Population Data to Benefit Individual People

Question 1

What does epidemiology look at?

Answer 1

Looks at nature and type of illness in society using numerical science of epidemiology.

Looks at the time, place and person affected

Question 2

What are the 3 main aims of epidemiology?

Answer 2

Description
-to describe the amount and distribution in human populations.

Explanation
-To elucidate the natural history and identify aetiological factors for disease usually by combining epidaemiological data with data from other disciplines such as biochemistry, occupational health and genetics.

Disease control
-To provide the basis on which preventive measures, public health practices and therapeutic strategies can be developed,
implemented, monitored and evaluated for the purposes of disease control.

Question 3

Epidemiology compares groups (study populations) in order to detect differences pointing to what?

Answer 3

Aetiological clues (what causes the problem)

The scope for prevention

The identification of high risk or priority groups in society

Question 4

How may study populations in epidemiological studies be defined?

Answer 4

By age/sex/location

Or even the same group over time

Question 5

Clinical medicine deals with the individual patient; epidemiology deals with populations.

It is essential to be quite clear which populations we are talking about when we carry out a survey, conduct a study or formulate a hypotheses about disease and risk.

In order to do this we talk in terms of ratios.

Explain ratios.

Answer 5

Numerator/ Denominator = Events/ Population at risk

eg, Deaths from IHD in men aged 55-64 in Grampian in 1990/
All men aged 55-64 in Grampian in 1990

The numerator is the top line, the number of events (in this example deaths). The denominator is the bottom line, the population at risk.

It is usual to convert such ratios into rates by expressing them in terms of a specified time period (eg, per year) and a notional ‘at risk’ population of 10n (eg, %; per 1000; per 100,000).

Question 6

Risk the the crucial part with ratios.

Explain what is vital about the ratio

Answer 6

What this means is that everyone in the denominator must have the possibility of entering the numerator, and conversely those people in the numerator must have come from the denominator population.

Question 7

Define incidence

Answer 7

The number of new cases of a disease in a population in a specified period of time.

Incidence tells us something about trends in causation and the aetiology of disease

Question 8

Define prevalence

Answer 8

The number of people in a population with a specific disease at a single point in time or in a defined period of time

Prevalence tells us something about the amount of disease in a population. It is useful in assessing the workload for the health service but is less useful in studying the causes of disease.

Question 9

Define relative risk

Answer 9

This is the measure of the strength of an association between a suspected risk factor and the disease under study.

Relative risk (RR) = incidence of disease in exposed group/ incidence of disease in unexposed group

Question 10

Give 10 sources of epidemiological data

Answer 10

Disease

• Mortality Data
• Reproductive health statistics
• Cancer statistics
• Accident statistics
• General Practice morbidity
• Drug Misuse database

Healthcare

• Hospital Activity Statistics
• Health and household surveys
• Social security statistics
• Expenditure data from NHS

Question 11

What are descriptive studies?

Answer 11

Descriptive studies attempt to describe the amount and distribution of a disease in a given population

This kind of study does not provide definitive conclusions about disease causation, but may give clues to possible risk factors and candidate aetiologies.

Such studies are usually cheap, quick and give a valuable initial overview of a problem.

Question 12

Descriptive studies may attempt to describe the amount and distribution of a disease in a given population for the purposes of gaining insight into the aetiology of thecondition or for planning health services to meet the clinical need.

How do they do this?

Answer 12

Studies may look at the disease alone or may also examine one or more factors (exposures) thought to be linked to the aetiology. This kind of study does not provide definitive conclusions about disease causation, but may give clues to possible risk factors and candidate aetiologies.

Question 13

Descriptive studies follow the time, place, person framework.

Descriptive epidemiological studies are useful in what?

Answer 13

Identifying emerging public health problems through monitoring and surveillance of disease patterns.

Signalling the presence of effects worthy of further investigation.

Assessing the effectiveness of measures of prevention and control (eg, screening programmes).

Assessing needs for health services and service planning.
Generating hypotheses about disease aetiology.

Question 14

What DONT descriptive studies do?

Answer 14

They do not provide evidence about the causes of disease. They do not test hypotheses.

Question 15

Give 3 types of analytic studies

Answer 15

Cross sectional

Case control

Cohort studies

Question 16

What are cross sectional studies?

Answer 16

Cross-Sectional (disease frequency, survey, prevalence study)

In cross-sectional studies, observations are made at a single point in time.

Conclusions are drawn about the relationship between diseases (or other health-related characteristics) and other variables of interest in a defined population.

A strength of this method is its ability to provide results quickly; however, it is usually impossible to infer causation.

Question 17

What are case control studies?

Answer 17

In case control studies, two groups of people are compared: a group of individuals who have the disease of interest are identified (cases), and a group of individuals who do not have the disease (controls).

Data are then gathered on each individual to determine whether or not he or she has been exposed to the suspected aetiological factor(s). The average exposure in the two groups, cases and controls, is compared to identify significant differences, give clues to factors which elevate (or reduce) risk of the disease under investigation.

The results obtained from case control studies are expressed as ‘odds ratios’ or ‘relative risks’ (see above). Be aware that relative risks are also presented for cohort studies and randomised trials. Sometimes confidence intervals or ‘p values’ are presented as a guide as to whether the result could be a chance finding.

Question 18

What are cohort studies?

Answer 18

In cohort studies, baseline data on exposure are collected from a group of people who do not have the disease under study. The group is then followed through time until a sufficient number have developed the disease to allow analysis.

The original group is separated into subgroups according to original exposure status and these subgroups are compared to determine the incidence of disease according to exposure. Cohort studies allow the calculation of cumulative incidence, allowing for differences in follow up time.

The results are usually expressed as relative risks (see above), with confidence intervals or p values.

Question 19

What are trials?

Answer 19

Trials are experiments used to test ideas about aetiology or to evaluate interventions.

Question 20

What is the definitive method of assessing any new treatment in medicine?

Answer 20

The radomised control trial.

Question 21

How is a randomised control trial carried out?

Answer 21

Two groups at risk of developing a disease are assembled, a study (intervention) group and a control group. An alteration is made to the intervention group (eg, a suspected causative factor is removed or neutralised), whilst no alteration is made to the control group. Data on subsequent outcomes (eg, disease incidence) are collected in the same way from both groups, and the relative risk is calculated. The aim is to determine whether modification of the factor (removing, reducing or increasing exposure) alters the incidence of the disease.

In a trial of a new treatment, the underlying design is the same: the intervention group receive the new therapy, the control group receive the current standard therapy (or a placebo) and the treatment outcomes (eg, reduction in symptoms) are compared in the two groups.

Question 22

Give 6 factors to consider when interpreting results

Answer 22

Standardisation

Standardised Mortality Ratio

Quality of Data

Case Definition

Coding and Classification

Ascertainment

Question 23

What is standardisation in relation to interpreting results?

Answer 23

A set of techniques used to remove (or adjust for) the effects of differences in age or other confounding variables, when comparing two or more populations.

An age-sex standardised rate represents what the unstandardised (crude) rate would have been in the study population if that population had the same proportion of males and females, and of people in different age groups, as the standard population.

Rates can be standardised for any other relevant confounding factor (eg, social class).

Comparisons of incidence or mortality rates in a population over time, or between two different populations, or between population subgroups, should always be based on standardised rates, never on crude rates.

Question 24

What is standardised mortality ratio in terms of interpreting results?

Answer 24

This is a special kind of standardisation which you may encounter in your reading.

It is simply a standardised death rate converted into a ratio for easy comparison. The figure for a standard reference population (eg, Scotland) is taken to be 100 and the standardised death rates for the comparison (study) populations (eg, Grampian) are expressed as a proportion of 100.

A figure below one hundred means fewer than expected deaths, and above 100 means more. For example, an SMR of 120 means that 20% more deaths occurred than expected in the study population, allowing for differences in the age and sex structure of the standard and study populations and an SMR of 83 means 17% fewer deaths occurred.

Question 25

What do we mean by quality of data in terms of interpreting results?

Answer 25

In working with data about health and disease, we must be careful to ensure that the data are trustworthy. There are some questions you can ask yourself which can help you decide whether to believe the results of analyses based on the data.

Question 26

Why do we need to consider case definition when interpreting results?

Answer 26

The purpose of case definition is to decide whether an individual has the condition of interest or not.

It is important in because not all doctors or investigators mean the same thing when they use medical terms.

Differences in incidence of disease over time or in different populations may be artefact, due to differences in case definition, rather than differences in true incidence.

Question 27

Why do we need to consider coding and classification when interpreting results?

Answer 27

This is related to the issue of case definition. When data are being collected routinely (eg, death certificates), it is normal to convert disease information to a set of codes, to assist in data storage and analysis. Rules are drawn up to dictate how clinical information is converted to a code. If these rules change, it sometimes appears that a disease has become more common, or less common, when in fact it has just been coded under a new heading.

Question 28

Why do we need to consider ascertainment when interpreting results?

Answer 28

Is the data complete - are any subjects missing? If researchers in one country look harder for cases of a given disease than researchers in any other, it might not be surprising that they come up with higher incidence rates.

Question 29

What is bias?

Answer 29

Bias is any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth. There are very many types of bias which can creep into epidemiological studies.

Question 30

Give 4 important types of bias

Answer 30

Selection bias
Information bias
Follow up bias
Systematic error

Question 31

What is selection bias?

Answer 31

Occurs when the study sample is not truly representative of the whole study population about which conclusions are to be drawn.

For example, in a randomised controlled trial of a new drug, subjects should be allocated to the intervention (study) group and control group using a random method.

If certain types of people (eg, older, more ill) were deliberately allocated to one of these groups then the results of the trial would reflect these differences, not just the effect of the drug.

Question 32

What is information bias?

Answer 32

arises from systematic errors in measuring exposure or disease.

For example, in a case control study, a researcher who was aware of whether the patient being interviewed was a ‘case’ or a ‘control’ might encourage cases more than controls to think hard about past exposures to the factors of interest.

Any differences in exposure would then reflect the enthusiasm of the researcher as well as any true difference in exposure between the two groups.

Question 33

What is follow up bias?

Answer 33

arises when one group of subjects is followed up more assiduously than another to measure disease incidence or other relevant outcome.

For example, in cohort studies, subjects sometimes move address or fail to reply to questionnaires sent out by the researchers.

If greater attempts are made to trace these missing subjects from the group with greater initial exposure to a factor of interest than from the group with less exposure, the resulting relative risk would be based on a (relative) underestimate of the incidence in the less exposed group compared with the more exposed group.

Question 34

What is systematic error?

Answer 34

A form of measurement bias where there is a tendency for measurements to always fall on one side of the true value.

It may be because the instrument (eg, a blood pressure machine) is calibrated wrongly, or because of the way a person uses an instrument.

This problem may occur with interviews, questionnaires etc, as well as with medical instruments.

Question 35

What is a confounding factor?

Answer 35

A confounding factor is one which is associated independently with both the disease and with the exposure under investigation and so distorts the relationship between the exposure and disease. In some cases the confounding factor may be the true causal factor, and not the exposure that is under consideration.

Age, sex and social class are common cofounders

Question 36

How do we deal with confounding factors?

Answer 36

In trials, the process of randomisation (in effect the play of chance leads to similar proportions of subjects with particular
confounding in the intervention and control groups).

Restriction of eligibility criteria to only certain kinds of study subjects.

Subjects in different groups can be matched for likely confounding factors.

Results can be stratified according to confounding factors.

Results can be adjusted (using multivariate analysis techniques) to take account of suspected confounding factors.

Question 37

It is difficult to prove causation between an exposure and disease.

Often the best that can be achieved is to demonstrate a weight of evidence in favour of a causal relationship.

A number of criteria have been devised to help investigators assess the available evidence, known as the criteria for causality.

What are these criteria?

Answer 37

Strength of association
-As measured by relative risk or odds ratio.

Consistency
-Repeated observation of an association in different populations under different circumstances.

Specificity
-A single exposure leading to a single disease.

Temporality
-The exposure comes before the disease.

Biological gradient
-Dose-response relationship. As the exposure increases so does the risk of disease.

Biological plausibility
-The association agrees with what is known about the biology of the disease.

Coherence
-The association does not conflict with what is known about the biology of the disease.

Analogy
-Another exposure-disease relationship exists which can act as a model for the one under investigation.
For example, it is known that certain drugs can cross the placenta and cause birth defects
- it might be possible for viruses to do the same.

Experiment
-A suitably controlled experiment to prove the association as causal - very uncommon in human populations.

Question 38

What is the only absolute criterion for causality?

Answer 38

Temporality

Failing to fulfil any of the others does not necessarily rule out a causative association.

For example, specificity: few diseases have a single cause.

It is known that lung cancer can be caused by agents other than smoking and that smoking causes diseases other than lung cancer.

This does not rule out the association between smoking and lung cancer as causative.