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Activity Number: 35 - Imputing Race/Ethnicity to Understand Health Care Disparities Among Older Adults
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Social Statistics Section
Abstract #312227
Title: The Contribution of First-Name Information to the Accuracy of Racial and Ethnic Imputations Varies by Gender, Race, and Ethnicity
Author(s): Amelia M Haviland*
Companies: Carnegie Mellon University
Keywords: Race and Ethnicity Imputation; First Names

Race/ethnicity data is often not present in healthcare and other administrative datasets. One solution is to impute race/ethnicity, which surname has long been used for. Although less predictive, first-name information improves imputations that already include surname information.

We compare the marginal contribution of first-name information to the accuracy of racial/ethnic imputations in a sample of Medicare beneficiaries with known race/ethnicity. We analyze two scenarios: a sparse set of predictors and a rich set of predictors to assess whether gains in accuracy from first names differ by gender and race/ethnicity.

Among non-Hispanic white, Hispanic, and Asian/Pacific Islander beneficiaries, first-name information improves accuracy more for women and narrows the gender gap in accuracy. Gains in accuracy from adding first-name information are similar for black men and women. For all groups, the addition of first-name information improves prediction accuracy more under our sparse predictor scenario.

Thus, first-name information increases the accuracy of racial/ethnic imputations, especially when there are only a sparse set of predictors, and especially for women.

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

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