Exploring the Causal Impact of Age and Nighttime Driving on Road Traffic Injuries among Elderly Drivers: A Bayesian LASSO Approach

Abstract

Objective: To determine the causal relationship between aging and nighttime driving and the odds of injuryamong elderly drivers.Methods: In this cross-sectional study, 5460 car accidents were investigated from 2015 to 2016. The data wereextracted from the Iranian Integrated Road Traffic Injury Registry System. Pedestrian accidents, motorcyclecrashes, and fatalities were excluded from the study. To account for major confounders, Bayesian-LASSO, andtreatment-effect cutting-edge approaches were used.Results: Overall, 801 injuries (14.67%) were evaluated. The results of the univariable analysis indicated thataging and nighttime had adverse effects on the odds of road traffic injuries (RTIs), even after adjusting forthe effect of other variables, these effects remained statistically significant. According to a newly developedapproach, the overall effects of aging and nighttime were significantly and directly correlated with the odds ofbeing injured for older adults (both p<0.001). Our findings indicated that drivers over 75 years old experienced23% higher injury odds (OR=1.23, 95% CI:1.11 to 1.39; p<0.001), while driving at night increased the odds by1.78 times (OR=1.78, 95% CI:1.51 to 1.83; p<0.001).Conclusion: Aging and nighttime driving are significant risk factors for RTIs among elderly drivers. Thishighlights the importance of implementing targeted interventions to enhance road safety for this vulnerablepopulation. Furthermore, the use of advanced Bayesian-LASSO and treatment-effect statistical methodshighlights the importance of utilizing sophisticated methodologies in epidemiological research to effectivelycapture and adjust for potential confounding factors.

Keywords

• accident
• traffic accidents
• causal effect
• bayesian estimation
• regularization algorithm