w w w . e l s e v ie r . c o m / l o c a t e / b j i d
The
Brazilian
Journal
of
INFECTIOUS
DISEASES
Original
article
HIV-1
mother-to-child
transmission
in
Brazil
(1994–2016):
a
time
series
modeling
Antonio
Victor
Campos
Coelho
a,b,∗,
Hemílio
Fernandes
Campos
Coelho
c,
Luiz
Cláudio
Arraes
d,e,
Sergio
Crovella
baUniversidadeFederaldaParaíba.DepartamentodeBiologiaMolecular,JoãoPessoa,PB,Brazil
bUniversidadeFederaldePernambuco,DepartamentodeGenética,Recife,PE,Brazil
cUniversidadeFederaldaParaíba.DepartamentodeEstatística,JoãoPessoa,PB,Brazil
dUniversidadeFederaldePernambuco,DepartamentodeMedicinaTropical,Recife,PE,Brazil
eInstitutodeMedicinaIntegralProfessorFernandoFigueira,Recife,PE,Brazil
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received13February2019
Accepted24June2019
Availableonline22July2019
Keywords: HIV-1 Verticaltransmission Perinataltransmission Biostatistics Epidemiology Forecasting
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HIV-1mother-to-childtransmission(HIV-1MTCT),isanimportantcauseofchildren
mortal-ityworldwide.BrazilhasbeentraditionallypraisedbyitsHIV/Aidsprogram,whichprovides
free-of-chargecareforpeoplelivingwith HIV-1. Usingpublicepidemiology and
demo-graphicdatabases,weaimedatmodelingHIV-1MTCTprevalenceinBrazilthroughtheyears
(1994–2016)andelaborateastatisticalmodelforforecasting,contributingtoHIV-1
epidemi-ologicsurveillanceandhealthcaredecision-making.Wedownloadedsetsoflivebirthsand
mothers’dataalongsideHIV-1casesnotificationinchildrenoneyearoldorless.Through
timeseriesmodeling,weestimatedprevalencealongtheyearsinBrazil,andobserveda
remarkabledecreaseofHIV-1MTCTbetween1994(10casesper100,000livebirths)and
2016(fivecasesper100,000livebirths),areductionof50%.Usingourmodel,weelaborated
aprognosisforeachBrazilianstatetohelpHIV-1surveillancedecisionmaking,indicating
whichstatesareintheoryinriskofexperiencingariseinHIV-1MTCTprevalence.Tenstates
hadgood(37%),ninehadmild(33%),andeighthadpoorprognostics(30%).Stratifyingthe
prognosticsbyBrazilianregion,weobservedthattheNortheastregionhadmorestateswith
poorprognosis,followedbyNorthandMidwest,SoutheastandSouthwithonestateofpoor
prognosiseach.BrazilundoubtedlyadvancedinthefightagainstHIV-1MTCTinthepast
twodecades.WehopeourmodelwillhelpindicatingwhereHIV-1MTCTprevalencemay
riseinthefutureandsupportgovernmentdecisionmakersregardingHIV-1surveillance
andprevention.
©2019SociedadeBrasileiradeInfectologia.PublishedbyElsevierEspa ˜na,S.L.U.Thisis
anopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/
licenses/by-nc-nd/4.0/).
∗ Correspondingauthorat:UniversidadeFederaldaParaíba.DepartamentodeBiologiaMolecular.CidadeUniversitária,JoãoPessoa,PB
58051-900,Brazil.
E-mailaddress:antoniocampos@dbm.ufpb.br(A.V.CamposCoelho).
https://doi.org/10.1016/j.bjid.2019.06.012
1413-8670/©2019SociedadeBrasileiradeInfectologia.PublishedbyElsevierEspa ˜na,S.L.U.ThisisanopenaccessarticleundertheCC
Introduction
The human immunodeficiency virus type 1 (HIV-1) is a
retrovirus that infectsT CD4+ lymphocytes, resulting in a
progressivedeteriorationoftheimmunesystem, leadingto
acquiredimmunodeficiencysyndrome(Aids),iftheinfection
isleftundiagnosedanduntreatedwithantiretroviraldrugs.
HIV-1transmissionoccursthroughunprotectedsexual
inter-course,bloodtransfusionsandneedlesharing,andthrough
an HIV-1 infected mother to her child during pregnancy
orbreastfeeding.1 HIV-1 mother-to-childtransmission
(HIV-1MTCT),alsoknownasverticalorperinataltransmission,is
animportantcauseofchildmortality,anditisestimatedthat
abouttwomillionchildrenareinfectedwithHIV-1worldwide.2
Useofantiretroviraldrugsduringpregnancyisessentialfor
HIV-1 MTCT prevention. The rate of transmission without
treatmentrangesbetween15%and45%,fallingbelow2%with
propertreatment with plasma HIV-1 suppression to
unde-tectablelevels.3
BrazilhasbeentraditionallypraisedbyitsHIV-1/Aids
pro-gram,whichprovidesfree-of-chargeanduniversalprevention
tools,diagnostictests,andcareforpeoplelivingwithHIV-1in
thepublichealthcarenetwork,knownasSUS(SistemaÚnico
deSaúde).4Inthelastfewyears,BrazilincreasedHIV-1testing
coverageamongpregnantwomen.5Thus,weaimedtomodel
HIV-1MTCTprevalenceinBrazilthroughtheyearsand
elab-orateastatisticalmodelforforecasting,contributingtoHIV-1
epidemiologicsurveillanceandhealthcaredecision-making.
Methods
Datacollectionandmanipulation
ToestimateHIV-1MTCThistoryinBrazil,wedownloadedtwo
setsofdatafromDATASUS,whichisapublicdatabasethat
integrateslivebirths,causes ofdeath,compulsory
notifica-tioncomorbidities(suchasHIV-1infectionandAidscases)and
demographicdata.6Thefirstdatasetcontainedthenumberof
allHIV/AidscasesdetectedamongBrazilianindividualsaged
oneyearorless,accordingtofederativeunit(26statesand
aFederalDistrict)andyearofdiagnosis,rangingfrom 1994
to2016.Thisfirstdatasetrepresentedtheprobablecasesof
HIV-1MTCT,asweassumedthatchildreninthisagegroup
wereprobablydiagnosedwiththeinfectionwithintheiryear
ofbirth.Inotherwords,theyearofdiagnosis=yearofbirth.
Theseconddatasetcontainedthenumberofalllivebirths,
alsoaccordingto Brazilianregion, federativeunitand year
ofdiagnosis,inadditiontoinformationaboutthe mothers:
race(white,black,admixed,Asiaticdescentandindigenous
descent, respectively followingthe classificationof Brazil’s
demographiccouncil,IBGE),7ageatthetimeofdelivery(10–14,
15–19,20–24,25–29,30–34,35–39,40–44,45–49,50–54,and55or
moreyearsold),typeofdelivery(vaginalorcesareansection),
numberofprenatalconsultations(zero,onetothree,fourto
sixandsevenormoreconsultations),andeducationallevel
(zero,onetothree,fourtoseven,eightto11and12ormore
yearsofeducation).
To simplifythe analysis, wedichotomized thevariables
race,age,educationallevelandnumberofprenatal
consul-tations level and summed the respective counts into new
categories.Racewasmodeledwiththecategories“white”or
“non-white”,ageatdeliverywith“lessthan25yearsold”or
“25yearsoldormore”,educationwith“noformaleducation”
or“anyyearsofeducation”,andnumberofprenatal
consulta-tionswith“idealnumberofconsultations(sevenormore)”or
“suboptimalnumberofconsultations(lessthanseven)”.
Usingthenumberofinfectedchildrenfromthefirstdataset
asnumeratorandalllivebirthsfromtheseconddatasetas
denominatorafterexcludingmissingdata,weestimatedthe
prevalence ofHIV-1 MTCT per 100,000live birthsper year,
hereaftertermed“HIV-1MTCTprevalence”anditwasour
vari-ableofinterest.Theexplanatoryvariableswerealsoconverted
thisway,givingaprofileofthemothershavingchildreneach
yearaccordingtotheirfederativeunitandnationwideBrazil.
Statisticalanalysis
All statistical analyses were performed with R software
version3.5.1.8All analysesweretwo-tailedandlevelof
sig-nificance(␣)wassetat5%.
Linearregressionanalysis
With the per 100,000live birthsdata, weperformed linear
regressionanalysisusingthebaseRpackagestats8tocheck
if any ofthe mother’scharacteristics were associatedwith
HIV-1MTCTprevalence.Forthisanalysis,weusednationwide
(aggregatedforBrazilasawhole)data.
Timeseriesanalysis
Wedescribed Brazil’sHIV-1MTCT prevalencethroughtime
seriesanalysisusingtheRpackagestseries9andforecast.10
We then considered ARIMAX (autoregressive integrated
moving average–ARIMA– withexogenousinputs) models
toforecastvaluesofHIV-1MTCTprevalenceatbothnational
andstatelevels.Briefly,anARIMAXmodelisatypeofARIMA
modelwithexternalpredictors.Ontheotherhand,anARIMA
modelisatimeseriesmodelthatrepresentslinear
phenom-enathatappeartobestationary–aseriesofvaluesordered
in time forwhich its present values depend onlyon their
pastvaluesandrandomnoise(withmeanequaltozeroand
constant variance), hence the term “moving average”.The
“autoregressive”termmeansthattheinterestvariablevalues
are aregression ofpresent against pastvalues. The
“inte-grated”termindicatesthatthedatavaluescanbereplaced
withthedifferencebetweentheirpresentandpastvalues.The
generalrepresentationofanARIMAmodelisthedenotation
ARIMA(p,d,q),inwhichprepresentsthenumberof
autore-gressiveparametersofthemodel,drepresentsthenumberof
differencingstepsnecessarytoremoveanynon-stationarity,
and q representsthe number of parametersrelated to the
movingaverages.11AnARIMAXmodelthereforepermitsthe
inclusionofexplanatoryvariables(inourcase,themother’s
characteristicsasmentionedabove)thatcouldbeusefulfor
improvingtheforecastofthevariableofinterest(HIV-1MTCT
prevalence).12
Wethenselectedthebestpredictivemodelbasedonthe
theforecastpackage,whichconsidersseveralmodelsand
auto-maticallyreturnsastationarymodelwiththesmallestAkaike
InformationCriterion(AIC)value,meaningithadthebestfit
tothedata.13
Morespecifically,weproduced28timeseriesmodels.The
firstwasmadeusingnationwidedata(theBrazilmodel)and
theremaining27weremadeusingdatafromeachBrazilian
federativeunit.Ineachpredictionweincludedthebest
exter-nalpredictorsasindicatedbycorrelationanalysistoimprove
thepredictivepowerofthetimeseriesmodels.
Themodeloutputswereextractedwiththepackagedplyr.14
Theplotsforeachmodelweremadeusingpackages,ggplot215
and ggforce.16 Briefly, time series are a sequence of
obser-vations ordered in time.17 With these models, we aimed
at predicting HIV-1 MTCT prevalence using data collected
between1994and2016.Totestthefitofthemodels,weused
onlythedatabetween1994and2012formodelingandthen
compared the predictions with their respective95%
confi-denceintervalstheactualobservations(“true”values)through
plotsforeachtimeseries.
Results
TheestimatedHIV-1MTCTprevalenceinBrazildecreasedto
fivecasesper100,000 livebirthsin2016from 10casesper
100,000casesin1994(Fig.1),a50%decrease.Thecorrelation
analysispointedthatmothers’loweducationallevelandbeing
youngerthan25yearsatdeliveryweresignificantlyassociated
withHIV-1MTCTprevalenceincrease(p=0.02andp=0.001,
respectively). Race (p=0.16), type of delivery(p=0.15), and
numberofprenatalconsultations(p=0.87)didnotinfluence
prevalence.Theregression modelhad anadjustedR2=0.88
anditwasstatisticallysignificant(F-statistic=34.0onfiveand
17degrees-of-freedom,p-value«0.001).Therefore,these
vari-ableswereusedasexternalpredictorsfortheARIMAXmodels
(Table1).
TheARIMAXmodelsforecastforyears2013,2014,2015,and
2016generallyhadgoodfittothedata,asdemonstratedby
the95%confidenceintervals,whichcoveredthetrue
observa-tions,asdisplayedinFig.1,forexample.Theonlyexception
wastheforecastforAlagoasstate,inwhichthetruevalues
werewellabovetheforecastvalues.Remarkably,Alagoasis
theBrazilianstatewiththelowesthumandevelopmentindex,
HDI=0.631.Forcomparison,Brazil’sHDIis0.754.7Thus,
gov-ernmentunderfundingmaybeassociatedwiththepredicted
riseofHIV-1MTCTprevalence.The27plotsforeachfederative
unitareavailableinSupplementaryFig.1.Additionally,some
forecastsyieldednegativevalues,sowereplacedthemwith
zero,sinceprevalencevaluescannotbenegative.
Wecomparedtheestimatedprevalencevalues(“true”
val-ues)withthevaluesprovidedbyeachforecast.Thesevalues
arereportedinSupplementaryTable1andelaborateda
tenta-tive“prognostic”regardingHIV-1MTCTprevalencebehaviorin
Brazilandeachfederativeunit.Wetermeda“good”prognostic
if(1)theforecastindicatedadecreasingtrendinthe
preva-lenceAND(2)the“true”valueswerelowerthanthepredicted.
A“mild”prognosticwasdefinedas(1)theforecastindicated
astabilizingorincreasingtrendintheprevalenceAND(2)the
“true”valueswerelowerthanthepredicted.Finally,a“poor”
prognosticwasdeemedwhen(1)theforecastindicateda
sta-bilizingorincreasingtrendintheprevalenceORdecreasing
trendAND(2)the“true”valueswerehigherthanthepredicted.
ResultsofthisrationalearesummarizedonTable2.
Tenstateshadgood(37%),ninehadmild(33%),andeight
had poor prognostic (30%). Stratifying the prognostics by
Brazilianregion,weobservedthattheNortheastregionhad
themoststateswithpoorprognosis(threestates,athirdofthe
statesintheregion:Alagoas,Ceará,andSergipe),followedby
Northregion(twostatesinaregionwithsevenstates:Acreand
Amapá). TheMidwest,(fourstates),Southeast(fourstates),
and South(threestates)had onestatewithpoorprognosis
each (Mato Grosso, SãoPaulo, and Santa Catarina,
respec-tively)(Table3). 15 HIV -1 MTCT pre v alence (b y 100,000 liv e bir ths) 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 19941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016 Year Data True values Forecast
Fig.1–Timeseriesfor1994–2015forBrazilHIV-1MTCTprevalenceper100,000livebirthsandARIMAXmodelforecastfor years2013–2015todemonstratethemodel’sfit.Atrendofdecreaseintheprevalenceisclearlyseen.
Table1–Multiplelinearregressionmodelforexplanatoryvariablesselectionusedintimeseriesmodeling.
Variables Linearregressionestimate() Standarderror p-value
Intercept(␣) −42.9 21.8 0.07
Non-whiterace 0.00002 0.00002 0.16
Lessthan25yearsoldatdelivery 0.0008 0.0003 0.02
Deliverybycesariansection 0.0003 0.0002 0.15
Noformaleducation 0.0001 0.00004 0.001
Sevenormoreprenatalconsultations −0.000002 0.00001 0.87
Thebolditalicvaluesarestatisticallysignificantvalues(levelofsignificancealpha=0.05)inamultiplelinearregressionmodel.
Table2–ResultssummaryforARIMAXmodelsforBrazil’sHIV-1MTCTprevalenceforecasting(years2013–2016).Regions andfederativeunits(states)arelistedalphabetically.
Region Federativeunit(state) Forecasttrend Prognostics ARIMAXmodelselected
Brazil (Allstates) Decrease Mild ARIMA(0,1,0)
Midwest DistritoFederal Increase Mild ARIMA(0,1,0)
Goiás Increase Mild ARIMA(0,1,0)
MatoGrosso Stabilization Poor ARIMA(0,1,0)
MatoGrossodoSul Decrease Good ARIMA(0,1,3)
Northeast Alagoas Stabilization Poor ARIMA(1,1,0)
Bahia Decrease Good ARIMA(0,1,0)
Ceará Increase Poor ARIMA(0,1,0)
Maranhão Decrease Good ARIMA(1,1,0)
Paraíba Decrease Good ARIMA(1,1,0)
Pernambuco Increase Mild ARIMA(1,1,0)
Piauí Decrease Good ARIMA(0,1,0)
RioGrandedoNorte Increase Mild ARIMA(0,1,0)
Sergipe Decrease Poor ARIMA(1,1,0)
North Acre Stabilization Poor ARIMA(1,1,0)
Amapá Increase Poor ARIMA(1,1,0)
Amazonas Increase Mild ARIMA(1,1,0)
Para Decrease Good ARIMA(1,1,0)
Rondônia Decrease Good ARIMA(2,1,0)
Roraima Decrease Good ARIMA(1,1,0)
Tocantins Decrease Good ARIMA(0,1,0)
Southeast EspíritoSanto Increase Mild ARIMA(0,1,0)
MinasGerais Increase Mild ARIMA(0,1,0)
RiodeJaneiro Increase Mild ARIMA(0,1,0)
SãoPaulo Decrease Poor ARIMA(0,1,0)
South Paraná Increase Mild ARIMA(0,1,0)
RioGrandedoSul Decrease Good ARIMA(0,1,0)
SantaCatarina Decrease Poor ARIMA(0,1,0)
Table3–SummaryofHIV-1mother-to-childtransmissionforecastprognosis,stratifiedbyBrazilianregion.
Brazilianregion Statesperregion Prognostics
Good Mild Poor
n % n % n % Midwest 4 1 25.0 2 50.0 1 25.0 Northeast 9 4 44.4 2 22.2 3 33.3 North 7 4 57.1 1 14.3 2 28.6 Southeast 4 0 0.0 3 75.0 1 25.0 South 3 1 33.3 1 33.3 1 33.3
Discussion
WeperformedatimeseriesmodelingofHIV-1MTCT preva-lenceevolutioninBrazilbetween1994and 2016,aimingto forecastthisprevalenceforfutureyears,basedonthe demo-graphiccharacteristicsofchildbearingwomen(race,age,type
ofdelivery,educationallevel,andnumberofprenatal consul-tationsreceived).
Weobservedasignificantassociationbetweendeliveryat youngerageandloweducationallevelwithprevalenceof HIV-1MTCT,agreeingwiththeavailableevidencethatinBrazil,the majorityofHIV-1 infectioncasesoccur amongpoorer indi-viduals,withlessaccesstoeducation(“pauperization”ofthe
epidemics).18Thus,itislogicaltothinkthatthesewomenthat
areathigherriskofcarryingpregnancieswhileundiagnosed
bytheinfection.Theuntreatedinfectionisstronglyassociated
withtherisktoHIV-1MTCT.2
Between 1996 and 2012, Brazil invested US$ 1.6 billion
for the provision of universal, free-of-charge,
antiretrovi-ral care and general healthcare for people living with
HIV-1. Thisinvestment payed off: there was a remarkable
reductionofmortalityandmorbidityrelatedwithHIV-1
infec-tions and providedanestimatedUS$ 2billion economyin
hospitalizations.19 Moreover,Brazil alsoenactedhealthcare
policiesthatguaranteefreeHIV-1rapidtestsandsubstitutes
for maternalmilk for pregnant and breastfeeding women.
Since2012,pregnantwomenareofferedantiretroviral
treat-mentregardlessofCD4+Tcellcountandcontinuetreatment
afterchildbirth.4
Consequently, according to our estimates, Brazil cut by
half the rate of new perinatal HIV-1 infection in the past
twodecades.Currently,withanestimatedfiveinfectionsper
100,000livebirths,it isapromisingcandidateforreaching
theWorldHealthOrganizationgoalsofeliminationofHIV-1
MTCT.20Forcomparison,themorerealisticgoalsetbytheUSA
CentersforDiseaseControl(CDC)islessthan1infectionper
100,000livebirths,sinceeliminatingHIV-1MTCTisvery
diffi-cult,sincewomencanstillbeinfected,asthepandemicsisstill
ongoing.21TheBrazilianMinistryofHealthisactively
commit-tedtotheattainmentofthisgoal,promotinginitiativessuch
ascertificationofmunicipalitiesandstatesthatachieve
inci-denceratesof0.3newcasesorlesscasesinthepastthree
yearsandaprevalenceof2%ofinfectedchildrenamongthe
exposedtoHIV-1inthepastthreeyears.22
Ourforecastpointedtoafurtherreductioninthe
Brazil-ianHIV-1MTCTprevalence,buttheactualvalueswerehigher
thanthepredictionsinthefinalyearofthetimeseries,2016.
Therefore,wesupposethatthereissomeyetunidentified
fac-torcontributingtoslowdowndecelerationintheprevalence.
Oneofthose could betherecentspike ofnewHIV-1 cases
amongyoungerindividuals.Forexample,therewasa13.4%
increaseinthe number ofHIV-1detections amongwomen
agedbetween15and19yearswhencomparingtheyearsof
2006and2016.5Therefore,ifmorewomeninthestartoftheir
childbearingagearebeinginfected,itisexpectedthatsome
oftheseinfectionscanbetransmittedtotheirfuturechildren.
Ourstudyhassomelimitations.Sinceweworkedwith
sec-ondary data,we aresubject totheir inescapableproblems,
suchasmissinginformationandsub-notification.
Neverthe-less,webelievethatwehavebeenabletocaptureimportant
trends in HIV-1 MTCT in Brazil. Moreover, it is possible
that the number ofprenatal consultationsand delivery by
cesariansectionwerenotsignificantlyassociatedwith
HIV-1MTCTduringourmultiplelinearregressionmodelduring
explanatoryvariablesselectionfortimeseriesmodelingdue
tosuboptimaldataintheconsultedDATASUSregistry.Proper
prenatalcareisobviouslyapowerfultoolagainstHIV-1MTCT,
reducingperinatalHIVtransmissionprobabilitytolessthan
1%.23 Apreviousstudy regardingthe NewYorkstate(USA)
investmentinprenatalcareofHIV-1infectedpregnantwomen
estimatedthat“forevery$1investedinpreventingperinatal
HIVinfections,almost$4inHIVtreatmentcostshavebeen
saved”. It is important topoint out that we included this
variableinourmodeldespitenotachievingstrictlystatistical
association.
Conclusion
BrazilundoubtedlyadvancedinthefightagainstHIV-1aswell
asinHIV-1MTCTcareinthepasttwodecades,providing
uni-versaltreatmentforallpeoplelivingwithHIV-1andreducing
HIV-1MTCTprevalence.Weelaboratedatimeseries
statisti-calmodelwiththeintentiontocontinuouslyimprovethese
efforts,indicatingwhereHIV-1MTCTprevalencemayrisein
thefuture,whichisundesirable,andwehopethatitcouldhelp
governmentdecisionmakingregardingHIV-1surveillanceand
prevention.
Funding
ThisworkwassupportedbyFundac¸ãodeAmparoàCiênciae
TecnologiadePernambuco,FACEPE(A.V.C.C,scholarshipgrant
numberBFP-0018-2.02/17).
Conflicts
of
interest
Theauthorsdeclarenoconflictsofinterest.
Appendix
A.
Supplementary
data
Supplementary material related to this article can be
found, in the online version,at doi:https://doi.org/10.1016/
j.bjid.2019.06.012.
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