Evidence map›Paper›PMID 36704281›Full record

ArticleHeliyon2023

Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population.

Tengfei Yang, Haotian Zeng, Xingan Yang, Jiangwei Kong, Xinzhe Chen, Nian Zhou, Ziwei Zhao, Qifeng Miao, Weidong Zhao, Fu Zhang and 5 more

Abstract read
In one paragraph

Article in Heliyon, 2023. 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.

  1. Article
  2. Article
  3. Article
  4. Overview of road traffic injuries among migrant workers in Guangzhou, China, from 2017 to 2021.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2024
    Article
  5. Epidemiological characteristics and factors influencing hospitalization burden among trauma patients: a retrospective analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024
    Article
  6. Article
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

15 authors.

Tengfei YangDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Haotian ZengGuangzhou Public Security Bureau, Guangzhou, China.
Xingan YangDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Jiangwei KongDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Xinzhe ChenSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Nian ZhouDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Ziwei ZhaoSchool of Pharmaceutical Science, Southern Medical University, Guangzhou, China.
Qifeng MiaoGuangdong Province Research Center of Traffic Accident Identification Engineering Technology, Guangzhou, China.
Weidong ZhaoDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Fu ZhangGuangdong Public Security Department, Guangzhou, China.
Xinbiao LiaoGuangdong Public Security Department, Guangzhou, China.
Yangeng YuGuangdong Public Security Department, Guangzhou, China.
Fengchong LanSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China.
Huijun WangDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.
Dongri LiDepartment of Forensic Evidence Science, School of Forensic Medicine, Southern Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to explore the trend and main influencing factors of road traffic accidents in Guangzhou, China, from 2007 to 2020 and to provide a reference and guidance for government decision-making. Methods: A retrospective cohort study was used to describe road traffic accidents in Guangzhou. According to the population types, all people with road traffic accidents were divided into migrant workers and the control population. We divided road users, administrative districts, motorcycle types and injury levels into subgroups to investigate the characteristics of road traffic accidents in Guangzhou. The road traffic accident data were derived from the Guangzhou Public Security Traffic Management Integrated System. Results: The incidence rate of road traffic accidents per 10,000 vehicles in Guangzhou decreased from 36.55 in 2007 to 10.07 in 2012, remained relatively stable at 9.47 in 2017, and finally rose to 11.12 in 2020. The injury rate showed the same trend as the incidence rate, while the mortality rate gradually decreased from 14.21 in 2007 to 5.19 in 2020. Vulnerable road users such as motorized two-to-three-wheeler drivers and migrant workers were casualties in more than 80% of the cases. The proportion of casualties involving mopeds and electric bicycles increased rapidly after 2018. Motor vehicle drivers frequently caused road traffic accidents and were most often uninjured. Conclusion: Road safety in Guangzhou has shown a clear trend of improvement, but casualties are uneven across administrative districts. More attention should be given to motorized two-to-three-wheelers, migrant workers, and road traffic violations by uninjured individuals.

Indexed as

CasualtyChinaMigrant workerRoad safetyRoad traffic accidentsTraffic violation

Identifiers

PMID36704281
PMCPMC9871230

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.