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.空气污染与流行病学的不良健康结果之间的一致关联 研究在制定监管标准和保护公众健康方面发挥了重要作用。暴露于- 评估，基于人口的研究通常使用室外监测网络的空气质量测量结果 因为它的公共可用性。然而，监测网络的空间覆盖范围有限，环境浓度高， 排放量可能并不反映人类暴露于户外来源的空气污染，因为个人花费了大部分时间， 在室内的时间。因此，暴露的不确定性可能来自未观测到的空气污染的空间变化 浓度，以及人口特征的空间变化，导致不同的暴露 (e.g.年龄和居住类型）。该项目的总体目标是开发和应用创新的 改进暴露评估和量化空气污染暴露不确定性的统计方法， 健康研究。通过整合额外的数据源来补充环境监测测量，我们将 (1)提高空气质量数据的空间覆盖率和分辨率;（2）研究 环境浓度和人体暴露;（3）系统地评估暴露措施的影响- 错误。Approach.在目标1中，通过监测测量和遥感卫星图像相结合， 数据，我们将开发一种时空数据融合方法来预测颗粒物的每日浓度 空气动力学直径小于2.5 m（PM2 5）。我们的模型允许监控之间的不同关系 . 和不同空间分辨率的遥感数据，同时解决相关的数据缺失问题， 卫星图像。在目标2中，我们将开发一个贝叶斯空间层次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 . 通过利用来自随机暴露模拟器的数据来完成，这些模拟器反映了最先进的人类暴露 科学该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，并作为一个模拟器，用于输入空间分辨的曝光，可以很容易地 用于健康研究。在目标3中，我们将进行流行病学时间序列分析，以估计短期内- 亚特兰大20个县每日PM2.5暴露与急诊就诊之间的关系 . 区我们将使用不同的暴露度量来检查风险-响应函数的鲁棒性， 评估暴露测量误差和生态偏差的影响。预期成果。研究AD- 衣服三个公认的来源暴露错误的空气污染流行病学，产生于：（1）空间 环境浓度的变化，（2）空间变异的浓度关系，和（3）空间ag- 健康结果的分类。改进暴露评估是提高准确性的关键一步 和健康研究结果的相关性。所提出的方法可适用于不同的空气污染物， 研究设计，这可能会扩展到我们其他正在进行的健康影响研究和健康影响分析。