Journal of Health and Safety at Work

Predictive and Diagnostic Modeling of Noise Exposure, Hearing Loss, and Systemic Health Impacts Using a Bayesian Network Model: A Case Study of Petrochemical Workers in the South Pars Region

2026-06-27T13:59:21+00:00

Introduction: Noise‑Induced Hearing Loss (NIHL) is one of the major occupational health concerns. Prolonged exposure to high noise levels not only damages auditory function but also contributes to systemic physiological disorders. This study employed the dual predictive and diagnostic capabilities of a Bayesian Network (BN) to explore the complex interactions between causal factors of NIHL and the physiological outcomes of occupational noise exposure.

Material and Methods: In this cross‑sectional study, medical and environmental records of 828 petrochemical workers were collected, including demographic, audiometric, noise, hematological, and biochemical variables. After preprocessing, an inferential BN model was developed using the Bayesian Search algorithm, enabling both Forward Inference (FI, predictive) and Backward Inference (BI, diagnostic) reasoning. Model performance was validated through Receiver Operating Characteristic (ROC) curve analysis and sensitivity testing.

Results: The FI results showed that exposure to SPL levels above 85 dB increased the risk of severe NIHL (warning level) from 9% to 57%. Also, the probability of systolic hypertension, the FBS above 100 mg/dL, and the total cholesterol above 200 mg/dL increased from 6%to10%, 8%to18%, 5% to 9% respectively. When multiple high‑risk conditions (e.g., high SPL, long work experience, noisy units) were combined, the probability of severe NIHL exceeded 70%, accompanied by cumulative metabolic disturbances. BI results indicated that the presence of severe NIHL significantly increased the posterior probability of previous exposure to high or borderline SPL levels. Moreover, metabolic indices such as triglycerides (TG) and fasting blood sugar (FBS) showed positive associations with noise exposure, even below conventional action thresholds.

Conclusion: Bayesian networks provide a powerful framework for identifying and modeling direct and indirect probabilistic dependencies between occupational noise exposure and health outcomes in industrial environments. Their bidirectional inference ability (FI and BI) enhances predictive surveillance, early diagnosis, and the design of evidence‑based preventive strategies in occupational health management.

Feasibility Study of Using PDLLA Polymer in the Production of Biodegradable Nanofibrous Media for Capturing Particulate Air Pollutants

2026-06-27T13:59:20+00:00

Introduction: Air filters are often made from non-biodegradable substrates, which can lead to significant environmental pollution. Therefore, the use of biodegradable polymers in the development of filtration media is of particular importance. The aim of the present study was to fabricate and evaluate a PDLLA nanofibrous substrate for the adsorption of airborne particulate matter.

Material and Methods: This experimental research was conducted in the Occupational Health Laboratory, School of Public Health, Tehran University of Medical Sciences. The optimal electrospinning conditions for the PDLLA nanofibrous substrate were determined based on a review of previous literature. Subsequently, its filtration efficiency in removing PM0.3, PM0.5, PM1, and PM3 particles was investigated. Tests were performed at three airflow rates: 1.67, 5.3, and 14.16 cm/s, using an FT200PS device. Furthermore, to assess performance stability in humid environments, samples were exposed to relative humidity levels of 75±5% and 55±5% for 30 minutes. The data were analyzed using the Bonferroni post-hoc test within a one-way ANOVA framework.

Results: The optimal electrospinning conditions were obtained at a concentration of 16%, a voltage of 20 kV, an injection rate of 0.5 ml/h, and a tip-to-collector distance of 17 cm. The results showed that the filtration efficiency for PM0.5, PM1, and PM3 particles was consistently above 98%. For PM0.3 particles, the efficiency was over 99% at a velocity of 1.67 cm/s, but it decreased to approximately 96% with an increase in flow velocity to 14.16 cm/s. The quality factor for PM1 and PM3 decreased from 0.529 pa⁻¹ to 0.038 pa⁻¹, and for PM0.3, it decreased from 0.22 pa⁻¹ to 0.011 pa⁻¹. Also, humidity had a slight effect on the filtration of PM0.5, but for PM0.3, a decrease in efficiency was evident.

Conclusion: The PDLLA nanofibrous substrate, under optimal conditions, demonstrated very high efficiency in filtering fine particles, particularly PM1 and PM3. However, increasing the airflow velocity led to a decrease in the quality factor, and the substrate’s performance was more limited when encountering very fine PM0.3 particles and high humidity.

Identification and Ranking of Factors Related to Mental Health Problems in Healthcare Workers from the Perspective of Stakeholders and Experts

2026-06-27T13:59:19+00:00

Introduction: Healthcare workers face high rates of depression and anxiety due to job-related stressors,which harm their well-being and compromise care quality and patient safety. This study aims to integrateevidence and expert/stakeholder insights to identify and prioritize factors affecting healthcare workers’mental health, enabling more targeted interventions and efficient resource allocation.

Material and Methods: This qualitative-ranking study used semi-structured interviews with purposivelysampled hospital occupational health managers to identify factors affecting healthcare workers’ mentalhealth, with interviews recorded, transcribed, and analyzed using content analysis in MAXQDA untilsaturation. The identified factors were then rated by industrial psychology experts and psychiatrists usinga five-point Likert survey.

Results: 12 occupational health managers were interviewed, and 18 experts completed the ranking. 51factors across 19 subcategories were identified in four main work-system levels: Individual (14 factors),Work/Task (16), Organizational (12), and External (9). In ranking, a history of mental illness was rated asthe most important factor (mean 4.36), while working with automation was rated as the least important(mean 2.84); 11 factors scored >4, and many factors scored 3.5–4.

Conclusion: Using a systemic approach and stakeholder input, this study identified and prioritized factorsaffecting healthcare workers’ mental health across four levels—individual, work/task, organizational, andexternal—enabling more targeted, evidence-based interventions.

A Novel Hybrid PFMEA-HFMEA Framework for Risk Assessment in Medical Imaging: Insights from Developing Healthcare Systems

2026-06-27T13:59:18+00:00

Introduction: Medical imaging modalities such as MRI and CT scans are indispensable for accurate diagnosis, yet they pose substantial operational and patient safety risks—particularly in resource-limited healthcare systems.

Material and Methods: This applied methodological study, conducted from July 2024 to April 2025, used a four-phase methodology: scoping, data collection, framework development, and risk analysis. Data were gathered through FGDs involving radiologists, technicians and HSE experts and also with semi-structured interviews and process mapping, which identified 125 failure modes across nine workflow stages. PFMEA assessed operational risks, whereas HFMEA focused on patient-centric hazards. A composite risk indicator that comprising 40% PFMEA RPN and 60% HFMEA hazard score, prioritized risks. Statistical analyses, including Shapiro-Wilk, Spearman’s correlation and Kruskal-Wallis tests, were used to evaluate risk distributions and inter-stage variability.

Results: The framework identified critical risks, such as insufficient operator training and staff fatigue, with post-process management and image reconstruction as high-risk phases. MRI and CT units showed distinct yet overlapping risk profiles that show significant inter-stage variability (p<0.001). The hybrid model integrated operational and clinical perspectives, which outperformed standalone FMEA methods.

Conclusion: This hybrid PFMEA-HFMEA framework offers a scalable and context-sensitive approach to enhance patient safety with operational resilience in medical imaging. Further studies should authenticate the framework in different settings and investigate long-term mitigation strategies to enhance radiology risk management.

Effects of Noise and Aluminum Co-Exposure on Behavioral and Cognitive Indices and Biomarkers in a Wistar Rat Model

2026-06-27T13:59:17+00:00

Introduction: Combined exposures, such as noise and aluminum exposure, are present in many occupational workplaces. Nevertheless, the neurocognitive effects resulting from this co-exposure have been scarcely investigated. The present study aimed to determine the effects of noise and aluminum co-exposure on behavioral and cognitive indices and biomarkers in a Wistar rat model.

Material and Methods: This experimental study investigated 20 adult male Wistar rats over a period of 45 days, divided into four groups: 1. Control group (no exposure), 2. Noise exposure group (95 dB, 4 hours daily), 3. Aluminum chloride exposure group (10 mg/kg, daily intraperitoneal injection), and 4. Combined noise and aluminum exposure group (a combination of Group 2 and Group 3). Spatial memory performance was assessed using the Morris Water Maze test. The serum concentrations of total tau protein and beta-amyloid 42 were measured in blood samples using the ELISA method. Data were analyzed using SPSS software, version 27.

Results: Behavioral test results indicated that the control group spent the least time searching for the platform. Exposure to aluminum and the combination of noise + aluminum led to a significant decrease in cognitive performance. Furthermore, serum levels of tau protein and beta-amyloid were significantly increased in all exposed groups (p<0.01), and a strong positive correlation was observed between these two biomarkers (r=0.70, p<0.001).

Conclusion: Findings demonstrated concurrent noise and aluminum exposure can synergistically impact cognitive performance and neurodegenerative biomarkers. These alterations likely occur through shared mechanisms such as oxidative stress, neuroinflammation, and disruption of protein homeostasis. Increased tau and amyloid, coupled with memory decline, underscore this combined role in worsening neurodegeneration. These results suggest monitoring combined exposure and using blood biomarkers for early cognitive assessment.

Prediction of Mineral Oil Concentrations Using Fourier Transform Infrared (FTIR) and Modeling Methods

2026-06-27T13:59:16+00:00

Introduction: Mineral oil, a key component of metalworking fluids, is a complex mixture that generatesaerosols during industrial processes, posing significant respiratory health risks such as laryngeal cancer,asthma, and lung cancer. The NIOSH 5026 method uses Fourier Transform Infrared Spectroscopy (FTIR)to assess exposure to mineral oils. However, the diverse and complex compositions of mineral oils causesignificant spectral interferences. Partial Least Squares (PLS) and Artificial Neural Networks (ANN) areadvanced modeling methods used to address these interferences without manual intervention. This studyaimed to predict mineral oil concentrations in an automotive industry using FTIR and modeling methods.

Material and Methods: FTIR spectral data (1500–4000 cm⁻¹) were recorded across 701 wave numbersand analyzed using PLS and ANN models. Input (matrix X) consisted of FTIR data, while output (matrix Y)represented mineral oil concentrations. Model performance was evaluated using Root Mean Square Error(RMSEp).

Results: The ANN model significantly outperformed the PLS model. The overall RMSEp for ANN was0.0036, compared to 5.01 for PLS. ANN achieved a regression of 0.997 in the test set, with an averageerror percentage of 3.01%, while PLS yielded an error of 4.792. ANN modeling used 15% of data forvalidation and required fewer than 11 hidden layers to achieve optimal performance.

Conclusion: ANN modeling effectively predicted mineral oil concentrations despite spectral interferences,outperforming PLS in accuracy and error reduction. Both methods are viable for evaluating mineral oilexposure, but ANN offers superior predictive capabilities

Identification and Prioritization of Key Factors Influencing Risk Management in Process Industries Using the Fuzzy DANP Method

2026-06-27T13:59:15+00:00

Introduction: Process industries are considered to be vital pillars of economic development, but thecomplexity of their performance and processes poses serious challenges to effective risk management.This study presents an integrated model based on Fuzzy DEMATEL and Fuzzy ANP methods to identify andprioritize key factors affecting risk management in these industries.

Material and Methods: First, in a systematic literature review, 54 eligible articles were selected fromreliable sources between 1995 and 2024, and 23 initial factors affecting risk management were extracted.Then, in three rounds of Delphi technique with the participation of 13 experts with an average workexperience of 12.6 years, 20 final factors were confirmed. Subsequently, the Fuzzy DEMATEL method wasused to analyze the cause-and-effect relationships between the factors, and the Fuzzy ANP method wasused to determine their weight and priority.

Results: The results indicated that 6 factors play a causal role, while 14 factors play an effect role.“Training” and “management commitment” were identified as the most effective causal factors, playing apivotal role in strengthening other risk management measures. In contrast, “maintenance and repair” and“contractor and stakeholder participation” were the most influenced by other factors. Additionally, “safetyculture and climate” obtained the highest weight in the fuzzy network analysis.

Conclusion: By providing a comprehensive picture of the influencing factors and their interactions, theproposed framework provides a practical and strategic tool for managers to improve process safety andmanage risks more effectively by focusing on priority factors

Evaluating the Impact of Respiratory Exposure to Hospital Chemicals on Lung Function and Blood Markers in Employees of a Healthcare Facility

2026-06-27T13:59:14+00:00

Introduction: Employees in various departments of hospital are inevitably exposed to toxic gases, including isoflurane, as well as BTEX, formaldehyde, nitric acid, and chlorine. Therefore, this study aims to evaluate the impact of chemical exposure on pulmonary and hematological parameters among hospital staff.

Material and Methods: The present cross-sectional study was conducted in 2024 at a hospital in Tehran, involving a sample size of 240 participants. Standard methods established by OSHA and NIOSH were employed for sampling, transfer, and laboratory analysis. A history of respiratory symptoms was obtained using the American Thoracic Society Questionnaire (ATSQ). Additionally, lung function was assessed through spirometry tests, and blood samples were collected to analyze the participants’ hematological factors.

Results: The exposure levels of benzene (0.3 ppm), ethylbenzene (12 ppm), toluene (9 ppm), xylene (50 ppm), nitric acid (0.9 ppm), isoflurane (1.6 ppm), and chlorine (0.06 ppm) were found to be below the maximum occupational exposure limits. The average occupational exposure of staff in the operating room and pathology laboratory over an 8-hour period was 0.16 ppm for formaldehyde, which exceeds the ACGIH-recommended acceptable occupational exposure limits (OEL). Findings revealed a correlation between the prevalence of respiratory symptoms and elevated liver enzymes in employees exposed to formaldehyde and isoflurane.

Conclusion: The results indicated a significantly higher prevalence of respiratory symptoms and liver enzyme disorders in individuals exposed to isoflurane and formaldehyde compared to the control group. Furthermore, significant differences were observed in pulmonary function tests and liver enzyme levels in the blood of subjects exposed to these substances, in contrast to the control group. It is recommended that individuals working in operating rooms and pathology laboratories implement engineering controls and management practices related to occupational health and safety, and utilize appropriate personal protective equipment, particularly due to exposure to isoflurane and formaldehyde gases.

Prioritizing Factors Influencing Failure in the Crisis Management System of a Process Industry Using the Analytic Hierarchy Process

2026-06-27T13:59:13+00:00

Introduction: This study aimed to identify and prioritize factors leading to the failure of crisis management systems in a process industry. An integrated approach combining Fault Tree Analysis (FTA) and the Analytic Hierarchy Process (AHP) was employed.

Material and Methods: Initially, FTA was used to systematically identify factors contributing to the failure, resulting in a hierarchical model with one top event, four main intermediate events, 16 sub-intermediate events, and 42 basic events. Subsequently, the AHP method was applied to prioritize these identified factors based on pairwise comparisons conducted by a panel of 11 industry and academic experts.

Results: The AHP results revealed that among the main phases of crisis management, failure in the prevention phase held the highest priority (weight = 0.380), followed by failure in the preparedness phase (0.280), response phase (0.245), and recovery phase (0.095). Key basic events identified included knowledge-skill gaps in leadership, inadequate periodic inspection programs, malfunctioning warning equipment, and untimely budget allocation.

Conclusion: The study findings confirm that prevention is the most pivotal phase in crisis management within process industries. By utilizing the integrated FTA-AHP framework, managers can systematically prioritize failure factors and align corrective actions with the most influential determinants, thereby enabling targeted resource allocation and strategic reinforcement of the crisis management system

Application of the Human Factors Analysis and Classification System (HFACS) in Accident Analysis and Safety Recommendation Prioritization: A Multi-Criteria Decision-Making Approach

2026-06-27T13:59:12+00:00

Introduction: The present study was conducted with the objectives of identifying the human and organizational factors contributing to a drum fall accident, classifying these contributing factors based on the Human Factors Analysis and Classification System (HFACS) framework, and prioritizing safety recommendations using Multi-Criteria Decision-Making (MCDM) techniques.

Material and Methods: The HFACS technique was initially applied across its four levels to determine the human and organizational factors involved in the HP drum fall accident from a rotator within a manufacturing industry. Subsequently, the proposed safety recommendations were prioritized using the Best-Worst Method (BWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), based on four criteria: effectiveness, initial cost, reliability, and maintainability.

Results: The contributing factors identified in this study included inattention to the longitudinal movement of the drum during rotation on the rotator, as well as non-compliance with the rotator’s loading capacity relative to its mechanical strength and associated equipment (HFACS Level 1); failure to conduct a design risk assessment to identify critical points related to structural balance and stability (HFACS Level 2); non-adherence to the manufacturing sequence (HFACS Level 3); and the omission of a mechanical locking system for the rotator on the rail and failure to use certified rotating equipment with specified capacities (HFACS Level 4). The three safety recommendations identified with the highest priority were “using certified rotating equipment with specified capacity limits (relative closeness coefficient = 0.822)”, “adhering to the standard drum fabrication sequence (0.749)”, and “the on-site presence of a supervisor or foreman (0.698)”.

Conclusion: The ultimate objective of safety management systems is to propose corrective safety measures based on findings from accident analysis. Implementing safety recommendations as a structured framework not only prevents accident occurrence but also establishes protective layers that mitigate the recurrence of similar accidents in the future.

Investigation of Occupational Stress, Job Burnout, and Musculoskeletal Discomfort Among Dentists and the Role of Ergonomics in Reducing Them: A Systematic Review

2026-06-27T13:59:11+00:00

Introduction: Dentistry is considered a highly stressful profession due to its nature, placing dentists at an increased risk of occupational burnout and musculoskeletal disorders. This study aimed to investigate stress, occupational burnout, and musculoskeletal discomfort among dentists, as well as the role of ergonomics in reducing these problems.

Material and Methods: This study is a systematic review of articles published from 2000 to March 2025 in three databases: Scopus, Web of Science, and PubMed. The inclusion criteria required original research in English—experimental, observational, or conference-based—addressing both the prevalence of occupational stress, burnout, and/or WMSD in dentists and the impact of ergonomic interventions. Unrelated articles, review papers, books, letters to the editor, and book chapters were excluded.

Results: Out of 366 identified articles, 28 met the inclusion criteria. The most frequently reported discomforts were in the neck, lower back, shoulders, wrists, upper back, forearms, and arms, respectively. Dentists with a higher risk of occupational burnout reported more health complaints, and patient care was identified as the main source of stress. Moreover, the use of ergonomic aids—such as dental magnification loupes, optimized hand tool designs, and prismatic glasses—played a significant role in reducing musculoskeletal discomfort among dentists.

Conclusion: Stress, burnout, and musculoskeletal disorders are common challenges in the dental profession. Strong evidence supports the effectiveness of ergonomic interventions in reducing the physical burden of these problems; however, implementation faces barriers such as high costs and insufficient training. Therefore, it is recommended that ergonomic principles and the use of assistive tools be integrated as essential components of dental education curricula and ongoing professional development programs.

Fungal Contaminants in Work Environments: A Threat to Occupational Health and Strategies for Their Management and Control

2026-06-27T13:59:09+00:00

Introduction: Fungal contaminants in workplace environments pose significant biological hazards to employee health across various industries. This narrative review aims to explore the types of contaminating fungi, their health impacts, and strategies for prevention and control in occupational settings.

Material and Methods: This study was designed as a narrative review, with scientific literature sourced from databases including PubMed, Scopus, Web of Science, and local databases like Magiran and SID, covering the period from 2000 to March 2025. Keywords such as “fungal contaminants,” “occupational health,” and “mycotoxins” were used. Data were qualitatively analyzed and organized into thematic categories.

Results: Fungi such as Aspergillus, Penicillium, Cladosporium, and Stachybotrys are prevalent in environments with high humidity, poor ventilation, and abundant organic material, causing respiratory diseases, allergies, fungal infections, and chronic toxic effects. Identification methods include air sampling, molecular analysis, and mycotoxin assessment. Effective control measures encompass humidity management, enhanced ventilation, and personal protective equipment.

Conclusion: Effective management of fungal contaminants requires integrated approaches, including accurate identification, environmental control, and employee training. These measures can enhance worker health and reduce economic and social costs.