Statistical Methods for Complex Enivronmental Health Data

复杂环境健康数据的统计方法

• 批准号: 8600272
• 负责人: ROGER PENG
• 金额: $ 35.69万
• 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/8600272
• 关键词: Accounting; Address; Air; Air Pollutants; Air Pollution; Ally; Bayesian Method; Bayesian Modeling; 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 chain Monte Carlo methodology; Measurement; Measures; Medicare; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; Nature; Outcome; Particulate Matter; Pollution; Population; 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; public health relevance; 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.

描述（由申请人提供）：复杂环境健康数据的统计方法 项目摘要 环境颗粒物（PM）空气污染是对公众健康的主要威胁，但目前制定空气质量标准的方法并未反映空气污染的复杂多污染物性质PM 化学混合物。最近的研究表明，通过针对产生最有害化学成分的 PM 来源，可能存在减少环境 PM 的公共健康负担的机会。目前，制定新的多污染物空气质量干预策略的科学依据不足，现有的统计方法无法充分应对数据带来的挑战。研究人员开发了广泛使用的统计方法来进行环境空气污染和健康的国家流行病学研究，并确定迫切需要一套新的统计方法来评估复杂空气污染物混合物对健康的影响。第一个目标是开发时空贝叶斯分层多元受体模型，用于识别空气污染化学混合物的来源并估计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全国综合评估； (b) 使用时空模型进行源解析； (c) 引入关于源概况和排放的国家数据库，为模型开发和参数估计提供信息。第二个目标将开发新颖的多元时空模型，用于估计周围环境暴露对社区级健康的影响，并考虑空间错位和测量误差。第三个目标是将新开发的统计方法应用于国家空气污染和健康结果研究（即医疗保险队列空气污染研究）的数据，以 (a) 估计 PM 源对国家、区域、和局部规模； (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。第四个目标是开发模块化和可扩展的开源软件，实施新的统计方法。通过在一项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学挑战，该应用程序的目标将为有针对性的干预措施和空气质量控制策略奠定基础，这些策略将具有对广大人群的公共卫生产生重大影响。