2018
DOI: 10.1111/1475-6773.13099
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Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity

Abstract: 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 predi… Show more

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Cited by 44 publications
(53 citation statements)
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“…We report estimates for all groups but AI/AN and multiracial due to their low precision. 34 Standard errors were clustered on PO. We used "recycled predictions" to estimate group means on the scale of percent (binary and binomial outcomes) or rates (count outcomes).…”
Section: Discussionmentioning
confidence: 99%
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“…We report estimates for all groups but AI/AN and multiracial due to their low precision. 34 Standard errors were clustered on PO. We used "recycled predictions" to estimate group means on the scale of percent (binary and binomial outcomes) or rates (count outcomes).…”
Section: Discussionmentioning
confidence: 99%
“…Beneficiary race and ethnicity were estimated using the Medicare Bayesian Improved Surname Geocoding (MBISG 2.0) methodology, which combines Medicare administrative data and US census data to derive probabilities of membership in each of six mutually exclusive racial and ethnic groups (white, black, Hispanic, API, American Indian/Alaska Native (AIAN), and multiracial) 34 . For the racial and ethnic groups included in the current analysis, correlations between the MBISG algorithm's estimated race/ethnicity and self‐reported race/ethnicity range from 0.88 to 0.95.…”
Section: Methodsmentioning
confidence: 99%
“…Note that the comparative predictiveness of different proxies for protected class labels, such as race, has been thoroughly studied (Consumer Financial Protection Bureau 2014, Dembosky et al 2019, Elliott et al 2008, Imai and Khanna 2016 as it was understood this impacts the quality of corresponding proxy assessments of disparity. The above result highlights that the proxies' predictiveness of outcomes is also crucial, or even sufficient.…”
Section: Identification Under Perfect Predictionmentioning
confidence: 99%
“…BISG estimates conditional race membership probabilities given surname and geolocation (e.g., census tract or ZIP code) using data from the US census, and then imputes the race labels based on the estimated probabilities. Since its invention (Elliott et al 2008(Elliott et al , 2009), the BISG method has been widely used in assessing racial disparities in health care (Brown et al 2016, Fremont et al 2016, 2005, Haas et al 2019, Weissman and Hasnain-Wynia 2011. In 2009, the Institute of Medicine also suggested it as an interim strategy until routine collection of relevant data is feasible (Nerenz et al 2009).…”
Section: Introductionmentioning
confidence: 99%
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