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Flashcards in Use of evidence Deck (33):
1

Checking association is not due to chance

p values, less than 0.05 is statistically significant
95% CI is range true value lies within, 95% sure

2

Checking association is not due to confounders

Can use multivariable analysis to adjust for confounding factors present

3

Types of bias, have to be addressed during study design stage

Measurement bias
Observer bias
Loss to follow up

4

Bradford Hill viewpoints for causation

Temporal relationship (rf must precede disease)
Biological plausibility
Consistency
Association strength
Biological gradient (dose-response relationship)
Specificity e.g. rf inc association of one disease, not many
Coherence - association seen across different measures
Experimental - association replicated in lab/trials
Reversibility - removal of rf results in reduced disease risk

5

Hierarchy of study designs for causality

Systematic reviews/meta-analyses
RCT w/ definitive result
RCT w/out definitive result
Cohort studies
Case-control studies
X-sectional studies
Ecological studies
Case reports

6

Critical appraisal of epidemiological findings

Bias (selection, measurement (observer/responder))
Confounding - dealt with by stratification by confounder status (e.g. age) or statistical modelling
Chance - p value and CI
Causality - Bradford Hill criteria
Generalisability - look at reverse causality being possible and whether these results could apply to your patient or more to another group

7

Types of outcomes of studies

Continuous
Categorical
Binary (type of categorical)
Time to event (type of binary)

8

RCT analysis of treatment effect

Risk ratio= risk in intervention/risk in control
Risk reduction= 1- risk ratio
Risk difference (ARR)= risk in control-risk in intervention
NNT= 1/ARR
Odds ratio= Odds in intervention/odds in control
Rate ratio= rate in intervention/rate in control (for time to event results)
Difference in means= mean in intervention-mean in control (for continuous results)

9

Observational study appraisal

Measurement/adjusting for confounders
Is interpretation of result reasonable given confounders

10

Systematic review process

Find relevant evidence in unbiased manner
Appraise each paper for methodological quality
Synthesise results in met-analysis

11

Cochrane collaboration in EBM

Cochrane database has register of all published RCTs and publishes SRs

12

PRISMA

Preferred reporting items for systematic review and meta-analysis, has checklist to help authors construct SRs

13

How to gather evidence for EBM

Posing a question using PICO (population, intervention, comparator, outcome)
Search for evidence (published and unpublished)
Appraise evidence (Forest plot and pooled estimate with 95% CI)
Applying evidence, see if more RCTs needed

14

SR limitations

Varying methodological quality assessed by risk of bias graph
Heterogeneity test (p>0.05 means no difference) for differences in results
Publication bias - statistically significant more likely to be published than insignificant

15

Assessing a measuring instrument

Validity - check sensitivity and specificity, measures continuum, correlation coefficient between a gold standard test and this
Reliability - Same results on repeated trials by same or different observers, kappa statistic measures how much more agreement than by chance alone
Responsiveness - Shows when true changes have occurred

16

Measures for malnutrition

Stunting - low height for age
Underweight - extremely low weight for age
Wasting - extremely low weight for height

17

Rapid weight gain during childhood can increase risk of

CHD/T2DM in later life

18

Growth stunting at population level indicates

Long-term nutritional deficiencies, individual stunting may be genetic

19

In europe growth patterns reflected by

incidence of wars

20

Ways to calculate gentiles for growth

Rank data- all data in order and no assumptions about distribution
Normal distribution - mean and SD used (mean ±1SD is 16th/84th centile, mean ±1.96SD is 2.5/97.5th centile)

21

Z score for height/weight

z=(value-mean)/SD, shows how many SDs away from average of a certain population an individual is

22

Interpreting z scores for height

0 is average
<0 is below average
>0 is above average

Adjusted for parental height

23

Use of z scores

Osteoporosis/penia - used to assess bone mineral density for age/gender/ethnicity
Educational attainment
Paediatric cardiology measurements

24

Risk definition

Proportion of people with specific event in specific time period
New cases/number in study population= risk (cumulative incidence)

25

Incidence rate definition

Number of events per person-time at risk
New cases/person-time at risk= incidence rate

26

Low prevalence can be due to

low incidence
rapid fatal disease progression
rapid cure

27

High prevalence can be due to

high incidence
prolonged survival without cure

28

point prevalence

no. of people with condition/no. in study at one point in time

29

odds

no.of people with condition/no. without

30

Standardised mortality ratio

observed no. of deaths/expected no. of deaths
Compares death rate in standard population vs pop of interest

31

X sectional study

Defined population surveyed
Can't measure incidence and reverse causality a problem

32

Case control study

Cases and control groups selected and look at previous exposure to rf
Can't assess risk/incidence, selection bias, recall bias but can use odds ratio to estimate risk ratio

33

Cohort study

Group selected and exposure status measured, sees who gets disease and who doesn't
Difficult to follow up so loss to follow up bias