Cohort, case-control & cross- sectional studies

Source: Alain Moren, EPIET Introductory courses

Kostas DanisEPIET Introductory course

Menorca, Spain16/9-12/10/2012

Epidemiological studies

Two types Observation Experiment

Experiment

Exposed

Not exposed

Disease occurrence

Unethical to perform experiments on people

if exposure is harmful

Exposure assigned

RandomisedControlledTrial

BlindedDosesTime periodRisk - effectNo bias

If exposure not harmful

TreatmentPreventive measure (vaccination)

If RCT not possible Left with observation of experiments designed by Nature

Cohort studiesCross-sectional studies

Case control studies

Cohort studies marching towards outcomes

What is a cohort?

One of 10 divisions of a Roman legion Group of individuals

- sharing same experience - followed up for specified period of time

Examples- EPIET cohort 2012- birth cohort- cohort of guests at barbecue- occupational cohort of chemical plant workers- influenza vaccinated in 2011-12

follow-up period

Calculate measure of frequency

Cumulative incidence- incidence proportion- attack rate (outbreak)

Incidence rate

end of follow-up

Cohort studies

Purpose- Study if an exposure is associated with outcome(s)- Estimate risk of outcome in

exposed and unexposed cohorts- Compare risk of outcome in two cohorts

Cohort membership- Being at risk of outcome(s) studied- Being alive and - Being free of outcome at start of follow-up

unexposed

exposed

Cohort studies

unexposed

exposed

Incidence amongexposed

Incidence amongunexposed

Cohort studies

ate ham

did not eat ham

ill not ill Total

a b a+b

c d c+d

Presentation of cohort data: 2x2 table

Risk in exposed= a/a+b

Risk in unexposed= c/c+d

Incidence rate

Number of NEW cases of disease

Total person - time of observation

Incidence rate

Number of NEW cases of disease

Total person - time of observation

Rate

Denominator:

- is a measure of time

- the sum of each individual’s time at risk and free from disease

A

B

C

D

E

90 91 92 93 94 95 96 97 98 99 Time at risk

x

x

6.0

6.0

10.0

8.5

5.0

Total years at risk 35.5

-- time followed

x disease onset

Person-time

A

B

C

D

E

90 91 92 93 94 95 96 97 98 99 00 Time at risk

x

x

6.0

6.0

10.0

8.5

5.0

Total years at risk 35.5

-- time followed

x disease onset

Incidence rate (IR)(Incidence density)

IR = 2 / 35.5 person years

= 0.056 cases / person year

= 5.6 cases / 100 person years

= 56 cases / 1000 person years

Person-years Cases

Smoke 102,600 133

Do not smoke 42,800 3

Presentation of cohort data: Person-years at risk

Tobacco smoking and lung cancer, England & Wales, 1951

Source: Doll & Hill

Presentation of data: Various exposure levels

time

Exposure Study startsDisease

occurrence

Prospective cohort study

time

ExposureStudy startsDisease

occurrence

Retrospective cohort study

Exposure

time

Diseaseoccurrence Study starts

Recipe: Cohort study

Identify group of - exposed subjects- unexposed subjects

Measure incidence of disease Compare incidence between exposed and

unexposed group

unexposed

exposed

Incidence amongexposed

Incidence amongunexposed

Cohort studies

Absolute measures

- Risk difference (RD) Ie - Iue

Relative measures- Relative risk (RR)

Rate ratio Risk ratio

Effect measures in cohort studies

Ie

Iue

Ie = incidence in exposedIue= incidence in unexposed

Exposed

Not exposed

CasesNoncases Risk %

Cohort study

50 50 50 %

10 90 10 %

Risk ratio 50% / 10% = 5

Total

100

100

ate ham

did not eat ham

ill not ill Incidence

49 49 98 50 %

4 6 10 40 %

Risk difference 50% - 40% = 10%

Relative risk 50% / 40% = 1.25

Interpretation of Risk Ratios

RR>1

RR=1

RR<1 Protective factor

Risk factor

No association

Exposure Population (f/u 2 years)

Cases Incidence

(%) Relative

Risk

HIV +

215

8

3.7

11

HIV - 298 1 0.3

Does HIV infection increase risk of developing TB among drug users?

Vaccine efficacy (VE)

Status Pop. Cases Cases per

1,000 RR

Vaccinated 301,545 150 0.49 0.28

Unvaccinated 298,655 515 1.72 Ref.

Total 600,200 665 1.11

VE = 1 - RR = 1 - 0.28

= 72%

Population Cases Incidence

a1High N1 I1

cUnexposed NneIue

at riskExposurelevel

a2Medium N2 I2

a3Low N3 I3

Various exposure levels

Population Cases Incidence RR

a1High N1 I1

cUnexposed NneIue

at riskExposurelevel

a2Medium N2 I2

a3Low N3 I3

RR1

RR2

RR3

Reference

Various exposure levels

Cohort study: Tobacco smoking and lung cancer,

England & Wales, 1951

Source: Doll & Hill

Disadvantages of cohort studies

Large sample size

Latency period

Cost

Time-consuming

Loss to follow-up

Exposure can change

Multiple exposure = difficult

Ethical considerations

Strengths of cohort studies

Can directly measure - incidence in exposed and unexposed groups- true relative risk

Well suited for rare exposure Temporal relationship exposure-disease is

clear Less subject to selection biases

- outcome not known (prospective)

Can examine multiple effects for a single exposure

Population Outcome 1 Outcome 2 Outcome 3

exposed Ne Ie1 Ie2 Ie3

unexposed Nne Iue1 Iue2 Iue3

RR1 RR2 RR3

Cohort studies

A cohort study allows to calculate indicators which have a clear, precise meaning.

The results are immediately understandable.

Cross-sectional (prevalence) studies

Cross-sectional studies

Observation of a cross-section of a population at a single point in time

Recruitment of study participants Population Population sample

Observation for the presence of: One or more outcomes One or more exposures

Sampling

Sample

Target Population

Sampling

Population

Uses of cross-sectional surveys in public health

Estimate prevalence of disease or their risk factors

Estimate burden Measure health status in a defined population Plan health care services delivery Set priorities for disease control Generate hypotheses Examine evolving trends

- Before / after surveys- Iterative cross-sectional surveys

Potential objectives of a cross sectional study

Descriptive Estimate prevalence

Analytic Compare the prevalence of a disease in

various subgroups, exposed and unexposed Compare the prevalence of an exposure in

various subgroups, affected and unaffected

Ill Non ill Total

Exposed a b a+b

Non exposed c d c+d

Presentation of the data of an analytical cross sectional study in a 2

x 2 table

Simultaneous measurement of outcomes and exposures

Exposed

Not exposed

CasesNoncases Prevalence %

Cross-sectional study

500 500 50 %

100 900 10 %

Prevalence ratio (PR) 50% / 10% = 5

Total

1,000

1,000

Measuring association in analytical cross-sectional

surveys Prevalence among exposed / prevalence

among unexposed Prevalence ratio Formula equivalent to risk ratio Concept different

- No incidence- Only prevalence

• depends on both occurrence of new cases & duration of disease

Prevalence of West Nile virus (WNV) infection by place of residence, Central Macedonia,Greece, 2010

Infected Total Prevalence Prevalence

ratio

Rural 38 491 7.7% 5.9

Urban 3 232 1.3% Ref

Prevalence of HIV infection by socioeconomic status, African country X, 1999

Infected Total Prevalence Prevalence

ratio

High class

15 235 6.4% 2.6

Low class

11 450 2.4% Ref

Prevalence of hepatitis C (HCV) infection by quantity of therapeutic injections, Hazabad, Pakistan, 1993

No.of injections

Infected Total Prevalence Prevalence

ratio

>10 9 41 22% 22

0-10 4 52 8% 8

0 1 82 1% Ref

Advantages of cross-sectional surveys

Fairly quick Easy to perform Less expensive Adapted to chronic diseases

Limitations of cross-sectional surveys

Limited capacity to document causality (exposure and outcome measured at the same time -difficult to establish time sequence of events) Not useful to study disease etiology

Not suitable for the study of rare / short diseases Not adapted to severe / acute diseases

Not adapted to incidence measurement.

Limitations of causal inference in analytical cross sectional

studies

• Prevalent cases• Exposure and outcome examined at the

same time

Principle of case control studies

Our objective is to compare

the incidence rate in the exposed population

to the rate that would have been observed

in the same population, at the same time

if it had not been exposed

Exposed

Unexposed

Source population

CasesExposed

Unexposed

Source population

CasesExposed

Unexposed

Source population

Sample

Controls

CasesExposed

Unexposed

Source population

Controls:Sample of the denominator Representative with regard to exposure

Controls

Sample

Intuitively

if the frequency of exposure is higher among cases than controls

then the incidence rate will probably be higher among exposed than non-exposed

Case control study

DiseaseControls

Exposure

??

Retrospective nature

Cases Controls

Exposed a b

Not exposed c d

Total a + c b + d

% exposed

Distribution of cases and controls according to exposure in a case control study

Cases Controls

Exposed a b

Not exposed c d

Total a + c b + d

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

Distribution of cases and controls according to exposure in a case control study

Oral Myocardialcontraceptives Infarction Controls

Yes 693 320

No 307 680

Total 1000 1000

% exposed 69.3% 32%

Distribution of myocardial infarction by oral contraceptive use

in cases and controls

Physical Myocardialactivity Infarction Controls

>= 2500 Kcal 190 230

< 2500 Kcal 176 136

Total 366 366

% exposed 51.9% 62.8%

Distribution of myocardial infarction by amount of physical activity

in cases and controls

Water Cases ControlsConsumption

YES 150 ?

NO 50 ?

Total 200 200

Volvo factory, Sweden, 3000 employees, Cohort study200 cases of gastroenteritis

Two types of case control studies

ExploratoryNew diseaseNew risk factorsSeveral exposures"Fishing expedition"

AnalyticalDefine a single hypothesisDose response

Cohort studies

Rate/risk Rate/risk difference Rate Ratio/Risk ratio (strength of association)

No calculation of rates/risks Proportion of exposure

Case control studies

Any way of estimating measures of association?

Odds

Probability that an event will happen

Probability that an event will not happen

Probability that cases/controls will be exposed

Probability that cases/controls will not be exposed

Exposed

Not exposed

Cases Controls Odds ratio

Case control study

a b

c d

Total a+c

OR= (a/c) / (b/d)

= ad / bc

a/c b/dOdds ofexposure

b+d

% exposed a/(a+c)

% unexposed c/(a+c)

b/(b+d)

d/(b+d)

Exposed

Not exposed

Cases Controls Odds ratio

Case control study

50 20 4

a b 50 80

c d

Total 100 100OR= (a/c) / (b/d)

= ad / bc

= (50x80) / (20x50)

= 4

50/50 20/80Odds ofexposure

Case control study design

Cases Controls

E

E

a b

c d

a b a x d---- --- = --- ---- c d b x c

Odds ratio

Frequency of chicken consumption in campylobacter cases and controls, Republic of Ireland and Northern

Ireland, 2003

Cases Controls Oddsratio

Ate chicken 181 251 2.1

Did not eat chicken

15 44 Ref

Frequency of contact with a dog in campylobacter cases and controls, Republic of Ireland and Northern

Ireland, 2003

Cases Controls Oddsratio

Contact with dog

Yes 29 93 0.40

No 158 201 Ref

Oral Myocardialcontraceptives Infarction Controls OR

Yes 693 320 4.8

No 307 680 Ref.

Total 1000 1000

Odds 693/307= 320/680=of exposure 2.2 0.5

Distribution of myocardial infarction by recent oral contraceptive use

in cases and controls

Physical Myocardialactivity Infarction Controls OR

>= 2500 Kcal 190 230 0.64

< 2500 Kcal 176 136 Ref.

Total 366 366

odds of 190/176= 230/136=exposure 1.1 1.7

Distribution of myocardial infarction by amount of physical activity

in cases and controls

Distribution of cases of endometrial cancer by oestrogen use in cases and controls

Oestrogen use Cases Controls Odds ratio

High a1 b1 a1d/b1c

Low a2 b2 a2d/b2c

None c d Reference

Relation of hepatocellular adenoma to duration of oral contraceptive use

in 79 cases and 220 controls

Months ofOC use Cases Controls Odds ratio

0-12 7 121 Ref.13-36 11 49 3.937-60 20 23 15.061-84 21 20 18.1>= 85 20 7 49.7Total 79 220

Source: Rooks et al. 1979

Advantages of case control studies

Rare diseases

Several exposures

Long latency

Rapidity

Low cost

Small sample size

Available data

No ethical problem

Limitations of case-control studies

Cannot compute directly risk Not suitable for rare exposure Temporal relationship exposure-disease

difficult to establish Biases +++

- control selection- recall biases when collecting data

Loss of precision due to sampling

The cohort study

is the gold-standard

of analytical

epidemiology

CASE-CONTROL STUDIES HAVE THEIR PLACE IN EPIDEMIOLOGY, but if cohort study possible,

do not settle for second best

Thank you!

Back-up slides

I1 = a / P1

I0 = c /P0

E

E

a

c

P1

P0

Populationdenominator

Cases

E

E

a

c

P1 /10

P0 /10

PopulationsampleCases

a/P1

I1/ I0 = ------

c/P0

}

aI1 = --------

P1/10

cI0 = --------

P0/10

} a/P1

I1/ I0 = ------

c/P0

I1 = a / P1

I0 = c /P0

Cases Controls

E

E

a b

c d

E

E

a

c

P1

P0

Source population

Pop.Cases

P1 b--- = ----P0 d

= sample

a/P1

I1/ I0 = ------

c/P0

}

I1 = a / P1

I0 = c /P0

Cases

= sample

E

E

a b

c d

Since d/b = P0 / P1

E

E

a

c

P1

P0

Source population

Pop.Cases

a/P1 a . P0 a . d I1 / I0 = ------ = ------- = ----- = c/P0 c . P1 c . b

}Controls

P1 b

--- = ----

P0 d

a / c------b / d