Cause Of Road Traffic Crashes Research Articles

Exploring the association between the Arabic version of the Traffic Locus of Control, driving behavior, and road traffic crashes: A multidimensional approach.

The Traffic Locus of Control scale (T-LOC) serves as a measure of drivers' personality attributes, providing insights into their perceptions of potential causes of road traffic crashes (RTCs). This study meticulously evaluated the psychometric properties of the Arabic version of T-LOC (T-LOC-A) among Lebanese drivers. Additionally, the study aimed to explore associations between the T-LOC scale and various driving variables, including driver behavior, accident involvement, and traffic offenses. A cross-sectional study was conducted among Lebanese drivers using a face-to-face approach. The validation of the Arabic version of T-LOC (T-LOC-A) occurred through a two-stage process: translating and culturally adapting T-LOC in the first stage, and testing its psychometric properties in the second stage. Data were collected using a comprehensive self-reported questionnaire in Arabic, covering demographic and travel-related variables, risk involvement, and measures such as the Driver Behavior Questionnaire (DBQ) and T-LOC. Exploratory factor analysis and confirmatory factor analysis were performed to scrutinize the factorial structure of T-LOC. Pearson correlation and chi-square tests were used for continuous and categorical variables, respectively. Two logistic regression analyses were executed to probe associations between T-LOC and involvement in road traffic crashes (RTCs) and T-LOC subscales with the occurrence of traffic offenses. The study included 568 drivers, predominantly male (69%) and aged between 30 and 49 years (42.1%). The findings revealed that T-LOC-A exhibited robust psychometric properties, with excellent reliabilities (α = 0.85) and adherence to the original four-factor structure, encompassing self (α = 0.88), other drivers (α = 0.91), vehicle/environment (α = 0.86), and fate (α = 0.66). The multidimensional structure was statistically supported by favorable fit indices. Gender differences revealed men attributing responsibility to other drivers, while women leaned towards fate and luck beliefs. Regarding driver behavior, the "other drivers" and self-dimensions of T-LOC-A correlated positively with aggressive violations. The fate dimension showed positive associations with aggressive violations and lapses. The "other drivers" subscale correlated positively with errors, and the vehicle/environment subscale with lapses. External T-LOC factors were positively associated with accident involvement, while the "LOC self" factor emerged as a protective element. In terms of traffic offenses, "LOC fate" displayed a positive association, while the "LOC self" factor showed a protective effect. In conclusion, the Arabic T-LOC is a reliable and valuable instrument, suggesting potential improvements in driving safety by addressing drivers' locus of control perceptions.

Evaluating Distraction Safety Performance Indicators in an Urban Area of a Low- or Middle-Income Country: A Case Study of Yaoundé, Cameroon

Distracted driving is a major cause of road traffic crashes in Yaoundé. This is partly due to the scarcity of enforcement and a lack of evidence and investigation using the distraction safety performance indicator (SPI), hindering evidence-based interventions. This study aimed to address this evidence gap by evaluating the distraction SPI using a proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observations on 36 randomly selected road sections carefully spread to cover the city. SPIs were computed and weighted with traffic volume to ensure the representativeness of the values. A total of 41,004 drivers were observed (38,248 in cars; 1116 in vans; 977 in trucks; 663 in buses). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024–2030) fighting strategy aimed at halving the prevalence was developed. The strategy contains interventions including legislation/enforcement, which have been proven to be effective. This study, pioneered in Yaoundé, provides stakeholders with evidence of the issue and measures to implement and can also be used when developing a road safety strategy. Future research should consider investigation at national level.

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

Edge-Empowered Communication-Based Vehicle and Pedestrian Trajectory Perception System for Smart Cities

Road traffic crashes are one of the prime issues in the world. Every year 1.35 million people die, 20 to 50 million fatal injuries, and many incur a disability due to road traffic crashes. 50% of the total death, injuries, and disabilities are among vulnerable road users (VRUs), such as motorcyclists, cyclists, and pedestrians. Among these VRUs, highly vulnerable is pedestrians. In this paper, we are dealing with providing safety and alert system to pedestrians and vehicles to reduce and/or avoid the causes of road traffic crashes in metropolitan areas. In this paper, we develop a cooperative communication framework between pedestrians and nearby vehicles as well as traffic light systems using mobile agent systems and apps. The proposed cooperative communication framework controls the mobility of vehicles as well as pedestrians to avoid accidents. We have developed an application that will notify the pedestrian if there is any automobile within the range of 200 meters of the pedestrian. If there are any vehicles in this range, using direct Wi-Fi, connect the vehicles and pedestrians to notify them about their presence of them. The proposed system is implemented and tested in real-time as well as simulated in the SUMO, Veins, OMNeT++, and MiXiM simulator. The proposed systems results (real-time, simulation, and comparison) show real-time deployability, accuracy, and reliability.

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

A Rapid Assessment of Road Crashes in Uganda: Notes from the Field

Abstract Background Road crashes continue to persist on Ugandan roads despite the government’s commendable efforts over many years. This study sought to explore the persistent causes of road crashes, suggest recommendations and evaluate the costs incurred by the government for treating accident victims. Methods A rapid cross-sectional study involving both quantitative and qualitative study approaches was performed. Desk reviews and six key informant interviews were conducted with traffic police officers and hospital administrators. Traffic road accident data were abstracted from the Uganda Police Force database for the period of January 2021 to February 2022, to ascertain the magnitude of the problem and its causes. Results The causes of road accidents are mainly caused by to human error, weather and the poor state of some roads. The government spends reasonable amounts of money in treating accident victims, at USD 245–3590, yet the funds could instead be used to promote socio-economic development in communities. Conclusion Road crashes continue to occur in Uganda, mainly because of human error. We recommend amelioration of the identified causes of road traffic crashes to save lives and conserve limited public resources.

Red-light-running in a Ghanaian metropolis

BackgroundRed-light-running (RLR) is a major cause of road traffic crashes in Ghana. Yet, there is limited scientific research on RLR in Ghana. This study employed the Theory of Planned Behaviour (TPB) and logistic regression models to understand RLR behaviour in the Cape Coast Metropolis, Ghana. An understanding of such behaviour would enable the design of behavioural and technological interventions including change communications and nudges to pro-driver behaviours. MethodsA naturalistic exploratory road-side observational design of drivers at three (3) signalized intersections in the Cape Coast Metropolis was conducted from 17th-23rdApril, 2021. A structured observational checklist was used to collect data on motorists in two (2) phases (7:00–9:00 am and 3:00–5:00 pm) each day for seven (7) days at the University of Cape Coast (UCC), Pedu Junction and Interbeton Junction traffic light intersections. Data captured included RLR, gender, age group of the driver, type of vehicle, and presence of other passengers. Data was entered and analyzed using STATA IC version 15. Bivariate and Multivariable Logistic regression analysis was done at p < 0.05 and 95% confidence interval. ResultsA total of 5,045 motorists were observed at three (3) traffic lights within the seven (7) days with the majority being male (94.4%), aged 25–45 years (77.4%), and on a Friday (18.6%). A low rate of RLR (13.3%) [Pedu Junction traffic lights (15.1%), UCC (12.7%) and Interbeton Junction (10.8%)] in the metropolis. Drivers entering the UCC Gate-Campus were 1.54 times more likely to engage in RLR than were those leaving campus, and vehicles without passengers were 1.27 times more likely to engage in RLR, compared to those with one or more passengers. Motorists were less likely to make an RLR offence on weekdays as compared to Saturday. ConclusionThe officials of the National Road Safety Authority (NRSA) and Motor Transport and Traffic Department of the Ghana Police Service should educate drivers on the danger associated with RLR and arrest and prosecute errant ones to serve as a deterrent.

Built Environment Analysis for Road Traffic Crash Hotspots in Kigali, Rwanda

Introduction: Road traffic injuries (RTIs) are a significant cause of morbidity and mortality in Rwanda. Investigations of the high risk areas for road traffic crashes (RTCs) are urgently needed to guide improvements in road safety. This study aims to identify RTC hotspots in Kigali, Rwanda, and to conduct a built environment analysis of these hotspots. Methods: RTC and RTC-prone locations were collected from the Kigali Traffic Police and high frequency road users, and hotspots were identified through kernel density estimation. Built environment characteristics (BEA), including road design, road safety, pedestrian safety, and traffic density, were collected for each hotspot. BEA characteristics were associated with risk of RTC using logistic regression and BEA scores were calculated using principal component analysis. Patterns of BEA were identified through exploratory cluster analysis and associated with risk for RTC using logistic regression. Results: 25 RTC hotspots were identified. High crash risk locations were less likely to have unpaved roads (21%, p=0.049) and road narrowing (21%, p=0.049). High crash risk locations were also more likely to have pedestrian walkways (100%, p=0.009), factors aiding pedestrian crossing (100%, p=0.026), and poor road surfaces (86%, p=0.005). Cluster analysis showed that hotspots with fewer urban characteristics, including road safety features, motor vehicle density, and pedestrian safety features, have significantly decreased odds of being a high mortality risk hotspot than a hotspot with more urban characteristics (OR=0.13, 95% CI 0.02-0.79). Conclusions: RTC hotspots were in the city center with high motor vehicle density but did have road and pedestrian safety features, suggesting that speeding is a major cause of RTCs. Effective traffic calming measures and enforcement of road safety laws may reduce the burden of road traffic injuries in Kigali but additional analyses are recommended.

Driver action recognition using deformable and dilated faster R-CNN with optimized region proposals

Distracted driver action is the main cause of road traffic crashes, which threatens the security of human life and public property. Based on the observation that cues (like the hand holding the cigarette) reveal what the driver is doing, a driver action recognition model is proposed, which is called deformable and dilated Faster R-CNN (DD-RCNN). Our approach utilizes the detection of motion-specific objects to classify driver actions exhibiting great intra-class differences and inter-class similarity. Firstly, deformable and dilated residual block are designed to extract features of action-specific RoIs that are small in size and irregular in shape (such as cigarettes and cell phones). Attention modules are embedded in the modified ResNet to reweight features in channel and spatial dimensions. Then, the region proposal optimization network (RPON) is presented to reduce the number of RoIs entering R-CNN and improves model efficiency. Lastly, the RoI pooling module is replaced with the deformable one, and the simplified R-CNN without regression layer is trained as the final classifier. Experiments show that DD-RCNN demonstrates state-of-the-art results on Kaggle-driving dataset and self-built dataset.

Animal-vehicle collisions in Victoria, Australia: An under-recognised cause of road traffic crashes.

Non-fatal injuries sustained from animal-vehicle collisions are a globally under-recognised road safety issue, with limited data on these crash types. The present study aimed to quantify the number and causes of major trauma events resulting from animal-vehicle collisions. The study was a retrospective analysis of major trauma cases occurring in Victoria, Australia, between 2007 and 2016, using data from the population-based Victorian State Trauma Registry. To identify animal-vehicle collisions, Victorian State Trauma Registry injury codes were combined with text-mining of the text description of the injury event. Over the 10 year period, there were 152 major trauma patients who were admitted to Victorian trauma-receiving hospitals due to vehicle collisions with animals. The crude population-based incidence rate for animal-vehicle collisions increased by 6.7% per year (incidence rate ratio 1.07; 95% confidence interval 1.01-1.13; P = 0.02). Development of systematic recording methods of animal-vehicle collisions will improve reporting of these crash types to assist future studies in implementing effective countermeasures.

Sustainable Development Goal 3.6 and road traffic injury surveillance, Nigeria West Africa,2013–2016

ObjectiveThis study aimed to describe RTC characteristics and trends in Nigeria and determine progress towards halving RTC-related deaths/injuries by 2020 [i.e., Sustainable Development Goal (SDG) Target 3.6].IntroductionGlobally, road traffic crashes (RTCs) annually kill 1.3 million people and injure 20-50 million others. Nigeria accounts for an estimated 15% of RTC deaths in the WHO African Region.MethodsWe abstracted, cleaned, and analyzed RTC surveillance data routinely collected from crash scenes from 2013-2016. Federal Road Safety Corps (FRSC) is the lead agency for RTC surveillance and regularly collates data from the 6 geopolitical zones (which contain the 36 Nigerian States and the Federal Capital Territory). We defined road traffic injury as a fatal or non-fatal injury incurred from collision on a public road involving ≥1 moving vehicle(s). We calculated descriptive statistics, frequencies, and proportions to describe RTC characteristics and trends.ResultsFrom 2013-2016, 283,949 persons were involved in 42,813 RTCs, resulting in 23,412 deaths and 127,264 injuries. Twenty-eight percent of RTCs involved ≥1 fatality. Ninety percent of persons involved in RTCs were ≥18 years old, with a male-female ratio of 3:1. The same proportion was also found for RTC deaths and for injuries. Despite containing only 15% of the country’s population, the North-Central geopolitical zone accounted for 37% of RTCs. The most common cause of RTCs was speed violations (26%). From 2013-2016, there were reductions of 30% for RTCs, 22% for number of fatalities, and 25% for number of injuries.ConclusionsNigeria reduced RTC deaths/injuries and achieved modest progress toward SDG Target 3.6. To further progress, FRSC can help by enhancing enforcement of speed violations and by educating road users about road safety practices. Also, they could investigate why certain geographical areas had disproportionate amounts of RTCs, deaths, and injuries.

The relationship between sleep quality and road traffic crashes of urban drivers in Hamadan, Iran.

:Background:Sleep quality is one of the main human factors related to urban road traffic crashes. This study aimed at determining the relationship between sleep quality and road traffic crashes in urban drivers. Methods:This correlational study was conducted in Hamadan, a city located in the western part of Iran. The study samples consisted of 309 Hamadan drivers (i.e., 103 with road traffic crashes (RTCs) and 206 without RTCs), who were referred to police centers to change or renew their driving licenses. The data collection tool was a two-part questionnaire including demographic information and the Pittsburgh Sleep Quality Index (PSQI). The questionnaire was filled out in a self-administered manner. Statistical analysis was done using the SPSS-16 software and applying logistic regression, Fisher’s exact test, and Chi-square test.Results:The comparison of sleep quality scores between two groups, using the adjusted logistic regression test, showed a statistically significant difference between them (P = 0.019). This means that the sleep quality of drivers without RTCs was 1.8 times better than drivers with RTCS (OR=1.8; 95% CI, 1.1 - 3.07). Conclusions:There was a significant association between poor sleep quality and the occurrence of RTCs in urban drivers. As a result, it is recommended paying more attention to the sleep quality of urban drivers to prevent and control RTCs.

The key risk indicators of road traffic crashes in Serbia, Niš region

ABSTRACTTraffic crashes are a serious public health and socio-economic problem, and they are one of leading causes of death of males in Serbia. In order to determine the main causes of road traffic crashes in Niš region, Republic of Serbia, nine key risk indicators of road traffic crashes are identified, organized into four groups (number and structure of crashes, consequences of traffic crashes, temporal and spatial location of traffic crashes, causes and sanctions), and described in detail. The indicators are ranked based on group fuzzy analytic hierarchy process (AHP), where experts from insurance companies, professionally engaged in the process of risk assessment, with different previous experience present different impact on the final ranking. In relation to the most influential factors in the assessment of traffic crashes, improper and unadjusted vehicle speed, overtaking and passing, as well as the physical condition of the driver have the highest effect.

Lived experiences of inter-urban commercial bus drivers involved in road traffic crashes in Central Region, Ghana

ABSTRACTInterurban commercial bus drivers are confronted with inherent risk of over speeding, road curvature, road geometry, and weather condition predisposing them to Road Traffic Crashes (RTCs). Lived experiences of inter-urban commercial bus drivers involved in RTCs in Ghana remains relatively underreported. This relates to their experiences and opinions on the causes of RTCs and the post-experiences as survivors. This paper is an exploratory qualitative study involving face-to-face in-depth interviews with 15 interurban commercial bus drivers who survived RTCs and still drive. The sample was a mix of purposive and snowball sampling techniques at the terminals/stations of interurban commercial bus drivers in Cape Coast, the Capital city of Central Region. The analysis revealed environmental factors (such as weather condition, road surface, road curvature) accounted for the RTCs. Survivors received poor pre-hospital trauma care and no welfare package. Measures to RTCs could include road/transport infrastructure improvements and survivors are to be provided with social welfare package.

Factors associated with road traffic injuries in Tanzania.

IntroductionInjuries represent a significant cause of morbidity and mortality worldwide and road traffic crashes accounts for a significant proportion of these injuries. Tanzania is among the countries with high rates of road traffic crashes. The aim of this study was to determine the pattern, associated factors and management of road traffic injury patients in Tanzania.MethodsA cross-sectional study of patients involved in motor traffic crashes and attended in six public hospitals of Tanzania mainland between April 2014 and September 2014.ResultsA total of 4675 road traffic injury patients were seen in studied hospitals, 76.6% were males. Majority (70.2%) were between 18 - 45 years age group. Motorcycles were the leading cause of road traffic crashes (53.4%), and drivers (38.3%) accounted for majority of victims. Fractures accounted for 34.1%, and injuries were severe in 2.2% as determined by the Kampala trauma score II (KTS II). Majorities 57.4% were admitted and 2.2% died at the casualty. Factors associated with mortality were; using police vehicles to hospital (P = 0.000), receiving medical attention within 2 to 10 hours after injury (P = 0.000), 18 - 45 years age group (P = 0.019), not using helmet (P = 0.007), severe injuries (P = 0.000) and sustaining multiple injury (P = 0.000).ConclusionRoad traffic Injuries in Tanzania are an important public health problem, predominantly in adult males, mostly due to motorcycle crashes. It is therefore important to reinforce preventive measures and pre-hospital emergency service is urgently needed.

DRIVER’S MENTAL WORKLOAD: TASK PERFORMANCE AND MENTAL WORKLOAD

It is known that unnecessary increase of driver’s mental workload may be a cause of road traffic crashes. In the recent decades, mental workload’s level detection has been one of the major interesting research subjects in today’s society. This paper discussed the relationship between driver’s performance and mental workload level. The results reveal that there is a significant effect on driving task performance to participants’ mental workload while performing different level of secondary task and driving task.

Traumatic spinal cord injury in Botswana: characteristics, aetiology and mortality.

Descriptive study with a cross-sectional design. To describe the epidemiology of traumatic spinal cord injuries (TSCIs) in Botswana, with a specific focus on road traffic crashes (RTC). Main public referral hospital, Gaborone, Botswana. Two samples were included. Sample one described the epidemiology and included patients admitted during a 2-year period with acute TSCI. Sample two included only patients with TSCI due to RTC. Annual incidence was 13 per million population. Epidemiology of TSCI: 49 patients were included, 71% male, age ranging from 4 to 81 years, 80% ⩽ 45 years. Tetraplegia was more common than paraplegia (59/41%), and 39% had C1-C4 level of injury. The main cause of TSCI was RTC (68%), followed by assault (16%) and falls (10%). Mortality was 20%, where all, but one, had tetraplegia (18%). Median time from injury to spinal surgery was 12 days, with longer time for women, 16 days compared with 8 for men. Burst tire was the primary cause of RTC resulting in a TSCI, followed by hitting animals on the road. The majority had been passengers and 72% were involved in single crashes. The most common cause for TSCI was RTC, followed by assault. In-hospital mortality was high and the waiting period from the time of accident to spinal surgery was long, especially for women. The aetiology and high mortality of TSCI in Botswana indicate that improvements in roadway safety and medical care may decrease the TSCI incidence and mortality.

Prevalence, knowledge, attitude and practice of speeding in two districts in Kenya: Thika and Naivasha

IntroductionIn Kenya, RTIs had the second highest increase in disability-adjusted life years between 1990 and 2010, compared to other conditions. This study aims to determine the prevalence, knowledge, attitudes and practices for speeding in Thika and Naivasha districts in Kenya. MethodsDirect observations of vehicle speed were conducted at various times during the day and different days of the week on six roads selected based on a multi-stage sampling method in two districts to determine the prevalence of speeding. Roadside KAP interviews were administered to drivers, at motorcycle bays, petrol stations, and rest areas. ResultsEight rounds of speed observations and four rounds of KAP interviews were conducted between July 2010 and November 2012. Results from the speeding observational studies show an overall high proportion of vehicles speeding above posted limits in both districts, with an average of 46.8% in Thika and 40.2% in Naivasha. Trend analysis revealed a greater decline in this prevalence in Thika (OR: 0.804, 95% CI: 0.793–0.814) than in Naivasha (OR: 0.932, 95% CI: 0.919–0.945) over the study period. On average, 58.8% of speeding vehicles in Thika and 57.2% of speeding vehicles in Naivasha travelled at 10 km/h or higher above speed limit. While the majority of respondents agreed that speeding is a cause of road traffic crashes in both Thika (70.3%) and Naivasha (68.7%), knowledge of speed limits at the location of the interview was limited. Enforcement levels also remained low, but subsequent rounds of data collection showed improvement, especially in Thika. ConclusionsThis study demonstrates an improvement in the prevalence of speeding in two districts of Kenya over 2010–2012. It also highlights the need for further action to be taken to address the problem, and represents new data on speeding in Kenya and Africa.

Road traffic crashes managed by Rescue 1122 in Lahore, Pakistan

The objective of this retrospective study was to describe demographic characteristics, injury patterns and causes of road traffic crashes (RTCs) managed by Rescue 1122 in Lahore, Pakistan during the period 2005–2010. In total 123,268 RTCs were reported and responded by Rescue 1122 ambulance service during the study period. Of the 132,504 victims of RTCs, there were 67% male and 33% female subjects, and the maximum share (65%) was reported among people aged 16–35 years. Motorcyclists were involved in 45% of crashes, with over-speeding (40%) found to be the major reason of these collisions. Similarly, minor injuries (65%) and fractures (25%) were the most reported outcome of these crashes. It is concluded that data from ambulance services, if appropriately collected, can provide valuable epidemiological information to monitor RTCs in developing countries. However, in Pakistan, the collection of data as well as the registration process needs further improvement.

Patterns of Injuries After Road Traffic Crashes Involving Bodabodas

Background Trauma is a leading cause of morbidity and mortality in Africa. Bodabodas are a main form of transport in Kampala and are becoming a major cause of road traffic crashes. We examined the pattern of injuries attributed to bodabodas. Patients and Methods We retrospectively reviewed the charts of all trauma patients who presented between June and August 2008 to the emergency ward of the Mulago hospital in Kampala and identified the patient characteristics and the nature of injuries sustained. Results Road Traffic Crashes (RTC’s) were the leading cause of trauma and bodabodas were involved in 41% of all trauma patients. Majority of the patients were young males below the age of 40. The commonest injuries were fractures, cranial trauma and soft tissue injuries. The lower limbs bore the brunt of the injuries. Conclusions Injuries seen as a result of bodabodas constitute a majority of trauma cases and are hence a pressing problem in need of urgent solutions. The relevant authorities should ensure preparedness of the health system to cater for the care of these patients.