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.

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.

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.