RESEARCHARTICLE Improving the quality of child anthropometry: Manual anthropometry in the Body Imaging for Nutritional Assessment Study (BINA) JoelConkle1*,UshaRamakrishnan1,2☯,RafaelFlores-Ayala1,3☯,Parminder S.Suchdev1,2,3,4☯,ReynaldoMartorell1,2☯ 1 DoctoralPrograminNutritionandHealthSciences,LaneyGraduateSchool,EmoryUniversity,Atlanta,GA, UnitedStatesofAmerica,2 HubertDepartmentofGlobalHealth,RollinsSchoolofPublicHealth,Emory University,Atlanta,GA,UnitedStatesofAmerica,3 DivisionofNutrition,PhysicalActivityandObesity; a1111111111 NationalCenterforChronicDiseasePreventionandHealthPromotion,U.S.CentersforDiseaseControland a1111111111 Prevention,Atlanta,GA,UnitedStatesofAmerica,4 DepartmentofPediatrics,SchoolofMedicine,Emory a1111111111 University.Atlanta,GA,UnitedStatesofAmerica a1111111111 a1111111111 ☯Theseauthorscontributedequallytothiswork. *joelconkle@gmail.com,joel.conkle@emory.edu Abstract OPENACCESS Anthropometricdatacollectedinclinicsandsurveysareofteninaccurateandunreliabledue Citation:ConkleJ,RamakrishnanU,Flores-Ayala R,SuchdevPS,MartorellR(2017)Improvingthe tomeasurementerror.TheBodyImagingforNutritionalAssessmentStudy(BINA)evalu- qualityofchildanthropometry:Manual atedtheabilityof3Dimagingtocorrectlymeasurestature,headcircumference(HC)and anthropometryintheBodyImagingforNutritional armcircumference(MUAC)forchildrenunderfiveyearsofage.Thispaperdescribesthe AssessmentStudy(BINA).PLoSONE12(12): protocolforandthequalityofmanualanthropometricmeasurementsinBINA,astudycon- e0189332.https://doi.org/10.1371/journal. pone.0189332 ductedin2016–17inAtlanta,USA.Qualitywasevaluatedbyexaminingdigitpreference, biologicalplausibilityofz-scores,z-scorestandarddeviations,andreliability.Wecalculated Editor:FasilTekolaAyele,NationalInstitutesof Health,UNITEDSTATES z-scoresandanalyzedplausibilitybasedonthe2006WHOChildGrowthStandards(CGS). Forreliability,wecalculatedintra-andinter-observerTechnicalErrorofMeasurement Received:June28,2017 (TEM)andIntraclassCorrelationCoefficient(ICC).Wefoundlowdigitpreference;99.6% Accepted:November22,2017 ofz-scoreswerebiologicallyplausible,withz-scorestandarddeviationsrangingfrom0.92 Published:December14,2017 to1.07.TotalTEMwas0.40forstature,0.28forHC,and0.25forMUACincentimeters. Copyright:Thisisanopenaccessarticle,freeofall ICCrangedfrom0.99to1.00.ThequalityofmanualmeasurementsinBINAwashighand copyright,andmaybefreelyreproduced, similartothatoftheanthropometricdatausedtodeveloptheWHOCGS.Weattributedhigh distributed,transmitted,modified,builtupon,or qualitytovigoroustraining,motivatedandcompetentfieldstaff,reductionofnon-measure- otherwiseusedbyanyoneforanylawfulpurpose. TheworkismadeavailableundertheCreative menterrorthroughtheuseoftechnology,andreductionofmeasurementerrorthroughade- CommonsCC0publicdomaindedication. quatemonitoringandsupervision.Ouranthropometrymeasurementprotocol,whichbuilds DataAvailabilityStatement:Dataandthestudy onandimprovesupontheprotocolusedfortheWHOCGS,canbeusedtoimproveanthro- measurementmanualareavailableatOpen pometricdataquality.Thediscussionillustratestheneedtostandardizeanthropometric ScienceFramework(identifiers:DOI10.17605/ dataqualityassessment,andweconcludethatBINAcanprovideavaluableevaluationof OSF.IO/WMBS2|ARKc7605/osf.io/wmbs2). 3Dimagingforchildanthropometrybecausethereiscomparisontogold-standard,manual Funding:TheBillandMelindaGatesFoundation measurements. (www.gatesfoundation.org)fundedthestudy(ID OPP1132308).Dr.ReynaldoMartorellwasthe PrincipalInvestigatorforthestudy.Allauthorsdo nothaveaffiliationswithorfinancialinvolvement withanyorganizationorentitywithafinancial PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 1/13 BINAmanualanthropometrymethodsandquality interestinthesubjectmatterormaterials Introduction discussedinthemanuscript.Fundingsourceshad TheMulticenterGrowthReferenceStudy(MGRS)andsubsequentdevelopmentofWorld noroleindataanalyses,datainterpretation,or reportwriting.Thecorrespondingauthorhadfull HealthOrganization(WHO)ChildGrowthStandards(CGS)in2006providedasinglesetof accesstoalldata,finalresponsibilityforthe referencemeasurementsforchildrenaroundtheglobe[1].TheWHOCGShavebeencom- decisiontosubmitforpublication,andhasno monlyadaptedforroutineuseinlow-andmiddle-incomecountriesaswellasinsomehigh- conflictsofinteresttodisclose.Thefindingsand incomecountries,includingtheUS,forpurposesofindividualgrowthmonitoring,clinical conclusionsinthisarticlearethoseoftheauthors research,andpublichealthprogrammonitoring[2].Withthenewreferencecameasingle anddonotnecessarilyrepresenttheofficial measuringprotocolthatcouldbeadoptedinhealthfacilitiesandsurveyseverywhere[3];how- positionoftheCentersforDiseaseControland Prevention. ever,theMGRSprotocolrequiresextensivetrainingandsupervision,andrepeatedmeasure- ments[3].ThefullMGRSprotocolisnotroutinelyfollowedinhealthfacilities,norisitused Competinginterests:Theauthorshavedeclared inlarge-scalesurveysthatevaluatenutritionsuchasDemographicandHealthSurveys(DHS) thatnocompetinginterestsexist. andMultipleIndicatorClusterSurveys(MICS). Thenon-digital,manualmeasurementscurrentlyinusearesusceptibletohumanerror[4], andtheuseofinadequatemeasuringprotocolscanincreasemeasurementerror.Poorquality childanthropometryiscommoninbothhealthfacilitiesandsurveys,withskinfoldthickness, circumferencesandchildlengththemostaffected[5–9].Measurementerrorcancausemis- classificationofnutritionalstatusattheindividuallevelandoverdispersionatthepopulation level;thelatterleadstooverestimationoftheprevalenceofabnormalnutritionalstatus. Thereisrecognitionthatthequalityofchildanthropometryshouldbeassessedbefore usinganddisseminatingthedata.DHS,MICSandStandardizedMonitoringandAssess- mentofReliefandTransitions(SMART)Surveymethodologiesincludeassessmentof anthropometricdataquality.Theassessmentsarealllooselybasedonrecommendations fromaWHOexpertcommitteeconvenedin1995[10],buttherearemethodologicaldiffer- ences.Recently,therehavebeencallstoimproveanthropometryqualitythroughtheuseof technology,andtorevisitthe1995WHOrecommendationstostandardizedataquality assessment[11]. ThedataweanalyzeispartoftheBodyImagingforNutritionalAssessmentStudy(BINA), whichcompareda3Dimagingsystemtocurrentlyrecommendednon-digital,manualmea- surementsofstature(lengthandheight),armcircumference(MUAC)andheadcircumference (HC)in474childrenunderfiveyearsofageinAtlanta,USA.BINAusedtheAutoAnthro3D imagingsystem,asystemcomprisedofanoff-the-shelf,low-cost,handheldscanner(Occipital Inc.StructureSensor,SanFrancisco,CA,USA)andcustomsoftwareforscanningchildren developedbyBodySurfaceTranslations,Inc.(BST)(AutoAnthro,BST,Inc.,Atlanta,GA, USA).Inordertodrawconclusionsontheabilityof3Dimagingtoreplicatemanualanthro- pometry,thestudyneededtocollectgold-standardmanualanthropometry.Athoroughanaly- sisofmanualanthropometryqualitywasparticularlyimportantforBINAbecause,unlike previousresearchon3Dimagingforanthropometry,thestudysampleconsistedofchildren underfiveyearsofage,anagegroupthatcanbedifficulttomeasurewithmanualmethods becauseofnoncooperation.Thispaperdescribesthetraining,standardizationanddatacollec- tionmethodsformanualanthropometryinBINA;evaluatesthequalityofBINAmanual anthropometry;andprovidessomerecommendationsforachievinggoldstandardmanual anthropometryandstandardizingdataqualityassessmenttoguideclinicians,researchersand publichealthprogrammanagers. Materialsandmethods PrimarycaregiversofallchildrenparticipatinginBINAgavewritten,informedconsentand thestudywasapprovedbytheEmoryInstitutionalReviewBoard. PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 2/13 BINAmanualanthropometrymethodsandquality Fieldstafftrainingandstandardization AllfivefieldstaffselectedfortheBINAStudyheldcollegedegrees,withthreeofthefivehold- ingamaster’sdegreeatthetimeofthestudy.InAugust2016fieldstaffcompletedathree- weektrainingledbytrainersfromEmoryUniversitywhohadextensiveexperiencewith anthropometryinclinic,survey,andresearchsettings;includingexperienceinthestudyused todevelopthe2006WHOCGS.Trainingconsistedoftheoreticalandpracticalsessionson3D imagingandmanualmeasurements,andfieldstaffweretrainedtofunctionasbothanthropo- metristsandassistants.EmoryUniversityfacultyandfieldstaffdevelopedtrainingmaterials andastudymanual. Trainingculminatedwithathree-daystandardizationtestformanualanthropometryata localdaycarecenter,whichconsistedofaleadanthropometristfromEmoryUniversityandall fieldstafftakingrepeatedmeasurementsoftenchildrenunderfiveyearsofage.Thelead anthropometristwasconsideredanexpertanthropometristbasedonpreviousexperienceand areliabilitytestcarriedoutbeforeBINA.Asanthropometrists,fieldstaffwereassessedonall measurementsexceptweight,anddatawasanalyzedusingENASoftware2011[12]anda MicrosoftExcelSpreadsheetfromtheUnitedStatesCentersforDiseaseControlandPreven- tion(USCDC)MicronutrientSurveyToolkit[13].Passingorfailingthestandardizationtest wasbasedonaccuracyandreliabilityresultsforthemainmeasurementsofinterest(length andheight).Wedeterminedaccuracybycomparingeachanthropometristtotheleadanthro- pometristandtothemeanofallanthropometrists.Forreliability,wecomputedtheTechnical ErrorofMeasurement(TEM),whichdeterminesifanthropometristsgetsimilarresultswhen carryingoutrepeatedmeasurements.WealsousedtheUSCDCspreadsheetstovisuallypres- entaccuracyandreliabilityresultstotheanthropometrists,andtoassesswhetherornotan anthropometrist’sfirstmeasurementwassystematicallylowerorhigherthanthesecondmea- surement,alsoknownasthemeasurementeffect[3].Therewasnoevidenceofsubstantial measurementeffect.Theaccuracyandreliabilityoftheleadanthropometristandallanthropo- metristsforlengthandheightweresimilartoresultsfromtheMGRS[3]andwereclassifiedas thehighestrankingof“good”accordingtotheSMARTsuggestedcut-offpointof<0.4cmfor bothintra-observerTEMandbiasfromanexpert[14].Basedonresultsofthestandardization test,weretainedallanthropometristsforthestudy. Sampling,measurementanddataentryprocedures Utilizingconveniencesamplingwerecruitedandmeasuredchildreninfacilities,primarily daycarecenters,inAtlanta,GA,USAfromSeptember2016toFebruary2017.Formanual anthropometrywefollowedmeasurementtechniquesusedtodevelopthe2006WHOGrowth Standards[3],includingmeasuringchildrenundertwoyearsofagelyingdownandmeasuring tothenearesttenthofakgandcm.Wemeasuredweightwithdigitalscaleswithtaringfunc- tion(RiceLakeWeighingSystems,Inc.,RiceLake,WI),staturewithaninfant/child/adult woodenboard(ShorrBoard,WeighandMeasure,LLC,MarylandUSA),andcircumferences withsyntheticmeasuringtapes(ShorrTape,WeighandMeasure,LLC,MarylandUSA).We routinelycheckedcalibrationofscalesusingknownweightsandreplaceddamagedmeasuring tapes. Duringdatacollectionsessions,thefivefieldstaffsplitintotwoteamsoftwo,withthefifth personactingasafloater.Thetechniquesforscansandmanualmeasurementsrequiredan assistant,andteammatesalternatedastheanthropometristandassistant.Anthropometrists alsotookturnsasafloater,andthemainrolesofthefloaterweretopreparechildrenformea- surementandtoactasasecondassistantforyoungerchildren.Anthropometryprocedures designedforhouseholdsurveysoftencontainaroleforthechild’sparent.However,sincewe PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 3/13 BINAmanualanthropometrymethodsandquality obtaineddataprimarilyindaycarecenterswherethecaregiverswerenotpresent,asecond assistantwasneededtoholdandpositionthechild. Aftertakingtimetoacclimatethechildandestablishrapport,andafterundressingthe child(totheirdiaperortoskin-tightshorts/leotardsprovidedbythestudy),thefieldteam startedwith3Dscanswhichwasthenfollowedbymanualmeasurements.Thefirstanthropo- metristcompletedonesession,measuringheadcircumference,MUAC,andlengthorheight. Insteadofoneanthropometristcompletingtwosessionsconcurrently,thefieldteamreversed rolesandanewanthropometristcompletedherfirstsessionofmanualmeasurements.After bothteammemberscompletedtheirfirstsession,theprocesswasrepeatedforthesecondses- sion.Westaggeredmanualmeasurementstoreducethelikelihoodofanthropometrists rememberingtheirfirstmeasurement.Tofurtherminimizebiasininter-observererror, anthropometristsenteredtheirownmeasurementresultsanddidnotinformtheassistantof thenumber,whichisdifferentfromthetypicalanthropometryprocedureoftheassistant recordingresultsonbehalfoftheanthropometrist. Qualitycontrolanddatacleaning Ourcustomsoftwareforelectronicdatacaptureincludedrangechecksfornon-digitalmea- surementstocatchdataentryerrors.Ifameasurementwasbelowthe0.01percentileorabove the99.9percentile,apop-upboxappearedonthescreenandtheanthropometristcouldeither re-enteroracceptthevalue.Thesoftwarealsoincludedautomatictriggeringofathirdmea- surementfornon-digitalmeasurements.ThetriggerswereprogrammedbasedontheMGRS standardsforthemaximumallowabledifference((cid:20)0.5cmforheadandarmcircumference, and(cid:20)0.7cmforlength)[3],butourstudydifferedfromMGRSinthatwetriggeredwithin observerdifferenceswhileMGRStriggeredbetweenobservers.Foreachchild,twoanthropo- metristsentereddemographicinformation,includingdateofbirth,separately.Doubledata entryallowedustoidentifydiscrepanciescausedbydataentryerror,andwereferredtothe originalconsentformtomakecorrections. Formanualanthropometry,fieldstaffreceivedongoingmonitoringandsupervision.This includedexaminingdigitpreference,thepercentofintra-observermeasurementsexceeding themaximumallowabledifference,inter-observerTEM,andestimatesofbiasbycomparing toanexpertanthropometristwhomeasuredasubsampleofchildren.Inadditiontocomparing toanexpertanthropometrist,fieldstaffalsoperiodicallycomparedwitheachother.Fieldstaff regularlyreceiveddataqualityreportscoveringallmeasuresofqualityandsupervisionfrom expertanthropometrists. Qualitytests Fordigitpreference,weexaminedtheproportionofnon-digital,manualmeasurements (height,headcircumferenceandarmcircumference)witheachofthepossibledigits(0–9)in thetenthsplace(mm).Weanalyzedthefirstmeasurementfromeachanthropometrist.Since eachchildwasmeasuredbytwodifferentanthropometrists,weincluded948observationsin theanalysis.WeusedtheStataSE13(StataCorp,CollegeStation,TX,USA)digdispackagefor analysis.WetestedforanydigitpreferencewithPearson’sChiSquaredTest.Wecalculated thedifferencebetweentheexpectedandobservedpercentagesandtestedforpreferenceof eachindividualdigitusingatwo-sidedBinomialTest.Todeterminehowcloseourobserved proportionsweretoauniformdistribution,wesummedthedifferencebetweenexpectedand observedforpositivedifferencesonly.Thesumgivesthepercentageofdigitsthatwouldneed tobereclassified(movedfromanoverrepresenteddigittoanunderrepresenteddigit)to achieveauniformdistribution,whichissimilartotheMyer’sBlendedIndex[7]andtheDigit PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 4/13 BINAmanualanthropometrymethodsandquality PreferenceScore[14].WecalculatedtheDigitPreferenceScoretoallowcomparisonswith otherresearch. Weusedthe2006WHOGrowthStandardstocalculatez-scoresforweight-for-length/ height(WHZ),length/height-for-age(HAZ),weight-for-age(WAZ),headcircumference-for- age(HCZ),andarmcircumference-for-age(ACZ).Weassessedtheplausibilityofz-scoresby determiningtheproportionofmeasurementsflaggedasfallingoutsideoftheplausiblerange asdefinedinWHOmacros(WHZ<-5|>5;HAZ<-6|>6;WAZ<-6|>5;HCZ<-5|>5;ACZ <-5|>5)[15].WealsoexaminedflagsusedinDemographicandHealthSurveys(DHS)forthe rangeofplausiblelengthandheightmeasurements.IntheWHOMacro,staturemeasure- mentsoutsideoftheDHSplausiblerange(lyingdown45–110cm,standingup65–120cm) arenotflagged,butWHZscorescannotbecalculatedandareautomaticallysettomissing.We calculatedz-scorestandarddeviations(SD)andanalyzedSDdisaggregatedbyage(underand overtwoyearsofage)todetermineifthequalityofmeasurementsdifferedbyage.Wealso analyzedz-scoresforboththeaverageofallmeasurements(repeated)andthefirstmeasure- mentofthefirstanthropometrist(single). Weanalyzedbothintra-andinter-observererrorforheight,HCandMUACusingrepeated measurements.Intra-observertechnicalerrorofmeasurement(TEM)wascalculatedwiththe followingformula: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P TEMintra¼ N ðM (cid:0) M Þ2= i¼1 i1 i2 2(cid:3)N ,whereNisthenumberofchildrenandM M aretheclosestrepeatedmanualmeasuresfor i1 i2 onechildbyoneobserver.Forinter-observerTEMwecomparedtheaveragemeasurementof oneobservertotheaveragemeasurementofanotherobserverforthesamechild,changingthe numeratorintheequationabove: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P TEMinter ¼ N ðM þM =2(cid:0) M þM =2Þ2= i¼1 ij1 ij2 ik1 ik2 2(cid:3)N WecalculatedRelativeTEM,alsoknownas%TEM,bydividingTEMbythemeanofallthe measurementsthatwentintocalculatingTEMandmultiplyingby100[16].TotalTEMcom- binesintraandinter-observerreliabilityinthefollowingformula: pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi TotalTEM¼ TEMintra2þTEMinter2 Forcorrelation,wecalculatedtheIntraclassCorrelationCoefficient(ICC)usingtwo-way mixedeffects[17]andanabsoluteagreementdefinitioninSPSS20(IBMCorp.,Armonk,NY, USA). Results SamplecharacteristicsinTable1showthatchildrenundertwoyearsofagewereoverrepre- sented,andthattherewassubstantialsize,racial,andethnicvariationinthesample. Qualitytests Digitpreference. Allmeasurements(stature,HC,andMUAC)showedevidenceoftermi- naldigitpreferencewhenwetestedthechild’sfirstmeasurementwithPearson’sChi-Squared Test(p<.01,n=948).Forallmeasurements,theterminaldigitfourwassignificantlyoverrep- resented.Inaddition,eightwasoverrepresentedforHC,andsixwasoverrepresentedfor MUAC(Table2).Thesumofthedifferencebetweenobservedandexpectedpercentagesfor PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 5/13 BINAmanualanthropometrymethodsandquality Table1. Samplecharacteristics. Ageinmonths,mean(range) 25.7 (0–59) AgeGroups Newborn(<1month) 82 (17%) 1–11.9months 66 (14%) 1year 75 (16%) 2years 85 (18%) 3–4.9years 166 (35%) Sex Female 228 (48%) Race Black 201 (42%) White 134 (28%) Asian 40 (8%) Multiple,OtherorNotReported 99 (21%) Ethnicity Non-Hispanic 385 (81%) Hispanic 77 (16%) NotReported 12 (3%) Statureincm,mean(range) 82.3 (42.5–119.0) HeadCircumferenceincm,mean(range) 45.7 (31.4–55.3) ArmCircumferenceincm,mean(range) 15.4 (8.8–25.3) https://doi.org/10.1371/journal.pone.0189332.t001 alloverrepresenteddigitsindicatedthat5.8%ofstaturemeasurementsand9.7%ofbothhead andarmcircumferencemeasurementswouldneedtobereclassifiedtoachieveauniformdis- tribution(Table2).Thedigitpreferencescorewas5.5forheight,8.7forHC,and8.3for MUAC. Plausibility&reliability. Usingmeansofrepeatedmeasuresandcutoffsdefinedby WHOforbiologicallyplausiblez-scoresonechild(0.2%)wasflagged,fallingabovetherange forWHZ,andACZ.Also,thelengthofonechild(0.2%)waslowerthantheplausiblestature range.Withsinglemeasures,thelengthofoneadditionalchildwaslowerthantheplausible staturerange,andnoadditionalchildrenhadimplausiblez-scores. Table2. Terminaldigitpreferenceexpressedaspercentageofanthropometrists’firstmeasurementforheight,headcircumferenceandarmcir- cumferenceendingin.0to.9comparedtotheexpected10%amongchildren0–4.9years(n=948observationsfrom474children),BINA2017. TerminalDigit Height HeadCircumference ArmCircumference (tenthsplace) % %Observed—% P-Value % %Observed—% P-Value % %Observed—% P-Value Observed Expected Observed Expected Observed Expected 0 8.5 -1.5 0.14 5.0 -5.0 0.00 5.9 -4.1 0.00 1 9.2 -0.8 0.45 9.2 -0.8 0.45 10.7 0.7 0.48 2 11.2 1.2 0.23 11.2 1.2 0.23 8.4 -1.6 0.12 3 10.2 0.2 0.79 10.1 0.1 0.87 9.8 -0.2 0.91 4 14.3 4.3 0.00 14.3 4.3 0.00 14.6 4.6 0.00 5 9.7 -0.3 0.83 6.8 -3.2 0.00 6.4 -3.6 0.00 6 9.8 -0.2 0.91 9.8 -0.2 0.91 12.3 2.3 0.02 7 9.8 -0.2 0.91 9.6 -0.4 0.75 11.6 1.6 0.10 8 9.1 -0.9 0.36 13.3 3.3 0.00 9.7 -0.3 0.83 9 8.1 -1.9 0.06 10.8 0.8 0.42 10.5 0.5 0.55 https://doi.org/10.1371/journal.pone.0189332.t002 PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 6/13 BINAmanualanthropometrymethodsandquality Formeansofrepeatedmeasures,thestandarddeviationforallz-scores(HAZ,WHZ, WAZ,ACZ,andHCZ)wascloseto1.0,rangingfrom0.92forWHZto1.07forHAZ.Standard deviationsusingsinglemeasureswereslightlyhigher,withthelargestdifferencesinACZ (0.03)andWHZ(0.02)(Table3).Withincreasedvariationwealsofoundsinglemeasureshad slightlyhigherprevalenceestimatesofchildrenbeloworabovesomeSDcutoffsforACZand WHZ(Fig1),butdifferenceswerenotstatisticallysignificantwhentestedwithChi-Square. Forbothrepeatedandsinglemeasures,z-scorestandarddeviationwasnotconsistentlyhigher forchildrenunder2yearsofagecomparedtochildren2yearsandolder.Levene’sTestfor EqualityofVariancesshowednostatisticaldifferenceinz-scorevariancebetweentheage groupsforallindices(Table3),indicatingthattherewasnodifferenceinmeasurementreli- abilitybyage. Thecorrelationbetweenananthropometrist’sfirstandsecondmeasurementofachild (intra-observer),andbetweentwoanthropometrists’averagemeasurementsofachild(inter- observer)wasnearperfect.TheIntraclassCorrelationCoefficientforallintra-andinter- observermeasurementswasexactly1.00(95%confidenceintervalof1.00,1.00)exceptfor inter-observerarmcircumference,withICCof0.99(CI:0.99,0.99).Intra-observerTEMin centimeterswas0.22,0.13,and0.16forstature,HC,andMUACrespectively.Intra-observer TEMvaluescorrespondedtorelativeTEMsof0.26%,0.29%,and1.04%respectively(Fig2). AsanillustrationofTEMinterpretation,thestatureintra-observerTEMof0.22cmmeans that2/3rdsofrepeatedmeasurementswerewithin±0.22cmand95%ofreplicatemeasure- mentswerewithin2(cid:3)TEM,or±0.44cm.Inter-observerTEMwashigherthanintra-observer forallmeasurements:statureTEM0.34cm,%TEM0.42%;HCTEM0.25cm,%TEM0.54%; andMUACTEM0.19cm,%TEM1.22%(Fig2).AlthoughMUACinterandintraTEMwas lowerinabsoluteterms,MUACrelativeTEMwasapproximatelytwotothreetimeshigher thanstatureandHC.TotalTEM,combininginter-andintra-observerreliability,was0.40for stature,0.28forHC,and0.25forMUACincentimeters. Table3. Meanz-score,standarddeviation,andtestofequalvariancebetweenagegroupsforheight-for-age(HAZ),weight-for-height(WHZ), weight-for-age(WAZ),armcircumference-for-age(ACZ),andheadcircumference-for-age(HCZ)frommanualmeasurements(n=474),BINA2017. Total(0–4.9years) Lessthan2years(U2) 2–4.9years(O2) DifferenceinVariance (Levene’sTest) N Meanz- Standard n Meanz- Standard n Meanz- Standard U2 F P-Value score Deviation score Deviation score Deviation SD-O2 (SD) SD Repeated Height-for-age 474 -0.29 1.07 223 -0.42 1.10 251 -0.18 1.03 0.08 0.12 0.73 measure Weight-for-height 472 0.34 0.92 222 0.32 0.88 250 0.35 0.96 -0.08 0.87 0.35 mean Weight-for-age 474 0.06 1.04 223 -0.05 1.02 251 0.17 1.05 -0.04 0.26 0.61 Arm 385 0.78 0.94 135 0.84 0.92 250 0.75 0.95 -0.03 <0.01 0.93 circumference- for-age Head 474 0.24 1.02 223 0.11 1.01 251 0.35 1.01 0.00 0.32 0.57 circumference- for-age Single Height-for-age 474 -0.30 1.08 223 -0.43 1.11 251 -0.17 1.02 0.09 <0.01 0.96 measure Weight-for-height 471 0.34 0.95 221 0.34 0.92 250 0.34 0.97 -0.05 0.38 0.54 Weight-for-age 474 0.06 1.04 223 -0.05 1.01 251 0.17 1.05 -0.04 0.31 0.58 Arm 385 0.78 0.97 135 0.85 0.98 250 0.75 0.96 0.02 0.25 0.62 circumference- for-age Head 474 0.24 1.04 223 0.11 1.04 251 0.36 1.03 0.01 0.52 0.47 circumference- for-age https://doi.org/10.1371/journal.pone.0189332.t003 PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 7/13 BINAmanualanthropometrymethodsandquality Fig1.Classificationcomparison.Prevalencebystandarddeviationcutoffsforweight-for-heightandarmcircumference-for-agez-scoresforsingle andmeansofrepeatedmeasuresamongchildren0–4.9yearsofage. https://doi.org/10.1371/journal.pone.0189332.g001 Discussion OurdatashowsthatmanualanthropometryinBINAwasofexcellentquality.Only0.4%ofthe samplehadbiologicallyimplausiblez-scoresormeasurements,whichiswellbelowthe1.0% cutoffrecommendedbyaWHOexpertcommitteeasanindicatorofdataqualityproblems [10].Ourz-scoreSDsalsoindicatedgoodqualitybecausetheywerebetween0.9SD—1.1SD [14,18],andtherewerenodifferencesinSDbetweenagegroups;ahigherSDamongchildren under2isanindicatorofpoorqualityandisattributedtodifficultyinmeasuringthelengthof young,uncooperativechildren[7,19].Wefoundnoterminaldigitpreferenceforzeroorfive forstature,andthepercentofmeasurementstheoreticallyrequiringreclassificationforanor- maldistributionwaslowforallmeasures.Wecouldobtainanormaldistributionbychanging 6%ofterminaldigitsforourstaturemeasurementscomparedtoanaverageof18%in52DHS from2005–2014[7].Biologicalplausibility,z-scoreSD,anddigitpreferencearemetricscom- monlyusedtoassessdataqualityofsingleanthropometricmeasures[3,7–9],andourstudy showedexcellentqualityaccordingtoallthreemetrics.Inaddition,therepeatedmeasuresin ourstudyenabledanalysisofinter-andintra-observerreliability.ForstaturetheBINAintra- andinter-observerTEMsof0.22and0.34cmrespectivelyarewithintherangeofinter-observer TEMatMGRSstudysites(0.15–0.41cm)[4].TheTEMsforallofourmeasurementswereon parwithTEMsobservedatMGRSstudysitesandwerewithinthe95%precisionmarginof MGRSexpertanthropometrists[4].ThequalityofmanualanthropometryinBINAwassimilar tothequalityofanthropometricdatausedtodevelopthe2006WHOgrowthstandards. Inourstudy,weachievedhighqualitymanualanthropometrythroughfollowingestab- lishedprotocolsandaddingadditionalqualitycontrolcomponents.Wefollowedadvicefrom PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 8/13 BINAmanualanthropometrymethodsandquality Fig2.Measurementreliability.Closesttwomanualmeasuresfromsingleobserver(intra-observer)andmeasurementreliabilityinaverageof closesttwomanualmeasuresbetweentwoobservers(inter-observer)forstature,headcircumference(HC)andarmcircumference(MUAC)among children0–4.9yearsofage(intra-observern=948,inter-observern=474). https://doi.org/10.1371/journal.pone.0189332.g002 MGRSprotocolauthorstodevelopastudyspecifictrainingmanual,trainandteststaffwith standardization,andprovideadequatesupervisionduringdatacollection[3].Formeasure- menttechniques,wedrewfrommaterialsusedinhouseholdsurveys[20–23],whicharelargely basedona1986UNpublicationonmeasuringchildren[24].Wealsomadesurethatourtech- niqueswerealignedwiththeproceduresusedtodeveloptheWHOChildGrowthStandards byreferringtoMGRSprotocolandtheWHOanthropometryvideousedtotrainMGRS anthropometrists[3,25].Withtheaimtostrengthentheirunderstandingof,anddedicationto adhereto,studyprotocols,weincludedourfieldstaffindevelopmentofthestudymanual. Fieldstaffproducedthefirstdraftofthe3Dimagingsectionofthemanualandparticipatedin revisionandrefinementofmanualanthropometrysections.Fieldstaffparticipationinstudy manualdevelopmentledtorecognitionoftheneedtoimproveuponsomeoftheborrowed materials.Forexample,theinstructionsforfindingthemid-upperarmpointdidnotade- quatelyexplainidentificationoftheacromionprocessandillustrationsincorrectlydepicted measuringthesideofthearm.Forheadcircumference,weaddedadditionalinstructionsfor theanthropometriststorepositionatboththefrontandbackoftheheadwhileremovingand replacingthetapemultipletimestofindthelargestcircumference.Electronicdatacapture allowedustoavoiddataentryerrorsthroughtheuseofrangechecksanddoubledataentryat thepointofdataacquisition;andfacilitatedadequatemonitoringandsupervision.Identifying errorsduringmeasurementallowedustore-measurechildren,reducingthenumberofim- plausiblemeasurements.LiketheMGRSandfollowingrecommendationsfroma1995expert committee[3,10],weroutinelyassessedfieldstaffaccuracyandprovidedregular,timelyfeed- backonthequalityofanthropometry.Assessmentofaccuracyduringdatacollectionisnot standardincommonsurveyssuchasDHSandMICS.Repeatedmeasurements,whicharerare outsideofresearchstudysettingsallowedustoincludereliabilityinqualitymonitoring;and PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 9/13 BINAmanualanthropometrymethodsandquality electronicdatacaptureprovidedanthropometristswithreal-timefeedbackontheirownreli- abilitythroughtheuseofautomatictriggers. Therehavebeensuggestionstotakerepeatedmeasuresinsurveys[11].Ourresultsshowlit- tledifferencebetweensingleandrepeatedmeasuresforz-scorestandarddeviationsandpreva- lence.However,withpoorqualityanthropometryrepeatedmeasuresshouldreducevariance. Also,triggeringadditionalmeasurementsprovidesanincentivetoanthropometriststo improvereliabilityandavoidadditionalwork.Forsurveyinstitutionswithaknownhistoryof poorqualityanthropometrytemporaryadoptionofduplicatemeasuresbytwoanthropome- trists,alongwithtriggeringofrepeatedmeasuresbasedoninter-observermaximumallowable difference,mayhelptoimprovequalityandprovideaccurateresults.Inter-observererrorwas higherinourstudy,whichisconsistentwithfindingsinadevelopingcountrysetting[19];the MGRSusedinter-observertriggeringandadoptionofinter-observertriggersinsurveysmay improvequalitymorethanintra-observertriggers.Inaddition,repeatedmeasurescouldbe restrictedtothemostunreliablemeasures,suchaslengthofchildrenundertwoyearsofage andMUAC. Thereissomeconsensusonwhattoassessforanthropometricdataquality.A1995WHO expertcommitteerecommendedlookingataccuracy,reliability,biologicalplausibility,z-score standarddeviation,anddigitpreference[10];andthesemetricsareincorporatedintomanuals andassessmentsforDHSandMICS.SMARTsurveymethodologyincludesallofthesemet- rics,alongwithadditionalmetricssuchasskewnessandkurtosis.The2011EmergencyNutri- tionSoftware(ENA)forSMARThasautomatedreportsforstandardizationtestsanddata quality,withthelatterreferredtoasaplausibilitycheck[14].Whilequalityassessmentsfor DHS,MICSandSMARTaresimilar,themethodsarenotexactlythesame.Forexample,z- scorerangesforbiologicalplausibilitycandifferandtherearemultipletestsfordigitprefer- ence.Ourstudyprovidesasimplifiedmethodforassessingdigitpreferencethatdoesnot requirespecializedsoftware.Standardizationofwhattomeasureandhowtomeasureanthro- pometricdataqualityisneeded,buttheremaybelargerinstitutionaldifferencesinhowto interpretandactondataqualityreports.Oneofthechallengesinthisstudywasdetermining thecriteriatojudgewhetherornotweachievedgoldstandardmanualanthropometry.DHS andMICSdonothaveindicatorthresholdsforacceptableanthropometricdataquality,andit isveryrareforeithertosuppressdatabecauseofpoorquality.TheENASMARTplausibility checkdeterminesifdataqualityisacceptablewithacompositescorebasedonthresholdsfor multipleindicators[14].AnothercompositescoringsystemwasdevelopedbyUNICEF-sup- portedresearch[9],butthesystemisnotregularlyused.Formanysurveysdataqualityis assessed,butthereisnostandardcriteriatojudgewhetherornotresultsshouldbereleased. Wecanconsiderz-scorestandarddeviationtoillustratetheimportanceofreachingconsen- susoninterpretationandaction.WHOandtheUSCDCpromotetheuseofnormativeranges ofSDtodetermineifsurveyqualityisacceptable[10,26],buttherangesarebasedonsurveys thathaveevidenceofpoordataquality[7,18].ThemostrecentDHSdataqualityassessment showedthat30of52countrieshadHAZSDgreaterthan1.5,butonlyonecountrysuppressed databecauseofpoorquality[7].AccordingtoSMARTdataqualityisnotacceptableifHAZ SDisabove1.2[14],andarecentmodelingstudyshowedthatSDof1.5canresultinsubstan- tialoverestimationofstuntingprevalence[18].Meanwhile,thepublishednormativerangefor HAZSDthatsomeorganizationsusetodeemdataqualityacceptableis1.35–1.95[10,26]. Digitpreferenceprovidesanotherexampleoftheneedtofocusoninterpretationandaction. Digitpreferenceisacommonphenomenoncausedbyhumantendencytoroundnumbers thathasbeenmonitoredforyearsasanindicatorofdataquality,butevenveryhighmeasure- mentdigitpreference(80%theoreticallyneedingreclassificationforanormaldistribution)has nomeaningfulimpactonz-scoremeansorprevalences[18].Weightandheightheapingmay PLOSONE|https://doi.org/10.1371/journal.pone.0189332 December14,2017 10/13

Manual anthropometry in the Body Imaging for Nutritional Assessment Study

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