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FoPC Year 3 > Population Data - Tutorial 1 > Flashcards

Population Data - Tutorial 1 Flashcards

1
Q

What does epidemiology look at?

A

Nature and type of illness in society using numerical science of epidemiology
Looks at the time, place and person affected

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2
Q

What are the three main aims of epidemiology? Describe these?

A

Description - to describe the amount and distribution of disease in human populations

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

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

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3
Q

What does epidemiology compare?

A

Groups (study populations) in order to detect differences pointing to

  • aetiological clues (what cases the problem)
  • the scope for prevention
  • the identification of high risk or priority groups in society

We compare how often an event appears in one group with another

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4
Q

What might the study population be defined by?

A

May be defined by age, sex, location or even be the same group over time

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5
Q

What does clinical medicine deal with in comparison to epidemiology?

A

Clinical medicine deals with the individual patient, epidemiology deals with populations

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6
Q

It is essential to be clear which populations we are talking about when we carry out studies or surveys or formulate hypotheses, what do we do in order to do this?

A

In order to do this we talk in terms of ratios

Number of events/Population at risk

It is usual to convert such ratios into rates by expressing them in terms of a specified time period e.g. per year and a notional ‘at risk’ population of 10n

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7
Q

What does the risk mean?

A

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

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8
Q

What is incidence?

A

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

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9
Q

What is prevalence?

A

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

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10
Q

What is the difference between incidence and prevalence in minor illnesses compared with chronic illnesses?

A

Minor illnesses might have a high incidence but a low prevalence e.g. a cold
Other illnesses may be chronic with low incidence but high prevalence e.g. diabetes

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11
Q

What does incidence tell you about?

A

Trends in causation and aetiology of disease

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12
Q

What does prevalence tell you about?

A

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

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13
Q

What does lifetime prevalence depend on?

A

Number developing disease, those dying from the disease and those recovering from the disease

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14
Q

What is the relative risk?

A

Measure of the strength of an association between a suspected risk factor and the disease under study

Relative risk = incidence of disease in exposed group/incidence of disease in unexposed group

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15
Q

What are the different types of risk?

A

Relative risk
Actual risk
Communicating risk

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16
Q

What are sources of epidemiological data?

A 
Mortality data 
Hospital activity statistics
Reproductive health statistics
Cancer statistics
Accident statistics
General practice morbidity 
Health and household surveys 
Social security statistics 
Drug misuse databases 
Expenditure data from NHS
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17
Q

What are the types of study?

A

Descriptive studies
Cross-sectional
Case control
Cohort

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18
Q

What are descriptive studies?

A

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

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

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19
Q

What framework do descriptive studies follow?

A

Time, place, person framework

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20
Q

Where are descriptive epidemiological studies useful?

A

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 e.g. screening programmes
Assessing the needs for health services and service planning
Generating hypotheses about disease aetiology

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21
Q

What are the advantages and disadvantages of descriptive studies?

A

Advantages

  • cheap
  • quick
  • give valuable initial overview of a problem

Disadvantages

  • do not provide evidence about the causes of disease
  • do no test hypotheses
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22
Q

What are cross-sectional studies?

A

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

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23
Q

What are the advantages and disadvantages of cross-sectional studies?

A

Advantages
- ability to provide results quickly

Disadvantages
- usually impossible to infer causation

24
Q

What are case control studies?

A

In case control studies, two groups of people are compared: a group of individuals who have the disease 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

25
Q

How are the results obtained from case control studies expressed?

A

As ‘odds ratios’ or ‘relative risks’

Be aware that relative risks are also presented for cohort studies and randomised trials

26
Q

What might be presented as a guide to whether the result could be a chance finding?

A

Confidence intervals or ‘p values’

27
Q

What are cohort studies?

A

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

28
Q

What do cohort studies allow?

A

Calculation of cumulative incidence, allowing for differences in follow-up time

29
Q

How are the results of cohort studies usually expressed?

A

As relative risks with confidence intervals or p values

30
Q

What are trials?

A

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

31
Q

What is the randomised control trial?

A

The randomised control trial is the definitive method of assessing any new treatment in medicine

32
Q

What are the features of the randomised control trial?

A

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 e.g. a suspected causative factor is removed or neutralised, whilst no alteration is made to the control group

Data on subsequent outcomes e.g. disease incidence are collected in the same way from both groups and the relative risk is calculated

33
Q

What is the aim of the randomised control trial?

A

To determine whether modification of the factor (removing, reducing or increasing exposure) alters the incidence of disease

34
Q

What are the features of a trial of new treatment?

A

Underlying design is the same as a randomised control trial: the intervention group receive the new therapy, the control group receive the current standard therapy (or a placebo) and the treatment outcomes (e.g. reduction in symptoms) are compared in the two groups

35
Q

What factors need to be considered when interpreting results?

A 
Standardisation 
Standardised mortality ratio
Quality of data 
Case definition 
Coding and classification
Ascertainment
36
Q

What is standardisation?

A

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

37
Q

What does an age-sex standardised rate represent?

A

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

38
Q

What can rates be standardised for?

A

Rates can be standardised for any other relevant confounding factors e.g. social class

39
Q

What should comparisons of incidence or mortality rates in a population over time, or between two different populations, or between population subgroups be based on?

A

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

40
Q

What is the standardised mortality ratio?

A

Special kind of standardisation

It is simply a standardised death rate converted into a ratio for easy comparison

The figure for a standard reference population is taken to be 100 and the standardised death rates for the comparison (study) populations are expressed as a proportion of 100

A figure below 100 means fewer than expected deaths, and above 010 means more e.g. an SMR of 120 means that 20% more deaths occurred than expected in the study population

41
Q

What do you need to do when considering quality of data?

A

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

42
Q

What is case definition?

A

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

It is important 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

43
Q

What is coding and classification?

A

This is related to the issue of case definition

When data are being collected routinely e.g. 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

44
Q

What is ascertainment?

A

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, they might come up with higher incidence

45
Q

What is bias?

A

Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth

46
Q

What are some of the types of bias?

A

Selection bias
Information bias
Follow up bias
Systematic error

47
Q

Describe selection bias

A

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 e.g. elderly, 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

48
Q

Describe information bias

A

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

49
Q

Describe follow up bias

A

Arises when one group of subjects is followed up more assiduously than another to measure disease incidence or other relevant outcomes

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

50
Q

Describe systematic error

A

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 (e.g. a BP 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

51
Q

What is a confounding factor?

A

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

52
Q

What are common confounders?

A

Age
Sex
Social Class

53
Q

What are some ways to deal with confounding factors?

A

Depends on the particular study design

In trials, the process of randomisation - in effect, the play of chance leads to similar proportions of subjects with particular confounding factors 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

54
Q

It is difficult to prove causation between an exposure and disease, what is often the best that can be achieved?

A

To demonstrate a weight of evidence in favour of a causal relationship

55
Q

What are the criteria for causality? Describe these

A

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

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

Experiment - a suitably controlled experiment to prove the association is causal, very uncommon in human populations

56
Q

What is the only absolute criterion for causality?

A

Temporality

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

FoPC Year 3 (6 decks)

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