Space-time Modeling for Linking Climate Change,Pollutant Exposure, Built Environm

描述（由申请人提供）：这是北卡罗来纳州立大学、杜克大学和北卡罗来纳大学教堂山分校的联合合作项目。三所院校的专业知识互补，形成合力。我们将实现以下目标：(1)发展广泛的时空统计模型，研究气候变化条件下空气污染对人类健康的影响。我们将改进现有方法，引入贝叶斯多变量时空统计模型，该模型同时表征环境压力源、气候变量和健康结果中复杂的时空依赖结构，同时考虑到模型和数据中不同的不确定性来源。我们将为气候和污染变量开发新的空间分位数回归模型，以更好地表征极端情况、尾部行为以及天气和污染之间的复杂依赖关系。(2)发展了贝叶斯层次收缩方法，用于评估复杂污染物混合物与健康结果之间的空间关联。我们将改进现有的方法，同时考虑到不同的污染物类型，如臭氧和颗粒物（PM）或特定的PM，表征胎儿发育易感期的时空结构（妊娠结果）和暴露滞后（死亡率结果），同时考虑到模型和数据中不同的不确定性来源。(3)构建邻里剥夺和环境指标，联动健康结果。我们将在妊娠、感染和营养（PIN）研究中使用上述统计框架以及出生体重和分娩时胎龄的数据，该研究考察了与妊娠结果相关的建筑和感知物理环境的邻里因素，将GIS功能、空气污染、气候和天气的确定性模型以及用于降维的新型空间统计建模方法结合在一起。(4)我们将结合目标1-3中的统计模型，在预测的未来气候条件下研究空气污染和极端天气对人类健康的影响。要审查的健康数据包括：2001-2006年美国县一级的每日死亡率（以及北卡罗来纳州和纽约州街道一级的地理编码）。出生体重（小于胎龄）和分娩时胎龄（早产）在美国10个州出生的婴儿样本中，这些婴儿作为对照参加了国家出生缺陷预防研究（NBDPS），他们出生时的地理编码纬度和经度是可用的。个人水平的心血管出生缺陷地理编码数据，以及NBDPS中15,000例和对照的个人水平心血管出生缺陷地理编码数据都是可用的。我们将通过开发免费获取的软件，对我们的方法进行广泛的验证和诊断，以及提出拟合优度的措施，使这种新方法得到广泛的应用和传播。小灵通SF424（09年12月更新）第1页延续格式页

DESCRIPTION (provided by applicant): This is a joint collaborative effort between North Carolina State University, Duke University, and the University of North Carolina at Chapel Hill. The expertise at the 3 institutions complements each other, and brings synergy. We will achieve the following objectives: (1) The development of broad spatial-temporal statistical models to study the impact under climatic change conditions of air pollution on human health. We will improve upon existing methods, by introducing Bayesian multivariate spatio-temporal statistical models that characterize simultaneously complex spatial and temporal dependence structures in the environmental stressors, climatic variables, and health outcomes, while taking into account different sources of uncertainty in models and data. We will develop novel spatial quantile regression models for the climatic and pollution variables for better characterization of extremes, tail behavior, and complex dependences between weather and pollution. (2) The development of Bayesian hierarchical shrinkage methods for assessing spatial associations between complex pollutant mixtures and health outcomes. We will improve upon existing approaches by simultaneously accounting for different pollutant types, such as ozone and particulate matter (PM) or speciated PM, characterizing the spatial temporal structure of the susceptible periods of fetal development (pregnancy outcomes) and the exposure lag (mortality outcome), while taking into account different sources of uncertainty in models and data. (3) We will build neighborhood deprivation and environment indices for linkage to health outcomes. We will use the statistical frameworks above and data on birth weight and gestational age at delivery in the Pregnancy, Infection, and Nutrition (PIN) study, which examines neighborhood factors concerning the built and perceived physical environment in relation to pregnancy outcomes, to bring together GIS capabilities, deterministic models for air pollution, climate and weather, and novel spatial statistical modeling approaches for dimension reduction. (4) We will combine the statistical models in aims 1-3 to study the impact of air pollution and extreme weather on human health under projected future climatic conditions. Health data to be examined include the following: U.S. daily mortality in 2001-2006 at the county level (and geocoded at the street level for the states of NC and NY). Birth weight (small-for-gestational age) and gestational age at delivery (preterm birth) in a sample of infants born in 10 U.S. states who participated as controls in the National Birth Defects Prevention Study (NBDPS), for whom geocoded latitude and longitude at delivery are available. Individual-level cardiovascular birth defects geocoded data are available, as well as individual-level geocoded cardiovascular birth defects data for 15,000 cases and controls in NBDPS. We will make this new methodology broadly applicable and disseminated by developing free-access software and conducting extensive validation and diagnostics of our approaches, as well as presenting measures of goodness-of-fit. PHS SF424 (Updated 12/09) Page 1 Continuation Format Page