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The impact of quadrivalent influenza vaccine (QIV) in Canada: Some insights from a dynamic model Ed Thommes, PhD Health Outcomes Manager GlaxoSmithKline.

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Presentation on theme: "The impact of quadrivalent influenza vaccine (QIV) in Canada: Some insights from a dynamic model Ed Thommes, PhD Health Outcomes Manager GlaxoSmithKline."— Presentation transcript:

1 The impact of quadrivalent influenza vaccine (QIV) in Canada: Some insights from a dynamic model Ed Thommes, PhD Health Outcomes Manager GlaxoSmithKline Canada & Department of Mathematics & Statistics, University of Guelph

2 4Strain dynamic influenza model team: Chris Bauch Professor, Dept. of Applied Mathematics University of Waterloo, ON Geneviève Meier Director, Health Economics, Vaccines GlaxoSmithKline Wavre, Belgium Ayman Chit Director, Health Outcomes and Economics North America Sanofi Pasteur Toronto, ON Afisi Ismaila Director Therapy Area GlaxoSmithKline Research Triangle Park, NC, USA 2

3 Outline Background: What is QIV? Overview of the 4Strain dynamic transmission model Calibrating the influenza “natural history” input parameters Test case: Ontario’s adoption of universal influenza immunization TIV  QIV switch results: outcomes prevented and cost-effectiveness Summary 3

4 Background: TIV Current trivalent influenza vaccines (TIV) contain 2 influenza A virus types: H3N2, H1N1 and one influenza B lineage Annual strain recommendation is based on surveillance  Recommended strains may not reflect current circulating strains Co-circulation of B/Victoria and B/Yamagata

5 Background: Influenza B Two main genetic lineages in circulation: 1. Victoria (1987) 2. Yamagata (1988) B Victoria and B Yamagata have co-circulated in recent years Mutation rate is slower compared to influenza A viruses

6 Vaccine mismatch for influenza B: Canada Adapted from Fluwatch and NACI

7 GSK’s QIV: FluLaval ® Tetra Quadrivalent split-virion, inactivated influenza vaccine Authorized for use in Canada Feb 6, 2014 Manufactured in Sainte-Foy, Quebec

8 A menagerie of modeling approaches… flu model static treeMarkov dynamic compartmental individual or “agent”-based (ABM) 8

9 Model structure: i) Simple S(usceptible)I(nfected)R(ecovered) model

10 Model structure: ii) Adding a second strain Approach of Castillo-Chavez et al. (1989) Introduces cross-protection into model dynamics Immunity waning: each strain sequentially, i.e.. R 1 R 2 →S 1 R 2 →S 1 S 2 or R 1 R 2 →R 1 S 2 →S 1 S 2

11 Success/failure determined at time of vaccination: Let ε 1, ε 2 be the efficacies. Then, e.g. for a person in S 1 S 2, possible outcomes of vaccinating, are, with probability P: P=ε 1 ε 2 : go to V 1 V 2 P= ε 1 (1- ε 2 ): go to V 1 S 2 P= ε 2 (1- ε 1 ): go to S 1 V 2 P=(1- ε 1 )(1- ε 2 ): stay in S 1 S 2 Waning of vaccinated immunity occurs analogously to waning of natural immunity NOTE: We assume that the natural immunity always lasts at LEAST as long as vaccine-conferred immunity. Thus, e.g., successfully vaccinating someone in compartment R 1 S 2 against strain 1 has no effect Model structure: iii) Adding vaccination

12 Calibrating the model to real-world data –Ideally, we’d like to use a given region’s influenza surveillance to calibrate model parameters –Problem: influenza surveillance very incomplete  instead, used Turner et al. (2003) HTA: Calculates unvaccinated (“natural”) attack rate of influenza from placebo arms of vaccine & antiviral RCTs –advantage of natural atk rate: Only indirectly (through herd immunity) depends on vaccination state of population (or: avoiding “Garbage In – Garbage Out”) 12

13 Our calibration approach: Approximate Bayesian computation (ABC) 13

14 Fitting simulations: Influenza in the US, influenza A influenza B Thommes et al., Vaccine, submitted

15 Testing the model: Ontario’s adoption of a universal influenza immunization program (UIIP) –Implemented in 2000; world’s first large-scale universal influenza immunization program –Resulting changes in both vaccine uptake and influenza- associated events have been studied in detail (Kwong et al., PLoS Medicine 2008). Events considered: –doctor’s office (GP) visits –emergency room (ER) visits –hospitalizations –deaths –  Objective: Assess how well our model agrees with Kwong et al.’s results

16 Testing the 4Strain model on Ontario’s UIIP: Kwong et al. (2008) 4Strain dynamic model Thommes et al., Vaccine, submitted Result: Model is overall conservative relative to Kwong et al. (2008) in predicting outcomes averted by UIIP Relative rate ratio: Reduction Ontario = Reduction Canada

17 Result: Canada-wide TIV  QIV switch brings about clear reduction in outcomes # simulations outcomes per season, TIV and QIV outcomes prevented per season by QIV influenza cases (50k-300k prevented) GP visits (20k-120k prevented) ER visits ( prevented) hospitalizations ( prevented) deaths ( prevented)

18 Sensitivity analysis: QIV highly cost- effective across all plausible inputs

19 Limitations: –Vaccine uptake extrapolated below 12 yrs in most provinces –Using mostly US attack rates in model calibration –Very little information about duration of vaccine-conferred immunity to influenza (we assume 1 yr on average) –No healthy vs. at-risk stratification in model population

20 Summary: What insights did we gain? –Much of the complexity in developing a dynamic transmission model lies in the calibration –A large-scale change in vaccination policy (e.g. targeted  universal transition) makes a great test case –A dynamic model is more challenging to work with than a static model, but can also give us deeper insights –Our result: A Canada-wide switch from TIV to QIV is projected to be highly cost-effective across all plausible inputs –Province-specific analyses (AB, MB, ON, QC, NS) yield very similar CE results

21 Backup slides

22 TIV and transmission dynamics: An interesting insight… –The WHO’s choice of B lineage to include in TIV matches the dominant circulating B lineage in only ~50% of seasons –Insight from 4Strain: The WHO actually does much better than this. –…Why? Because circulation of TIV-included B lineage preferentially suppressed, which in many seasons actually changes the dominant lineage! TIV actually works better than we think! Even with perfect prediction, TIV would not prevent as many outcomes as QIV OR

23 Modeling the impact of a Canada-wide switch from TIV to QIV

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26 Parameter fitting Overall approach (analogous to that of van der Velde et al for an HPV model): –Prior ranges chosen for input parameters to be varied –Allowable target ranges chosen for model outputs –Sets of input parameters drawn using Latin hypercube sampling –One simulation run for each parameter set –Posterior parameter distribution consists of all parameter sets which produce simulation outputs satisfying all the target ranges Above approach used to fit natural history parameters of the model. Fitting targets are: –“natural attack rate”, i.e. force of infection in the unvaccinated population, (Turner et al HTA, using placebo arms of vaccine/antiviral RCTs) –relative fraction of influenza A and B, by season (CDC surveillance data) –% of circulating influenza B covered by the B strain selected for vaccine (Reed et al. 2012) Can then perform simulations in different settings (i.e. with different demographics, vaccine uptake, etc.), each time drawing parameter sets from the above posterior distribution

27 Background: Ontario’s adoption of a universal influenza immunization program (UIIP) Implemented in 2000; world’s first large-scale universal influenza immunization program Resulting changes in both vaccine uptake and influenza- associated events have been studied in detail (Kwong et al., PLoS Medicine 2008). Events considered: –doctor’s office (GP) visits –emergency room (ER) visits –hospitalizations –deaths  Objective: Assess how well our model agrees with Kwong et al.’s results

28 Simulating Ontario’s universal influenza immunization program (UIIP): Model inputs I Population, birth, death rates from Statistics Canada, Simulated period is , as in Kwong (2008) (i.e. 3 yrs pre-introduction, 4 yrs post-introduction of universal influenza immunization Uptake rates: –age 6-23 months: Campitelli et al. (2012) –age 2-11 years: extrapolated using Moran et al. (2009) –age 12 yrs and up: Kwong et al. (2008) “natural attack rate”, i.e. force of infection in the unvaccinated population, (Turner et al. (2003) HTA, using placebo arms of vaccine/antiviral RCTs)

29 Simulating Ontario’s universal influenza immunization program (UIIP): Model inputs I Population, birth, death rates from Statistics Canada, Simulated period is , as in Kwong (2008) (i.e. 3 yrs pre-introduction, 4 yrs post-introduction of universal influenza immunization Uptake rates: –age 6-23 months: Campitelli et al. (2012) –age 2-11 years: extrapolated using Moran et al. (2009) –age 12 yrs and up: Kwong et al. (2008) “natural attack rate”, i.e. force of infection in the unvaccinated population, (Turner et al. (2003) HTA, using placebo arms of vaccine/antiviral RCTs)

30 fraction of circulating influenza B, and fraction of B covered by vaccine: FluWatch surveillance network vaccine efficacy: Tricco et al. (submitted), systematic review against influenza A against influenza B, lineage match against influenza B, lineage mismatch outcomes probabilities: Pr(GP visit|flu), Pr(hospitalization|flu), Pr(death|flu): Molinari et al. (2007) Pr(ER visit|flu): extrapolated from Kwong et al. (2008) Simulating Ontario’s universal influenza immunization program (UIIP): Model inputs II


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