Evidence map›Paper›PMID 27140214›Full record

ArticleChinese journal of traumatology = Zhonghua chuang shang za zhi2016

Prevalence and risk factors of alcohol and substance abuse among motorcycle drivers in Fars province, Iran.

Seyed Taghi Heydari, Mehrdad Vossoughi, Armin Akbarzadeh, Kamran B Lankarani, Yaser Sarikhani, Kazem Javanmardi, Ali Akbary, Maryam Akbari, Mojtaba Mahmoodi, Mohammad Khabaz Shirazi and 1 more

Abstract readComparative Study
In one paragraph

Article in Chinese journal of traumatology = Zhonghua chuang shang za zhi, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Seyed Taghi HeydariHealth Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Mehrdad Vossoughi
Armin Akbarzadeh
Kamran B Lankarani
Yaser Sarikhani
Kazem Javanmardi
Ali Akbary
Maryam Akbari
Mojtaba Mahmoodi
Mohammad Khabaz Shirazi
Reza Tabrizi

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this present study is to investigate the prevalence of alcohol and substance abuse (ASA) and its relationship with other risky driving behaviors among motorcycle drivers.

methodsThis is a cross sectional study which is performed at Shiraz city of Iran. Data from motorcycle drivers were collected using a standard questionnaire in eight major streets at different times of the day. The data includes consumption of alcohol and other substances two hours before driving and some of the risky behaviors during driving.

resultsA total of 414 drivers with a mean ± SD age of (27.0 ± 9.3) years participated in the study. Alcohol or substance consumptions two hours before driving was significantly associated with risky driving behaviors such as using mobile phone during driving, poor maneuvering, and driving over the speed limit (both p < 0.001). It was also associated with carelessness about safety such as driving with technical defects (p < 0.001) and not wearing a crash helmet (p=0.008).

conclusionScreening for alcohol and substance consumption among motorcycle drivers is an efficient way to identify drivers that are at a greater risk for road traffic accidents.

Indexed as

Risk-TakingAccidents, TrafficAdultAge DistributionAlcoholismChi-Square DistributionConfidence IntervalsCross-Sectional StudiesDeveloping CountriesHumansIranMaleMiddle AgedMotorcyclesOdds RatioPrevalence

Identifiers

PMID27140214
PMCPMC4897829

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.