Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight.
Bayesian G-Computation for Estimating Impacts of Interventions on Exposure Mixtures: Demonstration With Metals From Coal-Fired Power Plants and Birth Weight.
复制标题
用于估计干预措施对暴露混合物影响的贝叶斯 G 计算：用燃煤发电厂的金属和出生体重进行演示。
DOI：
10.1093/aje/kwab053
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发表时间：
2021
影响因子：
5
通讯作者：
Kalkbrenner,AmyE
中科院分区：
文献类型：
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作者：
Keil,AlexanderP;Buckley,JessieP;Kalkbrenner,AmyE
人们日益认识到研究接触混合物对健康影响的重要性，但这种研究在方法和解释方面存在许多困难。我们使用贝叶斯g计算来估计2011-2013年在威斯康星州的密尔沃基模拟公共卫生行动对暴露混合物和出生体重的影响。我们将出生记录的数据与环境保护局国家空气毒性评估模型的人口普查区水平的空气毒性数据联系起来。我们估计了观察到的出生体重和预期出生体重之间的差异，理论上，在2010年之前，通过关闭密尔沃基县的3座燃煤发电厂，进行假设干预，以减少6种空气中金属的暴露。使用贝叶斯g计算，我们估计在这种假设的干预后，出生体重增加68 g（95%可信区间：25，135）。这个例子证明了我们的方法的效用，使用观测数据来评估和对比可能的公共卫生行动。此外，贝叶斯g计算提供了一种灵活的策略，用于估计高度相关的暴露的影响，解决方差膨胀等统计问题，并解决独立效应缺乏可解释性等概念问题。
The importance of studying the health impacts of exposure mixtures is increasingly being recognized, but such research presents many methodological and interpretation difficulties. We used Bayesian g-computation to estimate effects of a simulated public health action on exposure mixtures and birth weights in Milwaukee, Wisconsin, in 2011–2013. We linked data from birth records with census-tract–level air toxics data from the Environmental Protection Agency’s National Air Toxics Assessment model. We estimated the difference between observed and expected birth weights that theoretically would have followed a hypothetical intervention to reduce exposure to 6 airborne metals by decommissioning 3 coal-fired power plants in Milwaukee County prior to 2010. Using Bayesian g-computation, we estimated a 68-g (95% credible interval: 25, 135) increase in birth weight following this hypothetical intervention. This example demonstrates the utility of our approach for using observational data to evaluate and contrast possible public health actions. Additionally, Bayesian g-computation offers a flexible strategy for estimating the effects of highly correlated exposures, addressing statistical issues such as variance inflation, and addressing conceptual issues such as the lack of interpretability of independent effects.