Evidence map›Paper›PMID 32758693›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2020

Asymptomatic infection by SARS-CoV-2 in healthcare workers: A study in a large teaching hospital in Wuhan, China.

Dong Zhao, Mengmei Wang, Ming Wang, Yang Zhao, Zhishui Zheng, Xiaochen Li, Yunting Zhang, Tao Wang, Shaolin Zeng, Weihua Hu and 2 more

Open access · goldAbstract read
In one paragraph

Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 5 pooled it
1.2field-weighted citation impact, top 12% 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

32 citing papers in PubMed, 5 syntheses or guidelines pooled it, 50 citations in OpenAlex.

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  4. Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis.Proceedings of the National Academy of Sciences of the United States of America · 2021
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  20. Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests.Proceedings of the National Academy of Sciences of the United States of America · 2021
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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 1 institution in 1 country.

Dong ZhaoDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Mengmei WangDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Ming WangDepartment of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, China.
Yang ZhaoDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Zhishui ZhengDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Xiaochen LiDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Yunting ZhangDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Tao WangDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Shaolin ZengDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Weihua HuDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Wenzhen YuDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
Ke HuDepartment of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China. Electronic address: huke-rmhospital@163.com.
Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate the proportion and characteristics of asymptomatic infection among healthcare workers (HCWs).

methodsThis study retrospectively investigated 1407 HCWs who were screened for COVID-19 by chest computed tomography (CT) scans and nasopharyngeal swabs for SARS-CoV-2 nucleic acid. Demographics, CT features, nasopharyngeal swabs, baseline symptoms, and laboratory data were collected.

resultsOf 1407 HCWs, 235 had symptoms and 1172 were asymptomatic close contacts, of which, 107 were symptomatic cases and 84 were close contacts who had abnormal CT findings. Of 152 symptomatic individuals and 908 close contacts tested for SARS-CoV-2 nucleic acid, 122 symptomatic cases and 38 close contacts had positive reverse-transcriptase real-time polymerase chain (RT-PCR) test results. The rate of confirmed asymptomatic infections was 4.2% (38/908). Both symptomatic and asymptomatic infected cases had high titrations of specific IgG or had ≥four-fold increase in IgG during convalescence compared with the acute phase. Combining the RT-PCR tests and serological findings, the rate of asymptomatic infections was 9.7% (88/908). In terms of the duration of viral shedding, there was no significant difference between symptomatic mild/moderate participants and asymptomatic infections.

conclusionsThe findings demonstrated that a high rate of asymptomatic SARS-CoV-2 carriers existed among healthcare worker close contacts during the outbreak of COVID-19.

Indexed as

BetacoronavirusHealth PersonnelHospitals, TeachingAdultAsymptomatic InfectionsCarrier StateChinaCoronavirus InfectionsCOVID-19FemaleHumansMalePandemicsPneumonia, ViralRetrospective StudiesSARS-CoV-2AsymptomaticCOVID-19EpidemiologyHealthcare workerPneumoniaSARS-CoV-2

Identifiers

PMID32758693
PMCPMC7836921
OpenAlexW3046371817

What OpenQuestion holds

Textmetadata
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.