Irtiqa Imini Pishgiri Masdumiyat (Safety Promotion and Injury Prevention)

Rational use of medicines and medical devices is a fundamental prerequisite for improving healthcare quality, enhancing patient safety, and increasing the efficiency of health systems. In recent years, Iran's healthcare system has faced major challenges, including self-medication, inappropriate prescribing, resource constraints, rising healthcare costs, inadequate supervision, and inefficient management of medical devices. These challenges have contributed to unnecessary expenditures, antimicrobial resistance, waste of valuable resources, and reduced access to essential healthcare services. Evidence from recent studies indicates that irrational use is influenced by a complex interaction of individual, cultural, organizational, and policy-related factors, highlighting the need for comprehensive evidence-based interventions. Key strategies include reforming provider payment systems, strengthening the referral and family physician programs, expanding electronic health records, implementing intelligent prescription monitoring, improving public health literacy, empowering pharmacists, and promoting strategic management of medical devices. This editorial emphasizes the critical role of research and evidence-informed policymaking in fostering rational resource utilization and achieving a more efficient, equitable, and sustainable healthcare system.

Background and Aim: The present study was conducted in 2023 with the aim of developing a model for predicting the risk of leakage accidents in two 42-inch and 48-inch oil transmission pipelines located in southwestern Iran through the integration of the Kent Muhlbauer risk assessment method and Artificial Neural Networks (ANNs).

Methods: In this study, five indices of the Kent Muhlbauer method, including third-party damage, corrosion, design, incorrect operation, and leak impact factor, were used as input variables. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural network models were developed to estimate accident risk levels across ten different pipeline zones. The neural network architectures consisted of an input layer, hidden layers, and an output layer. Model performances were evaluated and compared using the Mean Squared Error (MSE) and correlation coefficient (R).

Results: The results indicated that Zone 5 (the Jarahi River crossing section) exhibited the highest risk level according to the Kent Muhlbauer method, with priority numbers of 2.7684 and 2.8475 for the 42-inch and 48-inch pipelines, respectively. The risk levels associated with the 48-inch pipeline were generally higher than those of the 42-inch pipeline. The artificial neural network modeling results showed that the MSE values for the MLP and RBF networks were 0.00015891 and 0.0002917, respectively, while the corresponding R values were 0.7767 and 0.7227. These results demonstrated acceptable agreement with the risk data derived from qualitative expert judgments. Overall, the MLP network exhibited superior predictive performance compared with the RBF network.

Conclusion: Considering the presence of residential areas and populations living along the pipeline corridor, as well as environmentally sensitive ecosystems such as reservoir lakes, the Jarahi River, sparsely forested regional habitats, and urban areas, oil transmission pipelines possess considerable potential for accident occurrence and associated environmental consequence

Background and Aim: The aim of the present study was to identify and quantify the contribution of emission sources of heavy metals associated with atmospheric microplastics in plastic-mulched agricultural fields of the Dasht-e Azadegan Plain using the PMF model.

Methods: This study was conducted in the Dasht-e Azadegan Plain during 2024. Active sampling was performed using a systematic grid-based approach comprising 35 plastic-mulched fields and five control stations located 10 km away, during winter and summer, with an SKC air sampler in triplicate. Particles were identified by optical microscopy, surface morphology was examined by SEM, and polymer types were determined by FTIR. Heavy metals (Ni, Zn, Mn, Pb, Fe, Cu, Cr, and Cd) accumulated on airborne microplastics were quantified using ICP-OES.

Results: The results showed that polyethylene (53.7%) was the dominant polymer in atmospheric samples from agricultural fields, and that airborne microplastic abundance was higher in winter than in summer. Heavy metal concentrations were consistently higher in agricultural than control sites, with Cu and Fe exhibiting the greatest surface accumulation. PMF model performance (R² = 0.73–0.96; Qrobust/Qtrue ≈ 1.16) indicated satisfactory model reliability. Local/regional dust was the major contributor to Fe (61.4%) and Mn (56.3%), whereas traffic emissions, agricultural activities, and agricultural plastic degradation were the primary sources of Pb (31.8% and 22.4%) and Zn (22.4% and 27.7%), respectively.

Conclusion: Extensive use of agricultural plastics not only contributes directly to the release of polymer particles into the atmosphere but also provides a significant pathway for the atmospheric transport and dispersion of heavy metals via airborne microplastics.

Background and Aim: Shift work and sleep disorders are important and increasingly prevalent factors in healthcare settings that may compromise employees' occupational safety by impairing cognitive performance. This study aimed to investigate the relationship between shift work and sleep disorders with human errors and occupational accidents among emergency department staff at the Oil Hospital of Ahvaz. This descriptive–analytical study was conducted in 2025.

Methods: The study population included all emergency department staff (physicians, nurses, emergency technicians, and other healthcare personnel), who were assessed through a census method (n = 120). Data were collected using the Pittsburgh Sleep Quality Index (PSQI), a human error questionnaire, and the International Labour Organization (ILO) occupational accident checklist. A researcher-developed questionnaire was used to assess shift work. Data analysis was performed using Pearson correlation and linear regression tests in SPSS software.

Results: The results indicated that shift work and sleep disturbances had significant positive correlations with all components of human errors and occupational accidents (p<0.05). The strongest correlations were observed in the dimensions of reduced concentration, task forgetting, and attentional errors. In the domain of occupational accidents, the strongest relationships were found for accident-causing errors and the frequency of accidents.

Conclusion: Although shift work is an important organizational factor contributing to human errors and occupational accidents, sleep disorders appear to play a more substantial role in explaining these adverse outcomes. Therefore, improving sleep quality and implementing fatigue management programs, together with optimizing shift scheduling systems, may effectively reduce human errors and enhance occupational safety among emergency department personnel.

Background and Aim: Occupational safety in high-risk industries, particularly in the oil and gas sector, is one of the most significant challenges of public management. This study aimed to evaluate public management policies for improving occupational safety and reducing workplace accidents in the National Iranian South Oilfields Company.

Methods: This applied qualitative study collected data through semi-structured interviews with 15 experts specializing in occupational safety. The interview data were analyzed using thematic analysis based on Braun and Clarke's six-phase framework.

Results: The analysis yielded 78 initial codes, 8 organizing themes, and 4 overarching themes: "Safety Governance and Public Management in Operational Organizations"; "Human Capital Empowerment and Institutionalization of Safety Culture"; "Organizational Learning System and Risk Management for Safety Improvement"; and "Outcomes and Challenges of the Effectiveness of Public Management Policies in Occupational Safety". The findings indicated that top management commitment, implementation of the HSE management system, continuous employee training, and effective risk management were among the most influential factors in improving occupational safety and reducing workplace accidents.

Conclusion: Overall, the implementation of these policies has contributed to reducing the frequency and severity of workplace accidents, lowering accident-related costs, enhancing organizational productivity, and improving employees' sense of occupational security. Nevertheless, several challenges were identified, including conflicts between production objectives and safety priorities, resource constraints, resistance to organizational change, and insufficient interdepartmental coordination.

Background and Aim: Rapid urbanization in metropolitan areas in arid and semi-arid regions has had significant impacts on climatic conditions, public health, and the environment. This study aimed to investigate the relationship between the physical growth pattern of Ahvaz City and climatic and environmental factors, with an emphasis on air quality, using the Shannon Entropy Model over a long-term temporal framework.

Methods: This descriptive–analytical study employed a quantitative approach and was conducted in 2026 within the urban area of Ahvaz. Data were collected through documentary sources and remote sensing analyses. Landsat satellite imagery from 2005 to 2025, the NDVI, and LST were utilized for analysis. Urban growth patterns were calculated using the Shannon Entropy Model, and their relationships with LST, AQI, and PM₁₀ concentrations were examined. Subsequently, the compact urban development pattern, spatial distribution of air pollutants, and health impacts associated with population exposure to particulate matter were assessed using the Exceedance Factor.

Results: The results indicated that the built-up area of Ahvaz expanded from approximately 174 km² in 2005 to more than 202 km² in 2025. The relative Shannon entropy index (G = 0.197) indicated that urban development was predominantly characterized by a compact, infill growth pattern. During the same period, the mean Land Surface Temperature in August increased from 47.10°C to 49.28°C, while air quality indicators exhibited a consistent deteriorating trend. In addition, the Exceedance Factor increased from 8.06 in 2005 to 10.93 in 2025, indicating an increasing environmental health risk associated with population exposure to PM₁₀.

Conclusion: The results demonstrate that the compact physical expansion of Ahvaz has significantly intensified surface warming and degraded air quality.

Keywords: Ahvaz, Air

Background and Aim: This study aimed to design and validate a school-based safety education and accident prevention model grounded in the active learning approach.

Methods: This applied study was conducted in 2025 using a mixed-methods (qualitative–quantitative) approach. In the qualitative phase, data were collected through semi-structured interviews with 10 experts selected via purposive sampling and analyzed using thematic analysis, resulting in the identification of four overarching themes. In the quantitative phase, a researcher-developed questionnaire based on the qualitative findings was administered to 300 school principals, teachers, and education experts selected through convenience sampling. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software.

Results: The findings indicated that the factor loadings of the constructs ranged from 0.76 to 0.85. Composite Reliability (CR) values ranged from 0.86 to 0.90, while the Average Variance Extracted (AVE) ranged from 0.61 to 0.67, demonstrating satisfactory convergent validity and construct reliability. Furthermore, the model fit indices confirmed an acceptable fit for the proposed model, with an SRMR value of 0.061 and an NFI value of 0.91, both falling within the recommended thresholds.

Conclusion: Integrating active learning strategies, practical activities, and simulation-based training, together with strengthening safety culture, promoting family involvement, and providing appropriate educational infrastructure, can effectively enhance students' safety awareness, skills, and preparedness for accident prevention and emergency management in school settings. The proposed model provides a practical framework for the design, implementation, and evaluation of school-based safety education programs and can contribute to fostering a stronger safety culture and reducing risks in educational environments.