Bradford Hill criteria
The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing
In 1996, David Fredricks and David Relman remarked on Hill's criteria in their seminal paper on microbial pathogenesis.[2]
Definition
In 1965, the English statistician Sir
- Strength (effect size): A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.
- Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
- Specificity: Causation is likely if there is a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.[1]
- Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).
- Biological gradient (dose–response relationship): Greater exposure should generally lead to greater incidence of the effect. However, 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.[1]
- Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).
- Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".
- Experiment: "Occasionally it is possible to appeal to experimental evidence".
- Analogy: The use of analogies or similarities between the observed association and any other associations.
Some authors[3] consider, also, Reversibility: If the cause is deleted then the effect should disappear as well.
Debate in epidemiology
All scientific work is incomplete. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time.
—Bradford Hill, on the fallacy of persisting with existing research and rules.[4]
Bradford Hill's criteria had been widely accepted as useful guidelines for investigating
In addition, their method of application is debated.[citation needed] Some proposed options how to apply them include:
- Using a counterfactual consideration as the basis for applying each criterion.[6]
- Subdividing them into three categories: direct, mechanistic and parallel evidence, expected to complement each other. This operational reformulation of the criteria has been recently proposed in the context of evidence-based medicine.[7]
- Considering confounding factors and bias.[8]
- Using Hill’s criteria as a guide, but not considering them to give definitive conclusions.[9]
- Separating causal association and interventions, because interventions in public health are more complex than can be evaluated by use of Hill’s criteria[10]
An argument against the use of Bradford Hill criteria as exclusive considerations in proving causality is that the basic mechanism of proving causality is not in applying specific criteria—whether those of Bradford Hill or counterfactual argument—but in scientific common sense deduction.[11] Others argue that the specific study from which data has been produced is important, and while the Bradford Hill criteria may be applied to test causality in these scenarios, the study type may rule out deducing or inducing causality, and the criteria are only of use in inferring the best explanation of this data.[12]
Debate over the scope of application of the criteria includes, whether they can be applied to
Examples of application
Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between exposures to molds and infant pulmonary hemorrhage,
Since the description of the criteria, many methods to systematically evaluate the evidence supporting a causal relationship have been published, for example the five evidence-grading criteria of the World Cancer Research Fund (Convincing; Probable; Limited evidence – suggestive; Limited evidence – no conclusion; Substantial effect on risk unlikely).[25]
See also
- Causal inference – Branch of statistics concerned with inferring causal relationships between variables
- Granger causality – Statistical hypothesis test for forecasting
- Koch's postulates – Four criteria showing a causal relationship between a causative microbe and a disease
- Public health – Promoting health through organized efforts and informed choices of society and individuals
- MAGIC criteria – Set of guidelines for using statistical analysis
- Correlation does not imply causation – Refutation of a logical fallacy
References
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- ^ What the Continuous Update Conclusions Mean American Institute for Cancer Research, n.d., retrieved 13 June 2017