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

Francisco Diego Rabelo-da-Ponte; Juliana Nichterwitz Scherer; Vinicius Roglio; Eduardo Nunes Borges; Fabiana Galland; Tanara Sousa; Ives Cavalcante Passos; Lisia von Diemen; Felix Kessler; Flavio Pechansky

doi:10.1016/j.jpsychires.2020.11.025

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

J Psychiatr Res. 2021 Nov:143:556-562. doi: 10.1016/j.jpsychires.2020.11.025. Epub 2020 Nov 12.

Affiliations

¹ Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil; Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, R. Ramiro Barcelos, 2350, 90035-007, Porto Alegre, RS, Brazil. Electronic address: fdponte@hcpa.edu.br.
² Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil; Program in Collective Health, Universidade Do Vale Do Rio Dos Sinos, Unisinos Avenue, 950, 93022750, São Leopoldo, RS, Brazil.
³ Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil.
⁴ Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil; Center for Computational Sciences, Federal University of Rio Grande, Av. Itália, S/n - Km 8, Rio Grande, RS, Brazil.
⁵ Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil; Health Economics Unit, Centre for Health Policy, University of Melbourne, Level 4, 207 Bouverie Street, 3010, Melbourne, VIC, Australia.
⁶ Center for Drug and Alcohol Research, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, Rua Professor Álvaro Alvim, 400, 90420-020, Porto Alegre, RS, Brazil; Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande Do Sul, R. Ramiro Barcelos, 2350, 90035-007, Porto Alegre, RS, Brazil.

PMID: 33218750
DOI: 10.1016/j.jpsychires.2020.11.025

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.

Keywords: Driving behavior; Driving under influence; Drug use.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic*
Alcohol Drinking
Automobile Driving*
Ethanol
Humans
Male
Risk-Taking

Substances

Ethanol