Methods for Measuring Racial Differences in Hospitals Outcomes Attributable to Disparities in Use of High-Quality Hospital Care

Health Serv Res. 2017 Apr;52(2):826-848. doi: 10.1111/1475-6773.12514. Epub 2016 Jun 3.

Abstract

Objective: To compare two approaches to measuring racial/ethnic disparities in the use of high-quality hospitals.

Data sources: Simulated data.

Study design: Through simulations, we compared the "minority-serving" approach of assessing differences in risk-adjusted outcomes at minority-serving and non-minority-serving hospitals with a "fixed-effect" approach that estimated the reduction in adverse outcomes if the distribution of minority and white patients across hospitals was the same. We evaluated each method's ability to detect and measure a disparity in outcomes caused by minority patients receiving care at poor-quality hospitals, which we label a "between-hospital" disparity, and to reject it when the disparity in outcomes was caused by factors other than hospital quality.

Principal findings: The minority-serving and fixed-effect approaches correctly identified between-hospital disparities in quality when they existed and rejected them when racial differences in outcomes were caused by other disparities; however, the fixed-effect approach has many advantages. It does not require an ad hoc definition of a minority-serving hospital, and it estimated the magnitude of the disparity accurately, while the minority-serving approach underestimated the disparity by 35-46 percent.

Conclusions: Researchers should consider using the fixed-effect approach for measuring disparities in use of high-quality hospital care by vulnerable populations.

Keywords: Race; disparities; hospital readmissions.

MeSH terms

  • Black or African American / statistics & numerical data
  • Healthcare Disparities / statistics & numerical data*
  • Hospitals / standards*
  • Hospitals / statistics & numerical data
  • Humans
  • Models, Statistical
  • Patient Readmission / statistics & numerical data
  • Quality of Health Care / standards
  • Quality of Health Care / statistics & numerical data
  • Racial Groups / statistics & numerical data*
  • White People / statistics & numerical data