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流感在肺炎流行病学中的作用。

The role of influenza in the epidemiology of pneumonia.

作者信息

Shrestha Sourya, Foxman Betsy, Berus Joshua, van Panhuis Willem G, Steiner Claudia, Viboud Cécile, Rohani Pejman

机构信息

Department of Ecology &Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.

Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA.

出版信息

Sci Rep. 2015 Oct 21;5:15314. doi: 10.1038/srep15314.

Abstract

Interactions arising from sequential viral and bacterial infections play important roles in the epidemiological outcome of many respiratory pathogens. Influenza virus has been implicated in the pathogenesis of several respiratory bacterial pathogens commonly associated with pneumonia. Though clinical evidence supporting this interaction is unambiguous, its population-level effects-magnitude, epidemiological impact and variation during pandemic and seasonal outbreaks-remain unclear. To address these unknowns, we used longitudinal influenza and pneumonia incidence data, at different spatial resolutions and across different epidemiological periods, to infer the nature, timing and the intensity of influenza-pneumonia interaction. We used a mechanistic transmission model within a likelihood-based inference framework to carry out formal hypothesis testing. Irrespective of the source of data examined, we found that influenza infection increases the risk of pneumonia by ~100-fold. We found no support for enhanced transmission or severity impact of the interaction. For model-validation, we challenged our fitted model to make out-of-sample pneumonia predictions during pandemic and non-pandemic periods. The consistency in our inference tests carried out on several distinct datasets, and the predictive skill of our model increase confidence in our overall conclusion that influenza infection substantially enhances the risk of pneumonia, though only for a short period.

摘要

由相继发生的病毒和细菌感染引起的相互作用在许多呼吸道病原体的流行病学结果中起着重要作用。流感病毒与几种通常与肺炎相关的呼吸道细菌病原体的发病机制有关。尽管支持这种相互作用的临床证据确凿,但在大流行和季节性暴发期间,其在人群层面的影响——程度、流行病学影响和变化——仍不清楚。为了解决这些未知问题,我们使用了不同空间分辨率和不同流行病学时期的流感和肺炎发病率纵向数据,来推断流感与肺炎相互作用的性质、时间和强度。我们在基于似然性的推断框架内使用了一个机制性传播模型来进行正式的假设检验。无论所检查的数据来源如何,我们发现流感感染会使肺炎风险增加约100倍。我们没有发现支持这种相互作用会增强传播或严重程度影响的证据。为了进行模型验证,我们让拟合模型在大流行和非大流行期间进行样本外肺炎预测。我们在几个不同数据集上进行的推断测试的一致性以及模型的预测能力,增加了我们对总体结论的信心,即流感感染会大幅增加肺炎风险,尽管只是在短时间内。

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