Intro to Epidemiology Flashcards Preview

MD2001 > Intro to Epidemiology > Flashcards

Flashcards in Intro to Epidemiology Deck (45):
1

What does a meaningful statistic need?

1. A denominator population

2. A time frame

2

What are examples of denominators?

+ Health board
+ City
+ Hospital
+ Disease register
+ Recruited to a study

Denominator must correspond to numerator - without denominator pop. and time dath rate are meaningless

3

What is timeframe?

+ Person-time
+ n-year follow-up

4

What is incidence?

+ Number of new cases
+ A rate or proportion
+ Useful for identifying causes of diseases
+ Occurs, by definition, only in people without the disease

5

What is prevalence?

+ Proportion of population that has disease:
- point
- period
+ Identifies disease burden
+ Useful for planning services
+ Depends partly on incidence

6

Sporadic:

Occasional cases occuring irregularly

7

Endemic:

Persistent background levels of occurence (low to moderate levels)

8

Epidemic:

Occurence in excess of the expected level for a given time period

9

Pandemic:

Epidemic occuring in or spreading over more than one continent

10

What are non-modifiable exposures?

+ Age
+ Sex
+ Genotype

11

What are modifiable exposures?

+ Smoking
+ Weight
+ Diet
+ Alcohol consumption

12

What is risk?

(No. outcomes in group / No. people in group) x 100

13

What is relative risk (RR/risk ratio)?

Risk in exposed / Risk in unexposed

14

What is the relative risk reduction (RRR)?

(1 - Relative risk) x 100

15

What is the absolute risk reduction (ARR/risk difference)?

Risk in unexposed - Risk in exposed

16

What is number needed to treat (NNT)

1 / Absolute risk reduction

17

Odds ratio (OR)?

Commonly used estimate of risk ratio (non-RCT study designs)

18

Rate ratio (RR)?

Ratio between two mortality rates, hospitalisation rates stc.

19

Hazard ratio (HR)?

A special kind of rate ratio (survival analysis)

20

What are confidence intervals?

+ Represents range of plausible values

+ Can be presented for any statistic/effect measure

+ Values near the limits/more extreme values less plausible than those in the middle

+ The wider the interval the greater the uncertainty

+ Very useful in appraising published research

21

Steps in cross-sectional study?

+ Sample population

+ Estimate the population:
- different exposures
- different signs/symptoms
- different outcomes

+ Use data:
- to describe prevalence/burden
- to explore associations

22

Steps in case-control study?

+ Select cases with an outcome

+ Select controls without the outcome

+ Explore EXPOSURES in cases and controls

+ Compare exposures in cases and controls

+ Identify association

23

Steps in a cohort study?

+ Select people without an outcome

+ Classify according to an exposure

+ Follow-up:
- prospective
- retrospective

+ Compare RISK od disease in exposed and unexposed

24

Steps in randomised controlled trial (RCT)?

+ Random allocation:
- intervention
- control/comparator

+ Compare RISK of outcome in interventon and control groups

25

Study design? Objective: Treatment effect

RCT

26

Study design? Objective: Cause

+ Cohort
+ Case-control

27

Study design? Objective: Prognosis

Cohort

28

Study design? Objective: Incidence

Cohort

29

Study design? Objective: Prevalence

Cross-sectional

30

Study design? Time-frame: Future

+ RCT
+ Cohort: prospective

31

Study design? Time-frame: Past

+ Cohort: retrospective
+ Case-control
+ Cross-sectional

32

What is confounding?

When a true relationship gets "confused" by another factor

33

What is bias?

Systematic error:
- what data are collected
- how data are collected
- how data are analysed
- how data are interpreted
- how data are reported

34

What does bias lead to?

Wrong conclusions concerning:
- effectiveness
- causation

35

What is the hierarchy of evidence, from least from to confounding and bias to most?

1. Systematic reviews and meta-analyses
2. Experimental designs; RCTs; pseudo-RCTs
3. Quasi-experimental designs
4. Observational-analytic designs: cohort study, case-controlled study
5. Observational-descriptive designs: Cross-sectional studies, case series, case study
6. Background information/expert opinion

36

What are the 9 criteria for causality?

1. Strength (effect size)
2. Consistency (reproducibility)
3. Specificity
4. Temporality
5. Biological gradient
6. Plausability
7. Coherence
8. Experiment
9. Analogy

37

Describe criteria (strength) for inferring causality:

A causal link is more likely with STRONG associations (RR or OR)

However, small association does not mean that there is not causal effect

38

Describe criteria (consistency) for inferring causality:

A causal link is more likely if the association is observed in DIFFERENT STUDIES and different sub-groups

39

Describe criteria (specificity) for inferring causality:

A causal link is more liekly when a disease is associated with ONE SPECIFIC FACTOR

The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship

40

Describe criteria (temporality) for inferring causality:

A causal link is more likely if EXPOSURE to the putative vause has been shown to PRECEDE THE OUTCOME
(i.e RCT, prospective cohort)

41

Describe criteria (biological gradient) for inferring causality:

A causal link is more likely if DIFFERENT LEVELS OF EXPOSURE to the putative factor lead to DIFFERENT RISK of acquiring the
outcome
- Greater exposure should generally lead to greater
incidence of the effect.
- In some cases, the mere presence of the factor can
trigger the effect.
- In other cases, an inverse proportion is observed:
greater exposure leads to lower incidence

42

Describe criteria (plausability) for inferring causality:

A causal link is more likely if a BIOLOGICALLY PLAUSIBLE MECHANISM is likely or demonstrated

However, knowledge of the mechanism is limited by current knowledge

43

Describe criteria (coherence) for inferring causality:

A causal link is more likely if the observed association CONFORMS WITH CURRENT KNOWLEDGE
- epidemiological and laboratory findings
- But, lack of laboratory evidence cannot
invalidate an epidemiological association

44

Describe criteria (experiment) for inferring causality:

A causal link is very likely if REMOVAL or prevention of the putative factor leads to a REDUCED OR NON-EXISTENT RISK of acquiring the outcome
- Experimental evidence

45

Describe criteria (analogy) for inferring causality:

A causal link is more likely if an analogy exists with OTHER DISEASES, SPECIES OR SETTINGS