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[Preprint]. 2020 Oct 2:2020.09.30.20203315.
doi: 10.1101/2020.09.30.20203315.

Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data

Katie Labgold  1 , Sarah Hamid  1 , Sarita Shah  1   2   3 , Neel R Gandhi  1   2   3 , Allison Chamberlain  1 , Fazle Khan  4 , Shamimul Khan  4 , Sasha Smith  4 , Steve Williams  4 , Timothy L Lash  1 , Lindsay J Collin  1   5
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Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data

Katie Labgold et al. medRxiv. .

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Abstract

Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

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