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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：
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
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/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

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.