Medicare
Geographic Areas Used to Adjust Physician Payments for Variation in Practice Costs Should Be Revised Gao ID: GAO-07-466 June 29, 2007The 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.
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. 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.
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Director: Team: Phone:GAO-07-466, Medicare: Geographic Areas Used to Adjust Physician Payments for Variation in Practice Costs Should Be Revised
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for Variation in Practice Costs Should Be Revised' which was released
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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:
GAO Highlights:
Highlights of GAO-07-466, a report to the Chairman, Subcommittee on
Health, Committee on Ways and Means, House of Representatives
Why GAO Did This Study:
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.
What GAO Found:
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.
Figure: 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:
[See PDF for Image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 Department of Housing and Urban Development data.
[End of figure]
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.
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.
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-07-466].
To view the full product, including the scope and methodology, click on
the link above. For more information, contact A. Bruce Steinwald at
(202) 512-7114 or steinwalda@gao.gov.
[End of section]
Contents:
Letter:
Results in Brief:
Background:
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:
More Than Half of the Physician Payment Localities Had Counties within
Them with Large Payment Differences:
Several Alternative Approaches to the Physician Payment Localities
Could Improve Payment Accuracy While Generally Imposing Minimal
Additional Administrative Burden:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Appendix I: Scope and Methodology:
Appendix II: Information on Configuration of the Current Medicare
Physician Payment Localities and the Alternative Approaches:
Appendix III: Comments from the Centers for Medicare & Medicaid
Services:
Appendix IV: GAO Contact and Staff Acknowledgments:
Tables:
Table 1: Selected Alternative Approaches to Current Medicare Physician
Payment Localities:
Table 2: Medicare Physician Payment Localities, by State:
Table 3: Physician Payment Localities Created Using the County-Based
Iterative Alternative Approach, by State:
Table 4: Physician Payment Localities Created Using the County-Based
GAF Ranges Alternative Approach, by State:
Table 5: Physician Payment Localities Created Using the Metropolitan
Statistical Area (MSA)-Based Iterative Alternative Approach, by State:
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:
Figure 2: Calculation of the GAF for the South Carolina and District of
Columbia Medicare Physician Payment Localities, 2007:
Figure 3: Approaches Used to Establish and Revise Geographic Boundaries
of Medicare Physician Payment Localities as of May 2007:
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:
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:
Figure 6: Average Payment Difference for the Current Medicare Physician
Payment Localities and Selected Alternative Approaches:
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:
Figure 8: Average Adjacent-Locality GAF Difference, for the Current
Medicare Physician Payment Localities and Selected Alternative
Approaches:
Figure 9: Number of Statewide Physician Payment Localities for the
Current Medicare Physician Payment Localities and Selected Alternative
Approaches:
Figure 10: Configuration of Minnesota's Physician Payment Localities
under the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
Figure 11: Configuration of Ohio's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
Figure 12: Configuration of Florida's Physician Payment Localities
under the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
Figure 13: Number of Physician Payment Localities for the Current
Medicare Physician Payment Localities and Selected Alternative
Approaches:
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:
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:
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.[Footnote 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.[Footnote 2]
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.[Footnote 3] We also interviewed
officials at CMS; five Medicare Part B[Footnote 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.
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."[Footnote 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.[Footnote 6]
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.
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,[Footnote 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.[Footnote 8] In addition, each service on the fee
schedule has three relative value units (RVU) that correspond to the
three components of physician payment:
* Physician work--the financial value of physicians' time, skill, and
effort that are associated with providing the service.
* Practice expense--the costs incurred by physicians in employing
office staff, renting office space, and buying supplies and equipment.
* 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.[Footnote 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.[Footnote 10]
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.[Footnote 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.
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:
[See PDF for image]
Source: GAO analysis of CMS data.
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.
[End of figure]
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.[Footnote 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[Footnote 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.[Footnote 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.[Footnote 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.[Footnote 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.
Figure 2: Calculation of the GAF for the South Carolina and District of
Columbia Medicare Physician Payment Localities, 2007:
[See PDF for image]
Source: GAO analysis of CMS data.
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.
[End of figure]
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:
[See PDF for image]
Source: GAO analysis of Federal Register notices.
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.
[End of figure]
In 1989, OBRA required the establishment of a national Medicare
physician fee schedule, replacing the charge-based payment
system.[Footnote 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,[Footnote 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.[Footnote 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.
In 1996, CMS cited a lack of consistency among the carrier-defined
payment localities[Footnote 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.[Footnote 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."[Footnote 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.[Footnote 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.
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.[Footnote 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.[Footnote 25]
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.[Footnote 26]
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.[Footnote 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.
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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: County-based iterative;
Methodology used to construct localities: 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].
Alternative approach: County-based GAF ranges;
Methodology used to construct localities: 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.
Alternative approach: MSA-based iterative;
Methodology used to construct localities: 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.
Alternative approach: Statewide;
Methodology used to construct localities: All states have one statewide
payment locality.
Alternative approach: County-based unique GAF;
Methodology used to construct localities: 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.
[A] For 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.
[B] For 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.
[C] For 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.
[End of table]
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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.[Footnote 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.
Figure 9: Number of Statewide Physician Payment Localities for the
Current Medicare Physician Payment Localities and Selected Alternative
Approaches:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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.[Footnote 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.[Footnote 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).
Figure 10: Configuration of Minnesota's Physician Payment Localities
under the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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.[Footnote
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).
Figure 11: Configuration of Ohio's Physician Payment Localities under
the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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.[Footnote 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).
Figure 12: Configuration of Florida's Physician Payment Localities
under the Current Medicare Physician Payment Localities and Selected
Alternative Approaches:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
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.
[End of figure]
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.[Footnote 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.
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:
[See PDF for image]
Source: GAO analysis of 2005 CMS, 2000 Census Bureau, and fiscal year
2006 HUD data.
[End of figure]
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,
Signed by:
A. Bruce Steinwald:
Director, Health Care:
[End of section]
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.[Footnote 34] 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.[Footnote 35] 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[Footnote 36] minus its
county-specific GAF, divided by its county-specific GAF.
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,[Footnote 37] 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.[Footnote 38] 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.
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.[Footnote 39]
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.
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.[Footnote 40] 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.[Footnote 41]
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.[Footnote
42] 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.[Footnote 43]
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.
[End of section]
Appendix II: Information on Configuration of the Current Medicare
Physician Payment Localities and the Alternative Approaches:
Table 2: Medicare Physician Payment Localities, by State:
State: Alabama;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 67;
Locality geographic adjustment factor (GAF)[B]: 0.918;
Average payment difference in percentage points[C]: 2.38.
State: Alaska;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 27;
Locality geographic adjustment factor (GAF)[B]: 1.081;
Average payment difference in percentage points[C]: 1.34.
State: Arizona;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 15;
Locality geographic adjustment factor (GAF)[B]: 0.991;
Average payment difference in percentage points[C]: 1.99.
State: Arkansas;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 75;
Locality geographic adjustment factor (GAF)[B]: 0.885;
Average payment difference in percentage points[C]: 2.73.
State: California;
Locality number[A]: 1;
Counties in locality: San Francisco;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.239;
Average payment difference in percentage points[C]: 2.03.
State: California;
Locality number[A]: 2;
Counties in locality: San Mateo;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.230;
Average payment difference in percentage points[C]: 1.03.
State: California;
Locality number[A]: 3;
Counties in locality: Santa Clara;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.224;
Average payment difference in percentage points[C]: 4.21.
State: California;
Locality number[A]: 4;
Counties in locality: Alameda, Contra Costa;
Number of counties in locality: 2;
Locality geographic adjustment factor (GAF)[B]: 1.144;
Average payment difference in percentage points[C]: 0.24.
State: California;
Locality number[A]: 5;
Counties in locality: Marin, Napa, Solano;
Number of counties in locality: 3;
Locality geographic adjustment factor (GAF)[B]: 1.128;
Average payment difference in percentage points[C]: 4.44.
State: California;
Locality number[A]: 6;
Counties in locality: Orange;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.109;
Average payment difference in percentage points[C]: 3.23.
State: California;
Locality number[A]: 7;
Counties in locality: Los Angeles;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.088;
Average payment difference in percentage points[C]: 2.39.
State: California;
Locality number[A]: 8;
Counties in locality: Ventura;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.072;
Average payment difference in percentage points[C]: 4.28.
State: California;
Locality number[A]: 9;
Counties in locality: Rest of California;
Number of counties in locality: 47;
Locality geographic adjustment factor (GAF)[B]: 1.012;
Average payment difference in percentage points[C]: 3.73.
State: Colorado;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 64;
Locality geographic adjustment factor (GAF)[B]: 0.986;
Average payment difference in percentage points[C]: 3.54.
State: Connecticut;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 8;
Locality geographic adjustment factor (GAF)[B]: 1.091;
Average payment difference in percentage points[C]: 2.19.
State: Delaware;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 3;
Locality geographic adjustment factor (GAF)[B]: 1.016;
Average payment difference in percentage points[C]: 4.25.
State: District of Columbia;
Locality number[A]: 1;
Counties in locality: District of Columbia;
Alexandria City, Arlington, Fairfax, Fairfax City, Falls Church City in
Virginia;
Montgomery, Prince George's in Maryland;
Number of counties in locality: 8;
Locality geographic adjustment factor (GAF)[B]: 1.114;
Average payment difference in percentage points[C]: 1.54.
State: Florida;
Locality number[A]: 1;
Counties in locality: Miami- Dade, Monroe;
Number of counties in locality: 2;
Locality geographic adjustment factor (GAF)[B]: 1.075;
Average payment difference in percentage points[C]: 0.43.
State: Florida;
Locality number[A]: 2;
Counties in locality: Broward, Collier, Indian River, Lee, Martin, Palm
Beach, St. Lucie;
Number of counties in locality: 7;
Locality geographic adjustment factor (GAF)[B]: 1.024;
Average payment difference in percentage points[C]: 2.94.
State: Florida;
Locality number[A]: 3;
Counties in locality: Rest of Florida;
Number of counties in locality: 58;
Locality geographic adjustment factor (GAF)[B]: 0.971;
Average payment difference in percentage points[C]: 2.24.
State: Georgia;
Locality number[A]: 1;
Counties in locality: Butts, Cherokee, Clayton, Cobb, DeKalb, Douglas,
Fayette, Forsyth, Fulton, Gwinnett, Henry, Newton, Paulding, Rockdale,
Walton;
Number of counties in locality: 15;
Locality geographic adjustment factor (GAF)[B]: 1.036;
Average payment difference in percentage points[C]: 2.10.
State: Georgia;
Locality number[A]: 2;
Counties in locality: Rest of Georgia;
Number of counties in locality: 144;
Locality geographic adjustment factor (GAF)[B]: 0.934;
Average payment difference in percentage points[C]: 2.17.
State: Hawaii;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality geographic adjustment factor (GAF)[B]: 1.045;
Average payment difference in percentage points[C]: 3.60.
State: Idaho;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 44;
Locality geographic adjustment factor (GAF)[B]: 0.905;
Average payment difference in percentage points[C]: 2.26.
State: Illinois;
Locality number[A]: 1;
Counties in locality: Cook;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.096;
Average payment difference in percentage points[C]: 0.11.
State: Illinois;
Locality number[A]: 2;
Counties in locality: DuPage, Kane, Lake, Will;
Number of counties in locality: 4;
Locality geographic adjustment factor (GAF)[B]: 1.072;
Average payment difference in percentage points[C]: 1.38.
State: Illinois;
Locality number[A]: 3;
Counties in locality: Bond, Calhoun, Clinton, Jersey, Macoupin,
Madison, Monroe, Montgomery, Randolph, St. Clair, Washington;
Number of counties in locality: 11;
Locality geographic adjustment factor (GAF)[B]: 0.993;
Average payment difference in percentage points[C]: 1.63.
State: Illinois;
Locality number[A]: 4;
Counties in locality: Rest of Illinois;
Number of counties in locality: 86;
Locality geographic adjustment factor (GAF)[B]: 0.939;
Average payment difference in percentage points[C]: 2.86.
State: Indiana;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 92;
Locality geographic adjustment factor (GAF)[B]: 0.932;
Average payment difference in percentage points[C]: 2.57.
State: Iowa;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 99;
Locality geographic adjustment factor (GAF)[B]: 0.909;
Average payment difference in percentage points[C]: 2.92.
State: Kansas;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 105;
Locality geographic adjustment factor (GAF)[B]: 0.922;
Average payment difference in percentage points[C]: 3.42.
State: Kentucky;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 120;
Locality geographic adjustment factor (GAF)[B]: 0.918;
Average payment difference in percentage points[C]: 2.72.
State: Louisiana;
Locality number[A]: 1;
Counties in locality: Jefferson, Orleans, Plaquemines, St. Bernard;
Number of counties in locality: 4;
Locality geographic adjustment factor (GAF)[B]: 0.979;
Average payment difference in percentage points[C]: 3.85.
State: Louisiana;
Locality number[A]: 2;
Counties in locality: Rest of Louisiana;
Number of counties in locality: 60;
Locality geographic adjustment factor (GAF)[B]: 0.924;
Average payment difference in percentage points[C]: 2.61.
State: Maine;
Locality number[A]: 1;
Counties in locality: Cumberland, York;
Number of counties in locality: 2;
Locality geographic adjustment factor (GAF)[B]: 0.978;
Average payment difference in percentage points[C]: 2.07.
State: Maine;
Locality number[A]: 2;
Counties in locality: Rest of Maine;
Number of counties in locality: 14;
Locality geographic adjustment factor (GAF)[B]: 0.921;
Average payment difference in percentage points[C]: 0.68.
State: Maryland;
Locality number[A]: 1;
Counties in locality: Anne Arundel, Baltimore, Baltimore City, Carroll,
Harford, Howard;
Number of counties in locality: 6;
Locality geographic adjustment factor (GAF)[B]: 1.033;
Average payment difference in percentage points[C]: 1.61.
State: Maryland;
Locality number[A]: 2;
Counties in locality: Rest of Maryland, except Montgomery and Prince
George's counties;
Number of counties in locality: 16;
Locality geographic adjustment factor (GAF)[B]: 0.974;
Average payment difference in percentage points[C]: 4.63.
State: Massachusetts;
Locality number[A]: 1;
Counties in locality: Middlesex, Norfolk, Suffolk;
Number of counties in locality: 3;
Locality geographic adjustment factor (GAF)[B]: 1.136;
Average payment difference in percentage points[C]: 0.84.
State: Massachusetts;
Locality number[A]: 2;
Counties in locality: Rest of Massachusetts;
Number of counties in locality: 11;
Locality geographic adjustment factor (GAF)[B]: 1.049;
Average payment difference in percentage points[C]: 3.28.
State: Michigan;
Locality number[A]: 1;
Counties in locality: Macomb, Oakland, Washtenaw, Wayne;
Number of counties in locality: 4;
Locality geographic adjustment factor (GAF)[B]: 1.109;
Average payment difference in percentage points[C]: 0.22.
State: Michigan;
Locality number[A]: 2;
Counties in locality: Rest of Michigan;
Number of counties in locality: 79;
Locality geographic adjustment factor (GAF)[B]: 0.987;
Average payment difference in percentage points[C]: 2.00.
State: Minnesota;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 87;
Locality geographic adjustment factor (GAF)[B]: 0.968;
Average payment difference in percentage points[C]: 5.13.
State: Mississippi;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 82;
Locality geographic adjustment factor (GAF)[B]: 0.897;
Average payment difference in percentage points[C]: 2.53.
State: Missouri;
Locality number[A]: 1;
Counties in locality: Clay, Jackson, Platte;
Number of counties in locality: 3;
Locality geographic adjustment factor (GAF)[B]: 0.979;
Average payment difference in percentage points[C]: 1.16.
State: Missouri;
Locality number[A]: 2;
Counties in locality: Jefferson, St. Charles, St. Louis, St. Louis
City;
Number of counties in locality: 4;
Locality geographic adjustment factor (GAF)[B]: 0.971;
Average payment difference in percentage points[C]: State: 0.78.
State: Missouri;
Locality number[A]: 3;
Counties in locality: Rest of Missouri;
Number of counties in locality: 108;
Locality geographic adjustment factor (GAF)[B]: 0.887;
Average payment difference in percentage points[C]: 2.03.
State: Montana;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 56;
Locality geographic adjustment factor (GAF)[B]: 0.909;
Average payment difference in percentage points[C]: 0.83.
State: Nebraska;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 93;
Locality geographic adjustment factor (GAF)[B]: 0.900;
Average payment difference in percentage points[C]: 3.65.
State: Nevada;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 17;
Locality geographic adjustment factor (GAF)[B]: 1.023;
Average payment difference in percentage points[C]: 0.93.
State: New Hampshire;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 10;
Locality geographic adjustment factor (GAF)[B]: 1.002;
Average payment difference in percentage points[C]: 3.06.
State: New Jersey;
Locality number[A]: 1;
Counties in locality: Bergen, Essex, Hudson, Hunterdon, Middlesex,
Morris, Passaic, Somerset, Sussex, Union, Warren;
Number of counties in locality: 11;
Locality geographic adjustment factor (GAF)[B]: 1.120;
Average payment difference in percentage points[C]: 0.93.
State: New Jersey;
Locality number[A]: 2;
Counties in locality: Rest of New Jersey;
Number of counties in locality: 10;
Locality geographic adjustment factor (GAF)[B]: 1.068;
Average payment difference in percentage points[C]: 2.54.
State: New Mexico;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 33;
Locality geographic adjustment factor (GAF)[B]: 0.935;
Average payment difference in percentage points[C]: 3.09.
State: New York;
Locality number[A]: 1;
Counties in locality: New York;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.203;
Average payment difference in percentage points[C]: 1.68.
State: New York;
Locality number[A]: 2;
Counties in locality: Bronx, Kings, Nassau, Richmond, Rockland,
Suffolk, Westchester;
Number of counties in locality: 7;
Locality geographic adjustment factor (GAF)[B]: 1.178;
Average payment difference in percentage points[C]: 1.91.
State: New York;
Locality number[A]: 3;
Counties in locality: Queens;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.151;
Average payment difference in percentage points[C]: 0.26.
State: New York;
Locality number[A]: 4;
Counties in locality: Columbia, Delaware, Dutchess, Greene, Orange,
Putnam, Sullivan, Ulster;
Number of counties in locality: 8;
Locality geographic adjustment factor (GAF)[B]: 1.046;
Average payment difference in percentage points[C]: 4.29.
State: New York;
Locality number[A]: 5;
Counties in locality: Rest of New York;
Number of counties in locality: 45;
Locality geographic adjustment factor (GAF)[B]: 0.956;
Average payment difference in percentage points[C]: 1.89.
State: North Carolina;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 100;
Locality geographic adjustment factor (GAF)[B]: 0.938;
Average payment difference in percentage points[C]: 2.91.
State: North Dakota;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 53;
Locality geographic adjustment factor (GAF)[B]: 0.901;
Average payment difference in percentage points[C]: 1.68.
State: Ohio;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 88;
Locality geographic adjustment factor (GAF)[B]: 0.967;
Average payment difference in percentage points[C]: 2.81.
State: Oklahoma;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 77;
Locality geographic adjustment factor (GAF)[B]: 0.899;
Average payment difference in percentage points[C]: 2.47.
State: Oregon;
Locality number[A]: 1;
Counties in locality: Clackamas, Multnomah, Washington;
Number of counties in locality: 3;
Locality geographic adjustment factor (GAF)[B]: 1.001;
Average payment difference in percentage points[C]: 0.66.
State: Oregon;
Locality number[A]: 2;
Counties in locality: Rest of Oregon;
Number of counties in locality: 33;
Locality geographic adjustment factor (GAF)[B]: 0.929;
Average payment difference in percentage points[C]: 1.27.
State: Pennsylvania;
Locality number[A]: 1;
Counties in locality: Bucks, Chester, Delaware, Montgomery,
Philadelphia;
Number of counties in locality: 5;
Locality geographic adjustment factor (GAF)[B]: 1.069;
Average payment difference in percentage points[C]: 0.43.
State: Pennsylvania;
Locality number[A]: 2;
Counties in locality: Rest of Pennsylvania;
Number of counties in locality: 62;
Locality geographic adjustment factor (GAF)[B]: 0.951;
Average payment difference in percentage points[C]: 2.63.
State: Rhode Island;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality geographic adjustment factor (GAF)[B]: 1.025;
Average payment difference in percentage points[C]: 2.63.
State: South Carolina;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 46;
Locality geographic adjustment factor (GAF)[B]: 0.919;
Average payment difference in percentage points[C]: 1.61.
State: South Dakota;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 66;
Locality geographic adjustment factor (GAF)[B]: 0.890;
Average payment difference in percentage points[C]: 2.81.
State: Tennessee;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 95;
Locality geographic adjustment factor (GAF)[B]: 0.925;
Average payment difference in percentage points[C]: 2.73.
State: Texas;
Locality number[A]: 1;
Counties in locality: Dallas;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.035;
Average payment difference in percentage points[C]: 2.11.
State: Texas;
Locality number[A]: 2;
Counties in locality: Harris;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.026;
Average payment difference in percentage points[C]: 0.04.
State: Texas;
Locality number[A]: 3;
Counties in locality: Travis;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.003;
Average payment difference in percentage points[C]: 0.17.
State: Texas;
Locality number[A]: 4;
Counties in locality: Brazoria;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.002;
Average payment difference in percentage points[C]: State: 0.96.
State: Texas;
Locality number[A]: 5;
Counties in locality: Tarrant;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 0.992;
Average payment difference in percentage points[C]: 0.07.
State: Texas;
Locality number[A]: 6;
Counties in locality: Galveston;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 0.989;
Average payment difference in percentage points[C]: 1.12.
State: Texas;
Locality number[A]: 7;
Counties in locality: Jefferson;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 0.951;
Average payment difference in percentage points[C]: 0.36.
State: Texas;
Locality number[A]: 8;
Counties in locality: Rest of Texas;
Number of counties in locality: 247;
Locality geographic adjustment factor (GAF)[B]: 0.932;
Average payment difference in percentage points[C]: 2.36.
State: Utah;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 29;
Locality geographic adjustment factor (GAF)[B]: 0.948;
Average payment difference in percentage points[C]: 2.69.
State: Vermont;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 14;
Locality geographic adjustment factor (GAF)[B]: 0.956;
Average payment difference in percentage points[C]: 3.26.
State: Virginia;
Locality number[A]: 1;
Counties in locality: Statewide, except Alexandria City, Arlington,
Fairfax, Fairfax City, Falls Church City;
Number of counties in locality: 130;
Locality geographic adjustment factor (GAF)[B]: 0.948;
Average payment difference in percentage points[C]: 3.72.
State: Washington;
Locality number[A]: 1;
Counties in locality: King;
Number of counties in locality: 1;
Locality geographic adjustment factor (GAF)[B]: 1.049;
Average payment difference in percentage points[C]: 0.34.
State: Washington;
Locality number[A]: 2;
Counties in locality: Rest of Washington;
Number of counties in locality: 38;
Locality geographic adjustment factor (GAF)[B]: 0.974;
Average payment difference in percentage points[C]: 2.72.
State: West Virginia;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 55;
Locality geographic adjustment factor (GAF)[B]: 0.932;
Average payment difference in percentage points[C]: 1.99.
State: Wisconsin;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 72;
Locality geographic adjustment factor (GAF)[B]: 0.950;
Average payment difference in percentage points[C]: 2.89.
State: Wyoming;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 23;
Locality geographic adjustment factor (GAF)[B]: 0.922;
Average payment difference in percentage points[C]: 1.79.
State: Nation;
Locality number[A]: 87;
Counties in locality: [Empty];
Number of counties in locality: [Empty];
Locality geographic adjustment factor (GAF)[B]: [Empty];
Average payment difference in percentage points[C]: 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.
[A] The 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.
[B] The locality GAF is Medicare's 2005 locality GAF without the work
GPCI floor or Alaska adjustments.
[C] Payment 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).
[End of table]
Table 3: Physician Payment Localities Created Using the County-Based
Iterative Alternative Approach, by State:
State: Alabama; Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 67;
Locality GAF[B]: 0.921;
Average payment difference in percentage points[C]: 2.38.
State: Alaska; Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 27;
Locality GAF[B]: 1.082;
Average payment difference in percentage points[C]: 1.31.
State: Arizona;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 15;
Locality GAF[B]: 0.986;
Average payment difference in percentage points[C]: 2.09.
State: Arkansas;
Locality number[A]: 1;
Counties in locality: Pulaski;
Number of counties in locality: 1;
Locality GAF[B]: 0.932;
Average payment difference in percentage points[C]: 0.00.
State: Arkansas; Locality number[A]: 2;
Counties in locality: Rest of Arkansas;
Number of counties in locality: 74;
Locality GAF[B]: 0.879;
Average payment difference in percentage points[C]: 1.56.
State: California;
Locality number[A]: 1;
Counties in locality: San Mateo;
Number of counties in locality: 1;
Locality GAF[B]: 1.217;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 2;
Counties in locality: San Francisco;
Number of counties in locality: 1;
Locality GAF[B]: 1.214;
Average payment difference in percentage points[C]: State: 0.00.
State: California;
Locality number[A]: 3;
Counties in locality: Marin;
Number of counties in locality: 1;
Locality GAF[B]: 1.183;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 4;
Counties in locality: Santa Clara;
Number of counties in locality: 1;
Locality GAF[B]: 1.175;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 5;
Counties in locality: Contra Costa;
Number of counties in locality: State: 1; Locality GAF[B]: State:
1.151; Average payment difference in percentage points[C]: State: 0.00.
State: California;
Locality number[A]: 6;
Counties in locality: Orange;
Number of counties in locality: 1;
Locality GAF[B]: 1.146;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 7;
Counties in locality: Alameda;
Number of counties in locality: 1;
Locality GAF[B]: 1.144;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 8;
Counties in locality: Ventura;
Number of counties in locality: 1;
Locality GAF[B]: 1.120;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 9;
Counties in locality: Santa Cruz;
Number of counties in locality: 1;
Locality GAF[B]: 1.119;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 10;
Counties in locality: Los Angeles;
Number of counties in locality: 1;
Locality GAF[B]: 1.115;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 11;
Counties in locality: Napa;
Number of counties in locality: 1;
Locality GAF[B]: 1.097;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 12;
Counties in locality: Sonoma;
Number of counties in locality: 1;
Locality GAF[B]: 1.097;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 13;
Counties in locality: Monterey;
Number of counties in locality: 1;
Locality GAF[B]: 1.094;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 14;
Counties in locality: San Benito;
Number of counties in locality: 1;
Locality GAF[B]: 1.081;
Average payment difference in percentage points[C]: 0.00.
State: California;
Locality number[A]: 15;
Counties in locality: Rest of California;
Number of counties in locality: 44;
Locality GAF[B]: 1.018;
Average payment difference in percentage points[C]: 3.23.
State: Colorado;
Locality number[A]: 1;
Counties in locality: Boulder;
Number of counties in locality: 1;
Locality GAF[B]: 1.038;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 2;
Counties in locality: Denver;
Number of counties in locality: 1;
Locality GAF[B]: 1.033;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 3;
Counties in locality: Arapahoe;
Number of counties in locality: 1;
Locality GAF[B]: 1.028;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 4;
Counties in locality: Jefferson;
Number of counties in locality: 1;
Locality GAF[B]: 1.015;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 5;
Counties in locality: Adams;
Number of counties in locality: 1;
Locality GAF[B]: 1.008;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 6;
Counties in locality: Broomfield;
Number of counties in locality: 1;
Locality GAF[B]: 1.007;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 7;
Counties in locality: Douglas;
Number of counties in locality: 1;
Locality GAF[B]: 1.006;
Average payment difference in percentage points[C]: 0.00.
State: Colorado;
Locality number[A]: 8;
Counties in locality: Rest of Colorado;
Number of counties in locality: 57;
Locality GAF[B]: 0.957;
Average payment difference in percentage points[C]: 1.72.
State: Connecticut;
Locality number[A]: 1;
Counties in locality: Fairfield;
Number of counties in locality: 1;
Locality GAF[B]: 1.149;
Average payment difference in percentage points[C]: 0.00.
State: Connecticut;
Locality number[A]: 2;
Counties in locality: Rest of Connecticut;
Number of counties in locality: 7;
Locality GAF[B]: 1.083;
Average payment difference in percentage points[C]: 1.03.
State: Delaware;
Locality number[A]: 1;
Counties in locality: New Castle;
Number of counties in locality: 1;
Locality GAF[B]: 1.054;
Average payment difference in percentage points[C]: 0.00.
State: Delaware;
Locality number[A]: 2;
Counties in locality: Rest of Delaware;
Number of counties in locality: 2;
Locality GAF[B]: 0.962;
Average payment difference in percentage points[C]: 0.63.
State: District of Columbia;
Locality number[A]: 1;
Counties in locality: District of Columbia;
Number of counties in locality: 1;
Locality GAF[B]: 1.162;
Average payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 1;
Counties in locality: Miami- Dade;
Number of counties in locality: 1;
Locality GAF[B]: 1.073;
Average payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 2;
Counties in locality: Palm Beach;
Number of counties in locality: 1;
Locality GAF[B]: 1.056;
Average payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 3;
Counties in locality: Broward;
Number of counties in locality: 1;
Locality GAF[B]: 1.051;
Average payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 4;
Counties in locality: Collier;
Number of counties in locality: 1;
Locality GAF[B]: 1.025;
verage payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 5;
Counties in locality: Rest of Florida;
Number of counties in locality: 63;
Locality GAF[B]: 0.974;
Average payment difference in percentage points[C]: 2.04.
State: Georgia;
Locality number[A]: 1;
Counties in locality: Fulton;
Number of counties in locality: 1;
Locality GAF[B]: 1.028;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 2; Counties in locality: DeKalb; Number of counties
in locality: 1; Locality GAF[B]: 1.018; Average payment difference in
percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 3;
Counties in locality: Cobb;
Number of counties in locality: 1;
Locality GAF[B]: 1.012;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 4;
Counties in locality: Gwinnett;
Number of counties in locality: 1;
Locality GAF[B]: 1.010;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 5;
Counties in locality: Fayette;
Number of counties in locality: 1;
Locality GAF[B]: 1.000;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 6;
Counties in locality: Clayton;
Number of counties in locality: 1;
Locality GAF[B]: 0.997;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 7;
Counties in locality: Cherokee;
Number of counties in locality: 1;
Locality GAF[B]: 0.996;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 8;
Counties in locality: Rockdale;
Number of counties in locality: 1;
Locality GAF[B]: 0.996;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 9;
Counties in locality: Forsyth;
Number of counties in locality: 1;
Locality GAF[B]: 0.995;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 10;
Counties in locality: Bartow;
Number of counties in locality: 1;
Locality GAF[B]: 0.994;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 11;
Counties in locality: Coweta;
Number of counties in locality: 1;
Locality GAF[B]: 0.986;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 12;
Counties in locality: Henry;
Number of counties in locality: 1;
Locality GAF[B]: 0.985;
Average payment difference in percentage points[C]: 0.00.
State: Georgia;
Locality number[A]: 13;
Counties in locality: Rest of Georgia;
Number of counties in locality: 147;
Locality GAF[B]: 0.937;
Average payment difference in percentage points[C]: 2.14.
State: Hawaii;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality GAF[B]: 1.084;
Average payment difference in percentage points[C]: 1.40.
State: Idaho;
Locality number[A]: 1;
Counties in locality: Ada;
Number of counties in locality: 1;
Locality GAF[B]: 0.949;
Average payment difference in percentage points[C]: 0.00.
State: Idaho;
Locality number[A]: 2;
Counties in locality: Rest of Idaho;
Number of counties in locality: 43;
Locality GAF[B]: 0.902;
Average payment difference in percentage points[C]: 1.27.
State: Illinois;
Locality number[A]: 1;
Counties in locality: Cook;
Number of counties in locality: 1;
Locality GAF[B]: 1.095;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 2;
Counties in locality: DuPage;
Number of counties in locality: 1;
Locality GAF[B]: 1.087;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 3;
Counties in locality: Lake;
Number of counties in locality: 1;
Locality GAF[B]: 1.085;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 4;
Counties in locality: Kane;
Number of counties in locality: 1;
Locality GAF[B]: 1.065;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 5;
Counties in locality: Will;
Number of counties in locality: 1;
Locality GAF[B]: 1.049;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 6;
Counties in locality: McHenry;
Number of counties in locality: 1;
Locality GAF[B]: 1.037;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 7;
Counties in locality: Grundy;
Number of counties in locality: 1;
Locality GAF[B]: 1.022;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 8;
Counties in locality: Kendall;
Number of counties in locality: 1;
Locality GAF[B]: 0.999;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 9;
Counties in locality: St. Clair;
Number of counties in locality: 1;
Locality GAF[B]: 0.997;
Average payment difference in percentage points[C]: 0.00.
State: Illinois;
Locality number[A]: 10;
Counties in locality: Rest of Illinois;
Number of counties in locality: 93;
Locality GAF[B]: 0.945;
Average payment difference in percentage points[C]: 2.51.
State: Indiana;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 92;
Locality GAF[B]: 0.939;
Average payment difference in percentage points[C]: 2.47.
State: Iowa;
Locality number[A]: 1;
Counties in locality: Polk;
Number of counties in locality: 1;
Locality GAF[B]: 0.959;
Average payment difference in percentage points[C]: 0.00.
State: Iowa;
Locality number[A]: 2;
Counties in locality: Rest of Iowa;
Number of counties in locality: 98;
Locality GAF[B]: 0.904;
Average payment difference in percentage points[C]: 2.33.
State: Kansas;
Locality number[A]: 1;
Counties in locality: Linn;
Number of counties in locality: 1;
Locality GAF[B]: 1.021;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 2;
Counties in locality: Johnson;
Number of counties in locality: 1;
Locality GAF[B]: 0.975;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 3;
Counties in locality: Wyandotte;
Number of counties in locality: 1;
Locality GAF[B]: 0.972;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 4;
Counties in locality: Leavenworth;
Number of counties in locality: 1;
Locality GAF[B]: 0.970;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 5;
Counties in locality: Miami;
Number of counties in locality: 1;
Locality GAF[B]: 0.961;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 6;
Counties in locality: Sedgwick;
Number of counties in locality: 1;
Locality GAF[B]: 0.944;
Average payment difference in percentage points[C]: 0.00.
State: Kansas;
Locality number[A]: 7;
Counties in locality: Rest of Kansas;
Number of counties in locality: 99;
Locality GAF[B]: 0.898;
Average payment difference in percentage points[C]: 2.00.
State: Kentucky;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 120;
Locality GAF[B]: 0.923;
Average payment difference in percentage points[C]: 2.72.
State: Louisiana;
Locality number[A]: 1;
Counties in locality: St. Charles;
Number of counties in locality: 1;
Locality GAF[B]: 1.058;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 2;
Counties in locality: Orleans;
Number of counties in locality: 1;
Locality GAF[B]: 1.031;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 3;
Counties in locality: Plaquemines;
Number of counties in locality: 1;
Locality GAF[B]: 1.026;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 4;
Counties in locality: West Feliciana;
Number of counties in locality: 1;
Locality GAF[B]: 1.025;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 5;
Counties in locality: Jefferson;
Number of counties in locality: 1;
Locality GAF[B]: 1.012;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 6;
Counties in locality: St. John the Baptist;
Number of counties in locality: 1;
Locality GAF[B]: 1.010;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 7;
Counties in locality: St. Tammany;
Number of counties in locality: 1;
Locality GAF[B]: 1.007;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 8;
Counties in locality: St. Bernard;
Number of counties in locality: 1;
Locality GAF[B]: 1.004;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 9;
Counties in locality: Ascension;
Number of counties in locality: 1;
Locality GAF[B]: 0.991;
Average payment difference in percentage points[C]: 0.00.
State: Louisiana;
Locality number[A]: 10;
Counties in locality: Rest of Louisiana;
Number of counties in locality: 55;
Locality GAF[B]: 0.930;
Average payment difference in percentage points[C]: 2.09.
State: Maine;
Locality number[A]: 1;
Counties in locality: Cumberland;
Number of counties in locality: 1;
Locality GAF[B]: 1.002;
Average payment difference in percentage points[C]: 0.00.
State: Maine;
Locality number[A]: 2;
Counties in locality: York;
Number of counties in locality: 1;
Locality GAF[B]: 0.968;
Average payment difference in percentage points[C]: 0.00.
State: Maine;
Locality number[A]: 3;
Counties in locality: Rest of Maine;
Number of counties in locality: 14;
Locality GAF[B]: 0.919;
Average payment difference in percentage points[C]: 0.66.
State: Maryland;
Locality number[A]: 1;
Counties in locality: Montgomery;
Number of counties in locality: 1;
Locality GAF[B]: 1.122;
Average payment difference in percentage points[C]: 0.00.
State: Maryland;
Locality number[A]: 2;
Counties in locality: Prince George's;
Number of counties in locality: 1;
Locality GAF[B]: 1.113;
Average payment difference in percentage points[C]: 0.00.
State: Maryland;
Locality number[A]: 3;
Counties in locality: Calvert;
Number of counties in locality: 1;
Locality GAF[B]: 1.088;
Average payment difference in percentage points[C]: 0.00.
State: Maryland;
Locality number[A]: 4;
Counties in locality: Rest of Maryland;
Number of counties in locality: 21;
Locality GAF[B]: 1.029;
Average payment difference in percentage points[C]: 3.47.
State: Massachusetts;
Locality number[A]: 1;
Counties in locality: Suffolk;
Number of counties in locality: 1;
Locality GAF[B]: 1.150;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 2;
Counties in locality: Middlesex;
Number of counties in locality: 1;
Locality GAF[B]: 1.130;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 3;
Counties in locality: Norfolk;
Number of counties in locality: 1;
Locality GAF[B]: 1.128;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 4;
Counties in locality: Essex;
Number of counties in locality: 1;
Locality GAF[B]: 1.105;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 5;
Counties in locality: Plymouth;
Number of counties in locality: 1;
Locality GAF[B]: 1.092;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 6;
Counties in locality: Dukes, Nantucket;
Number of counties in locality: 2;
Locality GAF[B]: 1.088;
Average payment difference in percentage points[C]: 0.00.
State: Massachusetts;
Locality number[A]: 7;
Counties in locality: Rest of Massachusetts;
Number of counties in locality: 7;
Locality GAF[B]: 1.022;
Average payment difference in percentage points[C]: 1.77.
State: Michigan;
Locality number[A]: 1;
Counties in locality: Wayne;
Number of counties in locality: 1;
Locality GAF[B]: 1.112;
Average payment difference in percentage points[C]: 0.00.
State: Michigan;
Locality number[A]: 2;
Counties in locality: Washtenaw;
Number of counties in locality: 1;
Locality GAF[B]: 1.110;
Average payment difference in percentage points[C]: 0.00.
State: Michigan;
Locality number[A]: 3;
Counties in locality: Oakland;
Number of counties in locality: 1;
Locality GAF[B]: 1.109;
Average payment difference in percentage points[C]: 0.00.
State: Michigan;
Locality number[A]: 4;
Counties in locality: Macomb;
Number of counties in locality: 1;
Locality GAF[B]: 1.103;
Average payment difference in percentage points[C]: 0.00.
State: Michigan;
Locality number[A]: 5;
Counties in locality: Livingston;
Number of counties in locality: 1;
Locality GAF[B]: 1.041;
Average payment difference in percentage points[C]: 0.00.
State: Michigan;
Locality number[A]: 6;
Counties in locality: Rest of Michigan;
Number of counties in locality: 78;
Locality GAF[B]: 0.990;
Average payment difference in percentage points[C]: 1.90.
State: Minnesota;
Locality number[A]: 1;
Counties in locality: Ramsey;
Number of counties in locality: 1;
Locality GAF[B]: 1.024;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 2;
Counties in locality: Hennepin;
Number of counties in locality: 1;
Locality GAF[B]: 1.021;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 3;
Counties in locality: Anoka;
Number of counties in locality: 1;
Locality GAF[B]: 1.019;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 4;
Counties in locality: Carver;
Number of counties in locality: 1;
Locality GAF[B]: 1.008;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 5;
Counties in locality: Scott;
Number of counties in locality: 1;
Locality GAF[B]: 1.007;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 6;
Counties in locality: Dakota;
Number of counties in locality: 1;
Locality GAF[B]: 1.006;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 7;
Counties in locality: Washington;
Number of counties in locality: 1;
Locality GAF[B]: 1.002;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 8;
Counties in locality: Olmsted;
Number of counties in locality: 1;
Locality GAF[B]: 0.987;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 9;
Counties in locality: Wright;
Number of counties in locality: 1;
Locality GAF[B]: 0.972;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 10;
Counties in locality: Chisago;
Number of counties in locality: 1;
Locality GAF[B]: 0.966;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 11;
Counties in locality: Sherburne;
Number of counties in locality: 1;
Locality GAF[B]: 0.964;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 12;
Counties in locality: Isanti;
Number of counties in locality: 1;
Locality GAF[B]: 0.960;
Average payment difference in percentage points[C]: 0.00.
State: Minnesota;
Locality number[A]: 13;
Counties in locality: Rest of Minnesota;
Number of counties in locality: 75;
Locality GAF[B]: 0.906;
Average payment difference in percentage points[C]: 1.31.
State: Mississippi;
Locality number[A]: 1;
Counties in locality: Hinds;
Number of counties in locality: 1;
Locality GAF[B]: 0.953;
Average payment difference in percentage points[C]: 0.00.
State: Mississippi;
Locality number[A]: 2;
Counties in locality: DeSoto;
Number of counties in locality: 1;
Locality GAF[B]: 0.944;
Average payment difference in percentage points[C]: 0.00.
State: Mississippi;
Locality number[A]: 3;
Counties in locality: Hancock;
Number of counties in locality: 1;
Locality GAF[B]: 0.943;
Average payment difference in percentage points[C]: 0.00.
State: Mississippi;
Locality number[A]: 4;
Counties in locality: Madison;
Number of counties in locality: 1;
Locality GAF[B]: 0.941;
Average payment difference in percentage points[C]: 0.00.
State: Mississippi;
Locality number[A]: 5;
Counties in locality: Rest of Mississippi;
Number of counties in locality: 78;
Locality GAF[B]: 0.895;
Average payment difference in percentage points[C]: 1.46.
State: Missouri;
Locality number[A]: 1;
Counties in locality: Jackson;
Number of counties in locality: 1;
Locality GAF[B]: 0.991; Average payment difference in percentage
points[C]: 0.00.
State: Missouri;
Locality number[A]: 2;
Counties in locality: St. Louis City;
Number of counties in locality: 1;
Locality GAF[B]: 0.981;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 3;
Counties in locality: St. Louis;
Number of counties in locality: 1;
Locality GAF[B]: 0.975;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 4;
Counties in locality: Clay;
Number of counties in locality: 1;
Locality GAF[B]: 0.968;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 5;
Counties in locality: Platte;
Number of counties in locality: 1;
Locality GAF[B]: 0.967;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 6;
Counties in locality: Cass;
Number of counties in locality: 1;
Locality GAF[B]: 0.959;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 7;
Counties in locality: St. Charles;
Number of counties in locality: 1;
Locality GAF[B]: 0.953;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 8;
Counties in locality: Lafayette;
Number of counties in locality: 1;
Locality GAF[B]: 0.948;
Average payment difference in percentage points[C]: 0.00.
State: Missouri;
Locality number[A]: 9;
Counties in locality: Rest of Missouri;
Number of counties in locality: 107;
Locality GAF[B]: 0.895;
Average payment difference in percentage points[C]: 2.12.
State: Montana;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 56;
Locality GAF[B]: 0.909;
Average payment difference in percentage points[C]: 0.84.
State: Nebraska;
Locality number[A]: 1;
Counties in locality: Douglas;
Number of counties in locality: 1;
Locality GAF[B]: 0.947;
Average payment difference in percentage points[C]: 0.00.
State: Nebraska;
Locality number[A]: 2;
Counties in locality: Sarpy;
Number of counties in locality: 1;
Locality GAF[B]: 0.938;
Average payment difference in percentage points[C]: 0.00.
State: Nebraska;
Locality number[A]: 3;
Counties in locality: Rest of Nebraska;
Number of counties in locality: 91;
Locality GAF[B]: 0.893;
Average payment difference in percentage points[C]: 2.69.
State: Nevada;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 17;
Locality GAF[B]: 1.031;
Average payment difference in percentage points[C]: 0.34.
State: New Hampshire;
Locality number[A]: 1;
Counties in locality: Hillsborough;
Number of counties in locality: 1;
Locality GAF[B]: 1.047;
Average payment difference in percentage points[C]: 0.00.
State: New Hampshire;
Locality number[A]: 2;
Counties in locality: Rockingham;
Number of counties in locality: 1;
Locality GAF[B]: 1.030;
Average payment difference in percentage points[C]: 0.00.
State: New Hampshire;
Locality number[A]: 3;
Counties in locality: Rest of New Hampshire;
Number of counties in locality: 8;
Locality GAF[B]: 0.979;
Average payment difference in percentage points[C]: 0.90.
State: New Jersey;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 21;
Locality GAF[B]: 1.109;
Average payment difference in percentage points[C]: 2.35.
State: New Mexico;
Locality number[A]: 1;
Counties in locality: Santa Fe;
Number of counties in locality: 1;
Locality GAF[B]: 0.994;
Average payment difference in percentage points[C]: 0.00.
State: New Mexico;
Locality number[A]: 2;
Counties in locality: Rest of New Mexico;
Number of counties in locality: 32;
Locality GAF[B]: 0.940;
Average payment difference in percentage points[C]: 2.94.
State: New York;
Locality number[A]: 1;
Counties in locality: Westchester;
Number of counties in locality: 1;
Locality GAF[B]: 1.218;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 2;
Counties in locality: Nassau;
Number of counties in locality: 1;
Locality GAF[B]: 1.204;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 3;
Counties in locality: New York;
Number of counties in locality: 1;
Locality GAF[B]: 1.183;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 4;
Counties in locality: Suffolk;
Number of counties in locality: 1;
Locality GAF[B]: 1.182;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 5;
Counties in locality: Richmond;
Number of counties in locality: 1;
Locality GAF[B]: 1.156;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 6;
Counties in locality: Bronx;
Number of counties in locality: 1;
Locality GAF[B]: 1.156;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 7;
Counties in locality: Kings;
Number of counties in locality: 1;
Locality GAF[B]: 1.155;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 8;
Counties in locality: Rockland;
Number of counties in locality: 1;
Locality GAF[B]: 1.152;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 9;
Counties in locality: Queens;
Number of counties in locality: 1;
Locality GAF[B]: 1.148;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 10;
Counties in locality: Putnam;
Number of counties in locality: 1;
Locality GAF[B]: 1.105;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 11;
Counties in locality: Dutchess;
Number of counties in locality: 1;
Locality GAF[B]: 1.079;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 12;
Counties in locality: Orange;
Number of counties in locality: 1;
Locality GAF[B]: 1.076;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 13;
Counties in locality: Ulster;
Number of counties in locality: 1;
Locality GAF[B]: 1.003;
Average payment difference in percentage points[C]: 0.00.
State: New York;
Locality number[A]: 14;
Counties in locality: Rest of New York;
Number of counties in locality: 49;
Locality GAF[B]: 0.954;
Average payment difference in percentage points[C]: 1.83.
State: North Carolina;
Locality number[A]: 1;
Counties in locality: Durham;
Number of counties in locality: 1;
Locality GAF[B]: 1.006;
Average payment difference in percentage points[C]: 0.00.
State: North Carolina;
Locality number[A]: 2;
Counties in locality: Wake;
Number of counties in locality: 1;
Locality GAF[B]: 1.000;
Average payment difference in percentage points[C]: 0.00.
State: North Carolina;
Locality number[A]: 3;
Counties in locality: Rest of North Carolina;
Number of counties in locality: 98;
Locality GAF[B]: 0.935;
Average payment difference in percentage points[C]: 2.43.
State: North Dakota;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 53;
Locality GAF[B]: 0.894;
Average payment difference in percentage points[C]: 1.70.
State: Ohio;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 88;
Locality GAF[B]: 0.968;
Average payment difference in percentage points[C]: 2.80.
State: Oklahoma;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 77;
Locality GAF[B]: 0.897;
Average payment difference in percentage points[C]: 2.51.
State: Oregon;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 36;
Locality GAF[B]: 0.954;
Average payment difference in percentage points[C]: 2.83.
State: Pennsylvania;
Locality number[A]: 1;
Counties in locality: Philadelphia;
Number of counties in locality: 1;
Locality GAF[B]: 1.073;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 2;
Counties in locality: Montgomery;
Number of counties in locality: 1;
Locality GAF[B]: 1.071;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 3;
Counties in locality: Delaware;
Number of counties in locality: 1;
Locality GAF[B]: 1.070;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 4;
Counties in locality: Chester;
Number of counties in locality: 1;
Locality GAF[B]: 1.069;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 5;
Counties in locality: Bucks;
Number of counties in locality: 1;
Locality GAF[B]: 1.050;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 6;
Counties in locality: Lehigh;
Number of counties in locality: 1;
Locality GAF[B]: 1.010;
Average payment difference in percentage points[C]: 0.00.
State: Pennsylvania;
Locality number[A]: 7;
Counties in locality: Rest of Pennsylvania;
Number of counties in locality: 61;
Locality GAF[B]: 0.955;
Average payment difference in percentage points[C]: 2.39.
State: Rhode Island;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality GAF[B]: 1.053;
Average payment difference in percentage points[C]: 0.38.
State: South Carolina;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 46;
Locality GAF[B]: 0.925;
Average payment difference in percentage points[C]: 1.53.
State: South Dakota;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 66;
Locality GAF[B]: 0.889;
Average payment difference in percentage points[C]: 2.82.
State: Tennessee;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 95;
Locality GAF[B]: 0.930;
Average payment difference in percentage points[C]: 2.71.
State: Texas;
Locality number[A]: 1;
Counties in locality: Harris;
Number of counties in locality: 1;
Locality GAF[B]: 1.026;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 2;
Counties in locality: Collin;
Number of counties in locality: 1;
Locality GAF[B]: 1.015;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 3;
Counties in locality: Dallas;
Number of counties in locality: 1;
Locality GAF[B]: 1.014;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 4;
Counties in locality: Chambers;
Number of counties in locality: 1;
Locality GAF[B]: 1.009;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 5;
Counties in locality: Travis;
Number of counties in locality: 1;
Locality GAF[B]: 1.005;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 6;
Counties in locality: Rockwall;
Number of counties in locality: 1;
Locality GAF[B]: 1.004;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 7;
Counties in locality: Fort Bend;
Number of counties in locality: 1;
Locality GAF[B]: 1.004;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 8;
Counties in locality: Galveston;
Number of counties in locality: 1;
Locality GAF[B]: 1.000;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 9;
Counties in locality: Tarrant;
Number of counties in locality: 1;
Locality GAF[B]: 0.993;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 10;
Counties in locality: Brazoria;
Number of counties in locality: 1;
Locality GAF[B]: 0.992;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 11;
Counties in locality: Williamson;
Number of counties in locality: 1;
Locality GAF[B]: 0.991;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 12;
Counties in locality: Denton;
Number of counties in locality: 1;
Locality GAF[B]: 0.985;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 13;
Counties in locality: Montgomery;
Number of counties in locality: 1;
Locality GAF[B]: 0.983;
Average payment difference in percentage points[C]: 0.00.
State: Texas;
Locality number[A]: 14;
Counties in locality: Rest of Texas;
Number of counties in locality: 241;
Locality GAF[B]: 0.935;
Average payment difference in percentage points[C]: 2.01.
State: Utah;
Locality number[A]: 1;
Counties in locality: Summit;
Number of counties in locality: 1;
Locality GAF[B]: 0.985;
Average payment difference in percentage points[C]: 0.00.
State: Utah;
Locality number[A]: 2;
Counties in locality: Salt Lake;
Number of counties in locality: 1;
Locality GAF[B]: 0.965;
Average payment difference in percentage points[C]: 0.00.
State: Utah;
Locality number[A]: 3;
Counties in locality: Rest of Utah;
Number of counties in locality: 27;
Locality GAF[B]: 0.917;
Average payment difference in percentage points[C]: 1.67.
State: Vermont;
Locality number[A]: 1;
Counties in locality: Chittenden;
Number of counties in locality: 1;
Locality GAF[B]: 0.997;
Average payment difference in percentage points[C]: 0.00.
State: Vermont;
Locality number[A]: 2;
Counties in locality: Franklin;
Number of counties in locality: 1;
Locality GAF[B]: 0.984;
Average payment difference in percentage points[C]: 0.00.
State: Vermont;
Locality number[A]: 3;
Counties in locality: Addison, Bennington, Caledonia, Essex, LaMoille,
Orleans, Orange, Rutland, Washington, Windham, Windsor;
Number of counties in locality: 11;
Locality GAF[B]: 0.932;
Average payment difference in percentage points[C]: 0.00.
State: Vermont;
Locality number[A]: 4;
Counties in locality: Rest of Vermont;
Number of counties in locality: 1;
Locality GAF[B]: 0.826;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 1;
Counties in locality: Arlington;
Number of counties in locality: 1;
Locality GAF[B]: 1.142;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 2;
Counties in locality: Fairfax;
Number of counties in locality: 1;
Locality GAF[B]: 1.130;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 3;
Counties in locality: Alexandria City;
Number of counties in locality: 1;
Locality GAF[B]: 1.126;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 4;
Counties in locality: Fairfax City;
Number of counties in locality: 1;
Locality GAF[B]: 1.121;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 5;
Counties in locality: Falls Church City;
Number of counties in locality: 1;
Locality GAF[B]: 1.113;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 6;
Counties in locality: Manassas City;
Number of counties in locality: 1;
Locality GAF[B]: 1.085;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 7;
Counties in locality: Prince William;
Number of counties in locality: 1;
Locality GAF[B]: 1.082;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 8;
Counties in locality: Loudoun;
Number of counties in locality: 1;
Locality GAF[B]: 1.079;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 9;
Counties in locality: Fauquier;
Number of counties in locality: 1;
Locality GAF[B]: 1.052;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 10;
Counties in locality: Fredericksburg City;
Number of counties in locality: 1;
Locality GAF[B]: 1.046;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 11;
Counties in locality: Clarke;
Number of counties in locality: 1;
Locality GAF[B]: 1.038;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 12;
Counties in locality: Stafford;
Number of counties in locality: 1;
Locality GAF[B]: 1.037;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 13;
Counties in locality: Spotsylvania;
Number of counties in locality: 1;
Locality GAF[B]: 1.012;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 14;
Counties in locality: New Kent;
Number of counties in locality: 1;
Locality GAF[B]: 0.997;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 15;
Counties in locality: Richmond City;
Number of counties in locality: 1;
Locality GAF[B]: 0.995;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 16;
Counties in locality: Henrico;
Number of counties in locality: 1;
Locality GAF[B]: 0.992;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 17;
Counties in locality: Hopewell City;
Number of counties in locality: 1;
Locality GAF[B]: 0.992;
Average payment difference in percentage points[C]: 0.00.
State: Virginia;
Locality number[A]: 18;
Counties in locality: Rest of Virginia;
Number of counties in locality: 118;
Locality GAF[B]: 0.941;
Average payment difference in percentage points[C]: 2.98.
State: Washington;
Locality number[A]: 1;
Counties in locality: King;
Number of counties in locality: 1;
Locality GAF[B]: 1.045;
Average payment difference in percentage points[C]: 0.00.
State: Washington;
Locality number[A]: 2;
Counties in locality: Rest of Washington;
Number of counties in locality: 38;
Locality GAF[B]: 0.982;
Average payment difference in percentage points[C]: 2.75.
State: West Virginia;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 55;
Locality GAF[B]: 0.937;
Average payment difference in percentage points[C]: 1.95.
State: Wisconsin;
Locality number[A]: 1; C
counties in locality: Statewide;
Number of counties in locality: 72;
Locality GAF[B]: 0.959;
Average payment difference in percentage points[C]: 2.91.
State: Wyoming;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 23;
Locality GAF[B]: 0.912;
Average payment difference in percentage points[C]: 1.23.
State: Nation;
Locality number[A]: 219;
Counties in locality: [Empty];
Number of counties in locality: [Empty];
Locality GAF[B]: [Empty];
Average payment difference in percentage points[C]: 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.
[A] The 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.
[B] We 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.
[C] Payment 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.
[End of table]
Table 4: Physician Payment Localities Created Using the County-Based
GAF Ranges Alternative Approach, by State:
State: Alabama;
Locality number[A]: 1;
Counties in locality: Autauga, Jefferson, Limestone, Madison, Shelby;
Number of counties in locality: 5;
Locality GAF[B]: 0.948;
Average payment difference in percentage points[C]: 0.33.
Locality number[A]: : 2;
Counties in locality: : Rest of Alabama;
Number of counties in locality: : 62;
Locality GAF[B]: : 0.908;
Average payment difference in percentage points[C]: : 1.71.
State: Alaska;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 27;
Locality GAF[B]: 1.082;
Average payment difference in percentage points[C]: 1.31.
State: Arizona;
Locality number[A]: 1;
Counties in locality: Coconino, Maricopa;
Number of counties in locality: 2;
Locality GAF[B]: 1.003;
Average payment difference in percentage points[C]: 0.01.
Locality number[A]: : 2;
Counties in locality: : Rest of Arizona;
Number of counties in locality: : 13;
Locality GAF[B]: : 0.960;
Average payment difference in percentage points[C]: : 1.24.
State: Arkansas;
Locality number[A]: 1;
Counties in locality: Crittenden, Jefferson, Miller, Pulaski;
Number of counties in locality: 4;
Locality GAF[B]: 0.930;
Average payment difference in percentage points[C]: 0.41.
Locality number[A]: : 2;
Counties in locality: : Rest of Arkansas;
Number of counties in locality: : 71;
Locality GAF[B]: : 0.876;
Average payment difference in percentage points[C]: : 1.32.
State: California;
Locality number[A]: 1;
Counties in locality: Marin, San Francisco, San Mateo;
Number of counties in locality: 3;
Locality GAF[B]: 1.211;
Average payment difference in percentage points[C]: 0.67.
Locality number[A]: State: 2;
Counties in locality: State: Alameda, Contra Costa, Orange, Santa
Clara, Santa Cruz, Ventura;
Number of counties in locality: State: 6;
Locality GAF[B]: State: 1.147;
Average payment difference in percentage points[C]: State: 0.89.
Locality number[A]: State: 3;
Counties in locality: State: Los Angeles, Monterey, Napa, Sacramento,
San Benito, Solano, Sonoma;
Number of counties in locality: State: 7;
Locality GAF[B]: State: 1.109;
Average payment difference in percentage points[C]: State: 0.85.
Locality number[A]: State: 4;
Counties in locality: State: El Dorado, Placer, Riverside, San
Bernardino, San Diego, San Joaquin, San Luis Obispo, Santa Barbara,
Yolo;
Number of counties in locality: State: 9;
Locality GAF[B]: State: 1.040;
Average payment difference in percentage points[C]: State: 1.35.
Locality number[A]: : 5;
Counties in locality: : Rest of California;
Number of counties in locality: : 33;
Locality GAF[B]: : 0.973;
Average payment difference in percentage points[C]: : 1.19.
State: Colorado;
Locality number[A]: 1;
Counties in locality: Adams, Arapahoe, Boulder, Broomfield, Denver,
Douglas, Jefferson;
Number of counties in locality: 7;
Locality GAF[B]: 1.027;
Average payment difference in percentage points[C]: 0.73.
Locality number[A]: : 2;
Counties in locality: : Rest of Colorado;
Number of counties in locality: : 57;
Locality GAF[B]: : 0.957;
Average payment difference in percentage points[C]: : 1.72.
State: Connecticut;
Locality number[A]: 1;
Counties in locality: Fairfield;
Number of counties in locality: 1;
Locality GAF[B]: 1.149;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: State: 2;
Counties in locality: State: Hartford, Middlesex;
Number of counties in locality: State: 2;
Locality GAF[B]: State: 1.095;
Average payment difference in percentage points[C]: State: 0.03.
Locality number[A]: : 3;
Counties in locality: : Rest of Connecticut;
Number of counties in locality: : 5;
Locality GAF[B]: : 1.073;
Average payment difference in percentage points[C]: : 1.32.
State: Delaware;
Locality number[A]: 1;
Counties in locality: New Castle;
Number of counties in locality: 1;
Locality GAF[B]: 1.054;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: of Columbia: 2;
Counties in locality: of Columbia: Rest of Delaware;
Number of counties in locality: of Columbia: 2;
Locality GAF[B]: of Columbia: 0.962;
Average payment difference in percentage points[C]: of Columbia:
0.63.
State: District of Columbia;
Locality number[A]: 1;
Counties in locality: District of Columbia;
Number of counties in locality: 1;
Locality GAF[B]: 1.162;
Average payment difference in percentage points[C]: 0.00.
State: Florida;
Locality number[A]: 1;
Counties in locality: Broward, Miami-Dade, Palm Beach;
Number of counties in locality: 3;
Locality GAF[B]: 1.061;
Average payment difference in percentage points[C]: 0.85.
Locality number[A]: State: 2;
Counties in locality: State: Collier, Duval, Hillsborough, Jefferson,
Lee, Manatee, Martin, Nassau, Orange, Pinellas, St. Johns, Sarasota,
Seminole;
Number of counties in locality: State: 13;
Locality GAF[B]: State: 0.995;
Average payment difference in percentage points[C]: State: 0.69.
Locality number[A]: : 3;
Counties in locality: : Rest of Florida;
Number of counties in locality: : 51;
Locality GAF[B]: : 0.954;
Average payment difference in percentage points[C]: : 1.61.
State: Georgia;
Locality number[A]: 1;
Counties in locality: Cobb, DeKalb, Fulton, Gwinnett;
Number of counties in locality: 4; Locality GAF[B]: 1.020;
Average payment difference in percentage points[C]: 0.65.
Locality number[A]: State: 2;
Counties in locality: State: Barrow, Bartow, Burke, Carroll, Chatham,
Cherokee, Clayton, Coweta, Douglas, Fayette, Forsyth, Hall, Henry,
Houston, Newton, Paulding, Pickens, Rockdale, Spalding, Walton;
Number of counties in locality: State: 20;
Locality GAF[B]: State: 0.978;
Average payment difference in percentage points[C]: State: 1.17.
Locality number[A]: : 3;
Counties in locality: : Rest of Georgia;
Number of counties in locality: : 135;
Locality GAF[B]: : 0.927;
Average payment difference in percentage points[C]: : 1.66.
State: Hawaii;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality GAF[B]: 1.084;
Average payment difference in percentage points[C]: 1.40.
State: Idaho;
Locality number[A]: 1;
Counties in locality: Ada;
Number of counties in locality: 1;
Locality GAF[B]: 0.949;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: : 2;
Counties in locality: : Rest of Idaho;
Number of counties in locality: : 43;
Locality GAF[B]: : 0.902;
Average payment difference in percentage points[C]: : 1.27.
State: Illinois;
Locality number[A]: 1;
Counties in locality: Cook, DuPage, Lake;
Number of counties in locality: 3;
Locality GAF[B]: 1.093;
Average payment difference in percentage points[C]: 0.28.
Locality number[A]: State: 2;
Counties in locality: State: Grundy, Kane, McHenry, Will;
Number of counties in locality: State: 4;
Locality GAF[B]: State: 1.051;
Average payment difference in percentage points[C]: State: 0.90.
Locality number[A]: State: 3;
Counties in locality: State: DeKalb, Kankakee, Kendall, Madison,
McLean, Peoria, Rock Island, St. Clair, Sangamon, Winnebago;
Number of counties in locality: State: 10;
Locality GAF[B]: State: 0.972;
Average payment difference in percentage points[C]: State: 0.95.
Locality number[A]: : 4;
Counties in locality: : Rest of Illinois;
Number of counties in locality: : 85;
Locality GAF[B]: : 0.922;
Average payment difference in percentage points[C]: : 1.43.
State: Indiana;
Locality number[A]: 1;
Counties in locality: Hamilton, Hancock, Hendricks, Lake, Marion,
Porter;
Number of counties in locality: 6;
Locality GAF[B]: 0.968;
Average payment difference in percentage points[C]: 0.67.
Locality number[A]: : 2;
Counties in locality: : Rest of Indiana;
Number of counties in locality: : 86;
Locality GAF[B]: : 0.921;
Average payment difference in percentage points[C]: : 1.72.
State: Iowa;
Locality number[A]: 1;
Counties in locality: Johnson, Linn, Polk, Pottawattamie;
Number of counties in locality: 4;
Locality GAF[B]: 0.950;
Average payment difference in percentage points[C]: 0.95.
Locality number[A]: : 2;
Counties in locality: : Rest of Iowa;
Number of counties in locality: : 95;
Locality GAF[B]: : 0.894;
Average payment difference in percentage points[C]: : 1.51.
State: Kansas;
Locality number[A]: 1;
Counties in locality: Linn;
Number of counties in locality: 1;
Locality GAF[B]: 1.021;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: State: 2;
Counties in locality: State: Butler, Johnson, Leavenworth, Miami,
Sedgwick, Wyandotte;
Number of counties in locality: State: 6;
Locality GAF[B]: State: 0.958;
Average payment difference in percentage points[C]: State: 1.58.
Locality number[A]: : 3;
Counties in locality: : Rest of Kansas;
Number of counties in locality: : 98;
Locality GAF[B]: : 0.897;
Average payment difference in percentage points[C]: : 1.93.
State: Kentucky;
Locality number[A]: 1;
Counties in locality: Boone, Campbell, Fayette, Jefferson, Jessamine,
Kenton, Meade;
Number of counties in locality: 7;
Locality GAF[B]: 0.950;
Average payment difference in percentage points[C]: 0.22.
Locality number[A]: : 2;
Counties in locality: : Rest of Kentucky;
Number of counties in locality: : 113;
Locality GAF[B]: : 0.901;
Average payment difference in percentage points[C]: : 1.32.
State: Louisiana;
Locality number[A]: 1;
Counties in locality: St. Charles;
Number of counties in locality: 1;
Locality GAF[B]: 1.058;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: State: 2;
Counties in locality: State: Jefferson, Orleans, Plaquemines, St.
Bernard, St. John the Baptist, St. Tammany, West Feliciana;
Number of counties in locality: State: 7;
Locality GAF[B]: State: 1.015;
Average payment difference in percentage points[C]: State: 0.81.
Locality number[A]: State: 3;
Counties in locality: State: Ascension, Caddo, East Feliciana, East
Baton Rouge, Iberville, Livingston, West Baton Rouge;
Number of counties in locality: State: 7;
Locality GAF[B]: State: 0.956;
Average payment difference in percentage points[C]: State: 1.21.
Locality number[A]: : 4;
Counties in locality: : Rest of Louisiana;
Number of counties in locality: : 49;
Locality GAF[B]: : 0.916;
Average payment difference in percentage points[C]: : 1.42.
State: Maine;
Locality number[A]: 1;
Counties in locality: Cumberland, , York;
Number of counties in locality: 3;
Locality GAF[B]: 0.993;
Average payment difference in percentage points[C]: 1.26.
Locality number[A]: : 2;
Counties in locality: : Rest of Maine;
Number of counties in locality: : 13;
Locality GAF[B]: : 0.918;
Average payment difference in percentage points[C]: : 0.61.
State: Maryland;
Locality number[A]: 1;
Counties in locality: Calvert, Montgomery, Prince George's;
Number of counties in locality: 3;
Locality GAF[B]: 1.118;
Average payment difference in percentage points[C]: 0.49.
Locality number[A]: State: 2;
Counties in locality: State: Anne Arundel, Baltimore, Baltimore City,
Carroll, Cecil, Charles, Frederick, Harford, Howard;
Number of counties in locality: State: 9;
Locality GAF[B]: State: 1.050;
Average payment difference in percentage points[C]: State: 0.67.
Locality number[A]: : 3;
Counties in locality: : Rest of Maryland;
Number of counties in locality: : 12;
Locality GAF[B]: : 0.947;
Average payment difference in percentage points[C]: : 1.80.
State: Massachusetts;
Locality number[A]: 1;
Counties in locality: Suffolk;
Number of counties in locality: 1;
Locality GAF[B]: 1.150;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: : 2;
Counties in locality: : Rest of Massachusetts;
Number of counties in locality: : 13;
Locality GAF[B]: : 1.076;
Average payment difference in percentage points[C]: : 4.45.
State: Michigan;
Locality number[A]: 1;
Counties in locality: Macomb, Oakland, Washtenaw, Wayne;
Number of counties in locality: 4;
Locality GAF[B]: 1.109;
Average payment difference in percentage points[C]: 0.22.
Locality number[A]: State: 2;
Counties in locality: State: Genesee, Ingham, Livingston, Monroe;
Number of counties in locality: State: 4;
Locality GAF[B]: State: 1.014;
Average payment difference in percentage points[C]: State: 0.35.
Locality number[A]: : 3;
Counties in locality: : Rest of Michigan;
Number of counties in locality: : 75;
Locality GAF[B]: : 0.984;
Average payment difference in percentage points[C]: : 1.81.
State: Minnesota;
Locality number[A]: 1;
Counties in locality: Anoka, Carver, Hennepin, Ramsey;
Number of counties in locality: 4;
Locality GAF[B]: 1.021;
Average payment difference in percentage points[C]: 0.12.
Locality number[A]: State: 2;
Counties in locality: State: Chisago, Dakota, Isanti, Olmsted, Scott,
Sherburne, Washington, Wright;
Number of counties in locality: State: 8;
Locality GAF[B]: State: 0.989;
Average payment difference in percentage points[C]: State: 0.39.
Locality number[A]: : 3;
Counties in locality: : Rest of Minnesota;
Number of counties in locality: : 75;
Locality GAF[B]: : 0.906;
Average payment difference in percentage points[C]: : 1.31.
State: Mississippi;
Locality number[A]: 1;
Counties in locality: DeSoto, Hancock, Hinds, Madison, Rankin;
Number of counties in locality: 5;
Locality GAF[B]: 0.949;
Average payment difference in percentage points[C]: 0.59.
Locality number[A]: : 2;
Counties in locality: : Rest of Mississippi;
Number of counties in locality: : 77;
Locality GAF[B]: : 0.893;
Average payment difference in percentage points[C]: : 1.27.
State: Missouri;
Locality number[A]: 1;
Counties in locality: Clay, Jackson, St. Louis, St. Louis City;
Number of counties in locality: 4;
Locality GAF[B]: 0.980;
Average payment difference in percentage points[C]: 0.67.
Locality number[A]: State: 2;
Counties in locality: State: Boone, Cass, Clinton, Cole, Franklin,
Jefferson, Lafayette, Lincoln, Moniteau, Platte, Ray, St. Charles;
Number of counties in locality: State: 12;
Locality GAF[B]: State: 0.934;
Average payment difference in percentage points[C]: State: 1.29.
Locality number[A]: : 3;
Counties in locality: : Rest of Missouri;
Number of counties in locality: : 99;
Locality GAF[B]: : 0.886;
Average payment difference in percentage points[C]: : 1.38.
State: Montana;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 56;
Locality GAF[B]: 0.909;
Average payment difference in percentage points[C]: 0.84.
State: Nebraska;
Locality number[A]: 1;
Counties in locality: Cass, Douglas, Lancaster, Sarpy, Washington;
Number of counties in locality: 5;
Locality GAF[B]: 0.936;
Average payment difference in percentage points[C]: 1.27.
Locality number[A]: : 2;
Counties in locality: : Rest of Nebraska;
Number of counties in locality: : 88;
Locality GAF[B]: : 0.872;
Average payment difference in percentage points[C]: : 0.04.
State: Nevada;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 17;
Locality GAF[B]: 1.031;
Average payment difference in percentage points[C]: 0.34.
State: New Hampshire;
Locality number[A]: 1;
Counties in locality: Hillsborough, Rockingham;
Number of counties in locality: 2;
Locality GAF[B]: 1.041;
Average payment difference in percentage points[C]: 0.79.
Locality number[A]: Jersey: 2;
Counties in locality: Jersey: Rest of New Hampshire;
Number of counties in locality: Jersey: 8;
Locality GAF[B]: Jersey: 0.979;
Average payment difference in percentage points[C]: Jersey: 0.90.
State: New Jersey;
Locality number[A]: 1;
Counties in locality: Bergen, Middlesex, Somerset;
Number of counties in locality: 3;
Locality GAF[B]: 1.137;
Average payment difference in percentage points[C]: 0.56.
Locality number[A]: State: 2;
Counties in locality: State: Essex, Hudson, Hunterdon, Mercer,
Monmouth, Morris, Ocean, Passaic, Salem, Union;
Number of counties in locality: State: 10;
Locality GAF[B]: State: 1.115;
Average payment difference in percentage points[C]: State: 0.86.
Locality number[A]: Mexico: 3;
Counties in locality: Mexico: Rest of New Jersey;
Number of counties in locality: Mexico: 8;
Locality GAF[B]: Mexico: 1.056;
Average payment difference in percentage points[C]: Mexico: 0.77.
State: New Mexico;
Locality number[A]: 1;
Counties in locality: Bernalillo, Sandoval, Santa Fe;
Number of counties in locality: 3;
Locality GAF[B]: 0.974;
Average payment difference in percentage points[C]: 0.59.
Locality number[A]: York: 2;
Counties in locality: York: Rest of New Mexico;
Number of counties in locality: York: 30;
Locality GAF[B]: York: 0.915;
Average payment difference in percentage points[C]: York: 0.35.
State: New York;
Locality number[A]: 1;
Counties in locality: Westchester;
Number of counties in locality: 1;
Locality GAF[B]: 1.218;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: State: 2;
Counties in locality: State: Bronx, Kings, Nassau, New York, Queens,
Richmond, Rockland, Suffolk;
Number of counties in locality: State: 8;
Locality GAF[B]: State: 1.176;
Average payment difference in percentage points[C]: State: 1.50.
Locality number[A]: State: 3;
Counties in locality: State: Dutchess, Orange, Putnam;
Number of counties in locality: State: 3;
Locality GAF[B]: State: 1.081;
Average payment difference in percentage points[C]: State: 0.48.
Locality number[A]: State: 4;
Counties in locality: State: Albany, Schenectady, Ulster;
Number of counties in locality: State: 3;
Locality GAF[B]: State: 0.994;
Average payment difference in percentage points[C]: State: 0.35.
Locality number[A]: Carolina: 5;
Counties in locality: Carolina: Rest of New York;
Number of counties in locality: Carolina: 47;
Locality GAF[B]: Carolina: 0.948;
Average payment difference in percentage points[C]: Carolina: 1.53.
State: North Carolina;
Locality number[A]: 1;
Counties in locality: Durham, Franklin, Forsyth, Guilford, Johnston,
Mecklenburg, Orange, Wake;
Number of counties in locality: 8;
Locality GAF[B]: 0.979;
Average payment difference in percentage points[C]: 1.44.
Locality number[A]: Dakota: 2;
Counties in locality: Dakota: Rest of North Carolina;
Number of counties in locality: Dakota: 92;
Locality GAF[B]: Dakota: 0.922;
Average payment difference in percentage points[C]: Dakota: 1.40.
State: North Dakota;
Locality number[A]: 1;
Counties in locality: Cass;
Number of counties in locality: 1;
Locality GAF[B]: 0.910;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: : 2;
Counties in locality: : Rest of North Dakota;
Number of counties in locality: : 52;
Locality GAF[B]: : 0.884;
Average payment difference in percentage points[C]: : 1.83.
State: Ohio;
Locality number[A]: 1;
Counties in locality: Butler, Clermont, Cuyahoga, Delaware, Franklin,
Geauga, Greene, Hamilton, Lake, Lorain, Madison, Montgomery, Ottawa,
Pickaway, Portage, Summit, Union, Warren;
Number of counties in locality: 18;
Locality GAF[B]: 0.990;
Average payment difference in percentage points[C]: 0.88.
Locality number[A]: : 2;
Counties in locality: : Rest of Ohio;
Number of counties in locality: : 70;
Locality GAF[B]: : 0.935;
Average payment difference in percentage points[C]: : 1.54.
State: Oklahoma;
Locality number[A]: 1;
Counties in locality: Oklahoma, Osage, Rogers, Tulsa, Wagoner;
Number of counties in locality: 5;
Locality GAF[B]: 0.915;
Average payment difference in percentage points[C]: 0.53.
Locality number[A]: : 2;
Counties in locality: : Rest of Oklahoma;
Number of counties in locality: : 72;
Locality GAF[B]: : 0.869;
Average payment difference in percentage points[C]: : 1.14.
State: Oregon;
Locality number[A]: 1;
Counties in locality: Clackamas, Multnomah, Washington;
Number of counties in locality: 3;
Locality GAF[B]: 0.994;
Average payment difference in percentage points[C]: 0.18.
Locality number[A]: : 2;
Counties in locality: : Rest of Oregon;
Number of counties in locality: : 33;
Locality GAF[B]: : 0.934;
Average payment difference in percentage points[C]: : 1.14.
State: Pennsylvania;
Locality number[A]: 1;
Counties in locality: Bucks, Chester, Delaware, Montgomery,
Philadelphia;
Number of counties in locality: 5;
Locality GAF[B]: 1.069;
Average payment difference in percentage points[C]: 0.44.
Locality number[A]: State: 2;
Counties in locality: State: Allegheny, Beaver, Cumberland, Dauphin,
Lehigh, Northampton, Washington;
Number of counties in locality: State: 7;
Locality GAF[B]: State: 0.988;
Average payment difference in percentage points[C]: State: 1.03.
Locality number[A]: Island: 3;
Counties in locality: Island: Rest of Pennsylvania;
Number of counties in locality: Island: 55;
Locality GAF[B]: Island: 0.941;
Average payment difference in percentage points[C]: Island: 1.70.
State: Rhode Island;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 5;
Locality GAF[B]: 1.053;
Average payment difference in percentage points[C]: 0.38.
State: South Carolina;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 46;
Locality GAF[B]: 0.925;
Average payment difference in percentage points[C]: 1.53.
State: South Dakota;
Locality number[A]: 1;
Counties in locality: Minnehaha, Pennington, Union;
Number of counties in locality: 3;
Locality GAF[B]: 0.912;
Average payment difference in percentage points[C]: 0.54.
Locality number[A]: : 2;
Counties in locality: : Rest of South Dakota;
Number of counties in locality: : 63;
Locality GAF[B]: : 0.862;
Average payment difference in percentage points[C]: : 0.92.
State: Tennessee;
Locality number[A]: 1;
Counties in locality: Anderson, Davidson, Hamilton, Rutherford, Shelby,
Williamson, Wilson;
Number of counties in locality: 7;
Locality GAF[B]: 0.956;
Average payment difference in percentage points[C]: 0.81.
Locality number[A]: : 2;
Counties in locality: : Rest of Tennessee;
Number of counties in locality: : 88;
Locality GAF[B]: : 0.906;
Average payment difference in percentage points[C]: : 1.71.
State: Texas;
Locality number[A]: 1;
Counties in locality: Chambers, Collin, Dallas, Harris;
Number of counties in locality: 4;
Locality GAF[B]: 1.020;
Average payment difference in percentage points[C]: 0.57.
Locality number[A]: State: 2;
Counties in locality: State: Bastrop, Bexar, Brazoria, Caldwell,
Denton, Ellis, Fort Bend, Galveston, Hays, Hunt, Kendall, Montgomery,
Rockwall, Tarrant, Travis, Waller, Williamson;
Number of counties in locality: State: 17;
Locality GAF[B]: State: 0.986;
Average payment difference in percentage points[C]: State: 1.26.
Locality number[A]: : 3;
Counties in locality: : Rest of Texas;
Number of counties in locality: : 233;
Locality GAF[B]: : 0.927;
Average payment difference in percentage points[C]: : 1.45.
State: Utah;
Locality number[A]: 1;
Counties in locality: Salt Lake, Summit, Tooele;
Number of counties in locality: 3;
Locality GAF[B]: 0.965;
Average payment difference in percentage points[C]: 0.04.
Locality number[A]: : 2;
Counties in locality: : Rest of Utah;
Number of counties in locality: : 26;
Locality GAF[B]: : 0.916;
Average payment difference in percentage points[C]: : 1.64.
State: Vermont;
Locality number[A]: 1;
Counties in locality: Chittenden, Franklin;
Number of counties in locality: 2;
Locality GAF[B]: 0.996;
Average payment difference in percentage points[C]: 0.22.
Locality number[A]: : 2;
Counties in locality: : Rest of Vermont;
Number of counties in locality: : 12;
Locality GAF[B]: : 0.932;
Average payment difference in percentage points[C]: : 0.00.
State: Virginia;
Locality number[A]: 1;
Counties in locality: Alexandria City, Arlington, Fairfax, Fairfax
City, Falls Church City;
Number of counties in locality: 5;
Locality GAF[B]: 1.131;
Average payment difference in percentage points[C]: 0.28.
Locality number[A]: State: 2;
Counties in locality: State: Fauquier, Fredericksburg City, Loudoun,
Manassas City, Prince William;
Number of counties in locality: State: 5;
Locality GAF[B]: State: 1.065;
Average payment difference in percentage points[C]: State: 1.61.
Locality number[A]: State: 3;
Counties in locality: State: Clarke, New Kent, Richmond City,
Spotsylvania, Stafford;
Number of counties in locality: State: 5;
Locality GAF[B]: State: 0.999;
Average payment difference in percentage points[C]: State: 0.69.
Locality number[A]: State: 4;
Counties in locality: State: Albemarle, 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;
Number of counties in locality: State: 25;
Locality GAF[B]: State: 0.969;
Average payment difference in percentage points[C]: State: 1.13.
Locality number[A]: : 5;
Counties in locality: : Rest of Virginia;
Number of counties in locality: : 95;
Locality GAF[B]: : 0.907;
Average payment difference in percentage points[C]: : 1.24.
State: Washington;
Locality number[A]: 1;
Counties in locality: King;
Number of counties in locality: 1;
Locality GAF[B]: 1.045;
Average payment difference in percentage points[C]: 0.00.
Locality number[A]: State: 2;
Counties in locality: State: Benton, Clark, Kitsap, Pierce, Snohomish,
Thurston;
Number of counties in locality: State: 6;
Locality GAF[B]: State: 1.010;
Average payment difference in percentage points[C]: State: 0.84.
Locality number[A]: Virginia: 3;
Counties in locality: Virginia: Rest of Washington;
Number of counties in locality: Virginia: 32;
Locality GAF[B]: Virginia: 0.957;
Average payment difference in percentage points[C]: Virginia: 1.01.
State: West Virginia;
Locality number[A]: 1;
Counties in locality: Berkeley, Jefferson, Morgan, Putnam;
Number of counties in locality: 4;
Locality GAF[B]: 0.968;
Average payment difference in percentage points[C]: 0.18.
Locality number[A]: : 2;
Counties in locality: : Rest of West Virginia;
Number of counties in locality: : 51;
Locality GAF[B]: : 0.935;
Average payment difference in percentage points[C]: : 1.89.
State: Wisconsin;
Locality number[A]: 1;
Counties in locality: Dane, Kenosha, Milwaukee, Ozaukee, Pierce,
Racine, St. Croix, Washington, Waukesha;
Number of counties in locality: 9;
Locality GAF[B]: 0.987;
Average payment difference in percentage points[C]: 0.38.
Locality number[A]: : 2;
Counties in locality: : Rest of Wisconsin;
Number of counties in locality: : 63;
Locality GAF[B]: : 0.931;
Average payment difference in percentage points[C]: : 1.17.
State: Wyoming;
Locality number[A]: 1;
Counties in locality: Statewide;
Number of counties in locality: 23;
Locality GAF[B]: 0.912;
Average payment difference in percentage points[C]: 1.23.
State: Nation;
Locality number[A]: 119;
Counties in locality: [Empty];
Number of counties in locality: [Empty];
Locality GAF[B]: [Empty];
Average payment difference in percentage points[C]: 1.09.
[End of table]
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.
[A] The 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.
[B] We 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.
[C] Payment 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:
State: Alabama;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 67;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Alaska;
Locality number[A]: 18;
MSA in locality[B]: Anchorage, AK;
Number of state's counties in locality: 2;
Locality GAF[C]: 1.085;
Average payment difference in percentage points[D]: 1.20.
Locality number[A]: State: 28;
MSA in locality[B]: State: Fairbanks, AK;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.056;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 24;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Arizona;
Locality number[A]: 60;
MSA in locality[B]: Flagstaff, AZ;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.004;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: State: 63;
MSA in locality[B]: State: Phoenix-Mesa- Scottsdale, AZ;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.002;
Average payment difference in percentage points[D]: State: 0.13.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 12;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Arkansas;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 75;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: California;
Locality number[A]: 1;
MSA in locality[B]: San Francisco-Oakland-Fremont, CA;
Number of state's counties in locality: 5;
Locality GAF[C]: 1.179;
Average payment difference in percentage points[D]: 2.71.
Locality number[A]: State: 2;
MSA in locality[B]: State: San Jose- Sunnyvale-Santa Clara, CA;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.173;
Average payment difference in percentage points[D]: State: 0.25.
Locality number[A]: State: 7;
MSA in locality[B]: State: Los Angeles- Long Beach-Santa Ana, CA;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.121;
Average payment difference in percentage points[D]: State: 0.91.
Locality number[A]: State: 8;
MSA in locality[B]: State: Oxnard- Thousand Oaks-Ventura, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.120;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 9;
MSA in locality[B]: State: Santa Cruz- Watsonville, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.119;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 13;
MSA in locality[B]: State: Napa, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.097;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 14;
MSA in locality[B]: State: Santa Rosa- Petaluma, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.097;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 16;
MSA in locality[B]: State: Salinas, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.094;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 23;
MSA in locality[B]: State: Vallejo- Fairfield, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.066;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 27;
MSA in locality[B]: State: Sacramento- Arden-Arcade-Roseville, CA;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 1.057;
Average payment difference in percentage points[D]: State: 1.11.
Locality number[A]: State: 29;
MSA in locality[B]: State: Santa Barbara-Santa Maria, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.056;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 30;
MSA in locality[B]: State: San Diego- Carlsbad-San Marcos, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.055;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 40;
MSA in locality[B]: State: San Luis Obispo-Paso Robles, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.030;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 42;
MSA in locality[B]: State: Riverside-San Bernardino-Ontario, CA;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.026;
Average payment difference in percentage points[D]: State: 0.32.
Locality number[A]: State: 45;
MSA in locality[B]: State: Stockton, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.025;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 69;
MSA in locality[B]: State: Modesto, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.996;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 93;
MSA in locality[B]: State: Fresno, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.984;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 94;
MSA in locality[B]: State: Bakersfield, CA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.984;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 30;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Colorado;
Locality number[A]: 36;
MSA in locality[B]: Boulder, CO;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.038;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: State: 43;
MSA in locality[B]: State: Denver- Aurora, CO;
Number of state's counties in locality: State: 10;
Locality GAF[C]: State: 1.025;
Average payment difference in percentage points[D]: State: 0.78.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 53;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Connecticut;
Locality number[A]: 4;
MSA in locality[B]: Bridgeport-Stamford-Norwalk, CT;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.149;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: State: 17;
MSA in locality[B]: State: Hartford-West Hartford-East Hartford, CT;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 1.093;
Average payment difference in percentage points[D]: State: 0.34.
Locality number[A]: State: 19;
MSA in locality[B]: State: New Haven- Milford, CT;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.084;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 22;
MSA in locality[B]: State: Norwich-New London, CT;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.067;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 2;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Delaware;
Locality number[A]: 24;
MSA in locality[B]: Philadelphia-Camden-Wilmington, PA-NJ-DE-MD;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.064;
Average payment difference in percentage points[D]: 0.75.
Locality number[A]: of Columbia: 98;
MSA in locality[B]: of Columbia: Rest of Nation;
Number of state's counties in locality: of Columbia: 2;
Locality GAF[C]: of Columbia: 0.934;
Average payment difference in percentage points[D]: of Columbia:
2.63.
State: District of Columbia;
Locality number[A]: 10;
MSA in locality[B]: Washington-Arlington-Alexandria, DC-VA-MD-WV;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.116;
Average payment difference in percentage points[D]: 2.22.
State: Florida;
Locality number[A]: 25;
MSA in locality[B]: Miami-Fort Lauderdale-Miami Beach, FL;
Number of state's counties in locality: 3;
Locality GAF[C]: 1.061;
Average payment difference in percentage points[D]: 0.85.
Locality number[A]: State: 44;
MSA in locality[B]: State: Naples-Marco Island, FL;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.025;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 67;
MSA in locality[B]: State: Sarasota- Bradenton-Venice, FL;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.997;
Average payment difference in percentage points[D]: State: 0.42.
Locality number[A]: State: 80;
MSA in locality[B]: State: Cape Coral- Fort Myers, FL;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.988;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 84;
MSA in locality[B]: State: Jacksonville, FL;
Number of state's counties in locality: State: 5;
Locality GAF[C]: State: 0.988;
Average payment difference in percentage points[D]: State: 0.37.
Locality number[A]: State: 85;
MSA in locality[B]: State: Tampa-St. Petersburg-Clearwater, FL;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 0.987;
Average payment difference in percentage points[D]: State: 1.10.
Locality number[A]: State: 86;
MSA in locality[B]: State: Orlando- Kissimmee, FL;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 0.987;
Average payment difference in percentage points[D]: State: 0.93.
Locality number[A]: State: 92;
MSA in locality[B]: State: Port St. Lucie-Fort Pierce, FL;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.985;
Average payment difference in percentage points[D]: State: 0.84.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 45;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Georgia;
Locality number[A]: 54;
MSA in locality[B]: Atlanta- Sandy Springs-Marietta, GA;
Number of state's counties in locality: 28;
Locality GAF[C]: 1.011;
Average payment difference in percentage points[D]: 1.43.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 131;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Hawaii;
Locality number[A]: 15;
MSA in locality[B]: Honolulu, HI;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.094;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 4;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Idaho;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 44;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Illinois;
Locality number[A]: 21;
MSA in locality[B]: Chicago- Naperville-Joliet, IL-IN-WI;
Number of state's counties in locality: 9;
Locality GAF[C]: 1.072;
Average payment difference in percentage points[D]: 3.10.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 93;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Indiana;
Locality number[A]: 21;
MSA in locality[B]: Chicago- Naperville-Joliet, IL-IN-WI;
Number of state's counties in locality: 4;
Locality GAF[C]: 1.072;
Average payment difference in percentage points[D]: 3.10.
Locality number[A]: State: 96;
MSA in locality[B]: State: Cincinnati- Middletown, OH-KY-IN;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 0.982;
Average payment difference in percentage points[D]: State: 1.49.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 85;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Iowa;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 99;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Kansas;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 105;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Kentucky;
Locality number[A]: 96;
MSA in locality[B]: Cincinnati-Middletown, OH-KY-IN;
Number of state's counties in locality: 7;
Locality GAF[C]: 0.982;
Average payment difference in percentage points[D]: 1.49.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 113;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Louisiana;
Locality number[A]: 51;
MSA in locality[B]: New Orleans-Metairie-Kenner, LA;
Number of state's counties in locality: 7;
Locality GAF[C]: 1.016;
Average payment difference in percentage points[D]: 0.87.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 57;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Maine;
Locality number[A]: 74;
MSA in locality[B]: Portland- South Portland-Biddeford, ME;
Number of state's counties in locality: 3;
Locality GAF[C]: 0.993;
Average payment difference in percentage points[D]: 1.26.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 13;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Maryland;
Locality number[A]: 10;
MSA in locality[B]: Washington-Arlington-Alexandria, DC-VA-MD-WV;
Number of state's counties in locality: 5;
Locality GAF[C]: 1.116;
Average payment difference in percentage points[D]: 2.22.
Locality number[A]: State: 24;
MSA in locality[B]: State: Philadelphia- Camden-Wilmington, PA-NJ-DE-
MD;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.064;
Average payment difference in percentage points[D]: State: 0.75.
Locality number[A]: State: 31;
MSA in locality[B]: State: Baltimore- Towson, MD;
Number of state's counties in locality: State: 7;
Locality GAF[C]: State: 1.050;
Average payment difference in percentage points[D]: State: 0.58.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 11;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Massachusetts;
Locality number[A]: 6;
MSA in locality[B]: Boston-Cambridge-Quincy, MA-NH;
Number of state's counties in locality: 5;
Locality GAF[C]: 1.121;
Average payment difference in percentage points[D]: 2.15.
Locality number[A]: State: 33;
MSA in locality[B]: State: Providence- New Bedford-Fall River, RI-MA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.046;
Average payment difference in percentage points[D]: State: 0.90.
Locality number[A]: State: 34;
MSA in locality[B]: State: Worcester, MA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.040;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 35;
MSA in locality[B]: State: Barnstable Town, MA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.039;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 59;
MSA in locality[B]: State: Springfield, MA;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 1.005;
Average payment difference in percentage points[D]: State: 1.00.
Locality number[A]: State: 97;
MSA in locality[B]: State: Pittsfield, MA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.981;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 2;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Michigan;
Locality number[A]: 11;
MSA in locality[B]: Ann Arbor, MI;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.110;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: State: 12;
MSA in locality[B]: State: Detroit- Warren-Livonia, MI;
Number of state's counties in locality: State: 6;
Locality GAF[C]: State: 1.104;
Average payment difference in percentage points[D]: State: 0.95.
Locality number[A]: State: 48;
MSA in locality[B]: State: Monroe, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.022;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 53;
MSA in locality[B]: State: Flint, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.011;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 55;
MSA in locality[B]: State: Lansing-East Lansing, MI;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 1.010;
Average payment difference in percentage points[D]: State: 0.30.
Locality number[A]: State: 56;
MSA in locality[B]: State: Grand Rapids- Wyoming, MI;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 1.007;
Average payment difference in percentage points[D]: State: 0.47.
Locality number[A]: State: 64;
MSA in locality[B]: State: Holland-Grand Haven, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.000;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 66;
MSA in locality[B]: State: Battle Creak, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.000;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 73;
MSA in locality[B]: State: Jackson, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.994;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 75;
MSA in locality[B]: State: Kalamazoo- Portage, MI;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.993;
Average payment difference in percentage points[D]: State: 0.15.
Locality number[A]: State: 76;
MSA in locality[B]: State: Saginaw- Saginaw Township North, MI;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.993;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 61;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Minnesota;
Locality number[A]: 50;
MSA in locality[B]: Minneapolis-St. Paul-Bloomington, MN-WI;
Number of state's counties in locality: 11;
Locality GAF[C]: 1.019;
Average payment difference in percentage points[D]: 0.47.
Locality number[A]: State: 88;
MSA in locality[B]: State: Rochester, MN;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 0.986;
Average payment difference in percentage points[D]: State: 0.24.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 73;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Mississippi;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 82;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Missouri;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 115;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Montana;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 56;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Nebraska;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 93;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Nevada;
Locality number[A]: 38;
MSA in locality[B]: Reno-Sparks, NV;
Number of state's counties in locality: 2;
Locality GAF[C]: 1.033;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: State: 39;
MSA in locality[B]: State: Las Vegas- Paradise, NV;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.033;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 46;
MSA in locality[B]: State: Carson City, NV;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.024;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: Hampshire: 98;
MSA in locality[B]: Hampshire: Rest of Nation;
Number of state's counties in locality: Hampshire: 13;
Locality GAF[C]: Hampshire: 0.934;
Average payment difference in percentage points[D]: Hampshire: 2.63.
State: New Hampshire;
Locality number[A]: 6;
MSA in locality[B]: Boston-Cambridge-Quincy, MA-NH;
Number of state's counties in locality: 2;
Locality GAF[C]: 1.121;
Average payment difference in percentage points[D]: 2.15.
Locality number[A]: State: 32;
MSA in locality[B]: State: Manchester- Nashua, NH;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.047;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: Jersey: 98;
MSA in locality[B]: Jersey: Rest of Nation;
Number of state's counties in locality: Jersey: 7;
Locality GAF[C]: Jersey: 0.934;
Average payment difference in percentage points[D]: Jersey: 2.63.
State: New Jersey;
Locality number[A]: 3;
MSA in locality[B]: New York- Northern NJ-Long Island, NY-NJ-PA;
Number of state's counties in locality: 12;
Locality GAF[C]: 1.158;
Average payment difference in percentage points[D]: 2.58.
Locality number[A]: State: 5;
MSA in locality[B]: State: Trenton-Ewing, NJ;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.127;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 24;
MSA in locality[B]: State: Philadelphia- Camden-Wilmington, PA-NJ-DE-
MD;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 1.064;
Average payment difference in percentage points[D]: State: 0.75.
Locality number[A]: State: 26;
MSA in locality[B]: State: Atlantic City, NJ;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.059;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 41;
MSA in locality[B]: State: Vineland- Millville-Bridgeton, NJ;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.028;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 47;
MSA in locality[B]: State: Ocean City, NJ;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.022;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: Mexico: 57;
MSA in locality[B]: Mexico: Allentown-Bethlehem-Easton, PA-NJ;
Number of state's counties in locality: Mexico: 1;
Locality GAF[C]: Mexico: 1.007;
Average payment difference in percentage points[D]: Mexico: 1.56.
State: New Mexico;
Locality number[A]: 72;
MSA in locality[B]: Santa Fe, NM;
Number of state's counties in locality: 1;
Locality GAF[C]: 0.994;
Average payment difference in percentage points[D]: 0.00.
Locality number[A]: York: 98;
MSA in locality[B]: York: Rest of Nation;
Number of state's counties in locality: York: 32;
Locality GAF[C]: York: 0.934;
Average payment difference in percentage points[D]: York: 2.63.
State: New York;
Locality number[A]: 3;
MSA in locality[B]: New York- Northern NJ-Long Island, NY-NJ-PA;
Number of state's counties in locality: 10;
Locality GAF[C]: 1.158;
Average payment difference in percentage points[D]: 2.58.
Locality number[A]: State: 20;
MSA in locality[B]: State: Poughkeepsie- Newburgh-Middletown, NY;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.078;
Average payment difference in percentage points[D]: State: 0.15.
Locality number[A]: State: 61;
MSA in locality[B]: State: Kingston, NY;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.003;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 83;
MSA in locality[B]: State: Albany- Schenectady-Troy, NY;
Number of state's counties in locality: State: 5;
Locality GAF[C]: State: 0.988;
Average payment difference in percentage points[D]: State: 0.72.
Locality number[A]: Carolina: 98;
MSA in locality[B]: Carolina: Rest of Nation;
Number of state's counties in locality: Carolina: 44;
Locality GAF[C]: Carolina: 0.934;
Average payment difference in percentage points[D]: Carolina: 2.63.
State: North Carolina;
Locality number[A]: 71;
MSA in locality[B]: Raleigh-Cary, NC;
Number of state's counties in locality: 3;
Locality GAF[C]: 0.995;
Average payment difference in percentage points[D]: 0.86.
Locality number[A]: State: 77;
MSA in locality[B]: State: Durham, NC;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 0.992;
Average payment difference in percentage points[D]: State: 1.84.
Locality number[A]: Dakota: 98;
MSA in locality[B]: Dakota: Rest of Nation;
Number of state's counties in locality: Dakota: 93;
Locality GAF[C]: Dakota: 0.934;
Average payment difference in percentage points[D]: Dakota: 2.63.
State: North Dakota;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 53;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Ohio;
Locality number[A]: 68;
MSA in locality[B]: Cleveland- Elyria-Mentor, OH;
Number of state's counties in locality: 5;
Locality GAF[C]: 0.997;
Average payment difference in percentage points[D]: 0.97.
Locality number[A]: State: 87;
MSA in locality[B]: State: Akron, OH;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.987;
Average payment difference in percentage points[D]: State: 0.30.
Locality number[A]: State: 89;
MSA in locality[B]: State: Columbus, OH;
Number of state's counties in locality: State: 8;
Locality GAF[C]: State: 0.986;
Average payment difference in percentage points[D]: State: 0.95.
Locality number[A]: State: 96;
MSA in locality[B]: State: Cincinnati- Middletown, OH-KY-IN;
Number of state's counties in locality: State: 5;
Locality GAF[C]: State: 0.982;
Average payment difference in percentage points[D]: State: 1.49.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 68;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Oklahoma;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 77;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Oregon;
Locality number[A]: 78;
MSA in locality[B]: Portland- Vancouver-Beaverton, OR-WA;
Number of state's counties in locality: 5;
Locality GAF[C]: 0.991;
Average payment difference in percentage points[D]: 0.50.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 31;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Pennsylvania;
Locality number[A]: 3;
MSA in locality[B]: New York-Northern NJ-Long Island, NY-NJ-PA;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.158;
Average payment difference in percentage points[D]: 2.58.
Locality number[A]: State: 24;
MSA in locality[B]: State: Philadelphia- Camden-Wilmington, PA-NJ-DE-
MD;
Number of state's counties in locality: State: 5;
Locality GAF[C]: State: 1.064;
Average payment difference in percentage points[D]: State: 0.75.
Locality number[A]: State: 57;
MSA in locality[B]: State: Allentown- Bethlehem-Easton, PA-NJ;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 1.007;
Average payment difference in percentage points[D]: State: 1.56.
Locality number[A]: State: 81;
MSA in locality[B]: State: Harrisburg- Carlisle, PA;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 0.988;
Average payment difference in percentage points[D]: State: 1.06.
Locality number[A]: Island: 98;
MSA in locality[B]: Island: Rest of Nation;
Number of state's counties in locality: Island: 55;
Locality GAF[C]: Island: 0.934;
Average payment difference in percentage points[D]: Island: 2.63.
State: Rhode Island;
Locality number[A]: 33;
MSA in locality[B]: Providence-New Bedford-Fall River, RI-MA;
Number of state's counties in locality: 5;
Locality GAF[C]: 1.046;
Average payment difference in percentage points[D]: 0.90.
State: South Carolina;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 46;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: South Dakota;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 66;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Tennessee;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 95;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Texas;
Locality number[A]: 49;
MSA in locality[B]: Houston-Sugar Land-Baytown, TX;
Number of state's counties in locality: 10;
Locality GAF[C]: 1.019;
Average payment difference in percentage points[D]: 1.11.
Locality number[A]: State: 62;
MSA in locality[B]: State: Dallas-Fort Worth-Arlington, TX;
Number of state's counties in locality: State: 12;
Locality GAF[C]: State: 1.002;
Average payment difference in percentage points[D]: State: 1.34.
Locality number[A]: State: 65;
MSA in locality[B]: State: Austin-Round Rock, TX;
Number of state's counties in locality: State: 5;
Locality GAF[C]: State: 1.000;
Average payment difference in percentage points[D]: State: 0.77.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 227;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Utah;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 29;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Vermont;
Locality number[A]: 70;
MSA in locality[B]: Burlington- South Burlington, VT;
Number of state's counties in locality: 3;
Locality GAF[C]: 0.996;
Average payment difference in percentage points[D]: 0.22.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 11;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Virginia;
Locality number[A]: 10;
MSA in locality[B]: Washington-Arlington-Alexandria, DC-VA-MD-WV;
Number of state's counties in locality: 15;
Locality GAF[C]: 1.116;
Average payment difference in percentage points[D]: 2.22.
Locality number[A]: State: 91;
MSA in locality[B]: State: Richmond, VA;
Number of state's counties in locality: State: 20;
Locality GAF[C]: State: 0.986;
Average payment difference in percentage points[D]: State: 1.08.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 100;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Washington;
Locality number[A]: 37;
MSA in locality[B]: Seattle- Tacoma-Bellevue, WA;
Number of state's counties in locality: 3;
Locality GAF[C]: 1.034;
Average payment difference in percentage points[D]: 1.30.
Locality number[A]: State: 52;
MSA in locality[B]: State: Olympia, WA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.015;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 58;
MSA in locality[B]: State: Bremerton- Silverdale, WA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 1.006;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: State: 78;
MSA in locality[B]: State: Portland- Vancouver-Beaverton, OR-WA;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.991;
Average payment difference in percentage points[D]: State: 0.50.
Locality number[A]: State: 79;
MSA in locality[B]: State: Kennewick- Richland-Pasco, WA;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 0.991;
Average payment difference in percentage points[D]: State: 1.19.
Locality number[A]: State: 90;
MSA in locality[B]: State: Mount Vernon- Anacortes, WA;
Number of state's counties in locality: State: 1;
Locality GAF[C]: State: 0.986;
Average payment difference in percentage points[D]: State: 0.00.
Locality number[A]: Virginia: 98;
MSA in locality[B]: Virginia: Rest of Nation;
Number of state's counties in locality: Virginia: 29;
Locality GAF[C]: Virginia: 0.934;
Average payment difference in percentage points[D]: Virginia: 2.63.
State: West Virginia;
Locality number[A]: 10;
MSA in locality[B]: Washington-Arlington-Alexandria, DC-VA-MD-WV;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.116;
Average payment difference in percentage points[D]: 2.22.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 54;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Wisconsin;
Locality number[A]: 21;
MSA in locality[B]: Chicago- Naperville-Joliet, IL-IN-WI;
Number of state's counties in locality: 1;
Locality GAF[C]: 1.072;
Average payment difference in percentage points[D]: 3.10.
Locality number[A]: State: 50;
MSA in locality[B]: State: Minneapolis- St. Paul-Bloomington, MN-WI;
Number of state's counties in locality: State: 2;
Locality GAF[C]: State: 1.019;
Average payment difference in percentage points[D]: State: 0.47.
Locality number[A]: State: 82;
MSA in locality[B]: State: Milwaukee- Waukesha-West Allis, WI;
Number of state's counties in locality: State: 4;
Locality GAF[C]: State: 0.988;
Average payment difference in percentage points[D]: State: 0.27.
Locality number[A]: State: 95;
MSA in locality[B]: State: Madison, WI;
Number of state's counties in locality: State: 3;
Locality GAF[C]: State: 0.983;
Average payment difference in percentage points[D]: State: 0.95.
Locality number[A]: : 98;
MSA in locality[B]: : Rest of Nation;
Number of state's counties in locality: : 62;
Locality GAF[C]: : 0.934;
Average payment difference in percentage points[D]: : 2.63.
State: Wyoming;
Locality number[A]: 98;
MSA in locality[B]: Rest of Nation;
Number of state's counties in locality: 23;
Locality GAF[C]: 0.934;
Average payment difference in percentage points[D]: 2.63.
State: Nation;
Locality number[A]: 98;
MSA in locality[B]: [Empty];
Number of state's counties in locality: [Empty];
Locality GAF[C]: [Empty];
Average payment difference in percentage points[D]: 1.89.
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. 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.
[A] The 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.
[B] In the case that an MSA crosses state lines, it is listed under
each state that it is part of. MSA names are those published by the
Office of Management and Budget as of December 2005.
[C] We 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.
[D] Payment 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.
[End of table]
[End of section]
Appendix III: Comments from the Centers for Medicare & Medicaid
Services:
Department Of Health & Human Services:
Centers for Medicare & Medicaid Services:
Administrator:
Washington, DC 20201:
Date: May - 4 2007:
To: A. Bruce Steinwald:
Director, Health Care:
Government Accountability Office:
From: Leslie V. Norwalk, Esq:
Acting Administrator:
Subject: Government Accountability Office (GAO) Draft Report:
"Medicare: Geographic Areas Used to Adjust Physician Payments for
Variation in Practice Costs Should Be Revised." (GAO-07-466):
Thank you for the opportunity to review and comment on the subject GAO
draft report.
The Medicare statute requires that physician fee schedule payments be
adjusted for certain differences in the relative costs among areas.
Specifically, the statute requires an adjustment which reflects
differences among areas for the relative costs of the mix of goods and
services comprising practice expenses (other than malpractice expenses)
compared to the national average. The statute also requires adjustment
for the relative costs of malpractice expenses among areas compared to
the national average. The statute also requires adjustment for one-
quarter of the difference between the relative value of physicians'
work effort among areas and the national average of such work effort.
The physician work component represents 52.466 percent of the national
average fee schedule payment amount. Thus, the statutory requirement
for geographic adjustment of only one-quarter to the physician work
component means that, on average, only 13.117 percentage points of
physician work are geographically adjusted, and 39.349 percentage
points are not adjusted and represent a national fee schedule.
In addition, the practice expense component represents 43.669 percent
of the national average fee schedule payment amount. Practice expenses
are comprised of nonphysician employee compensation, office expenses
(including rent), medical equipment, drugs and supplies, and other
expenses. Only the categories of nonphysician employee compensation and
rents are geographically adjusted. Such categories represent, on
average, 30.862 percentage points of the total practice expense, and
12.807 percent of practice expenses are not geographically adjusted.
In total, more than half (52.I56 percent) of the average physician fee
schedule amount is a national payment and not geographically adjusted.
This is an important note to place into context any discussion of
physician payment localities.
Currently there are 89 Medicare physician payment localities to which
geographic practice cost indices (GPCIs) are applied. The structure of
the payment localities has been in place since I998. Over time,
changing demographics and local economic conditions may have lead to
variations in practice costs within payment locality boundaries. The
Centers for Medicare & Medicaid Services (CMS) is concerned about the
potential impact of these variations and has been studying this issue
and potential alternatives for a number of years. However, because
changes to the GPCIs must be applied in a budget neutral manner (and
under the current locality system, budget neutrality results in
aggregate payments within each State remaining the same), there are
significant redistributive effects to any change. Therefore, because of
this redistributive impact, we have looked for support from an impacted
state, such as from a State medical association, before proposing to
make changes to payment localities in a state. The GAO report considers
these issues and offers recommendations to CMS. We have some concerns
about the recommendations and specific points made in the report.
Analytic Basis:
The report uses county level data as the "gold standard" for
comparison. The report compares a GPCI for each county to the GPCI of
the locality in which the county in located. The standard of "accurate"
payment is the degree of congruence between these two figures. Use of
the county as the gold standard implies that county level data are
measured with absolute precision. Several caveats are important. First,
the data used are only "proxies" for physician work, employee
compensation and rents. That is, wage data for various categories of
employees are used to proxy the wages of physician employees. Second,
even the data used for such proxies are based on actual Census data
only for a limited number of counties. Data for more than 90 percent of
counties are based on proxies based on larger geographic areas (e.g.,
data for all rural areas in a state combined are used to proxy the
values for each rural county in a state). We are concerned that the
report purports to present such definitive conclusions about payment
"accuracy" without any caveats to indicate that the underlying data are
necessarily proxies for actual costs.
Impact of the GAO proposals:
The report finds that I4 percent of counties are affected by what are
characterized as "inaccurate" payments, and makes recommendations about
possible changes to the payment localities. The GAO's characterization
of these payments as "inaccurate" is highly inappropriate and
potentially problematic. We are concerned that a finding by the GAO
that certain payments are "inaccurate" could be construed to mean that
there has been an overpayment for which recoupment and other possible
remedies and sanctions should be pursued. The GAO study did not review
or consider whether claims submitted by physicians in these counties
are paid properly. Rather, there is every indication that such claims
were paid in accordance with current Medicare policy, including
policies in effect regarding the use of geographic areas in the
calculation of GPCIs. We believe it would be more appropriate and
technically accurate for the report to indicate that the proxies for
costs in these counties are above or below the proxies for average
costs for the area analyzed.
We are also concerned that the report does not sufficiently account for
the impact the recommended changes would have on physicians. "Budget
neutrality" using the existing data sources is applied at the State
level.
Thus, if we make changes that increase payments to physicians in some
counties in a State, those same changes will reduce payments to
physicians in other counties in the State. This report does not
sufficiently convey the extent to which physician payments in certain
areas would be reduced under the various options. We are concerned that
neither the summary of "What GAO Found" nor the "Conclusion" makes
clear that any change to increase payments in some areas would result
in significant reductions in payments in others. We believe that the
report should be transparent about the nature and extent of the payment
reductions that would occur, particularly at the county level, under
the options analyzed. We believe it would be particularly important to
point out the impact of reductions in rural areas, i.e., the urban-
rural payment differences that would result in rural states that are
currently statewide localities. Since GAO uses the county as the basis
far analysis, GAO should have the data to determine and discuss the
changes that would occur at the county level for both the counties that
would receive higher and lower payments. The report should present both
state and county level impacts of the options analyzed.
Administrative Burden:
In the report, the GAO references telephone conversations that GAO
staff had with carrier and CMS staff regarding the administrative
burden its recommendations would have on the agency. GAO concluded that
there would be a one time, minimal administrative burden. CMS believes
that the burden would be more significant than what is presented in
this draft report. Changing localities requires reprogramming systems
and extensive provider education, both of which are expensive and
burdensome administrative activities that can last for a significant
period of time. Because we receive claims for payment that cross
calendar years, carriers must maintain payment files for the two
different years. Locality changes present administrative challenges to
ensure that the pricing file for the correct locality for a physician
in each year is used to make payment.
In addition, the GAO report does not point out the potential
implications of an increased number of localities. In contrast to an
institutional provider that furnishes services in a fixed location,
physicians (and other health care professionals paid under the
physician fee schedule) often practice in multiple locations. Thus,
there are different considerations when evaluating the effect of
locality changes for physicians than for institutional providers. The
more localities that exist, the more borders exist. Physicians often
practice in multiple office settings which often cross localities. The
more localities, the more opportunities exist to inappropriately submit
bills with the place of service being the higher paid locality.
Consider an ophthalmologist who works in three different offices on
different days of the week. If the different offices are located in
three different payment localities, each claim should be filed based on
the specific location where the service is furnished. Thus, a physician
with an office in each of three different localities should be filing
claims based on three different localities. An increase in the number
of localities will increase the likelihood of this scenario, thereby
increasing administrative costs for physicians, especially if they have
a single billing function for their practice. Even if a physician tried
to bill properly, locality changes and an increased number of
localities could increase administrative costs and lead to more
incorrect billings. It would be difficult for carriers to monitor and
audit the accuracy of payment based on the specific branch of a
physician's office in which each service is furnished. Thus, creation
of a larger number of localities creates more opportunities for
erroneous billing, unintended or intended. We believe it is important
for the report to point out these potential on-going administrative
expenses.
California Medical Association Proposal:
In discussing that there have been no recent changes to the payment
locality structure, the report refers to a proposal made to CMS by the
California Medical Association (CMA) to change certain aspects of the
payment locality structure in California, and indicates that the CMA
proposal was rejected by CMS. Specifically, this proposal by the CMA
suggested that CMS remove ten high cost counties from the "Rest of
California" payment locality. We believe there are a number of
significant problems with the CMA proposal as we outlined in the
November 2I, 2005 physician fee schedule final rule (70 FR 7015I) that
prevented us from implementing these suggested changes--most notably
that the proposal is inconsistent with our statutory authority. Thus,
we do not believe the CMS rejection of the CMA proposal demonstrates a
reluctance on the part of the agency to consider and adopt changes in
payment localities.
GAO Recommendation:
The GAO recommends that 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.
CMS Response:
The CMS considers payment locality issues as concerns are raised to us
by interested parties and based on our own initiative. Because locality
changes are redistributive, we have looked to State Medical
Associations for leadership and support, but we also seek input from a
broad range of stakeholders, including urban and rural physicians in a
State. In the future, we will evaluate and consider applying changes
uniformly to the locality structure across all the states; however, we
note that we will also give full consideration to the redistributive
impacts and administrative burdens of such an approach.
GAO Recommendation:
The GAO recommends that CMS review the payment locality structure every
ten years and make changes accordingly.
CMS Response:
The CMS considers the payment locality issue as concerns are raised to
us by interested parties and based on our own initiative. We believe
this is a more flexible and efficient approach than doing a review
every ten years.
We appreciate the work that GAO has done in examining this issue. The
analysis will serve as a helpful resource as we continue to examine
payment locality alternatives.
[End of section]
Appendix IV: GAO Contact and Staff Acknowledgments:
GAO Contact:
A. Bruce Steinwald, (202) 512-7114 or steinwalda@gao.gov:
Acknowledgments:
In addition to the contact named above, Thomas A. Walke, Assistant
Director; Margaret S. Colby; Jennifer DeYoung; and Joanna L. Hiatt made
major contributions to this report.
FOOTNOTES
[1] See 61 Fed. Reg. 34,616-17 (1996).
[2] See 61 Fed. Reg. 34,616 (1996).
[3] Health Economics Research, Inc., Assessment and Redesign of
Medicare Fee Schedule Areas (Localities) (Waltham, Mass., 1995).
[4] Medicare Part B provides coverage for certain physician, outpatient
hospital, laboratory, and other services to beneficiaries who pay
monthly premiums.
[5] Specifically, 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.
[6] Of 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.
[7] See Pub. L. No. 101-239, § 6102(a), 103 Stat. 2106, 2169-84 (adding
section 1848 of the Social Security Act) (codified at 42 U.S.C. § 1395w-
4 (2000)).
[8] By 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, §
601(a)(1), 117 Stat. 2066, 2300-01, Pub. L. No. 109-171, § 5104, 120
Stat. 4, 40-41, Pub. L. No. 109-432, Div. B, Tit. I, § 101, 120 Stat.
2922, 2975.
[9] A 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.
[10] The 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."
[11] In 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, GAO-05-119 (Washington, D.C.: Mar. 11, 2005).
[12] In 2005, we reported on CMS's methods for calculating the GPCIs.
See GAO-05-119.
[13] By 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, § 412, 117 Stat.
at 2274 (codified at 42 U.S.C. § 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, § 102, 120 Stat. 2922, 2981.
[14] 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, § 602, 117
Stat. at 2301 (codified at 42 U.S.C. § 1395w-4(e)(1)(G)).
[15] Across 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.
[16] These percentages do not total to 100 percent due to rounding. The
percentages correspond to shares of the average cost of running a
physician practice.
[17] See Pub. L. No. 101-239, § 6102(a), 103 Stat. 2106, 2169-84
(adding section 1848 of the Social Security Act) (codified at 42 U.S.C.
§ 1395w-4 (2000)).
[18] These six states were: Iowa (1995), Minnesota (1992), Nebraska
(1992), North Carolina (1994), Ohio (1994), and Oklahoma (1992).
[19] CMS 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).
[20] Specifically, 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).
[21] In addition, the District of Columbia locality currently consists
of the District, five Virginia counties, and two Maryland counties.
[22] See 58 Fed. Reg. 38,003 (1993).
[23] The average GAF was weighted by locality RVUs.
[24] CMS'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).
[25] CMS 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.
[26] Since 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.
[27] Our analysis excluded 2 of the 89 physician payment localities:
Puerto Rico and the U.S. Virgin Islands.
[28] GAO-05-119.
[29] These four states are: Montana, Rhode Island, South Carolina, and
Wyoming.
[30] These nine states are: Colorado, Connecticut, Delaware, Minnesota,
New Hampshire, New Mexico, North Carolina, Vermont, and Virginia.
[31] These 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.
[32] These 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.
[33] The 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.
[34] See Health Economics Research, Inc., Assessment and Redesign of
Medicare Fee Schedule Areas (Localities) (Waltham, Mass., 1995).
[35] In 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.
[36] 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, § 412, 117 Stat. at 2274 (codified at 42
U.S.C. § 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, § 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, § 602, 117 Stat. at 2301 (codified at 42 U.S.C. §1395w-
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.
[37] These 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.
[38] The CMS and HUD data we obtained are more recent than the data CMS
used to calculate the 2005 GPCIs.
[39] Our analysis excluded 2 of the 89 physician payment localities:
Puerto Rico and the U.S. Virgin Islands.
[40] Although 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.
[41] Although 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.
[42] Although 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.
[43] In 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.
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