Center of Excellence for Occupational Health (CEOH) and Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Center of Excellence for Occupational Health (CEOH) and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.
Epidemiol Health. 2019;41:e2019017. doi: 10.4178/epih.e2019017. Epub 2019 May 11.
Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.
The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents.
Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents.
Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.
职业伤害是已知的职业事故的主要不良后果。本研究的目的是使用贝叶斯网络（BN）分析确定控制策略，以降低采矿业职业伤害的严重程度。
BN 结构是使用焦点小组技术创建的。收集了 425 起采矿事故的数据，并提取了所需的信息。使用期望最大化算法估计条件概率表。置信度更新用于确定哪些因素对事故严重程度的影响最大。
基于 BN 的敏感性分析，培训、事故类型和工人的活动类型是影响事故严重程度的最重要因素。在个体因素中，工人的经验对事故的严重程度影响最大。
在所检查的因素中，安全培训是影响事故严重程度的最重要因素。组织可以通过举办根据工人活动类型准备的安全培训课程来降低职业伤害的严重程度。
