To determine accurately the number of serious injuries at EU level and to compare serious injury rates between different countries it is essential to use a common definition. In January 2013, the High Level Group on Road Safety established the definition of serious injuries as patients with an injury level of MAIS3+(Maximum Abbreviated Injury Scale). Whatever the method used for estimating the number or serious injuries, at some point it is always necessary to use hospital records. The aim of this paper is to understand the implications for (1) in/exclusion criteria applied to case selection and (2) a methodological approach for converting ICD (International Classification of Diseases/Injuries) to MAIS codes, when estimating the number of road traffic serious injuries from hospital data. A descriptive analysis with hospital data from Spain and the Netherlands was carried out to examine the effect of certain choices concerning in- and exclusion criteria based on codes of the ICD9-CM and ICD10. The main parameters explored were: deaths before and after 30 days, readmissions, and external injury causes. Additionally, an analysis was done to explore the impact of using different conversion tools to derive MAIS3 + using data from Austria, Belgium, France, Germany, Netherlands, and Spain. Recommendations are given regarding the in/exclusion criteria and when there is incomplete data to ascertain a road injury, weighting factors could be used to correct data deviations and make more real estimations.

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Introduction Costs related to road crashes represent an important societal burden. Additionally they constitute an essential input variable to assess the cost efficiency of road safety measures. While most attention is usually spent on costs related to fatal crashes, this paper focuses on costs related to serious injuries. Method A review of these costs is presented based on different data sets and methods. Results A survey collecting crash cost estimates in European countries shows considerable variation in the costs related to serious injuries. The reported cost per serious injury varies between €28,205 and €975,074 and the total costs related to serious injuries vary between 0.04% and 2.7% of a country’s GDP. The applied methodology to estimate human costs appears to have a large influence. Other potential explanations are the applied definition for seriously injured victims, the registration procedure of crashes with serious injuries and the cost components that are included. Detailed analyses of medical costs and production loss that are based on country-specific datasets show the importance of assessing medical costs on the long term and taking into account the variation of these costs for different subgroups of traffic victims. A comparison of approaches to estimate monetary values for human costs shows that most countries use the Willingness To Pay method. While having a sound theoretical background, this method is rather limited in the specification of injuries. The use of Quality Adjusted Life Years gives the possibility to provide values for a larger diversity of injury types.

BackgroundElectrically assisted bicycles (e-bikes) have become increasingly popular and may facilitate active commuting and recreational cycling.ObjectiveTo evaluate the physical activity levels and usage characteristics of e-bikers and conventional cyclists under real-world conditions.MethodsWe conducted a prospective observational study in Germany to examine the effects of e-biking compared with conventional cycling on reaching the World Health Organization (WHO) target for physical activity—at least 150 min of moderate-to-vigorous physical activity (MVPA) per week. Study participants (1250 e-bikers and 629 conventional bike users) were equipped with activity trackers to assess the time, distance and heart rate during cycling over four consecutive weeks. Questionnaires were used to assess any traffic accidents incurred over 12 months.ResultsThe proportion of participants reaching 150 min of MVPA per week was higher for conventional bike users than for e-bike users (35.0% vs 22.4%, p<0.001). In a multiple regression model, the odds of reaching the physical activity target were lower for e-biking than for conventional biking (OR=0.56; 95% CI 0.43 to 0.72) with age, sex, comorbidities and bike usage patterns as confounding factors. No significant differences were observed between bike groups for traffic accidents, yet when controlled for cycling time and frequency of cycling e-bikers had a higher risk of a traffic accident (OR=1.63; 95% CI 1.02 to 2.58).ConclusionE-bikes are associated with a lower probability of reaching WHO targets for MVPA due to reduced duration and a reduced cardiovascular effort during riding. However, e-bikes might facilitate active transportation, particularly in older individuals or those with pre-existing conditions.