Burden of injury of serious road injuries in six EU countries.

BACKGROUND: Information about the burden of (non-fatal) road traffic injury is very useful to further improve road safety policy. Previous studies calculated the burden of injury in individual countries. This paper estimates and compares the burden of non-fatal serious road traffic injuries in six EU countries/regions: Austria, Belgium, England, The Netherlands, the Rhône region in France and Spain. METHODS: It is a cross-sectional study based on hospital discharge databases. POPULATION: of study are patients hospitalized with MAIS3+ due to road traffic injuries. The burden of injury (expressed in years lived with disability (YLD)) is calculated applying a method that is developed within the INTEGRIS study. The method assigns estimated disability information to the casualties using the EUROCOST injury classification. RESULTS: The average burden per MAIS3+ casualty varies between 2.4 YLD and 3.2 YLD per casualty. About 90% of the total burden of injury of MAIS3+ casualties is due to lifelong consequences that are experienced by 19% to 33% of the MAIS3+ casualties. Head injuries, spinal cord injuries and injuries to the lower extremities are responsible for more than 90% of the total burden of MAIS3+ road traffic injuries. Results per transport mode differ between the countries. Differences between countries are mainly due to differences in age distribution and in the distribution over EUROCOST injury groups of the casualties. CONCLUSION: The analyses presented in this paper can support further improvement of road safety policy. Countermeasures could for example be focused at reducing skull and brain injuries, spinal cord injuries and injuries to the lower extremities, as these injuries are responsible for more than 90% of the total burden of injury of MAIS3+ casualties.

Impact of alcohol checks and social norm on driving under the influence of alcohol (DUI).

This study investigated the influence of alcohol checks and social norm on self-reported driving under the influence of alcohol above the legal limit (DUI). The analysis was based on the responses of 12,507 car drivers from 19 European countries to the SARTRE-4 survey (2010). The data were analysed by means of a multiple logistic regression-model on two levels: (1) individual and (2) national level. On the individual level the results revealed that driving under the influence (DUI) was positively associated with male gender, young age (17-34), personal experience with alcohol checks, the perceived likelihood of being checked for alcohol, perceived drunk driving behaviour of friends (social norm) and was negatively associated with higher age (55+). On a national level, the results showed a negative association with a lower legal alcohol limit (BAC 0.2g/l compared with BAC 0.5g/l) and the percentage of drivers checked for alcohol. DUI was positively associated with the percentage of respondents in the country that reported that their friends drink and drive (social norm). The comparison of the results obtained on national and individual levels shows a paradoxical effect of alcohol checks: Countries with more alcohol checks show lower DUI (negative association) but respondents who have been personally checked for alcohol show a higher chance of DUI (positive association). Possible explanations of this paradox are discussed. The effects of the social norm variable (perceived drunk driving behaviour of friends) are positively associated with DUI on both levels.

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries.

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.

Multilevel analysis in road safety research.

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.

Comparing single vehicle and multivehicle fatal road crashes: a joint analysis of road conditions, time variables and driver characteristics.

The difference between single vehicle crashes and multivehicle crashes was investigated in a collection of fatal crashes from six European countries. Variables with respect to road conditions, time variables, and participant characteristics were studied separately at first and then jointly in a logistic multiple regression model allowing to weigh different accounts of single vehicle as opposed to multivehicle crash occurrence. The most important variables to differentiate between single and multivehicle crashes were traffic flow, the presence of a junction and the presence of a physical division between carriageways. Heavy good vehicles and motorcycles were less likely to be involved in single vehicle crashes than cars. Moreover crashes of impaired drivers with more passengers were more likely to be single vehicle crashes than those of other drivers. Young drivers, rural roads, nights and weekends were all shown to have a higher proportion of single vehicle crashes but in the multivariate analysis these effects were demonstrated to be mediated by the road conditions named above.

Estimation of fatality and injury risk by means of in-depth fatal accident investigation data.

OBJECTIVE: In this article the factors affecting fatality and injury risk of road users involved in fatal accidents are analyzed by means of in-depth accident investigation data, with emphasis on parameters not extensively explored in previous research. METHODS: A fatal accident investigation (FAI) database is used, which includes intermediate-level in-depth data for a harmonized representative sample of 1300 fatal accidents in 7 European countries. The FAI database offers improved potential for analysis, because it includes information on a number of variables that are seldom available, complete, or accurately recorded in road accident databases. However, the fact that only fatal accidents are examined requires for methodological adjustments, namely, the correction for two types of effects on a road user's baseline risk: "accident size" effects, and "relative vulnerability" effects. Fatality and injury risk can be then modeled through multilevel logistic regression models, which account for the hierarchical dependences of the road accident process. RESULTS: The results show that the baseline fatality risk of road users involved in fatal accidents decreases with accident size and increases with the vulnerability of the road user. On the contrary, accident size increases nonfatal injury risk of road users involved in fatal accidents. Other significant effects on fatality and injury risk in fatal accidents include road user age, vehicle type, speed limit, the chain of accident events, vehicle maneuver, and safety equipment. In particular, the presence and use of safety equipment such as seat belt, antilock braking system (ABS), and electronic stability program (ESP) are protection factors for car occupants, especially for those seated at the front seats. CONCLUSIONS: Although ABS and ESP systems are typically associated with positive effects on accident occurrence, the results of this research revealed significant related effects on accident severity as well. Moreover, accident consequences are more severe when the most harmful event of the accident occurs later within the accident chain.

Risk and protection factors in fatal accidents.

This paper aims at addressing the interest and appropriateness of performing accident severity analyses that are limited to fatal accident data. Two methodological issues are specifically discussed, namely the accident-size factors (the number of vehicles in the accident and their level of occupancy) and the comparability of the baseline risk. It is argued that - although these two issues are generally at play in accident severity analyses - their effects on, e.g., the estimation of survival probability, are exacerbated if the analysis is limited to fatal accident data. As a solution, it is recommended to control for these effects by (1) including accident-size indicators in the model, (2) focusing on different sub-groups of road-users while specifying the type of opponent in the model, so as to ensure that comparable baseline risks are worked with. These recommendations are applied in order to investigate risk and protection factors of car occupants involved in fatal accidents using data from a recently set up European Fatal Accident Investigation database (Reed and Morris, 2009). The results confirm that the estimated survival probability is affected by accident-size factors and by type of opponent. The car occupants' survival chances are negatively associated with their own age and that of their vehicle. The survival chances are also lower when seatbelt is not used. Front damage, as compared to other damaged car areas, appears to be associated with increased survival probability, but mostly in the case in which the accident opponent was another car. The interest of further investigating accident-size factors and opponent effects in fatal accidents is discussed.