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

Practice patterns vary widely for similar patients Source: Wennberg and Gittelsohn, Science 183(4117): Surgical Procedure Lowest Two Areas Entire State Highest Two Areas Tonsillectomy Appendectomy Males Prostatectomy Prostatectomy Females Cholecystectomy Cholecystectomy Hysterectomy Hysterectomy Mastectomy Mastectomy Number of Procedures per 10,000 for 13 Vermont hospital service areas, 1969

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

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

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?

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

Empirical implications Spending im = X i *  1 + Z im *  2 + M m +  i M m 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

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

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)

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

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

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 McLaughlin et al, 1989 Source: McLaughlin et al, 1989

Physician Composition and Spending Source: Chernew et al, 2009

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

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

Commercial vs. Medicare Correlation Level(2006)Growth Hospital and Physician Reimbursements (2006) -.17 (p<0.01) (p<0.01).20(p<.01) Per Capita Inpatient Days (2004) 0.59 (p<.01) (p<.01).13(P<.01) Source: Chernew et al, 2010

Concentration and spending Population Hospital Concentration Top third Commercial spending markets, , Bottom third commercial spending markets, ,081, Top third Medicare spending markets, ,365, Bottom third Medicare spending markets, , Source: Chernew et al, 2010

Provider variation more complex Selection issues more salient –Health status adjustments difficult Attribution issues are complex