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Activity Number: 119 - SPEED: Government and Health Policy
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #329005 Presentation
Title: Developing and Evaluating Methods for Estimating Race/Ethnicity in an Incomplete Dataset Using Address, Surname and Family Race
Author(s): Gabriella Christine Silva* and Roee Gutman
Companies: Brown University and Brown University
Keywords: multiple imputation; missing race/ethnicity; geocoding; Bayesian regression; health disparities

The availability of race data is essential for identifying and addressing racial/ethnic disparities in the health care system; however, patient self-reported racial/ethnic information is often missing. Indirect methods for estimating race have been developed, yet these perform poorly among racial minorities, usually only consider geocoded and surname data as predictors, and are unable to provide race estimates for subjects missing this information. The objective of this study was to develop a race estimation method that addressed these limitations and achieved higher predictive performance than previous methods. A subset of Rhode Island Medicaid beneficiaries was used to explore multiple imputation methods for race. The general location model was used to implement joint model imputation while Bayesian multinomial regression models were explored for fully conditional specification imputation. Our method outperformed existing ones, especially among American Indians and Asians, using evaluation criteria like area under the curve and racial composition estimates. Family race was identified as an important predictor of race and should be included in race estimation models when possible.

Authors who are presenting talks have a * after their name.

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