Statistical Methods for Complex Enivronmental Health Data

复杂环境健康数据的统计方法

• 批准号: 8019720
• 负责人: ROGER PENG
• 金额: $ 37.29万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-18 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8019720
• 关键词: Accounting; Address; Air; Air Pollutants; Air Pollution; Ally; Bayesian Method; Chemicals; Cities; Communities; Complex; Computer software; Data; Databases; Detection; Environmental Exposure; Environmental Health; Epidemiologic Studies; Epidemiology; Equation; Factor Analysis; Foundations; Health; Health Status; Individual; Intervention; Link; Location; Markov Chains; Measurement; Measures; Medicare; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Nature; Outcome; Particulate Matter; Pollution; Population; Procedures; Public Health; Quality Control; Relative (related person); Research Personnel; Resolution; Risk; Source; Statistical Methods; Statistical Models; Toxic effect; Variant; Work; ambient particle; base; cohort; design; innovation; model development; mortality; novel; open source; planetary Atmosphere; pollutant; population health; prevent; receptor

DESCRIPTION (provided by applicant): Statistical Methods for Complex Environmental Health Data Project Summary Ambient particulate matter (PM) air pollution is a major threat to public health, but current approaches to setting air quality standards do not reflect the complex multi-pollutant nature of the PM chemical mixture. Recent work indicates that opportunities may exist to reduce the public health burden of ambient PM by targeting the sources of PM that produce the most harmful chemical constituents. Currently, the scientific basis for developing new multi-pollutant air quality intervention strategies is insufficient and available statistical methods do not adequately address the challenges presented by the data. The investigators have developed widely-used statistical methodology for conducting national epidemiological studies of ambient air pollution and health and have identified the critical need for a new set of statistical methods for assessing the health effects of complex air pollutant mixtures. The first aim will develop a spatial-temporal Bayesian hierarchical multivariate receptor model for identifying sources of air pollution chemical mixtures and estimating their effect on population health outcomes. Innovation focuses on (a) conducting an integrated national assessment of the health effects of pollution sources; (b) the use of spatial-temporal models for source apportionment; and (c) the introduction of national databases on source profiles and emissions to inform model development and parameter estimation. The second aim will develop novel multivariate spatial-temporal models for estimating community-level health effects of ambient environmental exposures, accounting for spatial misalignment and measurement error. The third aim will apply the newly developed statistical methods to data from a national study of air pollution and health outcomes, the Medicare Cohort Air Pollution Study, to (a) estimate short-term population health effects of PM sources on a national, regional, and local scale; (b) estimate short- and long-term health effects of PM constituents and identify the sources of toxic constituents. The fourth aim will develop modular and extensible open source software implementing new statistical methods. By providing critical evidence about the relative toxicities of PM constituents and sources in a national study and by developing novel statistical approaches to overcome current methodological challenges, the aims of this application will lay the foundation for targeted interventions and air quality control strategies that will have a substantial public health impact across broad populations. PUBLIC HEALTH RELEVANCE: Relevance Ambient particle air pollution is a major public health problem and current approaches to regulating pollutant levels are sub-optimal. This project will develop novel statistical methods to be applied to national databases for estimating the health effects of ambient particle air pollution chemical constituents and sources. The evidence generated by this work will serve as the foundation for more targeted air quality control strategies.

描述（由申请人提供）：复杂环境健康数据项目的统计方法摘要环境颗粒物（PM）空气污染是对公共卫生的主要威胁，但是当前设定空气质量标准的方法并不能反映PM化学混合物的复杂多污染物性质。最近的工作表明，可能存在机会，以通过针对产生最有害的化学成分的PM来源来减轻环境PM的公共卫生负担。当前，开发新的多污染物空气质量干预策略的科学基础不足，可用的统计方法无法充分解决数据所面临的挑战。研究人员开发了广泛使用的统计方法，用于对环境空气污染和健康进行全国流行病学研究，并确定了对评估复杂空气污染物混合物的健康影响的新统计方法的关键需求。第一个目标将开发出空间贝叶斯分层多元受体模型，用于鉴定空气污染的化学混合物的来源并估算其对人口健康结果的影响。创新的重点是（a）对污染源的健康影响进行综合评估； （b）使用时空模型用于源分配； （c）引入有关源资料和排放的国家数据库，以告知模型开发和参数估计。第二个目标将开发新型的多元时空模型，以估计环境环境暴露的社区水平健康影响，考虑空间未对准和测量误差。第三个目的将把新开发的统计方法应用于国家对空气污染和健康结果研究的数据，《医疗保险队列空气污染研究》，（a）估计PM来源对国家，地区，地区和地方规模的短期人口健康影响； （b）估计PM成分的短期和长期健康影响，并确定有毒成分的来源。第四目标将开发实施新统计方法的模块化和可扩展的开源软件。通过在国家研究中提供有关PM成分和来源的相对毒性的关键证据，并通过开发新的统计方法来克服当前的方法论挑战，该应用的目的将为有针对性的干预措施和空气质量控制策略奠定基础，这些策略将在广泛的人群中产生实质性的公共卫生影响。 公共卫生相关性：相关环境粒子空气污染是一个主要的公共卫生问题，当前调节污染物水平的方法是最佳的。该项目将开发新的统计方法，以应用于国家数据库，以估计环境颗粒空气污染化学成分和来源的健康影响。这项工作产生的证据将成为更具针对性的空气质量控制策略的基础。