A Spatial-Temporal Modleing Approach for Environmental Epidemiological Data

描述(由申请人提供)：环境流行病学数据需要随着时间的推移和跨不同地理区域收集。需要对这些数据进行分析，以确定国家环境政策的重要方面，即保护公民健康和防止破坏基础设施和环境的方面。这项研究的目的是建立一个统计框架和方法，用于综合分析关于空气污染浓度和其他环境因素、暴露、健康结果和协变量信息的时空数据。一般来说，这些不同的数据层在时间上是错位的，并且是在不同的空间尺度上观察到的。本研究的重点是： [1]在两个案例研究的背景下，开发新的统计方法和模型，在考虑到人类活动的情况下，调查环境应激源与不利的人类健康结果之间的时空联系：*研究臭氧和PM(精细、过程和超精细)对美国各地心血管死亡率的影响*研究臭氧和PM(精细、过程和超精细)对威斯康星州哮喘、心脑血管疾病的影响。 [2]制定一个广泛的统计框架，研究环境因素与不良健康后果之间的关系。该框架结合了环境过程的参数和非参数依赖结构，考虑了空间错位、支持度的时空变化以及时空协方差函数的平稳性和可分性。暴露模拟器模型用于表征人群暴露水平。 [3]多变量时空环境流行病学数据的模型拟合、估计和预测。 [4]确定性和随机性模型性能的统计评估，以及模型诊断。在目标2-4中，我们建立了一般统计框架，这些框架将用于目标1中介绍的案例研究。

DESCRIPTION (provided by applicant): Environmental epidemiological data need to be collected over time and across different geographic domains. These data need to be analyzed in order to determine important aspects of national environmental policy, aspects that protect the health of citizens and prevent damage to infrastructure and the environment. The purpose of this research is to develop a statistical framework and methodology for integrated analyses of spatial temporal data on air pollution concentrations and other environmental agents, exposure, health outcomes and covariate information. Generally, these various data layers are temporally misaligned and are observed at different spatial scales. The focus of this research is: [1] the development of new statistical methods and models for the investigation of the spatial and temporal association between environmental stressors, taking into account human activity, and adverse human health outcomes in the context of two case studies: *study of the impact of ozone and PM (fine, course and ultrafine) on cardiovascular mortality across the conterminuous U.S. *study of the impact of ozone and PM (fine, course and ultra fine) on asthma, cardiovascular and cerebrovascular diseases in the state of Wisconsin. [2] The development of a broad statistical framework to study the association of environmental factors and adverse health outcomes. This general framework incorporates parametric and nonparametric ial dependence structure for environmental processes, taking into account spatial misalignment, spatial and temporal change of support, and lack of stationarity and lack of separability in the space-time covariance function. An exposure simulator model is used to characterize population exposure levels. [3] The model fitting, estimation and prediction of multivariate space-time environmental epidemiological data. [4] The statistical assessment of the performance of deterministic and stochastic models, and model diagnostics. In aims 2-4 we establish general statistical frameworks that will be implemented to the case studies introduced in aim 1.