Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage
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CitationChang, Qiuzhi, Marc Lipsitch, and William P. Hanage. 2015. “Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus Aureus Carriage.” Infection Control & Hospital Epidemiology (November 24): 1–8. doi:10.1017/ice.2015.269.
AbstractBACKGROUND 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.
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