Data Integration Methods for Environmental Exposures with Applications to Air Pollution and Asthma Morbidity

环境暴露数据集成方法及其在空气污染和哮喘发病率中的应用

• 批准号: 9291306
• 负责人: Howard H Chang
• 金额: $ 60.76万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9291306
• 关键词: Address; Air Pollutants; Air Pollution; Algorithms; Area; Asthma; Biomass; Cessation of life; Chemicals; Cities; Climate; Coal; Complex; Computer Simulation; Data; Data Set; Data Sources; Databases; Dependence; Development; Documentation; Dust; Elderly; Emergency department visit; Ensure; Environmental Epidemiology; Environmental Exposure; Environmental Health; Environmental Pollution; Epidemiology; Exposure to; Future; Gasoline; Goals; Health; Heterogeneity; Imagery; Individual; Joints; Knowledge; Link; Location; Measurement; Meteorology; Methods; Modeling; Monitor; Morbidity - disease rate; National Institute of Environmental Health Sciences; Outcome; Ozone; Particulate Matter; Play; Policies; Pollution; Population; Prevention; Process; Proxy; Public Health; Reproducibility; Research; Research Priority; Resolution; Retrieval; Risk; Risk Assessment; Risk Reduction; Role; Soil; Source; Statistical Methods; Statistical Models; Technology; Time; Toxicology; Uncertainty; Work; World Health Organization; age group; ambient air pollution; atmospheric sciences; base; climate change; data integration; evidence base; experience; extreme heat; fine particles; improved; metropolitan; models and simulation; pollutant; population based; programs; remote sensing; simulation; spatiotemporal; success; tool; tropospheric ozone; Address; Air Pollutants; Air Pollution; Algorithms; Area; Asthma; Biomass; Cessation of life; Chemicals; Cities; Climate; Coal; Complex; Computer Simulation; Data; Data Set; Data Sources; Databases; Dependence; Development; Documentation; Dust; Elderly; Emergency department visit; Ensure; Environmental Epidemiology; Environmental Exposure; Environmental Health; Environmental Pollution; Epidemiology; Exposure to; Future; Gasoline; Goals; Health; Heterogeneity; Imagery; Individual; Joints; Knowledge; Link; Location; Measurement; Meteorology; Methods; Modeling; Monitor; Morbidity - disease rate; National Institute of Environmental Health Sciences; Outcome; Ozone; Particulate Matter; Play; Policies; Pollution; Population; Prevention; Process; Proxy; Public Health; Reproducibility; Research; Research Priority; Resolution; Retrieval; Risk; Risk Assessment; Risk Reduction; Role; Soil; Source; Statistical Methods; Statistical Models; Technology; Time; Toxicology; Uncertainty; Work; World Health Organization; age group; ambient air pollution; atmospheric sciences; base; climate change; data integration; evidence base; experience; extreme heat; fine particles; improved; metropolitan; models and simulation; pollutant; population based; programs; remote sensing; simulation; spatiotemporal; success; tool; tropospheric ozone

PROJECT SUMMARY Accurate and reliable exposure estimates are crucial to the success of any environmental health study. The overarching goal of this project is to develop and apply statistical methods to improve exposure assessment and exposure uncertainty quantification for spatio-temporal environmental pollution fields. This is accomplished by statistically integrating observations with additional data sources, including state-of-the-art computer model simulations and satellite imagery. We will develop methods motivated by three current research priorities in air pollution epidemiology: a) identifying susceptible sub-populations most at risk to air pollution exposures; (b) quan- tifying health impacts of air pollution under a changing climate; and (c) understanding sources of air pollution to develop control strategies. In Aim 1, we will develop multi-resolutional and multivariate data integration methods for ambient air pollution concentrations. We will supplement sparse observations from monitoring networks with simulations from a chemical transport model and multiple satellite retrieval parameters. The proposed methods will exploit the between-pollutant dependence and the spatio-temporal autocorrelation within each pollutant for better predictions. In Aim 2, we will develop multivariate bias-correction methods for climate model simulations using historical observations. The goal is to perform joint bias-correction across multiple variables such that the observed dependence is retained in future projections. In Aim 3, we will develop ensemble source apportionment methods for fine particulate matter pollution (PM2.5). The methods will estimate emission source contributions by combining results from several algorithms that incorporate different types of external information and assump- tions. We will further utilize computer model simulations to spatially interpolate source information to locations without monitors. Methods developed from Aims 1, 2, and 3 will be used to create national databases of (1) daily concentration estimates for criteria pollutants and major constituents of PM2.5, (2) projections of ozone levels due to climate change under different future emission scenarios, and (3) daily estimates of contributions from multiple PM2.5 sources, including coal combustion, on-road diesel and gasoline combustion, biomass burning, and resuspended soil/dust. We will also provide uncertainty estimates, detailed documentation, and R packages to ensure these methods and estimates can be used in other environmental health studies. In Aim 4, we will acquire individual-level emergency department (ED) visit data from 25 cities during the period 2005-2014. The data integration products will be used to estimate short-term associations between asthma ED visits and multiple air pollutants and pollutant sources. The proposed health study fills a major gap by considering both elderly and non-elderly susceptible populations to support the development of targeted, effective risk reduction and preven- tion activities. While air pollution serves as the motivating application in this project, the methods proposed are highly applicable to other environmental exposures.

项目摘要 准确可靠的暴露估计对于任何环境健康研究的成功至关重要。这 该项目的总体目标是开发和应用统计方法来改善暴露评估 和暴露不确定性量化，用于空间周期性环境污染场。这是完成的 通过将观察值与其他数据源（包括最先进的计算机模型）整合在一起 模拟和卫星图像。我们将开发以当前三个空中研究重点的激励方法 污染流行病学：a）确定最容易受到空气污染风险的敏感亚人群； （b）quan- 在气候不断变化的气候下，会影响空气污染的健康影响； （c）了解空气污染的来源 制定控制策略。在AIM 1中，我们将开发多分辨率和多元数据集成方法 对于环境空气污染的浓度。我们将补充与监视网络的稀疏观察 化学传输模型和多个卫星检索参数的模拟。提出的方法 将探索每种污染物内的污染物间依赖性和时空自相关 更好的预测。在AIM 2中，我们将开发用于气候模型模拟的多元偏差校正方法 使用历史观察。目的是在多个变量上执行关节偏差纠正，以便 观察到的依赖性保留在未来的项目中。在AIM 3中，我们将开发合奏源分配 特定物质污染的方法（PM2.5）。这些方法将估计排放源贡献 通过结合几种算法的结果，这些算法结合了不同类型的外部信息和假设 tions。我们将进一步利用计算机模型模拟来空间插值源信息到位置 没有监视器。 AIMS 1、2和3开发的方法将用于创建（1）每天的国家数据库 标准污染物的浓度估计值和PM2.5的主要构成，（2）臭氧水平的预测 由于气候变化在不同的未来排放情况下，以及（3）每日估计 多个PM2.5来源，包括煤层，道路柴油和汽油组合，生物质燃烧， 和解决的土壤/灰尘。我们还将提供不确定性估计，详细文档和R软件包 为了确保这些方法和估计可以在其他环境健康研究中使用。在AIM 4中，我们将 在2005 - 2014年期间，收购个人级别急诊室（ED）访问来自25个城市的数据。这 数据集成产品将用于估计哮喘访问与多次访问之间的短期关联 空气污染物和污染物来源。拟议的健康研究填补了一个重大差距 非易感人群支持有效的，有效的风险降低和预防 活动。尽管空气污染是该项目的动机应用，但提出的方法是 高度适用于其他环境暴露。