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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

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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

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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)

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“Survival” of marriages contracted in 1960, 1970 and 1980

Duration of marriage (years)

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Treatment of acute myocardial infarction Analyzed

by Cox model, adjusted hazard ratio 2.31

Propor-tionality?