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Novel and innovative approaches for measuring influenza VE - Anke Stuurman P95

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DRIVE Annual Forum
17th -18th September 2018, Rome

Published in: Health & Medicine
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Novel and innovative approaches for measuring influenza VE - Anke Stuurman P95

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 12. • Do you agree with the methods and prioritization? • Additional possible innovative methods? Questions to you!
  13. 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!

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