Cost-utility of quadrivalent versus trivalent influenza vaccine in Brazil – comparison of outcomes from different static model types

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w w w . e l s e v i e r . c o m / l o c a t e / b j i d

The

Brazilian

Journal

of

INFECTIOUS

DISEASES

Original

article

Cost-utility

of

quadrivalent

versus

trivalent

inﬂuenza

vaccine

in

Brazil

–

comparison

of

outcomes

from

different

static

model

types

Laure-Anne

Van

Bellinghen

a,∗

_,

_Alen

_Marijam

b

_,

_Gabriela

_Tannus

_Branco

_de

_Araujo

c

_,

Jorge

Gomez

d

_,

_Ilse

_Van

_Vlaenderen

a

a_CHESS_in_Health,_Bonheiden,_Belgium

b_GSK,_Wavre,_Belgium

c_AxiaBio,_São_Paulo,_SP,_Brazil

d_GSK,_Victoria,_Buenos_Aires,_Argentina

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received17July2017 Accepted14November2017 Availableonline18January2018

Keywords: Inﬂuenza Brazil Cost-effectiveness Vaccination QIV TIV

a

b

s

t

r

a

c

t

Background:InﬂuenzaburdeninBrazilisconsiderablewith4.2–6.4millioncasesin2008

andinﬂuenza-like-illnessresponsiblefor16.9%ofhospitalizations.Cost-effectivenessof inﬂuenzavaccinationmaybeassessedbydifferenttypesofmodels,withlimitationsdueto dataavailability,assumptions,andmodellingapproach.

Objective:Tounderstandtheimpactofmodelcomplexity,thecost-utilityofquadrivalent

versustrivalentinﬂuenzavaccinesinBrazilwasestimatedusingthreedistinctmodels:a 1-yeardecisiontreepopulationmodelwiththreeagegroups(FLOU);amoredetailed1-year populationmodelwithﬁveagegroups(FLORA);andamorecomplexlifetimemulti-cohort Markovmodelwithnineagegroups(FLORENCE).

Methods:Analysis1(impactofmodelstructure)comparedeachmodelusingthesamedata

inputs(i.e.,bestavailabledataforFLOU).Analysis2(impactofincreasinggranularity) com-paredeachmodelpopulatedwiththebestavailabledataforthatmodel.

Results:Usingthebestdataforeachmodel,thediscountedcost-utilityratioofquadrivalent

versustrivalentinﬂuenzavaccinewasR$20,428withFLOU,R$22,768withFLORA(versus R$20,428inAnalysis1),and,R$19,257withFLORENCE(versusR$22,490inAnalysis1)using alifetimehorizon.ConceptualdifferencesbetweenFLORAandFLORENCEmeantthesame assumptionregardingincreasedall-causemortalityinat-riskindividualshadanopposite effectontheincrementalcost-effectivenessratioinAnalysis2versus1,andaproportionally highernumberofvaccinatedelderlyinFLORENCEreducedthisratioinAnalysis2.

∗ _{Corresponding}_author.

E-mailaddress:lvanbellinghen@chessinhealth.com(L.VanBellinghen). https://doi.org/10.1016/j.bjid.2017.11.004

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Discussion: FLOUprovidedadequatecost-effectivenessestimateswithdatainbroadage groups.FLORAincreasedinsights(e.g.,inhealthyversusat-risk,paediatric, respiratory/non-respiratory complications). FLORENCE provided greater insights and precision (e.g., in elderly,costsandcomplications,lifetimecost-effectiveness).

Conclusion:Allthreemodelspredictedacostperquality-adjustedlifeyeargainedfor

quadri-valentversustrivalentinﬂuenzavaccineintherangeofR$19,257(FLORENCE)toR$22,768 (FLORA)withthebestavailabledatainBrazil(AppendixA).

Background

Theannual number ofconfirmed influenza casesinBrazil was estimated tobe between 4.2 and 6.4 million cases in 2008.1_While_data_were_limited_due_to_likely_{underreporting,} theinfluenzasurveillancesystemreportedthatinfluenza-like illnesswasresponsiblefor4.4–16.9%ofhospitalconsultations between2000and2008.2_Data_on_mortality_were_reported_for influenzaandpneumoniacombined,andwerehighestamong the youngest and eldest age groups in most Latin Ameri-cancountries.Thehighestproportionofdeathsinchildren underfiveyearsintheregionwerereportedforEcuador(14.4% in2003) andBrazil (13.5%in2004).1 _The_vaccination_target groupinBrazilhasbecomeprogressivelybroadersince1999, andnowincludespeopleover60yearsold,childrenagedsix monthstofouryears,andarangeofvulnerablepeople.2

Byinvestingindiseaseprevention,influenzavaccination programscanincrease thehealth oftheentirepopulation. Policy-makers need to choose which age- and risk-groups tovaccinate inorder toachieve thebest health outcomes. Investmentcostsneedtobeweighedupagainstthecurrent healthburden, expected healthgains,and cost savings, to evaluatewhethervaccinationprogramsoffervalueformoney comparedtoexistingdiseasemanagementoptions. Epidemi-ologicandeconomicmodels,combining currentknowledge ofthediseaseburden,transmissionandimpactonhealthcare resourcesarefrequentlyusedtopredictthehealthand eco-nomicconsequencesofvaccination.Arangeofmodeltypes are availablewithmany differentdata requirements;more detailedapproachestypicallyrequire moredata,whichcan oftenbeunavailableleadingtoincreaseduseofassumptions, andultimatelyreducingvalidityofamorecomplexapproach. Regardingthecost-effectivenessofinfluenzavaccination,a rangeofmodeltypeshavebeenused,butwithcaveatsrelating tolimitationsinthemodellingapproach,lackofinputdata, anduseofassumptions.Healthpolicyguidelinesrecommend annualinfluenzavaccinationinconsecutiveseasons,yetmany influenzamodelsconsidercost-effectivenessinoneyearand applyalifetimehorizontoassessquality-adjustedlife-years (QALYs)lostduetoprematuremortality.3–5_These_1-year mod-elsmayartificiallyoversimplifylifetimeeffectsbyassuming allinfluenzamortalityoccursatoneaverageagewithinan agegroup,andthosewhosurviveinfluenzawouldlivetheir remaininglifeexpectancyataconstantbaselineutility.The populationofthese1-yearmodelsisoftenbroadlysubdivided

(e.g.children,adults,andelderly),however,thereis consider-ableheterogeneitywithinthosebroadagebands,especially amongtheelderly(e.g.,duetonaturalmortality,baseline util-ity and costs). Multi-cohortmodels inwhichcohorts enter the model atmany different ages and are followed over a lifetime of consecutive influenza seasons, provide a more directapproachtoinfluenzamanagementthan1-year mod-els,considerheterogeneityinthepopulation,andallowforan appropriateattributionofQALYsovertime.6_However,_detailed age-specificdatamayprovedifficulttofind.

This paperaimsto understand the impactmodel com-plexity hasonpredictingresults,andthe prosandconsof differentapproaches.Inordertodoso,theimpactof introduc-inginfluenzavaccinationinBrazilwasestimatedusingthree distinctmodels,froma1-yeardecisiontreepopulationmodel (FLOU) to a more complex life-time multi-cohort Markov model (FLORENCE), and witha moderatelycomplex 1-year populationmodel(FLORA).ThusFLOU,FLORA,andFLORENCE, each used increasingdata and modellingcomplexity, were compared intermsofpredictingoutcomes withincreasing precision.Themodelscomparedcases,costsandhealth out-comes ofthe following influenza vaccination strategies in Brazil:novaccination, trivalentinfluenzavaccination (TIV), andquadrivalentinfluenzavaccination(QIV).

Methods

Modeldescriptions

FLOU(i.e.inFLuenzacOst-Utility)model

TheFLOUmodelisadecisiontreepopulationmodel divid-ing the populationinto threeage groups(paediatric,adult, and elderly; <18,18–64, and ≥65 years,respectively), each subdivided into two risk groups (healthy and at-risk). The modelusesa1-yeartimehorizon,whileattributinglifetime QALYlossestoprematuredeaths.Inﬂuenzacasescouldlead togeneralpractitioner(GP)visits,hospitalization,anddeath followinghospitalizationornohospitalization.

Adistinctionwasmadebetweenhealthyandat-risk popu-lations for vaccination coverage,and the probability of GP visits and hospitalization. The model calculated vaccina-tion costs (vaccine price and administration), GP visit and hospitalizationcostsaswellasbaselineutilities,QALYloss due to inﬂuenza, hospitalizations, and mortality for each strategy.

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Toavoidoversimplificationoflifetimebenefitcalculations observedinother 1-yearmodels,alifetableapproachwas implementedto estimate(quality-adjusted) lifeexpectancy ((QA)LE)atageofprematuredeath;age-dependentbaseline utilities,probabilitiesofall-causemortality,anddiscounting were included, with a defineddiscount rate for outcomes. Finally,theweightedaverageof(QA)LEwasestimatedforeach agegroup.

FLORAmodel

To increase the sensitivity of the analysis, another 1-year decisiontreepopulationmodelcalledFLORAwasdeveloped, with more granularity than FLOU. Influenza could lead to GP visits, antiviraltreatment withor withoutresistance in at-risk populations, respiratory or non-respiratory compli-cationsleading to outpatienttreatment or hospitalization, and influenza death with or without prior complications. TheFLORAmodelstructureincludedfiveage-groups(i.e.,<7 months, 7–24 months, 3–17 years,18–64 years, 65+ years). Additional parameters compared to FLOU allowed for fur-therdifferentiationbetweenhealthyandat-riskpopulations (e.g.,probabilitiesofinfluenza,probabilitiesofcomplications (respiratoryandnon-respiratory),deathfollowing hospitaliza-tion,outpatienttreatment, nocomplication,and QALYloss per premature death). The societal perspective considered directandindirectmedicalcosts.Disutilitiesassociatedwith influenzacomplicationstreatedinanoutpatientsettingwere alsoincluded.

The calculation of (QA)LE was adapted to account for typicallylowerbaselineutilitiesandthehigher riskof nat-uralmortalityinat-risk individuals,andthusashorterlife expectancy in this risk-group. In addition, the life table approachallowedforaproportionofhealthypeopletobecome at-riskwithage,tomaintaintheactualpopulation distribu-tionovertime/agedespitethehighermortalityintheat-risk population.Moreaccurateestimatesoflifeexpectancyinthe healthypopulationwerethusobtained.

ThedecisiontreemodelstructuresofFLOU(Fig.A1)and FLORA(Fig.A2)arepresentedinAppendixB.

FLORENCE(i.e.InFLuenzacOmpREhENsive Cost-Effectiveness)model

TheFLORENCEmodelisamulti-cohortMarkovmodelwith nine age groups (including fiveage groups forthe elderly) followedoveralifetimehorizonwithconsecutiveinfluenza seasonsinannualmodelcycles.Themodelwaspreviously usedtoassessinfluenzavaccinationintheUK.6In compar-isontoFLORA,FLORENCEaddedmoregranularityinmodel structure(e.g., post-exposure prophylaxis (PEP), emergency room(ER)visitsforinfluenza,threerespiratorycomplications and fivenon-respiratory complications)and indata inputs (e.g.,probabilities,costsand/orhealthoutcomes) relatedto vaccination adverse events,PEP, antibiotics, nursing home visits, herd immunity (static approach) and more societal costs(e.g.,non-reimbursed medicalcosts and non-medical costs).

Vaccinationwasappliedineachannualcycle,and cumu-lative QALYs lost due to inﬂuenza mortality were directly calculated via the cohort approach.Hence, unlike inFLOU and FLORA, age-dependent life expectancy estimates were

not required as proxies for QALYs lost due to inﬂuenza mortality.

Modelanalyses

Twoanalyseswereconducted:theﬁrsttoassesstheimpact ofchanging modelstructureon results, andthe second to assess the impact of increasing granularity in inputs and outputs.

Analysis1estimatedtheimpactofmodelstructureand compared outcomes when the same data inputsand sett-ings were used forall threemodels. TheFLOU model was populatedwiththebestavailabledataforBrazilianchildren, adults, and elderly, and FLORA and FLORENCEwere popu-lated withthe sameFLOU datainputs(e.g.,if12.8% ofthe paediatric groupwere at-risk ofclinical inﬂuenzain FLOU, then12.8% wasusedforallagegroups<18yearsinFLORA andFLORENCE).Onlythepopulationdistributionacrossage groups differedbetween models, calculatedas the popula-tionsizeoftheage-groupdividedbythetotalpopulationin Brazil.Sincedatainputswere thesameacrossthe models, anyobserveddifferenceswouldbeattributabletothemodel structure.

Analysis2estimatedtheimpactofincreasingthelevelof granularity(e.g.,assessingheterogeneity inthepopulation) and comparedoutcomesfrom eachmodelbeing populated withthebestavailabledataforBrazilaccordingtotheir spe-cificdatarequirements.Inthefirstinstance,outcomesinthe armwithnovaccinationwerecomparedacrossmodels. Iden-ticalcasesandcostswereexpectedwithFLOUandFLORAand, whenconsideringa1-yeartimehorizon,inFLORENCE.Then, theresultswithvaccinationwerecompared,withdifferences reflectingtheincreasedprecisionofdatainputs.

Setting,perspective,discountrate,currencyandpricedate

Thepublichealthcarepayer perspectiveinBrazil wasused in FLORA and FLORENCEto allowcomparisons withFLOU whichdidnotincludethesocietalperspective.Similarly,the analysis withFLORENCEexcluded costsand effects dueto PEP, vaccination adverse events, nursing home visits and herd immunity, to allowcomparisons toFLOU and FLORA outputs.

Allcosts(2015BrazilianReal,R$)andhealthoutcomeswere discountedat5%.CostswerebasedontheBrazilianUniﬁed HealthSystem(SistemaUnicodeSaude,SUS)whichapplies thesamereimbursementcoststoallregionsofthecountry. DiscountinghadnoimpactoncostsintheFLOUandFLORA modelsgiventheir1-yeartimehorizon,butdidhaveanimpact onhealthoutcomes,aslifetimeQALYslostwere calculated forprematuredeaths.IntheFLORENCEmodel,futurecosts (avoided)werediscountedintheyearinwhichtheyoccurred and annuallyrecurrentcostsassociatedwithoneinﬂuenza case(e.g.long-termsequelae)were notincluded.Regarding mortality,(QA)LYslostwerediscountedeveryyearofthetime horizon ofthe model. Discounting therefore had agreater impactonhealthoutcomesthanoncosts.

Outcomesassessed

Theepidemiologic outcomes assessedwere:number vacci-nated,inﬂuenzacases,GPvisits(andERvisitsinFLORENCE)

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for uncomplicated influenza, influenza complications with hospitaloroutpatienttreatment(thelatteronlyforFLORAand FLORENCE),andinfluenzadeathswithorwithoutprior com-plications.Theeconomicoutcomesassessedwerevaccination costs (vaccine price and administration), neuraminidase inhibitors(NI)treatmentcosts(i.e.NIdrugsandantibiotics, onlyforFLORAandFLORENCE),GPvisitcosts(ERcosts,only forFLORENCE),influenzacomplicationtreatmentcosts (hos-pitaloroutpatienttreatment,thelatteronlyforFLORAand FLORENCE).Thehealthoutcomes measuredwere life-years gained during timehorizon and life-yearslost due to pre-maturemortality,QALYsgained,usingbaselineutilitiesand utility decrements due toinfluenza, hospital or outpatient treatmentforcomplications(thelatteronlyforFLORAand FLORENCE),and prematuremortality.Differencesin health outcomesandcostsbetweentheQIVandTIVarmswere com-paredintermsofincrementalcostperLYgainedandperQALY gained.

Whencomparingoutcomesusinga1-yeartimehorizon, theaccumulated(QA)LYsgainedintheFLORENCEmodeldid notreflect the impact of premature mortalitybecause the FLORENCEmodelisamulti-cohortlifetimemodelwhereLYs andQALYsareaccumulatedovertime.Noresultscouldthus be produced in terms of health outcomes associated with influenzamortalitywiththeFLORENCE1-yeartimehorizon. Thismeans thatthe lifetime(QA)LYs lost due tomortality couldnotbeconsideredwitha1-yeartimehorizon.However, thebaselineutilitiesgainedandQALYslostduetoinfluenza andhospitalizationcouldbeextractedandcomparedtothe 1-yearFLOUandFLORAmodels.

ModelinputsforAnalyses1and2

AppendixCpresentstheinputtablesusedinAnalysis2(=best availabledatabymodel).ThedatainputsusedforAnalysis1 inthethreemodelsaretheFLOUdatainputsofAppendixC. AppendixDpresentstheassumptionsandsourcedataused forthecalculationofthedatainputs(i.e.,demographics, vac-cineinputs,resourceuse,complicationsandhospitalization, costs,andutilities).

Duringdatainputcalculations,twoimportantadjustments were taken into consideration with regard to collect-ing/calculatingdatainputsforage-groupscoveringdifferent ageboundaries,aswasthecaseintheFLOU,FLORA,and FLO-RENCEmodels.

Firstly,acommonlyusedapproachtoobtainingaweighted averageforamodelledagegroupcoveringmultipleagegroups of the population in the source reference was to use the crudepopulationdistributionofthemodel tocalculatethe proportionalage-distribution. Thisiscorrectaslongasthe crude population distribution in the model is exactly the same as the modelled age distribution of the parameter investigated.Forinstance,tocalculatetheprobabilitythata complicationisarespiratorycomplicationinhealthy individ-uals,the proportional age-distribution should consider not onlythecrudepopulationdistributionbutalsotheprobability ofbeinghealthy,theprobabilityofinﬂuenza,andthe prob-abilityofinﬂuenza complicationsin healthy individuals.It wasthereforeimportantthatweightedaveragecalculations

considered the correct proportional age-distribution of the population considered in the source reference, e.g. the age-distribution of respiratory complications can be sub-stantially different from the age distribution of the total population.

Secondly, reported pooled results in publications rarely matchedthe modelledpopulation. Age-stratiﬁeddata were selectedabovepooleddata.Forinstance,whendatawas avail-ableforafullage-group(i.e.,children)aswellasforsubgroups (i.e., 0–5,6–12 and 13–17years), the calculationswere per-formedonthesubgroupsinsteadofthepooleddata. Using thelatterimpliesthatthepopulationdistributionofthe pub-licationmatchesthemodelledpopulationdistribution,which isnotthecase.

Results

Analysis1:impactofmodelstructureonvaccination outcomeswithidenticaldatainputsforallthreemodels

Table1presents thecasesandcostsavoidedwitha1-year timehorizonforQIVversusTIVwithallthreemodelsina sin-glecolumn,astheseresultswereidenticalacrossthemodels. Inaseparatecolumn,FLORENCElifetimeresults(forQIV ver-susTIV)arereported.Disaggregatedresultsintermsofcases, costs,andQALYswithTIVandQIVarepresentedinAppendix E.

Allthreemodelsusedthesameinputs(fromFLOU).The resultsshowedanidenticalnumberofcasesandcostsinall threemodelswitha1-yearhorizon.Overall,QIVprevented more influenza cases, complications and deaths than TIV, resultinginfewerGPandhospitalizationcosts.Despite sav-ing disease management costs, vaccinationcosts withQIV werehigherthanTIVduetothehigherpriceofQIVvaccine. InFLORENCEwithalifetimehorizon,QIVresultedinfewer influenzacasesandassociateddeaths,resultinginhigher sur-vival andtherefore moreindividuals tobevaccinated.The (QA)LYs gained with FLOU and FLORA were identical, and matchedtheQALYsduetoinfluenza,outpatienttreatment, andhospitalizationwhichcouldbeextractedfromthe FLO-RENCE 1-year model. Overall, QIV resulted in moreQALYs gainedthanTIVfrompreventinginfluenzacases, complica-tionsanddeaths.

ThediscountedcostperLYgainedwithQIVversusTIVwas R$16,263withtheFLOU/FLORA1-yearmodels,andR$18,000 withFLORENCEusingalifetimehorizon.Thediscounted cost-utilityratiowasR$20,428perQALYgainedwithFLOU/FLORA 1-yearmodels,andR$22,490withFLORENCEusingalifetime horizon.GiventhatFLOUandFLORAusedonebroadagegroup torepresenttheelderly,allsubjectsaccruedthesamenumber ofLYslostperprematuredeath(i.e.,8.22years).InFLORENCE withﬁveelderlyagegroups,subjectsdiedonaverageatan olderageandthushadfewerLYslostduetoprematuredeath, resulting ina lower (QA)LY gain due to mortalityand the highercostper(QA)LYobserved.Thiseffectwasreinforcedby highersurvivalwithQIV,resultinginmorepersonsbeing vac-cinatedatlaterages,andwithfewerLYslostduetopremature death.

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Table1–Differenceincasesanddiscountedcosts,QALYsandcost-utilityratiowithQIVversusTIVforallthreemodels usingFLOUinputsforBrazil.

Allmodels1-year FLORENCElifetime

Casesavoided(N)(QIVvsTIV)

Vaccinated 0 −794,333

Inﬂuenzacases 145,169 8,650,885

Uncomplicatedinﬂuenza1st-lineMA 74,509 5,015,454

NItreatmenta ₀ ₀

Inﬂuenzacomplications 4986 416,479

Hospitalization 4986 416,479

Outpatienttreatmenta ₀ ₀

Inﬂuenzadeaths 990 99,870

Withoutpriorcomplicationsa ₀ ₀

Withpriorcomplicationsa ₉₉₀ _99,870

Costsavoided(R$)(QIVvsTIV),5%disc

Totalvaccinationcosts −145,682,900 −2,588,372,448

Vaccineprice −145,682,900 −2,588,216,523

Administration 0 −155,925

Uncomplicatedinﬂuenzatreatment 745,089 12,574,131

NItreatmenta ₀ ₀

Antibioticsa ₀ ₀

Medicalvisits 745,089 12,574,131

Inpatientcomplicationtreatment 5,371,315 97,664,516

GPvisita ₀ ₀

Hospitalizations 5,371,315 97,664,516

Outpatientcomplicationtreatmenta ₀ ₀

Totalcosts −139,566,496 −2,478,133,802

(QA)LYsgained(QIVvsTIV),5%disc

LYsgainedb ₈₅₈₂ _137,674

LYsgainedduringtimehorizonb ₀ _NA

LYsgainedfromdeathsavoidedb ₈₅₈₂ _NA

QALYsgainedb ₆₈₃₂ _110,188

Baselineutilitiesb ₀ _101,382

Frominﬂuenzaavoided 310 5146

Fromhospitalizationavoided 50 890

Fromoutpatienttreatmentavoideda ₀ ₀

Fromdeathsavoidedb ₆₄₇₂ _NA

CostperLYgainedb _16,263 _18,000

CostperQALYgainedb _20,428 _22,490

QIVvsTIV,quadrivalentversustrivalentinﬂuenzavaccine;MA,medicaladvice;NI,neuraminidaseinhibitors;GP,generalpractitioner;LY, life-year;QALY,quality-adjustedlife-year;NA,notapplicable;disc,discounted.

a _Not_applicable_in_FLOU.

b _Not_applicable_in_FLORENCE_1-year.

Analysis2:impactofincreasedgranularityonmodel outcomeswithallthreemodelspopulatedwithbest availabledata

Analysisofthenovaccinationarmsinthe1-yearmodels populatedwithbestavailabledata

The ‘no vaccination’ arms of the FLOU, FLORA and FLO-RENCE1-yearmodelspopulatedwiththebestavailabledata forBrazilwere comparedintermsofcases,costs,and out-comes(Table2).Overall,resultsdifferedwithFLOUcompared toFLORAandFLORENCE,duetoFLOU’smodelstructurewith fewerage-groups.

Allthreemodelspredictedthesamenumberofinfluenza casesinoneyear. However,FLOU hadfewer casesseeking firstlinemedicaladvice(MA).Thisdifferenceisattributable tothe different datainputs inFLOU versus FLORA forthe probability of influenza. FLORA distinguished between the

probability ofinﬂuenza in healthy and at-risk individuals. A weighted average probability for each group was calcu-latedbasedontheirrespectiveproportionalage-distribution, obtainedbymultiplying the correspondingpopulation age-distributionwiththehealthy/at-riskdistribution.InFLOU,the proportionalage-distributiononlyconsideredthepopulation distribution(sincenodistinctionwasmadebetweenthe prob-abilityofinﬂuenzainhealthyandat-riskindividuals).

FLOUhadahighernumberofhospitalizationsanddeaths comparedtoFLORA and FLORENCE,sinceFLORAand FLO-RENCEincludedtheuseofNItreatmentforat-riskindividuals reducing therisk ofdevelopinginﬂuenza-related complica-tions.

The cost differences between the models reﬂected the different number of cases, i.e., lower costs for outpatient treatmentofuncomplicatedinﬂuenzaandhigher hospitaliza-tion costsforFLOU. FLORENCEconveyedhighercoststhan

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Table2–Cases,costsandhealthoutcomeswithnovaccinationinthe1-yearmodelspopulatedwithbestavailabledata inputs.

FLOU FLORA/FLORENCE1-year

Cases(N)

Inﬂuenzacases 17,942,592 17,942,592

Uncomplicatedinﬂuenza1st-lineMA 6,956,421 6,958,277

NItreatment NA 135,342

Inﬂuenzacomplications 263,679 2,204,037

Hospitalization 263,679 252,149

Outpatienttreatment NA 1,951,888

Inﬂuenzadeaths 38,961 36,358

Withoutpriorcomplications NA 0

Withpriorcomplications NA 36,358

Costs(R$)

Uncomplicatedinﬂuenzatreatment 69,564,211 448,208,731a_/281,747,312

NItreatment NA 7,878,816

Antibiotics NA 0a_/204,285,731

Medicalvisits 69,564,211 69,582,765

Inpatientcomplicationtreatment 266,485,899 249,658,698

GPvisit NA 2,521,490

Hospitalizations 266,485,899 247,137,209

Outpatientcomplicationtreatment NA 121,088,451

Totalcosts(diseasemanagement) 336,050,109 448,208,731a_/652,494,462

Healthoutcomes

LYsgained(total) 202,410,073 202,467,694/NA

LYsgainedduringtimehorizon 202,768,562 202,768,562/NA

LYslostduetomortality 358,489 300,868/NA

QALYsgained(total) 179,897,884 179,924,731/NA

Baselineutilities 180,213,312 180,213,312/NA

QALYSlostfrominﬂuenza 37,667 37,615

QALYSlostfromhospitalization 2759 2646

QALYSlostfromoutpatienttreatment NA 16,744

QALYSlostfrommortality 275,002 231,576/NA

NI,neuraminidaseinhibitors;GP,generalpractitioner;LY,life-year;QALY,quality-adjustedlife-year;NA,notapplicable.

a _No_antibiotics_in_FLORA_therefore_different_{uncomplicated}_inﬂuenza_treatment_cost_and_total_costs.

FLORA due to the inclusion of antibiotics and associated costs.

ThereweremoreLYsandQALYslostduetomortalityin FLOUcomparedwithFLORAduetothedistinctionbetween healthyandat-riskindividuals,withfewerQALYslostinthe caseofprematuredeathinat-risk individualscomparedto healthy individuals. Since at-risk individuals had a higher probability of influenza death, they were overrepresented (compared to the population distribution) in the average influenzadeaths,inturn leadingtofewer(QA)LYs lost due toinfluenzamortalityinFLORAversusFLOU. FewerQALYs werelostduetoinfluenzainFLORAandFLORENCEcompared withFLOU,asNItreatmentreducedthedurationofinfluenza andthereforetheQALYloss.Where1-yeardatacouldbe cal-culated fromthe FLORENCEmodel,these wereidentical to FLORA(e.g.,QALYlossduetoinfluenza,hospitalizationand outpatienttreatment)(Table2).

Analysisofthevaccinationarmspopulatedwithbest availabledata

Inthisanalysis,thebestdataavailableforBrazilineachmodel wasusedtocomparevaccinationwithQIVversus TIV. Dis-aggregatedresultsintermsofcases,costs,andQALYswith

TIVandQIVarepresentedinAppendixF,andQIVversusTIV outcomesarepresentedinTable3.

In all three models, QIV prevented more influenza cases and deaths, and reduced resource utilization (e.g., in/outpatient visits and treatment of un/complicated influenza)comparedwithTIV.Whencomparingthemodels withina1-yeartimehorizon,thebestoutcomesforQIVwere mainlyachievedintheFLORENCEmodel;i.e.,mostinfluenza cases prevented, mostvisits prevented inGP, hospital and outpatientsettings.ThenumberofNItreatmentsprevented washigherwithFLORA.NItreatmentwasonlyprovidedto at-risk individualsseekingMA. Therewere fewerinfluenza cases avoidedwithQIVin at-riskindividuals inFLORENCE (despite ahigher total number ofinfluenza casesavoided) andthuslessNItreatmentscouldbeavoidedcomparedwith FLORA.

Giventhattheinfluenzacasesinthe‘novaccination’armof FLORAandFLORENCE1-yearmodelswereidentical(Table2), differencesinnumbersofinfluenzacasesinthe‘vaccination’ armswereduetovaccinationcoverageandvaccineefficacies (VE). Slightly morehospitalizationsand deathsare avoided in the FLOU model compared to FLORA and FLORENCE 1-year.ThisisbecauseNItreatmentisnotconsideredinthe FLOU modeland hencethere isno associatedreductionin riskofcomplications.Therefore,theimpactofvaccinationon

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Table3–CasesavoidedwithQIVvsTIVinthemodelspopulatedwithbestavailabledatainputs.

FLOU FLORA FLORENCE1-year FLORENCElifetime

Casesavoided(N)(QIVvsTIV)

Vaccinated 0 0 0 −1,124,732

Inﬂuenzacases 145,169 153,539 161,299 10,473,230

Uncomplicatedinﬂuenza1st-lineMA 74,509 79,682 84,518 6,498,228

NItreatment NA 3922 3857 395,321 Inﬂuenzacomplications 4986 23,142 25,336 1,982,201 Hospitalization 4986 4923 5040 664,739 Outpatienttreatment NA 18,219 20,296 1,317,462 Outpatientvisits 74,509 102,824 109,854 8,480,430 Inﬂuenzadeaths 990 962 964 166,233

Withoutpriorcomplications NA NA 0 0

Withpriorcomplications NA NA 964 166,233

QIVvsTIV,quadrivalentversustrivalentinﬂuenzavaccine;MA,medicaladvice;NI,neuraminidaseinhibitors;NA,notapplicable.

Table4–CostsavoidedwithQIVversusTIVinthemodelspopulatedwithbestavailabledatainputs.

Costs(R$)avoided(QIVvsTIV)

Vaccinationcosts −145,682,900 −145,682,900 −145,682,900 −2,582,045,940

Vaccineprice −145,682,900 −145,682,900 −145,682,900 −2,581,917,946

Administration 0 0 0 −127,994

Diseasemanagementcosts 6,116,403 7,143,967 9,663,447 197,280,934

Uncomplicatedinﬂuenzatreatment 745,089 1,025,150 3,395,349 63,263,085

NItreatment NA 228,335 224,533 4,413,394

Antibiotics NA NA 2,325,639 45,740,323

Medicalvisits 745,089 796,815 845,177 13,109,368

Inpatientcomplicationtreatment 5,371,315 5,059,479 5,149,362 114,199,731

GPvisit NA 49,233 50,399 1,063,336

Hospitalizations 5,371,315 5,010,246 5,098,962 113,136,395

Outpatientcomplicationtreatment NA 1,059,337 1,118,737 19,818,119

Totalcosts −139,566,496 −138,538,933 −136,019,453 −2,384,765,005

Negativevalues=incrementalcosts,positivevalues=costsavings.

QIVvsTIV,quadrivalentversustrivalentinﬂuenzavaccine;NI,neuraminidaseinhibitors;GP,generalpractitioner;NA,notapplicable.

complications and further consequences thereof will be higherinFLOU.

AswasthecaseinAnalysis1,theFLORENCElifetime hori-zonanalysisfoundmoreindividualsvaccinatedwithQIVthan TIV,duetobettersurvivalintheQIVarmwhichresultedin morepeopleeligibleforvaccination.

Vaccinationcostswereidenticalacrossmodelsandhigher withQIVversusTIV(AppendixF).Overall,thehighestdisease managementcostsavingswithQIVversusTIVwereshown intheFLORENCE1-yearmodel,mainlyduetocostsavingsin antibioticsandoutpatientcomplicationtreatment(included inthismodel’sstructureandinputs)(Table4).

ComparedtoFLORA,thereweremoreLYsandQALYslost duetomortalitywithTIVandQIVinFLOU,asthelatter pre-dictedmoredeathsandmadenodistinctionbetweenhealthy andat-risk individuals’lifeexpectancy.Influenza mortality wasthekeydriverofhealthoutcomes.TheQALYslostwith TIVandQIVduetoinfluenzaandhospitalizationwerealso higherinFLOUvsFLORAreflectingthehighernumberofcases. InFLORAcomparedtoFLORENCE1-yearmodel,therewere moreQALYslostduetoinfluenza,hospitalizations,and outpa-tienttreatmentwithQIVandTIVreflectingthehighernumber ofcases(AppendixF).

TheLYsandQALYsgainedwithQIVversusTIVwerehigher inFLOUversusFLORAbecauseofmoredeathsareavoidedin FLOUand becausetheFLORAmodelincludedalower aver-age (QA)LEper prematuredeathin at-riskindividuals.The FLORENCE1-yearmodel,however,gainedmostQALYsdueto inﬂuenza, hospitalization,andoutpatienttreatment,dueto highernumbersofcasesavoidedwithQIV(Table5).

ThediscountedcostperLYgainedwithQIVversusTIVwas R$16,263 withFLOU,R$18,843withFLORA (versusR$16,263 inAnalysis1),andR$15,072withFLORENCE(versusR$18,000 inAnalysis1)usingalifetimehorizon.Thediscounted cost-utility ratio wasR$20,428 withFLOU, R$22,768 withFLORA (versusR$20,428inAnalysis1),andR$19,257withFLORENCE (versusR$22,490inAnalysis1)usingalifetimehorizon.

Withincreasingmodelcomplexityandusingmoredetailed data inputs, the FLORA model produced less favourable results,whileFLORENCEproducedmorefavourableresultsfor QIV.ThekeydriversofimprovedoutcomesinFLORENCEwith bestavailabledatainputswereall-causemortalityinhealthy and at-riskindividuals(witha10-foldincreaseinmortality rateinat-riskindividuals),addingantibioticsandassociated costs,andﬁnallyprovidingmoregranularityinotherinputs. ThelessfavourableoutcomeswithFLORA,usingbestavailable

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Table5–LYsandQALYsgainedwithQIVvsTIVinthemodelspopulatedwithbestavailabledatainputs.

Healthoutcomesgained(QIVvs.TIV)

Lifeyearsgained 8582 7352 NA 158,221

LYsgainedduringtimehorizon 0 0 0 NA

LYsgainedfromdeathsavoided 8582 7352 NA NA

QALYsgained 6832 6085 NA 123,837

Baselineutilities 0 0 0 115,407

Frominﬂuenzaavoided 310 326 342 5050

Fromhospitalizationavoided 50 49 51 1045

FromoutpatientTxavoided NA 150 168 2335

Fromdeathsavoided 6472 5560 NA NA

QIVvsTIV,quadrivalentversustrivalentinﬂuenzavaccine;LY,life-year;QALY,quality-adjustedlife-year;NA,notapplicable;Tx,treatment.

datainputsversususingFLOUinputswereduetothe10-fold increaseinmortalityofat-riskindividualswhichledtoshorter lifeexpectancyofat-riskindividuals,resultinginlowerQALY gainsandahighercostperQALYgained.

Discussion

One model type may be more appropriate than another, dependingonthequestionsofthedecision-maker.For exam-ple, in the case of inﬂuenza in Brazil, the FLOU model was able to provide an adequate estimate of QIV versus TIVcost-effectivenessinoneyear,includinglifetimeeffects of premature mortality, with basic data and assumptions to ﬁt broad age groups. FLORA was able to provide more insightsabouthealthyversusat-riskpopulations,paediatric agegroupswithinthepopulationunder18yearsold,aswellas costdriverslinkedtorespiratoryornon-respiratory complica-tions.Finally,FLORENCEprovidedevengreatercertaintyand precisionthanFLORA, addingdetailsforagegroupswithin theelderlypopulation,moredetailedcostandcomplication breakdowns,andthelifetimecost-effectivenessof consecu-tiveannualvaccination.

Thechoiceofasuitablemodelisthereforecloselylinkedto thequestionsthatwillhelpdecision-makersunderstandthe valueoftheinterventionsbeingassessed.Anotherkey fac-tortobeconsidered,however,istheavailabilityofdata.The qualityofthedatathatgoesintothemodelhasanimpacton thequalityoftheoutcomes;i.e.,howcloselytheyreflectthe actualburdenofdiseaseandthetrueeffectofinterventions inthepopulation.Economicmodelstypicallycombinedata onepidemiology,efficacyandsafety,costsandresourceuse, toassesstheimpactonpatientsandsociety.Itisnot uncom-monformodelstorelyonmultipleassumptionsassomedata areoftenlacking.Inthisstudy,themodelwiththelowestdata requirementswasFLOU.However,eventhismodelreliedon assumptions(e.g.,theproportionoftheveryyoungandelderly groupsclinicallyat-risk)tofilldatagapsforBrazil.Additional assumptionswererequiredintheFLORAmodel(e.g., mortal-ityriskintheat-riskversushealthypopulationwerebased onUKdata),andintheFLORENCEmodel(e.g.,probabilities ofspecificcomplicationsineachagegroup).WhileFLORENCE attemptedtoaddressissuesrelatedtobroadagegroups,using

a1-yearhorizon versusvaccinationinconsecutiveseasons over lifetime,andoversimplificationoflifetimeeffects, this modelhadsomedrawbacks.Age-stratifieddatatopopulate FLORENCEweresometimeslimited, computingtimetorun sensitivityanalysestookseveralhours/days,andasdecision makersaremoreusedtointerpreting1-yearmodels,itmay bedifficultforhealthauthoritiestocompareoutcomesfrom thislifetimemulti-cohortmodel.

This study has shown that when using the same base casedatainallthreemodels,despitedifferencesinstructural complexity,themodelsallprovidedcomparableresults;i.e., acostperQALYratioforQIVversusTIVofR$20,428(FLOU and FLORA) and R$22,490(FLORENCE). While this outcome validatestheapproachandassumptionsmadeinthemore complexmodelstosomeextent,italsoshowsthataless com-plexapproachisabletosatisfytheneedsofdecision-makers consideringtheavailabledata.WhencomparingQIVandTIV, the driver of incremental costs was vaccination cost, and thedriverofincrementalQALYswasmortality.Aback-of-the envelopecalculationbasedsolelyontheseparameterscould provide aninitial indicationofwhether the costper QALY gainediswithingenerallyacceptedwillingness-to-pay thresh-olds, and if increasedgranularity in data inputs or model structureisneeded.Keydatasources(e.g.,vaccinecoverage andefﬁcacy)shouldoptimallymatchthemodelage-groupsto avoidcalculationsandassumptions;i.e.,themajorityofdata inputsforBrazilwere reportedfor0–4and 5–17years pae-diatricagegroupsand18–49,50–64,65–74,75+years.Theage groupsforwhichdatainputsareavailableandthedesiredlevel ofaccuracyintheresultsarethusdriversofwhichmodelto choose.Thedifferenceinall-causemortalityinputsbetween healthyandat-riskindividualswasacriticaldriverof differ-encesinoutcomesbetweenFLOUandFLORA.Hence,FLORA shouldbepreferredoverFLOUincasethedifferencebetween all-causemortalityissubstantiatedandwell-documented.

Inconclusion,abalance needstobedrawnbetweenthe levelofdetailandcertaintyoftheanswersrequired,andthe ensuinglevelofcomplexityanddataavailabilityforthemodel. Ofthemany modelsassessinginﬂuenzavaccination,some offermoregranularitythanothersbut atacost(i.e.,lossof transparencywithcomplexdatarequirements).Inthisstudy, the FLOU model provided transparency allowing decision-makerstobetterunderstandhowthemodelworksandwhat

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drivesthe outcomes. In addition, FLOU required less data andprovidedcomparableresultstoFLORAandFLORENCE.All threemodelspredictedacostperQALYgainedforQIVversus TIVintherangeofR$19,257(FLORENCE)toR$22,768(FLORA) withthebestavailabledatainBrazil.

Authorship

Allauthorsparticipatedinthedesignorimplementationor analysis,andinterpretationofthestudy,andthedevelopment ofthismanuscript.Allauthorshadfullaccesstothedataand gaveﬁnalapprovalbeforesubmission.

Funding

GlaxoSmithKline Biologicals S.A. funded this study (GSK identiﬁernumberHO-13-13962) andall costsrelatedtothe developmentofthemanuscript.

Conﬂicts

of

interest

ThecompanyofLAVBand IVV receivedfundingfrom GSK GroupofCompaniestocomplete thework disclosedinthis manuscript.GTAdeclaredtohavereceivedconsultancyfees fromGSKGroupofCompaniesforthesubmittedwork.AM andJGareemployedbyGSKGroupofCompaniesandJGhold sharesintheGSKGroupofCompanies.

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Appendices

7–38

Supplementary

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Supplementary data associated with this

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https://doi.org/10.1016/j.bjid.2017.11.004.

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