超越工作接触矩阵(JEM):任务接触矩阵(TEM)。
Beyond the job exposure matrix (JEM): the task exposure matrix (TEM).
作者信息
Benke G, Sim M, Fritschi L, Aldred G
机构信息
Department of Epidemiology and Preventive Medicine, Monash Medical School, Commercial Road, Victoria 3181, Prahran, Australia.
出版信息
Ann Occup Hyg. 2000 Sep;44(6):475-82.
The job exposure matrix (JEM) has been employed to assign cumulative exposure to workers in many epidemiological studies. In these studies, where quantitative data are available, all workers with the same job title and duration are usually assigned similar cumulative exposures, expressed in mgm(-3)xyears. However, if the job is composed of multiple tasks, each with its own specific exposure profile, then assigning all workers within a job the same mean exposure can lead to misclassification of exposure. This variability of exposure within job titles is one of the major weaknesses of JEMs. A method is presented for reducing the variability in the JEM methodology, which has been called the task exposure matrix (TEM). By summing the cumulative exposures of a worker over all the tasks worked within a job title, it is possible to address the variability of exposure within the job title, and reduce possible exposure misclassification. The construction of a TEM is outlined and its application in the context of a study in the primary aluminium industry is described. The TEM was found to assign significantly different cumulative exposures to the majority of workers in the study, compared with the JEM and the degree of difference in cumulative exposure between the JEM and the TEM varied greatly between contaminants.
在许多流行病学研究中,工作暴露矩阵(JEM)已被用于确定工人的累积暴露量。在这些有定量数据的研究中,通常会给所有具有相同职位和工作时长的工人分配相似的累积暴露量,以毫克/立方米×年表示。然而,如果一项工作由多个任务组成,每个任务都有其特定的暴露特征,那么给同一工作岗位的所有工人分配相同的平均暴露量可能会导致暴露分类错误。同一职位内暴露的这种变异性是工作暴露矩阵的主要弱点之一。本文提出了一种减少工作暴露矩阵方法变异性的方法,该方法被称为任务暴露矩阵(TEM)。通过将工人在一个职位内所从事的所有任务的累积暴露量相加,可以解决同一职位内暴露的变异性问题,并减少可能的暴露分类错误。本文概述了任务暴露矩阵的构建,并描述了其在原铝行业一项研究中的应用。结果发现,与工作暴露矩阵相比,任务暴露矩阵给该研究中的大多数工人分配了显著不同的累积暴露量,而且工作暴露矩阵和任务暴露矩阵之间累积暴露量的差异程度在不同污染物之间有很大差异。