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Bradford Hill criteria

The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence, established by the English epidemiologist Sir Austin Bradford Hill (1897–1991) in 1965.

The list of the criteria is as follows:

  1. Strength: 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.[1]
  2. Consistency: Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.[1]
  3. Specificity: Causation is likely if 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).[1]
  5. Biological gradient: 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).[1]
  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".[1]
  8. Experiment: "Occasionally it is possible to appeal to experimental evidence".[1]
  9. Analogy: The effect of similar factors may be considered.[1]

Debate in modern epidemiology

Bradford Hill's criteria are still widely accepted in the modern era as a logical structure for investigating and defining causality in epidemiological study. However, their method of application is debated. Some proposed options include:

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

Arguments against the use of Bradford Hill criteria as exclusive considerations in proving causality also exist. Some argue 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.[7] Others also 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.[8]

Debate over the scope of application of the criteria includes whether they can be applied to social sciences.[9] The argument proposed in this line of thought is that when considering the motives behind defining causality, the Bradford Hill criteria are important to apply to complex systems such as health sciences because they 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 as the instigation of causation, rather than the consequence, is needed for these models.

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,[10] [11] vitamin D and pregnancy and neonatal outcomes,[12] alcohol and cardiovascular disease outcomes,[13] infections and risk of stroke,[14] nutrition and biomarkers related to disease outcomes,[15] and sugar-sweetened beverage consumption and the prevalence of obesity and obesity-related diseases.[16] Referenced papers can be read to see how Hill’s criteria have been applied.

See also


  1. ^ a b c d e f g h i Hill, Austin Bradford (1965). "The Environment and Disease: Association or Causation?". Proceedings of the Royal Society of Medicine 58 (5): 295–300. PMC 1898525. PMID 14283879. 
  2. ^ Höfler M (2005). "The Bradford Hill considerations on causality: a counterfactual perspective?". Emerging themes in epidemiology 2 (1): 11. PMC 1291382. PMID 16269083. doi:10.1186/1742-7622-2-11. 
  3. ^ 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. PMC 2677430. PMID 19417051. doi:10.1258/jrsm.2009.090020. 
  4. ^ Glass TA, Goodman SN, Hernán MA, Samet JM (2013). "Causal inference in public health". Annu Rev Public Health 34: 61–75. PMC 4079266. PMID 23297653. doi:10.1146/annurev-publhealth-031811-124606. 
  5. ^ Potischman N, Weed DL (1999). "Causal criteria in nutritional epidemiology". Am J Clin Nutr 69 (6): 1309S–1314S. PMID 10359231. 
  6. ^ Rothman KJ, Greenland S (2005). "Causation and causal inference in epidemiology". Am J Public Health 95 (Suppl 1): S144–50. PMID 16030331. doi:10.2105/AJPH.2004.059204. 
  7. ^ Phillips, CV; Goodman KJ (2006). "Causal criteria and counterfactuals; nothing more (or less) than scientific common sense?". Emerging themes in epidemiology 3 (1): 5. PMC 1488839. PMID 16725053. doi:10.1186/1742-7622-3-5. 
  8. ^ Ward,AC (2009). "The role of causal criteria in causal inferences: Bradford Hill's "aspects of association". Epidemiological perspectives and innovations 6 (1): 2. PMC 2706236. PMID 19534788. doi:10.1186/1742-5573-6-2. 
  9. ^ Ward,AC (2009). "The Environment and Disease: Association or Causation?". Medicine, health care and philosophy 12 (3): 333–43. PMID 19219564. doi:10.1007/s11019-009-9182-2. 
  10. ^ 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. PMC 2715209. PMID 20046584. 
  11. ^ 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): 152–7. PMC 3427194. PMID 22928071. doi:10.4161/derm.20449. 
  12. ^ 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. PMID 23533188. doi:10.1136/bmj.f1169. 
  13. ^ 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. PMC 3043109. PMID 21343207. 
  14. ^ Grau AJ, Urbanek C, Palm F (2010). "Common infections and the risk of stroke". Nat Rev Neurol 6 (12): 681–94. PMID 21060340. doi:10.1038/nrneurol.2010.163. 
  15. ^ 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. PMID 23955424. doi:10.1007/s00394-013-0553-3. 
  16. ^ 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. PMID 23763695. doi:10.1111/obr.12040. 
  17. ^ Kleinberg, S. and Hripcsak, G. (2011) "A review of causal inference for biomedical informatics" J. Biomed Informatics