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
• TIVQIV 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 TIVQIV 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)