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Cooper Health Care And The Affluence Poverty Nexus Detroit

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  • 1. Health Care and the Affluence Poverty Nexus Richard A. Cooper, M.D. Leonard Davis Institute of Health Economics University of Pennsylvania WCMS Foundation Francis P. Rhoades, MD Memorial Lecture March 26, 2010
  • 2. Geographic Variation in Health Care DARTMOUTH ATLAS Peter Orszag, 2007
  • 3. Three Myths of Geography and Poverty 1. Hospital Referral Regions: Variation in health care utilization among hospital referral regions (HRRs) is due to the overuse of supply-sensitive services. 2. Academic Medical Centers: Variation in physician inputs among academic medical centers is a sign of waste and inefficiency. 3. HRR Quintiles: If the entire US could achieve spending equivalent to the lowest-spending region, 30% of health care spending could be saved.
  • 4. “Regional differences in poverty and income explain almost none of the observed variation.” Skinner and Fisher 2009
  • 5. The Inconvenient Truth ************************* Geographic variation in health care is principally the result of geographic differences in poverty. Payment changes made according to geographic norms will harm to low-income patients and the providers who care for them.
  • 6. Regional Poverty Poverty, 2000 0 - 20% 20 - 40% 40 - 60% 60 - 80% 80 - 100%
  • 7. Urban Poverty
  • 8. Philadelphia Income = 118% of US Average The Bruton Center The University of Texas at Dallas
  • 9. Baltimore Income = 114% of US Average
  • 10. Detroit Income = 96% of US Average
  • 11. Myth #1 “Unexplained geographic variation is due to the overuse of supply-sensitive specialty services.”
  • 12. Milwaukee Wisconsin Milwaukee HRR
  • 13. Hospital Days in Wisconsin HRRs 600 500 Milwaukee Hospital 30% excess 400 Days utilization per 1,000300 200 100 0 day/1000_1864 Days per 1,000 HRRs Wisconsin
  • 14. Milwaukee HRR Per Capita Income = 108% of US Average Milwaukee is the third most segregated city in the nation The Bruton Center The UT at Dallas
  • 15. Milwaukee Hospital Days vs. Per Capita Income 1,000 Poor Days per 1,000 750 4-fold 500 250 Rich 2 R = 0.65 0 $- $10,000 $20,000 $30,000 $40,000 $50,000 Per Capita Income ZIP Codes - Ages 18-64 Power
  • 16. Milwaukee’s “Poverty Corridor” “Poverty Corridor” 42% of total population 92% of Black population 74% of Latino population 33% of income
  • 17. Hospital Utilization in Wisconsin HRRs 600 Poverty Corridor 500 Milwaukee Hospital Days 400 Milwaukee minus “Poverty Corridor”  per 1,000300 200 100 0 day/1000_1864 Days per 1,000 HRRs Wisconsin
  • 18. “Preventable” Hospital Admissions Milwaukee 8 Ratio of 6 Poorest to 4 6-fold Wealthiest Zones 2 0 Diabetes Asthma COPD CHF 1999
  • 19. Los Angeles Los Angeles HRR
  • 20. Los Angeles County 7.5 million adults Average Income = 108% of US Average The Bruton Center The UT at Dallas
  • 21. Los Angeles Hospital Days Per Capita vs. Household Income 1,200 Poor Days Per 1,000 800 4-fold 400 2 R = 0.61 Rich 0 $- $50,000 $100,000 $150,000 $200,000 $250,000 Mean Household Income ZIP Codes - Ages 45-64
  • 22. Poverty Zone 1.8 million adults (25%) Poverty Zone 25%
  • 23. Poverty Core 375,000 (5%) Watts Poverty Core 5%
  • 24. Los Angeles Hospital Days vs. Household Income ZIP Codes - Ages 45-64 1,200 Days Per 1,000 800 Household Income >$100,000 400 1.4 million (18%) 0 $- $50,000 $100,000 $150,000 $200,000 $250,000 Mean Household Income
  • 25. Hospital Days in Los Angeles Per Cent of Days in ZIPs with Household Income >$100,000 % in Z IP C o d e s w ith M H I > $ 1 0 0 K Household Income >$100K 200% Poverty Core Days per 1,000 Poverty Zone w/o Core D a y s p e r 1 ,0 0 0 , in the Poverty Core 150% are double the rate Total County of ZIPs >$100K Days per 1,000 100% in all of LA County are 36% greater than in ZIPs >$100K 50% 0% All Ages .
  • 26. Hospital Days Among Eight California Counties Adults (18-64) 300 Variation LOS ANGELES Among SACRAMENTO 225 All Adults SAN FRANCISCO Days ALAMEDA Per 150 SAN DIEGO 1,000 ORANGE 75 SAN MATEO MARIN 0 Total Adult Income >$100K
  • 27. Hospital Days Among Eight California Counties Adults (18-64) ZIP Code Household Income 300 Variation Among the LOS ANGELES Wealthiest SACRAMENTO 225 SAN FRANCISCO Days ALAMEDA Per 150 SAN DIEGO 1,000 ORANGE SAN MATEO 75 MARIN 0 Total Adult Income >$100K
  • 28. Hospital Days in California Counties Adults (18-64) ZIP Code Household Income 300 34% greater use of hospital days below $100K income LOS ANGELES SACRAMENTO 225 SAN FRANCISCO Days ALAMEDA Per 150 SAN DIEGO 1,000 ORANGE SAN MATEO 75 MARIN 0 Total Adult Income >$100K
  • 29. Conclusion “Unexplained variation” is explained by poverty.
  • 30. Myth #2 Dartmouth’s Quintiles “The 30% Solution” “If the entire nation could bring its costs down to match the lower-spending regions, the country could cut perhaps 20 to 30 percent off its health care bill, a tremendous saving.” New York Times, 2007
  • 31. Medicare Spending Lowest Medicare Highest Medicare
  • 32. The Quintiles Study Compare With Boston Washington Chicago Oregon Detroit Idaho Houston Utah Los Angeles Wyoming McAllen Montana Miami  Nebraska Philadelphia North Dakota Pittsburgh South Dakota Newark Iowa New Orleans New York Minnesota Pensacola Wisconsin Texakana ...except for their Washington major cities
  • 33. Total Health Care Spending Highest Total Lowest Total
  • 34. No Differences 1-year Mortality No differences 5-year Mortality Lowest better; others the same Functional status No differences Satisfaction No consistent differences Access No better (one slightly worse)* Quality No better on most measures, worse for some preventive care * “Trouble seeing a doctor” 3.1% vs. 2.5%
  • 35. Dartmouth Doubletalk Outcomes were no different because differences could not be discerned. But since outcomes were no better, spending in “high spending” “regions” must have been wasted. And because this “wasted spending” could not be explained (by them), it must have been due to an over-supply of specialists providing low value care. “Waste” “Inefficiency” “Supply-sensitive” “Value”
  • 36. Conclusion The 30% solution is a mirage.
  • 37. Myth #3 Academic medical centers vary by more than 3-fold in the quantity of physician services at the end of life. Given this apparent inefficiency, the supply pipeline is sufficient to meet future needs for physicians through 2020.” Goodman et al, 2006
  • 38. 15 Cities with 15 Cities with “Highest Efficiency” “Lowest Efficiency” Hospitals Hospitals Cincinnati Indianapolis Boston Salt Lake City Chicago Augusta Detroit (2) Dartmouth Houston (2) Madison WI Los Angeles Richmond VA Philadelphia (3) Temple TX Pittsburgh Rochester NY Newark Jackson MS New York (2) Columbia MO Washington Lexington KY Oklahoma City Atlanta (Grady) Rochester MN (Mayo)
  • 39. 15 Cities with 15 Cities with “Highest Efficiency” “Lowest Efficiency” Hospitals Hospitals 250,000 Population 1,500,000 22% Blacks + Latinos 51% 8% Seniors in poverty 17%
  • 40. Medicare Spending/Decedent Mayo – 15 Most “Efficient” th (Last 2 years During Last 2 Years of Life Medicare Spending of life, 2001-2005) Douglas Wood, MD, Mayo Clinic 2001-2005 from Dartmouth Atlas, Appendix Table 1.
  • 41. Sinai-Grace Hospital, Detroit 9th Least “Efficient” AMC “Occupying a campus of red brick buildings amid abandoned houses, check-cashing stores and wig shops on the city’s West Side, Sinai-Grace is a classic urban hospital. It has eight hundred physicians, seven hundred nurses and two thousand other medical personnel to care for a population with the lowest median income of any city in the country.” Atul Gawande The New Yorker
  • 42. University of California Hospitals 18 Dartmouth: 15 The volume of care  during the last 6 months 45% 12 of life varies among  9 University of California Days hospitals by 45%. Unexplained 6 differences 3 0 Dartmouth UCLA Last 6 Months of Life 6 Months of Severe CHF .
  • 43. Frequently asked question: But how do you ensure that patients were not more severely ill at some hospitals than at others? Dartmouth: The study focused only on patients who died, so we could be sure that all patients were similarly ill. By definition, the prognosis was identical – all were dead. Therefore, variations among hospitals cannot be explained by differences in the severity of patient’s illnesses. Dartmouth Atlas Online
  • 44. University of California Hospitals 18 Dartmouth UCLA Similarly dead; All patients (dead or not) 15 similarly ill adjusted for income and illness 12 9 Days Unexplained Remarkably 6 differences similar 3 0 Last Six Months Six Months of life with CHF of life with severe CHF Circulation, Cardiovascular Quality and Outcomes, 2009
  • 45. Conclusion Variation is due to variation in patients’ income and burden of disease.
  • 46. Medicare Spending and Income National Medicare Spending by Income Groups $10,000 $7,500 Annual Medicare $5,000 Spending $2,500 $0 <$10,000 $10- $15- $20- $25- >$50,000 15,000 20,000 25,000 50,000 Income Groups Sutherland, Fisher, Skinner, 2009, from CMS
  • 47. Patients, Not Geography National Medicare Spending by Income Groups $10,000 34% of Medicare Expenditures $7,500 Annual Medicare $5,000 Spending $2,500 $0 <$10,000 $10- $15- $20- $25- >$50,000 15,000 20,000 25,000 50,000 Income Groups
  • 48. Health Care Reform Has Taken Off Dorothy to the Wizard: Come back! Come back! Don't leave without me! Come back! Wizard of Orszag: I can't come back! I don't know how it works! Good-bye folks!
  • 49. Payments for “Efficient Counties” An incentive payment of $400M for providers in the 25% of counties that have the lowest Medicare expenditures Medicare per Enrollee Lowest Spending Highest Spending
  • 50. Payments for “Value” Incentive payments of up to 2% for physicians and hospitals that attain “efficiency standards” developed by the Secretary. Advocacy states Other Low Medicare States
  • 51. Penalties for Hospital Readmissions Penalties of 3% to 5% for hospitals with “excess” levels of “preventable” readmissions.
  • 52. Reductions in Disproportionate Share Payments $20B reduction in DSH over 9 years, $10B yearly therafter Lowest DSH Highest DSH
  • 53. Institute of Medicine (IOM) Study of Geographic Variation “The IOM will recommend strategies for addressing geographic variation by altering payments for physicians and hospitals.”
  • 54. Conclusions ************************* Geographic variation in health care is principally related to geographic differences in poverty. Payment changes made according to geographic norms would result in major harm to low-income patients and the providers who care for them.
  • 55. Tho' a man may be in doubt of what he know, very quickly he will fight to prove that what he does not know is so. King of Siam
  • 56. Visit http://buzcooper.com PHYSICIANS AND HEALTH CARE REFORM Commentaries and Controversies