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Flashcards in Evidence sofia LOs Deck (28):
1

Describe what is meant by 'epidemiological transition'

The shift from infectious and deficient diseases to chronic non-communicable diseases as a result of socio-demographic changes among poorer countries

2

What is the role of Evidence based medicine (EBM) in the clinical setting

Conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients providing clinicians with a means to integrate continuing education and practice improvement in their day-to-day work

3

Criticism of EBM

Doctors regard EBM as an academic practice and find there isn't enough time in their jobs to critically appraise so many articles per consultation
Often not appropriate for General practice but conducted in teaching hospitals
Healthcare commissioners use EBM jargon to justify decisions seen inappropriate by clinicians

4

What is the hierarchy of evidence?

It is the hierarchy showing how well rated each type of article should be rated in terms of what it is portraying. The higher in the hierarchy, the more respected it will be when the information is presented.

5

What is the order of the hierarchy of evidence?

Systematic reviews and meta-analyses
Randomised controlled trials
Cohort studies
Case-control studies
Ecological studies
Descriptive/cross-sectional studies
Case report/series

6

Why Should EBM matter to clinicians?

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7

What are the tools for a clinical decision?

Clinical findings - Properly gather and interpret findings from history and physical examination

Aetiology - Identify cause for a disease

Clinical manifestations of disease - Knowing how often and when a disease causes its clinical manifestations

Differential diagnosis - Consider possible causes of a clinical problem & how to choose which are likely, which are serious and which respond to treatment

Diagnostic tests - Select and interpret diagnostic tests to confirm or exclude a diagnosis, considering their precision, accuracy, acceptability, expense and safety

Prognosis - how to estimate a patient's likely clinical course over time and anticipate complications

Therapy - select treatments to offer a patient that do more good than harm

Prevention - reduce chance of disease by identifying and modifying RFs and how to diagnose disease early by screening

8

What is chance?

Chance is an associated factor, is one reason for an observational association. Doesn't mean that there observed association actually means there is a link between the factor and disease or whatever youre actually testing

9

What is bias?

Bias is a consequence of defects in the design or execution of an epidemiological study
Bias cannot be controlled in the analysis of study but is eliminated with a large sample size

10

Types of bias

Selection
- Difference in the people selected for the study and those that are not
Measurement
- measurements or classifications of disease or exposure are inaccurate

11

What is a confounding factor?

A potential confounder is any factor which is believed to have an effect on risk of the disease and is also relation the risk factor of the disease

12

Common confounders

Age
Sex
Socio-economic status
Geography

13

What is a causal relationship?

Judgement based on a chain of logic that addresses two main areas: Observed association and Lots of evidence that supports it being a cause

14

What are the Bradford hill criteria?

They are factors to consider when deciding if a relationship is causal.
They include: Temporal relationship, Plausibility, Consistency with other investigations, Strength of association, Dose-response relationship, specificity, Experimental evidence, Coherence, Analogy

15

What is Odds ratio?

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

16

How is Odds ratio calculated?

(Number of cases who were exposed/ Number of cases who were not exposed) / (Number of exposed controls/number of unexposed controls)

Otherwise known: Odds of exposure in cases / Odds of exposure in controls

17

How is odds ratio understood?

If,
OR < 1; Decreased affect on incidence of disease
OR = 1; No affect on prevalence of disease
OR > 1; Increased affect on incidence of disease

18

What is attributable risk? and how is it calculated?

The attributable risk is the difference in rate of a condition between an exposed population and an unexposed population

(Incidence in exposed - incidence in unexposed)

19

What is relative risk? and how is it calculated?

Ratio of the probability of an event occurring in an exposed group compared to an unexposed group

Incidence in exposed / Incidence in the unexposed

20

How is relative risk interpreted?

If,
RR < 1; Exposure = lower risk
RR = 1; Exposure is not associated
RR > 1; Exposure = higher risk

21

What is sampling?

To take a sample of a population in order to get a response which represents the whole population

22

What is sampling variation?

Variation in response from sample to sample

23

What is a P value?

Probability of obtaining the study result if the null hypothesis is true.

24

How is a P value interpreted?

If P<0.05 it is considered statistically significant and we reject the null hypothesis

25

What are confidence intervals?

An estimated range of values calculated from a given set of sample data which are likely to contain the true population value

26

How are confidence intervals interpreted?

If a result is within 95% confidence intervals, it is considered that any difference in results is due to chance

27

How is confounding dealt with?

Confounding - a possible reason for a result if confounding variables were not considered in the study design.

28

How is confounding dealt with?

Stratification - risks are calculated separately for each confounding variable e.g. age and sex

Standardisation - Produce a standardised mortality rate,

Regression - statistical modelling is used to control for one or many confounding variables