Socio-demographic determinants of motorcycle speeding in Maha Sarakham, Thailand

• Published in: PloS one Vol. 15; no. 12; p. e0243930
• Main Authors: Hong, Vennis, Iwamoto, Sage K, Goto, Rei, Young, Sean, Chomduangthip, Sukhawadee, Weeranakin, Natirath, Nishi, Akihiro
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.12.2020; Public Library of Science (PLoS)
• Subjects: Accidents, Traffic - prevention & control; Accidents, Traffic - psychology; Adolescent; Automobile Driving - psychology; Behavior; Causes of; Computer and Information Sciences; Crashes; Demographic aspects; Demographics; Discount rates; Engineering and Technology; Fatalities; Female; Gender; Health aspects; Health behavior; Health risks; Humans; Male; Mediation; Medicine and Health Sciences; Motorcycles; Motorcycling; People and Places; Physical Sciences; Public health; Research and Analysis Methods; Risk Factors; Risk-Taking; Risk-taking (Psychology); Roads; Self Report; Sex Factors; Social classes; Social Networking; Social networks; Social organization; Social Sciences; Sociodemographics; Socioeconomic factors; Socioeconomic status; Socioeconomics; Speed limits; Thailand; Traffic; Traffic accidents; Traffic accidents & safety; Traffic safety; Traffic speed; Universities; Young Adult; Thailand; Los Angeles California; Maldives; United States > US; China; California; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0243930

Thailand has the highest road traffic fatality rate in Southeast Asia, making road safety a critical public health concern. A 2015 World Health Organization (WHO) Report showed that speeding behavior was the most important determinant for road traffic crashes in Thailand. Here, we aimed to examine associations of socio-demographic factors (gender, age, socioeconomic status) with self-reported motorcycle speeding behavior. Additionally, we examined a potential role of time discounting and risk preference as mediators in the association of socio-demographic factors with speeding. We used data obtained from the Mahasarakham University Social Network Survey 2018 (MSUSSS) (N = 150). We ran linear network autocorrelation models (lnam) to account for the data's social network structure. We found that males are more likely than females to engage in speeding behavior (β = 0.140, p = 0.001) and to discount the future (β = 5.175, p = 0.017). However, further causal mediation analysis showed that time discounting does not mediate the gender-speeding association (p for mediation = 0.540). Although socioeconomic status (subjective social class) was not associated with speeding (β = 0.039, p = 0.177), age was marginally associated with speeding (β = 0.005, p = 0.093). Future studies may consider using a larger sample.