Traffic Crash Injuries Research Articles

Aggression of electric bike riders in China: comparisons of the Cycling Anger Expression Inventory toward drivers, pedestrians, and other riders

ABSTRACT Electric bikes (e-bikes) account for an unexpected proportion of traffic crash injuries and deaths in China. There is an urgent need to study the factors influencing the aggressive behaviours of e-bike riders and reduce their potential involvement in crashes. In addition, aggression varies when directed at different types of road users, particularly when expressed by vulnerable groups. This study aimed to validate the Cycling Anger Expression Inventory (CAX) for e-bikers and explore whether the expression of aggression in e-bikers differs depending on the recipients, such as drivers, pedestrians, or other riders. The analysis examined the aggression specificity of e-bikers by employing tests of measurement invariance (MI). These findings indicate that adaptive/constructive expression has emerged as a mainstream strategy for managing e-biker anger. E-bikers exhibited the highest levels of aggression toward drivers. These results provide a strong basis for the prevention and intervention of aggression and accidents among Chinese e-bikers.

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama

Traffic crashes are the leading cause of injuries and deaths for police officers in the US. There has been limited research to investigate the characteristics of traffic crashes that involve police officers. This study aims to investigate the relationships between different independent variables and police officer injury severity in traffic crashes. Considering the inherent spatial correlation between traffic crashes and the geographical environment, this study employs the ordered logit regression and the Geographically Weighted Ordered Logistic Regression (GWOLR) approach to identify the contributing factors of police officers’ injuries in traffic crashes and to quantify the correlates of police injury severity in crashes. This study explored traffic crashes of five years (Number of observations = 4,231) that involved police officers in the State of Alabama. The results of the global ordered logit model illustrated that some independent variables, such as pre-crash behaviors, holiday, and road type, are statistically significantly related to police officer crash injury severity. Also, some variables, such as pre-crash behaviors and seatbelt usage, passed the non-stationarity tests. This suggests significant spatial variations in their relationships with the severity of injuries sustained by police officers in traffic crashes. The local correlates of police injury severity are valuable information for law enforcement agencies to localize strategies for improving officer safety on the road. For example, Move-over laws may need to be enhanced in the southeast Alabama area, where failing to yield (to police officers) seems to lead to increased traffic injury severities of police officers compared to other areas in Alabama.

Crash injury severity prediction considering data imbalance: A Wasserstein generative adversarial network with gradient penalty approach

For each road crash event, it is necessary to predict its injury severity. However, predicting crash injury severity with the imbalanced data frequently results in ineffective classifier. Due to the rarity of severe injuries in road traffic crashes, the crash data is extremely imbalanced among injury severity classes, making it challenging to the training of prediction models. To achieve interclass balance, it is possible to generate certain minority class samples using data augmentation techniques. Aiming to address the imbalance issue of crash injury severity data, this study applies a novel deep learning method, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), to investigate a massive amount of crash data, which can generate synthetic injury severity data linked to traffic crashes to rebalance the dataset. To evaluate the effectiveness of the WGAN-GP model, we systematically compare performances of various commonly-used sampling techniques (random under-sampling, random over-sampling, synthetic minority over-sampling technique and adaptive synthetic sampling) with respect to dataset balance and crash injury severity prediction. After rebalancing the dataset, this study categorizes the crash injury severity using logistic regression, multilayer perceptron, random forest, AdaBoost and XGBoost. The AUC, specificity and sensitivity are employed as evaluation indicators to compare the prediction performances. Results demonstrate that sampling techniques can considerably improve the prediction performance of minority classes in an imbalanced dataset, and the combination of XGBoost and WGAN-GP performs best with an AUC of 0.794 and a sensitivity of 0.698. Finally, the interpretability of the model is improved by the explainable machine learning technique SHAP (SHapley Additive exPlanation), allowing for a deeper understanding of the effects of each variable on crash injury severity. Findings of this study shed light on the prediction of crash injury severity with data imbalance using data-driven approaches.

Examining gender differences in gig worker safety

AbstractA critical but often overlooked road safety concern is gender differences in road safety issues. The limited investigations have reported gender differences in road safety issues such as traffic accident rates and crash injuries. An emerging road safety issue is the safety of gig workers. Despite the exponential growth of the gig economy, little is known about road safety issues faced by gig workers, including gender‐specific road safety issues. The present study aimed to investigate whether there are gender differences in gig workers' involvement in road safety incidents and the likelihood of reporting incidents when gig working. Gig workers who provide, or have provided, ride‐hailing, courier, and food delivery services via digital platforms in Australia were invited to participate in an online survey on their involvement in road safety incidents and the likelihood that they would report road and personal safety incidents when gig working. Data from 71 gig workers (35 female gig workers; 36 male gig workers) were reported in the study. No gender differences were found in their involvement in road safety incidents and the likelihood of reporting road and personal safety incidents when gig working. Despite the lack of gender differences observed, findings from the study contributed to the research field and to closing the gender data gap. A research agenda is proposed for further data collection in gig work using a systems thinking approach to better our understanding of factors influencing the gendered participation in the gig workforce, and the health, safety, and wellbeing of gig workers.

Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms

Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms

Parents’ knowledge and socio-demographic determinants toward child’s restraint system use

BackgroundLack of protection or improper protection, is one of the most important reasons of child passenger’s death and injury in traffic crashes. Based on what we see on the roads, Iranian children are unrestrained inside the car. The aim of this study was to investigate children restrained system (CRS) use rate, its socio-demographic determinants and parents’ knowledge toward CRS use among Iranian parents.MethodsUsing multi-stage cluster sampling and direct in filed method of observation, the behavior of 700 children in cars was observed in the current cross-sectional study. Socio-demographic determinants and parents’ knowledge, toward using the CRS were evaluated using questionnaires. The study was performed from July to August 2019 in Tabriz city, northwestern Iran.ResultsThe rate of child safety seat (CSS) use was 15.1% CI 95%:(12.5%,18.0%), and the rate of booster use was 0.6%; CI 95%:(4.3%,8.0%). The majority of parents [e.g. 64.3%; CI 95%: (60.7%,67.9%)], had low knowledge about the use of CRS. The most important reasons for not using CRS was lack of laws and policies [e.g. 59.7%; CI 95%:(12.5%,18.0%)], lack of knowledge [e.g.59.6%; CI 95%:(57.9%, 63.3%)] and the high cost of CRS [e.g. 57.6%; CI 95%:(53.81%,61.2%)]. The most important predictors of not using CRS were the child's age, parental knowledge, and the socioeconomic status of the household (p < 0.05).ConclusionsMost children did not have CRS. The parents with higher education and those with higher socioeconomic status had higher rate of CRS use. Based on the low rate of CRS use and poor parental knowledge about it, education of parents toward boosters use and benefits of using CRS, enforcing mandatory laws and ploicies for CRS use in Iran, and allocation of government subsidies to low-income families for purchasing CRS are suggeted as essential strategies to increase CRS use.

Trends in alcohol-impaired crashes in California, 2016 to 2021: A time series analysis for alcohol involvement and crash distribution among demographic subgroups.

In 2020, the COVID-19 pandemic and control measures changed alcohol consumption in the United States (US) and globally. Before the pandemic, alcohol-impaired crashes contributed to approximately one-third of all road traffic crash injuries and fatalities nationally. We examined the impact of the COVID-19 pandemic on crashes and examined differences in alcohol-involved crashes across various subgroups. The University of California Berkeley Transportation Injury Mapping Systems provided information on all crashes reported to the California Highway Patrol from January 1, 2016 through December 31, 2021. Using autoregressive integrated moving average (ARIMA) models applied to weekly time series data, we estimated the effect of California's first mandatory statewide shelter-in-place order (March 19, 2020) on crashes per 100,000 population. We also examined crash subgroups according to crash severity, sex, race/ethnicity, age, and alcohol involvement. In California, the mean crash rate per week before the pandemic (January 1, 2016-March 18, 2020) was 9.5 crashes per 100,000 population, and 10.3% of those were alcohol-involved. After the initiation of the COVID-19 stay-at-home order, the percentage of crashes that were alcohol-involved rose to 12.7%. Overall, the crash rate across California decreased significantly (-4.6 crashes per 100,000; 95% CI: -5.3, -3.9), including across all examined subgroups, with the greatest decrease among the least severe crashes. However, there was a 2.3% absolute increase in the proportion of crashes that were alcohol-involved (0.02 crashes per 100,000; 95% CI: 0.02, 0.03). The initiation of a COVID-19 stay-at-home ordinance in California was associated with a substantial decrease in overall crash rates. While crashes have returned to pre-pandemic levels, alcohol-involved crashes remain elevated. The initiation of the stay-at-home order significantly increased alcohol-impaired driving, which has remained elevated.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents

BackgroundExisting studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents. MethodsThe traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models. Results(1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %. ConclusionsCompared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

Statistical approaches for assessing the effectiveness of safety devices use in preventing head injuries from motorcycle crashes

This study investigates the effectiveness of motorcyclist’s helmet use in preventing head injuries using the binary regression model and the mosaic plots. The association between head injuries and helmet use was modeled by applying binary logistic regression. The fitted model to the Changhua city accidents dataset has revealed that wearing a helmet had a positive impact in preventing the motorcyclist from sustaining head injuries in road traffic crashes. In addition to the association between head injuries and non-wearing of helmet, we found that alcohol impairment risk factor was increasing the likelihood of head injuries during crashes. Besides, the results were supported by using the mosaic plots which provide a visualization of the joint effect of Alcohol, helmet and heading injury for motorcyclists involved in crashes.

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

Implementation of a road traffic crash injury surveillance tool in a trauma referral hospital in Kisumu City, Western Kenya: Lessons learned from the pilot study

Objective Road traffic injury (RTI) is a major public health concern in Kenya with more than 13,000 deaths estimated annually. The primary objective of this study was to design and implement an injury surveillance tool for the collection of injury data, and assess the tool’s feasibility for the development of a hospital-based trauma registry in a leading Kenyan referral hospital. Secondarily, an epidemiologic profile was created to characterize RTI in the region. Methods An injury surveillance tool was developed and implemented, on a pilot basis, in a level 5 trauma hospital, Jaramogi Oginga Odinga Teaching and Referral Hospital (JOOTRH), in Kisumu City, Western Kenya, for a 4-week period from 2019-07-15 to 2019-08-11. A descriptive statistical analysis summarized injury frequency counts and percentages. Results Over the pilot phase, 371 patients had forms completed, but 488 official injury-related hospital registrations, indicating that 117 injury patients (24%) were missed. A process evaluation of the tool implementation revealed issues in the collection protocol that required revisions, resulting in improved data form completeness rates. For the 368 cases with cause documented, the most common mechanisms of injury were RTI (46.5%; n = 171), assaults (23.9%; n = 88), and falls (14.9%; n = 55). For RTI patients, the median age was 28 years (IQR = 16) and 77% (n = 132) were males, with motorbike collision injuries (n = 91; 53.2%) the leading RTI mechanism. There were 348 injuries for 171 patients. The most common anatomical regions for RTI were the lower limb 32.8% (n = 114), upper limb (15.2%; n = 53), followed by head lacerations 8.6% (n = 30) and concussions 7.2% (n = 25). Two-thirds of patients (n = 113; 66.1%) were discharged from ED, just over a quarter (n = 46; 26.9%) were admitted to hospital and 9 patients succumbed to RTI (5.3%). Conclusions This injury surveillance pilot study produced the first injury dataset in Kisumu City, demonstrating the significant magnitude of RTI in Western Kenya, the leading cause of injury for the region. This dataset can be replicated in other hospitals to create an injury surveillance system for the collection of trauma data, needed for the development of countermeasures for the reduction of trauma, as well as for quality initiatives to improve patient outcomes.

Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi

Pedestrian safety is a serious concern in the developing nations of the world. It is evident from the past studies that built-environment characteristics near bus-stops, play a crucial role on the frequency and overall share of pedestrian deaths and injuries in road traffic crashes. The present study aims to identify critical built-environment features around vulnerable bus-stops in an Indian city and evaluate the odds of risk that prevails on the safety of pedestrians near bus stops. Hotspot analysis was conducted to finalise 177 bus stop sites within high-crash clusters in the study area. Built-environment attributes considered were based on sidewalk, crosswalk and bus stop conditions near such vulnerable locations. This study includes a video graphic and manual field survey conducted during the day and night-time. Logistic regression was applied to estimate the impact of built environment features on pedestrian crashes. Width and disability friendliness of sidewalks, presence of bus bays and on-street parking have significant impacts on pedestrian fatalities at locations with a higher share of pedestrian fatalities during the day. On the other hand, presence of zebra crossings at junctions, proper bus stop lighting and high sidewalks reduce the odds of pedestrian crashes at night near bus stops.

Ride-Hailing and Road Traffic Crashes: A Critical Review.

Ride-hailing businesses, including Uber and Lyft, have reshaped road traffic since they first began operating in the United States approximately a decade ago. It follows that ride-hailing may also alter the incidence and distribution of road traffic crash injuries and deaths. The available evidence relating ride-hailing to crashes is critically reviewed in this article. We present a theoretical model that synthesizes the hypothesized mechanisms, and we identify common methodological challenges and suggest priorities for future research. Mixed results have been reported for the overall incidence of road traffic crash injuries and deaths, likely due to heterogeneous impacts on vehicular traffic flow (e.g., increasing the volume of vehicles); on vehicle-, person-, and event-level characteristics (e.g., reducing alcohol-impaired driver crashes); on road-user types (e.g., increasing pedestrian crashes); and on environmental conditions (e.g., reducing crashes most substantially where public transit access is poorest). The lack of a well-developed theory of human mobility and methodological challenges that are common to many ecological studies impede exploration of these sources of moderation. Innovative solutions are required to explicate ride-hailing's heterogeneous impacts, to guide policy that can take advantage of the public health benefits of ride-hailing, and to ensure that research keeps pace with technological advances that continue to reshape road traffic use.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

Introduction: The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria. Methods: This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose. Results: The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar. Practical Application: To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

A literature review of machine learning algorithms for crash injury severity prediction

Introduction: Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase of injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision making in road safety management, improving road safety, and reducing the severity of future crashes. Method: This paper presents a recent literature review of the methods that have been applied to road crash injury severity modeling. It includes 56 studies from 2001 to 2021 that consider more than 20 different statistical or machine learning techniques. Results: Random Forest was the algorithm with the best results, achieving the best performance in 70% of the times that it was applied and in 29% of all studies. Support Vector Machine and Decision Tree achieved the best performance in 53% and 31% of the times and in 16% and 14% of all studies, respectively. Bayesian Networks and K-Nearest Neighbors achieved the best performance in 67% and 40% of the times that were used but only achieved the best performance in 4% and 7% of all the studies analyzed, respectively. Conclusions: At this point, Random Forest revealed to be a good approach for road traffic crash injury severity prediction followed by Support Vector Machine, Decision Tree, and K-Nearest Neighbor. However, there is still a lot of room in this area to explore other techniques that can best suit this purpose as not only the model's performance should be considered but also causality issues, unobserved heterogeneity, and temporal instability. Practical Applications: This review enables researchers to understand the recent techniques applied in the analysis of injury severity modeling, and the ones that achieved the best performance results. Based on the reviewed studies, challenges and future research directions are presented.

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach.

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision.

The pattern of orthopedic fractures and visceral injury in road traffic crash victims, Addis Ababa, Ethiopia.

Road Traffic crash injury is one of the main public health problems resulting in premature death and disability particularly in low-income countries. However, there is limited evidence on the crash fractures in Ethiopia. The study was conducted to assess the magnitude of road traffic crash fractures and visceral injuries. A hospital-based cross-sectional study was conducted on 420 fracture patients. Participants were randomly selected from Addis Ababa City hospitals. The study was carried out between November 2019 and February 2020. Data were collected using a questionnaire and record of medical findings. Multilevel logistic regression analysis was carried out. Ethical clearance was obtained from the Addis Ababa University, College of Health Sciences Institutional Review Board. Confidentiality of participants' information was maintained. The study found out that the majority 265 (63. 1%) of fracture cases were younger in the age group of 18 to 34 years. Males were more affected-311(74.0%). The mortality rate was 59(14.1%), of those 50(85.0%) participants were males. The major road traffic victims were pedestrians-220(52.4%), mainly affected by simple fracture type -105(53.3%) and compound fracture type-92(46. 7%). Drivers mainly suffered from compound fracture type -23 (59.0%). One hundred eighty-two (43.3%) of fracture patients had a visceral injury. Homeless persons who sit or sleep on the roadside had a higher risk of thoracic visceral injury compared to traveler pedestrians (AOR = 4.600(95%CI: 1.215-17.417)); P = 0.025. Visceral injury, simple and compound fractures were the common orthopedic injury types reported among crash victims. Males, pedestrians, and young age groups were largely affected by orthopedic fracture cases. Homeless persons who sited or slept on the roadside were significant factors for visceral injury. Therefore, preventing a harmful crash and growing fracture care should be considered to reduce the burden of crash fracture.

Risky road behaviours cluster and share predictor variables with smoking and drinking, and anti-social behaviours during early adolescence

ContextThe prime cause of death among young adolescents - 12 to 15 years of age - are unintentional injuries, especially injuries in traffic crashes. In the Netherlands, they lose their lives as pedestrians, cyclists or as passengers of young drivers. The age-related rise in deliberate risk-taking is one of the contributing factors. To date, widely implemented strategies that solely target risky road behaviour are only marginally effective. To date, an unexplored but promising approach for effective strategies is to explicitly address risk-taking tendency, since this may not only result in risky decisions in traffic but also in health and social position. Further, these risky decisions may share the same underlying contributors, such as sensitivity to peer pressure. To date, these presumed relationships have not yet been tested. PurposeThis study contributes to the developmental of prevention strategies, by testing that a) risky road behaviour cluster with other types of risk behaviours and (b) risk behaviours share the same underlying factors, such as developmental adolescent characteristics (CADs) and presence of risk behaviours in the perceived social environment (parents and peers). MethodsSurveys were completed in class in several schools by 417 students, 46% male, all in the first two years of secondary school in the Netherlands. ResultsCorrelations showed strong associations between risky road behaviour, smoking and/or drinking and anti-social behaviour, and weaker associations with a poor diet. These behaviours also shared predictor variables in regression modelling: peers acting risky and riding as passengers with dangerous drivers. Age and gender did not play a significant role. Implications and contributionsThe effectiveness and efficiency of road safety programmes for young adolescents may benefit from an integrated rather than a silo approach, by addressing clusters of risk behaviours and the influence of underlying factors related to adolescent development and their social environment.

Prehospital Care and 24-hour Crash Injury Mortality Among Road Traffic Crash Victims in Addis Ababa, Ethiopia

Road traffic crash injuries are critical public health problems that demand on time post crash response to deliver pre-hospital care. Ethiopia is one of the African countries with the worst pre-hospital care delivery platform. This study describes the current practice of pre-hospital service provided for crash injured individuals. A cross-sectional study was conducted among 430 crash victims attending public hospitals in Addis Ababa. Participants were selected by random sampling upon their arrival to the selected hospitals from January 2019 to August 2020. The study described the data using the percentage, median, and interquartile range. The study used binary logistic regression to assess the relationship between pre-hospital care provided and 24-hour crash victim mortality. The median scene time was 20 minutes. More than half of respondents transported to hospital greater than 60 minutes. Among the 430 injured individuals, the fatal crash occurred on 21 (4.9%) individuals. The crash injured individuals received pre-hospital care involving wound care, resuscitations with IV (Intravenous) fluid, and immobilization. Using the binary logistic regression model crash injured patients who did not receive ambulance service were three times more chance of mortality during a 24-hour crash event ((P-value=0.002, AOR=3.1 (95%CI 1.5– 6.4). This study showed that 33.3% of crash-injured individuals received pre-hospital care. The majority of participants arrived at tertiary level trauma hospital with time greater than platinium 10 minutes scene time and golden an hour transport time. Being male, young, and not receiving pre-hospital care were significantly related to early crash injury mortality. A suitable basic and advanced life support strategy should be designed and implemented to enhance timely crash injury care.