Neighbourhood-level risk factors of COVID-19 incidence and mortality

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.