Eminent Scholar Lecture: A Discussion on Methodological Approaches to Studying Health Disparities

Eminent Scholar Lecture

Yan Ma, Ph.D., Professor and Chair of the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health.

Large, population based studies necessary to address healthcare disparities can be costly and difficult to perform, and may be compromised by sampling strategies and patient selection biases, an efficient alternative that is becoming increasingly attractive is the use of the Healthcare Cost & Utilization Project (HCUP) State Inpatient Databases (SID). A significant limitation of SID and other large databases is the quantity of missing data. In particular, “patient race”, a key indicator for health disparities research, has a high proportion of missingness. The goal of this study is to make SID a more useful and reliable resource for the study of racial disparity. A novel simulation study compared four imputation methods (random draw, hot deck, joint multiple imputation [MI], conditional MI) for missing values for multiple variables in the SID, including race, gender, admission source, median household income, and total charges. The simulation was built on real data from the SID to retain their hierarchical data structures and missing data patterns. Additional predictive information from the U.S. Census and American Hospital Association (AHA) database was incorporated into the imputation.

Organized by the Department of Sociology, Anthropology, and Public Health.

This event is open for full participation by all individuals regardless of race, color, religion, sex, national origin, or any other protected category under applicable federal law, state law, and the University's nondiscrimination policy.