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Foundations of Primary Care 2nd Year > The Use of Data > Flashcards

Flashcards in The Use of Data Deck (44):
1

What percentage of patients are passed on from primary care to secondary care?

3%

2

What is the difference between disease and illness?

Disease = symptoms and signs, biomedical perspective

Illness = ideas, concerns and expectations - experience. patients perspective

3

What are the factors affecting the uptake of care?

Concept of Lay Referral – “Granny knows best”, helpful?

Sources of info – Peers, family, internet TV, health pages of newspaper or women’s mag, “What should I do? Booklet, SHOW (scottish health on the web) website, Practice leaflet or website

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

Non medical factors – crisis, peer pressure “wife sent me”, patient beliefs, expectations, social class, economic, psychological, environmental, cultural, ethnic, age, gender, media etc

4

What are the three main aims of epidaemiology?

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

Explanation - to make clear the natural history and identify aetiological factors for disease

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

5

What is the point in epidemiology?

Helps isolate aetiologies

Provides a scope for prevention

Can allow the identification of high risk or priority groups in society

6

What is relative risk?

The 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)

Incidence of disease is measured as a fraction - the denominator is a smaller fraction than the denominator

7

How is a fundamental measure taken?

(Event)/(total population)

8

What are the sources of epidemiological data?

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

9

What is health literacy?

Health literacy is about people having the knowledge, skills, understanding and confidence to use health information, to be active partners in their care, and to navigate health and social care systems.

10

How is the Scottish government attempted to improve health literacy?

Published 'making it easy'

A health literacy action plan for Scotland

11

What does SIGN stand for?

Scottish Intercollegiate Guidelines Network.

12

What is the point of the SIGN guidance?

Aims to aid the transition of new knowledge into action

Helps health and social care professionals and patients to understand medical evidence and use it to make decisions about healthcare

Reduces unwarranted variations in practice and makes sure patients get the best care possible, no matter where they live

Improves healthcare across Scotland by focussing on patient - important outcomes

SIGN is also involved in assessing the quality of evidence

13

What is a descriptive study?

Attempts to describe the amount and distribution of a disease in a given population

Doesn't give conclusions about causation, might give clues about risk factors and candidate aetiologies

14

What are the benefits of descriptive studies?

Cheap, quick, valuable initial overview of a problem

15

What are descriptive studies useful for?

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.

16

What is a cross sectional study?

It is a measure of disease frequency , survey, prevalence study) - used to make an observation at a single point in time

17

What conclusions are drawn from cross sectional studies?

Conclusions are drawn about the relationship between diseases and other variables in a defined population

18

What is a strength and a limitation of cross sectional studies?

Strength - can provide results quickly

Limitation - usually impossible to infer causation

19

What is a case controlled study?

Two groups of people are compared - those who have the disease of interest (cases) and those who do not (controls)

20

How are conclusions drawn from case controlled studies?

The two groups have their exposure to a suspected aetiological factor measured. The average exposure between the two groups is compared to identify significant differences - giving clues as to what factors elevate or reduce the risk of the disease under investigation

21

What are the results in a case controlled study expressed as?

They are expressed as odds ratios or relative risks

Often have a P value associated - indicates how likely to results could just be a chance finding

Cohort studies and randomised trials also express results this way

22

What is a cohort study?

Baseline data on exposure are collected from a group of people who do not have the disease under study

The group is then followed until a sufficient number have developed the disease to allow analysis

23

What are trials?

Experiments to test ideas about aetiology or to evaluate interventions

24

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

Randomised control trial

25

How is a trial assessing aetiology conducted?

Two groups at risk of developing a disease are assembled.

An alteration is made to the intervention group (eg, a suspected causative factor is removed), 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.

26

How is a trial assessing new treatment conducted?

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.

27

What is meant by standardisation?

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

Another variable could be sex

28

What is the standard mortality ratio?

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.

29

What are the factors to consider when interpreting results?

Standardisation
Standard mortality ratio
Quality of data
Case definition
Coding and classification
Ascerteinment

30

How can issues in case definition affect results?

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.

31

How can coding and classification result in errors in results?

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.

32

How can ascerteinment result in differences in incidence rates?

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.

33

What is bias?

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.

34

What are the different types of bias?

Selection bias
Information bias
Follow up bias
Systematic error

35

What is selection bias?

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

36

What is information bias?

arises from systematic errors in measuring exposure or disease

37

What is follow up bias?

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

38

Define systematic error

Tendency for measurements to always fall on one side of the true value. Can occur with medical instruments as well as questionnaires

39

What is a cofounding factor?

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

40

What are common cofounders?

Age
Sex
Social class

41

What are the ways of dealing with cofounding factors?

Randomisation
Restriction of eligibility criteria
Subjects in different groups can be matched for likely cofounding factors
Results can be stratified according to cofounding factors
Results can be adjusted to take into account of suspected cofounding factors

42

What is the criteria for causality?

Strength of association (relative risk or odds ratio)

Consistency

Specificity

Temporality

Biological gradient

Biological plausability

Coherence

Analogy

Experiment

The only absolute criterion is temporality

43

Full note

Criteria for Causality
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.

44

What is a possible source of audit criteria?

NICE guidelines