Business Unit Quality & Safety, TNO Quality of Life, Zeist, The Netherlands.
J Occup Environ Hyg. 2010 Apr;7(4):216-23. doi: 10.1080/15459621003597488.
The web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control at the workplace. The tool uses a mechanistic model to arrive at a "Stoffenmanager score" for exposure. In a recent study it was shown that variability in exposure measurements given a certain Stoffenmanager score is still substantial. This article discusses an extension to the tool that uses a Bayesian methodology for quantitative workplace/scenario-specific exposure assessment. This methodology allows for real exposure data observed in the company of interest to be combined with the prior estimate (based on the Stoffenmanager model). The output of the tool is a company-specific assessment of exposure levels for a scenario for which data is available. The Bayesian approach provides a transparent way of synthesizing different types of information and is especially preferred in situations where available data is sparse, as is often the case in small- and medium sized-enterprises. Real-world examples as well as simulation studies were used to assess how different parameters such as sample size, difference between prior and data, uncertainty in prior, and variance in the data affect the eventual posterior distribution of a Bayesian exposure assessment.
基于网络的工具“Stoffenmanager”最初是为了帮助荷兰的中小企业进行定性风险评估,并为工作场所的控制提供建议而开发的。该工具使用一种机械模型来得出暴露的“Stoffenmanager 分数”。最近的一项研究表明,给定特定的 Stoffenmanager 分数,暴露测量的可变性仍然很大。本文讨论了该工具的扩展,该扩展使用贝叶斯方法进行定量的工作场所/特定场景的暴露评估。这种方法允许将感兴趣的公司中观察到的实际暴露数据与先验估计值(基于 Stoffenmanager 模型)相结合。该工具的输出是针对具有可用数据的场景的特定于公司的暴露水平评估。贝叶斯方法提供了一种综合不同类型信息的透明方法,特别是在可用数据稀缺的情况下,这在中小企业中经常发生。使用实际示例和模拟研究来评估不同参数(例如样本量、先验和数据之间的差异、先验的不确定性以及数据的方差)如何影响贝叶斯暴露评估的最终后验分布。