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 
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

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. 

Highlights of GAO-05-856, a report to the 

Honorable Paul Ryan, House of 

Representatives 

August 2005

FEDERAL EMPLOYEES HEALTH 

BENEFITS PROGRAM 

Competition and Other Factors Linked to 

Wide Variation in Health Care Prices 

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 

Source: GAO analysis of FEHBP data.

Lowest quartile Second quartile Third quartile Highest quartile

0

.50

1.50

.75

1.00

1.25

.25

1.75

2.00

Avg.

1.00

Avg.

1.00

Hospital price indices in 

232 metropolitan areas

Physician price indices 

in 319 metropolitan areas

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. 

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. 
Page i GAO-05-856 FEHBP Health Care Prices 

Letter 1

Results in Brief 4 

Background 5 

Large Differences in Hospital and Physician Prices across 

Metropolitan Areas 10 

Less Competition and Less HMO Capitation Linked to Higher 

Health Care Prices 18 

Total Spending Varied 112 Percent; Price Variation Contributed to 

One-third of the Variation in Hospital and Physician Spending 24 

Concluding Observations 30 

Agency and Other Comments 30 

Appendix I Scope and Methodology 32 

FEHBP Data and Study Eligibility Criteria 32 

Hospital and Physician Price Estimates 34 

Factors Affecting Health Care Prices 35 

Analytical Approach 39 

Spending Analysis 45 

Decomposing Spending Variation into Price and Utilization Effects 46 

Data Reliability 47 

Appendix II FEHBP PPO Adjusted Hospital Prices in U.S. 

Metropolitan Areas, 2001 49 

Appendix III FEHBP PPO Adjusted Physician Prices in U.S. 

Metropolitan Areas, 2001 56 

Appendix IV FEHBP PPO Adjusted Health Care Spending Per 

Enrollee in U.S. Metropolitan Areas, 2001 65 

Appendix V Comments from the Office of Personnel Management 72 

Contents 
Page ii GAO-05-856 FEHBP Health Care Prices 

Appendix VI GAO Contacts and Staff Acknowledgments 74

Tables 

Table 1: FEHBP PPO Hospital Price Indices in Metropolitan Areas 

Grouped by Census Region, 2001 14 

Table 2: Metropolitan Areas with the Highest and Lowest Hospital 

Price Indices in FEHBP PPOs, 2001 14 

Table 3: FEHBP PPO Physician Price Indices in Metropolitan Areas 

Grouped by Census Region, 2001 17 

Table 4: Metropolitan Areas with the Highest and Lowest Physician 

Price Indices in FEHBP PPOs, 2001 17 

Table 5: FEHBP PPO Price Indices in the Least and Most 

Competitive Metropolitan Areas, 2001 19 

Table 6: FEHBP PPO Price Indices in Metropolitan Areas with the 

Least and Most HMO Capitation, 2001 21 

Table 7: FEHBP PPO Price Indices in Metropolitan Areas in the 

Lowest and Highest Medicaid Payment Quartiles, 2001 23 

Table 8: FEHBP PPO Spending Per Enrollee Indices in 

Metropolitan Areas by Census Region, 2001 27 

Table 9: Price and Utilization Indices in Metropolitan Areas in the 

Highest and Lowest Quartiles of Hospital and Physician 

Spending, 2001 28 

Table 10: Example of the Offsetting Effect of Physician Price and 

 Utilization on Physician Spending in Two Metropolitan 

 Areas in the FEHBP, 2001 29 

Table 11: Factors Included in Analysis of Hospital and Physician 

 Price, 2001 36 

Table 12: Results for Hospital Price Regression—Estimated Effects 

 of Selected Factors on Hospital Prices in Metropolitan 

 Areas, 2001 41 

Table 13: Results for Physician Price Regression—Estimated 

 Effects of Selected Factors on Physician Prices in 

 Metropolitan Areas, 2001 42 

Table 14: Effects of Changes in Explanatory Variables on Prices 44 

Table 15: Ranking of Metropolitan Areas by Adjusted Hospital 

 Prices, 2001 49 

Table 16: Ranking of Metropolitan Areas by Adjusted Physician 

 Prices, 2001 56 

Table 17: Ranking of Metropolitan Areas by Adjusted Health Care 

 Spending Per Enrollee, 2001 65 
Page iii GAO-05-856 FEHBP Health Care Prices 

Figures 

Figure 1: Distribution of Hospital Price Indices across 232 

Metropolitan Areas, 2001 11 

Figure 2: Distribution of Physician Price Indices across 319 

Metropolitan Areas, 2001 12 

Figure 3: FEHBP PPO Adjusted Hospital Price Index Quartiles in 

232 Metropolitan Areas, 2001 13 

Figure 4: FEHBP PPO Adjusted Physician Price Index Quartiles in 

319 Metropolitan Areas, 2001 16 

Figure 5: Distribution of FEHBP PPO Spending Per Enrollee 

Indices across 232 Metropolitan Areas, 2001 25 

Figure 6: FEHBP Adjusted Spending Per Enrollee Quartiles in 232 

Metropolitan Areas, 2001 26 

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 

This is a work of the U.S. government and is not subject to copyright protection in the 

United States. It may be reproduced and distributed in its entirety without further 

permission from GAO. However, because this work may contain copyrighted images or 

other material, permission from the copyright holder may be necessary if you wish to 

reproduce this material separately. 
Page 1 GAO-05-856 FEHBP Health Care Prices 

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)1

 in Milwaukee compared to other 

metropolitan areas.2

 In this report, we have expanded that analysis to 

include geographic variation in prices and spending in metropolitan areas3

throughout the United States. This final report examines prices and 

spending in FEHBP PPOs to determine: (1) the extent to which hospital 

 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. 

United States Government Accountability Office

Washington, DC 20548 
Page 2 GAO-05-856 FEHBP Health Care Prices 

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.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.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.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.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.8

 The larger the share of the hospital service 

 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. 
Page 3 GAO-05-856 FEHBP Health Care Prices 

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.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.10 We also estimated average physician Medicaid payment 

rates in each metropolitan area based on Medicaid rates for 29 common 

procedures.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.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 

 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. 

10The 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. 

11J. Menges, et al., for The Lewin Group, Comparing Physician and Dentist Fees Among 

Medicaid Programs (Oakland, Calif.: Medi-Cal Policy Institute, 2001). 

12Total spending per enrollee includes both enrollee deductible and coinsurance obligations 

and PPO expenditures on behalf of the enrollee. 
Page 4 GAO-05-856 FEHBP Health Care Prices 

and utilization to spending for hospital stays and physician services.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. 

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.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.15 When we controlled for other 

 13Our 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. 

14We 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. 

15We 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. 

Results in Brief 
Page 5 GAO-05-856 FEHBP Health Care Prices 

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 lowestspending metropolitan area.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.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. 

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 

 16Total 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. 

17We 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. 

Background 

FEHBP and Participating 

PPOs 
Page 6 GAO-05-856 FEHBP Health Care Prices 

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 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.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.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 1995-199620 and more than five times as many hip 

and knee replacement surgeries in some markets as in others in 2000-

 18The 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). 

19GAO analysis of unadjusted 2003 Medicare spending per beneficiary data. 

20The Dartmouth Atlas of Health Care 1999, p. 74. 

Geographic Variation in 

Spending, Utilization, and 

Prices 
Page 7 GAO-05-856 FEHBP Health Care Prices 

2001.21 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.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.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 providers24 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. 

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. 

 21J.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). 

22The Dartmouth Atlas of Health Care 1999, pp. 11 and 27. 

23C.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. 

24We use the term providers to refer to hospitals, physicians, and other providers of health 

care services unless otherwise specified. 

Health Care Market 

Characteristics and Price 
Page 8 GAO-05-856 FEHBP Health Care Prices 

Some but not all studies have shown that recent decreases in competition 

among providers have been associated with increased prices.25,26 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.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.28 For 

example, consolidated hospitals can streamline operations by centralizing 

services, such as emergency care or intensive care units.29 However, other 

studies of hospital mergers and acquisitions have not found evidence that 

they result in any reductions in costs.30

Other research has shown that the presence of HMOs in a metropolitan 

area may also influence the price of health care services.31 HMOs have 

 25See 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). 

26Hospitals 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). 

27Cuellar and Gertler, “How the Expansion of Hospital Systems Has Affected Consumers,” 

p. 213. 

28See 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). 

29Bazzoli et al., “Hospital Reorganization and Restructuring Achieved Through Merger,” pp. 

2 and 6. 

30D. Dranove, A. Durkac, and M. Shanley, “Are Multihospital Systems More Efficient?” 

Health Affairs, vol. 15, no. 1 (1996). 

31L. 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. 
Page 9 GAO-05-856 FEHBP Health Care Prices 

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.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.33 Yet, other researchers have found no evidence of 

cost shifting.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.35 Without sufficient market power, providers 

 32D.A. Draper, et al., “The Changing Face of Managed Care,” Health Affairs, vol. 21, no. 1 

(2002). 

33J.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 ‘CostShift’?” (Washington, D.C.: 1993). 

34See 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). 

35See 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). 
Page 10 GAO-05-856 FEHBP Health Care Prices 

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.36

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. 

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,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. 

 36D. Dranove and W.D. White, “Medicaid-dependent Hospitals and Their Patients: How 

Have They Fared?” Health Services Research, (June 1998). 

37Quartiles 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. 

Large Differences in 

Hospital and 

Physician Prices 

across Metropolitan 

Areas 

Hospital Prices Varied 

More than Physician Prices 
Page 11 GAO-05-856 FEHBP Health Care Prices 

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

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. 

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,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. 2.39 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. 

 38The La Crosse, Wisconsin metropolitan area includes areas in Minnesota. 

39We had sufficient data to analyze more metropolitan areas for physician prices than for 

hospital prices. 

0.0 0.5 1.0 1.5 2.0

Source: GAO analysis of FEHBP data.

0

.51

1.83

.77

.86

.97

1.12

1.27

Lowest 

quartile

Highest 

quartile

Avg.

1.00

Second 

quartile

Third

 quartile
Page 12 GAO-05-856 FEHBP Health Care Prices 

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

2001 

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. 

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. 

Hospital and Physician 

Prices Were Generally 

Higher in the Midwest and 

Lower in the Northeast 

0.0 0.3 0.6 0.9 1.2 1.5

Source: GAO analysis of FEHBP data.

0

.73

.84

.91

1.00

1.07

1.15

1.48

Lowest 

quartile

Highest 

quartile

Avg.

1.00

Second 

quartile

Third

 quartile
Page 13 GAO-05-856 FEHBP Health Care Prices 

Figure 3: FEHBP PPO Adjusted Hospital Price Index Quartiles in 232 Metropolitan Areas, 2001 
Page 14 GAO-05-856 FEHBP Health Care Prices 

Table 1: FEHBP PPO Hospital Price Indices in Metropolitan Areas Grouped by 

Census Region, 2001 

Region 

Average hospital price indexa

 for 

region

Midwest 1.07

West 1.00

South 1.00

Northeast 0.94

Percent by which prices in the Midwest 

exceed prices in the Northeast 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. 

Table 2: Metropolitan Areas with the Highest and Lowest Hospital Price Indices in 

FEHBP PPOs, 2001 

Rank 

Highest-priced 

metropolitan areas Rank 

Lowest-priced 

metropolitan areas 

1 a 232 Orange County, Calif. 

2 Dover, Del. 231 Pueblo, Colo. 

3 Biloxi-Gulfport-Pascagoula, Miss. 230 Ventura, Calif. 

4 St. Joseph, Mo. 229 Albany-Schenectady-Troy, N.Y. 

5 Milwaukee-Waukesha, Wisc. 228 Newburgh, New York-Penn. 

6 Salinas, Calif. 227 New York, N.Y. 

7 Buffalo-Niagara Falls, N.Y. 226 Altoona, Penn. 

8 Grand Junction, Colo. 225 Decatur, Ala. 

9 a 224 Anniston, Ala. 

10 La Crosse, Wisconsin-Minn. 223 Saginaw-Bay City-Midland, Mich. 

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. 
Page 15 GAO-05-856 FEHBP Health Care Prices 

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. 
Page 16 GAO-05-856 FEHBP Health Care Prices 

Figure 4: FEHBP PPO Adjusted Physician Price Index Quartiles in 319 Metropolitan Areas, 2001 
Page 17 GAO-05-856 FEHBP Health Care Prices 

Table 3: FEHBP PPO Physician Price Indices in Metropolitan Areas Grouped by 

Census Region, 2001 

Region 

Average physician price 

indexa

 for region

Midwest 1.05

South 1.02

West 0.99

Northeast 0.91

Percent by which prices in the Midwest 

exceed prices in the Northeast 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. 

Table 4: Metropolitan Areas with the Highest and Lowest Physician Price Indices in 

FEHBP PPOs, 2001 

Rank 

Highest-priced 

metropolitan areas Rank 

Lowest-priced 

metropolitan areas 

1 La Crosse, Wisconsin-Minn. 319 Baltimore, Md. 

2 Wausau, Wisc. 318 Lowell, Massachusetts-N.H. 

3 Eau Claire, Wisc. 317 Nassau-Suffolk, N.Y. 

4 Madison, Wisc. 316 Washington, D.C. 

5 Jonesboro, Ark. 315 Fort Lauderdale, Fla. 

6 Janesville-Beloit, Wisc. 314 West Palm Beach-Boca Raton, 

Fla. 

7 Great Falls, Mont. 313 Miami, Fla. 

8 Green Bay, Wisc. 312 Providence-Fall River-Warwick, 

Rhode Island-Mass. 

9 Appleton-Oshkosh-Neenah, Wisc. 311 Dutchess County, N.Y. 

10 Racine, Wisc. 310 San Francisco, Calif. 

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. 
Page 18 GAO-05-856 FEHBP Health Care Prices 

FEHBP PPOs paid higher average hospital and physician prices in 

metropolitan areas with less competition among hospitals.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 onethird 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. 

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 

 40We 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. 

Less Competition and 

Less HMO Capitation 

Linked to Higher 

Health Care Prices 

Prices Were Higher in 

Metropolitan Areas with 

Less Competition 
Page 19 GAO-05-856 FEHBP Health Care Prices 

for physician prices.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 

Average 

hospital price 

indexa

Average 

physician price 

indexb

Least competitivec 1.10 1.04

Most competitivec 0.93 0.94

Percent by which prices in the least 

competitive areas exceed prices in the most 

competitive areasd 18.28 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. 

 41Other 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. 
Page 20 GAO-05-856 FEHBP Health Care Prices 

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. 

FEHBP PPO hospital and physician prices were higher, on average, in 

metropolitan areas with less HMO capitation.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 

 42Capitation 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. 

Prices Were Higher in 

Metropolitan Areas with 

Less HMO Capitation 
Page 21 GAO-05-856 FEHBP Health Care Prices 

reduced by about one-third for hospital prices and two-thirds for physician 

prices.43 See appendix I. 

Table 6: FEHBP PPO Price Indices in Metropolitan Areas with the Least and Most 

HMO Capitation, 2001 

HMO capitation quartile 

Average 

hospital price 

indexa

Average 

physician price 

indexb

Least HMO capitationc 1.05 1.06

Most HMO capitationc 0.95 0.96

Percent by which prices in areas with the 

least capitation exceed prices in areas with 

the most capitationd

10.53 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. 

 43Other 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. 
Page 22 GAO-05-856 FEHBP Health Care Prices 

Many of the metropolitan areas in our study had low levels of HMO 

capitation.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. 

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.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 

 44HMO 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. 

45We 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. 

No Evidence of Cost 

Shifting Due to Medicaid, 

Medicare, or the 

Uninsured 
Page 23 GAO-05-856 FEHBP Health Care Prices 

observed relationship between physician prices and Medicaid payments.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 

Average physician 

price indexa

Lowest 0.92

Highest 1.02

Percent by which prices in the lowest Medicaid 

payment areas were lower than prices in the 

highest Medicaid payment areasb

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. 

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.47 In this simulation, we found that the physician prices were 

6 percent lower, on average, in the quartile with the highest percent 

 46Other 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. 

47These 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. 
Page 24 GAO-05-856 FEHBP Health Care Prices 

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.48

FEHBP PPO total spending per enrollee was more than twice as high in 

some areas as in others.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. 

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 

 48The 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. 

49Total 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. 

Total Spending Varied 

112 Percent; Price 

Variation Contributed 

to One-third of the 

Variation in Hospital 

and Physician 

Spending 

Spending per Enrollee 

Varied by 112 Percent 

across Metropolitan Areas 
Page 25 GAO-05-856 FEHBP Health Care Prices 

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 

0.0 0.3 0.6 0.9 1.2 1.5

Source: GAO analysis of FEHBP data.

0

.67

.84

.90

.98

1.00

1.08

1.24

1.42

Lowest 

quartile

Highest 

quartile

Avg.

1.00

Second

quartile

Third

quartile

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. 

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). 
Page 26 GAO-05-856 FEHBP Health Care Prices 

Figure 6: FEHBP Adjusted Spending Per Enrollee Quartiles in 232 Metropolitan Areas, 2001 
Page 27 GAO-05-856 FEHBP Health Care Prices 

Table 8: FEHBP PPO Spending Per Enrollee Indices in Metropolitan Areas by 

Census Region, 2001 

Region 

Average spending per 

enrollee indexa or region

South 1.08

Midwest 0.95

West 0.94

Northeast 0.88

Percent by which spending in the South exceeds 

spending in the Northeast 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. 

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.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.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.52 Physician prices were 12 percent higher, on average, in the 

 50In 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. 

51We did not analyze factors associated with this variation in utilization as it was outside 

the scope of our research objectives. 

52The 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. 

Price Contributed to Onethird of the Variation in 

Spending, but the 

Contribution of Price to 

Spending Was Partially 

Offset by Utilization of 

Physician Services 
Page 28 GAO-05-856 FEHBP Health Care Prices 

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.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 Spending quartile 

Average 

price indexa

Average 

utilization indexb

Hospital stays Highest 1.12 1.24

 Lowest 0.89 0.80

Percent by which highest 

hospital spending areas 

exceed lowest hospital 

spending areas 25.84 55.00

Physician 

services 

Highest 1.05 1.12

 Lowest 0.94 0.89

Percent by which highest 

physician spending areas 

exceed lowest physician 

spending areas 11.70 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. 

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 

 53The 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. 
Page 29 GAO-05-856 FEHBP Health Care Prices 

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 

 Anchorage, Alaska Bakersfield, California

Physician price indexa 1.22 0.94

Physician utilization indexb 0.78 1.20

Physician spending indexc 1.23 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. 
Page 30 GAO-05-856 FEHBP Health Care Prices 

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. 

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. 

Concluding 

Observations 

Agency and Other 

Comments 
Page 31 GAO-05-856 FEHBP Health Care Prices 

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, 

A. Bruce Steinwald 

Director, Health Care 
Appendix I: Scope and Methodology 

Page 32 GAO-05-856 FEHBP Health Care Prices 

In this appendix we describe the data and methods we used to compare 

geographic variations in prices and spending in metropolitan areas1

 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. 

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.2,3

 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 

 1

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. 

2

Our study may also have included some federal retirees under the age of 65, whose 

primary insurer was an FEHBP PPO. 

3

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. 

Appendix I: Scope and Methodology 

FEHBP Data and 

Study Eligibility 

Criteria 
Appendix I: Scope and Methodology 

Page 33 GAO-05-856 FEHBP Health Care Prices 

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.4

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.5

 The population of these 232 metropolitan areas accounted for 

88 percent of the population living in all metropolitan areas. 

 4

We excluded metropolitan areas that had fewer than 38 hospital stays. 

5

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. 
Appendix I: Scope and Methodology 

Page 34 GAO-05-856 FEHBP Health Care Prices 

We calculated price indices for hospital and physician services. We 

selected these services because together they represented nearly twothirds 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.6

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.7

 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 

 6

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. 

7

The APR-DRG software was provided to GAO by 3M Health Information Systems in 

Murray, Utah. 

Hospital and 

Physician Price 

Estimates 
Appendix I: Scope and Methodology 

Page 35 GAO-05-856 FEHBP Health Care Prices 

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. 

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. 

Factors Affecting 

Health Care Prices 
Appendix I: Scope and Methodology 

Page 36 GAO-05-856 FEHBP Health Care Prices 

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

Factor Measurement 

Source of data to calculate 

measurement 

Competition Percent hospital beds of the two 

largest hospitals or hospital 

networksa

Verispan, L.L.C. 

HMO capitation Percent of primary care physicians’ 

compensation from capitationb

 

InterStudy Publications and 

U.S. Census Bureau 

Cost shifting Percent of population enrolled in 

Medicare 

InterStudy Publications and 

U.S. Census Bureau 

 Percent of population enrolled in 

Medicaid 

InterStudy Publications and 

U.S. Census Bureau 

 Percent of population uninsuredc InterStudy Publications and 

U.S. Census Bureau 

 Average Medicaid payment The Lewin Group, Centers 

for Medicare and Medicaid 

Services, and U.S. Census 

Bureau 

Supply of providers Hospital beds per capita Verispan, L.L.C. and U.S. 

Census Bureau 

Per capita income Population’s real per capita 

incomed

Bureau of Economic 

Analysis and Centers for 

Medicare and Medicaid 

Services 

Hospital ownership 

status 

Percent beds in for-profit hospitals Verispan, L.L.C. 

Census division Indicator of the presence or 

absence of the metropolitan area 

in the census divisions 

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. 
Appendix I: Scope and Methodology 

Page 37 GAO-05-856 FEHBP Health Care Prices 

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.8

 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.9

 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.10 We 

then weighted each Medicaid service using utilization estimates from the 

 8

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. 

9

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. 

10Some 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. 
Appendix I: Scope and Methodology 

Page 38 GAO-05-856 FEHBP Health Care Prices 

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.11

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.12

 11We 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. 

12In 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. 
Appendix I: Scope and Methodology 

Page 39 GAO-05-856 FEHBP Health Care Prices 

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.13 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.14

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.15 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 

 13Quartiles 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. 

14We did not perform an analysis comparing prices inside and outside of those census 

divisions that were significant in our regressions. 

15P. Kennedy, A Guide to Econometrics, 5th ed. (Cambridge, Mass. MIT Press, 2003), p. 188. 

Analytical Approach 

Price Regression 

Analysis—Methods and 

Results 
Appendix I: Scope and Methodology 

Page 40 GAO-05-856 FEHBP Health Care Prices 

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. 
Appendix I: Scope and Methodology 

Page 41 GAO-05-856 FEHBP Health Care Prices 

Table 12: Results for Hospital Price Regression—Estimated Effects of Selected Factors on Hospital Prices in Metropolitan 

Areas, 2001 

Dependent variable is the logarithm of adjusted hospital stay pricea

Factor Variable used to measure factor Parameter estimate t-value

Competition Percent hospital beds of the two largest hospitals or hospital networks 0.1337 2.11**

HMO capitation Percent of primary care physicians’ compensation from capitation -0.3213 -2.22**

Percent of population uninsured -0.3621 -0.68

Average Medicaid payment 0.0026 1.58

Cost-shifting 

Percent of population enrolled in Medicaid -0.0538 -0.20

 Percent of population enrolled in Medicare -0.5267 -1.14

Supply of providers Hospital beds per capita 21.5968 0.50

Per capita income Population’s real per capita income 0.0000 -0.52

Hospital ownership status Percent of beds in for profit hospitals 0.0767 0.86

Census Division 1 – New England 0.0625 0.78

Census Division 2 – Middle Atlantic -0.1158 -1.43

Census Division 3 – East North Central -0.0572 -0.73

Census Division 4 – West North Central 0.0418 0.33

Dummy variable indicator 

showing the Census 

Division in which the 

metropolitan area was 

located 

Census Division 5 – South Atlantic -0.0258 -0.35

 Census Division 6 – East South Central -0.1845 -1.80*

 Census Division 7 – West South Central -0.1077 -1.14

 Census Division 8 – Mountain -0.0428 -0.63

 Census Division 9 – Pacificb

 Intercept 8.8972 45.67***

 R-squared 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. 
Appendix I: Scope and Methodology 

Page 42 GAO-05-856 FEHBP Health Care Prices 

Table 13: Results for Physician Price Regression—Estimated Effects of Selected Factors on Physician Prices in Metropolitan 

Areas, 2001 

Dependent variable is the logarithm of adjusted physician services pricea

Factor Variable used to measure factor Parameter estimate t-value

Competition Percent hospital beds of the two largest hospitals or hospital networks 0.1234 4.36***

HMO capitation Percent of primary care physicians’ compensation from capitation -0.1393 -2.24**

Percent of population uninsured -0.5328 -2.22**

Average Medicaid payment 0.0041 5.24***

Percent of population enrolled in Medicaid 0.1081 0.91

Cost-shifting 

Percent of population enrolled in Medicare 0.0217 0.10

Hospital ownership status Percent of beds in for profit hospitals -0.0536 -1.34

Per capita income Population’s real per capita income 0.0000 0.00

Supply of providers Physicians per capita (physicians per 1000 population) -0.0002 -0.91

Census Division 1 – New England -0.1112 -2.79***

Census Division 2 – Middle Atlantic -0.0346 -1.01

Census Division 3 – East North Central 0.0041 0.14

Dummy variable indicator 

showing the Census 

Division in which the 

metropolitan area was 

located 

Census Division 4 – West North Central 0.0120 0.32

 Census Division 5 – South Atlantic -0.0470 -1.58

 Census Division 6 – East South Central -0.0558 -1.61

 Census Division 7 – West South Central 0.0947 3.24***

 Census Division 8 – Mountain -0.0240 -0.77

 Census Division 9 – Pacificb

 Intercept 3.7808 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. 
Appendix I: Scope and Methodology 

Page 43 GAO-05-856 FEHBP Health Care Prices 

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. 
Appendix I: Scope and Methodology 

Page 44 GAO-05-856 FEHBP Health Care Prices 

Table 14: Effects of Changes in Explanatory Variables on Prices 

Significant explanatory variable 

Percent impact on 

physician price 

Percent impact on 

hospital price

Percent hospital beds of the two largest 

hospitals or hospital networks 6.64 7.62

Percent of primary care physicians’ 

compensation from capitation -3.31 -7.17

Average Medicaid payment 9.69 a 

Percent of population uninsured -6.05 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. 

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.16 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-ofneed 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. 

 16A 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. 
Appendix I: Scope and Methodology 

Page 45 GAO-05-856 FEHBP Health Care Prices 

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,17 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.18

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.19 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.20

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 

 17Medicare 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. 

18We excluded mental health, chemical dependency services, and pharmaceuticals from our 

spending analysis. 

19There 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. 

20There 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. 

Spending Analysis 
Appendix I: Scope and Methodology 

Page 46 GAO-05-856 FEHBP Health Care Prices 

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 ageand 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 costadjusted 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. 

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 

Decomposing 

Spending Variation 

into Price and 

Utilization Effects 
Appendix I: Scope and Methodology 

Page 47 GAO-05-856 FEHBP Health Care Prices 

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. 

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 

Data Reliability 
Appendix I: Scope and Methodology 

Page 48 GAO-05-856 FEHBP Health Care Prices 

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.21

We performed our work from September 2002 through July 2005 in 

accordance with generally accepted government auditing standards. 

 21Where 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. 
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 49 GAO-05-856 FEHBP Health Care Prices 

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 Metropolitan area Predominant statea Adjusted hospital price index

1 b b 1.829

2 Dover DE 1.680

3 Biloxi-Gulfport-Pascagoula MS 1.591

4 St. Joseph MO 1.578

5 Milwaukee-Waukesha WI 1.568

6 Salinas CA 1.499

7 Buffalo-Niagara Falls NY 1.451

8 Grand Junction CO 1.431

9 b b 1.419

10 La Crosse, WI-MN WI 1.385

11 Wichita KS 1.379

12 Manchester NH 1.365

13 Bakersfield CA 1.361

14 Sioux Falls SD 1.357

15 Bangor ME 1.340

16 Owensboro KY 1.326

17 Fort Walton Beach FL 1.322

18 Portsmouth-Rochester, NH-ME NH 1.318

19 Lakeland-Winter Haven FL 1.310

20 South Bend IN 1.285

21 Honolulu HI 1.277

22 Albany GA 1.270

23 Oklahoma City OK 1.270

24 Nashua NH 1.266

25 Olympia WA 1.262

26 Omaha, NE-IA NE 1.256

27 Duluth-Superior, MN-WI MN 1.252

28 Rapid City SD 1.249

29 Terre Haute IN 1.244

30 Charleston WV 1.243

31 Wilmington-Newark, DE-MD DE 1.239

32 Lynchburg VA 1.237

Appendix II: FEHBP PPO Adjusted Hospital 

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

Prices in U.S. Metropolitan Areas, 2001 

Page 50 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

33 Billings MT 1.235

34 b b 1.233

35 Myrtle Beach SC 1.231

36 Columbia MO 1.230

37 Topeka KS 1.225

38 Evansville-Henderson, IN-KY IN 1.193

39 Lawton OK 1.192

40 Missoula MT 1.187

41 Daytona Beach FL 1.186

42 Medford-Ashland OR 1.177

43 Roanoke VA 1.176

44 Bismarck ND 1.173

45 Charleston-North Charleston SC 1.161

46 Portland ME 1.158

47 Sioux City, IA-NE IA 1.157

48 Jackson MS 1.151

49 Hattiesburg MS 1.148

50 Provo-Orem UT 1.147

51 Fort Collins-Loveland CO 1.144

52 Boise City ID 1.138

53 Salt Lake City-Ogden UT 1.137

54 Enid OK 1.137

55 Gainesville FL 1.136

56 San Antonio TX 1.132

57 Parkersburg-Marietta, WV-OH WV 1.127

58 Boston, MA-NH MA 1.123

59 Memphis, TN-AR-MS TN 1.117

60 Cedar Rapids IA 1.113

61 Jackson TN 1.111

62 Houston TX 1.103

63 Huntington-Ashland, WV-KY-OH WV 1.102

64 Fayetteville NC 1.102

65 Springfield MA 1.101

66 Melbourne-Titusville-Palm Bay FL 1.099

67 Portland-Vancouver, OR-WA OR 1.098

68 Iowa City IA 1.092

69 Florence SC 1.087
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 51 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

70 Fort Pierce-Port St. Lucie FL 1.086

71 Tacoma WA 1.086

72 Grand Forks, ND-MN ND 1.083

73 Lubbock TX 1.078

74 New Haven-Meriden CT 1.071

75 Great Falls MT 1.068

76 Columbus, GA-AL GA 1.065

77 Fort Myers-Cape Coral FL 1.061

78 Fargo-Moorhead, ND-MN ND 1.061

79 Des Moines IA 1.060

80 Minneapolis-St. Paul, MN-WI MN 1.057

81 Fort Smith, AR-OK AR 1.052

82 Bremerton WA 1.048

83 Richmond-Petersburg VA 1.041

84 Lincoln NE 1.040

85 Phoenix-Mesa AZ 1.039

86 Laredo TX 1.033

87 Salem OR 1.031

88 Bloomington IN 1.029

89 Lexington KY 1.029

90 Reading PA 1.028

91 Augusta-Aiken, GA-SC GA 1.027

92 Fort Worth-Arlington TX 1.025

93 b b 1.024

94 Austin-San Marcos TX 1.019

95 Asheville NC 1.016

96 Wichita Falls TX 1.015

97 Little Rock-North Little Rock AR 1.015

98 Las Vegas, NV-AZ NV 1.013

99 McAllen-Edinburg-Mission TX 1.011

100 Jonesboro AR 1.006

101 Miami FL 1.006

102 Charlotte-Gastonia-Rock Hill, NC-SC NC 1.002

103 Orlando FL 1.001

104 Seattle-Bellevue-Everett WA 0.993

105 Pensacola FL 0.986

106 Odessa-Midland TX 0.983
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 52 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

107 Lansing-East Lansing MI 0.983

108 Johnson City-Kingsport-Bristol, TN-VA TN 0.981

109 Charlottesville VA 0.980

110 Knoxville TN 0.978

111 Fayetteville-Springdale-Rogers AR 0.978

112 Clarksville-Hopkinsville, TN-KY TN 0.975

113 Dayton-Springfield OH 0.974

114 San Angelo TX 0.971

115 Tucson AZ 0.970

116 Tampa-St. Petersburg-Clearwater FL 0.967

117 Ann Arbor MI 0.965

118 Scranton—Wilkes-Barre—Hazleton PA 0.964

119 Eugene-Springfield OR 0.964

120 Atlantic-Cape May NJ 0.963

121 Anchorage AK 0.962

122 Bridgeport CT 0.961

123 San Francisco CA 0.960

124 Panama City FL 0.957

125 Baltimore MD 0.953

126 Greenville-Spartanburg-Anderson SC 0.950

127 Trenton NJ 0.946

128 Redding CA 0.946

129 York PA 0.942

130 Amarillo TX 0.941

131 Lawrence, MA-NH MA 0.933

132 Springfield MO 0.932

133 Washington, DC-MD-VA-WV VA 0.931

134 Las Cruces NM 0.930

135 Indianapolis IN 0.928

136 Gary IN 0.927

137 Detroit MI 0.927

138 Tulsa OK 0.921

139 Greensboro—Winston-Salem—High Point NC 0.919

140 Nashville TN 0.914

141 Santa Fe NM 0.912

142 Raleigh-Durham-Chapel Hill NC 0.911

143 Grand Rapids-Muskegon-Holland MI 0.906
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 53 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

144 Baton Rouge LA 0.905

145 Columbia SC 0.900

146 Middlesex-Somerset-Hunterdon NJ 0.899

147 Sarasota-Bradenton FL 0.896

148 Cumberland, MD-WV MD 0.895

149 Waterbury CT 0.894

150 Atlanta GA 0.891

151 b b 0.889

152 Macon GA 0.888

153 Birmingham AL 0.886

154 Harrisburg-Lebanon-Carlisle PA 0.885

155 Sacramento CA 0.884

156 Fort Wayne IN 0.883

157 New London-Norwich, CT-RI CT 0.876

158 Toledo OH 0.875

159 New Orleans LA 0.873

160 Florence AL 0.870

161 West Palm Beach-Boca Raton FL 0.870

162 Mobile AL 0.870

163 Columbus OH 0.868

164 Hartford CT 0.867

165 Fort Lauderdale FL 0.866

166 Corpus Christi TX 0.866

167 Savannah GA 0.865

168 Monroe LA 0.864

169 Montgomery AL 0.864

170 Houma LA 0.864

171 Galveston-Texas City TX 0.862

172 Dallas TX 0.861

173 Richland-Kennewick-Pasco WA 0.861

174 Norfolk-Virginia Beach-Newport News, VA-NC VA 0.861

175 Pittsburgh PA 0.861

176 Bergen-Passaic NJ 0.860

177 Denver CO 0.859

178 Bryan-College Station TX 0.859

179 Colorado Springs CO 0.859

180 Monmouth-Ocean NJ 0.859
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 54 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

181 Reno NV 0.858

182 Texarkana, TX-Texarkana TX 0.857

183 Punta Gorda FL 0.853

184 Waco TX 0.853

185 Flint MI 0.847

186 Kansas City, MO-KS MO 0.838

187 Oakland CA 0.836

188 Killeen-Temple TX 0.830

189 Tuscaloosa AL 0.826

190 Philadelphia, PA-NJ PA 0.820

191 Chattanooga, TN-GA TN 0.814

192 Providence-Fall River-Warwick, RI-MA RI 0.813

193 Sherman-Denison TX 0.812

194 Kalamazoo-Battle Creek MI 0.808

195 Jacksonville FL 0.807

196 Boulder-Longmont CO 0.804

197 Cleveland-Lorain-Elyria OH 0.803

198 Shreveport-Bossier City LA 0.799

199 Syracuse NY 0.797

200 Wilmington NC 0.794

201 Erie PA 0.790

202 Jersey City NJ 0.787

203 Yakima WA 0.786

204 Los Angeles-Long Beach CA 0.785

205 Chicago IL 0.785

206 Huntsville AL 0.780

207 Hagerstown MD 0.779

208 Johnstown PA 0.777

209 Cincinnati, OH-KY-IN OH 0.776

210 Lafayette LA 0.772

211 Gadsden AL 0.769

212 Lake Charles LA 0.764

213 Louisville, KY-IN KY 0.761

214 Allentown-Bethlehem-Easton PA 0.754

215 Spokane WA 0.746

216 Athens GA 0.745

217 Albuquerque NM 0.743
Appendix II: FEHBP PPO Adjusted Hospital 

Prices in U.S. Metropolitan Areas, 2001 

Page 55 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted hospital price index

218 Nassau-Suffolk NY 0.740

219 Dothan AL 0.728

220 San Diego CA 0.727

221 Riverside-San Bernardino CA 0.727

222 Newark NJ 0.725

223 Saginaw-Bay City-Midland MI 0.712

224 Anniston AL 0.709

225 Decatur AL 0.709

226 Altoona PA 0.678

227 New York NY 0.676

228 Newburgh, NY-PA NY 0.675

229 Albany-Schenectady-Troy NY 0.674

230 Ventura CA 0.635

231 Pueblo CO 0.609

232 Orange County CA 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. 
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 56 GAO-05-856 FEHBP Health Care Prices 

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 Metropolitan area Predominant statea Adjusted physician price index

1 La Crosse, WI-MN WI 1.484

2 Wausau WI 1.459

3 Eau Claire WI 1.418

4 Madison WI 1.414

5 Jonesboro AR 1.348

6 Janesville-Beloit WI 1.324

7 Great Falls MT 1.287

8 Green Bay WI 1.279

9 Appleton-Oshkosh-Neenah WI 1.267

10 Racine WI 1.239

11 Sheboygan WI 1.231

12 Billings MT 1.230

13 Wichita Falls TX 1.224

14 Anchorage AK 1.221

15 Corvallis OR 1.220

16 Milwaukee-Waukesha WI 1.217

17 Jacksonville NC 1.216

18 Kenosha WI 1.213

19 Fayetteville-Springdale-Rogers AR 1.206

20 Texarkana, TX-Texarkana TX 1.204

21 Fort Smith, AR-OK AR 1.202

22 Monroe LA 1.198

23 Pine Bluff AR 1.194

24 Missoula MT 1.190

25 Salem OR 1.187

26 St. Cloud MN 1.187

27 Eugene-Springfield OR 1.184

28 Duluth-Superior, MN-WI MN 1.178

29 Medford-Ashland OR 1.165

30 Alexandria LA 1.162

31 Houma LA 1.159

32 Sherman-Denison TX 1.159

Appendix III: FEHBP PPO Adjusted 

Physician Prices in U.S. Metropolitan Areas, 

2001 
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 57 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

33 Wheeling, WV-OH WV 1.157

34 Shreveport-Bossier City LA 1.145

35 Grand Junction CO 1.144

36 Omaha, NE-IA NE 1.143

37 Bryan-College Station TX 1.143

38 Little Rock-North Little Rock AR 1.142

39 Rocky Mount NC 1.136

40 Springfield MO 1.135

41 Lafayette LA 1.134

42 Lubbock TX 1.129

43 San Angelo TX 1.129

44 Lincoln NE 1.129

45 Pueblo CO 1.128

46 Abilene TX 1.121

47 Hattiesburg MS 1.119

48 Kankakee IL 1.119

49 Fayetteville NC 1.111

50 Parkersburg-Marietta, WV-OH WV 1.111

51 Jackson TN 1.106

52 Charleston WV 1.105

53 Longview-Marshall TX 1.103

54 Sioux City, IA-NE IA 1.101

55 Clarksville-Hopkinsville, TN-KY TN 1.101

56 Albany GA 1.098

57 Bismarck ND 1.097

58 Lawrence KS 1.096

59 Panama City FL 1.096

60 Rapid City SD 1.096

61 Lewiston-Auburn ME 1.096

62 Bangor ME 1.095

63 Muncie IN 1.093

64 Baton Rouge LA 1.093

65 Grand Forks, ND-MN ND 1.091

66 Portland-Vancouver, OR-WA OR 1.085

67 Huntington-Ashland, WV-KY-OH WV 1.085

68 Elmira NY 1.084

69 Tyler TX 1.084
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 58 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

70 Pocatello ID 1.083

71 Dubuque IA 1.082

72 Macon GA 1.081

73 Terre Haute IN 1.079

74 Goldsboro NC 1.078

75 Greenville NC 1.077

76 Columbus, GA-AL GA 1.075

77 McAllen-Edinburg-Mission TX 1.074

78 Brownsville-Harlingen-San Benito TX 1.072

79 Glens Falls NY 1.072

80 Johnson City-Kingsport-Bristol, TN-VA TN 1.072

81 Laredo TX 1.072

82 Waco TX 1.069

83 Cedar Rapids IA 1.067

84 Boise City ID 1.066

85 Greeley CO 1.065

86 Fort Walton Beach FL 1.065

87 Lawton OK 1.064

88 Iowa City IA 1.063

89 Hickory-Morganton-Lenoir NC 1.062

90 Asheville NC 1.060

91 Lake Charles LA 1.059

92 Sioux Falls SD 1.057

93 Enid OK 1.057

94 Portland ME 1.055

95 Pensacola FL 1.051

96 Yuma AZ 1.051

97 Fort Myers-Cape Coral FL 1.050

98 Joplin MO 1.049

99 South Bend IN 1.049

100 Fort Wayne IN 1.049

101 Lafayette IN 1.046

102 St. Joseph MO 1.046

103 Biloxi-Gulfport-Pascagoula MS 1.045

104 Auburn-Opelika AL 1.044

105 Fort Worth-Arlington TX 1.043

106 Odessa-Midland TX 1.043
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 59 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

107 Fargo-Moorhead, ND-MN ND 1.042

108 Flagstaff, AZ-UT AZ 1.042

109 Savannah GA 1.041

110 Knoxville TN 1.041

111 Colorado Springs CO 1.040

112 Elkhart-Goshen IN 1.038

113 Las Cruces NM 1.037

114 Evansville-Henderson, IN-KY IN 1.036

115 Beaumont-Port Arthur TX 1.034

116 Columbia MO 1.034

117 Topeka KS 1.034

118 Sharon PA 1.034

119 Fort Collins-Loveland CO 1.033

120 Killeen-Temple TX 1.033

121 Owensboro KY 1.032

122 Sumter SC 1.032

123 Corpus Christi TX 1.030

124 Yuba City CA 1.029

125 Victoria TX 1.029

126 Jackson MS 1.028

127 Waterloo-Cedar Falls IA 1.027

128 New Orleans LA 1.026

129 Yakima WA 1.024

130 Dallas TX 1.022

131 Austin-San Marcos TX 1.021

132 Utica-Rome NY 1.021

133 Portsmouth-Rochester, NH-ME NH 1.018

134 Brazoria TX 1.017

135 Memphis, TN-AR-MS TN 1.016

136 Charlotte-Gastonia-Rock Hill, NC-SC NC 1.016

137 Wichita KS 1.013

138 Lima OH 1.013

139 Amarillo TX 1.011

140 Minneapolis-St. Paul, MN-WI MN 1.011

141 Yolo CA 1.010

142 Dothan AL 1.010

143 Tallahassee FL 1.009
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 60 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

144 Des Moines IA 1.009

145 El Paso TX 1.008

146 Atlanta GA 1.008

147 San Antonio TX 1.006

148 Bloomington IN 1.006

149 Syracuse NY 1.006

150 Redding CA 1.005

151 Albany-Schenectady-Troy NY 1.005

152 Altoona PA 1.003

153 Indianapolis IN 1.002

154 Lakeland-Winter Haven FL 1.001

155 Roanoke VA 1.001

156 Modesto CA 0.999

157 Punta Gorda FL 0.999

158 Augusta-Aiken, GA-SC GA 0.998

159 Mansfield OH 0.998

160 Ocala FL 0.997

161 Athens GA 0.997

162 Anniston AL 0.994

163 Chico-Paradise CA 0.994

164 Burlington VT 0.994

165 Tuscaloosa AL 0.993

166 Binghamton NY 0.992

167 Florence SC 0.992

168 Boulder-Longmont CO 0.991

169 Naples FL 0.991

170 Spokane WA 0.991

171 Albuquerque NM 0.991

172 Merced CA 0.991

173 Chicago IL 0.990

174 Tulsa OK 0.988

175 Gainesville FL 0.983

176 Johnstown PA 0.983

177 Denver CO 0.983

178 Wilmington NC 0.982

179 Chattanooga, TN-GA TN 0.981

180 Lexington KY 0.980
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 61 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

181 Tacoma WA 0.979

182 Galveston-Texas City TX 0.979

183 Norfolk-Virginia Beach-Newport News, VA-NC VA 0.975

184 Houston TX 0.975

185 Gary IN 0.974

186 Oklahoma City OK 0.974

187 Kokomo IN 0.972

188 Raleigh-Durham-Chapel Hill NC 0.970

189 Sarasota-Bradenton FL 0.969

190 Mobile AL 0.966

191 Bremerton WA 0.965

192 Montgomery AL 0.964

193 Myrtle Beach SC 0.964

194 Fresno CA 0.963

195 Nashville TN 0.962

196 Bellingham WA 0.962

197 Florence AL 0.959

198 Scranton—Wilkes-Barre—Hazleton PA 0.959

199 Lynchburg VA 0.959

200 Daytona Beach FL 0.959

201 Steubenville-Weirton, OH-WV OH 0.958

202 Stamford-Norwalk CT 0.958

203 Charleston-North Charleston SC 0.956

204 Honolulu HI 0.956

205 Richland-Kennewick-Pasco WA 0.956

206 Gadsden AL 0.956

207 Greensboro—Winston-Salem—High Point NC 0.955

208 Visalia-Tulare-Porterville CA 0.954

209 Decatur AL 0.949

210 Danbury CT 0.949

211 New London-Norwich, CT-RI CT 0.948

212 Jacksonville FL 0.947

213 Erie PA 0.946

214 Rochester NY 0.946

215 Reno NV 0.944

216 Bakersfield CA 0.942

217 Olympia WA 0.941
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 62 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

218 Pittsfield MA 0.941

219 Santa Fe NM 0.939

220 Louisville, KY-IN KY 0.938

221 Benton Harbor MI 0.938

222 Williamsport PA 0.936

223 Charlottesville VA 0.935

224 Salinas CA 0.935

225 Kalamazoo-Battle Creek MI 0.935

226 Manchester NH 0.932

227 Youngstown-Warren OH 0.930

228 Dover DE 0.926

229 Hartford CT 0.923

230 Lancaster PA 0.923

231 Canton-Massillon OH 0.922

232 Sacramento CA 0.920

233 Seattle-Bellevue-Everett WA 0.919

234 Jackson MI 0.913

235 Springfield MA 0.913

236 Vallejo-Fairfield-Napa CA 0.911

237 Orlando FL 0.909

238 Huntsville AL 0.909

239 Grand Rapids-Muskegon-Holland MI 0.909

240 Provo-Orem UT 0.906

241 Stockton-Lodi CA 0.904

242 Fitchburg-Leominster MA 0.904

243 Tucson AZ 0.904

244 Birmingham AL 0.903

245 Akron OH 0.901

246 New Haven-Meriden CT 0.900

247 Waterbury CT 0.899

248 Columbus OH 0.899

249 Tampa-St. Petersburg-Clearwater FL 0.899

250 Jamestown NY 0.898

251 Richmond-Petersburg VA 0.898

252 Cincinnati, OH-KY-IN OH 0.897

253 Cumberland, MD-WV MD 0.895

254 York PA 0.894
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 63 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

255 Greenville-Spartanburg-Anderson SC 0.893

256 New Bedford MA 0.892

257 Riverside-San Bernardino CA 0.891

258 Saginaw-Bay City-Midland MI 0.890

259 Columbia SC 0.888

260 Nashua NH 0.888

261 Hamilton-Middletown OH 0.887

262 Harrisburg-Lebanon-Carlisle PA 0.886

263 Las Vegas, NV-AZ NV 0.885

264 Toledo OH 0.885

265 Kansas City, MO-KS MO 0.884

266 Cleveland-Lorain-Elyria OH 0.883

267 San Luis Obispo-Atascadero-Paso Robles CA 0.883

268 Vineland-Millville-Bridgeton NJ 0.882

269 Reading PA 0.876

270 Bridgeport CT 0.874

271 Monmouth-Ocean NJ 0.873

272 Los Angeles-Long Beach CA 0.870

273 Ann Arbor MI 0.870

274 Orange County CA 0.870

275 Melbourne-Titusville-Palm Bay FL 0.869

276 Santa Barbara-Santa Maria-Lompoc CA 0.866

277 Jersey City NJ 0.865

278 Lawrence, MA-NH MA 0.861

279 San Diego CA 0.861

280 Trenton NJ 0.861

281 State College PA 0.861

282 Lansing-East Lansing MI 0.861

283 Barnstable-Yarmouth MA 0.861

284 Phoenix-Mesa AZ 0.859

285 Allentown-Bethlehem-Easton PA 0.856

286 New York NY 0.854

287 Ventura CA 0.851

288 Santa Cruz-Watsonville CA 0.848

289 Worcester, MA-CT MA 0.846

290 Flint MI 0.844

291 Pittsburgh PA 0.841
Appendix III: FEHBP PPO Adjusted Physician 

Prices in U.S. Metropolitan Areas, 2001 

Page 64 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted physician price index

292 San Jose CA 0.837

293 Atlantic-Cape May NJ 0.835

294 Dayton-Springfield OH 0.833

295 Salt Lake City-Ogden UT 0.833

296 Fort Pierce-Port St. Lucie FL 0.830

297 Philadelphia, PA-NJ PA 0.828

298 Buffalo-Niagara Falls NY 0.823

299 Wilmington-Newark, DE-MD DE 0.823

300 Newburgh, NY-PA NY 0.822

301 Hagerstown MD 0.822

302 Newark NJ 0.818

303 Santa Rosa CA 0.817

304 Middlesex-Somerset-Hunterdon NJ 0.816

305 Oakland CA 0.813

306 Detroit MI 0.809

307 Bergen-Passaic NJ 0.807

308 Brockton MA 0.802

309 Boston, MA-NH MA 0.785

310 San Francisco CA 0.772

311 Dutchess County NY 0.768

312 Providence-Fall River-Warwick, RI-MA RI 0.763

313 Miami FL 0.755

314 West Palm Beach-Boca Raton FL 0.749

315 Fort Lauderdale FL 0.747

316 Washington, DC-MD-VA-WV VA 0.746

317 Nassau-Suffolk NY 0.744

318 Lowell, MA-NH MA 0.743

319 Baltimore MD 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. 
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 65 GAO-05-856 FEHBP Health Care Prices 

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 Metropolitan area Predominant statea Adjusted spending index

1 Biloxi-Gulfport-Pascagoula MS 1.422

2 Myrtle Beach SC 1.404

3 Monroe LA 1.393

4 Hattiesburg MS 1.393

5 Parkersburg-Marietta, WV-OH WV 1.343

6 Anniston AL 1.322

7 Florence SC 1.298

8 Terre Haute IN 1.297

9 Bakersfield CA 1.268

10 San Angelo TX 1.258

11 Gadsden AL 1.250

12 Wichita Falls TX 1.240

13 Houma LA 1.240

14 Sherman-Denison TX 1.235

15 Wilmington NC 1.216

16 Huntington-Ashland, WV-KY-OH WV 1.216

17 Macon GA 1.213

18 Lubbock TX 1.212

19 Dothan AL 1.211

20 Punta Gorda FL 1.211

21 Decatur AL 1.200

22 Milwaukee-Waukesha WI 1.197

23 Rapid City SD 1.195

24 Albany GA 1.194

25 Fort Walton Beach FL 1.187

26 Texarkana, TX-Texarkana TX 1.186

27 Oklahoma City OK 1.182

28 Charleston-North Charleston SC 1.180

29 Lake Charles LA 1.169

30 Panama City FL 1.167

31 La Crosse, WI-MN WI 1.163

32 Little Rock-North Little Rock AR 1.163

Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 66 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

33 Florence AL 1.161

34 Knoxville TN 1.157

35 Jacksonville NC 1.155

36 Yuma AZ 1.151

37 Shreveport-Bossier City LA 1.133

38 Pine Bluff AR 1.132

39 Lafayette LA 1.126

40 Galveston-Texas City TX 1.122

41 Charlotte-Gastonia-Rock Hill, NC-SC NC 1.120

42 Enid OK 1.119

43 Johnson City-Kingsport-Bristol, TN-VA TN 1.118

44 Fort Worth-Arlington TX 1.117

45 Lawton OK 1.116

46 Charleston WV 1.116

47 Jonesboro AR 1.115

48 McAllen-Edinburg-Mission TX 1.113

49 Melbourne-Titusville-Palm Bay FL 1.108

50 Nashville TN 1.103

51 Tuscaloosa AL 1.102

52 Dallas TX 1.101

53 Bryan-College Station TX 1.097

54 Waco TX 1.096

55 Omaha, NE-IA NE 1.092

56 Jackson MS 1.089

57 Savannah GA 1.088

58 Springfield MO 1.088

59 New Orleans LA 1.082

60 Las Vegas, NV-AZ NV 1.081

61 Chattanooga, TN-GA TN 1.079

62 Boulder-Longmont CO 1.078

63 Duluth-Superior, MN-WI MN 1.077

64 Greenville-Spartanburg-Anderson SC 1.077

65 Baton Rouge LA 1.076

66 Las Cruces NM 1.074

67 St. Joseph MO 1.074

68 Owensboro KY 1.073

69 Corpus Christi TX 1.073
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 67 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

70 Lakeland-Winter Haven FL 1.072

71 Sarasota-Bradenton FL 1.072

72 Jacksonville FL 1.070

73 San Antonio TX 1.067

74 Tulsa OK 1.060

75 Odessa-Midland TX 1.059

76 Portsmouth-Rochester, NH-ME NH 1.057

77 Topeka KS 1.056

78 Orange County CA 1.049

79 Pensacola FL 1.049

80 Amarillo TX 1.048

81 Fort Myers-Cape Coral FL 1.048

82 Houston TX 1.045

83 Indianapolis IN 1.039

84 Colorado Springs CO 1.036

85 Montgomery AL 1.034

86 Huntsville AL 1.033

87 Orlando FL 1.033

88 Wichita KS 1.030

89 Memphis, TN-AR-MS TN 1.027

90 Anchorage AK 1.025

91 Bloomington IN 1.022

92 Monmouth-Ocean NJ 1.021

93 Cumberland, MD-WV MD 1.020

94 Lincoln NE 1.020

95 Columbus, GA-AL GA 1.014

96 Fort Smith, AR-OK AR 1.012

97 Roanoke VA 1.012

98 Norfolk-Virginia Beach-Newport News, VA-NC VA 1.012

99 Mobile AL 1.011

100 Boise City ID 1.010

101 Louisville, KY-IN KY 1.008

102 Austin-San Marcos TX 1.007

103 Clarksville-Hopkinsville, TN-KY TN 1.004

104 Ventura CA 1.004

105 Birmingham AL 1.000

106 Manchester NH 0.999
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 68 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

107 Daytona Beach FL 0.996

108 Sioux Falls SD 0.994

109 Columbia SC 0.994

110 Richland-Kennewick-Pasco WA 0.992

111 Atlantic-Cape May NJ 0.988

112 Grand Forks, ND-MN ND 0.988

113 New London-Norwich, CT-RI CT 0.988

114 Trenton NJ 0.987

115 Olympia WA 0.984

116 Columbia MO 0.984

117 Atlanta GA 0.983

118 Killeen-Temple TX 0.982

119 Grand Junction CO 0.982

120 Kansas City, MO-KS MO 0.980

121 Gary IN 0.979

122 West Palm Beach-Boca Raton FL 0.977

123 Athens GA 0.977

124 Fayetteville-Springdale-Rogers AR 0.977

125 Billings MT 0.975

126 Fort Lauderdale FL 0.971

127 Great Falls MT 0.970

128 Dover DE 0.965

129 Jackson TN 0.965

130 Lynchburg VA 0.962

131 Des Moines IA 0.962

132 Gainesville FL 0.960

133 Laredo TX 0.959

134 Augusta-Aiken, GA-SC GA 0.959

135 Denver CO 0.958

136 Bremerton WA 0.957

137 Fort Pierce-Port St. Lucie FL 0.955

138 Salinas CA 0.952

139 Pueblo CO 0.952

140 Tampa-St. Petersburg-Clearwater FL 0.951

141 Fort Wayne IN 0.950

142 Hagerstown MD 0.949

143 Los Angeles-Long Beach CA 0.947
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 69 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

144 Lexington KY 0.946

145 Middlesex-Somerset-Hunterdon NJ 0.942

146 Redding CA 0.942

147 Bangor ME 0.941

148 Tacoma WA 0.941

149 Phoenix-Mesa AZ 0.935

150 Riverside-San Bernardino CA 0.935

151 Cedar Rapids IA 0.934

152 Greensboro—Winston-Salem—High Point NC 0.932

153 Fayetteville NC 0.930

154 Miami FL 0.928

155 Sacramento CA 0.927

156 Reading PA 0.927

157 Salt Lake City-Ogden UT 0.925

158 Cincinnati, OH-KY-IN OH 0.923

159 Richmond-Petersburg VA 0.920

160 Detroit MI 0.920

161 Chicago IL 0.918

162 Provo-Orem UT 0.918

163 Fort Collins-Loveland CO 0.913

164 Yakima WA 0.913

165 Goldsboro NC 0.913

166 Albany-Schenectady-Troy NY 0.913

167 Nashua NH 0.911

168 Asheville NC 0.911

169 Nassau-Suffolk NY 0.909

170 Santa Fe NM 0.908

171 Scranton—Wilkes-Barre—Hazleton PA 0.906

172 Missoula MT 0.904

173 York PA 0.904

174 Jersey City NJ 0.904

175 Raleigh-Durham-Chapel Hill NC 0.901

176 Columbus OH 0.901

177 Sioux City, IA-NE IA 0.899

178 Cleveland-Lorain-Elyria OH 0.899

179 Greenville NC 0.897

180 Wilmington-Newark, DE-MD DE 0.897
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 70 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

181 Tucson AZ 0.897

182 Waterbury CT 0.896

183 Portland ME 0.893

184 Salem OR 0.892

185 Bergen-Passaic NJ 0.891

186 Eugene-Springfield OR 0.883

187 Kalamazoo-Battle Creek MI 0.881

188 Washington, DC-MD-VA-WV VA 0.881

189 Bismarck ND 0.880

190 Flint MI 0.879

191 Newark NJ 0.878

192 Springfield MA 0.876

193 Baltimore MD 0.875

194 New Haven-Meriden CT 0.874

195 Minneapolis-St. Paul, MN-WI MN 0.873

196 Philadelphia, PA-NJ PA 0.870

197 San Diego CA 0.869

198 Albuquerque NM 0.868

199 Reno NV 0.866

200 Altoona PA 0.866

201 Lawrence, MA-NH MA 0.862

202 Dayton-Springfield OH 0.852

203 Portland-Vancouver, OR-WA OR 0.848

204 Newburgh, NY-PA NY 0.848

205 New York NY 0.845

206 Seattle-Bellevue-Everett WA 0.843

207 Medford-Ashland OR 0.841

208 Evansville-Henderson, IN-KY IN 0.836

209 Charlottesville VA 0.836

210 Providence-Fall River-Warwick, RI-MA RI 0.834

211 Lansing-East Lansing MI 0.833

212 Harrisburg-Lebanon-Carlisle PA 0.832

213 South Bend IN 0.830

214 Iowa City IA 0.827

215 Toledo OH 0.825

216 Allentown-Bethlehem-Easton PA 0.814

217 San Francisco CA 0.809
Appendix IV: FEHBP PPO Adjusted Health 

Care Spending Per Enrollee in U.S. 

Metropolitan Areas, 2001 

Page 71 GAO-05-856 FEHBP Health Care Prices 

Rank Metropolitan area Predominant statea Adjusted spending index

218 Hartford CT 0.809

219 Oakland CA 0.807

220 Erie PA 0.803

221 Syracuse NY 0.793

222 Spokane WA 0.789

223 Ann Arbor MI 0.778

224 Pittsburgh PA 0.776

225 Fargo-Moorhead, ND-MN ND 0.766

226 Saginaw-Bay City-Midland MI 0.753

227 Johnstown PA 0.746

228 Boston, MA-NH MA 0.746

229 Bridgeport CT 0.732

230 Buffalo-Niagara Falls NY 0.715

231 Honolulu HI 0.684

232 Grand Rapids-Muskegon-Holland MI 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. 
Appendix V: Comments from the Office of Personnel 

Management 

 Page 72 GAO-05-856 FEHBP Health Care Prices 

Appendix V: Comments from the Office of 

Personnel Management 
Appendix V: Comments from the Office of Personnel 

Management 

 Page 73 GAO-05-856 FEHBP Health Care Prices 
Appendix VI: GAO Contacts and 

Staff Acknowledgments 

Page 74 GAO-05-856 FEHBP Health Care Prices 

A. Bruce Steinwald, (202) 512-7101 or steinwalda@gao.gov 

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. 

Appendix VI: GAO Contacts and Staff 

Acknowledgments 

GAO Contacts 

Acknowledgments 

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