Developing a job-exposure matrix with exposure uncertainty from expert elicitation and data modeling
Developing a job-exposure matrix with exposure uncertainty from expert elicitation and data modeling
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DOI：
10.1038/jes.2015.37
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发表时间：
2017-01-01
影响因子：
4.5
通讯作者：
Kheifets, Leeka
中科院分区：
文献类型：
--
作者：
Fischer, Heidi J.;Vergara, Ximena P.;Kheifets, Leeka
工作暴露矩阵（Job exposure matrices, JEMs）是一种工具，用于在缺乏个人层面数据的情况下，根据一般工作任务对职位名称的暴露程度进行分类。然而，在JEM中，由于工人实践、工作条件和数据质量的变化而导致的暴露不确定性从未被系统地量化。我们描述了一种创建JEM的方法，该方法在连续尺度上定义职业暴露，并利用启发方法通过分配暴露概率分布和专家参与确定的参数来量化暴露不确定性。在缺乏可用的职业水平数据的情况下，专家利用他们的知识利用相关的暴露替代数据开发数学模型，并根据其他类似职业调整模型输出。正式的专家启发方法提供了一个一致的、有效的过程，将专家判断纳入一个大型的、基于共识的JEM。使用这些方法创建了一个基于人群的电击JEM，允许透明的暴露估计。
Job exposure matrices (JEMs) are tools used to classify exposures for job titles based on general job tasks in the absence of individual level data. However, exposure uncertainty due to variations in worker practices, job conditions, and the quality of data has never been quantified systematically in a JEM. We describe a methodology for creating a JEM which defines occupational exposures on a continuous scale and utilizes elicitation methods to quantify exposure uncertainty by assigning exposures probability distributions with parameters determined through expert involvement. Experts use their knowledge to develop mathematical models using related exposure surrogate data in the absence of available occupational level data and to adjust model output against other similar occupations. Formal expert elicitation methods provided a consistent, efficient process to incorporate expert judgment into a large, consensus-based JEM. A population-based electric shock JEM was created using these methods, allowing for transparent estimates of exposure.