1. Epidemiology 101: basic concepts
Dr Ike Anya
Specialist Registrar in Public Health Medicine,
Bristol Joint Directorate of Public Health UK and Visiting Lecturer London School of Hygiene and Tropical Medicine

2. Learning objectives To explore the definition of epidemiology
To introduce key concepts in epidemiology
To introduce the concepts of risk, risk measurement and standardization in epidemiology

3. What is epidemiology? The study of epidemics? The study of diseases?
The study of diseases of the skin?
Something scientists and academics use to confuse other people?

4. Definition of epidemiology
“The study of the distribution and
determinants of health related states or events in specified populations and the application of this study to control health problems”
- James Last
A Dictionary of Epidemiology

5. Unpacking that definition
Study: Observing,recording,experimenting
Distribution : Who, where, when
Determinants: Why?
Health related states
Specified populations
Application

6. Epidemiology asks or uses:
Person- Who?
Place- Where?
Time- When?
Helps us to understand: Why?

7. Specified populations
How many people in this room are infected with the HIV virus?
How many people in Toronto are infected ?
How many people in Canada are infected?

8. Why is it important to specify the population
In order to be able to compare between two populations, we need to know what the defined population is
For example,if we say 50 people in this room have an infection compared with 100 people in the next room, does it mean that infections are less common in this room?

9. Numerators and denominators
Numerator – the top half of the fraction
Denominator- the bottom number in the fraction
Numerator is usually number of people with the disease and the denominator is usually the total population at risk

10. Numerators and denominators 2
There may be fewer people in this room than in the next room
Let’s assume that there are 100 people in this room and 1000 people in the next room
So 50 people with infections out of 100 people in this room means half (50/100) of the people in this room have infections
100 people with infections out of a 1000 in the next room means only a tenth (100/1000) of the next room have colds
Similar argument for time – Winter vs summer

11. Measuring disease frequency
There are 2 main measures used
Prevalence
Incidence

12. Prevalence and incidence
Prevalence - the number of people with a particular condition, habit at a specified time within a defined population eg prevalence of colds,smoking
Incidence - the number of NEW cases of a condition/habit in a defined population over a specified period of time

13. Distinguishing between incidence and prevalence
Prevalence includes both old and new cases and is usually expressed as a percentage
Incidence includes only NEW cases and is expressed as the number of cases per population per year
Time period and population must be specified

14. Prevalence Prevalence of colds in this class
Number of cases (people with colds) = 3
Population of class = 30
Prevalence = 3/30
Expressed as a percentage = 3/30 X 100
=10%

15. Incidence
Number of cases of newly diagnosed HIV infection in a city in 2003 is 900
Population of the city is
Incidence of HIV is 900 per in 2003

16. Defining risk Probability that an event will occur
Different from causation
Chance that if exposed to certain risk factors will develop condition
Distinguish between

17. Risk and risk factors
Risk factors are factors that increase the probability that a disease will occur
Risk factors could be
environmental
behavioural/lifestyle
genetic
Ask for examples

18. Differentiating between risk and causation
Risk is about probability or likelihood
Causation is about “certainty”
Identifying a risk may be the first step to understanding causation eg smoking and lung cancer

19. Types of risk
Absolute risk
Relative risk
Attributable risk

20. Measures of risk – absolute risk
Number of cases in a defined population
Similar to incidence
If 100 people are infected with HIV in a town of 1000 people, the absolute risk of HIV in the town is 100 per 1000
But the people in the town have different lifestyles, genes,living conditions which absolute risk does not take note of

21. Measures of risk – relative risk
Going back to our example, we could divide the population of the town into injecting drug users (IDUs) and non-injecting drug users non-IDUs)
Count the number of cases of HIV in IDUs and count the number in non-IDUs
Relative risk (risk ratio) is the ratio between the two
I.e.Risk in the exposed /risk in the unexposed

22. Relative risk
In our example, there were 400 IDUs in the town, and 80 of them were diagnosed with HIV in the year of our study. The risk of HIV in IDUs was therefore 80/400 = 0.2
There were 20 diagnoses of HIV in the non-IDU population of 600, so the risk of HIV in non-IDUs was 20/600 = 0.033
The relative risk is therefore 0.2 divided by 0.033=6.06

23. What does the relative risk mean?
From the example, we obtained a relative risk of 6.06
In simple terms it means that IDUs in the town in that year were 6.06 times more likely to be diagnosed with HIV than non-IDUs

24. Attributable risk
Difference between risk in the exposed and risk in the unexposed
Risk in exposed minus risk in unexposed
From our example the attributable risk for smokers in the town was
=0.167

25. Rates Rates are another means of expressing measurement
3 broad types of rates commonly used in epidemiology
Crude rates
Specific rates
Standardized rates

26. Crude rates
Looking at the death records in Newtown which has a population of we find that 500 people died in 2005
In neighbouring OldTown with the same population of , there were 800 deaths in 2005

27. Comparing crude rates
Newtown had a crude death rate of 500 per
Oldtown had a crude death rate of 800 per
Oldtown appears to have a higher death rate than Newtown, but do the crude rates tell the whole story?

28. Newtown
Oldtown
Age group
Number of deaths
10-20
200 30
20-30
150 20
30-40 50 40
40-50
50-60 15 90
60-70 10
70-80
250
80-90 35
TOTAL
500
800

29. Delving deeper – specific rates
Looking at the number of deaths in different age groups we get a different picture
The majority of deaths in Oldtown occurred in people over the age of 60
The majority of deaths in Newtown occurred in people under the age of 40

30. Specific rates
Specific rates give us more detail by looking at the occurrence of events in a subgroup of the population
In the example, we used age groups, but could have used gender, ethnicity,occupation,etc

31. Comparing rates - standardisation
Going back to the example, we know that there were different patterns in the deaths recorded in the two towns
But we may find it difficult to compare rates between the two towns
Why?

32. Why standardize ?
Perhaps Oldtown is a retirement town with many old people and few young people?
Perhaps Newtown has very few old people and is a barracks town consisting largely of soldiers going to Iraq?
To enable valid comparison, we need to be comparing like with like – hence standardization

33. What are standardized rates?
Standardized rates are rates that take into account the structure of the population and adjust for differences in population structure
Rates can be age-standardized, sex-standardized, etc

34. Summary
Epidemiology uses person, time and place to study how illness and health are distributed in populations
In epidemiology, specifying populations and time periods is important
When interpreting epidemiology, always check that like is being compared with like