Reporting road victims: Assessing and correcting data issues through distinct injury scales |
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| Affiliation: | 1. Department of Civil Engineering, National Chi Nan University, No. 1 University Rd, Puli, Nantou County 54561, Taiwan;2. Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei City 110, Taiwan;1. Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;2. Tabriz Health Services Management Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;3. Department of Public Health Science, School of Health Sciences, Mid Sweden University, Sweden;1. Vietnamese-German Transport Research Centre (VGTRC), Vietnamese-German University, Binh Duong, Viet Nam;2. UHasselt, School of Transportation Sciences, Transportation Research Institute (IMOB), Agoralaan, 3590 Diepenbeek, Belgium;1. School of Transportation, Southeast University, China;2. Jiangsu Key Laboratory of Urban ITS, China;3. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China;4. Transport Strategy Centre, Imperial College London, UK |
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| Abstract: | IntroductionThe most common measurement for road accidents relies in police reports; however, there is a high portion of underreporting and misclassification, mainly concerning elderly causalities, urban accidents, slightly injured, users of two-wheeled vehicles, and car occupants.MethodsIn order to assess these issues, road accidents occurring in the Porto Metropolitan Area, Portugal, covering a 6-year period (2006–2011) were analyzed based on police and hospital datasets. By linking hospital data with police data, it is possible to evaluate the misclassification of the victims' severity by the police regarding the maximum abbreviated injury scale (MAIS) classification. Additionally, considering that 29% of the victims recorded by hospitals were not reported by the police, which is in line with the reality of other EU countries, underreporting is further investigated. Thus, we used econometric and statistics tools to measure the correlation between different available data to identify possible causes of underreporting and misclassification. In this sense, factors contributing to the misclassification of casualties by the police are identified using a univariate analysis. On the basis of the linked police–hospital data, and considering those factors and the police classification, a probabilistic model was developed to estimate a MAIS-based classification for all individuals included in the police accident records. Results: The results of misclassification indicate a significant over-classification of severe injury by the police. Additionally, a systematic police underreporting phenomenon of around 30% was found. Conclusions and Practical Applications: Finally, comparing estimated results and actual data, we were able to produce non-fatality adjustment coefficients to estimate the total casualties taking into account the underreporting and misclassification phenomena and to compare them with the Portuguese and European realities. |
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