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Bayesian integration of biomarkers and spatial exposure data

生物标志物和空间暴露数据的贝叶斯积分

基本信息

批准号：
8722555
负责人：
SCOTT Michael BARTELL
金额：
$ 17.22万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-16 至 2016-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8722555
关键词：
Accounting Address Bayesian Analysis Bayesian Method Bayesian Modeling Biochemical Biological Biological Markers Blood Blood specimen Communication Communities Complex Consent Data Development Diet Dose Environment Environmental Pollutants Epidemiologic Studies Epidemiology Exposure to Funding Opportunities General Population Goals Goretex Health Hepatotoxicity Individual Joints Laboratory Animals Life Link Location Logistic Regressions Malignant neoplasm of testis Maps Measurement Medical Methods Modeling Monte Carlo Method Movement Nutritional Occupational Outcome Participant Patient Self-Report Pattern Physical activity Population Population Study Pre-Eclampsia Probability Production Publishing Questionnaires Recording of previous events Renal carcinoma Reporting Review Literature Running Science Sentinel Serum Source Statistical Methods Statistical Models Surveys Techniques Teflon Testing Time Toxic effect Uncertainty United States National Institutes of Health Update Variant Water Water Supply Water consumption West Virginia base consumer product data modeling drinking water food environment hookah immunotoxicity improved pharmacokinetic model pollutant population based pregnancy hypertension public health relevance reconstruction research study spatiotemporal

项目摘要

DESCRIPTION (provided by applicant): Many environmental pollutants have distinct patterns in both space and time. One of the main challenges in epidemiologic studies of the health effects of these pollutants is characterizing those patterns accurately enough to rank study participants by the amount of pollutant exposure at any particular point in time or cumulatively over a given period of time. We propose new Bayesian statistical models to combine spatial and temporal exposure information from a variety of sources including source emissions, fate and transport models, exposure questionnaires, and biochemical measurements such as pollutant concentrations in participant's blood samples. Bayesian methods also produce quantitative estimates of the extent of uncertainty regarding each participant's amount of exposure. This characterization of uncertainty is critical for understanding the reliability of spatial epidemioloic analyses linking pollutant exposures to adverse health effects. We propose to further develop and implement these new Bayesian models using data from the C8 Health Project, a recent study of over 69,030 community residents exposed to perfluorooctanoate released from a major production facility in West Virginia. Previous analyses have associated perfluorooctanoate exposures in these study participants to pregnancy-induced hypertension/preeclampsia, testicular cancer, and kidney cancer; numerous other epidemiologic analyses are currently underway. These analyses all rely on the same set of exposure estimates, which estimate the extent of past exposure to each individual based on his/her residential and occupational history and tap water consumption, in conjunction with a complex set of linked spatial fate and transport models that estimate the past movement of perfluorooctanoate in the environment. Probability distributions will be used to characterize the uncertainty contributed by the various exposure model components for each consented participant in the C8 Health Project. Monte Carlo techniques will then be used to determine the combined effects of those uncertainties on the exposure estimates. These induced prior distributions will then be combined with perfluorooctanoate blood serum measurements obtained for the study participants in 2005-2006; the resulting combined posterior exposure estimates will be used to reanalyze the pregnancy-induced hypertension/preeclampsia association. These Bayesian models should 1.) improve exposure rankings for epidemiologic analyses and 2.) allow for direct characterization of the effects of spatial uncertainty on the reliability of epidemiologic findings.

描述（由申请人提供）：许多环境污染物在空间和时间上都有不同的模式。这些污染物对健康影响的流行病学研究的主要挑战之一是准确地描述这些模式，以便根据任何特定时间点或特定时间段内累积的污染物暴露量对研究参与者进行排名。我们提出了新的贝叶斯统计模型，结合联合收割机空间和时间的暴露信息，从各种来源，包括源排放，命运和运输模型，暴露问卷调查，和生化测量，如污染物浓度在参与者的血液样本。贝叶斯方法还可以对每个参与者的接触量的不确定程度进行定量估计。这种不确定性的表征对于理解将污染物暴露与不利健康影响联系起来的空间流行病学分析的可靠性至关重要。我们建议使用C8健康项目的数据进一步开发和实施这些新的贝叶斯模型，C8健康项目是一项最近对西弗吉尼亚州一家主要生产设施释放的全氟辛酸酯暴露于69，030多名社区居民的研究。先前的分析将这些研究参与者的全氟辛酸酯暴露与妊娠高血压/先兆子痫、睾丸癌和肾癌相关联;目前正在进行许多其他流行病学分析。这些分析都依赖于同一套接触估计值，即根据个人的居住和职业史以及自来水消费量来估计每个人过去的接触程度，并结合一套复杂的相互关联的空间归宿和迁移模型来估计全氟辛酸盐过去在环境中的移动情况。概率分布将用于表征C8健康项目中每位知情同意参与者的各种暴露模型组件所带来的不确定性。然后将使用蒙特卡罗技术来确定这些不确定性对接触估计数的综合影响。然后将这些诱导的先验分布与2005-2006年研究参与者获得的全氟辛酸血清测量值相结合;由此产生的合并后验暴露估计值将用于重新分析妊娠高血压/先兆子痫相关性。这些贝叶斯模型应该1.）改进流行病学分析的暴露等级; 2.）允许直接描述空间不确定性对流行病学调查结果可靠性的影响。