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MD2001 > Intro to Epidemiology > Flashcards

Intro to Epidemiology Flashcards

1
Q

What does a meaningful statistic need?

A
  1. A denominator population

2. A time frame

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2
Q

What are examples of denominators?

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

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

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3
Q

What is timeframe?

A

+ Person-time

+ n-year follow-up

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4
Q

What is incidence?

A

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

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5
Q

What is prevalence?

A 
\+ Proportion of population that has disease:
- point
- period
\+ Identifies disease burden
\+ Useful for planning services
\+ Depends partly on incidence
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6
Q

Sporadic:

A

Occasional cases occuring irregularly

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7
Q

Endemic:

A

Persistent background levels of occurence (low to moderate levels)

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8
Q

Epidemic:

A

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

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9
Q

Pandemic:

A

Epidemic occuring in or spreading over more than one continent

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10
Q

What are non-modifiable exposures?

A

+ Age
+ Sex
+ Genotype

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11
Q

What are modifiable exposures?

A

+ Smoking
+ Weight
+ Diet
+ Alcohol consumption

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12
Q

What is risk?

A

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

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13
Q

What is relative risk (RR/risk ratio)?

A

Risk in exposed / Risk in unexposed

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14
Q

What is the relative risk reduction (RRR)?

A

(1 - Relative risk) x 100

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15
Q

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

A

Risk in unexposed - Risk in exposed

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16
Q

What is number needed to treat (NNT)

A

1 / Absolute risk reduction

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17
Q

Odds ratio (OR)?

A

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

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18
Q

Rate ratio (RR)?

A

Ratio between two mortality rates, hospitalisation rates stc.

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19
Q

Hazard ratio (HR)?

A

A special kind of rate ratio (survival analysis)

20
Q

What are confidence intervals?

A

+ 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
Q

Steps in cross-sectional study?

A

+ Sample population

+ Estimate the population:

  • different exposures
  • different signs/symptoms
  • different outcomes

+ Use data:

  • to describe prevalence/burden
  • to explore associations
22
Q

Steps in case-control study?

A

+ 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
Q

Steps in a cohort study?

A

+ Select people without an outcome

+ Classify according to an exposure

+ Follow-up:

  • prospective
  • retrospective

+ Compare RISK od disease in exposed and unexposed

24
Q

Steps in randomised controlled trial (RCT)?

A

+ Random allocation:

  • intervention
  • control/comparator

+ Compare RISK of outcome in interventon and control groups

25
Q

Study design? Objective: Treatment effect

A

RCT

26
Q

Study design? Objective: Cause

A

+ Cohort

+ Case-control

27
Q

Study design? Objective: Prognosis

A

Cohort

28
Q

Study design? Objective: Incidence

A

Cohort

29
Q

Study design? Objective: Prevalence

A

Cross-sectional

30
Q

Study design? Time-frame: Future

A

+ RCT

+ Cohort: prospective

31
Q

Study design? Time-frame: Past

A

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

32
Q

What is confounding?

A

When a true relationship gets “confused” by another factor

33
Q

What is bias?

A

Systematic error:

  • what data are collected
  • how data are collected
  • how data are analysed
  • how data are interpreted
  • how data are reported
34
Q

What does bias lead to?

A

Wrong conclusions concerning:

  • effectiveness
  • causation
35
Q

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

A
  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
Q

What are the 9 criteria for causality?

A
  1. Strength (effect size)
  2. Consistency (reproducibility)
  3. Specificity
  4. Temporality
  5. Biological gradient
  6. Plausability
  7. Coherence
  8. Experiment
  9. Analogy
37
Q

Describe criteria (strength) for inferring causality:

A

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
Q

Describe criteria (consistency) for inferring causality:

A

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

39
Q

Describe criteria (specificity) for inferring causality:

A

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
Q

Describe criteria (temporality) for inferring causality:

A

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
Q

Describe criteria (biological gradient) for inferring causality:

A

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
Q

Describe criteria (plausability) for inferring causality:

A

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
Q

Describe criteria (coherence) for inferring causality:

A

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
Q

Describe criteria (experiment) for inferring causality:

A

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
Q

Describe criteria (analogy) for inferring causality:

A

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

MD2001 (52 decks)

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