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##### Ed Thommes, PhD Health Outcomes Manager GlaxoSmithKline Canada &

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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 Canada & Department of Mathematics & Statistics, University of Guelph**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 AfisiIsmaila Director Therapy Area GlaxoSmithKline Research Triangle Park, NC, USA**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 • TIVQIV switch results: outcomes prevented and cost-effectiveness • Summary**Background: TIV**• Current trivalent influenza vaccines (TIV) contain 2 influenza A virus types: H3N2, H1N1 and oneinfluenza 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**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**Vaccine mismatch for influenza B: Canada**Adapted from Fluwatch http://www.phac-aspc.gc.ca/fluwatch/ and NACI http://www.phac-aspc.gc.ca/naci-ccni/**GSK’s QIV: FluLaval® Tetra**• Quadrivalent split-virion, inactivated influenza vaccine • Authorized for use in Canada Feb 6, 2014 • Manufactured in Sainte-Foy, Quebec**Model structure:i) Simple S(usceptible)I(nfected)R(ecovered)**model**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.. R1R2→S1R2→S1S2 or • R1R2→R1S2→S1S2**Model structure:iii) Adding vaccination**• Success/failure determined at time of vaccination: Let ε1, ε2 be the efficacies. Then, e.g. for a person in S1S2, possible outcomes of vaccinating, are, with probability P: • P=ε1 ε2: go to V1V2 • P= ε1(1- ε2): go to V1S2 • P= ε2(1- ε1): go to S1V2 • P=(1- ε1)(1- ε2): stay in S1S2 • 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 R1S2 against strain 1 has no effect**Calibrating the model to real-world data**(or: avoiding “Garbage In – Garbage Out”) • 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**Our calibration approach: Approximate Bayesian computation**(ABC)**Fitting simulations: Influenza in the US, 1998-2008**influenza A influenza B Thommes et al., Vaccine, submitted**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**Testing the 4Strain model on Ontario’s UIIP:**Result: Model is overall conservative relative to Kwong et al. (2008) in predicting outcomes averted by UIIP Kwong et al. (2008) 4Strain dynamic model Relative rate ratio: ReductionOntario =-------------------------- ReductionCanada Thommes et al., Vaccine, submitted**Result: Canada-wide TIVQIV switch brings about clear**reduction in outcomes influenza cases (50k-300k prevented) GP visits (20k-120k prevented) ER visits (1000-8000 prevented) hospitalizations (500-4000 prevented) # simulations deaths (50-800 prevented) outcomes prevented per season by QIV outcomes per season, TIV and QIV**Sensitivity analysis: QIV highly cost-effective across all**plausible inputs**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**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**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! OR TIV actually works better than we think! Even with perfect prediction, TIV would not prevent as many outcomes as QIV**Parameter fitting**• Overall approach (analogous to that of van derVelde et al. 2007 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. 2003 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**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**Simulating Ontario’s universal influenza immunization**program (UIIP): Model inputs I • Population, birth, death rates from Statistics Canada, http://www5.statcan.gc.ca/cansim/ • Simulated period is 1997-2004, 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)**Simulating Ontario’s universal influenza immunization**program (UIIP): Model inputs I • Population, birth, death rates from Statistics Canada, http://www5.statcan.gc.ca/cansim/ • Simulated period is 1997-2004, 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)**Simulating Ontario’s universal influenza immunization**program (UIIP): Model inputs II • 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)