An informative Bayesian structural equation model to assess source-specific health effects of air pollution
An informative Bayesian structural equation model to assess source-specific health effects of air pollution
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DOI：
10.1093/biostatistics/kxl032
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发表时间：
2007-07-01
期刊：
影响因子：
2.1
通讯作者：
Godleski, John J.
中科院分区：
文献类型：
--
作者：
Nikolov, Margaret C.;Coull, Brent A.;Godleski, John J.
当前空气污染研究的一个主要目标是评估与空气颗粒或颗粒物质（PM）的特定来源有关的健康影响。量化特定污染源的风险是一项挑战，因为大多数PM健康研究没有直接观察污染源本身的贡献。相反，在了解了已知污染源的化学特性后，研究人员通过对大量观察到的元素浓度进行源分配或多变量受体分析来推断污染源的贡献。虽然来源分配方法已很好地用于暴露评估，但在评估在健康影响分析中描述不可观察来源特征的适当性方面所做的工作很少。在本文中，我们提出了一个结构方程框架，利用特定元素数据来评估特定源的健康影响。这种方法对应于联合拟合受体模型和健康结果模型，这样，关于健康影响的推论就可以解释不确定性与来源贡献相关的事实。由于结构方程模型（SEM）通常涉及大量参数，对于小样本设置，我们提出了一种利用以前相关暴露研究的历史暴露数据的全贝叶斯估计方法。我们通过模拟比较了我们的方法在估计源特定健康影响方面的性能与两种现有方法（示踪剂方法和两阶段方法）的性能。仿真结果表明，即使在暴露次数有限的情况下，所提出的信息量贝叶斯扫描电镜也能有效地消除两种现有方法所产生的偏差。我们采用建议的方法分析了一项集中剂研究，该研究调查了st段、心血管结局和波士顿PM主要来源之间的关系，并讨论了我们的研究结果对未来PM集中剂研究设计的影响。
A primary objective of current air pollution research is the assessment of health effects related to specific sources of air particles or particulate matter (PM). Quantifying source-specific risk is a challenge because most PM health studies do not directly observe the contributions of the pollution sources themselves. Instead, given knowledge of the chemical characteristics of known sources, investigators infer pollution source contributions via a source apportionment or multivariate receptor analysis applied to a large number of observed elemental concentrations. Although source apportionment methods are well established for exposure assessment, little work has been done to evaluate the appropriateness of characterizing unobservable sources thus in health effects analyses. In this article, we propose a structural equation framework to assess source-specific health effects using speciated elemental data. This approach corresponds to fitting a receptor model and the health outcome model jointly, such that inferences on the health effects account for the fact that uncertainty is associated with the source contributions. Since the structural equation model (SEM) typically involves a large number of parameters, for small-sample settings, we propose a fully Bayesian estimation approach that leverages historical exposure data from previous related exposure studies. We compare via simulation the performance of our approach in estimating source-specific health effects to that of 2 existing approaches, a tracer approach and a 2-stage approach. Simulation results suggest that the proposed informative Bayesian SEM is effective in eliminating the bias incurred by the 2 existing approaches, even when the number of exposures is limited. We employ the proposed methods in the analysis of a concentrator study investigating the association between ST-segment, a cardiovascular outcome, and major sources of Boston PM and discuss the implications of our findings with respect to the design of future PM concentrator studies.