Author(s): Elliott MN, Fremont A, Morrison PA, Pantoja P, Lurie N
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Abstract OBJECTIVE: To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable. DATA SOURCE: Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan. STUDY DESIGN: We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone. We assess accuracy with respect to estimating (1) individual race/ethnicity and (2) overall racial/ethnic prevalence in a population. PRINCIPAL FINDINGS: The Bayesian approach was 74 percent more efficient than geocoding alone in estimating individual race/ethnicity and 56 percent more efficient in estimating the prevalence of racial/ethnic groups, outperforming the non-Bayesian hybrid on both measures. The non-Bayesian hybrid was more efficient than geocoding alone in estimating individual race/ethnicity but less efficient with respect to prevalence (p<.05 for all differences). CONCLUSIONS: The Bayesian Surname and Geocoding (BSG) method presented here efficiently integrates administrative data, substantially improving upon what is possible with a single source or from other hybrid methods; it offers a powerful tool that can help health care organizations address disparities until self-reported race/ethnicity data are available. © Health Research and Educational Trust.
This article was published in Health Serv Res
and referenced in Journal of Biometrics & Biostatistics