Abstract
BACKGROUND: Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19. METHODS: We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PM(2.5)) between 2000 and 2016 and cumulative COVID-19 mortality counts through June 2020, December 2020, and April 2021. To show that spurious associations can be obtained in ecological data, we modeled the association between PM(2.5) and the prevalence of human immunodeficiency virus (HIV). We fitted negative binomial models, with a logarithmic offset for county-specific population, to these data. Natural cubic splines were used to describe the shape of the exposure-response curves. RESULTS: Our analyses revealed that the shape of the exposure-response curve between PM(2.5) and COVID-19 changed substantially over time. Analyses of COVID-19 mortality through June 30, 2021, suggested a positive linear relationship. In contrast, an inverse pattern was observed using county-level concentrations of PM(2.5) and the prevalence of HIV. CONCLUSIONS: Our analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.