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410 Motorcycle type, familarity and rider age: a conditinal process analysis
  1. Brown Julie1,
  2. Baldock Matthew2,
  3. Albanese Bianca1,
  4. Meredith Lauren1,
  5. Fitzharris Michael3,
  6. Robin Turner4
  1. 1NeuRA Injury Prevention Research Centre and UNSW NSW
  2. 2CASR, University of Adelaide
  3. 3MUARC, Monash University
  4. 4School of Public Health and Community Medicine, UNSW


Background Motorcyclists are vulnerable road users and with increasing registrations, the number of motorcyclists killed and injured continues to rise. Commonly reported risk factors for crash involvement include the type of motorcycle, the rider’s familiarity with the motorcycle and rider age. However, identifying potential risk factors is only the first step. To develop effective interventions, there is a need to understand how risk factors work together. This paper aims to examine the relationship between the type of motorcycle, the rider’s familiarity with the motorcycle and rider age as risk factors for crash involvement using a case control sample and conditional process analysis.

Methods A case control sample consisting of 100 seriously injured motorcyclists and 500 controls was collected in NSW, Australia between 2012 and 2014 using in-depth crash investigation and survey. Condiitonal process analysis was used to test a moderated mediation effect of key risk factors; motorcycle type (sports motercycle versus other), rider familiarity with the motorcycle (km ridden on the motorcycle) and rider age (years) on crash involvement while controlling for gender and most common type of riding (recreation versus other). This was acheived using the PROCESS macro in SAS that implements a series of regression analyses to estimate direct and indirect effects of the risk factors and interactions, as well as testing the significance of these effects.

Results Riders of sports motorcycles were more likely to be in the crash sample than those riding other types of motorcycles, however this effect is mediated by the rider’s familiarity with motorcycle. Furthermore, this indirect effect is moderated by rider age, with the effect being more pronounced in older riders.

Conclusions This analysis provides the first insight into how commonly reported risk factors related to motorcycle type, familiarity with a motorcycle and rider age work together. Specifically, this analysis identifies high priority targets for interventions aimed at mitigating crash risk through these risk factors.

  • motorcycles
  • risk factor
  • crashes

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