The Implications of Regional Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care

By E. Fisher, D. Wennberg, T. Stukel, D. Gottlieb, F. Lucas, Etoile Pinder, Strasenburgh Hall. Annals of Internal Medicine, 2003

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Context Per capita Medicare spending varies considerably from region to region. The effect of greater Medicare spending on mortality, functional status, and satisfaction is not known. Contribution Using end-of-life care spending as an indicator of Medicare spending, the researchers categorized geographic regions into five quintiles of spending and examined costs and outcomes of care for hip fracture, colorectal cancer, and acute myocardial infarction. Residents of high-spending regions received 60% more care but did not have lower mortality rates, better functional status, or higher satisfaction. Implications Medicare beneficiaries who live in higher Medicare spending regions do not necessarily have better health outcomes or satisfaction with health care than those in lower-spending regions. The Editors The inexorable growth of health care spending in the United States is widely believed to be due to the greater use of advanced technology of clear-cut benefit (1). Policymakers argue (and the public assumes) that any constraints on growth are likely to be harmful (1, 2). Studies of regional variations in spending and medical practice, however, call these assumptions into question. Earlier research has indicated that the nearly twofold differences in Medicare spending observed across U.S. regions are not due to differences in the prices paid for medical services (3, 4) or to differences in health or socioeconomic status (3, 5, 6). Recent research, some of which is presented in Part 1 of our study, indicates that regional variations in average per capita Medicare spending are not due to more frequent performance of major surgery (7, 8) and that regions with higher per capita spending are no more likely to provide higher-quality care, whether defined in terms of specific evidence-based services or in terms of greater access to basic health care (7, 8). The additional utilization in high-spending regions is largely devoted to discretionary services that have previously been demonstrated to be associated with the local supply of physicians and hospital resources (5, 6). These include the frequency and type of evaluation and management services provided by physicians, the use of specialist consultations, the frequency of diagnostic tests and minor procedures, and the likelihood of treating patients with chronic disease in the inpatient or intensive care unit setting. Whether the specialist-oriented, more inpatient-based practice observed in high-spending regions offers important health benefits, however, is unknown. Although recent studies have found no benefit in terms of mortality (5, 9, 10), they had limited ability to adjust for possible case-mix differences, inadequate individual-level clinical detail, and limited outcome measures. Our study was designed to address these concerns. In Part 1, we reported on the relationship between regional differences in spending and the content of care, quality of care, and access to care provided to four cohorts of Medicare beneficiaries. In this article, Part 2, we describe associations between increased spending and mortality, functional status, and satisfaction with care. Methods Design Overview As described in greater detail in Part 1, we carried out a cohort study in four parallel populations using a natural randomization approach (11). In this approach, one or more exposure variables allow assignment of patients into treatment groups (different levels of average spending), as would a randomized trial. Because some of the regional differences in Medicare spending are due to differences in illness levels (enrollees in Louisiana are sicker than those in Colorado) and price (Medicare pays more for the same service in New York than in Iowa), we could not use Medicare spending itself as the exposure. We therefore assigned U.S. hospital referral regions (HRRs), and thus the cohort members residing within them, to different exposure levels using a measure that reflects the component of regional variation in Medicare spending due to physician practice rather than regional differences in illness or pricethe End-of-Life Expenditure Index (EOL-EI). Because regional differences in end-of-life spending are unrelated to underlying illness levels, it is reasonable to consider residence in HRRs with differing levels of end-of-life spending as a random event. The index was calculated as spending on hospital and physician services provided to a reference cohort distinct from the study cohorts: Medicare enrollees in their last 6 months of life. In the current paper, we also present several analyses with an alternative exposure measure, the Acute Care Expenditure Index (AC-EI), to decrease concern about possible residual confounding. We confirmed that the exposures used to assign the HRRs achieved the goals of natural randomization: 1) Study samples assigned to different levels of the exposure [the EOL-EI] were similar in baseline health status, and 2) the actual quantity of services delivered to the individuals within the study samples nevertheless differed substantially across exposure levels and was highly correlated with average per capita Medicare spending in the HRRs. We followed the cohorts for up to 5 years after their initial hospitalizations and compared the processes of care (Part 1) and health outcomes (Part 2) across HRRs assigned to different exposure levels. Study Cohorts The four study cohorts are described in detail in Part 1. Briefly, we studied fee-for-service Medicare enrollees, ages 65 to 99 years, who were eligible for Medicare Parts A and B. The acute myocardial infarction (MI) cohort was drawn from patients included in the Cooperative Cardiovascular Project, who had index hospitalizations between February 1994 and November 1995. The hip fracture and colorectal cancer cohorts were identified based on an incident hospitalization between 1993 and 1995. The general population sample included participants in the Medicare Current Beneficiary Survey (MCBS) who had initial interviews between 1991 and 1996 (for the survival analysis) or between 1992 and 1995 (for the other analyses) (see Appendix, Section C). Each cohort member was placed in a spending group according to the EOL-EI (as defined in detail in Part 1) in their HRR of residence at the time of the index hospitalization (chronic disease cohorts), or initial interview (MCBS cohort). Characteristics of the study cohorts were ascertained from a variety of sources, as described in detail in Part 1, including Medicare administrative files and claims (all four cohorts), chart reviews (acute MI cohort), in-person interview (MCBS cohort), U.S Census data (attributes of ZIP code of residence, such as income, for the three chronic disease cohorts), and American Hospital Association data (to characterize hospitals). Assignment to Exposure Levels As we summarized here and described in detail in Part 1, we used two approaches to determine cohort members' exposure to different levels of Medicare spending in their HRR of residence. Previous research has shown that the dramatic differences in end-of-life treatment across U.S. regions are highly predictive of differences in total spending (8, 12) but are not due to differences in case mix or patient preferences (13). Our primary measure of exposure was the EOL-EI, which was calculated as age-sex-raceadjusted spending (measured with standardized national prices) on hospital and physician services provided to Medicare enrollees who were in their last 6 months of life in each of the 306 U.S. HRRs in mid-1994 to 1997, excluding any members of the study cohorts (Appendix, Section E) We also repeated the major analyses with an alternative exposure measure, the AC-EI, which was based on differences across HRRs in risk-adjusted spending during an acute illness episode (Appendix, Section F). Both measures were highly predictive of average age-sex-raceadjusted Medicare spending at the HRR level (r = 0.81 for the EOL-EI and 0.79 for the AC-EI in the acute MI cohort) and, as was shown in Part 1, of the regional differences in utilization experienced by the study cohorts. For many analyses, we grouped HRRs into quintiles of increasing exposure to the expenditure indices. Statistical Analyses To assess the aggregate impact of any differences in individual attributes on average baseline risk for death across regions of increasing EOL-EI, we used logistic regression to determine each individual's predicted 1-year risk for death as a function of his or her baseline characteristics. The models had modest to excellent predictive ability (c-statistics were 0.61 for the colorectal cancer cohort, 0.68 for the hip fracture cohort, 0.77 for the acute MI cohort, and 0.82 for the MCBS cohort). We used these models to determine the average predicted risk for death across quintiles of Medicare expenditure indices. Mortality Analyses The association between the HRR-level expenditure index and survival was assessed by using Cox proportional-hazards regression models (14), with the expenditure index measured both as a categorical variable (in which each HRR was assigned to a quintile of Medicare spending based on the EOL-EI) and a continuous variable (using the value of the EOL-EI in the HRR of residence as the exposure). The survival models included independent variables to adjust for patient characteristics, hospital characteristics, and attributes of the HRR. Model fit was assessed by using methods for Cox model residuals to examine overall model fit, to test proportional hazards assumptions, and to identify influential observations. The main survival models underpredicted mortality in the first 6 months, possibly because of short-term complications that could not be adequately predicted with the available data; however, the models provided excellent prediction of 1-year mortality rates for each cohort. The models are presented in Appendix Tables 6, 7, 8, and 9. To test whether the overall findings were consis

Subfields: Economics and Econometrics, General Health Professions

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