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A Bayesian model for studying urban air pollution and respiratory symptoms in children

机译:用于研究儿童城市空气污染和呼吸道症状的贝叶斯模型

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摘要

The association between traffic-related air pollution and long-term respiratory health problems has been extensively studied. In this work we evaluated the effect of traffic-related air pollution on respiratory symptoms in children living in Florence, Italy. Children were selected from different schools part of the Italian Studies on Respiratory Disorders in Children and the Environment 2. Exposure to traffic air pollution was assessed through a dispersion model and weighted by distance using four different criteria. A Bayesian hierarchical logistic regression model was specified to assess the impact of traffic air pollution on cough or phlegm and asthma. Potential confounders were included in the analysis. Familiarity of asthma and exposure to second-hand smoking showed the strongest positive association with respiratory symptoms. No evidence of increasing risk of asthma with urban air pollution was found, while some evidence of an association was observed for carbon dioxide and cough or phlegm.
机译:与交通有关的空气污染与长期呼吸健康问题之间的联系已得到广泛研究。在这项工作中,我们评估了与交通有关的空气污染对生活在意大利佛罗伦萨的儿童呼吸道症状的影响。儿童是从《意大利儿童与环境呼吸系统疾病研究》的不同学校中选出的。2通过分布模型评估交通空气污染的暴露程度,并使用四个不同的标准按距离加权。贝叶斯分层逻辑回归模型被指定来评估交通空气污染对咳嗽或痰和哮喘的影响。分析中包括潜在的混杂因素。熟悉哮喘和接触二手烟显示出与呼吸道症状最强的正相关。没有发现增加城市空气污染引起哮喘风险的证据,而观察到一些证据表明二氧化碳与咳嗽或痰有关联。

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