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.

项目概要/摘要 空气污染暴露是一个普遍关注的问题，与各种不良的环境空气后果有关。 污染是一种复杂的环境暴露，由多种不同来源引起，并随时间变化； 然而，许多空气污染对健康的影响研究未能一次考虑多种污染物，并且依赖于 多因素统计方法的最新进展。 共线暴露导致关于多污染物对人类健康影响的研究数量增加 混合物，但这些方法仍然经常导致难以解释的效果估计，并且不能扩展到 因此，需要进一步改进混合物方法，以便能够 研究随时间变化的暴露并进行可解释的暴露效应估计。 这项研究的总体目标是改进空气污染混合物的研究方法 通过使用两阶段时间序列聚类方法，初始工作重点是补充当前的方法。 将聚类方法扩展到时间序列数据的可解释分析。 开发工作将为以后识别和转化多污染物的应用提供坚实的基础 在目标 1 中，我将通过扩展电流来确定结束簇的最佳数量。 静态数据的方法并评估其在时间序列数据上的性能识别最佳簇。 在没有外部信息（例如密钥）的情况下，数字是不平凡的，并且当前的方法无法提供证据 在目标 2 中，我将线性统计模型扩展到时间序列数据的正（或负）性能。 适当地将多元聚类方法转化为污染物混合物对健康影响的研究。 按簇分组的曝光本身在视觉上很直观，但可以通过添加解释性来改进 代表性聚类中心的特征与各个聚类成员之间的距离。 系列聚类方法将在两个应用中进行演示：（目标 3a）识别典型的 南加州多污染物的昼夜特征，以及（目标 3b）评估它们与呼出气体的关联 南加州儿童健康研究中的一氧化氮 (FeNO) 每小时监测数据。 <2.5μm (PM2.5) 和 <10μm (PM10)、二氧化氮 (NO2) 和臭氧 (O3) 的物质用于识别典型的 每日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，并为时变研究提供新工具 随着数据量的不断增加，对随时间变化的风险的分析变得越来越重要。 对最新技术进步的响应随时间变化的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。