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Exposure to Traffic-related Air Pollution During Pregnancy and Term Low Birth Weight: Estimation of Causal Associations in a Semiparametric Model

机译:妊娠和足月低出生体重期间与交通有关的空气污染:半参数模型中的因果关系估计

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摘要

Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.
机译:与交通有关的空气污染被认为是造成健康问题的重要因素。流行病学分析表明,产前暴露于交通相关的空气污染物可能与不良的出生结局有关。但是,没有足够的证据得出这种关系是因果关系的结论。空气污染,遗传学和早期生命事件研究涵盖了2000-2006年间加利福尼亚州圣华金河谷4个县的所有妇女分娩。使用机器学习和针对怀孕期间每个交通四分位数的有针对性的最大似然估计,可以估计人口中足月婴儿低出生体重的可能性。如果每个人都住在大容量高速公路附近(大约是交通密度的四分之一),则估计的低出生体重的概率为2.27%(95%置信区间:2.16、2.38),而2.02%(95%置信度)时间间隔:1.90、2.12),如果每个人都住在较小的本地道路附近(交通密度的第一个四分位数)。在没有适用于数据的任意模型假设的情况下,对潜在因果关系的评估应导致相对无偏的估计。目前的结果支持以前的研究结果,即产前暴露于交通相关的空气污染可能会对足月婴儿的出生体重产生不利影响。

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