Spatial patterns of multidrug resistant tuberculosis and relationships to socio-economic

14 Pages·2017·1.37 MB·English

RESEARCHARTICLE
Spatial patterns of multidrug resistant
tuberculosis and relationships to socio-
economic, demographic and household
factors in northwest Ethiopia
KefyalewAddisAlene1,2*,KerriViney1,EmmaS.McBryde3,4,5,ArchieC.A.Clements1
1 ResearchSchoolofPopulationHealth,CollegeofMedicine,BiologyandEnvironment,TheAustralian
NationalUniversity,Canberra,AustralianCapitalTerritory,Australia,2 InstituteofPublicHealth,Collegeof
MedicineandHealthSciences,UniversityofGondar,Gondar,Ethiopia,3 CentreforPopulationHealth,
BurnetInstitute,Melbourne,Victoria,Australia,4 DepartmentofMedicine,TheUniversityofMelbourne,
Parkville,Victoria,Australia,5 AustralianInstituteofTropicalHealthandMedicine,JamesCookUniversity,
a1111111111 Townsville,Queensland,Australia
a1111111111
*[email protected]
a1111111111
a1111111111
a1111111111
Abstract
Background
OPENACCESS
Understandingthegeographicaldistributionofmultidrug-resistanttuberculosis(MDR-TB)in
Citation:AleneKA,VineyK,McBrydeES,Clements highTBburdencountriessuchasEthiopiaiscrucialforeffectivecontrolofTBepidemicsin
ACA(2017)Spatialpatternsofmultidrugresistant
thesecountries,andthusglobally.Wepresentthefirstspatialanalysisofmultidrugresistant
tuberculosisandrelationshipstosocio-economic,
tuberculosis,anditsrelationshiptosocio-economic,demographicandhouseholdfactorsin
demographicandhouseholdfactorsinnorthwest
Ethiopia.PLoSONE12(2):e0171800.doi:10.1371/ northwestEthiopia.
journal.pone.0171800
Editor:OlivierNeyrolles,InstitutdePharmacologie Methods
etdeBiologieStructurale,FRANCE
AnecologicalstudywasconductedusingdataonpatientsdiagnosedwithMDR-TBatthe
Received:November28,2016 UniversityofGondarHospitalMDR-TBtreatmentcentre,fortheperiod2010to2015.Dis-
Accepted:January26,2017 trictlevelpopulationdatawereextractedfromtheEthiopiaNationalandRegionalCensus
Report.SpatialautocorrelationwasexploredusingMoran’sIstatistic,LocalIndicatorsof
Published:February9,2017
SpatialAssociation(LISA),andtheGetis-Ordstatistics.AmultivariatePoissonregression
Copyright:©2017Aleneetal.Thisisanopen
modelwasdevelopedwithaconditionalautoregressive(CAR)priorstructure,andwithpos-
accessarticledistributedunderthetermsofthe
CreativeCommonsAttributionLicense,which teriorparametersestimatedusingaBayesianMarkovchainMonteCarlo(MCMC)simula-
permitsunrestricteduse,distribution,and tionapproachwithGibbssampling,inWinBUGS.
reproductioninanymedium,providedtheoriginal
authorandsourcearecredited.
Results
Dataavailabilitystatement:Allrelevantdataare
Atotalof264MDR-TBpatientswereincludedintheanalysis.Theoverallcrudeincidence
withinthepaperanditsSupportingInformation
files. rateofMDR-TBforthesix-yearperiodwas3.0casesper100,000population.Thehighest
incidenceratewasobservedinMetema(21casesper100,000population)andHumera(18
Funding:Theauthorsreceivednospecificfunding
forthiswork. casesper100,000population)districts;whereasninedistrictshadzerocases.Spatialclus-
teringofMDR-TBwasobservedindistrictslocatedintheEthiopia-SudanandEthiopia-Eri-
Competinginterests:Theauthorshavedeclared
thatnocompetinginterestsexist. treaborderregions,wherelargenumbersofseasonalmigrantslive.Spatialclusteringof
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
MDR-TBwaspositivelyassociatedwithurbanization(RR:1.02;95%CI:1.01,1.04)andthe
percentageofmen(RR:1.58;95%CI:1.26,1.99)inthedistricts;afteraccountingforthese
factorstherewasnoresidualspatialclustering.
Conclusion
SpatialclusteringofMDR-TB,fullyexplainedbydemographicfactors(urbanizationandper-
centmale),wasdetectedintheborderregionsofnorthwestEthiopia,inlocationswhere
seasonalmigrantsliveandwork.Cross-borderinitiativesincludingoptionsformobileTB
treatmentandfollowupareimportantfortheeffectivecontrolofMDR-TBintheregion.
Introduction
Tuberculosis(TB),adiseasethathaskilledapproximately2billionpeopleoverthelast200
years,remainsathreattohumankind[1].Itdisproportionallyaffectsthoselivinginlowand
middleincomecountries,andwithincountries,peoplefromlowsocio-economicgroups[1–
3].ThemostrecentglobalTBreportestimatedthattherewere10.4millionnewcasesglobally
(equivalenttoanincidencerateof142casesper100,000population)and1.4milliondeathsin
2015[4].Thegeographicdistributionofthediseasevariesacrosstheglobe,aswellaswithin
countries,withpovertyandanumberofotherriskfactorsbeingstrongpredictorsofincidence
[5–7].ThecontinentofAfricareportsparticularlyhighincidencerates;itaccountsfor26%of
allTBcasesintheworldandthehighestreportedincidencerateof275casesper100,000popu-
lation[4].Approximately87%oftheglobalTBburdenisreportedfrom30highTBburden
countries[4,8].Ethiopiaisoneofthese30high-burdencountriesandhasbeenclassifiedas
havinghighburdensofTB,multidrugresistantTB(MDR-TB)andTB-HIVco-infection[4,
8].Thecountryisstrivingtoreducethemagnitudeofthediseaseinlinewiththeobjectivesof
theglobalEndTBStrategy[9].
Multidrug-resistantTBisdefinedasTBthatisresistanttoatleastisoniazidandrifampicin
[10].TheemergenceofMDR-TBhasposedanadditionalchallengeforglobalandnationalTB
controlefforts.Globally,in2015,therewereanestimated481,400newcasesofMDR-TB,and
approximately250000deathsfromMDR-TB[4].Accordingtothe2016WHOglobalTB
report,inEthiopiaanestimated2.7%and14%ofnewandpreviouslytreatedTBcaseshad
MDR-TB[11].AmongnotifiedpulmonaryTBcases,morethan3300MDR-TBpatientswere
estimatedtooccurin2015(equivalenttoanincidencerateof3.4casesper100,000popula-
tion).TheMDR-TBcase-detectionrateisverylow;lessthanaquarteroftheestimated3300
MDR-TBpatientswereidentifiedin2015.However,itisimprovingandthereportednumber
ofpatientswithMDR-TBhasincreasedfrom140in2010to597in2015[11,12].Thereasons
behindtheemergenceofMDR-TBaremulti-factoral,andareassociatedwithsocio-economic,
demographic,cultural,behavioural,clinicalandenvironmentalfactors.Previousspatialstudies
onMDR-TBconductedinPeru[13–17],Moldova[18]andGeorgia[19]haveshownthat
MDR-TBisclusteredinspecificgeographicalareasandclustersareassociatedwithlocation,
socio-economicstatusandpopulationdensity[20,21].
TheidentificationofareaswhereMDR-TBisconcentratedcouldallowpolicymakersto
implementtargetedinterventionsaimedatpreventionandmanagement.Thismightbepartic-
ularlyimportantinresource-constrainedsettingsandinhighMDR-TBburdencountries.Tar-
getedinterventionsmaybenecessaryincountriesthatfinditimpossibletoprovideuniversal
MDR-TBservicesacrossthecountryasthediagnosisandtreatmentofMDR-TBisexpensive.
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
TheEthiopian2013nationalTBreportindicatedthatasignificantproportionofMDR-TB
patientsdidnotgettheopportunitytoaccessMDR-TBtreatmentduetothescarcityoftreat-
mentcentersandinadequatediagnosticfacilities[22].
Theuseofgeographicalinformationsystems(GIS)andspatialanalysistoidentifyhotspots
ofdisease[23],maybehelpfultoidentifythegeographicalpatternsandecologicalpredictors
ofMDR-TBinhighTBburdencountriessuchasEthiopia.However,thereareveryfewstudies
inAfricathathaveassessthespatialdistributionofTB.Hence,theaimsofthisstudywere:1)
todeterminethespatialdistributionofMDR-TBinnorthwestEthiopiausingasix-yearcohort;
and2)toidentifydistrict-levelsocio-economic,demographic,householdandenvironmental
variablesassociatedwiththespatialdistributionofMDR-TBinthisregion.
Methods
Studydesignandarea
Thiswasanecologicalstudy,usingMDR-TBdatafromnorthwestEthiopiaaggregatedatthe
districtlevel.NorthwestEthiopiahasavarietyofgeographicalfeatures,includingthehighest
mountainandthelargestlakeinthecountry,andageographicalboundarywithbothSudan
andEritrea(Fig1).
Morethan8millionpeopleliveinthearea,in45differentdistrictsandtowns;approxi-
mately85%ofthepopulationlivesinruralareas[24].TheUniversityofGondarHospitalis
oneoftheoldestreferralhospitalsintheareaandtheonlyhospitalthatprovidesMDR-TBser-
vicesinnorthwestEthiopia.IndividualswhoarelivinginnorthwestEthiopiaandwhodevel-
opedresistancetofirstlineanti-TBdrugsorwhoaresuspectedofhavingMDR-TBare
referredtoUniversityofGondarHospitalfordiagnosisandtreatment.
WhileTBpatientscanbediagnosedusingsputumsmearmicroscopyandtreatedwithfirst
lineanti-TBdrugsatthelocallevel,MDR-TBpatientsarediagnosed(basedonthelaboratory
resultsobtainedfromtheregionalornationallaboratorycentre)andtreatedatGondarUni-
versityHospital.IntheEthiopiancontext,thenationalTBguidelinerecommendsthatdrug-
susceptibilitytesting(DST)isusedforhighriskgroupswhoareatincreasedriskofhaving
drugresistantTB.Highriskgroupsareidentifiedbasedonpatienthistory,suchas:previous
Fig1.Mapofthestudyarea,northwestEthiopia.
doi:10.1371/journal.pone.0171800.g001
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
exposuretoanti-TBtreatment(i.e.atreatmentoutcomeoffailure,treatmentafterrelapse,
treatmentafterlosstofollowupandprevioustreatmentwithanunknowntreatmentout-
come),exposuretoaknownMDR-TBcase,co-morbidconditionssuchasHIV/AIDSand
otherconditionsassociatedwithmal-absorption[22,25].Patientssuspectedofhaving
MDR-TBarereferredtoUniversityofGondarHospitalforfurtherdiagnosisandtreatment.
Thehospitalstaffcollectasputumsamplefromthepatientandsendittothenationalor
regionallaboratoryforconfirmationofthediagnosis.PhenotypicDST,lineprobeassay(i.e
GenoTypeMTBDRplusV.2.o,HAINLife,Science,Nehren,Germanuny)andGeneXpertare
performedatnationalandregionalreferencelaboratoriestoidentifyMDR-TBcases.Patients
diagnosedwithMDR-TBaretreatedatGondarUniversityHospitalfreeofcharge.Details
aboutthetreatmentregimenusedhavebeendescribedelsewhere[26].
Studypopulation
ThestudypopulationincludedallMDR-TBcasesregisteredandlivinginthecatchmentarea
oftheUniversityofGondarHospitalMDR-TBtreatmentcentre(45contiguousdistrictsin
northwestEthiopia),registeredbetweentheyears2010and2015.Patientswhoseresidencelay
outsideofthehospital’scatchmentarea(i.e.fromadjacentregionsanddistricts)wereexcluded
fromthestudy.
Datasourceandvariablesofthestudy
Dependentvariable. ThenumberofMDR-TBpatientsineachdistrictwasthedependent
variable.TheMDR-TBdatasetincludedindividual-leveldemographic,laboratoryandclinical
variables.ThedatawerecollectedbystafffromtheUniversityofGondarMDR-TBtreatment
centre.TheseMDR-TBdatawereaggregatedtothedistrictleveltoobtainthenumberof
MDR-TBpatientsineachdistrictforeachyear.
Independentvariables. District-levelecologicaldatawereincludedasindependentvari-
ables.AnumberofvariableswereextractedfromtheEthiopiannationalandregionalcensus
report[24,27].Thesevariablesincludeddemographicfactors(i.e.percentageofmaleand
female,percentageofpeoplelivinginruralandurbancommunities);socioeconomicvariables
(i.e.illiteracyrate,economicinactivityandtheunemploymentrate);housingconditions(i.e.
theaveragenumberofhouseholdsperhousingunitandtheaveragenumberofroomsper
house);measuresofindoorairpollution(i.e.percentageofhouseswithtraditionalkitchens
andusingcharcoal,firewoodanddungforcooking);andmigrationvariables(i.e.thepercent-
ageofallmigranttypesintheareaandthepercentageofnewmigrants(lessthan5years)in
thearea).Thetermeconomicinactivity,accordingtotheEthiopianPopulationandHousing
Censusreport,referstotheproportionofpeopleagedtenyearsandabovewhowereneither
engagednoravailabletobeengagedintheproductionofeconomicgoodsandservicesduring
agivenreferenceperiod[24].
ApolygonshapefilesforEthiopia’sadministrativeboundariesatthedistrictlevelwas
obtainedfromtheOpenAfricawebsite[28].Theboundariesofthedistrictsrequiredforthe
studywereselected.Alloftheextracteddatawerelinkedandgeoreferencedwiththedistrict
polygonsusingtheGIS,ArcGIS10.1.3[29].Thepopulationdensityofeachdistrictwascalcu-
latedusingArcGISbydividingthetotalpopulationineachdistrictbytheareaofthatdistrict
insquarekilometres.
Dataanalysis
MDR-TBincidencerate. TheoverallcrudeincidencerateofMDR-TBwascalculatedby
takingthetotalnumberofMDR-TBcasesreportedinthesix-yearstudyperiodasthe
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
numeratorandthemid-pointtotalpopulationduringthesametimeperiodasthedenomina-
tor.Thesexandresidenceadjustedstandardizedmorbidityratio(SMR)wascalculatedfor
eachdistrictusingtheformula:Y =[O/E];whereYistheSMRindistricti,Oistheobserved
i i i
numberofMDR-TBcasesinthedistrictandEistheexpectednumberofMDR-TBcasesin
thedistrictacrossthestudyperiod.TheexpectednumberofMDR-TBcasesforeachdistrict
wascalculatedbymultiplyingthemid-pointpopulationofeachdistrictbytheoverallcrude
MDR-TBincidencerateforthestudyareaandperiod.
Spatialautocorrelationanalysis. Spatialautocorrelationwasexploredataglobalscale
usingMoran’sIstatisticandatalocalscaleusingLocalindicatorsofspatialassociation
(LISA),estimatedusingtheAnselinLocalMoran’sIstatistic,andtheGetis-Ordstatistic.The
globalMoran’sIstatisticwasusedtoassessthepresence,strengthanddirectionofspatialauto-
correlationoverthewholestudyareaandtotesttheassumptionofspatialindependenceinthe
implementationofthespatialpatternanalysis.TheLISAandtheGetis-Ordstatisticswere
usedtodetectlocalclusteringofMDR-TBandtoidentifythelocationsofhot-spots.These
analyseswereconductedusingtoolsprovidedinArcGIS.
Non-spatialPoissonregressionanalysis. Becausethedependentvariable(i.e.thenumber
ofMDR-TBcasesineachdistrict)wasacountvariableandararedisease,weassumedthatit
followedaPoissondistribution.UnivariateandmultivariatePoissonregressionmodelswere
initiallycomputedusingSTATAversion14software(StataCorp.2015.StataStatisticalSoft-
ware:Release14.CollegeStation,TX:StataCorpLP),bytakingthenumberofMDR-TBcases
recordedbydistrictsduring2010–2015asthedependentvariableandallothervariablesas
independentvariables.Thoseindependentvariablesthathadap-valuelessthan0.2intheuni-
variatePoissonregressionmodelwerefittedtothefinalmultivariatePoissonregressionmodel
andwereconsideredforfurtherspatialanalysis.Allsignificantvariablesweretestedformulti-
collinearityandthosevariableswithavarianceinflationfactor(VIF)greaterthan6were
excludedfromthefinalmodel.
SpatialPoissonregressionanalysis. WeconstructedthreedifferentBayesianspatial
modelsusingWinBUGSversion1.4.3software(MedicalResearchCouncilBiostatisticsUnit,
Cambridge,UnitedKingdom).Inthefirstmodel(ModelI),weassumedthatspatialautocorre-
lationwasnotpresentintherelativeriskofMDR-TBandamodelwithoutaspatialcomponent
wascomputed.Allofthecovariatesselectedaboveinthenon-spatialmultivariatePoisson
regressionanalysiswereincorporatedasfixedeffects,andunstructuredrandomeffectsfordis-
trictswereincludedinthemodel.Theassumptionsofthismodelwerethat:1)thenumberof
MDR-TBcasesthatoccurredinonedistrictwereindependentofthenumberofMDR-TB
casesintheotherdistricts,afteraccountingforthecovariates;and2)thevariancewashomoge-
neousacrossthestudyarea.Tohandlethepossiblespatialdependencyofobservationsandthe
violationofhomogeneityofvariancewithineachdistrictduetothespatialnatureofthedata,a
secondmodel(ModelII)containingspatiallystructuredrandomeffectswasconstructedusing
aBayesiansmoothingconditionalautoregressive(CAR)modelfortherandomeffects.
Finally,athirdmodel(ModelIII),aconvolutionmodel,containingthecovariatesandboth
theunstructuredandspatiallystructuredrandomeffects,wasconstructed.Thismodel
assumedthattheobservednumberofMDR-TBcases(Y)forithdistrictfollowedaPoissondis-
tributionwithamean(μ):
YePoissonðmÞ;
i i
LogðmÞ¼LogðEÞþy
i i i
yi¼aþb X þ...::b X þU þV
li li ni ni i i
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
WhereE isthesexandresidence-adjustedexpectednumberofMDR-TBcasesindistricti;θ
i i
isthelogrelativerisk(RR)determinedbytheintercept(α),thecoefficientsofthecovariates
(β ...β ),theunstructuredrandomeffects(U)andthespatiallystructuredrandomeffects
1i ni i
(V).Thespatiallystructuredrandomeffects(V)werecomputedusingaCARpriorstructure,
i i
whichisdefinedusinganadjacencymatrixtodeterminethespatialrelationshipsbetweendis-
tricts.TheadjacencymatrixforeachdistrictwasgeneratedusingArcGIS.Aweightof1was
giveniftwodistrictswereneighbouringandaweightof0wasgiveniftwodistrictswerenot
neighbouring.Twodistrictswereconsideredtobeneighbouringiftheysharedthesameedges
orcorners(i.e.queencontiguity).Priorprobabilitydistributionsforthecoefficients(β)were
assumedtohavenormaldistributionswithamean=0andaprecision(i.e.inverseofvariance)
=1x10−6.Fortheintercept(α)flatpriordistributionswasused(i.e.anon-informative,
improperpriorwithbounds-1and+1).Theunstructuredrandomeffects(U)andspatially
i
structuredrandomeffects(V)wereassumedtohaveameanofzeroandaprecision(inverse
i
ofvariance)of1/σ 2and1/σ 2respectively.Thepriorsfortheprecisionoftheunstructured
u v
andspatiallystructuredrandomeffectswereassignedanon-informativegammadistribution
withashapeandscaleparametersof0.001(S1Table).
Theposteriorparameterswereestimatedfromtheprioranddatalikelihoodinformation
usingaBayesianMarkovChainMonteCarlo(MCMC)simulationapproachwithGibbssam-
pling,employedbyWinBUGS.Afteraninitialburn-inof1,000iterations,themodelswere
runsubsequentlyfor1,000,000iterations.Forallmodels(asevidencedfromvisualinspection
ofposteriorkerneldensitiesandhistoryplots),convergenceoccurredwithinthefirst10,000
iterations.Onehundredthousandvaluesfromtheposteriordistributionofeachparameter
werestoredforsummarymeasuressuchastheposteriormean,standarddeviationandthe
95%credibleinterval(CrI).TheDevianceInformationCriterion(DIC)wasalsostoredfor
modelselection,wherealowerDICindicatesapreferredmodel.
Ethicalclearance
EthicalclearancewasobtainedfromtheInstitutionalReviewBoardoftheUniversityofGon-
darandfromtheAustralianNationalUniversityHumanResearchEthicsCommittee(protocol
number2016/219).Further,theUniversityofGondarHospitalprovidedpermissiontoaccess
thedata.Asthisstudyusedsecondarydata,informedconsentwasnotobtainedfromeachof
thestudyparticipants.
Results
Descriptiveanalysis
Amongatotalof282MDR-TBpatientsnotifiedduringthesixyearperiodinnorthwestEthio-
pia,17(6.0%)patientsresidedoutofthestudyareaandwerethereforeexcludedfromtheanal-
ysis.Ofthe264patientswhowereincluded,159(60.2%)weremaleand115(56.4%)were
fromurbanlocations.ThehighestnumberofMDR-TBcaseswerereportedinthetwohighly
populatedandurbanareasoftheregion,namelyGondar(60cases;23.0%),theplacewherethe
MDR-TBtreatmentcenterislocatedandBahirdar(31cases;12.0%),thecapitalcityofthe
region.Ninedistricts,manyofthemlocatedfarawayfromthetreatmentcentre,didnotreport
anyMDR-TB.TheoverallcrudeincidencerateofMDR-TBforthesixyearperiodwas3.0
casesper100,000population,rangingfrom0to21casesper100,000population,perdistrict.
WeobservedstrongspatialvariationintheMDR-TBincidencerateacrossthedistricts.
ThemapinFig2showsthespatialdistributionoftheMDR-TBSMRinnorthwestEthiopiaat
thedistrictlevel.ThehighestSMRwasobservedinMetema(7.0)andHumera(6.1)districts,
bothlocatedintheEthiopia-SudanandEthiopia-Eritreaborderregionsofthecountry(Fig2).
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
Fig2.Choroplethmapshowingthegeographicaldistributionofmultidrug-resistanttuberculosisstandardized
morbidityratiosacrosseachdistrictinthenorthwestEthiopia,2010to2015.
doi:10.1371/journal.pone.0171800.g002
SpatialdistributionofMDR-TB
TheglobalMoran’sindexstatisticfortheMDR-TBincidencerateper100,000populationwas
0.14(p-value=0.04),indicatingthepresenceofsignificant,positivespatialautocorrelationin
MDR-TBincidencerateoverthewholestudyarea.IntheLISAanalysis,districtssuchas
Quara,MetemaandMirabArmacho(alllocatedintheborderregions)showedahigh-high
typeofrelationship,meaningthatthesedistrictshadahighincidenceofMDR-TBandthesur-
roundingdistrictshadalsohadhighMDR-TBincidence(Fig3a).Humeradistrict,theother
districtwhichisalsofoundintheborderregionofthecountry,hadahigh-lowtypeofrelation-
shipwhichindicatedthattherewasahighincidenceofMDR-TBinthisdistrict,surrounded
bydistrictswithlowincidenceofMDR-TB.UsingtheGetis-Ordstatistic(Fig3b),weobserved
thatMetemaandHumerawerehotspotsat99%and95%confidencelevels,respectively.
Therewerenostatisticallysignificantrelationshipsforallotherdistricts(Fig3).
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
Fig3.Spatialclusteringofmultidrug-resistanttuberculosisincidenceinnorthwestEthiopia,2010to2015,basedon:a)
LocalindicatorsofspatialassociationusingAnselinLocalMoran’sIstatistic;andb)andtheGetis-OrdGi*statistic.
doi:10.1371/journal.pone.0171800.g003
FactorsassociatedwithMDR-TBclusters
ThemultivariablePoissonregressionresultsarepresentedinTable1.Atthearealevel,the
incidenceofMDR-TBper100,000populationwaspositivelyassociatedwithpopulationden-
sitypersquarekilometer(RR:1.01;95%CI:1.00,1.01),theproportionofthepopulationwho
wereeconomicallyinactive(RR:1.05;95%CI:1.03,1.07),theproportionofpeoplelivingin
urbanareas(RR:1.02;95%CI:1.01,1.04)andtheproportionofmalesinthepopulation(RR:
1.58;95%CI:1.26,1.99).
Table1. AmultivariablefixedeffectsPoissonregressionmodelofsocio-economicanddemographicfactorsinfluencingdistrict-levelincidence
ofmultidrugresistanttuberculosisper100,000populationinnorthwestEthiopia,2010to2015.
Variables Coefficients Relativerisk(95%CI*) P-value
Populationdensitypersquarekilometre 0.01 1.01(1.00,1.01) 0.001
Economicallyinactivepopulation(%) 0.05 1.05(1.03,1.07) <0.001
Migrantpopulation(%) 0.001 1.00(0.99,1.01) 0.887
Malepopulation(%) 0.46 1.58(1.26,1.99) 0.01
Urbanresidence(%) 0.02 1.02(1.01,1.04) 0.03
*Confidenceinterval.
doi:10.1371/journal.pone.0171800.t001
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SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
Table2. Poissonregressionmodelfortheassociationofsocio-economic,demographic,housingconditionandspatiallystructuredrandom
effectatthedistrictlevelwithcasesofmultidrug-resistanttuberculosisinnorthwestEthiopia,2010to2015.
ModelI: ModelII: ModelIII:
unstructured structured structured&unstructured
Variable Coefficient,posterior RRb,Posterior Coefficient,posterior RRb,Posterior Coefficient,posterior RRb,Posterior
mean(95%CrIa) Mean(95% mean(95%CrIa) Mean(95%CrIa) mean(95%CrIa) Mean(95%CrIa)
CrIa)
α(Intercept) -1.2(-1.67,-0.78) -1.20(-1.52,-0.90) -1.21(-1.66,-0.81)
Male(%) 0.29(0.05,0.54) 1.35(1.05, 1.16(0.88,1.44) 3.22(2.41,4.23) 0.29(0.05,0.54) 1.35(1.05,1.72)
1.71)
Urbanresidence(%) 1.16(0.88,1.44) 3.22(2.40, 0.29(0.05,0.54) 1.35(1.05,1.72) 1.16(0.88,1.44) 3.21(2.40,4.22)
4.23)
Populationdensityd 0.19(-0.23,0.62) 1.24(0.79, 0.18(-0.35,0.71) 1.24(0.70,2.04) 0.19(-0.28,0.66) 1.24(0.76,1.94)
1.87)
Migrants(%) -0.10(-0.55,0.34) 0.93(0.58, -0.26(-0.72,0.20) 0.79(0.49,1.22) -0.14(-0.61,0.32) 0.90(0.54,1.38)
1.41)
Economicallyinactive -0.15(-0.53,0.23) 0.87(0.59, -0.17(-0.58,0.26) 0.87(0.56,1.29) -0.15(-0.53,0.27) 0.88(0.59,1.31)
(%) 1.26)
Heterogeneity
Unstructuredvariance 1.06(0.57,2.29) - 0.03(0.002,2.18)
Structuredvariance - 3.70(1.96,8.33) 0.008(0.001,5.56)
DICc 561.7 568.5 564.1
aCredibleinterval,
brelativerisk,
cdevianceinformationcriterion,
dpopulationdensitypersquarekilometer
doi:10.1371/journal.pone.0171800.t002
AsindicatedbythelowDICvalueinTable2,themodelwithoutaspatialcomponenthada
betterfitthanthemodelscontainingspatiallystructuredrandomeffects.Thissuggestedthat
despitethepresenceofspatialautocorrelationforthedependantvariablesintheexploratory
phaseofthestudy,andasevidencedbytheglobalandlocalMoran’sIstatistic,theinclusionof
covariatesresultedinnoresidualspatialautocorrelation,andthustheCARrandomeffects
modelswereredundant.Inotherwords,thecovariatesincludedinthemodelsexplainedthe
spatialautocorrelationandspatialclusteringevidentintheMDR-TBdata.
ThegeographicaldistributionofMDR-TBinthedistrictswaspositivelyassociatedwiththe
percentageofthepopulationthatwasmaleandthepercentageofthepopulationlivingin
urbanlocations.Theothervariablesincludedinthemodelswerenotsignificantlyassociated
withMDR-TBincidence.Mapsofthepercentageofthepopulationwhoweremaleandwho
werelivinginurbanlocationsareprovidedinFig4,demonstratingthatthespatialdistribution
ofthediseaseandthesepredictorsweresimilar.
Discussion
WefoundthatMDR-TBwasgeographicallyclusteredinthenorth-westborderdistrictsof
northwestEthiopia.Wealsofoundseveralcharacteristicsofthedistrictsthatwereassociated
withhigherratesofMDR-TBincludingtheproportionofmalesinthepopulation,urbanisa-
tionandpopulationdensity.Ofinterest,spatialclusteringwasnotapparentoncethesedistrict
characteristicsweretakenintoaccount,suggestingthattheproportionofmalesandurbanisa-
tionexplainedthespatialdistributionofMDR-TBinthisregionofEthiopia.
PLOSONE|DOI:10.1371/journal.pone.0171800 February9,2017 9/14
SpatialpatternsofMDR-TBandsocio-economicanddemographicfactors
Fig4.Choroplethmapsshowingthegeographicaldistributionofthepercentageofthedistrict(a)whoaremaleand(b)who
liveinurbancommunitiesacrossnorthwestEthiopia,2010to2015.
doi:10.1371/journal.pone.0171800.g004
SpatialclustersweredetectedinMetemaandHumeradistricts,bothofwhicharelocatedin
theEthiopia-SudanandEthiopia-Eritreaborderregionsofthecountry.Thesedistrictsare
locatedfarfromreferralhospitalsandcontainahighnumberofseasonalmigrants(mostly
male)duetothepresenceofagriculturalinvestmentsintheseareas.Thedetectionofclusters
ofMDR-TBinagriculturalinvestmentareasandintheborderregionsofthecountrymay
meanthatMDR-TBispoorlycontrolledintheseitinerantandhardtoreachpopulations.This
highlightstheriskofcross-bordertransmissionofMDR-TBinEthiopia,EritreaandSudan,
particularlyinpredominantlymalemigrantpopulations.Therefore,wesuggestthatmobileTB
andMDR-TBservicesshouldbeconsideredtoaddresstheissueofTBamongtheseasonal
migrantworkerpopulationintheborderregions.
Inpreviousstudies,similarcross-borderproblemsofMDR-TBineastAfricaalongthe
Somalia-Kenyaborder[30]andinMongoliaalongtheTrans-SiberianRailwayline[31]have
beenreported.Thisreinforcestheneedforcross-bordercollaborationfortheeffectivecontrol
ofMDR-TB[32].
ThemajorityofMDR-TBpatientsinourstudyweremale.Previousstudiesconductedin
EthiopiahavealsoreportedthatMDR-TBismorecommonamongmales[33,34].Thiscould
berelatedtotheconvergenceofotherriskfactorsforTB,suchascigarettesmokingandthe
needforalongdurationoftreatment(notingthat,asmalesaremorelikelytotravelfrom
placetoplaceinEthiopia,particularlyasseasonalmigrants,theymayhaveahigherlikelihood
ofbeinginfectedwithTBorexperiencingtreatmentinterruptions)[35,36].Poorcompliance
withanti-TBdrugshaspreviouslybeendocumentedamongseasonalmigrants[37]and
MDR-TBismoreprevalentamongpeoplewhohavenotcompletedacourseofTBtreatment
[38].
PLOSONE|DOI:10.1371/journal.pone.0171800 February9,2017 10/14