Estimating the Per-Exposure Effect of Infectious Disease Interventions
Estimating the Per-Exposure Effect of Infectious Disease Interventions
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DOI：
10.1097/ede.0000000000000003
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发表时间：
2014-01-01
期刊：
影响因子：
5.4
通讯作者：
Hernan, Miguel A.
中科院分区：
文献类型：
--
作者：
O'Hagan, Justin J.;Lipsitch, Marc;Hernan, Miguel A.
感染性疾病干预（例如疫苗）的平均效果在不同程度暴露于病原体的人群中有所不同。因此，许多研究人员倾向于采用一种独立于人群暴露水平的暴露效应测量方法，这种方法可以用于模拟，以估计不同人群通过干预措施避免的总疾病负担。然而，虽然经常估计每次暴露的影响，但感兴趣的数量往往定义不清，并且其计算中的假设通常是隐含的。在这篇文章中，我们建立在Halloran和Struchiner（流行病学。1995;6：142-151）以开发每次暴露效应的正式定义并讨论其无偏估计的必要条件。如果更加注意传播模式的参数化，就可以更好地理解其结果，从而对决策者具有更大的价值。
The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.