Bradford Hill criteria

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The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research. They were established in 1965 by the English epidemiologist Sir Austin Bradford Hill.[1]

In 1996, Fredricks and Relman remarked on Hill's criteria in their seminal paper on microbial pathogenesis.[2]


In 1965, the English statistician Sir Austin Bradford Hill proposed a set of nine criteria to provide epidemiologic evidence of a causal relationship between a presumed cause and an observed effect. (For example, he demonstrated the connection between cigarette smoking and lung cancer.) The list of the criteria is as follows:[1]

  1. 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.
  2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
  3. 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]
  4. 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).
  5. 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]
  6. Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).
  7. 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".
  8. Experiment: "Occasionally it is possible to appeal to experimental evidence".
  9. Analogy: The use of analogies or similarities between the observed association and any other associations.

Some authors[which?] consider, also, Reversibility: If the cause is deleted then the effect should disappear as well.

Debate in epidemiology[edit]

Bradford Hill's criteria had been widely accepted as useful guidelines for investigating causality in epidemiological studies but their value has been questioned because they have become somewhat outdated.[3]

In addition, their method of application is debated.[citation needed] Some proposed options how to apply them include:

  1. Using a counterfactual consideration as the basis for applying each criterion.[4]
  2. 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.[5]
  3. Considering confounding factors and bias.[6]
  4. Using Hill’s criteria as a guide, but not considering them to give definitive conclusions.[7]
  5. Separating causal association and interventions, because interventions in public health are more complex than can be evaluated by use of Hill’s criteria[8]

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.[9] 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.[10]

Debate over the scope of application of the criteria includes, whether they can be applied to social sciences.[11] The argument proposes that there are different motives behind defining causality; the Bradford Hill criteria applied to complex systems such as health sciences are useful in prediction models where a consequence is sought; explanation models as to why causation occurred are deduced less easily from Bradford Hill criteria because the instigation of causation, rather than the consequence, is needed for these models.

Examples of application[edit]

Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between ultraviolet B radiation, vitamin D and cancer,[12][13] vitamin D and pregnancy and neonatal outcomes,[14] alcohol and cardiovascular disease outcomes,[15] infections and risk of stroke,[16] nutrition and biomarkers related to disease outcomes,[17] and sugar-sweetened beverage consumption and the prevalence of obesity and obesity-related diseases.[18] They have also been used in non-human epidemiological studies, such as on the effects of neonicotinoid pesticides on honey bees.[19] Their use in quality improvement of health care services has been proposed, highlighting how quality improvement methods can be used to provide evidence for the criteria.[20]

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).[21]

See also[edit]

  • 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 – Preventing disease, prolonging life and 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


  1. ^ a b c d Hill, Austin Bradford (1965). "The Environment and Disease: Association or Causation?". Proceedings of the Royal Society of Medicine. 58 (5): 295–300. doi:10.1177/003591576505800503. PMC 1898525. PMID 14283879.
  2. ^ Fredricks, David; Relman, David (January 1996). "Sequence-Based Identification of Microbial Pathogens: a Reconsideration of Koch's Postulates". Clinical Microbiology Reviews. 9 (1): 18–33. doi:10.1128/CMR.9.1.18. PMC 172879. PMID 8665474.
  3. ^ Schünemann H, Hill S, Guyatt G; et al. (2011). "The GRADE approach and Bradford Hill's criteria for causation". Journal of Epidemiology & Community Health. 65 (5): 392–95. doi:10.1136/jech.2010.119933. PMID 20947872.CS1 maint: multiple names: authors list (link)
  4. ^ Höfler M (2005). "The Bradford Hill considerations on causality: a counterfactual perspective?". Emerging Themes in Epidemiology. 2 (1): 11. doi:10.1186/1742-7622-2-11. PMC 1291382. PMID 16269083.
  5. ^ Howick J, Glasziou P, Aronson JK (2009). "The evolution of evidence hierarchies: what can Bradford Hill's 'guidelines for causation' contribute?". Journal of the Royal Society of Medicine. 102 (5): 186–94. doi:10.1258/jrsm.2009.090020. PMC 2677430. PMID 19417051.
  6. ^ Glass TA, Goodman SN, Hernán MA, Samet JM (2013). "Causal inference in public health". Annu Rev Public Health. 34: 61–75. doi:10.1146/annurev-publhealth-031811-124606. PMC 4079266. PMID 23297653.
  7. ^ Potischman N, Weed DL (1999). "Causal criteria in nutritional epidemiology". Am J Clin Nutr. 69 (6): 1309S–14S. doi:10.1093/ajcn/69.6.1309S. PMID 10359231.
  8. ^ Rothman KJ, Greenland S (2005). "Causation and causal inference in epidemiology". Am J Public Health. 95 (Suppl 1): S144–50. doi:10.2105/AJPH.2004.059204. hdl:10.2105/AJPH.2004.059204. PMID 16030331.
  9. ^ Phillips, CV; Goodman KJ (2006). "Causal criteria and counterfactuals; nothing more (or less) than scientific common sense?". Emerging Themes in Epidemiology. 3 (1): 5. doi:10.1186/1742-7622-3-5. PMC 1488839. PMID 16725053.
  10. ^ Ward, AC (2009). "The role of causal criteria in causal inferences: Bradford Hill's "aspects of association". Epidemiological Perspectives and Innovations. 6 (1): 2. doi:10.1186/1742-5573-6-2. PMC 2706236. PMID 19534788.
  11. ^ Ward, AC (2009). "The Environment and Disease: Association or Causation?". Medicine, Health Care and Philosophy. 12 (3): 333–43. doi:10.1007/s11019-009-9182-2. PMID 19219564.
  12. ^ Grant WB (2009). "How strong is the evidence that solar ultraviolet B and vitamin D reduce the risk of cancer? An examination using Hill's criteria for causality". Dermatoendocrinology. 1 (1): 17–24. doi:10.4161/derm.1.1.7388. PMC 2715209. PMID 20046584.
  13. ^ Mohr SB, Gorham ED, Alcaraz JE, Kane CI, Macera CA, Parsons JE, Wingard DL, Garland CF (2012). "Does the evidence for an inverse relationship between serum vitamin D status and breast cancer risk satisfy the Hill criteria?". Dermatoendocrinology. 4 (2): 152–57. doi:10.4161/derm.20449. PMC 3427194. PMID 22928071.
  14. ^ Aghajafari F, Nagulesapillai T, Ronksley PE, Tough SC, O'Beirne M, Rabi DM (2013). "Association between maternal serum 25-hydroxyvitamin D level and pregnancy and neonatal outcomes: systematic review and meta-analysis of observational studies". BMJ. 346 (Mar 26): f1169. doi:10.1136/bmj.f1169. PMID 23533188.
  15. ^ Ronksley PE, Brien SE, Turner BJ, Mukamal KJ, Ghali WA (2011). "Association of alcohol consumption with selected cardiovascular disease outcomes: a systematic review and meta-analysis". BMJ. 342 (Feb 22): d671. doi:10.1136/bmj.d671. PMC 3043109. PMID 21343207.
  16. ^ Grau AJ, Urbanek C, Palm F (2010). "Common infections and the risk of stroke". Nat Rev Neurol. 6 (12): 681–94. doi:10.1038/nrneurol.2010.163. PMID 21060340.
  17. ^ de Vries J, Antoine JM, Burzykowski T, Chiodini A, Gibney M, Kuhnle G, Méheust A, Pijls L, Rowland I (2013). "Markers for nutrition studies: review of criteria for the evaluation of markers". Eur J Nutr. 52 (7): 1685–99. doi:10.1007/s00394-013-0553-3. PMID 23955424.
  18. ^ Hu FB (2013). "Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases". Obes Rev. 14 (8): 606–19. doi:10.1111/obr.12040. PMC 5325726. PMID 23763695.
  19. ^ Cresswel, James E; Desneux, Nicolas; VanEngelsdorp, Dennis (24 January 2012). "Dietary traces of neonicotinoid pesticides as a cause of population declines in honey bees: an evaluation by Hill's epidemiological criteria". Pest Management Science. 68 (6): 819–827. doi:10.1002/ps.3290. PMID 22488890.
  20. ^ Poots, Alan J; Reed, Julie E; Woodcock, Thomas; Bell, Derek; Goldmann, Don (2 August 2017). "How to attribute causality in quality improvement: lessons from epidemiology". BMJ Quality & Safety. 26 (11): 933–937. doi:10.1136/bmjqs-2017-006756. hdl:10044/1/49089. PMID 28768711.
  21. ^ the Continuous Update Conclusions Mean[permanent dead link] American Institute for Cancer Research, n.d., retrieved 13 June 2017