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Li Y, Yu J, Wang Y, Yi J, Guo L, Wang Q, Zhang G, Xu Y, Zhao Y. Cocirculation and coinfection of multiple respiratory viruses during autumn and winter seasons of 2023 in Beijing, China: A retrospective study. J Med Virol 2024; 96:e29602. [PMID: 38597349 DOI: 10.1002/jmv.29602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
China experienced severe epidemics of multiple respiratory pathogens in 2023 after lifting "Zero-COVID" policy. The present study aims to investigate the changing circulation and infection patterns of respiratory pathogens in 2023. The 160 436 laboratory results of influenza virus and respiratory syncytial virus (RSV) from February 2020 to December 2023, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from June 2020 to December 2023, Mycoplasma pneumoniae, adenovirus, and human rhinovirus from January 2023 to December 2023 were analyzed. We observed the alternating epidemics of SARS-CoV-2 and influenza A virus (IAV), as well as the out-of-season epidemic of RSV during the spring and summer of 2023. Cocirculation of multiple respiratory pathogens was observed during the autumn and winter of 2023. The susceptible age range of RSV in this winter epidemic (10.5, interquartile range [IQR]: 5-30) was significantly higher than previously (4, IQR: 3-34). The coinfection rate of IAV and RSV in this winter epidemic (0.695%) was significantly higher than that of the last cocirculation period (0.027%) (p < 0.001). Similar trend was also found in the coinfection of IAV and SARS-CoV-2. The present study observed the cocirculation of multiple respiratory pathogens, changing age range of susceptible population, and increasing coinfection rates during the autumn and winter of 2023, in Beijing, China.
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Affiliation(s)
- Yi Li
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
- Graduate School, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
| | - Jinhan Yu
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
- Graduate School, Peking Union Medical College, Chinese Academy of Medical Science, Beijing, China
| | - Yao Wang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Jie Yi
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Lina Guo
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Qing Wang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Ge Zhang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Yingchun Xu
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Ying Zhao
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
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Qian C, Chen Q, Lin W, Li Z, Zhu J, Zhang J, Luan L, Zheng B, Zhao G, Tian J, Zhang T. Incidence of community-acquired pneumonia among children under 5 years in Suzhou, China: a hospital-based cohort study. BMJ Open 2024; 14:e078489. [PMID: 38171617 PMCID: PMC10773396 DOI: 10.1136/bmjopen-2023-078489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVES To depict the seasonality and age variations of community-acquired pneumonia (CAP) incidence in the context of the COVID-19 impact. DESIGN Retrospective cohort study. PARTICIPANTS The observational cohort study was conducted at Soochow University Affiliated Children's Hospital from January 2017 to June 2021 and involved 132 797 children born in 2017 or 2018. They were followed and identified CAP episodes by screening on the Health Information Systems of outpatients and inpatients in the same hospital. OUTCOME The CAP episodes were defined when the diagnoses coded as J09-J18 or J20-J22. The incidence of CAP was estimated stratified by age, sex, birth year, health status group, season and month, and the rate ratio was calculated and adjusted by a quasi-Poisson regression model. Stratified analysis of incidence of CAP by birth month was conducted to understand the age and seasonal variation. RESULTS The overall incidence of CAP among children aged ≤5 years was 130.08 per 1000 person years. Children aged ≤24 months have a higher CAP incidence than those aged >24 months (176.84 vs 72.04 per 1000 person years, p<0.001). The CAP incidence increased from October, peaked at December and January and the highest CAP incidence was observed in winter (206.7 per 1000 person years, 95% CI 204.12 to 209.28). A substantial decline of CAP incidence was observed during the COVID-19 lockdown from February to August 2020, and began to rise again when the communities reopened. CONCLUSIONS The burden of CAP among children is considerable. The incidence of CAP among children ≤5 years varied by age and season and decreased during COVID-19 lockdown.
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Affiliation(s)
| | - Qinghui Chen
- Soochow University Affiliated Children's Hospital, Suzhou, Jiangsu, China
| | | | | | - Jun Zhu
- Soochow University Affiliated Children's Hospital, Suzhou, Jiangsu, China
| | - Jun Zhang
- Suzhou Centers for Disease Control, Suzhou, Jiangsu, China
| | - Lin Luan
- Suzhou Centers for Disease Control, Suzhou, Jiangsu, China
| | - Benfeng Zheng
- Suzhou Centers for Disease Control, Suzhou, Jiangsu, China
| | | | - Jianmei Tian
- Soochow University Affiliated Children's Hospital, Suzhou, Jiangsu, China
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Zhang X, Du J, Li G, Chen T, Yang J, Yang J, Zhang T, Wang Q, Yang L, Lai S, Feng L, Yang W. Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study. Infect Dis Poverty 2023; 12:11. [PMID: 36797765 PMCID: PMC9933034 DOI: 10.1186/s40249-023-01061-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/28/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.
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Affiliation(s)
- Xingxing Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
| | - Jing Du
- Beijing Centre for Disease Prevention and Control, Beijing, 100013, China
| | - Gang Li
- Beijing Centre for Disease Prevention and Control, Beijing, 100013, China
| | - Teng Chen
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794-3600, USA
| | - Jin Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
| | - Jiao Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
| | - Ting Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
| | - Qing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
| | - Liuyang Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China
- Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, 650506, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China.
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100073, China.
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