introduction to epidemiologypublicifsv.sund.ku.dk/~pka/epi06/hhj1.pdfj. m. last, 2001: a dictionary...

1

Epidemiology

Spring semester 2006

Introduction to epidemiology

• Background, definitions & applications(chapter 1)

• Different epidemiological study designs (chapter 2)

• Statistical and/or causal relationships(chapter 3)

William Farr

• Physician, Compiler of Astracts in the General Register Office

• In 35th annual report notes difference in childhoodmortality rates between rich and poor

• What are the causes? Do they admit to removal? Ifthey do admit to removal, is the destruciton of life to be allowed to go on indefinitely?

Assumption about cause & effect

• Diseases do not occur at random

• Diseases are preventable!

H&B 4

2

Epidemiology: Definition & objectives

”…the study of the distribution and determinants

of disease frequency”

”…the study of the distribution and determinants of

health related states or events in specified

populations, and the application of this study to

control of health problems”

J.M. Last, 1988; A Dictionary of Epidemiology, Oxf University Press

H&B 3

Epidemiologist’s tools

Design Statistics

Problem

Data

Time

Economy

Power

Bias & kon

Operation.

Analyses

Descriptive & analytic epidemiology

Randomised intervention

Epidemiologic focus

Case control study

Cohort study

Case report

Case series

Ecological study(correlational study))

Cross sectional study

generating testing

Hypothesis-

Incidence report

A patient series

Carcinoma of the penis and cervix

“… Case 3. – Presented with 5-year history in November, 1969, aged 47. He had massive penile condylomata with squamous carcinomatous change and invaded ingual nodes. Died in 1977. His wife presented with carcinoma of the cervix in 1971 at the age of 43. She had a squamous cell carcinoma and stage III disease. Died 27 months later.”

Cartwright and Sinson, 1980; Lancet: 1: 97

3

Occurrence of gonorrhoea

Beral, Lancet, 25 May 1974

1922 1932 1942 1952 1962 19720

25

50

75

Calendar year

Go

no

rrh

oea i

ncid

en

ce

per

100,0

00 w

om

en

Exercise

• Insert data for cervical cancer mortality in hand-outs

• Discuss what is shown in the figure

• Can this method be used to test hypotheses?

Birth cohort England Scotland1902-6 91 98

1907-11 88 92

1912-16 90 110

1917-21 102 100

1922-26 112 100

1927-31 100 90

1932-36 68 85

1937-42 65 86

1943-47 82 170

1948-52 130

Measure of mortality from cervicalcancer in England/Wales & Skotland per birth cohort

Adapted from Beral, Lancet, 1974

Two different situations

Exposure

COHORT

CASE-CONTROL

Case control studies

Population

Outcome

Healthy

Tid

Beaglehole et al., 1993

Exposed

Unexposed

Exposed

Unexposed

4

Gonorrhoea & cervical cancer

Gonorrhoea Patients Controls

Yes 13 49

No 45 560

Odds ratio 3,30(13/45)/(49/560)

Study of women with cervical cancer og controls

• Were you ever diagnosed with gonorrhoea?

Kjaer et al., Cancer Causes Control. 1992 Jul;3(4):339-48.

Cohort studies

Population Persons without outcome

Exposed Outcome

Healthy

Lost

Unexposed Outcome

Healthy

Lost

Tid


Cohort study

• Occurrence of cervical cancer in 4.440 kvinder hospitalised with gonorrhoea and followed for 54.576 person-years at risk

CIN III

• 227 cases observed

• 102.6 cases expected

• Relative risk 2.2

Cervical cancer




Johansen et al., Acta Obstet Gynecol Scand. 2001 Aug;80(8):757-61

Applications

Epidemiological methods can be used to

• Identify (new) diseases

• Characterise the natural history of diseases

• Characterise disease occurrence in populations

• Identify causes of diseases

• Evaluate the efficacy and effectiveness of interventions

5

WHO issues a global alert about cases of atypical pneumonia

“In Viet Nam the outbreak began with a single initial case who was hospitalized for treatment of severe, acute respiratory syndrome of unknown origin. He felt unwell during his journey and fell ill shortly after arrival in Hanoi from Shanghai and Hong Kong SAR, China.Following his admission to the hospital, approximately 20 hospital staff became sick with similar symptoms.”

http://www.who.int/csr/sars/archive/2003_03_12/en/

Outcomes and Prognostic Factors in 267 Patients with Severe Acute

Respiratory Syndrome in Hong Kong

Choi KW et al., Ann Intern Med. 2003 Nov 4;139(9):715-23

http://www.who.int/whr/2003/chapter5/en/index2.html

Identification of severe acute respiratory syndrome in Canada

Poutanen et al., N Engl J Med. 2003 May 15;348(20):1995-2005

6

Gonorrhoea in Denmark

1980 1985 1990 1995 20000

2000

4000

6000

8000

10000

12000

0

1

2

3

4

5

6

7

8

Kalenderår

Til

fæld

e

Man

d:K

vin

de ra

tio

Hoffmann S., Euro Surveill. 2001 May;6(5):86-90

EpiNyt

Gonorrhoea occurrence

BMJ 2002 Jun 1;324(7349):1324-7

Changes in causes of death

100.0%Total100.0%Total

18.0%Other7.2%Other

1.7%Diabetes1.9%Diphtheria

1.9%Chronic liver disease4.2%Accidents

2.0%Pneumonia./influenza4.5%Cancer

2.1%Suicide5.9%Nephritis

2.9%Chronic lung disease6.3%Diarrhea/enteritis

6.5%Stroke7.6%Stroke

6.6%Accidents9.4%Heart disease

23.9%Cancer11.2%Tuberculosis

34.4%Heart disease11.8%Pneumonia/influenza

19821900

H&B 9

Smoking and lung cancer

Non-smokers

27 (4,2%)633 (95,8%)Controls

2 (0,3%)647 (99,7%)Lung cancer

32 (53,3%)28 (46,7%)Controls

19 (31,7%)41 (68,3%)Lung cancer

Women

Men

Smokers

Doll & Hill, BMJ, 2:739, 1950H&B pp 45 & 90

7

Bradford Hills criteria

Is there a valid statisticalassociation?

• Is there a strong association?

• Is there biological credibility to

the hypothesis?

• Is there consistency with otherstudies?

• Is the time sequencecompatible?

• Is there evidence of a dose-response relation-ship?

Can this valid statistical associa-tion be judged as cause & effect?

• Is the association likely to bedue to chance?

• Is the association likely to bedue to bias?

• Is the association likely to be

due to confounding?

H&B 45

The result of the study

• Association refers to the statistical dependencebetween two variables, that is, the degree to whichthe rate of disease in persons with a specificexposure is either higher or lower than the rate ofdisease among those without that exposure

• A causal association is one in which a change in thefrequency or quality of an exposure or characteristicresults in a corresponding change in the frequency ofthe disease or outcome of interest.

Excercise

Obesity

Stress

Cardiovascular disease

Inherited factors

Hypertension

Smoking

Interpretation of epidemiologic data

Is the observed association a chance phenomenon?

• In epidemiology population samples are typically used

• Samples are characterised by random variation

• The magnitude of this variation can be quantified

• The p-value reflects both samples size and magnitude ofeffect

• The confidence interval renders the impression of botheffect and statistical significance

H&B 32-3

8

Gonorrhoea & cervical cancer

GonorrhoeaPatients Controls

Yes 13 49

No 45 560

Odds ratio 3.30(13/45)/(49/560)

Study of women with cervical cancer og controls

• Were you ever diagnosed with gonorrhoea?

95% Confidence interval: 1.67 to 6.53

Cohort study

• Occurrence of cervical cancer in 4 440 kvinder hospitalised with gonorrhoea and followed for 54,576 person-years at risk

CIN III




Cervical cancer




95% CI 0.6 til 2.2

Johansen et al., Acta Obstet Gynecol Scand. 2001 Aug;80(8):757-61

Power calculations

The necessary study size depends on

• The magnitude of the hypothesised effect

• Levels of type 1 & type 2 errors

– Type 1: The chance of erronously rejecting the null-hypothesis

– Type 2: The chance of erronously accepting the null-hypothesis

• The incidence of outcome/prevalence of exposure in population

• Ratio of numbers of exposed and unexposed or cases and controls, respectively

Samples size calculations (OR/RR = 2)

Cohort study

• 1:1 exposed/uexposed

• 1% outcome in unexposed

• 2514 exposed/unexposed





Case-kontrol study

• 1:1 cases/controls

• 1% expsoure in controls

• 2597 cases/controls





9

Bias definition

”Any trend in the collection, analysis, interpretation, publication or review of data thatcan lead to conclusions that are systematicallydifferent from the truth”.

J. M. Last, 2001: A Dictionary of Epidemiology, Oxf University Press

Different types of bias

• Selection bias – Systematic difference between the persons, who are or are not

enrolled in an investigation

– If loss to follow-up differs between exposed and un-exposedparticipants

• Measurement- or information bias– Interviewer bias: If the interviewer knows about the study

hypotheses and inadvertently bias the respondent

– Recall bias: When cases and controls recall exposuredifferently

• Evaluate/assess the significance and direction ofsuspected bias

Example of bias

Case Control

Smoker 900 100

Non-smoker 100 900

OR = (900*900)/(100*100) = 81

What happens if 10% of the controls are misclasssified with respect to exposure

Confounding

”The third alternative explanation that must beconsidered is that an observed association (orlack of one) is in fact due to a mixing of effectsbetween the exposure, the disease, and a thirdfactor that is associated with the exposure and independently affect the risk of developing thedisease.”

H&B p 35-6

10

Confounding

• Confounding may occur if some ”other” risk factor is prevalent in the study population, which is associatedwith both exposure and outcome.

Risk factor(Stress)

Outcome(Heart disease)

Confounder(Smoking)


Generalizability

• Are the results of the investigation generalizable?– I.e., can it be assumed that associations like the observed will

also apply to the rest of the population and in other populations?

– This question must be asked already in the planning phase

From sample to population Between populations

Interpretation of epidemiological data

• Magnitude of effect

• Great effect hardly unknown confounder

• Is there consistency with other studies?

• Have others made similar observations?

• Biologic credibility

• Is the time sequence sound?

• Does exposure precede outcome?

• Is there evidence of a dose-response pattern?

11

Time sequence


Ylitalo et al., Lancet, 355; 2194-8

On epidemiologic studies

It is more important to increase the quality ofdata in the collection phase than to applysophisticated statistics

A. Bradford Hill

12

Take home messages

• Epidemiology is based on the assumption thatdiseases have causes that can be identified

• There are different types of epidemiologic designs that differ with respect to strengths and weaknesses

• Statististical association does not necessary reflectcausality, but may result from chance, bias and confounding

introduction to epidemiologypublicifsv.sund.ku.dk/~pka/epi06/hhj1.pdfj. m. last, 2001: a dictionary...

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