Causal Vaccine Effects on Post-infection Outcomes in the Naturally Infected

2026-03-31

AI Summary

This study addresses the common underestimation of vaccine efficacy in existing evaluations, which often neglect indirect benefits arising from infection prevention. Focusing on “natural infectees”—individuals who would become infected in the absence of vaccination—the authors propose a novel causal estimand within the principal stratification framework to quantify the vaccine’s effect on post-infection outcomes. Under exclusion restrictions and a partial principal ignorability assumption, they achieve point identification and develop a robust, efficient one-step estimator. Simulation studies demonstrate favorable finite-sample performance of the proposed method. In a reanalysis of a rotavirus vaccine trial, the approach reveals a significant reduction in antibiotic use among natural infectees, highlighting an underappreciated indirect benefit of vaccination.

Abstract

Understanding vaccine effects on post-infection outcomes is critical for evaluating the full value proposition of a vaccine. However, defining appropriate causal effects on such outcomes is challenging because infection is affected by vaccination. Existing principal stratification approaches focus on the \emph{Doomed} stratum, individuals who would be infected regardless of vaccine receipt. For many relevant outcomes, however, this estimand will understate vaccine benefit by excluding individuals whose adverse post-infection outcomes are improved because vaccination prevented infection. We therefore propose causal estimands for post-infection outcomes in the \emph{Naturally Infected}, individuals who would be infected in absence of vaccine. We derive bounds under minimal assumptions and give point identification results under an exclusion restriction and/or a partial principal ignorability assumption. For point-identified settings, we develop efficient one-step estimators with robustness properties under inconsistent nuisance parameter estimation. We further show under what conditions the same identification functional can be interpreted as targeting an effect among individuals exposed to a sufficiently infectious dose of the pathogen, thereby avoiding direct reliance on cross-world parameters and fundamentally untestable causal assumptions. Simulations show that the bounds are valid but often wide, and that the point estimators perform well when their identifying assumptions hold. In a reanalysis of a rotavirus vaccine trial, marginal and Doomed-stratum analyses showed little evidence of an effect on antibiotic use, whereas analyses targeting the Naturally Infected suggested a protective effect under principal ignorability-based assumptions.

Problem

• causal inference
• vaccine effects
• post-infection outcomes
• principal stratification
• naturally infected

Innovation

• principal stratification
• causal inference
• vaccine efficacy
• post-infection outcomes
• natural infection