HIV-1 mother-to-child transmission in Brazil (1994–2016): a time series modeling

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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

b

a_Universidade_Federal_da_Paraíba._Departamento_de_Biologia_Molecular,_João_Pessoa,_PB,_Brazil

b_Universidade_Federal_de_Pernambuco,_Departamento_de_Genética,_Recife,_PE,_Brazil

c_Universidade_Federal_da_Paraíba._Departamento_de_{Estatística,}_João_Pessoa,_PB,_Brazil

d_Universidade_Federal_de_Pernambuco,_Departamento_de_Medicina_Tropical,_Recife,_PE,_Brazil

e_Instituto_de_Medicina_Integral_Professor_Fernando_Figueira,_Recife,_PE,_Brazil

a

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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.

anopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/

licenses/by-nc-nd/4.0/).

∗ _{Corresponding}_author_at:_Universidade_Federal_da_Paraíba._Departamento_de_Biologia_Molecular._Cidade_{Universitária,}_João_Pessoa,_PB

58051-900,Brazil.

E-mailaddress:antoniocampos@dbm.ufpb.br(A.V.CamposCoelho).

https://doi.org/10.1016/j.bjid.2019.06.012

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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-child}_transmission

(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.5_Thus,_we_aimed_to_model

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.6_The_ﬁrst_dataset_contained_the_number_of

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),7_age_at_the_time_of_delivery_(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

version3.5.1.8_All _analyses_were_two-tailed_and_level_of

sig-niﬁcance(␣)wassetat5%.

Linearregressionanalysis

With the per 100,000live birthsdata, weperformed linear

regressionanalysisusingthebaseRpackagestats8_to_check

if any ofthe mother’scharacteristics were associatedwith

HIV-1MTCTprevalence.Forthisanalysis,weusednationwide

(aggregatedforBrazilasawhole)data.

Timeseriesanalysis

Wedescribed Brazil’sHIV-1MTCT prevalencethroughtime

seriesanalysisusingtheRpackagestseries9_and_forecast.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.11_An_ARIMAX_model_therefore_permits_the

inclusionofexplanatoryvariables(inourcase,themother’s

characteristicsasmentionedabove)thatcouldbeusefulfor

improvingtheforecastofthevariableofinterest(HIV-1MTCT

prevalence).12

Wethenselectedthebestpredictivemodelbasedonthe

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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’sHDIis0.754.7_Thus,

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

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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).

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epidemics).18_Thus,_it_is_logical_to_think_that_these_women_that

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 _also_enacted_healthcare

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.20_For_comparison,_the_more_realistic_goal_set_by_the_USA

CentersforDiseaseControl(CDC)islessthan1infectionper

100,000livebirths,sinceeliminatingHIV-1MTCTisvery

difﬁ-cult,sincewomencanstillbeinfected,asthepandemicsisstill

ongoing.21_The_Brazilian_Ministry_of_Health_is_actively

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

2006and2016.5_Therefore,_if_more_women_in_the_start_of_their

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 _A_previous_study _regarding_the _New_York_state_(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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