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

• 批准号: 7815451
• 负责人: Sudipto Banerjee
• 金额: $ 30.67万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7815451
• 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

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）：本申请解决了广泛的挑战领域（15）转化科学和具体挑战主题：（15-TW-101）预测气候变化对健康影响的模型。我们建议开发贝叶斯分层统计方法和软件，帮助空间分析师建立健康结果与大气和气候预测因素之间的关系。我们提出了一个全面的建模框架来适应不同来源和类型的时空数据。在目标 1 中，我们提出了一个统计建模框架，用于对暴露、气候和健康结果数据进行建模，该框架集成了点级空间错位数据和使用贝叶斯分层空间模型的支持回归变化的方法。我们确定了三种健康结果：哮喘住院、非黑色素瘤皮肤癌的发病率和食源性疾病沙门氏菌病。目标 2 修改了这些模型，使用称为“预测过程”的降维随机过程来适应大型数据集。最后，在目标 3 中，我们承诺提供一套软件包，帮助集成必要的空间数据库和显示组件以及贝叶斯统计建模功能，从而将我们的方法论提供给比目前更广泛的受众，包括健康和环境研究人员和管理员。 。确定明显更有害的环境和气候相关因素将改善卫生研究人员、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。通过使调查人员不再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可以在公共卫生研究中产生深远的有益影响，这可能会触及社会意想不到的角落。