Evidence map›Paper›PMID 33845915›Full record

ArticleInfectious diseases of poverty2021

Risk factors for developing severe COVID-19 in China: an analysis of disease surveillance data.

Meng-Jie Geng, Li-Ping Wang, Xiang Ren, Jian-Xing Yu, Zhao-Rui Chang, Can-Jun Zheng, Zhi-Jie An, Yu Li, Xiao-Kun Yang, Hong-Ting Zhao and 3 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
4.0field-weighted citation impact, top 5% 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, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.

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  16. Clinical heterogeneity in patients with myoclonus associated to COVID-19.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 1 country.

Meng-Jie GengDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Li-Ping WangDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiang RenDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Jian-Xing YuNational Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhao-Rui ChangDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Can-Jun ZhengDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhi-Jie AnNational Immunization Program, Chinese Center for Disease Control and Prevention, Beijing, China.
Yu LiDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Xiao-Kun YangDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Hong-Ting ZhaoDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
Zhong-Jie LiDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China. lizj@chinacdc.cn.
Guang-Xue HeNational Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. hegx@chinacdc.cn.
Zi-Jian FengDivision of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China. fengzj@chinacdc.cn.
Chinese Center For Disease Control and Prevention · CNNational Institute for Communicable Disease Control and Prevention · CNNational Institute for Viral Disease Control and Prevention · CN

Funding

Ministry of Science and Technology of the People's Republic of China 2018ZX10713001National Natural Science Foundation of China 91846302
6 · The paper itself

Abstract

backgroundCOVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19.

methodsThe data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity.

resultsThe total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46-68] and the median age of non-severe cases was 43 years (IQR: 32-54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2-1.5]; fever (aOR = 2.3, 95% CI: 2.0-2.7), cough (aOR = 1.4, 95% CI: 1.2-1.6), fatigue (aOR = 1.3, 95% CI: 1.2-1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4-4.6), hypertension (aOR = 1.5, 95% CI: 1.2-1.8) and diabetes (aOR = 1.96, 95% CI: 1.6-2.4). With the increase of age, risk for the severity was gradually higher [20-39 years (aOR = 3.9, 95% CI: 1.8-8.4), 40-59 years (aOR = 7.6, 95% CI: 3.6-16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5-43.7)], and longer time from symtem onset to diagnosis [3-5 days (aOR = 1.4, 95% CI: 1.2-1.7), 6-8 days (aOR = 1.8, 95% CI: 1.5-2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6-2.3)].

conclusionsOur study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.

Indexed as

Age FactorsComorbiditySeverity of Illness IndexSex FactorsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolChinaCOVID-19Early DiagnosisFemaleHumansInfantCOVID-19Non-severe caseRisk factorSevere case

Identifiers

PMID33845915
PMCPMC8040359
OpenAlexW3154353458

What OpenQuestion holds

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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.