Prediction Methods for Antimicrobial Resistance Trends in China

Area of Science:

• * Infectious Diseases
• * Microbiology
• * Public Health

Background:

• * Antimicrobial resistance (AMR) poses a significant global health threat.
• * Accurate prediction of AMR trends is crucial for effective intervention strategies.
• * China's extensive antibiotic usage necessitates surveillance and modeling of resistance patterns.

Purpose of the Study:

• * To develop and validate robust prediction models for AMR trends in China.
• * To identify specific pathogens and antibiotics exhibiting significant resistance changes.
• * To evaluate the performance of different modeling techniques for AMR surveillance.

Main Methods:

• * Utilized data from the China Antimicrobial Resistance Surveillance System (2014-2021).
• * Developed prediction models using GM (1,1), support vector machine, polynomial fitting, and time series analysis.
• * Compared model accuracy and robustness for predicting resistance trends.

Main Results:

• * GM (1,1) model showed superior accuracy and robustness compared to other methods.
• * Observed increasing resistance trends for carbapenem-resistant Klebsiella pneumoniae and erythromycin-resistant Streptococcus pneumoniae.
• * Decreasing resistance trends were noted for most other investigated pathogens.

Conclusions:

• * The study highlights the need for enhanced antibiotic stewardship in China, particularly concerning macrolide overuse.
• * Rising resistance in specific pathogens like E. coli and P. aeruginosa warrants targeted interventions.
• * The COVID-19 pandemic may have exacerbated AMR trends, necessitating continued global monitoring.