Predictive ability of underlying factors of motorcycle rider behavior: an application of logistic quantile regression for bounded outcomes

Abstract

Background: The human factors are of great importance, especially Motorcycle Rider BehaviorQuestionnaire (MRBQ) and attention deficit hyperactivity disorder (ADHD) in motorbike riders in road traffic injuries. This study aimed to predict MRBQ score by ADHD score and the underlying predictors by the logistic quantile regression (LQR), as a new strategy.Methods: In this cross-sectional study, 311 motorbike riders were randomly sampled by a clustering method in Bukan, northwest of Iran. The data were collected by MRBQ and ADHDstandard surveys. To assess the relationship at all levels of MRBQ distribution, LQR in 5th, 25th,50th, 75th and 95th quantiles of MRBQ score was utilized to assess the predictability of ADHDscore and its subscales in addition to the underlying predictors of MRBQ score. To do this, an unadjusted and as well as adjusted 4-step hierarchical modeling was used.Results: Almost in all quantiles of MRBQ scores, direct and significant relationships were observed between MRBQ score and ADHD score and its subscales (coefficients: 0.02 to 0.10, all P < 0.05). Besides, the driving period (coefficients: -0.58 to -0.95, P < 0.05) and hour driving(coefficients: 0.42 to 0.52, P < 0.05) also came to be the significant predictors of MRBQ score.Conclusion: ADHD score and driving parameters can be taken into the consideration when planning actions on the motorcycle rider behaviors at all levels of the MRBQ.

Keywords: Logistic quantile regression, Bounded outcomes, Motorcycle traffic accidents, ADHD, MRBQ