Epidemiology

1. What is epidemiology and what are its uses?

2. Descriptive epidemiology– Incidence and prevalence– Qualitative information

3. Analytical epidemiology – Types of studies– Association and causation

What is Epidemiology?

“The study of the frequency, distribution and determinants of health problems and disease in human populations”

The unit of interest is the population

Purpose of epidemiology

“To obtain, interpret and use health information to promote health and reduce disease”

How does Epidemiology help?

It allows the distribution of health and illness in a population to be described in terms of:

– WHERE and WHEN does this health problem occur in the population?

– WHAT is the problem and its frequency?

– WHO is affected?

– WHY does it occur in this particular population?

How does Epidemiology help? (2)

Epidemiological information is used to:

– Prevent illness and promote health

– To help treat people with existing disease

– Evaluate existing health services

Epidemiological Studies

Descriptive Analytic

Observational Experimental

What is descriptive epidemiology?

Frequency (of disease) (incidence & prevalence)

+Distribution (of disease)

=“descriptive epidemiology”

Descriptive epidemiology

• Usually makes use of routinely collected data, (e.g. death certification data, hospital episode statistics, infectious disease notifications)

• May require special surveys (usually cross sectional)

• Can’t answer ‘why?’ but can raise a causal hypothesis

• Can often provide sufficient information for public health action to be taken

TIME, PLACE, PERSON

• Time: Trends, seasonal variations, cohort effects…

• Place: Variations between geographical areas – local, national, international…

• Person: Variations in health by age, sex, ethnicity, occupation, leisure interests...

– All help us examine variations (inequalities) in health

Example 1: Pneumococcal meningitis incidence rate per 100,000 population by age group, England and Wales, 1996-2005 (Source HPA surveillance data)

Public Health ActionOn 4th September 2006 Pneumococcal

vaccinationintroduced into childhood immunisation

schedule!

Cumulative weekly number of reports of Invasive Pneumococcal Disease due to any of the seven serotypes in Prevenar™ : Children aged < 2 Years in England and Wales by

Epidemiological Year: July-June (2003- To Date)

Example 2

Why might death rates in the UK be high?

Descriptive epidemiology

• By studying frequency and distribution we can describe patterns of disease

• This can raise further questions and help us to generate hypotheses for the causes of disease

• It also helps us to respond to public health problems

Measures of disease frequency

There are 2 major types of measure of disease frequency:

Incidence

Prevalence

What is incidence?

The incidence is the number of NEW CASES of disease that develop, in a population of individuals at risk, during a specified time period

Usually expressed as the number of new cases, per 100,000 population per year

Example 3: Measuring incidence

Incidence of cervical cancer in a PCT during 2002

– Number of new cases during 2002 = 18

– Number of disease-free persons (‘population at risk’) at the beginning of 2002 = 200,000

Incidence is (18/200,000) x 100,000

– 9 cases of cervical cancer per 100,000 in 2002

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

What is prevalence?

Prevalence is the total number of EXISTING CASES of disease in a population at one point in time.

It is expressed as a proportion of the total number of persons in that population.

Also referred to as “point prevalence”

Period prevalence is a variation which represents the number of persons who were a case at any time during a specified period, as a proportion of the total number of persons in that population

Prevalence

• Prevalence is expressed as a proportion(0-100%)

…or as a rate (e.g. X cases per 100,000 population)

• It does not take into account WHEN people became infected / diseased

Example 4: Labouring the point!Incidence and prevalence

Cases of cold infections in class 4J. Class size: 20January February March

What is the period prevalence during February?

What is the point prevalence on the 28th February?

What is the incidence in February?

6/20 = 30%

1/20 = 5%

4/?

Incidence and prevalence

Sick population

(Prevalence)Healthy

population

Incidence (new cases)

die (mortality)

recover

Epidemiological Studies

Descriptive Analytic

Observational Experimental

Analytic epidemiology

Descriptive epidemiology (who, what, when)

+Analysis of cause and effect

(why, how)=

“analytic epidemiology”

Example 5: 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)

What did he do?

Analysis by place: he mapped the cases – most were near Broad Street (…miasma would predict even spread)

Anecdote: People had complained that the water smelt bad. Cases from further afield had water delivered by cart from Broad Street.

Public health action

He removed the handle from the Broad Street pump and the number of infections fell.

What did he do?

Recorded deaths by water supplier

The Lambeth company had started to pump water from 20 miles upstream from the Thames

Conclusion: Risk of infection is highest in people using water from the Southwark and Vauxhall water company.

Types of analytical study

Observational studies– Cross-sectional study

– (may be descriptive or analytical)

– Case control study– Cohort study

Intervention study (experimentation)– Randomised controlled trial (RCT)

Cross-sectional study

• Information on health status and other characteristics is collected from each subject at one point in time

• Cross-sectional studies can be descriptive…(eg. the prevalence of cough in a population)

• Or analytical…(eg. the association between cough and risk factors such as type of house lived in or whether person is a smoker)

Example 6: cross sectional study

A cross sectional survey of adult dental health in Cornwall, in 1996.Aims:

• to provide a baseline measure of dental health status (descriptive)

• to compare dental health status in deprived and affluent neighbourhoods (analytical)

Sampling method

+………..….Townsend score ……...…..…-

deprivede.d.s

randomly selectede.d.s

affluente.d.s

Using deprivation data from the 1991 census, participants were selected from the most deprived and the most affluent enumeration districts, and a random selection of e.d.s in between.

Survey of adult dental health in Cornwall

Proportion of each subgroup in "poor dental health"

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

25-34 years 45-54 years

Age

deprived

aff luent

county

Deprived people in both age groups were more likely than affluent people to be in poor dental health.

After difficulty in finding a dentist, what was the outcome?

0

10

20

30

40

50

60

70

deprived 25-34 yrs

deprived 45-54 yrs

affluent 25-34 yrs

affluent 45-54 yrs

county 25-34 yrs

county 45-54 yrs

%

gave up private NHS

PH action: grants for service development targeting high need areas.

Most people in deprived areas eventually found an NHS dentist, or gave up.

People in more affluent areas were more likely to pay privately for treatment.

Case-control study

• 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

• Often used to investigate the source of an outbreak of disease.

Example 7: Case control study

What caused an outbreak of Salmonella in south east Wales?

• A case control study of people in SE Wales examined their diet and behaviour during the 3 days before illness

• Those who were ill were found to have been 4.5 x more likely to have eaten sliced ham than those who were not ill

• Further investigations revealed that those who were ill were 25 x more likely to have eaten ham supplied by “producer A”

Outcome

Exposure 1

Exposure 2

Exposure 3

Exposure 4

Case (Person with outcome)

Exposure

Control (Person without the outcome

Cohort Study

• Follow up two groups of people over time and compare the occurrence of disease

• One group is exposed to a possible risk factor for the disease, while the other is not (the control group)

• The exposure is the starting point, the disease is the outcome of interest

Example 8: Cohort study

• 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 respiratory cancer than the control group.

Population

Exposed

Unexposed

Outcome

Outcome

Exposure Outcomes

• Cohorts can be retrospective too

• The starting point is still the EXPOSURE

• Outbreak of salmonella amongst guests at a wedding

• Use wedding menu to identify potential exposures and then survey the guests

• Identify most likely source of the outbreak

Cohort Study (2)

Measures of Disease Frequency

The most basic measure of disease frequency is a simple count of affected individuals.

However, counting is not enough!

Why is a simple count not enough?

• 3 cases of cancer per year from a city of 1,000 people is very different than 3 cases per year from a city of 100,000 people

So, in epidemiology we must know:

• the size of the population from which the affected individuals come, and

• the time period the information was collected.

Basic Measure of Disease Frequency

• Rate – an example: Incidence

Rate

• basic measure in epidemiology

• the frequency with which an event occurs in a group of people

• used to compare the occurrence of disease in different groups

Example: Rates

Rate = Number of events in a specified time period Average population during the time period

***the measure of time is a critical part of a rate!

Such as, the number of newly diagnosed cases of breast cancer per 100,000 women during 1999.

Common Rates

• mortality (death) rate is the number of deaths in a defined group of people during a specified time period.

• birth rate is the number of live births in a defined group of people over a specified time period.

Incidence

• a type of rate

• the number of new cases that develop in a group of individuals during a specific time period

Incidence (cont.)

I = number of new events during a period of time

number of persons at risk during this time period

Example: Incidence

If there were 150,00 new cases of lung cancer in the United States during 1997, the incidence rate would be:

(150,000/260,000,000) = 0.000058 0.000058 x 100,000 people

= 58 cases per 100,000 people, per year

Measures of Association

• How much greater the frequency of disease is in one group compared with another.

• Often presented in the form of a two-by-two table.

Two-By-Two Table

a b

c d

Disease

Yes No

Yes

Exposure

No

Total a+c b+d

Total

a+b

c+d

a+b+c+d

Hypothetical Two-By-Two Table

70 300

15 700

Lung cancer

Yes No

Yes

Smoking

No

Total 85 1,000

Total

370

715

1,085

Relative Risk (RR)

• Measures how likely the exposed group will develop a disease compared to the unexposed group.

RR = incidence in the exposed = a/(a+b)incidence in the unexposed

c/(c+d)

Example: Hypothetical Study

Lung cancer

Yes No Total

Yes 70 300 370

Smoking No 15 700 715

Total 85 1,000 1,085

Relative Risk = 70/(70+300) = 9.0 15/(15+700)

Which means… participants who smoked were 9 times more likely to develop lung cancer than those who did not smoke.

Interpreting Measures of Association

RR of 1.0 indicates that the occurrence of disease in the exposed an unexposed groups are identical:

–No association observed between exposed and unexposed groups.

Interpreting Measures of Association (Continued)

• RR greater than 1.0 indicates a positive association, or an increased risk among the exposed.

• RR less than 1.0 means that there is a decreased risk among the exposed group.

Standardized Mortality Ratios (SMRs)

• the ratio of the observed number of deaths to the expected number of deaths

• a standard group of people is used to determine the expected number of deaths – the standard is often the US population, a state, or

a county.– serves as the comparison group

SMRs (Continued)

Calculation:

SMR = observed deathsexpected deaths

Example: Study of Smoking and Lung Cancer

Our hypothetical study found 58 lung cancer deaths between 1948 and 1963:

– based on US population rates, we know that 42.9 cancer deaths were expected in a similar population.

SMR = (58/42.9) =1.35

Example: Study of Smoking and Lung Cancer (cont.)

Our study group had a risk of cancer mortality approximately 35% greater than those in the general population.

Measuring Exposure and Measuring Outcomes in

Environmental Epidemiology

• Contaminants in air, water, food, or soil come in contact with people through swallowing, breathing, or skin contact

• Once the contaminant gets in the body, it can act at that point of entry or can be distributed throughout the body where it can act upon a target organ or organs

Measuring Exposure• We can measure exposures or their surrogates along the

entire path from emissions to body burden

Types of Data Approximation to actual exposure_________________________________________________________________________1) Residence in a defined geographical Poorestarea (e.g. county) of a site |2) Residence in a geographical area in |proximity to to a site where exposure is assumed |3) Distance or duration of residence |4) Distance and duration of residence |5) Quantified surrogate of exposure (e.g. estimate |of drinking-water use |6) Quantified area measurements in vicinity of the |residence |7) Quantified personal measurement Best

Baker D, et al., Environmental Epidemiology: A Textbook on Study Methods and Public Health Applications, 1999

What is an Outcome or Adverse Health Effect?

• Any change in health status or body function.

– Wide spectrum of effects such as wheezing, change in immune function, changes in blood chemistry, DNA adducts, adverse birth outcomes, developmental disabilities, clinical disease and death

These changes can be…

Randomised Controlled Trial

• Compares effectiveness of a new intervention against the best current alternative (or a placebo)

• Can be for clinical or behaviour change interventions

Randomised Controlled Trial

• Select people with the same disease or characteristics (a defined target population)

• Randomly allocate these people to ‘intervention’ or ‘control’ groups

• Intervention group receives the new treatment, the control group receives the standard or placebo treatment

• The benefits of each treatment are assessed by comparing the health gain in each group

Randomised controlled trial

population

group 1

group 2

Outcome

Outcome

intervention

control

Example 11: RCT

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.

In the news…..BBC website

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.

Oily fish is a source of vitamin D Grapefruit is full of vitamin C

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.

Heart disease may run in the family

Interpreting results of analytical studies

• No association found

• Association may be artifactual (false)– Due to chance – Due to bias in the study

• Association may be real, but indirect– Apparent relationship due to a confounding factor

• Association is direct (causal, true)

An ASSOCIATION between a risk factor (smoking) and a disease (lung cancer)

DOES NOT INDICATE a CAUSAL relationship

Central dogma of epidemiology

Association is not proof of causeBradford Hill’s Criteria for Causation

• Strength of association• Temporal relationship • Geographical distribution • Dose-response relationship • Consistency of results• Biological plausibility (but remember John Snow)• Specificity (if a single causal agent)• Reversibility

Assessing the relationship between a possible cause and an outcome

A p p ly g u id e lin es an d m ake ju d g em en t

C ou ld it b e cau sa l?

C ou ld it b e a resu lt o f ch an ce?

C ou ld it b e d u e to con fou n d in g ?

C ou ld it b e d u e to se lec tion o r m easu rem en t b ias?

Horses for courses

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.

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

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 http://www.healthknowledge.org.uk/Epidemiology/Epidemiology%201.htm