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2įor example, the first criterion 'strength of association' does not take into account the fact that not every component cause will have a strong association with the disease it produces, or that strength of association also depends on the prevalence of other factors. He contends that the Bradford Hill criteria fail to deliver on the hope of clearly distinguishing causal from non-causal relations. Rothman argues that Hill did not propose these criteria as a checklist for evaluating whether a reported association might be interpreted as causal, but they have been widely applied in this way. For example, knowing of the teratogenic effects of thalidomide, we may accept a cause-effect relationship for a similar agent based on slighter evidence.Īlthough widely used, the criteria are not without criticism. analogous to) other established cause-effect relationships.
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Analogy – The relationship is in line with (i.e.Experiment – Removal of the exposure alters the frequency of the outcome.Coherence – The relationship found agrees with the current knowledge of the natural history/biology of the disease.Biological plausibility – There is a potential biological mechanism which explains the association.Biological gradient – Changes in the intensity of the exposure results in a change in the severity or risk of the outcome (i.e.Temporal sequence – The exposure must precede outcome (to exclude reverse causation).Specificity – There is a one-to-one relationship between the exposure and outcome.Consistency – The same findings have been observed among different populations, using different study designs and at different times.Strength of association – The stronger the association, or magnitude of the risk, between a risk factor and outcome, the more likely the relationship is thought to be causal.The Bradford Hill criteria, listed below, are widely used in epidemiology as a framework with which to assess whether an observed association is likely to be causal. A reverse causation explanation could be that people with poor mental wellbeing are more likely to use recreational drugs as, say, a means of escapism.Īn observed statistical association between a risk factor and a disease does not necessarily lead us to infer a causal relationship conversely, the absence of an association does not necessarily imply the absence of a causal relationship.Ī judgment about whether an observed statistical association represents a cause-effect relationship between exposure and disease requires inferences far beyond the data from a single study. For example, a study may find an association between using recreational drugs (exposure) and poor mental wellbeing (outcome) and thus conclude that using drugs is likely to impair wellbeing. Reverse causality describes the event where an association between an exposure and an outcome is not due to direct causality from exposure to outcome, but rather because the defined “outcome” actually results in a change in the defined “exposure”. An observed association may in fact be due to the effects of one or more of the following:Ī discussion of chance, bias and confounding can be found in the subsequent chapters and in the chapter “ Sources of variation”. Specifically, causation needs to be distinguished from mere association – the link between two variables (often an exposure and an outcome). However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer that a cause-effect relationship exists. We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed.Ī principal aim of epidemiology is to assess the causes of disease.