Methodological Framework for World Health Organization Estimates of the Global Burden of

20 Pages·2015·1.62 MB·English

RESEARCHARTICLE
Methodological Framework for World Health
Organization Estimates of the Global Burden
of Foodborne Disease
BrechtDevleesschauwer1,2,3,4*,JuanitaA.Haagsma5,FrederickJ.Angulo6,David
C.Bellinger7,8,DanaCole6,DörteDöpfer9,AamirFazil10,EricM.Fèvre11,12,Herman
J.Gibb13,TineHald14,MartynD.Kirk15,RobinJ.Lake16,CharlineMaertensdeNoordhout2,
ColinD.Mathers17,ScottA.McDonald18,SaraM.Pires14,NikoSpeybroeck2,M.
KateThomas10,PaulR.Torgerson19,FeliciaWu20,ArieH.Havelaar4,21,22,NicolasPraet3
1 DepartmentofVirology,ParasitologyandImmunology,FacultyofVeterinaryMedicine,GhentUniversity,
Merelbeke,Belgium,2 InsituteofHealthandSociety(IRSS),UniversitécatholiquedeLouvain,Brussels,
Belgium,3 DepartmentofBiomedicalSciences,InstituteofTropicalMedicine,Antwerp,Belgium,
4 DepartmentofAnimalSciencesandEmergingPathogensInstitute,UniversityofFlorida,Gainesville,FL,
UnitedStatesofAmerica,5 DepartmentofPublicHealth,ErasmusUniversityMedicalCenter,Rotterdam,
TheNetherlands,6 CentersforDiseaseControlandPrevention,Atlanta,GA,UnitedStatesofAmerica,
7 DepartmentofNeurology,BostonChildren'sHospital,HarvardMedicalSchool,Boston,MA,UnitedStates
ofAmerica,8 DepartmentofEnvironmentalHealth,HarvardSchoolofPublicHealth,Boston,MA,United
OPENACCESS StatesofAmerica,9 DepartmentofMedicalSciences,SchoolofVeterinaryMedicine,Universityof
WisconsininMadison,Wisconsin,UnitedStatesofAmerica,10 PublicHealthAgencyofCanada,Guelph,
Citation:DevleesschauwerB,HaagsmaJA,Angulo
Ontario,Canada,11 InstituteofInfectionandGlobalHealth,UniversityofLiverpool,LeahurstCampus,
FJ,BellingerDC,ColeD,DöpferD,etal.(2015)
Neston,UnitedKingdom,12 InternationalLivestockResearchInstitute,Nairobi,Kenya,13 Gibb
MethodologicalFrameworkforWorldHealth EpidemiologyConsulting,Arlington,VA,UnitedStatesofAmerica,14 NationalFoodInstitute,Danish
OrganizationEstimatesoftheGlobalBurdenof TechnicalUniversity,Lyngby,Denmark,15 NationalCentreforEpidemiologyandPopulationHealth,
FoodborneDisease.PLoSONE10(12):e0142498. ResearchSchoolofPopulationHealth,TheAustralianNationalUniversity,Canberra,Australia,16 Institute
doi:10.1371/journal.pone.0142498 ofEnvironmentalScienceandResearch,Christchurch,NewZealand,17 DepartmentofHealthStatistics
andInformationSystems,WorldHealthOrganization,Geneva,Switzerland,18 CentreforInfectiousDisease
Editor:MirjamE.E.Kretzschmar,TheNational Control,NationalInstituteforPublicHealthandtheEnvironment(RIVM),Bilthoven,TheNetherlands,
InstituteforPublicHealthandtheEnvironment, 19 SectionofEpidemiology,VetsuisseFaculty,UniversityofZurich,Zurich,Switzerland,20 Departmentof
NETHERLANDS FoodScienceandHumanNutrition,DepartmentofAgricultural,Food,andResourceEconomics,Michigan
StateUniversity,EastLansing,MI,UnitedStatesofAmerica,21 NationalInstituteforPublicHealthandthe
Received:July20,2015
Environment(RIVM),Bilthoven,theNetherlands,22 UtrechtUniversity,Utrecht,Netherlands
Accepted:October22,2015
*[email protected]
Published:December3,2015
Copyright:©2015WorldHealthOrganization.This
isanopenaccessarticledistributedunderthe Abstract
CreativeCommonsAttributionIGOLicense,which
permitsunrestricteduse,distribution,and
reproductioninanymedium,providedtheoriginal
Background
workisproperlycited.http://creativecommons.org/
licenses/by/3.0/igo/,Thisarticleshouldnotbe
TheFoodborneDiseaseBurdenEpidemiologyReferenceGroup(FERG)wasestablished
reproducedforuseinassociationwiththepromotion
in2007bytheWorldHealthOrganizationtoestimatetheglobalburdenoffoodbornedis-
ofcommercialproducts,servicesoranylegalentity..
eases(FBDs).ThispaperdescribesthemethodologicalframeworkdevelopedbyFERG's
DataAvailabilityStatement:Allrelevantdataare
ComputationalTaskForcetotransformepidemiologicalinformationintoFBDburden
withinthepaperanditsSupportingInformationfiles.
TheRsourcecodeisalsoavailablefromhttps:// estimates.
github.com/brechtdv/FERG.
Funding:Thisstudywascommissionedandpaidfor MethodsandFindings
bytheWorldHealthOrganization(WHO).Copyright
Theglobalandregionalburdenof31FBDswasquantified,alongwithlimitedestimatesfor
intheoriginalworkonwhichthisarticleisbased
belongstoWHO.Theauthorshavebeengiven 5otherFBDs,usingDisability-AdjustedLifeYearsinahazard-andincidence-based
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WHO/FERGMethodologicalFramework
permissiontopublishthisarticle.GibbEpidemiology approach.Toaccomplishthistask,thefollowingworkflowwasdefined:outlineofdisease
Consulting,LLC,providedsupportintheformof modelsandcollectionofepidemiologicaldata;designandcompletionofadatabasetem-
salariesforauthor[HJG],butdidnothaveany
plate;developmentofanimputationmodel;identificationofdisabilityweights;probabilistic
additionalroleinthestudydesign,datacollectionand
analysis,decisiontopublish,orpreparationofthe burdenassessment;andestimatingtheproportionofthediseaseburdenbyeachhazard
manuscript.Thespecificrolesofthisauthorare thatisattributabletoexposurebyfood(i.e.,sourceattribution).Allcomputationswereper-
articulatedinthe‘authorcontributions’section.
formedinRandthedifferentfunctionswerecompiledintheRpackage'FERG'.Traceability
CompetingInterests:Allauthorsserveasmembers andtransparencywereensuredbysharingresultsandmethodsinaninteractivewaywith
oftheWorldHealthOrganizationadvisorybody—the
allFERGmembersthroughouttheprocess.
FoodborneDiseaseBurdenEpidemiologyReference
Group—withoutremuneration.Theauthorsdeclare
nocompetinginterests.Co-authorHermanJ.Gibbis Conclusions
theownerofGibbEpidemiologyConsulting.There
WedevelopedacomprehensiveframeworkforestimatingtheglobalburdenofFBDs,in
arenopatents,productsindevelopmentormarketed
whichmethodologicalsimplicityandtransparencywerekeyelements.Allthetoolsdevel-
productstodeclare.Thisdoesnotaltertheauthors'
adherencetoallthePLOSONEpoliciesonsharing opedhavebeenmadeavailableandcanbetranslatedintoauser-friendlynationaltoolkit
dataandmaterials. forstudyingandmonitoringfoodsafetyatthelocallevel.
Introduction
TheFoodborneDiseaseBurdenEpidemiologyReferenceGroup(FERG)wasestablishedin
2007bytheWorldHealthOrganization(WHO)toestimatetheglobalburdenoffoodborne
diseases(FBDs)[1].In2012,FERGestablishedaComputationalTaskForce(CTF)toderive
FBDburdenestimatesusingepidemiologicalinformationgeneratedbythehazard-basedand
sourceattributiontaskforces[2].Theaimofthispaperistodescribethemethodological
frameworkdevelopedbytheCTFforthistask.
Variationinmethodologicalchoiceshasbeenidentifiedbothingeneralandinfoodborne-
specificburdenofdiseasestudies,impedingcomparabilityofburdenestimatesacrossstudies
[3,4].Toensureaccuracy,utilityandcomparabilitywithotherexistinghealthmetricinitiatives,
FERGdecidedtoquantifytheburdenofFBDsintermsofDisability-AdjustedLifeYears
(DALYs),ahealthgapmeasureexpressingthenumberofhealthylifeyearslostduetoreduc-
tionofhealthanddeath.Furthermethodologicalchoiceswerediscussedduringthefourthfor-
malmeetingofFERGin2010[5],andconfirmedattheFERGStrategicPlanningMeetingin
2011andatthefifthFERGmeetingin2013[6,7].Themainmethodologicaldecisionwasthat
DALYsweretobecalculatedinahazard-andincidence-basedapproach[8].
BuildingontheFERGapproach,theCTFtaskswere(1)todevelopthenecessarytoolsfor
DALYcalculationand(2)toimplementthesetoolsforestimatingtheburdenof36FBDs,
including21entericdiseasescausedbybacteria,virusesandprotozoa,11non-entericparasitic
diseases,and4diseasescausedbychemicalsandtoxins.
Inthispaper,we(1)describetheapproachtakenforquantifyingtheburdenofFBD;(2)
describethestepsandmethodologicalchoicestocalculateDALYsresultingfromFBDs;(3)
describetraceabilityandtransparencyduringtheprocess;and(4)discusslimitationsand
futuredirections.AlltoolsthatweredevelopedbytheCTFareavailableasSupportingInfor-
mation,allowingthereadertoaccess,exploreandusethesedifferenttools.
Hazard-andIncidence-BasedDALYApproach
Strongandreliableburdenofdiseaseestimatesarecrucialforsettingprioritiesinpublichealth
andbiomedicalresearch[9,10].Rankingdiseaseimpactmaybebasedondiseaseoccurrence
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(prevalenceorincidence)oronthenumberofdeaths(mortality).However,thesesimplemea-
suresofpopulationhealthdonotprovideafullpictureoftheimpactofspecificdiseaseson
humanhealth.Indeed,whilecertaindiseasesmaybeverycommon,theirclinicalimpactmay
belimited.InfectionwithahighlyprevalentparasitesuchasEnterobiusvermicularis(also
knownaspinwormorthreadworm)forinstancehasaverylowburdenbecausemostofthe
casesaremildtoasymptomaticandself-limiting[11].Likewise,ignoringtheageatwhichpeo-
pledie,andthusnotconsideringhowmanyyearsofhealthylifemightbelostduetoaprema-
turedeathdoesnotfairlycapturetheimpactofmortality.Diseaseseverity,definedbythe
healthimpactanddurationoftheconcernedsymptomsandthelifeexpectancyattheageof
death,shouldthusalsobetakenintoaccountwhenquantifyingburdenofdisease.Further-
more,simplemeasuresofpopulationhealthdonotcombinetheimpactsofmorbidityand
mortality.Thisprohibitsacomparativerankingofhighlymorbid,butnotnecessarilyfataldis-
easessuchaschorioretinitisandhighlylethaldiseasessuchaslivercancer,complicatingdeci-
sionsonresourceallocationpriorities.
Toovercomethelimitationsofsimplemeasuresofpopulationhealth,summarymeasures
ofpopulationhealth(SMPHs)havebeendevelopedasanadditionalsourceofinformationfor
measuringdiseaseburden.AmongtheseSMPHs,theDisability-AdjustedLifeYear(DALY)is
currentlythemostwidelyusedinpublichealthresearch.Originallydevelopedtoquantifyand
comparetheburdenofdiseases,injuriesandriskfactorsacrosscountries,theDALYsumma-
rizestheoccurrenceandimpactofmorbidityandmortalityinasinglemeasure[12,13].The
DALYisthekeymeasureintheGlobalBurdenofDisease(GBD)studiesandisofficially
adoptedbyWHOforreportingonhealthinformation[14,15].
TheDALYisahealthgapmeasure.Itmeasuresthehealthylifeyearslostduetoadiseaseor
injury.DALYsarecalculatedbyaddingtheadjustednumberofyearslivedwithdisability
(YLDs)andthenumberofyearsoflifelostduetoprematuremortality(YLLs):
YLD=NumberofincidentcasesxDurationuntilremissionordeathxDisabilityWeight
YLL=NumberofdeathsxResiduallifeexpectancyattheageofdeath
DifferentapproachescanbetakenforcalculatingDALYs,dependingonwhethertheinter-
estliesinquantifyingtheburdenofahealthoutcome(suchasdiarrhea),ahazard(asdefined
bytheCodexAlimentariusCommission,e.g.abiologicalagentthatmaycauseillnessin
humanssuchasSalmonellaenterica),orariskfactor(e.g.anexposurethatincreasesthelikeli-
hoodofillnesssuchasunsafewater)[16].SinceFERGisconcernedwiththeburdenofFBDs,
whicharecausedbyawiderangeofhazards(bacteria,viruses,parasites,chemicals,andtox-
ins),anaturalchoiceisthehazard-basedapproach.Thisapproachdefinestheburdenofaspe-
cificfoodbornehazardasthatresultingfromthehealthstates,i.e.,symptomsandsequelae,
includingdeath,thatarecausallyrelatedtotheconcernedhazardtransmittedthroughfood,
andwhichmaybecomemanifestatdifferenttimescalesorhavedifferentseveritylevels[8].
Thisapproachthusallowsforacomprehensiveestimateoftheburdenofdiseaseduetoacer-
tainhazard,includingsequelae,whichmayhaveahigherburdenthanacuteillnessalone[17–
19].Table1showsthehazardsandrelatedhealthstatesthatwereincludedinFERG'sglobal
burdenofFBDestimates.
DALYs,andmorespecificallytheirYLDcomponent,maybecalculatedfromanincidence
oraprevalenceperspective.Whileincidence-basedYLDsaredefinedastheproductofthe
numberofincidentcasesandthedurationanddisabilityweight(DW)oftheconcernedhealth
state,prevalence-basedYLDsaredefinedastheproductofthenumberofprevalentcasesand
thecorrespondingDW[13,14].Intheincidence-basedapproach,allhealthoutcomes,includ-
ingthoseinfutureyears,areassignedtotheinitialevent(e.g.exposuretoacertainhazard).
Thisapproachthereforereflectsthefutureburdenofdiseaseresultingfromcurrentevents.In
theprevalence-basedapproach,ontheotherhand,thehealthstatusofapopulationisassessed
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Table1. FERGhazards,causallyrelatedhealthstatesandcorrespondingdisabilityweights(DWs). DetailsonthederivationoftheDWsareprovided
inS2Table.
Hazard Healthstate DW
Diarrhealdiseaseagents
Norovirus Diarrhealdiseasea 0.074
Campylobacterspp. Diarrhealdiseasea 0.101
Guillain-Barrésyndrome 0.445
EnteropathogenicE.coli Diarrhealdiseasea 0.074
EnterotoxigenicE.coli Diarrhealdiseasea 0.074
Shigatoxin-producingE.coli Diarrhealdiseasea 0.091
Hemolyticuremicsyndrome 0.210
End-stagerenaldisease 0.573
Non-typhoidalS.enterica Diarrhealdiseasea 0.101
Invasivesalmonellosis 0.210
Shigellaspp. Diarrhealdiseasea 0.101
Vibriocholerae Diarrhealdiseasea 0.194
Cryptosporidiumspp. Diarrhealdiseasea 0.074
Entamoebahistolytica Diarrhealdiseasea 0.074
Giardiaspp. Diarrhealdiseasea 0.074
Invasiveinfectiousdiseaseagents
HepatitisAVirus Hepatitis 0.108
Brucellaspp. Acutebrucellosis 0.108
Chronicbrucellosis 0.079
Orchitis 0.097
Listeriamonocytogenes,perinatal Sepsis 0.210
Centralnervoussysteminfection 0.426
Neurologicalsequelae 0.292
Listeriamonocytogenes,acquired Sepsis 0.210
Centralnervoussysteminfection 0.426
Neurologicalsequelae 0.292
Mycobacteriumbovis Tuberculosis 0.331
SalmonellaParatyphi Paratyphoidfever 0.210
Liverabscessesandcysts 0.254
SalmonellaTyphi Typhoidfever 0.210
Liverabscessesandcysts 0.254
Toxoplasmagondii,congenital Intracranialcalcification 0.010
Hydrocephalus 0.360
Chorioretinitis,earlyinlife 0.033
Chorioretinitis,laterinlife 0.033
CNSabnormalities 0.360
Toxoplasmagondii,acquired Chorioretinitis,mild 0.004
Chorioretinitis,moderate 0.033
Chorioretinitis,severe 0.191
Acuteillness 0.053
Post-acuteillness 0.254
Entericintoxications
Bacilluscereusb Acuteintoxication 0.061
Clostridiumbotulinumb Moderate/mildbotulism 0.198
Severebotulism 0.445
(Continued)
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Table1. (Continued)
Hazard Healthstate DW
Clostridiumperfringensb Acuteintoxication 0.061
Staphylococcusaureusb Acuteintoxication 0.061
Cestodes
Echinococcusgranulosus,casesseekingtreatment Pulmonarycysticechinococcosis 0.192
Hepaticcysticechinococcosis 0.123
Centralnervoussystemcysticechinococcosis 0.221
Echinococcusgranulosus,casesnotseekingtreatment Pulmonarycysticechinococcosis 0.015
Hepaticcysticechinococcosis 0.012
Centralnervoussystemcysticechinococcosis 0.054
Echinococcusmultilocularis Alveolarechinococcosis 0.123
Taeniasolium Epilepsy:treated,seizurefree 0.072
Epilepsy:treated,withrecentseizures 0.319
Epilepsy:severe 0.657
Epilepsy:untreated 0.420
Nematodes
Ascarisspp. Ascariasisinfestation 0.030
Mildabdominopelvicproblemsduetoascariasis 0.012
Severewastingduetoascariasis 0.127
Trichinellaspp. Acuteclinicaltrichinellosis 0.637
Trematodes
Clonorchissinensis Abdominopelvicproblemsduetoheavyclonorchiosis 0.123
Fasciolaspp. Abdominopelvicproblemsduetoheavyfasciolosis 0.123
Intestinalflukesc Abdominopelvicproblemsduetoheavyintestinalflukeinfections 0.123
Opisthorchisspp. Abdominopelvicproblemsduetoheavyopisthorchiosis 0.123
Paragonimusspp. Centralnervoussystemproblemsduetoheavyparagonimosis 0.420
Pulmonaryproblemsduetoheavyparagonimosis 0.132
Organicpollutants
Dioxin Infertility 0.056
Hypothyroidyduetoprenatalexposure 0.019
Hypothyroidyduepostnatalexposure 0.019
Toxinsandallergens
Aflatoxin Hepatocellularcarcinoma:diagnosisandprimarytherapy 0.294
Hepatocellularcarcinoma:metastatic 0.484
Hepatocellularcarcinoma:terminalphasewithmedication 0.508
Hepatocellularcarcinoma:terminalphasewithoutmedication 0.519
Cyanideincassava Konzo 0.065
Peanutallergensb Livingwithpeanut-inducedallergy 0.012
aThedisabilityweightsfordiarrhealdiseaseweredefinedasaweightedaverageofthedisabilityweightsformild,moderateandseverediarrhea,with
differentrelativecontributionsoftheseseveritylevelsleadingtodifferentweightedaveragesfordifferentdiarrhealdiseaseagents.
bExcludedfromglobalburdenassessments.
cIncludesEchinostomaspp.,Fasciolopsisbuski,Heterophyesspp.,Metagonimusspp.andotherfoodborneintestinaltrematodespecies.
doi:10.1371/journal.pone.0142498.t001
ataspecificpointintime,andprevalentdiseasesareattributedtoinitialeventsthathappened
inthepast.Thisapproachthereforereflectsthecurrentburdenofdiseaseresultingfromprevi-
ousevents.ForburdenofFBDstudies,theincidence-basedYLDapproachwasdeemedthe
mostappropriateapproach,because(1)thisapproachismoresensitivetocurrent
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epidemiologicaltrends[20];(2)ismoreconsistentwiththehazard-basedapproach,sinceit
hasthepointofinfection(orprimaryhealtheffectfromexposure)asstartingpointforthecal-
culations;and(3)ismoreconsistentwiththeestimationofYLLs,whichbydefinitionfollows
anincidence-basedapproach,asmortalitycanbeseenastheincidenceofdeath[21].Neverthe-
less,theprevalence-andincidence-basedapproachesyieldsimilaroverallresultsiftheepide-
miologyofdisabilitiesandthepopulationage-structureareconstantovertime[20].However,
burdenestimatesforspecificagegroupswillalwaysdifferbetweentheprevalence-andinci-
dence-basedapproaches,becausetheformerassignstheburdentotheageatwhichtheburden
isexperienced,whilethelatterassignstheburdentotheageofdiseaseonset[15].
CTFStructureandWorkflow
Fig1showsaschematicoverviewoftheCTFworkflow.TheCTFstructureisdefinedaround
thesixdistinctivecomponentsofthisworkflow,i.e.,outlineofdiseasemodelsandcollectionof
epidemiologicaldata;designofadatabasetemplate;developmentofanimputationmodel;
identificationofdisabilityweights;probabilisticburdenassessment;andsourceattributionof
Fig1.ComputationalTaskForceworkflow.CTF=ComputationalTaskForce;YLDs=YearsLivedwithDisability;YLLs=YearsofLifeLostdueto
mortality;DALYs=Disability-AdjustedLifeYears;UNWPP2012=UnitedNationsWorldPopulationProspects2012Revision.
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thediseaseburden.AllcomputationswereimplementedinRversion3.2.0[22],andthevari-
ousfunctionswerecompiledintheRpackage'FERG',whichisavailableasSupportingInfor-
mationtothismanuscript(S1File)andonlineathttps://github.com/brechtdv/FERG.
Diseasemodelsandepidemiologicaldata
ThestartingpointoftheCTFworkflowwastheoutlineofdiseasemodelsforeachofthe
includedhazards(aschosenbythehazard-basedtaskforces),andtheepidemiologicaldata
inputsthatparameterizedthesediseasemodels.Toobtainthisinformation,systematicreviews
werecommissionedandmanagedbythreehazard-basedtaskforces,i.e.,theEntericDiseases
TaskForce(EDTF),theParasiticDiseasesTaskForce(PDTF),andtheChemicalsandToxins
TaskForce(CTTF).Detailsarethereforeprovidedelsewhere[23–25].
Thecourseofdiseaseischaracterizedbyvarioushealthstates(e.g.acuteorchronicphases,
short-termorlong-termsequelae),possiblyhavingdifferentseveritylevels[8].Adisease
model,alsoreferredtoasanoutcometree,isaschematicrepresentationofthevarioushealth
statesassociatedwiththeconcernedhazard,andthepossibletransitionsbetweenthesestates.
Adiseasemodelforeachhazardwasdefinedbythemembersandcommissionedexpertsof
eachhazard-basedtaskforce,consideringrelevanthealthoutcomessupportedbyevidence
identifiedintherespectivereviews.Discussionsofthechoicesanduncertaintiesinderivingthe
diseasemodelsforspecifichazardsareprovidedelsewhere[23–25].
InthecontextoftheCTF,diseasemodelsweredefinedascomputationaldiseasemodels,
andnotmerelyasbiologicaldiseasemodels.Whilebiologicaldiseasemodelsmerelyreflectthe
naturalhistoryofdisease,computationaldiseasemodelsalsoreflecttheinputparameters
neededtocalculateincidenceandmortalityofeachoftheconcernedhealthstates.Assuch,
computationaldiseasemodelsareacombinationofdiseasebiologyanddataavailability.
Computationaldiseasemodelsmayberepresentedasdirectedacyclicgraphs,definedby
parentandchildnodesanddirectededges(arrows)definingtherelationshipsbetweennodes.
IntheCTFframework,parentnodeswereeitherincidence,mortality,YLDorYLLrates,while
childnodesweremultiplicativeelements,suchasproportionsorratios(reflectinge.g.theprob-
abilityofdevelopingaspecificsymptomfollowinginfection,ortheproportionofillnesses
attributabletotheconcernedhazard).Aspecificdiseasemodel"language"wasdevelopedto
denotetherelationshipandcontributionofthedifferentnodes.Rectanglesdefinedparent
nodes,androundedrectanglesdefinedchildnodes.Greynodesdidnotcontributedirectlyto
theDALYs,greennodescontributedYLDs,andrednodescontributedYLLs.Nodesthatcon-
tributedtotheincidenceoftheindexdiseasewereidentifiedbyathickborder.S1Figgivesthe
diseasemodelsforall36FERGhazards.
Ingeneral,threemainapproachescanbedistinguishedforestimatingtheburdenduetoa
specifichazardinfood,i.e.,categoricalattribution,counterfactualanalysis,andriskassessment.
S1Tablegivesanoverviewofthemodellingstrategyappliedforeachincludedhazard.Asthe
choiceofthemodellingstrategywasmainlydrivenbythetypeofdataavailable,nosensitivity
analysescouldbeperformedtotriangulatedifferentmodellingapproaches.Modellingchoices
werefurtherdrivenbyastriveforconsistencywithexistingWHOGlobalHealthEstimates.
Categoricalattributioncanbeusedwhenafoodbornehazardresultsinanoutcome(death
oraspecificsyndrome)thatisidentifiableascausedbythehazardinindividualcases[26].Fol-
lowingthetypologyofDevleesschauweretal.[16],theburdenduetoaspecifichazardcan
thenbecalculatedusinganattributionalmodel(inwhichtheincidenceofthesymptomismul-
tipliedwiththeattributableproportionforagivenhazard)oratransitionalmodel(inwhich
theincidenceofinfectionwithorexposuretothehazardismultipliedwiththeprobabilityof
developingagivensymptom).Categoricalattributionwasapplicableforallviral,bacterialand
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Fig2.ComputationaldiseasemodelforMycobacteriumbovis.Rectanglesdefineparentnodes,whileroundedrectanglesdefinedchildnodes.Green
nodescontributeYearsLivedwithDisability,andrednodescontributeYearsofLifeLost.Nodescontributingtotheincidenceoftheindexdiseaseare
identifiedbyathickborder.INC=country-specificincidence;PROB–local=country-specificprobability.
doi:10.1371/journal.pone.0142498.g002
parasitichazards,andforcyanideincassavaandpeanutallergens,andwasthereforethestan-
dardmethodusedbyFERG.Fig2showsthecomputationaldiseasemodelforMycobacterium
bovis,whichischaracteristicfortheattributionalmodels.Inthismodel,theoverallincidence
andmortalityoftuberculosisismultipliedwiththeproportionattributabletoM.bovis,result-
ingintheincidenceandmortalityofM.bovistuberculosis.Fig3showsthecomputationaldis-
easemodelforEchinococcusgranulosus,whichischaracteristicforthetransitionalmodels.In
thismodel,theoverallincidenceofinfectionbythisparasitewasmultipliedwithchildnodes
reflectingtheprobabilityofdevelopingtheconcernedhealthstates,resultingintheincidences
ofthespecifichealthstates.
Whenthehazardelevatestheriskofadiseaseordisabilityoutcomethatoccursinthepopu-
lationfromothercausesaswell,causalattributioncanonlybemadestatistically,andnotonan
individualbasis.Thisisthecaseformanychemicals,includingaflatoxinanddioxin.Aflatoxin
forinstancemayincreasetheriskofhepatocellularcarcinoma,butitisnotpossibletospecify
thataspecificlivercancercasewascausedbyaflatoxin.Inthissituation,thestandardapproach
forcalculatingtheburdenofenvironmentalexposuresistouseacounterfactualanalysisin
whichthecurrentdiseaseoutcomeswithcurrentexposurearecomparedtothediseaseout-
comesunderanalternateexposure(aminimumriskexposurewhichcouldbezero,orsome
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Fig3.ComputationaldiseasemodelforEchinococcusgranulosus.Rectanglesdefineparentnodes,whileroundedrectanglesdefinedchildnodes.Grey
nodesdonotcontributedirectlytotheDALYs,greennodescontributeYLDs,andrednodescontributeYLLs.Nodescontributingtotheincidenceoftheindex
diseaseareidentifiedbyathickborder.INC=country-specificincidence;PROB–global=probabilityappliedtoallcountries;CE=cysticechinococcosis;
CNS=centralnervoussystem.
doi:10.1371/journal.pone.0142498.g003
acceptedbackgroundlevel)[27].Thisallowscalculationofapopulationattributablefraction
(PAF)thatcanbeappliedtotheall-causeburdenestimatesfortherelevantdiseaseoutcome
(theso-calledburdenenvelope),leadingtoaspecialcaseoftheattributionalmodel[16].Inthe
contextofFERG,counterfactualanalysiswasusedtoestimatetheburdenofaflatoxin-related
hepatocellularcarcinoma.
Inadditiontocategoricalattributionandcounterfactualanalysis,whichcanbeconsidered
top-downapproaches,FBDburdencanalsobeestimatedbyariskassessmentapproach,which
canbeconsideredabottom-upapproach.Inthisapproach,theincidencesofthespecifichealth
states(e.g.impairedmalefertilityduetoprenataldioxinexposure)areestimatedbycombining
exposureanddose-responsedata.Thedose-responsemodelmayforinstancedefinetheproba-
bilityofillnessatagivenexposurelevel,whichcanthenbetranslatedintoanestimateofthe
numberofincidentorprevalentcasesexpectedtooccurintheexposedpopulation[25,27].As
thisapproachdoesnotinvolveburdenattribution,itdoesnotnecessarilyensureconsistency
withexistinghealthstatistics.However,riskassessmentmaybeavalidalternativewhenno
burdenenvelopesexistorwhenitcanbedemonstratedthattheestimatedexcessriskisadditive
tothebackgroundrisk.InthecontextofFERG,riskassessmentwasusedtoestimatethebur-
denofdioxin-relatedhypothyroidyandimpairedfertility.
CTFdatabasetemplate
AdatabasetemplatewasdevelopedinExcel(MicrosoftCorp.,Redmond,Washington,USA)
tocollectthedataresultingfromthesystematicreviewsinastandardizedway(S2File).The
structureofthedatabasewasbasedonthediseasemodels,withonesheetpernode.Three
genericsheetsweredefined:(1)a"RATE"sheet,forratesbycountry;(2)a"PROB-local"sheet,
PLOSONE|DOI:10.1371/journal.pone.0142498 December3,2015 9/20
WHO/FERGMethodologicalFramework
forproportionsorratiosbycountry;and(3)a"PROB-global"sheet,forasingleproportionor
ratiothatappliedtoallcountries.
Eachsheetconsistedoffourtablesforentering(1)therateorproportion/ratiodata;(2)the
agedistribution;(3)thesexdistribution;and(4),ifapplicable,theduration.Usingadrop-
downmenu,differentformatscouldbeselectedforenteringtheinputparameters,includinga
meanand95%confidenceinterval;aminimum,mostlikelyandmaximum;differentpercen-
tiles;theshapeandrateofaGammadistribution(forrates);andtheshapeparametersofa
Betadistribution(forproportions).GammaandBetadistributionswerechosenbecausetheir
domainscorrespondtothoseofratesandproportions,respectively,andbecausetheirparame-
tershaveanintuitiveinterpretation(i.e.,numberofcasesandsamplesizeorobservationtime,
respectively,numberofpositivesandnumberofnegatives).Likewise,differentlevelsofstratifi-
cationcouldbeselectedforthedurationparameters(i.e.,none,byageonly,bysexonly,byage
andsex).Agedistribution,sexdistributionanddurationwereallowedtovarybycountry,by
definingdifferent"groups"andassigningcountriesto"groups".Fulldetailsontheparameters
usedforquantifyingtheburdenofthedifferenthazardsisavailableintheappendicestothe
EDTF,PDTFandCTTFmanuscripts[23–25].
Imputation
Extrapolationorimputationmodelsmaybeneededwhenliteraturesearchescannotprovide
essentialepidemiologicaldatasuchasincidenceormortalityrates[28].Thesemodelsestimate
parametersbasedondataofneighboringregionsorothertimeperiods.Theexternaldataused
mustthusberepresentativeoftheselectedpopulation,regionandtime.TheCTFdeveloped,
testedandevaluatedseveralpossibleapproachestoimputemissingincidencedataatthecoun-
trylevel[29].Thisexerciseidentifiedseveralpitfallsintheuseofexplanatorycovariates,such
asthepotentialforoverfittingandthearbitrarinessintheselectionofcovariates.Therefore,
andfurthermotivatedbyastriveforparsimonyandtransparency,wedecidedtousealog-Nor-
malrandomeffectsmodelasthedefaultmodelforimputingmissingcountry-levelincidence
data.WeusedthesubregionsasdefinedinTable2astherandomeffectorclustervariable.
Thismodelassumesthatthelog-transformedincidencerateincountryjbelongingtosubre-
gioniarisesfromaNormaldistributionwithsubregionspecificmeanμ andawithin-region
i
(=between-country)variances2.Eachsubregionspecificmeanμ isinturnassumedtoarise
w i
fromaNormaldistributionwithmeanμ andabetween-regionvariances2:
0 b
logðy Þ(cid:2)Normalðm;s2Þ
ij i w
m (cid:2)Normalðm ;s2Þ
i 0 b
Afterfittingthishierarchicalrandomeffectsmodeltotheavailabledata,incidencevalues
forcountrieswithnodatawereimputedbasedontheresultingposteriorpredictivedistribu-
tions.Inotherwords,werepresentedmissingincidencedatabylog-Normaldistributions
basedonthefittedmeanandvarianceparameters.Forcountriesinasubregionwherenoneof
thecountrieshaddata,thelog-incidencewasimputedasmultiplerandomdrawsfromaNor-
maldistributionwithmeanequaltothefittedglobalinterceptμ andvarianceequaltothesum
0
ofthefittedbetween-regionvariances2andthefittedwithin-regionvariances2 (thusimput-
b w
ingthelog-incidenceasthatofa“random”countrywithina“random”subregion,withthe
PLOSONE|DOI:10.1371/journal.pone.0142498 December3,2015 10/20