• Understanding Research: Causation

    by Cian O'Brien. Last modified: 27/04/14

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    The aim of epidemiology is to assess the cause of disease. However, since most of epidemiological studies are by nature observation rather than experimental, a number of possible explanations for an observed association need to be considered before you can infer cause-effect relationship

    The observed association may be because of:

    • Chance (random error)
    • Bias (systematic error)
    • Confounding

    An observed statistical association between a risk factor and a disease does not necessarily lead to a causal relationship. The absence of an association does not necessarily imply absence of a causal relationship.

    Bradford-Hill Criteria

    Assess whether an observed association is likely to be causal

    1. Strength of the association – the stronger the association between a risk factor and outcome, the more likely the relationship is to be causal
    2. Consistency of findings – have the same findings been observed among different populations, in different study designs and different times?
    3. Specificity of the association – there must be a one to one relationship between cause and outcome
    4. Temporal sequence of association – exposure must precede outcome
    5. Biological gradient – change in disease rates should follow from corresponding changes in exposure (dose-response)
    6. Biological plausibility – presence of a potential biological mechanism
    7. Coherence – does the relationship agree with the current knowledge of the natural history/biology of the disease
    8. Experiment – does the removal of the exposure alter the frequency of the outcome?
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    Cian O'Brien

    Cian O'Brien

    Cian is an Irish trained Emergency Medical Technician, Registered General Nurse and holds a Masters degree in Public Health from University College Cork, Ireland. His research interests include prehospital care and marathon medicine.

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