Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004.
Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004.
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DOI：
10.1097/ede.0000000000000146
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发表时间：
2014-09
期刊：
影响因子：
--
通讯作者：
Tolbert P
中科院分区：
文献类型：
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作者：
Winquist A;Kirrane E;Klein M;Strickland M;Darrow LA;Sarnat SE;Gass K;Mulholland J;Russell A;Tolbert P
由于环境空气污染暴露是以混合物的形式发生的，考虑多种污染物的联合影响可能会促进我们对空气污染健康影响的理解。我们评估了1998-2004年间，在亚特兰大儿科哮喘急诊科就诊中，空气污染物在选定的组合(代表氧化性气体、二次气体、交通、发电厂和标准污染物；使用标准污染物和细颗粒物(PM2.5)的组合构成)中的联合影响。使用控制时间趋势、气象和每日非哮喘上呼吸道急诊次数的多污染物泊松广义线性模型来评估联合效应。比率比率(RR)是根据每种污染物浓度四分位数范围内增加的综合效应计算的。所有选定污染物组合的增加与暖季儿童哮喘急诊就诊的增加相关[例如，标准污染物(包括臭氧、一氧化碳、二氧化氮、二氧化硫和PM2.5)的联合有效率比=1.13(95%可信区间1.06-1.21)]。没有非线性效应的模式的冷季联合效应一般弱于暖季效应。由于控制混杂，多污染物模型的联合效应估计值往往小于基于单污染物模型结果计算的估计值。与没有相互作用的模型相比，包括一阶污染物相互作用的模型的联合效应估计值基本相似。有证据表明，冷季效应是非线性的。我们的分析说明了对联合影响的考虑如何增加了我们对多污染物暴露对健康影响的理解，也说明了计算和解释多污染物联合影响所涉及的一些复杂性。
Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of air pollution health effects. We assessed the joint effect of air pollutants in selected combinations (representative of oxidant gases, secondary, traffic, power plant, and criteria pollutants; constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components) on pediatric asthma emergency department (ED) visits in Atlanta during 1998–2004. Joint effects were assessed using multi-pollutant Poisson generalized linear models controlling for time trends, meteorology and daily non-asthma upper respiratory ED visit counts. Rate ratios (RR) were calculated for the combined effect of an interquartile-range increment in each pollutant’s concentration. Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma ED visits [e.g., joint effect rate ratio=1.13 (95% confidence interval 1.06–1.21) for criteria pollutants (including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5)]. Cold-season joint effects from models without non-linear effects were generally weaker than warm-season effects. Joint effect estimates from multi-pollutant models were often smaller than estimates calculated based on single-pollutant model results, due to control for confounding. Compared with models without interactions, joint effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of non-linear cold-season effects. Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multi-pollutant exposures, and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.