Optimizing Data on Race and Ethnicity for Veterans Affairs Patients.

Introduction: Maintaining accurate race and ethnicity data among patients of the Veterans Affairs (VA) healthcare system has historically been a challenge. This work expands on previous efforts to optimize race and ethnicity values by combining multiple VA data sources and exploring race- and ethnicity-specific collation algorithms.

Materials And Methods: We linked VA patient data from 2000 to 2018 with race and ethnicity data from four administrative and electronic health record sources: VA Medical SAS files (MedSAS), Corporate Data Warehouse (CDW), VA Centers for Medicare extracts (CMS), and VA Defense Identity Repository Data (VADIR). To assess the accuracy of each data source, we compared race and ethnicity values to self-reported data from the Survey of Health Experiences of Patients (SHEP). We used Cohen's Kappa to assess overall (holistic) source agreement and positive predictive values (PPV) to determine the accuracy of sources for each race and ethnicity separately.

Results: Holistic agreement with SHEP data was excellent (K > 0.80 for all sources), while race- and ethnicity-specific agreement varied. All sources were best at identifying White and Black users (average PPV = 0.94, 0.93, respectively). When applied to the full VA user population, both holistic and race-specific algorithms substantially reduced unknown values, as compared to single-source methods.

Conclusions: Combining multiple sources to generate race and ethnicity values improves data accuracy among VA patients. Based on the overall agreement with self-reported data, we recommend using non-missing values from sources in the following order to fill in race values-SHEP, CMS, CDW, MedSAS, and VADIR-and in the following order to fill in ethnicity values-SHEP, CDW, MedSAS, VADIR, and CMS.