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

• Published in: Environment international Vol. 146; p. 106316
• Main Authors: Konstantinoudis, Garyfallos, Padellini, Tullia, Bennett, James, Davies, Bethan, Ezzati, Majid, Blangiardo, Marta
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.01.2021; The Authors. Published by Elsevier Ltd; Elsevier
• Subjects: Air Pollutants - analysis; Air Pollutants - toxicity; Air Pollution - adverse effects; Air Pollution - analysis; Air-pollution; autocorrelation; Bayes Theorem; Bayesian spatial models; Bayesian theory; chronic exposure; climate; COVID-19; COVID-19 infection; Cross-Sectional Studies; disease transmission; England; England - epidemiology; environment; Environmental Exposure - adverse effects; Environmental Exposure - analysis; Humans; Mortality; Nitrogen dioxide; Nitrogen Dioxide - analysis; Nitrogen Dioxide - toxicity; Particular matter; Particulate Matter - analysis; Particulate Matter - toxicity; relative risk; SARS-CoV-2; Spatial Analysis; England; COVID-19; Nitrogen dioxide; Mortality; Particular matter; Bayesian spatial models; Air-pollution
• ISSN: 0160-4120; 1873-6750; 1873-6750
• DOI: 10.1016/j.envint.2020.106316

•We downscaled the coarse spatial resolution of deaths to mitigate ecological bias.•We accounted for confounding, spatial autocorrelation and pre-existing conditions.•We found some evidence of an effect of NO2 on COVID-19 mortality.•The effect of long-term PM2.5 exposure remains more uncertain.•Our spatial model captured strong patterns likely reflecting disease spread. Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.