Online Program

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Thursday, January 11
Thu, Jan 11, 2:00 PM - 3:45 PM
Crystal Ballroom B
Improving Measurement and Assessment Related to Disparities

Validation and Application of the Medicare Bayesian Improved Surname Geocoding (MBISG) Version 2.0 (304208)

John Lloyd Adams, Kaiser Permanente Center for Effectiveness and Safety Research 
Jacob W. Dembosky, RAND Corporation 
*Marc Nathan Elliott, RAND Corporation 
Sarah Gaillot, Centers for Medicare & Medicaid Services 
Ann Haas, RAND Corporation 
Samuel C. Haffer, Centers for Medicare & Medicaid Services 
Amelia Haviland, Carnegie Mellon University  
Joshua Mallet, RAND Corporation 
Shondelle Wilson-Frederick, Centers for Medicare & Medicaid Services 

Keywords: race/ethnicity, imputation, Medicare, disparities

Race/ethnicity (R/E) is often unavailable or inaccurate. Medicare files contain a Social Security Administration (SSA) R/E variable with known limitations. The Medicare Bayesian Improved Surname Geocoding (MBISG 1.0) method estimates a vector of 6 R/E probabilities (White, Black, Hispanic, American Indian/Alaska Native, and Asian/Pacific Islander (API), and multiracial) by augmenting SSA R/E with surname and address-based R/E Census information. Using data from 284,627 Medicare beneficiaries, we improve MBISG 1.0 by (a) allowing the association of SSA data with self-reported R/E to vary by age, (b) disaggregating compound surnames, (c) better accounting for Puerto Rican residence, (d) incorporating additional data elements (e.g., first names), and (e) allowing more flexible multinomial logistic regression modeling, resulting in MBISG 2.0. MBISG 2.0 significantly improves accuracy for Hispanic, White, and API beneficiaries by removing about 1/3 of remaining error in cross-validated results. The R/E group with the lowest MBISG 1.0 performance (Hispanic) improved the most Thus, MBISG 2.0 performance is higher and more uniform.