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

Abstract

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. RÉSUMÉ Les renseignements fondés sur la race et d'autres données démographiques sur les patients atteints duThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Strengthening the Collection and Use of Disaggregated Data to Understand and Monitor the Risk and Burden of COVID-19 Among Racialized Populations

“…Despite this evidence, the Province of Ontario was initially reluctant to collect and release demographic and socioeconomic indices on COVID-19 infections and deaths, considering “all groups of people are equally important to us” (Siddiqi et al, 2020 ). By contrast to the government response, academic and community groups increasingly viewed the COVID-19 pandemic and its outcomes as a structural issue in Canada that was disproportionately affecting equity-seeking communities, such as immigrants, refugees, women, and people who are from low-income background, have disabilities, and/or are housing insecure (Choi et al, 2021 , Etowa & Hyman, 2021 ). By the end of 2020, the Province of Ontario invested in the possibility of sociodemographic data collection for the vaccine roll-out, and the Federal Government announced a national sociodemographic data collection initiative and a pandemic equity model (McKenzie, 2021 ).…”

A tale of two epicentres: Lombardy and New York City at the outbreak of the Coronavirus pandemic

“…mortality and fatality rate, diffusion of the virus among the population) are not conclusive. Therefore, it is not yet possible to build a complete picture of the links between the various phases of the pandemic (Choi et al 2021;Esteve, Permanyer and Boertien 2020). Yet the analysis of the combination of factors characterising some of the first epicentres of the pandemic might help identify specific micro-and meso-institutional and governance reactions useful for mitigating the persistence and recrudescence of the virus.…”

COVID-19 Vaccination for People with Disabilities

“…Pop-up locations have been an effective strategy to reach individuals in 'hot spot' areas of high COVID-19 incidence and transmission, and have generally used a walk-in model. 25 Data from Toronto show that allowing walk-in appointments through popup vaccination clinics, in some areas, have helped to reduce race-based disparities in COVID-19 vaccine access. 26,27 However, while these have improved access for residents of high SARS-CoV-2 risk areas, many images of these locations reveal opportunities to improve accessibility, particularly for at-risk people with disabilities.…”