RETROSPECTIVE EXPOSURE ASSESSMENT IN INDUSTRIAL SETTINGS

建立Long所致疾病的剂量反应关系 长期接触污染物，至关重要的是要确定 作为时间的函数的个人或群体。最现有的职业 暴露数据库不包含历史记录的连续记录 暴露在空气中的污染物。历史记录中的这些差距 可以通过使用专家和专业人员 在田野里拥有。这项研究建议开发一个贝叶斯 获取机载暴露历史估计的框架 来自有限历史测量的颗粒物，使用主观 专家的判断。专家判断将根据以下知识进行通知 历史工厂条件和工作实践，以及描述 依赖于过程的气溶胶产生、通风和工人活动 模式。该方法还将结合关于取样器的知识 性能、不同类型的测量之间的关系、 测量中的不确定性和系统性偏差。结果就是 综合将是任务暴露的概率分布-- 以暴露的形式在每个过去的时间间隔内的工人群 矩阵。该矩阵随后可潜在地用于流行病学 学习。

To establish dose response relationships for diseases caused by long term exposures to pollutants, it is vital to determine exposures of individuals or cohorts as functions of time. Most existing occupational exposure databases do not contain continuous records of historical exposures to airborne contaminants. These gaps in the historical record may be filled by using the knowledge-base that experts and professionals in the field possess. This research proposes to develop a Bayesian framework obtaining estimates of exposure histories for airborne particulates from limited historical measurements, using subjective expert judgment. The expert judgments will be informed by knowledge of historical plant conditions and work practices, and models describing process-dependent aerosol generation, ventilation, and worker activity patterns. The method will also incorporate knowledge about sampler performance, relationships between different types of measurements, uncertainties in measurements, and systematic biases. The result of the synthesis will be probability distributions of the exposure of task- groups of workers at each past time interval, in the form of an exposure matrix. This matrix can then be potentially used in epidemiological studies.