Evidence map›Paper›PMID 36451170›Full record

ArticleBMC public health2022

Modeling road traffic fatalities in Iran's six most populous provinces, 2015-2016.

Fatemeh Jahanjoo, Homayoun Sadeghi-Bazargani, Mohammad Asghari-Jafarabadi

Erratum issuedOpen access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 3 countries.

Fatemeh JahanjooRoad Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, 5167846311, East Azerbaijan, Islamic Republic of Iran.
Homayoun Sadeghi-BazarganiRoad Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, 5167846311, East Azerbaijan, Islamic Republic of Iran. homayoun.bazargani@gmail.com.
Mohammad Asghari-JafarabadiRoad Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, 5167846311, East Azerbaijan, Islamic Republic of Iran. m.asghari862@gmail.com.
Monash University · AUTabriz University of Medical Sciences · IRTurku University Hospital · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Administrative PersonnelAutomobile DrivingAzerbaijanCross-Sectional StudiesHumansIranDriving behaviourIranRoad factorsRoad traffic injuryStatistical modelling

Identifiers

PMID36451170
PMCPMC9710022
OpenAlexW4310384359

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.