Leveraging Multiple Administrative Data Sources to Reduce Missing Race and Ethnicity Data: A Descriptive Epidemiology Cross-Sectional Study of COVID-19 Case Relative Rates.

Background: Understanding race and ethnicity (RE) differentials improves health outcomes. However, RE data are consistently missing from electronic laboratory reports, the primary source of COVID-19 case metrics. We addressed the missing RE differentials and compared vaccinated and unvaccinated cases from March 1, 2020, to May 30, 2023, in New York State (NYS), excluding New York City.

Methods: This descriptive epidemiology cross-sectional study linked the NYS Electronic Clinical Laboratory Reporting System (ECLRS) with NYS Immunization Information System (NYSIIS) to address the missing RE data in the ECLRS system. The primary metric was the COVID-19 case relative risk (RR) for each RE relative to white individuals.

Results: There were 4,212,741 COVID-19 cases with 39% (1,624,818) missing RE data in ECLRS; missing RE data declined to 17% (726,023) after matching with NYSIIS. For those aged 65 years or older (after matching), 42% were missing in 2020, which declined by 17% by 2023. In May 2021, COVID-19 RRs for vaccinated individuals were 1.09 (95% CI 0.90-1.32), 1.11 (95% CI 0.87-1.43), 1.13 (95% CI 0.93-1.39), and 1.89 (95% CI 1.01-3.52), and for unvaccinated individuals were 1.73 (95% CI 1.66-1.82), 0.84 (95% CI 0.78-0.92), 3.10 (95% CI 2.98-3.22), and 3.49 (95% CI 3.05-3.98) respectively for Hispanic, Asian/Pacific Islander, Black people, and American Indian/Alaska Native individuals.

Conclusion: Matching case data with vaccine registries reduce missing RE data for COVID-19 cases. Disparity was lower in vaccinated than in unvaccinated individuals indicating that vaccination mitigated RE disparities early in the pandemic. This underscores the value of interoperable systems with automated matching for disparity analyses.