Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis.


Journal

BMJ open
ISSN: 2044-6055
Titre abrégé: BMJ Open
Pays: England
ID NLM: 101552874

Informations de publication

Date de publication:
22 07 2021
Historique:
entrez: 24 7 2021
pubmed: 25 7 2021
medline: 30 7 2021
Statut: epublish

Résumé

The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown. We examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020. Counties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 μm, temperature and precipitation. Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented ('third wave'). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis. Except for the winter 'third wave', when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.

Sections du résumé

BACKGROUND
The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown.
OBJECTIVE
We examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020.
METHODS
Counties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 μm, temperature and precipitation.
RESULTS
Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented ('third wave'). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis.
CONCLUSION
Except for the winter 'third wave', when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.

Identifiants

pubmed: 34301657
pii: bmjopen-2020-048086
doi: 10.1136/bmjopen-2020-048086
pmc: PMC8300549
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e048086

Subventions

Organisme : NHLBI NIH HHS
ID : K01 HL149982
Pays : United States
Organisme : NHLBI NIH HHS
ID : L30 HL148912
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL007745
Pays : United States
Organisme : NHLBI NIH HHS
ID : T32 HL130025
Pays : United States

Commentaires et corrections

Type : UpdateOf

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: None declared.

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Auteurs

Shabatun J Islam (SJ)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Aditi Nayak (A)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Yingtian Hu (Y)

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Anurag Mehta (A)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Katherine Dieppa (K)

City Operations, Getaround Incorporated, San Francisco, California, USA.

Zakaria Almuwaqqat (Z)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Yi-An Ko (YA)

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Shivani A Patel (SA)

Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Abhinav Goyal (A)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Samaah Sullivan (S)

Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Tené T Lewis (TT)

Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Viola Vaccarino (V)

Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Alanna A Morris (AA)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA.

Arshed A Quyyumi (AA)

Department of Medicine, Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia, USA aquyyum@emory.edu.

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Classifications MeSH