Evidence map›Paper›PMID 29875516›Full record

ArticleBulletin of the World Health Organization2018

Poisoning deaths in China, 2006-2016.

Lijun Wang, Yue Wu, Peng Yin, Peixia Cheng, Yunning Liu, David C Schwebel, Jinlei Qi, Peishan Ning, Jiangmei Liu, Xunjie Cheng and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Bulletin of the World Health Organization, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
4.1field-weighted citation impact, top 6% 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

27 citing papers in PubMed, 43 citations in OpenAlex.

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  19. Deaths and hospitalizations of victims of non-drug toxicological events in Brazil, 2009 to 2018.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2022
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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

12 authors at 3 institutions in 2 countries.

Lijun WangNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yue WuDepartment of Occupational and Environmental Health, Xiangya School of Public Health, Central South University, Changsha, China.
Peng YinNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Peixia ChengDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Yunning LiuNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
David C SchwebelDepartment of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Jinlei QiNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Peishan NingDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Jiangmei LiuNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Xunjie ChengDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
Maigeng ZhouNational Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Guoqing HuDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
National Center for Chronic and Noncommunicable Disease Control and Prevention · CNCentral South University · CNUniversity of Alabama at Birmingham · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo provide a comprehensive overview of poisoning mortality patterns in China.

methodsUsing mortality data from the Chinese national disease surveillance points system, we examined trends in poisoning mortality by intent and substance from 2006 to 2016. Differences over time between urban and rural residents among different age groups and across external causes of poisoning were quantified using negative binomial models for males and females separately.

resultsIn 2016, there were 4936 poisoning deaths in a sample of 84 060 559 people (5.9 per 100 000 people; 95% confidence interval: 5.6-6.2). Age-adjusted poisoning mortality dropped from 9.2 to 5.4 per 100 000 people between 2006 and 2016. Males, rural residents and older adults consistently had higher poisoning mortality than females, urban residents and children or young adults. Most pesticide-related deaths (34 996 out of 39 813) were suicides among persons older than 15 years, although such suicides decreased between 2006 and 2016 (from 6.1 per 100 000 people to 3.6 for males and from 5.8 to 3.0 for females). In 2016, alcohol caused 29.3% (600/2050) of unintentional poisoning deaths in men aged 25-64 years. During the study period, unintentional fatal drug poisoning by narcotics and psychodysleptics in individuals aged 25-44 years increased from 0.4 per 100 000 people to 0.7 for males and from 0.05 to 0.13 for females.

conclusionDespite substantial decreases in mortality, poisoning is still a public health threat in China. This warrants further research to explore causative factors and to develop and implement interventions targeting at-risk populations.

Indexed as

SuicideAdultAgedCause of DeathChildChinaFemaleHumansMaleMiddle AgedMortalityPoisoningRural PopulationYoung Adult

Identifiers

PMID29875516
PMCPMC5985423
OpenAlexW2800011819

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.