Improving knowledge of cyclist crashes based on hospital data including crash descriptions from open text fields

In this study the authors explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. The authors also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period. The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. The authors performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories: bicyclist, road, bicycle, and the other party. The authors employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash. The open-ended text fields in hospital records provide detailed information about crash factors not available in police records, including riding speed, inattention, clothing, specific road conditions, and bicycle defects. The factors alcohol and curb had the highest odds of being identified in relation to a single-bicycle crash. Crash data registered at the hospital included a larger number of bicycle crashes, particularly single-bicycle crashes and crashes with slight injuries only. Crash information registered at the hospital in Aarhus Municipality contributes to a better understanding of bicycle crashes due to detailed information about crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication: Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.


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  • Accession Number: 01762154
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 24 2020 3:13PM