Joint spatial modelling of COVID-19 severity among seniors: a Bayesian shared component approach using health administrative data from Ontario, Canada

Purpose — Jointly monitoring adverse COVID-19 outcomes among seniors is critical for assessing outbreak severity. These outcomes are often influenced by socioeconomic and demographic conditions and may co-occur in space, indicating shared structural risks that inform targeted responses.

Methods — We analyzed severe COVID-19 outcomes among adults aged 65 + in Ontario (January 2020–March 2022) using data from the Ontario Health Data Platform supported by ICES. A Bayesian shared component model with Integrated Nested Laplace Approximation at the forward sortation area level included socioeconomic and demographic covariates.

Results — The shared component explained ∼75 % of the total modeled spatial variability. High risks clustered in southern Ontario, while lower risks occurred in central and northern regions. Material deprivation was positively associated with death (RR 1.12, 95 % CrI: 1.04–1.21) and multiple hospitalizations (RR 1.20, 95 % CrI: 1.13–1.29). Racialized/newcomer population concentration was positively associated with death (RR 1.25, 95 % CrI: 1.14–1.38) and with single hospitalizations (RR 1.18, 95 % CrI: 1.11–1.24). The percentage of seniors was inversely associated with hospitalization (RR 0.98, 95 % CrI: 0.96–0.99) but not death.

Conclusions — Findings highlight structural inequities in pandemic severity and suggest targeted, equity-oriented strategies in guiding pandemic preparedness and response.