Developing an Algorithm for Combining Race and Ethnicity Data Sources in the Veterans Health Administration

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.

Disparities in Anticoagulant Therapy Initiation for Incident Atrial Fibrillation by Race/Ethnicity Among Patients in the Veterans Health Administration System

“…We used VA administrative data on race/ethnicity, supplemented by data from the Centers for Medicare and Medicaid Services when missing, to define this variable as non-Hispanic White (ie, White), non-Hispanic Black (ie, Black), Hispanic, Asian, or American Indian/Alaska Native (AI/AN). 36,37 We identified additional baseline patient sociodemographic characteristics, including a categorized variable for age at index diagnosis, sex, and VA priority group (groups 1-8), which conveys patients' level of eligibility for VA services given their financial means, service-connected health conditions, and disabilities (lower groups have increased eligibility for VA benefits). 38 We assessed geographic census region and level of rurality based on each patients' residence.…”

Model of Organizational Commitment Applied to Health Management Systems

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US veterans administration diabetes risk (VADR) national cohort: cohort profile

“…The missing race/ethnicity variable in VA data is widely known. 35 Screening for diabetes with Hgb A1c became more common after the recommendation was published in 2009. 36 …”