Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.
Black or African American
/ statistics & numerical data
Asian
/ statistics & numerical data
COVID-19
/ ethnology
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
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 03 2021
01 03 2021
Historique:
pubmed:
17
12
2020
medline:
12
2
2021
entrez:
16
12
2020
Statut:
ppublish
Résumé
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.
Sections du résumé
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.
Identifiants
pubmed: 33323745
pii: 00001648-202103000-00001
doi: 10.1097/EDE.0000000000001314
pmc: PMC8641438
mid: NIHMS1754433
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
157-161Subventions
Organisme : NLM NIH HHS
ID : R01 LM013049
Pays : United States
Organisme : NCI NIH HHS
ID : F31 CA239566
Pays : United States
Organisme : NIAID NIH HHS
ID : K24 AI114444
Pays : United States
Organisme : NCATS NIH HHS
ID : TL1 TR002540
Pays : United States
Organisme : NHLBI NIH HHS
ID : UM1 HL134590
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest.
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