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. 2021 Mar 1;32(2):157-161.
doi: 10.1097/EDE.0000000000001314.

Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and 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
Affiliations

Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data

Katie Labgold et al. Epidemiology. .

Abstract

Background: 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. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data.

Methods: We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification.

Results: The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons.

Conclusions: These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.

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Conflict of interest statement

The authors report no conflicts of interest.

Update of

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