Environmental Epidemiology最新文献

Prenatal air pollution exposure has been associated with adverse childhood cardiometabolic outcomes. It is unknown whether evidence of metabolic disruption associated with air pollution is identifiable at birth. We examined exposure to prenatal ambient air pollution and cord blood cardiometabolic biomarkers among 812 mother-infant pairs in the Healthy Start study.

Methods: Using inverse-distance-weighted interpolation of ambient concentrations obtained from stationary monitors, we estimated daily particulate matter ≤2.5 micrometers (PM_2.5) and ozone (O₃) concentrations at participant residences. Daily estimates were averaged by trimester, full-pregnancy, and the 7 and 30 days prior to delivery. Associations of air pollution with the following cord blood biomarkers were estimated via multivariable linear regression: glucose, insulin, glucose/insulin ratio (GIR), leptin, high-density lipoprotein (HDL) cholesterol, non-HDL cholesterol, free fatty acids, and triglycerides.

Results: In this Denver-based cohort, PM_2.5 concentrations were lower than in many US urban areas, but O₃ concentrations regularly exceeded federal air quality standards. Higher O₃ concentrations during pregnancy were consistently associated with higher insulin and lower GIR in cord blood. For example, an interquartile range increase in full pregnancy O₃ (6.3 parts per billion [ppb]) was associated with 0.13 log-µIU/ml (95% confidence interval [CI] = 0.04, 0.22) higher cord blood insulin, after adjusting for PM_2.5 and other confounders. We found positive, but generally nonsignificant, associations between PM_2.5 and leptin and isolated associations between pollutants during certain exposure periods and lipids.

Conclusions: In this cohort with moderately high O₃ exposure, prenatal concentrations of O₃ were positively associated with cord blood insulin. Future studies should examine the implications for offspring long-term health.

[This corrects the article DOI: 10.1097/EE9.0000000000000170.].

Background: Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19.

Methods: We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PM_2.5) between 2000 and 2016 and cumulative COVID-19 mortality counts through June 2020, December 2020, and April 2021. To show that spurious associations can be obtained in ecological data, we modeled the association between PM_2.5 and the prevalence of human immunodeficiency virus (HIV). We fitted negative binomial models, with a logarithmic offset for county-specific population, to these data. Natural cubic splines were used to describe the shape of the exposure-response curves.

Results: Our analyses revealed that the shape of the exposure-response curve between PM_2.5 and COVID-19 changed substantially over time. Analyses of COVID-19 mortality through June 30, 2021, suggested a positive linear relationship. In contrast, an inverse pattern was observed using county-level concentrations of PM_2.5 and the prevalence of HIV.

Conclusions: Our analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.