Disparities in Inpatient Quality of
Care Measures by Race and Ethnicity
____________________________
Academy Health
June 27, 2005
Boston, MA
Romana Hasnain-Wynia, Ph.D.
Health Research and Educational Trust
Co-authors
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•
•
•
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David W.Baker, MD, MPH
Raj Behal, MD, MPH
Joe Feinglass, PhD
David Nerenz, PhD
Joel S. Weissman, PhD
PROJECT
Linking Race and Ethnicity Data to
Inpatient Quality of Care Measures
Funding: The Commonwealth Fund
Background
• Hospital Quality Alliance
– One of many efforts in CMS’s overall Hospital Quality
Initiative to foster hospital quality improvement
through a variety of quality measurement and
improvement opportunities
– >4,000 hospitals participating
• Focus on Three Conditions
– Acute Myocardial Infarction (AMI)
– Heart Failure
– Pneumonia
Background
• Evidence indicates that quality improvement
efforts, when linked to data on race and ethnicity,
can reduce disparities in care and improve quality
– Mukamel and Mushlin “Quality of Care Information Makes a
Difference: An Analysis of Market Share and Price Changes
Following Publication of the New York State Cardiac Surgery
Report Care.” Medical Care; 36:1998
– Schneider and Lieberman “Publicly Disclosed Information
About the Quality of Healthcare: Response to the US Public.”
Quality in Health Care. 2001
Background
• Health care disparities should be brought into the
mainstream quality assurance and continuous
quality improvement discussions
– Fiscella, et al. “Inequality in Quality: Addressing
Socioeconomic, Racial, and Ethnic Disparities in Health Care.
JAMA. 2000
Data Source
• University Health System Consortium
(UHC)
– UHC is an alliance of academic health centers in the
United States aimed at improving performance
levels in clinical, operational, and financial areas.
– UHC is collecting the quality measures for the three
conditions with patient race and ethnicity
information for 123 hospitals.
– We are working with UHC to conduct analyses.
– >7,000 cases per condition
Methods
• Create performance quintiles
• Present data by % racial minorities seen at
hospitals in each quintile
• Exclusion if <50 total cases or <15 minority cases
• Develop multivariate models
– Model 1: unadjusted
– Model 2: adjusted for individual characteristics,
including co-morbidities, payer, age, gender
– Model 3: Model 2 + adjusted for organizational effects
(between hospital variation)
Performance quintiles by
% minority patients seen
AMI measures
% Minority
70
60
50
40
30
20
10
0
Smoking
cessation
counseling
1st quintile
ASA at
arrival
2nd quintile
ASA at DC
3rd quintile
PCI w/in
120 min
4th quintile
Rate-based measures
(higher quintile = better performance)
5th quintile
Top and bottom quintiles by
% minority patients seen
AMI measures
% Minority
70
60
50
40
30
20
10
0
Smoking
cessation
counseling
ASA at
arrival
1st quintile
ASA at DC
PCI w/in
120 min
5th quintile
Rate-based measures
(higher quintile = better performance)
Top and bottom quintiles by
% minority patients seen
Heart Failure measures
% Minority
70
60
50
40
30
20
10
0
Beta-blocker
on arrival
ACEI for
LVSD
DC
instructions
1st quintile
5th quintile
LVF
assessment
Rate-based measures
(higher quintile = better performance)
Top and bottom quintiles by
% minority patients seen
% Minority
Pneumonia measures
60
50
40
30
20
10
0
O2
assessment
Vaccination
Blood
cultures
1st quintile
5th quintile
Smoking
cessation
Rate-based measures
(higher quintile = better performance)
Top and bottom quintiles by
% minority patients seen
% Minority
Pneumonia measures
70
60
50
40
30
20
10
0
Abx w/in 8
hrs
Abx w/in 4 Abx selection Abx selection
hours
in ICU
non-ICU
1st quintile
5th quintile
Rate-based measures
(higher quintile = better performance)
Top and bottom quintiles by
% minority patients seen
% Minority
70
60
50
40
1st quintile
5th quintile
30
20
10
0
time to
thrombolysis
Time to PCI
Time to
antibiotics
Time-based measures
(higher quintile = worse performance)
Multivariate models adjusting for
individual factors and hospital effects
AMI
Measures
Model 1
Unadjusted
Smoking
Cessation
-0.47 (-0.56—0.38)
-0.47 (-0.58—0.37)
-0.20 (-0.32—0.09)
B-Blocker at
arrival
-0.18 (-0.30 --0.06)
-0.20 (-.32—0.07)
0.03 (0.08—0.12)
B-Blocker at
discharge
-0.29 (-0.39—0.19)
-0.31 (-0.42—0.21)
-0.05 (-0.14- 0.07)
0.05 (-0.16-0.21)
0.11 (-0.06-0.28)
0.23 (0.03-0.44)
-0.21 (-0.34--0.08)
-0.17 (-.030—0.04)
0.11 (-0.04-0.26)
Aspirin at arrival
Aspirin at
discharge
Model 2
Model 3
Adj. for demos,
Adj. for between
incl. co morbidities hospital effects
Multivariate models adjusting for
individual factors and hospital effects
Heart
Failure
Measures
Model 1
Unadjusted
Model 2
Model 3
Adj. for demos,
Adj. for between
incl. co morbidities hospital effects
Smoking
Cessation
-0.34 (-0.42—0.26)
-0.33 (-0.41—0.25)
-0.14 (-0.24—0.04)
D/C Instructions
-0.44 (-0.47—0.40)
-0.41 (-0.45—0.37)
-0.02 (-0.07-0.03)
Assess LV
Function
-0.24 (-0.30—0.18)
-0.25 (-0.31—0.18)
0.06 (-0.02-0.15)
Multivariate models adjusting for
individual factors and hospital effects
Pneumonia Model 1
Unadjusted
Measures
Model 2
Model 3
Adj. for demos,
Adj. for between
incl. co morbidities hospital effects
Smoking
Cessation
-0.60 (-0.70—0.50)
-0.57 (-0.67—0.47)
-0.20 (-0.33—0.08)
Antibiotics w/in 4
hours
-0.28 (-0.32—0.23)
-0.16 (-0.21—0.13)
0.10 (0.05 – 0.15)
Quality Challenges for the
Underserved
Where You Go
Quality in
Underserved
Settings
Who You Are
Pt Centered Care for the
Underserved
Slide by A. Beal
Considerations
• There is some within hospital variation
• There is clearly variation between hospitals with the data showing
that performance on some of the CMS quality measures is poorer
in hospitals serving a large number of minorities
• Examine hospital characteristics (payer mix, urban location, age of
facility, etc…)
• Be careful. For example, what will be the outcome of Pay for
Performance?
• Should quality improvement efforts focus on hospitals serving a
large % of minority patients. Focus on factors amenable to
improvement.
Policy Focus
“Policies designed to equalize patients’ treatment within
hospitals will not erase disparities at the national level.
What is necessary to erase health care disparities is to
implement national policies designed to improve the
overall treatment of all patients, which in turn will have a
disproportionate effect on reducing racial,ethnic,and
geographic disparities in health care and health
outcomes.”
K. Baicker, A. Chandra, and J. S. Skinner (2005).“Geographic Variation in Health Care
and the Problem of Measuring Racial Disparities.” Perspectives in Biology and Medicine.