Visual Analytics of Image-Centric Cohort Studies in Epidemiology

1 MB·English

by  Bernhard Preim

Visual Analytics of Image-Centric Cohort
Studies in Epidemiology
BernhardPreim,PaulKlemm,HelwigHauser,KatrinHegenscheid,SteffenOeltze,
KlausToennies,andHenryVo¨lzke
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5 AbstractEpidemiologycharacterizestheinfluenceofcausestodiseaseandhealth
1 conditionsof defined populations.Cohortstudies are population-basedstudies in-

volvingusuallylargenumbersofrandomlyselectedindividualsandcomprisingnu-
]
V merousattributes,rangingfromself-reportedinterviewdatatoresultsfromvarious
medicalexaminations,e.g.,bloodandurinesamples.Sincerecently,medicalimag-
C
ing has been used as an additional instrument to assess risk factors and potential
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s prognosticinformation.Inthischapter,wediscusssuchstudiesandhowtheevalu-
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ation maybenefitfromvisualanalytics. Cluster analysis to definegroups,reliable
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image analysis of organsin medical imaging data and shape space explorationto
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characterizeanatomicalshapesareamongthevisualanalyticstoolsthatmayenable
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epidemiologiststofullyexploitthepotentialoftheirhugeandcomplexdata.Togain
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0 acceptance,visualanalyticstoolsneedtocomplementmoreclassicalepidemiologic
0 tools,primarilyhypothesis-drivenstatisticalanalysis.
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0 1 Introduction
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Epidemiologyisascientificdisciplinethatprovidesreliableknowledgeforclinical
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medicinefocusingonprevention,diagnosisandtreatmentofdiseases[14].Research
i
X inepidemiologyaimsatcharacterizingriskfactorsfortheoutbreakofdiseasesand
r at evaluating the efficiency of certain treatment strategies, e.g., to compare a new
a treatment with an established gold standard. This research is strongly hypothesis-
drivenandstatisticalanalysisisthemajortoolforepidemiologistssofar.Correla-
tions between genetic factors, environmentalfactors, life style-related parameters,
age and diseases are analyzed. The data are acquired by a mixture of interviews
(self-reported data, e.g., about nutrition and previous infections) and clinical ex-
BernhardPreim
Otto-von-Guericke University Magdeburg, 39106 Magdeburg, e-mail:
[email protected]
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aminations,suchasmeasurementofbloodpressure.Statisticalcorrelations,evenif
theyarestrong,maybemisleadingbecausetheydonotrepresentcausalrelations.
As an example, the slightly reducedrisk of heart infarctand cardiac mortality for
elderlypeoplereportingtodrinkoneglassofwineeveryevening(comparedtopeo-
pledrinkingnoalcoholatall)maybeduetotheinvolvedlowlevelofalcoholbut
may also be a consequence of a very regular and stress-free lifestyle [14]. When
something happened, before an event, it is an indicator for a causal relationship.
However, care is necessary, since many things happen in the life of an individual
before,e.g.,aheartattack,butdonotcauseit.
Thus, statistical correlationsare the starting point for investigatingwhy certain
factorsincreasetheriskofgettingdiseases.Epidemiologyisnotapurelyacademic
endeavorbuthashugeconsequencesforestablishingandevaluatingpreventivemea-
sures even outside of medicine. The protection of people from passive smoking,
recommendationsforvariousvaccinationsandtheintroductionofearlycancerde-
tection strategies, e.g., mammographyscreening, are all based on large-scale epi-
demiological studies. Also the official guidelines for the treatment of widespread
diseases,suchasdiabetes,arebasedonevidencefromepidemiologicalstudies[14].
Whilethisallmaysoundobvious,itisaratherrecentdevelopment.Evidence-based
medicineoftenstillhasto“fight”againstrecommendationsofafewopinionleaders
arguingbasedontheirpersonalexperienceonly.
Theanalysistechniquesusedsofararelimitedtoinvestigatinghypothesesbased
onknownorsuspectedrelations,e.g.hypothesesrelatedtoobservationsorprevious
publications.Theavailabletoolssupporttheanalysisofafewdimensions,butnotof
thehundredsofattributesacquiredperindividualinacohortstudy.Bothtypicalvi-
sualizationtechniquesaswellasanalysistechniques,e.g.,supportvectormachines,
donotscalewellforhundredsofattributes[41].Whilewearenotabletodescribe
solutions for these challenging problems, we give a survey on recent approaches
aimingalsoathypothesisgeneration.
Organization.Thischapteris organizedasfollows.In Sect.2 we describeim-
portantconceptsandtermsofepidemiologyincludingobservationsfromepidemi-
ologic workflows. This discussion is restricted to those terms that are crucial for
communicatingwith epidemiologists,understandingrequirementsand for design-
ingsolutionsthatfitintheirprocess.InSect.3,wediscusshow(general)informa-
tionvisualizationanddataanalysistechniquesmaybeusedforepidemiologicdata.
Section 4 describes the analysis of image data from cohort studies and how this
analysisiscombinedwiththeexplorationofnon-imageattributedata.Thissection
representsthecoreofthechapterandemploysacasestudywhereMRIdataofthe
lumbar spine are analyzed along with attributes characterizing life-style, working
habits,andbackpainhistory.
VisualAnalyticsofImage-CentricCohortStudiesinEpidemiology 3
2 Background inEpidemiology
Population-based studies. Epidemiological studies are based on a sample of the
population.Thereliabilityoftheresultsobviouslydependsonthesizeofthatsample
but also stronglyon the selection criteria. Often, data from patientstreated in one
hospitalare analyzed.While this may be a large numberof patients, the selection
maybeheavilybiased,e.g.,sincethehospitalishighlyspecializedanddiseasesare
oftenmoresevereorinalaterstagecomparedtothegeneralpopulation.
Population-basedstudies,whererepresentativeportionsofapopulation(without
known diseases) are examined, have the potential to yield highly reliable results.
The source population may be from a city, a region or a country. Individuals are
randomlyselected,e.g.,approachingdatabasesofpopulationregistries.Thehigher
thepercentageofpeoplewhoaccepttheinvitationandactuallytakepartinthestudy,
themorereliabletheresultsare.
In this chapter, we focus on longitudinal population-based studies. The sheer
amountanddiversityintermsoftypeofdatamakesitdifficulttofullyidentifyand
analyzeinterestingrelations.Wewillshowthatinformationvisualizationandvisual
analyticstechniquesmayprovidesubstantialsupportthatcomplementsthestatisti-
caltoolswiththeirrathersimplestatisticalgraphics.Mostepidemiologicalstudies
wererestrictedtonominal(oftencalledcategorical)andscalardata,e.g.,relatedto
alcoholconsumption,andbodymassindexasonemeasureofobesity.
Image-centricepidemiologicalstudies.Morerecently,forexample,intheRot-
terdam study [22], also non-invasiveimaging data, primarily ultrasound and MRI
data, are employed. Petersen and colleagues [32] report on six studies involving
cardiacMRIfromatleast1000individualsinpopulation-basedstudies.Thesehigh-
dimensionaldata enableto answer analysisquestions,e.g.,how doesthe shape of
thespinechangesasaconsequenceofage,lifestyleanddiseases?Wefocusonsuch
image-centricepidemiologicalstudies.
Epidemiologyandpublichealth.Therearedifferentbranchesofepidemiology.
Onebranchdealswithpredictionstoinformpublichealthactivities.Theseinclude
measuresin case of an epidemic– an acute publichealth problem,mostly related
to infectious diseases. The recent article ”computational epidemiology” [29] was
focussedonthisbranchofepidemiology.Anotherbranchofepidemiologyaimsat
long-termstudies and at findingsprimarilyessential forprevention.Image-centric
cohortstudies,thefocusofthisarticle,belongtothissecondbranch.Thetargetuser
groupconsistsofepidemiologistswhocanbeexpectedto havea highlevelofex-
pertise in statistics. Thus,their findingsinvolvestatistical significance,confidence
intervalsandothermeasuresofstatisticalpower.
Healthyagingandpathologicchanges.Anessentialprobleminthedailyclini-
calroutineisthediscriminationbetweenhealthyage-relatedmodifications(thatmay
notbereversedbytreatment)andearlystagediseases(thatmaybenefitfromimme-
diatetreatment).Asaconsequence,elderlypeopleareoftennotadequatelytreated.
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Asageneralgoalforepidemiologicalstudies,betterandmorereliablemarkersfor
early stage diseases are searched for. The cardiovascularbranchof the Rotterdam
study,forexample,aimsatanunderstandingofatherosclerosis,coronaryheartdis-
easeand“cardiovascularconditionsatolderage”[22].
Modern epidemiology. Epidemiology faces new challenges due to the rapid
progress,e.g., in geneticsand sequencingtechnologyas well as medicalimaging.
Acquisitionofhealthdatathusbecomescheaperandmoreprecise.Incohortstud-
ies, as much potentiallyrelevantdata as possible are acquired as a basis for an as
broadaspossiblespectrumofanalysisquestions.Thisincludesblood,urineandtis-
suesamples,informationaboutenvironmentalconditionsandthesocialmilieu.
Visualanalyticsformodernepidemiology.Inthepast,epidemiologyprimarily
dealtwithhypothesesaimingtoprovethem,e.g.,theefficiencyofearlycancerde-
tectionprogramsintermsofmortalityandlongtermsurvival[14].Sincerecently,
more and more data mining is performedto identify correlations. Results of such
analyses, however,need to be very carefully interpreted.If thousandsof potential
correlationsare analyzed automatically,just by chance some of them will reach a
highlevelofstatisticalsignificance.
Anessentialsupportforepidemiologyresearchisto definerelevantsubgroups.
Toperformseparateanalysesforwomenandmenaswellasfordifferentagegroups
isacommonpracticeinepidemiology.However,relevantsubgroupsmaybedefined
byanon-obviouscombinationofseveralattributesthatmaybedetectedbyacom-
binationofclusteranalysisandappropriatevisualization.
Sincetheinformationspaceisgrowingwitheachexaminationcycle,Pearceand
Merletti[31]pointedoutin2006thatmethodsareneededwhichcancopewiththis
complexityandenabletheanalysisofunderlyingcausesofacertaindisease.Visual
analytics (VA) methods can support epidemiological data assessment in different
ways, e.g. by defining subgroupsbased on a multitude of attributes that exhibit a
certaincharacteristic.Fortheanalysisofscalarandcategoricaldata,establishedin-
formationvisualizationtechniquescombinedwithclusteringanddimensionreduc-
tion are a good starting point, but need to be tightly integratedwith statistic tools
epidemiologists that are more familiar with. For image-centric studies, however,
newvisualization,(image)analysisandinteractiontechniquesareneeded.
Inthefollowing,wedefineessentialtermsinepidemiologyandgiveanoverview
oncohortstudiesthatemploymedicalimagedataasanessentialelement.Finally,
wedescribehowimagedata,derivedinformationandotherdatacomplementeach
othertoidentifyandcharacterizerisks.
2.1 ImportantTerms
Prevalence and incidence. Epidemiology investigates how often certain diseases
orclinicalevents,suchasacerebralstrokeorsuddenheartdeath,occurinthepop-
VisualAnalyticsofImage-CentricCohortStudiesinEpidemiology 5
ulation.Twotermsareimportanttocharacterizethisfrequency.Theprevalencein-
dicatestheportionofpeoplesufferingfromadiseaseatagivenpointintime.The
incidencerepresentshowmanypeoplesufferfromadiseaseoreventinacertainin-
terval,usuallyoneyear.Highprevalenceisusuallyassociatedwithhigheconomic
costs. Population-basedstudies focus on diseases with a high prevalence, such as
diabetes,coronaryheartdiseaseorneurodegenerativediseases.Eventhesediseases
do not occur frequently in a random population including many younger people
(wheretheprevalenceofthesediseasesislow).Araredisease,suchasamyotrophic
lateral sclerosis, may have a prevalence of 5 from 100,000. Thus, even in a large
population-basedstudyprobablynoindividualsuffersfromthisdisease.
Absoluteandrelativerisks.Anotheressentialepidemiologicaltermistherisk
foraclinicalevent,suchasoutbreakofacertaindisease,severity(stage)ordeath.
Asanexample,astudyrelatedtocardiacriskmayinvestigateanginapectoris,my-
ocardial infarction, atrial fibrillation depending on attributes such as age and sex.
Theabsoluterisk characterizesthelikelihoodofgettingadiseaseinlifetime.The
absoluterisk fora womantodevelopbreastcancerintheWestern worldispartic-
ularly high for women aged 50-60 (2.6%) and 60-70 (3.7%). Therefore,for these
agegroups,mammographyscreening–aimingatearlydetectionandthusoptimal
treatment–wasintroduced.
Therelativerisk(RR)characterizestheincreasedriskifanindividualisexposed
toacertainriskfactor,e.g.,smoking,excessiveweight,oralcoholabuse.Itisbased
on a comparison with a control group not exposed to that risk factor. A value of
RR<1 represents a factor that protects, e.g., moderate physicalactivity. Exciting
observationsareoftenthecombinedeffectsofseveralparameters.Acertainfactor
maybeprotectiveforsomepeople(younger,slimwomen)andisinvolvedwithan
increasedriskforothers.Thecombinedriskmaybesignificantlysmallerorlarger
thancouldbeexpectedfromindividualfactors.
Moreover,relationships are often distinctly non-linear or even non-monotonic.
Dose-response relationships are often non-linear. RR increases slowly (almost no
effectfora smalldose)andincreasesmuchfasterforhigherlevelsofadose,e.g.,
exposure to toxicity. A typical non-monotonic relation is U-shaped, that is both
verylowandveryhighinstancesofanattributeinvolveanincreasedrisk,whereas
values in between are associated with a reduced risk. Examples are weight (both
verylowandveryhighweightareassociatedwithanincreasedriskformortality)
andsleepingtime(bothveryshortandverylongsleepershaveanincreasedriskfor
developingpsychiatricdisorders[22]).Suchrelationscannotbecharacterizedbya
global RR value. Instead, tools are necessary that support the hypothesis of a U-
shapedrelationbyestimatingtheirparameterswithsomekindofbest-fitalgorithm.
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2.2 Image-CentricCohort Studies
Image datain epidemiology.The acquisitionof image data is determinedby the
available time, by financial resources, by the epidemiological importance and by
ethic considerations. Epidemiological studies require approval by a local ethics
committee.As a consequence,healthyindividualsin a cohortstudy shouldnotbe
exhibitedtoariskassociatedtotheexaminationscarriedout.Thus,MRIshouldbe
preferredoverX-rayorCTimagingforitsnon-radiationnature.Petersenandcol-
leagues[32]explainwhycardiacCTislessfeasibleinacohortstudyandevenMR
isonlyusedwithoutacontrastagentintheirstudyduetoethicalreasons.MRIdata
and ultrasound data are the prevailing modalities in both the SHIP as well as the
Rotterdamstudy.Unfortunately,MRIandultrasounddatadonotexhibitstandard-
ized intensity values(in contrastto CT data). Moreover,MRI andultrasounddata
suffer frominhomogeneitiesand variousartifacts. Thus,they are moredifficultto
interpretforhumansandmoredifficulttoanalyzewithcomputationalmeans.These
data are used to measure, e.g., the thickness of vessel walls, the abdominal aorta
diameter and plaque vulnerability in the coronary vessels [22]. The intensive use
of MRI in epidemiologicalresearch also explains to some extentwhich questions
areanalyzed:MRIisthebestmodalityfortheanalysisofbrainstructuresandthus
servesto exploreearly signs of Parkinson’s,Alzheimer’sand otherneurodegener-
ativediseases.Epidemiologicalresearchaimsatidentifyingsuchbrainpathologies
inapre-symptomaticstage.AmongthesourcesforsuchinvestigationsareMRDif-
fusionTensorImagingdatathatenableanassessmentofwhitematterintegrity[22].
The selection of imaging parameters is always a trade-off between conflicting
goalsrelatedtoquality,e.g.,imageresolution,signal-to-noiseratio,patientcomfort,
e.g., examination time and associated costs. As a consequence, to shorten overall
examinationtimesincohortstudyexaminations,notthehighestpossiblequalityis
available,i.e.,aslicedistanceof4mmismoretypicalthan1mm.Agreatadvantage
ofMRIisthatthismethodisveryflexibleandenablestodisplaydifferentstructures
indifferentsequences,suchasT1-,T2-andprotondensity-weightedimaging.MRI
dataincohortstudiesoftencomprisemorethantendifferentsequences.
Standardization in image acquisition. Due to the rapid progress in medical
imaging, sequences, protocols and even (MR) scanners are frequently updated in
clinical routine (similar to the update frequency on a computer). These updates
wouldseverelyhamperthecomparisonofimagingresultsandthustheassessment
ofnaturalchangesanddiseaseoutbreak.Thus,differencesinacquisitionparameters
areessentialconfoundingvariables.Therefore,foronecohortandexaminationcy-
clethatmaylastuptoseveralyears,noupdatesareallowed.Moreover,allinvolved
physiciansandradiologytechniciansarecarefullyinstructedtousethesamestan-
dardized imaging parameters. This point is even more important for longitudinal
studieswithrepeatedimagingexaminations.EvenifMRscannersandprotocolsare
notupdated,thelifecycleofMRcoilsleadstochangesofimagequalitythatneed
tobemonitoredandcompensated.
VisualAnalyticsofImage-CentricCohortStudiesinEpidemiology 7
2.3 Examplesfor Image-CentricCohort Study Data
Inthefollowing,wedescribeselectedcomprehensiveandon-goinglongitudinalco-
hortstudies.Bothuseanumberof(epidemiologic)instrumentsthatareinnovative
in cohort studies and thus lead already to a large number of insights documented
inhundredsof(medical)publications.Aconsiderableportionofthesepublications
employ results from imaging data. However,the full potential of analyzing organ
shapes,texturesandspatialrelationsquantitativelyisnotexploitedsofar.
TheRotterdamstudy.AprominentexampleistheRotterdamStudy1,initiated
in1990inthecityofRotterdam,intheNetherlands.Similartolaterstudies,itwas
motivatedbythedemographicchangewithmoreandmoreelderlypeoplesuffering
fromdifferentdiseasesandtheirinteractions.Aftertheinitialstudyinvolvingalmost
8,000menandwomen,follow-upsatfourpointsintimewereperformed—themost
recent examinations took place in the 2009-2011period. In the later examination
cycles,alsonewindividualswereinvolvedleadingtodatasetsfromalmost15,000
patients[22].
The original focus of the Rotterdam Study was on neurological diseases, but
meanwhileit hasbeenextendedto othercommondiseases includingcardiovascu-
larandmetabolicdiseases.Thestudyhasanenormousimpactonepidemiological
andrelatedmedicalresearch,documentedin797journalpublicationsregisteredin
thepubmeddatabase(searchwithkeyword“RotterdamStudy”,January30,2014).
Amongthem are predictionsfor the futureprevalenceof heartdiseases and many
studies on potentialrisk factorsfor neurodegenerativediseases. For a comprehen-
sive overviewof the findings, see [22] that summarizesthe findings of more than
240 papers related to the Rotterdam Study. In a similar way, [23] is a significant
updateofthesefindingswithmorerecentdata.
Norwegian Aging Study. A long-term study in Norway investigates the rela-
tions between brain anatomy (as well as brain function), cognitive function, and
geneticsinnormallyagingpeople.2 Intotal170individuals(120ofthemfemale),
aged between 46 and 77 (mean 62), were examined in Bergen and Oslo in by
nowthreewaves(1stwavein2004/2005,nextin 2008/2009,andmostrecentlyin
2011/2012)[48].Whilenaturallynotallofthesesubjectscouldbefollowedthrough
allthreewaves,stillmostofthemweresubjectedtoanextensivecombinationof
1. neuropsychologicaltests,includingtestsoftheintellectual,language(memory),
sensory/motor,andattention/executivefunction,
2. MRI data, including co-registered T1-weighted anatomical imaging, diffusion
tensorimaging,and–fromthe2ndwaveon–alsoresting-statefunctionalMRI,
aswellas
3. genotyping(1stwaveonly)[46].
1 http://www.erasmus-epidemiology.nl/research/ergo.htm, accessed:
1/31/2014
2http://org.UiB.no/aldringsprosjektet/,accessed:1/31/2014
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Thesubstantiallyheterogeneousimagingandtest data are usedto study aging-
relatedquestionsaboutthemodernNorwegianpopulation,forexample,howanatom-
icalandfunctionalchangesinthehumanbrainpossiblyrelatetothelaterdevelop-
mentof Alzheimerand dementia. Importantfindingsinclude the relation between
hippocampalvolumesandmemoryfunctioninelderlywomen[48]andtherelation
between subcortical functional connectivity and verbal episodic memory function
inhealthyelderly[47].
SHIP. The Study of Health in Pommerania (SHIP) is another cohort study
broadlyinvestigatingfindingsandtheirpotentialprognosticvalueforawiderange
of diseases. The SHIP tries to explain health-related differencesafter the German
reunionbetweenEastandWestGermany.Itwasinitiatedin theextremenortheast
ofGermany,aregionwithhighunemploymentandarelativelylowlifeexpectancy.
Inthefirstexaminationcycle(1997-2001)4,308adultsofallagegroupswereex-
amined,followedbyasecondandathirdcyclethatwasfinishedattheendof2012.
The instruments used changed over time with some initial image data (liver and
gallbladderultrasound)availablealreadyin the firstcycleandothers,in particular
wholebodyMRI,addedlater.TheuseofwholebodyMRIwasuniquein2008when
the thirdexaminationcyclestarted.Breast MRI forwomenisperformed,whereas
for men MR angiographydata are acquired,since men suffer fromcardiovascular
diseasessignificantlyearlierthanwomen[42].Inaddition,asecondcohort(SHIP-
Trend)wasestablishedcomprising4,420adultparticipants.
Diagnosticreportsarecreatedbytwoindependentradiologistswhofollowstrict
guidelinestoreporttheirfindingsinastandardizedmanner.Thepilotstudytodis-
cusstheviabilityandpotentialofsuchacomprehensiveMRexamisdescribedby
[20].Theoveralltimefortheinvestigationistwo(complete)dayswith90minutes
fortheMRexam.TheSHIPhelpedtoreliablydeterminetheprevalenceofriskfac-
tors,suchasobesity,anddiseases.MajorfindingsoftheSHIPareincreasedlevelsof
obesityandhighbloodpressure(comparedtotheGermanpopulation)inthecohort.
TheMRexamsaloneidentifiedpathologicalfindingsin35%ofthesamplepopula-
tion.Morethan400publicationsinpeer-reviewedjournalsarebasedonSHIPdata
(January2014).
UKBiobank.TheUKBiobankstartedrecentlyandrepresentsacomprehensive
approachtostudydiseaseswithahighprevalenceinanagingsociety,suchashear-
ingloss,diabetesandlungdiseases.Halfamillionindividualswillbeinvestigated
inoneexaminationcyclefromwhich100,000receiveanMRIfrom2014onwards.
TherationaleforthenumberofindividualstobeincludedisexplainedbyPeterson
andcolleagues[32]:theyaimatareliableidentificationofevenmoderateriskfac-
tors(RRbetween1,3and1,5)fordiseaseswithaprevalenceof5%.Theprospective
studyshouldhaveacomprehensiveprotocolofcardiacMRI,brainMRIandabdom-
inalMRI.Thisprospectivecohortstudyalsoinvolvesgeneticinformation.3
3http://www.ukbiobank.ac.uk,accessed:1/31/2014
VisualAnalyticsofImage-CentricCohortStudiesinEpidemiology 9
TheGermanNationalCohort.Therecentlystarted“GermanNationalCohort”
inGermanyisbasedonexperienceswitha numberofmoderate-sizestudies,such
asSHIP,andexaminessome200,000individualsoveraperiodof10-20years.In-
dividualswill be invited in three waves to characterizechanges.Due to the large-
scale character,imagingis distributedoverfive cities. Thus,the subtle differences
in imaging within differentscanners have to be considered.4 It explicitly aims at
improvementsin thetreatmentof chronicdiseasesandinvolvesa varietyof tissue
samples,e.g.,lymphocytes.Imagingin30,000individualsisagainperformedwith
MRI,comprisingwholebody,brainandheart.
2.4 EpidemiologicalData
Epidemiologicaldataarehugeandveryheterogeneous.Asanexample,intheUK
biobank329attributesrelatetophysicalmeasures,suchaspulserate,systolicand
diastolic blood pressure, and variousmeasures relate to vision or hearing. 471 at-
tributesrelatetointerviews(socio-demographics,healthhistory,lifestyle,...).
The data that are stored per individual is standardized but not completely the
same, e.g., childbirth status and menstrual period are available for women only.
Image data and derived information, e.g., segmentation results, significantly in-
creasedboththeamountandcomplexityofdata.Longitudinalcohortstudydataare
time-dependent.Whilesomeinstruments,suchasbloodpressuremeasurements,are
availableforallexaminationcycles,otherswereaddedlaterorremoved.Individuals
dropout,becausetheymove,dieorjustdonotaccepttheinvitationtoasecondor
thirdexaminationcycle.Itisimportanttoconsideralsosuchincompletedatabutto
beawareofpotentiallymisleadingconclusions.
The great potential of image-centric studies is that image data and associated
laboratory data as well as data from interviews are available. An epidemiological
study, such as the SHIP, has a large data dictionary that precisely defines all at-
tributes and their ranges. While laboratory data are scalar values, most data from
interviewsarenominalorordinalvalues.Inparticular,datafrominterviewsexhibit
anessentialamountofuncertainty.Self-reportswithrespecttoalcoholanddruguse,
cigarettesmokingandsexualpracticesmaybebiasedtowards“expectedorsocially
accepted”answers.Epidemiologistsarenotonlyawareoftheseproblemsbutdevel-
opedstrategiestominimizethenegativeeffects,e.g.,byaskingredundantquestions.
Afterdatacollection,expertsspendalotofefforttoimprovethequalityofthedata.
Despitetheseefforts,visualanalyticstechniqueshavetoconsideroutliers,missing
anderroneousdata.
Geographic data. Geographic data play a central role in public health where
the dynamics of local infections are visualized and analyzed (disease mapping).
Chuiandcolleagues[9]presentedavisualanalyticssolutiondirectlyaddressingthis
4http://www.nationale-kohorte.de/,accessed:1/31/2014
10 AuthorsSuppressedDuetoExcessiveLength
problembycombiningthreededicatedviews.Alsoincohortstudydata,geographic
dataarepotentiallyinterestingtounderstandlocaldifferencesinthefrequencyand
severityofdiseasesasaninteractionbetweenenvironmentalfactorsandgeneticdif-
ferences.Thisbranchofepidemiologyisreferredtoasspatialepidemiology.Beale
andcolleagues[5]investigateddifferencesbetweenruralandurbanpopulations.In
theircomprehensivesurvey,Jerrettandcolleagues[24]consideredspatialepidemi-
ologyasanemergingarea.However,wedonotfocusonspatialepidemiologysince
cohortstudydata typicallycompriserathernarrowregionsandthusmay notfully
supportsuchanalysisquestions.
2.5 Analysis ofEpidemiologicalWorkflow
Thefollowingdiscussionofobservationsandrequirementsforcomputersupportis
largelybasedondiscussionswithepidemiologistsaswellastheinspiringpublica-
tionbyThewandcolleagues.Accordingto[40]
• epidemiologicalhypothesesaremostlyobservationsmadebyphysiciansinclin-
icalroutine,
• correspondingattributesarechosenbasedontheobservationsandfurtherexpe-
rience,and
• regressionanalysis is frequentlyused to determinewhetherthe investigatedat-
tributeisariskfactorornot.
Majorrequirementsforanepidemiologicalworkflow(againbasedon[40])are:
• Results have to be reproducible.Due to the iterative data assessment, methods
needtobeappliedtonewdatasetsaswellandtheresultsneedtobecomparable
betweendifferentassessmenttimestocharacterizethechange.Userinputneeds
tobemonitoredallthetimetoenablereproducibleresults.
• Amajorresultofanepidemiologicalanalysisiswhethercertainfactorsinfluence
adiseasesignificantly.Relativerisk(asameasureofeffectsize)andp-valuesas
statisticalsignificancelevelareparticularlyimportant.
Although these requirements neither consider image data nor visual analytics,
theyhavetobeconsideredalso inthesemoreinnovativesettings.Reproducibility,
forexample,meansthatclusteringwithrandominitializationisnotfeasible.More-
over, reports must be generated that clearly revealall settings, e.g., parameters of
clusteringalgorithmsthatwereusedforgeneratingtheresults.
Sincestatisticalanalysisplayssuchanimportantrole,statisticspackages,suchas
SPSS5,R6 andSTATA7 dominateinepidemiology.Theyprovidevariousstatistical
testsalso incaseswhereassumptions,suchasa normaldistribution,arenotvalid.
5http://www-01.ibm.com/software/analytics/spss/products/statistics/,
accessed:1/31/2014
6http://www.r-project.org/,accessed:1/31/2014
7http://www.stata.com/,accessed:1/31/2014