1. 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

2. Co-authors David W.Baker, MD, MPH Raj Behal, MD, MPH Joe Feinglass, PhD David Nerenz, PhD Joel S. Weissman, PhD

3. PROJECT Linking Race and Ethnicity Data to Inpatient Quality of Care Measures Funding: The Commonwealth Fund

4. 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

5. 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

6. 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

7. 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

8. 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)

9. Performance quintiles by % minority patients seen Rate-based measures (higher quintile = better performance) % Minority AMI measures

10. Top and bottom quintiles by % minority patients seen Rate-based measures (higher quintile = better performance) % Minority AMI measures

11. Top and bottom quintiles by % minority patients seen Rate-based measures (higher quintile = better performance) % Minority Heart Failure measures

12. Top and bottom quintiles by % minority patients seen Rate-based measures (higher quintile = better performance) % Minority Pneumonia measures

13. Top and bottom quintiles by % minority patients seen Rate-based measures (higher quintile = better performance) % MinorityPneumonia measures

14. Top and bottom quintiles by % minority patients seen Time-based measures (higher quintile = worse performance) % Minority

15. 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)

16. Multivariate models adjusting for individual factors and hospital effects Heart Failure Measures Model 1 Unadjusted Model 2 Adj. for demos, incl. co morbidities Model 3 Adj. for between 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)

17. Multivariate models adjusting for individual factors and hospital effects Pneumonia Measures Model 1 Unadjusted Model 2 Adj. for demos, incl. co morbidities Model 3 Adj. for between 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)

18. 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

19. 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.

20. 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.