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
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