Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study

Abstract

Background: Inequities in the burden of COVID-19 observed across Canada suggest heterogeneity within community transmission. Objectives: To quantify the magnitude of heterogeneity in the wider community (outside of long-term care homes) in Toronto, Canada and assess how the magnitude in concentration evolved over time (January 21 to November 21, 2020). Design: Retrospective, population-based observational study using surveillance data from Ontario's Case and Contact Management system. Setting: Toronto, Canada. Participants: Laboratory-confirmed cases of COVID-19 (N=33,992). Measurements: We generated epidemic curves by SDOH and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 cases by social determinants of health (SDOH) and estimated the crude Gini coefficient. We examined the correlation between SDOH using Pearson correlation coefficients. Results: The Gini coefficient of cumulative cases by population size was 0.41 (95% CI: 0.36-0.47) and were estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI: 0.16-0.28); recent immigration (0.12, 95%CI: 0.09-0.16); suitable housing (0.21, 95%CI: 0.14-0.30); multi-generational households (0.19, 95%CI: 0.15-0.23); and essential workers (0.28, 95% CI: 0.23-0.34). Most SDOH were highly correlated. Locally acquired cases were concentrated in higher income neighbourhoods in the early phase of the epidemic, and then concentrated in lower income neighbourhoods. Mirroring the trajectory of epidemic curves by income, the Lorenz curve shifted over time from below to above the line of equality with a similar pattern across SDOH. Limitations: Study relied on area-based measures of the SDOH and individual case counts of COVID-19. We cannot infer concentration of cases by specific occupational exposures given limitation to broad occupational categories. Conclusion: COVID-19 is increasingly concentrated by SDOH given socioeconomic inequities and structural racism. Primary Funding Source: Canadian Institutes of Health Research.

The Role of a Resilient Information Infrastructure in COVID-19 Vaccine Uptake in Ontario

“…In Canada, areas with the highest proportions of visible minorities had COVID-19-related death rates that were close to two times higher than areas with the lowest proportions; in Quebec and Ontario, the comparable death rate was three times higher, and in British Columbia, it was more than 10 times higher (Statistics Canada 2021). In Ontario, individuals have been at increased odds of a COVID-19 diagnosis and related hospitalizations and deaths if they live in areas with high household density, low educational attainment and larger proportions of recent immigrants (Sundaram et al 2020), challenging social determinants of health and measures of income inequality (Mishra et al 2021), higher ethno-cultural diversity (Ontario Agency for Health Protection and Promotion 2020a) and neighbourhood material deprivation (Ontario Agency for Health Protection and Promotion 2020b). Immigrants, refugees and other newcomers, who make up roughly 25% of the Ontario population and are more likely than the Canadian-born to be essential workers, accounted for 44% of all COVID-19 cases in the first wave (Guttmann et al 2020).…”

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“…2 Risk factors for heightened acquisition and transmission in these neighborhoods include lower-income, multigenerational households, high density housing, precarious working conditions, a lack of paid sick leave, and difficulty in access to or uptake of interventions such as isolation, quarantine and testing. [3][4][5] Ontario's initial mass COVID-19 vaccination strategy in place until April 8, 2021 was based on per capita regional allocation of vaccines with subsequent distribution -in order of relative priority -by age, chronic health conditions and high-risk congregate care settings, COVID-19 hotspots, and essential workers in nonhealthcare settings.…”

“…The top third represents the 33% of neighbourhoods (by overall population size) with the highest proportion of essential non-healthcare workers; the bottom third represents the 33% of neighbourhoods (by overall population size) with the lowest proportion of essential non-healthcare workers. Neighbourhoods are defined by dissemination areas of 400 to 700 residents 4,15. Data sourced from Case and Contact Management Solutions (CCM)+ and the Statistics Canada 2016 Census.…”

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“…Ontario data show a higher risk for SARS-CoV-2 exposure and infection among racialized and low-income communities. 7 York, Peel and Toronto have seen the highest rates of cases per capita. 7 Workplace outbreaks have contributed to a significant number of cases.…”