Hierarchical spatial process models for estimating and predicting health effects

用于估计和预测健康影响的分层空间过程模型

基本信息

批准号：
7943904
负责人：
Sudipto Banerjee
金额：
$ 30.36万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7943904
关键词：
Accounting Address Administrator Adverse effects Area Asthma Attention Cities Climate Computer software Data Data Aggregation Data Set Databases Decision Making Dependency Development Dimensions Disease Environmental Exposure Environmental Health Environmental Risk Factor Epidemiologic Studies Funding Health Healthcare Systems Hospitalization Incidence Investigation Joints Maps Methodology Methods Modeling Monitor Occupations Outcome Output Patients Policy Maker Positioning Attribute Process Public Health Qualitative Methods Reporting Research Personnel Role Salmonella infections Skin Cancer Skin Carcinoma Societies Solutions Source Statistical Methods Statistical Models Stimulus Stochastic Processes Time Touch sensation Translational Research Uncertainty base climate change flexibility foodborne improved mortality novel public health research socioeconomics

项目摘要

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (15) Translational science and Specific Challenge Topic: (15-TW-101) Models to predict health effects of climate change. We propose to develop Bayesian hierarchical statistical methods and software that will help spatial analysts to establish relationships among health outcomes and atmospheric and climate predictors. We propose a comprehensive modeling framework to accommodate disparate sources and types of spatial-temporal data. In Aim 1 we propose a statistical modelling framework for modelling exposure, climate and health outcome data that integrates methods for point-level spatially mis- aligned data and change of support regression using Bayesian hierarchical spatial models. We identify three health outcomes: asthma hospitalizations, incidence of nonmelanoma skin cancer and a food borne disease salmonellosis. Aim 2 modifies adapts these models for use with large datasets using a dimension reduction stochastic process called the "predictive process". Finally, in Aim 3, we promise a suite of software packages that help integrate necessary spatial databases and display components with Bayesian statistical modeling ca- pability, thus delivering our methodology to a far broader audience of health and environmental researchers and administrators than is currently accessible. Identifying environmental and climate-related factors that are pronouncedly more detrimental will improve the understanding and decision making process of health researchers, policy makers and patients, thereby having far-reaching beneficial effects on the health care system and society. By redeeming the investigators from using ad-hoc and qualitative methods that often reveal deceptive stories, our proposed statistical methods can have far reaching beneficial effects in public health research that will potentially touch unexpected corners of society.

描述（由申请人提供）：此申请应解决广泛的挑战领域（15）转化科学和特定挑战主题：（15-TW-101）模型，以预测气候变化的健康影响。我们建议开发贝叶斯分层统计方法和软件，以帮助空间分析师建立健康结果与大气和气候预测因子之间的关系。我们提出了一个全面的建模框架，以适应不同的时空数据的不同来源和类型。在AIM 1中，我们提出了一个用于建模暴露，气候和健康结果数据的统计建模框架，该框架可以使用贝叶斯分层空间模型整合了点级空间误差数据的方法和支持回归的变化。我们确定了三个健康结果：哮喘住院，非甲状腺瘤皮肤癌的发病率和食物传播疾病沙门氏菌病。 AIM 2使用降低缩小随机过程（称为“预测过程”）修改这些模型，以与大型数据集一起使用。最后，在AIM 3中，我们保证了一套软件包，有助于整合必要的空间数据库，并将组件与贝叶斯统计建模的可容纳性显示，从而将我们的方法传达给对健康和环境研究人员和管理人员更广泛的受众，而不是目前可访问的。确定明显有害的环境和气候相关因素将改善健康研究人员，政策制定者和患者的理解和决策过程，从而对医疗保健系统和社会产生深远的有益影响。通过赎回调查人员使用经常揭示欺骗性故事的临时和定性方法，我们提出的统计方法在公共卫生研究中可能会产生巨大的有益效果，这可能会遇到社会意外的角落。