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 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 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 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); multigenerational 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 neighborhoods 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.

Association between nursing home crowding and outbreak-associated respiratory infection and death prior to the COVID-19 pandemic between 2014 and 2019 in Ontario, Canada

“…16 Evidence suggests that crowding is associated with increased SARS-CoV-2 incidence across a range of residential settings, including nursing homes, prisons and households. 4,[17][18][19][20] Our results extend these findings, suggesting that crowding is not only a risk factor for SARS-CoV-2 virus, but also associated with increased incidence of other acute respiratory infections. We hypothesize that crowded sleeping quarters have increased transmission across a range of mechanisms including aerosol, droplet, and direct and indirect contact.…”

Factors Influencing Mobility During the COVID-19 Pandemic in Community-Dwelling Older Adults

“…48,49 In the context of the restrictions put in place due to COVID-19, our results suggest older adults who rated their neighborhood environment as more pleasant to walk in had higher mobility scores. COVID-19 infection rates are closely linked to community 50 and geographical influences, 51 which in turn may affect older adults' perceived walkability. Recognizing that these restrictions might have deleterious effects, one strategy used during COVID-19 was to regulate and create outdoor spaces to promote physical activity without compromising safety (e.g., mixed land use, pedestrian, and bicycle systems, compliant with disability needs).…”

“…14,53,54 Yet, these factors were not significant in our model, suggesting physical and environmental factors become of greater importance to mobility when public health restrictions are in place. That said, given the inequities made apparent as a result of COVID-19, 51 it was surprising that the socioeconomic status indicator (i.e., education) was not associated with mobility. 43,55 This may be because close to half of our sample completed post-secondary education.…”

Effects of socio-economic factors on elementary school student COVID-19 infections in Ontario, Canada

“…Thus, student susceptibility to SARS-CoV-2 must be viewed through a social determinant framework for public health interventions to prevent onward transmission in schools and their communities. 38 Finally, increasing inequities in prevalence have also been noted over time, and associated with households with lower incomes, crowding, and essential workers. 39,40 Household size has been shown to be associated with increased odds of a positive test result for SARS-CoV-2, likely related to more intense exposure compared to other settings.…”