Practice of Epidemiology Potential for BiasWhen Estimating Critical Windows for Air Pollution in Children’s Health
Practice of Epidemiology Potential for BiasWhen Estimating Critical Windows for Air Pollution in Children’s Health
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发表时间：
2017
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通讯作者：
A. Wilson;Y. Chiu;H. Hsu;R. Wright;Rosalind J Wright;B. Coull
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作者：
A. Wilson;Y. Chiu;H. Hsu;R. Wright;Rosalind J Wright;B. Coull
有证据表明，孕妇在怀孕期间暴露于空气污染与儿童的健康状况之间存在关联。最近的兴趣集中在确定脆弱性的关键窗口上。基于分布式滞后模型(DLM)的分析可以产生与回归三个月平均暴露(TAE)结果的分析不同的对临界窗口的估计。使用模拟研究，我们评估了使用3种回归方法获得的临界窗口估计的偏差：1)3个单独的模型来估计与3个TEE中的每一个的关联；2)单个模型来联合估计结果与所有3个TEE之间的关联；以及3)DLm。我们使用了马萨诸塞州波士顿及其周边地区出生队列中238名新生儿每周的细颗粒物暴露数据，并模拟了结果和随时间变化的暴露效应。对每个TAE使用单独模型的估计是有偏差的，并确定了不正确的窗口。这种偏差源于颗粒物的季节性趋势，这种趋势导致了Te之间的相关性。在单一模型中包含所有tae可以减少偏差。DLM提供了公正的估计，并增加了确定窗口的灵活性。对同一队列中的体重指数、z得分和脂肪质量的分析突出了3种方法估计的不一致。
Evidence supports an association between maternal exposure to air pollution during pregnancy and children’s health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAEwere biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a singlemodel reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods.