ArticleBMC public health2022
Modeling road traffic fatalities in Iran's six most populous provinces, 2015-2016.
Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- A Hybrid of Random Forests and Generalized Path Analysis: A Causal Modeling of Crashes in 52,524 Suburban Areas.Journal of research in health sciences · 2023Article
- Exploring the Causal Impact of Age and Nighttime Driving on Road Traffic Injuries among Elderly Drivers: A Bayesian LASSO Approach.Bulletin of emergency and trauma · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.