disparities in inpatient quality of care measures by race and ethnicity ____________________________...

Disparities in Inpatient Quality of Care Measures by Race and Ethnicity

____________________________

Academy HealthJune 27, 2005Boston, MA

Romana Hasnain-Wynia, Ph.D.Health Research and Educational Trust

Co-authors

• 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

0

1020

3040

5060

70

Smokingcessation

counseling

ASA atarrival

ASA at DC PCI w/in120 min

1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile

Rate-based measures(higher quintile = better performance)

% Minority AMI measures

Top and bottom quintiles by % minority patients seen

0

1020

3040

5060

70

Smokingcessation

counseling

ASA atarrival

ASA at DC PCI w/in120 min

1st quintile 5th quintile


% Minority AMI measures


0

10

20

30

40

50

60

70

Beta-blockeron arrival

ACEI forLVSD

DCinstructions

LVFassessment



% Minority Heart Failure measures


0

10

20

30

40

50

60

O2assessment

Vaccination Bloodcultures

Smokingcessation



% Minority Pneumonia measures


0

10

20

30

40

50

60

70

Abx w/in 8hrs

Abx w/in 4hours

Abx selectionin ICU

Abx selectionnon-ICU



% Minority Pneumonia measures


0

10

20

30

40

50

60

70

time tothrombolysis

Time to PCI Time toantibiotics

1st quintile5th quintile

Time-based measures(higher quintile = worse performance)

% Minority

Multivariate models adjusting for individual factors and hospital effects

AMI

Measures

Model 1

Unadjusted

Model 2

Adj. for demos, incl. co morbidities

Model 3

Adj. for between hospital effects

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)

Aspirin at arrival 0.05 (-0.16-0.21) 0.11 (-0.06-0.28) 0.23 (0.03-0.44)

Aspirin at discharge

-0.21 (-0.34--0.08) -0.17 (-.030—0.04) 0.11 (-0.04-0.26)


Heart Failure Measures

Model 1

Unadjusted

Model 2


Model 3


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)


Pneumonia Measures

Model 1

Unadjusted

Model 2


Model 3


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

Who You Are

Where You Go

Pt Centered Care for the Underserved

Quality in Underserved

Settings

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 Careand the Problem of Measuring Racial Disparities.” Perspectives in Biology and Medicine.

disparities in inpatient quality of care measures by race and ethnicity ____________________________...

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