Final Report 1 Notes

8/3/2019 Final Report 1 Notes

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2 Weemploy DESCRIPTIVE EPIDEMIOLOGY to describe the health-related conditionsand behaviorsinpopulations. To do this, wemayconduct SURVEILLANCE onasampleofthepopulation

Descriptiveepidemiologyprovidesinformationon the distributionsofhealth or diseasein termsofPERSON, PLACE,and TIME (PPT)

PPT information helpsus FORMULATE HYPOTHESES about what might explain these distributions Hypothesescanbe TESTED usingvarious ANALYTCIAL EPIDEMIOLOGIC STUDY DESIGNS Nomatter which study designisused,a 2x2 TABLE can tally theinformationonexposureand disease

forindividualstudysubjects Calculationsbased onnumbersin the 2x2 tableyield RATES OF DISEASE inexposed versus

unexposed groups,and acomparisonoftheseratesyieldsa RELATIVE RISK.

Therelativeriskmeasures thestrength ofthe ASSOCIATION between theexposureand theoutcome. Is the association causal? Inother words,ifanassociationisfound, what doesit mean? It isacausal

associationoris theresomeotherexplanationforobserving theassociation?

Is the association causal? Inother words,ifanassociationisfound, what doesit mean? It isacausalassociationoris theresomeotherexplanationforobserving theassociation?

This Lecture willexplain how to thinkabout whyanexposureand anoutcome turned up together. Thisiscalled interpretationofastudy,or discussionofwhat thestudyresultsmean. Inother words,

how to we explain theobserved association.

3 Ask:

What do wemean when wesay that two thingsare tied,linked,related,orassociated with eachother?

What doesit mean tosay that thereisanassociation between two things? Tellstudents that one definitionofassociationis that things that areassociated arelinked insome

way that makes them turnup together.

4 Inepidemiology, the 2x2 tableisanimportant toolforcalculatingand demonstrating how things turnup

together. Inbrief, therelativeriskmeasureshows how strongly theexposureand theoutcome turnup

together. Forexample,arelativeriskof10 means that theoutcome turnsup 10 timesmorefrequently

amongpeople with theexposurecompared topeople without theexposure. Thisisastrong association.

A relativeriskofonly 1.5isamuch weakerassociation; theoutcome doesnot turnupasoftenamong theexposed versus theunexposed.

5 The headlines that follow werefound on the Internet. These headlinesarefromreportsofinvestigationsof

possibleassociationsbetweenexposuresand outcomes.

Theseassociationsarebased onsinglestudiesand arenot necessarilycausaloreven true.

6 Explain that thisslideshowsacartoonabout the headlines. Thenewscasterin thecartoonisspinning three

wheels to decide which news he willreport.

What is thepoint ofthecartoon? (Given thefrequency with which we hearabout associationson thenews,

it is hard toknow which oftheseassociationsshould bebelieved / takenseriously. Weneed toknow more

about thescientificbasisfor thereported associations; weneed toknow what explanationsmight explain

associations)

While Theresultsofanepidemiologicalstudymayreflect the trueeffect ofanexposure (s) on the

development oftheoutcomeunderinvestigation,it should alwaysbeconsidered that thefindingsmayin

fact be due toanalternativeexplanation

7 Ask why theseexposuresand outcomes turned up together.

Inbrief, thefivereasonsanexposureand anoutcomemight turnup togetherare:

1. Causal - theexposureis theunderlyingreason (oroneoftheunderlyingreasons) that theoutcome(e.g.,a disease) occurred

2. Chance - occurrenceoftheoutcomeisacoincidenceunrelated to theexposure


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3. Bias - amistakein thestudyis thereasonfor theobserved association4. Confounding - a third factor (that isacause) is thereasonfor theobserved association

8 Thefirst possibleexplanationis that they turned up togetherbecause theexposurecauses theoutcome.

9 Review the definitionofCause.

Example:avirus that causesacold several daysafterexposure

10 Thesecond possibleexplanationis that anexposureand anoutcome turned up togetherbychance.

11 Review the definitionsofchance with theclass.Askstudents to describesomeactivitiesinlife that involvechance. (flippingacoin tosee which football

teamgets to decide whether tokickofforreceive;participatinginalottery;playingaslot machine)

Explain tostudents that inepidemiology,chanceplaysaroleinasimilar wayasit doesinreportingresultsof

anelectionpoll. Toavoid CHANCE asapossibleexplanation,pollsters try topollalargeenough sampleso

that achancefindingisunlikely. (It stands toreason that themorepeopleyoupoll, themorelikely this

represents thereal distributionsofopinionsofeveryone (caseinpoint is that ifyoupolleveryoneyou will

get the rightresult)

Remind studentsofthephrase, leavenothing tochance.

Inepidemiology, CHANCE might explainanobserved associationifthenumberofpeople/health eventsin

thestudyissolow that aresult popsupaccidentally,and it DOES NOT represent what isgoingonin the

wholepopulation (it doesnot meananything). (See thenext sectionon BIAS for discussionofotheraspectsofstudyconduct that canlead toanerroneousresult / aresult that doesnot meananything.)

12 The third possibleexplanationis that anexposureand anoutcome turned up togetherbecauseofbias.

13 Review the definitionofbias with theclass.

Teacher Note: Biasmeanssystematicerror,and inepidemiologyit meanserrorin how thestudyisset upor

conducted. Onesourceofbiasisinselectionofstudyparticipants;errorsmayoccurinselectingasampleof

studysubjects that isrepresentativeofthe wholepopulationofpotentialsubjects. Referringback to the

exampleofanelectionpoll (Slide #18),another taskofpollsters (besidesgettingalargeenough sample) is to

samplecarefully toobtainanaccuratecross-sectionofthepopulation. Ifthisfails, theresult ofthepoll will

not beaccurate. Similarly,inanepidemiologystudy,selectionbias willlead toamistakenresult. Any

association that isfound willnot reallyexist,it wasonlyfound becauseofabias (systematicerror) in the

study.

Anothersourceoferror/biasinanepidemiologystudyismeasurement error. Unless theexposuresand

outcomesinastudyaremeasured accurately,anyassociations that arefound donot necessarilyexist,but

rather, werefound becauseofmeasurement error.

14 Thefourth possibleexplanationis that anexposureand anoutcome turned up togetherbecauseof

confounding.

15 sometimes wearefooled because weobserved anassociationbetweenanon-causalfactorand anoutcome,

in which that non-causalfactor happens toberelated to therealcausalfactor. Thecausalfactor,actingasa

confounder,canfoolusinto thinking that thenon-causalfactorisactually thecause

16 Reinforce theidea that associationisnot necessarilycausation. Thisisoneofthecentralpointsin

understandingepidemiology. Testinga hypothesisand finding that anexposureand a disease turnup

togetherisanimportant step. However,interpreting what thisassociationmeansischallengingbecausefiveexplanationsarepossiblefor why theassociation hasbeenfound

17 Weareexploringfourpossibleexplanationsfor whyaexposureand a health outcome turnup together.

We willexamine the third possibleexplanation; that they turned up togetherbecauseofbias.

18 Review the definitionofbias. Bias doesnot mean that theinvestigatoris prejudiced. Askstudents:

What does the word biasmean toyou? What comes tomind whenyou thinkofbias? A line diagonal to thegrainofafabric Highlypersonaland unreasoned distortionofjudgment Systematicerrorintroduced encouragingoneoutcomeoverothers


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19 Several typesofbiasexist inresearch. Sackett et al havelisted 60++ typesofbias which exist inresearch.

Thesebiasescanbeclassified eitherinto SELECTION or INFORMATION BIAS.

Biasisaresult ofanerrorin the designorconduct ofastudy. Effortsshould thereforebemade toreduceor

eliminatebiasorat theveryleast, torecognizeit and takeit intoaccount wheninterpreting thefindingsofa

study

20 SELECTION BIAS

Resultsfromproceduresused toselect subjectsintoastudy that lead toaresult different from what would

havebeenobtained from theentirepopulation targeted forstudy

Most likely tooccurincase-controlorretrospectivecohort becauseexposureand outcome haveoccurred attimeofstudyselection

A. Response Bias - those whoagree tobeinastudymaybeinsome way different from those whorefuse toparticipate. Sinceinmanystudiesnoinformationisobtained from thenonresponders,

nonresponsemayintroduceaseriousbias that may difficult toassess. Thus theyshould bekept toa

minimumorshould becharacterized asmuch aspossiblebyusing whateverinformationisavailable

to determine waysin which they differfromrespondersand togauge thelikelyimpact oftheir

nonresponseon theresultsofthestudy.

B. Exclusionbias resultsfrom theinvestigatorsapplying different eligibilitycriteria to thecasesand tothecontrolsinregard to which clinicalconditionsin thepast would permit eligibilityin thestudyand

which would serveas thebasisforexclusion.

WHAT ARE THE SOLUTIONS?

Littleornothingcanbe done tofix thisbiasonceit hasoccurred. Youneed toavoid it whenyou designand conduct thestudyby,forexample,using thesamecriteria

forselectingcasesand controls,obtainingallrelevant subject records,obtaining high participation

rates,and takinginaccount diagnosticand referralpatternsofdisease.

Clear definition of study population Explicit case and control definitions Cases and controls from same population Selection independent of exposure Selection of exposed and non-exposed without knowing disease status

EXAMPLE: Retrospective Cohort Study

Outcome: COPDExposure: Employment in tire

manufacturing

Exposed: Plant assemblyline workers

Non-exposed: Plant administrativepersonnel

Bias: Theexposed werecontacted (selected) at alocalpub while watching Mondaynight football; the

non-exposed wereidentified through review ofplant personnelfiles. Exposed personsmay havebeenmore

likely tobesmokers (related to COPD)

EXAMPLE: Case Control Study

Outcome: Hemorrhagicstroke

Exposure: Appetitesuppressant products that contain Phenylpropanolamine (PPA)Cases: Persons whoexperienced astroke

Controls: Personsin thecommunity without stroke

Bias: Controlsubjects wererecruited byrandom-digit dialingfrom 9:00 AM to

5:00 PM. Thisresulted inover- representationofunemployed persons whomaynot represent the

studybase in termsofuseofappetitesuppressant products.

21 INFORMATION BIAS alsoknownas MEASUREMENT/OBSERVATIONAL BIAS

22 Anerror that arisesfromsystematic differencesin the wayinformationonexposureor diseaseisobtained from thestudygroups

Resultsinparticipants whoareincorrectlyclassified aseitherexposed orunexposed oras diseasedornot diseased


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Occursafter thesubjects haveentered thestudy Several typesofobservationbias:

23 Biasmaybeintroduced in the way that informationisabstracted frommedical,employment,orother

records

24 Biasfrom themannerin which interviewersaskquestions

y Interviewers knowledge of subjects disease status may result in differential probing of exposurehistory

y Similarly, interviewers knowledge of subjects exposure history may result in differential probingand recording of the outcome under examination

Investigatoraskscasesand controls differently about exposure

e.g:soft cheeseand listeriosis

Investigatormayprobelisteriosiscases about consumptionofsoft cheese (knows

hypothesis)

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26 Studygroupparticipantssystematically differin the way dataonexposureoroutcomearerecalled

Particularlyproblematicincase-controlstudies Individuals who haveexperienced a diseaseoradverse health outcomemay tend to thinkabout

possible causesoftheoutcome. Thiscanlead to differentialrecall

EXAMPLE: Case-Control Study

Outcome: Cleft palate

Exposure: Systemicinfection duringpregnancy

Cases: Mothersgivingbirth tochildren with cleft palate

Controls: Mothersgivingbirth tochildrenfree

ofcleft palate

Bias: Mothers who havegivenbirth toachild with cleft palatemayrecallmore thoroughlycoldsand other

infectionsexperienced duringpregnancy

Cases remember exposure differently than controls

e.g.riskofmalformation

Mothersofchildren with malformationsremember past exposuresbetter thanmothers withhealthychildren

27 Individuals with severe disease tends to havecompleterecords thereforemorecompleteinformationabout

exposuresand greaterassociationfound

Thisresultsinunderreporting. Wish Biasoccursinsubjects who developed a diseaseand whoinattempting

toanswer thequestion Whyme?seek toshow,oftenunintentionally, that the diseaseisnot theirfault.

Thus, theymay denycertainexposuresrelated to theirlifestyle (such assmokingor drinking)28 Informationbias:misclassification

Measurement errorleads toassigning wrongexposureoroutcomecategory

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thefourth possibleexplanation; that they turned up togetherbecauseofconfounding

31 Tobring toruin (archaic) Consume, waste Toput toshame


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Damn To throw intoconfusion Tofail to discern differencesbetween: mixupsometimes wearefooled because weobserved anassociationbetweenanon-causalfactorand an

outcome,in which that non-causalfactor happens toberelated to therealcausalfactor. Thecausal

factor,actingasaconfounder,canfoolusinto thinking that thenon-causalfactorisactually thecause.

32 Note that aconfounder has twocharacteristics: 1) it isacauseoftheoutcome;and 2) it isassociated with,

but not caused by, theexposureofinterest.

33 Scenario: Wearestudyinglungcancer. Ourexposureofinterest ismatch-carrying (inonespocket).

Analysisofthe datarevealsanassociationbetweenpeople who havelungcancerand match-carrying.

But match-carryingisnot acauseoflungcancer. A realcauseis tobaccosmoking.

Iftheresearchers donot askabout smoking,but onlyabout match-carrying, theassociation theyfind isreal

but needs tobeinterpreted correctly (it isnot acausalrelationship,but ratherisobserved becauseof

confoundingby therealcause,smoking.

In thisexample,smokingisapotentialconfounder that should havebeenmeasured.

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Final Report 1 Notes