What question did this study set out to answer?

This research aims to explore the patterns and driving factors of antimicrobial resistance in China during the COVID-19 pandemic.

February 11, 2026Open Access

Spatiotemporal dynamics and multiple driving factors of antimicrobial resistance in China during the COVID-19 pandemic (2019–2023): a provincial panel data analysis

Key Points

This research aims to explore the patterns and driving factors of antimicrobial resistance in China during the COVID-19 pandemic.
Analyzed provincial panel data from 2019 to 2023
Utilized spatial autocorrelation (Global Moran's I)
Applied multimodel methods including fixed-effects regression and random forest
Identified drivers spanning healthcare, agriculture, environment, and socioeconomic factors
Significant spatial autocorrelation found for CRKP and CRAB
MRSA linked to livestock and air pollution (PM2.5)
CRKP associated with healthcare spending and pig inventory
CRAB correlates with healthcare resources but paradoxically with aging population
COVID-19 had an indirect influence on AMR dynamics

Abstract

ABSTRACT Antimicrobial resistance (AMR) poses a critical and growing global health threat, directly causing millions of deaths, with China bearing a significant burden. Understanding the provincial dynamics and multifactorial one health drivers of AMR, especially amidst the transformative 2019–2023 coronavirus disease 2019 (COVID-19) pandemic, remains crucial but underexplored. This comprehensive study investigated the spatiotemporal patterns and multisectoral drivers of methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Klebsiella pneumoniae (CRKP), and carbapenem-resistant Acinetobacter baumannii (CRAB) prevalence across Chinese provinces using a robust 2019–2023 panel data set. Utilizing spatial autocorrelation (Global Moran’s I) and a multimodel approach, including panel fixed-effects regression, least absolute shrinkage and selection operator, and random forest, we identified robust drivers across healthcare, agricultural, environmental, and socioeconomic domains. Significant positive spatial autocorrelation was found for CRKP (Moran’s I = 0.225; P < 0.05) and CRAB (Moran’s I = 0.159; P < 0.05), indicating geographical clustering, whereas MRSA exhibited no significant pattern. Pathogen-specific drivers emerged. MRSA prevalence was linked to livestock inventory and PM2.5; CRKP to healthcare expenditure and pig inventory; and CRAB to healthcare expenditure and hospital beds, alongside counterintuitive negative associations with population aging and average length of hospital stay. The direct annual effect of COVID-19 was not statistically significant. We conclude that Chinese AMR is a spatially heterogeneous challenge driven by complex one health factors. A striking “paradox of progress” suggests higher healthcare capacity correlates with dangerously increased carbapenem-resistant pathogens, emphasizing the urgent need for robust infection prevention and control. The pandemic’s influence was predominantly indirect. These findings demand multisectoral, regionally tailored AMR strategies integrating healthcare, agricultural, and environmental policies for effective control.

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