Epidemiology and Health

Original Articles

Analysis of the severity of occupational injuries in the mining industry using a Bayesian network: Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, Asghar Nikravesh; Epidemiol Health. 2019;41:e2019017. Published online May 11, 2019; DOI: https://doi.org/10.4178/epih.e2019017

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232 Download
11 Web of Science
7 Crossref

Abstract

OBJECTIVES
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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Summary

Citations

Citations to this article as recorded by

A fuzzy Bayesian network DEMATEL model for predicting safety behavior
Mohsen Mahdinia, Iraj Mohammadfam, Ahmad Soltanzadeh, Mostafa Mirzaei Aliabadi, Hamed Aghaei
International Journal of Occupational Safety and Ergonomics.2023; 29(1): 36. CrossRef
Analysis of occupational accidents among nurses working in hospitals based on safety climate and safety performance: a Bayesian network analysis
Fakhradin Ghasemi, Hamed Aghaei, Taleb Askaripoor, Farhad Ghamari
International Journal of Occupational Safety and Ergonomics.2022; 28(1): 440. CrossRef
Retrospective assessment of the association between noise exposure and nonfatal and fatal injury rates among miners in the United States from 1983 to 2014
Abas Shkembi, Lauren M. Smith, Richard L. Neitzel
American Journal of Industrial Medicine.2022; 65(1): 30. CrossRef
Contributing effects of individual characteristics, behavioural and job-related factors on occurrence of mining-related injuries: A systematic review
Michael Mayom Ajith, Apurna Kumar Ghosh, Janis Jansz
Work.2022; 71(1): 87. CrossRef
Human Error Analysis for Hydraulic Engineering: Comprehensive System to Reveal Accident Evolution Process with Text Knowledge
Dan Tian, Hao Liu, Shu Chen, Mingchao Li, Chengzhao Liu
Journal of Construction Engineering and Management.2022;[Epub] CrossRef
Analysis of occupational accidents in Spain using shrinkage regression methods
Vicente Gallego, Ana Sánchez, Isabel Martón, Sebastián Martorell
Safety Science.2021; 133: 105000. CrossRef
Research trends in mining accidents study: A systematic literature review
Siti Noraishah Ismail, Azizan Ramli, Hanida Abdul Aziz
Safety Science.2021; 143: 105438. CrossRef

Effects of human and organizational deficiencies on workers’ safety behavior at a mining site in Iran: Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid Kalatpour, Ali Reza Soltanian, Maryam SeyedTabib; Epidemiol Health. 2018;40:e2018019. Published online May 18, 2018; DOI: https://doi.org/10.4178/epih.e2018019

13,253 View
204 Download
22 Web of Science
24 Crossref

Abstract PDF

Abstract

OBJECTIVES
Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents.

METHODS

In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers’ unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies.

RESULTS

Organizational deficiencies had a direct positive effect on workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.23). The effect of unsafe supervision on workers’ violations and workers’ errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers’ violations (path coefficient, 0.16) and workers’ errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers’ unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers’ unsafe acts.

CONCLUSIONS

Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

Summary

Citations

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Exploring the application of PLS-SEM in construction management research: a bibliometric and meta-analysis approach
Sachin Batra
Engineering, Construction and Architectural Management.2024;[Epub] CrossRef
Analysis of characteristics and causes of gas explosion accidents: a historical review of coal mine accidents in China
Yunxin Wang, Gui Fu, Qian Lyu, Chenhui Yuan
International Journal of Occupational Safety and Ergonomics.2024; 30(1): 168. CrossRef
A fuzzy Bayesian network DEMATEL model for predicting safety behavior
Mohsen Mahdinia, Iraj Mohammadfam, Ahmad Soltanzadeh, Mostafa Mirzaei Aliabadi, Hamed Aghaei
International Journal of Occupational Safety and Ergonomics.2023; 29(1): 36. CrossRef
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Isik Ates Kiral, Sevilay Demirkesen
Engineering, Construction and Architectural Management.2023; 30(9): 4435. CrossRef
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Wang Yuxin, Fu Gui, Lyu Qian, Li Xiao, Chen Yiran, Wu Yali, Xie Xuecai
Process Safety and Environmental Protection.2023; 170: 28. CrossRef
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Saira Sherin, Salim Raza, Ishaq Ahmad
Safety.2023; 9(2): 31. CrossRef
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Mostafa Mirzaei Aliabadi, Iraj Mohammadfam, Keyvan Salimi
International Journal of Occupational Safety and Ergonomics.2022; 28(2): 1291. CrossRef
Influencing Factors, Formation Mechanism, and Pre-control Methods of Coal Miners′ Unsafe Behavior: A Systematic Literature Review
Li Yang, Xue Wang, Junqi Zhu, Zhiyuan Qin
Frontiers in Public Health.2022;[Epub] CrossRef
Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS
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Frontiers in Public Health.2022;[Epub] CrossRef
Application of AHP and DEMATEL for Identifying Factors Influencing Coal Mine Practitioners’ Unsafe State
Lei Chen, Hongxia Li, Shuicheng Tian
Sustainability.2022; 14(21): 14511. CrossRef
Integrated Method for Assessing Occupational Risks at Oil and Gas Production Facilities
N V Gorlenko, M A Murzin
IOP Conference Series: Earth and Environmental Science.2021; 666(6): 062141. CrossRef
Integrated Method for Assessing Occupational Risks at Oil and Gas Production Facilities
N V Gorlenko, M A Murzin
IOP Conference Series: Materials Science and Engineering.2021; 1079(6): 062078. CrossRef
Occupational Risk Assessment for Workers of Aluminum Production Using the Example of RUSAL Bratsk OJSC
M A Murzin, M S Tepina, N V Gorlenko
IOP Conference Series: Materials Science and Engineering.2021; 1079(6): 062080. CrossRef
Zero-Emission Water Cycle When Developing Underground Gas Storage in Rock Salt Formation
E A Lokshina, A V Kolchin, B N Mastobaev
IOP Conference Series: Materials Science and Engineering.2021; 1079(7): 072039. CrossRef
Research trends in mining accidents study: A systematic literature review
Siti Noraishah Ismail, Azizan Ramli, Hanida Abdul Aziz
Safety Science.2021; 143: 105438. CrossRef
Investigation of the Relationship among Human Factors in Mining Accidents Using a Systematic Approach
Mostafa Mirzaei Aliabadi, Taleb Askaripoor, Hamed Aghaei
Journal of Occupational Hygiene Engineering.2021; 8(2): 8. CrossRef
Contributory factors interactions model: A new systems‐based accident model
Linlin Jing, Qingguo Bai, Weiqun Guo, Yan Feng, Lin Liu, Yingyu Zhang
Systems Research and Behavioral Science.2020; 37(2): 255. CrossRef
Game Modelling and Strategy Research on Trilateral Evolution for Coal-Mine Operational Safety Production System: A Simulation Approach
Yan Li, Yan Zhang, Haifeng Dai, Ziyan Zhao
Complexity.2020; 2020: 1. CrossRef
A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review
Justin Pence, Zahra Mohaghegh
Risk Analysis.2020; 40(6): 1183. CrossRef
Occupational Risks in the Extraction and Processing of Mineral Raw Materials
N V Gorlenko, M S Leonova, M A Murzin
IOP Conference Series: Earth and Environmental Science.2020; 459(3): 032023. CrossRef
Structural equation modeling of risk-taking behaviors based on personality dimensions and risk power
MostafaMirzaei Aliabadi, Elnaz Taheri, Kamran Najafi, Farzaneh Mollabahrami, Sajjad Deyhim, Maryam Farhadian
International Archives of Health Sciences.2020; 7(3): 119. CrossRef
The Relationships Among Occupational Safety Climate, Patient Safety Climate, and Safety Performance Based on Structural Equation Modeling
Hamed Aghaei, Zahra Sadat Asadi, Mostafa Mirzaei Aliabadi, Hassan Ahmadinia
Journal of Preventive Medicine and Public Health.2020; 53(6): 447. CrossRef
Analysis of the severity of occupational injuries in the mining industry using a Bayesian network
Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, Asghar Nikravesh
Epidemiology and Health.2019; 41: e2019017. CrossRef
Cause Analysis of Unsafe Behaviors in Hazardous Chemical Accidents: Combined with HFACs and Bayesian Network
Xiaowei Li, Tiezhong Liu, Yongkui Liu
International Journal of Environmental Research and Public Health.2019; 17(1): 11. CrossRef

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors: Shiva Borzouei, Ali Reza Soltanian; Epidemiol Health. 2018;40:e2018007. Published online March 10, 2018; DOI: https://doi.org/10.4178/epih.e2018007

13,435 View
287 Download
13 Web of Science
11 Crossref

Abstract PDF

Abstract

OBJECTIVES
To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.

METHODS

This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.

RESULTS

Variables found to be significant at a level of p<0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.

CONCLUSIONS

In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Summary

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