracy by reducing the average payment difference between the
county-specific GAF and the locality GAF (see fig. 6). For example,
compared to the current localities, the county-based GAF ranges approach
would reduce the national average payment difference by 52 percent--from
2.3 to 1.1 percent. The statewide approach, however, would increase the
average payment difference by 74 percent--from 2.3 to 4.0 percent.

Figure 6: Average Payment Difference for the Current Medicare Physician
Payment Localities and Selected Alternative Approaches

Note: The dotted line represents the national average payment difference
for the current localities. Payment difference is the absolute value of
the locality GAF minus the county-specific GAF, divided by the
county-specific GAF. In calculating the average payment difference, each
county's payment difference was weighted by county RVUs. The county-based
unique GAF approach has an average payment difference of 0 percent
because, according to the methodology for this approach, locality GAFs
always equal county-specific GAFs.

In addition, four of our five approaches would substantially reduce or
eliminate relative underpayments to physicians (see fig. 7). For example,
under the three county-based approaches, 0 percent of physicians would be
underpaid by 5 percent or more, relative to their county-specific GAF.
Thus, the number of counties that could potentially experience difficulty
attracting and retaining physicians as a result of relative underpayments
would also decrease.

Figure 7: Percentage of Medicare Payments to Physicians Who Were Overpaid
or Underpaid by 5 Percent or More Relative to Their County-Specific GAF,
for the Current Medicare Physician Payment Localities and Selected
Alternative Approaches

Note: We calculated county-specific GAFs as a measure of the costs
physicians incur for providing services in a particular county. Under the
county-based unique GAF approach, 0 percent of payments would be to
physicians who were overpaid or underpaid by 5 percent or more relative to
their county-specific GAF because, according to the methodology for this
approach, locality GAFs always equal county-specific GAFs.

Compared to the current localities, the three county-based approaches
would also reduce the percentage of payments to physicians who were
overpaid by 5 percent or more, relative to their county-specific GAF.
However, the statewide and MSA-based iterative approaches would
substantially increase relative overpayments. The statewide approach would
increase relative overpayments because statewide localities frequently
group together counties with very different costs. The MSA-based iterative
approach does so because MSAs, which are based on commuting patterns, also
frequently group together counties with dissimilar costs. For example, the
Atlanta MSA contains 28 counties. The county-specific GAF of the
lowest-cost county was 0.821, while the county-specific GAF of the
highest-cost county was 1.028. Under the MSA-based approach, however, all
counties in the Atlanta MSA would belong to the same payment locality and
have the same locality GAF, leading to large payment differences for
physicians in certain counties.

Improvements in payment accuracy often lead to increased differences in
the GAFs of adjacent payment localities. For example, the county-based
unique GAF approach, which minimizes large payment differences, generates
the highest average adjacent-locality GAF difference among our alternative
approaches (see fig. 8). In general, large differences in
adjacent-locality GAFs may be problematic. According to officials from
several state medical associations we spoke with, such differences create
incentives for physicians to relocate to the higher-GAF payment locality,
potentially creating beneficiary access problems in the lower-GAF payment
locality. However, the specific instances of high adjacent-locality GAF
differences that these officials cited result from payment localities that
have large differences between Medicare's geographic adjustment and
physicians' practice costs. Therefore, in these cases, improvements in
payment accuracy actually reduce problematic boundary differences.

Figure 8: Average Adjacent-Locality GAF Difference, for the Current
Medicare Physician Payment Localities and Selected Alternative Approaches

Note: The dotted line represents the average adjacent-locality GAF
difference for the current localities. We calculated adjacent-locality GAF
differences as the absolute value of the difference in locality GAFs
between all unique, contiguous, county pairs. We weighted the average
adjacent-locality GAF difference by the sum of the RVUs of the contiguous
counties.

For instance, officials from California's state medical association cited
Santa Cruz County, California, as an example, stating that the county is
having difficulty recruiting and retaining physicians. This county had a
county-specific GAF of 1.119, but is currently part of the
Rest-of-California payment locality, which had a GAF of 1.012. Therefore,
physicians in Santa Cruz County had a relative underpayment of 10 percent.
The adjacent county of Santa Clara has its own, single-county, payment
locality, with a GAF of 1.224. Because physicians in Santa Cruz County had
such a high relative underpayment, the difference in the locality GAFs
between these two counties was very large--21 percent. If physicians in
both counties were paid their county-specific GAF, however, the difference
between the two county-specific GAFs would be only 5 percent.

We previously reported that income, and therefore GAFs, is only one of
several factors that drive physicians' location decisions.^28 Nonfinancial
factors, such as the quality of local schools or a spouse's employment
opportunities, and other financial factors, such as a community's average
income level, are also major influences in physicians' decisions to locate
and remain in certain geographic areas. Accordingly, small increases in
the average adjacent-locality GAF difference may not create substantial
relocation incentives.

Several Alternative Approaches to the Payment Localities Would Substantially
Reduce the Number of Statewide Localities

Four of our five approaches would substantially reduce the number of
statewide payment localities (see fig. 9). Statewide payment localities
tend to have higher payment differences than nonstatewide payment
localities because most states have substantial cost variation among their
counties.

^28 [26]GAO-05-119 .

Figure 9: Number of Statewide Physician Payment Localities for the Current
Medicare Physician Payment Localities and Selected Alternative Approaches

Note: The dotted line represents the number of statewide localities for
the current localities. For the current localities, the District of
Columbia payment locality consists of the District, two Maryland counties,
and five Virginia counties; for the MSA-based iterative approach, it would
consist of the Washington, D.C., MSA; and for all other approaches it
would consist of only the District of Columbia. However, we do not
consider it a statewide locality for any of these approaches.

Of the 34 current statewide payment localities, all would remain so under
the statewide approach. In contrast, all of the current statewide payment
localities would become multiple-locality states under the county-based
unique GAF approach.

Under the remaining three approaches, the number of states that would
remain statewide localities varies. Four current statewide payment
localities would remain statewide under all three approaches, 9 would
become multiple-locality states under all three approaches, and 21 would
remain statewide under some approaches, but not others. The 16 states that
currently have multiple localities would generally also have multiple
payment localities under the three approaches.

  Statewide Payment Localities That Would Remain Statewide under All Three
  Approaches

The four current statewide payment localities that would remain statewide
under each of the county-based iterative, county-based GAF ranges, and
MSA-based iterative approaches had relatively low cost variation among
their counties.^29 For example, county-specific GAFs in Rhode Island
ranged from 1.043 to 1.057, a variation of only 1 percent.

  Statewide Payment Localities That Would Become Multiple-Locality States under
  All Three Approaches

The nine current statewide payment localities that would become
multiple-locality states under each of these three approaches had
substantial cost variation among their counties.^30 For example,
county-specific GAFs in Minnesota ranged from 0.870 to 1.024, a variation
of 18 percent. Accordingly, under the county-based iterative approach,
Minnesota would have thirteen payment localities; under the county-based
GAF ranges approach, it would have three payment localities; and under the
MSA-based approach, it would have three payment localities (see fig. 10).

^29These four states are: Montana, Rhode Island, South Carolina, and
Wyoming.

^30These nine states are: Colorado, Connecticut, Delaware, Minnesota, New
Hampshire, New Mexico, North Carolina, Vermont, and Virginia.

Figure 10: Configuration of Minnesota's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected Alternative
Approaches

Note: Under each approach, each distinct number represents a payment
locality. Under the county-based GAF ranges approach, each area labeled as
locality 2 belongs to the same payment locality.

  Statewide Payment Localities That Would Become Multiple-Locality States under
  Some Approaches, but Not Others

There were 21 current statewide payment localities that would become
multiple-locality states under some approaches, but not others. These
states generally had more cost variation than states that remained
statewide in all three approaches, but less than those that were converted
to multiple-locality states in all three approaches.^31 For example,
county-specific GAFs in Ohio range from 0.888 to 1.003, a variation of 13
percent. Under the county-based iterative approach, Ohio would remain a
statewide payment locality; under the county-based GAF ranges approach,
Ohio would have two payment localities; and under the MSA-based iterative
approach, it would have five payment localities (see fig. 11).

^31These 21 states are: Alabama, Alaska, Arkansas, Arizona, Hawaii, Idaho,
Indiana, Iowa, Kansas, Kentucky, Mississippi, Nebraska, Nevada, North
Dakota, Ohio, Oklahoma, South Dakota, Tennessee, Utah, West Virginia, and
Wisconsin.

Figure 11: Configuration of Ohio's Physician Payment Localities under the
Current Medicare Physician Payment Localities and Selected Alternative
Approaches

Note: Under each approach, each distinct number represents a payment
locality. Under the county-based GAF ranges approach, each area labeled as
locality 1 belongs to the same payment locality.

  States That Currently Have, and Would Generally Retain, Multiple Payment
  Localities

The 16 states that currently have multiple payment localities would
generally also have multiple payment localities under each of the
county-based iterative, county-based GAF ranges, and MSA-based iterative
approaches.^32 However, depending on the specific state, and approach, the
number of payment localities may increase, decrease, or stay the same.
This occurs because almost all multiple-locality states had substantial
cost variation among their counties. For example, county-specific GAFs in
Florida ranged from 0.910 to 1.073, a variation of 18 percent. Florida
currently has three payment localities. Under the county-based iterative
approach, the state would have five payment localities; under the
county-based GAF ranges approach, it would have three payment localities;
and under the MSA-based iterative approach, it would have nine payment
localities (see fig. 12).

^32These 16 states are: California, Florida, Georgia, Illinois, Louisiana,
Maine, Maryland, Massachusetts, Michigan, Missouri, New Jersey, New York,
Oregon, Pennsylvania, Texas, and Washington. Although most of these states
retain multiple localities under each of these three approaches, there are
several exceptions: New Jersey and Oregon become statewide localities
under the county-based iterative approach, and Missouri becomes a
statewide locality under the MSA-based iterative approach.

Figure 12: Configuration of Florida's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected Alternative
Approaches

Note: Under each approach, each distinct number represents a payment
locality. Under the county-based GAF ranges approach, each area labeled as
locality 2 belongs to the same payment locality.

Several Alternative Approaches to the Payment Localities Would Generally Impose
a Minimal Amount of Additional Administrative Burden on CMS, Medicare Carriers,
and Physicians

Four of our approaches would generally impose a minimal amount of
additional administrative burden on CMS, Medicare carriers, and
physicians. This occurs because these four approaches would generally
create three or fewer additional localities in each state. In total, these
four approaches create from 36 fewer to 132 more payment localities than
currently exist (see fig. 13). For example, the county-based iterative
approach would generate 132 additional localities, for a total of 219. The
statewide approach would generate 36 fewer localities, for a total of 51.
The county-based unique GAF approach, however, would generate 1,054
additional localities, for a total of 1,141--over 13 times the current
number.

Figure 13: Number of Physician Payment Localities for the Current Medicare
Physician Payment Localities and Selected Alternative Approaches

Note: The dotted line represents the current number of payment localities.
Our analysis excluded 2 of the 89 payment localities: Puerto Rico and the
U.S. Virgin Islands.

The number of localities generated by the county- and MSA-based iterative
approaches, however, could be reduced with very little loss in payment
accuracy by regrouping single-county and single-MSA payment localities
with similar GAFs, respectively, into larger payment localities. For
example, by combining localities with county-specific GAFs that vary by 1
percent or less, the total number of payment localities under the
county-based iterative approach could be reduced from 219 to 139, while
only increasing the average payment difference from 1.5 to 1.6 percent.^33
For example, in Kansas, under the county-based iterative approach,
Wyandotte County, which has a county-specific GAF of 0.972, and Johnson
County, which has a county-specific GAF of 0.975, would both become
distinct single-county payment localities. However, under a regrouping
methodology, these counties could be regrouped into a two-county payment
locality while increasing the average payment differences of these
counties from 0 percent to about one-tenth of 1 percent.

CMS officials we spoke with stated they would experience onetime upfront
costs if the current payment localities were modified, regardless of the
number of localities generated by the approach chosen. Specifically, CMS
creates a distinct physician fee schedule for each payment locality and
would have to perform data reliability checks on the localities' physician
fee schedules to ensure their accuracy. Agency officials stated that they
would have to reprogram CMS systems, update its files that assign carriers
and physicians to a payment locality, and provide physicians with
extensive education on the payment locality modifications. However, CMS
officials stated that they did not anticipate that significant
modifications to the payment localities would require a substantial amount
of additional ongoing administrative burden.

In addition, CMS officials stated that any change to the payment
localities would cause Medicare carriers to incur upfront costs.
Representatives from the five Medicare carriers that we spoke with each
stated that a moderate increase in the number of payment localities would
not require a substantial amount of additional resources. They each
indicated that modifying the payment localities would cause onetime
transitional costs. Specifically, they would be required to create new
data files that assigned each physician to a new payment locality. Carrier
representatives also indicated that an increase in the number of payment
localities would increase their ongoing operational costs. Specifically,
the carriers must load each of the distinct physician fee schedules CMS
sends them into their data systems and then perform data reliability
checks on them to ensure they are accurate.

^33The method we used regrouped payment localities into GAF ranges using a
1-percent threshold. Under this method, the lowest county-specific GAF
that qualified to become a single-county payment locality becomes the
lower boundary for the first regrouped GAF range. This lower boundary is
increased by 1 percent to create the upper boundary of the first regrouped
GAF range. All single-county payment localities with a GAF in that GAF
range are grouped into the same locality. The first GAF that exceeds the
upper boundary of the first regrouped GAF range becomes the lower boundary
of a second regrouped GAF range and is increased by 1 percent to create
the upper boundary of this range. The process is repeated until all
single-county payment localities in the state are assigned to new
regrouped payment localities.

Physicians would not incur additional administrative burden if their
payment locality changed. In addition, physicians in California we spoke
with stated that if the current localities were modified, they would not
experience an increase in administrative burden and would complete the
same paperwork as they do currently. CMS officials we spoke with agreed
that physicians' paperwork requirements would remain the same. In
addition, representatives from the Medicare carriers we spoke with stated
that they do not anticipate having to provide physicians with significant
additional training about payment locality modifications, since most
carriers already routinely send each physician a complete fee schedule
specific to their payment locality.

Modifying the payment localities will cause physicians' locality GAFs to
change, and accordingly, physicians will have to transition to new
reimbursement rates. Representatives from the American Medical Association
we spoke with expressed concern that transitioning to new reimbursement
rates could be burdensome to physicians. However, we found that under four
of our five approaches, locality GAFs would neither increase nor decrease
substantially, relative to current locality GAFs (see fig. 14). For
example, under the county-based GAF ranges approach, locality GAFs for
one-half of 1 percent of Medicare physician payments would experience a
decrease of 5 percent or more, while locality GAFs for about 4 percent of
payments would experience an increase of 5 percent or more. Under the
statewide approach, however, locality GAFs for about 15 percent of
Medicare physician payments would experience a decrease of 5 percent or
more, while locality GAFs for about 10 percent of payments would
experience an increase of 5 percent or more. Rural counties would
generally account for most of the counties with a decrease of 5 percent or
more in Medicare's geographic adjustment.

Figure 14: Percentage of Medicare Physician Payments for Which the
Locality GAF Would Change by 5 Percent or More, Relative to the Current
Locality GAF, under the Selected Alternative Approaches

Conclusions

Adjusting Medicare payments for the costs physicians incur in operating a
private medical practice in different parts of the country is important to
ensure that Medicare accurately accounts for variations in physicians'
costs of providing care, and that beneficiaries have sufficient access to
physician care. However, more than half of the current physician payment
localities had counties within them with large payment differences--that
is, there was a payment difference of 5 percent or more between
physicians' costs and Medicare's geographic adjustment for an area. In
addition, CMS's lack of a uniform approach to revising payment localities
has resulted in localities where there is substantial cost variation, a
particular problem among the 34 statewide localities. We have identified
three alternative approaches to the current payment localities that, if
uniformly applied to all states, could be used to improve payment accuracy
while generally imposing a minimal amount of additional administrative
burden. This is consistent with the goal that CMS has stated in setting
the geographic boundaries of payment localities.

While, under four of our five alterative approaches, payments to
physicians would not change substantially overall, rural counties would
generally account for most of the counties with a large decrease in
Medicare's geographic adjustment. However, CMS has other payment policies
specifically designed to ensure that physicians practicing in rural areas,
such as those designated as physician scarcity areas, are able to recruit
and retain physicians, helping ensure beneficiary access. Other approaches
are possible as well and CMS could phase in implementation over several
years, for example, to lessen the effect on physician payments in areas
negatively affected by changes to the current physician payment
localities. Using an approach that would be uniformly applied to all
states would likely have a large redistributive effect on the payment
localities the first time the approach was applied, especially given that
many of the localities, particularly the statewide localities, have not
been reexamined recently, and in some cases since they were created in
1966. Subsequent changes to the payment localities, if made periodically,
would likely be smaller.

Currently, CMS has no mechanism in place to periodically update the
physician payment localities to ensure that the geographic boundaries of
the payment localities accurately address variations in the costs of
operating a private medical practice. Other components of the physician
fee schedule are routinely reviewed--the RVUs every 5 years, and the GPCIs
every 3 years. Updating the geographic boundaries of physician payment
localities at least every 10 years when new decennial census data become
available--the major data source used in the calculation of the
GPCIs--would ensure that Medicare appropriately accounted for changes in
the geographic distribution of physicians' costs of operating a private
medical practice.

Recommendations for Executive Action

To help ensure that Medicare's payments to physicians more accurately
reflect geographic differences in physicians' costs of operating a private
medical practice, we recommend the following two actions. First, we
recommend that the Administrator of CMS examine and revise the physician
payment localities using an approach that is uniformly applied to all
states and based on the most current data. Second, the Administrator
should examine and, if necessary, update the physician payment localities
on a periodic basis with no more than 10 years between updates.

Agency Comments and Our Evaluation

CMS reviewed a draft of this report and provided comments, which appear in
appendix III. CMS stated that it appreciated the work we had done in
examining this issue and that our analysis would serve as a helpful
resource as it continues to examine payment locality alternatives.

CMS stated it would consider our first recommendation--to examine and
revise the physician payment localities using an approach that is
uniformly applied to all states and based on the most current data. The
agency also stated that, in doing so, it would give full consideration to
the redistributive effects and administrative burdens of any change to the
payment locality structure. We agree that redistributive effects and
administrative burden should be considered when making the necessary
changes to the physician payment localities.

Regarding our second recommendation--that CMS examine and, if necessary,
update the payment localities on a periodic basis--the agency stated that
it considers payment locality issues when concerns are raised by
interested parties and based on its own initiative, an approach that it
believes is more flexible and efficient than examining the payment
localities every 10 years. Reviewing payment localities in response to
concerns raised by interested parties, however, could result in CMS
examining only selected physician payment localities, rather than
examining all payment localities using a uniform approach. Updating the
payment localities at least every 10 years when new decennial census data
become available would ensure that Medicare appropriately accounts for
changes in the geographic distribution of physicians' costs of operating a
private medical practice.

CMS also stated several concerns about specific points in the report. The
agency asserted that our use of counties as the basis for comparing
physician costs and Medicare's geographic adjustment implies that
county-level data are measured with absolute precision but the data we
used to calculate county-specific physician costs are proxies for actual
costs. We recognize that the data we used to calculate county-specific
physician costs are proxy measures. As noted in the draft report, we
calculated our measure of physician costs using the same data sources and
methodology CMS uses to calculate the GPCIs, which are the agency's proxy
measures of physicians' costs. In 1991, the year before the GPCI's
implementation, CMS noted that the cost would be prohibitive to collect
the detailed locality-level data needed to measure every area's staff
costs and other expenses compared to the national average. The agency
therefore limited data sources to those that existed and were readily
available, selecting data proxies for each GPCI. As the agency uses the
GPCIs to adjust physician fees for variations in physicians' costs of
providing care in different geographic areas, we determined that this
measure was sufficient for our purposes. CMS also asserted that the data
we used to calculate county-specific physician costs are proxies because,
for more than 90 percent of counties, the Census Bureau data we obtained
were based on data for larger geographic areas. As noted in the draft
report, although Census Bureau data were not available at the county level
for all counties, we were able to obtain county-specific data for 1,091 of
the 3,142 counties in the United States--about 35 percent. Also as noted
in the draft report, these 1,091 counties represented 83 percent of the
U.S. population in 2000, and 88 percent of Medicare's payments to
physicians in 2005. We have, however, clarified in our report that the
data we used to calculate physician costs are proxy measures.

CMS commented that the draft report's characterization of payments to 14
percent of counties as "inaccurate" was highly inappropriate and
potentially problematic. The agency stated that it was concerned that a
finding that payments were inaccurate could be construed to mean that
there has been an overpayment for which recoupment of the overpayment, as
well as other actions, should be pursued. As a result, we have deleted the
term and instead define counties with a payment difference of 5 percent or
more as having a "large payment difference." As we did in the draft
report, however, we use the term "payment accuracy" to refer to the extent
to which the payment localities accurately measure variations in
physicians' costs of providing care in different geographic areas.

CMS expressed a concern that our report did not sufficiently account for
the effect our recommended changes would have on physicians. Specifically,
the agency stated that increasing payments to physicians in some counties
in a state would reduce payments to physicians in other counties in a
state, and that our report did not sufficiently convey the extent to which
our alternative approaches would reduce physician payments in certain
areas. As noted throughout the draft report, because GPCIs measure
physician costs relative to the national average costs, an increase in the
GPCIs of one area will result in a decrease in the GPCIs of other areas.
With the exception of the MSA-based iterative approach, each of our
alternative approaches examines physicians' costs within a state and was
therefore in accordance with the principal of within-state "budget
neutrality," which provides that adjusting Medicare payments should
neither increase nor decrease the total amount of Medicare payments to
physicians. We recognize that the potential for large payment reductions
is an important issue and have added information to the report to address
it.

CMS commented on our finding that several alternative approaches to the
payment localities would generally impose a minimal amount of additional
administrative burden. Specifically, the agency stated that it believes
the level of administrative burden would be more significant than what we
presented in our draft report. We believe that our report accurately
portrays the level of administrative burden that CMS would incur if the
payment localities were modified. In the draft report, we stated that the
agency would experience onetime upfront costs if the current payment
localities were modified, regardless of the number of localities
generated, but that they did not anticipate that significant modifications
to the payment localities would require a substantial amount of additional
ongoing administrative burden. In addition, using an approach that is
uniformly applied to all states would likely have a large redistributive
effect on the payment localities the first time the approach was applied,
especially given that many of the localities have not been reexamined
recently, but if subsequent changes were made periodically, they would
likely be smaller. However, we have modified the report to include
additional information on the types of upfront costs CMS would incur if
the payment localities were changed.

CMS also stated that our draft report did not point out the potential
implications an increased number of payment localities would have on
physicians' administrative burden. Specifically, the agency stated that
increasing the number of payment localities also increases the likelihood
that physicians will practice in multiple localities and therefore have to
file claims based on multiple localities. However, physicians are already
required to include the address of the facility where services were
rendered on the claim. As noted in the draft report, physicians we spoke
with stated they would not incur additional administrative burden and
would complete the same paperwork as they currently do if the payment
localities were modified; CMS officials we spoke with concurred with this
statement.

CMS commented on our description of the agency's denial of California's
state medical association's 2004 proposal for a change to the payment
localities. Specifically, CMS stated that it does not believe that its
denial of the California proposal demonstrates reluctance on the part of
the agency to consider and adopt changes to the payment localities. We did
not state in the draft report that the agency's denial of the California
proposal demonstrated a reluctance to consider and adopt changes to the
payment localities. Rather, we stated that, since 1997, CMS has indicated
that only one state medical association has petitioned for a change to the
payment localities--California's state medical association. CMS denied its
petition, stating that the agency did not have the statutory authority to
make the specific change the association had requested.

As agreed with your office, unless you publicly announce the contents of
this report earlier, we plan no further distribution of it until 30 days
from the date of this letter. We will then send copies to the
Administrator of CMS, appropriate congressional committees, and other
interested parties. We will also make copies available to others upon
request. This report is also available at no charge on GAO's Web site at
http://www.gao.gov.

If you or your staff have any questions about this report, please contact
me at (202) 512-7114 or [email protected]. Contact points for our Offices
of Congressional Relations and Public Affairs may be found on the last
page of this report. GAO staff who made contributions to this report are
listed in appendix IV.

Sincerely yours,

A. Bruce Steinwald
Director, Health Care

Appendix I: Scope and Methodology

In conducting this study, we analyzed data obtained from the Census
Bureau, the Department of Housing and Urban Development (HUD), and the
Centers for Medicare & Medicaid Services (CMS). We interviewed officials
from CMS and representatives from five Medicare Part B carriers that
process physician claims in 27 states. We also interviewed representatives
from the American Medical Association and the state medical associations
from California, Colorado, Florida, Iowa, Minnesota, New York, North
Carolina, Ohio, Texas, Virginia, and Washington. These states represent
geographically diverse areas, as well as Medicare physician payment
localities that were established in 1966 using carrier definitions,
localities that were revised from 1992 through 1995 using a physician
overwhelming support approach for a statewide locality, and localities
that were revised in 1997 using a CMS approach designed to consolidate
carrier-defined localities. In addition, we interviewed county medical
associations and 11 physicians from San Diego, Santa Cruz, and Sonoma
Counties in California, and Albany County, New York, which were referred
to us by representatives from the state medical associations we spoke
with.

To determine how CMS has revised the physician payment localities since
they were established and the approaches the agency used, we reviewed
relevant documents published in the Federal Register to determine when and
how the boundaries of the localities have changed, and a CMS-contracted
report on the payment localities that was used as the basis for the
agency's 1997 modifications.^1 To determine the extent to which the
current payment localities reflect the costs of providing care in
different geographic areas, we used the geographic adjustment factor
(GAF). The GAF is a weighted average of the three geographic practice cost
indices (GPCI)--work, practice expense, and malpractice expense.^2 We
constructed a proxy measure of the costs physicians incur for providing
services in a particular county (the county-specific GAF) and compared
this measure with Medicare's geographic adjustment for the locality to
which that county is assigned and is a proxy for physicians' costs in a
locality (the locality GAF). We compared the two by calculating the
"payment difference," the absolute value of the county's 2005 locality
GAF^3 minus its county-specific GAF, divided by its county-specific GAF.

^1See Health Economics Research, Inc., Assessment and Redesign of Medicare
Fee Schedule Areas (Localities) (Waltham, Mass., 1995).

^2In calculating the GAF, each of the GPCIs is weighted by the percentage
of costs accounted for by its corresponding relative value unit--a measure
of the relative costliness of providing a particular service. On average,
across all services, work represents 52.5 percent of costs, practice
expense represents 43.7 percent, and malpractice expense represents 3.9
percent. These percentages do not total to 100 percent due to rounding.

To calculate county-specific GAFs, we calculated GPCIs using the same
methodology CMS used for the most recent GPCI update, in 2005.
Specifically, we computed county-level work and practice expense GPCIs
using 2000 Census Bureau data on the median earnings of six categories of
nonphysician professional occupations,^4 fiscal year 2006 HUD data on fair
market rents, and 2005 CMS data on county-level relative value units
(RVU)--a measure of the relative costliness of providing a particular
service. These data were the most recent data available at the time of our
analysis.^5 Although we refer to these data and GPCIs as
"county-specific," we were not able to compute unique county GAFs for each
of the 3,142 counties in the United States because Census Bureau data are
not available at this level. Specifically, it is Census Bureau protocol to
suppress statistics for which less than three people report values and, in
certain cases, nonmetropolitan counties had less than three persons
reporting earnings for a profession. Therefore, we were able to obtain
data that allowed us to calculate individual work and practice expense
GPCIs for the 1,091 counties that were part of a metropolitan statistical
area (MSA) and one composite work and one composite practice expense GPCI
for each non-MSA area per state. In 2000, counties in MSAs represented 83
percent of the population, and in 2005, they represented 88 percent of
Medicare's payments to physicians. We used the Office of Management and
Budget's MSA definitions as of December 2005.

^3From 2004 through 2006, the Medicare Prescription Drug, Improvement, and
Modernization Act of 2003 (MMA) established a floor of 1.0 for any
locality where the work GPCI would otherwise fall below 1.0. Pub. L. No.
108-173, S 412, 117 Stat. at 2274 (codified at 42 U.S.C. S
1395w-4(e)(1)(E)). This provision was extended through 2007 by the Tax
Relief and Health Care Act of 2006, Pub. L. No. 109-432, Div. B, Tit. I, S
102, 120 Stat. 2922, 2981. From 2004 through 2005, MMA set the work,
practice expense, and malpractice expense GPCIs for the state of Alaska at
1.67 if any GPCI would otherwise be less than 1.67. Pub. L. No. 108-173, S
602, 117 Stat. at 2301 (codified at 42 U.S.C. S1395w-4(e)(1)(G)). We used
the 2005 locality GAF before the work GPCI floor and Alaska adjustments
were put into place because the work GPCI floor is set to expire at the
end of 2007 and the Alaska adjustments expired in 2005.

^4These six categories are: architecture and engineering; computer,
mathematical, and natural sciences; social scientists, social workers, and
lawyers; education, training, and library; registered nurses and
pharmacists; and writers, artists, and editors.

^5The CMS and HUD data we obtained are more recent than the data CMS used
to calculate the 2005 GPCIs.

The data CMS uses to calculate the malpractice expense GPCIs are not
available at the county level. However, the malpractice expense GPCI is
weighted by only 3.9 percent when calculating the GAF. Thus, to calculate
the county-specific GAFs, we computed the weighted average of the
county-level work and practice expense GPCIs and the locality-level
malpractice expense GPCI. In addition, we defined a county as urban if it
was part of an MSA and as rural if it was not part of an MSA. Our analysis
was limited to the 87 payment localities within the 50 states and the
District of Columbia.^6

We assessed the reliability of the CMS, Census Bureau, and HUD data in
several ways. First, we performed tests of data elements. For example, we
examined the Census Bureau data on the median earnings of certain
professions to determine whether these data were complete. Second, we
reviewed existing information about the data elements. For example, we
compared the county-level work and practice expense GPCIs we calculated to
less-recent county-level work and practice expense GPCIs provided by CMS.
Third, we interviewed a CMS official and a Census Bureau official
knowledgeable about the data and reviewed documentation related to the
data. We determined that the data used in our analyses were sufficiently
reliable for our purposes.

To evaluate whether alternative approaches to the Medicare payment
localities could improve payment accuracy without imposing a substantial
amount of additional administrative burden, we used the county-specific
GAFs to construct five different payment locality configurations. We
evaluated the payment accuracy of each approach, the extent to which each
approach accurately measures variations in physicians' costs of providing
care, based on its payment difference, that is, the absolute value of the
county's 2005 locality GAF minus its county-specific GAF, divided by its
county-specific GAF. Because improvements in payment accuracy may increase
the differences in the GAFs of adjacent payment localities, which could
potentially create beneficiary access problems, we examined the
differences between the GAFs of adjacent payment localities. We calculated
adjacent-locality GAF differences as the absolute value of the difference
in locality GAFs between all unique, contiguous, county pairs. We weighted
the average adjacent-locality GAF difference by the sum of the RVUs of the
contiguous counties. We evaluated the administrative burden of each
approach based on the number of payment localities that it generated as
well as interviews with CMS officials, Medicare carrier representatives,
and physicians.

^6Our analysis excluded 2 of the 89 physician payment localities: Puerto
Rico and the U.S. Virgin Islands.

Although many alternatives exist, in this report we present five possible
approaches for constructing the payment localities. Three of our
approaches are designed to balance payment accuracy with administrative
burden. We also present two approaches that are useful for comparison
because they illustrate the tradeoffs between payment accuracy and
administrative burden.

Of the three approaches that balance payment accuracy with administrative
burden, two are based on counties, the smallest geographic unit for which
GAFs can be constructed from the data sources available, and one is based
on MSAs. There are two important general distinctions between our two
county-based approaches and our MSA-based approach. First, under the
county-based approaches, it is possible for adjacent counties in an MSA to
belong to different payment localities. In addition, as CMS has done in
the past, our county-based approaches create payment localities within a
state: no payment locality crosses state lines.^7 In contrast, under our
MSA-based approach, in order to keep MSAs intact, all the counties in an
MSA belong to the same payment locality and wherever an MSA crosses state
lines, its payment locality crosses state lines as well.^8

Our three approaches that balance payment accuracy with administrative
burden use two distinct methodologies: the iterative methodology and the
range methodology. The iterative methodology creates single-county or
single-MSA payment localities for the highest-cost areas and "Rest-of"
localities for the remaining areas. Specifically, the county-based
approach creates one payment locality for the moderate- and low-cost
counties in each state, which we refer to as the "Rest-of-State" payment
localities. The MSA-based approach creates a single payment locality that
combines moderate-cost MSAs, low-cost MSAs, and non-MSA areas from many
different states, which we refer to as the "Rest-of-Nation" payment
locality. The range methodology creates a payment locality for each group
of similar-cost counties within a state. Generally, under this
methodology, moderate- and low-cost counties within a state are assigned
to different payment localities.^9 For each of these approaches, we used a
5-percent threshold because that is what CMS used for its 1997
consolidation methodology. However, a different percentage threshold may
also be feasible.^10

^	7Although our county-based approaches generate localities that do not
cross state lines, it would also be possible to create county-based
localities that do cross state lines.

^8Although our MSA-based approach generates payment localities that do
cross state lines, it would also be possible to create MSA-based payment
localities that do not cross state lines.

Of the two approaches that illustrate the tradeoffs between payment
accuracy and administrative burden, under the statewide approach, each
state has one statewide payment locality. This approach minimizes
administrative burden, but maximizes large payment differences. In
contrast, under the county-based unique GAF approach, each group of
counties in a state with a unique county-specific GAF is a distinct
payment locality. This approach minimizes large payment differences, but
maximizes administrative burden.

We conducted our work from June 2006 through May 2007 in accordance with
generally accepted government auditing standards.

^9Although our range methodology did not require that all counties in a
payment locality be contiguous, it would be possible to make geographic
contiguity a priority.

^10In general, lower thresholds generate more payment localities and
further improve payment accuracy. Although the specific results would
differ if an alternate threshold were used, the general advantages and
disadvantages of each approach would remain the same.

Appendix II: Information on Configuration of the Current Medicare
Physician Payment Localities and the Alternative Approaches

Table 2: Medicare Physician Payment Localities, by State

                                                                      Average 
                                                          Locality    payment 
                                              Number of geographic difference 
                                               counties adjustment         in 
                  Locality Counties in               in     factor percentage 
State          number^a locality            locality    (GAF)^b   points^c 
Alabama               1 Statewide                 67      0.918       2.38 
Alaska                1 Statewide                 27      1.081       1.34 
Arizona               1 Statewide                 15      0.991       1.99 
Arkansas              1 Statewide                 75      0.885       2.73 
California            1 San Francisco              1      1.239       2.03 
                         2 San Mateo                  1      1.230       1.03 
                         3 Santa Clara                1      1.224       4.21 
                         4 Alameda, Contra            2      1.144       0.24 
                           Costa                                              
                         5 Marin, Napa,               3      1.128       4.44 
                           Solano                                             
                         6 Orange                     1      1.109       3.23 
                         7 Los Angeles                1      1.088       2.39 
                         8 Ventura                    1      1.072       4.28 
                         9 Rest of California        47      1.012       3.73 
Colorado              1 Statewide                 64      0.986       3.54 
Connecticut           1 Statewide                  8      1.091       2.19 
Delaware              1 Statewide                  3      1.016       4.25 
District of           1 District of                8      1.114       1.54 
Columbia                Columbia;                                          
                           Alexandria City,                                   
                           Arlington,                                         
                           Fairfax, Fairfax                                   
                           City, Falls Church                                 
                           City in Virginia;                                  
                           Montgomery, Prince                                 
                           George's in                                        
                           Maryland                                           
Florida               1 Miami-Dade, Monroe         2      1.075       0.43 
                         2 Broward, Collier,          7      1.024       2.94 
                           Indian River, Lee,                                 
                           Martin, Palm                                       
                           Beach, St. Lucie                                   
                         3 Rest of Florida           58      0.971       2.24 
Georgia               1 Butts, Cherokee,          15      1.036       2.10 
                           Clayton, Cobb,                                     
                           DeKalb, Douglas,                                   
                           Fayette, Forsyth,                                  
                           Fulton, Gwinnett,                                  
                           Henry, Newton,                                     
                           Paulding,                                          
                           Rockdale, Walton                                   
                         2 Rest of Georgia          144      0.934       2.17 
Hawaii                1 Statewide                  5      1.045       3.60 
Idaho                 1 Statewide                 44      0.905       2.26 
Illinois              1 Cook                       1      1.096       0.11 
                         2 DuPage, Kane,              4      1.072       1.38 
                           Lake, Will                                         
                         3 Bond, Calhoun,            11      0.993       1.63 
                           Clinton, Jersey,                                   
                           Macoupin, Madison,                                 
                           Monroe,                                            
                           Montgomery,                                        
                           Randolph, St.                                      
                           Clair, Washington                                  
                         4 Rest of Illinois          86      0.939       2.86 
Indiana               1 Statewide                 92      0.932       2.57 
Iowa                  1 Statewide                 99      0.909       2.92 
Kansas                1 Statewide                105      0.922       3.42 
Kentucky              1 Statewide                120      0.918       2.72 
Louisiana             1 Jefferson,                 4      0.979       3.85 
                           Orleans,                                           
                           Plaquemines, St.                                   
                           Bernard                                            
                         2 Rest of Louisiana         60      0.924       2.61 
Maine                 1 Cumberland, York           2      0.978       2.07 
                         2 Rest of Maine             14      0.921       0.68 
Maryland              1 Anne Arundel,              6      1.033       1.61 
                           Baltimore,                                         
                           Baltimore City,                                    
                           Carroll, Harford,                                  
                           Howard                                             
                         2 Rest of Maryland,         16      0.974       4.63 
                           except Montgomery                                  
                           and Prince                                         
                           George's counties                                  
Massachusetts         1 Middlesex,                 3      1.136       0.84 
                           Norfolk, Suffolk                                   
                         2 Rest of                   11      1.049       3.28 
                           Massachusetts                                      
Michigan              1 Macomb, Oakland,           4      1.109       0.22 
                           Washtenaw, Wayne                                   
                         2 Rest of Michigan          79      0.987       2.00 
Minnesota             1 Statewide                 87      0.968       5.13 
Mississippi           1 Statewide                 82      0.897       2.53 
Missouri              1 Clay, Jackson,             3      0.979       1.16 
                           Platte                                             
                         2 Jefferson, St.             4      0.971       0.78 
                           Charles, St.                                       
                           Louis, St. Louis                                   
                           City                                               
                         3 Rest of Missouri         108      0.887       2.03 
Montana               1 Statewide                 56      0.909       0.83 
Nebraska              1 Statewide                 93      0.900       3.65 
Nevada                1 Statewide                 17      1.023       0.93 
New Hampshire         1 Statewide                 10      1.002       3.06 
New Jersey            1 Bergen, Essex,            11      1.120       0.93 
                           Hudson, Hunterdon,                                 
                           Middlesex, Morris,                                 
                           Passaic, Somerset,                                 
                           Sussex, Union,                                     
                           Warren                                             
                         2 Rest of New Jersey        10      1.068       2.54 
New Mexico            1 Statewide                 33      0.935       3.09 
New York              1 New York                   1      1.203       1.68 
                         2 Bronx, Kings,              7      1.178       1.91 
                           Nassau, Richmond,                                  
                           Rockland, Suffolk,                                 
                           Westchester                                        
                         3 Queens                     1      1.151       0.26 
                         4 Columbia,                  8      1.046       4.29 
                           Delaware,                                          
                           Dutchess, Greene,                                  
                           Orange, Putnam,                                    
                           Sullivan, Ulster                                   
                         5 Rest of New York          45      0.956       1.89 
North Carolina        1 Statewide                100      0.938       2.91 
North Dakota          1 Statewide                 53      0.901       1.68 
Ohio                  1 Statewide                 88      0.967       2.81 
Oklahoma              1 Statewide                 77      0.899       2.47 
Oregon                1 Clackamas,                 3      1.001       0.66 
                           Multnomah,                                         
                           Washington                                         
                         2 Rest of Oregon            33      0.929       1.27 
Pennsylvania          1 Bucks, Chester,            5      1.069       0.43 
                           Delaware,                                          
                           Montgomery,                                        
                           Philadelphia                                       
                         2 Rest of                   62      0.951       2.63 
                           Pennsylvania                                       
Rhode Island          1 Statewide                  5      1.025       2.63 
South Carolina        1 Statewide                 46      0.919       1.61 
South Dakota          1 Statewide                 66      0.890       2.81 
Tennessee             1 Statewide                 95      0.925       2.73 
Texas                 1 Dallas                     1      1.035       2.11 
                         2 Harris                     1      1.026       0.04 
                         3 Travis                     1      1.003       0.17 
                         4 Brazoria                   1      1.002       0.96 
                         5 Tarrant                    1      0.992       0.07 
                         6 Galveston                  1      0.989       1.12 
                         7 Jefferson                  1      0.951       0.36 
                         8 Rest of Texas            247      0.932       2.36 
Utah                  1 Statewide                 29      0.948       2.69 
Vermont               1 Statewide                 14      0.956       3.26 
Virginia              1 Statewide, except        130      0.948       3.72 
                           Alexandria City,                                   
                           Arlington,                                         
                           Fairfax, Fairfax                                   
                           City, Falls Church                                 
                           City                                               
Washington            1 King                       1      1.049       0.34 
                         2 Rest of Washington        38      0.974       2.72 
West Virginia         1 Statewide                 55      0.932       1.99 
Wisconsin             1 Statewide                 72      0.950       2.89 
Wyoming               1 Statewide                 23      0.922       1.79 
Nation               87                                               2.28 

Source: GAO analysis of 2005 Centers for Medicare & Medicaid (CMS), 2000
Census Bureau, and fiscal year 2006 Department of Housing and Urban
Development (HUD) data.

Notes: Our analysis includes the 87 payment localities within the 50
states and District of Columbia and excludes the Puerto Rico and the U.S.
Virgin Islands payment localities. We consider independent cities, such as
Alexandria City in Virginia, as county equivalents, because this is how
the Census Bureau considers them. The District of Columbia locality
consists of the District, five Virginia counties, and two Maryland
counties. These Virginia and Maryland counties are excluded from the
Virginia and Rest-of-Maryland localities.

aThe locality number is relative on a state basis. That is, locality 1 has
the highest GAF in the state, locality 2 has the second-highest GAF, and
so on.

bThe locality GAF is Medicare's 2005 locality GAF without the work GPCI
floor or Alaska adjustments.

cPayment difference compares the costs physicians incur for providing
services in different geographic areas (the county-specific GAF) with the
geographic adjustment that Medicare applies to those areas (the locality
GAF). We calculated payment difference as the absolute value of the
locality GAF minus the county-specific GAF, divided by the county-specific
GAF. In calculating the average payment difference, each county's payment
difference was weighted by county relative value units (RVU).

Table 3: Physician Payment Localities Created Using the County-Based
Iterative Alternative Approach, by State

                                                                                                     Average payment 
                                                                       Number of                       difference in 
                                         Locality Counties in        counties in                          percentage 
State                                    number^a locality              locality     Locality GAF^b         points^c 
Alabama                                         1 Statewide                   67              0.921             2.38 
Alaska                                          1 Statewide                   27              1.082             1.31 
Arizona                                         1 Statewide                   15              0.986             2.09 
Arkansas                                        1 Pulaski                      1              0.932             0.00 
                                                2 Rest of                     74              0.879             1.56 
                                                  Arkansas                                                           
California                                      1 San Mateo                    1              1.217             0.00 
                                                2 San Francisco                1              1.214             0.00 
                                                3 Marin                        1              1.183             0.00 
                                                4 Santa Clara                  1              1.175             0.00 
                                                5 Contra Costa                 1              1.151             0.00 
                                                6 Orange                       1              1.146             0.00 
                                                7 Alameda                      1              1.144             0.00 
                                                8 Ventura                      1              1.120             0.00 
                                                9 Santa Cruz                   1              1.119             0.00 
                                               10 Los Angeles                  1              1.115             0.00 
                                               11 Napa                         1              1.097             0.00 
                                               12 Sonoma                       1              1.097             0.00 
                                               13 Monterey                     1              1.094             0.00 
                                               14 San Benito                   1              1.081             0.00 
                                               15 Rest of                     44              1.018             3.23 
                                                  California                                                         
Colorado                                        1 Boulder                      1              1.038             0.00 
                                                2 Denver                       1              1.033             0.00 
                                                3 Arapahoe                     1              1.028             0.00 
                                                4 Jefferson                    1              1.015             0.00 
                                                5 Adams                        1              1.008             0.00 
                                                6 Broomfield                   1              1.007             0.00 
                                                7 Douglas                      1              1.006             0.00 
                                                8 Rest of                     57              0.957             1.72 
                                                  Colorado                                                           
Connecticut                                     1 Fairfield                    1              1.149             0.00 
                                                2 Rest of                      7              1.083             1.03 
                                                  Connecticut                                                        
Delaware                                        1 New Castle                   1              1.054             0.00 
                                                2 Rest of                      2              0.962             0.63 
                                                  Delaware                                                           
District of Columbia                            1 District of                  1              1.162             0.00 
                                                  Columbia                                                           
Florida                                         1 Miami-Dade                   1              1.073             0.00 
                                                2 Palm Beach                   1              1.056             0.00 
                                                3 Broward                      1              1.051             0.00 
                                                4 Collier                      1              1.025             0.00 
                                                5 Rest of Florida             63              0.974             2.04 
Georgia                                         1 Fulton                       1              1.028             0.00 
                                                2 DeKalb                       1              1.018             0.00 
                                                3 Cobb                         1              1.012             0.00 
                                                4 Gwinnett                     1              1.010             0.00 
                                                5 Fayette                      1              1.000             0.00 
                                                6 Clayton                      1              0.997             0.00 
                                                7 Cherokee                     1              0.996             0.00 
                                                8 Rockdale                     1              0.996             0.00 
                                                9 Forsyth                      1              0.995             0.00 
                                               10 Bartow                       1              0.994             0.00 
                                               11 Coweta                       1              0.986             0.00 
                                               12 Henry                        1              0.985             0.00 
                                               13 Rest of Georgia            147              0.937             2.14 
Hawaii                                          1 Statewide                    5              1.084             1.40 
Idaho                                           1 Ada                          1              0.949             0.00 
                                                2 Rest of Idaho               43              0.902             1.27 
Illinois                                        1 Cook                         1              1.095             0.00 
                                                2 DuPage                       1              1.087             0.00 
                                                3 Lake                         1              1.085             0.00 
                                                4 Kane                         1              1.065             0.00 
                                                5 Will                         1              1.049             0.00 
                                                6 McHenry                      1              1.037             0.00 
                                                7 Grundy                       1              1.022             0.00 
                                                8 Kendall                      1              0.999             0.00 
                                                9 St. Clair                    1              0.997             0.00 
                                               10 Rest of                     93              0.945             2.51 
                                                  Illinois                                                           
Indiana                                         1 Statewide                   92              0.939             2.47 
Iowa                                            1 Polk                         1              0.959             0.00 
                                                2 Rest of Iowa                98              0.904             2.33 
Kansas                                          1 Linn                         1              1.021             0.00 
                                                2 Johnson                      1              0.975             0.00 
                                                3 Wyandotte                    1              0.972             0.00 
                                                4 Leavenworth                  1              0.970             0.00 
                                                5 Miami                        1              0.961             0.00 
                                                6 Sedgwick                     1              0.944             0.00 
                                                7 Rest of Kansas              99              0.898             2.00 
Kentucky                                        1 Statewide                  120              0.923             2.72 
Louisiana                                       1 St. Charles                  1              1.058             0.00 
                                                2 Orleans                      1              1.031             0.00 
                                                3 Plaquemines                  1              1.026             0.00 
                                                4 West Feliciana               1              1.025             0.00 
                                                5 Jefferson                    1              1.012             0.00 
                                                6 St. John the                 1              1.010             0.00 
                                                  Baptist                                                            
                                                7 St. Tammany                  1              1.007             0.00 
                                                8 St. Bernard                  1              1.004             0.00 
                                                9 Ascension                    1              0.991             0.00 
                                               10 Rest of                     55              0.930             2.09 
                                                  Louisiana                                                          
Maine                                           1 Cumberland                   1              1.002             0.00 
                                                2 York                         1              0.968             0.00 
                                                3 Rest of Maine               14              0.919             0.66 
Maryland                                        1 Montgomery                   1              1.122             0.00 
                                                2 Prince George's              1              1.113             0.00 
                                                3 Calvert                      1              1.088             0.00 
                                                4 Rest of                     21              1.029             3.47 
                                                  Maryland                                                           
Massachusetts                                   1 Suffolk                      1              1.150             0.00 
                                                2 Middlesex                    1              1.130             0.00 
                                                3 Norfolk                      1              1.128             0.00 
                                                4 Essex                        1              1.105             0.00 
                                                5 Plymouth                     1              1.092             0.00 
                                                6 Dukes,                       2              1.088             0.00 
                                                  Nantucket                                                          
                                                7 Rest of                      7              1.022             1.77 
                                                  Massachusetts                                                      
Michigan                                        1 Wayne                        1              1.112             0.00 
                                                2 Washtenaw                    1              1.110             0.00 
                                                3 Oakland                      1              1.109             0.00 
                                                4 Macomb                       1              1.103             0.00 
                                                5 Livingston                   1              1.041             0.00 
                                                6 Rest of                     78              0.990             1.90 
                                                  Michigan                                                           
Minnesota                                       1 Ramsey                       1              1.024             0.00 
                                                2 Hennepin                     1              1.021             0.00 
                                                3 Anoka                        1              1.019             0.00 
                                                4 Carver                       1              1.008             0.00 
                                                5 Scott                        1              1.007             0.00 
                                                6 Dakota                       1              1.006             0.00 
                                                7 Washington                   1              1.002             0.00 
                                                8 Olmsted                      1              0.987             0.00 
                                                9 Wright                       1              0.972             0.00 
                                               10 Chisago                      1              0.966             0.00 
                                               11 Sherburne                    1              0.964             0.00 
                                               12 Isanti                       1       0.960 Oregon           0.0078 
5                                        13 0.991 Rest of                     75              0.906           1.3198 
                                                  Minnesota 0.50                                                     
Mississippi 31                            1 0.934 Hinds 2.63                   1 0.953 Pennsylvania            0.003 
1                                         2 1.158 DeSoto 2.58                  1              0.944           0.0024 
5                                         3 1.064 Hancock 0.75                 1              0.943           0.0057 
3                                         4 1.007 Madison 1.56                 1              0.941           0.0081 
3                                         5 0.988 Rest of                     78              0.895           1.4698 
                                                  Mississippi                                                        
                                                  1.06                                                               
Missouri 55                               1 0.934 Jackson 2.63                 1 0.991 Rhode Island           0.0033 
5                                         2 1.046 St. Louis City               1        0.981 South           0.0098 
                                                  0.90                                     Carolina                  
46                                        3 0.934 St. Louis 2.63               1 0.975 South Dakota           0.0098 
66                                        4 0.934 Clay 2.63                    1    0.968 Tennessee           0.0098 
95                                        5 0.934 Platte 2.63                  1        0.967 Texas           0.0049 
10                                        6 1.019 Cass 1.11                    1              0.959           0.0062 
12                                        7 1.002 St. Charles                  1              0.953           0.0065 
                                                  1.34                                                               
5                                         8 1.000 Lafayette 0.77               1              0.948           0.0098 
227                                       9 0.934 Rest of                    107         0.895 Utah           2.1298 
                                                  Missouri 2.63                                                      
Montana 29                                1 0.934 Statewide 2.63              56      0.909 Vermont           0.8470 
Nebraska 3                                1 0.996 Douglas 0.22                 1              0.947           0.0098 
11                                        2 0.934 Sarpy 2.63                   1     0.938 Virginia           0.0010 
15                                        3 1.116 Rest of                     91              0.893           2.6991 
                                                  Nebraska 2.22                                                      
Nevada 20                                 1 0.986 Statewide 1.08              17              1.031           0.3498 
New Hampshire 100                         1 0.934 Hillsborough                 1   1.047 Washington           0.0037 
                                                  2.63                                                               
3                                         2 1.034 Rockingham 1.30              1              1.030           0.0052 
1                                         3 1.015 Rest of New                  8              0.979           0.9058 
                                                  Hampshire 0.00                                                     
New Jersey 1                              1 1.006 Statewide 0.00              21              1.109           2.3578 
New Mexico 2                              1 0.991 Santa Fe 0.50                1              0.994           0.0079 
2                                         2 0.991 Rest of New                 32              0.940           2.9490 
                                                  Mexico 1.19                                                        
New York 1                                1 0.986 Westchester                  1              1.218           0.0098 
                                                  0.00                                                               
29                                        2 0.934 Nassau 2.63                  1         1.204 West           0.0010 
                                                                                           Virginia                  
1                                         3 1.116 New York 2.22                1              1.183           0.0098 
54                                        4 0.934 Suffolk 2.63                 1    1.182 Wisconsin           0.0021 
1                                         5 1.072 Richmond 3.10                1              1.156           0.0050 
2                                         6 1.019 Bronx 0.47                   1              1.156           0.0082 
4                                         7 0.988 Kings 0.27                   1              1.155           0.0095 
3                                         8 0.983 Rockland 0.95                1              1.152           0.0098 
62                                        9 0.934 Queens 2.63                  1      1.148 Wyoming           0.0098 
23                                       10 0.934 Putnam 2.63                  1       1.105 Nation           0.0098 
                                               11 Dutchess 1.89                1  1.079 Source: GAO   0.00Notes: Our 
                                                                                   analysis of 2005         analysis 
                                                                                   CMS, 2000 Census  includes the 50 
                                                                                 Bureau, and fiscal       states and 
                                                                                      year 2006 HUD      District of 
                                                                                              data.     Columbia and 
                                                                                                     excludes Puerto 
                                                                                                        Rico and the 
                                                                                                         U.S. Virgin 
                                                                                                        Islands. The 
                                                                                                           MSA-based 
                                                                                                           iterative 
                                                                                                    approach creates 
                                                                                                        a single-MSA 
                                                                                                    payment locality 
                                                                                                         for any MSA 
                                                                                                           whose GAF 
                                                                                                         exceeds the 
                                                                                                    weighted average 
                                                                                                          GAF of all 
                                                                                                     counties in the 
                                                                                                         nation with 
                                                                                                     lower GAFs by 5 
                                                                                                    percent or more. 
                                                                                                       All remaining 
                                                                                                        counties are 
                                                                                                    grouped into the 
                                                                                                    "Rest-of-Nation" 
                                                                                                      locality. If a 
                                                                                                      state does not 
                                                                                                       have any MSAs 
                                                                                                           whose GAF 
                                                                                                         exceeds the 
                                                                                                    weighted average 
                                                                                                          GAF of all 
                                                                                                     counties in the 
                                                                                                         nation with 
                                                                                                     lower GAFs by 5 
                                                                                                    percent or more, 
                                                                                                    the entire state 
                                                                                                     is grouped into 
                                                                                                                 the 
                                                                                                    "Rest-of-Nation" 
                                                                                                           locality. 
^bIn the case that an MSA crosses         12 ^cWe Orange                       1     1.076              0.00 
state lines, it is listed under    calculated the ^dPayment                                        
each state that it is part of.    locality GAF as difference                                          
MSA names are those published by      the average compares the                                             
the Office of Management and      county-specific costs                                             
Budget as of December 2005.       GAF of counties physicians                                            
                                           in the incur for                                         
                                        locality, providing                                               
                                      weighted by services in                                              
                                     county RVUs. different                                         
                                  Our formula for geographic                                                         
                                  calculating the areas (the                                                         
                                  locality GAF is county-specific                                                    
                                      the same as GAF) with the                                                      
                                     that used by geographic                                                         
                                             CMS. adjustment that                                                    
                                                  Medicare                                                           
                                                  applies to                                                         
                                                  those areas                                                        
                                                  (the locality                                                      
                                                  GAF). We                                                           
                                                  calculated                                                         
                                                  payment                                                            
                                                  difference as                                                      
                                                  the absolute                                                       
                                                  value of the                                                       
                                                  locality GAF                                                       
                                                  minus the                                                          
                                                  county-specific                                                    
                                                  GAF, divided by                                                    
                                                  the                                                                
                                                  county-specific                                                    
                                                  GAF. In                                                            
                                                  calculating the                                                    
                                                  average payment                                                    
                                                  difference,                                                        
                                                  each county's                                                      
                                                  payment                                                            
                                                  difference was                                                     
                                                  weighted by                                                        
                                                  county RVUs.                                                       
                                               13 Ulster                       1              1.003             0.00 
                                               14 Rest of New      49     0.954              1.83 
                                                  York 
																  
North Carolina       1 (290533) Durham                       1        1.006           0.00
                                                         
North Dakota                                    1 Statewide                   53              0.894             1.70 
Ohio                                            1 Statewide                   88              0.968             2.80 
Oklahoma                                        1 Statewide                   77              0.897             2.51 
Oregon                                          1 Statewide                   36              0.954             2.83 
Pennsylvania                                    1 Philadelphia                 1              1.073             0.00 
                                                2 Montgomery                   1              1.071             0.00 
                                                3 Delaware                     1              1.070             0.00 
                                                4 Chester                      1              1.069             0.00 
                                                5 Bucks                        1              1.050             0.00 
                                                6 Lehigh                       1              1.010             0.00 
                                                7 Rest of                     61              0.955             2.39 
                                                  Pennsylvania                                                       
Rhode Island                                    1 Statewide                    5              1.053             0.38 
South Carolina                                  1 Statewide                   46              0.925             1.53 
South Dakota                                    1 Statewide                   66              0.889             2.82 
Tennessee                                       1 Statewide                   95              0.930             2.71 
Texas                                           1 Harris                       1              1.026             0.00 
                                                2 Collin                       1              1.015             0.00 
                                                3 Dallas                       1              1.014             0.00 
                                                4 Chambers                     1              1.009             0.00 
                                                5 Travis                       1              1.005             0.00 
                                                6 Rockwall                     1              1.004             0.00 
                                                7 Fort Bend                    1              1.004             0.00 
                                                8 Galveston                    1              1.000             0.00 
                                                9 Tarrant                      1              0.993             0.00 
                                               10 Brazoria                     1              0.992             0.00 
                                               11 Williamson                   1              0.991             0.00 
                                               12 Denton                       1              0.985             0.00 
                                               13 Montgomery                   1              0.983             0.00 
                                               14 Rest of Texas              241              0.935             2.01 
Utah                                            1 Summit                       1              0.985             0.00 
                                                2 Salt Lake                    1              0.965             0.00 
                                                3 Rest of Utah                27              0.917             1.67 
Vermont                                         1 Chittenden                   1              0.997             0.00 
                                                2 Franklin                     1              0.984             0.00 
                                                3 Addison,                    11              0.932             0.00 
                                                  Bennington,                                                        
                                                  Caledonia,                                                         
                                                  Essex,                                                             
                                                  LaMoille,                                                          
                                                  Orleans,                                                           
                                                  Orange,                                                            
                                                  Rutland,                                                           
                                                  Washington,                                                        
                                                  Windham,                                                           
                                                  Windsor                                                            
                                                4 Rest of Vermont              1              0.826             0.00 
Virginia                                        1 Arlington                    1              1.142             0.00 
                                                2 Fairfax                      1              1.130             0.00 
                                                3 Alexandria City              1              1.126             0.00 
                                                4 Fairfax City                 1              1.121             0.00 
                                                5 Falls Church                 1              1.113             0.00 
                                                  City                                                               
                                                6 Manassas City                1              1.085             0.00 
                                                7 Prince William               1              1.082             0.00 
                                                8 Loudoun                      1              1.079             0.00 
                                                9 Fauquier                     1              1.052             0.00 
                                               10 Fredericksburg               1              1.046             0.00 
                                                  City                                                               
                                               11 Clarke                       1              1.038             0.00 
                                               12 Stafford                     1              1.037             0.00 
                                               13 Spotsylvania                 1              1.012             0.00 
                                               14 New Kent                     1              0.997             0.00 
                                               15 Richmond City                1              0.995             0.00 
                                               16 Henrico                      1              0.992             0.00 
                                               17 Hopewell City                1              0.992             0.00 
                                               18 Rest of                    118              0.941             2.98 
                                                  Virginia                                                           
Washington                                      1 King                         1              1.045             0.00 
                                                2 Rest of                     38              0.982             2.75 
                                                  Washington                                                         
West Virginia                                   1 Statewide                   55              0.937             1.95 
Wisconsin                                       1 Statewide                   72              0.959             2.91 
Wyoming                                         1 Statewide                   23              0.912             1.23 
Nation                                        219                                                               1.51 

Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year 2006
HUD data.

Notes: Our analysis includes the 50 states and District of Columbia and
excludes Puerto Rico and the U.S. Virgin Islands. We consider independent
cities, such as Alexandria City in Virginia, as county equivalents,
because this is how the Census Bureau considers them. The county-based
iterative approach creates a single-county payment locality for any county
whose GAF exceeds the weighted average GAF of all counties in the state
with lower GAFs by 5 percent or more. The remaining counties in each state
are grouped into a "Rest-of-State" locality.

aThe locality number is relative on a state basis. That is, locality 1 has
the highest GAF in the state, locality 2 has the second-highest GAF, and
so on.

bWe calculated the locality GAF as the average county-specific GAF of
counties in the locality, weighted by county RVUs. Our formula for
calculating the locality GAF is the same as that used by CMS.

cPayment difference compares the costs physicians incur for providing
services in different geographic areas (the county-specific GAF) with the
geographic adjustment that Medicare applies to those areas (the locality
GAF). We calculated payment difference as the absolute value of the
locality GAF minus the county-specific GAF, divided by the county-specific
GAF. In calculating the average payment difference, each county's payment
difference was weighted by county RVUs.

Table 4: Physician Payment Localities Created Using the County-Based GAF
Ranges Alternative Approach, by State

                                                                      Average 
                                                                      payment 
                                                Number of          difference 
                                                 counties                  in 
                      Locality Counties in             in Locality percentage 
State              number^a locality          locality    GAF^b   points^c 
Alabama                   1 Autauga,                 5    0.948       0.33 
                               Jefferson,                                     
                               Limestone,                                     
                               Madison, Shelby                                
                             2 Rest of Alabama         62    0.908       1.71 
Alaska                    1 Statewide               27    1.082       1.31 
Arizona            Medicare Coconino,                2    1.003       0.01 
                   Payment for Maricopa                                       
                     Physician                                                
                    Services 1                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                      Medicare                                                
                   Payment for                                                
                     Physician                                                
                      Services                                                
                             2 Rest of Arizona         13    0.960       1.24 
Arkansas                  1 Crittenden,              4    0.930       0.41 
                               Jefferson,                                     
                               Miller, Pulaski                                
                             2 Rest of Arkansas        71    0.876       1.32 
California                1 Marin, San               3    1.211       0.67 
                               Francisco, San                                 
                               Mateo                                          
                             2 Alameda, Contra          6    1.147       0.89 
                               Costa, Orange,                                 
                               Santa Clara,                                   
                               Santa Cruz,                                    
                               Ventura                                        
                             3 Los Angeles,             7    1.109       0.85 
                               Monterey, Napa,                                
                               Sacramento, San                                
                               Benito, Solano,                                
                               Sonoma                                         
                             4 El Dorado,               9    1.040       1.35 
                               Placer,                                        
                               Riverside, San                                 
                               Bernardino, San                                
                               Diego, San                                     
                               Joaquin, San                                   
                               Luis Obispo,                                   
                               Santa Barbara,                                 
                               Yolo                                           
                             5 Rest of                 33    0.973       1.19 
                               California                                     
Colorado                  1 Adams, Arapahoe,         7    1.027       0.73 
                               Boulder,                                       
                               Broomfield,                                    
                               Denver, Douglas,                               
                               Jefferson                                      
                             2 Rest of Colorado        57    0.957       1.72 
Connecticut               1 Fairfield                1    1.149       0.00 
                             2 Hartford,                2    1.095       0.03 
                               Middlesex                                      
                             3 Rest of                  5    1.073       1.32 
                               Connecticut                                    
Delaware                  1 New Castle               1    1.054       0.00 
                             2 Rest of Delaware         2    0.962       0.63 
District of               1 District of              1    1.162       0.00 
Columbia                    Columbia                                       
Florida                   1 Broward,                 3    1.061       0.85 
                               Miami-Dade, Palm                               
                               Beach                                          
                             2 Collier, Duval,         13    0.995       0.69 
                               Hillsborough,                                  
                               Jefferson, Lee,                                
                               Manatee, Martin,                               
                               Nassau, Orange,                                
                               Pinellas, St.                                  
                               Johns, Sarasota,                               
                               Seminole                                       
                             3 Rest of Florida         51    0.954       1.61 
Georgia                   1 Cobb, DeKalb,            4    1.020       0.65 
                               Fulton, Gwinnett                               
                             2 Barrow, Bartow,         20    0.978       1.17 
                               Burke, Carroll,                                
                               Chatham,                                       
                               Cherokee,                                      
                               Clayton, Coweta,                               
                               Douglas,                                       
                               Fayette,                                       
                               Forsyth, Hall,                                 
                               Henry, Houston,                                
                               Newton,                                        
                               Paulding,                                      
                               Pickens,                                       
                               Rockdale,                                      
                               Spalding, Walton                               
                             3 Rest of Georgia        135    0.927       1.66 
Hawaii                    1 Statewide                5    1.084       1.40 
Idaho                     1 Ada                      1    0.949       0.00 
                             2 Rest of Idaho           43    0.902       1.27 
Illinois                  1 Cook, DuPage,            3    1.093       0.28 
                               Lake                                           
                             2 Grundy, Kane,            4    1.051       0.90 
                               McHenry, Will                                  
                             3 DeKalb,                 10    0.972       0.95 
                               Kankakee,                                      
                               Kendall,                                       
                               Madison, McLean,                               
                               Peoria, Rock                                   
                               Island, St.                                    
                               Clair, Sangamon,                               
                               Winnebago                                      
                             4 Rest of Illinois        85    0.922       1.43 
Indiana                   1 Hamilton,                6    0.968       0.67 
                               Hancock,                                       
                               Hendricks, Lake,                               
                               Marion, Porter                                 
                             2 Rest of Indiana         86    0.921       1.72 
Iowa                      1 Johnson, Linn,           4    0.950       0.95 
                               Polk,                                          
                               Pottawattamie                                  
                             2 Rest of Iowa            95    0.894       1.51 
Kansas                    1 Linn                     1    1.021       0.00 
                             2 Butler, Johnson,         6    0.958       1.58 
                               Leavenworth,                                   
                               Miami, Sedgwick,                               
                               Wyandotte                                      
                             3 Rest of Kansas          98    0.897       1.93 
Kentucky                  1 Boone, Campbell,         7    0.950       0.22 
                               Fayette,                                       
                               Jefferson,                                     
                               Jessamine,                                     
                               Kenton, Meade                                  
                             2 Rest of Kentucky       113    0.901       1.32 
Louisiana                 1 St. Charles              1    1.058       0.00 
                             2 Jefferson,               7    1.015       0.81 
                               Orleans,                                       
                               Plaquemines, St.                               
                               Bernard, St.                                   
                               John the                                       
                               Baptist, St.                                   
                               Tammany, West                                  
                               Feliciana                                      
                             3 Ascension,               7    0.956       1.21 
                               Caddo, East                                    
                               Feliciana, East                                
                               Baton Rouge,                                   
                               Iberville,                                     
                               Livingston, West                               
                               Baton Rouge                                    
                             4 Rest of                 49    0.916       1.42 
                               Louisiana                                      
Maine                     1 Cumberland,              3    0.993       1.26 
                               Sagadahoc, York                                
                             2 Rest of Maine           13    0.918       0.61 
Maryland                  1 Calvert,                 3    1.118       0.49 
                               Montgomery,                                    
                               Prince George's                                
                             2 Anne Arundel,            9    1.050       0.67 
                               Baltimore,                                     
                               Baltimore City,                                
                               Carroll, Cecil,                                
                               Charles,                                       
                               Frederick,                                     
                               Harford, Howard                                
                             3 Rest of Maryland        12    0.947       1.80 
Massachusetts             1 Suffolk                  1    1.150       0.00 
                             2 Rest of                 13    1.076       4.45 
                               Massachusetts                                  
Michigan                  1 Macomb, Oakland,         4    1.109       0.22 
                               Washtenaw, Wayne                               
                             2 Genesee, Ingham,         4    1.014       0.35 
                               Livingston,                                    
                               Monroe                                         
                             3 Rest of Michigan        75    0.984       1.81 
Minnesota                 1 Anoka, Carver,           4    1.021       0.12 
                               Hennepin, Ramsey                               
                             2 Chisago, Dakota,         8    0.989       0.39 
                               Isanti, Olmsted,                               
                               Scott,                                         
                               Sherburne,                                     
                               Washington,                                    
                               Wright                                         
                             3 Rest of                 75    0.906       1.31 
                               Minnesota                                      
Mississippi               1 DeSoto, Hancock,         5    0.949       0.59 
                               Hinds, Madison,                                
                               Rankin                                         
                             2 Rest of                 77    0.893       1.27 
                               Mississippi                                    
Missouri                  1 Clay, Jackson,           4    0.980       0.67 
                               St. Louis, St.                                 
                               Louis City                                     
                             2 Boone, Cass,            12    0.934       1.29 
                               Clinton, Cole,                                 
                               Franklin,                                      
                               Jefferson,                                     
                               Lafayette,                                     
                               Lincoln,                                       
                               Moniteau,                                      
                               Platte, Ray, St.                               
                               Charles                                        
                             3 Rest of Missouri        99    0.886       1.38 
Montana                   1 Statewide               56    0.909       0.84 
Nebraska                  1 Cass, Douglas,           5    0.936       1.27 
                               Lancaster,                                     
                               Sarpy,                                         
                               Washington                                     
                             2 Rest of Nebraska        88    0.872       0.04 
Nevada                    1 Statewide               17    1.031       0.34 
New Hampshire             1 Hillsborough,            2    1.041       0.79 
                               Rockingham                                     
                             2 Rest of New              8    0.979       0.90 
                               Hampshire                                      
New Jersey                1 Bergen,                  3    1.137       0.56 
                               Middlesex,                                     
                               Somerset                                       
                             2 Essex, Hudson,          10    1.115       0.86 
                               Hunterdon,                                     
                               Mercer,                                        
                               Monmouth,                                      
                               Morris, Ocean,                                 
                               Passaic, Salem,                                
                               Union                                          
                             3 Rest of New              8    1.056       0.77 
                               Jersey                                         
New Mexico                1 Bernalillo,              3    0.974       0.59 
                               Sandoval, Santa                                
                               Fe                                             
                             2 Rest of New             30    0.915       0.35 
                               Mexico                                         
New York                  1 Westchester              1    1.218       0.00 
                             2 Bronx, Kings,            8    1.176       1.50 
                               Nassau, New                                    
                               York, Queens,                                  
                               Richmond,                                      
                               Rockland,                                      
                               Suffolk                                        
                             3 Dutchess,                3    1.081       0.48 
                               Orange, Putnam                                 
                             4 Albany,                  3    0.994       0.35 
                               Schenectady,                                   
                               Ulster                                         
                             5 Rest of New York        47    0.948       1.53 
North Carolina            1 Durham,                  8    0.979       1.44 
                               Franklin,                                      
                               Forsyth,                                       
                               Guilford,                                      
                               Johnston,                                      
                               Mecklenburg,                                   
                               Orange, Wake                                   
                             2 Rest of North           92    0.922       1.40 
                               Carolina                                       
North Dakota              1 Cass                     1    0.910       0.00 
                             2 Rest of North           52    0.884       1.83 
                               Dakota                                         
Ohio                      1 Butler,                 18    0.990       0.88 
                               Clermont,                                      
                               Cuyahoga,                                      
                               Delaware,                                      
                               Franklin,                                      
                               Geauga, Greene,                                
                               Hamilton, Lake,                                
                               Lorain, Madison,                               
                               Montgomery,                                    
                               Ottawa,                                        
                               Pickaway,                                      
                               Portage, Summit,                               
                               Union, Warren                                  
                             2 Rest of Ohio            70    0.935       1.54 
Oklahoma                  1 Oklahoma, Osage,         5    0.915       0.53 
                               Rogers, Tulsa,                                 
                               Wagoner                                        
                             2 Rest of Oklahoma        72    0.869       1.14 
Oregon                    1 Clackamas,               3    0.994       0.18 
                               Multnomah,                                     
                               Washington                                     
                             2 Rest of Oregon          33    0.934       1.14 
Pennsylvania              1 Bucks, Chester,          5    1.069       0.44 
                               Delaware,                                      
                               Montgomery,                                    
                               Philadelphia                                   
                             2 Allegheny,               7    0.988       1.03 
                               Beaver,                                        
                               Cumberland,                                    
                               Dauphin, Lehigh,                               
                               Northampton,                                   
                               Washington                                     
                             3 Rest of                 55    0.941       1.70 
                               Pennsylvania                                   
Rhode Island              1 Statewide                5    1.053       0.38 
South Carolina            1 Statewide               46    0.925       1.53 
South Dakota              1 Minnehaha,               3    0.912       0.54 
                               Pennington,                                    
                               Union                                          
                             2 Rest of South           63    0.862       0.92 
                               Dakota                                         
Tennessee                 1 Anderson,                7    0.956       0.81 
                               Davidson,                                      
                               Hamilton,                                      
                               Rutherford,                                    
                               Shelby,                                        
                               Williamson,                                    
                               Wilson                                         
                             2 Rest of                 88    0.906       1.71 
                               Tennessee                                      
Texas                     1 Chambers,                4    1.020       0.57 
                               Collin, Dallas,                                
                               Harris                                         
                             2 Bastrop, Bexar,         17    0.986       1.26 
                               Brazoria,                                      
                               Caldwell,                                      
                               Denton, Ellis,                                 
                               Fort Bend,                                     
                               Galveston, Hays,                               
                               Hunt, Kendall,                                 
                               Montgomery,                                    
                               Rockwall,                                      
                               Tarrant, Travis,                               
                               Waller,                                        
                               Williamson                                     
                             3 Rest of Texas          233    0.927       1.45 
Utah                      1 Salt Lake,               3    0.965       0.04 
                               Summit, Tooele                                 
                             2 Rest of Utah            26    0.916       1.64 
Vermont                   1 Chittenden,              2    0.996       0.22 
                               Franklin                                       
                             2 Rest of Vermont         12    0.932       0.00 
Virginia                  1 Alexandria City,         5    1.131       0.28 
                               Arlington,                                     
                               Fairfax, Fairfax                               
                               City, Falls                                    
                               Church City                                    
                             2 Fauquier,                5    1.065       1.61 
                               Fredericksburg                                 
                               City, Loudoun,                                 
                               Manassas City,                                 
                               Prince William                                 
                             3 Clarke, New              5    0.999       0.69 
                               Kent, Richmond                                 
                               City,                                          
                               Spotsylvania,                                  
                               Stafford                                       
                             4 Albemarle,              25    0.969       1.13 
                               Charlottesville                                
                               City, Chesapeake                               
                               City,                                          
                               Chesterfield,                                  
                               Colonial Heights                               
                               City, Dinwiddie,                               
                               Goochland,                                     
                               Hampton City,                                  
                               Hanover,                                       
                               Henrico,                                       
                               Hopewell City,                                 
                               Isle of Wight,                                 
                               James City,                                    
                               Louisa, Newport                                
                               News City,                                     
                               Norfolk City,                                  
                               Petersburg City,                               
                               Portsmouth City,                               
                               Salem City,                                    
                               Suffolk City,                                  
                               Virginia Beach                                 
                               City, Warren,                                  
                               Williamsburg                                   
                               City, Winchester                               
                               City, York                                     
                             5 Rest of Virginia        95    0.907       1.24 
Washington                1 King                     1    1.045       0.00 
                             2 Benton, Clark,           6    1.010       0.84 
                               Kitsap, Pierce,                                
                               Snohomish,                                     
                               Thurston                                       
                             3 Rest of                 32    0.957       1.01 
                               Washington                                     
West Virginia             1 Berkeley,                4    0.968       0.18 
                               Jefferson,                                     
                               Morgan, Putnam                                 
                             2 Rest of West            51    0.935       1.89 
                               Virginia                                       
Wisconsin                 1 Dane, Kenosha,           9    0.987       0.38 
                               Milwaukee,                                     
                               Ozaukee, Pierce,                               
                               Racine, St.                                    
                               Croix,                                         
                               Washington,                                    
                               Waukesha                                       
                             2 Rest of                 63    0.931       1.17 
                               Wisconsin                                      
Wyoming                   1 Statewide               23    0.912       1.23 
Nation                  119                                1.09            

Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year 2006
HUD data.

Notes: Our analysis includes the 50 states and District of Columbia and
excludes Puerto Rico and the U.S. Virgin Islands. We consider independent
cities, such as Alexandria City in Virginia, as county equivalents,
because this is how the Census Bureau considers them. The county-based GAF
ranges approach groups counties with similar GAFs into one locality.

aThe locality number is relative on a state basis. That is, locality 1 has
the highest GAF in the state, locality 2 has the second-highest GAF, and
so on.

bWe calculated the locality GAF as the average county-specific GAF of
counties in the locality, weighted by county RVUs. Our formula for
calculating the locality GAF is the same as that used by CMS.

cPayment difference compares the costs physicians incur for providing
services in different geographic areas (the county-specific GAF) with the
geographic adjustment that Medicare applies to those areas (the locality
GAF). We calculated payment difference as the absolute value of the
locality GAF minus the county-specific GAF, divided by the county-specific
GAF. In calculating the average payment difference, each county's payment
difference was weighted by county RVUs.

Table 5: Physician Payment Localities Created Using the Metropolitan
Statistical Area (MSA)-Based Iterative Alternative Approach, by State

                                                            Number             Average 
                                                                of             payment 
                                                           state's          difference 
                                                          counties                  in 
              Locality                                          in Locality percentage 
State         number^a MSA in locality^b                  locality    GAF^c   points^d 
Alabama             98 Rest of Nation                           67    0.934       2.63 
Alaska              18 Anchorage, AK                             2    1.085       1.20 
                    28 Fairbanks, AK                             1    1.056       0.00 
                    98 Rest of Nation                           24    0.934       2.63 
Arizona             60 Flagstaff, AZ                             1    1.004       0.00 
                    63 Phoenix-Mesa-Scottsdale, AZ               2    1.002       0.13 
                    98 Rest of Nation                           12    0.934       2.63 
Arkansas            98 Rest of Nation                           75    0.934       2.63 
California           1 San Francisco-Oakland-Fremont, CA         5    1.179       2.71 
                     2 San Jose-Sunnyvale-Santa Clara, CA        2    1.173       0.25 
                     7 Los Angeles-Long Beach-Santa Ana,         2    1.121       0.91 
                       CA                                                              
                     8 Oxnard-Thousand Oaks-Ventura, CA          1    1.120       0.00 
                     9 Santa Cruz-Watsonville, CA                1    1.119       0.00 
                    13 Napa, CA                                  1    1.097       0.00 
                    14 Santa Rosa-Petaluma, CA                   1    1.097       0.00 
                    16 Salinas, CA                               1    1.094       0.00 
                    23 Vallejo-Fairfield, CA                     1    1.066       0.00 
                    27 Sacramento-Arden-Arcade-Roseville,        4    1.057       1.11 
                       CA                                                              
                    29 Santa Barbara-Santa Maria, CA             1    1.056       0.00 
                    30 San Diego-Carlsbad-San Marcos, CA         1    1.055       0.00 
                    40 San Luis Obispo-Paso Robles, CA           1    1.030       0.00 
                    42 Riverside-San Bernardino-Ontario,         2    1.026       0.32 
                       CA                                                              
                    45 Stockton, CA                              1    1.025       0.00 
                    69 Modesto, CA                               1    0.996       0.00 
                    93 Fresno, CA                                1    0.984       0.00 
                    94 Bakersfield, CA                           1    0.984       0.00 
                    98 Rest of Nation                           30    0.934       2.63 
Colorado            36 Boulder, CO                               1    1.038       0.00 
                    43 Denver-Aurora, CO                        10    1.025       0.78 
                    98 Rest of Nation                           53    0.934       2.63 
Connecticut          4 Bridgeport-Stamford-Norwalk, CT           1    1.149       0.00 
                    17 Hartford-West Hartford-East               3    1.093       0.34 
                       Hartford, CT                                                    
                    19 New Haven-Milford, CT                     1    1.084       0.00 
                    22 Norwich-New London, CT                    1    1.067       0.00 
                    98 Rest of Nation                            2    0.934       2.63 
Delaware            24 Philadelphia-Camden-Wilmington,           1    1.064       0.75 
                       PA-NJ-DE-MD                                                     
                    98 Rest of Nation                            2    0.934       2.63 
District of         10 Washington-Arlington-Alexandria,          1    1.116       2.22 
Columbia               DC-VA-MD-WV                                                     
Florida             25 Miami-Fort Lauderdale-Miami Beach,        3    1.061       0.85 
                       FL                                                              
                    44 Naples-Marco Island, FL                   1    1.025       0.00 
                    67 Sarasota-Bradenton-Venice, FL             2    0.997       0.42 
                    80 Cape Coral-Fort Myers, FL                 1    0.988       0.00 
                    84 Jacksonville, FL                          5    0.988       0.37 
                    85 Tampa-St. Petersburg-Clearwater,          4    0.987       1.10 
                       FL                                                              
                    86 Orlando-Kissimmee, FL                     4    0.987       0.93 
                    92 Port St. Lucie-Fort Pierce, FL            2    0.985       0.84 
                    98 Rest of Nation                           45    0.934       2.63 
Georgia             54 Atlanta-Sandy Springs-Marietta, GA       28    1.011       1.43 
                    98 Rest of Nation                          131    0.934       2.63 
Hawaii              15 Honolulu, HI                              1    1.094       0.00 
                    98 Rest of Nation                            4    0.934       2.63 
Idaho               98 Rest of Nation                           44    0.934       2.63 
Illinois            21 Chicago-Naperville-Joliet,                9    1.072       3.10 
                       IL-IN-WI                                                        
                    98 Rest of Nation                           93    0.934       2.63 
Indiana             21 Chicago-Naperville-Joliet,                4    1.072       3.10 
                       IL-IN-WI                                                        
                    96 Cincinnati-Middletown, OH-KY-IN           3    0.982       1.49 
                    98 Rest of Nation                           85    0.934       2.63 
Iowa                98 Rest of Nation                           99    0.934       2.63 
Kansas              98 Rest of Nation                          105    0.934       2.63 
Kentucky            96 Cincinnati-Middletown, OH-KY-IN           7    0.982       1.49 
                    98 Rest of Nation                          113    0.934       2.63 
Louisiana           51 New Orleans-Metairie-Kenner, LA           7    1.016       0.87 
                    98 Rest of Nation                           57    0.934       2.63 
Maine               74 Portland-South Portland-Biddeford,        3    0.993       1.26 
                       ME                                                              
                    98 Rest of Nation                           13    0.934       2.63 
Maryland            10 Washington-Arlington-Alexandria,          5    1.116       2.22 
                       DC-VA-MD-WV                                                     
                    24 Philadelphia-Camden-Wilmington,           1    1.064       0.75 
                       PA-NJ-DE-MD                                                     
                    31 Baltimore-Towson, MD                      7    1.050       0.58 
                    98 Rest of Nation                           11    0.934       2.63 
Massachusetts        6 Boston-Cambridge-Quincy, MA-NH            5    1.121       2.15 
                    33 Providence-New Bedford-Fall River,        1    1.046       0.90 
                       RI-MA                                                           
                    34 Worcester, MA                             1    1.040       0.00 
                    35 Barnstable Town, MA                       1    1.039       0.00 
                    59 Springfield, MA                           3    1.005       1.00 
                    97 Pittsfield, MA                            1    0.981       0.00 
                    98 Rest of Nation                            2    0.934       2.63 
Michigan            11 Ann Arbor, MI                             1    1.110       0.00 
                    12 Detroit-Warren-Livonia, MI                6    1.104       0.95 
                    48 Monroe, MI                                1    1.022       0.00 
                    53 Flint, MI                                 1    1.011       0.00 
                    55 Lansing-East Lansing, MI                  3    1.010       0.30 
                    56 Grand Rapids-Wyoming, MI                  4    1.007       0.47 
                    64 Holland-Grand Haven, MI                   1    1.000       0.00 
                    66 Battle Creak, MI                          1    1.000       0.00 
                    73 Jackson, MI                               1    0.994       0.00 
                    75 Kalamazoo-Portage, MI                     2    0.993       0.15 
                    76 Saginaw-Saginaw Township North, MI        1    0.993       0.00 
                    98 Rest of Nation                           61    0.934       2.63 
Minnesota           50 Minneapolis-St. Paul-Bloomington,        11    1.019       0.47 
                       MN-WI                                                           
                    88 Rochester, MN                             3    0.986       0.24 
                    98 Rest of Nation                           73    0.934       2.63 
Mississippi         98 Rest of Nation                           82    0.934       2.63 
Missouri            98 Rest of Nation                          115    0.934       2.63 
Montana             98 Rest of Nation                           56    0.934       2.63 
Nebraska            98 Rest of Nation                           93    0.934       2.63 
Nevada              38 Reno-Sparks, NV                           2    1.033       0.00 
                    39 Las Vegas-Paradise, NV                    1    1.033       0.00 
                    46 Carson City, NV                           1    1.024       0.00 
                    98 Rest of Nation                           13    0.934       2.63 
New Hampshire        6 Boston-Cambridge-Quincy, MA-NH            2    1.121       2.15 
                    32 Manchester-Nashua, NH                     1    1.047       0.00 
                    98 Rest of Nation                            7    0.934       2.63 
New Jersey           3 New York-Northern NJ-Long Island,        12    1.158       2.58 
                       NY-NJ-PA                                                        
                     5 Trenton-Ewing, NJ                         1    1.127       0.00 
                    24 Philadelphia-Camden-Wilmington,           4    1.064       0.75 
                       PA-NJ-DE-MD                                                     
                    26 Atlantic City, NJ                         1    1.059       0.00 
                    41 Vineland-Millville-Bridgeton, NJ          1    1.028       0.00 
                    47 Ocean City, NJ                            1    1.022       0.00 
                    57 Allentown-Bethlehem-Easton, PA-NJ         1    1.007       1.56 
New Mexico          72 Santa Fe, NM                              1    0.994       0.00 
                    98 Rest of Nation                           32    0.934       2.63 
New York             3 New York-Northern NJ-Long Island,        10    1.158       2.58 
                       NY-NJ-PA                                                        
                    20 Poughkeepsie-Newburgh-Middletown,         2    1.078       0.15 
                       NY                                                              
                    61 Kingston, NY                              1    1.003       0.00 
                    83 Albany-Schenectady-Troy, NY               5    0.988       0.72 
                    98 Rest of Nation                           44    0.934       2.63 
North               71 Raleigh-Cary, NC                          3    0.995       0.86 
Carolina                                                                               
                    77 Durham, NC                                4    0.992       1.84 
                    98 Rest of Nation                           93    0.934       2.63 
North Dakota        98 Rest of Nation                           53    0.934       2.63 
Ohio                68 Cleveland-Elyria-Mentor, OH               5    0.997       0.97 
                    87 Akron, OH                                 2    0.987       0.30 
                    89 Columbus, OH                              8    0.986       0.95 
                    96 Cincinnati-Middletown, OH-KY-IN           5    0.982       1.49 
                    98 Rest of Nation                           68    0.934       2.63 
Oklahoma            98 Rest of Nation                           77    0.934       2.63 

Portland-Vancouver-Beaverton, OR-WA

Rest of Nation

New York-Northern NJ-Long Island, NY-NJ-PA

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

Allentown-Bethlehem-Easton, PA-NJ

Harrisburg-Carlisle, PA

Rest of Nation

Providence-New Bedford-Fall River, RI-MA

Rest of Nation

Rest of Nation

Rest of Nation

Houston-Sugar Land-Baytown, TX

Dallas-Fort Worth-Arlington, TX

Austin-Round Rock, TX

Rest of Nation

Rest of Nation

Burlington-South Burlington, VT

Rest of Nation

Washington-Arlington-Alexandria, DC-VA-MD-WV

Richmond, VA

Rest of Nation

Seattle-Tacoma-Bellevue, WA

Olympia, WA

Bremerton-Silverdale, WA

Portland-Vancouver-Beaverton, OR-WA

Kennewick-Richland-Pasco, WA

Mount Vernon-Anacortes, WA

Rest of Nation

Washington-Arlington-Alexandria, DC-VA-MD-WV

Rest of Nation

Chicago-Naperville-Joliet, IL-IN-WI

Minneapolis-St. Paul-Bloomington, MN-WI

Milwaukee-Waukesha-West Allis, WI

Madison, WI

Rest of Nation

Rest of Nation

^aThe locality number is relative on a national basis. That is, locality 1
has the highest GAF in the United States, locality 2 has the
second-highest GAF, and so on. Locality 98 represents counties that were
grouped into the "Rest-of-Nation" locality.

Appendix III: Comments from the Centers for Medicare & Medicaid Services

GAO Contact

A. Bruce Steinwald, (202)512-7114 or [email protected]

Acknowledgments

In addition to the contact named above, Thomas A. Walker, Assistant
Director; Margaret S. Colby; Jennifer DeYoung; and Joanna L. Hiatt
made major contributions to this report.

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Highlights of [28]GAO-07-466 , a report to the Chairman, Subcommittee on
Health, Committee on Ways and Means, House of Representatives

June 2007

MEDICARE

Geographic Areas Used to Adjust Physician Payments for Variation in
Practice Costs Should Be Revised

The Centers for Medicare & Medicaid Services (CMS) adjusts Medicare
physician fees for geographic differences in the costs of operating a
medical practice. CMS uses 89 physician payment localities among which
fees are adjusted. Concerns have been raised that the boundaries of some
payment localities do not accurately address variations in physicians'
costs. GAO was asked to examine how CMS has revised the localities; the
extent to which they accurately reflect variations in physicians' costs;
and alternative approaches to constructing the localities. To do so, GAO
reviewed selected Federal Register documents; compared data on the costs
physicians incur in different areas with the Medicare geographic
adjustment; and used the physician cost data to construct and evaluate
alternative approaches.

[29]What GAO Recommends

GAO recommends that CMS (1) examine and revise the payment localities
using an approach that is uniformly applied to all states and based on the
most current data and (2) update the payment localities on a periodic
basis. CMS stated it will consider GAO's first recommendation, but
continue its approach of updating the localities when interested parties
raise concerns and on its own initiative. GAO notes that updating the
localities in this manner may result in updating only select localities,
rather than all localities using a uniform approach.

The current 89 physician payment localities are primarily consolidations
of the 240 localities that Medicare carriers--CMS contractors responsible
for processing physician claims--established in 1966. Since then, CMS has
revised the payment localities using three different approaches that were
not uniformly applied. From 1992 through 1995, CMS permitted state medical
associations to petition to consolidate into a statewide locality if the
state's physicians demonstrated "overwhelming support" for the change. In
1997, CMS revised the 28 states with multiple payment localities using two
approaches: CMS consolidated carrier-defined localities in 25 states and
created entirely new localities in 3 states.

More than half of the current physician payment localities had counties
within them with a large payment difference--that is, a payment difference
of 5 percent or more between GAO's measure of physicians' costs and
Medicare's geographic adjustment for an area. These 447
counties--representing 14 percent of all counties--were located across the
United States, but a disproportionate number were located in California,
Georgia, Minnesota, Ohio, and Virginia. Large payment differences occur
because certain localities combine counties with different costs, which
may be due to several factors. For example, although substantial
population growth has occurred in certain areas, potentially leading to
increased costs, CMS has not revised the payment localities in accordance
with these changes.

Counties in Which Physicians Had a Payment Difference of Less than 5
Percent, or 5 Percent or More, between Their Costs and Medicare's
Geographic Adjustment

Many alternative approaches could be used to revise the geographic
boundaries of the current payment localities. GAO identified three
possible approaches that would improve payment accuracy while generally
imposing a minimal amount of additional administrative burden on CMS,
Medicare carriers, and physicians. One approach, for example, would
improve payment accuracy, the extent to which each approach accurately
measures variations in physicians' costs, by 52 percent over the current
localities.

References

Visible links
  24. http://www.gao.gov/cgi-bin/getrpt?GAO-05-119
  25. http://www.gao.gov/cgi-bin/getrpt?GAO-05-119
  26. http://www.gao.gov/cgi-bin/getrpt?GAO-05-119
  27. http://www.gao.gov/cgi-bin/getrpt?GAO-07-466
  28. http://www.gao.gov/cgi-bin/getrpt?GAO-07-466
*** End of document. ***