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The risk of COVID-19 severity and mortality differs markedly by age, socio-demographic characteristics and pre-existing health status. Various studies have suggested that higher air pollution exposures also increase the likelihood of dying from COVID-19.
Objectives: To assess the association between long-term outdoor air pollution (NO 2 , NOx, PM 10 and PM 2.5 ) concentrations and the risk of death involving COVID-19, using a large individual-level dataset.
We used comprehensive individual-level data from the Office for National Statistics’ Public Health Data Asset for September 2020 to January 2022 and London Air Quality Network modelled air pollution concentrations available for 2016. Using Cox proportional hazard regression models, we adjusted for potential confounders including age, sex, vaccination status, dominant virus variants, geographical factors (such as population density), ethnicity, area and household-level deprivation, and health comorbidities.
There were 737,356 confirmed COVID-19 cases including 9,315 COVID-related deaths. When only adjusting for age, sex, and vaccination status, there was an increased risk of dying from COVID-19 with increased exposure to all air pollutants studied (NO 2 : HR 1.07 [95% confidence interval: 1.04-1.12] per 10 μg/m 3 ; NOx: 1.05[1.02-1.09] per 20 μg/m 3 ; PM 10 : 1.32[1.15-1.51] per 10 μg/m 3 ; PM 2.5 : 1.29[1.12-1.49] per 5 μg/m 3 ). However, after adjustment including ethnicity and socio-economic factors the HRs were close to unity (NO 2 : 0.98[0.90-1.06]; NOx: 0.99[0.94-1.04]; PM 10 : 0.95[0.74-1.22]; PM 2.5 : 0.90[0.67-1.20]). Additional adjustment for dominant variant or pre-existing health comorbidities did not alter the results.
Observed associations between long-term outdoor air pollution exposure and COVID-19 mortality in London are strongly confounded by geography, ethnicity and deprivation.
Using a large individual-level dataset, we found that a positive association between long-term outdoor air pollution and COVID-19 mortality in London did not persist after adjusting for confounders including population density, ethnicity and deprivation.
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Reviewers: Reviewers: Sophie M. Woodward, Xiao Wu | 📒📒📒◻️◻️