Evidence map›Paper›PMID 35547223›Full record

ArticleFrontiers in medicine2022

Molecular Epidemiology of Herpangina Children in Tongzhou District, Beijing, China, During 2019-2020.

Ming-Zhu Xie, Lin-Yi Chen, Yan-Na Yang, Yan Cui, Si-Hui Zhang, Tian-Shuo Zhao, Wan-Xue Zhang, Juan Du, Fu-Qiang Cui, Qing-Bin Lu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Ming-Zhu XieDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Lin-Yi ChenDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Yan-Na YangInstitute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Disease Control and Prevention, Beijing, China.
Yan CuiInstitute for Infectious Diseases and Endemic Diseases Prevention and Control, Beijing Tongzhou Center for Disease Control and Prevention, Beijing, China.
Si-Hui ZhangDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Tian-Shuo ZhaoDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Wan-Xue ZhangDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Juan DuDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Fu-Qiang CuiDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Qing-Bin LuDepartment of Laboratorial Science and Technology and Vaccine Research Center, School of Public Health, Peking University, Beijing, China.
Peking University · CNBeijing Center for Disease Prevention and Control · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The changing pattern of pathogen spectrum causing herpangina in the time of coronavirus disease 2019 (COVID-19) pandemic was unknown. The purpose of this study was to investigate the changes on the molecular epidemiology of herpangina children during 2019-2020 in Tongzhou district, Beijing, China. Method: From January 2019 to December 2020, children diagnosed with herpangina were recruited by the staff from Tongzhou Center for Disease Control and Prevention (CDC) in Beijing. Viral RNA extraction from pharyngeal swabs was used for enterovirus (EV) detection and the complete VP1 gene was sequenced. The phylogenetic analysis was performed based on all VP1 sequences for EV genotypes. Result: A total of 1,331 herpangina children were identified during 2019-2020 with 1,121 in 2019 and 210 in 2020, respectively. The predominant epidemic peak of herpangina children was in summer and autumn of 2019, but not observed in 2020. Compared to the number of herpangina children reported in 2019, it decreased sharply in 2020. Among 129 samples tested in 2019, 61 (47.3%) children were detected with EV, while 22.5% (20/89) were positive in 2020. The positive rate for EV increased since June 2019, peaked at August 2019, and decreased continuously until February 2020. No cases were observed from February to July in 2020, and the positive rate of EV rebounded to previous level since August 2020. Four genotypes, including coxsackievirus A6 (CV-A6, 9.3%), CV-A4 (7.8%), CV-A10 (2.3%) and CV-A16 (10.1%), were identified in 2019, and only three genotypes, including CV-A6 (9.0%), CV-A10 (6.7%) and CV-A16 (1.1%), were identified in 2020. The phylogenetic analysis showed that all CV-A6 strains from Tongzhou located in Group C, and the predominant strains mainly located in C2-C4 subgroups during 2016-2018 and changed into C1 subgroup during 2018-2020. CV-A16 strains mainly located in Group B, which consisting of strains widely distributed around the world. Conclusions: The predominant genotypes gradually shifted from CV-A16, CV-A4 and CV-A6 in 2019 to CV-A6 in 2020 under COVID-19 pandemic. Genotype-based surveillance will provide robust evidence and facilitate the development of public health measures.

Indexed as

childrenChinaCOVID-19 pandemicherpanginamolecular epidemiology

Identifiers

PMID35547223
PMCPMC9082675
OpenAlexW4224861490

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.