Investigation of predictors of injury leading to motorcyclist hospitalization: A case-control study

Background and Aim: Traffic and its related problems are among the most important public health concerns in the world and prevention of these problems are necessary. GSEM model is a method to test of theoretical models and causal modeling exactly. The objective of the present study was to investigate the predictors of injuries leading to hospitalization of motorcyclists considering MRBQ as a mediator in this case-control study, using a generalized structural equation modeling (GSEM). Methods: In this case-control study, we selected 303 cases (motorcyclists admitted for a traumatic condition) and 153 controls (motorcyclists admitted for a non-traumatic condition) using a cluster random sampling method in Tabriz, Iran. We used motorcycle-riding behavior questionnaire (MRBQ), Attention-deficit/hyperactivity disorder (ADHD) questionnaire, and a researcher-made checklist. GSEM model was used to examine the direct linear and indirect linear relationships of variables in the conceptual model, considering the binary response variable of the model. Data analysis was performed by STATA14 software. Results: The predictors of injury were: MRBQ, ADHD, and demographic charcteristics. The results indicated significant linear and direct relationships between odds of injury and cell phone answering (OR= 2.22, P= 0.010), hyperactive child (OR= 1.65, P= 0.057), dark hour riding (OR= 1.01, P= 0.001) and MRBQ (OR= 1.27, P= 0.092), while there were significant inverse relationships between injury and marital status (OR= 0.43, P= 0.002), and academic education (OR= 0.29, P= 0.001). Conclusions: According to the results of our study, intervention programs on the ADHD, use of cell phone while driving, and dark hour riding are highly recommended.