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.
journal_name
Health Serv Resjournal_title
Health services researchauthors
Elliott MN,Fremont A,Morrison PA,Pantoja P,Lurie Ndoi
10.1111/j.1475-6773.2008.00854.xsubject
Has Abstractpub_date
2008-10-01 00:00:00pages
1722-36issue
5 Pt 1eissn
0017-9124issn
1475-6773pii
HESR854journal_volume
43pub_type
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doi：10.1111/1475-6773.13570
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journal_title：Health services research
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doi：
更新日期：1990-04-01 00:00:00
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doi：
更新日期：1995-06-01 00:00:00
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doi：10.1111/j.1475-6773.2010.01115.x
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更新日期：2008-08-01 00:00:00
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