Citation

Mirzaei Aliabadi M, Aghaei H, Kalatpuor O, Soltanian AR, Nikravesh A. Epidemiol. Health 2019; 41: e2019017.

Affiliation

Golgohar mining & industrial company, Sirjan, Kerman, Iran.

Copyright

(Copyright © 2019, Korean Society of Epidemiology)

DOI

10.4178/epih.e2019017

PMID

31096750

Abstract

OBJECTIVES: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSION: 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.

Language: en

Keywords

Bayesian Network; Lost Work Days; Mining Industry; Occupational Injuries