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Ensemble-Based Source Apportionment of Fine Particulate Matter and Emergency Department Visits for Pediatric Asthma

机译:儿科哮喘的细颗粒物和急诊就诊的基于集合的源分配

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摘要

Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002–2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17).
机译:流行病学研究利用细颗粒物的源分配(SA)表明,某些来源的颗粒比其他来源的颗粒对健康的危害更大。但是,很难量化与给定的SA方法相关的不确定性。在本研究中,我们使用一种基于集合的新SA技术,研究了细颗粒物的来源贡献与佐治亚州亚特兰大市急诊就诊的小儿哮喘(2002-2010年)之间的关联。来自4种SA方法的六种每日来源贡献被合并为一个整体来源贡献。为了更好地说明暴露的不确定性,从其后分布中采样了10个源剖面,得出10个具有每日SA浓度的时间序列。对于这些时间序列中的每个时间序列,使用具有不同滞后结构的Poisson广义线性模型来估计6个来源的健康关联。将来自10个估算源贡献时间序列的特定于源的健康关联的比率进行合并,从而得出具有较高置信区间的健康关联,以更好地说明暴露的不确定性。观察到与儿童哮喘有不良关联,即暴露于柴油车(速率比= 1.06,95%置信区间:1.01,1.10)和汽油车(速率比= 1.10,95%置信区间)产生的颗粒中暴露8天。 :1.04,1.17)。

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