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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
Beaglehole et al., 1993
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
• 11 cases observed
• 8.9 cases expected
• Relative risk 1.2
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
• 227 cases observed
• 102.6 cases expected
• Relative risk 2.2
Cervical cancer
• 11 cases observed
• 8.9 cases expected
• Relative risk 1.2
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
• 5% outcome in unexposed
• 474 exposed/unexposed
• 15% outcome in unexposed
• 133 exposed/unexposed
Case-kontrol study
• 1:1 cases/controls
• 1% expsoure in controls
• 2597 cases/controls
• 5% expsoure in controls
• 559 cases/controls
• 15% expsoure in controls
• 225 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)
Beaglehole et al., 1993
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
Beaglehole et al., 1993
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