Uncovering road traffic crashes typologies using multiple correspondence analysis (MCA), in a low-resource setting

Abstract

The main focus of this study was to investigate self-reported road traffic crashes (RTC) among drivers from Loja Ecuador and to illustrate the applicability of multiple correspondence analysis (MCA) in detecting and representing typologies of individuals who have experienced an RTC as well as the typology of RTC events, upon information collected through a web-based survey carried in 2021. Overall, 754 drivers were investigated and we estimated a life prevalence (LP) of RTC of 41.5% (95% CI 36.9 to 46.2). Typology of drivers who reported involvement in an RTC is characterized by a predominance of people of 25 to 40 years of age, who drive mainly cars and frequently experience distraction and use of a mobile phone when driving. Additionally, MCA indicated two distinctive typologies of RTC events. One is characterized by collision vehicle-to-vehicle, due to behavioral factors, occurring at low-speed limit roads during the afternoon.  The second one is characterized by collision vehicle-to-surrounding occurring at medium-speed limit roads during the evening and late evening. Our study revealed major determinants of RTC are modifiable behavioral factors and that MCA is both a valid exploratory tool to identify individual and event typologies of those under at most risk to suffer an RTC and a feasible technique to be implemented in low-income countries such as Ecuador.