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In the past decades, extremely serious road accidents with a death toll over ten in each have become a severe public health problem in China. This study investigates risk factors contributing to extremely serious road accidents, which will be crucial for accident prevention. Collecting data from The Road Accident Statistical Annual Report openly issued by China's Traffic Management Bureau of the Public Security Ministry for the time period 2004-2015, we used the monthly case number of extreme serious road accidents as the dependent variable. We then selected ten risk factors as primary independent variables: professional driver, driving under influence (alcohol or drug), fatigue, vehicle type, overload, brake problem, weather, road classification, terrain, and region. The method of negative binominal regression was implemented to investigate the association between these risk factors and extremely serious road accidents. A total of 346 extremely serious road accidents were included in our analysis. On a national scale, we found that professional driver [incidence rate ratio (IRR): 1.10, 95% CI: 1.02-1.19], fatigue (IRR: 1.15, 95% CI: 1.03-1.29), large vehicle type (IRR: 1.11, 95% CI: 1.03-1.21), overload (IRR: 1.09, 95% CI: 1.03-1.16), and terrain (IRR: 1.09, 95% CI: 1.01-1.18) were significantly associated with extremely serious road accidents. Besides, separate analyses on western and non-western region indicated that both regions had shared risk factors as well as distinct factors. Our study identifies professional driver, fatigue, large vehicle type, overload, and terrain as significant risk factors of extremely serious road accidents in China, and targeted and preventative measures could be taken based on our findings.

Mortality statistics are critical to determine the burden of disease. Certain causes of death are prone to being misclassified on cause of death certificates. This poses a serious risk for public health and safety, as accurate death certificates form the basis for mortality statistics, which in turn are crucial for research, funding allocation and health interventions. This study uses generalised estimating equations and regression modelling to investigate for which cause of death categories suicide and accident deaths are misclassified as. National mortality statistics and autopsy rates from North America and Europe covering the past forty years were analysed to determine the associations between the different causes of death in cross-sectional and longitudinal models. We find that suicides and deaths by accidents are frequently mutually misclassified. We also find that suicides are frequently misclassified as drug use disorder deaths, in contrast to accident deaths, which are not misclassified as drug use disorder deaths. Furthermore, suicides do not seem to be misclassified as undetermined deaths or ill-defined deaths. The frequency of misclassification shows that the quality of death certificates should be improved, and autopsies may be used systematically to control the quality of death certificates.

Background With the rapid development of China’s chemical industry, although researchers have developed many methods in the field of chemical safety, the situation of chemical safety in China is still not optimistic. How to prevent accidents has always been the focus of scholars’ attention. Methods Based on the characteristics of chemical enterprises and the Heinrich accident triangle, this paper developed the organizational-level accident triangle, which divides accidents into group-level, unit-level, and workshop-level accidents. Based on 484 accident records of a large chemical enterprise in China, the Spearman correlation coefficient was used to analyze the rationality of accident classification and the occurrence rules of accidents at different levels. In addition, this paper used TF-IDF and K-means algorithms to extract keywords and perform text clustering analysis for accidents at different levels based on accident classification. The risk factors of each accident cluster were further analyzed, and improvement measures were proposed for the sample enterprises. Results The results show that reducing unit-level accidents can prevent group-level accidents. The accidents of the sample enterprises are mainly personal injury accidents, production accidents, environmental pollution accidents, and quality accidents. The leading causes of personal injury accidents are employees’ unsafe behaviors, such as poor safety awareness, non-standard operation, illegal operation, untimely communication, etc. The leading causes of production accidents, environmental pollution accidents, and quality accidents include the unsafe state of materials, such as equipment damage, pipeline leakage, short-circuiting, excessive fluctuation of process parameters, etc. Conclusion Compared with the traditional accident classification method, the accident triangle proposed in this paper based on the organizational level dramatically reduces the differences between accidents, helps enterprises quickly identify risk factors, and prevents accidents. This method can effectively prevent accidents and provide helpful guidance for the safety management of chemical enterprises.

Official suicide statistics for England are based on deaths given suicide verdicts and most cases given an open verdict following a coroner's inquest. Previous research indicates that some deaths given accidental verdicts are considered to be suicides by clinicians. Changes in coroners' use of different verdicts may bias suicide trend estimates. We investigated whether suicide trends may be over- or underestimated when they are based on deaths given suicide and open verdicts. Method Possible suicides assessed by 12 English coroners in 1990/91, 1998 and 2005 and assigned open, accident/misadventure or narrative verdicts were rated by three experienced suicide researchers according to the likelihood that they were suicides. Details of all suicide verdicts given by these coroners were also recorded. In 1990/91, 72.0% of researcher-defined suicides received a suicide verdict from the coroner, this decreased to 65.4% in 2005 (p trend < 0.01); equivalent figures for combined suicide and open verdicts were 95.4% (1990/91) and 86.7% (2005). Researcher-defined suicides with a verdict of accident/misadventure doubled over that period, from 4.6% to 9.1% (p < 0.01). Narrative verdict cases rose from zero in 1990/91 to 25 in 2005 (4.2% of researcher-defined suicides that year). In 1998 and 2005, 50.0% of the medicine poisoning deaths given accidental/misadventure verdicts were rated as suicide by the researchers. Between 1990/91 and 2005, the proportion of researcher-defined suicides given a suicide verdict by coroners decreased, largely due to an increased use of accident/misadventure verdicts, particularly for deaths involving poisoning. Consideration should be given to the inclusion of 'accidental' deaths by poisoning with medicines in the statistics available for monitoring suicides rates.

To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machine learning approaches have become an important aspect in predicting the severity of road traffic injuries. The paper presents a hybrid feature selection-based machine learning classification approach for detecting significant attributes and predicting injury severity in single and multiple-vehicle accidents. To begin, we employed a Random Forests (RF) classifier in conjunction with an intrinsic wrapper-based feature selection approach called the Boruta Algorithm (BA) to find the relevant important attributes that determine injury severity. The influential attributes were then fed into a set of four classifiers to accurately predict injury severity (Naive Bayes (NB), K-Nearest Neighbor (K-NN), Binary Logistic Regression (BLR), and Extreme Gradient Boosting (XGBoost)). According to BA’s experimental investigation, the vehicle type was the most influential factor, followed by the month of the year, the driver’s age, and the alignment of the road segment. The driver’s gender, the presence of a median, and the presence of a shoulder were all found to be unimportant. According to classifier performance measures, XGBoost surpasses the other classifiers in terms of prediction performance. Using the specified attributes, the accuracy, Cohen’s Kappa, F1-Measure, and AUC-ROC values of the XGBoost were 82.10%, 0.607, 0.776, and 0.880 for single vehicle accidents and 79.52%, 0.569, 0.752, and 0.86 for multiple-vehicle accidents, respectively.

South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens' relief since in our daily lives road accidents are getting more diverse.

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers.

The aviation system is safety-critical by nature, and any occurrence of an incident or accident can lead to the loss of human life and significant operational disruptions. The International Civil Aviation Organization (ICAO) emphasizes that every flight must take off and land safely—a goal achieved over 126,000 times daily. Despite major advancements,mishaps and accidents continue to occur, underscoring the need for robust safety management systems. The accurate classification of aviation occurrences (Incident or Accident) reports is essential for safety management, yet manual review is time-consuming and prone to inconsistency. While incident/accident labels are assigned during reporting, automated classification enables rapid triage, detection of potential mislabeling, and support for severity assessment in high-volume aviation safety operations. To address this,we developed and compared three machine learning classifiers—Multinomial Naive Bayes, Random Forest, and Support Vector Machine—using TF-IDF vectorization on an 80-year dataset of 53,770 aviation occurrence summaries obtained from the Transportation Safety Board of Canada. A two-stage evaluation strategy was employed, consisting of an initial 80/20 train–test split to create an independent test set, followed by 5-fold cross-validation applied exclusively to the training data to ensure robustness and prevent optimistic bias. The Support Vector Machine (SVM) classifier achieved the highest classification performance, attaining an accuracy of 98.06% during 5-fold cross-validation, with consistent results across folds, demonstrating its effectiveness in managing high-dimensional textual data and dataset complexity. The proposed framework provides a robust foundation for automated aviation safety report processing, offering practical value for (1) early triage of safety reports, (2) identification of potentially mislabeled cases requiring expert review, and (3) integration into downstream severity assessment pipelines. This work advances beyond prior classification studies by establishing a benchmark on the largest historical aviation safety dataset while delivering a deployable and operationally relevant framework for real-world safety management applications. The findings offer valuable insights for regulatory authorities and airline operators, contributing to enhanced safety oversight, improved response strategies, and safer aviation operations.

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors.

Abstract With the aging of society, the increase in the number of elderly drivers poses a potential hazard to road traffic safety. Therefore, accurately predicting the severity of possible traffic accidents of elderly drivers is crucial to ensure the safety of drivers and passengers. In this paper, a hybrid model based on the CTGAN-ResNet-XGBoost network is proposed for classifying the severity of the accidents of elderly drivers. The model was trained and tested using traffic accident data of the United States from 2018–2022. The hybrid model first generates a small amount of categorical data via the Conditional Tabular Generative Adversarial Network to address the dataset's category imbalance. Then, the balanced dataset is transformed into feature images using the DeepInsight method and feature extraction is performed using the residual neural network to improve the feature recognition ability of the classification model. Finally, the XGBoost model is used to classify the severity of the accident and the SHAP method is used to analyse the main features affecting the accident. The superior performance of the hybrid model is verified through experimental comparative analysis. The experimental results show that the hybrid model has a significant advantage in the prediction of driving risk for elderly drivers, that the causes of accidents for elderly drivers are different from those for younger drivers and that the characteristics of speed, seat belt use and driver's age are the main factors affecting the severity of accidents. The results of this study improve the accuracy and reliability of traffic accident severity prediction and provide more scientific support for traffic safety management.