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 RAND.org on September 17, 2021
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Access further information on this document at Health Services ResearchThis article was published outside of RAND. The full text of the article can be found at the link above.
Objective
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.
Conclusions
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.
This article was published outside of RAND. The full text of the article can be found at the link above.
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