Principal Component Pursuit to Assess Exposure to Environmental Mixtures in Epidemiologic Studies

项目摘要 传统上，环境流行病学研究的重点是评估与单一污染物有关的风险。 一次来一次。然而，这并不反映现实，因为我们经常暴露在多种污染物中 一次。因此，非常重要的是，能够在进行 环境流行病学方法。然而，这样做特别具有挑战性，主要是因为 多污染物暴露矩阵的维度(如果感兴趣的暴露包括例如5或10以上 化学物质)，而且这些污染物通常彼此高度相关。尽管有些人 解决这些问题的方法是可用的，它们通常需要强有力的假设，并具有严重的 限制。通过这项研究，我们建议通过改编和扩展一部小说来绕过大多数这些限制 以及评估多种污染物暴露的稳健方法，称为主成分追踪法(PCP)。我们 将评估代表多个潜在场景和研究的PCP合成数据集的性能 设计，并将我们的结果与现有方法得到的结果进行比较。随后，我们将把PCP应用于 三个重要的公共卫生问题，即评估(I)宫内暴露于 多氯联苯混合物与神经发育，(Ii)接触金属混合物与心血管健康，以及(Iii) 暴露于空气污染混合物和心血管急诊入院。最后，我们将发展和 共享软件，以便其他研究人员可以在过多的 研究设计和研究问题。我们拟议的工作将具有重要意义，因为它将为流行病学家提供 使用一种新的和强大的工具来评估环境污染物混合物的暴露情况。

Project Summary Traditionally, environmental epidemiologic studies have focused on assessing risks related to a single pollutant at a time. This, however, does not reflect reality, since we are constantly exposed to multiple pollutants at once. It is very important, therefore, to be able to assess exposure to pollutant mixtures when conducting environmental epidemiologic methods. Doing so, however, is especially challenging, mainly due to the high dimension of the multi-pollutant exposure matrix (if the exposure of interest includes more than e.g. 5 or 10 chemicals) and because these pollutants are usually very highly correlated with each other. Although some methods are available to address these issues, they usually require strong assumptions and have severe limitations. With this study we propose to bypass most of these limitations by adapting and extending a novel and robust method to assess exposure to multiple pollutants, called Principal Component Pursuit (PCP). We will assess the performance of PCP synthetic datasets representing multiple potential scenarios and study designs, and compare our results to those obtained by existing methods. Subsequently, we will apply PCP to three important Public Health issues, i.e. to evaluate the associations between (i) in utero exposure to a mixture of PCBs and neurodevelopment, (ii) exposure to a metals mixture and cardiovascular health, and (iii) exposure to an air pollution mixture and emergency cardiovascular admissions. Finally, we will develop and share software so other researchers can freely use this novel, robust and flexible tool across a plethora of study designs and research questions. Our proposed work will be significant as it will provide epidemiologists with a novel and robust tool to assess exposure to environmental pollutant mixtures.