Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage.
Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage.
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DOI：
10.1017/ice.2015.269
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发表时间：
2016-02
影响因子：
4.5
通讯作者：
Hanage WP
中科院分区：
文献类型：
--
作者：
Chang Q;Lipsitch M;Hanage WP
金黄色葡萄球菌是全世界细菌感染的常见原因。它最常见的是从前鼻孔进入和传播。众所周知，宿主对金黄色葡萄球菌鼻腔携带的倾向不同，根据携带时间的不同，可分为持续性、间歇性和非携带者。然而，预测干预结果的金黄色葡萄球菌的数学模型通常假定所有人都同样容易被殖民。为了刻画同质宿主群体在估计控制干预效果时产生的偏差，我们在同质假设下建立了医疗环境中金黄色葡萄球菌携带量的模型，并建立了一个异质性模型来解释三种类型的金黄色葡萄球菌携带者。在这两个模型中，我们计算了均衡携带率，以预测控制措施(减少接触和非殖民化)的影响。与同质模型相比，同质模型几乎总是低估金黄色葡萄球菌的传播率，而高估干预策略在降低携带率方面的影响。这一发现大体上是一致的，而不考虑模型设置的变化，以改变不同携带者在人口中的比例和这些携带者类型的运输持续时间。如果不考虑宿主的异质性，就会在预测干预策略的效果时产生系统性和实质性的偏差。通过建模进一步了解异质性对临床的影响，有助于有针对性的控制措施和更有效地分配资源。
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, intermittent, and non- carriers 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 being colonized. To characterize biases created by assuming a homogeneous host population in estimating efficacy of control interventions Mathematical modeling 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 three 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). 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 to vary the proportions of various carriers in the population and the duration of carriage for these carrier types. 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.