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entitled 'Federal Employees Health Benefits Program: Competition and 
Other Factors Linked to Wide Variation in Health Care Prices' which was 
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Report to the Honorable Paul Ryan, House of Representatives: 

United States Government Accountability Office: 

GAO: 

August 2005: 

Federal Employees Health Benefits Program: 

Competition and Other Factors Linked to Wide Variation in Health Care 
Prices: 

GAO-05-856: 

GAO Highlights: 

Highlights of GAO-05-856, a report to the Honorable Paul Ryan, House of 
Representatives: 

Why GAO Did This Study: 

Congress is concerned about the health care spending burden facing the 
Federal Employees Health Benefits Program (FEHBP), the largest private 
health insurance program in the country. Health care spending per 
person varies geographically, and the underlying causes for the 
spending variation have not been fully explored. Understanding market 
forces and other factors that may influence health care spending may 
contribute to efforts to moderate health care spending. 

Health care spending varies across the country due to differences in 
its components, the utilization and price of health care services. A 
wide body of research describes extensive geographic variation in 
utilization. However, less is known about private sector geographic 
variation in prices. 

This report examined prices and spending in FEHBP Preferred Provider 
Organizations (PPOs) to determine (1) the extent to which hospital and 
physician prices varied geographically, (2) which factors were 
associated with geographic variation in hospital and physician prices, 
and (3) the extent to which hospital and physician price variation 
contributed to geographic variation in spending. 

We analyzed claims data from several large national PPOs participating 
in FEHBP. We used 2001 data, the most current data available at the 
time of the study. 

What GAO Found: 

FEHBP PPOs paid substantially different prices for hospital inpatient 
and physician services across metropolitan areas in the United States. 
Hospital prices varied by 259 percent and physician prices varied by 
about 100 percent across metropolitan areas. While there were some 
areas with very high or low prices, most had prices that were closer to 
the average. 

The variation in prices appeared to be affected by market 
characteristics. Metropolitan areas with the least competition, areas 
with a higher percentage of hospital beds in the two largest hospitals 
or hospital networks, had hospital prices that were 18 percent higher 
and physician prices that were 11 percent higher than areas with the 
most competition. The percent of primary care physicians’ reimbursement 
that was paid on a capitation basis in health maintenance organizations 
(HMO), a proxy for HMO price bargaining leverage, was also associated 
with geographic variation in prices. Metropolitan areas with the least 
HMO capitation tended to have hospital and physician prices that were 
about 10 percent higher than areas with the most HMO capitation. When 
GAO controlled for other factors that might be associated with 
geographic variation in prices, more hospital competition and HMO 
capitation were still associated with lower prices, but the effect was 
reduced. GAO did not find any evidence that price variation was due to 
cost shifting, where providers raise private sector prices to 
compensate for lower prices from other payers. 

Total health care spending per enrollee varied by over 100 percent 
across metropolitan areas. For hospital and physician services, price 
contributed to about one-third and utilization to about two-thirds of 
the variation in spending between metropolitan areas in the highest and 
lowest spending quartiles. Higher physician prices were also associated 
with lower physician utilization, but higher prices were still typical 
in higher spending areas. 

The Office of Personnel Management provided comments on a draft of this 
report and agreed with our findings. 

Distribution of Hospital and Physician Price Indices, 2001: 

[See PDF for image] 

Note: GAO converted prices to an index by dividing the average price in 
a metropolitan area by the average price in all study metropolitan 
areas. 

[End of figure] 

www.gao.gov/cgi-bin/getrpt?GAO-05-856. 

To view the full product, including the scope and methodology, click on 
the link above. For more information, contact A. Bruce Steinwald, (202) 
512-7101 or steinwalda@gao.gov. 

[End of section] 

Contents: 

Letter: 

Results in Brief: 

Background: 

Large Differences in Hospital and Physician Prices across Metropolitan 
Areas: 

Less Competition and Less HMO Capitation Linked to Higher Health Care 
Prices: 

Total Spending Varied 112 Percent; Price Variation Contributed to One- 
third of the Variation in Hospital and Physician Spending: 

Concluding Observations: 

Agency and Other Comments: 

Appendix I: Scope and Methodology: 

FEHBP Data and Study Eligibility Criteria: 

Hospital and Physician Price Estimates: 

Factors Affecting Health Care Prices: 

Analytical Approach: 

Spending Analysis: 

Decomposing Spending Variation into Price and Utilization Effects: 

Data Reliability: 

Appendix II: FEHBP PPO Adjusted Hospital Prices in U.S. Metropolitan 
Areas, 2001: 

Appendix III: FEHBP PPO Adjusted Physician Prices in U.S. Metropolitan 
Areas, 2001: 

Appendix IV: FEHBP PPO Adjusted Health Care Spending Per Enrollee in 
U.S. Metropolitan Areas, 2001: 

Appendix V: Comments from the Office of Personnel Management: 

Appendix VI: GAO Contacts and Staff Acknowledgments: 

Tables: 

Table 1: FEHBP PPO Hospital Price Indices in Metropolitan Areas Grouped 
by Census Region, 2001: 

Table 2: Metropolitan Areas with the Highest and Lowest Hospital Price 
Indices in FEHBP PPOs, 2001: 

Table 3: FEHBP PPO Physician Price Indices in Metropolitan Areas 
Grouped by Census Region, 2001: 

Table 4: Metropolitan Areas with the Highest and Lowest Physician Price 
Indices in FEHBP PPOs, 2001: 

Table 5: FEHBP PPO Price Indices in the Least and Most Competitive 
Metropolitan Areas, 2001: 

Table 6: FEHBP PPO Price Indices in Metropolitan Areas with the Least 
and Most HMO Capitation, 2001: 

Table 7: FEHBP PPO Price Indices in Metropolitan Areas in the Lowest 
and Highest Medicaid Payment Quartiles, 2001: 

Table 8: FEHBP PPO Spending Per Enrollee Indices in Metropolitan Areas 
by Census Region, 2001: 

Table 9: Price and Utilization Indices in Metropolitan Areas in the 
Highest and Lowest Quartiles of Hospital and Physician Spending, 2001: 

Table 10: Example of the Offsetting Effect of Physician Price and 
Utilization on Physician Spending in Two Metropolitan Areas in the 
FEHBP, 2001: 

Table 11: Factors Included in Analysis of Hospital and Physician Price, 
2001: 

Table 12: Results for Hospital Price Regression--Estimated Effects of 
Selected Factors on Hospital Prices in Metropolitan Areas, 2001: 

Table 13: Results for Physician Price Regression--Estimated Effects of 
Selected Factors on Physician Prices in Metropolitan Areas, 2001: 

Table 14: Effects of Changes in Explanatory Variables on Prices: 

Table 15: Ranking of Metropolitan Areas by Adjusted Hospital Prices, 
2001: 

Table 16: Ranking of Metropolitan Areas by Adjusted Physician Prices, 
2001: 

Table 17: Ranking of Metropolitan Areas by Adjusted Health Care 
Spending Per Enrollee, 2001: 

Figures: 

Figure 1: Distribution of Hospital Price Indices across 232 
Metropolitan Areas, 2001: 

Figure 2: Distribution of Physician Price Indices across 319 
Metropolitan Areas, 2001: 

Figure 3: FEHBP PPO Adjusted Hospital Price Index Quartiles in 232 
Metropolitan Areas, 2001: 

Figure 4: FEHBP PPO Adjusted Physician Price Index Quartiles in 319 
Metropolitan Areas, 2001: 

Figure 5: Distribution of FEHBP PPO Spending Per Enrollee Indices 
across 232 Metropolitan Areas, 2001: 

Figure 6: FEHBP Adjusted Spending Per Enrollee Quartiles in 232 
Metropolitan Areas, 2001: 

Abbreviations: 

APR-DRG: All Patient Refined/Diagnosis Related Group: 
FEHBP: Federal Employees Health Benefits Program: 
GPCI: Geographic Practice Cost Index: 
HMO: Health Maintenance Organization: 
OPM: Office of Personnel Management: 
PPO: Preferred Provider Organization: 

United States Government Accountability Office: 

Washington, DC 20548: 

August 15, 2005: 

The Honorable Paul Ryan: 
House of Representatives: 

Dear Mr. Ryan: 

Congress is concerned about the health care spending burden facing the 
Federal Employees Health Benefits Program (FEHBP), the largest private 
health insurance program in the country. Previous research has shown 
that health care spending varies geographically, but has not fully 
explored the underlying causes. A better understanding of market and 
other forces that may influence health care spending could assist 
efforts to moderate health care spending. 

Geographic differences in health care spending are due to differences 
in utilization--the amount and type of health services used--and price-
-the amount paid to physicians, hospitals, and other providers. Most of 
the geographic variations research has focused on the utilization of 
services. However, less is known about the variation in prices, factors 
that affect price variation, or how price variation contributes to 
spending variation. 

You asked us to analyze geographic variation in prices and spending in 
FEHBP. In August 2004, we provided you with an interim report about how 
hospital and physician prices and spending in FEHBP Preferred Provider 
Organizations (PPO)[Footnote 1] in Milwaukee compared to other 
metropolitan areas.[Footnote 2] In this report, we have expanded that 
analysis to include geographic variation in prices and spending in 
metropolitan areas[Footnote 3] throughout the United States. This final 
report examines prices and spending in FEHBP PPOs to determine: (1) the 
extent to which hospital and physician prices varied geographically, 
(2) which factors were associated with geographic variation in hospital 
and physician prices, and (3) the extent to which hospital and 
physician price variation contributed to geographic variation in 
spending. 

To estimate the extent to which hospital and physician prices varied 
geographically, we analyzed health claims data from several large 
national insurers participating in FEHBP in 2001, all of which were 
PPOs.[Footnote 4] These 2001 data were the most recent that were 
available at the time we began our study. We grouped all claims by the 
metropolitan area where care was delivered. For hospital and physician 
prices, we removed the effect of geographic differences in the costs of 
doing business (such as wages and rents) and the mix of services 
provided, using the same methodology Medicare uses to geographically 
adjust payments for hospital stays and physician services with some 
modifications.[Footnote 5] We then computed an average adjusted price 
for hospital stays and an average adjusted price for physician services 
for each metropolitan area in our study.[Footnote 6] Finally, we 
created hospital and physician price indices that showed how prices in 
each metropolitan area compared to the average of all the metropolitan 
areas in our study. The average value for each index was set at 1.00. 

To determine which factors might be associated with geographic 
differences in price, we examined the relationship between price and 
indicators of market competition, health maintenance organization (HMO) 
price bargaining leverage, and cost-shifting pressures for each 
metropolitan area.[Footnote 7] To measure competition among hospitals 
for each metropolitan area, we estimated the percentage of beds in the 
two largest hospitals or hospital networks as a percent of all acute 
care hospital beds in the metropolitan area.[Footnote 8] The larger the 
share of the hospital service market controlled by a few providers, the 
greater the likelihood that insurers will have to contract with those 
providers to ensure enrollee access to care. We used hospital 
competition as a proxy for physician competition because many 
physicians are affiliated with hospitals and hospital networks. We also 
measured the percent of primary care physician compensation from HMOs 
that was capitated.[Footnote 9] Because physicians generally prefer fee-
for-service to capitation payments, the use of capitation by HMOs 
demonstrates that they have the leverage to negotiate capitation 
contracts with physicians. Therefore, we used HMO capitation as a proxy 
measure for the strength of HMO presence in a community, and HMOs' 
ability to negotiate prices with physicians, hospitals, and other 
providers. We also developed indicators of cost shifting--hospitals and 
physicians charging higher prices to privately insured patients to 
compensate for lower payments from other patients. For each 
metropolitan area, we estimated the proportions of the population who 
were without insurance or who were enrolled in Medicare or 
Medicaid.[Footnote 10] We also estimated average physician Medicaid 
payment rates in each metropolitan area based on Medicaid rates for 29 
common procedures.[Footnote 11] We examined the relationships of these 
variables to our hospital and physician price variables. 

To examine how prices affected spending, we computed the average 
spending for all covered health care services per enrollee for each 
metropolitan area, excluding pharmaceuticals, mental health services, 
and chemical dependency services.[Footnote 12] We adjusted total 
spending per enrollee, hospital spending, and physician spending, for 
differences in the costs of doing business and for differences in the 
age and sex of the enrollees in each metropolitan area. We calculated 
the relative contribution of prices and utilization to spending for 
hospital stays and physician services.[Footnote 13] See appendix I for 
a more detailed description of our methodology. 

We tested the data we obtained from FEHBP and other sources for 
consistency and reliability, and determined that they were adequate for 
our purposes. Our analysis is limited to geographic variation in 2001 
spending and prices in the FEHBP PPOs in our study and to the factors 
listed in appendix I. We performed our work from September 2002 through 
July 2005 in accordance with generally accepted government auditing 
standards. 

Results in Brief: 

We found that FEHBP PPO hospital prices differed by 259 percent and 
physician prices differed by about 100 percent across metropolitan 
areas in the United States, after we removed the geographic variation 
associated with the costs of doing business such as rents and salaries, 
and differences in the types of services provided. While some 
metropolitan areas had hospital or physician prices that were very low 
or very high, most had prices that were much closer to the average. 
Hospital and physician prices tended to vary together, such that areas 
with higher hospital prices tended to also have higher physician 
prices. Prices for hospital stays and physician services tended to be 
higher in metropolitan areas in the Midwest and lower in the Northeast. 

In general, less competition and less HMO capitation were associated 
with higher prices. Metropolitan areas where there was less 
competition--areas with a higher percentage of beds in the two largest 
hospitals or hospital networks--had higher prices, on average. 
Metropolitan areas with the least competition had, on average, 18 
percent higher hospital prices and 11 percent higher physician prices 
than areas with the most competition.[Footnote 14] Metropolitan areas 
with the least HMO capitation had hospital and physician prices that 
were both close to 10 percent higher, on average, than areas with the 
most HMO capitation.[Footnote 15] When we controlled for other factors 
that might be associated with geographic variation in prices, we found 
that less hospital competition and HMO capitation were still associated 
with higher prices, but the effect was reduced. We found no evidence of 
cost shifting--hospital and physician prices were no higher, on 
average, in areas with lower Medicaid payments, a higher proportion of 
the uninsured, or a higher percent of the population enrolled in 
Medicaid or Medicare. Rather, we found that physician prices were, on 
average, lower in areas with lower Medicaid payments and a higher 
percentage of uninsured. We did not find a relationship between 
hospital prices and Medicaid payments or between hospital prices and 
the percentage uninsured. 

Total adjusted health care spending per enrollee was more than twice as 
high in the highest-spending metropolitan area as it was in the lowest- 
spending metropolitan area.[Footnote 16] Spending in metropolitan areas 
in the South was about 23 percent higher, on average, than in 
metropolitan areas in the Northeast. For hospital and physician 
services, prices contributed to about one-third of the variation in 
spending between the areas with the highest spending and the areas with 
the lowest spending, such that higher prices tended to be associated 
with higher hospital and physician spending.[Footnote 17] The 
contribution of physician prices to variation in physician spending was 
partially offset by utilization of physician services; we found higher 
prices in areas with lower utilization and lower prices in areas with 
higher utilization. We did not find a similar offsetting relationship 
between price and utilization for hospital spending. 

Background: 

FEHBP and Participating PPOs: 

In 2004, the federal government spent more than $21 billion on FEHBP, 
which provides health insurance to federal civilian employees, their 
families, and retirees. Administered by the Office of Personnel 
Management (OPM), FEHBP contracts with private insurers to provide 
health benefits. As such, it is the largest private health insurance 
program in the country, covering nearly 8 million enrollees. Federal 
employees enrolled in FEHBP can select from a number of private 
insurance plans. In 2004, 183 private health insurance plans, including 
both local HMOs and national PPOs, contracted with FEHBP to provide 
health insurance. Nearly 75 percent of FEHBP beneficiaries were 
enrolled in national PPOs in 2004; the remainder were enrolled in local 
HMOs. The national PPOs offered the same benefits and charged the same 
premiums regardless of where enrollees lived or obtained their health 
care. However, the prices the national PPOs paid to the hospitals and 
physicians in their networks varied across the country depending on the 
prices negotiated between the PPOs and their hospital and physician 
providers. Enrollee coinsurance payments, which are based on a 
percentage of the negotiated prices, also varied. 

Geographic Variation in Spending, Utilization, and Prices: 

Geographic variation in prices and spending in private sector plans, 
such as those participating in FEHBP, have not been extensively 
researched. However, a well-established body of research has shown wide 
variation in fee-for-service Medicare spending and utilization per 
beneficiary, even after accounting for differences in population 
demographics and illness.[Footnote 18] In 1996, Medicare spending per 
beneficiary was higher in the Midwest and the South, especially in 
parts of Texas and Louisiana, than in the North and West. Across the 
country, Medicare spending per beneficiary varied by a factor of 2.9. A 
more recent examination of Medicare spending showed continued 
geographic differences in spending per beneficiary across the 
nation.[Footnote 19]

Geographic differences in utilization have also been found, though the 
amount of utilization variation depends upon the type of service. For 
instance, Medicare beneficiaries had more than twice as many 
nonsurgical hospital discharges in 1995-1996[Footnote 20] and more than 
five times as many hip and knee replacement surgeries in some markets 
as in others in 2000-2001.[Footnote 21] Geographic differences in the 
use of inpatient services do not appear to be caused by the 
substitution of other, less costly services; markets with higher 
Medicare spending per enrollee for acute care hospital services in 1996 
also tended to have higher outpatient and physician spending per 
enrollee.[Footnote 22] Studies of other populations, such as veterans 
and enrollees in Blue Cross Blue Shield of Michigan, also showed that 
regional variation in hospital use occurred in those 
populations.[Footnote 23]

Unlike in the private sector, where prices may be subject to 
negotiation, the prices paid to hospitals and physician providers by 
Medicare are not subject to negotiation. Medicare establishes national 
prices and adjusts them by using formulas that incorporate estimates of 
differences in input costs, such as wages and rents across geographic 
areas. In the private sector, prices are negotiated between 
providers[Footnote 24] and health insurers. Insurers may negotiate 
discounted rates with providers in exchange for an anticipated share of 
patient volume from the insurers' enrollees. The negotiated price may 
take into account the costs of doing business faced by providers as 
well as other market characteristics affecting the geographic area. 
Thus, the geographic differences in price in the Medicare program may 
not be the same as in the private sector. 

Health Care Market Characteristics and Price: 

Characteristics of the health care markets across the country may 
affect the prices that private sector insurers pay for health care 
services. Market characteristics such as the extent of competition 
among providers, the prevalence of managed care, and whether private 
sector providers shift costs to compensate for lower reimbursements 
from some payers all may contribute to variations in prices across the 
country. 

Some but not all studies have shown that recent decreases in 
competition among providers have been associated with increased 
prices.[Footnote 25],[Footnote 26] Research shows that since 1995, the 
hospital industry has become increasingly consolidated, and physicians 
have become increasingly aligned with health systems and hospital 
networks. For example, in 1995, 51 percent of all private acute care 
hospitals were part of a hospital system. By 2000, the percent of 
hospitals in systems had risen to 57 percent.[Footnote 27] 
Consolidation reduces the number of competitors in a market, giving the 
consolidated competitors a larger market share. Competition also may be 
limited in markets with small populations because less populated 
markets naturally have fewer hospitals or providers and hence few 
competitors. Some studies have shown that consolidation is associated 
with cost savings achieved by generating efficiencies and reducing 
excess capacity.[Footnote 28] For example, consolidated hospitals can 
streamline operations by centralizing services, such as emergency care 
or intensive care units.[Footnote 29] However, other studies of 
hospital mergers and acquisitions have not found evidence that they 
result in any reductions in costs.[Footnote 30]

Other research has shown that the presence of HMOs in a metropolitan 
area may also influence the price of health care services.[Footnote 31] 
HMOs have typically attempted to moderate spending by introducing 
controls on both utilization and price. One of the controls HMOs have 
used is to compensate their primary care physicians with a capitated 
payment--a fixed, predetermined payment for caring for an enrollee for 
a specified period of time, regardless of the number or type of 
services ultimately provided. In addition, research indicates HMOs have 
been able to secure deeper discounts from hospitals and physicians than 
other insurers. HMOs have tended to have smaller, exclusive provider 
networks and have been able to channel their enrollees to a limited 
number of providers in exchange for the lower rates. Toward the end of 
the 1990s, in response to resistance against managed care from 
providers and patients alike, HMOs relaxed the policies they had 
imposed to control utilization, price, and spending. For example, one 
study reported a sharp decline from 1999 to 2001 in the controls 
typically used by HMOs. Of more than 50 HMOs in the study, virtually 
all reported a trend toward broader provider networks and some reported 
decreased use of financial incentives, such as capitation.[Footnote 32]

Cost shifting--the theory that providers charge higher prices to one 
set of payers to compensate for lower revenues from other payers--has 
been debated for decades. Some researchers, for example, have found 
that when Medicare and Medicaid reimbursements fall, private sector 
reimbursements rise.[Footnote 33] Yet, other researchers have found no 
evidence of cost shifting.[Footnote 34] More recent articles on this 
subject note that cost shifting is possible, but only when providers 
have had sufficient and untapped market power to raise prices.[Footnote 
35] Without sufficient market power, providers that cost shift and 
raise private sector prices might lose privately insured patients. 
Alternatively, providers might also react to a decrease in prices from 
payers by lowering private sector prices, as was reported to be the 
case for Medicaid dependent hospitals in California.[Footnote 36]

Large Differences in Hospital and Physician Prices across Metropolitan 
Areas: 

Prices paid by FEHBP PPOs varied by 259 percent for hospital stays and 
by about 100 percent for physician services across the metropolitan 
areas in our study. Prices for both hospital stays and physician 
services tended to be higher in metropolitan areas in the Midwest and 
lower in metropolitan areas in the Northeast. 

Hospital Prices Varied More than Physician Prices: 

Adjusted hospital prices paid by FEHBP PPOs varied considerably across 
metropolitan areas. In the lowest-priced metropolitan area, hospital 
prices were 51 percent of the national average (index value of 0.51) 
and in the highest-priced metropolitan area, they were 83 percent above 
the national average (index value of 1.83)--a difference of 259 
percent. In five of the 232 metropolitan areas, FEHBP PPOs paid 
hospital prices that were more than 50 percent above the national 
average. While there were other metropolitan areas with very high and 
very low prices, most had prices much closer to the average. Half of 
the metropolitan areas in our study, those in the second and third 
quartiles, had hospital prices that were no more than 14 percent above 
or below the national average,[Footnote 37] and 80 percent had hospital 
prices ranging from 22 percent below average to 27 percent above 
average. The distribution of hospital price indices among 232 
metropolitan areas is presented in fig. 1. 

Figure 1: Distribution of Hospital Price Indices across 232 
Metropolitan Areas, 2001: 

[See PDF for image]

Note: We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. We converted hospital prices to an index by 
dividing the average price for a hospital stay in a metropolitan area 
by the average price for all hospital stays in 232 metropolitan areas. 
The average hospital price index value is 1.00. 

[End of figure]

Prices paid by FEHBP PPOs for physician services also varied 
substantially but less than hospital prices, after adjusting them for 
geographic differences in the costs of doing business and the mix of 
services. In the lowest-priced metropolitan area, Baltimore, Maryland, 
physician prices were 73 percent of the national average (index value 
of 0.73), and in the highest-priced metropolitan area, La Crosse, 
Wisconsin,[Footnote 38] they were nearly 50 percent above the national 
average (index value of 1.48). Overall, the percentage difference in 
prices between the lowest-and the highest-priced metropolitan areas was 
about 100 percent. Half of the metropolitan areas in our study, those 
in the second and third quartiles, had physician prices that were no 
more than 9 percent above or below the national average, and 80 percent 
had physician prices that were no more than 16 percent above or below 
the national average. The distribution of physician prices among 319 
metropolitan areas is presented in fig. 2.[Footnote 39] In addition, 
metropolitan areas with higher physician prices tended to have higher 
hospital prices, and metropolitan areas with lower physician prices 
tended to have lower hospital prices. 

Figure 2: Distribution of Physician Price Indices across 319 
Metropolitan Areas, 2001: 

[See PDF for image]

Notes: We adjusted physician prices to remove the effect of geographic 
variation in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00. 

We had sufficient data to analyze more metropolitan areas for physician 
prices than for hospital prices. 

[End of figure]

Hospital and Physician Prices Were Generally Higher in the Midwest and 
Lower in the Northeast: 

On average, FEHBP PPOs paid higher prices for hospital stays in 
metropolitan areas in the Midwest and lower prices in the Northeast. 
(See fig. 3.) Hospitals in the Midwest were paid about 14 percent more, 
on average, than hospitals in the Northeast (table 1), but there was a 
considerable range of hospital prices within regions. In fact, several 
metropolitan areas with hospital prices in the highest quartile were 
located in the same state as metropolitan areas with hospital prices in 
the lowest quartile. For example, hospital prices in Buffalo-Niagara 
Falls, New York were 45 percent higher than average, but prices in 
Syracuse, New York were 20 percent below average. Similarly, prices in 
Salinas, California were 50 percent higher than average, but prices in 
Orange County, California were 48 percent below average. The 10 
metropolitan areas with the highest and lowest hospital prices are 
listed in table 2. Appendix II presents the complete rankings of 
metropolitan areas by hospital price. 

Figure 3: FEHBP PPO Adjusted Hospital Price Index Quartiles in 232 
Metropolitan Areas, 2001: 

[See PDF for image]

[End of figure]

Table 1: FEHBP PPO Hospital Price Indices in Metropolitan Areas Grouped 
by Census Region, 2001: 

Region: Midwest; 
Average hospital price index[A] for region: 1.07. 

Region: West; 
Average hospital price index[A] for region: 1.00. 

Region: South; 
Average hospital price index[A] for region: 1.00. 

Region: Northeast; 
Average hospital price index[A] for region: 0.94. 

Region: Percent by which prices in the Midwest exceed prices in the 
Northeast; 
Average hospital price index[A] for region: 13.83. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. We converted hospital prices to an index by 
dividing the average hospital price in a metropolitan area by the 
average hospital price for all 232 metropolitan areas. The average 
hospital price index is 1.00. 

[End of table]

Table 2: Metropolitan Areas with the Highest and Lowest Hospital Price 
Indices in FEHBP PPOs, 2001: 

Highest-priced metropolitan areas: 

Rank: 1: [A]
Rank: 2: Dover, Del.
Rank: 3: Biloxi-Gulfport-Pascagoula, Miss.
Rank: 4: St. Joseph, Mo.
Rank: 5: Milwaukee-Waukesha, Wisc.
Rank: 6: Salinas, Calif.
Rank: 7: Buffalo-Niagara Falls, N.Y.
Rank: 8: Grand Junction, Colo.
Rank: 9: [A]; 
Rank: 10: La Crosse, Wisconsin-Minn.

Lowest-priced metropolitan areas: 

Rank: 223: Saginaw-Bay City-Midland, Mich.
Rank: 224: Anniston, Ala.
Rank: 225: Decatur, Ala.
Rank: 226: Altoona, Penn.
Rank: 227: New York, N.Y.
Rank: 228: Newburgh, New York-Penn.
Rank: 229: Albany-Schenectady-Troy, N.Y.
Rank: 230: Ventura, Calif.
Rank: 231: Pueblo, Colo.
Rank: 232: Orange County, Calif.

Source: GAO analysis of FEHBP data. 

Note: We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. 

[A] Name withheld to protect proprietary data where the metropolitan 
area had only one hospital in 2001. 

[End of table]

As with hospital prices, FEHBP PPOs paid higher average physician 
prices in metropolitan areas in the Midwest and lower average physician 
prices in metropolitan areas in the Northeast (see fig. 4). Prices for 
physician services were 15 percent higher, on average, in metropolitan 
areas in the Midwest than in metropolitan areas in the Northeast (table 
3). Metropolitan areas in Wisconsin had physician prices ranked among 
the highest in our study: of the 10 metropolitan areas with the highest 
physician prices, eight were located in Wisconsin (table 4). About 80 
percent of the metropolitan areas in the Northeast had below-average 
prices for physician services. Also, physician prices tended to be less 
variable within states than hospital prices. For example, among 
metropolitan areas in New Jersey, physician prices ranged from 12 
percent below average to 19 percent below average, but hospital prices 
ranged from about 4 percent below average to about 27 percent below 
average. Appendix III contains a complete ranking of physician prices 
in 319 metropolitan areas. 

Figure 4: FEHBP PPO Adjusted Physician Price Index Quartiles in 319 
Metropolitan Areas, 2001: 

[See PDF for image]

[End of figure]

Table 3: FEHBP PPO Physician Price Indices in Metropolitan Areas 
Grouped by Census Region, 2001: 

Region: Midwest; 
Average physician price index[A] for region: 1.05. 

Region: South; 
Average physician price index[A] for region: 1.02. 

Region: West; 
Average physician price index[A] for region: 0.99. 

Region: Northeast; 
Average physician price index[A] for region: 0.91. 

Region: Percent by which prices in the Midwest exceed prices in the 
Northeast; 
Average physician price index[A] for region: 15.38. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00. 

[End of table]

Table 4: Metropolitan Areas with the Highest and Lowest Physician Price 
Indices in FEHBP PPOs, 2001: 

Highest-priced metropolitan areas: 

Rank: 1: La Crosse, Wisconsin-Minn.
Rank: 2: Wausau, Wisc.
Rank: 3: Eau Claire, Wisc.
Rank: 4: Madison, Wisc.
Rank: 5: Jonesboro, Ark.
Rank: 6: Janesville-Beloit, Wisc.
Rank: 7: Great Falls, Mont.
Rank: 8: Green Bay, Wisc.
Rank: 9: Appleton-Oshkosh-Neenah, Wisc.
Rank: 10: Racine, Wisc.

Lowest-priced metropolitan areas: 

Rank: 310: San Francisco, Calif.
Rank: 311: Dutchess County, N.Y.
Rank: 312: Providence-Fall River-Warwick, Rhode Island-Mass.
Rank: 313: Miami, Fla.
Rank: 314: West Palm Beach-Boca Raton, Fla.
Rank: 315: Fort Lauderdale, Fla.
Rank: 316: Washington, D.C.
Rank: 317: Nassau-Suffolk, N.Y.
Rank: 318: Lowell, Massachusetts-N.H.
Rank: 319: Baltimore, Md.

Source: GAO analysis of FEHBP data. 

Note: We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. 

[A] The Washington, District of Columbia metropolitan area includes 
parts of Maryland, Virginia, and West Virginia. 

[End of table]

Less Competition and Less HMO Capitation Linked to Higher Health Care 
Prices: 

FEHBP PPOs paid higher average hospital and physician prices in 
metropolitan areas with less competition among hospitals.[Footnote 40] 
Many metropolitan areas we studied had low levels of competition; about 
one in four metropolitan areas had only one or two hospitals or 
hospital networks serving the entire market. Also, FEHBP PPOs paid 
higher average hospital and physician prices in metropolitan areas with 
less HMO capitation. HMOs did not have capitated arrangements in more 
than one-third of the metropolitan areas we studied. We found no 
evidence of cost shifting--higher hospital or physician prices where 
there were lower Medicaid payments or larger uninsured, Medicare, or 
Medicaid populations. 

Prices Were Higher in Metropolitan Areas with Less Competition: 

FEHBP PPO hospital and physician prices were higher, on average, in 
metropolitan areas with less competition among hospitals. In the least 
competitive metropolitan areas--those in the quartile with the least 
competition--hospital prices tended to be about 18 percent higher and 
physician prices tended to be nearly 11 percent higher than in the most 
competitive metropolitan areas--those in the quartile with the most 
competition. See table 5. For example, Rapid City, South Dakota, was in 
the quartile with the least competition; its hospital prices were 25 
percent above average, and its physician prices were 10 percent above 
average. In contrast, Pittsburgh, Pennsylvania, a metropolitan area in 
the quartile with the most competition, had hospital prices 14 percent 
below average and physician prices 16 percent below average. When we 
conducted a separate analysis that simulated the effect of increasing 
the level of competition while controlling for the effects of other 
factors, we found that less competition was still associated with 
higher prices, although the difference was reduced by 58 percent for 
hospital prices and 38 percent for physician prices.[Footnote 41] See 
appendix I for a complete description of the other factors we analyzed. 

Table 5: FEHBP PPO Price Indices in the Least and Most Competitive 
Metropolitan Areas, 2001: 

Competition quartile: Least competitive[C]; 
Average hospital price index[A]: 1.10; 
Average physician price index[B]: 1.04. 

Competition quartile: Most competitive[C]; 
Average hospital price index[A]: 0.93; 
Average physician price index[B]: 0.94. 

Competition quartile: Percent by which prices in the least competitive 
areas exceed prices in the most competitive areas[D]; 
Average hospital price index[A]: 18.28; 
Average physician price index[B]: 10.64. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. We converted hospital prices to an index by 
dividing the average price for a hospital stay in a metropolitan area 
by the average price for all hospital stays in 232 metropolitan areas. 
The average hospital price index value is 1.00. 

[B] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00: 

[C] The competition quartiles were based on 232 metropolitan areas for 
the hospital price analysis and 319 metropolitan areas for the 
physician price analysis. 

[D] We simulated the effect of increasing competition in these 
metropolitan areas from the average level of competition in the lowest 
quartile to the average level of competition in the highest quartile, 
while controlling for other factors such as our measures of 
competition, HMO capitation, cost shifting, per capita income, percent 
of for-profit beds, provider supply, and census divisions. We found 
that, on average, the effect of increasing competition was to reduce 
the hospital price index in a metropolitan area by 7.62 percent and the 
physician price index in a metropolitan area by 6.64 percent. See app. 
I for a complete list of control factors. 

[End of table]

Overall, many metropolitan areas in our study had low levels of 
competition. Several of the metropolitan areas in our study had few 
competing hospitals or hospital networks. In approximately one quarter 
of the 319 metropolitan areas in our study, 100 percent of the market 
share was held by one or two hospitals or hospital networks. In the 
most competitive metropolitan areas, about 44 percent of the market 
share, on average, was held by the two largest hospitals or hospital 
networks. Across all metropolitan areas, about 75 percent of the market 
share, on average, was held by the two largest hospitals or hospital 
networks. The least competitive metropolitan areas also tended to have 
smaller populations. In the quartile with the least competition, the 
average population was about 160,000. The average population of the 
metropolitan areas in the quartile with the most competition was more 
than 1.8 million. 

Prices Were Higher in Metropolitan Areas with Less HMO Capitation: 

FEHBP PPO hospital and physician prices were higher, on average, in 
metropolitan areas with less HMO capitation.[Footnote 42] On average, 
both hospital prices and physician prices were more than 10 percent 
higher in metropolitan areas in the quartile with the least HMO 
capitation than in the quartile with the most HMO capitation (table 6). 
For example, Hattiesburg, Mississippi, which had no HMO capitation, had 
both hospital and physician prices in the highest quartile. In 
contrast, Philadelphia, Pennsylvania, was in the highest quartile of 
HMO capitation and in the lowest quartiles of both hospital and 
physician prices. When we conducted a separate analysis that simulated 
the effect of increasing the level of HMO capitation while controlling 
for the effects of other factors, less HMO capitation was still 
associated with higher prices, but the difference was reduced by about 
one-third for hospital prices and two-thirds for physician 
prices.[Footnote 43] See appendix I. 

Table 6: FEHBP PPO Price Indices in Metropolitan Areas with the Least 
and Most HMO Capitation, 2001: 

HMO capitation quartile: Least HMO capitation[C]; 
Average hospital price index[A]: 1.05; 
Average physician price index[B]: 1.06. 

HMO capitation quartile: Most HMO capitation[C]; 
Average hospital price index[A]: 0.95; 
Average physician price index[B]: 0.96. 

HMO capitation quartile: Percent by which prices in areas with the 
least capitation exceed prices in areas with the most capitation[D]; 
Average hospital price index[A]: 10.53; 
Average physician price index[B]: 10.42. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. We converted hospital prices to an index by 
dividing the average price for a hospital stay in a metropolitan area 
by the average price for all hospital stays in 232 metropolitan areas. 
The average hospital price index value is 1.00. 

[B] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00. 

[C] HMO capitation quartiles were based on 232 metropolitan areas for 
the hospital price analysis. HMO capitation data were not available in 
4 of the 319 metropolitan areas in physician price analysis, and the 
HMO capitation quartiles were based on 315 metropolitan areas for the 
physician price analysis. 

[D] We simulated the effect of increasing HMO capitation in these 
metropolitan areas from the average level of HMO capitation in the 
lowest quartile to the average level of HMO capitation in the highest 
quartile, while controlling for other factors such as the level of 
competition, cost shifting, income, percent of for-profit beds, 
provider supply, and census divisions. We found that, on average, the 
effect of increasing HMO capitation was to reduce the hospital price 
index in a metropolitan area by 7.17 percent and the physician price 
index in a metropolitan area by 3.31 percent. See app. I for a complete 
list of control factors. 

[End of table]

Many of the metropolitan areas in our study had low levels of HMO 
capitation.[Footnote 44] More than a third of the metropolitan areas 
had almost no HMO capitation; on average, less than 1 percent of the 
payments to primary care physicians in these areas were paid on a 
capitated basis. In the metropolitan areas in the highest quartile of 
HMO capitation, 23 percent of primary care physicians' compensation was 
capitated, on average. Among all metropolitan areas, about 8 percent of 
primary care physicians' compensation was capitated, on average. As we 
found with competition, metropolitan areas with the least HMO 
capitation tended to be the less populated areas. Of the metropolitan 
areas that had almost no HMO capitation, the average population was 
about 250,000, while those in the highest quartile of HMO capitation 
had an average population of nearly 1.1 million. 

No Evidence of Cost Shifting Due to Medicaid, Medicare, or the 
Uninsured: 

We found no evidence of cost shifting. FEHBP PPOs did not pay higher 
prices in metropolitan areas with a higher percentage of Medicaid or 
Medicare beneficiaries, a larger uninsured population, or lower 
Medicaid payments.[Footnote 45] When we controlled for other factors 
that might have been associated with price, none of our cost-shifting 
factors were significantly related to higher prices. See appendix I. 

While none of these cost-shifting factors were significantly associated 
with higher hospital or physician prices, physician prices were 
actually lower, on average, in metropolitan areas with lower adjusted 
Medicaid payment rates and proportionately larger uninsured 
populations. Physician prices were nearly 10 percent lower in the 
metropolitan areas in the quartile with the lowest Medicaid payment 
index (average of 0.65) than in the quartile with the highest Medicaid 
payment index (average of 1.29). See table 7. When we conducted a 
separate analysis that simulated the effect of increasing the level of 
Medicaid payments, while controlling for the effects of other factors, 
we found that other factors did not significantly affect the observed 
relationship between physician prices and Medicaid payments.[Footnote 
46] There was no significant association between Medicaid payments and 
hospital prices. See appendix I. 

Table 7: FEHBP PPO Price Indices in Metropolitan Areas in the Lowest 
and Highest Medicaid Payment Quartiles, 2001: 

Medicaid payment quartile: Lowest; 
Average physician price index[A]: 0.92. 

Medicaid payment quartile: Highest; 
Average physician price index[A]: 1.02. 

Medicaid payment quartile: Percent by which prices in the lowest 
Medicaid payment areas were lower than prices in the highest Medicaid 
payment areas[B]; 
Average physician price index[A]: 9.80. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00. 

[B] We simulated the effect of increasing Medicaid payments in these 
metropolitan areas from the average Medicaid payment in the lowest 
quartile to the average Medicaid payment in the highest quartile, while 
controlling for other factors such as our measures of competition, HMO 
capitation, other cost-shifting variables, income, percent of for-
profit beds, provider supply, and census divisions. We found that, on 
average, the effect of increasing Medicaid payments was to increase the 
physician price index in a metropolitan area by 9.69 percent. However, 
there was no significant association between the Medicaid payments and 
hospital prices. See app. I for a complete list of control factors. 

[End of table]

The relationship between the percentage of the population uninsured and 
physician price was only evident when we controlled for other factors. 
We simulated the effect of increasing the percentage of the population 
uninsured from the average percent uninsured in the lowest quartile to 
the average percent uninsured in the highest quartile, while 
controlling for other factors.[Footnote 47] In this simulation, we 
found that the physician prices were 6 percent lower, on average, in 
the quartile with the highest percent uninsured (average uninsured 
percent of 19.5) than in the quartile with the lowest percent uninsured 
(average percent uninsured of 8.5). There was no significant 
association between the percent uninsured and hospital prices. See 
appendix I for a complete list of control factors.[Footnote 48]

Total Spending Varied 112 Percent; Price Variation Contributed to One- 
third of the Variation in Hospital and Physician Spending: 

FEHBP PPO total spending per enrollee was more than twice as high in 
some areas as in others.[Footnote 49] Metropolitan areas in the South 
tended to have higher spending per enrollee, while metropolitan areas 
in the Northeast tended to have lower spending per enrollee. For both 
hospital and physician services, variation in price contributed about 
one-third of the difference in spending per enrollee between 
metropolitan areas in the highest and lowest quartiles of spending. 
Metropolitan areas with higher physician prices tended to have lower 
physician utilization, which offset the impact of physician price on 
physician spending to some extent. We found no such offsetting 
relationship between hospital prices and hospital utilization. 

Spending per Enrollee Varied by 112 Percent across Metropolitan Areas: 

We found that total spending per enrollee varied by 112 percent across 
the 232 metropolitan areas in this analysis. Total spending per 
enrollee was the amount spent by FEHBP PPOs per person for all health 
care services except pharmaceuticals, mental health services, and 
substance abuse services, after adjusting for enrollee age and sex 
differences as well as geographic differences in the costs of doing 
business. Spending per enrollee in the metropolitan area with the 
lowest spending per enrollee, Grand Rapids-Muskegon-Holland, Michigan, 
was 67 percent of the national average (index value of 0.67). Spending 
per enrollee in the metropolitan area with the highest spending per 
enrollee, Biloxi-Gulfport-Pascagoula, Mississippi, was 42 percent above 
the average (index value of 1.42). Half of the metropolitan areas in 
our study, those in the second and third quartiles, had spending per 
enrollee that was no more than 10 percent above or below the national 
average, and 80 percent had spending per enrollee ranging from about 16 
percent below average to about 19 percent above average. The 
distribution of spending per enrollee indices among 232 metropolitan 
areas is presented in figure 5. Appendix IV contains the spending per 
FEHBP enrollee ranking for 232 metropolitan areas. 

Figure 5: Distribution of FEHBP PPO Spending Per Enrollee Indices 
across 232 Metropolitan Areas, 2001: 

[See PDF for image]

Note: Total spending per enrollee includes spending for all services 
except mental health, chemical dependency, and pharmaceuticals. We 
adjusted total spending per enrollee to remove the effect of geographic 
differences in enrollee age and sex, as well as geographic differences 
in the costs of doing business (such as wages and rents). The spending 
per enrollee index compares spending per enrollee in a metropolitan 
area to the average spending per enrollee in all study metropolitan 
areas, adjusted for patients' age and sex composition, and costs. The 
average spending index was 1.00. 

[End of figure]

Total spending per enrollee in FEHBP PPOs was, on average, highest 
among metropolitan areas in the South and lowest in metropolitan areas 
in the Northeast. About 86 percent of the metropolitan areas in the 
highest spending quartile were located in the South (see fig. 6). 
Nearly 38 percent of the metropolitan areas in the lowest spending 
quartile were located in the Northeast, and none of the metropolitan 
areas in the highest spending quartile were in the Northeast. Spending 
per enrollee was about 23 percent higher in metropolitan areas in the 
South than in the Northeast, on average (see table 8). 

Figure 6: FEHBP Adjusted Spending Per Enrollee Quartiles in 232 
Metropolitan Areas, 2001: 

[See PDF for image]

[End of figure]

Table 8: FEHBP PPO Spending Per Enrollee Indices in Metropolitan Areas 
by Census Region, 2001: 

Region: South; 
Average spending per enrollee index[A] or region: 1.08. 

Region: Midwest; 
Average spending per enrollee index[A] or region: 0.95. 

Region: West; 
Average spending per enrollee index[A] or region: 0.94. 

Region: Northeast; 
Average spending per enrollee index[A] or region: 0.88. 

Region: Percent by which spending in the South exceeds spending in the 
Northeast; 
Average spending per enrollee index[A] or region: 22.73. 

Source: GAO analysis of FEHBP data. 

[A] Total spending per enrollee includes spending for all services 
except mental health, chemical dependency, and pharmaceuticals. We 
adjusted total spending per enrollee to remove the effect of geographic 
differences in enrollee age and sex, as well as geographic differences 
in the costs of doing business (wages, rents, etc.) The spending per 
enrollee index compares spending per enrollee in a metropolitan area to 
the average spending per enrollee in all study metropolitan areas, 
adjusted for patients' age and sex composition, and costs. The average 
spending index value was 1.00. 

[End of table]

Price Contributed to One-third of the Variation in Spending, but the 
Contribution of Price to Spending Was Partially Offset by Utilization 
of Physician Services: 

In FEHBP PPOs, hospital price variation contributed to about one-third 
of the difference in average hospital spending per enrollee between the 
highest and lowest hospital spending quartiles.[Footnote 50] Similarly, 
physician price variation contributed to about one-third of the 
difference in average physician spending per enrollee between the 
highest and lowest physician spending quartiles. Variation in 
utilization contributed about two-thirds of the difference between 
metropolitan areas in the highest and lowest quartiles of spending per 
enrollee for both hospital and physician services.[Footnote 51] 
Hospital prices and hospital utilization (hospital stays per enrollee) 
were, on average, 26 percent higher and 55 percent higher, 
respectively, in metropolitan areas in the highest hospital spending 
quartile compared to metropolitan areas in the lowest hospital spending 
quartile.[Footnote 52] Physician prices were 12 percent higher, on 
average, in the metropolitan areas in the highest than in the lowest 
physician spending quartile. Physician utilization was 26 percent 
higher in the highest physician spending quartile than it was in the 
lowest.[Footnote 53] See table 9. 

Table 9: Price and Utilization Indices in Metropolitan Areas in the 
Highest and Lowest Quartiles of Hospital and Physician Spending, 2001: 

Type of spending: Hospital stays; 
Spending quartile: Highest; 
Average price index[A]: 1.12; 
Average utilization index[B]: 1.24. 

Spending quartile: Lowest; 
Average price index[A]: 0.89; 
Average utilization index[B]: 0.80. 

Spending quartile: Percent by which highest hospital spending areas 
exceed lowest hospital spending areas; 
Average price index[A]: 25.84; 
Average utilization index[B]: 55.00. 

Type of spending: Physician services; 
Spending quartile: Highest; 
Average price index[A]: 1.05; 
Average utilization index[B]: 1.12. 

Spending quartile: Lowest; 
Average price index[A]: 0.94; 
Average utilization index[B]: 0.89. 

Spending quartile: Percent by which highest physician spending areas 
exceed lowest physician spending areas; 
Average price index[A]: 11.70; 
Average utilization index[B]: 25.84. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted physician and hospital prices to remove the effect of 
geographic differences in the costs of doing business (wages, rents, 
etc.) and differences in the mix of services among metropolitan areas. 
For this analysis, we converted both hospital and physician prices to 
an index by dividing the average price in a metropolitan area by the 
average price in 232 metropolitan areas. The average price index is 
1.00. 

[B] We removed the effect of geographic variation in enrollee age and 
sex in metropolitan areas from utilization. The utilization of hospital 
and physician services indices compare utilization of hospital and 
physician services in a metropolitan area to the average utilization of 
hospital and physician services in all study metropolitan areas, 
adjusted for age and sex. The average utilization index for both 
hospital and physician utilization was 1.00. 

[End of table]

Although metropolitan areas with higher hospital and physician FEHBP 
PPO spending per enrollee also tended to have higher hospital and 
physician prices, respectively we found a modestly sized but 
statistically significant inverse relationship between physician prices 
and physician utilization. In general, there was lower utilization of 
physician services where the price of physician services was higher, 
and higher utilization of physician services where the price of 
physician services was lower. For example, Anchorage, Alaska and 
Bakersfield, California had similar physician spending per 
enrollee,with both ranked in the highest spending per enrollee 
quartile. Yet, Anchorage had below average utilization of physician 
services and above average physician prices, while Bakersfield had 
above average utilization of physician services and below average 
physician prices. See table 10. The similar spending per enrollee in 
Anchorage and Bakersfield occurred despite these areas having different 
prices and utilization levels because of the offsetting relationship 
between physician prices and physician utilization. While the off 
setting relationship between physician price and physician utilization 
dampened slightly the overall effect of physician price on spending, 
there was still a statistically significant relationship between higher 
prices and higher spending for both physician and hospital inpatient 
sectors. For hospital services, we did not find an offsetting 
relationship between price and utilization. 

Table 10: Example of the Offsetting Effect of Physician Price and 
Utilization on Physician Spending in Two Metropolitan Areas in the 
FEHBP, 2001: 

Physician price index[A]; 
Anchorage, Alaska: 1.22; 
Bakersfield, California: 0.94. 

Physician utilization index[B]; 
Anchorage, Alaska: 0.78; 
Bakersfield, California: 1.20. 

Physician spending index[C]; 
Anchorage, Alaska: 1.23; 
Bakersfield, California: 1.34. 

Source: GAO analysis of FEHBP data. 

[A] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 232 metropolitan areas. The average physician price 
index is 1.00. 

[B] We removed the effect of geographic variation in enrollee age and 
sex in metropolitan areas from utilization. The utilization of 
physician services index compares utilization of physician services in 
a metropolitan area to the average utilization of physician services in 
all study metropolitan areas, adjusted for age and sex. The average 
utilization index is 1.00. 

[C] We removed the effect of geographic differences in enrollee age and 
sex, as well as geographic differences in the costs of doing business 
(wages, rents etc.) from physician spending. The physician spending per 
enrollee index compares physician spending per enrollee in a 
metropolitan area to the average physician spending per enrollee in all 
study metropolitan areas, adjusted for patients' age and sex, and 
costs. The average physician spending index is 1.00. 

[End of table]

Concluding Observations: 

Our analysis shows that an understanding of price variation is 
essential to understanding geographic variation in health care spending 
in the private sector. We found that market forces, not just the 
underlying costs of doing business providers face, help to determine 
the prices FEHBP PPOs ultimately pay hospitals and physicians. In 
metropolitan areas where there was less competition among hospitals, 
FEHBP PPOs paid a higher price to hospitals and physicians than in 
metropolitan areas where hospitals and physicians had more competition. 
In metropolitan areas with less HMO capitation, FEHBP PPOs paid higher 
prices,which also suggests that hospitals and physicians in those 
metropolitan areas had less competition for patient share. We found no 
evidence that hospitals or physicians shifted costs, which suggests 
that FEHBP PPOs may have been influenced by market forces when 
establishing prices, regardless of the amount of uncompensated or 
undercompensated care in a metropolitan area. Further investigation may 
help to explain why there were regional patterns that appeared to be 
associated with private sector price variation. 

Agency and Other Comments: 

In written comments on a draft of this report, OPM officials agreed 
with our findings that competition and other factors were linked to 
variation in prices, stating that the findings confirm a long-held view 
of the agency. In addition, they suggested that several issues 
warranted further study and discussion. They pointed out that it would 
have been interesting to examine the relationships between physician 
prices, Medicaid payments, percentage of the population uninsured, and 
physician-prescribing patterns. They also noted that it would be 
instructive to investigate unexplained regional variations and 
intraregional variations. They thought some findings could have been 
addressed in greater detail within the text and in the concluding 
observations. 

Representatives of the FEHBP PPOs were also given an opportunity to 
comment on a draft of the report. Representatives of one PPO noted that 
market dynamics and prices could have changed since 2001. 

We agree this report addressed important issues but investigating them 
in further detail was beyond the scope of our work. We agree that 
market dynamics and prices could have changed since 2001, but we used 
the most recent data available at the start of the study and maintain 
that the relationship among the variables, specifically the linkage 
between competition, HMO capitation, and prices is less likely to have 
changed. Other comments provided by OPM and representatives of the 
FEHBP PPOs were incorporated into the draft, as appropriate. 

As arranged with your office, unless you publicly announce the contents 
of this report earlier, we plan no further distribution of it until 30 
days after its issue date. At that time, we will send copies of this 
report to the Director of the Office of Personnel Management and other 
interested parties. We will also provide copies to others upon request. 
In addition, the report is available at no charge on the GAO Web site 
at http://www.gao.gov. 

If you or your staff have any questions about this report, please 
contact me at (202) 512-7101 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 major contributions 
to this report are listed in Appendix VI. 

Sincerely yours,

Signed by: 

A. Bruce Steinwald: 
Director, Health Care: 

[End of section]

Appendix I: Scope and Methodology: 

In this appendix we describe the data and methods we used to compare 
geographic variations in prices and spending in metropolitan 
areas[Footnote 54] across the United States, and to analyze patterns in 
the factors that affect hospital and physician prices in these areas. 
We compared differences in hospital and physician prices and in per- 
enrollee spending across metropolitan areas using medical claims data 
from enrollees in selected national preferred provider organizations 
(PPO) participating in the Federal Employees Health Benefits Program 
(FEHBP). We identified potential factors that contributed to hospital 
price and physician price variation. We then examined the relationship 
between these factors and our measures of hospital and physician 
prices. Finally, we compared total spending per enrollee across 
metropolitan areas, and we examined the contribution of hospital and 
physician prices to hospital and physician spending. 

FEHBP Data and Study Eligibility Criteria: 

We compared hospital prices, physician prices, and health care spending 
per enrollee in metropolitan areas using 2001 health claims data from 
FEHBP. These 2001 data were the most recent that were available at the 
time we began our study. FEHBP, the health insurance program 
administered by the Office of Personnel Management for federal civilian 
employees and retirees, covered about 8.5 million people in 2001. FEHBP 
negotiates with private insurers to provide health benefits. It is the 
largest employer-sponsored insurance program in the United States. 

Our study included claims data from federal civilian employees under 
the age of 65 and their dependents who enrolled in selected national 
PPOs as their primary insurers.[Footnote 55],[Footnote 56] We selected 
these PPOs because they had a similar benefit structure with respect to 
coverage and out-of-pocket requirements. We prorated the data for 
enrollees with partial year enrollment based on their days of 
eligibility during 2001. We checked the dates of service on claims to 
ensure that they were included only if the service was delivered during 
a period when the member had insurance coverage. We excluded 
pharmaceutical claims from the study, as well as mental health and 
chemical dependency claims, because these services were subcontracted 
to other organizations by at least one of the PPOs in our study, and 
the associated claims for all service types were not available. 

We aggregated payments from our claims data to metropolitan areas. 
Metropolitan areas are designed to approximate market areas in general. 
Actual health care markets may include larger or smaller geographic 
areas and may not coincide exactly with metropolitan areas. However, we 
chose metropolitan areas for our analysis because they correspond 
fairly closely with heath care markets and we were able to obtain 
claims and other data (see table 11) at the metropolitan area level. We 
did not examine prices or spending outside of metropolitan areas 
because nonmetropolitan areas are expansive and could include multiple 
markets that we would not be able to distinguish between. 

In 2001, there were 331 metropolitan areas in the 50 states and the 
District of Columbia. We excluded some metropolitan areas from our 
study because we could not obtain complete claims information due to 
payment adjustments that occurred outside of the claims system or 
because there was an insufficient number of hospital stays to support 
our price analyses.[Footnote 57] In addition, we excluded one 
metropolitan area because it had a high proportion of claims from 
enrollees that lived outside of the area. In our physician price 
analyses, we had adequate data to make comparisons among 319 
metropolitan areas. The population of these 319 metropolitan areas 
accounted for 98 percent of the population living in all metropolitan 
areas. In all other analyses, including physician spending and 
utilization, we had adequate data to make comparisons among 232 
metropolitan areas.[Footnote 58] The population of these 232 
metropolitan areas accounted for 88 percent of the population living in 
all metropolitan areas. 

Hospital and Physician Price Estimates: 

We calculated price indices for hospital and physician services. We 
selected these services because together they represented nearly two- 
thirds of total health care spending and we could identify standard 
units of service--hospital stays and physician procedures--to which we 
could link prices. We derived our price estimates for each metropolitan 
area by aggregating payments from individual claims to the metropolitan 
area where the service was provided.[Footnote 59]

To estimate the price of a hospital stay, we first aggregated payments 
from separate hospital claims to determine the total payments for that 
stay. This involved combining hospital claims for the same enrollee 
that had contiguous dates of service from the same provider. We 
excluded stays that involved multiple hospital providers, and mental 
health or chemical dependency services. 

To account for differences in the types of hospital stay cases--known 
as "case mix"--across metropolitan areas, we first classified each stay 
into an All Patient Refined/Diagnosis Related Group (APR-DRG), using 
information on length of stay, diagnoses, procedures, and the patients' 
demographic characteristics.[Footnote 60] Each APR-DRG is associated 
with a weight that reflects the expected resources required to treat a 
typical privately insured patient under age 65 in the same APR-DRG, 
relative to the average resources required for that representative 
group. We used the APR-DRG weight to adjust the hospital price for case 
mix. We excluded stays from the analysis for which there was 
insufficient information on the claim to assign a valid APR-DRG. 

We adjusted hospital prices for differences in local costs of doing 
business by applying Medicare's methodology of cost-adjusting hospital 
payments. We applied the Medicare hospital wage index to 65 percent of 
the price, which is Medicare's estimate of the wage-related component 
of the costs, and applied the geographic adjustment factor to 9 percent 
of the price, which is Medicare's estimate of the capital cost 
component. We excluded hospital stays that had either extremely high or 
low prices, because these high or low prices could distort average 
prices in an area. We trimmed the cost-and service-mix-adjusted data 
for outliers using a standard statistical distribution (the lognormal) 
to remove observations more than three standard deviations above or 
below the mean. 

For our physician price analysis, we excluded laboratory, radiology, 
anesthesiology, mental health and chemical dependency, unspecified 
services, and services billed with certain modifiers and codes, because 
these services were not uniformly classified or billed across the PPOs 
in our study. This minimized the potential for aberrant billing 
practices in some areas to inappropriately affect our results. We 
aggregated the prices for the remaining services to the metropolitan 
area based on the provider's place of service. To account for 
differences in the mix of physician services across metropolitan areas, 
we applied the Medicare methodology used to adjust physician payments. 
For each service, we applied the appropriate relative value unit to 
reflect the resources required to perform a specific service relative 
to an intermediate office visit. 

To adjust physician prices for geographic differences in the cost of 
doing business, we applied the Medicare methodology used to adjust 
physician payments. We applied the appropriate Geographic Practice Cost 
Index (GPCI) to each physician payment. However, instead of applying 
the GPCIs used for Medicare payments, which are often based on 
geographic areas larger than a metropolitan area, we aggregated county- 
level cost indices to metropolitan areas and then applied them. We 
trimmed the cost and service-mix-adjusted data using the same method we 
used to trim our hospital price data, namely, using the lognormal 
distribution to identify and remove observations more than three 
standard deviations above or below the mean. 

Factors Affecting Health Care Prices: 

We identified factors that might explain geographic differences in 
hospital and physician prices to use in our analysis, including 
measures that approximated provider competition and health maintenance 
organization (HMO) capitation. We also included measures sometimes 
associated with cost shifting, measures of provider supply, per capita 
income, and hospital ownership status. See table 11 for a list of 
factors and data sources. 

Table 11: Factors Included in Analysis of Hospital and Physician Price, 
2001: 

Factor: Competition; 
Measurement: Percent hospital beds of the two largest hospitals or 
hospital networks[A]; 
Source of data to calculate measurement: Verispan, L.L.C. 

Factor: HMO capitation; 
Measurement: Percent of primary care physicians' compensation from 
capitation[B]; 
Source of data to calculate measurement: InterStudy Publications and 
U.S. Census Bureau. 

Factor: Cost shifting; 
Measurement: Percent of population enrolled in Medicare; 
Source of data to calculate measurement: InterStudy Publications and 
U.S. Census Bureau. 

Measurement: Percent of population enrolled in Medicaid; 
Source of data to calculate measurement: InterStudy Publications and 
U.S. Census Bureau. 

Measurement: Percent of population uninsured[C]; 
Source of data to calculate measurement: InterStudy Publications and 
U.S. Census Bureau. 

Measurement: Average Medicaid payment; 
Source of data to calculate measurement: The Lewin Group, Centers for 
Medicare and Medicaid Services, and U.S. Census Bureau. 

Factor: Supply of providers; 
Measurement: Hospital beds per capita; 
Source of data to calculate measurement: Verispan, L.L.C. and U.S. 
Census Bureau. 

Factor: Per capita income; 
Measurement: Population's real per capita income[D]; 
Source of data to calculate measurement: Bureau of Economic Analysis 
and Centers for Medicare and Medicaid Services. 

Factor: Hospital ownership status; 
Measurement: Percent beds in for- profit hospitals; 
Source of data to calculate measurement: Verispan, L.L.C. 

Factor: Census division; 
Measurement: Indicator of the presence or absence of the metropolitan 
area in the census divisions; 
Source of data to calculate measurement: U.S. Census Bureau. 

Source: GAO analysis of FEHBP data. 

[A] If a hospital was a member of more than one hospital network in a 
metropolitan area, we averaged the percent of hospital beds in the two 
largest hospitals or hospital networks across each combination of 
network affiliation. 

[B] We estimated the percent of primary care physicians' compensation 
from capitation in each metropolitan area by multiplying the percent of 
HMO compensation to primary care physicians on a capitation basis by 
the percent of the population enrolled in HMOs. 

[C] InterStudy Publications based the percent uninsured in a 
metropolitan area on state uninsured rates. 

[D] We computed real income by dividing per capita income by the 
Centers for Medicare and Medicaid Services hospital wage index for each 
metropolitan area. 

[End of table]

We measured health care provider competition by the percentage of 
hospital beds in a metropolitan area that were owned by the two largest 
hospitals or hospital networks.[Footnote 61] While this value 
specifically measures concentration in the hospital services market, we 
used this same variable to explain both hospital and physician prices 
because physicians are often aligned with health systems and hospital 
networks. 

We measured HMO capitation by the percentage of physician compensation 
that came from capitated payments.[Footnote 62] Physicians generally 
tend to prefer fee-for-service arrangements to capitation, which 
requires them to assume the financial risk of treating patients whose 
costs may exceed the capitation amount paid by the insurer. Therefore, 
we assumed that areas that had a higher percentage of physicians paid 
under capitation had a strong HMO presence with leverage to negotiate 
prices with physicians. 

We examined our data for evidence of cost shifting--hospitals and 
physicians charging higher prices for privately insured patients in 
order to offset lower payments from other patients. We used several 
variables to determine whether there was cost shifting. To estimate 
Medicare's influence on prices, we analyzed the relationship between 
hospital and physician prices, and the percentage of the metropolitan 
area's population who were Medicare beneficiaries. To measure 
Medicaid's impact, we analyzed the relationship between prices, and 
both the percentage of Medicaid beneficiaries and the average Medicaid 
payment. Our measure of the average Medicaid payment in an area was 
constructed by first identifying commonly provided physician services 
and Medicaid payment rates for those services using data reported by 
The Lewin Group, and then applying the GPCI and relative value units 
unique to each service.[Footnote 63] We then weighted each Medicaid 
service using utilization estimates from the state of California. Our 
analysis assumed that the relative difference in payments across 
metropolitan areas for common procedures included in our Medicaid price 
variable was similar to that for other procedures not included in our 
analysis. We used the statewide percentages of people without health 
insurance in an area to estimate the impact of uncompensated or charity 
care on hospital and physician prices.[Footnote 64]

We included variables to account for the effect that the supply of 
health services or health service providers had on hospital and 
physician prices. Metropolitan areas with larger numbers of physicians 
or hospital beds per capita may have lower prices because larger 
numbers of providers compete for a given amount of business. In our 
analysis of hospital prices, we used hospital beds per capita to 
estimate this effect, and in our physician price analysis, we used the 
number of physicians per capita. We also experimented with other 
measures of supply, in particular, teaching hospital beds per capita 
and the number of physician specialists per capita. 

We included a measure of income because variations in income can affect 
beneficiaries' ability to pay and thus may affect prices. Income data 
were unavailable for FEHBP enrollees, so we used per capita income in 
the metropolitan area. However, to account for geographic differences 
in purchasing power, specifically that the cost of living was higher in 
some metropolitan areas than others, we used the Centers for Medicare 
and Medicaid Services wage index as a proxy for the cost of living and 
divided this into dollar per capita income to calculate our income 
variable. We also included hospital ownership status in our analysis. 
We included the percent of hospital beds in for-profit hospitals and 
determined whether this had an impact on hospital and physician prices. 
Finally, we included dummy variables for each of the U.S. census 
divisions to account for regional effects.[Footnote 65]

Analytical Approach: 

We conducted two analyses to examine the relationship between our price 
variables and the factors described above. First, we grouped the 
metropolitan areas into quartiles for each of the factors.[Footnote 66] 
This enabled us to then compare the average prices in metropolitan 
areas, for example, with the highest levels of competition to those 
with the lowest. In addition, we also conducted regression analyses to 
examine the effect of each of the factors on price. To simplify the 
presentation of our results in the body of the report, we presented 
only those factors that were statistically significant in our 
regression analysis.[Footnote 67]

Price Regression Analysis--Methods and Results: 

We used separate regression models to estimate the impact of our 
variables on hospital and physician prices. To simplify the calculation 
of independent variables' effects and to match the statistical 
distribution assumption we made in our data trimming of prices, we used 
a log-linear model: that is, we regressed the logarithm of price 
(hospital price and physician price) on the levels of our independent 
variables. We were concerned that our measures of provider supply-- 
hospital beds per capita and physicians per capita in the case of 
hospital and physician price, respectively--were endogenous. For 
example, larger numbers of physicians could lead to lower physician 
prices, but lower physician prices could also make a metropolitan area 
less attractive to physicians and reduce their number. In order to 
address this issue we used the method of instrumental variables: a 
standard method to account for an endogenous explanatory 
variable.[Footnote 68] We also tested whether the HMO capitation 
variable was endogenous and found that it was not. 

Tables 12 and 13 show the results for estimating the determinants of 
hospital and physician prices, respectively. The set of explanatory 
variables was the same for both hospital and physician prices except 
that we used hospital beds per capita and physicians per capita to 
measure provider supply in the hospital and physician price models, 
respectively. Our regression results for hospital price showed 
significant effects of provider market share and managed care presence 
on prices: both of these effects were consistent with the idea that 
raising market competitiveness lowers prices. Our variable measuring 
the market share of the two largest networks was positively related to 
price: that is, when the market became more concentrated (less 
competitive), price tended to be higher. Also, our HMO presence 
variable, the percentage of physician compensation from capitation 
payments, was negatively associated with price: that is, less HMO 
presence tended to increase price. 

Table 12: Results for Hospital Price Regression--Estimated Effects of 
Selected Factors on Hospital Prices in Metropolitan Areas, 2001: 

Factor: Competition; 
Variable used to measure factor: Percent hospital beds of the two 
largest hospitals or hospital networks; 
Parameter estimate: 0.1337; 
t-value: 2.11**. 

Factor: HMO capitation; 
Variable used to measure factor: Percent of primary care physicians' 
compensation from capitation; 
Parameter estimate: -0.3213; 
t-value: -2.22**. 

Factor: Cost-shifting; 
Variable used to measure factor: Percent of population uninsured; 
Parameter estimate: -0.3621; 
t-value: -0.68. 

Variable used to measure factor: Average Medicaid payment; 
Parameter estimate: 0.0026; 
t-value: 1.58. 

Variable used to measure factor: Percent of population enrolled in 
Medicaid; 
Parameter estimate: -0.0538; 
t-value: -0.20. 

Variable used to measure factor: Percent of population enrolled in 
Medicare; 
Parameter estimate: -0.5267; 
t-value: -1.14. 

Factor: Supply of providers; 
Variable used to measure factor: Hospital beds per capita; 
Parameter estimate: 21.5968; 
t-value: 0.50. 

Factor: Per capita income; 
Variable used to measure factor: Population's real per capita income; 
Parameter estimate: 0.0000; 
t-value: -0.52. 

Factor: Hospital ownership status; 
Variable used to measure factor: Percent of beds in for profit 
hospitals; 
Parameter estimate: 0.0767; 
t-value: 0.86. 

Factor: Dummy variable indicator showing the Census Division in which 
the metropolitan area was located; 
Variable used to measure factor: Census Division 1 - New England; 
Parameter estimate: 0.0625; 
t-value: 0.78. 

Variable used to measure factor: Census Division 2 - Middle Atlantic; 
Parameter estimate: -0.1158; 
t-value: -1.43. 

Variable used to measure factor: Census Division 3 - East North 
Central; 
Parameter estimate: -0.0572; 
t-value: -0.73. 

Variable used to measure factor: Census Division 4 - West North 
Central; 
Parameter estimate: 0.0418; 
t-value: 0.33. 

Variable used to measure factor: Census Division 5 - South Atlantic; 
Parameter estimate: -0.0258; 
t-value: -0.35. 

Variable used to measure factor: Census Division 6 - East South 
Central; 
Parameter estimate: -0.1845; 
t-value: -1.80*. 

Variable used to measure factor: Census Division 7 - West South 
Central; 
Parameter estimate: -0.1077; 
t-value: -1.14. 

Variable used to measure factor: Census Division 8 - Mountain; 
Parameter estimate: -0.0428; 
t-value: -0.63. 

Variable used to measure factor: Census Division 9 - Pacific[B]; 
Parameter estimate: [Empty]; 
t-value: [Empty]. 

Intercept; 
Parameter estimate: 8.8972; 
t-value: 45.67***. 

: 0.25. 

Observations: 228. 

*** significant at the 1% level; 
** significant at the 5% level; 
* significant at the 10% level; 

Source: GAO analysis. 

[A] We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. 

[B] The Pacific Census Division was the excluded category. In order for 
the regression model's parameters to be estimated, we needed to exclude 
one of the Census Divisions. 

[End of table]

Table 13: Results for Physician Price Regression--Estimated Effects of 
Selected Factors on Physician Prices in Metropolitan Areas, 2001: 

Factor: Competition; 
Variable used to measure factor: Percent hospital beds of the two 
largest hospitals or hospital networks; 
Parameter estimate: 0.1234; 
t-value: 4.36***. 

Factor: HMO capitation; 
Variable used to measure factor: Percent of primary care physicians' 
compensation from capitation; 
Parameter estimate: -0.1393; 
t-value: -2.24**. 

Factor: Cost-shifting; 
Variable used to measure factor: Percent of population uninsured; 
Parameter estimate: -0.5328; 
t-value: -2.22**. 

Variable used to measure factor: Average Medicaid payment; 
Parameter estimate: 0.0041; 
t-value: 5.24***. 

Variable used to measure factor: Percent of population enrolled in 
Medicaid; 
Parameter estimate: 0.1081; 
t-value: 0.91. 

Variable used to measure factor: Percent of population enrolled in 
Medicare; 
Parameter estimate: 0.0217; 
t-value: 0.10. 

Factor: Hospital ownership status; 
Variable used to measure factor: Percent of beds in for profit 
hospitals; 
Parameter estimate: -0.0536; 
t-value: -1.34. 

Factor: Per capita income; 
Variable used to measure factor: Population's real per capita income; 
Parameter estimate: 0.0000; 
t-value: 0.00. 

Factor: Supply of providers; 
Variable used to measure factor: Physicians per capita (physicians per 
1000 population); 
Parameter estimate: -0.0002; 
t-value: -0.91. 

Factor: Dummy variable indicator showing the Census Division in which 
the metropolitan area was located; 
Variable used to measure factor: Census Division 1 - New England; 
Parameter estimate: - 0.1112; 
t-value: -2.79***. 

Variable used to measure factor: Census Division 2 - Middle Atlantic; 
Parameter estimate: -0.0346; 
t-value: -1.01. 

Variable used to measure factor: Census Division 3 - East North 
Central; 
Parameter estimate: 0.0041; 
t-value: 0.14. 

Variable used to measure factor: Census Division 4 - West North 
Central; 
Parameter estimate: 0.0120; 
t-value: 0.32. 

Variable used to measure factor: Census Division 5 - South Atlantic; 
Parameter estimate: -0.0470; 
t-value: - 1.58. 

Variable used to measure factor: Census Division 6 - East South 
Central; 
Parameter estimate: -0.0558; 
t-value: -1.61. 

Variable used to measure factor: Census Division 7 - West South 
Central; 
Parameter estimate: 0.0947; 
t-value: 3.24***. 

Variable used to measure factor: Census Division 8 - Mountain; 
Parameter estimate: -0.0240; 
t-value: - 0.77. 

Variable used to measure factor: Census Division 9 - Pacific[B]; 
Parameter estimate: [Empty]; 
t-value: [Empty]. 

Variable used to measure factor: Intercept; 
Parameter estimate: 3.7808; 
t-value: 35.48***. 

R-squared: 0.46. 

Observations: 315. 

*** significant at the 1% level. 
** significant at the 5% level. 
* significant at the 10% level. 

Source: GAO analysis. 

[A] We adjusted physician prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. 

[B] The Pacific Census Division was the excluded category. In order for 
the regression model's parameters to be estimated, we needed to exclude 
one of the Census Divisions. 

[End of table]

Our measures of cost-shifting effects were mostly not significant and 
none of the results supported the claim that more Medicaid enrollees, 
lower Medicaid payments, more Medicare enrollees, or more uninsured 
people were associated with higher hospital or physician prices. 
Ideally, we would have included an indicator of Medicare price levels 
for each area, such as the wage index or the GPCI. However, we did not 
include these as separate explanatory variables in the regression 
models because we had used the wage index and the GPCI to adjust the 
hospital and physician prices, respectively, for differences in the 
cost of doing business in different areas. Therefore, our sole measure 
of the impact of the Medicare program on prices was the percent of the 
population who were Medicare beneficiaries. In the physician price 
regression, the average Medicaid payment was significant. However, 
Medicaid payments were positively associated with prices, which was 
inconsistent with the negative association we would have expected if 
cost shifting were occurring. In the physician price analysis, the 
percent of people uninsured was significantly related to price and the 
result showed that where there were more uninsured people, prices were 
actually lower, rather than higher, as would have been predicted by the 
cost-shifting hypothesis. 

Our inclusion of the set of census division dummy variables allowed us 
to measure factors affecting price that were due simply to location and 
that were not accounted for by the other variables included in the 
model. In both price regression models, we ran an F-test that showed 
that the set of census division dummy variables was jointly 
significant. 

In the cases where our explanatory variables in the regression were 
significant, we calculated the significant variables' impact on prices 
by using our regression results to calculate the percent change in 
price for a given increase in the explanatory variable. To do this, we 
simulated the effect of increasing the significant explanatory variable 
from its average in its lowest quartile to its average in its highest 
quartile, while controlling for other factors. This was accomplished 
using the following steps: (1) we calculated the average value of the 
statistically significant explanatory variable for its lowest quartile, 
and input that value into our estimated regression equation to 
calculate price, (2) we calculated the average value of the key 
explanatory variable in its highest quartile, and used that value in 
our estimated regression model to calculate price again, and (3) we 
calculated the percent difference in price using the results from (1) 
and (2). See table 14. 

Table 14: Effects of Changes in Explanatory Variables on Prices: 

Significant explanatory variable: Percent hospital beds of the two 
largest hospitals or hospital networks; 
Percent impact on physician price: 6.64; 
Percent impact on hospital price: 7.62. 

Significant explanatory variable: Percent of primary care physicians' 
compensation from capitation; 
Percent impact on physician price: -3.31; 
Percent impact on hospital price: -7.17. 

Significant explanatory variable: Average Medicaid payment; 
Percent impact on physician price: 9.69; 
Percent impact on hospital price: [A]. 

Significant explanatory variable: Percent of population uninsured; 
Percent impact on physician price: -6.05; 
Percent impact on hospital price: [A]. 

Source: GAO analysis. 

Note: The percent impact is the change in price that would follow an 
increase in the explanatory variable from its average value in its 
lowest quartile to its average value in its highest quartile. 

[A] The average cost-adjusted Medicaid fee and the percent uninsured 
explanatory variables were not statistically significant in the 
hospital price regression. 

[End of table]

We also tested and opted not to include other variables in our 
regression: specifically, we tried to explain price variations by 
including the percent of the labor force in the metropolitan area 
covered by a labor union contract; the mortality rate for persons aged 
more than one but less than 65 years in the metropolitan area--a proxy 
for health status; and the effect of certificate-of-need laws.[Footnote 
69] We also used the number of teaching hospital beds per capita to see 
if this had an independent effect on price, separate from the effects 
of supply. We included this variable because it was possible that more 
teaching hospital beds in a metropolitan area might indicate more 
cutting-edge and higher quality services, or teaching hospitals might 
conduct more tests or services, which might in turn affect prices. We 
ultimately excluded labor union, mortality rates, certificate-of-need 
laws, and teaching hospital variables from our explanatory variables 
because they were not the focus of our analysis, they were not 
statistically significant, and their inclusion did not affect the 
significance of most of the other explanatory variables in the model. 

Spending Analysis: 

To determine average total spending per enrollee in each metropolitan 
area, we summed all payments for each enrollee, assigned enrollees to 
their metropolitan areas of residence, and then calculated the average 
for each metropolitan area. We adjusted spending service categories for 
geographic input costs, removed outliers, and accounted for differences 
in the age and sex distributions across metropolitan areas. After 
applying our eligibility criteria and removing outliers, we had about 
2.1 million enrollees in our study. 

We accounted for geographic differences in the costs of providing 
hospital inpatient,[Footnote 70] hospital outpatient, home health, 
rehabilitation, skilled nursing facility, other outpatient, and 
ambulatory surgery center services by first summing the payments per 
enrollee by service categories and then applying Medicare's hospital 
wage index to the labor-related portion of the total payment for each 
type of service. This approach is similar to the methodology used by 
Medicare to adjust such provider payments.[Footnote 71]

We accounted for geographic differences in the cost of providing 
physician services using a different methodology, but one that 
generally follows the basic methodology used by Medicare. We applied 
the appropriate GPCIs to the total physician payments.[Footnote 72] 
However, our method varied slightly from Medicare's in that instead of 
applying the GPCIs at the carrier/locality level, we calculated 
separate cost indices for each metropolitan area.[Footnote 73]

We excluded enrollees with high total health care spending because 
spending for those enrollees could distort average spending in an area 
with low enrollment. To identify enrollees with high spending, we used 
a standard statistical distribution (the lognormal). We removed 
enrollees from this analysis whose spending was at least three standard 
deviations above the mean. 

We adjusted spending for the age and sex distribution of each 
metropolitan area's population. To do this, we calculated the average 
age-and sex-specific spending rates of all 232 metropolitan areas 
combined, and applied these averages to the actual age and sex 
distribution in each metropolitan area. This yielded an "expected" 
spending rate for each metropolitan area: the spending in that 
metropolitan area if it had the study average spending rate, given the 
age and sex distribution of that metropolitan area's population. We 
then calculated the ratio of actual cost-adjusted spending to expected 
cost-adjusted spending. This yielded an index of how much higher or 
lower spending in the specific metropolitan area was from what would be 
expected if it had average spending rates, given its age and sex 
composition. An index value greater than 1.00 implies spending was 
higher than expected and an index value less than 1.00 implies spending 
was lower than expected. 

Decomposing Spending Variation into Price and Utilization Effects: 

We estimated the relative contribution of price and utilization 
variation to spending variation in 232 metropolitan areas. To do this, 
we first computed measures of price, spending, and utilization for 
hospital and physician services. We then analyzed price and utilization 
differences between metropolitan areas in the highest and lowest 
spending quartiles to decompose spending into its component parts. 

We used the same method to adjust hospital and physician spending as we 
did for total spending. That is, we used the appropriate Medicare cost 
adjustments and adjustments for age and sex. To estimate hospital and 
physician prices, we used prices we had computed from our price 
analysis for the same 232 metropolitan areas. 

We defined hospital utilization as the count of hospital stays. We 
excluded mental health and chemical dependency stays, and other 
nonacute hospital stays, such as nursing home and rehabilitation 
services, in each of the 232 metropolitan areas. Our measure of 
physician utilization was simply the count of services provided by 
physicians, excluding pathology, radiology, anesthesia, and psychiatric 
services. We aggregated the data for service use per enrollee up to the 
metropolitan area, and we then adjusted these data in a similar way to 
the spending data: that is, we adjusted for age and sex composition of 
the area by calculating the ratio of actual utilization to expected 
utilization. We calculated the physician and hospital utilization 
indices using the 232 metropolitan areas as the population basis. 

For both hospital and physician services, we compared the simple 
average adjusted spending per enrollee in the highest spending quartile 
metropolitan areas with the lowest spending quartile metropolitan 
areas. Similarly, we compared the average adjusted price and the 
average adjusted utilization per enrollee in the highest versus the 
lowest spending quartile. The proportional difference in spending 
between the highest and lowest quartiles can be divided into (1) the 
proportional difference in price between the highest and lowest 
spending quartiles, and (2) the proportional difference in utilization 
between the highest and lowest spending quartiles. In order to divide 
the variation in spending between price and utilization differences, we 
compared the values of (1) to (2) above. We estimated the relative 
contribution of physician price and utilization to spending by 
analyzing the percentage difference between the average prices and 
utilization in the highest and lowest spending quartiles, relative to 
the summed total of the percentage differences, as shown in table 9. 

Data Reliability: 

We used multiple data sources for this report. We obtained 2001 health 
care claims data from several PPOs participating in FEHBP. In addition, 
we obtained data describing characteristics of metropolitan areas from 
several other sources. See table 11. We determined that the data were 
sufficiently reliable to address the study objectives. 

We verified that our claims data were sufficiently reliable and 
unbiased in several ways. First, we interviewed staff from each of the 
FEHBP PPOs participating in the study to obtain an understanding of the 
completeness and accuracy of the data we had requested. Upon receipt of 
the data from the PPOs, we conducted numerous tests and edit checks to 
ensure that our data were complete and accurate: we reviewed the 
documentation that accompanied the data; we checked that essential 
elements of the data were populated with credible values; we excluded 
enrollees and claims records that did not match study eligibility 
criteria; and we examined the internal consistency and validity of the 
data, coordinating with any PPO that submitted data that required 
clarification or resubmission of corrected data. To test the validity 
of the hospital location variable from our claims data, we examined the 
proportion of hospital stays that occurred outside of the enrollee's 
state of residence or an adjacent state. For one metropolitan area, we 
conducted a sensitivity analysis to quantify the impact on our price 
estimate of removing the admissions from enrollees in another state. We 
concluded that our location data were sufficiently reliable for the 
purposes of our study. 

Ultimately, we excluded 12 of the 331 metropolitan areas for one of two 
reasons. First, in some metropolitan areas, some PPOs made additional 
"reconciliation" payments that were not recorded in the claims system, 
and price estimates would have been understated in these areas. Second, 
if a disproportionate number of enrollees traveled into a metropolitan 
area to receive care, we excluded the metropolitan area. We also 
excluded some hospital stays and physician services from our hospital 
and physician price estimates, respectively, either because there were 
insufficient data to case-mix adjust these services or because hospital 
or physician billing conventions were inconsistent across metropolitan 
areas for those services. 

We verified that the data describing market forces and other factors in 
a metropolitan area were sufficiently reliable and unbiased using 
methods similar to those we used to verify the claims data. We 
discussed data quality issues with data suppliers, reviewed the 
suppliers' documentation and internal data testing, and conducted our 
own tests for data completeness and credibility. Some limitations came 
to light through these processes. First, because direct estimates of 
uninsured rates were unavailable for all metropolitan areas in the 
study, we used the InterStudy Publications' estimates of the uninsured 
for metropolitan areas, which were based on statewide uninsured 
estimates. Similarly, metropolitan area specific Medicaid payment rates 
were not available, and Medicaid utilization rates were not available 
to weight the average of Medicaid payments in metropolitan areas. 
Consequently, we used statewide payment and utilization estimates for 
California's Medicaid program, which were reported by The Lewin 
Group.[Footnote 74]

We performed our work from September 2002 through July 2005 in 
accordance with generally accepted government auditing standards. 

[End of section]

Appendix II: FEHBP PPO Adjusted Hospital Prices in U.S. Metropolitan 
Areas, 2001: 

The adjusted hospital price indices based on FEHBP PPO payments for 
hospital stays in 232 metropolitan areas are presented below ranked in 
order from highest to lowest price. 

Table 15: Ranking of Metropolitan Areas by Adjusted Hospital Prices, 
2001: 

Rank: 1; 
Metropolitan area: [B]; 
Predominant state[A]: [B]; 
Adjusted hospital price index: 1.829. 

Rank: 2; 
Metropolitan area: Dover; 
Predominant state[A]: DE; 
Adjusted hospital price index: 1.680. 

Rank: 3; 
Metropolitan area: Biloxi-Gulfport-Pascagoula; 
Predominant state[A]: MS; 
Adjusted hospital price index: 1.591. 

Rank: 4; 
Metropolitan area: St. Joseph; 
Predominant state[A]: MO; 
Adjusted hospital price index: 1.578. 

Rank: 5; 
Metropolitan area: Milwaukee-Waukesha; 
Predominant state[A]: WI; 
Adjusted hospital price index: 1.568. 

Rank: 6; 
Metropolitan area: Salinas; 
Predominant state[A]: CA; 
Adjusted hospital price index: 1.499. 

Rank: 7; 
Metropolitan area: Buffalo-Niagara Falls; 
Predominant state[A]: NY; 
Adjusted hospital price index: 1.451. 

Rank: 8; 
Metropolitan area: Grand Junction; 
Predominant state[A]: CO; 
Adjusted hospital price index: 1.431. 

Rank: 9; 
Metropolitan area: [B]; 
Predominant state[A]: [B]; 
Adjusted hospital price index: 1.419. 

Rank: 10; 
Metropolitan area: La Crosse, WI-MN; 
Predominant state[A]: WI; 
Adjusted hospital price index: 1.385. 

Rank: 11; 
Metropolitan area: Wichita; 
Predominant state[A]: KS; 
Adjusted hospital price index: 1.379. 

Rank: 12; 
Metropolitan area: Manchester; 
Predominant state[A]: NH; 
Adjusted hospital price index: 1.365. 

Rank: 13; 
Metropolitan area: Bakersfield; 
Predominant state[A]: CA; 
Adjusted hospital price index: 1.361. 

Rank: 14; 
Metropolitan area: Sioux Falls; 
Predominant state[A]: SD; 
Adjusted hospital price index: 1.357. 

Rank: 15; 
Metropolitan area: Bangor; 
Predominant state[A]: ME; 
Adjusted hospital price index: 1.340. 

Rank: 16; 
Metropolitan area: Owensboro; 
Predominant state[A]: KY; 
Adjusted hospital price index: 1.326. 

Rank: 17; 
Metropolitan area: Fort Walton Beach; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.322. 

Rank: 18; 
Metropolitan area: Portsmouth-Rochester, NH-ME; 
Predominant state[A]: NH; 
Adjusted hospital price index: 1.318. 

Rank: 19; 
Metropolitan area: Lakeland-Winter Haven; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.310. 

Rank: 20; 
Metropolitan area: South Bend; 
Predominant state[A]: IN; 
Adjusted hospital price index: 1.285. 

Rank: 21; 
Metropolitan area: Honolulu; 
Predominant state[A]: HI; 
Adjusted hospital price index: 1.277. 

Rank: 22; 
Metropolitan area: Albany; 
Predominant state[A]: GA; 
Adjusted hospital price index: 1.270. 

Rank: 23; 
Metropolitan area: Oklahoma City; 
Predominant state[A]: OK; 
Adjusted hospital price index: 1.270. 

Rank: 24; 
Metropolitan area: Nashua; 
Predominant state[A]: NH; 
Adjusted hospital price index: 1.266. 

Rank: 25; 
Metropolitan area: Olympia; 
Predominant state[A]: WA; 
Adjusted hospital price index: 1.262. 

Rank: 26; 
Metropolitan area: Omaha, NE-IA; 
Predominant state[A]: NE; 
Adjusted hospital price index: 1.256. 

Rank: 27; 
Metropolitan area: Duluth-Superior, MN-WI; 
Predominant state[A]: MN; 
Adjusted hospital price index: 1.252. 

Rank: 28; 
Metropolitan area: Rapid City; 
Predominant state[A]: SD; 
Adjusted hospital price index: 1.249. 

Rank: 29; 
Metropolitan area: Terre Haute; 
Predominant state[A]: IN; 
Adjusted hospital price index: 1.244. 

Rank: 30; 
Metropolitan area: Charleston; 
Predominant state[A]: WV; 
Adjusted hospital price index: 1.243. 

Rank: 31; 
Metropolitan area: Wilmington-Newark, DE-MD; 
Predominant state[A]: DE; 
Adjusted hospital price index: 1.239. 

Rank: 32; 
Metropolitan area: Lynchburg; 
Predominant state[A]: VA; 
Adjusted hospital price index: 1.237. 

Rank: 33; 
Metropolitan area: Billings; 
Predominant state[A]: MT; 
Adjusted hospital price index: 1.235. 

Rank: 34; 
Metropolitan area: [B]; 
Predominant state[A]: [B]; 
Adjusted hospital price index: 1.233. 

Rank: 35; 
Metropolitan area: Myrtle Beach; 
Predominant state[A]: SC; 
Adjusted hospital price index: 1.231. 

Rank: 36; 
Metropolitan area: Columbia; 
Predominant state[A]: MO; 
Adjusted hospital price index: 1.230. 

Rank: 37; 
Metropolitan area: Topeka; 
Predominant state[A]: KS; 
Adjusted hospital price index: 1.225. 

Rank: 38; 
Metropolitan area: Evansville-Henderson, IN-KY; 
Predominant state[A]: IN; 
Adjusted hospital price index: 1.193. 

Rank: 39; 
Metropolitan area: Lawton; 
Predominant state[A]: OK; 
Adjusted hospital price index: 1.192. 

Rank: 40; 
Metropolitan area: Missoula; 
Predominant state[A]: MT; 
Adjusted hospital price index: 1.187. 

Rank: 41; 
Metropolitan area: Daytona Beach; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.186. 

Rank: 42; 
Metropolitan area: Medford-Ashland; 
Predominant state[A]: OR; 
Adjusted hospital price index: 1.177. 

Rank: 43; 
Metropolitan area: Roanoke; 
Predominant state[A]: VA; 
Adjusted hospital price index: 1.176. 

Rank: 44; 
Metropolitan area: Bismarck; 
Predominant state[A]: ND; 
Adjusted hospital price index: 1.173. 

Rank: 45; 
Metropolitan area: Charleston-North Charleston; 
Predominant state[A]: SC; 
Adjusted hospital price index: 1.161. 

Rank: 46; 
Metropolitan area: Portland; 
Predominant state[A]: ME; 
Adjusted hospital price index: 1.158. 

Rank: 47; 
Metropolitan area: Sioux City, IA-NE; 
Predominant state[A]: IA; 
Adjusted hospital price index: 1.157. 

Rank: 48; 
Metropolitan area: Jackson; 
Predominant state[A]: MS; 
Adjusted hospital price index: 1.151. 

Rank: 49; 
Metropolitan area: Hattiesburg; 
Predominant state[A]: MS; 
Adjusted hospital price index: 1.148. 

Rank: 50; 
Metropolitan area: Provo-Orem; 
Predominant state[A]: UT; 
Adjusted hospital price index: 1.147. 

Rank: 51; 
Metropolitan area: Fort Collins-Loveland; 
Predominant state[A]: CO; 
Adjusted hospital price index: 1.144. 

Rank: 52; 
Metropolitan area: Boise City; 
Predominant state[A]: ID; 
Adjusted hospital price index: 1.138. 

Rank: 53; 
Metropolitan area: Salt Lake City-Ogden; 
Predominant state[A]: UT; 
Adjusted hospital price index: 1.137. 

Rank: 54; 
Metropolitan area: Enid; 
Predominant state[A]: OK; 
Adjusted hospital price index: 1.137. 

Rank: 55; 
Metropolitan area: Gainesville; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.136. 

Rank: 56; 
Metropolitan area: San Antonio; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.132. 

Rank: 57; 
Metropolitan area: Parkersburg-Marietta, WV-OH; 
Predominant state[A]: WV; 
Adjusted hospital price index: 1.127. 

Rank: 58; 
Metropolitan area: Boston, MA-NH; 
Predominant state[A]: MA; 
Adjusted hospital price index: 1.123. 

Rank: 59; 
Metropolitan area: Memphis, TN-AR-MS; 
Predominant state[A]: TN; 
Adjusted hospital price index: 1.117. 

Rank: 60; 
Metropolitan area: Cedar Rapids; 
Predominant state[A]: IA; 
Adjusted hospital price index: 1.113. 

Rank: 61; 
Metropolitan area: Jackson; 
Predominant state[A]: TN; 
Adjusted hospital price index: 1.111. 

Rank: 62; 
Metropolitan area: Houston; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.103. 

Rank: 63; 
Metropolitan area: Huntington-Ashland, WV-KY-OH; 
Predominant state[A]: WV; 
Adjusted hospital price index: 1.102. 

Rank: 64; 
Metropolitan area: Fayetteville; 
Predominant state[A]: NC; 
Adjusted hospital price index: 1.102. 

Rank: 65; 
Metropolitan area: Springfield; 
Predominant state[A]: MA; 
Adjusted hospital price index: 1.101. 

Rank: 66; 
Metropolitan area: Melbourne-Titusville-Palm Bay; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.099. 

Rank: 67; 
Metropolitan area: Portland-Vancouver, OR-WA; 
Predominant state[A]: OR; 
Adjusted hospital price index: 1.098. 

Rank: 68; 
Metropolitan area: Iowa City; 
Predominant state[A]: IA; 
Adjusted hospital price index: 1.092. 

Rank: 69; 
Metropolitan area: Florence; 
Predominant state[A]: SC; 
Adjusted hospital price index: 1.087. 

Rank: 70; 
Metropolitan area: Fort Pierce-Port St. Lucie; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.086. 

Rank: 71; 
Metropolitan area: Tacoma; 
Predominant state[A]: WA; 
Adjusted hospital price index: 1.086. 

Rank: 72; 
Metropolitan area: Grand Forks, ND-MN; 
Predominant state[A]: ND; 
Adjusted hospital price index: 1.083. 

Rank: 73; 
Metropolitan area: Lubbock; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.078. 

Rank: 74; 
Metropolitan area: New Haven-Meriden; 
Predominant state[A]: CT; 
Adjusted hospital price index: 1.071. 

Rank: 75; 
Metropolitan area: Great Falls; 
Predominant state[A]: MT; 
Adjusted hospital price index: 1.068. 

Rank: 76; 
Metropolitan area: Columbus, GA-AL; 
Predominant state[A]: GA; 
Adjusted hospital price index: 1.065. 

Rank: 77; 
Metropolitan area: Fort Myers-Cape Coral; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.061. 

Rank: 78; 
Metropolitan area: Fargo-Moorhead, ND-MN; 
Predominant state[A]: ND; 
Adjusted hospital price index: 1.061. 

Rank: 79; 
Metropolitan area: Des Moines; 
Predominant state[A]: IA; 
Adjusted hospital price index: 1.060. 

Rank: 80; 
Metropolitan area: Minneapolis-St. Paul, MN-WI; 
Predominant state[A]: MN; 
Adjusted hospital price index: 1.057. 

Rank: 81; 
Metropolitan area: Fort Smith, AR-OK; 
Predominant state[A]: AR; 
Adjusted hospital price index: 1.052. 

Rank: 82; 
Metropolitan area: Bremerton; 
Predominant state[A]: WA; 
Adjusted hospital price index: 1.048. 

Rank: 83; 
Metropolitan area: Richmond-Petersburg; 
Predominant state[A]: VA; 
Adjusted hospital price index: 1.041. 

Rank: 84; 
Metropolitan area: Lincoln; 
Predominant state[A]: NE; 
Adjusted hospital price index: 1.040. 

Rank: 85; 
Metropolitan area: Phoenix-Mesa; 
Predominant state[A]: AZ; 
Adjusted hospital price index: 1.039. 

Rank: 86; 
Metropolitan area: Laredo; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.033. 

Rank: 87; 
Metropolitan area: Salem; 
Predominant state[A]: OR; 
Adjusted hospital price index: 1.031. 

Rank: 88; 
Metropolitan area: Bloomington; 
Predominant state[A]: IN; 
Adjusted hospital price index: 1.029. 

Rank: 89; 
Metropolitan area: Lexington; 
Predominant state[A]: KY; 
Adjusted hospital price index: 1.029. 

Rank: 90; 
Metropolitan area: Reading; 
Predominant state[A]: PA; 
Adjusted hospital price index: 1.028. 

Rank: 91; 
Metropolitan area: Augusta-Aiken, GA-SC; 
Predominant state[A]: GA; 
Adjusted hospital price index: 1.027. 

Rank: 92; 
Metropolitan area: Fort Worth-Arlington; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.025. 

Rank: 93; 
Metropolitan area: [B]; 
Predominant state[A]: [B]; 
Adjusted hospital price index: 1.024. 

Rank: 94; 
Metropolitan area: Austin-San Marcos; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.019. 

Rank: 95; 
Metropolitan area: Asheville; 
Predominant state[A]: NC; 
Adjusted hospital price index: 1.016. 

Rank: 96; 
Metropolitan area: Wichita Falls; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.015. 

Rank: 97; 
Metropolitan area: Little Rock-North Little Rock; 
Predominant state[A]: AR; 
Adjusted hospital price index: 1.015. 

Rank: 98; 
Metropolitan area: Las Vegas, NV-AZ; 
Predominant state[A]: NV; 
Adjusted hospital price index: 1.013. 

Rank: 99; 
Metropolitan area: McAllen-Edinburg-Mission; 
Predominant state[A]: TX; 
Adjusted hospital price index: 1.011. 

Rank: 100; 
Metropolitan area: Jonesboro; 
Predominant state[A]: AR; 
Adjusted hospital price index: 1.006. 

Rank: 101; 
Metropolitan area: Miami; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.006. 

Rank: 102; 
Metropolitan area: Charlotte-Gastonia-Rock Hill, NC-SC; 
Predominant state[A]: NC; 
Adjusted hospital price index: 1.002. 

Rank: 103; 
Metropolitan area: Orlando; 
Predominant state[A]: FL; 
Adjusted hospital price index: 1.001. 

Rank: 104; 
Metropolitan area: Seattle-Bellevue-Everett; 
Predominant state[A]: WA; 
Adjusted hospital price index: 0.993. 

Rank: 105; 
Metropolitan area: Pensacola; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.986. 

Rank: 106; 
Metropolitan area: Odessa-Midland; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.983. 

Rank: 107; 
Metropolitan area: Lansing-East Lansing; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.983. 

Rank: 108; 
Metropolitan area: Johnson City-Kingsport-Bristol, TN-VA; 
Predominant state[A]: TN; 
Adjusted hospital price index: 0.981. 

Rank: 109; 
Metropolitan area: Charlottesville; 
Predominant state[A]: VA; 
Adjusted hospital price index: 0.980. 

Rank: 110; 
Metropolitan area: Knoxville; 
Predominant state[A]: TN; 
Adjusted hospital price index: 0.978. 

Rank: 111; 
Metropolitan area: Fayetteville-Springdale-Rogers; 
Predominant state[A]: AR; 
Adjusted hospital price index: 0.978. 

Rank: 112; 
Metropolitan area: Clarksville-Hopkinsville, TN-KY; 
Predominant state[A]: TN; 
Adjusted hospital price index: 0.975. 

Rank: 113; 
Metropolitan area: Dayton-Springfield; 
Predominant state[A]: OH; 
Adjusted hospital price index: 0.974. 

Rank: 114; 
Metropolitan area: San Angelo; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.971. 

Rank: 115; 
Metropolitan area: Tucson; 
Predominant state[A]: AZ; 
Adjusted hospital price index: 0.970. 

Rank: 116; 
Metropolitan area: Tampa-St. Petersburg-Clearwater; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.967. 

Rank: 117; 
Metropolitan area: Ann Arbor; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.965. 

Rank: 118; 
Metropolitan area: Scranton--Wilkes-Barre--Hazleton; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.964. 

Rank: 119; 
Metropolitan area: Eugene-Springfield; 
Predominant state[A]: OR; 
Adjusted hospital price index: 0.964. 

Rank: 120; 
Metropolitan area: Atlantic-Cape May; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.963. 

Rank: 121; 
Metropolitan area: Anchorage; 
Predominant state[A]: AK; 
Adjusted hospital price index: 0.962. 

Rank: 122; 
Metropolitan area: Bridgeport; 
Predominant state[A]: CT; 
Adjusted hospital price index: 0.961. 

Rank: 123; 
Metropolitan area: San Francisco; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.960. 

Rank: 124; 
Metropolitan area: Panama City; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.957. 

Rank: 125; 
Metropolitan area: Baltimore; 
Predominant state[A]: MD; 
Adjusted hospital price index: 0.953. 

Rank: 126; 
Metropolitan area: Greenville-Spartanburg-Anderson; 
Predominant state[A]: SC; 
Adjusted hospital price index: 0.950. 

Rank: 127; 
Metropolitan area: Trenton; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.946. 

Rank: 128; 
Metropolitan area: Redding; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.946. 

Rank: 129; 
Metropolitan area: York; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.942. 

Rank: 130; 
Metropolitan area: Amarillo; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.941. 

Rank: 131; 
Metropolitan area: Lawrence, MA-NH; 
Predominant state[A]: MA; 
Adjusted hospital price index: 0.933. 

Rank: 132; 
Metropolitan area: Springfield; 
Predominant state[A]: MO; 
Adjusted hospital price index: 0.932. 

Rank: 133; 
Metropolitan area: Washington, DC-MD-VA-WV; 
Predominant state[A]: VA; 
Adjusted hospital price index: 0.931. 

Rank: 134; 
Metropolitan area: Las Cruces; 
Predominant state[A]: NM; 
Adjusted hospital price index: 0.930. 

Rank: 135; 
Metropolitan area: Indianapolis; 
Predominant state[A]: IN; 
Adjusted hospital price index: 0.928. 

Rank: 136; 
Metropolitan area: Gary; 
Predominant state[A]: IN; 
Adjusted hospital price index: 0.927. 

Rank: 137; 
Metropolitan area: Detroit; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.927. 

Rank: 138; 
Metropolitan area: Tulsa; 
Predominant state[A]: OK; 
Adjusted hospital price index: 0.921. 

Rank: 139; 
Metropolitan area: Greensboro--Winston-Salem--High Point; 
Predominant state[A]: NC; 
Adjusted hospital price index: 0.919. 

Rank: 140; 
Metropolitan area: Nashville; 
Predominant state[A]: TN; 
Adjusted hospital price index: 0.914. 

Rank: 141; 
Metropolitan area: Santa Fe; 
Predominant state[A]: NM; 
Adjusted hospital price index: 0.912. 

Rank: 142; 
Metropolitan area: Raleigh-Durham-Chapel Hill; 
Predominant state[A]: NC; 
Adjusted hospital price index: 0.911. 

Rank: 143; 
Metropolitan area: Grand Rapids-Muskegon-Holland; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.906. 

Rank: 144; 
Metropolitan area: Baton Rouge; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.905. 

Rank: 145; 
Metropolitan area: Columbia; 
Predominant state[A]: SC; 
Adjusted hospital price index: 0.900. 

Rank: 146; 
Metropolitan area: Middlesex-Somerset-Hunterdon; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.899. 

Rank: 147; 
Metropolitan area: Sarasota-Bradenton; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.896. 

Rank: 148; 
Metropolitan area: Cumberland, MD-WV; 
Predominant state[A]: MD; 
Adjusted hospital price index: 0.895. 

Rank: 149; 
Metropolitan area: Waterbury; 
Predominant state[A]: CT; 
Adjusted hospital price index: 0.894. 

Rank: 150; 
Metropolitan area: Atlanta; 
Predominant state[A]: GA; 
Adjusted hospital price index: 0.891. 

Rank: 151; 
Metropolitan area: [B]; 
Predominant state[A]: [B]; 
Adjusted hospital price index: 0.889. 

Rank: 152; 
Metropolitan area: Macon; 
Predominant state[A]: GA; 
Adjusted hospital price index: 0.888. 

Rank: 153; 
Metropolitan area: Birmingham; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.886. 

Rank: 154; 
Metropolitan area: Harrisburg-Lebanon-Carlisle; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.885. 

Rank: 155; 
Metropolitan area: Sacramento; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.884. 

Rank: 156; 
Metropolitan area: Fort Wayne; 
Predominant state[A]: IN; 
Adjusted hospital price index: 0.883. 

Rank: 157; 
Metropolitan area: New London-Norwich, CT-RI; 
Predominant state[A]: CT; 
Adjusted hospital price index: 0.876. 

Rank: 158; 
Metropolitan area: Toledo; 
Predominant state[A]: OH; 
Adjusted hospital price index: 0.875. 

Rank: 159; 
Metropolitan area: New Orleans; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.873. 

Rank: 160; 
Metropolitan area: Florence; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.870. 

Rank: 161; 
Metropolitan area: West Palm Beach-Boca Raton; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.870. 

Rank: 162; 
Metropolitan area: Mobile; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.870. 

Rank: 163; 
Metropolitan area: Columbus; 
Predominant state[A]: OH; 
Adjusted hospital price index: 0.868. 

Rank: 164; 
Metropolitan area: Hartford; 
Predominant state[A]: CT; 
Adjusted hospital price index: 0.867. 

Rank: 165; 
Metropolitan area: Fort Lauderdale; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.866. 

Rank: 166; 
Metropolitan area: Corpus Christi; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.866. 

Rank: 167; 
Metropolitan area: Savannah; 
Predominant state[A]: GA; 
Adjusted hospital price index: 0.865. 

Rank: 168; 
Metropolitan area: Monroe; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.864. 

Rank: 169; 
Metropolitan area: Montgomery; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.864. 

Rank: 170; 
Metropolitan area: Houma; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.864. 

Rank: 171; 
Metropolitan area: Galveston-Texas City; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.862. 

Rank: 172; 
Metropolitan area: Dallas; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.861. 

Rank: 173; 
Metropolitan area: Richland-Kennewick-Pasco; 
Predominant state[A]: WA; 
Adjusted hospital price index: 0.861. 

Rank: 174; 
Metropolitan area: Norfolk-Virginia Beach-Newport News, VA- NC; 
Predominant state[A]: VA; 
Adjusted hospital price index: 0.861. 

Rank: 175; 
Metropolitan area: Pittsburgh; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.861. 

Rank: 176; 
Metropolitan area: Bergen-Passaic; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.860. 

Rank: 177; 
Metropolitan area: Denver; 
Predominant state[A]: CO; 
Adjusted hospital price index: 0.859. 

Rank: 178; 
Metropolitan area: Bryan-College Station; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.859. 

Rank: 179; 
Metropolitan area: Colorado Springs; 
Predominant state[A]: CO; 
Adjusted hospital price index: 0.859. 

Rank: 180; 
Metropolitan area: Monmouth-Ocean; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.859. 

Rank: 181; 
Metropolitan area: Reno; 
Predominant state[A]: NV; 
Adjusted hospital price index: 0.858. 

Rank: 182; 
Metropolitan area: Texarkana, TX-Texarkana; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.857. 

Rank: 183; 
Metropolitan area: Punta Gorda; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.853. 

Rank: 184; 
Metropolitan area: Waco; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.853. 

Rank: 185; 
Metropolitan area: Flint; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.847. 

Rank: 186; 
Metropolitan area: Kansas City, MO-KS; 
Predominant state[A]: MO; 
Adjusted hospital price index: 0.838. 

Rank: 187; 
Metropolitan area: Oakland; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.836. 

Rank: 188; 
Metropolitan area: Killeen-Temple; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.830. 

Rank: 189; 
Metropolitan area: Tuscaloosa; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.826. 

Rank: 190; 
Metropolitan area: Philadelphia, PA-NJ; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.820. 

Rank: 191; 
Metropolitan area: Chattanooga, TN-GA; 
Predominant state[A]: TN; 
Adjusted hospital price index: 0.814. 

Rank: 192; 
Metropolitan area: Providence-Fall River-Warwick, RI-MA; 
Predominant state[A]: RI; 
Adjusted hospital price index: 0.813. 

Rank: 193; 
Metropolitan area: Sherman-Denison; 
Predominant state[A]: TX; 
Adjusted hospital price index: 0.812. 

Rank: 194; 
Metropolitan area: Kalamazoo-Battle Creek; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.808. 

Rank: 195; 
Metropolitan area: Jacksonville; 
Predominant state[A]: FL; 
Adjusted hospital price index: 0.807. 

Rank: 196; 
Metropolitan area: Boulder-Longmont; 
Predominant state[A]: CO; 
Adjusted hospital price index: 0.804. 

Rank: 197; 
Metropolitan area: Cleveland-Lorain-Elyria; 
Predominant state[A]: OH; 
Adjusted hospital price index: 0.803. 

Rank: 198; 
Metropolitan area: Shreveport-Bossier City; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.799. 

Rank: 199; 
Metropolitan area: Syracuse; 
Predominant state[A]: NY; 
Adjusted hospital price index: 0.797. 

Rank: 200; 
Metropolitan area: Wilmington; 
Predominant state[A]: NC; 
Adjusted hospital price index: 0.794. 

Rank: 201; 
Metropolitan area: Erie; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.790. 

Rank: 202; 
Metropolitan area: Jersey City; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.787. 

Rank: 203; 
Metropolitan area: Yakima; 
Predominant state[A]: WA; 
Adjusted hospital price index: 0.786. 

Rank: 204; 
Metropolitan area: Los Angeles-Long Beach; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.785. 

Rank: 205; 
Metropolitan area: Chicago; 
Predominant state[A]: IL; 
Adjusted hospital price index: 0.785. 

Rank: 206; 
Metropolitan area: Huntsville; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.780. 

Rank: 207; 
Metropolitan area: Hagerstown; 
Predominant state[A]: MD; 
Adjusted hospital price index: 0.779. 

Rank: 208; 
Metropolitan area: Johnstown; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.777. 

Rank: 209; 
Metropolitan area: Cincinnati, OH-KY-IN; 
Predominant state[A]: OH; 
Adjusted hospital price index: 0.776. 

Rank: 210; 
Metropolitan area: Lafayette; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.772. 

Rank: 211; 
Metropolitan area: Gadsden; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.769. 

Rank: 212; 
Metropolitan area: Lake Charles; 
Predominant state[A]: LA; 
Adjusted hospital price index: 0.764. 

Rank: 213; 
Metropolitan area: Louisville, KY-IN; 
Predominant state[A]: KY; 
Adjusted hospital price index: 0.761. 

Rank: 214; 
Metropolitan area: Allentown-Bethlehem-Easton; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.754. 

Rank: 215; 
Metropolitan area: Spokane; 
Predominant state[A]: WA; 
Adjusted hospital price index: 0.746. 

Rank: 216; 
Metropolitan area: Athens; 
Predominant state[A]: GA; 
Adjusted hospital price index: 0.745. 

Rank: 217; 
Metropolitan area: Albuquerque; 
Predominant state[A]: NM; 
Adjusted hospital price index: 0.743. 

Rank: 218; 
Metropolitan area: Nassau-Suffolk; 
Predominant state[A]: NY; 
Adjusted hospital price index: 0.740. 

Rank: 219; 
Metropolitan area: Dothan; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.728. 

Rank: 220; 
Metropolitan area: San Diego; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.727. 

Rank: 221; 
Metropolitan area: Riverside-San Bernardino; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.727. 

Rank: 222; 
Metropolitan area: Newark; 
Predominant state[A]: NJ; 
Adjusted hospital price index: 0.725. 

Rank: 223; 
Metropolitan area: Saginaw-Bay City-Midland; 
Predominant state[A]: MI; 
Adjusted hospital price index: 0.712. 

Rank: 224; 
Metropolitan area: Anniston; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.709. 

Rank: 225; 
Metropolitan area: Decatur; 
Predominant state[A]: AL; 
Adjusted hospital price index: 0.709. 

Rank: 226; 
Metropolitan area: Altoona; 
Predominant state[A]: PA; 
Adjusted hospital price index: 0.678. 

Rank: 227; 
Metropolitan area: New York; 
Predominant state[A]: NY; 
Adjusted hospital price index: 0.676. 

Rank: 228; 
Metropolitan area: Newburgh, NY-PA; 
Predominant state[A]: NY; 
Adjusted hospital price index: 0.675. 

Rank: 229; 
Metropolitan area: Albany-Schenectady-Troy; 
Predominant state[A]: NY; 
Adjusted hospital price index: 0.674. 

Rank: 230; 
Metropolitan area: Ventura; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.635. 

Rank: 231; 
Metropolitan area: Pueblo; 
Predominant state[A]: CO; 
Adjusted hospital price index: 0.609. 

Rank: 232; 
Metropolitan area: Orange County; 
Predominant state[A]: CA; 
Adjusted hospital price index: 0.515. 

Source: GAO analysis of FEHBP data. 

Note: We adjusted hospital prices to remove the effect of geographic 
differences in the costs of doing business (wages, rents, etc.) and 
differences in the severity of illnesses and mix of diagnoses among 
metropolitan areas. We converted hospital prices to an index by 
dividing the average price for a hospital stay in a metropolitan area 
by the average price for all hospital stays in 232 metropolitan areas. 
The average hospital price index value is 1.00. 

[A] Some metropolitan areas spanned more than one state. In those 
cases, we assigned the state that contained the largest proportion of 
the population of the metropolitan area. 

[B] Metropolitan area name withheld because there was only one hospital 
in the metropolitan area and the data were proprietary. 

[End of table]

[End of section]

Appendix III: FEHBP PPO Adjusted Physician Prices in U.S. Metropolitan 
Areas, 2001: 

[End of section]

The adjusted physician price indices based on FEHBP PPO payments for 
physician services in 319 metropolitan areas are presented below ranked 
in order from highest to lowest price. 

Table 16: Ranking of Metropolitan Areas by Adjusted Physician Prices, 
2001: 

Rank: 1; 
Metropolitan area: La Crosse, WI-MN; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.484. 

Rank: 2; 
Metropolitan area: Wausau; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.459. 

Rank: 3; 
Metropolitan area: Eau Claire; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.418. 

Rank: 4; 
Metropolitan area: Madison; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.414. 

Rank: 5; 
Metropolitan area: Jonesboro; 
Predominant state[A]: AR; 
Adjusted physician price index: 1.348. 

Rank: 6; 
Metropolitan area: Janesville-Beloit; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.324. 

Rank: 7; 
Metropolitan area: Great Falls; 
Predominant state[A]: MT; 
Adjusted physician price index: 1.287. 

Rank: 8; 
Metropolitan area: Green Bay; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.279. 

Rank: 9; 
Metropolitan area: Appleton-Oshkosh-Neenah; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.267. 

Rank: 10; 
Metropolitan area: Racine; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.239. 

Rank: 11; 
Metropolitan area: Sheboygan; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.231. 

Rank: 12; 
Metropolitan area: Billings; 
Predominant state[A]: MT; 
Adjusted physician price index: 1.230. 

Rank: 13; 
Metropolitan area: Wichita Falls; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.224. 

Rank: 14; 
Metropolitan area: Anchorage; 
Predominant state[A]: AK; 
Adjusted physician price index: 1.221. 

Rank: 15; 
Metropolitan area: Corvallis; 
Predominant state[A]: OR; 
Adjusted physician price index: 1.220. 

Rank: 16; 
Metropolitan area: Milwaukee-Waukesha; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.217. 

Rank: 17; 
Metropolitan area: Jacksonville; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.216. 

Rank: 18; 
Metropolitan area: Kenosha; 
Predominant state[A]: WI; 
Adjusted physician price index: 1.213. 

Rank: 19; 
Metropolitan area: Fayetteville-Springdale-Rogers; 
Predominant state[A]: AR; 
Adjusted physician price index: 1.206. 

Rank: 20; 
Metropolitan area: Texarkana, TX-Texarkana; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.204. 

Rank: 21; 
Metropolitan area: Fort Smith, AR-OK; 
Predominant state[A]: AR; 
Adjusted physician price index: 1.202. 

Rank: 22; 
Metropolitan area: Monroe; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.198. 

Rank: 23; 
Metropolitan area: Pine Bluff; 
Predominant state[A]: AR; 
Adjusted physician price index: 1.194. 

Rank: 24; 
Metropolitan area: Missoula; 
Predominant state[A]: MT; 
Adjusted physician price index: 1.190. 

Rank: 25; 
Metropolitan area: Salem; 
Predominant state[A]: OR; 
Adjusted physician price index: 1.187. 

Rank: 26; 
Metropolitan area: St. Cloud; 
Predominant state[A]: MN; 
Adjusted physician price index: 1.187. 

Rank: 27; 
Metropolitan area: Eugene-Springfield; 
Predominant state[A]: OR; 
Adjusted physician price index: 1.184. 

Rank: 28; 
Metropolitan area: Duluth-Superior, MN-WI; 
Predominant state[A]: MN; 
Adjusted physician price index: 1.178. 

Rank: 29; 
Metropolitan area: Medford-Ashland; 
Predominant state[A]: OR; 
Adjusted physician price index: 1.165. 

Rank: 30; 
Metropolitan area: Alexandria; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.162. 

Rank: 31; 
Metropolitan area: Houma; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.159. 

Rank: 32; 
Metropolitan area: Sherman-Denison; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.159. 

Rank: 33; 
Metropolitan area: Wheeling, WV-OH; 
Predominant state[A]: WV; 
Adjusted physician price index: 1.157. 

Rank: 34; 
Metropolitan area: Shreveport-Bossier City; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.145. 

Rank: 35; 
Metropolitan area: Grand Junction; 
Predominant state[A]: CO; 
Adjusted physician price index: 1.144. 

Rank: 36; 
Metropolitan area: Omaha, NE-IA; 
Predominant state[A]: NE; 
Adjusted physician price index: 1.143. 

Rank: 37; 
Metropolitan area: Bryan-College Station; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.143. 

Rank: 38; 
Metropolitan area: Little Rock-North Little Rock; 
Predominant state[A]: AR; 
Adjusted physician price index: 1.142. 

Rank: 39; 
Metropolitan area: Rocky Mount; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.136. 

Rank: 40; 
Metropolitan area: Springfield; 
Predominant state[A]: MO; 
Adjusted physician price index: 1.135. 

Rank: 41; 
Metropolitan area: Lafayette; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.134. 

Rank: 42; 
Metropolitan area: Lubbock; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.129. 

Rank: 43; 
Metropolitan area: San Angelo; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.129. 

Rank: 44; 
Metropolitan area: Lincoln; 
Predominant state[A]: NE; 
Adjusted physician price index: 1.129. 

Rank: 45; 
Metropolitan area: Pueblo; 
Predominant state[A]: CO; 
Adjusted physician price index: 1.128. 

Rank: 46; 
Metropolitan area: Abilene; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.121. 

Rank: 47; 
Metropolitan area: Hattiesburg; 
Predominant state[A]: MS; 
Adjusted physician price index: 1.119. 

Rank: 48; 
Metropolitan area: Kankakee; 
Predominant state[A]: IL; 
Adjusted physician price index: 1.119. 

Rank: 49; 
Metropolitan area: Fayetteville; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.111. 

Rank: 50; 
Metropolitan area: Parkersburg-Marietta, WV-OH; 
Predominant state[A]: WV; 
Adjusted physician price index: 1.111. 

Rank: 51; 
Metropolitan area: Jackson; 
Predominant state[A]: TN; 
Adjusted physician price index: 1.106. 

Rank: 52; 
Metropolitan area: Charleston; 
Predominant state[A]: WV; 
Adjusted physician price index: 1.105. 

Rank: 53; 
Metropolitan area: Longview-Marshall; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.103. 

Rank: 54; 
Metropolitan area: Sioux City, IA-NE; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.101. 

Rank: 55; 
Metropolitan area: Clarksville-Hopkinsville, TN-KY; 
Predominant state[A]: TN; 
Adjusted physician price index: 1.101. 

Rank: 56; 
Metropolitan area: Albany; 
Predominant state[A]: GA; 
Adjusted physician price index: 1.098. 

Rank: 57; 
Metropolitan area: Bismarck; 
Predominant state[A]: ND; 
Adjusted physician price index: 1.097. 

Rank: 58; 
Metropolitan area: Lawrence; 
Predominant state[A]: KS; 
Adjusted physician price index: 1.096. 

Rank: 59; 
Metropolitan area: Panama City; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.096. 

Rank: 60; 
Metropolitan area: Rapid City; 
Predominant state[A]: SD; 
Adjusted physician price index: 1.096. 

Rank: 61; 
Metropolitan area: Lewiston-Auburn; 
Predominant state[A]: ME; 
Adjusted physician price index: 1.096. 

Rank: 62; 
Metropolitan area: Bangor; 
Predominant state[A]: ME; 
Adjusted physician price index: 1.095. 

Rank: 63; 
Metropolitan area: Muncie; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.093. 

Rank: 64; 
Metropolitan area: Baton Rouge; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.093. 

Rank: 65; 
Metropolitan area: Grand Forks, ND-MN; 
Predominant state[A]: ND; 
Adjusted physician price index: 1.091. 

Rank: 66; 
Metropolitan area: Portland-Vancouver, OR-WA; 
Predominant state[A]: OR; 
Adjusted physician price index: 1.085. 

Rank: 67; 
Metropolitan area: Huntington-Ashland, WV-KY-OH; 
Predominant state[A]: WV; 
Adjusted physician price index: 1.085. 

Rank: 68; 
Metropolitan area: Elmira; 
Predominant state[A]: NY; 
Adjusted physician price index: 1.084. 

Rank: 69; 
Metropolitan area: Tyler; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.084. 

Rank: 70; 
Metropolitan area: Pocatello; 
Predominant state[A]: ID; 
Adjusted physician price index: 1.083. 

Rank: 71; 
Metropolitan area: Dubuque; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.082. 

Rank: 72; 
Metropolitan area: Macon; 
Predominant state[A]: GA; 
Adjusted physician price index: 1.081. 

Rank: 73; 
Metropolitan area: Terre Haute; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.079. 

Rank: 74; 
Metropolitan area: Goldsboro; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.078. 

Rank: 75; 
Metropolitan area: Greenville; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.077. 

Rank: 76; 
Metropolitan area: Columbus, GA-AL; 
Predominant state[A]: GA; 
Adjusted physician price index: 1.075. 

Rank: 77; 
Metropolitan area: McAllen-Edinburg-Mission; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.074. 

Rank: 78; 
Metropolitan area: Brownsville-Harlingen-San Benito; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.072. 

Rank: 79; 
Metropolitan area: Glens Falls; 
Predominant state[A]: NY; 
Adjusted physician price index: 1.072. 

Rank: 80; 
Metropolitan area: Johnson City-Kingsport-Bristol, TN-VA; 
Predominant state[A]: TN; 
Adjusted physician price index: 1.072. 

Rank: 81; 
Metropolitan area: Laredo; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.072. 

Rank: 82; 
Metropolitan area: Waco; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.069. 

Rank: 83; 
Metropolitan area: Cedar Rapids; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.067. 

Rank: 84; 
Metropolitan area: Boise City; 
Predominant state[A]: ID; 
Adjusted physician price index: 1.066. 

Rank: 85; 
Metropolitan area: Greeley; 
Predominant state[A]: CO; 
Adjusted physician price index: 1.065. 

Rank: 86; 
Metropolitan area: Fort Walton Beach; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.065. 

Rank: 87; 
Metropolitan area: Lawton; 
Predominant state[A]: OK; 
Adjusted physician price index: 1.064. 

Rank: 88; 
Metropolitan area: Iowa City; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.063. 

Rank: 89; 
Metropolitan area: Hickory-Morganton-Lenoir; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.062. 

Rank: 90; 
Metropolitan area: Asheville; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.060. 

Rank: 91; 
Metropolitan area: Lake Charles; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.059. 

Rank: 92; 
Metropolitan area: Sioux Falls; 
Predominant state[A]: SD; 
Adjusted physician price index: 1.057. 

Rank: 93; 
Metropolitan area: Enid; 
Predominant state[A]: OK; 
Adjusted physician price index: 1.057. 

Rank: 94; 
Metropolitan area: Portland; 
Predominant state[A]: ME; 
Adjusted physician price index: 1.055. 

Rank: 95; 
Metropolitan area: Pensacola; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.051. 

Rank: 96; 
Metropolitan area: Yuma; 
Predominant state[A]: AZ; 
Adjusted physician price index: 1.051. 

Rank: 97; 
Metropolitan area: Fort Myers-Cape Coral; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.050. 

Rank: 98; 
Metropolitan area: Joplin; 
Predominant state[A]: MO; 
Adjusted physician price index: 1.049. 

Rank: 99; 
Metropolitan area: South Bend; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.049. 

Rank: 100; 
Metropolitan area: Fort Wayne; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.049. 

Rank: 101; 
Metropolitan area: Lafayette; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.046. 

Rank: 102; 
Metropolitan area: St. Joseph; 
Predominant state[A]: MO; 
Adjusted physician price index: 1.046. 

Rank: 103; 
Metropolitan area: Biloxi-Gulfport-Pascagoula; 
Predominant state[A]: MS; 
Adjusted physician price index: 1.045. 

Rank: 104; 
Metropolitan area: Auburn-Opelika; 
Predominant state[A]: AL; 
Adjusted physician price index: 1.044. 

Rank: 105; 
Metropolitan area: Fort Worth-Arlington; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.043. 

Rank: 106; 
Metropolitan area: Odessa-Midland; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.043. 

Rank: 107; 
Metropolitan area: Fargo-Moorhead, ND-MN; 
Predominant state[A]: ND; 
Adjusted physician price index: 1.042. 

Rank: 108; 
Metropolitan area: Flagstaff, AZ-UT; 
Predominant state[A]: AZ; 
Adjusted physician price index: 1.042. 

Rank: 109; 
Metropolitan area: Savannah; 
Predominant state[A]: GA; 
Adjusted physician price index: 1.041. 

Rank: 110; 
Metropolitan area: Knoxville; 
Predominant state[A]: TN; 
Adjusted physician price index: 1.041. 

Rank: 111; 
Metropolitan area: Colorado Springs; 
Predominant state[A]: CO; 
Adjusted physician price index: 1.040. 

Rank: 112; 
Metropolitan area: Elkhart-Goshen; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.038. 

Rank: 113; 
Metropolitan area: Las Cruces; 
Predominant state[A]: NM; 
Adjusted physician price index: 1.037. 

Rank: 114; 
Metropolitan area: Evansville-Henderson, IN-KY; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.036. 

Rank: 115; 
Metropolitan area: Beaumont-Port Arthur; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.034. 

Rank: 116; 
Metropolitan area: Columbia; 
Predominant state[A]: MO; 
Adjusted physician price index: 1.034. 

Rank: 117; 
Metropolitan area: Topeka; 
Predominant state[A]: KS; 
Adjusted physician price index: 1.034. 

Rank: 118; 
Metropolitan area: Sharon; 
Predominant state[A]: PA; 
Adjusted physician price index: 1.034. 

Rank: 119; 
Metropolitan area: Fort Collins-Loveland; 
Predominant state[A]: CO; 
Adjusted physician price index: 1.033. 

Rank: 120; 
Metropolitan area: Killeen-Temple; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.033. 

Rank: 121; 
Metropolitan area: Owensboro; 
Predominant state[A]: KY; 
Adjusted physician price index: 1.032. 

Rank: 122; 
Metropolitan area: Sumter; 
Predominant state[A]: SC; 
Adjusted physician price index: 1.032. 

Rank: 123; 
Metropolitan area: Corpus Christi; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.030. 

Rank: 124; 
Metropolitan area: Yuba City; 
Predominant state[A]: CA; 
Adjusted physician price index: 1.029. 

Rank: 125; 
Metropolitan area: Victoria; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.029. 

Rank: 126; 
Metropolitan area: Jackson; 
Predominant state[A]: MS; 
Adjusted physician price index: 1.028. 

Rank: 127; 
Metropolitan area: Waterloo-Cedar Falls; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.027. 

Rank: 128; 
Metropolitan area: New Orleans; 
Predominant state[A]: LA; 
Adjusted physician price index: 1.026. 

Rank: 129; 
Metropolitan area: Yakima; 
Predominant state[A]: WA; 
Adjusted physician price index: 1.024. 

Rank: 130; 
Metropolitan area: Dallas; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.022. 

Rank: 131; 
Metropolitan area: Austin-San Marcos; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.021. 

Rank: 132; 
Metropolitan area: Utica-Rome; 
Predominant state[A]: NY; 
Adjusted physician price index: 1.021. 

Rank: 133; 
Metropolitan area: Portsmouth-Rochester, NH-ME; 
Predominant state[A]: NH; 
Adjusted physician price index: 1.018. 

Rank: 134; 
Metropolitan area: Brazoria; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.017. 

Rank: 135; 
Metropolitan area: Memphis, TN-AR-MS; 
Predominant state[A]: TN; 
Adjusted physician price index: 1.016. 

Rank: 136; 
Metropolitan area: Charlotte-Gastonia-Rock Hill, NC-SC; 
Predominant state[A]: NC; 
Adjusted physician price index: 1.016. 

Rank: 137; 
Metropolitan area: Wichita; 
Predominant state[A]: KS; 
Adjusted physician price index: 1.013. 

Rank: 138; 
Metropolitan area: Lima; 
Predominant state[A]: OH; 
Adjusted physician price index: 1.013. 

Rank: 139; 
Metropolitan area: Amarillo; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.011. 

Rank: 140; 
Metropolitan area: Minneapolis-St. Paul, MN-WI; 
Predominant state[A]: MN; 
Adjusted physician price index: 1.011. 

Rank: 141; 
Metropolitan area: Yolo; 
Predominant state[A]: CA; 
Adjusted physician price index: 1.010. 

Rank: 142; 
Metropolitan area: Dothan; 
Predominant state[A]: AL; 
Adjusted physician price index: 1.010. 

Rank: 143; 
Metropolitan area: Tallahassee; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.009. 

Rank: 144; 
Metropolitan area: Des Moines; 
Predominant state[A]: IA; 
Adjusted physician price index: 1.009. 

Rank: 145; 
Metropolitan area: El Paso; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.008. 

Rank: 146; 
Metropolitan area: Atlanta; 
Predominant state[A]: GA; 
Adjusted physician price index: 1.008. 

Rank: 147; 
Metropolitan area: San Antonio; 
Predominant state[A]: TX; 
Adjusted physician price index: 1.006. 

Rank: 148; 
Metropolitan area: Bloomington; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.006. 

Rank: 149; 
Metropolitan area: Syracuse; 
Predominant state[A]: NY; 
Adjusted physician price index: 1.006. 

Rank: 150; 
Metropolitan area: Redding; 
Predominant state[A]: CA; 
Adjusted physician price index: 1.005. 

Rank: 151; 
Metropolitan area: Albany-Schenectady-Troy; 
Predominant state[A]: NY; 
Adjusted physician price index: 1.005. 

Rank: 152; 
Metropolitan area: Altoona; 
Predominant state[A]: PA; 
Adjusted physician price index: 1.003. 

Rank: 153; 
Metropolitan area: Indianapolis; 
Predominant state[A]: IN; 
Adjusted physician price index: 1.002. 

Rank: 154; 
Metropolitan area: Lakeland-Winter Haven; 
Predominant state[A]: FL; 
Adjusted physician price index: 1.001. 

Rank: 155; 
Metropolitan area: Roanoke; 
Predominant state[A]: VA; 
Adjusted physician price index: 1.001. 

Rank: 156; 
Metropolitan area: Modesto; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.999. 

Rank: 157; 
Metropolitan area: Punta Gorda; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.999. 

Rank: 158; 
Metropolitan area: Augusta-Aiken, GA-SC; 
Predominant state[A]: GA; 
Adjusted physician price index: 0.998. 

Rank: 159; 
Metropolitan area: Mansfield; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.998. 

Rank: 160; 
Metropolitan area: Ocala; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.997. 

Rank: 161; 
Metropolitan area: Athens; 
Predominant state[A]: GA; 
Adjusted physician price index: 0.997. 

Rank: 162; 
Metropolitan area: Anniston; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.994. 

Rank: 163; 
Metropolitan area: Chico-Paradise; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.994. 

Rank: 164; 
Metropolitan area: Burlington; 
Predominant state[A]: VT; 
Adjusted physician price index: 0.994. 

Rank: 165; 
Metropolitan area: Tuscaloosa; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.993. 

Rank: 166; 
Metropolitan area: Binghamton; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.992. 

Rank: 167; 
Metropolitan area: Florence; 
Predominant state[A]: SC; 
Adjusted physician price index: 0.992. 

Rank: 168; 
Metropolitan area: Boulder-Longmont; 
Predominant state[A]: CO; 
Adjusted physician price index: 0.991. 

Rank: 169; 
Metropolitan area: Naples; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.991. 

Rank: 170; 
Metropolitan area: Spokane; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.991. 

Rank: 171; 
Metropolitan area: Albuquerque; 
Predominant state[A]: NM; 
Adjusted physician price index: 0.991. 

Rank: 172; 
Metropolitan area: Merced; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.991. 

Rank: 173; 
Metropolitan area: Chicago; 
Predominant state[A]: IL; 
Adjusted physician price index: 0.990. 

Rank: 174; 
Metropolitan area: Tulsa; 
Predominant state[A]: OK; 
Adjusted physician price index: 0.988. 

Rank: 175; 
Metropolitan area: Gainesville; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.983. 

Rank: 176; 
Metropolitan area: Johnstown; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.983. 

Rank: 177; 
Metropolitan area: Denver; 
Predominant state[A]: CO; 
Adjusted physician price index: 0.983. 

Rank: 178; 
Metropolitan area: Wilmington; 
Predominant state[A]: NC; 
Adjusted physician price index: 0.982. 

Rank: 179; 
Metropolitan area: Chattanooga, TN-GA; 
Predominant state[A]: TN; 
Adjusted physician price index: 0.981. 

Rank: 180; 
Metropolitan area: Lexington; 
Predominant state[A]: KY; 
Adjusted physician price index: 0.980. 

Rank: 181; 
Metropolitan area: Tacoma; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.979. 

Rank: 182; 
Metropolitan area: Galveston-Texas City; 
Predominant state[A]: TX; 
Adjusted physician price index: 0.979. 

Rank: 183; 
Metropolitan area: Norfolk-Virginia Beach-Newport News, VA- NC; 
Predominant state[A]: VA; 
Adjusted physician price index: 0.975. 

Rank: 184; 
Metropolitan area: Houston; 
Predominant state[A]: TX; 
Adjusted physician price index: 0.975. 

Rank: 185; 
Metropolitan area: Gary; 
Predominant state[A]: IN; 
Adjusted physician price index: 0.974. 

Rank: 186; 
Metropolitan area: Oklahoma City; 
Predominant state[A]: OK; 
Adjusted physician price index: 0.974. 

Rank: 187; 
Metropolitan area: Kokomo; 
Predominant state[A]: IN; 
Adjusted physician price index: 0.972. 

Rank: 188; 
Metropolitan area: Raleigh-Durham-Chapel Hill; 
Predominant state[A]: NC; 
Adjusted physician price index: 0.970. 

Rank: 189; 
Metropolitan area: Sarasota-Bradenton; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.969. 

Rank: 190; 
Metropolitan area: Mobile; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.966. 

Rank: 191; 
Metropolitan area: Bremerton; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.965. 

Rank: 192; 
Metropolitan area: Montgomery; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.964. 

Rank: 193; 
Metropolitan area: Myrtle Beach; 
Predominant state[A]: SC; 
Adjusted physician price index: 0.964. 

Rank: 194; 
Metropolitan area: Fresno; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.963. 

Rank: 195; 
Metropolitan area: Nashville; 
Predominant state[A]: TN; 
Adjusted physician price index: 0.962. 

Rank: 196; 
Metropolitan area: Bellingham; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.962. 

Rank: 197; 
Metropolitan area: Florence; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.959. 

Rank: 198; 
Metropolitan area: Scranton--Wilkes-Barre--Hazleton; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.959. 

Rank: 199; 
Metropolitan area: Lynchburg; 
Predominant state[A]: VA; 
Adjusted physician price index: 0.959. 

Rank: 200; 
Metropolitan area: Daytona Beach; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.959. 

Rank: 201; 
Metropolitan area: Steubenville-Weirton, OH-WV; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.958. 

Rank: 202; 
Metropolitan area: Stamford-Norwalk; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.958. 

Rank: 203; 
Metropolitan area: Charleston-North Charleston; 
Predominant state[A]: SC; 
Adjusted physician price index: 0.956. 

Rank: 204; 
Metropolitan area: Honolulu; 
Predominant state[A]: HI; 
Adjusted physician price index: 0.956. 

Rank: 205; 
Metropolitan area: Richland-Kennewick-Pasco; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.956. 

Rank: 206; 
Metropolitan area: Gadsden; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.956. 

Rank: 207; 
Metropolitan area: Greensboro--Winston-Salem--High Point; 
Predominant state[A]: NC; 
Adjusted physician price index: 0.955. 

Rank: 208; 
Metropolitan area: Visalia-Tulare-Porterville; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.954. 

Rank: 209; 
Metropolitan area: Decatur; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.949. 

Rank: 210; 
Metropolitan area: Danbury; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.949. 

Rank: 211; 
Metropolitan area: New London-Norwich, CT-RI; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.948. 

Rank: 212; 
Metropolitan area: Jacksonville; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.947. 

Rank: 213; 
Metropolitan area: Erie; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.946. 

Rank: 214; 
Metropolitan area: Rochester; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.946. 

Rank: 215; 
Metropolitan area: Reno; 
Predominant state[A]: NV; 
Adjusted physician price index: 0.944. 

Rank: 216; 
Metropolitan area: Bakersfield; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.942. 

Rank: 217; 
Metropolitan area: Olympia; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.941. 

Rank: 218; 
Metropolitan area: Pittsfield; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.941. 

Rank: 219; 
Metropolitan area: Santa Fe; 
Predominant state[A]: NM; 
Adjusted physician price index: 0.939. 

Rank: 220; 
Metropolitan area: Louisville, KY-IN; 
Predominant state[A]: KY; 
Adjusted physician price index: 0.938. 

Rank: 221; 
Metropolitan area: Benton Harbor; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.938. 

Rank: 222; 
Metropolitan area: Williamsport; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.936. 

Rank: 223; 
Metropolitan area: Charlottesville; 
Predominant state[A]: VA; 
Adjusted physician price index: 0.935. 

Rank: 224; 
Metropolitan area: Salinas; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.935. 

Rank: 225; 
Metropolitan area: Kalamazoo-Battle Creek; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.935. 

Rank: 226; 
Metropolitan area: Manchester; 
Predominant state[A]: NH; 
Adjusted physician price index: 0.932. 

Rank: 227; 
Metropolitan area: Youngstown-Warren; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.930. 

Rank: 228; 
Metropolitan area: Dover; 
Predominant state[A]: DE; 
Adjusted physician price index: 0.926. 

Rank: 229; 
Metropolitan area: Hartford; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.923. 

Rank: 230; 
Metropolitan area: Lancaster; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.923. 

Rank: 231; 
Metropolitan area: Canton-Massillon; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.922. 

Rank: 232; 
Metropolitan area: Sacramento; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.920. 

Rank: 233; 
Metropolitan area: Seattle-Bellevue-Everett; 
Predominant state[A]: WA; 
Adjusted physician price index: 0.919. 

Rank: 234; 
Metropolitan area: Jackson; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.913. 

Rank: 235; 
Metropolitan area: Springfield; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.913. 

Rank: 236; 
Metropolitan area: Vallejo-Fairfield-Napa; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.911. 

Rank: 237; 
Metropolitan area: Orlando; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.909. 

Rank: 238; 
Metropolitan area: Huntsville; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.909. 

Rank: 239; 
Metropolitan area: Grand Rapids-Muskegon-Holland; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.909. 

Rank: 240; 
Metropolitan area: Provo-Orem; 
Predominant state[A]: UT; 
Adjusted physician price index: 0.906. 

Rank: 241; 
Metropolitan area: Stockton-Lodi; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.904. 

Rank: 242; 
Metropolitan area: Fitchburg-Leominster; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.904. 

Rank: 243; 
Metropolitan area: Tucson; 
Predominant state[A]: AZ; 
Adjusted physician price index: 0.904. 

Rank: 244; 
Metropolitan area: Birmingham; 
Predominant state[A]: AL; 
Adjusted physician price index: 0.903. 

Rank: 245; 
Metropolitan area: Akron; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.901. 

Rank: 246; 
Metropolitan area: New Haven-Meriden; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.900. 

Rank: 247; 
Metropolitan area: Waterbury; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.899. 

Rank: 248; 
Metropolitan area: Columbus; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.899. 

Rank: 249; 
Metropolitan area: Tampa-St. Petersburg-Clearwater; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.899. 

Rank: 250; 
Metropolitan area: Jamestown; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.898. 

Rank: 251; 
Metropolitan area: Richmond-Petersburg; 
Predominant state[A]: VA; 
Adjusted physician price index: 0.898. 

Rank: 252; 
Metropolitan area: Cincinnati, OH-KY-IN; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.897. 

Rank: 253; 
Metropolitan area: Cumberland, MD-WV; 
Predominant state[A]: MD; 
Adjusted physician price index: 0.895. 

Rank: 254; 
Metropolitan area: York; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.894. 

Rank: 255; 
Metropolitan area: Greenville-Spartanburg-Anderson; 
Predominant state[A]: SC; 
Adjusted physician price index: 0.893. 

Rank: 256; 
Metropolitan area: New Bedford; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.892. 

Rank: 257; 
Metropolitan area: Riverside-San Bernardino; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.891. 

Rank: 258; 
Metropolitan area: Saginaw-Bay City-Midland; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.890. 

Rank: 259; 
Metropolitan area: Columbia; 
Predominant state[A]: SC; 
Adjusted physician price index: 0.888. 

Rank: 260; 
Metropolitan area: Nashua; 
Predominant state[A]: NH; 
Adjusted physician price index: 0.888. 

Rank: 261; 
Metropolitan area: Hamilton-Middletown; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.887. 

Rank: 262; 
Metropolitan area: Harrisburg-Lebanon-Carlisle; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.886. 

Rank: 263; 
Metropolitan area: Las Vegas, NV-AZ; 
Predominant state[A]: NV; 
Adjusted physician price index: 0.885. 

Rank: 264; 
Metropolitan area: Toledo; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.885. 

Rank: 265; 
Metropolitan area: Kansas City, MO-KS; 
Predominant state[A]: MO; 
Adjusted physician price index: 0.884. 

Rank: 266; 
Metropolitan area: Cleveland-Lorain-Elyria; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.883. 

Rank: 267; 
Metropolitan area: San Luis Obispo-Atascadero-Paso Robles; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.883. 

Rank: 268; 
Metropolitan area: Vineland-Millville-Bridgeton; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.882. 

Rank: 269; 
Metropolitan area: Reading; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.876. 

Rank: 270; 
Metropolitan area: Bridgeport; 
Predominant state[A]: CT; 
Adjusted physician price index: 0.874. 

Rank: 271; 
Metropolitan area: Monmouth-Ocean; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.873. 

Rank: 272; 
Metropolitan area: Los Angeles-Long Beach; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.870. 

Rank: 273; 
Metropolitan area: Ann Arbor; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.870. 

Rank: 274; 
Metropolitan area: Orange County; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.870. 

Rank: 275; 
Metropolitan area: Melbourne-Titusville-Palm Bay; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.869. 

Rank: 276; 
Metropolitan area: Santa Barbara-Santa Maria-Lompoc; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.866. 

Rank: 277; 
Metropolitan area: Jersey City; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.865. 

Rank: 278; 
Metropolitan area: Lawrence, MA-NH; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.861. 

Rank: 279; 
Metropolitan area: San Diego; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.861. 

Rank: 280; 
Metropolitan area: Trenton; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.861. 

Rank: 281; 
Metropolitan area: State College; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.861. 

Rank: 282; 
Metropolitan area: Lansing-East Lansing; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.861. 

Rank: 283; 
Metropolitan area: Barnstable-Yarmouth; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.861. 

Rank: 284; 
Metropolitan area: Phoenix-Mesa; 
Predominant state[A]: AZ; 
Adjusted physician price index: 0.859. 

Rank: 285; 
Metropolitan area: Allentown-Bethlehem-Easton; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.856. 

Rank: 286; 
Metropolitan area: New York; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.854. 

Rank: 287; 
Metropolitan area: Ventura; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.851. 

Rank: 288; 
Metropolitan area: Santa Cruz-Watsonville; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.848. 

Rank: 289; 
Metropolitan area: Worcester, MA-CT; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.846. 

Rank: 290; 
Metropolitan area: Flint; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.844. 

Rank: 291; 
Metropolitan area: Pittsburgh; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.841. 

Rank: 292; 
Metropolitan area: San Jose; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.837. 

Rank: 293; 
Metropolitan area: Atlantic-Cape May; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.835. 

Rank: 294; 
Metropolitan area: Dayton-Springfield; 
Predominant state[A]: OH; 
Adjusted physician price index: 0.833. 

Rank: 295; 
Metropolitan area: Salt Lake City-Ogden; 
Predominant state[A]: UT; 
Adjusted physician price index: 0.833. 

Rank: 296; 
Metropolitan area: Fort Pierce-Port St. Lucie; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.830. 

Rank: 297; 
Metropolitan area: Philadelphia, PA-NJ; 
Predominant state[A]: PA; 
Adjusted physician price index: 0.828. 

Rank: 298; 
Metropolitan area: Buffalo-Niagara Falls; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.823. 

Rank: 299; 
Metropolitan area: Wilmington-Newark, DE-MD; 
Predominant state[A]: DE; 
Adjusted physician price index: 0.823. 

Rank: 300; 
Metropolitan area: Newburgh, NY-PA; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.822. 

Rank: 301; 
Metropolitan area: Hagerstown; 
Predominant state[A]: MD; 
Adjusted physician price index: 0.822. 

Rank: 302; 
Metropolitan area: Newark; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.818. 

Rank: 303; 
Metropolitan area: Santa Rosa; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.817. 

Rank: 304; 
Metropolitan area: Middlesex-Somerset-Hunterdon; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.816. 

Rank: 305; 
Metropolitan area: Oakland; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.813. 

Rank: 306; 
Metropolitan area: Detroit; 
Predominant state[A]: MI; 
Adjusted physician price index: 0.809. 

Rank: 307; 
Metropolitan area: Bergen-Passaic; 
Predominant state[A]: NJ; 
Adjusted physician price index: 0.807. 

Rank: 308; 
Metropolitan area: Brockton; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.802. 

Rank: 309; 
Metropolitan area: Boston, MA-NH; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.785. 

Rank: 310; 
Metropolitan area: San Francisco; 
Predominant state[A]: CA; 
Adjusted physician price index: 0.772. 

Rank: 311; 
Metropolitan area: Dutchess County; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.768. 

Rank: 312; 
Metropolitan area: Providence-Fall River-Warwick, RI-MA; 
Predominant state[A]: RI; 
Adjusted physician price index: 0.763. 

Rank: 313; 
Metropolitan area: Miami; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.755. 

Rank: 314; 
Metropolitan area: West Palm Beach-Boca Raton; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.749. 

Rank: 315; 
Metropolitan area: Fort Lauderdale; 
Predominant state[A]: FL; 
Adjusted physician price index: 0.747. 

Rank: 316; 
Metropolitan area: Washington, DC-MD-VA-WV; 
Predominant state[A]: VA; 
Adjusted physician price index: 0.746. 

Rank: 317; 
Metropolitan area: Nassau-Suffolk; 
Predominant state[A]: NY; 
Adjusted physician price index: 0.744. 

Rank: 318; 
Metropolitan area: Lowell, MA-NH; 
Predominant state[A]: MA; 
Adjusted physician price index: 0.743. 

Rank: 319; 
Metropolitan area: Baltimore; 
Predominant state[A]: MD; 
Adjusted physician price index: 0.729. 

Source: GAO analysis of FEHBP data. 

Note: We adjusted physician prices to remove the effect of geographic 
variation in the costs of doing business (wages, rents, etc.) and 
differences in the mix of services among metropolitan areas. We 
converted physician prices to an index by dividing the average 
physician price per service in a metropolitan area by the average 
physician price in 319 metropolitan areas. The average physician price 
index value is 1.00. 

[A] Some metropolitan areas spanned more than one state. In those 
cases, we assigned the state that contained the largest proportion of 
the population of the metropolitan area. 

[End of table]

[End of section]

Appendix IV: FEHBP PPO Adjusted Health Care Spending Per Enrollee in 
U.S. Metropolitan Areas, 2001: 

The adjusted spending per enrollee indices based on FEHBP PPO spending 
in 232 metropolitan areas are presented below ranked in order from 
highest to lowest spending per enrollee. 

Table 17: Ranking of Metropolitan Areas by Adjusted Health Care 
Spending Per Enrollee, 2001: 

Rank: 1; 
Metropolitan area: Biloxi-Gulfport-Pascagoula; 
Predominant state[A]: MS; 
Adjusted spending index: 1.422. 

Rank: 2; 
Metropolitan area: Myrtle Beach; 
Predominant state[A]: SC; 
Adjusted spending index: 1.404. 

Rank: 3; 
Metropolitan area: Monroe; 
Predominant state[A]: LA; 
Adjusted spending index: 1.393. 

Rank: 4; 
Metropolitan area: Hattiesburg; 
Predominant state[A]: MS; 
Adjusted spending index: 1.393. 

Rank: 5; 
Metropolitan area: Parkersburg-Marietta, WV-OH; 
Predominant state[A]: WV; 
Adjusted spending index: 1.343. 

Rank: 6; 
Metropolitan area: Anniston; 
Predominant state[A]: AL; 
Adjusted spending index: 1.322. 

Rank: 7; 
Metropolitan area: Florence; 
Predominant state[A]: SC; 
Adjusted spending index: 1.298. 

Rank: 8; 
Metropolitan area: Terre Haute; 
Predominant state[A]: IN; 
Adjusted spending index: 1.297. 

Rank: 9; 
Metropolitan area: Bakersfield; 
Predominant state[A]: CA; 
Adjusted spending index: 1.268. 

Rank: 10; 
Metropolitan area: San Angelo; 
Predominant state[A]: TX; 
Adjusted spending index: 1.258. 

Rank: 11; 
Metropolitan area: Gadsden; 
Predominant state[A]: AL; 
Adjusted spending index: 1.250. 

Rank: 12; 
Metropolitan area: Wichita Falls; 
Predominant state[A]: TX; 
Adjusted spending index: 1.240. 

Rank: 13; 
Metropolitan area: Houma; 
Predominant state[A]: LA; 
Adjusted spending index: 1.240. 

Rank: 14; 
Metropolitan area: Sherman-Denison; 
Predominant state[A]: TX; 
Adjusted spending index: 1.235. 

Rank: 15; 
Metropolitan area: Wilmington; 
Predominant state[A]: NC; 
Adjusted spending index: 1.216. 

Rank: 16; 
Metropolitan area: Huntington-Ashland, WV-KY-OH; 
Predominant state[A]: WV; 
Adjusted spending index: 1.216. 

Rank: 17; 
Metropolitan area: Macon; 
Predominant state[A]: GA; 
Adjusted spending index: 1.213. 

Rank: 18; 
Metropolitan area: Lubbock; 
Predominant state[A]: TX; 
Adjusted spending index: 1.212. 

Rank: 19; 
Metropolitan area: Dothan; 
Predominant state[A]: AL; 
Adjusted spending index: 1.211. 

Rank: 20; 
Metropolitan area: Punta Gorda; 
Predominant state[A]: FL; 
Adjusted spending index: 1.211. 

Rank: 21; 
Metropolitan area: Decatur; 
Predominant state[A]: AL; 
Adjusted spending index: 1.200. 

Rank: 22; 
Metropolitan area: Milwaukee-Waukesha; 
Predominant state[A]: WI; 
Adjusted spending index: 1.197. 

Rank: 23; 
Metropolitan area: Rapid City; 
Predominant state[A]: SD; 
Adjusted spending index: 1.195. 

Rank: 24; 
Metropolitan area: Albany; 
Predominant state[A]: GA; 
Adjusted spending index: 1.194. 

Rank: 25; 
Metropolitan area: Fort Walton Beach; 
Predominant state[A]: FL; 
Adjusted spending index: 1.187. 

Rank: 26; 
Metropolitan area: Texarkana, TX-Texarkana; 
Predominant state[A]: TX; 
Adjusted spending index: 1.186. 

Rank: 27; 
Metropolitan area: Oklahoma City; 
Predominant state[A]: OK; 
Adjusted spending index: 1.182. 

Rank: 28; 
Metropolitan area: Charleston-North Charleston; 
Predominant state[A]: SC; 
Adjusted spending index: 1.180. 

Rank: 29; 
Metropolitan area: Lake Charles; 
Predominant state[A]: LA; 
Adjusted spending index: 1.169. 

Rank: 30; 
Metropolitan area: Panama City; 
Predominant state[A]: FL; 
Adjusted spending index: 1.167. 

Rank: 31; 
Metropolitan area: La Crosse, WI-MN; 
Predominant state[A]: WI; 
Adjusted spending index: 1.163. 

Rank: 32; 
Metropolitan area: Little Rock-North Little Rock; 
Predominant state[A]: AR; 
Adjusted spending index: 1.163. 

Rank: 33; 
Metropolitan area: Florence; 
Predominant state[A]: AL; 
Adjusted spending index: 1.161. 

Rank: 34; 
Metropolitan area: Knoxville; 
Predominant state[A]: TN; 
Adjusted spending index: 1.157. 

Rank: 35; 
Metropolitan area: Jacksonville; 
Predominant state[A]: NC; 
Adjusted spending index: 1.155. 

Rank: 36; 
Metropolitan area: Yuma; 
Predominant state[A]: AZ; 
Adjusted spending index: 1.151. 

Rank: 37; 
Metropolitan area: Shreveport-Bossier City; 
Predominant state[A]: LA; 
Adjusted spending index: 1.133. 

Rank: 38; 
Metropolitan area: Pine Bluff; 
Predominant state[A]: AR; 
Adjusted spending index: 1.132. 

Rank: 39; 
Metropolitan area: Lafayette; 
Predominant state[A]: LA; 
Adjusted spending index: 1.126. 

Rank: 40; 
Metropolitan area: Galveston-Texas City; 
Predominant state[A]: TX; 
Adjusted spending index: 1.122. 

Rank: 41; 
Metropolitan area: Charlotte-Gastonia-Rock Hill, NC-SC; 
Predominant state[A]: NC; 
Adjusted spending index: 1.120. 

Rank: 42; 
Metropolitan area: Enid; 
Predominant state[A]: OK; 
Adjusted spending index: 1.119. 

Rank: 43; 
Metropolitan area: Johnson City-Kingsport-Bristol, TN-VA; 
Predominant state[A]: TN; 
Adjusted spending index: 1.118. 

Rank: 44; 
Metropolitan area: Fort Worth-Arlington; 
Predominant state[A]: TX; 
Adjusted spending index: 1.117. 

Rank: 45; 
Metropolitan area: Lawton; 
Predominant state[A]: OK; 
Adjusted spending index: 1.116. 

Rank: 46; 
Metropolitan area: Charleston; 
Predominant state[A]: WV; 
Adjusted spending index: 1.116. 

Rank: 47; 
Metropolitan area: Jonesboro; 
Predominant state[A]: AR; 
Adjusted spending index: 1.115. 

Rank: 48; 
Metropolitan area: McAllen-Edinburg-Mission; 
Predominant state[A]: TX; 
Adjusted spending index: 1.113. 

Rank: 49; 
Metropolitan area: Melbourne-Titusville-Palm Bay; 
Predominant state[A]: FL; 
Adjusted spending index: 1.108. 

Rank: 50; 
Metropolitan area: Nashville; 
Predominant state[A]: TN; 
Adjusted spending index: 1.103. 

Rank: 51; 
Metropolitan area: Tuscaloosa; 
Predominant state[A]: AL; 
Adjusted spending index: 1.102. 

Rank: 52; 
Metropolitan area: Dallas; 
Predominant state[A]: TX; 
Adjusted spending index: 1.101. 

Rank: 53; 
Metropolitan area: Bryan-College Station; 
Predominant state[A]: TX; 
Adjusted spending index: 1.097. 

Rank: 54; 
Metropolitan area: Waco; 
Predominant state[A]: TX; 
Adjusted spending index: 1.096. 

Rank: 55; 
Metropolitan area: Omaha, NE-IA; 
Predominant state[A]: NE; 
Adjusted spending index: 1.092. 

Rank: 56; 
Metropolitan area: Jackson; 
Predominant state[A]: MS; 
Adjusted spending index: 1.089. 

Rank: 57; 
Metropolitan area: Savannah; 
Predominant state[A]: GA; 
Adjusted spending index: 1.088. 

Rank: 58; 
Metropolitan area: Springfield; 
Predominant state[A]: MO; 
Adjusted spending index: 1.088. 

Rank: 59; 
Metropolitan area: New Orleans; 
Predominant state[A]: LA; 
Adjusted spending index: 1.082. 

Rank: 60; 
Metropolitan area: Las Vegas, NV-AZ; 
Predominant state[A]: NV; 
Adjusted spending index: 1.081. 

Rank: 61; 
Metropolitan area: Chattanooga, TN-GA; 
Predominant state[A]: TN; 
Adjusted spending index: 1.079. 

Rank: 62; 
Metropolitan area: Boulder-Longmont; 
Predominant state[A]: CO; 
Adjusted spending index: 1.078. 

Rank: 63; 
Metropolitan area: Duluth-Superior, MN-WI; 
Predominant state[A]: MN; 
Adjusted spending index: 1.077. 

Rank: 64; 
Metropolitan area: Greenville-Spartanburg-Anderson; 
Predominant state[A]: SC; 
Adjusted spending index: 1.077. 

Rank: 65; 
Metropolitan area: Baton Rouge; 
Predominant state[A]: LA; 
Adjusted spending index: 1.076. 

Rank: 66; 
Metropolitan area: Las Cruces; 
Predominant state[A]: NM; 
Adjusted spending index: 1.074. 

Rank: 67; 
Metropolitan area: St. Joseph; 
Predominant state[A]: MO; 
Adjusted spending index: 1.074. 

Rank: 68; 
Metropolitan area: Owensboro; 
Predominant state[A]: KY; 
Adjusted spending index: 1.073. 

Rank: 69; 
Metropolitan area: Corpus Christi; 
Predominant state[A]: TX; 
Adjusted spending index: 1.073. 

Rank: 70; 
Metropolitan area: Lakeland-Winter Haven; 
Predominant state[A]: FL; 
Adjusted spending index: 1.072. 

Rank: 71; 
Metropolitan area: Sarasota-Bradenton; 
Predominant state[A]: FL; 
Adjusted spending index: 1.072. 

Rank: 72; 
Metropolitan area: Jacksonville; 
Predominant state[A]: FL; 
Adjusted spending index: 1.070. 

Rank: 73; 
Metropolitan area: San Antonio; 
Predominant state[A]: TX; 
Adjusted spending index: 1.067. 

Rank: 74; 
Metropolitan area: Tulsa; 
Predominant state[A]: OK; 
Adjusted spending index: 1.060. 

Rank: 75; 
Metropolitan area: Odessa-Midland; 
Predominant state[A]: TX; 
Adjusted spending index: 1.059. 

Rank: 76; 
Metropolitan area: Portsmouth-Rochester, NH-ME; 
Predominant state[A]: NH; 
Adjusted spending index: 1.057. 

Rank: 77; 
Metropolitan area: Topeka; 
Predominant state[A]: KS; 
Adjusted spending index: 1.056. 

Rank: 78; 
Metropolitan area: Orange County; 
Predominant state[A]: CA; 
Adjusted spending index: 1.049. 

Rank: 79; 
Metropolitan area: Pensacola; 
Predominant state[A]: FL; 
Adjusted spending index: 1.049. 

Rank: 80; 
Metropolitan area: Amarillo; 
Predominant state[A]: TX; 
Adjusted spending index: 1.048. 

Rank: 81; 
Metropolitan area: Fort Myers-Cape Coral; 
Predominant state[A]: FL; 
Adjusted spending index: 1.048. 

Rank: 82; 
Metropolitan area: Houston; 
Predominant state[A]: TX; 
Adjusted spending index: 1.045. 

Rank: 83; 
Metropolitan area: Indianapolis; 
Predominant state[A]: IN; 
Adjusted spending index: 1.039. 

Rank: 84; 
Metropolitan area: Colorado Springs; 
Predominant state[A]: CO; 
Adjusted spending index: 1.036. 

Rank: 85; 
Metropolitan area: Montgomery; 
Predominant state[A]: AL; 
Adjusted spending index: 1.034. 

Rank: 86; 
Metropolitan area: Huntsville; 
Predominant state[A]: AL; 
Adjusted spending index: 1.033. 

Rank: 87; 
Metropolitan area: Orlando; 
Predominant state[A]: FL; 
Adjusted spending index: 1.033. 

Rank: 88; 
Metropolitan area: Wichita; 
Predominant state[A]: KS; 
Adjusted spending index: 1.030. 

Rank: 89; 
Metropolitan area: Memphis, TN-AR-MS; 
Predominant state[A]: TN; 
Adjusted spending index: 1.027. 

Rank: 90; 
Metropolitan area: Anchorage; 
Predominant state[A]: AK; 
Adjusted spending index: 1.025. 

Rank: 91; 
Metropolitan area: Bloomington; 
Predominant state[A]: IN; 
Adjusted spending index: 1.022. 

Rank: 92; 
Metropolitan area: Monmouth-Ocean; 
Predominant state[A]: NJ; 
Adjusted spending index: 1.021. 

Rank: 93; 
Metropolitan area: Cumberland, MD-WV; 
Predominant state[A]: MD; 
Adjusted spending index: 1.020. 

Rank: 94; 
Metropolitan area: Lincoln; 
Predominant state[A]: NE; 
Adjusted spending index: 1.020. 

Rank: 95; 
Metropolitan area: Columbus, GA-AL; 
Predominant state[A]: GA; 
Adjusted spending index: 1.014. 

Rank: 96; 
Metropolitan area: Fort Smith, AR-OK; 
Predominant state[A]: AR; 
Adjusted spending index: 1.012. 

Rank: 97; 
Metropolitan area: Roanoke; 
Predominant state[A]: VA; 
Adjusted spending index: 1.012. 

Rank: 98; 
Metropolitan area: Norfolk-Virginia Beach-Newport News, VA- NC; 
Predominant state[A]: VA; 
Adjusted spending index: 1.012. 

Rank: 99; 
Metropolitan area: Mobile; 
Predominant state[A]: AL; 
Adjusted spending index: 1.011. 

Rank: 100; 
Metropolitan area: Boise City; 
Predominant state[A]: ID; 
Adjusted spending index: 1.010. 

Rank: 101; 
Metropolitan area: Louisville, KY-IN; 
Predominant state[A]: KY; 
Adjusted spending index: 1.008. 

Rank: 102; 
Metropolitan area: Austin-San Marcos; 
Predominant state[A]: TX; 
Adjusted spending index: 1.007. 

Rank: 103; 
Metropolitan area: Clarksville-Hopkinsville, TN-KY; 
Predominant state[A]: TN; 
Adjusted spending index: 1.004. 

Rank: 104; 
Metropolitan area: Ventura; 
Predominant state[A]: CA; 
Adjusted spending index: 1.004. 

Rank: 105; 
Metropolitan area: Birmingham; 
Predominant state[A]: AL; 
Adjusted spending index: 1.000. 

Rank: 106; 
Metropolitan area: Manchester; 
Predominant state[A]: NH; 
Adjusted spending index: 0.999. 

Rank: 107; 
Metropolitan area: Daytona Beach; 
Predominant state[A]: FL; 
Adjusted spending index: 0.996. 

Rank: 108; 
Metropolitan area: Sioux Falls; 
Predominant state[A]: SD; 
Adjusted spending index: 0.994. 

Rank: 109; 
Metropolitan area: Columbia; 
Predominant state[A]: SC; 
Adjusted spending index: 0.994. 

Rank: 110; 
Metropolitan area: Richland-Kennewick-Pasco; 
Predominant state[A]: WA; 
Adjusted spending index: 0.992. 

Rank: 111; 
Metropolitan area: Atlantic-Cape May; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.988. 

Rank: 112; 
Metropolitan area: Grand Forks, ND-MN; 
Predominant state[A]: ND; 
Adjusted spending index: 0.988. 

Rank: 113; 
Metropolitan area: New London-Norwich, CT-RI; 
Predominant state[A]: CT; 
Adjusted spending index: 0.988. 

Rank: 114; 
Metropolitan area: Trenton; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.987. 

Rank: 115; 
Metropolitan area: Olympia; 
Predominant state[A]: WA; 
Adjusted spending index: 0.984. 

Rank: 116; 
Metropolitan area: Columbia; 
Predominant state[A]: MO; 
Adjusted spending index: 0.984. 

Rank: 117; 
Metropolitan area: Atlanta; 
Predominant state[A]: GA; 
Adjusted spending index: 0.983. 

Rank: 118; 
Metropolitan area: Killeen-Temple; 
Predominant state[A]: TX; 
Adjusted spending index: 0.982. 

Rank: 119; 
Metropolitan area: Grand Junction; 
Predominant state[A]: CO; 
Adjusted spending index: 0.982. 

Rank: 120; 
Metropolitan area: Kansas City, MO-KS; 
Predominant state[A]: MO; 
Adjusted spending index: 0.980. 

Rank: 121; 
Metropolitan area: Gary; 
Predominant state[A]: IN; 
Adjusted spending index: 0.979. 

Rank: 122; 
Metropolitan area: West Palm Beach-Boca Raton; 
Predominant state[A]: FL; 
Adjusted spending index: 0.977. 

Rank: 123; 
Metropolitan area: Athens; 
Predominant state[A]: GA; 
Adjusted spending index: 0.977. 

Rank: 124; 
Metropolitan area: Fayetteville-Springdale-Rogers; 
Predominant state[A]: AR; 
Adjusted spending index: 0.977. 

Rank: 125; 
Metropolitan area: Billings; 
Predominant state[A]: MT; 
Adjusted spending index: 0.975. 

Rank: 126; 
Metropolitan area: Fort Lauderdale; 
Predominant state[A]: FL; 
Adjusted spending index: 0.971. 

Rank: 127; 
Metropolitan area: Great Falls; 
Predominant state[A]: MT; 
Adjusted spending index: 0.970. 

Rank: 128; 
Metropolitan area: Dover; 
Predominant state[A]: DE; 
Adjusted spending index: 0.965. 

Rank: 129; 
Metropolitan area: Jackson; 
Predominant state[A]: TN; 
Adjusted spending index: 0.965. 

Rank: 130; 
Metropolitan area: Lynchburg; 
Predominant state[A]: VA; 
Adjusted spending index: 0.962. 

Rank: 131; 
Metropolitan area: Des Moines; 
Predominant state[A]: IA; 
Adjusted spending index: 0.962. 

Rank: 132; 
Metropolitan area: Gainesville; 
Predominant state[A]: FL; 
Adjusted spending index: 0.960. 

Rank: 133; 
Metropolitan area: Laredo; 
Predominant state[A]: TX; 
Adjusted spending index: 0.959. 

Rank: 134; 
Metropolitan area: Augusta-Aiken, GA-SC; 
Predominant state[A]: GA; 
Adjusted spending index: 0.959. 

Rank: 135; 
Metropolitan area: Denver; 
Predominant state[A]: CO; 
Adjusted spending index: 0.958. 

Rank: 136; 
Metropolitan area: Bremerton; 
Predominant state[A]: WA; 
Adjusted spending index: 0.957. 

Rank: 137; 
Metropolitan area: Fort Pierce-Port St. Lucie; 
Predominant state[A]: FL; 
Adjusted spending index: 0.955. 

Rank: 138; 
Metropolitan area: Salinas; 
Predominant state[A]: CA; 
Adjusted spending index: 0.952. 

Rank: 139; 
Metropolitan area: Pueblo; 
Predominant state[A]: CO; 
Adjusted spending index: 0.952. 

Rank: 140; 
Metropolitan area: Tampa-St. Petersburg-Clearwater; 
Predominant state[A]: FL; 
Adjusted spending index: 0.951. 

Rank: 141; 
Metropolitan area: Fort Wayne; 
Predominant state[A]: IN; 
Adjusted spending index: 0.950. 

Rank: 142; 
Metropolitan area: Hagerstown; 
Predominant state[A]: MD; 
Adjusted spending index: 0.949. 

Rank: 143; 
Metropolitan area: Los Angeles-Long Beach; 
Predominant state[A]: CA; 
Adjusted spending index: 0.947. 

Rank: 144; 
Metropolitan area: Lexington; 
Predominant state[A]: KY; 
Adjusted spending index: 0.946. 

Rank: 145; 
Metropolitan area: Middlesex-Somerset-Hunterdon; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.942. 

Rank: 146; 
Metropolitan area: Redding; 
Predominant state[A]: CA; 
Adjusted spending index: 0.942. 

Rank: 147; 
Metropolitan area: Bangor; 
Predominant state[A]: ME; 
Adjusted spending index: 0.941. 

Rank: 148; 
Metropolitan area: Tacoma; 
Predominant state[A]: WA; 
Adjusted spending index: 0.941. 

Rank: 149; 
Metropolitan area: Phoenix-Mesa; 
Predominant state[A]: AZ; 
Adjusted spending index: 0.935. 

Rank: 150; 
Metropolitan area: Riverside-San Bernardino; 
Predominant state[A]: CA; 
Adjusted spending index: 0.935. 

Rank: 151; 
Metropolitan area: Cedar Rapids; 
Predominant state[A]: IA; 
Adjusted spending index: 0.934. 

Rank: 152; 
Metropolitan area: Greensboro--Winston-Salem--High Point; 
Predominant state[A]: NC; 
Adjusted spending index: 0.932. 

Rank: 153; 
Metropolitan area: Fayetteville; 
Predominant state[A]: NC; 
Adjusted spending index: 0.930. 

Rank: 154; 
Metropolitan area: Miami; 
Predominant state[A]: FL; 
Adjusted spending index: 0.928. 

Rank: 155; 
Metropolitan area: Sacramento; 
Predominant state[A]: CA; 
Adjusted spending index: 0.927. 

Rank: 156; 
Metropolitan area: Reading; 
Predominant state[A]: PA; 
Adjusted spending index: 0.927. 

Rank: 157; 
Metropolitan area: Salt Lake City-Ogden; 
Predominant state[A]: UT; 
Adjusted spending index: 0.925. 

Rank: 158; 
Metropolitan area: Cincinnati, OH-KY-IN; 
Predominant state[A]: OH; 
Adjusted spending index: 0.923. 

Rank: 159; 
Metropolitan area: Richmond-Petersburg; 
Predominant state[A]: VA; 
Adjusted spending index: 0.920. 

Rank: 160; 
Metropolitan area: Detroit; 
Predominant state[A]: MI; 
Adjusted spending index: 0.920. 

Rank: 161; 
Metropolitan area: Chicago; 
Predominant state[A]: IL; 
Adjusted spending index: 0.918. 

Rank: 162; 
Metropolitan area: Provo-Orem; 
Predominant state[A]: UT; 
Adjusted spending index: 0.918. 

Rank: 163; 
Metropolitan area: Fort Collins-Loveland; 
Predominant state[A]: CO; 
Adjusted spending index: 0.913. 

Rank: 164; 
Metropolitan area: Yakima; 
Predominant state[A]: WA; 
Adjusted spending index: 0.913. 

Rank: 165; 
Metropolitan area: Goldsboro; 
Predominant state[A]: NC; 
Adjusted spending index: 0.913. 

Rank: 166; 
Metropolitan area: Albany-Schenectady-Troy; 
Predominant state[A]: NY; 
Adjusted spending index: 0.913. 

Rank: 167; 
Metropolitan area: Nashua; 
Predominant state[A]: NH; 
Adjusted spending index: 0.911. 

Rank: 168; 
Metropolitan area: Asheville; 
Predominant state[A]: NC; 
Adjusted spending index: 0.911. 

Rank: 169; 
Metropolitan area: Nassau-Suffolk; 
Predominant state[A]: NY; 
Adjusted spending index: 0.909. 

Rank: 170; 
Metropolitan area: Santa Fe; 
Predominant state[A]: NM; 
Adjusted spending index: 0.908. 

Rank: 171; 
Metropolitan area: Scranton--Wilkes-Barre--Hazleton; 
Predominant state[A]: PA; 
Adjusted spending index: 0.906. 

Rank: 172; 
Metropolitan area: Missoula; 
Predominant state[A]: MT; 
Adjusted spending index: 0.904. 

Rank: 173; 
Metropolitan area: York; 
Predominant state[A]: PA; 
Adjusted spending index: 0.904. 

Rank: 174; 
Metropolitan area: Jersey City; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.904. 

Rank: 175; 
Metropolitan area: Raleigh-Durham-Chapel Hill; 
Predominant state[A]: NC; 
Adjusted spending index: 0.901. 

Rank: 176; 
Metropolitan area: Columbus; 
Predominant state[A]: OH; 
Adjusted spending index: 0.901. 

Rank: 177; 
Metropolitan area: Sioux City, IA-NE; 
Predominant state[A]: IA; 
Adjusted spending index: 0.899. 

Rank: 178; 
Metropolitan area: Cleveland-Lorain-Elyria; 
Predominant state[A]: OH; 
Adjusted spending index: 0.899. 

Rank: 179; 
Metropolitan area: Greenville; 
Predominant state[A]: NC; 
Adjusted spending index: 0.897. 

Rank: 180; 
Metropolitan area: Wilmington-Newark, DE-MD; 
Predominant state[A]: DE; 
Adjusted spending index: 0.897. 

Rank: 181; 
Metropolitan area: Tucson; 
Predominant state[A]: AZ; 
Adjusted spending index: 0.897. 

Rank: 182; 
Metropolitan area: Waterbury; 
Predominant state[A]: CT; 
Adjusted spending index: 0.896. 

Rank: 183; 
Metropolitan area: Portland; 
Predominant state[A]: ME; 
Adjusted spending index: 0.893. 

Rank: 184; 
Metropolitan area: Salem; 
Predominant state[A]: OR; 
Adjusted spending index: 0.892. 

Rank: 185; 
Metropolitan area: Bergen-Passaic; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.891. 

Rank: 186; 
Metropolitan area: Eugene-Springfield; 
Predominant state[A]: OR; 
Adjusted spending index: 0.883. 

Rank: 187; 
Metropolitan area: Kalamazoo-Battle Creek; 
Predominant state[A]: MI; 
Adjusted spending index: 0.881. 

Rank: 188; 
Metropolitan area: Washington, DC-MD-VA-WV; 
Predominant state[A]: VA; 
Adjusted spending index: 0.881. 

Rank: 189; 
Metropolitan area: Bismarck; 
Predominant state[A]: ND; 
Adjusted spending index: 0.880. 

Rank: 190; 
Metropolitan area: Flint; 
Predominant state[A]: MI; 
Adjusted spending index: 0.879. 

Rank: 191; 
Metropolitan area: Newark; 
Predominant state[A]: NJ; 
Adjusted spending index: 0.878. 

Rank: 192; 
Metropolitan area: Springfield; 
Predominant state[A]: MA; 
Adjusted spending index: 0.876. 

Rank: 193; 
Metropolitan area: Baltimore; 
Predominant state[A]: MD; 
Adjusted spending index: 0.875. 

Rank: 194; 
Metropolitan area: New Haven-Meriden; 
Predominant state[A]: CT; 
Adjusted spending index: 0.874. 

Rank: 195; 
Metropolitan area: Minneapolis-St. Paul, MN-WI; 
Predominant state[A]: MN; 
Adjusted spending index: 0.873. 

Rank: 196; 
Metropolitan area: Philadelphia, PA-NJ; 
Predominant state[A]: PA; 
Adjusted spending index: 0.870. 

Rank: 197; 
Metropolitan area: San Diego; 
Predominant state[A]: CA; 
Adjusted spending index: 0.869. 

Rank: 198; 
Metropolitan area: Albuquerque; 
Predominant state[A]: NM; 
Adjusted spending index: 0.868. 

Rank: 199; 
Metropolitan area: Reno; 
Predominant state[A]: NV; 
Adjusted spending index: 0.866. 

Rank: 200; 
Metropolitan area: Altoona; 
Predominant state[A]: PA; 
Adjusted spending index: 0.866. 

Rank: 201; 
Metropolitan area: Lawrence, MA-NH; 
Predominant state[A]: MA; 
Adjusted spending index: 0.862. 

Rank: 202; 
Metropolitan area: Dayton-Springfield; 
Predominant state[A]: OH; 
Adjusted spending index: 0.852. 

Rank: 203; 
Metropolitan area: Portland-Vancouver, OR-WA; 
Predominant state[A]: OR; 
Adjusted spending index: 0.848. 

Rank: 204; 
Metropolitan area: Newburgh, NY-PA; 
Predominant state[A]: NY; 
Adjusted spending index: 0.848. 

Rank: 205; 
Metropolitan area: New York; 
Predominant state[A]: NY; 
Adjusted spending index: 0.845. 

Rank: 206; 
Metropolitan area: Seattle-Bellevue-Everett; 
Predominant state[A]: WA; 
Adjusted spending index: 0.843. 

Rank: 207; 
Metropolitan area: Medford-Ashland; 
Predominant state[A]: OR; 
Adjusted spending index: 0.841. 

Rank: 208; 
Metropolitan area: Evansville-Henderson, IN-KY; 
Predominant state[A]: IN; 
Adjusted spending index: 0.836. 

Rank: 209; 
Metropolitan area: Charlottesville; 
Predominant state[A]: VA; 
Adjusted spending index: 0.836. 

Rank: 210; 
Metropolitan area: Providence-Fall River-Warwick, RI-MA; 
Predominant state[A]: RI; 
Adjusted spending index: 0.834. 

Rank: 211; 
Metropolitan area: Lansing-East Lansing; 
Predominant state[A]: MI; 
Adjusted spending index: 0.833. 

Rank: 212; 
Metropolitan area: Harrisburg-Lebanon-Carlisle; 
Predominant state[A]: PA; 
Adjusted spending index: 0.832. 

Rank: 213; 
Metropolitan area: South Bend; 
Predominant state[A]: IN; 
Adjusted spending index: 0.830. 

Rank: 214; 
Metropolitan area: Iowa City; 
Predominant state[A]: IA; 
Adjusted spending index: 0.827. 

Rank: 215; 
Metropolitan area: Toledo; 
Predominant state[A]: OH; 
Adjusted spending index: 0.825. 

Rank: 216; 
Metropolitan area: Allentown-Bethlehem-Easton; 
Predominant state[A]: PA; 
Adjusted spending index: 0.814. 

Rank: 217; 
Metropolitan area: San Francisco; 
Predominant state[A]: CA; 
Adjusted spending index: 0.809. 

Rank: 218; 
Metropolitan area: Hartford; 
Predominant state[A]: CT; 
Adjusted spending index: 0.809. 

Rank: 219; 
Metropolitan area: Oakland; 
Predominant state[A]: CA; 
Adjusted spending index: 0.807. 

Rank: 220; 
Metropolitan area: Erie; 
Predominant state[A]: PA; 
Adjusted spending index: 0.803. 

Rank: 221; 
Metropolitan area: Syracuse; 
Predominant state[A]: NY; 
Adjusted spending index: 0.793. 

Rank: 222; 
Metropolitan area: Spokane; 
Predominant state[A]: WA; 
Adjusted spending index: 0.789. 

Rank: 223; 
Metropolitan area: Ann Arbor; 
Predominant state[A]: MI; 
Adjusted spending index: 0.778. 

Rank: 224; 
Metropolitan area: Pittsburgh; 
Predominant state[A]: PA; 
Adjusted spending index: 0.776. 

Rank: 225; 
Metropolitan area: Fargo-Moorhead, ND-MN; 
Predominant state[A]: ND; 
Adjusted spending index: 0.766. 

Rank: 226; 
Metropolitan area: Saginaw-Bay City-Midland; 
Predominant state[A]: MI; 
Adjusted spending index: 0.753. 

Rank: 227; 
Metropolitan area: Johnstown; 
Predominant state[A]: PA; 
Adjusted spending index: 0.746. 

Rank: 228; 
Metropolitan area: Boston, MA-NH; 
Predominant state[A]: MA; 
Adjusted spending index: 0.746. 

Rank: 229; 
Metropolitan area: Bridgeport; 
Predominant state[A]: CT; 
Adjusted spending index: 0.732. 

Rank: 230; 
Metropolitan area: Buffalo-Niagara Falls; 
Predominant state[A]: NY; 
Adjusted spending index: 0.715. 

Rank: 231; 
Metropolitan area: Honolulu; 
Predominant state[A]: HI; 
Adjusted spending index: 0.684. 

Rank: 232; 
Metropolitan area: Grand Rapids-Muskegon-Holland; 
Predominant state[A]: MI; 
Adjusted spending index: 0.672. 

Source: GAO analysis of FEHBP data. 

Note: Total spending per enrollee includes spending for all services 
except mental health, chemical dependency, and pharmaceuticals. We 
adjusted total spending per enrollee to remove the effect of geographic 
differences in enrollee age and sex, as well as geographic differences 
in the costs of doing business (such as wages and rents). The spending 
per enrollee index compares spending per enrollee in a metropolitan 
area to the average spending per enrollee in all study metropolitan 
areas, adjusted for patients' age and sex composition, and costs. The 
average spending index was 1.00. 

[A] Some metropolitan areas spanned more than one state. In those 
cases, we assigned the state that contained the largest proportion of 
the population of the metropolitan area. 

[End of table]

[End of section]

Appendix V: Comments from the Office of Personnel Management: 

UNITED STATES OFFICE OF PERSONNEL MANAGEMENT: 
WASHINGTON, DC 20415-1000: 
OFFICE OF THE DIRECTOR: 

A. Bruce Steinwald: 
Director, Health Care: 
U.S. Government Accountability Office: 
Washington, DC 20548: 

Dear Mr. Steinwald: 

Thank you for providing us with a copy of your proposed report entitled 
FEDERAL EMPLOYEES HEALTH BENEFITS PROGRAM: Competition and Other 
Factors Linked to Wide Variation in Health Care Prices (GAO-05-856). We 
appreciate the opportunity to comment on the draft report. 

Overall, your finding, confirm a longstanding healthcare principle at 
the U.S. Office of Personnel Management (OPM) which is that market- 
based competition contributes to the affordable healthcare options 
available to Federal enrollees. The Federal Employees Health Benefits 
(FEHB) Program now offers almost 250 health. plan choices, including 
both the fee-for-service preferred provider networks and the health 
maintenance organizations (HMOs) discussed in your report. 

The report discusses geographic variations in spending for hospital and 
physician serVices and provides interesting observations about provider 
price and utilization as factors contributing to the variations. In 
addition, we note that it shows increased competition at the healthcare 
delivery level contributes to a lowering of healthcare spending. While 
most of the FEHB enrollment is in the fee-for-service plans, we have 
long supported HMO arrangements and contract with a far greater number 
of HMOs than fee-for-service plans. Therefore, we are pleased that your 
report shows the capitated arrangements commonly found in HMOs 
contributed to a lowering of both hospital and physician prices in the 
metropolitan areas you. studied. For reasons discussed in the report, 
the study omits spending for pharmaceuticals. We estimate this 
represents about 25 percent of FEHB Program costs. 

We have the following comments: 

* The report indicates that the national Preferred Provider 
Organizations (PPOS) offered the sauce benefits and charged the same 
premiums regardless of where enrollees lived. or obtained their health 
care. However, the prices that PPOs paid to hospitals and physicians 
varied. Although this is true, it may be worth noting that in most of 
the; PPO cases, the enrolls pays a percentage of the costs so that as 
the PPO's charges rise, the enrollee's charges also rise. In other 
words, enrollees received the same benefit as a percentage of covered 
cost; however, they generally do not receive the same services for the 
same price across the regions. 

* The report indicates that physician spending levels appear to be 
mitigated sornewhat in geographic areas where there are higher 
uninsured populations and lower Medicaid payments. Physicians' prices 
appear to be more closely linked to consumer (patient) expectations 
than those of hospitals. It would have been interesting to have 
observed any such linkage with physician prescribing patterns as well. 

* On page 16, the report indicates there was a considerable range of 
hospital prices within regions. Page 35 of the report indicates as part 
of the concluding observations that further investigation may help to 
explain why there were regional patterns which appeared to be 
associated with private sector price variations (i.e., prices for both 
hospital stays and physician services tended to be higher in the 
Midwest and lower in the Northeast). It would also be instructive to 
investigate the variations within regions mentioned on page 16. 

* On page 24, the statement that "the effect of increasing HMO 
capitation was to reduce the hospital price index in a metropolitan 
area by 7.17 percent and the physician price index in a metropolitan 
area by 3.31 percent" is found in a footnote to Table 6 and in footnote 
43. We would suggest that this is sufficiently relevant to include in 
the discussion section of the report as well. 

* We noted on page 26, the report states "..physician prices were 
actually lower, on average, in metropolitan areas with lower adjusted 
Medicaid payment rates and proportionately larger uninsured 
populations." This appears to be a relevant finding which may merit 
inclusion in the final discussion in Concluding Observations on page 
35. 

We also have provided some technical comments in the attachment. We 
appreciate this opportunity to comment. 

Sincerely,

Signed by: 

Linda M. Springer: 

Attachment: 

[End of section]

Appendix VI: GAO Contacts and Staff Acknowledgments: 

GAO Contacts: 

A. Bruce Steinwald, (202) 512-7101 or steinwalda@gao.gov: 

Acknowledgments: 

In addition to the contact named above, Christine Brudevold, Assistant 
Director; Jennie F. Apter; Leslie Gordon; Michael Kendix; Daniel Lee; 
Jennifer M. Rellick; Holly Stockdale; Ann Tynan; and Suzanne Worth made 
key contributions to this report. 

FOOTNOTES

[1] PPOs in our study refer to fee-for-service plans with preferred 
provider networks. PPOs generally allow enrollees to obtain care from 
any provider, but charge enrollees less if they obtain care from the 
plans' networks of preferred providers.

[2] GAO, Milwaukee Health Care Spending Compared to Other Metropolitan 
Areas: Geographic Variation in Spending for Enrollees in the Federal 
Employees Health Benefits Program, GAO-04-1000R (Washington, D.C.: Aug. 
18, 2004).

[3] A metropolitan area refers to a metropolitan statistical area, 
which the Office of Management and Budget defines as a core population 
of at least 50,000 people with adjacent communities linked socially and 
economically with that core.

[4] Price throughout this report includes both the amount the PPO pays 
directly and the amount the enrollee is obligated to pay through 
deductibles and coinsurance.

[5] See app. I for a description of how we adjusted prices.

[6] We had a sufficient volume of hospital stays to analyze hospital 
prices in 232 metropolitan areas, and we had a sufficient volume of 
physician services to analyze physician prices in 319 metropolitan 
areas.

[7] See app. I for a description of all of our data measures and 
sources.

[8] Hospital networks were defined by the vendor supplying the data, 
Verispan, L.L.C., as an affiliation between three or more health care 
organizations, at least one of which is a hospital, with a unified 
marketing strategy. Where one or both of the two largest hospitals was 
not affiliated with a network, the percentage of beds in the hospital 
was used instead of the percentage of beds in a network.

[9] Capitation is a payment method used by managed care organizations 
where physicians are paid a fixed, predetermined payment for caring for 
an enrollee for a specified period of time, regardless of the number or 
type of services ultimately provided.

[10] The number of individuals without health insurance in each 
metropolitan area was obtained from InterStudy Publications, Inc., and 
was based on statewide data; it does not include differences in the 
uninsured among metropolitan areas in the same state.

[11] J. Menges, et al., for The Lewin Group, Comparing Physician and 
Dentist Fees Among Medicaid Programs (Oakland, Calif.: Medi-Cal Policy 
Institute, 2001).

[12] Total spending per enrollee includes both enrollee deductible and 
coinsurance obligations and PPO expenditures on behalf of the enrollee.

[13] Our analysis of hospital spending and utilization may have been 
limited by the small number of enrollees and admissions in some areas. 
Ten of the 232 metropolitan areas in this analysis had between 500 and 
1,000 enrollees.

[14] We defined areas in the lowest 25 percent of competition as having 
the least competition, and areas in the highest 25 percent of 
competition as having the most competition.

[15] P. Kennedy, A Guide to Econometrics, 5th ed. (Cambridge, Mass. MIT 
Press, 2003), p. 188. 

[16] We defined areas in the lowest 25 percent of HMO capitation as the 
having the least HMO capitation, and areas in the highest 25 percent of 
HMO capitation as having the most HMO capitation.

[17] Total spending per enrollee includes spending for all health care 
services except mental health, chemical dependency, and 
pharmaceuticals. We adjusted total spending per enrollee for 
differences in costs of providing service and in the age and sex of 
enrollees across metropolitan areas.

[18] We defined areas in the highest 25 percent of spending as areas 
with the highest spending and areas in the lowest 25 percent of 
spending as areas with the lowest spending.

[19] The Center for the Evaluative Clinical Sciences, Dartmouth Medical 
School, The Dartmouth Atlas of Health Care 1999: The Quality of Medical 
Care in the United States: A Report on the Medicare Program (Chicago, 
Ill.: AHA Press, 1999).

[20] GAO analysis of unadjusted 2003 Medicare spending per beneficiary 
data.

[21] The Dartmouth Atlas of Health Care 1999, p. 74.

[22] J.N. Weinstein et al., "Trends and Geographic Variations in Major 
Surgery for Degenerative Diseases of the Hip, Knee and Spine," Health 
Affairs, Web Exclusive, (Oct. 7, 2004). 
http://content.healthaffairs.org/cgi/content/full/hlthaff.var.81 
(downloaded June 21, 2005).

[23] The Dartmouth Atlas of Health Care 1999, pp. 11 and 27.

[24] C.M. Ashton, et al., "Geographic Variations in Utilization Rates 
in Veterans Affairs Hospitals and Clinics," The New England Journal of 
Medicine, vol. 340, no. 1 (1999). The Center for the Evaluative 
Clinical Sciences, Dartmouth Medical School and The Center for Outcomes 
Research and Evaluation, Maine Medical Center, The Dartmouth Atlas of 
Health Care in Michigan, 2000, pp. 46 and 47.

[25] We use the term providers to refer to hospitals, physicians, and 
other providers of health care services unless otherwise specified.

[26] See for example, A.E. Cuellar and P.J. Gertler, "How the Expansion 
of Hospital Systems Has Affected Consumers," Health Affairs, vol. 24, 
no. 1 (2005); C. Capps and D. Dranove, "Hospital Consolidation and 
Negotiated PPO Prices," Health Affairs, vol. 23, no. 2 (2004); L.M. 
Nichols, et al., "Are Market Forces Strong Enough to Deliver Efficient 
Health Care Systems? Confidence is Waning," Health Affairs, vol. 23, 
no. 2 (2004); and H.R. Spang, G.J. Bazzoli, and R.J. Arnould, "Hospital 
Mergers and Savings for Consumers: Exploring New Evidence," Health 
Affairs, vol. 20, no. 4 (2001).

[27] Hospitals may compete on dimensions other than price, such as 
services, amenities, and quality. See for example, M.A. Morrisey, 
"Competition in Hospital and Health Insurance Markets: A Review and 
Research Agenda," Health Services Research, vol. 36, no. 1 (2001).

[28] Cuellar and Gertler, "How the Expansion of Hospital Systems Has 
Affected Consumers," p. 213.

[29] See for example, Spang, Bazzoli, and Arnould, "Hospital Mergers 
and Savings for Consumers," p. 150; and G.J. Bazzoli et al., "Hospital 
Reorganization and Restructuring Achieved Through Merger," Health Care 
Management Review, vol. 27, no. 1 (2002).

[30] Bazzoli et al., "Hospital Reorganization and Restructuring 
Achieved Through Merger," pp. 2 and 6.

[31] D. Dranove, A. Durkac, and M. Shanley, "Are Multihospital Systems 
More Efficient?" Health Affairs, vol. 15, no. 1 (1996).

[32] L. Baker, "Measuring Competition in Health Care Markets," Health 
Services Research, April (2001); and M.A. Morrisey, "Competition in 
Hospital and Health Insurance Markets," p. 191.

[33] D.A. Draper, et al., "The Changing Face of Managed Care," Health 
Affairs, vol. 21, no. 1 (2002).

[34] J.S. Lee, et al., "Medicare Payment Policy: Does Cost Shifting 
Matter?" Health Affairs, Web Exclusive, (Oct. 8, 2003). 
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.480v1 
(downloaded June 21, 2005); and Congressional Budget Office, "Responses 
to Uncompensated Care and Public Program Controls on Spending: Do 
Hospitals 'Cost-Shift'?" (Washington, D.C.: 1993).

[35] See for example, T. Rice, et al., "Do Physicians Cost Shift," 
Health Affairs, vol. 15, no. 3 (1996); and J. Hadley, S. Zuckerman, 
L.I. Iezzoni, "Financial Pressure and Competition: Changes in Hospital 
Efficiency and Cost-Shifting Behavior," Medical Care, vol. 34, no. 3 
(1996).

[36] See for example, M.A. Morrisey, "Cost Shifting: New Myths, Old 
Confusion, and Enduring Reality," Health Affairs, Web Exclusive (Oct. 
8, 2003). 
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.489v1 
(downloaded June 21, 2005); and P.B. Ginsburg, "Can Hospitals and 
Physicians Shift the Effects of Cuts in Medicare Reimbursement to 
Private Payers?" Health Affairs, Web Exclusive (Oct. 8, 2003). 
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.472v1 
(downloaded June 21, 2005).

[37] D. Dranove and W.D. White, "Medicaid-dependent Hospitals and Their 
Patients: How Have They Fared?" Health Services Research, (June 1998).

[38] Quartiles divide the distribution of prices from lowest to highest 
into four equal groups. The lowest quartile represents metropolitan 
areas ranked in the lowest 25 percent of price, and the highest 
quartile represents metropolitan areas ranked in the highest 25 percent 
of price.

[39] The La Crosse, Wisconsin metropolitan area includes areas in 
Minnesota.

[40] We had sufficient data to analyze more metropolitan areas for 
physician prices than for hospital prices.

[41] We measured competition as the percentage of hospital beds in a 
metropolitan area (market share) held by the two largest hospitals or 
hospital networks, where higher percentages indicated less competition 
and lower percentages indicated more. Physicians are often aligned with 
health systems and hospital networks. Therefore, we approximated 
physician competition by measuring competition among hospitals and 
hospital networks in a metropolitan area.

[42] Other factors included in our analysis were our measures of 
competition, HMO capitation, cost shifting, per capita income, percent 
of for-profit beds, provider supply, and census division. See app. I 
for a detailed description of each factor. When we simulated the effect 
of increasing competition from the average level of competition in the 
lowest quartile to the average level of competition in the highest 
quartile, while controlling for other factors, our estimate of the 
percent difference in the average hospital price index between the 
highest and lowest competition quartiles was 7.62 percent, and our 
estimate of the percent difference in the average physician price index 
between the highest and lowest quartiles was 6.64 percent.

[43] Capitation is a payment method where physicians are paid a fixed, 
predetermined payment for caring for an enrollee for a specified period 
of time, regardless of the number or type of services provided. 
Physicians often try to resist capitation payments. The use of 
capitation by HMOs demonstrates that they have the leverage to 
negotiate capitation contracts with physicians. We used HMO capitation 
as a proxy measure for the strength of the HMO presence in a community, 
and its ability to negotiate prices with physicians, hospitals, and 
other providers.

[44] Other factors included in our analysis were our measures of 
competition, HMO capitation, cost shifting, per capita income, percent 
of for-profit beds, provider supply, and census division. See app. I 
for a detailed description of each factor. When we simulated the effect 
of increasing the level of HMO capitation from the average level of HMO 
capitation in the lowest quartile to the average level of HMO 
capitation in the highest quartile, while controlling for other 
factors, our estimate of the percent difference in the average hospital 
price index between the highest and lowest HMO capitation quartiles was 
7.17 percent and our estimate of the percent difference in the average 
physician price index between the highest and lowest quartiles was 3.31 
percent.

[45] HMO capitation data were not available in 4 of the 319 
metropolitan areas in our study. Accordingly, our analysis of HMO 
capitation was based on 315 metropolitan areas.

[46] We estimated Medicaid payment rates for each metropolitan area by 
taking the average physician payment for a set of common services. 
Medicaid payment rate estimates for metropolitan areas were based on 
statewide payment rates. We adjusted Medicaid payment rates to remove 
the effect of geographic differences in input costs and in the mix of 
services across metropolitan areas. See app. I.

[47] Other factors included in our analysis were measures of 
competition, HMO capitation, cost shifting, per capita income, provider 
supply, and census division. See app. I for a detailed description of 
each factor. When we simulated the effect of increasing Medicaid 
payments from the average Medicaid payment in the lowest quartile to 
the average Medicaid payment in the highest quartile, while controlling 
for other factors, we found that the physician price index was 9.69 
percent higher, on average.

[48] These factors included our measures of competition, HMO 
capitation, other cost-shifting variables, per capita income, percent 
of for-profit beds, provider supply, and census divisions.

[49] The percent of the population that was uninsured was based on 
statewide data and does not include differences in uninsured rates 
among metropolitan areas in the same state. See app. I for a 
description of our regression methodology and results.

[50] Total spending per enrollee includes spending for all health care 
services except mental health, chemical dependency, and 
pharmaceuticals. We adjusted total spending per enrollee for 
differences in costs of providing service and in the age and sex of 
enrollees across metropolitan areas.

[51] In order to analyze the contribution of price to geographic 
variation in spending, we focused on hospital and physician spending 
(not total spending), price, and utilization.

[52] We did not analyze factors associated with this variation in 
utilization as it was outside the scope of our research objectives.

[53] The 26 percent difference between hospital prices in the highest 
and lowest quartiles contributed to about one-third of the difference 
in hospital spending. The 55 percent difference between hospital 
utilization in the highest and lowest hospital spending quartiles 
contributed to about two-thirds of the difference in hospital spending.

[54] The 12 percent difference between physician prices in the highest 
and lowest quartiles contributed to about one-third of the difference 
in physician spending. The 26 percent difference between physician 
utilization in the highest and lowest physician spending quartiles 
contributed to about two-thirds of the difference in physician spending.

[55] Metropolitan areas refer to metropolitan statistical areas, which 
the Office of Management and Budget defines as a core population of at 
least 50,000 people and the adjacent communities linked socially and 
economically with that core.

[56] Our study may also have included some federal retirees under the 
age of 65, whose primary insurer was an FEHBP PPO.

[57] We excluded PPO enrollees age 65 and over because Medicare, not 
FEHBP, was their primary insurer, and consequently the PPOs did not 
have records of all claim payments.

[58] We excluded metropolitan areas that had fewer than 38 hospital 
stays.

[59] Our analysis of hospital spending and utilization may have been 
limited by the small number of enrollees and admissions in some areas. 
Ten of the 232 metropolitan areas in this analysis had between 500 and 
1,000 enrollees.

[60] Price throughout this report includes both the amount the PPO pays 
directly and the amount the enrollee is obligated to pay through 
deductibles and coinsurance.

[61] The APR-DRG software was provided to GAO by 3M Health Information 
Systems in Murray, Utah.

[62] If a hospital was a member of more than one hospital network in a 
metropolitan area, we averaged the percent of hospital beds in the two 
largest hospitals or hospital networks across each combination of 
network affiliation.

[63] We estimated the percent of primary care physicians' compensation 
by multiplying the percent of HMO compensation to primary care 
physicians on a capitation basis by the percent of the population 
enrolled in HMOs.

[64] Some Medicaid payments for a given service varied depending on 
criteria such as patient age, sex, provider specialty, and practice 
setting. Researchers at The Lewin Group, who developed the statewide 
payments that we used in estimating metropolitan area Medicaid prices, 
reported that they focused on the payments most commonly made to a 
physician in private practice.

[65] We were unable to find uninsured data at the metropolitan area 
level. Therefore we used the number of uninsured from InterStudy 
Publications. The estimates from InterStudy Publications of the 
uninsured are based on state numbers.

[66] In order for the regression to be estimated we had to omit one of 
the census division dummies from our model: we chose to omit Census 
Division 9.

[67] Quartiles divide the distribution of prices from lowest to highest 
into four equal groups. The lowest quartile represents metropolitan 
areas ranked in the lowest 25 percent of price, and the highest 
quartile represents metropolitan areas ranked in the highest 25 percent 
of price.

[68] We did not perform an analysis comparing prices inside and outside 
of those census divisions that were significant in our regressions.

[69] A certificate-of-need law generally requires that a hospital or 
nursing home obtain approval from the state in which it is located 
before hospital construction or capital improvements occur.

[70] Medicare adjusts hospital inpatient payments for labor and capital-
related variations in costs. In our study, we applied labor and capital 
adjustments to the hospital inpatient portion of spending and to 
hospital inpatient price.

[71] We excluded mental health, chemical dependency services, and 
pharmaceuticals from our spending analysis.

[72] There are three GPCIs reflecting the cost of three different types 
of inputs to physician services: physician work, physician practice 
expenses, and expenses for physician liability insurance. Each GPCI is 
used to adjust for the price level for related inputs in the local 
market where the service is furnished.

[73] There are 89 carrier/locality regions nationwide and 331 
metropolitan areas in the 50 states and District of Columbia. Thus, a 
carrier/locality area is, on average, much larger than a metropolitan 
area. We used county-level data for the GPCIs and aggregated those data 
to the metropolitan area level.

[74] Where metropolitan areas overlapped several states, we prorated 
state Medicaid payment rates based on U.S. census estimates of Medicaid 
enrollment in each component county of the metropolitan area. We used 
utilization rates in California to weight the average Medicaid payment 
in each metropolitan area because utilization rates were not readily 
available for any other state.

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