Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data
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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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Race data assigned by an observer can have significant misclassification compared with how an individual self-identifies.
“This is important because there can be significant misclassification in race data 178 assigned by an observer compared with how an individual selfidentifies.”
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- Authors
- Labgold, K., Hamid, S., Shah, S., Gandhi, N. R., Chamberlain, A., Khan, F., Khan, S., Smith, S., Williams, S., Lash, T. L., & Collin, L. J.
- Affiliations
- Emory University (US)
- Georgia Department of Public Health (US)
- Corresponding author
- Collin, Lindsay J.
- ORCID
- Venue
- Epidemiology
- Published
- 2020-11-30
- Volume
- 32
- Issue
- 2
- Pages
- 157-161
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC8641438
- OpenAlex
- W3111489730
- Cited by
- 89
- References
- 16
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 12 · 2023: 17 · 2022: 20 · 2021: 26
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Epidemiology (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- Achievement Rewards for College Scientists Foundation
- Robert W. Woodruff Foundation
- Georgia Department of Public Health
- National Institutes of Health (TL1TR002540)
- National Heart, Lung, and Blood Institute (UM1HL134590)
- National Cancer Institute (F31CA239566)
- National Institute of Allergy and Infectious Diseases (K24AI114444)
- U.S. National Library of Medicine (R01LM013049)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Keywords
- COVID-19 pandemic, SARS-CoV-2, racial and ethnic disparities, missing data imputation, misclassification bias, notification rates, hospitalization, case fatality rate, health equity, Black and Hispanic populations, indigenous peoples, urban counties
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