1. HSTAT1101: 27. oktober 2004 Odd Aalen
Epidemiology
HSTAT1101: 27. oktober 2004
Odd Aalen

2. 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

3. Types of study Cross-sectional study Cohort (or follow-up) 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)

4. Epidemiology 2004;15: 653–659
Lönn et al

5. Prevalence
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)

6. 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

7. Incidence rate
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

8. 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

9. 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

10. Illustration of basic concepts
Incidence
Prevalence
Recovery
Death

11. 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

12. 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

13. Example From the Cancer Registry of Norway:
During there were 460 cases of breast cancer among women in the age group years
The population in this age group in 1985 was 302,501. Number of person-years are 302,501 × 5
The incidence rate is:

14. 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

15. Incidence of malignant melanoma among women in Norway 1956-1995

16. 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

17. Sampling
Total population
Random sampling
Study population

18. 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

19. 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

20. 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

21. 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

22. Small example Data set 26, 17, 7*, 41, 34*, 9, 13, 25*, 37, 18
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

23. 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

24. Example: Hazard rate (incidence rate) of divorce in Norway

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

26. Treatment of acute myocardial infarction
Analyzed by Cox model, adjusted hazard ratio 2.31
Propor-tionality?