Road Traffic Database Research Articles

A Support Vector Machine Approach in Predicting Road Traffic Mortality in Malaysia


 
 
 The traffic mortality rate is the baseline through which road safety plans of a country could be evaluated. A reliable and reasonable analysis of road traffic-related injuries and their leading causes is vital to the road safety investigation, evaluation as well as policymaking. Malaysia has the third highest fatality rate from road traffic accidents in Asia as well as in Southeast Asia. Although many researchers have attempted to provide predictive models of Road Traffic Mortality (RTM) in the country, the predictions are found to be rather unsatisfactory in forecasting the causes as well as the future road fatality. It is hypothesized that the inability of the previous models to provide a good prediction of the RTM might be attributed to the complicated and non-linear data relationship of the underlying causes of road traffic accidents. A Support Vector Machine (SVM) is demonstrated to be effective in solving both classifications as well as regression problems owing to its efficacy to cater for the non-linear relationship of a dataset. The present investigation proposed the application of SVM based model variations namely the Linear, Quadratic, Cubic, Fine, Medium, as well as Coarse Gaussian-based SVM in predicting the RTM. A dataset from 1972 to 1994 was obtained from the Malaysian road traffic database. The data were trained on the SVM model variations. It was demonstrated that the Linear-based SVM model can provide a reasonable prediction of the RTM with only a 12 % error. It is, therefore, inferred that a reasonable prediction of RTM in Malaysia could be achieved through the employment of non-conventional statistical techniques.
 
 

Cost-effectiveness of mandatory bicycle helmet use to prevent traumatic brain injuries and death

BackgroundTraumatic brain injury (TBI) is the main cause of mortality and severe morbidity in cyclists admitted to Dutch emergency departments (EDs). Although the use of bicycle helmets is an effective way of preventing TBI, this is uncommon in the Netherlands. An option to increase its use is through a legal enforcement. However, little is known about the cost-effectiveness of such mandatory use of helmets in the Dutch context.The current study aimed to assess the cost-effectiveness of a law that enforces helmet use to reduce TBI and TBI-related mortality.MethodsThe cost-effectiveness was estimated through decision tree modelling. In this study, wearing bicycle helmets enforced by law was compared with the current situation of infrequent voluntary helmet use. The total Dutch cycling population, consisting of 13.5 million people, was included in the model. Model data and parameters were obtained from Statistics Netherlands, the National Road Traffic Database, Dutch Injury Surveillance System, and literature. Effects included were numbers of TBI, death, and disability-adjusted life years (DALY). Costs included were healthcare costs, costs of productivity losses, and helmet costs. Sensitivity analysis was performed to assess which parameter had the largest influence on the incremental cost-effectiveness ratio (ICER).ResultsThe intervention would lead to an estimated reduction of 2942 cases of TBI and 46 deaths.Overall, the incremental costs per 1) death averted, 2) per TBI averted, and 3) per DALY averted were estimated at 1) € 2,002,766, 2) € 31,028 and 3) € 28,465, respectively. Most favorable were the incremental costs per DALY in the 65+ age group: € 17,775.ConclusionsThe overall costs per DALY averted surpassed the Dutch willingness to pay threshold value of € 20,000 for cost-effectiveness of preventive interventions. However, the cost per DALY averted for the elderly was below this threshold, indicating that in this age group largest effects can be reached. If the price of a helmet would reduce by 20%, which is non-hypothetical in a situation of large-scale purchases and use of these helmets, the introduction of this regulation would result in an intervention that is almost cost-effective in all age groups.

Qualifying information on the deaths and serious injuries caused by road traffic in five Brazilian capitals using record linkage

IntroductionRoad traffic crashes (RTCs) represent an public health problem, accounting for 1.2 million deaths per year. However, these figures may be even greater if health databases are linked to police records.
 Objectives and ApproachMeasure the numbers of deaths and seriously injured victims and to estimate the percentage of corrections of the underlying cause of death, cause of hospitalization, and injury severity of the victims in five representative state capitals of the five macro-regions of Brazil. This was cross-sectional, study using the Hospital Information System, Mortality Information System and Police Road Traffic database. The RecLink III was used for the record linkage by identifying true pairs to calculate the correction percentage of the underlying cause of death, cause of hospitalization, and injury severity of the victims in the traffic records.
 ResultsChange in the diagnosis of hospitalization in the Hospital Information System with percentage of correction of the cause at 24.4% for Belo Horizonte, 96.9% for Campo Grande, 100.0% for Palmas, and 33.2% for Teresina. The correction of the underlying cause of death in the Mortality Information System were 29.9%, 11.9%, 4.2%, and 33.5% for Belo Horizonte, Campo Grande, Curitiba, and Teresina, respectively. Change in the classification of injury severity was observed with an overall percentage of 100.0% for Belo Horizonte and Teresina, 48.0% for Campo Grande, and 51.4% for Palmas.
 Conclusion/ImplicationsThe results showed the importance of the record linkage between health and police databases for the qualification of the RTCs and the victims in the five capital cities studied. This implies that deaths and serious injuries caused by RTCs are underestimated in the absence of integration of databases that record

Qualifying information on deaths and serious injuries caused by road traffic in five Brazilian capitals using record linkage

IntroductionRoad traffic crashes (RTC) are an important public health problem, accounting for 1.2 million deaths per year worldwide. In Brazil, approximately 40,000 deaths caused by RTC occur every year, with different trends in the Federal Units. However, these figures may be even greater if health databases are linked to police records. In addition, the linkage procedure would make it possible to qualify information from the health and police databases, improving the quality of the data regarding underlying cause of death, cause of injury in hospital records, and injury severity. ObjectiveThis study linked different data sources to measure the numbers of deaths and serious injuries and to estimate the percentage of corrections regarding the underlying cause of death, cause of injury, and the severity injury in victims in matched pairs from record linkage in five representative state capitals of the five macro-regions of Brazil. MethodsThis cross-sectional, population-based study used data from the Hospital Information System (HIS), Mortality Information System (MIS), and Police Road Traffic database of Belo Horizonte, Campo Grande, Curitiba, Palmas, and Teresina, for the year 2013 for Teresina, and 2012 for the other capitals. RecLink III was used to perform probabilistic record linkage by identifying matched pairs to calculate the global correction percentage of the underlying cause of death, the circumstance that caused the road traffic injury, and the injury severity of the victims in the police database. ResultsThere was a change in the cause of injury in the HIS, with an overall percentage of correction estimated at 24.4% for Belo Horizonte, 96.9% for Campo Grande, 100.0% for Palmas, and 33.2% for Teresina. The overall percentages of correction of the underlying cause of death in the MIS were 29.9%, 11.9%, 4.2%, and 33.5% for Belo Horizonte, Campo Grande, Curitiba, and Teresina, respectively. The correction of the classification of injury severity in police database were 100.0% for Belo Horizonte and Teresina, 48.0% for Campo Grande, and 51.4% for Palmas after linkage with hospital database. The linkage between mortality and police database found a percentage of correction of 29.5%, 52.3%, 4.4%, 74.3 and 72.9% for Belo Horizonte, Campo Grande, Palmas, Curitiba and Teresina, respectively in the police records. ConclusionsThe results showed the importance of linking records of the health and police databases for estimating the quality of data on road traffic injuries and the victims in the five capital cities studied. The true causes of death and degrees of severity of the injuries caused by RTC are underestimated in the absence of integration of health and police databases. Thus, it is necessary to define national rules and standards of integration between health and traffic databases in national and state levels in Brazil.