Evidence map›Paper›PMID 29546203›Full record

ArticleAIMS public health2016

Risk Factors for Motorcycle-related Severe Injuries in a Medium-sized City in China.

Lili Xiong, Yao Zhu, Liping Li

Abstract read
In one paragraph

Article in AIMS public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

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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

3 authors.

Lili XiongHunan Province Maternal and Children Health Care Hospital, 53 Xiang Chun Road, Changsha, Hunan Province, China 410000.
Yao ZhuInjury Prevention Research Center, Shantou University Medical College, 22 Xin Ling Road Shantou, Guangdong Province, China 515041.
Liping LiInjury Prevention Research Center, Shantou University Medical College, 22 Xin Ling Road Shantou, Guangdong Province, China 515041.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMotorcycle vehicles are frequent in China, especially in the small and medium sized cities. Road traffic collisions involving motorcycles often result in severe injuries. We aimed to identify risk factors for severe injuries in inpatients sustaining motorcycle collisions.

methodsPatients with road traffic injuries involving motorcycles who presented to the neurosurgery and orthopedic departments of three major comprehensive hospitals in Shantou city were reviewed from October 2012 to June 2013. Data from 349 patients was investigated. Crash and injury characteristics were documented by interviewing patients, their family members, and their doctors. Binary logistic regression was used to determine risk factors for severe injuries.

resultsThere were 253 males (72.49%) and 96 females (27.51%), with a male to female ratio of 2.64:1. The mean age was 38.21±17.32 years. One-hundred and fifty patients were in the severe injury group with a mean injury severity score (ISS) of 15.34±9.13. The simple and multiple logistic model showed that males, lack of safeguards, morning and night hours, non-urban areas, collision of a motorcycle with a cycle, ambulance transportation to hospital, admission to a neurosurgery department, lack of traffic control, unobstructed traffic, and poor visibility were all the risk factors.

conclusionsThis research highlights some problems: less helmet wearing in motorcyclists and cyclists, rural injuries being more serious than urban ones, and head injuries being the main diagnosis in severe injuries. The result of this research is predictable. If the safety equipment is required to be used, such as helmets, and the traffic environment is improved, such as traffic flow, medical resources to injuries and deaths is seasonable, then traffic safety will be improved and accidents will be reduced.

Indexed as

injuriesmotorcyclesrisk factors

Identifiers

PMID29546203
PMCPMC5690413

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LicenceCC BY
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Registered trials

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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.