Epidemiology Key Concepts Flashcards Preview

Semester 2 Year 1 > Epidemiology Key Concepts > Flashcards

Flashcards in Epidemiology Key Concepts Deck (30)
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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

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
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
4
Q

What are the two broadest categories of epidemiological study designs?

A

Observational

Experimental

5
Q

How may experimental epidemiological study designs be split up?

A

Quasi-experimental

RCT

6
Q

How may observational epidemiological study designs be split up?

A

Into studies on populations and individuals

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
8
Q

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

A

Descriptive studies

- “Ecological” population case series

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’

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
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.

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

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.

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.”

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

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

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