running head: micro-level malaria exposure & morbidity · 87 the study was carried out in webuye...

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Mosquitoexposureandmalariamorbidity;amicro-levelanalysisofhouseholdmosquito1

populationsandmalariainapopulation-basedlongitudinalcohortinwesternKenya2

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Runninghead: Micro-levelmalariaexposure&morbidity4

Summary:Inthisstudy,wemeasuretherelationshipbetweenfine-scalespatio-temporal5

heterogeneityinexposuretoinfectedandsuccessfully-fedmalariavectors,theincidenceof6

malaria,andtheirinteractionwithITNuseinapopulation-basedcohort.7

AuthorsandAffiliations8

WendyPrudhommeO’Meara1,2,3,RyanSimmons1,4,PaigeBullins1,BetsyFreedman2,LucyAbel5,9

JudithMangeni6,SteveM.Taylor1,2,AndrewA.Obala710

1. DukeGlobalHealthInstitute,DukeUniversity,Durham,NC2770811

2. DepartmentofMedicine,SchoolofMedicine,DukeUniversity,Durham,NC2770812

3. SchoolofPublicHealth,CollegeofHealthSciences,MoiUniversity,Eldoret,Kenya13

4. DepartmentofBiostatisticsandBioinformatics,SchoolofMedicine,DukeUniversity,14

Durham,NC2770815

5. AcademicModelProvidingAccesstoHealthcare,MoiTeachingandReferralHospital,16

Eldoret,Kenya17

6. SchoolofNursing,CollegeofHealthSciences,MoiUniversity,Eldoret,Kenya18

7. SchoolofMedicine,CollegeofHealthSciences,MoiUniversity,Eldoret,Kenya19

20

CorrespondingAuthor21

All rights reserved. No reuse allowed without permission. not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint (which wasthis version posted October 18, 2019. ; https://doi.org/10.1101/19008854doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

https://doi.org/10.1101/19008854

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WendyPrudhommeO’MearaPhD,AssociateProfessorinMedicineatDukeUniversitySchoolof22

MedicineandAssociateResearchProfessorofGlobalHealthatDukeGlobalHealthInstitute,23

DukeUniversity,Durham,NC27708;[email protected];919-681-771124

Abstract2526Background:Malariamorbidityishighlyoverdispersedinthepopulation.Fine-scaledifferences27

inmosquitoexposuremaypartiallyexplainthisheterogeneity.However,exposurevariability28

hasnotbeenrelatedtoindividualmalariaoutcomes.29

Methods:Weestablishedacohortof38householdstoexploretheeffectofhousehold-level30

mosquitoexposureandindividualinsecticidetreatednet(ITN)useonrelativerisk(RR)of31

diagnostically-confirmedmalaria.Weconductedmonthlyactivesurveillance(n=254;2,62432

person-months)andweeklymosquitocollectioninallhouseholds(2,092household-daysof33

collection).Weusedmoleculartechniquestoconfirmhumanbloodfeedingandexposureto34

infectiousmosquitoes.35

Results:Of1,494femaleanopheles(89.8%Anophelesgambiaes.l.).88.3%werefed,51.9%had36

ahumanbloodmeal,and9.2%weresporozoite-infected.168laboratory-confirmedmalaria37

episodeswerereported(incidencerate0.064episodesperperson-monthatrisk,95%38

confidenceinterval[CI]:0.055,0.074).Malariariskwasdirectlyassociatedwithexposureto39

sporozoite-infectedmosquitoes(RR=1.24,95%CI:1.11,1.38).Nodirecteffectwasmeasured40

betweenITNuseandmalariamorbidity,however,ITNusedidmoderatetheeffectofmosquito41

exposureonmorbidity.42



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Conclusions:Malariariskincreaseslinearlywithvectordensityandfeedingsuccessforpersons43

withlowITNuse.Incontrast,malariariskamonghighITNusersisconsistentlylowand44

insensitivetovariationinmosquitoexposure.45

46Keywords:malaria,anopheles,cohort,insecticidetreatednet4748Wordcount49Abstract:20050Manuscript:2871 51



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Background52

53

Cases of malaria are highly over-dispersed in human populations [1-4]: as little as 20% of54

individuals experience 80% of the disease burden. The reason for this heterogeneity is55

undoubtedlymultifactorial,andevenafteradjustingforageandbehavioraldifferencessuchas56

ITNuse,theimbalanceremains[2-4].Quantifyingtheheterogeneityattributabletovariabilityin57

entomological exposure requires fine-scale measurement of bothmosquito populations and58

malariaincidenceatahouseholdorindividuallevel.Thishasrarelybeendoneandinsteadhuman59

cohortstudiesthatincorporateexposuresubstituteeitherlocalinfectionprevalenceinhumans60

asaproxyforexposure[3,5]orextrapolateentomologicalindicatorsfromsentinelvillagesor61

households [4, 6-9]. Both approaches have limitations with regards to understanding the62

relationship between exposure and disease risk owing to the fact that disease risk is63

heterogeneous below the village level [10-13]. Such studies present inconsistent findings64

regarding exposure and incidence. Although cross-sectional studies of vector biting65

heterogeneitydemonstratesignificantover-dispersion inexposuretomosquitobites, thishas66

notbeenlinkedtosubsequentinfectionordiseasedistribution[14].67

68

Recentstudiessupporttheroleofmosquitoexposureheterogeneityinunderstandingevolving69

malaria transmission dynamics, particularly in areas where endemicity is declining [14-18].70

Mathematicalmodels predict that exposure heterogeneity could reduce the effectiveness of71

malaria control measures [19]. Linking individual mosquito exposure to morbidity outcomes72



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would enablemore accurate estimation of the effects of vector control interventions at the73

populationlevel.However,cohortstudiesrarelymeasurefine-scaleentomologicalexposure.74

75

Here we offer a high-resolution picture of individual-level incidence of symptomatic disease76

relatedtoweeklyhouseholdmosquitoexposuremetrics.Weconductedalongitudinalstudyof77

36 households in a moderate-transmission setting in Western Kenya in which we collected78

weeklyindoor-restingmosquitoesandpassively-detectedcasesofmalaria.Wehypothesizedthat79

excessvariability in individualmalariariskwouldbeat leastpartlyexplainedbymoreprecise80

measures of mosquito exposure and that the relationship between disease and mosquito81

exposurewouldincreasewithincreasingspecificityofexposuremetric;fromvectorabundance,82

tofeedingsuccess,tohumanbitingrate,andfinallyvectorinfectiousness.83

84

Methods85

Studysite86

The studywas carried out inWebuye East andWest sub-counties located inwestern Kenya87

approximately400kilometersnorthwestofNairobiand60fromtheborderwithUganda.The88

sub-countiesareruralandmostfamiliesengageinsmall-scalefarmingandanimalhusbandry.89

Veryfewfamilieshaveaccesstoamenitiessuchasmunicipalwaterorelectricity(

6

distributionanduse; IRShasnot takenplace in thisareaandtheuseof individualprotective95

measuressuchasspraysandcoilsisveryuncommon.96

97

Cohort98

InJuly2017,weestablishedacohortof36householdsinthreevillagesthatexhibiteddifferent99

levelsofmalariatransmissionbasedonpreviouswork[21,22].Weenrolledanindexhousehold100

at random and then neighboring households radiating outwards until 12 households were101

enrolled per village in a natural grouping. Two households were replaced with neighboring102

households when the entire household migrated. All residents older than 12 months who103

regularlysleptinthehouseholdwereenrolled.Children

7

If theparticipanthadanegative test, theywere referredwith the test results to thenearest117

healthfacility.Ifthetestwasapositive,theparticipantcollectedquality-assuredACTfromthe118

localpharmacyatnochargetotheparticipant.119

120

Mosquitocollectionandidentification121

Onceperweek,onthesamedayeachweek,thestudyteamvisitedeachhouseholdearlyinthe122

morning to collect indoor resting mosquitoes via aspiration with Prokopacks (JohnW. Hock123

Company).Participantswereaskedtoleavewindowsanddoorscloseduntiltheteamarrived.124

Mosquitoeswerestoredincollectioncupsincoolboxeswithicepacksuntiltheyweretransported125

backtothelaboratory.Mosquitoeswereseparatedbygenusandsex,andfemalesweregraded126

according to theirabdominal status. FemaleAnopheleswere imagedundermagnification for127

post-hocspeciation,firstbystudystaffandthenbyaseniorentomologist inasubsetof25%.128

After imaging,mosquitoesweredissectedbetweenthethoraxandabdomen,andtheseparts129

werestoredinseparatetubespackedwithdesiccantatroomtemperature.130

131

Moleculardetectionofparasitesandhumanbloodinmosquitoes132

Mosquitospecimensweremanuallydisrupted in individualtubesandthengenomicDNAwas133

extractedusingaChelex-100protocol.EachgDNAextractwastestedinaduplexTaqManreal-134

timePCRassaytargetingbothP.falciparumandhumanbeta-tubulin[23].Samplesweretested135

in duplicate in 384-well plates on an ABI Quantstudio 6 platform; threshold lines were set136

manuallyintheexponentialphaseofamplification.Reactionplateswerepreparedindedicated137

workspaceswithfilteredpipettips,andeachincludedP.falciparumgDNAandhumanDNAas138



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positivecontrolsandmolecular-gradewaterasanegativecontrol.GenomicDNAwasextracted139

fromhumandriedbloodspotsandtestedinthesamereal-timePCRassay.140

141

Dataanalysis142

Theprimaryoutcomewasmalaria,whichwasdefinedasself-reportedillnessleadingtoapositive143

malariaRDTresultinthepreceding30days.Testresultswereconfirmedbyreviewingthemedical144

recordwheneverpossible.Followingapositivetest,individualswerecensoredfor21days.The145

primaryobjectiveofouranalysiswastoestimatetheimpactofhousehold-levelmosquitoindices146

onanindividual’sriskofmalaria.Weinvestigatedtheimpactoffourdifferentmosquitoindices,147

calculatedforeachhouseholdineachmonthasthesumofthecountsfromweeklymosquito148

collectionvisits: the totalnumberof1) femaleAnopheles,2)blood-fed femaleAnopheles,3)149

femaleAnophelesmosquitoescollectedwithhumanblooddetectedbyPCRintheabdomen,and150

4) femaleAnopheleswith P falciparum sporozoites detected in the head.Due to collinearity151

betweentheexposures(e.g.thenumberofsporozoitepositivemosquitoesbeingasubsetofthe152

total number ofmosquitoes collected, etc.), separatemodelswere fit for the effect of each153

exposureoneachmorbidityoutcome.154

155

Weusedgeneralizedlinearmixedmodels(GLMM),withtheoutcomedefinedasthepresenceor156

absenceofmalariaforeachindividualateachfollow-upmonth.Toestimaterelativerisks,we157

usedarobust(modified)Poissonmodel[24].Toaccountforclusteringbyhousehold,themodel158

includeda random interceptonhouseholdwitha compoundsymmetric covariance structure159

(notethatthisassumeshouseholdclustering isconstantovertime).Toaccountforclustering160



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withinindividualovertime,themodelincludedarandominterceptonindividual,nestedwithin161

household, with a homogenous first-order autoregressive covariance structure. Each model162

includedafixed-effectforvillage,ageatbaseline(codedasacategoricalvariablewithfive-levels:163

=18years),gender,andnumberofnightsofITNuseinthepastweek164

(dichotomized as >=6 and

10

All heads of household were asked to consent for participation in mosquito collection and182

household data collection. Each individual provided consent for monthly dried blood spot183

collectionandRDTtesting.Aparentorlegalguardianprovidedconsentonbehalfofachildunder184

18yearsofage.Childrentenyearsandabovewerealsoaskedtoprovideassentforparticipation.185

ThestudywasreviewedandapprovedbyDukeUniversityInstitutionalReviewBoardandMoi186

UniversityandMoiTeachingandReferralHospitalInstitutionalResearchEthicsCommittee.187

188

Results189

FromJune12,2017untilJuly31,2018,254participantsfrom38householdswerefollowedby190

monthlyactivesurveillanceandweeklyentomologicalsurveillanceforatotalof2,624person191

months of observation. The cohort demographics are described in Table 1. Sixty percent of192

participantswerechildrenlessthan18yearsofageatenrollmentandeachpersonprovidedan193

average of 11.1 months of follow-up. At enrollment, 54.9% (133/242) of participants were194

infectedwithP.falciparumasdetectedbyahighly-sensitivePCRassay.72%ofparticipantshad195

anITNfortheirsleepingspace;thisproportionwasslightlylowerinVillageS(59%)comparedto196

Village K andM (82% and 78%, respectively). In 76.7% (2,013/2,624) ofmonthly follow-ups,197

participantsreportedsleepingundertheirneteachdayoftheprevioussevendays.198

199

Mosquitoindices200

Across2,092household-days,wecollectedatotalof1,494femaleanophelesmosquitoes(Table201

2, Figure 1). Nearly 90% (n=1,023) were identified as Anopheles gambiae group and this202

proportion varied only slightly across the villages. VillageM recorded the highest number of203



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malariavectorsoverall(66.5perhousehold)andthehighestproportionofAn.funestus(9.3%).204

OtherAnophelesspp.wereobservedatlowfrequencies.Bymicroscopicevaluation,themajority205

ofmosquitoeshadrecentlyfed:11.2%(n=168)werefreshlybloodfed,31.9%(n=477)werehalf206

gravid,and39%(n=587)weregravid.InPCRtesting,wedetectedhumanbloodin51.9%ofall207

abdomens(n=776),including79.8%(n=134)ofthefreshlybloodfedspecimens.Theprevalence208

ofP.falciparumsporozoitesintheheadsoffemaleAnopheleswas9.2%(n=134).Theproportions209

ofmosquitoeswithhumanbloodorsporozoitesdidnotdifferbyspeciesorvillage.210

211

Malariaincidence212

During2,624person-monthsoffollow-up,therewere168casesofmalariain107people(Figure213

1),yieldinganoverall incidencerateof0.064(95%CI:0.055,0.075)casesperperson-month.214

Amongthese107people,66(61.7%)reportedonlyasingleepisode,26(24.3%)reportedtwo215

episodes,and15(14.0%)reported3ormoreepisodes(themaximumwas5).Themediannumber216

of days between sequential episodes was 37 (IQR: 62). Overall, 30% (n=74) of participants217

experienced80%(n=135)ofepisodes(SupplementalFigure1)and58%(n=147)ofparticipants218

neverexperiencedanepisodeofmalaria.219

220

Associationsbetweenmosquitoindicesandmalaria221

We estimated the relative risk ofmalaria inmodelswithout andwith each of ourmosquito222

indices(Table3).Asexpected,theriskofmalariadeclinedwithincreasingage,andtherewasno223

associationwithgender(RR1.00;95%CI0.72–1.40).RelativetohighITNusers,thosewithlow224

usewereatsimilarrisk(RR1.16;95%CI0.76–1.79).Inmodelswithindividualmosquitoindices,225



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malariariskwasnotsignificantlyassociatedwiththemonthlyhouseholdabundanceofmalaria226

vectors(RR1.02;95%CI0.97–1.08)northenumberofmosquitoeswhohadhumanbloodin227

theirabdomen(RR1.04;95%CI0.97–1.12).However,theriskofmalariaincreasedby24%with228

eachadditionalinfectiousmosquitocollectedthepreviousmonth(RR1.24;95%CI1.11–1.38).229

230

BecausehighITNusewasnotassociatedwithareducedriskofmalaria,wehypothesizedthat231

ITNusemaymodifytheeffectofmosquitoexposureonmalariarisk.Wetestedthishypothesis232

byexaminingtheinteractionbetweenITNuseandeachmosquitoexposurevariable(Table4).233

Wedemonstratesignificantcorrelationbetweenbothmosquitoabundanceandmalariarisk,and234

mosquitofeedingsuccessandmalariarisk,inmodelsthatincludetheexposure:ITNinteraction235

term.TherelativeriskofmalariaamonghighITNuserscomparedtolowITNusersdecreasedby236

6%(RR=0.94,95%CI:0.89-0.99)foreachadditionalanophelesmosquitointhehouseholdandby237

10%(RR=0.90,95%CI:0.82-0.99)foreachadditionalhuman-fedmosquito.ITNusenowappears238

positivelycorrelatedwithmalariariskinmodelsthatincludetheinteractionterm,however,we239

can understand this when we examine the results graphically (Figure 2). At low mosquito240

abundance,individualswithhighITNusehaveequalorslightlyelevatedmalariariskcompared241

tothosewhohavelowerITNuse.However,theriskofmalariaincreaseslinearlywithincreasing242

mosquitoexposureamongthosewithlowITNuse,butremainslowandconstantforthosewho243

useanITNconsistently.Similartrendsareseenformosquitofeedingsuccessandhumanfeeding244

success.245

246



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InanalysesofmarginalR2with incorporationofmosquito indices inmalariariskmodels,age,247

household size, and exposure to sporozoite-infected mosquitoes accounted for the largest248

increaseinR2(2.6%,2.9%and2.3%,respectively;Figure3).Othermosquitoexposuresaccounted249

foronlyaverysmallproportionoftheheterogeneity.DespitethesmallabsolutechangeinR2,it250

is interesting to note that household exposure to infectiousmosquitoes and age contribute251

nearlyequallytoexplainingthedistributionofmalariaincidenceinthepopulation.252

253

Discussion:254

Inthishigh-resolutionpairedentomologicalandepidemiologicalcohort,wemeasuredweekly255

household-levelmosquitoexposurealongsidelaboratory-confirmedmalariaepisodesina256

population-basedsampleof254individualsin38households.Weshowthat,amongputative257

entomologicalindices,theriskofmalariainhumansismediatedbyneithertheabundanceof258

mosquitoesoverallnorthosethatareblood-fedmosquitoesbutratheronlybytheriskof259

malariainmosquitoes(sporozoites).Wefurtherdemonstrateaninteractionbetweentheeffect260

ofITNuseandmosquitoexposureonmalariaepisodes,inthat,comparedtosuboptimalITN261

use,highITNuseattenuatesthedeleteriouseffectsofvectordensityandfeedingsuccesson262

theriskofmalaria.263

264

Therelationshipbetweenmosquitoexposureandhumanmalariariskisdifficulttoquantifydue265

tothetimelagbetweenaninfectiousbiteandpatentinfection,andtheuncertaintyinassigning266

individualexposurebasedonentomologicalvariables.Somestudiescomparepopulationbased267

measuresofdiseaseprevalenceorincidencewithvectordensityobservedatsentinellocations268



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andareessentiallyecologicalcomparisonsoftwopopulationsovertime[6,15].Alternatively,269

valuesfromsentinelhouseholdsarelocallyextrapolatedtounmeasuredhouseholds[4,7-9].270

Althoughwefallshortofassigningindividualexposure,whichmayvarywithinahousehold[26,271

27],ourweeklyhousehold-levelentomologicalmeasurementsgivenewinsightintothenature272

oftherelationshipbetweenmosquitoexposureanddiseaseandameanstoestimatethe273

changeinriskunderdifferentexposurecontrolstrategies.Forexample,sinceboththeeffectof274

mosquitoabundanceandfeedingsuccessonmalariamorbidityaremoderatedbyindividualITN275

use,wecanestimateapersonalprotectiveefficacyofITNuseofupto30%asexposure276

increases.Inhouseholdswhereasfewas8mosquitoeswerecollectedinamonth,ITNsoffer277

measurableprotectionrelativetothosewhodonotuseanITN.Thelow,butnon-zero,malaria278

riskexperiencedbyconsistentITNusersmayreflectexposureoutsideofthenet,dueto279

modifiedmosquitobehaviorssuchasbitingearlyintheeveningoroutdoors[28-32].280

281

Ourfindingsrelatingriskofmalariatobasicdemographicfactorsareconsistentwithprevious282

studies.Weandothers[3,4,33-35]measuredecliningincidenceofclinicalmalariawith283

increasingage.Theobservationthatincreasinghouseholdsizeisassociatedwithdecreasing284

individualriskcanbeinterpretedasreducedindividualriskofexposurewhenmorehostsare285

available.AlthoughindividualITNusehasbeenassociatedwithreducedriskofmalariain286

longitudinalstudiesofyoungchildreninthecontextofhightransmissionandlowITNcoverage287

[1,13],weandothersfailtodetectadirect,individualprotectiveeffect[3,7].Overall,288

behavioralanddemographicdifferencesexplainrelativelylittleoftheheterogeneityinmalaria289

cases.AfterincorporatingentomologicalexposuresandtheeffectofITNuseonthose290



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exposures,theproportionalamountofheterogeneityexplainedincreasedby36%,howeverthe291

absolutevalueofR2remainedsmall.292

293

Therearenotablelimitationstothisstudy.First,thetimespanisrelativelyshort(13months);a294

longerdurationofobservationwouldbedesirabletocaptureimportantannualvariationin295

transmissionandexposure.Second,themalariamorbidityoutcomeisbasedonrecallovera296

one-monthperiod.However,wemitigatedpossiblerecallbiasandmissinginformationby297

reviewingmedicalrecordstoconfirmtestresultsandensuringaffordably,timelyandaccurate298

malariadiagnosiswasavailabletoparticipants.299

300

Despitetheselimitations,wedemonstratethatinfectionsinmosquitoeswithinahouseholdare301

adirectriskfactorforinfectionsinindividuals.ThisriskisnotmitigatedbyITNuse.Other302

entomologicalexposuresmeasuredatthehousehold-level,includingmalariavectordensityand303

human-fedanopheles,increasetheriskofmalariaepisodesdifferentiallyinindividualswhodo304

notuseanITNconsistently.WeshowthatindividualprotectiveefficacyofITNsscaleswith305

exposureandfine-scaledentomologicalmeasurescanexplainasmuchheterogeneityinmalaria306

riskinthepopulationasage.307

308



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Footnotepage309

310

Fundingsource:ResearchreportedinthispublicationwassupportedbytheNationalInstitute311

ofAllergyandInfectiousDiseasesoftheNationalInstitutesofHealthunderAwardNumber312

R21AI126024.Thecontentissolelytheresponsibilityoftheauthorsanddoesnotnecessarily313

representtheofficialviewsoftheNationalInstitutesofHealth314

315

Acknowledgements:Wewouldliketothankourexceptionalfieldteam(I.Khaoya,L.Marango,316

E.Mukeli,E.Nalianya,J.Namae,L.Nukewa,E.Wamalwa,andA.Wekesa)andallthefamilies317

whogavetheirvaluabletimeforthisstudy.318

319

Conflictofinterestdisclosure:Theauthorsdeclarenoconflictsofinterestexist.320

321

322 323



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Table1:Participantcharacteristics,baselinebednetuse,follow-uptimeandmalaria

episodesreportedbyvillageandtotalnumbers

VillageK VillageM VillageS Total

Demographics

Households 12 13 13 38

Participants 80 78 96 254

MedianHouseholdsize

(range)6.5(5-13) 6(3-15) 8(3-14) 6.5(3-15)

Female 45(51.7%) 46(57.5%) 57(57.0%) 148(55.4%)

18

Suspectedmalariaepisodes

withpositivemalariatest51(37.5%) 55(37.9%) 62(40.8%) 168(38.8%)

a2peopleinVillageK,2invillageMand4invillageSweremissingsleepingspaceinformation 324



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Table2:Abundanceandcharacteristicsofanophelesmosquitoescollectedinthecohorthouseholds

VillageK VillageM VillageS Total

No.ofhouseholds

TotalfemaleAnoph.collected 404 858 232 1494

AnophelesperHH,mean(range) 33.7(8-72) 66(7-198) 17.8(4-45) 38.4(4-201)

Microscopicanalysis,no.(%)

Unfed 67(16.7%) 81(9.4%) 20(8.6%) 168(11.2%)

Freshlyfed 111(27.6%) 297(34.6%) 69(29.7%) 477(31.9%)

Halfgravid 165(41.0%) 320(37.3%) 102(44.0%) 587(39.3%)

Gravid 58(14.4%) 158(18.4%) 39(16.8%) 255(17.1%)

Undetermined 3(0.7%) 2(0.2%) 2(0.9%) 7(0.5%)

HumanbloodinabdomenbyPCR,

%(n/N)

53.1

(207/390)

53.5

(447/835)

54.7

(122/223)

53.6

(776/1448)

P.falciparumsporozoites,%(n/N)10.2

(40/393)

9.3

(78/839)

7.0

(16/230)

9.2

(134/1462)

Species1 319 631 189 1139

An.gambiaes.l 298(93.4%) 544(86.2%) 181(95.8%) 1023(89.8%)

An.funestuscomplex 14(4.4%) 55(8.7%) 2(1.1%) 71(6.2%)

An.demeilloni 3(0.9%) 6(1.0%) 3(1.6%) 12(1.1%)

An.pretoriensis 1(0.3%) 11(1.7%) 1(0.5%) 13(1.2%)

Other2 3(1.0%) 15(2.4) 2(1.1%) 20(1.8%)

3251355mosquitoeswerenotidentifiedtospeciesduetotechnicalproblemswiththemicroscope.326

Totalsandpercentageshereonlyincludeidentifiedmosquitoes.3272OtherspeciesincludeAn.maculipalpis(3),An.salbaii(4),An.rufipes(2),An.dancalicus(5),328

An.coustani(6)329

330



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Table3:Relativeriskofmalariaintheprior30dinbasemodelandinmodelsincluding331

mosquitoindices.TheeffectofmonthlyindividualITNuseandmonthlyhousehold-level332

mosquitoexposureontherelativeriskofmalariainthelast30days.Eachmodelisadjustedfor333

householdandindividual-leveldemographiccharacteristics.334

Basemodel Individualmosquitoindexmodels

1 2 3 4

Village

(Kinesamovs.Sitabich)

0.70

(0.43,1.14)

0.68

(0.42,1.10)

0.68

(0.42,1.11)

0.68

(0.42,1.1)

0.63

(0.39,1.00)

Village

(Marutivs.Sitabich)

0.91

(0.51,1.62)

0.81

(0.43,1.53)

0.81

(0.43,1.55)

0.81

(0.43,1.51)

0.77

(0.41,1.42)

21

No.ofAnoph.mosquitoes 1.02

(0.97,1.08)

No.BloodfedAnoph.

mosquitoes

1.03

(0.97,1.08)

No.HumanbloodfedAnoph.

mosquitoes

1.04

(0.97,1.12)

No.Sporozoitepositive

mosquitoes

1.24

(1.11,1.38)

335

336



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337Table4:TheeffectofindividualITNuse,mosquitoexposure,andtheinteractionbetweenITN338

useandmosquitoexposureontherelativeriskofmalariainthelast30days.Eachcolumn339

representsaseparatemodeladjustedforage,householdsize,gender,andvillage.340

341

Relativeriskofmalaria

inthelast30daysModel1 Model2 Model3 Model4

ITNUse 1.69 1.66 1.71 1.44

(Highvs.Low) (1.05,2.72) (1.04,2.64) (1.07,2.74) (0.90,2.30)

Total#ofAnoph.

Mosquitoes

1.08

(1.0131.12)

#ofFedAnoph.

Mosquitoes

1.08

(1.03,1.13)

#ofAnoph.Mosquitoes

FedonHumanBlood

1.13

(1.04,1.22)

#ofSporozoitePositive

Anoph.Mosquitoes

1.24

(1.14,1.36)

ITNUse*Exposure

Interaction

0.94 0.94 0.90 0.99

(0.89,0.99) (0.88,0.99) (0.82,0.99) (0.85,1.16)

342

343

344



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345 346



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running head: micro-level malaria exposure & morbidity · 87 the study was carried out in webuye...

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