Geographic Variation in Health Care

1. Geographic Variationin Health Care Presentation for: IOM Panel on Geographic Variation in Healthcare Spending and Promotion of High-Value Care Michael Chernew

2. Practice patterns vary widely for similar patients Number of Procedures per 10,000 for 13 Vermont hospital service areas, 1969 Source: Wennberg and Gittelsohn, 1973. Science 183(4117): 1102-1108.

3. Voluminous literature • 4x variation in cesarean delivery (Baicker et al 2006) • 1.6x variation in antibiotic fills PMPY, 5th-95th percentile (Steinman 2009) • 13.5x variation in odds ratio for type of vascular access for dialysis patients (Hirth et al 1996)

4. Variation in spending as well as in use Source: Fisher et al, 2009

5. Why is this important? • Changes beliefs away from notion that physicians are always right • Weakens notion that practice of medicine is purely science • Quantifies potential waste in the system • Not sure how to best get rid of ‘waste’ • Translating the population based results to the bedside is hard • Helps identify ‘efficient’ markets • Is Minneapolis more efficient than Miami?

6. Explaining variation Explanations that we typically accept Explanations with uncertain implications Explanations that generate concern • Health Status • Health behaviors • Patient Preferences • Input Prices • Income • Cost shifting • Physician supply • Infrastructure • Insurance • Output prices • Beliefs • Greed • Culture

7. Empirical implications Spendingim = Xi*b1 + Zim *b2 + Mm + ei Mm denotes mean spending at the market level after adjusting for personal traits (X) and market traits (Z) • Adding X’s and Z’s will shrink variation in M’s if: • X or Z affects spending AND varies across markets

8. Health status • Clearly important at individual level • Varies across markets (implying important at market level) • Hard to measure • Conceptually circular • If more treatment improves health, areas with aggressive practice styles will seem healthier

9. Controversy in measuring health status • More aggressive places code more illness, so populations appear ‘sicker’ • Are they really sicker or do they just code more? • If just coding, the health status adjustments ‘over’ adjust. • People who move have ‘increases’ in measured illness • Moving to area with 1 quartile higher spending associated with a 5.9% increase in HCC score (Song et al, 2010)

10. Other approaches to health status adjustments • Look at specific diseases • Examine end of life

11. Variation in price adjusted spending by Medicare beneficiaries. Source, Sutherland et al. 2010, NEJM

12. SES matters • 50% of variation in discharges explained by SES, crude health status measures, and physician supply measures • Robust by area definition (county vs health care market) • Robust to methods Source: McLaughlin et al, 1989

13. Physician Composition and Spending Source: Chernew et al, 2009

14. What do we get for extra spending Source: Baicker and Chandra, Health Affairs (April 2004)

15. Limitations • Tendency for Medicare focus • Cost shifting • Erroneous inference about areas • Salience of LTC services and maybe fraud • Uncertain policy solutions • Area focus obscures within area provider heterogeneity

16. Commercial vs. Medicare Correlation Source: Chernew et al, 2010

17. Concentration and spending Source: Chernew et al, 2010

18. Provider variation more complex • Selection issues more salient • Health status adjustments difficult • Attribution issues are complex