Race and Ethnicity Data Quality and Imputation Using U.S. Census Data in an Integrated Health System

Abstract

Research on racial and ethnic disparities using health system databases can shed light on the usual health care and outcomes of large numbers of individuals so that health inequities can be better understood and addressed. Such research often suffers from limitations in race/ethnicity data quality. We examined the quality of race/ethnicity data in a large, diverse, integrated health system that repeatedly collects these data on utilization of services. We tested the accuracy of Bayesian Improved Surname Geocoding for imputation of race/ethnicity data. Administrative race/ethnicity data were accurate as judged by comparison with self-report in adults. The Bayesian Improved Surname Geocoding method produced imputation results far better than chance assignment for the four most common race/ethnicity groups in the health system: Whites, Hispanics, Blacks, and Asians. These results support renewed efforts to conduct studies of racial and ethnic disparities in large health systems.