Open AccessWithin-City Variation in Reactive Oxygen Species from Fine Particle Air Pollution and COVID-19
Author(s) -
David M. Stieb,
Greg J. Evans,
Teresa To,
Pascale S. J. Lakey,
Manabu Shiraiwa,
Marianne Hatzopoulou,
Laura Minet,
Jeffrey R. Brook,
Richard T. Burnett,
Scott Weichenthal
Publication year - 2021
Publication title -
american journal of respiratory and critical care medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.272
H-Index - 374
eISSN - 1535-4970
pISSN - 1073-449X
DOI - 10.1164/rccm.202011-4142oc
Subject(s) - interquartile range , air pollution , environmental health , particulates , confidence interval , population , rate ratio , environmental science , atmospheric sciences , medicine , toxicology , ecology , biology , geology
Rationale: Evidence linking outdoor air pollution with coronavirus disease (COVID-19) incidence and mortality is largely based on ecological comparisons between regions that may differ in factors such as access to testing and control measures that may not be independent of air pollution concentrations. Moreover, studies have yet to focus on key mechanisms of air pollution toxicity such as oxidative stress. Objectives: To conduct a within-city analysis of spatial variations in COVID-19 incidence and the estimated generation of reactive oxygen species (ROS) in lung lining fluid attributable to fine particulate matter (particulate matter with an aerodynamic diameter ⩽2.5 μm [PM 2.5 ]). Methods: Sporadic and outbreak-related COVID-19 case counts, testing data, population data, and sociodemographic data for 140 neighborhoods were obtained from the City of Toronto. ROS estimates were based on a mathematical model of ROS generation in lung lining fluid in response to iron and copper in PM 2.5 . Spatial variations in long-term average ROS were predicted using a land-use regression model derived from measurements of iron and copper in PM 2.5 . Data were analyzed using negative binomial regression models adjusting for covariates identified using a directed acyclic graph and accounting for spatial autocorrelation. Measurements and Main Results: A significant positive association was observed between neighborhood-level ROS and COVID-19 incidence (incidence rate ratio = 1.07; 95% confidence interval, 1.01-1.15 per interquartile range ROS). Effect modification by neighborhood-level measures of racialized group membership and socioeconomic status was also identified. Conclusions: Examination of neighborhood characteristics associated with COVID-19 incidence can identify inequalities and generate hypotheses for future studies.