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

Epidemiology. 2020 Nov 30. doi: 10.1097/EDE.0000000000001314. Online ahead of print.

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.

PMID:33323745 | DOI:10.1097/EDE.0000000000001314