International Journal of Injury Control and Safety Promotion最新文献

The achievement of Sustainable Development Goals 3 (Good Health and Well-Being) and 8 (Decent Work and Economic Growth) requires addressing the occupational health challenges and unsafe working conditions faced by industrial workers in slums, particularly migrant laborers lacking adequate training and literacy. This study examines health challenges among 320 slum-dwelling workers across 17 industries in West Bengal, categorized into civil/mechanical, textile, consumable, and chemical sectors. employed across 17 industries in West Bengal, categorized into civil/mechanical, textile, consumable, and chemical sectors. Using multi-stage random sampling, findings reveal that chronic illnesses are more prevalent in textile and consumable industries, while acute injuries dominate civil/mechanical and chemical sectors due to hazardous conditions. It may create a significant financial burden exacerbated by the absence of sick leave or insurance benefits. The study underscores the urgent need for industry-specific interventions, including accessible healthcare, safety training, and comprehensive insurance schemes. .

The promotion of cycling as one of the most popular measures to drive sustainable urban mobility, and the rise in bicycle usage have triggered a debate on the consequences of the coexistence of different transportation modes. This interaction process has been developed rapidly in countries like Spain, where cycling has been fast-tracked. This paper analyzes the safety effect of coexistence in terms of urban traffic accidents. Our case study comprises 50 Spanish NUTS-3 regions, with an econometric model applied to a data panel based on Big Data for the period 2008-2019. The results point to a lack of equilibrium between the rapidly rising number of urban cycling trips and the adaptation of specific regulations and road user behavior. The development of specific awareness strategies, investment in clear and specific signage, traffic surveillance, and a more standardized regulation framework during the first month of bicycle promotion policies is recommended, with the appropriate data collection and monitoring of bicycle trips.

Active traveller (including pedestrians and bicyclists) crashes pose significant challenges to sustainable transportation. Active traveller injury severities not only demonstrate temporal variations, but also differ across different functional zones within the city. Therefore, conducting a spatiotemporal analysis to understand the impact of various factors on active traveller injury severities can help develop effective strategies aimed at mitigating these severities. However, most existing studies mainly focus on temporal instability from year to year, ignoring the spatial difference between rural and urban areas. To examine spatiotemporal instability, this study uses North Carolina as a case study and divides the six-year (2017-2022) active traveller crashes into four sub-datasets according to distinct spatial and temporal characteristics. An explainable and balanced machine learning framework is designed to address the challenges associated with small sample size and imbalanced crash data and explore factors affecting active traveller injury severities. Results demonstrate that spatial instability has a greater impact than temporal instability. For instance, non-intersection, bicycle and travel lanes, medium speed limit and dark with light conditions are important in urban areas, but crosswalk areas are significant in rural areas. These results can help policymakers develop region-specific countermeasures to promote the reliability of active transportation systems.

A principal safety issue at highway-rail grade crossings (HRGCs) is the severity of crashes. Although many studies have analyzed crash severity at HRGCs, they often rely on national datasets or a narrow set of variables, frequently overlooking region-specific factors such as roadway design, driver behavior, and local environmental conditions. However, this study contributes to the existing body of literature by providing additional insights into the factors associated with injury severity in HRGC crashes. This study aimed to model HRGC crash severity using statistical and machine learning methods, specifically Ordinal Logistic Regression (OLR) and Random Forest (RF) algorithms, to determine significant factors associated with severe injury HRGC crashes. The statistical modeling and analyses were based on five years of HRGC crash data (2017-2021) at state-maintained HRGCs in Florida. Based on the OLR statistical model, ten variables were significant at a 95% confidence interval: crashes that occurred in the morning peak hours, no lighting condition, adverse weather conditions, railway vehicle (i.e. train or train engine), driver action (i.e. disregarded signs, signals, markings as well as other contributing actions), a speed limit of greater than 45 mph, four-lane highways, driver younger than 25, female drivers, crashes that occurred at the railroad crossings, and estimated vehicle damage of more than $1,000. Results from the OLR model indicate that all significant variables increase the likelihood of an HRGC crash being more severe, except for the time of crash occurrence (morning peak), adverse weather conditions, and drivers under 25 years of age. According to the RF model, the most important (top five) factors affecting the injury severity of HRGC crashes include estimated vehicle damage, posted speed limit, type of shoulder, driver action, and crash type. Except for the type of shoulder and crash type, the RF model results are consistent with those of the OLR model. Finally, based on the model results, potential countermeasures to mitigate fatalities and injuries at HRGCs were presented.

Minimizing the losses that occur after traffic accidents is a primary duty for all humanity. To do so, it is necessary to examine and analyse the potential risk factors that affect the severity of traffic accidents. In this article, a new spatial decision-making-based statistical solution methodology is proposed to determine the accident risk factors that occur in three different accident types using 5-year (2015-2019) accident data. (i) 22 independent variables and 157 sub-variables were determined for the traffic accident categories where vehicle-vehicle, vehicle-pedestrian and vehicle-other collision types occurred, (ii) the fuzzy simple weight calculation method was preferred to determine the effects of risk factors on accident categories, (iii) spatial analyses of risk factors were provided via geographical information system and combined with the obtained effect values, (iv) the current effect of risk factors on accident categories was tested with the multinomial logistic regression model. The multinomial logistic regression model results revealed a strong model fit (McFadden R² = 0.749) and identified the variables that significantly increase or decrease the probability of each crash type compared to the reference category. For instance, while the geo-intersection had the highest effect for vehicle-vehicle crashes, the pedestrian defect had the highest impact for vehicle-pedestrian crashes. Spatial analysis results also showed that accident severity tends to be higher in the western, southern, and central regions of Türkiye. The proposed methodology offers a comprehensive framework that supports evidence-based policy development for improving traffic safety. The resulting findings serve as a guide for local administrators, policy makers, and traffic safety experts with regard to vehicle and pedestrian safety.

Work zones are widely recognized as major contributors to road fatalities and traffic congestion. Although extensive research has explored the relationship between work zone crashes and contributing factors, a comprehensive systematic review and meta-analysis remain absent. This study addresses this gap by exploring four key research questions: (i) Which elements of work zones are most crash-prone? (ii) What factors affect work zone severity and crash frequency? (iii) Which methods are used to predict crash occurrences and crash severity? (iv) How does the traffic volume affect crash occurrence with different severity levels in the work zone? The review identifies factors influencing crashes, including work zone characteristics, environmental conditions, roadway features, temporal aspects, driver characteristics, and crash attributes, and evaluates various modeling approaches. Moreover, a meta-analysis quantifies the association between traffic volume and crash severity, highlighting key findings for safety measures and developing targeted strategies for improving work zone safety.

Falls are considered one of the important causes of injuries and fatalities among children and adolescents. Students are continuously exposed to the risks of falls, in the school environment. Therefore, a thorough examination of student behaviours and the environmental conditions of schools is of significant importance for preventing fall-related injuries in students. This study aims to investigate the influential factors for preventing fall-related injuries among primary school students using the PRECEDE model. This cross-sectional study has been done among 428 primary school students from grades one to six in schools of Hamadan city, located in western Iran. The students were selected randomly through a multi-stage cluster sampling method and data collection has been done between December 2023 and February 2024. The data collection tool was a researcher-made questionnaire based on the PRECEDE model. The questionnaire was included three sections including demographic questions, questions related to the constructs of the PRECEDE model (preventive behaviour constructs; predisposing factors including knowledge and attitude; reinforcing factors; enabling factors; and environmental factors), and questions concerning the history of fall-related injuries at school. The data collection has been done through interviews with the students. Data were analyzed using SPSS24 software after collection. The results of this study revealed that out of 428 students, 131(30.6%) experienced falls, with ages ranging from 7 to 12 years and an average age of 9.5 ± 1.70 years. Among these, 54 (41.2%) were females and 77 (58.7%) were males. The findings indicate that males experienced more falls than females, and females exhibited better preventive behaviours than males (p = 0.002). Most falls occurred in the schoolyard (37.4%) and during recess time (40.5%). The most common types of injuries were abrasions (28.2%) and head injuries (24.4%). Additionally, the findings showed that parents' education level was significantly associated with preventive fall behaviours among students. Hence, the students with parents who had higher education levels (mothers' education with (p = 0.02) and fathers' education with (p = 0.03) demonstrated better preventive behaviours and were less at risk of falls. Among the constructs of the PRECEDE model, the predisposing factors of knowledge (p = 0.04) and attitude (p = 0.001), enabling factors (p = 0.02), and environmental factors (p = 0.03) had a significant relationship with fall-preventive behaviours. According to the statistical results, the attitude construct was the predictor of students' fall- preventive behaviours. The study results indicated that fall-related injuries in the studied group are high. Additionally, the PRECEDE model can help identify factors associated with fall prevention among students. Given the significant role of behaviour and the school environment in fall prev

Traffic accidents continue to be a major cause of death in urban areas. While recent research has demonstrated the utility of predictive modelling in rural, express and highway environments, a gap remains in understanding the factors that influence accidents in urban areas, particularly on arterial roads. This study developed multilayer perceptron (MLP), random forest (RF) and multinomial logistic regression (MLR) models to predict accident severity on urban arterial roads in Amman, Jordan's capital. The MLP demonstrates clear superiority over RF and MLR, achieving 97.3% training accuracy and 96.55% testing accuracy. Additionally, a Sobol Global Sensitivity Analysis (GSA) for the MLP model identified critical interactions between variables, especially between collision types and weather conditions. This study provides an in-depth understanding of the key factors influencing accident severity, which can be used to develop new safety regulations and countermeasures to prevent crashes.

The risk level of alcohol-involved traffic crashes is closely related to alcohol consumption. However, research on different influencing factors for DUI (Driving Under Influence) and DWI (Driving While Intoxicated) remains limited. This study analyzed data from 3,365 alcohol-related traffic crashes in Tianjin, China. The crashes were categorized into DUI and DWI based on drivers' Blood Alcohol Concentration. Four machine learning models were enhanced and compared. The accuracy, precision, recall and F1-score were used to evaluate the performance of the models. Shapley additive explanations were used to interpret model outputs and quantify risk factors and interaction effects on DUI and DWI crashes. The enhanced CatBoost model performed the best, with an AUC-ROC value of 0.953. The time period of crashes, intersection control or not, and the density of companies were identified as significant factors affecting DUI and DWI crashes. Interaction analysis indicated that drivers aged between 40 and 50 had a higher risk of DWI in areas with high intersection density; two-wheeled motorcycle riders exhibited higher DWI risk compared to car drivers between 21:00 and 24:00. These findings provide valuable insights for the traffic management department to implement targeted and refined control measures for DUI and DWI violations.