From the aDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; bCenter for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA; and cDepartment of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA.
Epidemiology. 2014 Jan;25(1):134-8. doi: 10.1097/EDE.0000000000000003.
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.
传染病干预（例如疫苗）的平均效果因病原体暴露程度不同而在不同人群中存在差异。因此，许多研究人员倾向于采用一种基于暴露次数的效应测量方法，这种方法被认为与人群的暴露水平无关，并且可以在模拟中用于估计干预措施在不同人群中避免的总疾病负担。然而，尽管经常估计基于暴露次数的效应，但感兴趣的数量通常定义不明确，并且在计算中通常隐含了其假设。在本文中，我们基于 Halloran 和 Struchiner 的工作（Epidemiology. 1995;6:142-151），对基于暴露次数的效应进行了正式定义，并讨论了其无偏估计所需的条件。通过更加关注传播模型的参数化，他们的结果可以得到更好的理解，从而对决策者更有价值。
