Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth. (December 2018)

• Record Type: Journal Article
• Authors: Wang, Qiong; Benmarhnia, Tarik; Zhang, Huanhuan; Knibbs, Luke D.; Sheridan, Paige; Li, Changchang; Bao, Junzhe; Ren, Meng; Wang, Suhan; He, Yiling; Zhang, Yawei; Zhao, Qingguo; Huang, Cunrui
• Is Part Of: Environment international. Volume 121(2018)Part 1
• Page Start: 317
• Page End: 324
• Publication Date: 2018-12
• Subjects: Air pollution -- Preterm birth -- Distributed lag model -- Susceptible exposure window; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2018.09.021
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population ~14.5 million), including 469, 975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM2.5, PM10, NO2, SO2, and O3 from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonality, and individual-level covariates. We also considered moderate PTB (32–36 gestational weeks) and very PTB (28–31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM2.5 exposure during the 20th to 28th gestational weeks (27.0 μg/m 3 ) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010–1.059). The significant exposure windows were the 19th–28th weeks for PM10, the 18th–31st weeks for NO2, and the 23rd–31st weeks for O3, respectively. The strongest associations were observed in the 25th week for PM10 (IQR = 37.0 μg/m 3 ; HR = 1.048, 95% CI:1.034–1.062), the 26th week for NO2 (IQR = 29.0 μg/m 3 ; HR = 1.060, 95% CI:1.028–1.094), and in the 28th week for O3 (IQR = 90.0 μg/m 3 ; HR = 1.063, 95% CI:1.046–1.081). Similar patterns were observed for moderate PTB (32–36 gestational weeks) and very PTB (28–31 gestational weeks) for PM2.5, PM10, NO2 exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO2 exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China. Highlights: Limited studies identified susceptible windows for prenatal air pollution and preterm birth at a weekly level. Prenatal PM2.5, PM10, NO2, and O3 exposure is associated with preterm birth. The most susceptible exposure window for each pollutant is gestational weeks 18–31. Distributed lag models are applicable for identifying the susceptible windows.