Medicare: Geographic Areas Used to Adjust Physician Payments for Variation in Practice Costs Should Be Revised
Published: 2007-06-29T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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.
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: 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. 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.
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. We also interviewed officials at  CMS; five Medicare Part B 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.” 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.
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.
subsections: section_title: Background
paragraphs: 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, 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. 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. 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.
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. 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.
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. 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 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. 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. 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. 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.
section_title: 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
paragraphs: 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.
In 1989, OBRA required the establishment of a national Medicare physician  fee schedule, replacing the charge-based payment system. 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, 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. 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 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.
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.” 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. 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. 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.
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.
section_title: More Than Half of the Physician Payment Localities Had Counties within Them with Large Payment Differences
paragraphs: 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. 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.
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.
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.
section_title: Several Alternative Approaches to the Physician Payment Localities Could Improve Payment Accuracy While Generally Imposing Minimal Additional Administrative Burden
paragraphs: 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.
subsections: section_title: Alternative Approaches Could Be Used to Modify the Current Payment Localities
paragraphs: 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.
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.
section_title: Several Alternative Approaches to the Payment Localities Would Improve Payment Accuracy
paragraphs: 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.
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.
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.
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. 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.
section_title: Several Alternative Approaches to the Payment Localities Would Substantially Reduce the Number of Statewide Localities
paragraphs: 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.
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.
subsections: section_title: Statewide Payment Localities That Would Remain Statewide under All Three Approaches
paragraphs: 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. For example, county-specific GAFs in Rhode Island  ranged from 1.043 to 1.057, a variation of only 1 percent.
section_title: Statewide Payment Localities That Would Become Multiple- Locality States under All Three Approaches
paragraphs: 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. 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).
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. 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).
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. 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).
section_title: Several Alternative Approaches to the Payment Localities Would Generally Impose a Minimal Amount of Additional Administrative Burden on CMS, Medicare Carriers, and Physicians
paragraphs: 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.
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. 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.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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.
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section_title: Appendix I: Scope and Methodology
paragraphs: 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. 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. 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  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, 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. 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.
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. 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.
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. 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.
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.
section_title: Appendix II: Information on Configuration of the Current Medicare Physician Payment Localities and the Alternative Approaches
paragraphs: Appendix II: Information on Configuration of  the Current Medicare Physician Payment  Localities and the Alternative Approaches City, Arlington, Fairfax, Fairfax  City, Falls Church City in Virginia;  Montgomery, Prince George’s in  Maryland   2  Broward, Collier, Indian River,  Lee, Martin, Palm Beach,   St. Lucie   1  Butts, Cherokee, Clayton, Cobb,  DeKalb, Douglas, Fayette,  Forsyth, Fulton, Gwinnett, Henry,  Newton, Paulding, Rockdale,  Walton   2.26 Average payment  difference in  percentage  factor (GAF)
2  DuPage, Kane, Lake, Will   3  Bond, Calhoun, Clinton, Jersey,  Macoupin, Madison, Monroe,  Montgomery, Randolph, St. Clair,  Washington   1  Jefferson, Orleans, Plaquemines,  1  Anne Arundel, Baltimore,  Baltimore City, Carroll, Harford,  Howard   2  Rest of Maryland, except  Montgomery and Prince George’s  counties  1  Middlesex, Norfolk, Suffolk  1  Macomb, Oakland, Washtenaw,  1  Clay, Jackson, Platte  2  Jefferson, St. Charles, St. Louis,  3.06 Average payment  difference in  percentage  factor (GAF)
1  Bergen, Essex, Hudson,  Hunterdon, Middlesex, Morris,  Passaic, Somerset, Sussex,  Union, Warren  2  Rest of New Jersey  2  Bronx, Kings, Nassau, Richmond,  Rockland, Suffolk, Westchester   4  Columbia, Delaware, Dutchess,  Greene, Orange, Putnam,  Sullivan, Ulster   5  Rest of New York  1  Clackamas, Multnomah,  1  Bucks, Chester, Delaware,  Montgomery, Philadelphia   2.69 Average payment  difference in  percentage  factor (GAF)
1  Statewide, except Alexandria City,  Arlington, Fairfax, Fairfax City,  Falls Church City  The locality GAF is Medicare’s 2005 locality GAF without the work GPCI floor or Alaska adjustments.
Average payment  difference in  percentage  50  Minneapolis–St. Paul–Bloomington, MN– 82  Milwaukee–Waukesha–West Allis, WI   represents counties that  were grouped into the “Rest-of-Nation” locality.
section_title: Appendix III: Comments from the Centers for Medicare & Medicaid Services
section_title: Appendix IV: GAO Contact and Staff Acknowledgments
subsections: section_title: Acknowledgments
paragraphs: 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.