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Epidemiology. HSTAT1101: 27. oktober 2004 Odd Aalen. 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: - PowerPoint PPT Presentation
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Epidemiology
HSTAT1101: 27. oktober 2004Odd 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 non-
smokers 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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Epidem
iology 2004;15: 653–659Lönn et al
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Prevalence
P = number of people with disease or condition
number of people in population at risk100,000
Prevalence: The proportion of a population that has a certain condition at a specified time
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
I = number of new cases of disease
number of people in population at risk100,000
Incidence: Rate of new cases per year of a certain condition:
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
By person-years we mean the sum of the observation times for all individuals
I = number of cases
person- years
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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
302,501 5100,000 = 30.4
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
Calendar year in 5 year intervals
91-9586-9081-8576-8071-7566-7061-6556-60
Age
adj
. inc
iden
ce p
er 1
00,0
00 p
erso
n ye
ars
18
16
14
12
10
8
6
4
2
0
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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 cityAll people in the country suffering from tuberculosisPeople 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
Study population
Random sampling
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Association and causation
Epidemiology gives us statistical associationsExample: smokers have much higher risk of lung
cancers than non-smokersExample: 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
Survival curve: Describing proportion that survives up to some time
Hazard rate: Describing risk of event (death, relapse etc) as function of time
x 543210
1
0.8
0.6
0.4
0.2
0
x 543210
1.2
1
0.8
0.6
0.4
0.2
0
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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
Duration of marriage (years)
252321191715131197531
Div
orc
e r
ate
(p
er
10
00
ma
rria
ge
s) 25
20
15
10
5
0
1980
1970
1960
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“Survival” of marriages contracted in 1960, 1970 and 1980
Duration of marriage (years)
252321191715131197531
Su
rviv
al o
f m
arr
iag
e1.0
.9
.8
.7
.6
.5
1960
1970
1980
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Treatment of acute myocardial infarction Analyzed
by Cox model, adjusted hazard ratio 2.31
Propor-tionality?