International Journal of Injury Control and Safety Promotion最新文献

{"title":"Examining fatal and non-fatal injuries of drivers in single-vehicle-involved crashes on urban roadways using random parameter logit model.","authors":"Charles Atombo, Raymond Akuh, Richard Fiifi Turkson, Franklin Liggie-Kudonoo","doi":"10.1080/17457300.2025.2487637","DOIUrl":"https://doi.org/10.1080/17457300.2025.2487637","url":null,"abstract":"<p><p>Urban areas significantly impact crash injury severity due to high traffic density and complex road patterns. This study analysed factors influencing fatal and non-fatal injuries in single-vehicle crashes on urban roads in Ghana from 2017 to 2020, using data from the Driver and Vehicle Licensing Authority (DVLA). The Random Parameter Logit Model revealed that younger drivers (under 20) are at higher risk for both fatal and non-fatal injuries. Crashes involving saloon cars, pickups, and minibuses had higher injury risks. Severe frontal damage increases the likelihood of both non-fatal and fatal injuries. Newer vehicles (under 5 years) showed lower injury risks. Crashes at controlled intersections and daytime crashes were less likely to result in fatal injuries. Failure to yield the right of way and mechanical failures were significant contributors to injury severity. The study highlights the need for targeted road safety interventions.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-15"},"PeriodicalIF":2.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stacking models for analyzing traffic injury severity on two-lane, two-way rural roads.","authors":"Ali Tavakoli Kashani, Parsa Soleyman Farahani, Hamzeh Mansouri Kargar","doi":"10.1080/17457300.2025.2487635","DOIUrl":"https://doi.org/10.1080/17457300.2025.2487635","url":null,"abstract":"<p><p>The analysis of injury severity in accidents allows traffic management agencies to assess crash risk more effectively and develop cost-effective interventions. The aim of this research is to present a two-layer stacking model as a means of forecasting accident severity. In the initial layer, the system incorporates benefits derived from many base classification algorithms through a three-stage process to evaluate the outcomes of each model configuration. These base algorithms include Random Forests, Decision Tree, K Nearest Neighborhood and Support Vector Machine; in the second layer, Logistic Regression and Random Forest algorithms are used to classify crash injury severity. In total, 24,141 traffic accidents were recorded on 135 two-way, two-lane roads. The process of model calibration entails the optimization of several parameters, such as the number of trees in three fundamental methods of classification, the learning rate and the regularization coefficient which is achieved by the utilization of a systematic grid search strategy. To validate the model, the Stacking model's performance is assessed in comparison to other conventional models. The results indicate that the Stacking model has greater performance. Consequently, each component included in the prediction of severity is categorized into distinct groups according to its impact on results.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-12"},"PeriodicalIF":2.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analysis of occupational illness and injuries of the industrial workers in slums.","authors":"Shashwati Banerjee, Kishor Goswami","doi":"10.1080/17457300.2025.2486618","DOIUrl":"https://doi.org/10.1080/17457300.2025.2486618","url":null,"abstract":"<p><p>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. .</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-10"},"PeriodicalIF":2.3,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of IRAP at reducing road traffic injuries: urgent need for research on what works in road design in LMICs.","authors":"","doi":"10.1080/17457300.2025.2488043","DOIUrl":"https://doi.org/10.1080/17457300.2025.2488043","url":null,"abstract":"<p><p>A recently published impact evaluation overstates the benefits of IRAP protocols in reducing traffic injuries. This ICoRSI Position Statement clarifies the biases in the methods used in this study and how its findings should be interpreted. It further describes the potential value of road design in improving road safety in LMICs but highlights the urgent need for research on developing infrastructure interventions and evaluating them using real-world data from LMICs.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-3"},"PeriodicalIF":2.3,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of motorcyclist injury severities in motorcyclist violation crash on suburban roads of China: accommodating temporal instability and the unobserved heterogeneity in means and variances.","authors":"Yuntao Ye, Jie He, Xintong Yan","doi":"10.1080/17457300.2025.2487649","DOIUrl":"https://doi.org/10.1080/17457300.2025.2487649","url":null,"abstract":"<p><p>This study analysed motorcyclist violation (MV) crashes on suburban roads of China to investigate how determinants affect MV crash injury severity and explore the temporal stability of determinants. Crash data from Xi'an, China (2015-2018) were utilized to investigate three MV crash injury categories: no injury, minor injury and severe injury. Motorcyclist-related, crash-related, roadway-related, environment-related and time-related characteristics were analysed utilizing a group of random parameters multinomial logit models with heterogeneity in means and variances. The temporal instability was measured by performing likelihood ratio tests. Marginal effects were calculated to further illustrate the temporal variations of these factors. The study found an overall temporal instability, with some violations like alcohol-impaired riding, speeding, and unlicensed riding having significant effects on MV crash injury severity. Additionally, the study revealed a significant risk compensation mechanism of riders under adverse riding conditions. The findings provided insights and recommendations for suburban motorcycle crash prevention strategies.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-15"},"PeriodicalIF":2.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical outcomes of patients with crush syndrome in the Kahramanmaras earthquake.","authors":"Umit Cakmak, Suleyman Akkaya, Ramazan Danis, Enver Yuksel, Jehat Kilic, Ozgur Merhametsiz","doi":"10.1080/17457300.2025.2488040","DOIUrl":"https://doi.org/10.1080/17457300.2025.2488040","url":null,"abstract":"<p><p>Earthquakes are among the most devastating natural disasters, often resulting in significant loss of life and widespread injuries. Crush syndrome (CS), a systemic manifestation of muscle injury due to prolonged compression, is a critical condition commonly seen in earthquake survivors. This study examines the clinical outcomes of patients with crush syndrome (CS) and acute kidney injury (AKI) following the 2023 Kahramanmaras earthquake in Turkey. Of the 321 survivors hospitalized, 143 required intensive care. The study found that children were more likely to develop CS, while adults had longer hospital stays. CS was associated with higher rates of complications, including compartment syndrome, the need for fasciotomy, and mortality. The findings highlight the importance of early detection and treatment of CS and AKI in disaster survivors to improve outcomes and reduce mortality in future earthquakes.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-7"},"PeriodicalIF":2.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pattern of road traffic fatalities in India: a case study of Chhattisgarh State.","authors":"Arunabha Banerjee, Geetam Tiwari, Asha S Viswanathan, Rahul Goel, Kavi Bhalla","doi":"10.1080/17457300.2025.2486625","DOIUrl":"https://doi.org/10.1080/17457300.2025.2486625","url":null,"abstract":"<p><p>India does not have a national crash-level surveillance system. Instead, police stations report crashes in standardized tables that are summarized at the state level. Since tabulations provide limited insights into crash patterns, we developed a crash database from police First Information Reports (FIRs) on all (<i>n</i> = 11,175) fatalities in Chhattisgarh during 2017-2019. The data show that not only were motorcycle riders the most common victims (59% of fatalities), but they also posed a substantial threat to other road users. Motorcycle impacts caused 16% of all fatalities (37% of pedestrians). Although truck occupants comprised only 5% of fatalities, trucks were the most common striking vehicle. Remarkably, 94% of tractor occupants were killed in single-vehicle crashes, and more than were rollovers. The FIR database provides a richer description of crashes than tabulations and an important information source for safety management. India and other LMICs will benefit substantially by investing in crash surveillance systems.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-7"},"PeriodicalIF":2.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the interdependence of rider fault-status and injury severity in motorcycle rear-end crashes: insights from bivariate probit and XGBoost-SHAP models.","authors":"Chamroeun Se, Thanapong Champahom, Kestsirin Theerathitichaipa, Manlika Seefong, Sajjakaj Jomnonkwao, Vatanavongs Ratanavaraha, Tassana Boonyoo, Ampol Karoonsoontawong","doi":"10.1080/17457300.2025.2485032","DOIUrl":"https://doi.org/10.1080/17457300.2025.2485032","url":null,"abstract":"<p><p>This study examines the interdependent relationship between fault status and injury severity in motorcycle rear-end crashes in Thailand using data from 1,549 crashes (2011-2015) integrated from the Department of Highway's Accident Information Management System and Traffic Information Movement System. This article employs a bivariate probit model alongside various boosting techniques for simultaneous estimation of injury severity and at-fault status. Among the tested models (AdaBoost, CatBoost and LightGBM), both the bivariate probit and XGBoost-Endogenous models demonstrate superior performance in accuracy and F1-score. The bivariate probit model reveals that injury severity is significantly influenced by rider characteristics (age, gender), road features, and traffic conditions. Riders under 55 years old, female riders and those on roads with depressed medians or higher traffic volume show lower injury severity risk. Conversely, drunk riding, nighttime crashes on unlit roads, and higher truck traffic percentages increase severe injury likelihood. The XGBoost model corroborates these findings, identifying traffic volume, truck percentage and nighttime conditions on unlit roads as the most crucial predictors of injury severity. Regarding fault status, younger riders and those using safety equipment show a higher probability of being at-fault. This novel analytical approach provides valuable insights for motorcycle safety policy development and future research directions.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-15"},"PeriodicalIF":2.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bicycle crash frequency modeling across different crash severities using a random-forest-based Shapley Additive explanations approach.","authors":"Tao Li, Ruiqi Wang, Hongliang Ding, Tiantian Chen, Hyungchul Chung","doi":"10.1080/17457300.2025.2485040","DOIUrl":"https://doi.org/10.1080/17457300.2025.2485040","url":null,"abstract":"<p><p>Statistical modeling and data-driven studies on bicycle accidents are widespread, however, explanations of the underlying mechanisms remain limited, particularly regarding the impact of key risk factors on the bicycle crash frequency across different crash severities. This study aims to examine the effects of various risk factors on the frequency of bicycle crashes using Random Forest and Shapley Additive Explanations (RF-SHAP), taking into account the different crash severity levels. Data from three years of London crash data (2017 to 2019) is utilized. Population demographics, land use, road infrastructure, and traffic flows, are collected in Greater London. In addition to providing superior predictive accuracy, our proposed method identified critical risk factors at different levels of severity associated with bicycle crashes. The distinct contribution of this study is the identification of the primary factors influencing the severity of bicycle collisions in London through the use of RF-SHAP. The study quantifies both the main and interactive effects of various severity risk factors on bicycle collisions. Results suggest that the proportion of building areas and population density are most critical to bicycle crash numbers in different severity levels. Also, the interaction effects of the risk factors on bicycle crashes are revealed. Specifically, results reveal a negative correlation between traffic flow and overall bicycle crash frequency when the average road network connectivity is below 2.25. After controlling the population density, the proportion of residential areas shows a three-stage pattern of influence on the slight injury crash frequency. Furthermore, a boundary value of 6.3 is identified for the safety impact of road density on fatal and severely-injured bicycle crashes. Study findings should provide insights into cost-effective safety countermeasures for bicycle infrastructures, traffic controls, and safety education. Bicycle safety can be improved through these measures over the long term.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-14"},"PeriodicalIF":2.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the effectiveness of addition of road humps as a road safety intervention.","authors":"Walid Abdullah Al Bargi, Joel Kironde","doi":"10.1080/17457300.2025.2485033","DOIUrl":"https://doi.org/10.1080/17457300.2025.2485033","url":null,"abstract":"<p><p>Evaluating the effectiveness of road humps is very essential in traffic safety and transportation planning. In Uganda, no study has assessed the effectiveness of road humps. This study evaluated the effectiveness of the addition of road humps as a safety intervention in Uganda. Before and after data of the injuries and death that occurred along Kansanga-Gabba and Mukwano road were obtained from Uganda Police Forces (UPF) and used during the analysis. Scikit-Learn library in python 3.7 was used to calculate descriptive statistics and Empirical Bayes (EB) method was used to estimate the effectiveness of the addition of road humps on the road. The results show that the addition of road humps led to a reduction of the road crash death by 38%, 63%, 21%, 31% and 93% for pedestrians, bicyclists, motorcyclists, Light-Duty Vehicles (LDVs), and Heavy-Duty Vehicles (HDVs) respectively. In addition, road crash injuries decreased by 56%, 17%, 13%, 32%, and 74% for pedestrians, bicyclists, motorcyclists, LDVs and HDVs respectively. The inferences from these results will be useful to reduce the continued road crash injuries and death on the road in Uganda.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"1-11"},"PeriodicalIF":2.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}