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Managed Care and Medicare Expenditures

Managed Care and Medicare Expenditures. Health Economics Interest Group Seattle, WA Michael Chernew Phil DeCicca Robert Town June 24, 2006. Overview. Background Data and Analysis Sample Empirical Strategy Results Tentative Conclusions. Background.

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Managed Care and Medicare Expenditures

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  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

  20. THE END

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