Concept of Causation A statistically significant association between an exposure and a disease does not necessarily imply that there is a cause-effect relationship between the exposure and illness. The association could reflect biases in the design, conduct, or analysis of the study. The association may also occur because the exposure and the disease are related to some common underlying condition. Bradford-Hill Criteria In 1965 Austin Bradford Hill presented criteria for evaluating whether an association is causal. This became one of the most widely cited criteria for assessing causation. According to Bradford-Hill, none of the criteria should be indisputable evidence for or against causal hypothesis. The nine criteria are: 1. 2. 3. 4. 5. 6. 7. 8. 9. Strength of Association Consistency Specificity Temporal Relationship Dose-Response Relationship Biological Plausibility Coherence Analogy Experimental Evidence In the Background Reading, Hoffler (2005) elaborates on these nine criteria. Elimination of All Known Alternative Explanations In an epidemiologic investigation, even if the presumed causal factor is associated statistically with the disease and occurs before the disease appears, it is necessary to demonstrate that there are no other likely explanations for the association. Proper research design can reduce the likelihood of competing causal explanations. For example, randomization, if done correctly, ensures that neither self-selection nor investigator bias will influence the allocation of subjects into experimental and control groups. Randomization also means that the treatment and control groups should be reasonably comparable with regard to disease susceptibility and disease severity. Summary Epidemiologists are concerned with discovering the causes of disease in the environment, nutrition, lifestyle, and genes of individuals and populations--that is, the causes or factors that when removed or modified will be followed by a reduction in the disease burden. Research to determine causation is therefore, complicated, particularly because epidemiologists usually do not have experimental control and must rely on observational methods. The missed lessons of Sir Austin Bradford Hill Carl V Phillips1,2,3 and Karen J Goodman1 Epidemiol Perspect Innov. 2004; 1: 3. Retrieved May 28, 2012 at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC524370/ Hoffler, M. (2005). The Bradford Hill considerations on causality: a counterfactual perspective. Emerging Themes in Epidemiology, 2(11). Retrieved May 28, 2012 at: http://www.ete- online.com/content/2/1/11 Messina, M. J. and Loprinzi, C.L (2001). Soy for Breast Cancer Survivors: A Critical Review of the Literature. American Society for Nutritional Sciences, 131(3095S-3108S). May 28, 2012 at: http://jn.nutrition.org/cgi/content/full/131/11/3095S Randolph, JJ (2009). A Guide to Writing the Dissertation Literature Review. Practical Assessment Research & Evaluation, 14(13). May 28, 2012 at: http://pareonline.net/pdf/v14n13.pdf Sacks F.M., Lichtenstein, A., Van Horn, L., Harris, W., Kris-Etherton, P., and Winston, M. (2006). Soy Protein, Isoflavones, and Cardiovascular Health. Circulation, 98(7). May 28, 2012 at: http://circ.ahajournals.org/cgi/content/full/113/7/1034 Trock, B.J., Hilakivi-Clarke, L., Clarke, R. (2006). Meta analysis of soy intake and breast cancer risk. Journal of the National Cancer Institute, 98(7).May 28, 2012 at: http://jnci.oxfordjournals.org/content/98/7/459.full.pdf+html Weed, D.L. (2000). Interpreting epidemiological evidence: how metaanalysis and causal inference methods are related. International Journal of Epidemiology, 29(387-390). May 28, 2012 at: http://ije.oxfordjournals.org/cgi/content/full/29/3/387 Wu, A.H., Yu, M.C, Tseng, C-C., Pike, M.C. (2006). Epidemiology of soy exposures and breast cancer risk. British Journal of Cancer, 98(9-14). May 28, 2012 at: http://www.nature.com/bjc/journal/v98/n1/full/6604145a.ht ml
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