Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.

中文翻译：

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暴露混合物与健康的综合统计方法

人类同时接触化学、结构和毒理学上不同的化学物质。环境流行病学面临的一个关键挑战是量化因暴露于此类化学混合物而导致的不良健康后果的风险，并确定哪些混合物成分可能导致病因学关联。人们提出了多种统计方法来解决这些关键的研究问题。然而，他们通常仅依赖于特定研究中可用的测量暴露和健康数据。通过利用来自多个学科的外部数据和知识以及创新的统计工具，可以更好地理解混合物对人类健康影响的作用。在本文中，我们开发了健康分析的新方法，其中结合了混合物中化学物质的辅助信息，例如物理化学、结构和/或毒理学数据。我们预计，使用辅助信息识别的成分将比仅利用测量暴露之间观察到的相关性的方法识别的成分更具生物学意义。我们通过指定暴露及其影响（包括辅助信息）的先验分布来开发灵活的贝叶斯模型，并通过从回归到因子分析的一系列分析来检验这一想法。这些方法用于研究挥发性有机化合物对亚特兰大急诊室就诊的影响。我们发现，包含有关暴露变量的化学信息信息可以改善预测，并为呼吸系统疾病的急诊室就诊提供更可解释的模型。