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.
Body Mass Index ; Diabetes Mellitus, Type 2* ; Diagnosis ; Epidemiology ; Fruit ; Humans ; Hypertension ; Iran ; Logistic Models ; Mass Screening ; Methods ; Models, Statistical ; Neural Networks (Computer)* ; Risk Assessment ; Risk Factors* ; Sedentary Lifestyle ; Sensitivity and Specificity ; Smoke ; Smoking ; Vegetables ; Waist Circumference ; Walking

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.
Body Mass Index ; Diabetes Mellitus, Type 2 ; Diagnosis ; Epidemiology ; Fruit ; Humans ; Hypertension ; Iran ; Logistic Models ; Mass Screening ; Methods ; Models, Statistical ; Neural Networks (Computer) ; Risk Assessment ; Risk Factors ; Sedentary Lifestyle ; Sensitivity and Specificity ; Smoke ; Smoking ; Vegetables ; Waist Circumference ; Walking

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.

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.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

OBJECTIVES: A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM). METHODS: A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84. RESULTS: Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources. CONCLUSION: These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.
Cross-Sectional Studies ; Delivery of Health Care ; Diabetes Mellitus, Type 2 ; Education ; Friends ; Humans ; Internet ; Male ; Medical Staff ; Methods ; Models, Statistical

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.
Accidents, Occupational ; Bayes Theorem ; Focus Groups ; Mining ; Occupational Injuries

Background: One of the important actions for enhancing human reliability in any industry is assessing human error probability (HEP). The HEART technique is a robust tool for calculating HEP in various industries. The traditional HEART has some weaknesses due to expert judgment. For these reasons, a hybrid model is presented in this study to integrate HEART with Best-Worst Method.Materials MethodIn this study, the blasting process in an iron ore mine was investigated as a case study. The proposed HEART-BWM was used to increase the sensitivity of APOA calculation. Then the HEP was calculated using conventional HEART formula. A consistency ratio was calculated using BWM. Finally, for verification of the HEART-BWM, HEP calculation was done by traditional HEART and HEART-BWM. Results: In the view of determined HEPs, the results showed that the mean of HEP in the blasting of the iron ore process was 2.57E-01. Checking the full blast of all the holes after the blasting sub-task was the most dangerous task due to the highest HEP value, and it was found 9.646E-01. On the other side, obtaining a permit to receive and transport materials was the most reliable task, and the HEP was 8.54E-04. Conclusion The results showed a good consistency for the proposed technique. Comparing the two techniques confirmed that the BWM makes the traditional HEART faster and more reliable by performing the basic comparisons.

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.

BACKGROUND: In comparison with other industries, the construction industry still has a higher rate of fatal injuries, and thus, there is a need to apply new and innovative approaches for preventing accidents and promoting safe conditions at construction sites. METHODS: In this study, the effectiveness of a new incentive system-the surprising incentive system-was assessed. One year after the implementation of this new incentive system, behavioral changes of employees with respect to seven types of activities were observed. RESULTS: The results of this study showed that there is a significant relationship between the new incentive system and the safety performance of frontline employees. The new incentive system had a greater positive impact in the first 6 months since its implementation. In the long term, however, safety performance experienced a gradual reduction. Based on previous studies, all activities selected in this study are important indicators of the safety conditions at workplaces. However, there is a need for a comprehensive and simple-to-apply tool for assessing frontline employees' safety performance. Shortening the intervals between incentives is more effective in promoting safety performance. CONCLUSION: The results of this study proved that the surprising incentive would improve the employees' safety performance just in the short term because the surprising value of the incentives dwindle over time. For this reason and to maintain the surprising value of the incentive system, the amount and types of incentives need to be evaluated and modified annually or biannually.
Construction Industry ; Motivation*

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.
Accidents, Occupational ; Humans* ; Iran* ; Mining* ; Models, Statistical ; Organization and Administration ; Systems Analysis