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. 2025 Apr 3;15(1):53.
doi: 10.1007/s44197-025-00398-7.

Epidemiology and Mortality Risk of Severe Viral Pneumonia During the Pre-Pandemic, COVID-19 Pandemic and Post-Pandemic Era: A Retrospective Study of Hospitalized Children in ShenZhen, China Between 2017 and 2023

Huabao Chen #  1 , Lidan Zhang #  1 , Xing Nie #  1 , Li Wang  1 , Liangliang Kang  2 , Yucong Zhang  3 , Zhuanggui Chen  4 , Yating Li  5 , Yuhui Wu  6
Affiliations

Epidemiology and Mortality Risk of Severe Viral Pneumonia During the Pre-Pandemic, COVID-19 Pandemic and Post-Pandemic Era: A Retrospective Study of Hospitalized Children in ShenZhen, China Between 2017 and 2023

Huabao Chen et al. J Epidemiol Glob Health. .

Abstract

Purpose: This study aims to investigate the spectrum of viruses leading to severe viral pneumonia (SVP) and the associated risk factors for mortality among pediatric patients in the pediatric intensive care unit (PICU).

Methods: Taking the outbreak and end of the COVID-19 pandemic as a aboundary, The pre-pandemic period of COVID-19 spans from 01/2017 to 12/2019, the pandemic period from 01/2020 to 12/2021, and the post-pandemic period from 01/2022 to 12/2023. Patients were subsequently stratified into survivor and non-survivor groups based on clinical outcomes.

Results: A total of 1007 patients (median age 1.42 years, range 0.58-4.00; male: female ratio 1.7:1) diagnosed with SVP. Cases were stratified into pre-pandemic (n = 419, 41.6%), pandemic (n = 272, 27.0%), and post-pandemic (n = 316, 31.4%) periods. Viral predominance varied across phases: Pre-pandemic: Influenza A (IVA, 37.0% [155/419]), respiratory syncytial virus (RSV, 29.8%), adenovirus (19.8%), and influenza B (15.5%). Pandemic phase: Human rhinovirus (HRV, 40.1% [109/272]), RSV (33.1%), parainfluenza viruses (11.4%), and bocavirus (HBoV, 10.7%). Post-pandemic: HRV (24.4% [77/316]), RSV (22.8%), HBoV (14.2%), and IVA (13.6%). Comparative analysis revealed significant intergroup differences in the proportion of patients aged < 3 years, primary immunodeficiency disorders (PIDs), and sepsis between pure viral infection deaths and coinfection-associated fatalities among SVP cases. Logistic regression identified eight independent mortality predictors: acute leukemia, other malignant tumors, PIDs, moderate-to-severe underweight, rhabdomyolysis, acute respiratory distress syndrome (ARDS), infection-related encephalopathy, and multiorgan dysfunction syndrome (MODS). The prediction model demonstrated robust discriminative capacity for SVP mortality: sensitivity 73.8%, specificity 90.2%, and AUC 0.888 (95%CI 0.838-0.938) via ROC curve analysis.

Conclusions: The COVID-19 pandemic has altered the landscape of respiratory viruses causing SVP in children. The presence of underlying health conditions, particularly acute leukemia, other malignancies, and immunodeficiency, significantly increases the risk of death in children with viral pneumonia. The risk prediction model offers a reliable tool for clinical practice to predict mortality in these patients.

Keywords: Children; Epidemiological Trends; Risk Factors; Severe Pneumonia; Viral Etiologies.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was conducted in compliance with the protocol approved by the Ethics Committee of the Shenzhen Children’s Hospital ( Research approval number: 2022033). Conflict of interest: The authors report no conflicts of interest associated with this work.

Figures

Fig. 1
Fig. 1
Flowchart consort diagram
Fig. 2
Fig. 2
Accumulation of SVP and death cases in the PICU from 2017 to 2023, by month
Fig. 3
Fig. 3
Accumulation of the number of severe viral pneumonia cases in the same month over three years before the COVID-19 epidemic
Fig. 4
Fig. 4
Accumulation of the number of severe viral pneumonia cases in the same month over two years during the COVID-19 epidemic
Fig. 5
Fig. 5
Accumulation of the number of severe viral pneumonia cases in the same month over two years after the COVID-19 epidemic
Fig. 6
Fig. 6
ROC curve of the severe viral pneumonia mortality prediction model
See this image and copyright information in PMC

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