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.
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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.
The authors report no conflicts of interest.
This work was supported by grant number U54GM088558 from the National Institute of General Medical Sciences (J.J.O.H. and M.L.) and National Institutes of Health grant R01 AI102634 (M.A.H.).
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.
Correspondence: Justin J. O’Hagan, Harvard School of Public Health, 677 Huntington Avenue, Kresge 506, Boston, MA 02115. E-mail: firstname.lastname@example.org.