Exposure → Disease

8/12/2019 Exposure Disease

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Exposure Disease?

Exposed cases from a population

If the same people, at the same time,had not been exposed would they still have become cases?

Observed

Expected

Comparison


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Analytical epidemiology Important considerations

Estimation of the right thing Internal validity: Bias, confounding

Retrieval of most information for least cost Efficieny: Precision, resources, time-scale

Means to obtain the right answer

Proper study design Proper analysis Proper interpretation


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Comparisons are fundamental

Different solutions with differentreference groups:

Experimental study design Randomized controlled trial (RCT)

Observational study design Cohort study Case-control study


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Cohort study

What is a cohort? Originally: one of 10 divisions of a Roman legion Group of individuals

Sharing some characteristics

Followed up for specified period of time Examples:

Birth cohort Cohort of guests at barbecue Occupational cohort of chemical plant workers The cohort of this course


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Cohort study

Cases

Compare disease in exposed to disease in unexposed

Source population

Exposed

Unexposed


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Reference group: Cohort study

Unexposed Information on disease in

unexposed Free of disease at beginning of

follow-up

At risk of disease (e.g. notimmune)


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Cohort study Relative risk (RR)

RR = AR in exposed

=a / (a+b)

AR in unexposed c / (c+d)

Disease No disease Total

Exposed a b a+b

Unexposed c d c+d

RR is a gold standard effect measure!

Attack rate, AR = diseased / total (AR is a risk)


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t i m e

Exposure Study starts Disease

Prospective cohort study

t i m e

ExposureStudy starts Disease


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Retrospective cohort study

Exposure

t i m e

Disease Study starts


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Case-control studySource population

Exposed

Unexposed

Cases

Controls =representative sampleof source population

Compare exposure in cases to exposure in controls


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Reference group: Case-control study

Controls Information on exposure in source

population Representative sample of source

population Same inclusion/exclusion criteria as

cases At risk of disease


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Case-control study Odds ratio (OR)

Cases Controls

Exposed a b

Unexposed c d

OR =

Odds of exposure incases

=a / c

=a d

Odds of exposure incontrols b / d b c

Odds of exposure = exposed / unexposed


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RR versus ORDisease No disease Total

Exposed a b a+b

Unexposed c d c+d

RR =a / (a+b)

=a (c+d)

c / (c+d) c (a+b)

OR = a / c = a db / d b c

Ifd

=c+d

then OR=RRb a+b


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Types of case-control studies

Retrospective Different control selection strategies

1. Traditional, exclusive / cumulative design

2. Case-cohort, inclusive design

3. Risk-set / density sampling


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Case-control study:Traditional, exclusive / cumulative

design Controls from disease-free people in

the end OR estimates RR when disease is rare

( 10%)

t i m e

Exposure Disease Cases

Controls

l d


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Case-control study:Case-cohort, inclusive design

Controls from all people in thebeginning

Controls could also be cases OR estimates RR

t i m e

Exposure CasesDisease

Controls


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Case-control study:Risk-set / density sampling

Controls from all people when caseoccurs

Controls could also be cases OR estimates RR

t i m e

ExposureDisease

Case

Control

DiseaseCase

Control

DiseaseCase

Control


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Matching in case-control studies

Frequency matching (n:m), e.g. age group Individual matching (1:1), e.g. birth date

Age strata(yrs)

CasesControls

Unmatched Matched

0-14 50 10 50

15-29 30 25 3030-44 15 25 15

45- 5 40 5

Total 100 100 100


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Matching in case-control studies Makes the distribution of the matching variable

(confounder) similar for cases and controls Stratified comparison when controlling for the matching

variable (confounder) become more efficient

Matched controls are a biased, not representative, sampleof the source population This bias must be taken into account with a matched

analyses Mantel-Haenszel formula for OR Conditional logistic regression

Matching variables cannot be studied Do not match unless you have to


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Sources of controls Population (e.g. register)

Reduce risk of selection bias for controls Neighbourhood, friends, family

Matching for social factors Overmatching underestimation?

Random digit dialling Selection bias?

Hospital Selection bias?


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Cohort or case-control study?

Cohort study Gold standard Risk of inefficieny (large

cohorts,long follow-up)

Use when: High attack rate

Rare exposure Multiple diseases

Case-control study Often more efficient Risk of selection bias

for controls

Use when: Low attack rate

Rare disease Multiple exposures


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Retrospective or prospective design?

Retrospective design Cases and healthy

people may reportexposure differently

(recall bias) Temporal relationship

exposure-disease maybe difficult to determine

(reversed causality)

Prospective design Maybe impossible Time- and resource-

consuming


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RR =29 / 53

=0.55

= 2.3 (95%CI 1.2-4.5)8 / 34 0.24

Outbreak of gastroenteritis after eatingin restaurant, 37 of 87 visitors ill

Disease No disease Total

Exposed 29 24 53

Unexposed 8 26 34


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Increase in Legionella cases in theNetherlands, 23 cases in a week

Cases ControlsExposed 17 21

Unexposed 6 30

OR =17 / 6

=17 30

= 4.0 (95%CI 1.2-14.5)21 / 30 21 6


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Further reading

Epidemiology: An Introduction, chapter 4Kenneth J. RothmanOxford University Press, 2002Acknowledgement:M. Kivi; EPIET

Documents

Exposure → Disease