Journal of Occupational Hygiene Engineering
Year:2021 | Volume:8 | Issue:2|Papers Count:8

Background and Objective: Physical work capacity is one of the significant issues in work physiology which is used to investigate compatibility between work and worker. Body composition is among the factors affecting maximal aerobic capacity. Accordingly, the present study aimed to examine the effect of anthropometric components and body composition on physical work capacity and to present a final model for estimating physical work capacity using direct and instrumental methods. Materials and Methods: A total of 50 male students were randomly selected in this cross-sectional study. The Bruce protocol and the h/p/cosmostreadmill (Saturn model) were used to estimate the maximal aerobic capacity. Body composition was also measured using the Body Composition Analyzer Device (Olympia 3. 3 model). The relationship between the variables was investigated using Pearson correlation coefficient and regression analysis. Finally, a model was presented to estimate maximum oxygen consumption based on linear regression analysis. Results: The mean maximal aerobic capacity (ml/kg/min) of students was estimated to be 44. 01 ± 4. 75. Also, the mean body mass index and body fat percentage of students were 24. 03 ± 4. 31 𝑘 𝑔 m2 and 20. 77 ± 7. 6%, respectively. The mean waist-to-hip ratio was 0. 803. The present study indicated that there was a significant and negative relationship between maximal aerobic capacity and body composition. Conclusion: A model is proposed in the present study since estimating the physical work capacity requires acceptable accuracy and valid relationships and equations. The variables of body mass index, body fat percentage, and waist-hip ratio which play an effective role in estimating physical work capacity and the maximum oxygen consumption can be estimated with high accuracy based on the presented model.

Background and Objective: Systematic analysis of accidents in sociotechnical systems, such as mining industry, is very important to reduce accidents. The present study aimed to analyze the relationship among human factors in mining accidents using a systematic approach. Materials and Methods: The accidents occurred during seven years in a large iron ore mine complex in the south of Iran were collected, and the most important events were selected subsequently. Afterward, all contributing factors in each accident were identified using an accident analysis team by Human Factors Analysis and Classification System (HFACS) framework. A total of 154 accidents were collected, and the data were analyzed in SPSS Software (version 16) using logistic regression. Results: Skill-based errors were the most frequent among the unsafe acts. Furthermore, the deficiencies in physical environment, as well as inappropriate operations and organizational procedures were the most frequently identified deficiency in the selected accidents. Throughout the overall system, the existence of some deficiencies in upper levels predicted those in lower levels, which included organizational climate, inadequate supervision, organizational procedures and inappropriate operations, inadequate supervision and physical environment, as well as inappropriate operations and physical environment. Conclusion: Skill-based errors, supervision problems, and organizational procedures are the most common deficiencies in mining industry accidents. Analysis of the relationship among the causal factors at different levels of HFACS is necessary to adopt appropriate strategies to reduce the accidents.

Background and Objective: In today's world, intentional accidents occur in many organizations due to numerous reasons. These intentional accidents usually aim to cause substantial damages to industries. To minimize the risk of these threats, it is essential to design and implement risk identification and risk assessment programs. The present study aimed to assess the risk associated with conscious threats with Federal Emergency Management Agency (FEMA) and fuzzy FEMA and compare the results of these two methods. Materials and Methods: In the present study, FMEA and fuzzy FMEA methods were used to identify and assess terrorist threats in a combined cycle power plant. The risks were identified using FEMA checklist. Risk assessment was performed through field observation, the examination of documents, and expert opinion. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was used for prioritizing and selecting the optimum approach. Data were analyzed in SPSS software (version 21). Results: Based on the results, although the fuzzy FEMA method requires more time, as well as higher cost of implementation and educational needs, this method allows a more accurate estimation of risk levels due to the high level of accuracy of the results, and therefore, it prioritizes the units more efficiently. Therefore, the fuzzy FEMA was introduced as the preferred method. Conclusion: As evidenced by the results of the current study, the fuzzy FEMA method could be applied to overcome the weakness of the traditional method of FEMA. Moreover, it reduces uncertainty and increases the efficiency of organizations.

Background and Objective: Industrial development accelerates the transportation of hazardous materials, especially in developing countries, which leads to an increased risk of accidents. Consequently, conducting further research is essential to reduce these consequences. The present study aimed to provide a simple, comprehensive and innovative model of risk management for transporting hazardous materials. Materials and Methods: The proposed model consisted of four steps of identification, analysis, evaluation, and control. Appropriate layouts and tools such as system dynamics and fuzzy AHP were used to ensure that all relevant elements were carefully examined. The model was implemented in a real example of hydrazine explosive transport. Results: In the risk analysis stage, it was found that population density is the most important factor in increasing the risk of transporting hazardous materials along with sub-criteria related to driver and management. Finally, ways to reduce risk to an acceptable level were examined during the presentation of risk control planning. Conclusion: Using system dynamics in the risk identification stage and fuzzy AHP in the risk analysis stage along with expert judgment can be effective in improving the risk management process.

Background and Objective: The analysis of system resilience is one of the ways to increase the factor of safety. The present study aimed to develop a model for the assessment of safety resilience in process industries with a passive defense approach based on the Delphi method. Materials and Methods: This cross-sectional study was conducted in Phase 19 of the South Pars Gas Field Development Project in 2018-2020. This three-round Delphi study was performed in three rounds stages with the participation of 18 experts in the fields of chemical and process engineering, safety, occupational health, and environment. Results: After three rounds of the Delphi study, the safety resilience assessment model was developed based on the three components of preparedness, the likelihood of occurrence, and consequence. Based on the results, the preparedness component included the variables of hardware, software, and defensive preparedness, as well as access to external resources. The findings showed that experimental data, technical inspection, and the professional competence of individuals were among effective parameters in the likelihood component. Moreover, the parameters of human damage, property damage, process damage, environmental damage, and strategic and defense damage were among the effective parameters in the consequence component. In this Delphi study, all members of the expert panel confirmed the items in the algorithm, including resilience components and the variables of each component with a 75% acceptance level. Conclusion: Based on the opinions of the expert panel, the results of this Delphi study indicated that this semi-quantitative model has good reliability for the assessment of safety resilience in process industries. Therefore, the use of this model can be of great in the provision of an acceptable estimate of safety resilience in the process industry.

Background and Objective: The importance of examining shift work, especially in the health sector, is due to the harmful and irreparable consequences that are attributed to it. Work shifts, especially night shifts, lead to fatigue and consequent adverse effects on health, alertness, and performance. This study aimed to investigate the effects of shift work and mental workload on fatigue of intensive care unit (ICU) nurses. Materials and Methods: This analytical and cross-sectional study was performed on 144 ICU nurses in three hospitals of Shiraz. The subjects were selected using the census sampling method. The Samn-Perelli Mental Fatigue Scale and NASA Mental Workload Scale were completed for 2 weeks at the beginning and end of each shift. Results: The results showed that the amount of mental fatigue and mental workload were significantly associated with the type of work shifts (P<0. 05). Moreover, mental fatigue had a significant association with the average hours of sleep during the last two weeks (r=-0. 24, P<0. 01), the average mental fatigue and mental workload (P<0. 01, r=0. 25), lack of leisure time (P<0. 01, r=-0. 22), and a regular exercise program (P <0. 01, r =-0. 3). Conclusion: The mental workload and shift schedule affect the fatigue of ICU nurses. However, there seem to be other factors that affect their fatigue. Hence, further studies are needed to investigate the effect of lifestyle and personal factors on fatigue.

Background and Objective: Investment in health and safety has long been considered costly and inevitable, and the cost has been always a barrier to the achievement of safety and health interventions. Decision-makers in this area need to demonstrate that such interventions are not only effective in the obtainment of their goals but also have high economic efficiency. The present study aimed to determine an optimal model for the economic evaluation of occupational safety and health investments. Materials and Methods: The Analytic Hierarchy Process (AHP) method and Expert Choice® software were used to determine the optimal model. To this end, the Occupational Safety and Health (OSH) economic evaluation models were initially identified based on a review of previous studies. Thereafter, considering seven main criteria, the models were evaluated and the optimal model was introduced. Results: A number of 20 models/tools were identified after literature review and screening of the findings. Firstly, the specifications of models were extracted and compared using a decision matrix; thereafter, four models were identified for prioritization with AHP. The accuracy and precision with a final weight of 0. 490 and the comprehensiveness with a final weight of 0. 076 were the most and least important criteria, respectively. After prioritizing the alternatives over the target, the ROHSEI model, ToolKit method, Productivity Assessment tool, and Potential Method weighed 0. 320, 0. 281, 0. 240, and 0. 159, respectively. Conclusion: As evidenced by the obtained results, HSE experts can use the ROHSEI model to demonstrate the financial incentives for their goals, allocate limited resources, and improve the economic efficiency of their projects.

Background and Objective: The outbreak of COVID 19 has caused excessive stress and anxiety among people in different communities. In Iran, as in other countries, this outbreak has led to adverse psychological consequences for people from all walks of society, including nurses. The present study aimed to assess the level of job stress in nurses exposed to COVID-19 in educational hospitals in Ahvaz, Iran. Materials and Methods: This case-control study was conducted on 335 nurses who were assigned to two groups of target (exposed to COVID-19 patients) and control (not exposed to COVID 19 patients). The Osipow questionnaire was used to evaluate job stress. Appropriate statistical analyzes were conducted at a significance level of 0. 05. Results: Based on the obtained results, the target and control groups significantly differed in the level of job stress with mean scores of 19. 0. 59± 22. 56 and 183. 07± 21. 52, respectively (P<0. 05), and job stress was higher in the target group (P<0. 05). The mean of all components of job stress was significantly different between the two groups (P<0. 05). Moreover, the mean of the job was significantly correlated with gender, physical activity, and employment status (P<0. 05). Conclusion: Due to the dramatic increase in job stress caused by COVID-19 exposure, the availability of effective prevention or treatment strategies and strengthening the psychological services system are recommended. Furthermore, it is suggested to develop appropriate planning to control demographic factors affecting job stress, such as physical activity, and improve the employment status of nurses.