Managed Care and Medicare Expenditures

1. Managed Care and Medicare Expenditures Health Economics Interest Group Seattle, WA Michael Chernew Phil DeCicca Robert Town June 24, 2006

2. Overview • Background • Data and Analysis Sample • Empirical Strategy • Results • Tentative Conclusions

3. Background • Investigate the existence and extent of managed care “spillovers” in Medicare • We examine the impact of county-specific Medicare HMO penetration on spending of FFS Medicare beneficiaries • In particular, we try to identify the impact of within-county changes in MC HMO penetration on spending

4. Background (con’t) • Existence of spillovers assumes “connected” markets • Many pathways for spillovers • Increased competition • Changes in structure of delivery system • Changes in practice patterns • Previous work suggests spillovers exist • Baker (1997, 1999); Bundorf et al. (2004)

5. Data and Sample Info • Medicare Current Beneficiary Study (MCBS) • Cost and Use Files, 1994 to 2001 • Analysis Sample • Exclude individuals administered a “Facility” interview • Exclude individuals enrolled in HMOs • Exclude counties that contribute less than two cases per year, on average • Yields 60,067 cases from 293 counties • Including 2.5% with zero expenditure

6. Key Variables • MCBS Variables • Per-Person Total Annual Spending • Various “Broad” Measures of Utilization • Covariates including “usual suspects” and more detailed measures of health status • County-level Variables • Medicare HMO Penetration • Payment Rates (AAPCC)

7. Empirical Strategy • We Estimate Models of the Form: Log(Spend)ict=δ(MCHMO)ct+Xβ+μc+αt+εict • X depends on specification • μ and α are County and Year effects • δ<0 implies the existence of spillover

8. Empirical Strategy (Details) • Two Models Estimated—“Short” & “Long” • Estimate models with and without “zeroes” • Models estimated via OLS and IV • We use the payment rate (AAPCC) and its square as instruments for HMO penetration • As will see, strong relationship between payment rate and penetration

9. Estimates • In general, OLS estimates practically small • For example, • Largest estimated effect suggests that a one percentage point increase in MC HMO penetration leads to an 0.3 percent decrease in spending by FFS beneficiaries • Reduction ranges from 0.2 to 0.3 percent, depending on specification

10. Estimates (con’t) • OLS estimates, however, may be biased • E.g., HMOs may enter areas based on cost growth or characteristics correlated with it • Sorting into high cost growth areas would tend to attenuate measured spillover effects • Sorting into low cost growth areas would tend to overstate the magnitude of spillovers

11. Estimates (con’t) • Overview of Remaining Estimates • IV (First Stage) • IV (Structural Equation) • Utilization Models • Sensitivity Checks • Where are savings being generated? • High-Use vs. Low-Use Beneficiaries

12. Estimates (con’t)

13. Estimates (con’t)

14. Estimates (con’t) • Interpretation • Estimates suggest a one pct. point increase in HMO penetration leads to between 1.3 and 1.8 percent reduction in spending by FFS beneficiaries • (Perceived) Magnitudes • Estimates perhaps not as large as seem when consider that a one pct point increase in penetration is off a base of 9-10 pct pts • Many IV Diagnostics • All suggest that IV strategy is legitimate

15. Estimates (con’t) • Next Step: Estimate Utilization Models • Here, we use “broad” utilization categories as dependent variables • We find increases in MC HMO penetration reduce “Inpatient” and “Outpatient” events, especially on intensive margins • Supports our spending estimates which suggest non-trivial spillover

16. Estimates (con’t) • Next: Check Sensitivity of Main Estimates • Est. models without CA and FL counties • Est. models with “Supplemental” HI controls • Compare effect on “High” vs. “Low-Use” • Define “high-use” as FFS with ≥1 “chronic” condition (CC) & “low-use” as those with no CC’s. • CC’s include: Diabetes, HBP, Arthritis, Heart Disease and “Other” Heart Problems • Est. Spending Models Separately for Two Groups

17. Estimates (con’t)

18. Estimates (con’t) • Chronic Conditions Models Details • Results suggest main spending estimates driven by relatively high-use individuals • In particular, estimates imply 1.6 to 2.3 percent drop in FFS spending for high-use and virtually no effect for those without CC’s • Perhaps not too surprising as high-use individuals’ spending ≈ 2X low-users

19. Summary • We find evidence MC HMO penetration reduces spending by FFS beneficiaries • Evidence that MC HMO penetration reduces utilization supports spending reductions • Spending reductions seem to be derived from high-use individuals

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