Journal of Health and Safety at Work

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Introduction: Hazard Identification, risk assessment and management have an important role in reducing potential risks in industrial settings. This research was done with the aim of identifying the hazard and evaluating HSE risks and providing a HSE management program for the Urea Unit of Shiraz Petrochemical Complex.
Method: Firstly, hazard identification and qualitative risk assessment was done using FMEA technique and risky units were identified. Then, main events were identified and analysed as inputs of the Bow-Tie method. By identifying the strength, weakness, opportunity and threat factors, ranking and weighting them using the ANP method and Super Decisions software, a SWOT matrix was prepared and HSE management strategies were extracted.
Findings: The results showed that chemical leakage, falling from height and slipping were the most important incidents, and defect of control equipment, lack of attention to instructions, non-compliance with safety principles and human error were causes of these events. Also, ranking of weaknesses, strengths, opportunities and threats were 0.58, 0.2, 0.16 and 0.05, respectively.
Conclusion: Organizational opportunities with a weight of 0.124 were 4.8% higher than threats with a weight of 0.076. This shows that the company had a good situation in terms of achieving its goals.

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Introduction: Prevention of occupational accidents is of a great importance. Analysis of accidents and their causes could prevent the accidents recurrence. The study objective was to investigate the causes of occupational accidents in a dispatching project.
Material and method:  This research is descriptive-analytic. Data were extracted from the reports of occupational near-misses and accidents recorded between 2013 and 2017 and analyzed using Tripod-Beta model. Then direct, indirect and root causes of the accidents were identified. Based on the causes identified, a questionnaire was developed to analyze the workers’ mental patterns in relation to the real causes, and Friedman test was used to rank the causes.
Results: The results showed the falling/dropped objects hit a person, inappropriate working conditions and lack of safety trainings were, respectively, the most important direct, indirect and root causes as per the analysis results. The leakage of flammable and explosive substances, hazardous conditions in the workplace and discrimination in rewards were the most important causes from the employees’ views.
Conclusion: Identifying and ranking the most important causes of accidents would help to prevent the occurrence of similar events. Also, the analysis of the causes based on the employees’ mental patterns could be effective in the prevention of accidents.

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Introduction: Burnout dimensions include the emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA).
Materials and Methods: The information was obtained from searching standard Persian and English keywords in accessible databases including: Scopus, Magiran, SID, Science Direct  and  PubMed from database commencement to April 2017. Data  Analysis was performed using Stata version 11.1., and p value was considered less than 0.05.
Results: In the systematic review 43 studies met the inclusion criteria, and 9456 people were evaluated. The prevalence of burnout was calculated in any of the three dimensions of burnout. The prevalence of the Emotional Exhaustion (EE) dimension of burnout was%36 (%95CI: 29-42), the Depersonalization (DP) dimension was%23 (%95CI: 18-29) and the Personal Accomplishment (PA) dimension was%46 (%95CI: 39-53). Based on the type of the job, the maximum prevalence of the EE, DP and PA dimensions was in librarians 67% (%95CI:40-93), university staff 51% (14-88) and dentists 72%(%95CI:65-79), respectively. Meta regression results showed that there was no significant relationship between the year of publication and the prevalence of burnout.
Conclusion: According to the prevalence of burnout in Iran and the high prevalence of the PA dimension, it is recommended to consider mediating strategies for reducing and controlling stressful occupational events and burnout in organizational schedule.

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Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities.
Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software.
Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations.
Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air.

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Introduction: Workers in car manufacturing industry are at risk of a high prevalence of musculoskeletal disorders, especially low back pain. Therefore, in the present study aimed to design and fabricate a portable device to evaluate the low back kinematics and to compare these variables in workers with and without low back pain (LBP) in assembly lines of an automotive industry.
Material and Methods: In the present research workers postures were assessed using OWAS direct observational method. Moreover, simultaneously, prevalence and intensity of low back pain were evaluated by Dutch Musculoskeletal Questionnaire (DMQ) and Visual Analogue Scale (VAS). After fabricating motion analysis device, a field study was conducted using the designed device among 16 volunteers to investigate low back kinematic variables in two groups of workers: LBP and non-LBP.
Results: The results showed that 62.1 percent of all working postures were high risk with corrective action levels of 3 and 4. On average, 86.1 percent of workers experienced LBP in the previous 12 months. Regarding comparison of kinematic variable in the two groups of LBP and non-LBP, workers without LBP had higher degree and duration (in second) of movements (forward flexion, lateral bending, extension, and twisting), as compared to those with LBP. However, only movement range of forward flexion in non-LBP group (mean: 64.29 and SD: 8.41), was significantly higher than those with LBP (mean: 58.97 and SD: 11.34).  
Conclusion: The device can be used as an effective tool in the ergonomics studies in the field of back pain, due to its potential to record the kinematics of the trunk, as well as its lightweight and non-interference with the task. Device’s validity was acceptable based on the comparison of the results of this device with those obtained from inclinometer.

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Introduction: Particulate matter (PM) is known as the most common cause of air pollution in the world. Activities of sand quarries are known as one of the emission sources in Tehran. This study aimed at investigating the geological and environmental factors of airborne particles in an active quarry in the west of Tehran.
Material and Methods: Three methods of dust sampling were used. totally, 32 samples were analyzed by Scanning Electron Microscope-Energy Dispersive X-ray (SEM-EDX). The data were analyzed through Principal Component Analysis (PCA), Enrichment Factor (EF) and Geo-accumulation Index (Igeo).
Results: The results showed the presence of Si, Ca, Al, Na, Fe, K, Zn, Pb, P, S, Mg, Cu, Ti, Mn, Cl and V in dust of the quarry. Also, the elements of Mn, V, Zn, Cu and Pb were shown to have moderate to extremely enrichment and contamination from anthropogenic origin. The silicon and potassium were found to have a natural source originated from igneous and alluvial rocks.
Conclusion: In this study, it was shown that fugitive dust generated from sand quarries and related activities have higher concentration of elements than those in the Earth crust due to anthropogenic activities. Further studies on transfer of fugitive dust from sand and gravel quarries to Tehran and assessment of its health impact are suggested.

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Introduction: Considering the accreditation of international standards of hospitals and the necessity to improve the safety and quality of patients’ care, this study aimed at evaluating reliability among nurses using predictive analysis of cognitive errors and human event analysis techniques.
Material and Methods: The analysis of nurses̓ tasks was done by HTA method. Then, the types of errors and their causes were identified by TRACER method. In the next step, the error probability of each task was calculated by ATHEANA method. In order to calculate the probability of total event, the probability of human error was imported to probabilistic risk assessment.
Results: Factors affecting performance of the nurses were included: the complexity of the work, high workload, nurse’s experience, work environment design, fatigue, anxiety, shortage of the workforce, insufficient time period for doing job, sleep disturbance, and poor lighting and noise pollution. According to the instruction of ATHEANA method, the error probability for each base event was considered 0.001. Given that there are 15 base events, the probability of human error in the heart attack event was calculated 0.015.
Conclusion: The finding of this study was indicated the need for providing required nursing workforce, reducing overtime, scientific planning for nurses’ work shifts and giving practical training and stress management methods in the emergency conditions.

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Introduction: Occupational accidents are one of the major challenges of the industrial workplaces. The identifying of the effective causes of the incidents occurrence, could be used to prevent them. This study was aimed to determine basic causes of occupational accidents in a vehicle manufacturing company.
Material and Methods: The occupational accidents leading to loss of time, which cause losing at least one working day, occurred from 2012 to 2017 were analyzed using the Tripod-Beta method and the causes of their occurrences were determined from the active failures to the root causes. The data were analyzed using the SPSS-22 software.
Results: Eighty percent of the occupational accidents that occurred in a vehicle manufacturing company were related to 6 root causes, i.e. the weakness of OR (20%), EC (17%), MM¬ (12%), CO (10%), IG (10%) and DE (9%). Absence of necessary authority to stop working is the most important reason for the occurrence of the weakness of Organization system with a rate of 28%.
Conclusion: By handling three root causes appropriately, i.e. improving OR, EC and MM, more than 50% of accidents can be prevented. Proper hiring of workers, exact definition of accountability and an accurate job description to the employees, proper monitoring and supervision, and near-miss recordings are suggested to reduce this incidence rate.

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Introduction: Chemical industries often have risks for the environment and communities, due to the use of complex facilities and processes. Also, in the ammonia tanks, the probability of risk of explosion is high, owing to their specific characteristics. The aim of this study is to evaluate the risks of explosion scenario at the ammonia tank in the Kermanshah petrochemical complex
Material and Methods: To achieve the purpose of this study, the Fuzzy Fault Tree Analysis (FTA) method was used to estimate the probability of reliability in the basic events. In this study, after drawing Fault Tree for identifying basic events, the probability of basic events was estimated by means of expert’s elicitation, and the probability of minimal cut sets was computed through Boolean logic gates.
Results: According to the results, the probability of occurrence of the top event was obtained equal to 0/054997. In the minimal cut set prioritizing, the failing of pressure safety valves identified as the most effective factor in the top event occurrence, and afterward failing the control valves and human errors were identified.
Conclusion: This study indicates that, based on expert elicitation, a fuzzy error tree method can be used to assess the risk of various scenarios in the industry. Overall, in assessing the risk of the explosion scenario in the ammonia reservoir, it was found that some minor defects, and even human error, could be considered as a major contributor to the explosion.
 

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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.

 

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Introduction: Failure Mode and Effects Analysis (FMEA) is a structured way to find and understand the states of a system’s failure and to calculate the resulting effects. In this method, which has been criticized by many researchers, the risk priority number is obtained for each failure mode based on the multiplication of the three parameters of occurrence (O), severity (S) and detection (D). In order to overcome the disadvantages of the traditional method of FMEA, such as ranking the failure and weighting the parameters, this research proposes a model in the fuzzy set.
Material and Methods: The model proposed in this paper is a nonlinear model for weighting the parameters of the FMEA and the revised TOPSIS method for ranking the failures, which is used for the first time to improve the FMEA method.
Results: The proposed model was presented in the Copper Complex of Shahr-e-Babak to assess safety risks. Based on the results of the study, it was found that in this proposed model, the weights of severity and detection were 0.479 and 0.186, respectively, and the results of the ranking showed that the risks of falling from height and getting stuck between objects had the highest and lowest priorities, respectively.
Conclusion: In the proposed model, based on Logarithmic Fuzzy Preference Programming and the revised TOPSIS method, the definite weights of the parameters were presented without any fuzzy number ranking and risk ranking with more criteria, respectively. Therefore, the proposed model has a higher ability compared to the traditional FMEA, and its application can be recommended to determine the ranking of risks.

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Introduction: Human factor analysis has been identified as the most common cause of accidents in natural gas transportation and distribution facilities. The occurrence of accidents at these systems, especially gas reduction stations located in residential and industrial areas, has had catastrophic consequences. Therefore, this study aimed at analyzing critical tasks and human error assessment using the system for predictive error analysis and reduction (SPEAR) method and providing the appropriate framework for error management in the operation and maintenance of city gate stations.
Material and Methods: This descriptive cross-sectional study was conducted using the SPEAR framework and safety critical task analysis guideline to evaluate errors in gas pressure reduction stations. First, critical tasks were screened and evaluated, followed by performing task analysis by the hierarchical task analysis and detecting performance-influencing factors (PIF). Then, human errors were predicted and assessed based on the predictive human error analysis. Finally, error management was developed at three process, equipment, and training improvement levels.
Results: In general, out of 23 operations and 164 sub-tasks, 12 critical tasks were identified based on the results. Criticality level percentages were about 67% high risk, 25% moderate, and 8% low risk. In addition, 134 errors were identified which were mostly related to action (42.53%) and checking (39.55%) errors, respectively. Eventually, communication, retrieval, and selection errors were 8.96, 5.22, and 3.74%, respectively.
Conclusion: The results revealed that action and checking errors had the highest percentages. This method can be applied to appropriate the systems approach to error reduction using the PIF assessment output. The privilege affecting factors include preparing standard operation procedures, implementing a comprehensive training program, and controlling environmental hazards.

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Introduction: Risk assessment is a scale for predicting reliability and can manage interactions between components and process variables. Moreover, the reliability of one component or barrier affects the overall risk of the system. Being one of the most critical safety barriers of the storage tank, the failures of Fixed Foam Systems (FFS) on demand can result in severe consequences. FFS, is of grave importance in decreasing the risks associated with fires and damages.
Material and Methods: This study aims to determine the probability of root causes related to FFS failure through Fuzzy Fault Tree Analysis (FFTA) to estimate system reliability. In conventional fault tree analysis, accurate data is usually used to assess the failure probability of basic events. Therefore, the introduced approaches were employed to quantify failure probabilities and uncertainty handling. Finally, system reliability was estimated according to the failure probability of the top event.
Results: The findings showed that 13 baseline events involved FFS performance. According to the results, failures of cable path and detection system (or resistance temperature detectors), set the activation switch (multi-position) incorrectly, and foam makers not continuously running are the three most critical basic events influencing the reliability of fixed foam systems. In addition, this paper estimated the system reliability at 0.8470.
Conclusion: The results showed that the FFTA could be used in matters such as reliability evaluation failure and risk assessment using experts’ judgment. This paper can also show the adaptation of the fuzzy approach to assess the failure probability of the basic event in the fault tree analysis (FTA).

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Introduction: Occupational accidents are one of the most important risk factors for developing countries. In addition to designing preventive measures to prevent accidents, comprehensive research of accidents is considered as an undeniable necessity to reduce the risk of accidents. Thus, the first step is to find the root causes of their occurrence, which will certainly be possible with the use of appropriate techniques.
Material and Methods: In this study, first, the appropriate criteria for designing the accident analysis method were collected. In the second step, commonly used techniques were collected through known databases. In the third step, the collected techniques were scaled based on the selected criteria using the TOPSIS method, and ultimately, the new method (FAM) was developed. Finally, by analyzing three different accidents with the developed technique and four other common techniques, as well as using the ANP method, the developed technique was tested and confirmed.
Results: Based on the studies conducted to identify appropriate criteria for comparing accident analysis techniques, finally 6 criteria were selected for to be used in the study process. According to the findings, the FAM method with a normal final weight of 0.2684 was considered the priority in occupational accident analysis.
Conclusion: The output of this study was the introduction of the FAM technique. Using the strengths of the four techniques and covering their weaknesses, this technique can help identify and determine the causes of accidents graphically, systematically, and by minimizing the work attitude of analysts at three levels.

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Introduction: Iran has the most extensive maritime transport fleet in the Middle East, with 2700 km of water border with other countries in the region. However, the complex and hazardous marine environment has turned this advantage into a disadvantage. On the other hand, technological advancement has added to the complexity. Thus, new accident analysis tools should be developed to bring unity to marine casualty analysis and improve the analyst’s power of discovery from incident information. The current project aims to develop a specialized AcciMap-based marine accident investigation method.
Material and Methods: The primary stages of this applied descriptive study include data collection, method development, and validation. The necessary information about the factors leading to marine accidents was initially gathered through a review of previous studies, expert interviews, and analysis of actual cases. The AcciMap technique was then partially developed, and marine experts approved the designed model.
Results: This study’s results included an AcciMap model established on three levels: external influences (national and international), intra-organizational factors, and environmental/individual conditions and individual activities. Whereas external factors (international and national) are categorized into three main layers, two sublayers, and 13 secondary sublayers, intra-organizational factors are categorized into two main layers, 11 sublayers, and 35 secondary sublayers, and environmental/individual conditions and individual activities are organized in one main layer, three sublayers, and 11 secondary sublayers.
Conclusion: The developed approach can identify flawed levels and determine who is responsible for implementing corrective action. Because it includes emerging components that are effective in accidents, the method used in this study can better examine data from marine accidents.

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Introduction: Strategic management involves determining the organization’s direction, preparing a strategic vision and mission statement, and providing the basis for growth, profitability, and production. It also includes the inclusion of employee safety and health programs throughout the organization. The existence of a strategic plan for the scientific and practical strengthening of occupational health and safety is one of the country’s academic and industrial priorities. The purpose of this study is to present a strategic plan for developing the specialized field of occupational health and safety engineering in Iran.
Material and Methods: The current study is a collaborative action research study that was conducted in 2021. The strategic planning committee consisted of 20 professors, experts, and doctoral students. Over the course of 14 weeks, they held regular weekly meetings, collected information from inside and outside the organization, analyzed the organization’s internal and external environment, and identified its strengths, weaknesses, opportunities, and threats. Based on this analysis, the committee determined the organization’s mission, perspective, values, and general and specific goals for 2021-2024. They also identified the necessary measures to achieve these goals and developed an operational plan to improve the performance of the specialized field of occupational health and safety.
Results: Conducting this applied research led to the strategy of internal and external analysis of the specialized OHS field, determining the direction of the basic strategy, mission, perspective, values, and general goals. Finally, seven specific goals and 286 actions were determined to improve the performance of OHS. The SWOT analysis of OHS’s internal and external environment identified 27 strengths, seven weaknesses, 26 opportunities, and 12 threats. According to the results of the SWOT matrix, the strategic position of the OHS field is to implement preventive strategies and maintain existing conditions.
Conclusion: This plan aligns with the 4-year OHS plan. In developing the program, attention has been paid to the documents and policies of upstream organizations. The strategic position of occupational health and safety engineering is a prudent strategy. In this situation, strategies for maintaining existing conditions can be applied. Therefore, it is suggested to reduce the weaknesses of OHS as much as possible and increase its strategic capabilities by focusing on prudent strategies. From the second year of implementing the strategic plan, the OHS field can gradually focus on developing activities.

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Introduction: Medical centers, as complex technical-social systems, are exposed to the risk of fire incidents. This study analyzes the causes and contributing factors of the fire accident at Sina Mehr Clinic to prevent similar accidents, resulting in 19 deaths and 14 injuries.
Material and Methods: The causes and contributing factors for accidents in medical centers are found through studies related to laws and regulations, official accident reports, expert reports of regulatory bodies, interviews with experts, and review of past studies, extraction, and categorization. Accident analysis methods included AcciMap and STAMP. Finally, experts’ opinions were used to confirm and strengthen the findings.
Results: The most critical root and hierarchical causes of the weakness of medical center management in the field of safety, dangerous conditions, fire accidents, and emergency response are the issuance of a legal building completion permit for a building that violates national building regulations and the issuance of a legal permit for a medical institution for a building with residential use, as well as the insufficiency of supervision by government and public institutions with horizontal relationships with each other and vertical relationships with universities of medical sciences, labor offices, and firefighting organizations, as the direct supervisors of medical centers.
Conclusion: The AcciMap and STAMP findings indicate that the priority is to amend the regulations for the establishment, operation, and activity of medical centers with an emphasis on safety regulations, as well as the frequency and shortening of feedback loops such as inquiring about the building completion permit from the municipality, announcing the establishment of a medical center to other governmental and public supervisory authorities, and the reporting of unsafe cases directly by supervisors to the Ministry of Health. Legal authorities are the most crucial cycle in the resilience of fire incidents and their consequences in medical centers.
 

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Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.