Developing an Algorithm for Combining Race and Ethnicity Data Sources in the Veterans Health Administration. Issue 3 (10th October 2019)

• Record Type: Journal Article
• Authors: Hernandez, Susan E; Sylling, Philip W; Mor, Maria K; Fine, Michael J; Nelson, Karin M; Wong, Edwin S; Liu, Chuan-Fen; Batten, Adam J; Fihn, Stephan D; Hebert, Paul L
• Is Part Of: Military medicine. Volume 185:Issue 3/4(2020)
• Page Start: e495
• Page End: e500
• Publication Date: 2019-10-10
• Subjects: Surgery, Military -- Societies, etc; Medicine, Military -- Societies, etc; Medicine, Military -- Periodicals; Surgery, Military -- Periodicals; Medicine, Military; Surgery, Military; Military Medicine -- Periodicals; Periodicals; Electronic journals; 616.98023
• DOI: 10.1093/milmed/usz322
• Languages: English
• ISSNs: 0026-4075

Abstract

Abstract: Introduction: Racial/ethnic disparities exist in the Veterans Health Administration (VHA), despite financial barriers to care being largely mitigated and Veterans Administration's (VA) organizational commitment to health equity. Accurately identifying minority veterans is critical to monitoring progress toward equity as the VHA treats an increasingly racially and ethnically diverse veteran population. Although the VHA's completeness of race and ethnicity data is generally better than its public sector and private counterparts, the accuracy of the race and ethnicity in the various databases available to VHA is variable, as is the accuracy in identifying specific minority groups. The purpose of this article was to develop an algorithm for constructing race and ethnicity variables from data sources available to VHA researchers, to present demographic differences cross the data sources, and to apply the algorithm to one study year. Materials and Methods: We used existing VHA survey data from the Survey of Healthcare Experiences of Patients (SHEP) and three commonly used administrative databases from 2003 to 2015: the VA Corporate Data Warehouse (CDW), VA Defense Identity Repository (VADIR), and Medicare. Using measures of agreement such as sensitivity, specificity, positive and negative predictive values, and Cohen kappa, we compared self-reported race and ethnicity from the SHEP and each of the other data sources. Based on these results, we propose an algorithm for combining data on race and ethnicity from these datasets. We included VHA patients who completed a SHEP and had race/ethnicity recorded in CDW, VADIR, and/or Medicare. Results: Agreement between SHEP and other sources was high for Whites and Blacks and substantially lower for other minority groups. The CDW demonstrated better agreement than VADIR or Medicare. Conclusions: We developed an algorithm of data source precedence in the VHA that improves the accuracy of the identification of historically under-identified minorities: (1) SHEP, (2) CDW, (3) Department of Defense's VADIR, and (4) Medicare.