Objective: To assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid.
Data Sources and StudySetting: 2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used. Study Design: We cross-sectionally assessed Hispanic-White, Black-White, and Asian-White disparities in 22 quality and utilization measures, comparing selfreported race and ethnicity to imputed values from the Bayesian Improved Surname Geocoding (BISG) algorithm.Data Collection: Race and ethnicity were obtained from self-reported data and imputed using BISG.Principal Findings: 42.5%/4.9% of claims/EHR were missing self-reported data; BISG estimates were available for >99% of each and had good concordance (0.87-0.95) with Asian, Black, Hispanic, and White self-report. All estimated racial and ethnic disparities were statistically similar in self-reported and imputed EHR-based measures.However, within claims, BISG estimates and incomplete self-reported data yielded substantially different disparities in almost half of the measures, with BISG-based Black-White disparities generally larger than self-reported race and ethnicity data.Conclusions: BISG imputation methods are feasible for Medicaid claims data and reduced missingness to <1%. Disparities may be larger than what is estimated using self-reported data with high rates of missingness.