Federal Employees Health Benefits Program: Competition and Other
Factors Linked to Wide Variation in Health Care Prices
(15-AUG-05, GAO-05-856).
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.
-------------------------Indexing Terms-------------------------
REPORTNUM: GAO-05-856
ACCNO: A32980
TITLE: Federal Employees Health Benefits Program: Competition
and Other Factors Linked to Wide Variation in Health Care Prices
DATE: 08/15/2005
SUBJECT: Comparative analysis
Cost analysis
Health insurance
Health insurance cost control
Health maintenance organizations
Hospitals
Medicaid
Medical fees
Medical services rates
Medicare
Physicians
Price regulation
Prices and pricing
Competition
Economic analysis
Federal Employees Health Benefits
Program
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GAO-05-856
* Results in Brief
* Background
* FEHBP and Participating PPOs
* Geographic Variation in Spending, Utilization, and Prices
* Health Care Market Characteristics and Price
* Large Differences in Hospital and Physician Prices across Me
* Hospital Prices Varied More than Physician Prices
* Hospital and Physician Prices Were Generally Higher in the M
* Less Competition and Less HMO Capitation Linked to Higher He
* Prices Were Higher in Metropolitan Areas with Less Competiti
* Prices Were Higher in Metropolitan Areas with Less HMO Capit
* No Evidence of Cost Shifting Due to Medicaid, Medicare, or t
* Total Spending Varied 112 Percent; Price Variation Contribut
* Spending per Enrollee Varied by 112 Percent across Metropoli
* Price Contributed to One-third of the Variation in Spending,
* Concluding Observations
* Agency and Other Comments
* Appendix I: Scope and Methodology
* FEHBP Data and Study Eligibility Criteria
* Hospital and Physician Price Estimates
* Factors Affecting Health Care Prices
* Analytical Approach
* Price Regression Analysis-Methods and Results
* Spending Analysis
* Decomposing Spending Variation into Price and Utilization Ef
* Data Reliability
* Appendix II: FEHBP PPO Adjusted Hospital Prices in U.S. Metr
* Appendix III: FEHBP PPO Adjusted Physician Prices in U.S. Me
* Appendix IV: FEHBP PPO Adjusted Health Care Spending Per Enr
* Appendix V: Comments from the Office of Personnel Management
* Appendix VI: GAO Contacts and Staff Acknowledgments
* GAO Contacts
* Acknowledgments
* Order by Mail or Phone
United States Government Accountability Office
GAO
House of Representatives
August 2005
FEDERAL EMPLOYEES HEALTH BENEFITS PROGRAM
Competition and Other Factors Linked to Wide Variation in Health Care Prices
GAO-05-856
FEDERAL EMPLOYEES HEALTH BENEFITS PROGRAM
Competition and Other Factors Linked to Wide Variation in Health Care
Prices
What GAO Found
FEHBP PPOs paid substantially different prices for hospital inpatient and
physician services across metropolitan areas in the United States.
Hospital prices varied by 259 percent and physician prices varied by about
100 percent across metropolitan areas. While there were some areas with
very high or low prices, most had prices that were closer to the average.
The variation in prices appeared to be affected by market characteristics.
Metropolitan areas with the least competition, areas with a higher
percentage of hospital beds in the two largest hospitals or hospital
networks, had hospital prices that were 18 percent higher and physician
prices that were 11 percent higher than areas with the most competition.
The percent of primary care physicians' reimbursement that was paid on a
capitation basis in health maintenance organizations (HMO), a proxy for
HMO price bargaining leverage, was also associated with geographic
variation in prices. Metropolitan areas with the least HMO capitation
tended to have hospital and physician prices that were about 10 percent
higher than areas with the most HMO capitation. When GAO controlled for
other factors that might be associated with geographic variation in
prices, more hospital competition and HMO capitation were still associated
with lower prices, but the effect was reduced. GAO did not find any
evidence that price variation was due to cost shifting, where providers
raise private sector prices to compensate for lower prices from other
payers.
Total health care spending per enrollee varied by over 100 percent across
metropolitan areas. For hospital and physician services, price contributed
to about one-third and utilization to about two-thirds of the variation in
spending between metropolitan areas in the highest and lowest spending
quartiles. Higher physician prices were also associated with lower
physician utilization, but higher prices were still typical in higher
spending areas.
The Office of Personnel Management provided comments on a draft of this
report and agreed with our findings.
Distribution of Hospital and Physician Price Indices, 2001
Source: GAO analysis of FEHBP data.
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.
United States Government Accountability Office
Contents
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
Appendix III FEHBP PPO Adjusted Physician Prices in U.S. Metropolitan
Areas, 2001
Appendix IV FEHBP PPO Adjusted Health Care Spending Per Enrollee in U.S.
Metropolitan Areas, 2001
Appendix V Comments from the Office of Personnel Management 72
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 44
Prices
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
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
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separately.
United States Government Accountability Office Washington, DC 20548
August 15, 2005
The Honorable Paul Ryan House of Representatives
Dear Mr. Ryan:
Congress is concerned about the health care spending burden facing the
Federal Employees Health Benefits Program (FEHBP), the largest private
health insurance program in the country. Previous research has shown that
health care spending varies geographically, but has not fully explored the
underlying causes. A better understanding of market and other forces that
may influence health care spending could assist efforts to moderate health
care spending.
Geographic differences in health care spending are due to differences in
utilization-the amount and type of health services used-and price-the
amount paid to physicians, hospitals, and other providers. Most of the
geographic variations research has focused on the utilization of services.
However, less is known about the variation in prices, factors that affect
price variation, or how price variation contributes to spending variation.
You asked us to analyze geographic variation in prices and spending in
FEHBP. In August 2004, we provided you with an interim report about how
hospital and physician prices and spending in FEHBP Preferred Provider
Organizations (PPO) 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 areas 3 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 OtherMetropolitan Areas:
Geographic Variation in Spending for Enrollees in the Federal
EmployeesHealth Benefits P rogram,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.
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.
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.
10
The number of individuals without health insurance in each metropolitan
area was obtained from InterStudy Publications, Inc., and was based on
statewide data; it does not include differences in the uninsured among
metropolitan areas in the same state.
11
J. Menges, et al., for The Lewin Group, Comparing Physician and Dentist
Fees Among Medicaid Programs(Oakland, Calif.: Medi-Cal Policy Institute,
2001).
12
Total spending per enrollee includes both enrollee deductible and
coinsurance obligations and PPO expenditures on behalf of the enrollee.
Results in Brief
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
13
Our analysis of hospital spending and utilization may have been limited by
the small number of enrollees and admissions in some areas. Ten of the 232
metropolitan areas in this analysis had between 500 and 1,000 enrollees.
14
We defined areas in the lowest 25 percent of competition as having the
least competition, and areas in the highest 25 percent of competition as
having the most competition.
15
We defined areas in the lowest 25 percent of HMO capitation as the having
the least HMO capitation, and areas in the highest 25 percent of HMO
capitation as having the most HMO capitation.
factors that might be associated with geographic variation in prices, we
found that less hospital competition and HMO capitation were still
associated with higher prices, but the effect was reduced. We found no
evidence of cost shifting-hospital and physician prices were no higher, on
average, in areas with lower Medicaid payments, a higher proportion of the
uninsured, or a higher percent of the population enrolled in Medicaid or
Medicare. Rather, we found that physician prices were, on average, lower
in areas with lower Medicaid payments and a higher percentage of
uninsured. We did not find a relationship between hospital prices and
Medicaid payments or between hospital prices and the percentage uninsured.
Total adjusted health care spending per enrollee was more than twice as
high in the highest-spending metropolitan area as it was in the
lowest-spending metropolitan area. 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.
Background
FEHBP and Participating In 2004, the federal government spent more than
$21 billion on FEHBP, which provides health insurance to federal civilian
employees, their
PPOs
families, and retirees. Administered by the Office of Personnel Management
(OPM), FEHBP contracts with private insurers to provide
16
Total spending per enrollee includes spending for all health care services
except mental health, chemical dependency, and pharmaceuticals. We
adjusted total spending per enrollee for differences in costs of providing
service and in the age and sex of enrollees across metropolitan areas.
17
We defined areas in the highest 25 percent of spending as areas with the
highest spending and areas in the lowest 25 percent of spending as areas
with the lowest spending.
Page 5 GAO-05-856 FEHBP Health Care Prices
Geographic Variation in Spending, Utilization, and 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 1995-1996 20 and more than five times as many hip
and knee replacement surgeries in some markets as in others in 2000
18
The Center for the Evaluative Clinical Sciences, Dartmouth Medical School,
The Dartmouth Atlas of Health Care 1999: The Quality of Medical Care in
the United States: A Report on theMedicare Program(Chicago, Ill.: AHA
Press, 1999).
19
GAO analysis of unadjusted 2003 Medicare spending per beneficiary data.
20The Dartmouth Atlas of Health Care 1999, p. 74.
Page 6 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 providers 24 and health insurers.
Insurers may negotiate discounted rates with providers in exchange for an
anticipated share of patient volume from the insurers' enrollees. The
negotiated price may take into account the costs of doing business faced
by providers as well as other market characteristics affecting the
geographic area. Thus, the geographic differences in price in the Medicare
program may not be the same as in the private sector.
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.
Health Care Market Characteristics and Price
21
J.N. Weinstein et al., "Trends and Geographic Variations in Major Surgery
for Degenerative Diseases of the Hip, Knee and Spine," Health Affairs, Web
Exclusive, (Oct. 7, 2004).
http://content.healthaffairs.org/cgi/content/full/hlthaff.var.81
(downloaded June 21, 2005).
22Th
e Dartmouth Atlas of Health Care 1999, pp. 11 and 27.
23
C.M. Ashton, et al., "Geographic Variations in Utilization Rates in
Veterans Affairs Hospitals and Clinics," The New England Journal of
Medicine,vol. 340, no. 1 (1999). The Center for the Evaluative Clinical
Sciences, Dartmouth Medical School and The Center for Outcomes Research
and Evaluation, Maine Medical Center, The Dartmouth Atlas of Health Care
in Michigan,2000, pp. 46 and 47.
24
We use the term providers to refer to hospitals, physicians, and other
providers of health care services unless otherwise specified.
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
25
See for example, A.E. Cuellar and P.J. Gertler, "How the Expansion of
Hospital Systems Has Affected Consumers," Health Affairs,vol. 24, no. 1
(2005); C. Capps and D. Dranove, "Hospital Consolidation and Negotiated
PPO Prices," Health Affairs,vol. 23, no. 2 (2004);
L.M. Nichols, et al., "Are Market Forces Strong Enough to Deliver
Efficient Health Care Systems? Confidence is Waning," Health Affairs, vol.
23, no. 2 (2004); and H.R. Spang, G.J. Bazzoli, and R.J. Arnould,
"Hospital Mergers and Savings for Consumers: Exploring New Evidence,"
Health Affairs,vol. 20, no. 4 (2001).
26
Hospitals may compete on dimensions other than price, such as services,
amenities, and quality. See for example, M.A. Morrisey, "Competition in
Hospital and Health Insurance Markets: A Review and Research Agenda,"
Health Services Research, vol. 36, no. 1 (2001).
27
Cuellar and Gertler, "How the Expansion of Hospital Systems Has Affected
Consumers,"
p. 213.
28
See for example, Spang, Bazzoli, and Arnould, "Hospital Mergers and
Savings for Consumers," p. 150; and G.J. Bazzoli et al., "Hospital
Reorganization and Restructuring Achieved Through Merger," Health Care
Management Review, vol. 27, no. 1 (2002).
29
Bazzoli et al., "Hospital Reorganization and Restructuring Achieved
Through Merger," pp. 2 and 6.
30
D. Dranove, A. Durkac, and M. Shanley, "Are Multihospital Systems More
Efficient?" Health Affairs,vol. 15, no. 1 (1996).
31
L. Baker, "Measuring Competition in Health Care Markets," Health Services
Research, April (2001); and M.A. Morrisey, "Competition in Hospital and
Health Insurance Markets,"
p. 191.
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
32
D.A. Draper, et al., "The Changing Face of Managed Care," Health
Affairs,vol. 21, no. 1 (2002).
33
J.S. Lee, et al., "Medicare Payment Policy: Does Cost Shifting Matter?"
Health Affairs, Web Exclusive, (Oct. 8, 2003).
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.480v1
(downloaded June 21, 2005); and Congressional Budget Office, "Responses to
Uncompensated Care and Public Program Controls on Spending: Do Hospitals
`Cost-Shift'?" (Washington, D.C.: 1993).
34
See for example, T. Rice, et al., "Do Physicians Cost Shift," Health
Affairs,vol. 15, no. 3 (1996); and J. Hadley, S. Zuckerman, L.I. Iezzoni,
"Financial Pressure and Competition: Changes in Hospital Efficiency and
Cost-Shifting Behavior," Medical Care,vol. 34, no. 3 (1996).
35
See for example, M.A. Morrisey, "Cost Shifting: New Myths, Old Confusion,
and Enduring Reality," Health Affairs, Web Exclusive (Oct. 8, 2003).
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.489v1
(downloaded June 21, 2005); and P.B. Ginsburg, "Can Hospitals and
Physicians Shift the Effects of Cuts in Medicare Reimbursement to Private
Payers?" Health Affairs,Web Exclusive (Oct. 8, 2003).
http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.472v1
(downloaded June 21, 2005).
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.
Large Differences in Hospital and Physician Prices across Metropolitan Areas
Hospital Prices Varied More than Physician Prices
Adjusted hospital prices paid by FEHBP PPOs varied considerably across
metropolitan areas. In the lowest-priced metropolitan area, hospital
prices were 51 percent of the national average (index value of 0.51) and
in the highest-priced metropolitan area, they were 83 percent above the
national average (index value of 1.83)-a difference of 259 percent. In
five of the 232 metropolitan areas, FEHBP PPOs paid hospital prices that
were more than 50 percent above the national average. While there were
other metropolitan areas with very high and very low prices, most had
prices much closer to the average. Half of the metropolitan areas in our
study, those in the second and third quartiles, had hospital prices that
were no more than 14 percent above or below the national average, 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.
36
D. Dranove and W.D. White, "Medicaid-dependent Hospitals and Their
Patients: How Have They Fared?" Health Services Research, (June 1998).
37
Quartiles divide the distribution of prices from lowest to highest into
four equal groups. The lowest quartile represents metropolitan areas
ranked in the lowest 25 percent of price, and the highest quartile
represents metropolitan areas ranked in the highest 25 percent of price.
Page 10 GAO-05-856 FEHBP Health Care Prices
Figure 1: Distribution of Hospital Price Indices across 232 Metropolitan Areas,
2001
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.
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.
38
The La Crosse, Wisconsin metropolitan area includes areas in Minnesota.
39
We had sufficient data to analyze more metropolitan areas for physician
prices than for hospital prices.
Page 11 GAO-05-856 FEHBP Health Care Prices
Figure 2: Distribution of Physician Price Indices across 319 Metropolitan
Areas, 2001
Source: GAO analysis of FEHBP data.
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.
Hospital and Physician Prices Were Generally Higher in the Midwest and Lower
in the Northeast
On average, FEHBP PPOs paid higher prices for hospital stays in
metropolitan areas in the Midwest and lower prices in the Northeast. (See
fig. 3.) Hospitals in the Midwest were paid about 14 percent more, on
average, than hospitals in the Northeast (table 1), but there was a
considerable range of hospital prices within regions. In fact, several
metropolitan areas with hospital prices in the highest quartile were
located in the same state as metropolitan areas with hospital prices in
the lowest quartile. For example, hospital prices in Buffalo-Niagara
Falls, New York were 45 percent higher than average, but prices in
Syracuse, New York were 20 percent below average. Similarly, prices in
Salinas, California were 50 percent higher than average, but prices in
Orange County, California were 48 percent below average. The 10
metropolitan areas with the highest and lowest hospital prices are listed
in table 2. Appendix II presents the complete rankings of metropolitan
areas by hospital price.
Figure 3: FEHBP PPO Adjusted Hospital Price Index Quartiles in 232 Metropolitan
Areas, 2001
Table 1: FEHBP PPO Hospital Price Indices in Metropolitan Areas Grouped by
Census Region, 2001
Average hospital price indexa for Region 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
Highest-priced Lowest-priced Rank metropolitan areas Rank metropolitan
areas
a
232 Orange County, Calif.
Dover, Del. 231 Pueblo, Colo.
Biloxi-Gulfport-Pascagoula, Miss. 230 Ventura, Calif.
St. Joseph, Mo. 229 Albany-Schenectady-Troy, N.Y.
Milwaukee-Waukesha, Wisc. 228 Newburgh, New York-Penn.
Salinas, Calif. 227 New York, N.Y.
Buffalo-Niagara Falls, N.Y. 226 Altoona, Penn.
Grand Junction, Colo. 225 Decatur, Ala.
a
224 Anniston, Ala.
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.
As with hospital prices, FEHBP PPOs paid higher average physician prices
in metropolitan areas in the Midwest and lower average physician prices in
metropolitan areas in the Northeast (see fig. 4). Prices for physician
services were 15 percent higher, on average, in metropolitan areas in the
Midwest than in metropolitan areas in the Northeast (table 3).
Metropolitan areas in Wisconsin had physician prices ranked among the
highest in our study: of the 10 metropolitan areas with the highest
physician prices, eight were located in Wisconsin (table 4). About 80
percent of the metropolitan areas in the Northeast had below-average
prices for physician services. Also, physician prices tended to be less
variable within states than hospital prices. For example, among
metropolitan areas in New Jersey, physician prices ranged from 12 percent
below average to 19 percent below average, but hospital prices ranged from
about 4 percent below average to about 27 percent below average. Appendix
III contains a complete ranking of physician prices in 319 metropolitan
areas.
Figure 4: FEHBP PPO Adjusted Physician Price Index Quartiles in 319 Metropolitan
Areas, 2001
Table 3: FEHBP PPO Physician Price Indices in Metropolitan Areas Grouped
by Census Region, 2001
Average physician price Region 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
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
Highest-priced Lowest-priced Rank metropolitan areas Rank metropolitan
areas
La Crosse, Wisconsin-Minn. 319 Baltimore, Md.
Wausau, Wisc. 318 Lowell, Massachusetts-N.H.
Eau Claire, Wisc. 317 Nassau-Suffolk, N.Y.
Madison, Wisc. 316 Washington, D.C.
Jonesboro, Ark. 315 Fort Lauderdale, Fla.
Janesville-Beloit, Wisc. 314 West Palm Beach-Boca Raton, Fla.
Great Falls, Mont. 313 Miami, Fla.
Green Bay, Wisc. 312 Providence-Fall River-Warwick, Rhode Island-Mass.
Appleton-Oshkosh-Neenah, Wisc. 311 Dutchess County, N.Y.
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.
Less Competition and Less HMO Capitation Linked to Higher Health Care Prices
FEHBP PPOs paid higher average hospital and physician prices in
metropolitan areas with less competition among hospitals. 40 Many
metropolitan areas we studied had low levels of competition; about one in
four metropolitan areas had only one or two hospitals or hospital networks
serving the entire market. Also, FEHBP PPOs paid higher average hospital
and physician prices in metropolitan areas with less HMO capitation. HMOs
did not have capitated arrangements in more than one-third of the
metropolitan areas we studied. We found no evidence of cost
shifting-higher hospital or physician prices where there were lower
Medicaid payments or larger uninsured, Medicare, or Medicaid populations.
Prices Were Higher in Metropolitan Areas with Less Competition
FEHBP PPO hospital and physician prices were higher, on average, in
metropolitan areas with less competition among hospitals. In the least
competitive metropolitan areas-those in the quartile with the least
competition-hospital prices tended to be about 18 percent higher and
physician prices tended to be nearly 11 percent higher than in the most
competitive metropolitan areas-those in the quartile with the most
competition. See table 5. For example, Rapid City, South Dakota, was in
the quartile with the least competition; its hospital prices were 25
percent above average, and its physician prices were 10 percent above
average. In contrast, Pittsburgh, Pennsylvania, a metropolitan area in the
quartile with the most competition, had hospital prices 14 percent below
average and physician prices 16 percent below average. When we conducted a
separate analysis that simulated the effect of increasing the level of
competition while controlling for the effects of other factors, we found
that less competition was still associated with higher prices, although
the difference was reduced by 58 percent for hospital prices and 38
percent
40
We measured competition as the percentage of hospital beds in a
metropolitan area (market share) held by the two largest hospitals or
hospital networks, where higher percentages indicated less competition and
lower percentages indicated more. Physicians are often aligned with health
systems and hospital networks. Therefore, we approximated physician
competition by measuring competition among hospitals and hospital networks
in a metropolitan area.
Page 18 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
Average Average hospital price physician price Competition quartile indexa
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
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.
Other factors included in our analysis were our measures of competition,
HMO capitation, cost shifting, per capita income, percent of for-profit
beds, provider supply, and census division. See app. I for a detailed
description of each factor. When we simulated the effect of increasing
competition from the average level of competition in the lowest quartile
to the average level of competition in the highest quartile, while
controlling for other factors, our estimate of the percent difference in
the average hospital price index between the highest and lowest
competition quartiles was 7.62 percent, and our estimate of the percent
difference in the average physician price index between the highest and
lowest quartiles was 6.64 percent.
Prices Were Higher in Metropolitan Areas with Less HMO Capitation
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
42
Capitation is a payment method where physicians are paid a fixed,
predetermined payment for caring for an enrollee for a specified period of
time, regardless of the number or type of services provided. Physicians
often try to resist capitation payments. The use of capitation by HMOs
demonstrates that they have the leverage to negotiate capitation contracts
with physicians. We used HMO capitation as a proxy measure for the
strength of the HMO presence in a community, and its ability to negotiate
prices with physicians, hospitals, and other providers.
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
Average Average hospital price physician price HMO capitation quartile
indexa 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.
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.
Other factors included in our analysis were our measures of competition,
HMO capitation, cost shifting, per capita income, percent of for-profit
beds, provider supply, and census division. See app. I for a detailed
description of each factor. When we simulated the effect of increasing the
level of HMO capitation from the average level of HMO capitation in the
lowest quartile to the average level of HMO capitation in the highest
quartile, while controlling for other factors, our estimate of the percent
difference in the average hospital price index between the highest and
lowest HMO capitation quartiles was 7.17 percent and our estimate of the
percent difference in the average physician price index between the
highest and lowest quartiles was 3.31 percent.
No Evidence of Cost Shifting Due to Medicaid, Medicare, or the Uninsured
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
44
HMO capitation data were not available in 4 of the 319 metropolitan areas
in our study. Accordingly, our analysis of HMO capitation was based on 315
metropolitan areas.
45
We estimated Medicaid payment rates for each metropolitan area by taking
the average physician payment for a set of common services. Medicaid
payment rate estimates for metropolitan areas were based on statewide
payment rates. We adjusted Medicaid payment rates to remove the effect of
geographic differences in input costs and in the mix of services across
metropolitan areas. See app. I.
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
Average physician Medicaid payment quartile 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
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
46
Other factors included in our analysis were measures of competition, HMO
capitation, cost shifting, per capita income, provider supply, and census
division. See app. I for a detailed description of each factor. When we
simulated the effect of increasing Medicaid payments from the average
Medicaid payment in the lowest quartile to the average Medicaid payment in
the highest quartile, while controlling for other factors, we found that
the physician price index was 9.69 percent higher, on average.
47
These factors included our measures of competition, HMO capitation, other
cost-shifting variables, per capita income, percent of for-profit beds,
provider supply, and census divisions.
Total Spending Varied 112 Percent; Price Variation Contributed to One-third of
the Variation in Hospital and Physician Spending
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.
Spending per Enrollee Varied by 112 Percent across Metropolitan Areas
We found that total spending per enrollee varied by 112 percent across the
232 metropolitan areas in this analysis. Total spending per enrollee was
the amount spent by FEHBP PPOs per person for all health care services
except pharmaceuticals, mental health services, and substance abuse
services, after adjusting for enrollee age and sex differences as well as
geographic differences in the costs of doing business. Spending per
enrollee in the metropolitan area with the lowest spending per enrollee,
Grand Rapids-Muskegon-Holland, Michigan, was 67 percent of the national
average (index value of 0.67). Spending per enrollee in the metropolitan
area with the highest spending per enrollee, Biloxi-Gulfport-Pascagoula,
Mississippi, was 42 percent above the average (index value of 1.42). Half
of the metropolitan areas in our study, those in the second and third
quartiles, had spending per enrollee that was no more than 10 percent
above or below the national average, and 80 percent had spending per
enrollee ranging from about 16 percent below average to about 19 percent
48
The percent of the population that was uninsured was based on statewide
data and does not include differences in uninsured rates among
metropolitan areas in the same state. See app. I for a description of our
regression methodology and results.
49
Total spending per enrollee includes spending for all health care services
except mental health, chemical dependency, and pharmaceuticals. We
adjusted total spending per enrollee for differences in costs of providing
service and in the age and sex of enrollees across metropolitan areas.
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
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.
Total spending per enrollee in FEHBP PPOs was, on average, highest among
metropolitan areas in the South and lowest in metropolitan areas in the
Northeast. About 86 percent of the metropolitan areas in the highest
spending quartile were located in the South (see fig. 6). Nearly 38
percent of the metropolitan areas in the lowest spending quartile were
located in the Northeast, and none of the metropolitan areas in the
highest spending quartile were in the Northeast. Spending per enrollee was
about 23 percent higher in metropolitan areas in the South than in the
Northeast, on average (see table 8).
Figure 6: FEHBP Adjusted Spending Per Enrollee Quartiles in 232 Metropolitan
Areas, 2001
Price Contributed to One-third of the Variation in Spending, but the
Contribution of Price to Spending Was Partially Offset by Utilization of
Physician Services
Table8: FEHBP PPO Spending Per Enrollee Indices in Metropolitan Areas by
Census Region, 2001
Average spending per Region 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
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
50
In order to analyze the contribution of price to geographic variation in
spending, we focused on hospital and physician spending (not total
spending), price, and utilization.
51
We did not analyze factors associated with this variation in utilization
as it was outside the scope of our research objectives.
52
The 26 percent difference between hospital prices in the highest and
lowest quartiles contributed to about one-third of the difference in
hospital spending. The 55 percent difference between hospital utilization
in the highest and lowest hospital spending quartiles contributed to about
two-thirds of the difference in hospital spending.
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 Average Average spending Spending quartile price indexa 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 Highest 1.05 1.12services
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
The 12 percent difference between physician prices in the highest and
lowest quartiles contributed to about one-third of the difference in
physician spending. The 26 percent difference between physician
utilization in the highest and lowest physician spending quartiles
contributed to about two-thirds of the difference in physician spending.
Page 28 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.
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.
Concluding Observations
Our analysis shows that an understanding of price variation is essential
to understanding geographic variation in health care spending in the
private sector. We found that market forces, not just the underlying costs
of doing business providers face, help to determine the prices FEHBP PPOs
ultimately pay hospitals and physicians. In metropolitan areas where there
was less competition among hospitals, FEHBP PPOs paid a higher price to
hospitals and physicians than in metropolitan areas where hospitals and
physicians had more competition. In metropolitan areas with less HMO
capitation, FEHBP PPOs paid higher prices,which also suggests that
hospitals and physicians in those metropolitan areas had less competition
for patient share. We found no evidence that hospitals or physicians
shifted costs, which suggests that FEHBP PPOs may have been influenced by
market forces when establishing prices, regardless of the amount of
uncompensated or undercompensated care in a metropolitan area. Further
investigation may help to explain why there were regional patterns that
appeared to be associated with private sector price variation.
Agency and Other Comments
In written comments on a draft of this report, OPM officials agreed with
our findings that competition and other factors were linked to variation
in prices, stating that the findings confirm a long-held view of the
agency. In addition, they suggested that several issues warranted further
study and discussion. They pointed out that it would have been interesting
to examine the relationships between physician prices, Medicaid payments,
percentage of the population uninsured, and physician-prescribing
patterns. They also noted that it would be instructive to investigate
unexplained regional variations and intraregional variations. They thought
some findings could have been addressed in greater detail within the text
and in the concluding observations.
Representatives of the FEHBP PPOs were also given an opportunity to
comment on a draft of the report. Representatives of one PPO noted that
market dynamics and prices could have changed since 2001.
We agree this report addressed important issues but investigating them in
further detail was beyond the scope of our work. We agree that market
dynamics and prices could have changed since 2001, but we used the most
recent data available at the start of the study and maintain that the
relationship among the variables, specifically the linkage between
competition, HMO capitation, and prices is less likely to have changed.
Other comments provided by OPM and representatives of the FEHBP PPOs were
incorporated into the draft, as appropriate.
As arranged with your office, unless you publicly announce the contents of
this report earlier, we plan no further distribution of it until 30 days
after its issue date. At that time, we will send copies of this report to
the Director of the Office of Personnel Management and other interested
parties. We will also provide copies to others upon request. In addition,
the report is available at no charge on the GAO Web site at
http://www.gao.gov.
If you or your staff have any questions about this report, please contact
me at (202) 512- 7101 or steinwalda@gao.gov. Contact points for our
Offices of Congressional Relations and Public Affairs may be found on the
last page of this report. GAO staff who made major contributions to this
report are listed in Appendix VI.
Sincerely yours,
A. Bruce Steinwald Director, Health Care
Appendix I: Scope and Methodology
FEHBP Data and Study Eligibility Criteria
In this appendix we describe the data and methods we used to compare
geographic variations in prices and spending in metropolitan areas 1
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.
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.
Page 33 GAO-05-856 FEHBP Health Care Prices
Hospital and Physician Price Estimates
We calculated price indices for hospital and physician services. We
selected these services because together they represented nearly
two-thirds of total health care spending and we could identify standard
units of service-hospital stays and physician procedures-to which we could
link prices. We derived our price estimates for each metropolitan area by
aggregating payments from individual claims to the metropolitan area where
the service was provided. 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.
Page 34 GAO-05-856 FEHBP Health Care Prices
Factors Affecting 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.
Table 11: Factors Included in Analysis of Hospital and Physician Price, 2001
Source of data to calculate Factor Measurement measurement
Competition Percent hospital beds of the two Verispan, L.L.C. largest
hospitals or hospital networksa
HMO capitation Percent of primary care physicians' InterStudy Publications
and compensation from capitationb U.S. Census Bureau
Cost shifting Percent of population enrolled in InterStudy Publications
and Medicare U.S. Census Bureau
Percent of population enrolled in InterStudy Publications and Medicaid
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 Bureau of Economic incomed
Analysis and Centers for Medicare and Medicaid Services
Hospital ownership Percent beds in for-profit hospitals Verispan, L.L.C.
status
Census division Indicator of the presence or U.S. Census Bureau absence of
the metropolitan area in the census divisions
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.
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.
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.
10
Some Medicaid payments for a given service varied depending on criteria
such as patient age, sex, provider specialty, and practice setting.
Researchers at The Lewin Group, who developed the statewide payments that
we used in estimating metropolitan area Medicaid prices, reported that
they focused on the payments most commonly made to a physician in private
practice.
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
11
We were unable to find uninsured data at the metropolitan area level.
Therefore we used the number of uninsured from InterStudy Publications.
The estimates from InterStudy Publications of the uninsured are based on
state numbers.
12
In order for the regression to be estimated we had to omit one of the
census division dummies from our model: we chose to omit Census Division
9.
Page 38 GAO-05-856 FEHBP Health Care Prices
Analytical Approach
We conducted two analyses to examine the relationship between our price
variables and the factors described above. First, we grouped the
metropolitan areas into quartiles for each of the factors. 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
Price Regression Analysis-Methods and Results
We used separate regression models to estimate the impact of our variables
on hospital and physician prices. To simplify the calculation of
independent variables' effects and to match the statistical distribution
assumption we made in our data trimming of prices, we used a log-linear
model: that is, we regressed the logarithm of price (hospital price and
physician price) on the levels of our independent variables. We were
concerned that our measures of provider supply-hospital beds per capita
and physicians per capita in the case of hospital and physician price,
respectively-were endogenous. For example, larger numbers of physicians
could lead to lower physician prices, but lower physician prices could
also make a metropolitan area less attractive to physicians and reduce
their number. In order to address this issue we used the method of
instrumental variables: a standard method to account for an endogenous
explanatory variable. 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
13
Quartiles divide the distribution of prices from lowest to highest into
four equal groups. The lowest quartile represents metropolitan areas
ranked in the lowest 25 percent of price, and the highest quartile
represents metropolitan areas ranked in the highest 25 percent of price.
14
We did not perform an analysis comparing prices inside and outside of
those census divisions that were significant in our regressions.
15
P. Kennedy, A Guide to Econometrics, 5th ed. (Cambridge, Mass. MIT Press,
2003), p. 188.
provider market share and managed care presence on prices: both of these
effects were consistent with the idea that raising market competitiveness
lowers prices. Our variable measuring the market share of the two largest
networks was positively related to price: that is, when the market became
more concentrated (less competitive), price tended to be higher. Also, our
HMO presence variable, the percentage of physician compensation from
capitation payments, was negatively associated with price: that is, less
HMO presence tended to increase price.
Table 12: Results for Hospital Price Regression-Estimated Effects of
Selected Factors on Hospital Prices in Metropolitan Areas, 2001
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**
Cost-shifting Percent of population uninsured -0.3621 -0.68
Average Medicaid payment 0.0026 1.58
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
Dummy variable indicator Census Division 1 - New England 0.0625 0.78
showing the Census
Census Division 2 - Middle Atlantic -0.1158 -1.43
Division in which the
metropolitan area was Census Division 3 - East North Central -0.0572 -0.73
located
Census Division 4 - West North Central 0.0418 0.33
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.
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**
Cost-shifting 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
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
Dummy variable indicator Census Division 1 - New England -0.1112 -2.79***
showing the Census Census Division 2 - Middle Atlantic -0.0346 -1.01
Division in which the metropolitan area was Census Division 3 - East North
Central 0.0041 0.14
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.
Our measures of cost-shifting effects were mostly not significant and none
of the results supported the claim that more Medicaid enrollees, lower
Medicaid payments, more Medicare enrollees, or more uninsured people were
associated with higher hospital or physician prices. Ideally, we would
have included an indicator of Medicare price levels for each area, such as
the wage index or the GPCI. However, we did not include these as separate
explanatory variables in the regression models because we had used the
wage index and the GPCI to adjust the hospital and physician prices,
respectively, for differences in the cost of doing business in different
areas. Therefore, our sole measure of the impact of the Medicare program
on prices was the percent of the population who were Medicare
beneficiaries. In the physician price regression, the average Medicaid
payment was significant. However, Medicaid payments were positively
associated with prices, which was inconsistent with the negative
association we would have expected if cost shifting were occurring. In the
physician price analysis, the percent of people uninsured was
significantly related to price and the result showed that where there were
more uninsured people, prices were actually lower, rather than higher, as
would have been predicted by the cost-shifting hypothesis.
Our inclusion of the set of census division dummy variables allowed us to
measure factors affecting price that were due simply to location and that
were not accounted for by the other variables included in the model. In
both price regression models, we ran an F-test that showed that the set of
census division dummy variables was jointly significant.
In the cases where our explanatory variables in the regression were
significant, we calculated the significant variables' impact on prices by
using our regression results to calculate the percent change in price for
a given increase in the explanatory variable. To do this, we simulated the
effect of increasing the significant explanatory variable from its average
in its lowest quartile to its average in its highest quartile, while
controlling for other factors. This was accomplished using the following
steps: (1) we calculated the average value of the statistically
significant explanatory variable for its lowest quartile, and input that
value into our estimated regression equation to calculate price, (2) we
calculated the average value of the key explanatory variable in its
highest quartile, and used that value in our estimated regression model to
calculate price again, and (3) we calculated the percent difference in
price using the results from (1) and (2). See table 14.
Table 14: Effects of Changes in Explanatory Variables on Prices
Percent impact on Percent impact on
Significant explanatory variable physician price 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-of-need laws, and teaching hospital variables from our
explanatory variables because they were not the focus of our analysis,
they were not statistically significant, and their inclusion did not
affect the significance of most of the other explanatory variables in the
model.
A certificate-of-need law generally requires that a hospital or nursing
home obtain approval from the state in which it is located before hospital
construction or capital improvements occur.
Page 44 GAO-05-856 FEHBP Health Care Prices
Spending Analysis
Appendix I: Scope and Methodology
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
17
Medicare adjusts hospital inpatient payments for labor and capital-related
variations in costs. In our study, we applied labor and capital
adjustments to the hospital inpatient portion of spending and to hospital
inpatient price.
18
We excluded mental health, chemical dependency services, and
pharmaceuticals from our spending analysis.
19
There are three GPCIs reflecting the cost of three different types of
inputs to physician services: physician work, physician practice expenses,
and expenses for physician liability insurance. Each GPCI is used to
adjust for the price level for related inputs in the local market where
the service is furnished.
20
There are 89 carrier/locality regions nationwide and 331 metropolitan
areas in the 50 states and District of Columbia. Thus, a carrier/locality
area is, on average, much larger than a metropolitan area. We used
county-level data for the GPCIs and aggregated those data to the
metropolitan area level.
Decomposing Spending Variation into Price and Utilization Effects
standard statistical distribution (the lognormal). We removed enrollees
from this analysis whose spending was at least three standard deviations
above the mean.
We adjusted spending for the age and sex distribution of each metropolitan
area's population. To do this, we calculated the average age-and
sex-specific spending rates of all 232 metropolitan areas combined, and
applied these averages to the actual age and sex distribution in each
metropolitan area. This yielded an "expected" spending rate for each
metropolitan area: the spending in that metropolitan area if it had the
study average spending rate, given the age and sex distribution of that
metropolitan area's population. We then calculated the ratio of actual
cost-adjusted spending to expected cost-adjusted spending. This yielded an
index of how much higher or lower spending in the specific metropolitan
area was from what would be expected if it had average spending rates,
given its age and sex composition. An index value greater than 1.00
implies spending was higher than expected and an index value less than
1.00 implies spending was lower than expected.
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
Data Reliability
Appendix I: Scope and Methodology
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
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.
Where metropolitan areas overlapped several states, we prorated state
Medicaid payment rates based on U.S. census estimates of Medicaid
enrollment in each component county of the metropolitan area. We used
utilization rates in California to weight the average Medicaid payment in
each metropolitan area because utilization rates were not readily
available for any other state.
Page 48 GAO-05-856 FEHBP Health Care Prices
Appendix II: FEHBP PPO Adjusted Hospital Prices in U.S. Metropolitan Areas, 2001
The adjusted hospital price indices based on FEHBP PPO payments for
hospital stays in 232 metropolitan areas are presented below ranked in
order from highest to lowest price.
Page 49 GAO-05-856 FEHBP Health Care Prices
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
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
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
Page 52 GAO-05-856 FEHBP Health Care Prices
Rank Metropolitan area Predominant Adjusted
statea 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
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 VA 0.861
News, VA-NC
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
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 0.813
RI-MA
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
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
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.
Page 56 GAO-05-856 FEHBP Health Care Prices
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
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
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 1.072
TN-VA
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
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 1.016
NC-SC
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
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
Page 61 GAO-05-856 FEHBP Health Care Prices
Rank Metropolitan area Predominant Adjusted
statea physician
price index
181 Tacoma WA 0.979
182 Galveston-Texas City TX 0.979
183 Norfolk-Virginia Beach-Newport News, VA 0.975
VA-NC
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
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
Page 63 GAO-05-856 FEHBP Health Care Prices
Rank Metropolitan area Predominant Adjusted
statea 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 CA 0.883
Robles
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
Rank Metropolitan area Predominant Adjusted
statea 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 0.763
RI-MA
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
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.
Page 65 GAO-05-856 FEHBP Health Care Prices
Table 17: Ranking of Metropolitan Areas by Adjusted Health Care Spending
Per Enrollee, 2001
Rank Metropolitan area Predominant Adjusted
statea 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 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 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 1.118
TN-VA
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
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 VA 1.012
News, VA-NC
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
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
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
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 0.834
RI-MA
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
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 73 GAO-05-856 FEHBP Health Care Prices
Appendix VI: GAO Contacts and Staff Acknowledgments
A. Bruce Steinwald, (202) 512-7101 or steinwalda@gao.gov
GAO Contacts
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.
(290213)
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