Spatial panel data analysis of antimicrobial resistance in Escherichia coli in China

Antimicrobial resistance has caused tremendous loss of life, health, and economic property, and Enterobacteriaceae bacteria have been classified by the World Health Organization as one of the key pathogens for which drug development is urgently needed. There is a lack of in-depth research on spatial effect analysis of Escherichia coli (E. coli) by researchers. The study was analyzed using spatial effect analysis with data from the China Antibiotic Resistance Surveillance System covering the prevalence of third-generation cephalosporins-resistant E. coli (3GCREC), carbapenem-resistant E. coli (CREC) and quinolone-resistant E. coli (QREC) in 30 provinces during 2014-2023. Spatial Durbin model and effect decomposition were used to determine the spatial effects of E. coli and their influencing factors, including Ambient temperature, Particulate matter (PM_2.5), Precipitation, Absolute Humidity, Hospital beds, Physicians, Health facility, and GDP per capita. 3GCREC, CREC, and QREC showed significant spatial autocorrelation and regional differences (p < 0.001). The results of the time-fixed spatial Durbin model showed that ambient temperature, PM_2.5, hospital beds, and healthcare facilities had significant effects on antimicrobial resistance of different E. coli strains with spatial spillover effects. Direct and indirect effects of ambient temperature, PM_2.5, and healthcare facilities on CREC and QREC were determined by effect decomposition. The prevalence of different E. coli strains exhibits spatial autocorrelation, with provinces in close geographical proximity often having more similar resistance rates. More attention should be paid to areas with higher E. coli prevalence and interregional dynamics. The effects of ambient temperature, PM_2.5, hospital beds, and healthcare facilities on E. coli prevalence should be fully recognized.