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The mechanism behind a cause usually has several components. These can be subdivided into two categories: necessary and sufficient components . Let us take the example of an infectious disease, invasive meningococcal disease. We say it is caused by Neisseria meningitidis (difficult to argue against, as this organism by definition must be present). So it is a "necessary" component of cause.
On the other hand, infection with Neisseria meningitidis does not always result in meningococcal disease. Indeed illness is a rare outcome of the infection. The infection by itself is not "sufficient" and other factors need to be present. Lack of antibodies against this infection, the breakdown of the mucosal barriers by respiratory infection, low humidity, and passive smoking may be other causal factors.
These concepts of necessary and sufficient component causes explain the apparent anomaly whereby attributable fractions in a population (AFpops) can add up to more than 100%. The AFpop for Neisseria meningitidis is 100%. If Neisseria meningitidis were to be eliminated as a coloniser of the human pharynx, there would be no more meningococcal disease. The AFpop for low immunity is probably also close to 100%. This means that vaccination may be highly effective at reducing disease rates even without reducing colonisation.
Various conceptual models aiming to simplify the representation of causal mechanisms have been developed in epidemiology. A well known model is that of infectious disease causation (the agent, host, environment pyramid). Another widely adopted model for chronic disease causation is Rothman’s “sufficient component cause model” whereby the cause of any effect must consist of a constellation of components that act in concert .
1.Rothman KR. Epidemiology: an introduction. Oxford University Press, Oxford 2002.
2. Rothman K, Greenland S. Causation and causal inference in epidemiology. Am J Public Health. 2005;95:S144–S150.
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jcabaj posted on 8/5/2011 12:20:08 AM:
The last sentence of the cohort study section ("Therefore, in this example, age is not a confounder of the relationship between magnetic field exposure and breast cancer.") seems to be in error (should read: gender not confounder for vaccination status and disease).
James Stuart replied on 8/5/2011 11:54:04 AM:
I am not sure as to which section this comment applies. Is this a different chapter?
Arnold Bosman replied on 8/10/2011 8:15:32 AM:
Your comment is correct, however it was placed by the wrong chapter. Your remark is about an error in the chapter on Confounding, not on Causal Inference.
Thanks a lot for your remark, I have modified the content accordingly. I want to invite you to make modifications and improvements directly to the text as well, This is why it is a WIKI.
sbpmebxu replied on 7/29/2015 7:29:25 PM: 1
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