RETROSPECTIVE EXPOSURE ASSESSMENT IN INDUSTRIAL SETTINGS

为建立长期使用放射性核素引起的疾病的剂量反应关系， 长期暴露于污染物，至关重要的是要确定 个人或群体作为时间的函数。 大多数现有职业 暴露数据库不包含连续的历史记录 暴露于空气污染物。 历史记录中的这些空白 可以通过使用专家和专业人员的知识库来填补 在外地拥有。 本研究建议开发一种贝叶斯方法 获得空气中暴露历史估计的框架 来自有限历史测量的颗粒，使用主观 专家判断。 专家的判断将通过了解以下信息来获得信息： 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. 该方法还将纳入有关采样器的知识 性能，不同类型测量之间的关系， 测量的不确定性和系统偏差。 的结果 合成将是任务暴露的概率分布- 在每个过去的时间间隔，以暴露的形式， 矩阵 该矩阵可以潜在地用于流行病学 问题研究

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.