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

Background — Inequities in the burden of COVID-19 were observed early in Canada and around the world suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time.

Purpose — To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January-November, 2020 using a retrospective, population-based observational study using surveillance data.

Methods — We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients.

Results — 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); 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).

Conclusions — There was rapid epidemiologic transition from higher to lower income neighbourhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.