Statistical Methods to Account for Exposure Uncertainty in Environmental Epidemiology

解释环境流行病学中暴露不确定性的统计方法

• 批准号: 10252032
• 负责人: DONNA L SPIEGELMAN
• 金额: $ 60.71万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252032
• 关键词: Address; Adult; Air Pollution; Area; Attention; Biological; Biological Markers; Body Weight; Buffers; Carbon Black; Cardiovascular Diseases; Cardiovascular system; Chemicals; Complex; Computer software; Data; Data Analyses; Development; Disease Marker; Elderly; Environment; Environmental Epidemiology; Environmental Exposure; Environmental Health; Environmental Risk Factor; Epidemiologist; Event; Exposure to; Goals; Grant; Hand; Health; Health Food; Health Policy; Impaired cognition; Incidence; Individual; Internet; Libraries; Life Cycle Stages; Longevity; Measurement; Measures; Methodology; Methods; Modeling; National Institute of Environmental Health Sciences; Nature; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Population; Principal Investigator; Recording of previous events; Relative Risks; Research; Risk; Source; Statistical Methods; Time; Toxic effect; Uncertainty; Validation; Work; cardiovascular disorder risk; coarse particles; disorder prevention; fine particles; high dimensionality; human old age (65+); improved; innovation; middle age; mortality; multidisciplinary; novel; obesity development; obesity in children; older women; pollutant; public health relevance; tool; toxicant; user friendly software; user-friendly; validation studies; walkability; web site

 DESCRIPTION (provided by applicant): Exposure measurement error is a likely source of bias in nearly all environmental health studies, typically leading to an under-estimation of relative risks and a loss of statistical power to detect effects. In this proposed research, we wil take a life course approach, as consistent with NIEHS strategic priorities, focusing on methodological needs in several critical areas of environmental health, including the effects of constituents of air pollution and of aspects of the neighborhood environment on cardiovascular disease and its precursors and consequences, including all-cause mortality, obesity, type 2 diabetes and subclinical cardiovascular biomarkers. Having assembled a strong multi-disciplinary team of leading theoretical and applied statisticians, environmental epidemiologists and environmental exposure assessment experts, consistent with another NIEHS strategic objective, we will make significant contributions to novel areas of pressing environmental health policy importance, with a major focus on developing methods to accurately quantify the effects of complex single and multiple, simultaneous exposure effects across space and time, responding to another NIEHS strategic priority, reducing if not eliminating the bias and loss of efficiency otherwise present due to the presence of substantial exposure measurement error. In this work, careful attention will be paid to removing bias due to spatial and temporal confounding as well as to adjusting for confounding by indoor sources of air pollution. Currently available validation data on air pollution constituents and on features of the neighborhood environment will be assembled and used to develop measurement error models relating personal exposure to measured ambient exposure as suitable for the data at hand. Mixed longitudinal and Cox survival data regression models will underlie the analytic framework. User-friendly software implementing the methods will be posted on the web, facilitating wide-scale application of the new methods to a broad range of environmental health problems.

 描述（由申请人提供）：暴露测量误差是几乎所有环境健康研究中可能出现的偏差来源，通常会导致相对风险的低估以及检测影响的统计能力的丧失。在这项拟议的研究中，我们将采取与 NIEHS 战略重点一致的生命全程方法，重点关注环境健康几个关键领域的方法学需求，包括空气污染成分和邻里环境各方面对心血管疾病及其先兆和后果的影响，包括全因死亡率、肥胖、2 型糖尿病和亚临床心血管生物标志物。我们组建了一支由领先的理论和应用统计学家、环境流行病学家和环境暴露评估专家组成的强大的多学科团队，与 NIEHS 的另一项战略目标相一致，我们将为紧迫的环境卫生政策重要性的新领域做出重大贡献，重点是开发方法来准确量化跨空间和时间的复杂的单一和多重、同时暴露效应的影响，以响应 NIEHS 的另一项战略目标。 优先考虑，如果不能消除的话，可以减少由于存在大量曝光测量误差而导致的偏差和效率损失。在这项工作中，将特别注意消除由于空间和时间混杂造成的偏差，以及调整室内空气污染源的混杂。目前可用的有关空气污染成分和邻近环境特征的验证数据将被汇总并用于开发适合手头数据的将个人暴露与测量的环境暴露相关的测量误差模型。混合纵向和 Cox 生存数据回归模型将构成分析框架的基础。实施这些方法的用户友好软件将发布在网络上，促进新方法在广泛的环境健康问题上的广泛应用。