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 Afisi Ismaila Director Therapy Area GlaxoSmithKline Research Triangle Park, NC, USA 2 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 3 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 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 90% 80% Mismatch Match 70% 60% 50% 40% 30% 20% 10% 201011 200910 200809 200708 200607 200506 200405 200304 200203 200102 0% 200001 Influenza B: % total characterised influenza isolates 100% Season 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 A menagerie of modeling approaches… flu model static tree dynamic individual or “agent”-based (ABM) Markov compartmental 8 Model structure: i) Simple S(usceptible)I(nfected)R(ecovered) model infection natural immunity natural immunity waning Model structure: ii) Adding a second strain infection •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 natural immunity natural cross-protection natural immunity waning 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 infection vaccination natural immunity natural cross-protection vaccinated immunity waning natural immunity waning 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 12 Our calibration approach: Approximate Bayesian computation (ABC) 13 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 influenzaassociated 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 per season, TIV and QIV outcomes prevented per season by QIV Sensitivity analysis: QIV highly costeffective 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 Backup slides 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 Modeling the impact of a Canada-wide switch from TIV to QIV age ALL 0-4 5-19 20-49 50-64 65-74 75-84 85-99 comparator intervention difference % difference mean 95% CI - L 95% CI - U mean 95% CI - L 95% CI - U mean 95% CI - L 95% CI - U mean 95% CI - L 95% CI - U 2,933,460 2,532,276 3,351,695 2,797,922 2,392,853 3,199,681 -135,538 -228,154 -76,677 -4.6% -7.7% -2.7% 266,218 235,144 302,789 252,960 223,195 287,226 -13,258 -20,646 -8,264 -5.0% -7.6% -3.2% 566,688 489,747 645,471 542,466 465,387 618,874 -24,221 -38,946 -14,626 -4.3% -6.8% -2.6% 1,316,489 1,136,295 1,503,404 1,263,216 1,081,597 1,444,672 -53,273 -89,103 -30,831 -4.0% -6.7% -2.4% 432,127 368,561 499,095 412,697 347,580 477,183 -19,430 -33,835 -9,993 -4.5% -7.8% -2.3% 190,464 162,214 220,556 177,776 150,287 206,369 -12,688 -22,542 -6,051 -6.6% -11.7% -3.2% 114,966 97,973 133,823 105,945 89,322 123,201 -9,021 -16,197 -4,119 -7.8% -13.7% -3.6% 46,508 39,434 53,898 42,861 36,056 49,865 -3,647 -6,533 -1,629 -7.8% -13.5% -3.6% GP visits ALL 0-4 5-19 20-49 50-64 65-74 75-84 85-99 1,066,568 121,129 179,920 412,061 135,256 118,088 71,279 28,835 ER visits ALL 0-4 5-19 20-49 50-64 65-74 75-84 85-99 59,704 6,794 988 10,008 15,095 14,514 8,761 3,544 51,257 6,001 848 8,638 12,875 12,361 7,466 3,005 68,574 7,727 1,131 11,429 17,435 16,807 10,197 4,107 56,309 6,456 948 9,603 14,417 13,547 8,073 3,266 47,987 5,696 806 8,222 12,142 11,452 6,806 2,747 hospitalizations ALL 0-4 5-19 20-49 50-64 65-74 75-84 85-99 32,986 3,754 546 5,529 8,340 8,019 4,840 1,958 28,319 3,316 469 4,772 7,113 6,829 4,125 1,660 37,886 4,269 625 6,314 9,633 9,285 5,634 2,269 31,110 3,567 523 5,306 7,965 7,484 4,460 1,804 deaths ALL 0-4 5-19 20-49 50-64 65-74 75-84 85-99 4,836 11 10 118 579 2,228 1,345 544 4,114 9 9 102 494 1,898 1,146 461 5,606 12 12 135 669 2,581 1,566 631 4,508 10 10 114 553 2,080 1,240 501 influenza cases 921,034 1,218,892 1,014,368 106,990 137,769 115,097 155,495 204,930 172,229 355,660 470,566 395,387 115,360 156,217 129,174 100,572 136,745 110,221 60,743 82,970 65,686 24,449 33,417 26,574 868,298 1,160,118 101,554 130,688 147,764 196,482 338,540 452,182 108,793 149,358 93,178 127,949 55,380 76,385 22,355 30,916 -52,200 -6,032 -7,691 -16,674 -6,082 -7,866 -5,593 -2,261 -88,460 -9,394 -12,366 -27,889 -10,590 -13,976 -10,042 -4,050 -29,055 -3,760 -4,645 -9,650 -3,128 -3,751 -2,553 -1,010 -4.9% -5.0% -4.3% -4.0% -4.5% -6.6% -7.8% -7.8% -8.2% -7.6% -6.8% -6.7% -7.8% -11.7% -13.7% -13.5% -2.8% -3.2% -2.6% -2.4% -2.3% -3.2% -3.6% -3.6% 64,721 7,330 1,087 10,982 16,669 15,726 9,388 3,800 -3,395 -338 -41 -405 -679 -967 -687 -278 -5,907 -527 -68 -677 -1,182 -1,718 -1,234 -498 -1,731 -211 -24 -234 -349 -461 -314 -124 -5.7% -5.0% -4.1% -4.0% -4.5% -6.6% -7.8% -7.8% -9.7% -7.6% -6.8% -6.7% -7.8% -11.7% -13.7% -13.5% -3.0% -3.2% -2.4% -2.4% -2.3% -3.2% -3.6% -3.6% 26,512 3,147 445 4,543 6,708 6,327 3,760 1,518 35,757 4,050 601 6,068 9,210 8,688 5,187 2,099 -1,876 -187 -23 -224 -375 -534 -380 -154 -3,264 -291 -37 -374 -653 -949 -682 -275 -956 -117 -13 -129 -193 -255 -173 -69 -5.7% -5.0% -4.1% -4.0% -4.5% -6.6% -7.8% -7.8% -9.7% -7.6% -6.8% -6.7% -7.8% -11.7% -13.7% -13.5% -3.0% -3.2% -2.4% -2.4% -2.3% -3.2% -3.6% -3.6% 3,811 9 8 97 466 1,758 1,045 422 5,230 11 11 130 639 2,415 1,441 583 -328 -1 0 -5 -26 -148 -106 -43 -584 -1 -1 -8 -45 -264 -190 -76 -156 0 0 -3 -13 -71 -48 -19 -6.8% -5.0% -4.1% -4.0% -4.5% -6.6% -7.8% -7.8% -11.9% -7.6% -6.8% -6.7% -7.8% -11.7% -13.7% -13.5% -3.2% -3.2% -2.4% -2.4% -2.3% -3.2% -3.6% -3.6% Cost of vaccination: Cost of GP visits: Cost of ER visits: Cost of hospitalizations: Total payer costs: QALYs lost: LYs lost: Cost of vaccination: Cost of GP visits: Cost of ER visits: Cost of hospitalizations: Total payer costs: QALYs lost: LYs lost: comparator intervention difference mean 95% CI - L 95% CI - U mean 95% CI - L 95% CI - U mean 95% CI - L 95% CI - U $112,089,969 $111,646,794 $112,605,730 $151,441,924 $150,843,161 $152,138,755 $39,351,954 $39,196,367 $39,533,025 $45,169,166 $39,005,771 $51,620,087 $42,958,488 $36,772,434 $49,130,978 -$2,210,678 -$3,746,274 -$1,230,469 $13,217,880 $11,347,784 $15,181,519 $12,466,233 $10,623,828 $14,328,485 -$751,647 -$1,307,831 -$383,222 $114,493,950 $98,131,051 $131,727,254 $107,859,049 $91,782,364 $124,123,339 -$6,634,900 -$11,578,189 -$3,344,257 $284,970,966 $260,842,138 $310,472,595 $314,725,695 $290,749,551 $338,668,374 $29,754,729 $22,687,791 $34,327,516 68,980 59,036 79,436 64,930 55,206 74,837 -4,050 -7,076 -2,033 45,675 38,909 52,852 42,732 36,152 49,573 -2,944 -5,215 -1,417 $851,459,123 $344,113,857 $100,348,798 $868,685,572 $2,164,607,350 522,596 344,912 $848,060,111 $295,469,942 $85,768,367 $741,808,220 $1,974,928,547 446,330 293,245 $855,366,082 $1,150,384,894 $1,145,792,576 $1,155,663,489 $298,925,772 $394,623,287 $326,401,246 $278,359,229 $374,775,557 -$17,712,612 $115,510,962 $94,421,737 $80,104,557 $108,840,130 -$5,927,061 $1,000,472,625 $816,473,915 $691,417,165 $942,481,425 -$52,211,657 $2,361,559,430 $2,387,681,792 $2,203,932,794 $2,577,321,287 $223,074,442 601,554 490,805 414,820 567,537 -31,791 398,169 322,013 270,885 374,069 -22,899 mean 95% CI - L 95% CI - U Cost per case averted: $227 $97 $421 Cost per GP visit averted: $596 $250 $1,126 Cost per ER visit averted: $9,520 $3,792 $19,199 Cost per hospitalization averted: $17,231 $6,863 $34,751 Cost per death averted: $102,420 $38,591 $218,186 Cost per QALY gained: $8,057 $3,175 $16,417 Cost per LY gained: $11,344 $4,311 $23,953 $297,732,465 $300,297,407 -$29,155,352 -$9,782,336 -$10,030,340 -$2,998,031 -$88,525,569 -$26,104,120 $171,667,499 $259,231,329 -54,079 -15,845 -39,878 -10,871 Parameter fitting Overall approach (analogous to that of van der Velde 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 influenzaassociated 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)