Milwaukee Health Care Spending Compared to Other Metropolitan Areas
Geographic Variation in Spending for Enrollees in the Federal Employees Health Benefits Program Gao ID: GAO-04-1000R August 18, 2004Health 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. Congress 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 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.
Health care spending and prices in Milwaukee were high relative to the averages for metropolitan statistical areas (MSA) 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.
GAO-04-1000R, Milwaukee Health Care Spending Compared to Other Metropolitan Areas: Geographic Variation in Spending for Enrollees in the Federal Employees Health Benefits Program
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Employees Health Benefits Program' which was released on August 23,
2004.
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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
High:
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
prices.
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 http://www.gao.gov.
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
service.
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
2002.
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
status;
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
Bureau.
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:
Acknowledgements:
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]
(290397):
FOOTNOTES
[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
furnished.
[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.
