A Spatial-Temporal Modleing Approach for Environmental Epidemiological Data

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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.