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Medicine Year 1 - Semester 2 - Part 2 > Epidemiology - Key Concepts > Flashcards

Epidemiology - Key Concepts Flashcards

1
Q

What is needed to calculate mortality rate ?

A

MEANINGFUL STATISTICS

A denominator population
A time frame

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2
Q

State some denominators

A

Health board
City
Hospital
Disease register
Recruited to a study

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3
Q

What must the denominator correspond to ?

A

The numerator

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4
Q

State the types of epidemiological study designs

A

2 major groups:

Observational Studies
Experimental Studies

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5
Q

What can observational studies be subdivided into ?

A

Studies that look at:

  • Populations (as a whole)
  • Individuals
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6
Q

Describe Observational Population studies

A

Descriptive study
“Ecological” population case series

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7
Q

Describe the subtypes of Observational Individual studies

A

Descriptive
- Looks at case studies
- Cross sectional studies

Analytic
- Cohort study
- Case-control study

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8
Q

State the types of Experimental study designs

A

Quasi-experimental studies

Randomised Clinical Trials

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9
Q

Describe ecological studies

A

The unit of study is a population.

Descriptive, retrospective (look several years back), observational (observe over a period of time)

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10
Q

What are ecological studies useful for ?

A

Useful to study signs and symptoms, look at characteristics of cases for casual hypothesis.

Important to create disease definitions, foundation for other studies.

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11
Q

What are case series ?

A

A series, often consecutive, of cases with the same disease.

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12
Q

Example of a case series

A

5 cases of Pneumocystis pneumonia and an unexpectedly high incidence of Kaposi’s sarcoma amongst young, previously healthy men in 1981 led to discovery of HIV.

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13
Q

Describe cross sectional study design

A

Sample a population

Estimate the proportion of:
- different exposures
- different signs/symptoms
- different outcomes

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

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14
Q

Describe case control study deigns

A

Select cases with an outcome

Select controls without an outcome

Explore EXPOSURES in cases and controls.

Compare exposures in cases and controls.

Identify association.

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15
Q

Describe cohort study deigns

A

Select people without an outcome

Classify according to an exposure (subjected to an exposure)

Follow-up
- Prospective
- Retrospective

Compare RISK of disease in exposed and unexposed

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16
Q

Describe quasi-experimental study design

A

Non-Random allocation
- Intervention
- Control not required though commonly used

Researcher not in control of treatments, depends on existing groups.

Establish cause- and effect- relationship between dependent and independent variables.

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17
Q

Describe randomised control trial study design

A

Random allocation
- Intervention
- Control/comparator

Compare RISK of outcome in intervention and control groups

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18
Q

Objective of randomised control trials

A

Important in terms of describing the treatment effect.

Effect of treatment vs control

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19
Q

Objective of cohort study design

A

Good for defining:

  • Cause
  • Prognosis
  • Incidence

of disease

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20
Q

Objective of quasi-experimental study design

A

Good for defining cause-effect relationship

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21
Q

Objective of case-control study design

A

Good for defining cause.

Is exposure the cause of outcome ?

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22
Q

Objective of cross-sectional study design

A

Good for determining prevalence of a condition/ disease.

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23
Q

Time-frame of RCT’s

A

Look towards the future

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24
Q

Time frame of cohort study designs

A

Can be:

  • Prospective : future
  • Retrospective : past
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25
Q

Time frame of quasi-experimental study designs

A

Look towards the future

26
Q

Time frame of case control study designs

A

Look in the past

27
Q

Time frame of cross-sectional study designs

A

Look in the past

28
Q

X-axis

A

Independent variable
(exposure)

29
Q

Y-axis

A

Dependent variable
(outcome)

30
Q

Function of scatter plot

A

Used to test association of exposures and outcomes.

31
Q

Robin Hood index

A

Inequality

  • Higher inequality
  • Higher mortality
32
Q

Why is age adjusted in a scatterplot ?

A

Age is a confounder as it varies between states and affects mortality rates.

33
Q

Function of standardisation of age

A

Streamlines the inequality associated mortality measurement across states.

34
Q

Linear positive association

A

Higher the prevalence of something associated with an increase in an outcome.

e.g. Increase in inequality is associated with an increase in mortality across states.

35
Q

Inverse (negative) correlation

A

Higher the exposure, the lower the outcome.

36
Q

Crude mortality

A

Number of people with an outcome / the population of the area

Multiply by 1,000 (to give per 1,000)

37
Q

Limitations of ‘crude’ rates

A

Of limited value when comparing 2 populations with different structures (i.e. confounding variables)

2 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 are different (e.g. age).

38
Q

What is important to do when comparing mortality rates between different populations ?

A

It is crucial to standardise

39
Q

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 2 or more populations.

40
Q

State the 2 types of standardisation

A
  • Direct standardisation
  • Indirect standardisation
41
Q

Direct standardisation

A

Population of interest
(known: age-specific mortality rates)

Standard population

Compare the age-adjusted mortality rates

42
Q

Standard Population

A

One in which all the confounders have been taken care of.

43
Q

Indirect standardisation

A

Reference population
(unknown: age-specific mortality rates)

Population of interest

Compare the standardised mortality ratios of the population of interest

Compare the adjusted morality rates

44
Q

Standardised mortality ratio

A

Number of observed deaths / Number of expected deaths

MULTIPLIED BY 100

45
Q

What is the standardised mortality ratio ?

EXAM Q - MEMORISE

A

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.

46
Q

How can you deal with confounding ?

A

Study Design

Data Analysis (standardisation) - free of bias

47
Q

Confounding

EXAM Q - MEMORISE DEFINITION

A

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

48
Q

Quick Definition of confounding

A

Unmeasured variable, which influences both the supposed cause and supposed effect.

49
Q

Bias

EXAM Q - MEMORISE DEFINITION

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 the truth.

50
Q

What can bias lead to ?

A

Wrong conclusions about:

  • Disease causation
  • Treatment effectiveness
51
Q

Errors that arise from bias

A

Systematic error in:

  • What data are collected

How data are:
- collected
- analysed
- interpreted
- reported

52
Q

Describe the hierarchy of evidence

A

(TOP to BOTTOM)

Systematic reviews
RCT’s
Cohort studies
Case control studies
Case series and case reports
Editorials and expert opinions

53
Q

Bradford Hill criteria for causality

EXAM Q

A
  1. Strength
  2. Consistency
  3. Specificity
  4. Temporality
  5. Biological gradient
  6. Plausibility
  7. Coherence
  8. Experiment
  9. Analogy
54
Q

Consistency

A

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

55
Q

Specificity

A

A casual 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 casual relationship.

56
Q

Temporality

A

A casual link is more likely if exposure to the putative cause has been shown to precede the outcome.

57
Q

Biological gradient

A

A casual link is more likely if different levels of exposure to the putative factor lead to different risk of acquiring the outcome.

58
Q

Plausibility

A

A casual link is more likely of a biologically plausible mechanism is likely or demonstrated.

But, knowledge of the mechanism is limited by current knowledge.

59
Q

Coherence

A

A casual link is more likely if the observed association conforms with current knowledge.

60
Q

Analogy

A

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

61
Q

Experiment

A

A casual link is very likely is removal or prevention of the putative factor leads to a reduced or non-existent risk of acquiring the outcome.

Medicine Year 1 - Semester 2 - Part 2 (53 decks)

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