From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions. (November 2021)

• Record Type: Journal Article
• Authors: Rabelo-da-Ponte, Francisco Diego; Scherer, Juliana Nichterwitz; Roglio, Vinicius; Borges, Eduardo Nunes; Galland, Fabiana; Sousa, Tanara; Passos, Ives Cavalcante; von Diemen, Lisia; Kessler, Felix; Pechansky, Flavio
• Is Part Of: Journal of psychiatric research. Volume 143(2021)
• Page Start: 556
• Page End: 562
• Publication Date: 2021-11
• Subjects: Driving under influence -- Driving behavior -- Drug use; Psychiatry -- Periodicals; Mental Disorders -- Periodicals; Maladies mentales -- Périodiques; Psychiatry; Electronic journals; Periodicals; 616.89005
• DOI: 10.1016/j.jpsychires.2020.11.025
• Languages: English
• ISSNs: 0022-3956

Abstract

Abstract: Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed to identify clusters of driving risk behavior (DRB) among car drivers, and to verify intra-cluster differences concerning clinical and sociodemographic variables. We approached a total of 12, 231 drivers and we included 6392 car drivers. A cluster algorithm was used to identify groups of car drivers in relation to the DRB: driving without a seat belt (SB), exceeding the speed limit (SPD), using a cell phone while driving (CELL), and driving after drinking alcohol (DUI). The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000–5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic.