Analysis of mycoplasma pneumoniae infection among children with respiratory tract infections in hospital in Chengdu from 2014 to 2020

Mycoplasma pneumoniae among Chinese Outpatient Children with Mild Respiratory Tract Infections during the Coronavirus Disease 2019 Pandemic

“…Second, the facts that the M. pneumoniae infection rate decreased dramatically from the peak to an extremely low level within 1 month and that the extremely low level lasted for almost 1 year could not be explained by normal seasonal trends. No similar seasonal distribution of positivity rates of M. pneumoniae among pediatric patients with RTIs could be seen in previous studies in China ( 3 , 18 , 19 ). Third, M. pneumoniae infections tend to peak during summer or early fall ( 3 , 5 , 18 , 19 ).…”

“…No similar seasonal distribution of positivity rates of M. pneumoniae among pediatric patients with RTIs could be seen in previous studies in China ( 3 , 18 , 19 ). Third, M. pneumoniae infections tend to peak during summer or early fall ( 3 , 5 , 18 , 19 ). However, the positivity rate of M. pneumoniae was extremely low in this period of 2020 in our study.…”

“… Mycoplasma pneumoniae is a common pathogen among children with upper or lower respiratory tract infections (RTIs) ( 1 – 3 ). Together with a high prevalence of M. pneumoniae infections, a high prevalence of macrolide resistance in M. pneumoniae strains was also observed among Chinese pediatric patients with RTIs ( 2 ).…”

“…Together with a high prevalence of M. pneumoniae infections, a high prevalence of macrolide resistance in M. pneumoniae strains was also observed among Chinese pediatric patients with RTIs ( 2 ). However, during the pandemic of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), pneumonia ( 4 ) and hospitalized RTI cases ( 3 ) caused by M. pneumoniae seemed to decrease rapidly. Under the circumstance of the ongoing pandemic of COVID-19, the epidemiology of M. pneumoniae among outpatient children with mild RTIs is unknown.…”

Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

“…The length of hospitalization is related to the severity of the disease and the effect of treatment, so it is necessary to improve the accuracy of treatment and rational use of antibacterial drugs in clinical practice. Different antibiotics can be selected for different patients at the beginning of admission according to their sputum culture results [ 27 , 28 ]. For example, for older patients who have been exposed to antibiotics for a long time and higher-grade antibiotics can be selected to improve the treatment effect.…”