Assessing COVID-19 Risk Factors in Toronto Using a Localized Spatio-Temporal Conditional Autoregressive Model

PurposeMost spatio-temporal models identify COVID-19 sociodemographic and socioeconomic risk factors using methods that assume a single spatial dependency pattern across the city, which may not reflect reality. The purpose of this study is to apply a spatially and temporally localized Bayesian model to identify COVID-19 risk factors that account for localized context. MethodsFor this study, a spatio-temporal localized Bayesian Hierarchical Model (ST-LCAR) was used to assess the relationships between population factors (age, sex, income, visible minority status, and education) and COVID-19 relative risk. The ST-LCAR model accounts for spatial and temporal autocorrelation through spatio-temporal random effects along with piecewise intercepts to capture step changes in relative risk patterns that might be reflective of underlying local contexts. This study focuses on the first four complete waves of the COVID-19 pandemic across Forward Sortation Areas (FSAs) in the City of Toronto. ResultsA 10-percentage-point increase in the proportion of residents who identify as visible minorities was associated with a 3% increase in COVID-19 relative risk; however, this association varied across different social contexts. On the other hand, a 10-percentage-point increase in the proportion of residents with post-secondary education was associated with a 22% decrease in relative risk. Beyond quantitative relationships, our model identified 3 times higher COVID-19 relative risk in the northwestern portion of the city, with patterns varying over time. ConclusionThe different COVID-19 patterns in the city of Toronto may have been shaped by the complex and diverse social contexts, products of ingrained systems of structural inequities that influence the living, working, and economic conditions of city residents. Public health interventions and pandemic preparedness should integrate an equity-focused lens that considers the diverse social contexts across the city and how it shapes health outcomes.