Peretz C, Goren A, Smid T, Kromhout H
Environmental and Occupational Health Group, Institute for Risk Assessment Sciences, Utrecht University, The Netherlands.
Ann Occup Hyg. 2002 Jan;46(1):69-77. doi: 10.1093/annhyg/mef009.
The benefits of using linear mixed-effects models for occupational exposure assessment were studied by re-analysing three data sets from two published surveys with repeated exposure measurements. The relative contributions of particular characteristics affecting exposure levels were assessed as in a multiple regression model, while controlling for the correlation between repeated measurements. While one-way ANOVA allows one only to estimate unconditioned variance components, a mixed model enables estimation of between- and within-worker variance components of exposure levels while accounting for the fixed effects of work characteristics. Consequently, we can identify the work characteristics affecting each variance component. Mixed models were applied to the data sets with repeated measurements and auxil iary information on work characteristics. The between-worker variance components were reduced by 35, 66 and 80%, respectively, in the three data sets when work characteristics were taken into account. The within-worker (day-to-day) variability was reduced only in the pig farmer data set, by 25%, when accounting for work activities. In addition, coefficients of work characteristics from the mixed model were compared with coefficients resulting from originally published multiple linear regression models. In the rubber manufacturing data, the coefficients of the mixed model showed similar relative importance, but were generally smaller than the coefficients from regression models. However, in the pig farm data, only the coefficients of work activities were somewhat reduced. The mixed model is a helpful tool for estimating factors affecting exposure and suitable variance components. Identifying the factors in the working environment that affect the between-worker variability facilitates a posteriori grouping of workers into more uniformly exposed groups. Identifying the factors that affect the within-worker variance is helpful for hazard control and in designing efficient sampling schemes with reference to time schedule.
通过重新分析来自两项已发表调查的三个数据集（这些数据集包含重复的暴露测量值），研究了使用线性混合效应模型进行职业暴露评估的益处。如同在多元回归模型中那样，在控制重复测量之间的相关性的同时，评估了影响暴露水平的特定特征的相对贡献。虽然单向方差分析仅允许估计无条件方差分量，但混合模型能够在考虑工作特征的固定效应的同时，估计工人之间和工人内部暴露水平的方差分量。因此，我们可以识别影响每个方差分量的工作特征。将混合模型应用于具有重复测量值以及关于工作特征的辅助信息的数据集。当考虑工作特征时，在这三个数据集中，工人之间的方差分量分别减少了35%、66%和80%。在考虑工作活动时，仅养猪场数据集的工人内部（每日）变异性降低了25%。此外，将混合模型中工作特征的系数与最初发表的多元线性回归模型得出的系数进行了比较。在橡胶制造数据中，混合模型的系数显示出相似的相对重要性，但通常小于回归模型的系数。然而，在养猪场数据中，只有工作活动的系数有所降低。混合模型是估计影响暴露的因素和合适的方差分量的有用工具。识别工作环境中影响工人之间变异性的因素有助于将工人事后分组为暴露更均匀的组。识别影响工人内部方差的因素有助于进行危害控制，并有助于参照时间表设计有效的抽样方案。
