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

时间序列聚类可识别和转化随时间变化的多污染物暴露以进行健康研究

基本信息

  • 批准号:
    10749341
  • 负责人:
  • 金额:
    $ 4.77万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2024
  • 资助国家:
    美国
  • 起止时间:
    2024-01-01 至 2025-12-31
  • 项目状态:
    未结题

项目摘要

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.
项目摘要/摘要 空气污染暴露是一个普遍关注的问题,与广泛的不良健康后果有关。环境空气 污染是一种复杂的环境风险,由许多不同的来源产生,并随时间而变化; 然而,许多空气污染对健康影响的研究未能同时考虑一种以上的污染物, 在一段时间内的平均暴露量上。多边基金统计方法的最新进展 共线暴露导致关于多种污染物对人类健康影响的研究数量增加 混合物,但这些方法仍然往往导致难以解释的影响估计,并没有延伸到 反复暴露的措施。因此,需要进一步改进混合物方法,以便能够 调查随时间变化的暴露,并有可解释的暴露效应估计。 本研究的总体目标是改进研究空气污染混合物的方法 通过使用两阶段时间序列聚类方法。初期工作侧重于补充现有的 通过扩展聚类方法对时间序列数据进行可解释的分析,这 开发工作将为以后应用于识别和转化多种污染物提供坚实的基础 昼夜暴露曲线。在目标1中,我将通过扩展当前 静态数据的方法,并评估其对时间序列数据的性能。最优聚类识别 数在没有外部信息的情况下是非平凡的(例如,一个关键),目前的方法无法提供证据, 时间序列数据的正(或负)性能。在目标2中,我将扩展线性统计模型, 适当地将多变量聚类方法转化为污染物混合物对健康影响的研究。 按聚类分组的曝光本身在视觉上是直观的,但通过添加解释性 代表性聚类中心的特征与各个聚类成员之间的距离。的时间 系列聚类方法将在两个应用程序中演示:(目标3a)识别典型的 在南加州的多污染物的昼夜配置文件,并(目标3b),以评估其与呼出 一氧化氮(FeNO)在南加州儿童健康研究。颗粒物的每小时监测数据 <2.5µm(PM2.5)和<10µm(PM10)的物质、二氧化氮(NO2)和臭氧(O3)被用来识别典型的 昼夜环境空气污染暴露,并将其与儿童健康。 这一工作将改进现有的混合方法,为研究时变的非线性系统提供新的工具 暴露。随着数据量的不断增加,时变暴露的分析越来越重要, 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) 制定适用的方法将有利于公共卫生和监管决定。

项目成果

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