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
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