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Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage

Published online by Cambridge University Press:  24 November 2015

Qiuzhi Chang ,
Marc Lipsitch  and
William P. Hanage
Qiuzhi Chang*
Affiliation:
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
Marc Lipsitch
Affiliation:
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
William P. Hanage
Affiliation:
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
*
Address correspondence to Qiuzhi Chang, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA, 02115 (qic716@mail.harvard.edu).
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Abstract

BACKGROUND

Staphylococcus aureus is a common cause of bacterial infections worldwide. It is most commonly carried in and transmitted from the anterior nares. Hosts are known to vary in their proclivity for S. aureus nasal carriage and may be divided into persistent carriers, intermittent carriers, and noncarriers, depending on duration of carriage. Mathematical models of S. aureus to predict outcomes of interventions have, however, typically assumed that all individuals are equally susceptible to colonization.

OBJECTIVE

To characterize biases created by assuming a homogeneous host population in estimating efficacy of control interventions.

DESIGN

Mathematical model.

METHODS

We developed a model of S. aureus carriage in the healthcare setting under the homogeneous assumption as well as a heterogeneous model to account for the 3 types of S. aureus carriers. In both models, we calculated the equilibrium carriage prevalence to predict the impact of control measures (reducing contact and decolonization).

RESULTS

The homogeneous model almost always underestimates S. aureus transmissibility and overestimates the impact of intervention strategies in lowering carriage prevalence compared to the heterogeneous model. This finding is generally consistent regardless of changes in model setting that vary the proportions of various carriers in the population and the duration of carriage for these carrier types.

CONCLUSIONS

Not accounting for host heterogeneity leads to systematic and substantial biases in predictions of the effects of intervention strategies. Further understanding of the clinical impacts of heterogeneity through modeling can help to target control measures and allocate resources more efficiently.

Infect. Control Hosp. Epidemiol. 2016;37(2):197–204

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Supplement 2 , February 2016 , pp. 197 - 204
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Footnotes

*

Contributed equally to this manuscript.

References

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