Imputing Missing Race/Ethnicity in Pediatric Electronic Health Records: Reducing Bias with Use of U.S. Census Location and Surname Data

Health Services Research
Robert W GrundmeierRussell Localio

Abstract

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. Electronic health record data from 30 pediatric practices with known race/ethnicity. 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. Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes. The new method reduces bias when race/ethnicity is partially, nonrandomly missing.

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Citations

Jul 25, 2015·Health Services Research·William Rhodes
Apr 27, 2018·PLoS Computational Biology·Ji-Sung KimAndrey Rzhetsky
Dec 7, 2018·Health Services Research·Ann HaasAmelia M Haviland
Dec 28, 2018·Journal of the American Medical Informatics Association : JAMIA·Nathaniel D MercaldoJonathan S Schildcrout
Jan 23, 2021·Family Practice·Miguel MarinoJohn D Heintzman
Dec 13, 2016·Pediatrics·David C KaelberUNKNOWN Comparative Effectiveness Research Through Collaborative Electronic Reporting (CER2) Consortium

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