International Journal of Injury Control and Safety Promotion最新文献

Introduction: Fourteen million children participate in summer camps annually, yet injury data for these settings remain outdated. Our study aimed to modernize camp injury data collection by leveraging an electronic national camp-specific database to analyze the epidemiology of camp-related injuries. Methods: Deidentified data from 89 residential summer camps (2016-2019) were abstracted. Descriptive statistics and multivariable logistic regression analysis were used to determine injury rates and identify risk factors. Results: We identified 13,934 injuries, with an injury rate of 575 injuries per 100,000 camp-days. Common injuries were lacerations/abrasions (37.6%), sprains/strains (27.8%), and head injury/concussions (14.1%). Lower and upper extremity injuries (49.4% and 25.7% respectively) were common. 2.6% (n = 363) of injuries required a higher level of medical care. Older age (adjusted odds ratio [AOR] = 1.2, 95% confidence interval [CI]: 1.1-1.2), male sex (AOR = 1.4, 95% CI: 1.1-1.9), upper extremity injuries (AOR = 3.0, 95% CI: 1.5-6.0), and injuries to head/face (AOR = 2.1, 95% CI: 1.0-4.4) had significantly higher odds of moderate or severe injury. Conclusion: Our study found a higher injury rate than previous research, reflecting the enhanced data collection made possible by utilizing a camp-specific database. Capturing a broad spectrum of injuries provides insights to guide camp stakeholders in developing tailored, data-informed injury prevention strategies.

This study introduces a novel method for assessing the risk of rear-end conflicts based on collision risk force indicator. Drawing on the theory of road user safety fields, we construct a collision risk force indicator tailored for rear-end conflict scenarios. By analyzing the impact of driver attributes and vehicle features on risk force, as well as key road environmental factors influencing rear-end accidents, we propose a specific calibration method for the collision risk force indicator. This is further refined using historical accident data for parameter calibration. We then employ the Peak-Over-Threshold (POT) model and utilize the collision risk force values as input indicator to predict the annual average accident frequency. As a case study, we select intersections in Guilin City and extract vehicle trajectory information. The calibrated collision risk force method is applied to obtain accident prediction values for the intersections. The results demonstrate that the predicted annual frequency of rear-end accidents, based on the collision risk force indicator, aligns with the 95% Poisson confidence interval of actual accidents, validating the accuracy of our research method. The calibrated collision risk force indicator serves as a non-accident indicator for evaluating the safety risks of rear-end conflicts at intersections.

Pedestrian fatalities remain a significant global concern, particularly in low- and middle-income countries, where road safety measures are often lacking. In urban areas across Africa, pedestrian safety is especially critical, yet research into pedestrian behaviour in these settings is limited. This gap is addressed by applying cluster analysis to explore the role of socio-demographic factors, such as gender and education, in shaping pedestrian safety and risk perceptions. Using the Pedestrian Behaviour Scale, we identified four distinct clusters: Cluster 1, the highest-risk group, exhibited high levels of irresponsibility, recklessness, and aggressive behaviours, particularly among males. Cluster 4 represented law-abiding pedestrians with high compliance with traffic regulations. To examine the influence of socio-demographic factors, we conducted independent sample t-tests and Analysis of Variance, revealing significant variations in violation and error scores across demographic groups. Valuable insights are provided for urban planners and policymakers, offering data-driven recommendations to improve pedestrian safety in rapidly urbanising regions. By filling a critical gap in pedestrian safety research, it lays the groundwork for more effective interventions to reduce pedestrian fatalities and promote safer road environments in developing countries.

Walking and cycling play crucial roles in reducing obesity and promoting health. However, pedestrians and cyclists are vulnerable road users, highlighting the need to implement policies to protect them. This study aimed to provide a systematic description of the Chinese mainland national policies regarding pedestrian and cyclist road injuries over the past two decades, while identifying potential gaps according to measures proposed by the World Health Organization (WHO) to enhance pedestrian and cyclist road safety. A total of 28649 policies were examined, and eventually, 106 policies issued by 44 organizations were included, among which 23 were jointly developed. The results show an overall upward trend in policy quantity and a stable trend in policy intensity. Most of the WHO interventions had corresponding policy support in China, except for promoting the 'walking school bus' program and strengthening bicycle helmet-wearing. The findings of this study offer valuable insights for future policy development.

Risky pedestrian behaviors, such as signal violations, crossing from undesignated points, walking on the main carriageway instead of footpaths, and waiting at undesignated locations for buses, contribute to a significant number of pedestrian-vehicular collisions at urban signalized junctions in Indian cities. Therefore, identifying the factors influencing risky pedestrian behavior is crucial in urban India. A total of 59,409 pedestrians' road-using behavior was analyzed using video surveillance, complemented by on-site questionnaire responses from 3840 pedestrians regarding their risk perception, self-reported behaviors, and knowledge of traffic rules. Binary and ordered logit models were employed to assess the impact of the built environment, sociodemographic factors, and traffic enforcement on unsafe pedestrian actions. Results reveal a strong association between unsafe behavior and commercial zones, with young males more prone to signal violations and unsafe crossings. Further, poor lighting, inaccessible zebra crossings, on-street parking, lack of enforcement, and longer waiting times influence the likelihood of signal violations. A 1% increase in footpath encroachment by street vendors leads to an 18% rise in footpath underutilization. The lack of essential amenities and poor accessibility at bus stops discourages pedestrians from waiting at designated locations. Low educational levels and limited awareness of traffic rules exacerbate unsafe behaviors.

Road traffic injuries (RTIs) are a leading cause of death globally, disproportionately affecting youth in low- and middle-income countries (LMICs). While behavioral factors significantly contribute to RTIs, the role of socio-cultural norms remains understudied. This scoping review examines 75 studies (2000-2020) to explore how social norms (descriptive, injunctive, subjective, and collective) and cultural factors influence road safety behaviors among young people. Findings reveal that norms shape behaviors such as risky driving, helmet/seatbelt use, and compliance with traffic laws, often moderated by cultural contexts like gender, media, and religion. Peer and familial influences emerged as both risk and protective factors, while collective norms in certain communities reinforced harmful practices like drunk driving. Gaps persist in understanding the interplay between culture and norms, particularly in LMICs. The review highlights the need for culturally tailored interventions and further research to address socio-cultural determinants of road safety.

Surrogate safety measures (SSMs) are widely used for proactive road safety assessments, reducing reliance on crash data. Despite their potential utility amid escalating road fatalities and lack of good quality crash data in developing countries, SSMs have been predominantly applied in developed countries, where traffic streams are homogeneous, and strict lane discipline is followed. In contrast, traffic in many developing countries (e.g. China and India) is characterized by vehicular heterogeneity and multi-vehicle interactions due to non-lane-based movements. This paper provides a systematic review of 102 peer-reviewed studies in developing countries focusing on vehicular conflicts in traffic streams with heterogeneous vehicle composition and disorderly movement. This review highlights the salient features and challenges associated with SSMs-based safety assessment in developing countries and outlines potential directions for future research. It examines data collection techniques, sample sizes, and the suitability of various conflict indicators for non-lane-based traffic. Additionally, the impact of vehicular heterogeneity on conflict modeling is analyzed. A detailed discussion of conflict segregation methodologies, threshold selection techniques, and modeling frameworks is provided. This review will likely assist in developing more efficient conflict-based safety assessment techniques in heterogeneous traffic, contributing to improved road safety in developing countries.

In India, motorized two-wheeler (TW) riders account for 44.5% of fatal road crashes. While factors affecting drivers have been studied, research on pillion riders' injury severity remains limited. The study aims to identify factors causing severe injuries to pillion riders by developing an accurate prediction model. The study includes machine learning (ML) models, such as conditional inference tree, random forest (RF), gradient boosting, support vector machine, and a statistical model ordered probit for comparison. The study accounts for the imbalance in injury severity crash data by adopting data balancing techniques. Also, it recommends a combination of ML techniques, variable importance charts, and individual conditional expectation plots for identifying key variables and their effects. The finding suggests that RF trained in up-sampled data performs better than the remaining models. The presence of a central divider on the road reduces fatal injuries to pillion riders. The likelihood of getting severe injury is higher during nighttime crashes, TW-HMV (truck or bus) collisions, and hit-and-run crash cases where the colliding vehicle is unidentified. Older pillion riders are more vulnerable to sustaining fatal injuries in a crash. Crashes involving TWs hitting stationary objects and skidding are more fatal for pillion riders than other collision types.

This study pursues two complementary objectives: first, evaluating machine learning approaches for crash severity prediction to address methodological gaps in pickup truck crash analysis; second, systematically comparing single- versus multi-vehicle crash outcomes to understand distinct risk factors. Using Thailand crash data, the research compares Logistic Regression, Random Forest, XGBoost, and Deep Neural Network models, optimized with K-fold cross-validation and Bayesian Optimization, with SHAP employed for model interpretability. Results demonstrate that model performance varies significantly with injury classification schemes: XGBoost performed best for multiclass injury classification in both crash types, while Random Forest and Deep Neural Networks excelled in binary classification for single- and multi-vehicle crashes, respectively. The methodological analysis reveals the importance of both model selection and classification scheme in achieving optimal predictive performance. When applied to analyze crash factors, the models identified that both crash types are influenced by 4-lane roads, unlit roads, and barriers. Severity in single-vehicle crashes increases with fatigue, 2-lane roads, intra-province highways, and long holidays; in multi-vehicle crashes, severity is influenced by involvement of motorcycles or trucks, head-on collisions, and specific times of day. Factors reducing severity in single-vehicle crashes-such as concrete roads, defective vehicles, and hitting guardrails-do not significantly affect multi-vehicle crashes.