Temporal Patterns of Road Traffic Injuries in Iran


1 Department of Epidemiology, School of Public Health, Ilam University of Medical Sciences, Ilam, IR Iran

2 Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran

3 Department of Epidemiology, Safety Promotion and Injury Prevention Research Center, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran



Road traffic injuries (RTIs) are the main causes of death and disability in Iran. However, very few studies about the temporal variations of RTIs have been published to date.

This study was conducted to investigate the temporal pattern of RTIs in Iran in 2012.

Materials and Methods
All road traffic accidents (RTAs) reported to traffic police during a one-year period (March 21, 2012 through March 21, 2013) were investigated after obtaining permission from the law enforcement force of the Islamic Republic of Iran. Distributions of RTAs were obtained for season, month, week, and hour scales, and for long holidays (more than one day) and the day prior to long holidays (DPLH). The final analysis was carried out using the Poisson regression model to calculate incidence rate ratios for RTIs. All analyses were conducted using STATA 13.1 and Excel software; statistical significance was set at P < 0.05.

A total of 452,192 RTAs were examined. The estimated rate of all accidents was 219 per 10,000 registered vehicles, or 595 per 100,000 people. About 28% of all RTAs, and more than one third of fatal RTAs, occurred during the summer months. The incidence rate for all traffic accidents on DPLH was 1.20, compared to workdays as a reference category, and it was 1.40 for fatal crashes. The rate of fatal road traffic accidents in outer cities was 3.2 times higher than in inner ones.

Our findings reveal that there are temporal variations in traffic accidents, and long holidays significantly influence accident rates. Traffic injuries have different patterns on outer/inner city roads, based on weekday and holiday status. Thus, these findings could be used to create effective initiatives aimed at traffic management.