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entitled 'Milwaukee Health Care Spending Compared to Other Metropolitan 
Areas: Geographic Variation in Spending for Enrollees in the Federal 
Employees Health Benefits Program' which was released on August 23, 

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August 18, 2004:

The Honorable Paul Ryan:

House of Representatives:

Subject: Milwaukee Health Care Spending Compared to Other Metropolitan 
Areas: Geographic Variation in Spending for Enrollees in the Federal 
Employees Health Benefits Program:

Dear Mr. Ryan:

Health care spending varies across the country due to differences in 
the use and price of health care services. Understanding the reasons 
for utilization and price variation may contribute to developing 
methods to control health care spending. This report provides 
preliminary results from our work on geographic variations in health 
care spending and prices.

You asked us to examine geographic variations in health care spending 
and prices in the Federal Employees Health Benefits Program (FEHBP). 
FEHBP is the health insurance program administered by the Office of 
Personnel Management (OPM) for federal civilian employees and retirees, 
which covered 8.5 million people in 2001. FEHBP contracts with private 
insurers to provide health benefits. It is the largest private 
insurance program in the United States. This report summarizes 
preliminary information provided to you at an interim briefing on July 
21, 2004. The enclosed briefing slides (see enc. I) highlight the 
results of our work comparing Milwaukee to other areas of the country. 
The objectives of the briefing were to (1) compare Milwaukee health 
care spending per enrollee, hospital inpatient prices, and physician 
prices with other metropolitan areas, and (2) examine factors 
identified by stakeholders in Milwaukee that may affect health care 
spending and prices.

To estimate spending and prices in Milwaukee and other metropolitan 
areas, we analyzed 2001 claims data for enrollees under the age of 65 
from the largest national insurers participating in FEHBP. We defined 
price as the payment by insurers and enrollees to a provider for a 
service. Spending was the sum of payments across all providers for each 
enrollee. We analyzed mean spending per enrollee, mean inpatient price, 
and mean physician price in Milwaukee and other metropolitan 
statistical areas (MSA) across the country. Out of a total of 331 MSAs, 
we included 239 MSAs in the spending per enrollee and inpatient price 
analyses and 319 in the physician price analysis. We also interviewed 
key stakeholders in Milwaukee to identify factors they thought affected 
health care spending and prices. Key stakeholders included 
representatives of health insurance companies, hospital networks, 
physician networks, and large employers. To determine if these factors 
could affect geographic:

differences in spending and prices, we evaluated quantitative 
indicators of some aspects of the identified factors. We tested our 
data for consistency and reliability, and determined that they were 
adequate for our purposes. Our analysis is limited to geographic 
variation in FEHBP spending and prices in 2001, and we did not consider 
all of the factors that could affect health care spending and prices. 
However, our analysis provides important information about selected 
factors identified by stakeholders. Enclosure II contains additional 
details about our scope and methodology. We performed our work from 
June 2004 through August 2004 in accordance with generally accepted 
government auditing standards.

Results in Brief:

Health care spending and prices in Milwaukee were high relative to the 
averages for MSAs in our study, and preliminary analyses point to 
providers' leverage in negotiating prices with insurers as one of the 
contributing factors. Milwaukee ranked among the top 20 MSAs for 
spending per enrollee, inpatient prices, and physician prices. Some 
stakeholders asserted that high spending and prices were caused in part 
by the leverage exerted by provider networks in Milwaukee, which 
limited insurers' ability to control the prices they pay. This 
assertion was supported by our examination of indicators of the 
relative strength of providers and payers. We provided a draft of this 
report to OPM for review. OPM informed us that it had no comments.

Milwaukee's Health Care Spending and Prices Compared to Other MSAs Were 

Milwaukee ranked 16TH in overall spending among the 239 MSAs in the 
analysis, after accounting for differences in age and sex of those 
covered and the underlying costs of conducting business across the 
areas. Health care spending in Milwaukee was about 27 percent higher 
than the average across all of the MSAs in this analysis. High hospital 
inpatient and physician prices likely contributed to high total 
spending. Inpatient prices, after adjusting for differences in 
underlying costs and the mix and severity of cases, were 63 percent 
higher than average hospital inpatient prices in the 239 study MSAs. 
Milwaukee had the 5TH highest hospital inpatient prices. Adjusted 
physician prices were 33 percent higher than the average across the 319 
MSAs in the analysis. Milwaukee ranked 16TH highest for physician 

Provider Leverage Relative to Insurers May Contribute to High Prices; 
Payment Shortfalls Do Not Appear to Explain the Discrepancy in Prices 
between Milwaukee and Other Metropolitan Areas:

Stakeholders asserted that high health care prices were due at least in 
part to Milwaukee hospitals and physicians having considerable leverage 
over insurers when negotiating prices. Stakeholders described highly 
consolidated provider networks in Milwaukee that included both 
hospitals and physicians. These networks had established markets in 
separate geographic areas, each with loyal consumers. Insurers 
contended that they had to contract with multiple hospital networks 
because of consumers' demands for access to their local hospitals and 
to ensure enrollees had the ability to use hospital services across 
Milwaukee. Insurers further asserted that because they had to contract 
with multiple networks, this restricted their ability to direct 
enrollees to specific networks for care, thereby limiting insurers' 
leverage to negotiate lower prices for health care services with 
providers in exchange for a larger share of the insurers' business.

We found some evidence to support the stakeholders' assertion that 
hospitals and physicians had more leverage than insurers in negotiating 
prices. The two largest hospital networks in Milwaukee had 14 percent 
more market share, that is, share of beds, than the average across MSAs 
of similar size. The larger the share of the hospital service market 
controlled by a few providers, the greater the likelihood that insurers 
will have to contract with those providers to ensure enrollee access to 
care. Another indicator of the relative negotiating leverage of 
providers and insurers is the estimated share of primary care 
physicians' income that was paid through a capitation arrangement. 
Under a capitation arrangement, the insurer pays a predetermined fee to 
a provider to render all of an enrollee's care for a given period, 
regardless of how much care the enrollee ultimately uses; thus, 
providers have to absorb costs above the predetermined fee. Paying 
physicians on a capitated basis indicates that insurers had the 
leverage to negotiate this payment arrangement, which providers often 
try to resist. Milwaukee was an estimated 89 percent below the mean in 
the percentage of physicians' income derived from capitation payments, 
indicating that the providers may have had leverage to resist this 
payment arrangement.

Some hospital and physician group administrators in Milwaukee stated 
that they needed to charge higher prices to private insurers to make up 
for low Medicare payments and to recoup costs of uncompensated care. 
Milwaukee hospitals in our analysis received Medicare payments above 
the median for a high-volume type of inpatient stay, and one hospital's 
payment was higher than 90 percent of all hospitals in the country. 
Medicare hospital payments differ because of adjustments to account for 
geographic differences in costs. Hospital inpatient payments may also 
differ because of the mix of teaching hospitals or hospitals that 
provide a disproportionate share of care to low-income patients, which 
both receive higher Medicare payments. In Milwaukee, the Medicare 
payment for a typical physician office visit, which is adjusted for 
geographic differences in costs, was 3 percent below the median of all 
payment areas in the country. The percentage of uninsured people in 
Milwaukee is half that found in our study MSAs, which suggests that 
recouping the costs of uncompensated care is less of a problem in 
Milwaukee than elsewhere.

In an upcoming report, we will complete our analysis of spending in 
FEHBP. This will involve evaluating the separate contribution of price 
and utilization to spending and further analyzing the factors that 
contribute to regional variations in spending in FEHBP.

Agency Comments:

We provided a draft of this report to OPM for review. OPM informed us 
that it had no comments.

As agreed with your office, unless you publicly announce its contents 
earlier, we plan no further distribution of this report until 30 days 
after its date. We will then send copies of this report to the 
Administrator, OPM, and to the insurers that provided us with claims 
data for FEHBP enrollees. We will make copies available to others upon 
request. In addition, the report will be available at no charge on the 
GAO Web site at

If you or your staff have any questions or need additional information, 
please contact me at (202) 512-8942. Another contact and key 
contributors are listed in enclosure III.

Sincerely yours,

Signed by: 

Laura A. Dummit:

Director, Health Care--Medicare Payment Issues:

Enclosures - 3:

Enclosure I: 

[See PDF for images]

[End of slide presentation]

[End of section]

Enclosure II: Scope and Methodology:

This enclosure describes the data and methods we used to compare 
geographic variations in spending and price in Milwaukee with those of 
other metropolitan areas, and to explore the factors affecting the 
health care market in Milwaukee. Our study group comprised enrollees in 
selected national preferred provider organizations (PPO) participating 
in the FEHBP. We compared differences in per enrollee spending and in 
inpatient and physician service prices across Milwaukee and other 
metropolitan areas using medical claims data. We interviewed 
stakeholders in Milwaukee to identify potential factors that contribute 
to spending and prices, and then analyzed data related to these factors 
to assess their likely relevance to spending and prices in Milwaukee.

FEHBP Data and Study Eligibility Criteria:

To compare health care spending, hospital inpatient prices, and 
physician prices for Milwaukee with other metropolitan areas, we 
analyzed 2001 health services claims data from FEHBP. FEHBP, the health 
insurance program administered by the Office of Personnel Management 
for federal civilian employees and retirees, covered 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 employees under the age of 
65 and their dependents who enrolled in selected national PPOs as their 
primary insurers.[Footnote 1] Data for enrollees with partial year 
enrollment were prorated based on days of eligibility during 2001. The 
dates of service on claims were checked so that they were only included 
if the service was delivered during a period of PPO eligibility. 
Pharmaceutical claims were excluded from the study, and mental health 
and chemical dependency claims were excluded from some analyses because 
these services were subcontracted to other organizations by at least 
one of the PPOs and the associated claims for all service types were 
not routinely available.

In our study, price was defined as the total payment made by insurers 
and enrollees to a provider for a service. Spending was defined as the 
total payments for health care services (including the enrollee share) 
for persons enrolled with the selected insurers participating in FEHBP.

We aggregated payments to the MSA to compare spending and prices across 
MSAs. We did not examine spending or prices outside of MSAs because 
their expansive areas could include multiple markets that we would not 
be able to distinguish between.

There are 331 MSAs in the 50 states and the District of Columbia. We 
excluded some MSAs 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 
inpatient hospital admissions to support our analyses. In addition, we 
excluded one MSA because it had a high proportion of claims from 
enrollees that were out of the area. For our spending and inpatient 
analyses, we had adequate data to make comparisons among 239 MSAs, 
which accounted for 89 percent of the population living in MSAs. In our 
physician price analyses, we included 319 MSAs, which accounted for 98 
percent of the population living in MSAs.

Spending Analysis:

To determine average spending per enrollee in each MSA, we summed all 
payments for each enrollee and then assigned enrollees to their MSAs of 
residence. We then adjusted spending for geographic cost differences, 
removed outliers, and accounted for differences in the age and sex 
distributions across MSAs. After applying our eligibility criteria and 
removing outliers, we had 2.1 million enrollees in our study.

We accounted for geographic differences in the costs of providing 
services by applying the methodologies used by Medicare to adjust 
provider payments. To adjust some provider payments for geographic 
differences in costs, Medicare applies the Medicare hospital wage index 
to the portion of payments that covers labor-related costs for a 
specific service. We summed the payments per enrollee by service 
categories and then applied the hospital wage index to the labor-
related portion of the total payment for each type of service. 
Categories of service that were adjusted for cost differences in this 
manner were hospital inpatient,[Footnote 2] hospital outpatient, home 
health, rehabilitation, skilled nursing facility, other outpatient, and 
ambulatory surgery center. Mental health and chemical dependency 
services were excluded from the spending analysis. We adjusted 
physician services using a different methodology, again following the 
basic methodology used by Medicare. We applied the appropriate 
geographic practice cost indexes (GPCI) to the total physician 
payments.[Footnote 3] However, our method differed slightly in that 
instead of applying the GPCIs at the carrier/locality level, we 
calculated cost indexes for each MSA.[Footnote 4] By applying the 
Medicare cost adjustments as specified above, we obtained what we refer 
to as cost-adjusted spending.

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

We adjusted spending for the age and sex distribution of each MSA's 
population. To do this, we calculated the average age-and sex-specific 
spending rates of all 239 MSAs combined, and applied these averages to 
the actual age and sex distribution in each MSA. This yielded an 
"expected" spending rate for each MSA: the spending in that MSA if it 
had the study average spending rate, given the age and sex distribution 
of that MSA'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 MSA was from 
what would be expected if it had average spending rates, given its age 
and sex composition. An index value greater than one implies spending 
was higher than expected and an index value less than one implies 
spending was lower than expected. We refer to the spending index as the 
adjusted average spending per enrollee.

Inpatient and Physician Price Analyses:

We calculated prices for hospital inpatient and physician service 
categories. We selected these service categories because they 
represented nearly two-thirds of total health care spending and we 
could identify standard units of service, inpatient stays, and 
physician procedures, to which we could link prices. We could also 
adjust the associated spending for the mix of services provided. We 
derived our price estimates by aggregating payments from individual 
claims for the respective category to the MSA based on the place of 

For our inpatient price estimates, we first aggregated payments from 
separate inpatient hospital claims to determine the total payments for 
a hospital admission. This involved combining inpatient claims for the 
same enrollee that had contiguous dates of service and the same 
provider. We excluded stays that involved multiple hospital providers.

To account for differences in the mix of inpatient admissions across 
MSAs, we first classified each admission into an All Patient Refined 
Diagnosis Related Group (APR-DRG), using information on length of stay, 
diagnoses, procedures, and the patients' demographic characteristics. 
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 all patients. We used the APR-DRG weight to adjust the inpatient 
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 inpatient prices for differences in local costs of doing 
business by applying 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 the geographic adjustment factor to 9 percent of the 
price, which is Medicare's estimate of the capital cost component.

We trimmed our adjusted inpatient price data for outliers using a 
method similar to that used for trimming the spending data. We used a 
lognormal distribution to identify and remove prices 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. 
We aggregated the prices for the remaining services to the MSA based on 
the provider's place of service.

To account for differences in the mix of physician services across 
MSAs, we applied the Medicare methodology used to adjust physician 
payments. For each service, we applied the appropriate relative value 
unit to reflect the value of the specific service relative to an 
intermediate office visit.

To adjust physician prices for geographic differences in costs, we 
applied the Medicare methodology used to adjust physician payments. We 
applied the appropriate GPCI to each physician payment. However, 
instead of applying the GPCIs used for Medicare payments, which are 
based on geographic areas larger than an MSA, we aggregated county-
level cost indexes to MSAs and then applied them.

We trimmed the cost and service-mix adjusted data for outliers using 
the same method used for trimming our inpatient price data, namely, 
using the lognormal distribution to remove observations more than three 
standard deviations above or below the mean.

Analysis of Factors Identified by Stakeholders in Milwaukee That May 
Contribute to High Health Care Spending and Prices:

We interviewed key stakeholders in Milwaukee, including representatives 
of health insurance companies, hospital networks, physician networks, 
and large employers, to identify factors that might affect heath care 
spending. In all, we interviewed individuals from 17 organizations. To 
determine whether the factors could affect spending and prices, we 
identified indicators that quantify some aspects of each factor. This 
methodology enabled us to compare Milwaukee with other areas across the 
indicators. Factors identified by stakeholders and our associated 
indicators and data sources are listed in table 1.

To calculate the Medicare payment rates for inpatient hospitals, we 
identified a frequent payment category, "Heart Failure and Shock," 
Diagnosis Related Group 127. We calculated the Medicare payments for 
all hospitals, using Medicare payment formulas for 2002. Similarly, we 
chose one of the procedures that is widely used by physicians, 
Intermediate Office Visit (Current Procedural Terminology code 99213), 
and calculated the Medicare payments for all physician localities for 

Table 1: Stakeholder Analysis: Factors, Indicators, and Data Sources:

Factors identified by stakeholders: Provider leverage; 
Indicators: Hospital concentration: market share[A] of the MSA's two 
biggest hospital networks; Primary care physician capitated 
payments[B] weighted by health maintenance organization enrollment per 
MSA population; 
Data source: Verispan, LLC; InterStudy Publications; United States 
Census Bureau.

Factors identified by stakeholders: Medicare payments; 
Indicators: Medicare hospital payments; Medicare physician payments; 
Data source: Centers for Medicare & Medicaid Services.

Factors identified by stakeholders: Uncompensated care; 
Indicators: Uninsured, percentage of population; 
Data source: InterStudy Publications; U.S. Census Bureau.

Factors identified by stakeholders: Population characteristics; health 
Indicators: Mortality, deaths per 100,000 population aged 1-64, as a 
health status proxy; 
Data source: National Center for Health Statistics; U.S. Census 

Source: GAO analysis of factors, indicators, and data sources.

[A] Market share is defined in this study as the ratio of a hospital 
network's staffed beds to the total number of staffed beds in the MSA. 
Hospitals unaffiliated with a network are treated as sole hospital 
networks for this analysis.

[B] Capitated payments to providers typically require providers to care 
for a group of patients, regardless of the volume of services they 
ultimately use, for a predetermined payment for each patient.

[End of table]

Enclosure III: GAO Contact and Staff Acknowledgements:

GAO Contact:

Christine Brudevold, (202) 512-2669:


Leslie Gordon, Michael Kendix, Vanessa Kuhn, Daniel Lee, Kathryn 
Linehan, Jennifer Rellick, Ann Tynan, and Suzanne Worth made key 
contributions to this report.

[End of section]



[1] We excluded PPO enrollees age 65 and over because FEHBP is not 
their primary insurer, and consequently the PPOs do not have records of 
all claim payments. For retirees age 65 and over, FEHBP supplements 
Medicare benefits.

[2] 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.

[3] There are three GPCIs reflecting the cost of three different types 
of inputs: physician services, practice expenses, and expenses for 
physician liability insurance. Each GPCI is used to adjust to the price 
level for related inputs in the local market where the service is 

[4] There are 92 carrier/locality regions nationwide and 331 MSAs in 
the 50 states and District of Columbia. Thus, a carrier/locality area 
is, on average, much larger than an MSA. We used county-level data for 
the GPCIs and aggregated those data to the MSA level.