Imputation of Race/Ethnicity to Enable Measurement of HEDIS Performance by Race/Ethnicity

Published in: Health Services Research, Volume 54, Issue 1, pages 13–23 (February 2019). doi: 10.1111/1475-6773.13099

Posted on on September 17, 2021

by Ann C. Haas, Marc N. Elliott, Jacob W. Dembosky, John L. Adams, Shondelle Wilson-Frederick, Joshua Mallett, Sarah J. Gaillot, Samuel C. Haffer, Amelia Haviland

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To improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity.

Data Sources/Study Setting

Data from 284,627 respondents to the 2014 Medicare CAHPS survey.

Study Design

We compared performance (cross-validated Pearson correlation of estimates and self-reported race/ethnicity) for several alternative models predicting self-reported race/ethnicity in cross-sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework.

Data Collection/Extraction Methods

We linked survey-reported race/ethnicity to CMS administrative and US census data.

Principal Findings

MBISG 2.0 removed 25–39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self-reported race/ethnicity for these groups.


MBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.

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