Defining and Estimating Outcomes Directly Averted by a Vaccination Program when Rollout Occurs Over Time

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• Estimating deaths averted by COVID-19 vaccination is crucial.
• Previous studies often focused on "directly averted" deaths, potentially underestimating overall impact.
• Accurate measurement of vaccine effectiveness is vital for public health policy.

Purpose of the Study:

• To define and quantify outcomes "directly averted" by vaccination, considering vaccinated individuals only.
• To develop an unbiased estimator for this causal estimand in randomized controlled trials (RCTs).
• To analyze the bias of existing estimators, like the "hazard difference" method.

Main Methods:

• Causal inference framework to define "directly averted" outcomes.
• Development of an unbiased estimator for a one-stage RCT.
• Simulation studies to compare the proposed estimator with the "hazard difference" estimator.
• Exploration of estimands and estimators for "avertible outcomes".

Main Results:

• The "directly averted" outcome estimand serves as a lower bound for total averted outcomes when indirect effects are non-negative.
• The "hazard difference" estimator is biased in RCTs if vaccination has a non-null effect due to "depletion of susceptibles".
• Bias in the "hazard difference" estimator is small for averted deaths with low infection-fatality rates but can be large for averted infections with high reproduction numbers.

Conclusions:

• The proposed causal estimand provides a more accurate measure of vaccination impact on vaccinated individuals.
• Awareness of "depletion of susceptibles" is necessary for unbiased estimation of vaccine effectiveness.
• Further research is needed to apply these causal methods in observational studies.