Modelling the impact of antibiotic use on antibiotic-resistant Escherichia coli using population-based data from a large hospital and its surrounding community
- PMID: 21393172
- DOI: 10.1093/jac/dkq525
Modelling the impact of antibiotic use on antibiotic-resistant Escherichia coli using population-based data from a large hospital and its surrounding community
Abstract
Objectives: To determine the temporal relationship between antibiotic use and incidence of antibiotic-resistant Escherichia coli in both the inpatient and outpatient setting of a large urban area.
Methods: A retrospective observational time-series analysis was performed to evaluate the incidence of non-duplicate clinical isolates of E. coli resistant to ciprofloxacin, trimethoprim/sulfamethoxazole and cefepime from January 2000 through December 2007, combined with a transfer function model of aggregated data on antibiotic use in both settings obtained from the hospital's pharmacy and outpatient billing offices.
Results: Ciprofloxacin resistance increased from 6.0% (2000) to 15.4% (2007; P<0.0001) and cefepime resistance from 0.9% (2002) to 3.2% (2007; P=0.01). Trimethoprim/sulfamethoxazole resistance remained stable (23.7%-25.8%). Total antibiotic use increased in both settings, while fluoroquinolone use increased significantly only among outpatients. A temporal effect between fluoroquinolone resistance in community E. coli isolates and outpatient use of ciprofloxacin (immediate effect and time lag 1 month) and moxifloxacin (time lag 4 months) was observed, explaining 51% of the variance over time. The incidence of cefepime resistance in E. coli was correlated with ciprofloxacin use in the inpatient (lag 1 month) and outpatient (lag 4 months) settings and with the use of ceftriaxone (lag 0 month), piperacillin/tazobactam (3 months) and cefepime (3 months) in the hospital (R2=51%).
Conclusions: These results support efforts to reduce prescribing of fluoroquinolones for control of resistant E. coli including extended-spectrum β-lactamase producers and show the added value of time-series analysis to better understand the interaction between community and hospital antibiotic prescribing and its spill-over effect on antibiotic resistance.
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