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Author Notes:

Corresponding author: Michael Haber (Email: mhaber@sph.emory.edu)

BRL and MH wrote the manuscript and conducted the deterministic and stochastic simulations, respectively.

PK helped with important comments related to the administration of antibiotic-resistant drugs.

All authors read and approved the final manuscript.

The authors wish to thank the Editor and three reviewers for their helpful comments.

The authors declare that they have no competing interests.


Research Funding:

This project was partially supported by grants AI40662 and GM091875 from the US National Institutes of Health (BRL).

Pfizer Inc. also generously provided financial support for this project without imposing review or any other constraints that could possibly be construed as a conflict of interest.

Antibiotic control of antibiotic resistance in hospitals: a simulation study


Journal Title:

BMC Infectious Diseases


Volume 10


, Pages 1-10

Type of Work:

Article | Final Publisher PDF


Background: Using mathematical deterministic models of the epidemiology of hospital-acquired infections and antibiotic resistance, it has been shown that the rates of hospital-acquired bacterial infection and frequency of antibiotic infections can be reduced by (i) restricting the admission of patients colonized with resistant bacteria, (ii) increasing the rate of turnover of patients, (iii) reducing transmission by infection control measures, and (iv) the use of second-line drugs for which there is no resistance. In an effort to explore the generality and robustness of the predictions of these deterministic models to the real world of hospitals, where there is variation in all of the factors contributing to the incidence of infection, we developed and used a stochastic model of the epidemiology of hospital-acquired infections and resistance. In our analysis of the properties of this model we give particular consideration different regimes of using second-line drugs in this process. Methods: We developed a simple model that describes the transmission of drug-sensitive and drug-resistant bacteria in a small hospital. Colonized patients may be treated with a standard drug, for which there is some resistance, and with a second-line drug, for which there is no resistance. We then ran deterministic and stochastic simulation programs, based on this model, to predict the effectiveness of various treatment strategies. Results: The results of the analysis using our stochastic model support the predictions of the deterministic models; not only will the implementation of any of the above listed measures substantially reduce the incidences of hospital-acquired infections and the frequency of resistance, the effects of their implementation should be seen in months rather than the years or decades anticipated to control resistance in open communities. How effectively and how rapidly the application of second-line drugs will contribute to the decline in the frequency of resistance to the first-line drugs depends on how these drugs are administered. The earlier the switch to second-line drugs, the more effective this protocol will be. Switching to second-line drugs at random is more effective than switching after a defined period or only after there is direct evidence that the patient is colonized with bacteria resistant to the first antibiotic. Conclusions: The incidence of hospital-acquired bacterial infections and frequencies of antibiotic resistant bacteria can be markedly and rapidly reduced by different readily implemented procedures. The efficacy using second line drugs to achieve these ends depends on the protocol used for their administration.

Copyright information:

© 2010 Haber et al; licensee BioMed Central Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/).

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