Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

BMC Public Health

Research

Homayoun Sadeghi-Bazargani¹  Mohammad Asghari-Jafarabadi²  Fatemeh Jahanjoo³ 

[1] Road Traffic Injury Research Center, Tabriz University of Medical Sciences, 5167846311, Tabriz, East Azerbaijan, Islamic Republic of Iran;Road Traffic Injury Research Center, Tabriz University of Medical Sciences, 5167846311, Tabriz, East Azerbaijan, Islamic Republic of Iran;Cabrini Research, Cabrini Health, 3144, Melbourne, VIC, Australia;School of Public Health and Preventative Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, 3800, Melbourne, VIC, Australia;Road Traffic Injury Research Center, Tabriz University of Medical Sciences, 5167846311, Tabriz, East Azerbaijan, Islamic Republic of Iran;Injury Epidemiology and Prevention Research Group, Turku Brain Injury Center, Turku University Hospital and the University of Turku, Turku, Finland;

关键词: Road traffic injury;    Statistical modelling;    Driving behaviour;    Road factors;    Iran;   

DOI  :  10.1186/s12889-022-14678-5

 received in 2022-07-26, accepted in 2022-11-21,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundPrevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety.MethodsIn this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015—March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression.ResultsAccording to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54–5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75–3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52–1.76)), rainy weather (OR = 1.32, 95%CI = (1.06–1.64)), no intersection control (OR = 1.40, 95%CI = (1.29–1.51)), double solid line(OR = 2.21, 95%CI = (1.31–3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39–2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93–2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50–1.92)), human factor (OR = 1.13, 95%CI = (1.03–1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04–3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27–1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20–1.51)), head-on collision(OR = 3.35, 95%CI = (2.85–3.93)), tourist destination(OR = 1.95, 95%CI = (1.69–2.24)), suburban areas(OR = 3.26, 95%CI = (2.65–4.01)), expressway(OR = 1.84, 95%CI = (1.59–2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63–2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26–1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67–2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26–1.56)), dark color (OR = 1.26, 95%CI = (1.17–1.35)), old vehicle(OR = 1.46, 95%CI = (1.27–1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42–3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72–5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33–1.88)), low income(OR = 2.48, 95%CI = (1.95–3.15)), old age (OR = 1.67, 95%CI = (1.44–1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51–6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44–1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44–1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29–2.76)).ConclusionThis study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

【 图 表 】

Fig. 1