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©InternationalBankforReconstructionandDevelopment/TheWorldBank 1818HStreetNW,WashingtonDC20433 Internet:www.worldbank.org;Telephone:2024731000

ThisworkisaproductoftheWorldBankGroup,withcontributionsfromitscontractedvendor,ImperialCollege, London andofficials of theGlobal Fund,UNFPA,WHOandUNAIDS. TheWorldBankdoes notwarrantthattheuseofthecontentcontainedintheworkwillnotinfringeontherightsofthirdparties.

Thefindings,interpretations,andconclusionsexpressedinthisworkdonotnecessarilyreflecttheviewsof the Executive Directors of The World Bank or other partner institutions or the governments theyrepresent.TheWorldBankdoesnotguaranteetheaccuracyofthedataincludedinthiswork.

Nothinghereinshallconstituteorbeconsideredtobea limitationuponorwaiveroftheprivilegesandimmunitiesofTheWorldBank,allofwhicharespecificallyreserved.

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Attribution–Pleasecitetheworkasfollows:TheWorldBank.2016.EvaluatingtheEvidence

for Historical Interventions Having Reduced HIV Incidence: A Retrospective Programmatic MappingModellingAnalysisWashingtonDC:WorldBank.License:CreativeCommonsAttributionCCBY3.0

Translations–Ifyoucreateatranslationofthiswork,pleaseaddthefollowingdisclaimeralongwiththeattribution:ThistranslationwasnotcreatedbyTheWorldBankandshouldnotbeconsideredanofficialWorldBanktranslation.TheWorldBankshallnotbeliableforanycontentorerrorinitstranslation.

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EVALUATING THE EVIDENCE FOR HISTORICAL INTERVENTIONS HAVING REDUCED HIV INCIDENCE: A RETROSPECTIVE PROGRAMMATIC MAPPING MODELLING ANALYSIS

SYNOPSIS REPORT 2016

World Bank authors: David Wilson, Jessica Taaffe, Marelize Gorgens, Nicole Fraser-Hurt, Pandu Harimurti, Rosalia Rodriguez Garcia Global Fund authors: Mehran Hosseini, Jinkou Zhao, Ade Fakoya and Ryuichi KomatsuUNFPA authors: Bidia Depertes, Matthew Cogan, Mareledi Segotso, Edward Chigwedere, Clemens Benedikt, Sonia Vasquez and Ana Teresa RodriguezWHO authors: Jesus M Garcia Calleja and Daniel Low-Beer

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iii

CONTENTS

Acknowledgements ................................................................................................................... iv

Abbreviations ........................................................................................................................... v

1. INTRODUCTION .............................................................................................................. 1 1.1Aims.......................................................................................................................................3

1.2Objectives..............................................................................................................................4

2. METHODOLOGY ............................................................................................................. 5 2.1Countryselection..................................................................................................................5

2.2Epidemiologicalmodelling....................................................................................................5

2.3Data.......................................................................................................................................6

2.4Historicalmapping................................................................................................................6

3. RESULTS ......................................................................................................................... 7 3.1Botswana...............................................................................................................................7

3.2DominicanRepublic..............................................................................................................8

3.3Kenya.....................................................................................................................................8

3.4Malawi...................................................................................................................................8

3.5Zambia...................................................................................................................................9

4. DISCUSSION ................................................................................................................. 11 4.1Limitations...........................................................................................................................11

4.2Futureimpactevaluations..................................................................................................12

Conclusions ........................................................................................................................... 13

References ............................................................................................................................ 14

Table 1 .................................................................................................................................. 14

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v

ACKNOWLEDGEMENTS ThisresearchwasconductedbetweenJune2013andMarch2016,withfinancialsupportandtechnicalinputsprovidedbytheWorldBank,GlobalFund,UNFPA,WHOandUNAIDS.TheworkwastechnicallyguidedbyasteeringcommitteeconvenedbytheWorldBankandthatincludedmembersfromUNAIDS,UNFPA,WHO,andtheGlobalFund.ThemodellinganalysiswasperformedbyImperialCollegeLondon.Anindependentconsultant,financedbyUNFPA,performedthehistoricalmapping,withvaluablecontributionsfromfocusgroupparticipants.

WorldBank: MarelizeGorgens DavidWilson PanduHarimurti JessicaTaaffeNicoleFraser-Hurt RosaliaRodriguezGarcia

UNAIDS:Headquarters:StrategicinformationandevaluationdepartmentCountryoffices:Botswana,DominicanRepublic,Kenya,MalawiandZambia

GlobalFund:MehranHosseiniJinkouZhaoAdeFakoyaRyuichiKomatsu

UNFPA: BidiaDepertesMatthewCoganMarelediSegotsoEdwardChigwedereClemensBenedikt SoniaVasquezAnaTeresaRodriguez

WHO: JesusMGarciaCallejaDanielLow-Beer

BotswanaNationalAIDSCouncil: RobertSelato

CONAVIHSIDA: VictorTerrero

IvelisseSabbaghRosaSanchez

DIGECITSS: LuisErnestoFelizBaez JoseLedesma

TessieCaballeroNationalHealthService,DominicanRepublic:

GregorioTapiaStefanoToddeRamónAlbaradoMendoza

NationalAIDSControlCouncil,Kenya: NdukuKilonzo JoshuaGitonga

MalawiNationalAIDSCommission:

DavieKalomba

ImperialCollegeLondon: ProfessorTimHallettMsEllaBarberDrSarah-JaneAndersonDrJessicaMcGillenDrKellySutton

Independentconsultant:

HelenJackson

vi

ABBREVIATIONS

AIDS Acquiredimmunedeficiencysyndrome

ANC Antenatalclinics

ART AntiretroviralTherapy

BSS BehaviourSurveillanceSurveys

CONAVIHSIDAConsejoNacionalparaelVIHyelSIDA

COPRESIDA ConsejoPresidencialdeSIDA

DHS DemographicandHealthSurveys

DIGECITSS DirecciónGeneraldeControldeInfeccionesdeTransmisiónSexualSIDA.

FSW FemaleSexWorker

HIV Humanimmunodeficiencysyndrome

MICS MultipleIndicatorClusterSurvey.

MSM Menwhohavesexwithmen

RCT RandomisedClinicalTrial

UNAIDS JointUnitedNationsProgrammeonHIVandAIDS

UNFPAUnitedNationsPopulationFund

VMMC Voluntarymedicalmalecircumcision

WHO WorldHealthOrganisation

1

1. INTRODUCTION

Between2001and2011,HIVincidencewasreportedtohavedeclinedby>50%across25countries(13ofwhichareinsub-SaharanAfrica).1Since2001,thesharpestdeclinesinHIVincidencehavebeenobservedacrosssub-SaharanAfrican(25%)andtheCaribbean(42%).1

AcomprehensivereviewofempiricalandmodelledHIVincidencetrendsacross20countriesinSub-SaharanAfricabetween1990and2012foundthatthedeclineinincidencecommencedpriortotheintroductionofARTprogrammesfrom2004.2

Priortotheavailabilityofantiretroviraltherapy(ART),changinganindividual’ssexualriskbehaviourwasthefocusofmanyinterventionprogrammesanddeterminingwhetherthesehaveresultedinreducedHIVtransmissionisessentialiflessonsaretobelearntandsuccessfulprogrammesidentified.

However,variationinprevalencetrendsobservedinsurveillancedatacanoccurasaconsequenceofchangesinnaturaltransmissiondynamicsovertime.Duringepidemicmaturation,HIVtransmissionsaturatesamongthosewithhighratesofpartnerchangeandtheaverageincidencesubsequentlydecreases.Adeclineinprevalenceobservedinsurveillancedatacanthereforeoccurasaconsequenceofnaturalepidemiologicaldynamicsintheabsenceofchangesinsexualriskbehaviourandcannotbeautomaticallyattributedtoasuccessfulinterventionprogramme.

Undernaturalepidemicdynamics,transmissionduringtheearlyphaseoftheepidemicoccursrapidlywithinasmallgroupofpeoplewithanincreasedrateofchangeinsexualpartners,whichplacesthemathigherriskofHIVacquisition.Astheepidemicmatures,transmissionwithinthissmallgroupofpeoplewithanincreasedriskofHIVacquisitionsaturatesandincidencebeginstostabilise.TransmissioncontinuestooccurwithinalargergroupofpeoplewithalowerriskofHIVacquisition(whoconstitutethemajorityofthegeneralpopulation),butatamuchslowerrateduetotheirlowerrateofchangeinsexualpartners.PeoplewithahigherriskofHIVacquisitionareselectivelyremovedfromthepopulationasaconsequenceofexperiencingAIDS-relatedmortalityatagreaterratethanpeoplewithalowerriskofHIVacquisition.However,clinicalprogressionandtheassociateddeclineinimmunefunctioncanspanadecade3andAIDS-relatedmortalityisthereforesignificantlydelayedafterinitialinfection.ThiscreatesatimelagbetweenanychangeinincidenceanditsassociatedchangeinAIDS-relatedmortality.HeterogeneityintheriskofHIVacquisitionthereforeinfluencesthetrajectoryoftheHIVepidemicintwoways;peoplewithahigherriskofHIVacquisitioncauseanearlypeakinprevalenceresultingfromrapidtransmissionandincreasedincidentinfectionsandastheepidemicmatures,adelayedreductioninprevalenceasaresultofsubsequentAIDS-relatedmortality.FollowingthispeakinAIDS-relatedmortalityandintheabsenceofanyotherinfluences,prevalencecanplateauasincidentinfectionsandAIDS-relatedmortalityreachequilibrium.Thisisconditionalonthereproductionnumber,definedastheaveragenumberofsecondarycasesthatatypicalcaseofaninfectiongeneratesoverthecourseoftheirinfectiousperiod,beinggreaterthanone.

However,therearemanycomplexforcesthatinteractsimultaneouslytoproducetheprevalencetrendsobservedinsurveillancedataandinordertoexploretheimpactofeach,theavailabilityofaccurateepidemiologicaldataisessential.

Ugandapresentsearlyandcompellingevidenceforchangesinsexualriskbehaviourhavingcontributedtosubstantialdeclinesinnationalprevalence.4Prevalencedeclineswere

2

consistentlyobservedacrossdifferentgeographicanddemographicsectionsofthepopulationinadditiontoanumberofpopulation-basedsurveysreportingchangesinsexualriskbehaviourindicators.4Between1989and1995,sexualriskbehaviourindicatorsshowedanincreaseintheageofsexualdebut,adecreaseincasualornon-regularpartnersandanincreaseincondomuse.5Acomparativeanalysisofthesetrendswithinneighbouringcountries(Kenya,MalawiandZambia)thathadsimilarepidemicdynamicsbutlackedHIVprevalencedeclinesonthesamescaleasUgandashowedthatonlyareductioninsexualpartnersandabstinencedistinguishedUgandafromcomparisoncountries.5Stoneburneretal.5subsequentlydemonstratedthata70%reductiononHIVincidencewasassociatedwitha60%reductionincasualsex.Ithasbeenproposedthatthisresultedfromhighlevelpoliticalsupportfacilitatingamulti-sectorialresponsethatinturnutilisedadecentralisedapproachtoplanandimplementasuccessfulbehaviouralchangecommunicationprogramme.4

Randomisedclinicaltrials(RCTs)thatrandomiseattheindividual-levelcanthereforebealessinformativemeansbywhichtoevaluatesuchinterventions.6Changesinsocialandculturalnormsalsorequiretimetoaccumulateandsubsequentlydiffuseacrossdifferentriskgroupspriortoexertinganimpactonincidence.Detecting

suchchangesthereforerequiresanextendedfollowupperiodandwhileRCTsareconsideredthegoldstandardforprovidingevidenceofefficacy,restrictionsintermsoftheirscale,durationandselectivestudypopulationcanlimittheirabilitytodetecttheimpactofaninterventionaimedatchangingsexualriskbehaviour.6,7Althoughobservationalstudiesareconsideredtoprovideaweakersourceofevidence,7theirscale,durationandstudypopulationarebroaderthanthatofRCTs,whichcanbeadvantageouswhenevaluatingthepopulation-leveleffectivenessofHIVpreventionprogrammes.6

CombiningobservationaldatawithepidemiologicalmodelshaspreviouslybeenusedtoevaluatetheimpactofHIVpreventionprogrammesaimedatchangingsexualriskbehaviour.8–11Epidemiologicalmodelsareabletoaccountforthenaturalepidemicdynamicsintheabsenceofinterventionsandsubsequentlydeducewhatimpactonprevalencetheseinterventionalprogrammeshaveachieved.ThisapproachwasabletoprovidestrongevidenceforchangesinsexualriskbehaviouraffectingthecourseoftheepidemicinUganda,ZimbabweandurbanareasofKenyaandHaiti.11ForZimbabwe,extensivehistoricalmappingofpreventionprogrammesandkeyinformantinterviewswereconducted;anincreasedawarenessofAIDS-relatedmortalityandsevereeconomicdeclinewereconcludedtohavestimulatedreductionsinmultipleconcurrentsexualpartnerships.10,12

1.1 Aims TheaimofthismulticountrystudywastoevaluatewhetherARTscale-upandchangesinsexualriskbehaviour,asidentifiedthroughself-reportedDemographicandHealthSurvey(DHS)dataandotherdatasourcesasavailable,havecontributedtothetrendsinHIVprevalenceobservedthroughnationalsurveillancedata.

3

1.2 Objectives Inselectedcountries,towhatextentareestimatedHIVincidencedeclinesrobustandtowhatextenthavesuchincidencedeclinescontributedtodeclinesinprevalence.

a) IfdeclinesinHIVincidencehavebeenexperiencedintheselectedcountries,what

causedchangesinincidence?Determineifthereisevidencethatchangesinsexualbehaviorchange(numberofsexualpartnersand/orcondomuse)and/orincreaseduptakeofbiomedicalinterventions(ARTand/orvoluntarymedicalmalecircumcision)havecontributedtochangesinHIVincidenceindifferentpopulationsandagegroups,andatwhatmomentintimethismighthaveoccurred.

b) DeterminewhichspecificHIVpreventionprogrammesineachselectedcountryhavecontributedtochangesin(a)and(b).

4

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5

2. METHODOLOGY

2.1 Country selection AsteeringcommitteewasconvenedbytheWorldBankandincludedrepresentativesfromUNAIDS,UNFPA,WHOandtheGlobalFund,withImperialCollegeLondonasthetechnicalpartner.Thefollowinginclusioncriteriawereagreedbythesteeringcommitteeandusedtoidentifycountrieswithanappropriateepidemiologicalcontextforparticipationinthisstudy:

- EvidenceofreductionsinmodelledHIVincidence,eitheramongthegeneralpopulationoramongkeypopulations.

- Evidenceofself-reportedchangesinsexualriskbehaviours,measuredoveratleastthreepopulation-basedsurveys.

- EvidenceofrapidincreasesinARTcoverage.- EvidenceofavailabledataonotherHIVpreventionprogrammes.

ThefivecountriesthatsubsequentlyengagedinthisstudywereBotswana,DominicanRepublic,Kenya,MalawiandZambia.

2.2 Epidemiological modelling Theframeworkwithinwhichthisworkwasconductedhasbeendescribedpreviously11andusedthemodellingapproachoutlinedbyHallettetal.10However,owingtothedifferentpatternsofriskofHIVexposurewithineachcountry,themodelusedanditsassociatedpatternofanalysisvariedaccordingly.Furtherdetailscanbefoundwithineachspecificcountryreport.

Foreachcountry,adeterministicmathematicalmodelofheterosexualHIVtransmissionwasdevelopedinordertoreplicatenaturalepidemicdynamicsintheabsenceofinterventionsorchangesinsexualriskbehaviour.Thiswastermedtheconstrainedmodel.Byproducingcounterfactualprojectionsinthisway,wewereabletoremovetheeffectoffactorssuchasARTscale-upandsimulateacontrolscenarioofhowwebelieveprevalencetrendsnaturallyevolveinHIVepidemics.Ifmodelprojectionsfitpoorlytoobservationaldata,thisindicatesthatcharacteristicswhichinfluencethecourseoftheepidemicaremissingfromthemodel.Inastepwiseapproach,interventionsthatmighthaveinfluencedtheepidemicwerebuiltintothemodelanditsfittodatare-evaluated.

Ifthefitofmodelprojectionstosurveillancedatawerenotimprovedthroughtheincorporationofanintervention,thedatawereconsideredtoprovidelittleevidencetosupportitseffectonthecourseoftheepidemic.However,ifthemodelincorporatinginterventionsprovidedabetterfit,thenthenature,timingandmagnitudeoftheireffectonprevalencetrendswereexplored.Thenumberofinfectionsavertedasaconsequenceofchangesinsexualriskbehaviourwasaparameterofparticularinterestandwascalculatedbycomparingepidemictrajectoriesfromtwosimulationsofthesamemodel;onewiththeeffectsofchangesinsexualriskbehaviourremovedandonewiththeminplace.Comparingnestedmodelsinthiswayenabledasystematicassessmentastowhichmostaccuratelyreplicatedobservedprevalencetrends.

6

2.3 Data ObservedprevalencetrendsweredeterminedfromHIVsentinelsurveillancedatafrompregnantwomenandkeypopulations.Forallcountries,population-basedsurveyswereusedtomakeadjustmentstosentinelsurveillancedatainordertoaddresstheoverestimationresultingfromsamplingbias(seelimitationsforfurtherdetails).

DataweredisaggregatedaccordingtothedifferentpatternsofriskofHIVexposurewithineachcountry.ForMalawiandBotswana,dataweredisaggregatedaccordingtourbanandrurallocation.ForZambiaandKenya,dataweredisaggregatedaccordingtosubnationalregions.ForDominicanRepublic,dataweredisaggregatedaccordingtokeypopulationsofinterestthatincludedmenwhohavesexwithmen(MSM),femalesexworkers(FSW)andruralcommunitiesofHaitiansugarcaneworkerscalled“Bateyes”.

TemporaltrendsinsexualriskbehaviourweredeterminedfromknowledgeandsexualriskbehaviourindicatorsreportedatmorethanonetimepointthroughDemographicandHealthSurveys(DHS),MultipleIndicatorClusterSurveys(MICS)andsimilarnationalsurveysasavailable.

2.4 Historical mapping HistoricalmappingofHIVprogrammes,conductedforBotswanaandMalawi,providedaretrospectiveanalysisofpolitical,economic,social,culturalanddemographicfactorsthatmayhaveinfluencedthecourseoftheepidemic.AreviewofHIVpreventionpolicies,campaigns,interventions,studiesandevaluationsconductedbetween1985and2012wasperformedalongsidekeyinformantinterviews,whichincludedpeopleinvolvedinresearch,implementationandmanagementofHIVprogrammesoverthesametimeperiod.

Acomparisonofthetemporalrelationshipbetweenthemodelledchangeinincidence,trendsinsexualriskandbehaviouralindicatorsandhistoricalmappingofHIVpreventionprogrammesenabledanassessmenttobemadeastowhethertherewasanylikelyassociation.

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3. RESULTS 3.1 Botswana StrongevidencewasfoundforthecombinedinfluenceofchangesinsexualriskbehaviourandARTonthecourseoftheepidemic,withtheimpactofavertingapproximately210,000infectionsinurbanareasand120,000infectionsinruralareasbetween1975and2012.Inaddition,therewasevidenceforARTexertinganindependenteffectonincidenceinbothurbanandruralareasandsomeevidencefortheindependentinfluenceofbehaviouralchangeonincidenceinurbanareas.

ThediscrepancybetweenurbanandruralresultscouldbeduetogeographicalheterogeneityinHIVepidemiologyoralackofpowerintheavailabledatatodetectthismoresubtlesignalinruralareas.As43%13ofBotswana’spopulationresidesinruralareas,thishighlightstheneedforfurtherinvestigationintofactorsdrivingepidemiologyatasubnationallevel.

FindingsfromthehistoricalmappingofHIVpreventionprogrammeswereconsistentwiththetotalcombinedactivitieshavingcontributedtochangesinsexualriskbehaviour.However,thisexercisewaslimitedbyalackofavailabledataduring1985-2000andthereforeheavilyreliedonkeyinformantinterviews.

KnowledgeandsexualriskbehaviourindicatorsreportedatmorethanonetimepointthroughtheBotswanaAIDSIndicatorSurveys14–17indicatedthattemporaltrendsinsexualriskbehaviourvariedaccordingtoindicator.Ofthosewithmorethanonepartnerina12-monthperiod,thepercentagethatusedacondomremainedstableoverthesametimeperiod.Quantifyingtheextenttowhichthesetwofactorsmaycounteracteachotherremainschallengingandseparatingtherelativeimpactofeachonincidencedeclineswasnotpossible.

3.2 Dominican Republic Therewasstrongevidenceforthecombinedinfluenceofchangesinsexualriskbehaviourand,toalesserextent,ARTonthecourseoftheepidemic,withtheimpactofavertingapproximately460,000cumulativeinfectionsoverthecourseoftheepidemic(between1982and2015).AmongFSW,approximately44,000cumulativeinfectionswereaverted.AmongBateyes,approximately33,000cumulativeinfectionswereavertedandapproximately28,000cumulativeinfectionswereavertedamongMSM.

TherewasreasonableevidencetosupporttheinfluenceofARTonprevalence.However,ARTalonewasfoundtobeinsufficienttoexplaintheobservedepidemictrend.Evidencefortheinfluenceofchangesinsexualriskbehaviouronprevalencewasalsopromisingandsupportedbytrendsinknowledgeandsexualriskbehaviourindicators.

Knowledgeandsexualriskbehaviourindicators18reportedatmorethanonetimepointindicatedthattemporaltrendsinsexualriskbehaviouramongthegeneralpopulationvariedaccordingtoindicator.Whileallindicatorsforcondomusewerefoundtoincreasebetween1996and2013,thepercentageofpeopleengaginginhigherrisksexalsoincreasedoverthesametimeperiod.Thistrendwasalsoobservedinyoungerrespondentsaged15-24.Oftherespondentswhoreportedengagementinhigherrisksex,asdefinedbysexwithanon-marital,

8

non-cohabitatingpartner,condomusewasfoundtoincrease.Ofthoserespondentswhoreportedengagementinhigherrisksex,asdefinedbysexwithmorethanonepartnerwithina12-monthperiod,thepercentagewhoreportedcondomuseremainedstable.Quantifyingtheextenttowhichthesetwofactorsmaycounteracteachotherremainschallengingandseparatingtherelativeimpactofeachonincidencedeclineswasthereforenotpossible.

WhenaskedtodescribeamethodofHIVprevention,>90%ofFSWand>90%ofMSMreportedcondomuseacrossallfiveprovincesandthiswasconsistentlyreportedwithinthe2008and2012behavioursurveillancesurveys(BSS).19,20ReportedcondomuseamongFSWvariedaccordingtothetypeofsexualpartner;morethan60%ofrespondentsreportedcondomusewiththelastclientwhereaslessthan10%ofrespondentsreportedcondomusewiththeirhusband.19,20TheproportionofFSWwhomentionedhavingonefaithfulpartnerasameansofHIVpreventionwas>69%acrossallfiveprovincesin2008butthisreducedto>58%in2012.19,20TheprevalenceofcommercialsexamongMSMwas<40%acrossallfiveprovincesin2008howeverthisincreasedto>65%in2012.19,20Reportedcondomuseamongthoseengagingincommercialsexwas>18%acrossallfiveprovincesin2008andincreasedto>40%in2012.19,20

3.3 Kenya Resultsprovidedevidencefortheinfluenceofchangesinsexualriskbehaviour,andtoamuchlesserextentART,onthecourseoftheepidemic,withtheircombinedimpactavertingapproximately4,107,000infectionsbetween1980and2015.Thiswasmostlyattributedtochangesinsexualriskbehaviour.

Therewasevidenceforachangeinsexualriskbehaviourexertinganindependenteffectonprevalencewhenevaluatedonanationalandsubnational(county)scale.Changesinsexualriskbehaviouralonewerepredictedtohaveavertedapproximately4,000,000cumulativeinfectionsnationallyoverthecourseoftheepidemic.ThisisconsistentwithearlierfindingsbyHallettetal.11whichfoundthatobserveddeclinesinprevalencecouldonlybereproducedbythemodelifitassumedareductioninsexualriskbehaviour.Encouragingly,temporaltrendsinknowledgeandsexualriskbehaviourindicators21suggestedincreasedcondomuseanddecreasedengagementinhigherrisksexoverthesametimeperiodandarethereforesupportiveofthisfinding.

TherewasreasonableevidencetosupporttheindependentinfluenceofARTonprevalencenationally,especiallyasacoincidingtrendinARTscaleupwasobserved.WhilethisstudyhasshownARTtohavesomeimpactonprevalencetrends,thishasyettobefullyoptimisedandthemagnitudeofimpactexertedthroughARTisconsiderablysmallerthanthemagnitudeofimpactexertedthroughchangesinsexualriskbehaviour.

Theobservedvariationineffectsizeacrosscountieshighlightstheneedforfurtherinvestigationatthisspatialresolution.Expandingdataavailabilityatthesubnationallevelwillenablethisanalysistoberefinedandthedistributionofriskfactorsdrivinglocalepidemiologytobeexplored.

3.4 Malawi SomenationalcounterpartsraisedconcernsaboutthevalidityofearlyAntenatalClinic(ANC)prevalencedatainMalawi.Astheconclusionsdrawnfromthismethodrelystronglyonthosedata,conclusionsshouldbeinterpretedwithcare.

9

StrongevidenceforthecombinedinfluenceofchangesinsexualriskbehaviourandARTonthecourseoftheepidemicwasfoundinurbanareas,withtheimpactofavertingapproximately340,000infectionsbetween1975and2012.Whenevaluatingtheinfluencefromeachoftheseinterventionsindependently,changesintheepidemictrajectoryweremorestronglyassociatedwithchangesinsexualriskbehaviourthanART.Inruralareas,therewassomeindicationofthecombinedinfluenceofchangesinsexualriskbehaviourandARTonthecourseoftheepidemicbutthisdidnotreachahighlevelofevidence.

ThediscrepancybetweenurbanandruralresultscouldbeduetogeographicalheterogeneityinHIVepidemiologyoralackofpowerintheavailabledatacouldhavepreventeddetectionofamoresubtlesignalinruralareas.As84%13ofMalawi’spopulationresidesinruralareas,thishighlightstheneedforfurtherinvestigationintofactorsdrivingepidemiologyatasubnationallevel.

FindingsfromthehistoricalmappingofHIVpreventionprogrammeswereconsistentwiththetotalcombinedactivitieshavingcontributedtochangesinsexualriskbehaviour.However,thisexercisewaslimitedbyalackofavailabledataduring1985-2002andthereforeheavilyreliedonkeyinformantinterviews.

However,temporaltrendsinknowledgeandsexualriskbehaviourindicatorsprovidesomeinsight.WhenaskedtodescribeamethodofHIVprevention,theproportionofbothmenandwomenmentioningcondomuseincreasedbetween1992and2000.22EquallytheproportionofbothmenandwomenmentioningabstinenceasamethodofHIVpreventionincreasedduringthesameperiod.22AlthoughtheproportionofwomenwhomentionedhavingonlyonepartnerasamethodofHIVpreventionwasstableoverthisperiod,theproportionofmenmentioningthismethodreduced.22

Encouraginglyallindicatorsforcondomusewerefoundtoincreasebetween1992and2010.Engagementinhigherrisksex,asdefinedbysexwithanon-marital,non-cohabitatingpartner,decreasedformalerespondentsandremainedstableforfemalerespondentsbetween2000and2010.Thepercentageofrespondentsreportingsexwithmultiplepartnersremainedstablebetween2004and2010.Thepercentageofmalerespondentsreportingcommercialsexwithinthelastyeardecreasedbetween2000and2010.

3.5 Zambia Resultsprovidedsomeevidenceforthecombinedinfluenceofchangesinsexualriskbehaviourandantiretroviraltherapy(ART)onthecourseoftheepidemic,withtheimpactofavertingapproximately909,000infectionsbetween1975and2015.

Inaddition,therewasevidenceforachangeinsexualriskbehaviourexertinganindependenteffectonprevalencewhenevaluatedonanationalandsubnational(provincial)scale.TemporaltrendsinknowledgeandsexualriskbehaviourindicatorsdemonstratedanincreaseintheproportionofbothmenandwomenmentioningcondomuseandabstinenceasamethodofHIVprevention.23Engagementinhigherrisksex,asdefinedbysexwithmultiplepartners,decreasedbetween1992and2014.Between1996and2014,condomuseincreasedamongthoserespondentsreportingsexwithanon-marital,non-cohabitatingpartner.Thepercentageofrespondentswhoreportedcondomusethelasttimetheyhadsexwithaspouseorcohabitatingpartneralsoincreasedbetween1996and2014.Thepercentageofmalerespondentsreportingcommercialsexwithinthelastyeardecreasedbetween1996and2014inadditiontocondomuseincreasingamongsuchrespondentsbetween2001and2014.

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TherewasreasonableevidencetosupporttheindependentinfluenceofARTonprevalencenationally,especiallyasdatashowedacoincidingtrendinARTscaleup.Theobservedvariationinprevalencetrendsacrosssubnationalregionshighlightstheneedforfurtherinvestigationatthisspatialresolution.Expandingdataavailabilityatthesubnationallevelwouldenablethisanalysistoberefinedandthedistributionofriskfactorsdrivinglocalepidemiologytobeexplored.

Table1:Summaryofimpactevaluationresults

Country:

Impactevaluation:

CumulativeInfectionsaverted(n)

ReductioninCumulativeIncidence(%)

Year in which decline inincidencecommenced.

BotswanaUrbanareas:Ruralareas:

210,000120,000

4735

20012001

DominicanRepublic 460,000 21 1997

Kenya 4,107,000 63 1995

Malawi* 340,000 48 2001

Zambia 909,000 23 1990

*Impactevaluationforurbanareasonly-insufficientevidenceofimpactinruralareas.Source:Authors

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4. DISCUSSION FindingsfromthisstudyareconsistentwiththoseofAwadetal.8,whousedepidemiologicalmodellingtodemonstratetheplausibilityofdeclinesinHIVprevalenceoccurringasaconsequenceofdeclinesinsexualriskbehaviouracross18countriesinsub-SaharanAfrica.ThisstudyhasextendedsuchworkbyincorporatingARTscale-upwithintheepidemiologicalmodelsandadditionallytriangulatingdatafrommultiplesourcesinordertoexploredifferentprogrammaticcontributionsthatmayhaveinstigatedobservedchangesinsexualriskbehaviour.

EvidencewasfoundforthecombinedimpactofchangesinsexualriskbehaviourandARTonthecourseoftheepidemic.ARTwasshowntopossiblyhavesomeeffectontheepidemicbutalone,wasinsufficienttoexplaintheobservedepidemictrendowingtothefactthatincidencedeclinespredatetheavailabilityofART.Equally,thisstudyevaluatesincidencedeclinesthatpredatetheintroductionofvoluntarymedicalmalecircumcision(VMMC)intoHIVpreventionprogrammes,whichthereforelimitsitsabilitytoevaluatetheimpactofsuchbiomedicalinterventions.

Triangulationofdatafromdifferentsourceshasprovidedacompellingnarrativeforadeclineinsexualriskbehaviour.Suchtemporalchangesinsexualriskbehaviourindicatorsprovideplausibleproximatedeterminantsalongthecausalpathway.Inordertodeterminewhetherchangesinsexualriskbehaviourweremotivatedbytargetedpreventionprogrammesorbymoreinformalmeansofinter-personalcommunication,strongerevidenceofappropriatecoverageandtimingofpreventionprogrammestoensuresufficientexposuretotheirtargetpopulationsarerequiredbeforeconclusivestatementscanprogressfromassociationtocausation.

WhileobservinganincreaseinAIDS-relatedmortalityduringepidemicmaturationmaymotivateanindividualtoaltertheirriskofHIVacquisition,HIVpreventionprogrammesprovidetheknowledgeandresourcestoenablethatsameindividualtotakeactivestepsindoingso.However,distinguishingtherelativecontributionofeachmechanismanddeterminingtheextenttowhichtheyinteractischallenging.

4.1 Limitations Findingsfromthisworkareencouragingbutrequireanoteofcaution.Conclusionsarelargelydrawnagainstobservationaldatafromsentinelsurveillancesites,whichprovideavaluableinsightintotemporalprevalencetrends.However,thetechnologyforHIVtestinghasundergonecontinuousimprovementoverthecourseoftheepidemic,resultinginchangestotestspecificityovertime;thesechangescouldconfoundtheresultssubstantially.Furthermore,inallepidemics,astheepidemicmatures,AIDS-relatedsub-fertilityandashiftingagedistributionofwomenpresentingtotheseclinicscouldalsoaccountfortheobserveddeclineinprevalenceandcannotberuledoutatthisstage.

FindingsfromthehistoricalmappingofHIVpreventionprogrammeswereconsistentwiththetotalcombinedactivitieshavingcontributedtochangesinsexualriskbehaviour.However,thisqualitativeexercisereliedonsubjectiverecollections15yearsaftertheeventorexposuretofactorsofinterest,whichmayengenderrecallbias.Furthermore,thereliabilityofself-reportedsexualriskbehaviourischallengedbysocialdesirabilitybias.Thesesourcesofbias,combinedwithlimiteddataavailabilityforearlyinterventions,thereforelimittheweightgiventosuchevidence.

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WhileresultsshowedanassociationbetweeninterventionprogrammesandreducedHIVtransmission,evaluatingwhethertherewasevidenceforacausalrelationshipwithspecificprogrammeswasbeyondthescopeoftheavailabledata.Futurestudiesmayormaynotbeabletobetterdeterminewhetherchangesinsexualriskbehaviourweremotivatedbytargetedpreventionprogrammesorbymoreinformalmeans,suchasinter-personalcommunicationgivingrisetogreaterHIVawareness,whichoccurredconcurrentlyastheepidemicmatured.

4.2 Future impact evaluations Thisanalysislargelyfocusedonexploringthedriversbehindincidencedeclinesthatwereobservedpriortothescale-upofARTandVMMC,andthereforefocusedlargelyontheimpactofprogrammesaimedatchangingsexualriskbehaviour.WhiletheimpactofARTorVMMCwouldthereforenotbestrongly‘visible’atthistime,withthebenefitofseveralyearsofadditionaldata,futureevaluationsmaybeabletodetectevidenceoftheirimpactontheepidemic.

OwingtoanincreaseinthenumberofinterventionsaimedatprovidingeducationandinformationonHIV/AIDS,socialdesirabilitybiasislikelytohavechangedovertime.AccuratelyquantifyingtheextenttowhichapopulationisexposedtoanymassmediabehaviouralchangecampaignischallengingandforHIV/AIDS,thisiscompoundedbythefactthatcertainsexualbehavioursareoftenhighlystigmatised.ReceivingAIDSinformationthroughmoreinformalchannelsofcommunication,suchasfriends,churchesandothersocialnetworks,hasbeenshowntobehugelyinfluential,12howeverremainsevenmorechallengingtoaccuratelyquantify.

Whilesocialdesirabilitybiaspertainstoanyself-reporteddata,greateropportunitiesexisttointerrogatetreatmentdatawithquantifiablebiomarkers.Thiscouldfacilitatemethodstoadjustforsuchbiasandthusimprovethereliabilityofsuchdata.

AsepidemiologicaltrendscontinuetobemonitoredduringthecurrentperiodofARTprogrammeexpansionandbroadeningeligibilitycriteria,prospectivelycollecteddataoutliningkeyindicators,suchascoverage,uptake,adherenceandviralsuppression,willbeimperativeforfuturemonitoringandevaluationanalyses.Incontrasttoquantifyingthenumberofpeopleexposedtoabehaviouralchangecommunicationcampaigndisseminatedthroughradio,television,communityandpersonalnetworks,ARTprogrammesintrinsicallylendthemselvestoquantitativedatacollection(forexample,throughenumeratingthenumberofpatientsreceivingART).

Measuresofthecoverageanduptakeofpublichealthinterventionsareessentialforanyimpactevaluation.Incorporatingsuchindicatorsasanintegralpartoffuturetreatmentandpreventionprogrammeswouldgreatlyfacilitateprospectivecollectionofsuchdata,therebyreducingtheinfluenceofrecallbiasandgreatlyincreasingtherobustnessoffindings.Accurateprospectivemonitoringofcurrentinterventions,atbothnationalandsubnationallevels,willthereforebeimperativetobetterinformfuturemonitoringandevaluationefforts.

IntheeraofARTscale-up,longitudinalprevalencetrendsalonecanbemisleadingowingtothefactthatARTextendssurvivaloftheHIVpopulationinreceiptoftreatment.24Thiswillresultinincreasedprevalence,whichinturncanobscureadeclineinincidentinfections.MathematicalmodellingoftheHIVepidemicwillthereforehavearoleinevaluatingtheimpactofARTprogrammesbyprovidingameansbywhichtotakeaccountofconfoundingfactors,suchastheextendedsurvivalexperiencedbythoseinreceiptofART,whengeneratingthecounterfactualscenario.

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CONCLUSIONS Inconclusion,declinesinsexualriskbehaviourhaveshownaconsistentassociationwithconcomitantdeclinesinHIVincidenceandhavebeenestimatedtoresultinhundredsofthousandsofnewinfectionsbeingavertedinthefivecountriesincludedinthisstudy(Table1).Ultimately,primarypreventionthroughchangesinsexualriskbehaviourmustremaincentraltotheAIDSresponse;expansionsinARTprogrammesneedtobematchedbycommensurateprogressinHIVpreventionprogrammesortheyriskbecomingunsustainable.12Conclusionsfromthisstudyproposethatnosinglepreventionprogrammehasbeenresponsibleforthedeclineinincidence,ratherthecombinedexposuretopublicandinterpersonalcommunicationexplainingtheincreasedAIDS-relatedmortalityinadditiontoarangeofHIVpreventionprogrammeshasservedtomotivateandenableindividualstoaltertheirriskofHIVacquisition.

Attributingchangesinsexualriskbehaviourtospecificinterventionsrequiresdataquantifyingexposuretosuchinterventionsandplausiblemechanismslinkingtheproposedinterventiontochangesinsexualbehaviour.However,changesinsexualriskbehaviouraremostlikelytheproductofcumulativeexposuretoawiderangeorpreventionprogrammesandowingtomultipleinterventionsandsocietalchangesoccurringsimultaneously,suchattributionremainschallenging.6

AstheHIVepidemiccontinuestomaturealongsidetheincreasingscopeandcoverageoftreatmentandpreventionprogrammes,inferringtrendsinincidencefromavailableprevalencedataandsubsequentlyidentifyingdriversbehindepidemictrendswillbecomeincreasinglycomplex.25Theavailabilityofprospectivelycollectedprogrammaticandepidemicsurveillancedataatagreatersub-nationalspatialresolutionwillgreatlyimprovetherobustnessoffutureimpactevaluations.

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