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

Objective: To determine the causal relationship between aging and nighttime driving and the odds of injury among elderly drivers. Methods: In this cross-sectional study, 5460 car accidents were investigated from 2015 to 2016. The data were extracted from the Iranian Integrated Road Traffic Injury Registry System. Pedestrian accidents, motorcycle crashes, and fatalities were excluded from the study. To account for major confounders, Bayesian-LASSO, and treatment-effect cutting-edge approaches were used. Results: Overall, 801 injuries (14.67%) were evaluated. The results of the univariable analysis indicated that aging and nighttime had adverse effects on the odds of road traffic injuries (RTIs), even after adjusting for the effect of other variables, these effects remained statistically significant. According to a newly developed approach, the overall effects of aging and nighttime were significantly and directly correlated with the odds of being injured for older adults (both p<0.001). Our findings indicated that drivers over 75 years old experienced 23% higher injury odds (OR=1.23, 95% CI:1.11 to 1.39; p<0.001), while driving at night increased the odds by 1.78 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. This highlights the importance of implementing targeted interventions to enhance road safety for this vulnerable population. Furthermore, the use of advanced Bayesian-LASSO and treatment-effect statistical methods highlights the importance of utilizing sophisticated methodologies in epidemiological research to effectively capture and adjust for potential confounding factors.

A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas.

BACKGROUND: Determining suburban area crashes' risk factors may allow for early and operative safety measures to find the main risk factors and moderating effects of crashes. Therefore, this paper has focused on a causal modeling framework. STUDY DESIGN: A cross-sectional study. METHODS: In this study, 52524 suburban crashes were investigated from 2015 to 2016. The hybrid-random-forest-generalized-path-analysis technique (HRF-gPath) was used to extract the main variables and identify mediators and moderators. RESULTS: This study analyzed 42 explanatory variables using a RF model, and it was found that collision type, distinct, driver misconduct, speed, license, prior cause, plaque description, vehicle maneuver, vehicle type, lighting, passenger presence, seatbelt use, and land use were significant factors. Further analysis using g-Path demonstrated the mediating and predicting roles of collision type, vehicle type, seatbelt use, and driver misconduct. The modified model fitted the data well, with statistical significance ( χ230 =81.29, P<0.001) and high values for comparative-fit-index and Tucker-Lewis-index exceeding 0.9, as well as a low root-mean-square-error-of-approximation of 0.031 (90% confidence interval: 0.030-0.032). CONCLUSION: The results of our study identified several significant variables, including collision type, vehicle type, seatbelt use, and driver misconduct, which played mediating and predicting roles. These findings provide valuable insights into the complex factors that contribute to collisions via a theoretical framework and can inform efforts to reduce their occurrence in the future.

Road safety lead agency challenges of three decades of intervention for road safety in Iran: implications for policy-makers.

BACKGROUND: Road traffic crashes and associated injuries and mortalities are one of the big public health challenges, especially in low/middle-income countries. Road safety lead agency (RSLA) is a vital factor in the sustainable prevention and promotes road safety. In the recent decades, various policy interventions have been conducted for road safety in Iran. This study aimed at exploring the challenges of RSLA from the perspectives of stakeholders at various levels. METHODS: A qualitative study was conducted. In-depth interviews and document reviews were used for data collection. To conduct interviews, different stakeholders at various levels such as policy-makers, senior national authorities, researchers and faculty members were selected. Data collection was conducted between November 2019 and June 2020. Thematic content analysis approach was used for data analysis. RESULTS: The experts' perspectives were analysed and then categorised under five main themes including the role and position of the lead agency, the role and duties of the actors and players, translating policy into practice, intrasectoral and intersectoral cooperation and coordination and evidence production and application and a total of 22 subthemes were identified. The current structure of the RSLA is one of the main challenges emphasised by the research participants. CONCLUSION: To achieve significant improvements in road safety at the national level, a strong management system and leadership body is a critical issue. Organisational reform to establish an effective unique lead agency is proposed to cope with RSLA challenges.