Growing concentration of COVID-19 by socio-economic determinants and geography in Toronto, Canada: an observational study
This article was originally published here
Anne Epidémiol. July 25, 2021: S1047-2797 (21) 00216-7. doi: 10.1016 / j.annepidem.2021.07.007. Online ahead of print.
BACKGROUND: Inequalities in the burden of COVID-19 were observed early in Canada and around the world, suggesting that economically marginalized communities face disproportionate risks. However, there has been a limited systematic assessment of how risk heterogeneity has evolved in large urban centers over time.
OBJECTIVE: To fill this gap, we quantified the extent of risk heterogeneity in Toronto, Ontario, from January to November 2020 using a retrospective, population-based observational study using surveillance data.
METHODS: We generated epidemic curves by social determinants of health (SDOH) and raw Lorenz curves by neighborhoods to visualize inequalities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson correlation coefficients.
RESULTS: The Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]: 0.36-0.47) and 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); adequate housing (0.21, 95% CI: 0.14-0.30); multigenerational households (0.19, 95% CI: 0.15-0.23); and essential workers (0.28, 95% CI: 0.23-0.34).
CONCLUSIONS: There was a rapid epidemiological transition from high-income to low-income neighborhoods, with the Lorenz curve moving from low to above the tie line across the SDOH. Moving forward requires integrating programs and policies to address socioeconomic inequalities and structural racism into COVID-19 prevention and vaccination programs.