Imputing missing race/ethnicity in pediatric electronic health records: reducing bias with use of U.S. census location and surname data

Objective. To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort. Data Sources/Study Setting. Electronic health record data from 30 pediatric practices with known race/ethnicity. Study Design. In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information. Principal Findings. Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes. Conclusions. The new method reduces bias when race/ethnicity is partially, nonrandomly missing. Key Words. Multiple imputation, U.S. Census location and surname data, race and ethnicity, health disparities
Comparative effectiveness research using electronic health record (EHR) data promises timely understanding of health disparities (Olsen, Aisner, and McGinnis 2007; Fiks et al. 2012) but requires accurate racial/ethnic data, which [...]