Statistical Methods for Exposure Uncertainty in Air Pollution and Health Studies

项目总结 目标。空气污染与来自流行病学的不良健康后果之间的一致关联 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。暴露为- 评估，基于人群的研究通常使用室外监测网络的空气质量测量 由于它的公众可获得性。然而，监测网络的空间覆盖范围有限，环境浓度- 污染指数可能不能反映人类暴露在室外来源的空气污染中，因为大多数人花在 他们在室内的时光。因此，空气污染中未被观测到的空间变化可能会引起暴露不确定性。 集中度，以及造成不同接触的人口特征的空间差异 (例如楼龄和住宅类型)。该项目的总体目标是开发和应用创新 改进暴露评估和量化空气污染和污染中暴露不确定性的统计方法 健康研究。通过合并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)研究空间关系 环境浓度和人体暴露；以及(3)系统地评估暴露措施的影响- 测距错误。接近。在目标1中，通过将监测测量和遥感卫星图像结合起来 数据，我们将开发一种时空数据融合方法来预测每天的颗粒物浓度。 空气动力学直径(PM25)小于2.5？m。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丢失问题 用卫星图像。在目标2中，我们将开发一个贝叶斯空间分层模型来估计每日人口 暴露于环境PM25，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实现，这些数据反映了最先进的人类暴露 科学。统计模型克服了与传统人类接触相关的计算工作 模拟实验，并作为模拟器，用于输入空间分辨率曝光，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AS- 亚特兰大20个县的每日PM25暴露与急诊科就诊之间的关系 。 区域。我们将使用不同的曝光度量来检查曝光-响应函数的稳健性，以及 评估暴露测量误差和生态偏差的影响。预期结果。这则研究广告- 描述了空气污染流行病学中公认的暴露误差的三个来源：(1)空间 环境浓度的变化，(2)暴露-浓度关系的空间变化，以及(3)空间分布。 健康结果的汇总。改进的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。建议的方法可适用于不同的空气污染物和 研究设计，这可能扩展到我们正在进行的其他健康影响研究和健康影响分析。

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.