The document discusses 14 proposed novel methods for measuring influenza vaccine effectiveness (IVE). Experts from the DRIVE project brainstormed and prioritized the methods. Rapid near patient diagnostic tests, estimating IVE against non-specific influenza outcomes, and using high-dimensional propensity scores were deemed high priority. Methods like case-only studies and adaptive design were lower priority. The document also lists potential sites for implementing the methods during the 2018/19 influenza season.

1. Acknowledgement
DRIVE project has received funding from the Innovative
Medicines Initiative 2 Joint Undertaking under grant
agreement No 777363, This Joint Undertaking receives
support from the European Union’s Horizon 2020
research and innovation programme and EFPIA.
Novel and innovative
approaches for
measuring influenza VE
Acknowledgement
DRIVE project has received funding from the Innovative
Medicines Initiative 2 Joint Undertaking under grant
agreement No 777363, This Joint Undertaking receives
support from the European Union’s Horizon 2020
research and innovation programme and EFPIA.
Anke Stuurman-P95
DRIVE Annual Forum
17th-18th September 2018, Rome

2. THL: Ritva Syrjänen, Ulrike Baum, Hanna
Nohynek
FISABIO: Ainara Mira-Iglesias, Topi Turunen
IRD: François Simondon
UNIFI: Miriam Levi, Roberto Bonaiuti, Alfredo
Vannacci
Surrey: Uy Hoang, Harshana Liyanage,
UCBL: Bruno Lina
ISS: Caterina Rizzo
P95: Anke Stuurman, Marga Riera, Kaatje
Bollaerts
Contributors

3. • Improving existing systems and the utilization of
existing data sources and new technologies
• Sustainable IVE assessment; filling knowlege gaps
Purpose and process
Brainstorm Literature
Experts in DRIVE
Method
Added value
Is it already being done?
Potential synergies
DRIVE integration
Pros & cons
Validation
Prioritization for
feasibility
exploration

4. Method
Diagno
stics
Partici
patory
Outco
mes
Proced
ures
Popula
tions
Statisti
cal
metho
ds
Design
Data
source
1
Rapid near patient
molecular tests
x x
2 Participatory surveillance x x x x
3 Small data x
4
Clinical cohort study in
specific population
x x x x
5
Consent from next of kin
of severely ill patients
x x
6
Systematic swabbing in
hospital
x x
7 Case-only studies x
Overview of 14 proposed methods

5. Method
Diagno
stics
Partici
patory
Outco
mes
Proced
ures
Populat
ions
Statisti
cal
method
s
Design
Data
source
8
Non-specific
influenza outcomes
x x
9 Adaptive design x x
10
High-dimensional
propensity scores
x
11
Negative control
outcomes
x
12 Vaccine failure x x
13 Hybrid systems x
14
Syndromic
surveillance
x x
Overview of 14 proposed methods

6. Rapid near patient molecular diagnostic assays in
primary care
• Cheaper and faster than RT-PCR
• Accurate (when PPV and NPV meet criteria)
• Influenza A/B (but not subtype/lineage)
• Lower sensitivity than RT-PCR (corrected for in
statistical analysis)
Self-swabbing
• Swabs taken by patients and sent for diagnostic
testing
• Participatory approaches
• Swab soon after symptom onset
• Validation needed (housekeeping gene)
High priority

7. Non-specific influenza outcomes to estimate IVE
against influenza
 Estimate IVE against ILI
 Obtain PPV of influenza given ILI over time or during peak
 Use time-specific PPV to correct VE estiamtes based on ILI
for outcome misclassification
• Reduce cost as fewer lab-tests are needed
IVE against non-specific influenza outcomes
• Hidden disease burden
• Large public health importance if disease if common or
severe
High priority

8. High-dimensional propensity scores
• Control for confounding
• Surrogate for unmeasured confounders
• Probability of being exposed conditional, on
measured covariates
• To be applied in large register-based studies
• Algorithm to do the initial variable selection and
categorisation of values within a variable. That’s why
the method is called ‘high-dimensional’.
• The researcher can then look through the
algorithm’s results and modify it according to
biologic plausibility.
High priority

9. Improving data on severely ill patients
• Consent from next of kin
• Systematic swabbing (register-based studies)
Using negative control outcomes to detect
residual confounding
Participatory surveillance
• Active involvement of study population
• E.g. Influenzanet
Clinical cohort study with novel diagnostic in
populations of special interest with healthcare
contact
• Pregnant women, HCW, healthy children
Medium priority

10. • Case-only studies
• Time-varying confounding
• Adaptive design
• Re-adjustment of final sample size – too many data?
• Hybrid systems
• Traditional surveillance + big data
• Syndromic surveillance
• School absenteeism, OTC drugs, social media
• Small data
• Minimal set high quality data
• Analysis of adverse events ‘Vaccine failure’
• VE?
Low priority

11. Sites in 2018/19
PHIs & NICs
• Austria
• Finland
• Italy
• Romania
• Spain-Valencia
• Spain-La Rioja
• [An additional
NPHI]
Universities & networks
• Bambino Gesu
children’s hospital (IT)
• CIRI-IT (IT)*
• Helsinki Uni. Central
Hospital (FI)
• Kapodistrian
University of Athens
(GR)*
• Uni. Surrey (UK)*
• Vall d’Hebron Uni.
Hospital (ES)
• [An European
network]
??

12. • Do you agree with the methods and prioritization?
• Additional possible innovative methods?
Questions to you!

13. www.drive-eu.org
Acknowledgement
DRIVE project has received funding from the Innovative
Medicines Initiative 2 Joint Undertaking under grant
agreement No 777363, This Joint Undertaking receives
support from the European Union’s Horizon 2020
research and innovation programme and EFPIA.
Thank you
for your attention!