Predictive Factors for Fatality After Traumatic Brain Injury Among Road Traffic Crash Victims in Addis Ababa City, Ethiopia

Background Traumatic brain injury (TBI) is one of the common preventable causes of mortality and disability among road traffic victims worldwide, most especially in low- and middle-income countries, including Ethiopia. Objective to determine risk factors of mortality after traumatic brain injury due to road traffic crash. Methods This study aimed to examine the predictive factors of short-term mortality after severe brain injury due to a road traffic crash. The study was done on a prospective cohort of 242 severely brain-injured patients selected using cluster sampling in Addis Ababa City hospitals. The study was conducted from February 2018 to November 2019. Data were collected from brain-injured patients using a questionnaire and recorded findings within the first 24 hours of admission, Survival Analysis was used for statistical analysis. Ethical clearance was obtained from the Addis Ababa University, College of Health Sciences Institutional Review Board (IRB). Confidentiality of information about injured patients was maintained. Results In this study, the death rate was 73(30.2%). The majority of TBI patients accounting for, 186(81%) were men. The median age of TBI patients was 29 years. The hazard for those patients with subnormal body temperature was 1.64 times that of normal temperature (AHR: 1.64; CI: 2.14-10.29). The estimated fatality hazard ratio for patients who experienced Glasgow Coma Scale (GCS)below six was 5.61 times higher compared to GCS six to eight (CI:3.1-10.24). Conclusion In conclusion, there was high early mortality of patients (30.2%) in Ethiopia. Being men, young and lower GCS were associated with higher mortality hazards. Hence, optimum advanced neuro-surgical pre-hospital care programs are urgently needed.

Analysis of Pedestrian-Vehicle Crashes Using Artificial Learning Methods: City of Sakarya Case Study

The lives of approximately 1.3 million people are cut short every year as a result of road traffic crashes. Between 20 and 50 million people suffer non-fatal injuries, with many incurring a disability as a result of their injury. The risk of dying in a road traffic crash is more than 3 times higher in low-income countries than in high-income countries [1]. In Turkey, 18% of traffic accidents was related to pedestrian-vehicle collisions in urban roads in 2020. In addition, 20% of death toll caused by accidents is pedestrians in 2020 [2]. This study deals with the some of classifiers to forecast the number of injuries as a result of traffic accidents. The classifier’s performance ratios were also examined.

Paediatric Trauma Burden in Tanzania: Analysis of Prospective Registry Data from Thirteen Health Facilities

BackgroundTrauma is among the leading causes of morbidity and mortality among paediatric and adolescent populations worldwide, with over ninety percent of childhood injuries occurring in low-income and middle-income countries. Lack of region-specific data on paediatric injuries is among the major challenges limiting ability to implement interventions to prevent injuries and improve outcomes. We aimed to characterise the burden of paediatric injuries seen at thirteen diverse health facilities in Tanzania.MethodsThis was a prospective descriptive cohort study of children aged up to 18 years, and presenting to emergency units (EUs) of thirteen multi-level health facilities in Tanzania from 1st October 2019 to 30th September 2020. We describe injury patterns, mechanisms and early interventions performed at the emergency units of these health facilities.ResultsAmong 18,553 trauma patients seen in all thirteen-health facilities, 4368 (23.5%) were children, of whom 2894 (66.7%) were male. The overall median age was 8 years (Interquartile range 4-12 years). Fall 1592 (36.5%) and Road Traffic Crash (RTC) 840 (19.2%) were the top mechanisms of injury. Most patients 3748 (85.8%) arrived at EU directly from the injury site, using motorized (two or three) wheeled vehicles 2401 (55%). At EU 651 (14.9%) were triaged as an emergency category. Multiple superficial injuries (14.4%), fracture of forearm (11.7%), and open wounds (11.1%) were the top EU diagnoses, while 223 (5.2%) had intracranial injuries. Children aged 0-4 years had the highest proportion (16.3%) of burn injuries. Being referred, and being triaged as an emergency category was associated with high likelihood of serious injuries with risk ratio 3.3 (95%CI 2.7-4.0) and 2.3 (95% CI 2.0-2.8) respectively. 1095 (25.1%) of patients were admitted to inpatient care and 25 (0.6%) died in the EU.ConclusionsIn these multilevel health facilities in Tanzania, paediatric injuries accounted for nearly one-quarter of all injuries. Over half of injuries occurred at home. Fall from height was the leading mechanism of injury, followed by RTC. Most patients sustained fractures of extremities. Future studies of paediatric injuries should focus on evaluating various preventive strategies that can be instituted at home to reduce the incidence and associated impact of such injuries.

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

Background 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. Objective The study was conducted to assess the magnitude of road traffic crash fractures and visceral injuries. Methods 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. Results 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. Conclusion 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.

Severity, Spatial Pattern and Statistical Analysis of Road Traffic Crash Hot Spots in Ethiopia

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.

Characteristics and Injury Patterns of Road Traffic Injuries in Urban and Rural Uganda—A Retrospective Medical Record Review Study in Two Hospitals

In the ongoing Second Decade of Action for Road Safety, road traffic crashes pose a considerable threat especially in low-income countries. Uganda shows a vast burden of non-fatal injuries and resides at the top range of countries with the highest death rates due to unsafe roads. However, little is known about the differences in road traffic associated injuries between urban and rural areas and potential influence factors. Here, we used a cross-sectional study conducted by a retrospective medical record review from trauma cases admitted in 2016 to hospitals in rural and urban areas in Uganda. Injury severity scores were calculated and descriptive analysis was carried out while multivariate logistic regression was applied to assess significant covariates. According to the 1683 medical records reviewed, the mean age of trauma patients in the dataset under investigation was 30.8 years with 74% male. The trauma in-hospital mortality was 4% while prevalence of traumatic injuries is 56.4%. Motorcycle users (49.6%) and pedestrians (33.7%) were identified as the most vulnerable groups in both urban and rural setting while mild injuries of extremities (61.6%) and the head/neck-region (42.0%) were registered most. The frequency of road traffic injuries was homogenous in the urban and rural hospitals investigated in this study; interventions should therefore be intensified ubiquitously. The identification of significant differences in road traffic crash and injury characteristics provides the opportunity for specific programmes to decrease the socio-economic and health burden of unsafe roads. In addition to law enforcement and introduction of a Systems Thinking approach to road safety including infrastructural and educational concepts, the strengthening of trauma care and health resources is recommended.

Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia

Despite an improvement in worldwide numbers, road traffic crashes still cause social, psychological, and financial damage and cost most countries 3% of their gross domestic product. However, none of the current commercial or open-source navigation systems contain spatial information about road traffic crash hot spots. By developing an algorithm that can adequately predict such spatial patterns, we can bridge these still existing gaps in road traffic safety. To that end, geographically weighted regression and regression tree models were fitted with five uncorrelated (environmental and socioeconomic) road traffic crash predictor variables. Significant regional differences in adverse weather conditions were identified; Slovenia lies at the conjunction of different climatic zones characterized by differences in weather phenomena, which further modify traffic safety. Thus, more attention to speed limits, safety distance, and other vehicles entering and leaving the system could be expected. In order to further improve road safety and better implement globally sustainable development goals, studies with applicative solutions are urgently needed. Modern vehicle-to-vehicle communication technologies could soon support drivers with real-time traffic data and thus potentially prevent road network crashes.

The Analysis of Road Traffic Fatality Pattern for Selangor, Malaysia Case Study

Road traffic fatality is a burden towards low- and middle-income countries including Malaysia. Seeing that Selangor has the highest number of road traffic fatalities in Malaysia for the year 2019, therefore the state is selected as a case study. The aim of the article is 1) to understand the road traffic crash pattern and road traffic fatality pattern in Selangor 2) to determine the ability of 16 road traffic features in classifying road traffic fatality occurrence. The preliminary data screening shows that road traffic crash patterns and road traffic fatality patterns in Selangor have many similarities. However, both of them also have few dissimilarities such as crash time of occurrence, day of occurrence, number of vehicles involved in a crash, and type of vehicle first hit for the crash. Supervised machine learning algorithm in Orange data mining software was considered in this analysis. The analysed algorithms among others are neural network, random forest, decision tree, logistic regression, naïve Bayes, and support vector machine. Neural network was seen as the best algorithm to classify road traffic fatality occurrence with 97.0% classification accuracy outperform other algorithms. The result of the article can be used by the relevant traffic stakeholders to execute safety intervention in a more focused manner in Selangor to reduce the number of road traffic fatalities.