Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks

Abstract

BACKGROUND: Studies have reported that ambient air pollution is associated with an increased risk of developing or dying from coronavirus-2 (COVID-19). Methodological approaches to investigate the health impacts of air pollution on epidemics should differ from those used for chronic diseases, but the methods used in these studies have not been appraised critically. OBJECTIVES: Our study aimed to identify and critique the methodological approaches of studies of air pollution on infections and mortality due to COVID-19 and to identify and critique the methodological approaches of similar studies concerning severe acute respiratory syndrome (SARS). METHODS: Published and unpublished papers of associations between air pollution and developing or dying from COVID-19 or SARS that were reported as of 10 May 2020 were identified through electronic databases, internet searches, and other sources. RESULTS: All six COVID-19 studies and two of three SARS studies reported positive associations. Two were time series studies that estimated associations between daily changes in air pollution, one was a cohort that assessed associations between air pollution and the secondary spread of SARS, and six were ecological studies that used area-wide exposures and outcomes. Common shortcomings included possible cross-level bias in ecological studies, underreporting of health outcomes, using grouped data, the lack of highly spatially resolved air pollution measures, inadequate control for confounding and evaluation of effect modification, not accounting for regional variations in the timing of outbreaks' temporal changes in at-risk populations, and not accounting for nonindependence of outcomes. DISCUSSION: Studies of air pollution and novel coronaviruses have relied mainly on ecological measures of exposures and outcomes and are susceptible to important sources of bias. Although longitudinal studies with individual-level data may be imperfect, they are needed to adequately address this topic. The complexities involved in these types of studies underscore the need for careful design and for peer review.

Long-term exposure to air-pollution and COVID-19 mortality in England: A hierarchical spatial analysis

“…We also could not account for non-residential air-pollution exposure. Spatiotemporal variation in the strains of COVID-19 can introduce bias ( Villeneuve and Goldberg 2020 ), however at the time of publication there was no evidence supporting that strain types can confound the relationship between COVID-19 mortality and air-pollution.…”

“…While in our analysis the inclusion of area-level prevalence of hypertension, diabetes and COPD did not change the results, the ecological nature of the pre-existing conditions data does not allow us to account for the mediation effect at the individual level. Our study focuses on the mortality after contracting SARS-CoV-2, however we cannot rule out individual susceptibility to becoming infected as an explanation to the uncertainty in the effect estimates ( Villeneuve and Goldberg 2020 ). Such susceptibility can reflect immunosuppression, leading to later increases in inflammation ( Edoardo Conticini et al 2020 ) and thus worse prognosis, or even disease spread, as recent studies have suggested that PM 2.5 can proliferate COVID-19 transmission ( Bianconi et al 2020 ).…”

“…They were based on data aggregated on large spatial units and thus suffer from ecological fallacy (grouped levels association do not reflect individual ones) ( Wakefield 2008 ). Air pollution is characterised by high spatial variability, making the availability of mortality data at the same high spatial resolution crucial ( Villeneuve and Goldberg 2020 ). In addition, a coarse geographical resolution might lead to inadequate adjustment for confounders, when these are available at higher resolution ( Villeneuve and Goldberg 2020 ).…”

“…Air pollution is characterised by high spatial variability, making the availability of mortality data at the same high spatial resolution crucial ( Villeneuve and Goldberg 2020 ). In addition, a coarse geographical resolution might lead to inadequate adjustment for confounders, when these are available at higher resolution ( Villeneuve and Goldberg 2020 ). Most previous studies assessed cumulative deaths until mid or end of April and thus the generalisability of their results is limited to the early stages of the epidemic ( Liang et al, 2020 , Travaglio et al, 2020 , Wu et al, 2020 ).…”

The changing risk and burden of wildfire in the United States

“…It is currently unknown but probable that the historic fire season, and consequent smoke impacts, has also worsened COVID-related health outcomes, as early evidence suggests that exposure to air pollution increases both COVID cases and deaths in the United States ( 40 , 41 ) (a finding consistent with the relationship between pollution and other viral respiratory illness) ( 42 , 43 ). A better causal understanding of the impact of air pollution on COVID outcomes, including that from wildfires, is a critically urgent research priority, and scholars have provided guidelines on how air pollution/COVID relationships might be best studied ( 44 ). Findings from this research could be important in guiding labor- and finance-constrained firefighting effort and fuels management strategies as the pandemic continues.…”