Estimating the per-exposure effect of infectious disease interventions

Abstract

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.

Figure 1

Causal diagram for a double-blind randomized trial of a Chlamydia vaccine A and Chlamydia infection Y. E represents exposure to infection and U unmeasured risk factors for infection. The subscripts denote time period. For simplicity, only two time periods are shown.

Figure 2

Causal diagram for a randomized trial like that in Figure 1 except that the risk factors U affect exposure E.