Metapopulation ecology links antibiotic resistance, consumption, and patient transfers in a network of hospital wards
Abstract
Antimicrobial resistance (AMR) is a global threat. A better understanding of how antibiotic use and between-ward patient transfers (or connectivity) impact population-level AMR in hospital networks can help optimize antibiotic stewardship and infection control strategies. Here, we used a metapopulation framework to explain variations in the incidence of infections caused by 7 major bacterial species and their drug-resistant variants in a network of 357 hospital wards. We found that ward-level antibiotic consumption volume had a stronger influence on the incidence of the more resistant pathogens, while connectivity had the most influence on hospital-endemic species and carbapenem-resistant pathogens. Piperacillin-tazobactam consumption was the strongest predictor of the cumulative incidence of infections resistant to empirical sepsis therapy. Our data provide evidence that both antibiotic use and connectivity measurably influence hospital AMR. Finally, we provide a ranking of key antibiotics by their estimated population-level impact on AMR that might help inform antimicrobial stewardship strategies.
Data availability
All data and software code that support the findings of this study are available at: https://github.com/rasigadelab/metapop.
Article and author information
Author details
Funding
Fondation Innovations en Infectiologie (R18037CC)
- Jean-Philippe Rasigade
French Laboratory of Excellence project ECOFECT (ANR-11-LABX-0048)
- Julie Teresa Shapiro
- Gilles Leboucher
- François Vandenesch
- Jean-Philippe Rasigade
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Ben S Cooper, Mahidol University, Thailand
Publication history
- Received: December 30, 2019
- Accepted: October 12, 2020
- Accepted Manuscript published: October 27, 2020 (version 1)
- Version of Record published: November 26, 2020 (version 2)
Copyright
© 2020, Shapiro et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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