Journal of Health and Safety at Work

---

Introduction: The carpet industry is one of the most important handicrafts in Iran. This industry is one of the most difficult and harmful professions, in which the employees are often working in a workplace with non-ergonomic, unsafe, and unhealthy conditions. The present study aimed to address the modeling of the individual, job characteristics, and workplace conditions with the general health of carpet weavers through an ergonomic approach.
Material and Methods: The current study was a descriptive-analytic survey. The study population was female workers, who have had a minimum of one-year work experience and working in the workshops located in Meshginshahr city villages. The data collection tool was a combination questionnaire including, Goldberg’s questionnaire (GHQ-28), NIOSH questionnaire (disease history section), and questions about workplace conditions. The obtained data were entered into SPSS-17 software and analyzed statistically using statistical modeling based on the general linear model, multivariate and other statistical tests.
Results: Unpleasant condition was observed among the 37.4% of the examined, in terms of general health. Logistic regression modeling was used to investigate the internal and external factors of carpet weavers with their general health status. According to the model, general health has a significant relationship (p<0.05) with most of the internal factors such as age, marital status, history of disease, etc. However, there was no significant relationship with the workplace conditions.
Conclusion: According to the results of this study, it can be concluded that demographic and job variables are of the most important factors affecting the general health of carpet weavers. Although in the model, the workplace conditions did not show any significant relationship with the components of general health, interpreting these findings requires more studies. More studies are required objectively to identify the effect causes of the general health of carpet weavers (especially workplace conditions).

---

Introduction: Nearly half of occupational accidents in Iran occur in construction sites. Therefore, modeling of occupational accidents in these sites is one of the solutions to design safety strategies to reduce occupational accidents in the field of construction. This study was designed and conducted with the aim of modeling the cause-consequence of accidents in construction sites.
Material and Methods: This study was conducted based on a retrospective analysis of 10-year accident data (2010-2019) in Iranian construction sites in 2020. The main variable included the types of occupational accidents in construction sites. The study tool included accidents checklist as well as a detailed report of the studiedaccidents. The required data were collected based on a conceptual model designed to model the cause-consequence of accidents in the construction sites. Cause-consequence modeling of the studied accidents has been done based on the structural equation modeling and using IBM SPSS AMOS v. 22.0.
Results: The frequency of the studied accidents was 3854 accidents. The annual averages of AFR and ASR indices were 17.27 ± 8.54 and 322.42 ± 44.23 days, respectively. The results of cause-consequence modeling of these construction accidents showed that individual and occupational, safety training and risk assessment factors as well as variables related to these factors have a negative and significant relationship with the indicators of the construction accidents, and the factors of environmental conditions and unsafe acts and variables belonged to these factors have a positive and significant relationship with these indicators (p < 0.05).
Conclusion: The findings of the study revealed that the highest impact factors on accident indicators were related to safety training, risk assessment and unsafe acts and their variables. Therefore, the results of this modeling can help to design safety strategies in construction sites.

 

---

Introduction: Occupational fatigue is one of the harmful factors in many work environments, including health centers, which can have adverse effects on the health and safety of staff. This study was designed and conducted to determine the relationship between occupational fatigue and elements of the systems engineering model for patient safety in nurses.
Material and Methods: This descriptive correlational study was conducted with the participation of 457 nurses of Shahid Beheshti University of Medical Sciences in 2018. Dimensions of fatigue were assessed by a Multidimensional Fatigue Inventory (MFI) and Systems Engineering Initiative for Patient Safety (SEIPS) model’s elements using the SEIPS model’s questionnaire. Validity was examined using the Lawshe method; calculating Content Validity Ratio (CVR) and Content Validity Index (CVI) was approved through the confirmation of experts. Reliability was assessed using the intraclass correlation coefficient (ICC) and Cronbach’s alpha. Data analysis was performed using SPSS version 21
Results: The findings of this study indicated that the highest score of fatigue was related to the general fatigue dimension with an average of 12.86 and SD of 3.23, and the lowest score was related to the reduction of the motivation dimension with an average of 9.11 and SD of 3.66. In this study, no significant relationship was observed between demographic characteristics and fatigue dimensions, but a significant relationship was observed between the dimensions of fatigue with the element of organization, task, technology/tools, and physical environment.
Conclusion: The results of this study showed that fatigue in nurses was moderate, and the factors of the work system play a greater role in the occurrence of fatigue than demographic factors. Therefore, planning to improve the work system can help reduce fatigue in nurses.

---

Introduction: Unit risk management is a critical component of gas refining management, as risks that are not well-managed may lead to trip production failures. The present study aimed to provide a structural model for investigating the role and effect of different variables on stopping the gas production process in the gas refinery.
Material and Methods: This study was a retrospective cross-sectional and systematic analysis, which was carried out on key risks in the trip gas sweetening unit in a gas refinery industry located in Asaluyeh, Iran. The systems analysis was applied by using Fishbone Diagram, and then data modeling was prepared by Structural Equation Modeling (SEM) for an incident that occurred during gas sweetening production. Tools for the data analysis included the SPSS 24 and Smart PLS 2 software.
Results: Results of this research indicate that “Environment Risk” with a path coefficient of 0.943 and T- Value of 103.791; “Cost Risk” with a path coefficient of 0.937 and T- Value of 95.168; “Implementation of management system Risk” with a path coefficient of 0.847 and T- Value of 35.23; “Accident Risk” with path coefficient of 0.577 and T- Value of 25.410; “Time Risk” with path coefficient of 0.758 and T- Value of 15.121; “Human Error Risk” with path coefficient of 0.712 and T- Value of 11.215 had the most important coefficients of the paths respectively, that are effective in stopping production concerning other risks. Also, by comparing the path coefficients of the risks we can see that the impact of each of the risks on stopping production is different.
Conclusion: The findings of the present study revealed that a combination of variables can affect stopping production in the gas industry. Therefore, the role of these risks in losses in the refinery system should be investigated.

---

Introduction: Nowadays, air pollution is now considered to be the largest environmental health threat. This study was conducted with the aim of determining occupational exposure to chemical pollutants, including sulfur dioxide (SO2) and hydrogen sulfide (H2S) and assessing the health risk of exposure to these compounds using a combination of AERMOD and SQRA methods.
Material and Methods: The present study is considered as a descriptive-analytical and cross-sectional research, which was conducted in 2002 in one of the gas air refineries of South Pars in the Persian Gulf region, in such a way that the amount of emissions coming out of refinery chimneys was measured by the Testo 350- XL. AERMOD model was used to simulate the dispersion of H2S and SO2 chemical pollutants. Respiratory exposure and health risk assessment of refinery personnel and nearby residents were performed using the recommended method by the Singapore Occupational Health Services Pte Ltd.
Results: Hydrogen sulfide and sulfur dioxide were introduced as the most dangerous chemicals. According to the results, the highest risk value for sulfur dioxide among the exposure groups was related to the sulfur recovery unit (SRU), the west side of the Train Gas unit and the gate pass building of the refinery, and the highest risk values for sulfur dioxide among the exposure groups were related to the HSE building, security door, fire stations building, tanks, steam generating unit, west side of Train Gas unit, dining hall and gate pass building of the refinery. Hydrogen sulfide obtained a low to medium risk level, and sulfur dioxide a low to high risk level in terms of frequency.
Conclusion: This model can be considered as a suitable and quick solution in the superior management of the concentration of pollutants and also a promising solution in order to increase the ability of decision makers to assess the health risk of industries’ personnel. Also, ensuring quality   monitoring results and reducing sampling costs are discussed.

---

Introduction: Reliability is always of particular importance in system design and planning; thus, improving reliability is among the approaches for achieving a safe system. Simulation methods are widely used in system reliability assessment. Therefore, this study aims to assess the reliability of the City Gate Gas Station (CGS) using Monte Carlo Simulation (MCS).
Material and Methods: This descriptive and analytical study was conducted in one of the CGSs of North Khorasan Province in 2021. The CGS process was carefully examined and its block diagram was plotted. Then, failure time data of CGS equipment were collected over 11 years and time between failures of subsystems was calculated. The failure probability distribution function of subsystems was determined using Easy Fit software and Kolmogorov-Smirnov test. Moreover, subsystems’ reliability was estimated by MCS. Finally, station reliability was calculated considering the series-parallel structure of the CGS.
Results: The results revealed that the failure probability density distribution function of CGS subsystems was based on gamma and normal functions. The reliabilities of filtration, heater, pressure reduction system, and odorize were calculated as 0.97, 0.987, 0.98, and 0.992 respectively, and their failure rates were 0.000003477, 0.0000014937, 0.0000023062, and 0.0000009169 failures per hour respectively. The station reliability was calculated as 0.93.
Conclusion: The failure probability distribution function and reliability assessment of subsystems were determined by data modeling and MCS respectively. Filtration and pressure reduction systems had the highest failure rate and required a proper maintenance program.

---

Introduction: Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.
Material and Methods: In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.
Results: The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.
Conclusion: The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.

---

Introduction: The COVID-19 pandemic has been a significant global health challenge. Primary care services, such as screening health centers, were crucial in identifying infected individuals. However, these centers were often crowded and posed a high risk to staff and non-COVID-19 patients. This study aims to assess the risk of airborne transmission of SARS-CoV-2 in such settings through simulation.
Material and Methods: In this study, waiting and sampling rooms of a COVID-19 healthcare center were simulated using different scenarios. Then, the Quanta emission rate was estimated using the viral load in the sputum of infected individuals. Finally, the airborne transmission risk of SARS-CoV-2 was determined using the Wells-Riley method for scenarios of wearing and without masks.
Results: The study showed that the Quanta emission rate in an unmodulated speaking activity was higher than other expiratory activities in both units (p <0.001). Also, the total amount of Quanta was slightly higher in the sampling room than in the waiting room, which was not statistically significant. On the other hand, the calculation of transmission risk showed that the probability of airborne virus transmission in the sampling room was higher (about 2 to 8%). In addition, wearing masks reduced the possibility of airborne transmission of the virus significantly (77 to 81%).
Conclusion: This study shows that the level of risk in the sampling and waiting rooms is moderate. Masks also significantly reduce the possibility of airborne transmission of SARS-CoV-2. Taking appropriate health and safety measures such as avoiding crowds, wearing masks, whispering, and monitoring social distancing can reduce the plausibility of airborne transmission of the SARS-CoV-2 virus.
 

---

Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network.
Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders.
Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results.
Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.

---

Introduction: Improving health and the environment is one of the components of development, social welfare, and economic growth. Another influential factor in increasing health costs and reducing social welfare is work-related accidents and diseases, which impose high costs on individuals, industries, and the national economies of countries. Therefore, using multi-criteria decision-making methods, the present study provided a conceptual model to identify and rank work-related diseases’ environmental and health costs.
Material and Methods: The present study was conducted in 2023. A classification model for the economic evaluation of environmental and health costs of occupational diseases was developed to achieve the study’s aim. In the current research, the Delphi method was used to identify health and environmental criteria, and the Analytic Network Process (ANP) was used to weight the sub-criteria. Finally, the cost of health and the environment was estimated based on the available information. Naft Tehran Hospital (NSHT) was also selected as a case study site.
Results: The results showed that the drug and medical equipment cost factor, with a weight of 0.312 in the treatment sector, and the particular and infectious waste cost factor, with a weight of 0.085, were the most critical factors in the economic evaluation. Also, the parametric model results showed that 99.84% of the total costs are related to health costs, and 0.16% are related to environmental costs. In general, the results of this research showed that 61.3% of the costs of the health sector are related to the two sectors of medicine and medical equipment and the cost of service personnel, and 91.7% of the costs of the environmental sector are related to wastewater treatment and the cost of electricity consumption.
Conclusion: This study presented a semi-quantitative model to estimate health and environmental costs caused by occupational diseases. The results can create a novel scientific insight into implementing control measures using the optimal point of cost-benefit parameters. Implementing this integrated model can be a practical and effective step in allocating resources and prioritizing interventions.
 

---

Introduction: Cognitive functions play a vital role in how tasks are performed; for this, temporary cognitive and mental dysfunctions could lead to grave consequences, especially when an accurate and prompt response is required. Attention and reaction time to noise are among the most effective exogenous factors on the brain processing mechanism. This study aimed to measure the sustained attention of workers in the steel industry exposed to different sound pressure levels. 
Material and Methods: The study was conducted in 4 general stages, including 1- Selecting predictive orientation variables (age, work history, different sound pressure levels); 2- Conducting the Cognitive Performance Test (CPT); 3 Conducting N-BACK Cognitive Performance Test and 4- Modeling cognitive performance changes using model precision methods.
Results: Continuous Performance Test (CPT) results indicated that all three groups’ omission error, commission error, and response time were affected by shift time. All three components increased significantly as the shift ended, decreasing individuals’ cognitive function. Also, the higher noise impact in modeling CPT and N-Back tests indicated reduced workers’ concentration.
Conclusion: These study findings suggested that greater noise weight obtained in test modeling in three-time intervals, i.e., in the beginning, middle, and end of the shift, affected the continuous performance components of the CPT and working memory performance of the N-back test, including workers’ response time and reaction time, with workers’ rate of error increasing and their focus decreasing during the shift.