Search

Considering the high burden of injuries, the Health Authority - Abu Dhabi developed a draft electronic and paper-based injury and poisoning notification system (IPNS) to generate better data on the nature and severity of injuries. The pilot testing and evaluation of IPNS was conducted with the specific objectives to (1) identify the characteristics of injury cases, (2) explore potential risk factors, (3) illustrate the nature and type of data, and (4) the working mechanism of data collection. Data were collected from selected hospitals on patient demographics, injury information and clinical assessment. Descriptive, bivariate and multivariate analyses were conducted. Of 4226 injury cases, nearly three-fourths were male, majority were non-UAE nationals, and the mean age was 21.9. Multivariate findings suggested that compared to UAE nationals, non-UAE nationals were 27% more likely to experience fatal, severe or moderate injuries (p= 0.01). Individuals with health insurance were 31% less likely to suffer a fatal, severe or moderate injury compared to those having no health insurance (p< 0.001). This is the first systematically standardised collection of injury data across three facilities in Abu Dhabi, and provides initial information on characteristics and injury risk factors that will help identify the need for evidence-based intervention for injury prevention and control. [ABSTRACT FROM PUBLISHER]

The recent advancements in the field of data mining have made vast progress in extracting new information and hidden patterns from large datasets which are often overlooked by the traditional statistical approaches. These methods focus on searching for new and interesting hypothesis which were previously unobserved. Road safety researchers working with the crash data from developed world have seen encouraging success in obtaining new insight into crash mechanism through data mining. An attempt was made in this study to apply these advance methods and evaluate their performance in manifesting crash causes for Bangladesh. The study applies hierarchical clustering to identify hazardous clusters, random forest to find important variables explaining each of these clusters, and classification and regression trees to unveil their respective crash mechanisms for the road crash data of Bangladesh. The results identified several new interesting relationships and acknowledged issues related to quality of data. [ABSTRACT FROM AUTHOR]

As road traffic accidents (RTAs) cause enormous economic and human losses, especially in developing countries, numerous research efforts are needed to identify the key risk factors that significantly influence accident and crash severity. Despite that Dhaka city is registering alarming rises in related deaths and severe injuries, Bangladesh has yet to collect significant RTAs data. Thus, this study adopts probit and Heckman selection probit models to investigate RTAs and injury severity levels using original data from an on-field survey collecting 786 participants' responses regarding their socio-economic and demographic characteristics, their knowledge of road traffic systems and rules, the roads and vehicles types, and the road infrastructure conditions. Probit model showed that the major risk factors that increase road accidents causing severe injuries were wrong-way driving, and lack of speed control signs and adequate street lights. RTAs resulting in severe injuries were significantly associated with being married, not having an educational degree, driving on highways and in city areas. Furthermore, the Heckman probit model's selection equation showed that respondents who were unaware of road accident risks, resided in rural areas, and with high household income had higher risks of being directly involved in RTAs. [ABSTRACT FROM AUTHOR]

Dhaka, the capital and megacity of the developing country Bangladesh, has experienced a sharp rise in motorcycle users in the last decade, especially after the introduction of ridesharing services. Therefore, the morbidity and mortality rates of motorcycle crash injuries have also increased and become one of the major safety concerns. However, there is scant empirical evidence on motorcycle crash severity in the context of developing countries. Hence, this study was conducted to identify the factors that influenced the severity of motorcycle crashes in Dhaka. A binary logistic regression model was developed using motorcycle crash data of Dhaka over the period of 2006–2015 to identify the contributing factors of motorcycle crash severity. The model output showed that eleven factors significantly increased the probability of fatal motorcycle crashes. These factors were crashes occurring on weekends, during the rainy season, during dawn and night period, at non-intersections, on straight and flat roads, on highways, hit pedestrian type crashes, crashes involving motorcycles with no defect, crashes with heavier vehicles, crashes involving motorcyclists not wearing helmets, and drivers with alcohol suspicion. These findings would help to formulate prevention strategies to reduce the injury severity of motorcycle crashes in the developing countries. [ABSTRACT FROM AUTHOR]

Although the rate of road crashes and their severity is relatively higher in developing countries, there is still a lack of research on pedestrian-vehicle crash severity in these contexts, particularly in Bangladesh. Therefore, this study aimed to identify the contributing environmental, road, and vehicular factors that influenced pedestrian-single-vehicle crash severity in Dhaka, a megacity and the capital of Bangladesh. A binary logistic regression model was developed in this study by analyzing a data set of pedestrian-single-vehicle crashes involving casualties in Dhaka from 2010 to 2015. The model identified seven significant factors influencing pedestrian-vehicle crash severity. Significant factors increasing the likelihood of fatal crashes included crashes during adverse weather, dawn/dusk period, night period (where street light was absent), off-peak period, crashes where road divider was unavailable, road geometry was straight and flat, and crashes those were occurred by heavier vehicles. Besides, crashes at three-legged intersections were less likely to be fatal. Both similarities and differences were found among the significant factors influencing pedestrian-vehicle crash severity in Dhaka from the findings of the developed countries. The findings of this study would help transport engineers and planners to design safer roadways for both pedestrians and vehicles. [ABSTRACT FROM AUTHOR]