BackgroundRacialized and low income communities face disproportionally high rates of coronavirus 2019 (COVID-19) infection and death. However, data on inequities in COVID-19 across granular categories of socio-demographic characteristics is more sparse.MethodsNeighbourhood-level counts of COVID-19 cases and deaths in Ontario, Canada recorded as of July 28th, 2020 were extracted from provincial and local reportable infectious disease surveillance systems. Associations between COVID-19 incidence and mortality and 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics were estimated with Poisson generalized linear mixed models. Housing characteristic variables were subsequently added to models to explore if housing may have a confounding influence on the relationships between immigration, race, and socio-economic status and COVID-19 incidence.ResultsThere were large inequities in COVID-19 incidence and mortality across the socio-demographic variables examined. Neighbourhoods having a higher proportion immigrants, racialized populations, large households and low socio-economic status were associated with COVID-19 risk. Adjusting for housing characteristics, especially unsuitably crowded housing, attenuated COVID-19 risks. However persistent risk remained for neighbourhoods having high proportions of immigrants, racialized populations, and proportion of Black, Latin American, and South Asian residents.ConclusionsSocio-demographic factors account for some of the neighbourhood-level differences in COVID-19 across Ontario. Housing characteristics account for a portion, but not all, of the excess burden of COVID-19 experienced by immigrant, racialized, low income and low education populations.

Objectives We aimed to estimate associations between COVID-19 incidence and mortality with neighbourhood-level immigration, race, housing, and socio-economic characteristics. Methods We conducted a population-based study of 28,808 COVID-19 cases in the provincial reportable infectious disease surveillance systems (Public Health Case and Contact Management System) which includes all known COVID-19 infections and deaths from Ontario, Canada reported between January 23, 2020 and July 28, 2020. Residents of congregate settings, Indigenous communities living on reserves or small neighbourhoods with populations <1,000 were excluded. Comparing neighbourhoods in the 90th to the 10th percentiles of socio-demographic characteristics, we estimated the associations between 18 neighbourhood-level measures of immigration, race, housing and socio-economic characteristics and COVID-19 incidence and mortality using Poisson generalized linear mixed models. Results Neighbourhoods with the highest proportion of immigrants (relative risk (RR): 4.0, 95%CI:3.5–4.5) and visible minority residents (RR: 3.3, 95%CI:2.9–3.7) showed the strongest association with COVID-19 incidence in adjusted models. Among individual race groups, COVID-19 incidence was highest among neighbourhoods with the high proportions of Black (RR: 2.4, 95%CI:2.2–2.6), South Asian (RR: 1.9, 95%CI:1.8–2.1), Latin American (RR: 1.8, 95%CI:1.6–2.0) and Middle Eastern (RR: 1.2, 95%CI:1.1–1.3) residents. Neighbourhoods with the highest average household size (RR: 1.9, 95%CI:1.7–2.1), proportion of multigenerational families (RR: 1.8, 95%CI:1.7–2.0) and unsuitably crowded housing (RR: 2.1, 95%CI:2.0–2.3) were associated with COVID-19 incidence. Neighbourhoods with the highest proportion of residents with less than high school education (RR: 1.6, 95%CI:1.4–1.8), low income (RR: 1.4, 95%CI:1.2–1.5) and unaffordable housing (RR: 1.6, 95%CI:1.4–1.8) were associated with COVID-19 incidence. Similar inequities were observed across neighbourhood-level sociodemographic characteristics and COVID-19 mortality. Conclusions Neighbourhood-level inequities in COVID-19 incidence and mortality were observed in Ontario, with excess burden experienced in neighbourhoods with a higher proportion of immigrants, racialized populations, large households and low socio-economic status.