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A New Method for Estimating Race/Ethnicity and Associated Disparities Where Administrative Records Lack Self‐Reported Race/Ethnicity
Author(s) -
Elliott Marc N.,
Fremont Allen,
Morrison Peter A.,
Pantoja Philip,
Lurie Nicole
Publication year - 2008
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2008.00854.x
Subject(s) - geocoding , ethnic group , race (biology) , bayesian probability , pacific islanders , race and health , health equity , geography , population , medicine , demography , computer science , public health , socioeconomic status , environmental health , political science , sociology , cartography , artificial intelligence , nursing , law , gender studies
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.