Lectures 1&2 - The importance of evidence in the practice of medicine Flashcards Preview

YR1 Epidemiology in Practice > Lectures 1&2 - The importance of evidence in the practice of medicine > Flashcards

Flashcards in Lectures 1&2 - The importance of evidence in the practice of medicine Deck (19)
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1

what is evidence based medicine?

the use of critically appraised info to determine strength of evidence for the use of treatments and medicine in clinical practice

2

outline the different stages of clinical medicine where EBM can be of use

CLINICAL FINDINGS- use of patient history and physical exam
AETIOLOGY - identifying cause of disease
CLINICAL MANIFESTATIONS OF DISEASE - how often & when a disease causes clinical problems
DIFFERENTIAL DIAGNOSIS - selecting likely, serious and treatment-responsive causes of a problem
DIAGNOSTIC TESTS - selecting and interpreting tests to confirm/exclude diagnoses
PROGNOSIS - estimating a patient's clinical course and anticipating complications
THERAPY - selecting treatments that are cost effective and where good > harm
PREVENTION - reducing chance of disease by identifying and modifying risk factors and use of screening in early diagnosis

3

what are the criticisms of EBM?

- not enough time for doctors to critically appraise info
- EBM has been inappropriately used by the gov't to justify decisions that clinicians disagree with

4

what is the importance of EBM?

- better service to patients
- better patient care & safety
- increased medical knowledge
- revalidation - clinicians are up to date with evidence
- professionalism
- better interpersonal & communication skills

5

list the order of the hierarchy of studies

1) meta-analyses & systematic reviews
2) RCTs
3) cohort studies
4) case-control studies
5) cross sectional studies
6) animal trials & in-vitro studies
7) case reports, opinion papers, letters

6

describe systematic reviews & meta-analyses

- systematic reviews answer a defined research question by collecting and summarising evidence
- meta-analysis refers to use of statistical techniques to integrate the results of studies that match the eligibility criteria
- cheap
- avoids issue of large sample size as data for the study is pooled from many smaller studies

7

describe RCTs

- strong inclusion/exclusion criteria
- requires a large sample size
- expensive

8

describe cohort studies

- involves a group of people before they develop a condition
- exposures and risk factors are observed
- group is followed up over a period of time to see who ends up suffering from disease
- more effective with common diseases
- less prone to bias
- can be prospective or retrospective

9

describe case-controlled studies

- involves people suffering from disease and people not suffering from disease (control)
- more useful for rare conditions
- quick and cost-efficient
- can investigate many exposures simultaneously
- selection bias
- recall (of information) bias
- poor for rare exposures

10

describe ecological studies

- descriptive/observational study
- used to measure prevalence and incidence of disease in different populations, particularly when disease is rare

11

describe cross sectional studies

- routinely collected data that helps to describe the status of individuals with respect to absence/presence of both exposure and disease assessed at the same point in time
- hard to establish causal effect

12

describe case reports

- description of single case
- not used to support clinical practice
- may be useful in picking out new syndromes/conditions

13

what is association?

the statistical dependence between 2 variables - the degree to which the rate of disease in persons with a specific exposure is higher/lower than the rate of disease without the exposure

14

what 4 things can be used to evaluate statistical association?

Chance
Bias
Confounding factors
Causal effect

15

how is chance used to evaluate statistical association?

- how big is the sample size?
- use of power calculations - minimum sample size required so that you can likely detect an effect of a given size
- p values - p < 0.05 = low p that it's due to chance = significant
confidence intervals - range within which the 'true' value is expected to lie with a given degree of certainty

16

how is bias used to evaluate statistical association?

bias is a consequence of defects in design/execution (systemic error)
- selection bias - selection of participants
- measurement bias - measurements/classifications of disease/exposure

17

how are confounding factors used to evaluate statistical association?

factors that distort risk of disease but do not alone cause disease
- age
- sex
- socio-economic
- geography

18

how is causal effect used to evaluate statistical significance?

judgement of a cause-effect relationship based on logic -> observed association between exposure and disease -> sufficient evidence

19

outline the factors to consider in the Bradford-Hill Criteria for causation

ANALOGY - there is a source of more elaborate hypotheses about the association
STRENGTH - of association, measured by magnitude of relative risk
SPECIFICITY - if an exposure increases the risk of 1 disease but not many diseases
EXPERIMENTAL EVIDENCE - evidence on humans/animals
TEMPORAL RELATIONSHIP(!) - risk factor causes disease if it precedes the disease
CONSISTENCY - similar results in different populations using different study designs
DOSE-RESPONSE RELATIONSHIP - increased exposure = increased risk of disease
PLAUSIBILITY - association is more likely to be causal if consistent with other knowledge
COHERENCE - information is not conflicting

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