Epidemic features and megagenomic analysis of childhood Mycoplasma pneumoniae post COVID-19 pandemic: a 6-year study in southern China.
Yi XuChen YangPanpan SunFansen ZengQian WangJianlong WuChunxiao FangChe ZhangJinping WangYiling GuXiaohuan WuXiaoxian ZhangBin YangJuhua YangHongwei ZhangJiacee LianJinqiu ZhangLi HuangQizhou LianPublished in: Emerging microbes & infections (2024)
With the atypical rise of Mycoplasma pneumoniae infection (MPI) in 2023, prompt studies are needed to determine the current epidemic features and risk factors with emerging trends of MPI to furnish a framework for subsequent investigations. This multicentre, retrospective study was designed to analyse the epidemic patterns of MPI before and after the COVID-19 pandemic, as well as genotypes and the macrolide-resistance-associated mutations in MP sampled from paediatric patients in Southern China. Clinical data was collected from 1,33,674 patients admitted into investigational hospitals from 1 June 2017 to 30 November 2023. Metagenomic next-generation sequencing (mNGS) data were retrieved based on MP sequence positive samples from 299 paediatric patients for macrolide-resistance-associated mutations analysis. Pearson's chi-squared test was used to compare categorical variables between different time frames. The monthly average cases of paediatric common respiratory infection diseases increased without enhanced public health measures after the pandemic, especially for influenza, respiratory syncytial virus infection, and MPI. The contribution of MPI to pneumoniae was similar to that in the outbreak in 2019. Compared to mNGS data between 2019-2022 and 2023, the severity of MP did not grow stronger despite higher rates of macrolide-resistance hypervariable sites, including loci 2063 and 2064, were detected in childhood MP samples of 2023. Our findings indicated that ongoing surveillance is necessary to understand the impact of post pandemic on MP transmission disruption during epidemic season and the severity of clinical outcomes in different scenarios.
Keyphrases
- public health
- end stage renal disease
- risk factors
- intensive care unit
- ejection fraction
- newly diagnosed
- chronic kidney disease
- sars cov
- coronavirus disease
- respiratory tract
- electronic health record
- prognostic factors
- big data
- clinical trial
- machine learning
- genome wide
- young adults
- artificial intelligence
- patient reported outcomes
- early life
- gene expression
- dna methylation
- open label