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

• Published in: Epidemiology (Cambridge, Mass.) Vol. 32; no. 2; p. 157
• Main Authors: Labgold, Katie, Hamid, Sarah, Shah, Sarita, Gandhi, Neel R, Chamberlain, Allison, Khan, Fazle, Khan, Shamimul, Smith, Sasha, Williams, Steve, Lash, Timothy L, Collin, Lindsay J
• Format: Journal Article
• Language: English
• Published: United States 01.03.2021
• Subjects: Asian - statistics & numerical data; Black or African American - statistics & numerical data; COVID-19 - ethnology; COVID-19 - mortality; Data Collection; Georgia - epidemiology; Health Status Disparities; Hispanic or Latino - statistics & numerical data; Hospitalization - statistics & numerical data; Humans; Indigenous Peoples - statistics & numerical data; Mortality - ethnology; Native Hawaiian or Other Pacific Islander - statistics & numerical data; SARS-CoV-2; Statistics as Topic; United States - epidemiology; White People - statistics & numerical data; United States; Georgia
• ISSN: 1531-5487
• DOI: 10.1097/EDE.0000000000001314

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. 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. 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. 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.