Assessing the spatial–temporal relationship between population factors and COVID-19 testing rates in the City of Toronto

Background The COVID-19 experience in Toronto, Canada, varied over time. However, the focus on spatial and temporal patterns of COVID-19 in the city and associated population factors has mainly been concentrated on case patterns with less exploration of testing. As testing is the first indicator of disease burden and a means to identify at-risk populations, we sought to address this research gap by exploring the spatial and temporal patterns of COVID-19 testing rates in the City of Toronto while assessing population factors associated with varied testing rate distribution. Methods This study uses spatial-temporal Bayesian hierarchical models with conditional autoregressive priors to visually present the changing trends of COVID-19 testing rates over space and time while quantifying the potential relationship between socio-economic and sociodemographic characteristics and COVID-19 testing rates. This study focuses on the first four waves of COVID-19 using Forward Sortation Areas (FSAs) as the spatial unit of analysis. Results Across the first four waves of the COVID-19 pandemic, the maps generated from our Bayesian models showed heterogeneity of relative testing rates across FSA and over time. Quantitatively, a 10-percentage point increase in visible minorities in an FSA was associated with up to 8% decrease in relative testing rate. Other factors, such as age, sex, unemployment rate, and education attainment were also associated with relative testing rates, but to varying extents. Conclusion As COVID-19 remains endemic in Toronto with emerging pandemics a continuous public health concern, understanding testing heterogeneity will help inform more equitable testing strategies and stronger pandemic preparedness.