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. 2021 May;58(2):146-164.
doi: 10.1111/cars.12336. Epub 2021 May 4.

Studying the social determinants of COVID-19 in a data vacuum

Kate H Choi  1 , Patrick Denice  1 , Michael Haan  1 , Anna Zajacova  1
Affiliations

Studying the social determinants of COVID-19 in a data vacuum

Kate H Choi et al. Can Rev Sociol. 2021 May.

Abstract
in English, Spanish

Race-based and other demographic information on COVID-19 patients is not being collected consistently across provinces in Canada. Therefore, whether the burden of COVID-19 is falling disproportionately on the shoulders of particular demographic groups is relatively unknown. In this article, we first provide an overview of the available geographic and demographic data related to COVID-19. We then make creative use of these existing data to fill the vacuum and identify key demographic risk factors for COVID-19 across Canada's health regions. Drawing on COVID-19 counts and tabular census data, we examine the association between communities' demographic composition and the number of COVID-19 infections. COVID-19 infections are higher in communities with larger shares of Black and low-income residents. Our approach offers a way for researchers and policymakers to use existing data to identify communities nationwide that are vulnerable to the pandemic in the absence of more detailed demographic and more granular geographic data.

Les renseignements fondés sur la race et d'autres données démographiques sur les patients atteints du COVID-19 ne sont pas recueillis de manière uniforme dans toutes les provinces du Canada. Par conséquent, si le fardeau du COVID-19 tombe de manière disproportionnée sur les épaules de groupes démographiques particuliers est relativement inconnu. Dans cet article, nous fournissons d'abord un aperçu des données géographiques et démographiques disponibles liées au COVID-19. Nous utilisons ensuite de manière créative ces données existantes pour combler le vide et identifier les principaux facteurs de risque démographiques du COVID-19 dans les régions sociosanitaires du Canada. En nous basant sur les dénombrements de COVID-19 et les données tabulaires du recensement, nous examinons l'association entre la composition démographique des communautés et le nombre d'infections au COVID-19. Les infections au COVID-19 sont plus élevées dans les communautés avec une plus grande proportion de résidents Noirs et à faible revenu. Notre approche offre aux chercheurs et aux décideurs un moyen d'utiliser les données existantes pour identifier les communautés à l'échelle nationale qui sont vulnérables à la pandémie en l'absence de données démographiques plus détaillées et géographiques plus granulaires.

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Figures

Figure 1
Figure 1
COVID‐19 infections in health regions and pseudo‐census subdivisions. Data: University of Toronto's COVID‐19 Canada Open Data Working Group Dashboard and Statistics Canada's 2016 census tabular estimates. Notes: Top panel shows the observed number of infections across health regions; bottom panel shows predicted infection counts across census subdivisions and divisions. Figures on the left plot infections during the first peak of the pandemic (April 20, 2020); figures on the right plot infections during the second peak (January 10, 2021). Provinces our outlined in black [Color figure can be viewed at wileyonlinelibrary.com]
Figure 2
Figure 2
Cumulative count of COVID‐19 infections in the 140 neighbourhoods in the City of Toronto, by percent Black terciles, Data: Toronto Public Health and Toronto's Open Data Catalogue. Notes: The 140 neighbourhoods in the City of Toronto are divided into categories based on terciles of the percent of its residents who are Black
See this image and copyright information in PMC

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