Table 1

Factors influencing estimated COVID-19 vaccine effectiveness: outcomes affected and type of influence

FactorsOutcomes affected*Influence on effectiveness estimate†
Pre-existing immunity
 Same in V and UNVI, H, DGenuine
 Different in V and UNVI, H, DSpurious (selection bias)
Vaccination misclassificationI, H, DSpurious (misclassification)
Exposure difference
Induced by perceived vaccine protectionI, H, DGenuine
Pre-existing, carried forwardI, H, DSpurious (selection bias)
Typical diagnosis biasI, H, DSpurious (selection bias, misclassification)
Affecting treatmentH, DGenuine
Disease risk factor confoundingH, DSpurious (confounding bias)
Hospital admission decision
Induced by perceived vaccine protectionH, (D)‡Genuine
Other reasonsH, (D)‡Spurious (selection bias, confounding)
Treatment use difference
Induced by perceived vaccine protectionD, (H)§Genuine
Other reasonsD, (H)§Spurious (selection bias, confounding)
Death attributionDSpurious (misclassification)
  • *The outcomes considered here are infection (I), hospitalisation (H) and death (D).

  • †There are two major types of influence: genuine impact represents effect modification (ie, the vaccine effectiveness is genuinely different in different groups, settings, etc) and conversely, spurious impact means bias (ie, the vaccine effectiveness is different from what is being estimated).

  • ‡May affect death outcome if hospital admission affects the risk of death (not necessarily so).

  • §For treatments that are applied before hospitalisation and may affect the need for hospitalisation.

  • UNV, unvaccinated; V, vaccinated.