School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa.
Global Biostatistics and Programming, Pharmaceutical Product Development, Part of Thermofisher Scientific, Woodmead, Johannesburg 2191, South Africa.
Int J Environ Res Public Health. 2023 Jan 31;20(3):2496. doi: 10.3390/ijerph20032496.
Occupational exposure assessment is important in preventing occupational coal worker's diseases. Methods have been proposed to assess compliance with exposure limits which aim to protect workers from developing diseases. A Bayesian framework with informative prior distribution obtained from historical or expert judgements has been highly recommended for compliance testing. The compliance testing is assessed against the occupational exposure limits (OEL) and categorization of the exposure, ranging from very highly controlled to very poorly controlled exposure groups. This study used a Bayesian framework from historical and expert elicitation data to compare the posterior probabilities of the 95th percentile (P95) of the coal dust exposures to improve compliance assessment and decision-making. A total of 10 job titles were included in this study. Bayesian framework with Markov chain Monte Carlo (MCMC) simulation was used to draw a full posterior probability of finding a job title to an exposure category. A modified IDEA ("Investigate", "Discuss", "Estimate", and "Aggregate") technique was used to conduct expert elicitation. The experts were asked to give their subjective probabilities of finding coal dust exposure of a job title in each of the exposure categories. Sensitivity analysis was done for parameter space to check for misclassification of exposures. There were more than 98% probabilities of the P95 exposure being found in the poorly controlled exposure group when using expert judgments. Historical data and non-informative prior tend to show a lower probability of finding the P95 in higher exposure categories in some titles unlike expert judgments. Expert judgements tend to show some similarity in findings with historical data. We recommend the use of expert judgements in occupational risk assessment as prior information before a decision is made on current exposure when historical data are unavailable or scarce.
职业性暴露评估对于预防职业性煤工尘肺至关重要。已经提出了多种方法来评估暴露限值的遵守情况，旨在保护工人免受疾病的侵害。基于历史数据或专家判断获得信息先验分布的贝叶斯框架已被高度推荐用于合规性测试。合规性测试是针对职业暴露限值（OEL）和暴露分类进行评估的，从高度受控到高度不受控的暴露组。本研究使用基于历史数据和专家启发式数据的贝叶斯框架，比较了煤炭粉尘暴露的第 95 百分位数（P95）的后验概率，以改进合规性评估和决策制定。共有 10 个职位纳入本研究。使用贝叶斯框架和马尔可夫链蒙特卡罗（MCMC）模拟来绘制发现某个职位属于某个暴露类别的后验概率的完整分布。使用修改后的 IDEA（“调查”、“讨论”、“估计”和“汇总”）技术进行专家启发式评估。专家们被要求给出他们对在每个暴露类别中找到某个职位的煤炭粉尘暴露的主观概率。进行了参数空间的敏感性分析，以检查暴露的分类错误。当使用专家判断时，P95 暴露在高度不受控的暴露组中被发现的概率超过 98%。与专家判断相比，历史数据和非信息先验倾向于显示在某些标题中，P95 在较高暴露类别中被发现的概率较低。专家判断在某些方面与历史数据的发现结果相似。我们建议在没有历史数据或历史数据稀缺的情况下，在做出关于当前暴露的决策之前，将专家判断用作职业风险评估中的先验信息。
