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Analysis of the severity of occupational injuries in the mining industry using a Bayesian network
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Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid kalatpuor, Ali Reza Soltanian, Asghar Nikravesh
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Epidemiol Health. 2019;41:e2019017. Published online May 11, 2019
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DOI: https://doi.org/10.4178/epih.e2019017
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15,090
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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.
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Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors
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Shiva Borzouei, Ali Reza Soltanian
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Epidemiol Health. 2018;40:e2018007. Published online March 10, 2018
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DOI: https://doi.org/10.4178/epih.e2018007
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16,665
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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.
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Effects of human and organizational deficiencies on workers’ safety behavior at a mining site in Iran
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Mostafa Mirzaei Aliabadi, Hamed Aghaei, Omid Kalatpour, Ali Reza Soltanian, Maryam SeyedTabib
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Epidemiol Health. 2018;40:e2018019. Published online May 18, 2018
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DOI: https://doi.org/10.4178/epih.e2018019
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16,967
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215
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23
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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.
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- Exploring the application of PLS-SEM in construction management research: a bibliometric and meta-analysis approach
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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
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