Epi Flashcards

1
Q

Primary prevention

A

Stop getting the disease

Eg wearing suncream

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2
Q

Secondary prevention

A

Early identification and treatment
Treat RFs
Cure / prevent progression

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3
Q

Tertiary prevention

A

Rehabilitation of people with established disease

Aims to reduce numbers/ impact of complications

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4
Q

When should primary prevention start

A

As early as possible. E.g. During or pre conception

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5
Q

Why cant do RCT for questions about prognosis

A

unethical to make them wait if there is a treatment

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6
Q

Sensitivity calculation

A

proportion of people truly positive with the disease = true positive / total

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7
Q

Specificity calculation

A

probabiluty of -ive test result in people without the disease= true -ive /tatal

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8
Q

How to tell whats true positive or negative

A

if gold standard test is positive and the new intervension is +, then true +
if gold standard test is negative and the new intervension is -, then true -

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9
Q

which one is good to rule things out? in? a specific or sensitive test?

A

SnNout Sensitivity; rules out (eg D-dimer) (if high and -, then they most likely dont have the condition)

SpPin- specific; rules in ( if high and + then they most likely have the disease)

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10
Q

Positive predictive value

A

the probability of having disease if you test positive

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11
Q

Negative predictive value

A

the probability of not having disease if you test negative

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12
Q

work up bias

A
  • when gold standard test is too expensive/invasive
  • so you only use it in advanced disease eg kidney biopsy
  • so you may end up overestimating the sensitivity
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13
Q

reporting bias

A

was it blinded to investigators?

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14
Q

publication bias

A

difficult to publish -ive results

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15
Q

spectrum bias

A

A type of sampling bias
Pt mix in one clinic may be completely different from another
Therefore performance of a diagnostic test may be completely different

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16
Q

+ive likelihood ratio

A

LR+= Sensitivity / 1-specificity

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17
Q

Likelihood ratio

A

values close to 1 indicating no better than random

if higher than 10, it is likely and conclusive of disease presence

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18
Q

Intension to treat analysis

A

Analyse data from everyone randomised no matter whether they dropped out or not throughout the trial

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19
Q

Per protocol analysis

A

Just analyse people who completed/complied with the study protocol, exclude dropouts

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20
Q

Sensitivity analysis

A

Doing the analysis using different assumptions, eg if seeing if certain drug gives you hyponatraemia, analyse for Na < 135 mmol/L, then Na < 130, then etc.
See if lower Na levels have an effect on the outcome

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21
Q

Pragmatic study

A

Very inclusive, generalisable

Assesses real world

22
Q

Explanatory RCT

A

Selected (exclusive) patients

Assesses best case, efficacy

23
Q

Cluster randomisation

A

Groups rather than individuals

24
Q

Advantages of cluster randomisation

A

Avoid performance bias (treating similar patients differently)
Avoid contamination

25
Q

Disadv of cluster randomisation

A

Greater sample size required

Randomised before individual consent taken

26
Q

Prevelance calc

A

No individuals / total population at risk

27
Q

Incidence calc

A

No of new cases/ population at risk (disease free)

28
Q

Incidence rate calc

A

No of new cases/ (population at risk * time interval)

29
Q

Risk ratio

A

risk in exposed/risk in unexposed

30
Q

Odds ratio

A

ods in exposed /odds in unexposed

31
Q

Risk vs odds calculation

A

Risk: 1 in 10 people gets MI , Risk 1/10
Odds: 1 (MI) / 9 (healthy)

32
Q

Precision vs accuracy

A

Precision - all the darts on a board in a similar area; big sample size shows true value
Accuracy - all the darts on bulls eye, low bias shows true value

33
Q

SD vs SE

A

SD of samples means is SE

34
Q

95% CI calc

A

+/- 1.96 SE

35
Q

Quality adjusted life year

A

measure of health= morbidity (quality of life) *mortality (quantity of life)

36
Q

QUALY interpretation

A

1= best imaginable health
0.5 intermediate health state
0 worst- death

37
Q

ICER calc

A

Incremental cost-effectiveness ratio

Difference in cost/difference in effect (QUALY)

38
Q

Problems with ICER

A

hard to interpret, is ICER negative because negative QUALY or negative cost

39
Q

Alternative to ICER

A

Net monetary benefit
If NMB is positive, intervention is cost effective
(takes into account how much health we get when we spend money)

40
Q

Horizontal equity

A

equal treatment of equals

41
Q

Vertical equitiy

A

unequal treatment of unequals

42
Q

Fixed effect meta analysis assumption and weighting

A

homogeneity

Larger studies get higher weighting in the ultimate conclusion

43
Q

When use a random effect meta analysis and not a fixed effect

A

If there is risk that there is heterogeneity of data (difference in results of studies due to methods used and not random error)
i.e. if I squared value is bigger than 25, a random effect meta analysis should be used

44
Q

Heterogeneity tests

A
Q score (if p value is significant, the heterogeneity)
I2 statistics (if above 25 then more likely for heterogeneity)
45
Q

Which one is better fixed or random effect meta analysis

A

fixed if possible provides stronger evidence

46
Q

NNTB

A

number needed to treat to benefit
1/ (Risk difference)
risk difference = R0 (risk of exposed) - R1(risk of unexposed)

47
Q

NNTH

A

number needed to treat to harm
1/ (Risk difference)
risk difference = R1 (risk of unexposed) - R0 (risk of exposed)

48
Q

type 1 error

A

suggests there is a statistical difference when there isnt any

49
Q

type 2 error

A

suggests there is no difference when there is

50
Q

Berksons bias

A

controls selected from hospital patients

51
Q

Attrition bias

A

loss from one group more than the other in RCT