Evidence map›Paper›PMID 27552266›Full record

ArticleAmerican journal of public health2016

One Outcome, Many Trends: Understanding National Data Sources for Road Traffic Fatalities in China.

Qingfeng Li, Huan He, Hailun Liang, David M Bishai, Adnan A Hyder

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Qingfeng LiAll authors are with the Johns Hopkins International Injury Research Unit, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Huan HeAll authors are with the Johns Hopkins International Injury Research Unit, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Hailun LiangAll authors are with the Johns Hopkins International Injury Research Unit, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
David M BishaiAll authors are with the Johns Hopkins International Injury Research Unit, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Adnan A HyderAll authors are with the Johns Hopkins International Injury Research Unit, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo better understand national data sources and evaluate time trends in road traffic fatalities (RTF) in China.

methodsWe reviewed national sources on RTF data. These included population-based report from the Ministry of Public Security (MPS), sample-based estimates from the Ministry of Health (MOH) and the Disease Surveillance Points System (DSP), as well as model-based estimates from the World Health Organization (WHO), and the Global Burden of Disease Study (GBD).

resultsAll data sources have limitations in coverage, representativeness or overreliance on model specifications. Despite the discrepancies in methodologies and estimates, all sources indicated an increase in RTF before 2005. Since then, MPS and GBD indicated a decrease, DSP showed plateauing, and MOH and WHO suggested increasing fatalities. However, despite any recent decline, RTF remain high.

conclusionsThe divergent trends in RTF across data sets in China implies a challenge to understanding China's experience with addressing RTF. China needs to reconcile data sources and further improve road safety.

Indexed as

Accidents, TrafficChinaHumansInformation Storage and RetrievalPopulation SurveillanceSafetyTime Factors

Identifiers

PMID27552266
PMCPMC5024363

What OpenQuestion holds

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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.