Manual anthropometry in the Body Imaging for Nutritional Assessment Study

13 Pages·2017·1.7 MB·English

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.
*[email protected],[email protected]
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.
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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
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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
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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
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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
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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
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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
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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
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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
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