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Health Care Health Care and the and the
Affluence Poverty NexusAffluence Poverty Nexus
Richard A. Cooper, M.DRichard A. Cooper, M.D..Leonard Davis Institute of Health EconomicsLeonard Davis Institute of Health Economics
University of PennsylvaniaUniversity of Pennsylvania
WCMS Foundation Francis P. Rhoades, MD Memorial Lecture
March 26, 2010
Geographic Variation in Health CareGeographic Variation in Health CareDARTMOUTH ATLASDARTMOUTH ATLASPeter Peter OrszagOrszag, 2007, 2007
Three Myths of Geography and Poverty
1. Hospital Referral Regions: Variation in health care Variation in health care utilization among hospital referral regions (HRRs) utilization among hospital referral regions (HRRs) is due to the overuse of supplyis due to the overuse of supply--sensitive services. sensitive services.
2. 2. Academic Medical CentersAcademic Medical Centers: Variation in physician : Variation in physician inputs among academic medical centers is a sign of inputs among academic medical centers is a sign of waste and inefficiency.waste and inefficiency.
3. 3. HRR QuintilesHRR Quintiles: If the entire US could achieve : If the entire US could achieve spending equivalent to the lowestspending equivalent to the lowest--spending region, spending region, 30% of health care spending could be saved.30% of health care spending could be saved.
“Regional differences in poverty and incomeexplain almost none of the observed variation.”
Skinner and Fisher 2009
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.
The Inconvenient TruthThe Inconvenient Truth**************************************************
Regional Poverty
Poverty, 20000 - 20%
20 - 40%40 - 60%60 - 80%80 - 100%
Urban Poverty
PhiladelphiaPhiladelphiaIncome = 118% of US Average
The Bruton CenterThe University of Texas at Dallas
BaltimoreBaltimoreIncome = 114% of US Average
DetroitDetroitIncome = 96% of US Average
““Unexplained geographic variation is due to Unexplained geographic variation is due to the overuse of supplythe overuse of supply--sensitive specialty services.sensitive specialty services.””
Myth #1Myth #1
Milwaukee HRRWisconsin
MilwaukeeMilwaukee
0
100
200
300
400
500
600
day/1000_1864Days per 1,000
MilwaukeeMilwaukee
Hospital Days in Wisconsin HRRsHospital Days in Wisconsin HRRs
Wisconsin HRRsWisconsin HRRs
HospitalDaysper
1,000
30% excess utilization
Milwaukee HRRMilwaukee HRRPer Capita Income = 108% of US Average
The Bruton CenterThe UT at Dallas
Milwaukee is the third most
segregated city in the nation
R2 = 0.65
0
250
500
750
1,000
$- $10,000 $20,000 $30,000 $40,000 $50,000
Per Capita Income
Day
s pe
r 1,0
00Milwaukee
Hospital Days vs. Per Capita Income
4-fold
Power
Poor
Rich
ZIP Codes -
Ages 18-64
“Poverty Corridor”42% of total population92% of Black population74% of Latino population33% of income
MilwaukeeMilwaukee’’s s ““Poverty CorridorPoverty Corridor””
0
100
200
300
400
500
600
day/1000_1864Days per 1,000
MilwaukeeMilwaukee
Hospital Utilization in Wisconsin HRRsHospital Utilization in Wisconsin HRRs
Wisconsin HRRsWisconsin HRRs
HospitalDaysper
1,000
Poverty CorridorPoverty Corridor
Milwaukee minus Milwaukee minus ““Poverty CorridorPoverty Corridor””
“Preventable”
Hospital Admissions Milwaukee
0
2
4
6
8
Diabetes Asthma COPD CHF
Ratio of Poorest
toWealthiest
Zones
6-fold
1999
Los Angeles HRR
Los Angeles
The Bruton CenterThe UT at Dallas
Los Angeles CountyLos Angeles County7.5 million adults
Average Income = 108% of US Average
R2 = 0.61
0
400
800
1,200
$- $50,000 $100,000 $150,000 $200,000 $250,000Mean Household Income
Day
s P
er 1
,000
Los Angeles Hospital Days Per Capita vs. Household Income
4-fold
Poor
Rich
ZIP Codes -
Ages 45-64
Poverty Zone 1.8 million adults (25%)
Poverty Zone25%
Watts
Poverty Core
Poverty Core375,000 (5%)
5%
0
400
800
1,200
$- $50,000 $100,000 $150,000 $200,000 $250,000Mean Household Income
Day
s P
er 1
,000
Los Angeles Hospital Days vs. Household Income
ZIP Codes -
Ages 45-64
Household Income >$100,0001.4 million
(18%)
0%
50%
100%
150%
200%
All Ages .
Day
s pe
r 1,0
00,
% in
ZIP
Cod
es w
ith M
HI >
$100
KHousehold Income >$100KPoverty CorePoverty Zone w/o CoreTotal County
Hospital Days in Los Angeles Per Cent of Days in ZIPs
with Household Income >$100,000
Days per 1,000 in all of LA County
are 36% greater thanin ZIPs >$100K
Days per 1,000 in the Poverty Core are double the rate
of ZIPs >$100K
Hospital Days Among Eight California Counties Adults (18-64)
0
75
150
225
300
Total Adult Income >$100K
Days Per
1,000
LOS ANGELES
SACRAMENTO
SAN FRANCISCO
ALAMEDA
SAN DIEGO
ORANGE
SAN MATEO
MARIN
Variation Among
All Adults
Hospital Days Among Eight California Counties Adults (18-64)
ZIP Code Household Income
0
75
150
225
300
Total Adult Income >$100K
Days Per
1,000
LOS ANGELES
SACRAMENTO
SAN FRANCISCO
ALAMEDA
SAN DIEGO
ORANGE
SAN MATEO
MARIN
Variation Among the
Wealthiest
Hospital Days in California Counties Adults (18-64)
ZIP Code Household Income
0
75
150
225
300
Total Adult Income >$100K
Days Per
1,000
LOS ANGELES
SACRAMENTO
SAN FRANCISCO
ALAMEDA
SAN DIEGO
ORANGE
SAN MATEO
MARIN
34% greater use of hospital days
below $100K income
“Unexplained variation”
is explained by poverty.
Conclusion
Myth #2Myth #2DartmouthDartmouth’’s Quintiless Quintiles
““The 30% SolutionThe 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
Lowest Medicare
Highest Medicare
Medicare SpendingMedicare Spending
BostonChicagoDetroitHoustonLos Angeles McAllenMiamiPhiladelphia PittsburghNewarkNew OrleansNew YorkPensacolaTexakanaWashington
WashingtonOregonIdahoUtahWyomingMontanaNebraskaNorth DakotaSouth DakotaIowaMinnesotaWisconsin...except for their
major cities
The Quintiles Study
Compare With
Total Health Care Spending Total Health Care Spending
Lowest Total
Highest Total
No DifferencesNo Differences
11--year Mortality year Mortality No differences No differences 55--year Mortalityyear Mortality
Lowest better; others the same Lowest better; others the same
Functional status Functional status No differencesNo differencesSatisfactionSatisfaction
No consistent differencesNo consistent differences
Access Access No better (one slightly worse)No better (one slightly worse)**
QualityQuality
No better on most measures, No better on most measures, worse for some preventive careworse for some preventive care
** ““Trouble seeing a doctorTrouble seeing a doctor”” 3.1% vs. 2.5% 3.1% vs. 2.5%
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”
The 30% solution is a mirage.The 30% solution is a mirage.
ConclusionConclusion
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
Myth #3
BostonChicagoDetroit (2)Houston (2)Los Angeles
Philadelphia (3)PittsburghNewarkNew York (2)Washington
15 Cities with 15 Cities with“Highest Efficiency”
“Lowest Efficiency”
Hospitals HospitalsCincinnatiCincinnatiIndianapolis Indianapolis Salt Lake CitySalt Lake CityAugusta Augusta DartmouthDartmouthMadison WIMadison WIRichmond VARichmond VATemple TXTemple TXRochester NYRochester NYJackson MSJackson MSColumbia MOColumbia MOLexington KYLexington KYOklahoma CityOklahoma CityAtlanta (Grady)Atlanta (Grady)Rochester MN (Mayo)Rochester MN (Mayo)
15 Cities with 15 Cities with“Highest
Efficiency”
“Lowest Efficiency”
Hospitals Hospitals
250,000250,000 PopulationPopulation 1,500,0001,500,000
22%22% Blacks + LatinosBlacks + Latinos 51%51%
8%8% Seniors in povertySeniors in poverty 17%17%
Medicare Spending/Decedent (Last 2 years of life, 2001-2005)
Douglas Wood, MD, Mayo Clinicfrom Dartmouth Atlas, Appendix Table 1.
Mayo Mayo ––
1515thth
Most Most ““EfficientEfficient””
Medicare Spending During Last 2 Years of Life Medicare Spending During Last 2 Years of Life
20012001--20052005
SinaiSinai--Grace Hospital, DetroitGrace Hospital, Detroit 99thth
Least Least ““EfficientEfficient””
AMCAMC
““Occupying a campus of red brick buildings amid Occupying a campus of red brick buildings amid abandoned houses, checkabandoned houses, check--cashing stores and wig cashing stores and wig shops on the cityshops on the city’’s West Side, Sinais West Side, Sinai--Grace is a Grace is a classic urban hospital. It has eight hundred classic urban hospital. It has eight hundred physicians, seven hundred nurses and two physicians, seven hundred nurses and two thousand other medical personnel to care for a thousand other medical personnel to care for a population with the lowest median income of any population with the lowest median income of any city in the country.city in the country.””
AtulAtul
GawandeGawandeThe New YorkerThe New Yorker
0
3
6
9
12
15
18
University of California HospitalsUniversity of California Hospitals
Days
Dartmouth UCLALast 6 Months of Life 6 Months of Severe
CHF .
Unexplained differences
Dartmouth:The volume of care during the last 6 months of life varies among University of California hospitals by 45%.
45%
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 in the severity of
patientpatient’’s illnesses.s illnesses. Dartmouth Atlas OnlineDartmouth Atlas Online
Frequently asked questionFrequently asked question::
But how do you ensure that patients were not more But how do you ensure that patients were not more severely ill at some hospitals than at others?severely ill at some hospitals than at others?
0
3
6
9
12
15
18
Days
Last Six Months Six Months of life with CHF of life with severe CHF
UCLA All patients (dead or not)
adjusted for income and illness
Circulation, Cardiovascular Quality and OutcomesCirculation, Cardiovascular Quality and Outcomes, 2009, 2009
University of California HospitalsUniversity of California Hospitals
DartmouthSimilarly dead;
similarly ill
Remarkablysimilar
Unexplained differences
VVariation is due to variation in patientsariation is due to variation in patients’’ income and burden of disease.income and burden of disease.
ConclusionConclusion
$0
$2,500
$5,000
$7,500
$10,000
<$10,000 $10-15,000
$15-20,000
$20-25,000
$25-50,000
>$50,000
Income Groups
Annual Medicare Spending
Medicare Spending and IncomeMedicare Spending and IncomeNational Medicare Spending by Income GroupsNational Medicare Spending by Income Groups
Sutherland, Fisher, Skinner, 2009, from CMS
$0
$2,500
$5,000
$7,500
$10,000
<$10,000 $10-15,000
$15-20,000
$20-25,000
$25-50,000
>$50,000
Income Groups
Annual Medicare Spending
Patients, Not GeographyPatients, Not GeographyNational Medicare Spending by Income GroupsNational Medicare Spending by Income Groups
34% of Medicare Expenditures
Health Care Reform Has Taken OffHealth 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!
An incentive payment of $400M for providers in the 25% of counties that have the lowest Medicare expenditures
Payments for Payments for ““Efficient CountiesEfficient Counties””
Lowest Spending
Highest Spending
Medicare per Enrollee
Incentive payments of up to 2% for physicians and Incentive payments of up to 2% for physicians and hospitals that attain hospitals that attain ““efficiency standardsefficiency standards””
developed by the Secretary.developed by the Secretary.
Payments for Payments for ““ValueValue””
Advocacy states
Other Low Medicare States
Penalties for Hospital ReadmissionsPenalties of 3% to 5% for hospitals withPenalties of 3% to 5% for hospitals with
““excessexcess””
levels of levels of ““preventablepreventable””
readmissions.readmissions.
Reductions in Disproportionate Share PaymentsReductions in Disproportionate Share Payments
Lowest DSH
Highest DSH
$20B reduction in DSH over 9 years, $10B yearly therafter
“The IOM will recommend strategies for addressing geographic variation by altering payments for physicians and hospitals.”
Institute of Medicine (IOM) Institute of Medicine (IOM) Study of Geographic VariationStudy of Geographic Variation
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.
ConclusionsConclusions**************************************************
ThoTho' a man may be in doubt of what he know,' a man may be in doubt of what he know, very quickly he will fight to prove very quickly he will fight to prove
that what he does not know is so.that what he does not know is so.
King of SiamKing of Siam
PHYSICIANS AND HEALTH CARE REFORM
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