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Abstract
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OBJECTIVE: Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, named the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score.
METHOD: In this study, the MAIS score was derived from the International Classification of Diseases. The EU adopted a MAIS score equal to or greater than three as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff, and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital datasets from the Porto metropolitan area of Portugal over a 6-year period (2006-2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyse their impact on the injury severity classified according to LHS.
RESULTS: This study showed the impact of variables, such as the presence of blood-alcohol, the use of protection devices, the type of crash and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analysed as risk factors, showing that the elderly and male road users are highly associated with MAIS3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the impact magnitude of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification.
CONCLUSIONS: The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.
Language: en |