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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.
Sec. Clinical Microbiology
Volume 14 - 2024 | doi: 10.3389/fcimb.2024.1486953
This article is part of the Research Topic Advances in the Diagnosis and Management of Infectious Diseases View all 5 articles

Prevalence of respiratory pathogens among hospitalised patients with acute respiratory infection during and after the COVID-19 pandemic in Shijiazhuang, China Authors

Provisionally accepted
Pan pan Zheng Pan pan Zheng 1Ya nan Zhao Ya nan Zhao 2*Zhi kai Wang Zhi kai Wang 3*Min zhen Wang Min zhen Wang 4*Rong Li Rong Li 1*Jing Zhang Jing Zhang 2*Nan Li Nan Li 2*Zi feng Zhang Zi feng Zhang 2*Rui juan Rong Rui juan Rong 2*Yi chan Sun Yi chan Sun 2*Zan chao Liu Zan chao Liu 1*
  • 1 Hebei Key Laboratory of Basic Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
  • 2 Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
  • 3 School of Clinical Medicine, Hebei Medical Universit, Shijiazhuang, China
  • 4 Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, China

The final, formatted version of the article will be published soon.

    The COVID-19 pandemic and the resulting non-pharmaceutical interventions (NPIs) have led to changes in the epidemiology of other respiratory pathogens. This study was conducted to explore the epidemiological characteristics of 13 respiratory pathogens, including 11 respiratory viruses and 2 non-classical microorganisms, in hospitalised patients with acute respiratory tract infections (ARTIs) and to compare the prevalence of respiratory pathogens during and after the COVID-19 pandemic.We conducted a single-centre retrospective study involving 8979 patients with an influenza-like illness (ILI) ARTIs in Shijiazhuang City from December 2019 to December 2023. The GeXP analysis platform and multiple reverse transcription-PCR (mRT-PCR) technology were used to simultaneously detect 13 respiratory pathogens. The ARIMA model was constructed to predict the pathogen detection rate in each quarter of Shijiazhuang City in the next 2 y.Among the 8979 patients, 4169 (46.43%) tested positive for respiratory pathogens. The total pathogen detection rate rebounded in the year after the COVID-19 pandemic. After the COVID-19 pandemic, the positive rates in men were slightly higher than those in women and the positive rates in spring and winter were significantly higher than those in summer. The dominant pathogens during the COVID-19 pandemic were Influenza A viru (InfA ; 24.08%) and Human Rhinovirus (HRV ; 21.77%), and after the COVID-19 pandemic were InfA (27.92%) and H3 (21.17%). During the COVID-19 pandemic, InfA and HRV frequently occurred in all age groups. After the COVID-19 pandemic, InfA and Seasonal Influenza virus H3N2 (H3) frequently occurred in all age groups.Conclusions A series of NPIs introduced by the Chinese government during the COVID-19 pandemic had a significant impact on acute upper respiratory pathogenic infections. After the withdrawal of the NPIs, the spectrum of respiratory pathogens changed.

    Keywords: COVID-19, respiratory pathogens, Non-classical microorganisms, ARTIs/ILI, Epidemiology

    Received: 27 Aug 2024; Accepted: 29 Oct 2024.

    Copyright: © 2024 Zheng, Zhao, Wang, Wang, Li, Zhang, Li, Zhang, Rong, Sun and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Ya nan Zhao, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Zhi kai Wang, School of Clinical Medicine, Hebei Medical Universit, Shijiazhuang, China
    Min zhen Wang, Institute of Epidemiology and Health Statistics, School of Public Health, Lanzhou University, Lanzhou, China
    Rong Li, Hebei Key Laboratory of Basic Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Jing Zhang, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Nan Li, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Zi feng Zhang, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Rui juan Rong, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Yi chan Sun, Shijiazhuang Technology Innovation Center of Precision Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China
    Zan chao Liu, Hebei Key Laboratory of Basic Medicine for Diabetes, Shijiazhuang Second Hospital, Shijiazhuang, China

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