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Epidemiology and Health 2019;e2019017.
DOI: https://doi.org/10.4178/epih.e2019017    [Accepted] Published online May 11, 2019.
Analysis of occupational injuries severity in the mining industry using Bayesian network, Iran
Mostafa Mirzaei Aliabadi1  , Hamed Aghaei1  , Omid kalatpuor1  , Ali Reza Soltanian2  , Asghar Nikravesh3 
1Center of Excellence for Occupational Health (CEOH) and Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
2Department of Biostatistics and Epidemiology, School of Public Health and Modeling of Non-communicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3Golgohar mining & industrial company, Sirjan, Kerman, Iran
Correspondence  Hamed Aghaei ,Tel: 00988638231255, Email: h.aghaei@umsha.ac.ir
Received: March 20, 2019  Accepted after revision: May 11, 2019
Occupational injuries have been known as the main adverse outcome of occupational accidents. The purpose of the current study was to find control strategies for decreasing the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis.
The BN structure was created using a focus group technique. The 425 mining accidents data was collected and required data was extracted. Expectation-Maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine that which factors had the highest effect on severity of accidents.
Based on sensitivity analyses of BN, training, type of accident, and activity type of workers were the most important factors influencing severity of accidents. Moreover, among individual factors, experience of workers had a highest influence on severity of accidents.
Among the examined factors, safety training was the most important factor influencing severity of accidents. Organizations would be able to decline the severity of occupational injuries by holding safety training courses that prepared based on activity type of workers.
Keywords: Occupational Injuries; Lost Work Days; Bayesian Network; Mining Industry


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