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

• 批准号: 10749341
• 负责人: Brittney Marian
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10749341
• 关键词: Air; Air Pollution; Big Data; Biological Markers; California; Categories; Child Health; Childhood; Cohort Studies; Complex; Complex Mixtures; Data; Development; Dimensions; Environmental Exposure; Environmental Health; Exhalation; Exposure to; Foundations; Gases; Goals; Health; Hour; Human; Individual; Information Sciences; Intuition; Investigation; Link; Literature; Maps; Measures; Methodological Studies; Methodology; Methods; Modeling; Modernization; Monitor; National Institute of Environmental Health Sciences; Nitric Oxide; Nitrogen Dioxide; Outcome; Ozone; Participant; Particulate Matter; Pattern; Performance; Principal Component Analysis; Public Health; Research Design; Research Personnel; Sea; Series; Soil; Source; Specific qualifier value; Statistical Models; Sum; Techniques; Technology; Time; Time Series Analysis; Time Study; Translating; United States Environmental Protection Agency; Visual; Work; airway inflammation; ambient air pollution; coarse particles; computer science; design; health organization; improved; interest; linear transformation; member; method development; novel strategies; pollutant; response; self organization; tool

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 空气污染暴露是与多种不良健康结果有关的普遍关注点。环境空气 污染是由许多不同的来源引起的复杂环境暴露，随着时间的流逝而变化。 但是，许多空气污染的健康效应研究都无法一次考虑单一污染物，并且依靠 随着时间的流逝，平均的曝光。统计方法的最新进展 联合性暴露导致对多币对人类健康影响的研究增加 混合物，但是这些方法仍然经常导致难以解释的效果估计，并且不会扩展到 重复的暴露措施。那是有必要进一步改进混合物方法的 研究时变的暴露并具有可解释的暴露效应估计值。 这项研究的总体目标是改善研究空气污染混合物的方法 通过使用两个阶段的时间序列聚类方法。最初的工作重点是补充电流 文献通过将聚类方法扩展到时间序列数据的可解释分析。这 发展工作将为以后的应用提供牢固的基础，以识别和翻译多币种 昼夜暴露概况。在AIM 1中，我将通过扩展当前来确定最佳的结束簇数 静态数据的方法并评估其在时间序列数据上的性能。最佳群集的识别 没有外部信息（例如，钥匙），当前方法没有提供数字，而当前方法无法提供证据 时间序列数据的正（或负）性能。在AIM 2中，我将将线性统计模型扩展到 适当地将多元聚类方法转化为污染物混合物健康影响的研究。 集群分组的暴露本身是视觉上直觉的，但通过添加解释性将得到改善 代表群集中心和单个群集成员的特征之间的距离。时间 系列聚类方法将在两个应用中证明：（目标3A）确定典型 南加州的多层面昼夜概况，（目标3B）评估他们与疲惫的关联 南加州儿童健康研究中的一氧化氮（Feno）。每小时监视特定的数据 物质<2.5µm（PM2.5）和<10µm（PM10），二氧化氮（NO2）和臭氧（O3）用于识别典型 昼夜环境空气污染暴露并将其与小儿健康联系起来。 这项工作将改善当前的混合方法，并为时间变化提供新的工具 暴露。随着时变暴露的分析是，随着越来越多的数据的进口 对最近的技术进步的回应。在许多地方（例如，空气，土壤）存在时变的混合物 适用方法的发展将使公共卫生和监管决策受益。