Time series clustering to identify and translate time-varying multipollutant exposures for health studies

项目摘要/摘要 空气污染暴露是一个普遍关注的问题，与一系列不利的健康后果有关。环境空气 污染是一种复杂的环境暴露，来自众多不同的来源，并随着时间的推移而变化； 然而，许多空气污染对健康的影响研究没有同时考虑到一种以上的污染物，而是依赖于 在一段时间内平均的风险敞口。多指标统计方法的最新进展 共线暴露导致了更多关于多污染物对人类健康影响的研究 混合方法，但这些方法仍然经常导致难以解释的效果估计，并且不能扩展到 反复测量暴露的程度。因此，需要进一步改进混合方法，以便能够 调查随时间变化的暴露，并做出可解释的暴露影响估计。 这项研究的总体目标是改进空气污染混合物的研究方法。 采用两阶段时间序列聚类方法。前期工作重点是补充电流 通过将聚类方法扩展到时间序列数据的可解释性分析来研究文献。这 开发工作将为以后应用识别和转化多污染物提供坚实的基础 一天中的暴露情况。在目标1中，我将通过扩展Current来确定最佳结束簇的数量 方法对静态数据进行处理，并对时间序列数据进行性能评价。最佳聚类群的识别 在没有外部信息(例如，密钥)的情况下，数字不是平凡的，并且当前的方法不能提供证据 时间序列数据的正面(或负面)表现。在目标2中，我将把线性统计模型扩展到 适当地将多元聚类法应用于污染物混合物对健康影响的研究。 按集群分组的曝光本身在视觉上是直观的，但可以通过添加解释性的 具有代表性的集群中心的特征与单个集群成员之间的距离。时间 系列聚类方法将在两个应用程序中进行演示：(目标3a)以确定典型的 南加州多种污染物的日分布，以及(目标3b)评估它们与呼气的关系 南加州儿童健康研究中的一氧化氮(FeNO)。颗粒物每小时监测数据 物质&lt；2.5微米(PM2.5)和&lt；10微米(PM10)、二氧化氮(NO2)和臭氧(03)用于识别典型的 每天环境空气污染暴露，并将其与儿科健康联系起来。 这项工作将改进现有的混合方法，为时变特性的研究提供新的工具 曝光。随着数据的增长，时变风险暴露的分析变得越来越重要 对最新技术进步的回应。时变混合物存在于许多地方(例如，空气、土壤) 开发适用的方法将有利于公共卫生和监管决策。

PROJECT SUMMARY/ABSTRACT Air pollution exposure is a universal concern linked to a wide range of adverse health outcomes. Ambient air pollution is a complex environmental exposure arising from numerous different sources and varies over time; however, many air pollution health effects studies fail to consider more than a single pollutant at a time and rely on an exposure that has been averaged over time. Recent advancements in statistical methodologies for multi- collinear exposures have resulted in an increased number of studies on human health impacts of multipollutant mixtures, but these methodologies still often result in hard-to-interpret effect estimates and do not extend to repeated measures of exposure. Thus, there is a need to further improve mixtures methodologies to be able to investigate time-varying exposures and have interpretable exposure effect estimates. The overall goal of this study is to improve methodologies for the study of air pollution mixtures by using a two-stage time series clustering approach. Initial work focuses on supplementing current literature by extending clustering methodologies to the interpretable analysis of time series data. This developmental work will provide a strong foundation for later application to identify and translate multipollutant diurnal exposure profiles. In Aim 1, I will identify the optimal number of ending clusters by extending current methods on static data and evaluating their performance on time series data. Identification of optimal cluster number is nontrivial without external information (e.g., a key) and current methods fail to provide evidence of positive (or negative) performance for time series data. In Aim 2, I will extend the linear statistical model to appropriately translate multivariate clustering methods to studies on health effects of pollutant mixtures. Exposures grouped by clusters are themselves visually intuitive but would be improved by adding interpretive distances between features of the representative cluster center and individual cluster members. The time series clustering methodology will be demonstrated in two applications: (Aim 3a) to identify typical multipollutant diurnal profiles in Southern California, and (Aim 3b) to evaluate their associations with exhaled nitric oxide (FeNO) in the Southern California Children’s Health Study. Hourly monitoring data for particulate matter <2.5µm (PM2.5) and <10µm (PM10), nitrogen dioxide (NO2), and ozone (O3) are used to identify typical diurnal ambient air pollution exposures and relate them to pediatric health. This work will improve current mixtures methods and provide new tools for the study of time-varying exposures. The analysis of time-varying exposures is of increasing import with the growing amounts of data in response to recent technological advances. Time-varying mixtures are present in many places (e.g., air, soil) and development of applicable methodologies would benefit public health and regulatory decisions.