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Description of road traffic related knee injuries in published investigations is very heterogeneous. The purpose of this study was to estimate the risk of knee injuries in real world car impacts in Germany focusing vulnerable road users (pedestrians, bicyclists and motorcyclists) and restrained car drivers. The accident research unit analyses technical and medical data collected shortly after the accident at scene. Two different periods (years 1985-1993 and 1995-2003) were compared focusing on knee injuries (Abbreviated Injury Scale (AISKnee) 2/3). In order to determine the influences type of collision, direction and speed as well as the injury pattern and different injury scores (AIS, MAIS, ISS) were examined. 1.794 pedestrians, 742 motorcyclists, 2.728 bicyclists and 1.116 car drivers were extracted. 2% had serious ligamentous or bony injuries in relation to all injured. The risk of injury is higher for twowheelers than for pedestrians, but knee injury severity is higher for the latter group. Overall the current knee injury risk is low and significant reduced comparing both time periods (27%, p<0,0001). Severe injuries (AISKnee 2/3) were below 1%). Improved aerodynamic design of car fronts reduced the risk for severe knee injuries significantly (p=0,0015). Highest risk of injury is for motorcycle followed by pedestrians, respectively. Knee protectors could prevent injuries by reducing local forces. The classically described dashboard injury was rarely identified. The overall injury risk for knee injuries in road traffic is lower than estimated and reduced comparing both periods. The aerodynamic shape of current cars compared to older types reduced the incidence and severity of knee injuries. Further modification and optimization of the interior and exterior design could be a proper measurement. Classic described injury mechanisms were rarely identified. It seems that the AIS is still underestimating extremity injuries and their long term results.
In the context of this study, different data sources for accident research were examined regarding their possible data access and evaluated concerning the individual quality and extent of the data. Analyses of accidents require detailed and comprehensive information in particular concerning vehicle damages, injury patterns and descriptions of the accident sequence. The police documentation supplies the basic accident statistics and is amended in the context of the forensic treatment by further information, e.g. by medical and technical appraisals and witness questionings. As a new approach to the data acquisition for the analysis of fatal traffic accidents, the information was made usable which was collected by the police and by the investigations of the public prosecutor. The best strategy for obtaining reliable, extensive and complete data consists of combining the information from these two sources: the very complete, but elementary statistic data of the Niedersächsisches Landesamt für Statistik (Lower Saxony State Authority of Statistics), based on the police documentation as well as the very extensive accident information resulting from the investigation documentation of the public prosecutor after conclusion of the procedure, the so-called Court Records. Of all 715 fatal traffic accidents, which happened in the year 2003 in the German State of Lower Saxony, 238 cases were selected by means of a statistically coincidental selective procedure based on a statistically representative manner (every third accident). These cases cover the investigation documents of the 11 responsible public prosecutor- offices, which were requested and evaluated while preserving the data security. Of the 238 cases 202 cases were available, which were individually coded and stored in a data base using 160 variables. Thus a data base of a sample of representative data for fatal accidents in Lower Saxony was set up. The data base contains extensive information concerning general accident data (35 variables), concerning road and road surface data (30 variables), concerning vehicle-specific data (68 variables) as well as concerning personal and injury data (27 variables).