Variation in Health Care Spending Target Decision Making, Not Geography (2013) / Chapter Skim
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2 Empirical Analysis of Geographic Variation
2 Empirical Analysis of Geographic Variation
Pages 39-78
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Committee on Geographic Variation in Health Care Spending and Promotion of High-Value Care with examining "geographic variation in intensity, cost, and growth of health care services and in per capita health care spending among the Medicare, Medicaid, privately insured, and uninsured U.S. populations." To this end, the committee commissioned new analyses to complement its evaluation of the existing literature.
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health care spending and utilization. To better understand the causes of variation in the health care system, the committee commissioned original empirical analyses of the complete database of Medicare beneficiaries (by Acumen, LLC; Dartmouth Institute of Health Policy and Clinical Practice; and the University of Pittsburgh)
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After briefly addressing the methodological issue of the unit of analysis, the chapter confirms the robust presence of regional variation in both Medicare and commercial health care spending and utilization across multiple geographic units as well as over time. It then explores the sources of this
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estimate that unadjusted Medicare spending per beneficiary is 50-55 percent higher in HRRs in the highest quintile of spending relative to those in the lowest quintile. Medicare service use (adjusted for demographics and beneficiary health)
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, the commit tee uses the term "metropolitan CBSA" throughout this report. Medicare and Commercial Spending Varies Across All Levels of Geography For the present study, variation was examined at three geographic units of measurement: hospital service area (HSA)
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The policy implications of increasing levels of variation for smaller geographic units are discussed in Chapter 4. The committee, however, has chosen to present analysis results at the HRR level in the remainder of this report, as the corresponding area served by a major tertiary care hospital is the most widely established unit of analysis in the literature on geographic variation.
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It is surprising that the correlation of Medicare spending with total spending is not higher (see Table 2-3) , as Medicare accounts for a substantial fraction of total health care spending.
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That analysis was limited in scope as individual-level claims data were not available for the 2007-2009 study period; therefore, the analysis examined spending variation based on total monthly Medicare reimbursement paid to Medicare Advantage plans (Acumen, LLC, 2013a) .3 In part because of a policy decision to raise reimbursements in HRRs with lower traditional Medicare spending, the analysis found somewhat less variation in Medicare Advantage spending compared with traditional Medicare: HRRs in the 90th percentile spent 36 percent more per Medicare Advantage beneficiary than HRRs in the 10th percentile, while Table 2-1 shows a slightly higher differential ratio for feefor-service beneficiaries.
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. The impact of post-acute care services on variation in total Medicare spending and utilization is discussed in greater detail later in this chapter.
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NOTE: Utilization figures have been adjusted for age, sex, and health status. aThe committee was limited in the number of utilization measures it could investigate across Medicare and commercial databases due to time and budget constraints.
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Utilization growth rates mirror the spending patterns presented in Figure 2-2. These results are consistent with the existing literature, which reports that variation in Medicare spending persists across areas over time (Cutler and Sheiner, 1999)
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100 50 50 10 5 (Logarithmic Scale, 1970 = 1.0) Dollar Amount Relative to 1970 1 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Calendar Year GDP Per Capita Medicare $ Per Enrollee Private Health Insurance $ Per Enrollee FIGURE 2-1 Cumulative growth in spending for Medicare and private health insurance per enrollee compared with growth in per capita gross domestic product (GDP)
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10% 8% 6% Quintile 1 Quintile 2 4% Quintile 3 Quintile 4 2% Quintile 5 0% 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 -2% -4% -6% FIGURE 2-2 Growth rates of Medicare spending, adjusted for demographics, health status, and input price, among quintilesa of hospital referral regions (HRRs) based on expenditure levels in 1992.
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More precisely, random variation in average HRR-level Medicare spending in any one year is small relative to the mean (ranging from 2 percent for the largest HRR to 4 percent for the smallest HRR) , suggesting that sample sizes per HRR are sufficiently large to support conclusions.
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Analyses conducted for this study support the results in the existing literature, which reports that adjusting for regional differences in prices has little effect on observed variation in Medicare spending (Cutler and Sheiner, 1999; Fuchs et al., 2001; Gottlieb et al., 2010)
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. Harvard reports that 70 percent of variation in total commercial spending is attributable to price markups, most likely reflecting the varying market 8 Note that this result differs from the finding in MedPAC's 2011 Report to Congress: Regional Variation in Medicare Service Use, which reports that input price adjustment decreased variation between metropolitan CBSAs in the 90th and 10th percentiles from 55 percent to 30 percent.
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2.1 1.9 1.7 Implied Price 1.5 1.3 R2 = 0.42 1.1 0.9 0.7 0.5 2,000 2,500 3,000 3,500 4,000 4,500 Quantity (indexed to dollars) FIGURE 2-3 Relationships among spending, implied price and quantity, with adjustment for age, sex, and health status.
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.10 As discussed earlier, variation in the utilization of health care services, particularly inpatient hospitalization and emergency department visits, does contribute to regional spending differences in the commercial population. As shown in Table 2-7, however, utilization and input prices have noticeably smaller effects than price markups on overall variation in commercial spending.
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Adjusting for health status has been shown to reduce variation in Medicare spending by 16 to 66 percent (CBO, 2008; Cutler and Sheiner, 1999) .11 A recent study found that health status, measured at the individual level, accounted for 18 percent of the geographic difference between the highest- and lowest-spending Medicare quintiles (Bernstein et al., 2011; Sutherland et al., 2009)
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Respecting practical limitations, ideally health status measures based on claims data should be enhanced with important behavioral and clinical data on Medicare and commercially insured beneficiaries. Previous studies have found that demographic variables such as age, sex, race, ethnicity, and income are common confounders of individual patient health (Adler and Newman, 2002; Case and Deaton, 2005; DeNavasWalt et al., 2009; Farley, 1985)
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In addition to population and patient characteristics, geographic variation in health care spending and utilization may be influenced by a host of local and regional market factors, such as the supply of providers and medical services, the percentage composition of the insured population, and provider and payer competition (Baicker et al., 2004; Fisher et al., 2003a; Reschovsky et al., 2011; Welch et al., 1993; Wennberg and Cooper, 1999)
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fall in the middle range of Medicare spending when age, sex, and health status are taken into account. Results of the Cluster 3 and 4 regressions in Table 2-8 demonstrate that when health status is excluded from the model, other demographic variables, such as race or income, provide little explanatory power.14 Cluster 5 results confirm that race and income have a trivial effect on reducing variation once beneficiary health status is included in the model.
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, health status, employer and Medicare analysis. insurance characteristics, and market-level factors.
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As a result, the regional variation attributable to patient preferences and access to care or to differences in physician discretion and practice patterns, for example, could not be measured. Patient access to care has been shown to influence Medicare spending and utilization.
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18 856 1,056 16 14 12 10 8 6 4 2 under 700 701–725 726–750 751–775 776–800 801–825 826–850 851–875 876–900 901–925 926–950 951–975 976–1,000 1,001–1,025 1,026–1,050 1,051–1,075 1,076–1,100 1,101–1,125 1,126–1,150 1,151–1,175 1,176–1,200 over 1,200 (2b) Medicare Spending, Adjusted for Age, Sex, and Health Status ($PMPM)
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SOURCE: PHE, 2013. variation in patient preferences and geographic variation in health care utilization, while others have established that patient preferences account for little regional variation in health care spending (Anthony et al., 2009; Barnato et al., 2007)
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In fact, after controlling for all factors measurable within the data used for this analysis, a large amount of variation remains unexplained. INFLUENCE OF POST-ACUTE CARE SERVICES ON REGIONAL VARIATION IN MEDICARE To determine the extent to which variation in particular health care services contributes to total variation in Medicare expenditures, the committee disaggregated price-standardized, risk-adjusted Medicare spending into seven types of services: (1)
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Figure 2-5a shows the total Medicare utilization across HRRs that remains unexplained after adjustment for input prices, demographics, and health status, while Figures 2-5b through 2-5h display the unexplained variation in utilization in specific service categories only. These residual charts suggest that variation in post-acute care utilization accounts for a large portion of the unexplained variation in total utilization.
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Although the amount of annual Medicare spending due to fraud is, by definition, unknown (Goldman, 2012) , recent estimates indicate that Medicare and Medicaid paid as much as $98 billion in fraudulent and abusive charges in 2011 (Berwick and Hackbarth, 2012)
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Post-Acute Care Monthly Adjusted Differences from the National Mean of Spending Across HRR the National Mean of Spending Across HRR $500 $500 $400 $400 $300 $300 $200 $200 $100 $100 $0 $0 -$100 -$100 -$200 -$200 (c) Acute Care Monthly Adjusted Differences from (d)
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Procedures Monthly Adjusted Differences from the National Mean of Spending Across HRR the National Mean of Spending Across HRR FIGURE 2-5a–h Medicare service category utilization (monthly cost residual) by hospital referral region (HRR)
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Conclusion 2.4. Variation in total Medicare spending across geographic areas is driven largely by variation in the utilization of post-acute care services, and to a lesser extent by variation in the utilization of acute care services.
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The results are not adjusted for differences in beneficiaries' health status or prices. In March 2007, the U.S.
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, greater emphasis has been placed on studying relationships among quality, overall spending, and "high value." In Chapter 4, the committee evaluates the use of a geographically based value index and further explores the empirical interrelationships among quality of care and health care spending and utilization across Medicare and private payers. RESEARCH AGENDA This study represents the largest-scale analysis of geographic variation in health care spending in the United States, covering Medicare and representative private payer populations.
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To date, many nationally established quality composite measures have been designed to measure process and outcomes in the Medicare population and are not necessarily applicable to privately insured beneficiaries. Although in its estimate of total health care spending in the United States, PHE attempted to include estimates from Medicare, Medicaid, and commercial payers, as well as the uninsured, the generalizability of this analysis is limited.
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2004. Medicare spending, the physician workforce, and benefi ciaries' quality of care.
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2010. Geo graphic correlation between large-firm commercial spending and Medicare spending.
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2003b. The implications of regional variations in Medicare spending.
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2012. Health care spending and the Medicare program.
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2010. Clarifying sources of geo graphic differences in Medicare spending.
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