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Semester 2 Year 1 > Epidemiology Key Concepts > Flashcards

Epidemiology Key Concepts Flashcards

1
Q

What is epidemiology?

A

Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control of health problems

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2
Q

What should we learn from this hypothetical:
“• Should I be worried?
A) 50 people died from flu
B) 50 people in Fife died from flu
C) 50 people in Fife died from flu in past year”

A

• Meaningfulstatisticsneed

  1. A denominator population
  2. A time frame
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3
Q

Give some examples of denominator populations

A 
• Health board
• City
• Hospital
• Disease register
• Recruited to a study
The denominator must correspond to the numerator
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4
Q

What are the two broadest categories of epidemiological study designs?

A

Observational

Experimental

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5
Q

How may experimental epidemiological study designs be split up?

A

Quasi-experimental

RCT

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6
Q

How may observational epidemiological study designs be split up?

A

Into studies on populations and individuals

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7
Q

How may observational epidemiological studies of individuals designs be split up?

A

Descriptive

  • Case series
  • Cross-sectional study

Analytic

  • Cohort study
  • Case-control study
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8
Q

How may observational epidemiological studies of populations designs be split up?

A

Descriptive studies

- “Ecological” population case series

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9
Q

What is a case series?

A

A series, often consecutive, of cases with the same disease
For example: 5 cases of Pneumocystis pneumonia and an unexpectedly high incidence of Kaposi’s sarcoma among young, previously healthy, men in 1981
Led to ‘discovery of HIV’

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10
Q

What are ecological studies as in population case series?

A
  • The unit of study is a population (NOT an individual)
  • Useful to study signs and symptoms, look at characteristics of cases for causal hypotheses
  • Create disease definitions, foundation for other studies
  • Descriptive, retrospective, observational
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11
Q

Give an example of exposures and outcomes

A

Inequality and mortality in US states (Kennedy et al. 1996)

  • Scatter plot used to test association of exposures and outcomes
  • Exposures = Inequality (Robin Hood Index)
  • Outcomes = Mortality

• Age is a confounder as it varies between states & affect mortality rates
• Standardisation of age streamlines the inequality- associated mortality
measurement across states.
• Other variations existing between states may need to be adjusted for.
• Interpretation: The increase in inequality is associated with increase in
mortality across states. This is termed as linear (positive) association.

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12
Q

Give an example of crude mortality

A

State

  1. NYC
  2. Florida

Deaths in 2013

  1. 48,000
  2. 190,000

Population in 2013

  1. 8,000,000
  2. 19,000,000

Crude annual mortality

  1. 6 per 1,000
  2. 10 per 1,000

Rate ratio - 1.67

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13
Q

What are the limitations of crude rates?

A

Of limited value when comparing two populations with different structures (i.e. confounding variable)

Two populations with the same crude rates for a particular outcome (e.g. death) will have different overall rates if the distribution of a confounder within the populations (e.g. age) are different.

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14
Q

What is standardisation?

A

“A set of techniques, based on weighted averaging, used to remove as much as possible the effects of differences in age or other confounding variables in comparing two or more populations.”

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15
Q

What is the standardised mortality ratio (SMR)?

A

Standardized mortality (death) rate is a weighted average of the age- specific mortality rates, where the weights are the proportions of persons in the corresponding age groups of a standard population

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16
Q

What is shown when SMR is used in the NYC/Florida example?

A

State

  1. NYC
  2. Florida

Deaths in 2013

  1. 48,000
  2. 190,000

Population in 2013

  1. 8,000,000
  2. 19,000,000

Crude annual mortality

  1. 6 per 1,000
  2. 10 per 1,000

Expected Deaths

  1. 50,000
  2. 220,000

SMR

  1. 96 per 1,000
  2. 86 per 1,000

• Calculate expected deaths based on
– Age-sex specific mortality rates in whole of US

– Age and sex of people in NY and Florida

Both places have lower than expected mortality

New York has a higher SMR than Florida

17
Q

As SMR shows a discrepancy from crude death rate in the NYC/Florida example what is this an example of?

A

NY vs Florida was an example of confounding
True relationship confused by a third factor
• Can deal with confounding
– Study design
– Data analysis (e.g. standardisation)

18
Q

What is confounding?

A

“…the distortion of a measure of the effect of an exposure on an outcome due to the association of the exposure with other factors that influence the occurrence of the outcome.”

19
Q

What is bias?

A

“An error in the conception and design of a study – or in the collection, analysis, interpretation, reporting, publication, or review of data – leading to results or conclusions that are systematically (as opposed to randomly) different from truth”

 • Systematic error in
– What data are collected 
– How data are collected
– How data are analysed
– How data are interpreted 
– How data are reported
• Bias leads to wrong conclusions about 
– Disease causation
– Treatment effectiveness
20
Q

What is the hierarchy of evidence?

A

Systematic reviews > Randomised control trial (RCT) > Cohort studies > Case control studies > Case series and case reports > Editorials and expert opinions

21
Q

What are the criteria for causality? (Hill, 1965)

A

Consistency (reproducibility)

Specificity

Temporality

Biological gradient

Plausibility

Coherence

Experiment

Analogy

22
Q

What are the OG Bradford Hill criteria for causation?

A
  1. Strength
  2. Consistency
  3. Specificity
  4. Temporality
  5. Biological gradient
  6. Plausibility
  7. Coherence
  8. Experiment
  9. Analogy
23
Q

Describe consistency (reproducibility) in the Bradford Hill criteria for causation

A

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

24
Q

Describe specificity in the Bradford Hill criteria for causation

A

A causal link is more likely 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
Should rise in ice cream sales concurrent with rise in murder in many places at different times be regarded as causal?

25
Q

Describe temporality in the Bradford Hill criteria for causation

A

A causal link is more likely if exposure to the putative cause has been shown to precede the outcome (i.e. RCT, prospective cohort)

26
Q

Describe biological gradient in the Bradford Hill criteria for causation

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

27
Q

Describe plausibility in the Bradford Hill criteria for causation

A

A causal link is more likely if a biologically plausible mechanism is likely or demonstrated
But, knowledge of the mechanism is limited by current knowledge

28
Q

Describe coherence in the Bradford Hill criteria for causation

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

29
Q

Describe experiment in the Bradford Hill criteria for causation

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

30
Q

Describe analogy in the Bradford Hill criteria for causation

A

A causal link is more likely if an analogy exists with other diseases, species or settings

Semester 2 Year 1 (82 decks)

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