The Impact of Air Pollution Exposure on COVID-19 Severity and Mortality

PROJECT SUMMARY/Abstract: The novel coronavirus disease 2019 (COVID-19) pandemic is currently the most emergent public healthdisaster of the entire world. COVID-19 disproportionately affects older adults, people having history of smokingand comorbidities like diabetes, obesity, and hypertension. There is no vaccine and treatment for COVID-19,therefore, public health interventions have been taken to control disease transmission. Also, searchingmodifiable environmental factors that can help reduce the COVID-19 severity and mortality is crucial. Airpollution exposure has been shown to have a systemic effect on the human body including lung functionimpairment and immune alterations. Recent two ecologic studies from Europe and the United States havesuggested that higher long-term ambient air pollution exposure (PM2.5 and NO2) significantly contributes to theCOVID-19 mortality. These findings from aggregated data of air pollution exposure and total number of deathsin large geographic areas need to be further verified by cohort study with individual data of air pollutionexposure and COVID-19 case progression and mortality. Also, susceptibility factors such as low socialeconomic status (SES), race/ethnicity, smoking exposures and comorbidities need to be accounted for in theanalysis. To address the urgent public health question about the role of air pollution exposure in COVID-19progression, we propose to conduct a retrospective cohort study based on the existing EMR data of all COVID-19 cases (n>7000) diagnosed at Kaiser Permanente Southern California (KPSC) medical centers. Specificaims of this proposal have been expanded from our ongoing NIEHS-supported R01 study investigatingprenatal air pollution exposure and children's autism risk among 440,000 KPSC mother-child pairs (APARstudy, 1R01ES029963). In this proposal, the main outcomes of interest are COVID-19 severity assessed byhospitalization, ICU admission and ventilator use, as well as death. Details dates will be available throughEMR. Natural language processing technologies will be applied to identify the date of first COVID-19 symptomonset for each case. Thereafter, the earliest date among COVID-19 symptom onset and clinical diagnosis willbe used as the study entry date. Then short- and long-term air pollution exposure will be estimated for eachcase by averaging air pollution exposure levels during one-month and one-year before the study entry date.Both ambient (PM2.5, PM10, NO2 and O3) and traffic-related (line dispersion model estimated NOx) air pollutionexposure will be assessed based on individual residential addresses. Detailed residential history of all KPSCmembers have been well-maintained by the KPSC system. Key covariates including age, sex, race/ethnicity,smoking exposure, body mass index, comorbidities, medication use, and meteorological data will be extractedto control for confounding and identify susceptible high risk subgroups. With the unique cohort data resourceand world-renowned epidemiologists and exposure scientists, this one-year study will greatly enhance ourknowledge about the effect of air pollution exposure on COVID-19 progression and death.