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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
Time frame of quasi-experimental study designs
Look towards the future
26
Time frame of case control study designs
Look in the past
27
Time frame of cross-sectional study designs
Look in the past
28
X-axis
Independent variable (exposure)
29
Y-axis
Dependent variable (outcome)
30
Function of scatter plot
Used to test association of exposures and outcomes.
31
Robin Hood index
Inequality - Higher inequality - Higher mortality
32
Why is age adjusted in a scatterplot ?
Age is a confounder as it varies between states and affects mortality rates.
33
Function of standardisation of age
Streamlines the inequality associated mortality measurement across states.
34
Linear positive association
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
Inverse (negative) correlation
Higher the exposure, the lower the outcome.
36
Crude mortality
Number of people with an outcome / the population of the area Multiply by 1,000 (to give per 1,000)
37
Limitations of 'crude' rates
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
What is important to do when comparing mortality rates between different populations ?
It is crucial to standardise
39
Standardisation
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
State the 2 types of standardisation
- Direct standardisation - Indirect standardisation
41
Direct standardisation
Population of interest (known: age-specific mortality rates) Standard population Compare the age-adjusted mortality rates
42
Standard Population
One in which all the confounders have been taken care of.
43
Indirect standardisation
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
Standardised mortality ratio
Number of observed deaths / Number of expected deaths MULTIPLIED BY 100
45
What is the standardised mortality ratio ? EXAM Q - MEMORISE
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
How can you deal with confounding ?
Study Design Data Analysis (standardisation) - free of bias
47
Confounding EXAM Q - MEMORISE DEFINITION
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
Quick Definition of confounding
Unmeasured variable, which influences both the supposed cause and supposed effect.
49
Bias EXAM Q - MEMORISE DEFINITION
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
What can bias lead to ?
Wrong conclusions about: - Disease causation - Treatment effectiveness
51
Errors that arise from bias
Systematic error in: - What data are collected How data are: - collected - analysed - interpreted - reported
52
Describe the hierarchy of evidence
(TOP to BOTTOM) Systematic reviews RCT's Cohort studies Case control studies Case series and case reports Editorials and expert opinions
53
Bradford Hill criteria for causality EXAM Q
1. Strength 2. Consistency 3. Specificity 4. Temporality 5. Biological gradient 6. Plausibility 7. Coherence 8. Experiment 9. Analogy
54
Consistency
A casual link is more likely if the association is observed in different studies and different sub-groups.
55
Specificity
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
Temporality
A casual link is more likely if exposure to the putative cause has been shown to precede the outcome.
57
Biological gradient
A casual link is more likely if different levels of exposure to the putative factor lead to different risk of acquiring the outcome.
58
Plausibility
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
Coherence
A casual link is more likely if the observed association conforms with current knowledge.
60
Analogy
A casual link is more likely if an analogy exists with other diseases, species or settings.
61
Experiment
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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