title: A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity.
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.
  
  
  METHODS
  Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: RAND Corporation, 1776 Main Street, Santa Monica, CA 90407, RAND Corporation, Nantucket, MA RAND Corporation, Arlington, VA, USA.
authors.forename: Marc N
authors.initials: MN
authors.lastname: Elliott
authors.forename: Allen
authors.initials: A
authors.lastname: Fremont
authors.forename: Peter A
authors.initials: PA
authors.lastname: Morrison
authors.forename: Philip
authors.initials: P
authors.lastname: Pantoja
authors.forename: Nicole
authors.initials: N
authors.lastname: Lurie
mesh_terms: D001499:Bayes Theorem; D003625:Data Collection / Q000379:methods*; D005006:Ethnicity; D006302:Health Services Research / Q000379:methods; D054625:Healthcare Disparities / Q000208:ethnology*; D006801:Humans; D044469:Racial Groups; D057566:Self Report