Spatial and Temporal Relationship Between Population Factors and COVID-19 Testing Rates in the City of Toronto.

Aggregate testing and population data along with R code to replicate analysis of "Assessing the Spatial and Temporal Relationship Between Population Factors and COVID-19 Testing Rates in the City of Toronto." Shapefile for the City of Toronto can be accessed through Statistics Canada. For more questions about analysis please contact me directly at [e-mail] is the first indicator of disease burden and a means to identify at-risk populations. To better understand spatial variation of COVID-19 cases and their risk factors, one needs to consider testing. This study explores the spatial and temporal patterns of COVID-19 testing rates in the City of Toronto while assessing population factors that can potentially explain the varied testing rate distribution.MethodsThis 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.ResultsAcross the first four waves of the COVID-19 pandemic, there was heterogeneity in relative testing rates across FSA and overtime. 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 with varying extents.ConclusionAs 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.Data SourcesCOVID-19 testing information: GitHub - ccodwg/CovidTimelineCanada: A definitive dataset for COVID-19 in CanadaShapefiles: 2016 Census Boundary filesCensus Information: Census Profile - Age, Sex, Type of Dwelling, Families, Households, Marital Status, Language and Income, 2016 Census - Open Government PortalCOVID-19 vaccination data: raw.githubusercontent.com/jeanpaulrsoucy/ices-vaccine-coverage-by-fsa-time-series/refs/heads/main/data/fsa_ts.csv