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

基本信息

批准号：
10023260
负责人：
DONNA L SPIEGELMAN
金额：
$ 61.06万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10023260
关键词：
Address Adult Age 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 high dimensionality 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

项目摘要

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。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前兆和后果的影响，包括根据 NIEHS 的另一个战略目标，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，将在全因死亡率、肥胖、2 型糖尿病和亚临床心血管生物标志物方面做出重大贡献。到环境卫生政策紧迫的新领域，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，响应另一个 NIEHS 战略重点，减少（如果不是消除的话）偏见和否则会损失效率由于存在大量的暴露测量误差，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，以及调整当前可用的空气污染验证数据。将汇总并用于开发与个人暴露与测量的环境暴露相关的测量误差模型，以适合手头的数据。混合纵向和 Cox 生存数据回归模型将成为用户友好的分析框架的基础。实施这些方法的软件将发布在网络，促进新方法在广泛的环境健康问题上的广泛应用。