An ecological analysis of long-term exposure to PM2.5 and incidence of COVID-19 in Canadian health regions

Abstract

BackgroundAmbient fine particulate matter (PM2.5) is associated with a wide range of acute and chronic health effects, including increased risk of respiratory infection. However, evidence specifically related to novel coronavirus disease (COVID-19) is limited. MethodsCOVID-19 case counts for 111 Canadian health regions were obtained from the COVID-19 Canada Open Data portal. Annual PM2.5 data for 2000–2016 were estimated from a national exposure surface based on remote sensing, chemical transport modelling and ground observations, and minimum and maximum temperature data for 2000–2015 were based on a national interpolated surface derived from thin-plate smoothing splines. Population counts and sociodemographic data by health region were obtained from the 2016 census, and health data (self-rated health and prevalence of smoking, obesity, and selected chronic diseases) by health region, were obtained from the Canadian Community Health Survey. Data on total number of COVID-19 tests and changes in mobility comparing post-vs. pre-introduction of social distancing measures were available by province. Data were analyzed using negative binomial regression models. ResultsAfter controlling for province, temperature, demographic and health characteristics and days since peak incidence by health region, long-term PM2.5 exposure exhibited a positive association with COVID-19 incidence (incidence rate ratio 1.07, 95% confidence interval 0.97–1.18 per μg/m3). This association was larger in magnitude and statistically significant in analyses excluding provinces that reported cases only for aggregated health regions, excluding health regions with less than median population density, and restricted to the most highly affected provinces (Quebec and Ontario). ConclusionsWe observed a positive association between COVID-19 incidence and long-term PM2.5 exposure in Canadian health regions. The association was larger in magnitude and statistically significant in more highly affected health regions and those with potentially less exposure measurement error. While our results generate hypotheses for further testing, they should be interpreted with caution and require further examination using study designs less prone to bias.