1. Introduction to epidemiology
Epidemiology, "the study of what is upon the people", is derived from the Greek terms epi = upon, among; demos = people, district; logos = study suggesting that it applies only to human populations but there is also the concept of veterinary epidemiology and even botanical epidemiology.
Contributors Paul Pilkington, Sara Roberts, Alice Walsh Adapted by Dave Jenner for East Midlands PHI&I Training Course 2009

2. The unit of interest is the population.
What is Epidemiology?
“the study of the distribution, frequency and determinants of health problems and disease in human populations”
The unit of interest is the population.
In other words, epidemiology is the study of how often and why diseases occur in different groups of people.

3. Epidemiology ...
.... allows the distribution of health and ill-health in a population to be described in terms of
WHAT is the problem and its frequency?
WHO is affected?
WHERE and WHEN does it occur?
WHY does it occur in this particular population?
Example Mumps
what is the problem? Mumps
What is its frequency? use to be quite low but recent surge in cases
Who is affected? students who missed out on immunisation as a child are a particularly high risk group
Where and When? university campuses – start of term
Why? large number of unimmunised students in close proximity - droplet transmission

4. Types of epidemiology and their uses
Descriptive epidemiology
Describing patterns and trends in health and disease in populations
Analytical epidemiology
Examining associations and causation
Experimental epidemiology
Testing population interventions
Epidemiologists employ a range of study designs from the observational to experimental and are generally categorized as descriptive, analytic (aiming to further examine known associations or hypothesized relationships, identifying causal relationships between exposures and outcomes), and experimental (a term often equated with clinical or community trials of treatments and other interventions).
Descriptive epidemiology - ?same as public health intelligence.
In PHI we do some of the first and use the results of the second and third.
Scope of PHI - a bit more tightly drawn. We don’t often get involved in, e.g. studies of causation - we tend to leave that to the academics.
We’re also mainly concerned with out local population and local services less about what’s going on in the country as a whole.
More about this later today.

5. Purpose of epidemiology
“To obtain, interpret and use health information to promote health and reduce disease”
Different types of epidemiology - epidemiology in the academic sector and the service sector (public health and public health intelligence) = THE PURPOSE IS FUNDAMENTALLY THE SAME.

6. In the news….. UK C.Diff deaths 'rising sharply'
There are 10 times more deaths across the UK from the superbug clostridium difficile among over 65-year-olds than in any other country in the world.
Grapefruit 'may cut gum disease'
Researchers found people with gum disease who ate two grapefruits a day for a fortnight showed significantly less bleeding from the gums.
All eyes on England's smoking ban
Controversy has surrounded the introduction of laws prohibiting smoking in public places and there is still debate over whether they work.

7. Descriptive Epidemiology: PERSON, TIME, PLACE
Person: Variations in health by age, sex, ethnicity, occupation, leisure interests...
Time: Trends, seasonal variations, cohort effects…
Place: Variations between geographical areas – local, national, international…
More on Descriptive Epidemiology.

8. Descriptive epidemiology
Often makes use of routinely collected data, e.g. death certification data, hospital episode statistics, infectious disease notifications
May require special surveys
Can’t answer ‘why?’ but can raise hypotheses about causes
Can often provide sufficient information for public health action to be taken

9. Descriptive Epidemiology: Example 1
E coli O157 in Cornwall
Summer 2004
Several cases over a short period
Escherichia coli is a bacterium found in the normal gut flora of warm blooded animals.
Most are harmless, but some strains eg E. coli 0157 can cause serious gastro-intestinal problems.
Source of data: routine surveillance of notified cases.

10. Analysis by person, time, place
Person: they were all young children visiting Cornwall on holiday
Time
Descriptive data – tells us that we have a cluster of cases, possibly with a common source.
On enquiry found to have all visited the same beach and played in the same stream.
PT = Phage Type. PT 21/28 is the type associated with the stream, so the others come from a different source.
Place: they had all visited the same beach and played in the same stream

11. Public health action The stream fenced off with warning signs
Stream - maybe run-off from agricultural land?
Not proof of causation but sufficient for public health action.
The stream fenced off with warning signs

12. Descriptive Epidemiology: Example 2
Here is an example of descriptive epidemiology being used to make INTERNATIONAL (Western European) comparisons.
Shows deaths rates from breast cancer in women under 65 per 100,000 population, standardised by age (this takes into account the different age profiles of each country – will cover standardisation in ?Day 3).
WHY MIGHT DEATH RATES IN THE UK BE HIGH? GROUP DISCUSSION
Descriptive epidemiology, by its very nature, can only describe patterns, it does not explain them!
However it can certainly generate ideas!
Why might death rates in the UK be high?

13. Descriptive epidemiology: applications
studying the frequency and distribution of disease
blue - this is the academic/research perspective on descriptive epidemiology.
green - this is the focus for large chunks of the rest of the course - particularly perhaps Days 4 and 5 when we look in more depth at how we use local descriptive epidemiology to inform local action.
to inform local public health action
(the service focus)
to generate hypotheses about causes
(the academic focus)

14. In the news…..
Blood pressure cases 'to top 1bn'
High blood pressure is out of control around the world, with the number of sufferers expected to exceed a billion within 20 years, experts warn.
One in four adults already has the condition, which increases the risk of heart disease, stroke and death.

15. Descriptive Epidemiology:
what counts as a case?
“... in practice the boundaries of almost all diseases are unclear, and a full range of severity exists from the hardly perceptible to the catastrophic. Establishing what will be counted as a “case” ... can have enormous impacts on the numbers included.”
Oxford Handbook of Public Health Practice
When measuring the frequency of a disease ... we may come across this issue.
Applies to ...
tummy bugs in Cornwall
not breast cancer
certainly hypertension
Eg changing the level at which blood pressure becomes hypertension has huge implications for the number of “cases”
140/90 normally regarded as threshold between normal BP and hypertension.

16. Measures of disease frequency
The two main measures of disease frequency are:
Incidence
Prevalence
Measuring the amount of disease in a population.
Incidence and Prevalence - we’ll have a quick look at this now and return to it in Day 3 with some hands on work.

17. What is incidence?
The incidence is the number of NEW CASES of disease that develop in a population during a specified time period
Usually expressed as the number of new cases per 100,000 population per year.
We’ve referred to the potential problems of defining what counts as a case. What about the denominator - any problems with that?
Denominator can be the population at risk at the beginning, or the mid-point of the year, or the total person-time at risk.

18. Example: Measuring incidence
Incidence of cervical cancer in a PCT during 2008:
Number of new cases of cervical cancer during 2008: 45
PCT Population in 2008 (mid-year estimate): 500,000
Incidence: 9 cases of cervical cancer per 100,000 during 2008.
N.B. The denominator might be taken as the population at risk at the beginning, or the mid-point of the year, or the total person-time at risk

19. What is prevalence?
Prevalence is the total number of cases of disease in a population at one point in time, taken as a proportion of the total number of persons in that population.
Also referred to as “point prevalence”
N.B. Period prevalence is a variation which represents the number of persons who were a case at any time during a specified (short) period as a proportion of the total number of persons in that population.
Example ... the proportion of children with untreated dental decay in an area at a particular point in time.
Prevalence is expressed as a proportion or more commonly as a %.
Is it “rate”?
It does not take into account WHEN people became infected / diseased.

20. Example: incidence and prevalence
Cases of cold infections in class 4J. Class size: 20
January
February
March
When was the highest point prevalence?
What is the incidence in February?
What is the point prevalence on the 28th February?
What is the period prevalence during February?

21. incidence and prevalence
the number of new cases of disease per n of population occurring in a specified time period
prevalence:
the number of persons with disease at one point in time as a proportion of the total number of persons in that population.
Examples of incidence and prevalence - on your tables - get completely familiar with the concepts - let us have your best examples.

22. Why might the prevalence of a condition appear to have changed?
Improved treatment resulting in longer survival time
High profile case, raises awareness
Increased incidence following increased exposure to a causal factor
New diagnostic technique
Changed surveillance system – broadens the definition of a case
Improved treatment – resulting in cure for some

23. This diagram is a modified version of one published in the teachers’ guide to Basic Epidemiology, a textbook published under the sponsorship of the World Health Organization.

24. In the news….. Sibling link to heart health risk
Having a brother or sister with cardiovascular disease (CVD) is bad news for your own odds of developing problems, research has found.
Vitamin D ‘can lower cancer risk’
High doses of vitamin D can reduce the risk of developing some common cancers by as much as 50%, US scientists claim.
Grapefruit 'may cut gum disease'
Researchers found people with gum disease who ate two grapefruits a day for a fortnight showed significantly less bleeding from the gums.
The stories are making a link between a health condition and a possible causal factor.
Oily fish is a source of vitamin D
Grapefruit is full of vitamin C
Heart disease may run in the family

25. Analytical epidemiology
Descriptive epidemiology +
Focus on cause and effect =
“analytical epidemiology”
Analytical epidemiology is concerned with determining causation of disease.
Descriptive epidemiology can be an extremely useful part of this, helping to generate hypotheses. But the focus is generally on cause and effect.
Analytical studies may also be called “aetiological studies” (aetiology - the study of disease causation).

26. Example: John Snow John Snow, physician(1813-1858)
Outbreaks of Cholera were common in London during the 19th century
But what was causing the cholera? The popular theory at the time was that bad gases caused it (‘miasma’ theory)

27. What did he do?
Analysis by place: he mapped the cases – most were near Broad Street
Anecdote: People had complained that the water smelt bad. Cases from further afield had water delivered by cart from Broad Street.
He did some descriptive epidemiology.
He pursued a hypothesis - that the local water was the cause of the problem.

28. What did he do? Recorded deaths by water supplier
Conclusion: Risk of infection is highest in people using water Southwark and Vauxhall water company .
Examined the association (a) by district and (b) by household

29. Public health action
He removed the handle from the Broad Street pump and the number of infections fell.

30. Analytical epidemiology: common study designs
Cross-sectional study
Case control study
Cohort study

31. Analytical epidemiology: Cross-sectional study - Example 1
Information on health and other characteristics is collected from each subject in a population at one point in time.
Example - the prevalence of cough
collect data on the health problem: cough (yes/no)
collect data (in same individuals) on possible determinants:
damp housing (yes/no)
smoker (yes/no)
smoker (number of cigarettes per day)
statistical tests of association
Cross-sectional studies can be descriptive ... but by collecting other data, e.g. on the possible determinants of cough then it’s possible to search for associations ... and start generating hypotheses ...

32. Analytical epidemiology: Cross-sectional study - Example 2
Adult dental health in Cornwall
Deprived people more likely than affluent people to have poor dental health.

33. Analytical epidemiology: Cross-sectional study - Example 2
Why do deprived groups have poorer dental health?
A cross-sectional study might focus on dental health and ....
fluoridation of water supply
diet
personal dental hygiene
uptake of dental care
Ask the audience

34. Analytical epidemiology: Case-control studies
Compares people with a condition (cases) to a similar group of people without the condition (controls).
The aim is to try and identify the risk factors which may have caused the cases to get the condition in the first place.
Case-control studies use people who already have a disease or other condition and look back to see if there are characteristics of these people that differ from those who don’t have the disease.

35. Analytical epidemiology: Case-control study - Example
Investigation of an outbreak of Salmonella in S.E.Wales
Two groups: Group 1 ill, Group 2 not ill
Examination of diet and behaviour during the 3 days before illness.
Those who were ill were found to have been 4.5x more likely to have eaten sliced ham than those who were not ill.
Further investigations revealed that those who were ill were 25x more likely to have eaten ham supplied by “producer A”.
The starting point is establishing two groups - one with the disease and one without.
The great triumph of the case-control study was the demonstration of the link between tobacco smoking and lung cancer, by Sir Richard Doll and others after him. Doll was able to show a statistically significant association between the two in a large case control study. Opponents, usually backed by the tobacco industry, argued (correctly) for many years that this type of study cannot prove causation, but the eventual results of double-blind prospective studies confirmed the causal link which the case-control studies suggested, and it is now accepted that tobacco smoking is the cause of about 87% of all lung cancer mortality in the US.

36. Analytical epidemiology: Cohort studies
Follow up two groups of people over time and compare the occurrence of disease.
One group has been exposed to a possible risk factor for the disease, while the other has not (the control group).
The exposure is the starting point, the disease is the outcome of interest
The starting point is the EXPOSURE.

37. Analytical epidemiology: Cohort study - Example
Does being exposed to asbestos cause respiratory cancer?
Asbestos miners were followed up for 6 years. These were compared to the control group.
Asbestos miners were 50% more likely to die of mesothelioma (a respiratory cancer) than the control group.
Cohort studies can be prospective.
Cohort studies can also be retrospective – i.e. if the information/data is good enough a cohort can be tracked back through time, using records. As with a prospective cohort, it is still the exposure that is the starting point.

38. Beware ...
An ASSOCIATION between a risk factor (e.g. history of exposure to a substance) and a disease
DOES NOT NECESSARILY INDICATE
a CAUSAL relationship
Link from those studies that seek to assess ASSOCIATIONS and POSSIBLE causal relationships…

39. evidence for causality comes from criteria such as ...
Strength of association
Consistency of findings
Temporal relationship
Geographical distribution
Dose-response relationship
Biological plausibility
Reversibility
Strength of the association. The stronger the association between a risk factor and outcome, the more likely the relationship is thought to be causal e.g. how highly correlated is hypertension with a high sodium diet.
Consistency of findings. Have the same findings been observed among different populations, in different study designs and different times.
Temporal sequence of association. Exposure must precede outcome 
Biological gradient. Change in disease rates with corresponding changes in exposure (dose response).
Plausibility. Presence of a potential biological mechanism.
Reversibility Does the removal of the exposure alter the frequency of the outcome?
adapted from Bradford Hill’s Criteria for Causation

40. Experimental epidemiology
Testing the effectiveness of population health interventions

41. Randomised Controlled Trial
Didgeridoo playing as alternative treatment for obstructive sleep apnoea syndrome: randomised controlled trial. Reported in BMJ Dec 2005.
25 adults with obstructive sleep apnoea, randomised to didgeridoo instructions and daily practice for 4 months (14), or placing on the waiting list for lessons (11).
Didgeridoo players reported less daytime sleepiness and their partners reported less night time disturbance , compared with waiting list group.
Comparing one treatment or health education intervention with another
Select people with the same disease or characteristics (defined target population)
Randomly allocate these people
One group receives the one treatment, the other group receives an alternative treatment
The benefits of each treatment is assessed by comparing the health gain in each group
This example is really no different from a drug trial.

42. Controlled Trials of Community Interventions

43. Horses for courses how many people are affected by ...?
what is the cause of ...?
how effective is ...?
Work with your neighbour.
Think up an epidemiological research question.
Try to find how you would answer the Q as a researcher.
is there a link between ... ?

44. Conclusions Epidemiology is a core part of public health.
It allows the distribution of health and ill-health in a population to be described, and possible causal factors to be identified.
It enables public health professionals to understand health problems and take appropriate action.

45. What we have covered: What is epidemiology and what are its uses?
Descriptive epidemiology
Incidence and prevalence
Analytical epidemiology
– types of studies
association and causation
Experimental epidemiology

46. References
Medical statistics at a glance – Petrie and Sabin. Blackwell.
Epidemiology in Medicine – Charles Hennekins. Little, Brown and Company.
Epidemiology for the uninitiated – G.Rose and D.Barker.
Health Knowledge website