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

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This summary is machine-generated.

Estimating vaccine impact requires careful definition. This study defines "directly averted" outcomes, providing a lower bound for total averted outcomes, and identifies bias in common estimation methods.

Keywords:
Differential depletion of susceptiblesDirect effectHazard differenceVaccine-averted outcomes

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Accurate estimation of vaccine effectiveness during pandemics like COVID-19 is crucial for public health.
  • Previous studies often estimated "directly averted" deaths, potentially underestimating the total impact by not fully accounting for indirect effects.

Purpose of the Study:

  • To define a causal estimand for "directly averted" outcomes by vaccination, representing the impact on vaccinated individuals while holding coverage constant.
  • To develop an unbiased estimator for this causal estimand in randomized controlled trials (RCTs).
  • To analyze the bias of the commonly used "hazard difference" estimator.

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.

Main Results:

  • The "hazard difference" estimator is biased in RCTs if vaccination has a non-null effect, due to failure to account for "depletion of susceptibles" among the unvaccinated.
  • Overestimation by the "hazard difference" estimator is minimal for deaths with low infection-fatality rates but can be substantial for infections with high vaccine efficacy and high transmissibility.
  • Defined and compared "avertible" outcomes (outcomes that could have been averted but were not due to lack of vaccination).

Conclusions:

  • The proposed causal estimand provides a lower bound for total vaccine-averted outcomes.
  • The "hazard difference" estimator can lead to significant overestimation, particularly for averted infections.
  • Further research is needed to identify causal estimands in observational studies.