Traffic Injuries Research Articles

The Interplay of Local- and State-Level Alcohol Policies in the United States.

Most research on alcohol control policies in the United States has focused on the state level. In this study, we assessed both local and state policy prevalence and restrictiveness in a nationwide sample of cities. We conducted original legal research to assess prevalence of local-level policies across 374 cities (48 states) in 2019 for the following seven policy areas: (a) drink specials; (b) beverage service training; (c) minimum age for on-premise servers and bartenders; (d) minimum age for off-premise sellers; (e) prohibitions against hosting underage drinking parties (i.e., social host provisions); (f) bans on off-premise Sunday sales; and (g) keg registration. We obtained parallel state-level policies from the Alcohol Policy Information System. We assessed the restrictiveness of existing policies and how these compared across local and state levels. We found that for six of the seven policy areas, the majority of cities (53% to 83%) had only a state-level policy. Few cities (0% to 8% across policy areas) had only a local-level policy. The percentage of cities that had an alcohol policy at both the local and state levels ranged from less than 1% to 19% across policy areas, and the policies were mostly equally restrictive at both levels. The lack of local policies may point to areas where these localities could strengthen their alcohol policy environments. Additional research is needed to understand how the prevalence and restrictiveness of local and state policies are associated with public health harms such as traffic crashes.

Graph convolutional LSTM algorithm for real-time crash prediction on mountainous freeways

Accurate real-time traffic crash prediction is crucial for proactive traffic safety management. Currently, the majority of real-time models forecast crashes every 5 min to support different intelligent transportation systems. However, these intervals might be too short for practical use in manually implementing proactive traffic safety measures such as deploying traffic law enforcement and emergency rescue resources. Therefore, this study develops hourly crash prediction models to provide network operators with sufficient time to take measures in advance. A section of a mountainous freeway in Guizhou province is divided into homogeneous segments, with crash data, traffic operations data, and meteorological data being collected hourly. As the result is an imbalanced dataset of crash and non-crash instances, the training dataset is resampled using synthetic minority over-sampling technique (SMOTE) to address the issue. To fully capture the complex spatiotemporal relationships in the data and achieve high crash prediction accuracy, a graph convolutional network-long short-term memory (GCN-LSTM) model is constructed for the first time, combining a graph convolutional network (GCN) and long short-term memory (LSTM) neural network. For comparison purposes, LSTM, Extreme Gradient Boosting (XGBoost), and logistic regression (LR) models are developed. The results show that the GCN-LSTM model outperforms other models in hourly traffic crash prediction, and the optimal prediction performance is achieved with the crash-to-non-crash ratio of 1:4. The GCN-LSTM method is found to effectively capture the complex spatiotemporal relationships in prediction data and to handle imbalanced traffic crash data.

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian–vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features’ local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle–pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

A study of the impact of traffic investment on traffic fatalities in China, 2004–2020

PurposeRoad traffic injuries (RTIs) have been one of the most serious public health problems in China. The purpose of this study was to investigate the extent to which traffic investment affects traffic fatalities in China as well as regional differences. MethodsThe study analyzed the correlation between traffic investment and traffic fatalities, incorporating additional factors such as economic conditions, road infrastructure, population density, and lighting. The selected variables included the number of traffic fatalities, traffic investment, urban per capita road area, urban road length, road mileage, urban road lighting, population size, and per capita gross domestic product. Relevant data between 2004 and 2020 were collected for an analysis using a fixed effect regression model. A p < 0.05 is considered statistically significant. To reduce the heterogeneity caused by regional differences, the provinces were divided into 6 groups according to administrative districts, and the clustering standard error analysis was carried out. ResultsOverall, there has been a significant improvement in road safety in China from 2004 to 2020, but some regions show an increase in traffic fatalities. The model reveals that traffic investment is significantly and positively correlated with the number of traffic fatalities. Holding all other factors constant, each 10,000 yuan increase in transport investment was associated with an average increase of 0.22 road traffic fatalities. In the analysis of regional differences, there was a significant positive correlation between traffic investment and traffic fatalities in the Northwest region and an increase of 10,000 yuan leads to an increase of 0.47. There was a significant negative correlation between road mileage, urban road lighting system, and population and traffic fatalities. For example, holding other factors constant, a 10,000 km reduction in road length would increase the number of traffic deaths by 45.56. The model results of urban per capita road area, urban road length, per capita gross domestic product, and the explained variables showed that p > 0.100, which was not statistically significant. ConclusionsTherefore, traffic investments are essential for governments to develop measures to enhance road safety and reduce the risk of road fatalities. Adjusting traffic road investment and other covariates is conducive to improving traffic safety and reducing the risk of road fatalities. The road safety situation in different regions of China varies greatly. Local governments should consider the actual conditions to provide better road safety configuration policies.

Current Status and Challenges of the Evidence for Cupping Therapy in Clinical Practice Guidelines in Korea

Clinical practice guidelines (CPGs) published in Korea were reviewed to evaluate up-to-date evidence and the recommendations for cupping therapy (CT) to inform clinicians and researchers for future studies. There were 14 CPGs (allergic rhinitis, ankle sprain, cervical pain, chronic low back pain, cold hands and feet, facial nerve palsy, Hwabyung, knee osteoarthritis, lumbar herniated intervertebral disk, migraine, osteoporosis, postoperative syndrome, shoulder pain, and traffic accident injury) with 29 recommendations for CT determined from “low” to “moderate” rated evidence. The levels of evidence were mostly downgraded due to the risk of bias and imprecision. The majority of recommendations for CT were graded as B or C. This comprehensive analysis underscores the imperative need for robust clinical research, including randomized controlled trials and observational studies using real-world data to enhance the quality of the evidence for CT. In addition, recommendations providing definite phases or scope of the target conditions/diseases and treatment regimens should be employed. This work lays a foundational step towards integrating CT into evidence-based clinical practice, emphasizing strategic directions for future research to bridge the gap between evidence and practice.

AN APPLICATION OF POISSON REGRESSION MODEL ON ROAD ACCIDENTS DATA IN KADUNA STATE, NIGERIA

The Poisson Regression model was used to analyze the data on road traffic crashes of Kaduna State from year 2014 to 2017. The data was collected from the Kaduna State Command of the Federal Road Safety Corps (FRSC). The analysis was carried out using the R (MASS Package) software. The variables considered are; Number of Persons Involved in accidents, Season (months of the year), Number of Crashes and Causes of Accidents. The results from the model were analyzed using the Akaike Information Criterion (AIC) and goodness of fit. Result shows that the Poisson Regression Model has an AIC value of 1185.7. The scatter-plots are clustered and it has few outliers from the predicted line. This is due to the good value of the deviance and the parameters of the model are very much estimated. The result of the Poisson Regression Model aids in overcoming the over-dispersion problem, resulting in a better fit. Thus, indicating the existence of significant dispersion.

How the cognitive load of simulated driving affects the brain dynamics underlying auditory attention

Objective Distracted driving is a primary contributor to for motor vehicle crashes, the leading cause for injuries and fatalities for youth. Although attention and working memory clearly underlie driving abilities, few studies explore these functions on the brain-level under the cognitive load of driving. To understand the load driving has on auditory attention processing, we examined the differences in dynamic brain response to auditory stimuli during LOAD (while driving in a high-fidelity driving simulator) and No-LOAD conditions (seated in simulator, parked on the side of the road). Methods Twenty-seven young adult drivers (18–27 y/o; 15 = women) completed a Selective Auditory Attention Task during both a LOAD (driving) and No-LOAD condition in a ½ cab miniSim® high-fidelity driving simulator. During the task, participants responded by pressing the volume control button on the steering wheel when a target tone was presented to a target ear. Electroencephalography-recorded event-related brain responses to the target tones were evaluated through alpha and theta oscillations for two response windows (early: 150–330ms; late: 350–540ms). Results During an early time window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD theta power in the right frontal cortical region (F(1, 24)= 5.4, p=.03, partial η 2=.18). During the later window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD alpha response in the posterior cortical region (F(1, 24)=11.81, p=.002, partial η 2=.15) and in the right temporal cortical region during the window (F(1, 24)=4.3, p=.05, partial η 2=.33). Conclusions Our data provide insight into the demand that driving has on cognitive faculties and how dual task engagement may draw resources away from driving. We suggest future research directly incorporate vehicle control abilities into study design to understand how brain-based measures relate to driving behaviors.

Sociodemographic and psychological factors affecting motor vehicle crashes (MVCs): a classification analysis based on the contextual-mediated model of traffic-accident involvement

The study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the ‘contextual-mediated model of traffic accident involvement’, the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma.

Unpacking the hazards: An analytic study of injury patterns and risk factors in urban instant delivery

The rapid growth of urban instant delivery, facilitated by digital platforms and characterized by on-demand, short-term, task-based labor, has raised concerns about safety, particularly with the increasing frequency of instant delivery crashes (IDCs). This study addresses knowledge gaps in understanding injury patterns and risk factors associated with IDCs. Utilizing data extracted from judicial verdicts on IDC disputes in China, encompassing demographic, contextual, crash, and injury information, the research employs ordered logit regression to identify significant factors affecting injury patterns, the number of injuries per person (IPP), and injury severity. Overall, traffic injuries related to instant delivery services have gradually improved since 2020, as evidenced by the severity of individual accidents, the number of injuries, and the economic losses. Analysis of 648 injuries among 448 non-fatal victims reveals a prevalence of lower extremity injuries, followed by external, upper extremity, and head injuries. While the majority of victims suffered a single injury, approximately 22 % experienced major injuries. Female delivery riders exhibited higher injury ratios across various body regions. Rider risk behavior, type of delivery vehicles, and the mode of transport of non-delivery travelers emerged as significant influencers of injury patterns. Notably, functional and physical intersection areas exhibited the highest injury ratios among facility types. Contrary to conventional wisdom, older riders and travelers aged above 50 were associated with higher injury severity, challenging the perception of young age as the primary risk factor. The prominence of lower extremity injuries underscores the necessity for heightened protective measures for delivery riders. Major injuries among victims emphasize potential long-term consequences and associated costs. The significance of gender, age, and risk behavior as determining factors highlights the need for targeted safety interventions. These findings offer crucial insights for stakeholders, guiding the formulation of precise safety measures and informed policy initiatives within the dynamic landscape of instant delivery safety.

Patterns and sources of spatial inequity in freight crashes: An application of decomposition analysis

Despite considerable increases in road freight traffic and associated crashes over the past decade, our understanding of their spatial distribution remains limited. This is concerning because freight vehicle crashes often lead to fatal and severe injuries. This study focuses on Seoul, South Korea and contributes to the literature by investigating the patterns and sources of spatial inequity in freight crashes. Specifically, it examines whether socioeconomically disadvantaged neighborhoods experience a higher concentration of freight crashes. Using the Gelbach’s decomposition technique, this study also aims to identify the factors contributing to differences in freight crashes between disadvantaged and less-disadvantaged neighborhoods and quantify their relative contributions. The regression results show that severe freight crashes are more prevalent in disadvantaged neighborhoods before adjusting for other factors—a pattern not observed in non-severe crashes. The decomposition analysis reveals that the observed disparities in severe freight crashes between disadvantaged and less-disadvantaged neighborhoods are fully explained by differences in several neighborhood characteristics, including local road density, truck traffic volume density, proximity to logistics terminals, and off-road bicycle lane density, between neighborhood types. Interestingly, differences in built environment characteristics between neighborhood types not only fail to explain but instead counteract the disparities in severe freight crashes. The findings of this study suggest detailed policy implications for mitigating freight crash occurrences and addressing related spatial inequities.

Road Traffic Injury Prevention: Standing Electric Scooters

Road Traffic Injury Prevention: Standing Electric Scooters

Evaluation of a novel head and neck restraint for harness-restrained children

Motor Vehicle Crashes (MVCs) are a common source of neck injury and, although rare, catastrophic neck injury occurs in restrained children up to age 10 years old. Recently, a head/neck support (HNS) that cradles the head during sleep was developed. The objective of this study was to determine if the HNS improves injury criteria measured on anthropomorphic test devices (ATDs) in front facing 5-point harness child restraint systems (FFCRS). Nineteen matched pair frontal impact sled tests were conducted with ATDs representing the 12 month old, or 3 or 6 year old child in FFCRS. HNS reduced HIC36 by median 22.2%, maximum resultant head acceleration (3ms clip) by 16.2%, head excursion by 8.2%, and neck tension by 12.2%. Providing supplemental restraint directly to the head has the potential to improve injury outcomes while not adding to harm during common misuse scenarios, but further research is needed to determine if these benefits persist in real-world crashes.

From parents to children: associations of traffic risks with impulsivity, family relationships and serotonin transporter genotype.

Road traffic injuries are the leading cause of death for young adults, and parents play a major role in shaping their traffic behaviour. Higher impulsivity (predictor of higher traffic risk) has been shown to be dependent on family relations and the serotonin transporter gene promoter polymorphism (5-HTTLPR). The specific mechanisms for the inheritance of risky traffic behaviour from parents to children are not clear, and the genetic aspect has not been studied before. We used data of Estonian Children Personality Behaviour and Health Study subjects (n = 596, mean age = 25.2 ± 0.6) and their parents (mothers, n = 460, mean age = 52.1 ± 5.8; fathers, n = 339, mean age = 54.1 ± 6.5). Family relationships scale, traffic risk questionnaires and Adaptive and Maladaptive Impulsivity Scale were filled out. The increased risk-taking behaviour of parents and worse quality of family relationship were significant predictors of higher traffic risk among subjects. Family support and impulsivity of fathers significantly predicted the subjects' traffic risk score in interaction with 5-HTTLPR genotype: l'/l' homozygous subjects with adaptively impulsive fathers had higher traffic risk, whereas for s'-allele carrying subjects family support was more significant. Parental role modelling and family relationships are significant predictors of future traffic behaviour of the child. Whether the behavioural example of the father or the influence of family relationships is more important in predicting future risky traffic behaviour, depends on the 5-HTTLPR genotype of the child.

Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

Jointly modeling the dependence of injury severity and crash size involved in motorcycle crashes in Cambodia using a copula-based approach

Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view

IntroductionWearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need for large-scale data collection for situation assessment and intervention evaluation.MethodsThis study proposes a scalable, low-cost algorithm to estimate helmet-wearing rates. Applying the state-of-the-art deep learning technique for object detection to images acquired from Google Street View, the algorithm has the potential to provide accurate estimates at the global level.ResultsTrained on a sample of 3995 images, the algorithm achieved high accuracy. The out-of-sample prediction results for all three object classes (helmets, drivers, and passengers) reveal a precision of 0.927, a recall value of 0.922, and a mean average precision at 50 (mAP50) of 0.956.DiscussionThe remarkable model performance suggests the algorithm’s capacity to generate accurate estimates of helmet-wearing rates from an image source with global coverage. The significant enhancement in the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally.

Latent class models for capturing unobserved heterogeneity in major global causes of mortality: The cases of traffic crashes and COVID-19

Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

Alcohol is a risk factor for helmet non-use and fatalities in off-road vehicle and motorcycle crashes.

Off-road vehicle (ORV) and motorcycle use is common in Canada; however, risk of serious injury is heightened when these vehicles are operated without helmets and under the influence of alcohol. This study evaluated the impact of alcohol intoxication on helmet non-use and mortality among ORV and motorcycle crashes. Using data collected from the Nova Scotia Trauma Registry, a retrospective analysis (2002-2017) of ORV and motorcycle crashes resulting in major traumatic brain injury was performed. Patients were grouped by blood alcohol concentration (BAC) as negative (< 2 mmol/L), legally intoxicated (2-17.3 mmol/L) or criminally intoxicated (> 17.3 mmol/L). Logistic regression models were constructed to test for helmet non-use and mortality. A total of 424 trauma patients were included in the analysis (220 ORV, 204 motorcycle). Less than half (45%) of patients involved in ORV crashes were wearing helmets and 65% were criminally intoxicated. Most patients involved in motorcycle crashes were helmeted at time of injury (88.7%) and 18% were criminally intoxicated. Those with criminal levels of intoxication had 3.7 times the odds of being unhelmeted and were 3 times more likely to die prehospital compared to BAC negative patients. There were significantly increased odds of in-hospital mortality among those with both legal (OR = 5.63), and criminal intoxication levels (OR = 4.97) compared to patients who were BAC negative. Alcohol intoxication is more frequently observed in ORV versus motorcycle crashes. Criminal intoxication is associated with helmet non-use. Any level of intoxication is a predictor of increased in-hospital mortality.

Investigating the immediate and mid-term effect of a gamified e-learning platform for the enhancement of traffic knowledge and skills among Vietnamese adolescents operating powered two-wheelers

Introduction: Traffic crashes caused by adolescents are being assessed as particularly serious and a common concern of society as a whole. Improving traffic knowledge and skills is crucial in reducing adolescent traffic crashes. Objectives: This study aimed to investigate the effects of a gamified e-learning platform on traffic knowledge and skills among adolescents (aged 15–18) in Vietnam. Method: Using a pretest–posttest design, this quasi-experimental study, included 350 participants within the intervention group and 350 participants within the control group. All participants were selected from three high schools in Ho Chi Minh City in Vietnam. Intervention group participants got a gamified traffic safety learning experience with a gamified e-learning platform, while control group participants received general traffic safety education through conventional methods using short videos. The effect was measured via tests focusing on traffic knowledge and skills. Data were subsequently collected from both groups before (i.e., pretest) and immediately after (i.e., posttest 1) following the education. In addition, within the intervention group also a second posttest (i.e., posttest 2) was conducted six months after following the education. Results: A significant increase in scores on posttest 1 compared to the pretest was found in the intervention group but not in the control group. Also, among the intervention group, the scores in posttest 2 were significantly better than those in the pretest, however, there was no difference in scores between posttest 1 and posttest 2. Conclusion: The results of this study indicated that the gamified e-learning platform cannot only improve participants’ knowledge of traffic safety but also help participants retain such knowledge for at least six months. Practical implications: The study findings can reinforce the important role of traffic safety education in improving adolescent traffic knowledge and skills.

Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020

BackgroundAlthough road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures.MethodsHere, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana.ResultsWe find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas.ConclusionsA few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

Presentations and management of hospitalized patients with upper extremity fractures at a level 1 trauma center: a 5-year observational study

BackgroundUpper extremity injuries (UEIs) are common in the emergency departments, yet they are under-reported in developing countries. This study examined the frequency, injury characteristics, and treatment approaches of upper extremity fractures (UEFs) among hospitalized trauma patients in a nationally representative population.MethodsWe conducted a retrospective, observational study including all the hospitalized patients with UEFs in the only level 1 trauma center in Qatar between July 2015 and August 2020. Comparative analyses were performed according to injury mechanisms, severity, and management approach.ResultsA total of 2,023 patients sustained UEIs with an average age of 34.4 ± 12.9 years, and 92% were males. Motor vehicle crashes (MVCs; 42.3%) were the primary cause of shoulder girdle injuries in 48.3% of cases. Fractures of the radius, ulna, and hands occurred in 30.8, 16.5 and 14.5%, respectively. Young adults were more involved in MVCs and motorcycle crashes (MCCs), while pedestrians who were typically older had a higher rate of humerus fractures. Patients with MCCs had a higher rate of clavicle and ulna fractures. Pedestrians were at risk of serious injuries, with a higher mean injury severity score and lower Glasgow Coma Scale.ConclusionMost UEFs patients were young males and mainly affected by MVCs. Shoulder girdle, particularly clavicle and scapula/glenoid fractures, emerged as common injury sites. The study highlighted the potential risk of pedestrian injuries, as reflected in higher injury severity, concomitant injuries, and higher mortality. Future studies are needed to optimize preventive measures by incorporating insights into specific injury mechanisms and patterns of UEIs.