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FOPC Y2 > The Use of Data > Flashcards

The Use of Data Flashcards

1
Q

Difference between disease and illness

A

Symptoms, signs - diagnosis: a bio-medical perspective

Ideas, concerns, expectations - experience: patients perspective

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2
Q

What factors affect the uptake of care (4) or

What factors prompt patients to present to healthcare centers?

A

Concept of Lay referral - i.e. granny knows best

Sources of info - peers, family, internet, TV, newspaper

Medical - new symptoms, visible symptoms, increasing severity, duration

Non-medical - crisis, peer pressure (wife sent me), patient beliefs, expectations, social class, economic, psychological, environmental, cultural, ethnic, age, gender, media

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3
Q

What are the 3 main epidemiological aims of being able to provide information to patients (3)

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 preventive measures, public health practices and therapeutic strategies can be developed, implemented, monitored and evaluated for the purposes of disease control.

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4
Q

What does epidemiology compare? (1 + 3)

A

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

  • Aetiological clues (what causes the problem)
  • The scope for prevention
  • The identification of high risk or priority groups in society.
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5
Q

What does the risk part in the ratios of studies of diseases mean

A

That everyone in the denominator has the potential to move into the numerator and in turn that everyone in the numerator has come from the denominator

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6
Q

What may have a high incidence but a low prevalence

A

Minor illnesses - i.e. cold

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7
Q

What may have a low incidence but a high prevalence

A

Chronic illnesses - i.e. diabetes

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8
Q

What is relative risk

A

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

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9
Q

What is the equation of relative risk =

A

incidence of disease in an exposed group / incidence of disease in an unexposed group

(see word document for example)

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10
Q

10 sources of epidemiological data [7]

A 
Mortality data, GP morbidity
Expenditure data from NHS
Hospital activity, reproductive health statistics
Cancer and accident statistics
Social security statistics
Health and household surveys
Drug misuse databases
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11
Q

What is health literacy [4]

NB Health literacy is being increasingly recognised as a significant health concern around the world

A

About people having the knowledge, skills, understanding and confidence [1] to use health information [1], to be active partners in their care [1], and to navigate health and social care systems.[1]

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12
Q

What are the different types of studies (1,2(3), 3)

A 
Descriptive 
Analytical 
- Cross-sectional 
- Case-Control 
- Cohort
Trials
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13
Q

What is a descriptive study [3]

A

Attempt to describe the amount and distribution of a disease in a given population. [1]

  • Follow the time, place, person framework [1]
  • May look at disease alone or also look into factors (exposures) thought to be linked to the aetiology [1]
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14
Q

When are descriptive epidemiological studies useful (5)

A
  1. Identifying emerging public health problems by monitoring and surveilling disease patterns
  2. Signalling effects worth of further investigation
  3. Assessing the effectiveness of measures of prevention and control (eg, screening programmes).
  4. Assessing needs for health services and service planning.
  5. Generating hypotheses about disease aetiology and risk factors
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15
Q

What 3 things does a descriptive study not provide (3)

A
  1. Definitive conclusions about disease causation
  2. Evidence about the causes of disease
  3. Do not test hypotheses.
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16
Q

What is a benefit of descriptive studies (3)

A
  1. Cheap
  2. Quick
  3. Give a valuable initial overview of a problem
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17
Q

What do cross-sectional studies look at and what do they conclude

A

Observations made at a single point in time - disease frequency, survey, prevalence study

Conclusions indicate relationship between diseases and other variables of interest in a defined population.

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18
Q

What is a pro and con to cross-sectional studies

A

Provide quick results

Is usually impossible to infer causation

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19
Q

What does a case-control study compare [2]

A

Two groups of people are compared:

  1. Group of individuals with the disease of interest are identified (cases)
  2. Group of individuals who do not have the disease (controls)
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20
Q

How is data gathered in a case-control study (3 steps)

A
  1. Data is gathered on each individual to determine whether or not they have been exposed to the suspected aetiological factor(s)
  2. The average exposure in the two groups, cases and controls is compared
  3. This identifies significant differences, give clues to factors which elevate (or reduce) risk of the disease under investigation.
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21
Q

How are the results of a case-control study expressed (3)

A
  1. ‘odds ratios’
  2. ‘relative risks’ - also used for cohort & randomised trials
  3. Confidence intervals / ‘p values’ may be presented to guide whether the result could be a chance finding
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22
Q

Steps of a cohort study (2)

A
  1. Baseline data on exposure is collected from a group of people who do not have the disease under study
  2. The group is then followed through time until a sufficient number have developed the disease to allow analysis.
  3. The original group is separated into subgroups according to original exposure status.
  4. These subgroups are compared to determine the incidence of disease according to exposure.
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23
Q

What do cohort studies allow for (2)

A
  1. The calculation of cumulative incidence

2. For differences in follow up time

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24
Q

What is a trial

A

Experiments used to test ideas about aetiology or to evaluate interventions.

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25
Q

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

A

Randomised control trial

26
Q

How does a randomised control trial work (4)

A
  1. Two groups at risk of developing a disease are assembled: a study (intervention) group and a control group.
  2. 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.
  3. Data on subsequent outcomes (eg, disease incidence) are collected in the same way from both groups
  4. The relative risk is calculated.
27
Q

What is the aim of randomised control trial

A

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

28
Q

How is a randomised control trial of new treatment used

A

The underlying design is the same:

  • the intervention group receive the new therapy
  • the control group receive the current standard therapy (or a placebo)
  • the treatment outcomes (eg, reduction in symptoms) are compared in the two groups.
29
Q

What 6 factors need to be considered when interpreting the results of a study

A
  1. Standardisation
  2. Standardised Mortality Ratio
  3. Quality of Data
  4. Case Definition
  5. Coding and Classification
  6. Ascertainment
30
Q

What is standardisation [3] and give an example

A

A set of techniques [1] used to remove or adjust for the effects of differences [1] when comparing two or more populations, in age, social class etc. [1]

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

31
Q

What is standardised mortality ratio (2) and give an example

A
  1. A standardised death rate converted into a ratio for easy comparison.
  2. 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.

i.e. 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

32
Q

What is quality of data

A

Ensuring that the data are trustworthy

33
Q

What is case definition [2]

A

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

Important as not all investigators mean the same thing when they use medical terms.

34
Q

What is coding and classification in relation to case definition, what does it assist in [2]

A
  1. When data is collected routinely, eg, death certificates, it is normal to convert disease information to a set of codes
  2. Assists in data storage and analysis
35
Q

What happens to the incidence of disease if the rules of how to code clinical information is changed

A

If these rules change, it sometimes appears that a disease has become more or less common, when in fact it has just been coded under a new heading.

36
Q

What is ascertainment and how may it effect incidence

A

Ensuring all data is accurate & that equal effort has been put into acquiring all aspects of information

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.

37
Q

What is bias [2]

A

Any trend in the collection, analysis, interpretation, publication or review of data [1] that can lead to conclusions that are systematically different from the truth [1]

38
Q

What are the four different types of bias

A
  1. Selection Bias
  2. Information Bias
  3. Follow up Bias
  4. Systematic Error
39
Q

What is selection bias [2]

A

Occurs when the study sample is not truly representative of the whole study population [1] about which conclusions are to be drawn [1]

40
Q

What is information bias [2]

A

Arises from systematic errors [1] in measuring exposure or disease [1]

41
Q

What is follow-up bias [2]

A

Arises when one group of subjects is followed up more assiduously than another [1] to measure disease incidence or other relevant outcome [1]

42
Q

What is systematic error bias [2]

A

A form of measurement bias [1] where there is a tendency for measurements to always fall on one side of the true value [1]

43
Q

Example of selection bias that could occur in a randomized controlled trial of a new drug

A

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.

44
Q

Example of information bias in a case control study where research was aware of which patients were case and which were control… [2]

A

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 [1]

Any differences in exposure would then reflect the enthusiasm of the researcher as well as any true difference in exposure between the two groups [1]

45
Q

Example of follow-up bias in cohort studies

A

In cohort studies, subjects sometimes move address or fail to reply to questionnaires sent out by the researchers [1]

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 [1], 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 [1]

46
Q

Example of systematic error bias [1]

A

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.

47
Q

What is a confounding factor [3]
NB In some cases, the confounding factor may be the true causal factor, and not the exposure that is under consideration.

A

One which is associated independently with both the disease [1] and with the exposure under investigation [1] and so distorts the relationship between the exposure and disease [1]

48
Q

What are common confounders (3)

A

Age
Sex
Social class

49
Q

What are 5 ways to deal with confounding

A
  1. Randomisation in trials
  2. Restriction of eligibility criteria to only certain kinds of study subjects .
  3. Subjects in different groups can be matched for likely confounding factors.
  4. Results can be stratified according to confounding factors.
  5. Results can be adjusted (using multivariate analysis techniques) to take account of suspected confounding factors.
50
Q

Criteria for causality are in place because its difficult to prove causation between an exposure and disease and the criteria help to assess the available evidence
What are the 9 criteria for causality

A
  1. Strength of association
  2. Consistency
  3. Specificity
  4. Temporality
  5. Biological gradient
  6. Biological plausibility
  7. Coherence
  8. Analogy
  9. Experiment
51
Q

What is strength of association measured by

A

As measured by relative risk or odds ratio.

52
Q

What is consistency

A

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

53
Q

What is specificity

A

A single exposure leading to a single disease.

54
Q

What is temporality

A

The exposure comes before the disease.

55
Q

What is biological gradient

A

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

56
Q

What is biological plausibility

A

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

57
Q

What is coherence

A

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

58
Q

What is analogy give an example in clinical practice

A

An exposure-disease relationship exists which can act as a model for the one under investigation.

i. e. it is known that certain drugs can cross the placenta and cause birth defects
- it might be possible for viruses to do the same.

59
Q

What is experiment [2]

A

A suitably controlled experiment [1] to prove the association as causal [1]

60
Q

Three intended aims of SIGN Guidelines

A
  1. Help patients. health and social care professional understand medical evidence and use it to make decisions about healthcare
  2. Reduce unwarranted variations in practice and make sure patients get the best care available, no matter where they live
  3. Improve healthcare across Scotland by focussing on patient-important outcomes

FOPC Y2 (5 decks)

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