Currently, measuring the effectiveness of influenza vaccines relies on observational studies which compare the occurrence of influenza in vaccinated and unvaccinated populations or the odds of vaccination in cases and non-cases. The different types of observational studies used include cohort, case-control (notably, the test-negative design) and screening method study designs.
“We present here an initial exploration of novel methods and additional tools to assess IVE. New ideas will be added as they come along over the next months and years”, tells Anke Stuurman from P95, a Belgian SME conducting the analysis.
A key question in assessing influenza vaccine effectiveness (IVE) is how to balance the inputs in terms of resources to the accuracy and generalizability of the IVE estimates. Many of the traditional observational study designs are relatively costly to establish and maintain, yet remain susceptible to bias and may not provide reliable information on all the desired outcomes.
Which are the most promising existing and potential innovative methods for studying influenza vaccine effectiveness?
DRIVE aims to improve existing systems and explore novel and innovative approaches to measure IVE in order to promote robust IVE assessment and improve the utilization of existing data sources and new technologies.
This report presents the results of an initial mapping of existing and potential innovative methods. We describe novel diagnostic methods, participatory approaches, ways to capture data on outcomes of specific interest, novel designs, non-traditional data sources, and relatively unexplored methods to control for confounding in IVE studies.
Proposed prioritization for further exploration and potential implementation include methods as systematic swabbing in hospital and non-specific influenza outcomes to estimate influenza VE.
We describe the potential approaches, identify the most promising ones, describe if they can be integrated in traditional data collection and how one might validate them and recommend prioritization for novel methods to be explored, within DRIVE.