Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California

Abstract

Background: Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models.

Methods: We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM_2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM_0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients.

Findings: We found significant associations between COVID-19 death and several air pollution exposures, including: PM_2.5 mass, PM_0.1 mass, PM_2.5 nitrates, PM_2.5 elemental carbon, PM_2.5 on-road diesel, and PM_2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM_2.5 mass and PM_2.5 nitrate to HR ∼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects.

Interpretation: Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

Keywords: Air pollution; COVID-19; Cohort study; Death; Southern California.

Fig. 1

Hospital admission dates, among patients in study.

Fig. 2

Predicted PM_2.5 mass exposure fields during four seasons in the year 2016. All units µg/m⁻³(−|-).

Fig. 3

Risk plots showing hazard ratios of all pollutants based on the interquartile range increment.

Fig. 4

Risk plots showing PM_2.5 stratified by tertile maximum temperature and relative humidity during the month of diagnosis. See ODS for stratified risk plots for other pollutants.

Fig. 5

Dose-response Functions for Pollutants and Metrological Variables. All pollutant concentrations expressed in μ/m³, temperature in degrees C, and relative humidity in percent.