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

连接气候变化、污染物暴露、建筑环境的时空模型

• 批准号: 8478101
• 负责人: Brian J. Reich
• 金额: $ 33.28万
• 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 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8478101
• 关键词: Accounting; Address; Air; Air Pollution; Behavior; Birth Weight; Cardiovascular system; Case-Control Studies; Centers for Disease Control and Prevention (U.S.); Cessation of life; Climate; Complement; Complex; Computer software; Congenital Abnormality; County; Crime; Data; Data Set; Dependence; Development; Diagnostic; Dimensions; Enrollment; Environment; Environmental Exposure; Environmental Risk Factor; Equation; Factor Analysis; Fetal Development; Future; Geographic state; Gestational Age; Goals; Health; Household; Human; Income; Individual; Infant; Infectious Pregnancy Complications; Institution; Interview; Joints; Link; Location; Measures; Methodology; Methods; Modeling; Montserrat; Mothers; Neighborhoods; North Carolina; Outcome; Output; Ozone; Particulate Matter; Physical environment; Policy Making; Pollution; Poverty; Pregnancy Outcome; Premature Birth; Prevention; Principal Investigator; Risk; Sampling; Small for Gestational Age Infant; Source; Statistical Models; Structure; Tail; Time; Uncertainty; Universities; Update; Validation; Weather; case control; climate change; data modeling; deprivation; environmental agent; environmental stressor; improved; indexing; mortality; novel; nutrition; pollutant; programs; respiratory; tool

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

描述（由申请人提供）：这是北卡罗来纳州州立大学，杜克大学和查佩尔山的北卡罗来纳州大学之间的联合合作努力。这三个机构的专业知识相辅相成，并产生协同作用。 我们将实现以下目标：（1）开发广泛的时空统计模型，以研究气候变化条件下空气污染对人类健康的影响。我们将通过引入贝叶斯多变量时空统计模型来改进现有方法，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空间和时间依赖结构，同时考虑模型和数据中不同来源的不确定性。我们将为气候和污染变量开发新的空间分位数回归模型，以更好地描述极端情况，尾部行为以及天气和污染之间的复杂依赖关系。(2)贝叶斯分层收缩方法的发展，用于评估复杂污染物混合物和健康结果之间的空间关联。我们将通过同时考虑不同的污染物类型（如臭氧和颗粒物（PM）或物种PM），表征胎儿发育（妊娠结局）和暴露滞后（死亡结局）的敏感期的时空结构，同时考虑模型和数据中不同来源的不确定性来改进现有方法。(3)我们将建立邻里贫困和环境指数，以便与健康结果联系起来。我们将在妊娠、感染和营养（PIN）研究中使用上述统计框架和分娩时出生体重和胎龄的数据，该研究考察了与妊娠结局有关的建成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)我们将联合收割机结合目标1-3中的统计模型，研究在预测的未来气候条件下，空气污染和极端天气对人类健康的影响。要检查的健康数据包括以下内容：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），来自美国10个州的婴儿样本，他们作为对照参加了国家出生缺陷预防研究（NBDPS），分娩时的地理编码纬度和经度可用。个人水平的心血管出生缺陷地理编码数据是可用的，以及个人水平的地理编码心血管出生缺陷数据的15,000例病例和对照NBDPS。我们将通过开发免费访问软件，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，使这种新方法广泛适用和传播。PHS SF 424（2009年12月更新）第1页

项目成果

Joint variable selection for fixed and random effects in linear mixed-effects models.

• DOI: 10.1111/j.1541-0420.2010.01391.x
• 发表时间: 2010-12
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Bondell HD;Krishna A;Ghosh SK
• 通讯作者: Ghosh SK

Statistical issues in health impact assessment at the state and local levels.

• DOI: 10.1007/s11869-009-0033-3
• 发表时间: 2009-03-01
• 期刊: AIR QUALITY ATMOSPHERE AND HEALTH
• 影响因子: 5.1
• 作者: Fuentes, Montserrat

Space-time data fusion under error in computer model output: an application to modeling air quality.

• DOI: 10.1111/j.1541-0420.2011.01725.x
• 发表时间: 2012-09
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Berrocal VJ;Gelfand AE;Holland DM
• 通讯作者: Holland DM

Nonparametric Bayesian models for a spatial covariance.

空间协方差的非参数贝叶斯模型。

• DOI: 10.1016/j.stamet.2011.01.007
• 发表时间: 2012
• 期刊: Statistical methodology
• 作者: Reich,BrianJ;Fuentes,Montserrat
• 通讯作者: Fuentes,Montserrat

A stochastic neighborhood conditional autoregressive model for spatial data.

空间数据的随机邻域条件自回归模型。

• DOI: 10.1016/j.csda.2008.08.010
• 发表时间: 2009
• 期刊: Computational statistics & data analysis
• 影响因子: 1.8
• 作者: White,Gentry;Ghosh,SujitK
• 通讯作者: Ghosh,SujitK