Background: It is unclear whether changes in antimicrobial resistance (AMR) in primary care influence AMR in hospital settings. Therefore, we investigated the dynamic association of AMR between primary care and hospitals.
Methods: We studied resistance percentages of Escherichia coli and Klebsiella pneumoniae isolates to co-amoxiclav, ciprofloxacin, fosfomycin, nitrofurantoin and trimethoprim submitted by primary care, hospital outpatient and hospital inpatient settings to the Dutch National AMR surveillance network (ISIS-AR) from 2008 to 2020. For each bacterium-antibiotic combination, we first conducted multivariable logistic regressions to calculate AMR odds ratios (ORs) by month and healthcare setting, adjusted for patient-related factors and a time term. Second, multiple time series analysis was done using vector autoregressive models including the (log) ORs for each bacterium-antibiotic combination. Models were interpreted by impulse response functions and Granger-causality tests.
Findings: The main AMR association was unidirectional from primary care to hospital settings with Granger-causality p-values between <0.0001 and 0.029. Depending on the bacterium-antibiotic combination, a 1% increase of AMR in E. coli and K. pneumoniae in primary care leads to an increase of AMR in hospital settings ranging from 0.10% to 0.40%. For ciprofloxacin resistance in K. pneumoniae, we found significant bidirectional associations between all healthcare settings with Granger-causality p-values between <0.0001 and 0.0075.
Interpretation: For the majority of bacterium-antibiotic combinations, the main AMR association was from primary care to hospital settings. These results underscore the importance of antibiotic stewardship at the community level.
Funding: ISIS-AR is supported by the Ministry of Health, Welfare and Sport of the Netherlands and the first author by the Central University of Ecuador to follow a PhD program in Erasmus MC.
Keywords: Antimicrobial resistance; General practitioners; Granger causality; Health care settings; Multiple time series.
© 2024 The Author(s).