Evidence map›Paper›PMID 36598677›Full record

ArticleInfectious diseases and therapy2023

Comparison of the Distribution and Changes in the Antibiotic Resistance of Clinical Bacterial Isolates from the Lower Respiratory Tract of Children in Shenzhen Before the Epidemic, During the Epidemic, and During the Period of Normalized Prevention and Control of COVID-19.

Qing Meng, Wujiao Li, Hanfang Jiang, Huimin Yan, Heping Wang, Binjun Ye, Lintao Zhou, Yunsheng Chen

Open access · goldAbstract read
In one paragraph

Article in Infectious diseases and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 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

8 authors at 1 institution in 1 country.

Qing MengDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Wujiao LiDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Hanfang JiangDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Huimin YanDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Heping WangDepartment of Respiratory Diseases, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Binjun YeDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China.
Lintao ZhouDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China. 273323267@qq.com.
Yunsheng ChenDepartment of Clinical Microbiology Laboratory, Shenzhen Children's Hospital, No. 7019 Yitian Road, Futian District, Shenzhen, 518038, Guangdong, China. chenyunshenglw@163.com.ORCID http://orcid.org/0000-0002-8520-9852
Shenzhen Children's Hospital · CN

Funding

Guangdong Provincial High-level Hospital Construction Special Fund YNKT2021-ZZ10
6 · The paper itself

Abstract

introductionThis study aimed to understand the impact of the coronavirus disease 2019 (COVID-19) epidemic on the distribution and antibiotic resistance of pathogenic bacteria isolated from the lower respiratory tract of children in our hospital.

methodsAntimicrobial susceptibility tests were performed on bacteria isolated clinically from the lower respiratory tracts of children in our hospital from 2018 to 2021 by the Kirby-Bauer method and automated systems.

resultsFrom 2018 to 2021, the top three lower respiratory tract clinical isolates in our hospital were Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae. These three species showed obvious seasonal epidemic patterns, and their numbers decreased significantly during the COVID-19 epidemic, from 4559 in 2019 to 1938 in 2020. Bacterial resistance to antibiotics also changed before and after the COVID-19 epidemic. The annual proportions of methicillin-resistant S. aureus (MRSA) were 41%, 37.4%, 26.2%, and 29.8%. The resistance rates of Klebsiella pneumoniae to ceftriaxone were 40.5%, 51.9%, 35.3%, and 53.3%, and the detection rates of carbapenem-resistant K. pneumoniae (CRKP) were 2.7%, 11.1%, 5.9%, and 4.4%. The detection rates of β-lactamase-producing H. influenzae were 51.9%, 59.2%, 48.9%, and 55.3%. The rate of MRSA, ceftriaxone-resistant K. pneumoniae, CRKP, and β-lactamase-producing H. influenzae decreased significantly in 2020 compared with 2019, whereas that of carbapenem-resistant P. aeruginosa and carbapenem-resistant A. baumannii increased. The detection rates of β-lactamase-negative ampicillin-resistant H. influenzae (BLNAR) gradually increased over the 4 years.

conclusionsProtective measures against COVID-19, including reduced movement of people, hand hygiene, and surgical masks, may block the transmission of S. pneumoniae, H. influenzae, and M. catarrhalis and reduce the detection rate of MRSA, ceftriaxone-resistant K. pneumoniae, CRKP, and β-lactamase-producing H. influenzae.

Indexed as

Antimicrobial susceptibility testingBacterial resistance surveillanceCarbapenem-resistant Enterobacteriaceae bacteriaβ-Lactamase-negative ampicillin-resistant Haemophilus influenzae

Identifiers

PMID36598677
PMCPMC9812007
OpenAlexW4313489170

What OpenQuestion holds

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LicenceCC BY-NC
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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.