Statistical Methods for Exposure Uncertainty in Air Pollution and Health Studies

空气污染和健康研究中暴露不确定性的统计方法

• 批准号: 8638270
• 负责人: Howard H Chang
• 金额: $ 25.37万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-10 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8638270
• 关键词: Accident and Emergency department; Accounting; Acute; Address; Aerosols; Age; Air; Air Pollutants; Air Pollution; Area; Caliber; Cardiovascular system; Characteristics; County; Data; Data Quality; Data Set; Data Sources; Databases; Development; Ecological Bias; Environmental Epidemiology; Epidemiologic Studies; Epidemiology; Etiology; Exposure to; Funding; Gender; Goals; Health; Heterogeneity; Housing; Human; Image; Individual; Measurement; Measures; Meteorology; Methods; Metric; Modeling; Monitor; Morbidity - disease rate; Optics; Outcome; Particulate Matter; Pattern; Play; Policies; Pollution; Population; Population Characteristics; Population Study; Public Health; Reporting; Reproducibility; Research; Research Design; Resolution; Retrieval; Risk; Risk Assessment; Role; Science; Series; Simulate; Source; Staging; Statistical Methods; Statistical Models; Techniques; Time; Time Series Analysis; Uncertainty; United States Environmental Protection Agency; Variant; Visit; atmospheric sciences; experience; exposed human population; improved; innovation; insight; land use; man; metropolitan; novel; population based; public health relevance; remote sensing; research study; respiratory; response; simulation; spatial relationship; urban area

PROJECT SUMMARY Objectives. Consistent associations between air pollution and adverse health outcomes from epidemiological studies have played a major role in setting regulatory standards and protecting public health. For exposure as- sessment, population-based studies routinely utilize air quality measurements from outdoor monitoring network due to its public availability. However the monitoring network has limited spatial coverage, and ambient concen- trations may not reflect human exposure to air pollution from outdoor sources since individuals spend the majority of their time indoors. Therefore exposure uncertainty can arise from unobserved spatial variation in air pollution concentration, as well as spatial variations in population characteristics that contribute to differential exposure (e.g. age and residential housing type). The overarching goal of this project is to develop and apply innovative statistical methods for improving exposure assessment and quantifying exposure uncertainties in air pollution and health studies. By incorporating additional data sources to supplement ambient monitor measurements, we will (1) increase the spatial coverage and resolution of air quality data; (2) examine the spatial relationship between ambient concentration and human exposure; and (3) systematically evaluate the impacts of exposure measure- ment errors. Approach. In Aim 1, by combining monitoring measurements and remotely sensed satellite image data, we will develop a spatio-temporal data fusion approach to predict daily concentrations of particulate matter less than 2.5 ¿m in aerodynamic diameter (PM2 5). Our model allows for distinct relationships between monitoring . and remotely sensed data at different spatial resolutions, while addressing the missing data problem associated with satellite images. In Aim 2, we will develop a Bayesian spatial hierarchical model to estimate daily population exposure to ambient PM2 5 as a function of ambient concentrations and human daily activity patterns. This is . accomplished by utilizing data from stochastic exposure simulators that reflect state-of-the-art human exposure science. The statistical model overcomes the computational effort associated with traditional human exposure simulation experiments and serves as an emulator for imputing spatially-resolved exposures that can be readily used in health studies. In Aim 3, we will conduct a epidemiological time series analysis to estimate short-term as- sociations between daily PM2 5 exposure and emergency department visits in the 20-county Atlanta metropolitan . area. We will examine the robustness of the exposure-response function using different exposure metrics, and assess the impacts of exposure measurement error and ecological bias. Expected Outcomes. The research ad- dresses three well-recognized sources of exposure error in air pollution epidemiology that arise from: (1) spatial variation in ambient concentration, (2) spatial variation in exposure-concentration relationship, and (3) spatial ag- gregation of health outcomes. Improved exposure assessment is a crucial step towards increasing the accuracy and relevance of health study results. The proposed approaches can be applied to different air pollutants and study designs, which may be extended to our other ongoing health effect studies and health impacts analyses.

项目摘要 目标。流行病学的空气污染与不利健康结果之间的一致关联 研究在制定监管标准和保护公共卫生方面发挥了重要作用。用于暴露 会议，基于人群的研究通常利用室外监控网络的空气质量测量 由于其公共可用性。但是，监视网络的空间覆盖范围有限，环境连接 - 由于个人花费大部分 他们的时间在室内。因此，暴露不确定性可能是由于空气污染未观察到的空间变化 浓度以及人口特征的空间变化，导致差异暴露 （例如，年龄和居民住房类型）。该项目的总体目标是开发和应用创新 改善暴露评估和量化空气污染中暴露不确定性的统计方法 健康研究。通过合并其他数据源以补充环境监视测量值，我们将 （1）增加空间覆盖范围和空气质量数据的分辨率； （2）检查 环境集中和人类暴露； （3）系统评估暴露测量的影响 - 菜单错误。方法。在AIM 1中，通过结合监测测量和远程感知的卫星图像 数据，我们将开发一种空间数据融合方法来预测特定物质的每日浓度 空气动力学直径（PM2 5）的小于2.5€。我们的模型允许监视之间建立不同的关系 。 并在不同的空间分辨率下远程感知的数据，同时解决丢失的数据问题 带有卫星图像。在AIM 2中，我们将开发贝叶斯空间层次结构模型来估计每日人口 暴露于环境PM2 5作为环境浓度和人类日常活动模式的函数。这是 。 通过使用反映最新人类暴露的随机曝光模拟器的数据来完成 科学。统计模型克服了与传统人类暴露相关的计算工作 模拟实验，并用作模拟器，用于弹出空间分辨的暴露 用于健康研究。在AIM 3中，我们将进行流行病学时间序列分析，以估计短期AS- 每日PM2 5曝光与急诊室访问之间的社会在20县亚特兰大大都会 。 区域。我们将使用不同的暴露指标检查暴露响应函数的鲁棒性，并且 评估暴露测量误差和生态偏见的影响。预期的结果。研究广告 连衣裙在空气污染流行病学中出现的三种公认的暴露误差来源：（1）空间 环境浓度的变化，（2）暴露浓度关系的空间变化，（3）空间Ag- 健康结果的差异。改善暴露评估是提高准确性的关键步骤 和健康研究结果的相关性。提出的方法可以应用于不同的空气污染物和 研究设计可能扩展到我们正在进行的其他健康效应研究和健康影响分析。