Key factors influencing canine heartworm, Dirofilaria immitis, in the United States

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Key factors influencing canine heartworm, Dirofilaria immitis, in the United States
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English
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Brown, Heidi E.
Harrington, Laura C.
Kaufman, Phillip E.
McKay, Tanja
Bowman, Dwight D.
Nelson, C Thomas
Wang, Dongmei
Lund, Robert
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BioMed Central (Parasites & Vectors)
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Abstract An examination of the Companion Animal Parasite Council’s (CAPC) canine heartworm data to clarify the spatial prevalence of heartworm in the United States. Factors thought to influence the spatial risk of disease, as identified in a recent CAPC workshop, are discussed. Keywords: Canine heartworm, Dirofilaria immitis, Mosquito vectors, Spatial prevalence
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Publication of this article was funded in part by the University of Florida Open-Access publishing Fund. In addition, requestors receiving funding through the UFOAP project are expected to submit a post-review, final draft of the article to UF's institutional repository, IR@UF, (www.uflib.ufl.edu/UFir) at the times of funding. The institutional Repository at the University of Florida community, with research, news, outreach, and educational materials.
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Brown et al. Parasites & Vectors 2012, 5:245 http://www.parasitesandvectors.com/content/5/1/245; Pages 1-9
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doi:10.1186/1756-3305-5-245 Cite this article as: Brown et al.: Key factors influencing canine heartworm, Dirofilaria immitis, in the United States. Parasites & Vectors 2012 5:245.

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Abstract
An examination of the Companion Animal Parasite Council’s (CAPC) canine heartworm data to clarify the spatial prevalence of heartworm in the United States. Factors thought to influence the spatial risk of disease, as identified in a recent CAPC workshop, are discussed.
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Brown, Heidi E
Harrington, Laura C
Kaufman, Phillip E
McKay, Tanja
Bowman, Dwight D
Nelson, C T
Wang, Dongmei
Lund, Robert
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MEETINGREPORTOpenAccessKeyfactorsinfluencingcanineheartworm, Dirofilariaimmitis ,intheUnitedStatesHeidiEBrown1,LauraCHarrington2,PhillipEKaufman3,TanjaMcKay4,DwightDBowman5*,CThomasNelson6, DongmeiWang7andRobertLund7AbstractAnexaminationoftheCompanionAnimalParasiteCouncil ’ s(CAPC)canineheartwormdatatoclarifythespatial prevalenceofheartwormintheUnitedStates.Factorsthoughttoinfluencethespatialriskofdisease,asidentified inarecentCAPCworkshop,arediscussed. Keywords: Canineheartworm, Dirofilariaimmitis ,Mosquitovectors,SpatialprevalenceBackgroundTheCompanionAnimalParasiteCouncil(CAPC)collectedresultsof4,769,403canineheartwormtestsduringthe2011calendaryearfromvariouscommercial testinglaboratoriesintheUnitedStates(US),andofthis nationalsamplingofdogs,56,612(1.187%)werepositive forheartwormantigen,indicativeofanactive D.immitis infection[1].Thisrichdatasetcanbeusedtoinferthe nationalprevalenceofheartwormdiseaseand/orverify theaccuracyofpredictionsmadefromforecastingmodels.Herein,we:1)presentamapofpositiveheartworm testprevalence,therebyupgradingexistingknowledge; and2)constructalistoffactorsthatwillbeusedto explaintheobservedratesofheartwormpositivetests overthecomingyearsofsimilardatacollection. ThisworkstemsfromameetingheldinAtlanta,GA, onJune9 – 10,2012,duringwhichvectorecologists, entomologists,andbiologistsworkedwithateamofstatisticianstoidentifyriskfactorswhichcouldbeuseful forthedevelopmentofspatialriskmappingforimportantvector-bornecaninediseasesforwhichCAPChad accesstocollecteddatawiththeoverarchingobjective beingtoidentifythemostimportantfactorsinfluencing Lyme,ehrlichiosis,anaplasmosis,andheartworminfectionratesintheUScaninepopulation[1].Thefocusof thispaperisononeofthesedatasets,i.e.,thedatarelativetocanineheartworm( Dirofilariaimmitis )infection. Heartworminfectionsareasignificanthealthriskto dogsasevenlightinfectionsarecapableofproducing profoundpulmonaryvascularandparenchymaldisease. Despiteimproveddiagnosticmethods,effectivepreventivesandincreasingawarenessamongveterinaryprofessionalsandpetowners,casesofheartworminfection continuetobediagnosedinhighnumbersandare becomingmoreprevalentinareaspreviouslyconsidered tobeatalowrisk[2].Asurveyofveterinaryclinicsin 2005reportedthatover250,000dogstestedpositivefor heartwormsduringthe2004calendaryear[3].When oneconsidersa48%responseratetothesurveyandthe factthatonly30%ofthedogpopulationintheUSwas tested,theactualnumbersofdogsinfectedaremuch higher,probablyinthe1to1.5millionrange. Thetestusedforantigendetectionindogshasahigh sensitivity(84%,n=175/208)andhighspecificity(97%, n=30/31)[4].Whileconcernsaboutfalsepositivesin areasoflowprevalenceexist,comparisonswithknown prevalenceratesandstudiesthathaveexamineddogs forthepresenceofmicrofilariaeindicatethatanyoverestimationoftheinfectionratesduetofalsepositivesis notlarge.Asurveyformicrofilarialpresenceratherthan antigenemiaconductedinColoradofoundanoverall prevalenceofmicrofilarialpositivedogsin1990tobe 0.77%for7,818dogstested[5];the2008datashoweda prevalenceinColoradoof0.4%[2].In1981 – 1982,asurveyof541dogsin12citiesandfourcountiesinNorthernCaliforniafoundthat31(5.7%)werepositivefor heartwormmicrofilariae[6];theprevalenceratesin thesecountiesbymicrofilariaeweresimilartoresults fromtheantigensurvey[2]. *Correspondence: ddb3@cornell.edu5DepartmentofMicrobiologyandImmunology,CollegeofVeterinary Medicine,CornellUniversity,Ithaca,NY14853,USA Fulllistofauthorinformationisavailableattheendofthearticle 2012Brownetal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and reproductioninanymedium,providedtheoriginalworkisproperlycited.Brown etal.Parasites&Vectors 2012, 5 :245 http://www.parasitesandvectors.com/content/5/1/245

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AnexampleoftheinformationthattheCAPCheartwormdatabasecontainsisrepresentedbyamapdisplayingspatiallysmoothedheartwormpositivetestsforthe 2011calendaryear(Figure1).Thegraphicshowsthat heartwormismostprominentinthelowerMississippi RiverValleyandnearlyabsentinNorthernMontana. Thismapwasgeneratedusingannualizeddata — no seasonalfeatureswereconsidered.Theresultsrepresent theprevalenceofpositivetestsforcirculatingheartworm antigenoverafullyearamongstapopulationofdogs thatarevisitingaveterinarianandreceivingcarethat includesattheleastatestforheartworminfection. Theproportionofpositiveheartwormtestswascomputedforeachcountyinthecontiguous48states.Proportionsareanalyzedinpreferencetototalpositivetest countsasthenumberofpositivetestscanbeinfluenced bylocaltestingpractices.Theseproportionswerethen spatiallysmoothedviaaprocedureknownastheheadbangingalgorithmandgroupedintonineprobabilistic categories.Themethodsaccountforthevaryingnumber oftestsmadeindifferentcounties;forexample,ten positivetestsinasampleofsizeonehundredstatistically suggestsmoreofaproblemthanonepositivetestinten, althoughthesampleinfectionproportionsarethesame. Themapwillbeupdatedinfutureyearsasadditional databecomeavailable.Whilethismapquantifiesthe baselineheartworminfectionratesforUScounties,itis desirabletounderstandwhatfactorsexplainheartworm risk.Ourimmediategoalwastoassemblealistoffactors,whichareavailableandquantifiable,thatmayinfluencecanineheartworminfectionrates. AttheJune2012CAPCmeeting,ateamofexperts provideduptotenmeasurablefactors,rankedinorder ofimportancethataremostlikelytoaffectheartworm prevalenceandcanbeusedforspatialriskmapping.It wasunderstoodthatsomeofthefactorsmightproveto beofnovalue,thatsomecouldbeofsignificantvalue, thattheremightbeinteractionsbetweenfactors,and thatsomeimportantfactorscouldbeomittedornonmeasurable.Becauseheartwormriskvariesbothspatially andtemporally,theteamincludedfactorswhichcould predictbothbaselineprevalenceratesandinter-annual variations.Beforeleavingthemeeting,arankedlistwas generated(Table1). River Lake Figure1 Spatiallysmoothedproportionsofpositivecanineheartworm antigentestsrecordedbyUSveterinariansin2011. Thefigure summarizes4,769,403testsperformedbyveterinariansforcirculatingheartwormantigenintheUSin2011;ofthesetests,56,612tests(1.187%) werepositive.Thepopulationstudiedisthosedogsthatareseenbyaveterinarianandaretested;about5%ofowneddogsintheUS.Themap displaysprobabilitiesofapositivetestaftersmoothingbythehead-bangingalgorithm(seetextfordetails).Thefigureismadebyassigningthe smoothedproportionstoninecolor-codedcategories[0.00,0.03],(0.03,0.06],...,(0.21,0.24],and(0.24,1.00].Thecolorsrangefromdarkgree nfor thelowerproportionstobrightredforhigherproportions. Brown etal.Parasites&Vectors 2012, 5 :245 Page2of9 http://www.parasitesandvectors.com/content/5/1/245

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FactorsandtheirselectionrationaleThefactorsselectedbytheheartwormworkinggroup arelistedanddiscussedhere,groupedbyvector,parasite,andhostfactors.Somefactors,suchastemperature, areimportanttomultiplefactorgroups(vectorand parasite).Twocriteriainselectingfactorswerethat 1)thefactorcanbeeasilymeasuredand2)dataforit areavailable.VectorfactorsDogheartwormisamosquito-transmitteddiseasewitha cosmopolitandistribution[7,8].Themosquitobecomes infectedwhenitingestsmicrofilariaduringtheactof bloodfeedingonaninfectioushost.Tobeacompetent vectorof D.immitis ,mosquitoesmustbeabletosupportnematodedevelopmentintotheinfectivestage(L3), andtheinfectivelarvaemustbeabletomigratetothe proboscisofthemosquito[9].Over60speciesofmosquitoesarecapableofsupportingthedevelopmentofL3 D.immitis [7].Summariesofmosquitospeciesnaturally infectedwith D.immitis in19stateshaveappearedin previouspublications[10-12].IntheUS,multiplestudiesonpotentialheartwormvectorshavebeenconducted; however,moststudieswererestrictedtocertainlocations.Withoutknownvectordistributiondatafromthe majorityofstates,estimatesareneededtoidentifyareas whereeachvectoroccursandtowhatabundance.This distributionaldatacanthenbeusedtoidentifyareas withgreaterriskforheartwormtransmission.Witha numberofmosquitospecieslikelyplayingdifferentroles acrossdifferentregionsoftheUS[9]andtakinginto accountmosquitovectorcompetencedataeitherobtained naturallyand/orexperimenta lly,ninespecieswereidentifiedasmajorpotentialvectorsfornationalforecastmodeling.Thesespeciesare Aedesaegypti,Aedesalbopictus Aedescanadensis Aedessierrensis,Aedestrivitattus,Aedes vexans Anophelespunctipennis Anophelesquadrimaculatus ,and Culexquinquefasciatus Formostoftheninemosquitospecieschosen,publishedmapsillustratingtheirgeographicalhomeranges withinNorthAmericaexist[12].Asmanyofthemosquitospecieshavevaryingfrequencydistributionswithin evensmallranges,thehistoricalliteraturecanserveas astartingpointforfine-tuningtherepresentationof theirranges.Forspecies,suchas Ae.albopictus and Ae.aegypti, wherecompetitionbetweenthespecieshas ledtochangesintheirhistoricalranges[13,14],new geographicaldistributiondatacanbemadeavailableas itisaccumulated. Additionalvectormapscanbegeneratedusingsurveillancedataandhabitatmodeling,butqualitysurveillancedataarenotoriouslydifficulttoacquire. Consequently,thereisaneedtousesurrogatefactors. Possiblesurrogatesforactualpresence/absenceorabundancemapsofthesemosquitospeciesinclude:1)vegetationindices(derivedfromsatelliteimageryorclassified landcovermaps);2)urbanizationasitrelatestorural, suburban,andurbanlandscapes;and3)meteorological datatocaptureinter-annualvariability.Asnewmosquitodistributionmapsaregenerated,theywillideally beexaminedbyentomologiststoverifythattheyrepresentcurrentspeciesranges.VegetationindicesAsynopsisofvariousmosquitospeciesthatbreedin differenthabitatscanbemadeavailableandoverlaid onlandcover,cropland,andirrigationmapsoftheUS. Forexample,previousstudiesthathavefocusedon mosquitoesassociatedwithforestedenvironments ( Ae.sierrensis and An.punctipennis )[15],riceproduction( An.quadrimaculatus )[16],andirrigationcould providedataformapgeneration.Thesedatacanbe includedbothasdominanttypeofvegetation/cropland orasapercentageofeachtypebycounty.Rural,suburban,andurbanlandscapesCertainmosquitospeciesaremoreprevalentinurban environmentsduetodifferencesintheirpreferencefor particularovipositionsites.Mosquitospeciesthatbreed inartificialcontainerstendtobemoreurbanized,such as Cx.quinquefasciatus and Ae.aegypti .Thespatialand temporaldistributionof Cx.quinquefasciatus inresidentialareas[17]andundergroundstormdrainshasbeen Table1Rankedfactorsinitiallyidentifiedbytheworking group1Vectorpresence(+) 2HDU(calculatedfromweatherwitha14Cthreshold)() 3Laggedweather(7monthsto24monthspriortodiagnosis) a.Precipitation,temperature,andorrelativehumidity() b.MoistureIndex(PrecipitationandEvaporation)() 4Presenceofcoyoteandferaldogpopulations – categoricaldata(+) 5Humanpopulationdensity(+) 6Landcover(DominanttypeorPercentageclassification)() 7Cropland(DominanttypeorPercentageclassification)() 8Socialeconomicstatus() a.Householdincome(-) b.Education(-) c.Foreclosurerates(+) 9Irrigation()Theseweretheninefactorstheworkinggroupthoughtmeritedtestingfor inclusioninthemodelfortheriskofheartworminfection.Thefactorswere rankedbygroupconsensusbasedonwhichwouldmostlikelycontribute significantlytothefinalmodel.Forsomefactors,e.g.,weather,subfactors werealsoidentified.Finally,foreachfactorahypothesiswasgivenasto wherethatfactorwouldhaveapositive(+),negative( ),orunknown() associationwithheartwormrisk.Brown etal.Parasites&Vectors 2012, 5 :245 Page3of9 http://www.parasitesandvectors.com/content/5/1/245

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welldocumented[18,19],andthistypeofinformation maybeusefulformodeling.Humanpopulationdata fromtheUSCensusBureauandlandcoverclassified imagerywithurban,rural,suburbanlanduseclasses maybehelpfulinputsforthemodeltocapturemeasures ofurbanization.MeteorologicaldataMosquitodevelopmentratesaretemperature-dependent andcompletionoftheimmaturestagesisdependenton wateravailability.Inaforecastingmodel,weather(daily andmonthly,andatvarioustimelags)isexpectedtoinfluenceintra-annualfluctuations.Inastaticspatialmodel, climate,ratherthanweather,maybettergovernthelikelihoodavectorcancompleteitslifecycle.Inanefforttoincorporatetheseinfluences,werecommendincluding temperature(minimum,maximum,mean,anddailyvariability)andmoistureindex(calculatedbasedonprecipitationandevaporationrates)[20].Themoistureindex M ( t ) atday t overthecurrentandprevious L daysis Mt t i t LPi Di ; where P ( i )andE( i )aretheprecipitationandevaporation fromday i ,respectively.Moistureindicesmaybemore meaningfulthanrainfallbecausetheytakeintoaccount themoisturedeficitpriortorainfallandestimatethenet moistureintermsofstandingwateravailabletoegg-laying mosquitoes. Relatedindirectfactorsthatcouldplausiblyinfluenceheartwormsinclude:1)relativehumidity,2) elevation,and3)dailycircadian-relatedeventsin mosquitobehavior.Relativehumidityinfluencesvectorbehaviorandsurvival.Elevationmayserveasa surrogateoftheclimateexpectedatagivenlocation. Circadianactivityisimportanttosomevectorspecies ’ behavior[21,22].ParasitefactorsSeveralissueswereidentifiedrelativetotheparasiteand itsdevelopmentwithinthevectorandhost.Heartworm DevelopmentUnits(HDUs)astheyrelatetonematode developmentinmosquitoesandthelagperiodbetween infectionanddetectioninthedataonthecurrentantigendetectionmapswereconsideredotherimportant factors. The D.immitis lifecycleconsistsofseveraldevelopmentalstagesinboththemosquitovectorandvertebratehosts[9,23-25].Heartwormtransmissionisdriven bytheambienttemperaturesexperiencedbytheir mosquitovectors.Asaconsequence,ambientairtemperatureisusedtopredictthetimingofdogheartworm vectorcompetence[26,27].Theheatrequirementfor heartwormstocompleteincubationtotheinfectivestage canbeexpressedindevelopmentdegreedays,orHDUs. AsimplewaytocalculateHDUsusingmaximumand minimumairtemperaturesistosubtracttheheartworm developmentthreshold(14C)fromtheaveragedaily temperature[9,26].AccumulatedHDUsaresummed fromadeterminedstartingday,similartothemoisture indexequationabove.Ifhourlytemperaturesareutilized,thesummationofthedifferenceinhourly temperaturefrom14Ccanbedividedby24.Thenumberofaccumulateddegreedaysrequiredfordevelopment toinfectiveparasitestageis130[27],althoughthisvalue mayvarybystrainoftheparasite. D.immitis larvalsurvivalanddevelopmentwithinamosquitoisconsequently dependentontemperatureanditsfluctuations — larval heartwormsarecapableofslowingandrecommencing developmentinatemperature-dependentfashion.In addition, D.immitis larvalsurvivalisdependentonsurvivalofitsinvertebratevectorastheinfectedmosquito mustsurvivetheparasiteburdenandingestanother bloodmealfromadoginordertotransmittheinfective larvalstages.Takentogether,thesefindingssuggestthat predictingfuturetemperaturevariationandaccumulated thermalunitsmaybekeyinpredictingheartworm. The D.immitis antigendoesnotappearintheblood ofheartworminfecteddogsuntil6to9monthsafterinfection[9,23,27,28].Therefore,apositivetestforheartwormoftenindicatesaninfectionacquiredsometime duringthepreviousyear.TheCAPCdatasetdoesnotincludeinformationonwhetherpositivecasesareregular ornewpatients.Therefore,allcasesareviewedtorepresentinfectionsacquiredmorethan6monthspreviously. Asthesedataareusedtodevelopforecastingmodels, itwillbecriticalthatfactorsareconsideredoveran appropriatehistory.Werecommendexaminingfactors between7monthsand2yearspriortothecurrentdate.HostfactorsHeartwormhosts,principallythedomesticdog, Canis familiaris ,butwildcanidsalsomaintaintheparasiteand infectmosquitoes[11].Effectiveheartwormprevention isavailablefordomesticdogs,butcomplianceisinsufficienttocontrolthedisease(only74%-79%ofdogs visitingtheveterinaryteachinghospitalattheUniversityofPennsylvania[VHUP]fromJanuary1999through December2006werebeinggivenpreventiveatanygiven timeofyear)[29]. Measuresofthesizeandinfectionstatusofthese susceptibleandat-riskpopulationswillbeimportantto estimatingthespatialriskofdisease.Althoughdogs evacuatedfromtheGulfcoaststatesfollowingthe2005 hurricanespresentedas48.8%dirofilariasis-positive[30], thepercentagepositiverateamongthegeneralpopulationofowneddogsisexpectedtobemuchless.Ideally, datawouldbeavailableonthesusceptiblepopulationBrown etal.Parasites&Vectors 2012, 5 :245 Page4of9 http://www.parasitesandvectors.com/content/5/1/245

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ofcompanionanimals,coyotes,andferaldogs.However, suchdataarelikelydifficulttoacquire.Surrogatesfor assessingthesusceptiblepopulationincludeestimatesof feral/wildpopulations,salesdataonHWpreventionfor companionanimals,andsocioeconomicfactors.UnprotectedreservoirsFeraldogsandcoyotesareperhapsthemostsignificant heartwormreservoirsinNorthAmericaasthesecompetentreservoirpopulationsarenotcoveredthrougha prophylacticprogram.Indeed,manyinfectionsinnonprotecteddomesticdogs(ordogsreceivinginadequate protection)areprobablyamajorsourceofinfectionvia mosquitoesforotherdomesticdogs.Thesepopulations supportparasitedevelopmentandroutinelyhavecirculatingmicrofilariaeinfectivetomosquitoes[31-33]. Unfortunately,accuratedataontheirpopulationsize andtheprevalenceofheartwormwithinagivenpopulationisdifficulttoobtain,however,theworkinggroup consideredthisinformationtobevaluable.Assuch, obtainingpopulationsizedataforcoyotesfromwildlife authoritiesandonstraydogsfromanimalcontrolagenciescouldbeachievedthroughtelephoneinquiries. Methodshavebeendevelopedthatprovideforcoyote populationestimatesandtheirrelativereliability[34]. Backgroundheartwormprevalenceinreservoirpopulationscouldbeestimatedfrompublishedcoyotesurvey results,aswelldatafromdogsinshelters.Manyreports oncoyoteandotherwildcanidspecies ’ Dirofilaria infectionshavebeenpublishedwithreportratesashigh as71%[35];however,mostsurveysoccurredinthe early1980 ’ s[36].Morerecentsurveysconductedin Oklahoma/Texas,Illinois,FloridaandCaliforniahave reported6.5,16,40and42-44%ofcoyotesbeingpositive [36-40].Incontrast,studiesinArizonaandeastern Washingtonstatereportednocoyoteinfections[41,42], whileaseparaterecentsurveyofArizonawildcanids (feraldogsandcoyotes)reportedthat14%werepositive forheartworm[43].Suchdivergentresultsemphasizethe careneededtoutilizeasentinelsurveyofferalcanines.Regionaldataonthepercentageofdogsonprevention ordosesofproductsoldTherearetwopopulationsofcanidsinanyheartworm infectedarea:canidsonpreventivetherapyandcanids thatarenot.Manyofthedogsbeingtestedarelikely fromtheprotectedpopulation.Astheprotectionstatus ofeachtesteddogisnotavailableintheCAPCdata, surrogatefactorsmightproveuseful.Forexample,data onthetotaldog-equivalentdosessoldinagivenarea couldserveasanindicatorfornumberofanimalsinan areaonheartwormprevention.Afewstudieshave attemptedtoestimatethepercentageofdogsonpreventativesthroughownersurveys[29],butitisdifficultto generalizenationallyfromgeographicallylocalizedsmallscalesurveys.Suchdata,therefore,arenotnecessarilya goodrepresentationofanycertainclinic ’ spatientdemographics.Othersourcesofpotentialheartwormpreventativeusewouldinvolvepharmaceuticalcompanies ’ salesdata.SocialeconomicstatusInastudyconductedofpatientspresentedataPennsylvaniateachinghospital,patientage,ownerhousehold income,andbeingneuteredwerefactorsthatwereassociatedwithanincreasedlikelihoodofheartwormpreventativecompliance[29].Atthehighestincomelevels inthestudy,compliancebegantodrop,sometimesconsiderably,therebyaddingalevelofcautionwhenusinga linearassociationwithsocioeconomicstatus.Inthecase ofthedogsshippedoutofLouisianaafterHurricane Katrina,intactdogswere1.6timesmorelikelytohave dirofilariasisthanneutereddogs[30]. ArelationshipbetweenpetrelinquishmentandforeclosureshasbeenestablishedinCalifornia[44].Areas withconcentratedforeclosureshadgreaterconcentrationsofpetrelinquishments.Furthermore,itwas reportedthatresidentsinlow-valuedhomesaremore likelytohaveun-neutereddogsandweremorelikely torelinquishtheseanimals,andthatneuterstatusand relinquishmentshiftedashomevaluesincreased[44]. Takenwiththelowercompliancebyownersofreported unneutereddogs[29],thesefactorsmaybevaluablein themodeldevelopment.AlternativefactorsInadditiontothefactorsdiscussedabove,alistof covariates,secondaryinimportancetotheonesabove, werediscussed.Thesesecondarycovariatesinclude:LatitudeWhiletheeffectoflatitudemaydependonthelocation, latitudecouldserveasasurrogateforgeneralweather, whichinfluencesthepresenceandabundanceofmosquitospecies[45].However,becausethemorenuanced andfinerresolutionmeteorologicaldataareavailablefor useaspotentialfactors,latitudemaybeduplicativeto theseotherfactors.Geographicmobilityormigrationbehaviorofhumans (clients)withinagivenareaAspetownerswithunprotectedpetsmovebetweenhigh andlowtransmissionregions,theymaybefacilitating heartwormspreadtonewareas(seehostfactorsabove). Bringinginfectiousorsusceptibledogsintonewareas mayinfluencethespreadandinter-annualincidenceof heartworm.Geographicmobilitydatamaybeimportant inidentifyingheartwormoutbreaks.Thissaid,thefactorBrown etal.Parasites&Vectors 2012, 5 :245 Page5of9 http://www.parasitesandvectors.com/content/5/1/245

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maybecapturedindemographicdataalreadydescribed above,andishenceconsideredsecondary.PollencountInregionswhereprecipitationislow(theSouthwestfor example)orwhereirrigationiscommon,pollenmay serveasasurrogateforvectorhabitat.Pollencounts maybeabettermeasureinregionswhereprecipitation underestimatestheavailabilityofimmaturehabitat. However,withtheinclusionofcroptypes,forestation coverage,andmeteorologicaldata,pollencountsseem lessimportant.FactorswithinsufficientdataforinclusionAnumberoffactorsconsideredimportantseemed unlikelytobesufficientlyquantifiedforuseinmodeling. Thesefactorscanbedividedintotwogroups:thoseimportanttovectorandparasitedevelopmentandthose importanttohostsusceptibility.Someofthevectorand parasitefactorsexcludedwere:vectorinfectionrates, detailedreservoirinfectionrates,vectorabundanceand flightrange,vectorcompetence(vectorefficiencyindex), vectorsurvival,temperature-dependentdevelopment ratesofvectors(undernaturalfluctuatingtemperature regimes),andvariationsinheartwormdevelopment thresholds.Thesefactorsareimportantforaccurate forecastingmodelsbecausetheydictatetheratesat whichvectorsemergeinthepopulationandbecome infectious.However,duetolackofdataavailability,we excludedthemfromthemodelfactorslist. Thenumberofsusceptiblehostsinapopulationis alsoimportant.Directmeasuresofsusceptiblepopulationsarenotavailable.Potentialsurrogatesforthese dataarepresentedabove.DatalimitationsConcernwasexpressedthatnotalloftheabovefactors areeasilyobtainableorrecordedtoafinegeographical resolution,whichmaybiasthemodelpredictions. County-leveldatamightbemisleadinginsomelocales — asinglecountycanencompassmultipleclimatezonesin westernpartsoftheUS.Countysizeandhomogeneityof theabovefactors(e.g.,humanpopulationdensity,crop types,andclimates)willvarygeographically.Therealso wasconcernoverwhatthedatatrulyrepresent. Differencesintestingprocedures,whichmayvary geographically,mayinfluencethemodelpredictions.In addition,testingratesarelikelytonotbeevenlydistributedacrosspopulationssuchthatcertainpopulations, whichmayhaveaconsiderableroleindiseasetransmission,maynotbeadequatelycaptured.Aspectsofthese limitationscanbeaddressedstatistically;otherswillbe discussedwithrespecttointerpretationofthemodel output.DatasourcesDataareavailableformanyofthekeyfactors.GeographicdataarereadilyavailablefromtheUSGeologic SurveyLandCoverInstituteandtheNationalAtlas oftheUS[46,47].Additionallandcoverdatacanbe acquiredfromtheUSDepartmentofAgricultureEconomicResearchServiceandtheUniversityofNebraska – LincolnNationalDroughtMitigationCenter[48,49]. DemographicdataareavailablefromtheUSCensus Bureau[50].MethodsAspatialsmoothingprocedurebasedontheheadbangingalgorithmmethoddescribedbyHansenisused tocreateafirstbaselinemap(Figure1)[51].Theheadbangingalgorithmisparticularlyusefulindescribing datawithhighlocalvariationsasitismedianpolished (noteasilyinfluencedbyoutliers).Thisalgorithmis namedfromachild ’ sgamewhereafaceispressed againstaboardofpinsprotrudingatvariouslengths, leavingageneralimpressionofthechild ’ sfacewhile smoothingawayanyexcessively-varyinglocalfeatures thataremoreattributabletorandomchance.Weprefer head-bangingtoclassicalKrigingsmoothingtechniques sincethelattercouldbeundulyinfluencedbyafew countieswithhighheartwormprevalence. Acomplicationisthatvaryingnumbersofdogswere testedindistinctcounties.Tohandlethisaspect,the countydataisconvertedtoacommonbasisviastandard normalZ-scores.Here, p ( s )istheprobabilitythatasingletesteddogisheartwormpositiveatlocation(county), s .If N ( s )testswereconductedinthiscounty, k ( s )of whicharepositive,then p ( s )issimplyestimatedas k ( s )/ N ( s ).ThestandardnormalZ-scoreisthisestimated probabilitydividedbyitsstandarderror: Zs ks = Ns ks = Ns 1 ks = Ns Ns no 1 2fg : Countieswherenotestsareperformeddonotinfluencetheanalysis.Ourconventionstake Z ( s )=10,000 whenalltestsinthecountyarepositive,and Z ( s )=0 whenalltestsinthecountyarenegative(thesesomewhatarbitraryconventionsareneededtopreventdivisionbyzero). AfterZ-scoresarecomputedforeachcounty,theheadbangingalgorithmisappliedtospatiallysmooththem. FromthesmoothedZ-scoresandthecounty-by-county valuesof N ,onecanthenconvertbacktoasmoothed probability,representingtheprobabilityofapositivetest ineachcounty.Figure1isageographicdisplayofthese smoothedprobabilitiesusing20nearestneighbors(these areviewedasadjacentcounties).Brown etal.Parasites&Vectors 2012, 5 :245 Page6of9 http://www.parasitesandvectors.com/content/5/1/245

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Thesmoothedcounty, s ,estimateof p ( s ),denoted by p *( s ),willbekeyinourfactoridentificationtask. Specifically,afterthesmoothedprobabilityestimatesare computed,wewillconsiderlogisticregressionmodels oftheform Logit ps 1f1s ::: LfLs s ; where L isthenumberoffactors 1..., Lareregressioncoefficients, f1( s ), ... fL( s )arevaluesoftheobserved regressionfactorsforcounty s isanoveralllocation parameter,and ( s )iszeromeanrandomerrorfor county, s .Here,logitisdefinedforvaluesin[0,1](that is,probabilities)via Logit ps log ps log1 ps : Factor j issignificantinthepredictionofapositive heartwormtestif j 0.Standardforwardandbackwards regressionmodelfactorselectionroutinescanbeused todeterminewhichofthe L factorsaresignificant andhowsignificantiseachfactor.Theinterestedreader isreferredtoCasellaandBerger(2002)forfurther elaboration[52]. Forecastsoffutureprevalencecanbeobtainedfrom theabovelogisticregressionmodelasvariouspredictors anddatafromfutureyearsareconsidered.Byinvertingtheinvertingthelogittransformoftheregression modelutilizingforecastedpredictorfactors,canbe anestimatedvalueof p *( s )isobtained.Forexample,if annualtemperatureisanimportantfactor,onecould usehistoricaltemperaturedatatoforecastnextyear ’ s annualaveragetemperature.Thisforecastedfactoris thenusedintheregressionequationalongwithits accompanyingestimatedvalueof .Rightnow,any suchforecastsareannualinnatureasnoseasonalityhas beenconsidered.However,afterafewyearsofdata arecollected,itmaybepossibletoquantifyseasonal effectsandmakemonthlyforecasts.TheCAPCdatais updatedmonthly.DiscussionIdentifyingfactorsinvolvedinheartwormdiseaseisnot anewendeavor.Initialworkinthisareaoccurredin CanadaandtheUS[26,27],andthishassinceledto studiesonthepredictionofthedifferenttransmissionseasonsinEurope[53],theUnitedKingdom[54] andArgentina[55].Thesestudiespredictthebeginning andendofseasonaltransmission,andsomeconsiderthe impactofclimatechange[56].UtilizingtherichCAPC databasewehaveaccessto,weexpecttoobtaina clearerunderstandingofcanineheartwormtransmission intheUS.Ourworkinggroupidentifiedseveralfactorsthatcouldbeusedinmodeldevelopment. Thesefactors,combinedwiththemodelingapproaches outlinedabove,willbefittedtothecomprehensive CAPCdatasetinthefuturetogeneratedetailedestimationofcanineheartwormriskatacounty-by-county resolutionintheUS.Abbreviations Ae : Aedes ; An : Anopheles ;CAPC:CompanionAnimalParasiteCouncil; Cx : Culex ; D : Dirofilaria ;HDU:HeartwormDegreeUnit;VHUP:Veterinary HospitalattheUniversityofPennsylvania. Competinginterests Theauthorshavenocompetinginterestsrelativetotheworkpresentedin thisreport. Authors ’ contributions DWandRLwereresponsibleforthestatisticalpresentationandthe productionofFigure1.HEB,LCH,PK,andTMwerethegroupparticipants whoidentifiedtheriskfactors,builttheframeworkfortheirinclusioninto themanuscript,andprovidedtherationaleinthepaperfortheirinclusion. DDBandCTNwereresponsibleforgeneratingtheinitialdraftdocument fromtheminutesofthemeetingforcirculationtothegroup,the compilationofthelaterdraftsofthedocument,incorporationofall comments,andassistancewithformattingforfinalsubmission.Allauthors readandapprovedthefinalversionofthemanuscript. Acknowledgments TheauthorswouldliketothanktheCompanionAnimalParasiteCouncilfor itsfacilitationofthemeetingthroughitsorganizationaleffortsandits financialsupportfortravel,food,andlodgingtomakethemeetinga possibility.RobertLund ’ sworkwassupportedbyNationalScience FoundationGrantDMS0905570. Authordetails1SchoolofGeographyandDevelopment,UniversityofArizona,Tucson,AZ 85721,USA.2DepartmentofEntomology,CornellUniversity,Ithaca,NY 14853,USA.3EntomologyandNematologyDepartment,UniversityofFlorida, Gainesville,FL32611,USA.4DepartmentofBiologicalSciences,Arkansas StateUniversity,StateUniversity,AR72467,USA.5Departmentof MicrobiologyandImmunology,CollegeofVeterinaryMedicine,Cornell University,Ithaca,NY14853,USA.6AnimalMedicalCenter,Anniston,AL 36201,USA.7DepartmentofMathematicalSciences,ClemsonUniversity, Clemson,SC29634-0975,USA. Received:10October2012Accepted:22October2012 Published:30October2012 References1. CAPCParasitePrevalenceMapsHeartworm .2011.http://www.capcvet.org/ parasite-prevalence-maps. 2.BowmanDD,LittleSE,LorentzenL,ShieldsJ,SullivanMP,CarlinEP: Prevalenceandgeographicdistributionof Dirofilariaimmitis Borrelia burgdorferi Ehrlichiacanis ,and Anaplasmaphagocytophilum indogsin theUnitedStates:resultsofanationalclinic-basedserologicsurvey. 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BullSocVectorEcol 1989, 14: 301 – 18. 18.SuT,WebbJP,MeyerRP,MullaMS: Spatialandtemporaldistributionof mosquitoesinundergroundstormdrainsystemsinOrangeCounty, California. JVecEcol 2003, 28: 79 – 89. 19.MllerGC,JunnilaA,QuallsW,RevayEE,KlineDL,AllanS,SchleinY,Xue RD: Controlof Culexquinquefasciatus inastormdrainsysteminFlorida usingattractivetoxicsugarbaits. MedVetEntomol 2010, 24: 346 – 51. 20.GongH,DeGaetanoAT,HarringtonLC: Climate-basedModelsforWest Nile Culex MosquitoVectorsintheNortheasternUSA. IntJBiometeorol 2011, 55: 435 – 46. 21.SpielmanA: StructureandseasonalityofNeartic Culexpipiens populations. ProcNYAcadSci 2001, 951: 220 – 234. 22.EldridgeBF: Environmentalcontrolofovariandevelopmentin mosquitoesofthe Culexpipiens complex. Science 1966, 151: 826 – 28. 23.GrieveRB,LokJB,GlickmanJT: Epidemiologyofcanineheartworm infection. EpidemiolRev 1983, 5: 220 – 46. 24.LokJB,KnightDH: Laboratoryverificationofaseasonalheartworm transmissionmodel .In Recentadvancesinheartwormdisease:Symposium '98,Tampa,Florida,USA,1 – 3May,1998 .EditedbySewardRL,KnightDH. Batavia:AmericanHeartwormSociety;1998:15 – 20. 25.NayarJK,BradleyTJ: Effectsofinfectionwith Dirofilariaimmitis ondiuresis andoocytedevelopmentin Aedestaeniorhynchus and Anophelesquadrimaculatus (Diptera:Culicidae). JMedEntomol 1987, 24: 617 – 22. 26.SlocombeJOD,SurgeonerGA,SrivastavaB: Determinationofthe heartwormtransmissionperiodanditsusedindiagnosisandcontrol .In ProceedingsoftheHeartwormSymposium'89,Charleston,SouthCarolina, USA,17 – 19March,1989 .EditedbyOttoGF,JacksonRF,KnightDH, CampbellWC,CourtneyCH,DillonR,HiteSC,JacksonRI,LevineBG,Lewis RE,NoyesJD.Washington,DC:AmericanHeartwormSociety; 1989:19 – 26. 27.KnightDH,LokJBL: Seasonalityofheartworminfectionandimplications forchemoprophylaxis. ClinTechSmAnimPract 1998, 13: 77 – 82. 28.FrankGR,PacePM,DonoghueAR: Antigenemiaandmicrofilaremiain canineexperimentaldirofilariasis .In Recentadvancesinheartwormdisease: Symposium01,SanAntonio,Texas,USA,20 – 22April,2001 .EditedbySeward RL,KnightDH.Batavia:AmericanHeartwormSociety;2001:211 – 14. 29.GatesMC,NolanTJ: Factorsinfluencingheartworm,flea,andtick preventativeuseinpatientspresentingtoaveterinaryteachinghospital. PreventativeVetMed 2010, 93: 193 – 200. 30.LevyJK,EdinboroCH,GlotfeltyC-S,DingmanPA,WestAL,Kirkland-Cady DK: Seroprevalenceof Dirofilariaimmitis ,felineleukemiavirus,andfeline immunodeficiencyvirusinfectionamongdogsandcatsexportedfrom the2005GulfCoasthurricanedisasterarea. JAmerVetMedAssoc 2007, 231: 218 – 25. 31.SacksBN: Increasingprevalenceofcanineheartwormincoyotesfrom California. JWildlifeDis 1998, 34: 386 – 9. 32.SacksBN,WoodwardDL,ColwellAE: Along-termstudyofnon-nativeheartwormtransmissionamongcoyotesinaMediterraneanecosystem. Oikos 2003, 102: 478 – 90. 33.HolzmanS,ConroyMJ,DavidsonWR: Diseases,parasitesandsurvivalof coyotesinsouth-centralGeorgia. JWildlifeDis 1992, 28: 572 – 80. 34.HenkeSE,KnowltonFF: Techniquesforestimatingcoyoteabundance .In CoyotesintheSouthwest:ACompendiumofOurKnowledge.Symposium Proceedings,December13 – 14,1995 .EditedbyRollinsD,RichardsonC, BlankenshipT,CanonK,HenkeS.SanAngelo:ICWDMwebsite, DigitalCommons@UniveristyofNebraskaLincoln;1996:71 – 78.http://digitalcommons.unl.edu/cgi/viewcontent.cgi? article=1027&context=coyotesw. 35.CusterJW,PenceDB: Dirofilariasisinwildcanidsfromthegulfcoastal prairiesofTexasandLouisiana,U.S.A. VetParasitol 1981, 8: 71 – 82. 36.AndersonRC: FilarioidNematodes .In ParasiticDiseasesofWildMammals EditedbySamuelWM,PybusMJ,KocanAA.Ames,IA:IowaStateUniversity Press;2001:342 – 356. 37.FosterGW,MainMB,KinsellaJM,DixonLM,TerrellSP,ForresterDJ: Parasitic helminthesandarthropodsofcoyotes( Canislatrans )fromFlorida,U.S.A. 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NorthwestSci 2008, 82: 319 – 22. 43.Olsen-MikitowiczVM: PrevalenceofDirofilariaimmitis,thecausativeagentin canineheartwormdisease,inArizonafrom2008to2011usingferalcaninesas asentinelspecies.B.S.Thesis .USA:UniversityofArizona;2011. 44.MorrisGD,StefflerJ: Waspetrelinquishmentrelatedtoforeclosure?:a spatialresearchnotefromCaliforniaduringtheheightofforeclosure. SocialSciJ 2011, 48: 739– 45. 45.EdilloF,KiszewskiA,ManjouridesJ,PaganoM,HutchinsonM,KyleA,AriasJ, GainesD,LampmanR,NovakR,FoppaI,LebelcyzkC,SimthR,MoncayoA, SpielmanA: Effectsoflatitudeandlongitudeonthepopulationstructure of Culexpipienss.l .,vectorsofWestNilevirusinNorthAmerica. AmJ TropMedHyg 2009, 81: 842 – 48. 46.TheUSGSLandCoverInstitute(LCI):http://landcover.usgs.gov/ landcoverdata.php. 47.Nationalatlas.gov:http://www.nationalatlas.gov/. 48. USDAEconomicResearchServiceMajorLandUses ;http://www.ers.usda.gov/ data-products/major-land-uses.aspx. 49.NationalDroughtMitigationCenter: U.S.DroughtMonitor .:;http://drought. unl.edu/MonitoringTools/USDroughtMonitor.aspx. 50.UnitedStatesCensusBureau:http://www.census.gov/#. 51.HansenKM: Head-banging:robustsmoothingintheplane. IEEETranson GeosciRemoteSens 1991, 29: 369 – 78. 52.CasellaG,BergerRL: StatisticalInference .Secondthedition.PacificGrove,CA USA:DuxburyPress;2002:700. 53.GenchiC,RinaldiL,CasconeC,MortarinoM,CringoliG: Isheartworm diseasereallyspreadinginEurope? VetParasitol 2005, 133: 137 – 48.Brown etal.Parasites&Vectors 2012, 5 :245 Page8of9 http://www.parasitesandvectors.com/content/5/1/245

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54.MedlockJM,BarrassI,KerrodE,TaylorMA,LeachS: Analysisofclimatic predictionsforextrinsicincubationof Dirofilaria intheUnitedKingdom. VectorBorneZoonoticDis 2007, 7: 4 – 14. 55.VezzaniD,CarbajoAE: Spatialandtemporaltransmissionriskof Dirofilaria immitis inArgentina. IntJParasitol 2006, 36: 1463 – 72. 56.GenchiC,KramerLH,RivasiF: DirofilarialInfectionsinEurope. VectorBorne ZoonoticDis 2011, 11: 1307 – 17.doi:10.1186/1756-3305-5-245 Citethisarticleas: Brown etal. : Keyfactorsinfluencingcanine heartworm, Dirofilariaimmitis ,intheUnitedStates. Parasites&Vectors 2012 5 :245. Submit your next manuscript to BioMed Central and take full advantage of: € Convenient online submission € Thorough peer review € No space constraints or color “gure charges € Immediate publication on acceptance € Inclusion in PubMed, CAS, Scopus and Google Scholar € Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Brown etal.Parasites&Vectors 2012, 5 :245 Page9of9 http://www.parasitesandvectors.com/content/5/1/245


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title
p Key factors influencing canine heartworm, it Dirofilaria immitis, in the United States
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au id A1 snm Brownmi Efnm Heidiinsr iid I1 email HeidiBrown@email.arizona.edu
A2 HarringtonCLauraI2 lch27@cornell.edu
A3 KaufmanEPhillipI3 pkaufman@ufl.edu
A4 McKayTanjaI4 tmckay@astate.edu
A5 ca yes BowmanDDwightI5 ddb3@cornell.edu
A6 Nelsonmnm ThomasCI6 drtomnelson@amcvets.com
A7 WangDongmeiI7 dongmew@clemson.edu
A8 LundRobertlund@clemson.edu
insg
ins School of Geography and Development, University of Arizona, Tucson, AZ, 85721, USA
Department of Entomology, Cornell University, Ithaca, NY, 14853, USA
Entomology and Nematology Department, University of Florida, Gainesville, FL, 32611, USA
Department of Biological Sciences, Arkansas State University, State University, AR, 72467, USA
Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, NY, 14853, USA
Animal Medical Center, Anniston, AL, 36201, USA
Department of Mathematical Sciences, Clemson University, Clemson, SC, 29634-0975, USA
source Parasites & Vectors
issn 1756-3305
pubdate 2012
volume 5
issue 1
fpage 245
url http://www.parasitesandvectors.com/content/5/1/245
xrefbib pubidlist pubid idtype doi 10.1186/1756-3305-5-245pmpid 23111089
history rec date day 10month 10year 2012acc 22102012pub 30102012
cpyrt 2012collab Brown et al.; licensee BioMed Central Ltd.note This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
kwdg
kwd Canine heartworm
Dirofilaria immitis
Mosquito vectors
Spatial prevalence
abs
sec
st
Abstract
An examination of the Companion Animal Parasite Council’s (CAPC) canine heartworm data to clarify the spatial prevalence of heartworm in the United States. Factors thought to influence the spatial risk of disease, as identified in a recent CAPC workshop, are discussed.
bdy
Background
The Companion Animal Parasite Council (CAPC) collected results of 4,769,403 canine heartworm tests during the 2011 calendar year from various commercial testing laboratories in the United States (US), and of this national sampling of dogs, 56,612 (1.187%) were positive for heartworm antigen, indicative of an active D. immitis infection
abbrgrp
abbr bid B1 1
. This rich data set can be used to infer the national prevalence of heartworm disease and/or verify the accuracy of predictions made from forecasting models. Herein, we: 1) present a map of positive heartworm test prevalence, thereby upgrading existing knowledge; and 2) construct a list of factors that will be used to explain the observed rates of heartworm positive tests over the coming years of similar data collection.
This work stems from a meeting held in Atlanta, GA, on June 9–10, 2012, during which vector ecologists, entomologists, and biologists worked with a team of statisticians to identify risk factors which could be useful for the development of spatial risk mapping for important vector-borne canine diseases for which CAPC had access to collected data with the overarching objective being to identify the most important factors influencing Lyme, ehrlichiosis, anaplasmosis, and heartworm infection rates in the US canine population
1
. The focus of this paper is on one of these data sets, i.e., the data relative to canine heartworm (Dirofilaria immitis) infection.
Heartworm infections are a significant health risk to dogs as even light infections are capable of producing profound pulmonary vascular and parenchymal disease. Despite improved diagnostic methods, effective preventives and increasing awareness among veterinary professionals and pet owners, cases of heartworm infection continue to be diagnosed in high numbers and are becoming more prevalent in areas previously considered to be at a low risk
B2 2
. A survey of veterinary clinics in 2005 reported that over 250,000 dogs tested positive for heartworms during the 2004 calendar year
B3 3
. When one considers a 48% response rate to the survey and the fact that only 30% of the dog population in the US was tested, the actual numbers of dogs infected are much higher, probably in the 1 to 1.5 million range.
The test used for antigen detection in dogs has a high sensitivity (84%, n= 175/208) and high specificity (97%, n= 30/31)
B4 4
. While concerns about false positives in areas of low prevalence exist, comparisons with known prevalence rates and studies that have examined dogs for the presence of microfilariae indicate that any overestimation of the infection rates due to false positives is not large. A survey for microfilarial presence rather than antigenemia conducted in Colorado found an overall prevalence of microfilarial positive dogs in 1990 to be 0.77% for 7,818 dogs tested
B5 5
; the 2008 data showed a prevalence in Colorado of 0.4%
2
. In 1981–1982, a survey of 541 dogs in 12 cities and four counties in Northern California found that 31 (5.7%) were positive for heartworm microfilariae
B6 6
; the prevalence rates in these counties by microfilariae were similar to results from the antigen survey
2
.
An example of the information that the CAPC heartworm database contains is represented by a map displaying spatially smoothed heartworm positive tests for the 2011 calendar year (Figure
figr fid F1 1). The graphic shows that heartworm is most prominent in the lower Mississippi River Valley and nearly absent in Northern Montana. This map was generated using annualized data --- no seasonal features were considered. The results represent the prevalence of positive tests for circulating heartworm antigen over a full year amongst a population of dogs that are visiting a veterinarian and receiving care that includes at the least a test for heartworm infection. The proportion of positive heartworm tests was computed for each county in the contiguous 48 states. Proportions are analyzed in preference to total positive test counts as the number of positive tests can be influenced by local testing practices. These proportions were then spatially smoothed via a procedure known as the head-banging algorithm and grouped into nine probabilistic categories. The methods account for the varying number of tests made in different counties; for example, ten positive tests in a sample of size one hundred statistically suggests more of a problem than one positive test in ten, although the sample infection proportions are the same. The map will be updated in future years as additional data become available. While this map quantifies the baseline heartworm infection rates for US counties, it is desirable to understand what factors explain heartworm risk. Our immediate goal was to assemble a list of factors, which are available and quantifiable, that may influence canine heartworm infection rates.
fig Figure 1caption Spatially smoothed proportions of positive canine heartworm-antigen tests recorded by US veterinarians in 2011text
b Spatially smoothed proportions of positive canine heartworm-antigen tests recorded by US veterinarians in 2011. The figure summarizes 4,769,403 tests performed by veterinarians for circulating heartworm antigen in the US in 2011; of these tests, 56,612 tests (1.187%) were positive. The population studied is those dogs that are seen by a veterinarian and are tested; about 5% of owned dogs in the US. The map displays probabilities of a positive test after smoothing by the head-banging algorithm (see text for details). The figure is made by assigning the smoothed proportions to nine color-coded categories [0.00,0.03], (0.03,0.06], .,(0.21,0.24], and (0.24, 1.00]. The colors range from dark green for the lower proportions to bright red for higher proportions.
graphic file 1756-3305-5-245-1
At the June 2012 CAPC meeting, a team of experts provided up to ten measurable factors, ranked in order of importance that are most likely to affect heartworm prevalence and can be used for spatial risk mapping. It was understood that some of the factors might prove to be of no value, that some could be of significant value, that there might be interactions between factors, and that some important factors could be omitted or non-measurable. Because heartworm risk varies both spatially and temporally, the team included factors which could predict both baseline prevalence rates and inter-annual variations. Before leaving the meeting, a ranked list was generated (Table
tblr tid T1 1).
table
Table 1
Ranked factors initially identified by the working group
tgroup align left cols 2
colspec colname c1 colnum 1 colwidth 1*
c2
thead
row rowsep
entry
tbody valign top
1
Vector presence (+)
2
HDU (calculated from weather with a 14°C threshold) (±)
3
Lagged weather (7 months to 24 months prior to diagnosis)
  a. Precipitation, temperature, and or relative humidity (±)
  b. Moisture Index (Precipitation and Evaporation) (±)
4
Presence of coyote and feral dog populations – categorical data (+)
5
Human population density (+)
6
Land cover (Dominant type or Percentage classification) (±)
7
Cropland (Dominant type or Percentage classification) (±)
8
Social economic status (±)
  a. Household income (-)
  b. Education (-)
  c. Foreclosure rates (+)
9
Irrigation (±)
Factors and their selection rationale
The factors selected by the heartworm working group are listed and discussed here, grouped by vector, parasite, and host factors. Some factors, such as temperature, are important to multiple factor groups (vector and parasite). Two criteria in selecting factors were that 1) the factor can be easily measured and 2) data for it are available.
Vector factors
Dog heartworm is a mosquito-transmitted disease with a cosmopolitan distribution
B7 7
B8 8
. The mosquito becomes infected when it ingests microfilaria during the act of blood feeding on an infectious host. To be a competent vector of D. immitis, mosquitoes must be able to support nematode development into the infective stage (L3), and the infective larvae must be able to migrate to the proboscis of the mosquito
B9 9
. Over 60 species of mosquitoes are capable of supporting the development of L3 D. immitis
7
. Summaries of mosquito species naturally infected with D. immitis in 19 states have appeared in previous publications
B10 10
B11 11
B12 12
. In the US, multiple studies on potential heartworm vectors have been conducted; however, most studies were restricted to certain locations. Without known vector distribution data from the majority of states, estimates are needed to identify areas where each vector occurs and to what abundance. This distributional data can then be used to identify areas with greater risk for heartworm transmission. With a number of mosquito species likely playing different roles across different regions of the US
9
and taking into account mosquito vector competence data either obtained naturally and/or experimentally, nine species were identified as major potential vectors for national forecast modeling. These species are Aedes aegypti, Aedes albopictus, Aedes canadensis, Aedes sierrensis, Aedes trivitattus, Aedes vexans, Anopheles punctipennis, Anopheles quadrimaculatus, and Culex quinquefasciatus.
For most of the nine mosquito species chosen, published maps illustrating their geographical home ranges within North America exist
12
. As many of the mosquito species have varying frequency distributions within even small ranges, the historical literature can serve as a starting point for fine-tuning the representation of their ranges. For species, such as Ae. albopictus and Ae. aegypti, where competition between the species has led to changes in their historical ranges
B13 13
B14 14
, new geographical distribution data can be made available as it is accumulated.
Additional vector maps can be generated using surveillance data and habitat modeling, but quality surveillance data are notoriously difficult to acquire. Consequently, there is a need to use surrogate factors. Possible surrogates for actual presence/absence or abundance maps of these mosquito species include: 1) vegetation indices (derived from satellite imagery or classified land cover maps); 2) urbanization as it relates to rural, suburban, and urban landscapes; and 3) meteorological data to capture inter-annual variability. As new mosquito distribution maps are generated, they will ideally be examined by entomologists to verify that they represent current species ranges.
Vegetation indices
A synopsis of various mosquito species that breed in different habitats can be made available and overlaid on land cover, cropland, and irrigation maps of the US. For example, previous studies that have focused on mosquitoes associated with forested environments (Ae. sierrensis and An. punctipennis)
B15 15
, rice production (An. quadrimaculatus)
B16 16
, and irrigation could provide data for map generation. These data can be included both as dominant type of vegetation/cropland or as a percentage of each type by county.
Rural, suburban, and urban landscapes
Certain mosquito species are more prevalent in urban environments due to differences in their preference for particular oviposition sites. Mosquito species that breed in artificial containers tend to be more urbanized, such as Cx. quinquefasciatus and Ae. aegypti. The spatial and temporal distribution of Cx. quinquefasciatus in residential areas
B17 17
and underground storm drains has been well documented
B18 18
B19 19
, and this type of information may be useful for modeling. Human population data from the US Census Bureau and land cover classified imagery with urban, rural, suburban land use classes may be helpful inputs for the model to capture measures of urbanization.
Meteorological data
Mosquito development rates are temperature- dependent and completion of the immature stages is dependent on water availability. In a forecasting model, weather (daily and monthly, and at various time lags) is expected to influence intra-annual fluctuations. In a static spatial model, climate, rather than weather, may better govern the likelihood a vector can complete its lifecycle. In an effort to incorporate these influences, we recommend including temperature (minimum, maximum, mean, and daily variability) and moisture index (calculated based on precipitation and evaporation rates)
B20 20
. The moisture index M(t) at day t over the current and previous L days is
display-formula
m:math name 1756-3305-5-245-i1 xmlns:m http:www.w3.org1998MathMathML m:mrow
m:mi M
m:mfenced open ( close )
t
m:mo =
m:msubsup
Σ
i
=
t

L
t
P
i

D
i
m:mtext ,
where P(i) and E(i) are the precipitation and evaporation from day i, respectively. Moisture indices may be more meaningful than rainfall because they take into account the moisture deficit prior to rainfall and estimate the net moisture in terms of standing water available to egg-laying mosquitoes.
Related indirect factors that could plausibly influence heartworms include: 1) relative humidity, 2) elevation, and 3) daily circadian-related events in mosquito behavior. Relative humidity influences vector behavior and survival. Elevation may serve as a surrogate of the climate expected at a given location. Circadian activity is important to some vector species’ behavior
B21 21
B22 22
.
Parasite factors
Several issues were identified relative to the parasite and its development within the vector and host. Heartworm Development Units (HDUs) as they relate to nematode development in mosquitoes and the lag period between infection and detection in the data on the current antigen detection maps were considered other important factors.
The D. immitis lifecycle consists of several developmental stages in both the mosquito vector and vertebrate hosts
9
B23 23
B24 24
B25 25
. Heartworm transmission is driven by the ambient temperatures experienced by their mosquito vectors. As a consequence, ambient air temperature is used to predict the timing of dog heartworm vector competence
B26 26
B27 27
. The heat requirement for heartworms to complete incubation to the infective stage can be expressed in development degree days, or HDUs. A simple way to calculate HDUs using maximum and minimum air temperatures is to subtract the heartworm development threshold (14°C) from the average daily temperature
9
26
. Accumulated HDUs are summed from a determined starting day, similar to the moisture index equation above. If hourly temperatures are utilized, the summation of the difference in hourly temperature from 14°C can be divided by 24. The number of accumulated degree days required for development to infective parasite stage is 130
27
, although this value may vary by strain of the parasite. D. immitis larval survival and development within a mosquito is consequently dependent on temperature and its fluctuations --- larval heartworms are capable of slowing and recommencing development in a temperature-dependent fashion. In addition, D. immitis larval survival is dependent on survival of its invertebrate vector as the infected mosquito must survive the parasite burden and ingest another blood meal from a dog in order to transmit the infective larval stages. Taken together, these findings suggest that predicting future temperature variation and accumulated thermal units may be key in predicting heartworm.
The D. immitis antigen does not appear in the blood of heartworm infected dogs until 6 to 9 months after infection
9
23
27
B28 28
. Therefore, a positive test for heartworm often indicates an infection acquired sometime during the previous year. The CAPC dataset does not include information on whether positive cases are regular or new patients. Therefore, all cases are viewed to represent infections acquired more than 6 months previously. As these data are used to develop forecasting models, it will be critical that factors are considered over an appropriate history. We recommend examining factors between 7 months and 2 years prior to the current date.
Host factors
Heartworm hosts, principally the domestic dog, Canis familiaris, but wild canids also maintain the parasite and infect mosquitoes
11
. Effective heartworm prevention is available for domestic dogs, but compliance is insufficient to control the disease (only 74%-79% of dogs visiting the veterinary teaching hospital at the University of Pennsylvania [VHUP] from January 1999 through December 2006 were being given preventive at any given time of year)
B29 29
.
Measures of the size and infection status of these susceptible and at-risk populations will be important to estimating the spatial risk of disease. Although dogs evacuated from the Gulf coast states following the 2005 hurricanes presented as 48.8% dirofilariasis-positive
B30 30
, the percentage positive rate among the general population of owned dogs is expected to be much less. Ideally, data would be available on the susceptible population of companion animals, coyotes, and feral dogs. However, such data are likely difficult to acquire. Surrogates for assessing the susceptible population include estimates of feral /wild populations, sales data on HW prevention for companion animals, and socioeconomic factors.
Unprotected reservoirs
Feral dogs and coyotes are perhaps the most significant heartworm reservoirs in North America as these competent reservoir populations are not covered through a prophylactic program. Indeed, many infections in non-protected domestic dogs (or dogs receiving inadequate protection) are probably a major source of infection via mosquitoes for other domestic dogs. These populations support parasite development and routinely have circulating microfilariae infective to mosquitoes
B31 31
B32 32
B33 33
. Unfortunately, accurate data on their population size and the prevalence of heartworm within a given population is difficult to obtain, however, the working group considered this information to be valuable. As such, obtaining population size data for coyotes from wildlife authorities and on stray dogs from animal control agencies could be achieved through telephone inquiries. Methods have been developed that provide for coyote population estimates and their relative reliability
B34 34
.
Background heartworm prevalence in reservoir populations could be estimated from published coyote survey results, as well data from dogs in shelters. Many reports on coyote and other wild canid species’ Dirofilaria infections have been published with report rates as high as 71%
B35 35
; however, most surveys occurred in the early 1980’s
B36 36
. More recent surveys conducted in Oklahoma/Texas, Illinois, Florida and California have reported 6.5, 16, 40 and 42-44% of coyotes being positive
36
B37 37
B38 38
B39 39
B40 40
. In contrast, studies in Arizona and eastern Washington state reported no coyote infections
B41 41
B42 42
, while a separate recent survey of Arizona wild canids (feral dogs and coyotes) reported that 14% were positive for heartworm
B43 43
. Such divergent results emphasize the care needed to utilize a sentinel survey of feral canines.
Regional data on the percentage of dogs on prevention or doses of product sold
There are two populations of canids in any heartworm infected area: canids on preventive therapy and canids that are not. Many of the dogs being tested are likely from the protected population. As the protection status of each tested dog is not available in the CAPC data, surrogate factors might prove useful. For example, data on the total dog-equivalent doses sold in a given area could serve as an indicator for number of animals in an area on heartworm prevention. A few studies have attempted to estimate the percentage of dogs on preventatives through owner surveys
29
, but it is difficult to generalize nationally from geographically localized small-scale surveys. Such data, therefore, are not necessarily a good representation of any certain clinic’s patient demographics. Other sources of potential heartworm preventative use would involve pharmaceutical companies’ sales data.
Social economic status
In a study conducted of patients presented at a Pennsylvania teaching hospital, patient age, owner household income, and being neutered were factors that were associated with an increased likelihood of heartworm preventative compliance
29
. At the highest income levels in the study, compliance began to drop, sometimes considerably, thereby adding a level of caution when using a linear association with socioeconomic status. In the case of the dogs shipped out of Louisiana after Hurricane Katrina, intact dogs were 1.6 times more likely to have dirofilariasis than neutered dogs
30
.
A relationship between pet relinquishment and foreclosures has been established in California
B44 44
. Areas with concentrated foreclosures had greater concentrations of pet relinquishments. Furthermore, it was reported that residents in low-valued homes are more likely to have un-neutered dogs and were more likely to relinquish these animals, and that neuter status and relinquishment shifted as home values increased
44
. Taken with the lower compliance by owners of reported unneutered dogs
29
, these factors may be valuable in the model development.
Alternative factors
In addition to the factors discussed above, a list of covariates, secondary in importance to the ones above, were discussed. These secondary covariates include:
Latitude
While the effect of latitude may depend on the location, latitude could serve as a surrogate for general weather, which influences the presence and abundance of mosquito species
B45 45
. However, because the more nuanced and finer resolution meteorological data are available for use as potential factors, latitude may be duplicative to these other factors.
Geographic mobility or migration behavior of humans (clients) within a given area
As pet owners with unprotected pets move between high and low transmission regions, they may be facilitating heartworm spread to new areas (see host factors above). Bringing infectious or susceptible dogs into new areas may influence the spread and inter-annual incidence of heartworm. Geographic mobility data may be important in identifying heartworm outbreaks. This said, the factor may be captured in demographic data already described above, and is hence considered secondary.
Pollen count
In regions where precipitation is low (the Southwest for example) or where irrigation is common, pollen may serve as a surrogate for vector habitat. Pollen counts may be a better measure in regions where precipitation underestimates the availability of immature habitat. However, with the inclusion of crop types, forestation coverage, and meteorological data, pollen counts seem less important.
Factors with insufficient data for inclusion
A number of factors considered important seemed unlikely to be sufficiently quantified for use in modeling. These factors can be divided into two groups: those important to vector and parasite development and those important to host susceptibility. Some of the vector and parasite factors excluded were: vector infection rates, detailed reservoir infection rates, vector abundance and flight range, vector competence (vector efficiency index), vector survival, temperature-dependent development rates of vectors (under natural fluctuating temperature regimes), and variations in heartworm development thresholds. These factors are important for accurate forecasting models because they dictate the rates at which vectors emerge in the population and become infectious. However, due to lack of data availability, we excluded them from the model factors list.
The number of susceptible hosts in a population is also important. Direct measures of susceptible populations are not available. Potential surrogates for these data are presented above.
Data limitations
Concern was expressed that not all of the above factors are easily obtainable or recorded to a fine geographical resolution, which may bias the model predictions. County-level data might be misleading in some locales --- a single county can encompass multiple climate zones in western parts of the US. County size and homogeneity of the above factors (e.g., human population density, crop types, and climates) will vary geographically. There also was concern over what the data truly represent. Differences in testing procedures, which may vary geographically, may influence the model predictions. In addition, testing rates are likely to not be evenly distributed across populations such that certain populations, which may have a considerable role in disease transmission, may not be adequately captured. Aspects of these limitations can be addressed statistically; others will be discussed with respect to interpretation of the model output.
Data sources
Data are available for many of the key factors. Geographic data are readily available from the US Geologic Survey Land Cover Institute and the National Atlas of the US
B46 46
B47 47
. Additional land cover data can be acquired from the US Department of Agriculture Economic Research Service and the University of Nebraska – Lincoln National Drought Mitigation Center
B48 48
B49 49
. Demographic data are available from the US Census Bureau
B50 50
.
Methods
A spatial smoothing procedure based on the head-banging algorithm method described by Hansen is used to create a first baseline map (Figure
1)
B51 51
. The head-banging algorithm is particularly useful in describing data with high local variations as it is median polished (not easily influenced by outliers). This algorithm is named from a child’s game where a face is pressed against a board of pins protruding at various lengths, leaving a general impression of the child’s face while smoothing away any excessively-varying local features that are more attributable to random chance. We prefer head-banging to classical Kriging smoothing techniques since the latter could be unduly influenced by a few counties with high heartworm prevalence.
A complication is that varying numbers of dogs were tested in distinct counties. To handle this aspect, the county data is converted to a common basis via standard normal Z-scores. Here, p(s) is the probability that a single tested dog is heartworm positive at location (county), s. If N(s) tests were conducted in this county, k(s) of which are positive, then p(s) is simply estimated as k(s)/N(s). The standard normal Z-score is this estimated probability divided by its standard error:
1756-3305-5-245-i2
Z
s
=
m:mfrac
k
s
/
N
s
m:msup
{ }
k
s
/
N
s
m:mn 1

k
s
/
N
s
N
s
1
2
.
Counties where no tests are performed do not influence the analysis. Our conventions take Z(s)=10,000 when all tests in the county are positive, and Z(s)=0 when all tests in the county are negative (these somewhat arbitrary conventions are needed to prevent division by zero).
After Z-scores are computed for each county, the head-banging algorithm is applied to spatially smooth them. From the smoothed Z-scores and the county-by-county values of N, one can then convert back to a smoothed probability, representing the probability of a positive test in each county. Figure
1 is a geographic display of these smoothed probabilities using 20 nearest neighbors (these are viewed as adjacent counties).
The smoothed county, s, estimate of p(s), denoted by p*(s), will be key in our factor identification task. Specifically, after the smoothed probability estimates are computed, we will consider logistic regression models of the form
1756-3305-5-245-i3
Logit
stretchy true (
p
*
(
s
))
=
μ
+
m:msub
β
1
f
1
s
+
.
.
.
+
β
L
f
L
s
+
α
s
,
where L is the number of factors β
sub 1 β
L are regression coefficients, f
1(s),…,f
L
(s) are values of the observed regression factors for county s, μ is an overall location parameter, and α(s) is zero mean random error for county, s. Here, logit is defined for values in [0,1] (that is, probabilities) via
1756-3305-5-245-i4
Logit
p
*
s
=
log
p
*
s

log
1

p
*
s
.
Factor j is significant in the prediction of a positive heartworm test if β
j
≠0. Standard forward and backwards regression model factor selection routines can be used to determine which of the L factors are significant and how significant is each factor. The interested reader is referred to Casella and Berger (2002) for further elaboration
B52 52
.
Forecasts of future prevalence can be obtained from the above logistic regression model as various predictors and data from future years are considered. By inverting the inverting the logit transform of the regression model utilizing forecasted predictor factors, can be an estimated value of p*(s) is obtained. For example, if annual temperature is an important factor, one could use historical temperature data to forecast next year’s annual average temperature. This forecasted factor is then used in the regression equation along with its accompanying estimated value of β. Right now, any such forecasts are annual in nature as no seasonality has been considered. However, after a few years of data are collected, it may be possible to quantify seasonal effects and make monthly forecasts. The CAPC data is updated monthly.
Discussion
Identifying factors involved in heartworm disease is not a new endeavor. Initial work in this area occurred in Canada and the US
26
27
, and this has since led to studies on the prediction of the different transmission seasons in Europe
B53 53
, the United Kingdom
B54 54
and Argentina
B55 55
. These studies predict the beginning and end of seasonal transmission, and some consider the impact of climate change
B56 56
. Utilizing the rich CAPC data base we have access to, we expect to obtain a clearer understanding of canine heartworm transmission in the US. Our working group identified several factors that could be used in model development. These factors, combined with the modeling approaches outlined above, will be fitted to the comprehensive CAPC data set in the future to generate detailed estimation of canine heartworm risk at a county-by-county resolution in the US.
Abbreviations
Ae: Aedes; An: Anopheles; CAPC: Companion Animal Parasite Council; Cx: Culex; D: Dirofilaria; HDU: Heartworm Degree Unit; VHUP: Veterinary Hospital at the University of Pennsylvania.
Competing interests
The authors have no competing interests relative to the work presented in this report.
Authors’ contributions
DW and RL were responsible for the statistical presentation and the production of Figure
1. HEB, LCH, PK, and TM were the group participants who identified the risk factors, built the framework for their inclusion into the manuscript, and provided the rationale in the paper for their inclusion. DDB and CTN were responsible for generating the initial draft document from the minutes of the meeting for circulation to the group, the compilation of the later drafts of the document, incorporation of all comments, and assistance with formatting for final submission. All authors read and approved the final version of the manuscript.
bm
ack
Acknowledgments
The authors would like to thank the Companion Animal Parasite Council for its facilitation of the meeting through its organizational efforts and its financial support for travel, food, and lodging to make the meeting a possibility. Robert Lund’s work was supported by National Science Foundation Grant DMS 0905570.
refgrp CAPC Parasite Prevalence Maps Heartworm2011
http://www.capcvet.org/parasite-prevalence-maps
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