A Spatial-Temporal Modleing Approach for Environmental Epidemiological Data

环境流行病学数据的时空建模方法

• 批准号: 7387727
• 负责人: Montse Fuentes
• 金额: $ 33.33万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-12-15 至 2010-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7387727
• 关键词: Accounting; Address; Air; Air Pollution; Asthma; Cardiovascular system; Case Study; Cerebrovascular Disorders; Complex; Count; County; Data; Dependence; Development; Diagnostic; Disease; Economic Factors; End Point; Environment; Environmental Impact; Environmental Policy; Environmental Risk Factor; Health; Human; Human Activities; Investigation; Location; Methodology; Modeling; Montserrat; Outcome; Ozone; Performance; Policy Making; Population; Principal Investigator; Process; Purpose; Research; Research Infrastructure; Son; Source; Statistical Methods; Statistical Models; Structure; Time; Title; Ultrafine; Uncertainty; Wisconsin; Zip Code; environmental agent; environmental stressor; improved; mortality; prevent; programs; tool

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.

描述（由申请人提供）：需要随时间跨不同地理区域收集环境流行病学数据。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、保护公民健康和防止基础设施和环境受损的方面。本研究的目的是开发一个统计框架和方法，用于对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进行综合分析。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。这项研究的重点是： [1] 开发新的统计方法和模型，用于调查环境压力因素之间的空间和时间关联，并在两个案例研究的背景下考虑人类活动和不利的人类健康结果：*臭氧影响研究和PM（细颗粒物、中颗粒物和超细颗粒物）对全美范围内心血管死亡率的影响 *在威斯康星州研究臭氧和PM（细颗粒物、中颗粒物和超细颗粒物）对哮喘、心脑血管疾病的影响。 [2] 制定广泛的统计框架来研究环境因素与不良健康结果之间的关联。该总体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、支持的空间和时间变化以及时空协方差函数缺乏平稳性和缺乏可分离性。暴露模拟器模型用于表征人群暴露水平。 [3]多元时空环境流行病学数据的模型拟合、估计与预测。 [4] 确定性和随机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，该框架将应用于目标 1 中介绍的案例研究。