International Journal of Injury Control and Safety Promotion最新文献

Motorcycles are a popular low-cost personal transport mode. Despite their convenience, motorcycles are significantly more dangerous than other modes of transport, accounting for up to 39% of road fatalities in low-income countries. Speeding is among the most common factors causing road accidents. Thus, this research extends the theory of planned behavior to investigate young motorcyclists' speeding behavior by incorporating the latent variables of hedonic motivation and transport policy interventions using data collected through a questionnaire survey conducted among young motorcyclists in Lahore, Pakistan. Purpose-based sampling method was deployed to collect 394 responses. The results indicated that speeding attitudes (SA), perceived behavioral control (PBC), hedonic motivation (HM), and policy intervention (PI) variables are strong predictors of speeding intentions (SI), which act as a mediator of speeding behavior (SB). While HM positively affects SB, and the PI variable negatively influences SB. Moreover, unmarried and employed respondents are positively associated with SB. This research has provided important insights on how to improve young motorcyclists' safe behavior, which can be utilized by policymakers to make informed decisions to enhance road safety in Pakistan and other developing economies with similar socio-economic dynamics, with motorcycles as a popular low-cost personal travel mode.

Signalized intersections are the areas where traffic crashes with severe injuries frequently happen. Although existing studies have explored the factors affecting crash injury severity at signalized intersections, intricate causal relationships between factors often fail to be captured. Thus, usage of Bayesian network reveals factors contributing to injury severity and the causal relationships between them, with the use of crash data extracted from the Crash Report Sampling System in 2021. The K2 algorithm and Expectation-Maximization algorithms are adopted for structure learning and parameter learning in Bayesian networks, respectively. The results indicate that 1) factors such as speeding, drunk driving, and use of airbags can significantly affect the injury severity, 2) causal relationships exist between distraction, running the red signal, collision type, and crash injury severity, and 3) compared to the random parameter logit model and random forest, Bayesian network has better accuracy in predicting the crash injury severity. The findings can serve to propose effective traffic safety intervention measures to reduce the injury severity of crashes at signalized intersections.

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.

Cyclist crashes are a growing concern in Malaysia due to cyclists' vulnerability and infrastructural risks. This study identifies demographic and contextual factors influencing risky cyclist behaviours in Kuala Lumpur, Cyberjaya, and Shah Alam, focusing on helmet non-use, reflective clothes non-use, light non-use, riding two abreast, and riding outside designated lanes. Field observations (2,665) were conducted at midblock and intersection locations. Results show helmet non-use is more common during morning peak hours, among road bike users, in parks, and on weekends. Riding two abreast is frequent on weekdays and less common among female cyclists. Riding outside designated lanes increases during rainy conditions and in areas without street parking, but decreases on weekdays, during recreational activities, and in parks. At intersections, helmet non-use is prevalent during morning peak hours, among mountain and road bike users, and during recreational activities. These findings can inform targeted interventions like awareness programs, infrastructure improvements, policy enforcement, and promoting safety gear to enhance cyclist safety in Malaysia.

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.

The management of road safety relies on data from road traffic crashes to identify priorities, monitor trends and evaluate interventions. Both police and hospital records are important sources of information on crashes that result in injury; however, both are known to be incomplete, with the quality and completeness of data being lower in low- and middle-income countries. The aim of this study is to estimate the magnitude of the underreporting of crashes in Dire Dawa City, Ethiopia, as a case study that may be applicable elsewhere. In addition, it gives an opportunity to understand the discrepancies between police and hospital records in Dire Dawa City and how the data systems work in the city. This research compared data on traffic collisions resulting in injury from July 2014 to February 2019 across police and hospital databases and used the capture-recapture technique to estimate the actual numbers of crashes and the degree of under-recording in both sources. It was found that there was substantial under-recording in both sources, with the degree of under-recording varying by urban/rural area, gender, age, road user category and injury severity, as well as by source within these variables. The police figures were lower than the hospital figures, and in all cases but three (rural areas, passengers and serious injury crashes), both sources had more unmatched than matched cases. In addition, the analysis discovered undocumented deaths and injuries in both databases. To summarize, police capture more death instances, but hospitals capture more serious injury cases. The capture-recapture strategy predicted a greater number of instances than currently recorded by police and hospitals. This demonstrates a major under-reporting of crash data from both sources. This level of under-recording can lead to less effective road safety management and evaluation. Replication of this research in other parts of Ethiopia could provide information on local practices that are more or less successful in reducing the level of under-recording, and such results may have implications for other countries with similar problems.

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.

Public buses and taxis play crucial roles in urban transportation. Ensuring their safety is of paramount importance to develop sustainable communities. This study investigated the significant factors contributing to the injury severity of bus-taxi crashes, using the crash data recorded by the police in Hong Kong from 2009 to 2019. To account for the unobserved heterogeneity, the random parameters logistic model with heterogeneity in means was elaborately developed. The results revealed that taxi driver age, bus age, traffic congestion, and taxi driver behavior had significantly heterogeneous effects on the injury severity of bus-taxi crashes and that the mean value of the random parameter for severe traffic congestion was likely to increase if the taxi's age was <5 years. Taxi driver gender, rainfall, time of day, crash location, bus driver behavior, and collision type were found to significantly affect the bus-taxi crash severity. Specifically, female taxi drivers, old taxis, rainfall, midnight, improper manipulation of bus and taxi drivers, head-on and sideswipe collision types, and non-intersections were associated with a higher likelihood of fatal and severe crashes. Based on our findings, targeted countermeasures were proposed to mitigate the injury severity of bus-taxi crashes.

Previous research solely employed a single type of conflict extremes for crash estimation, without considering the joint impact of multiple types of conflict extremes on crash risk. Therefore, two analysis frameworks based on conflict extremes were proposed: separate modeling and cooperative modeling. Based on the trajectories from five diverging areas, longitudinal and lateral conflicts were extracted. Then, a Bayesian hierarchical model for joint multi-location conflict extremes was constructed. Next, the threshold for conflict extremes was determined using automatic mean residual life plots, and a link function was established between the logarithmic scale parameter and dynamic and static variables. Finally, model parameters were estimated using the Markov Chain Monte Carlo simulation method, and a comparative analysis of crash probabilities and overall risks for diverging areas in the two frameworks was conducted by the fitted distributions. The results show that density differences, speed differences, and the ratio of large vehicles are important covariates explaining the non-stationarity of conflict extremes. In terms of crash probability, significant covariates exhibit stronger explanatory power for longitudinal conflicts compared to lateral conflicts. At the overall risk level, the accuracy of the separate modeling is higher compared to the cooperative modeling.

Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fatalities were collected from forensic and hospital records. K-means clustering grouped death locations and identified cluster centres. The Ball Tree algorithm and Google Directions API were used to find the nearest trauma centre hospital from the injury locations. Statistical tests, including chi-square and Kruskal-Wallis, examined relationships between clusters and demographic variables. The analysis identified 181 cases, mostly males (83.43%), with a median age of 37 years. Clustering the death locations into four high-risk areas resulted in a Silhouette Score of 0.94, indicating suitable EMS locations. While no significant correlation was found with demographic variables, distinct patterns were observed in road user types. Testing the prediction performance for the nearest hospital using forty new locations yielded an accuracy, precision, recall, and F1 score of 0.90. These findings emphasize the importance of targeted interventions and resource allocation in traffic injury prevention and emergency response planning, showcasing the potential of ML and geographic analysis in enhancing traffic injury management and emergency response systems.