Evidence map›Paper›PMID 34568176›Full record

ArticleIranian journal of public health2021

Characteristic and Introspection of Road Traffic Injuries in China from 2012 to 2017.

Huiting Yu, Chan Nie, Yanna Zhou, Xue Wang, Haiyan Wang, Xiuquan Shi

Open access · goldAbstract read
In one paragraph

Article in Iranian journal of public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 2 countries.

Huiting YuDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Chan NieDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Yanna ZhouDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Xue WangDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Haiyan WangDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Xiuquan ShiDepartment of Epidemiology and Health Statistics, School of Public Health, Zunyi Medical University, Zunyi, Guizhou, 563006, China.
Zunyi Medical University · CNThe Ohio State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo analyze whether the area differences of RTIs (road traffic injuries, RTIs) caused by unequal development in China, provide suggestions for the prevention of the RTIs.

methodsThe data of RTIs in China was collected from the authoritative official website and yearbook of China.

resultsTotal RTIs in the East was the highest (RTIs frequency: 591789; injured people: 600611; death toll: 168885; economic loss: 27.22 billion RMB), followed by the Center (RTIs frequency: 321807; injured people: 352769; death toll: 91966; economic loss: 23.90 billion RMB) and the lowest in the West (RTIs frequency: 289482; injured people: 332517; death toll: 101095; economic loss: 16.35 billion RMB). The multivariate linear correlation and regression showed that the characteristic of RTIs was highly related with GDP (r=0.99,

conclusionThe economically developed areas had a large amount of traffic damages. The government should focus on preventing high RTIs in the East and high death tolls in the West.

Indexed as

ChinaTraffic accident injuriesUnequal development

Identifiers

PMID34568176
PMCPMC8426775
OpenAlexW3179497878

What OpenQuestion holds

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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.