International Journal of Injury Control and Safety Promotion最新文献

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.

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.

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.

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 (n = 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.

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.

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.

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.

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.

The study of road traffic injuries (RTIs) is crucial for understanding the unique challenges faced by West Asia and North Africa (WANA) states. This research evaluates road safety practices in the WANA region, comparing them to global standards, and employs secondary data analysis from sources such as the Global Road Safety Status Report, Global Road Safety Facility, and the World Health Organization. The analysis examines epidemiological data, preventive measures like seatbelt and child-restraint use, and policy development, including national action plans, to estimate road traffic death rates per 10,000 vehicles and per 100,000 population. Data from 23 countries are analyzed, focusing on road traffic injury rates by user type, road safety laws, and global safety targets. Overall, WANA states account for 10.5% of global RTI fatalities, exceeding both world and European averages. Most pedestrian fatalities occur in Ethiopia (40.0%) and Afghanistan (34.0%). This indicates that low enforcement scores (averaging 5 out of 10) in most WANA countries contribute to the insufficient effectiveness of road safety laws in reducing injuries and deaths. Achieving the Sustainable Development Goal (SDG) to reduce global road traffic deaths by 50% by 2030 requires commitment and cooperation from governments, communities, and stakeholders in the WANA region.

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.