    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

    This is a work of the U.S. government and is not subject to copyright
    protection in the United States. It may be reproduced and distributed in
    its entirety without further permission from GAO. However, because this
    work may contain copyrighted images or other material, permission from the
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    separately.

    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 steinwalda@gao.gov. 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 steinwalda@gao.gov

    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. ***