Download Epidemiology

Epidemiology
HSTAT1101: 27. oktober 2004
Odd Aalen
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Measuring disease occurrence
The aim of epidemiology is to map disease
occurrence statistically, so that the disease
may be better understood and perhaps
prevented
 This requires measures of disease
occurrence. Two major measures:

 prevalence
 incidence
rate
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Types of study

Cross-sectional study
 assessing
the situation at one specific time (example: how
many smokers and non-smokers have asthma at the
present time)

Cohort (or follow-up) study
 looking
ahead in time (e.g. follow-up of smokers and nonsmokers to observe occurrence of asthma)

Case-control study
 looking
back in time (e.g.: patients with asthma are
compared with control group to look for previous risk
factors, e.g. smoking)
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Epidemiology 2004;15: 653–659
Lönn et al
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Prevalence

Prevalence: The proportion of a population that has a
certain condition at a specified time
P =

number of people with disease or condition
 100,000
number of people in population at risk
Example:


Prevalence of asthma in Norway: 2.4%
Prevalence of multiple sclerosis in Norway: 100 per 100,000
(Note: Sometimes another basis number than 100,000 may be used, e.g. 1 million)
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Estimating prevalence



Need an estimate of the population size
Need an estimate of the number of cases of
disease. Cross-sectional design is sufficient
Requires definition of case. This is often not
obvious:
 example:
asthma (dyspnea, wheezing, cough,
spirometric measurements)
 sometimes an apparent increase in prevalence is due to
a changing definition (or increased awareness) of
disease
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Incidence rate

Incidence: Rate of new cases per year of a
certain condition:
I =

number of new cases of disease
 100,000
number of people in population at risk
Examples:
 Incidence
of multiple sclerosis in Norway:
5 per 100,000 person years
 Incidence
of HIV infection in Oslo in 2000:
11 per 100,000 person years
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Estimating incidence
Need an estimate of the population size or
“person-years”
 Need an estimate of the number of new
cases of disease over some time period
(e.g. one year)

 requires
definition of when the disease started
(e.g. time of first diagnosis by a medical doctor)

Preferably a cohort (follow-up) study
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Prevalence vs. Incidence
Incidence measures risk of disease
 Prevalence measures burden of disease
 The burden may increase because the risk
increases, or because the disease lasts
longer, e.g. if mortality of disease decreases

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Illustration of basic concepts
Incidence
Prevalence
Death
Recovery
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Example:
HIV-infection
With new treatments progression to AIDS or
death has been strongly decreased
 No complete recovery takes place
 The incidence of HIV infection is largely
unchanged
 This results in considerably increased
prevalence of HIV infection

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Computing an incidence rate by the
person-years method

The incidence rate is estimated as
I =

number of cases
person - years
By person-years we mean the sum of the
observation times for all individuals
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Example

From the Cancer Registry of Norway:
 During
1983-87 there were 460 cases of breast cancer
among women in the age group 30-39 years
 The population in this age group in 1985 was 302,501.
Number of person-years are 302,501 × 5
 The incidence rate is:
I =
460
 100,000 = 30.4
302,501 5
per 100,000 women per year
or: per 100,000 person years
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Example
On the next slide is presented incidence of
malignant melanoma in Norway, a disease
which has become much more common over
the last few decades
 The incidence is age-adjusted, to correct for
changing age-composition. This is done by
standardization

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Incidence of
malignant
melanoma
among
women in
Norway
1956-1995
18
16
14
12
10
8
6
4
2
0
56-60
61-65
66-70
71-75
76-80
81-85
86-90
91-95
Calendar year in 5 year intervals
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Population and sample

The population consist of all the individuals we
want to study. Examples:
 All
people between 20 and 60 years of age in a
city
 All people in the country suffering from
tuberculosis
 People in a profession: e.g. bus drivers

The sample consist of those individuals that
are actually included in the study
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Sampling
Total population
Random sampling
Study population
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Association and causation

Epidemiology gives us statistical associations
 Example:
smokers have much higher risk of lung
cancers than non-smokers
 Example: People with high blood pressure have
increased risk of heart disease

Association does not necessarily imply that
the factor is a biological cause
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Confounding
Example: Cigarette smoking in mothers is
associated with sudden infant death (SIDS).
Is this causal?
 Smoking could be an indicator of other
lifestyle factors that influence the risk of
SIDS.
 Such “other factors” that could explain an
association are called confounders

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Survival analysis

Studying durations:
 duration
of disease
 duration of remission
 duration of marriage
 age at breast cancer diagnosis

Durations are important clinical and epidemiological
outcome parameters
 do
patients live longer
 does the remission period last longer
 can we postpone disease
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Censoring

Special problem of duration data: incompletely
observed times (censored data). Causes:
 study is terminated
 withdrawal
 observation ceases

Basic assumption: No selective censoring
 the
individuals which get censored at any given time
shall not differ, on the average, from those that are under
observation but not censored at that time
 can be modified for Cox-regression

Censoring precludes the use of ordinary statistical
methods for measurement data
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Small example

Data set
26, 17, 7*, 41, 34*, 9, 13, 25*, 37, 18
* censoring time

The same data ordered:
7*, 9, 13, 17, 18, 25*, 26, 34*, 37, 41
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Graphical presentation
1

Survival curve: Describing
proportion that survives up
to some time
0.8
0.6
0.4
0.2
00

Hazard rate: Describing risk
of event (death, relapse etc)
as function of time
1
2
x
3
4
5
3
4
5
1.2
1
0.8
0.6
0.4
0.2
00
1
2
x
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Example: Hazard rate (incidence rate) of
divorce in Norway
Norway: Rates of divorce for couples
married in 1960, 1970 and 1980
25
20
15
10
1980
5
1970
0
1960
1
3
5
7
9
11
13
Duration of marriage (years)
15
17
19
21
23
25
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“Survival” of marriages contracted in 1960,
1970 and 1980
1.0
.9
.8
.7
1960
.6
1970
.5
1980
1
3
5
7
9
11
13
Duration of marriage (years)
15
17
19
21
23
25
25
Treatment of acute myocardial
infarction


Analyzed
by Cox
model,
adjusted
hazard
ratio 2.31
Proportionality?
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