This is the accessible text file for GAO report number GAO-04-1000R 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, 2004. This text file was formatted by the U.S. Government Accountability Office (GAO) to be accessible to users with visual impairments, as part of a longer term project to improve GAO products' accessibility. Every attempt has been made to maintain the structural and data integrity of the original printed product. Accessibility features, such as text descriptions of tables, consecutively numbered footnotes placed at the end of the file, and the text of agency comment letters, are provided but may not exactly duplicate the presentation or format of the printed version. The portable document format (PDF) file is an exact electronic replica of the printed version. We welcome your feedback. Please E-mail your comments regarding the contents or accessibility features of this document to Webmaster@gao.gov. This is a work of the U.S. government and is not subject to copyright protection in the United States. It may be reproduced and distributed in its entirety without further permission from GAO. Because this work may contain copyrighted images or other material, permission from the copyright holder may be necessary if you wish to reproduce this material separately. 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  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.  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.  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.  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.