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Jean Claude Desenclos
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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ecdc posted on 10/17/2012 3:43:34 PM:
OJE published "Case-control studies: basic concepts"
Abs: The purpose of this article is to present in elementary mathematical and statistical terms a simple way to quickly and effectively teach and understand case-control studies, as they are commonly done in dynamic populations-without using the rare disease assumption. Our focus is on case-control studies of disease incidence (‘incident case-control studies'); we will not consider the situation of case-control studies of prevalent disease, which are published much less frequently.
Ref: Jan P Vandenbroucke and Neil Pearce: Case-control studies: basic concepts Int. J. Epidemiol. (2012) 41(5): 1480-1489 doi:10.1093/ije/dys147 http://ije.oxfordjournals.org/content/41/5/1480.full.pdf+html
Arnold Bosman replied on 10/21/2012 1:22:41 PM:
Good reading !
The need to use case control in prevalent diseases occurs often. In that case we need to understand that 'the rare disease assumption' does not really exist, yet we need to think carefully about where we select our controls. The core principle is that we select controls because we want to measure 'how the exposure of interest is distributed in the population that gave rise to the cases.
On this topic, a very readable article appeared in Eurosurveillance. Olivier le Polain de Waroux and his co-authors do a great job describing and explaining the principles of case-cohort, with very useful examples.
I can invite everyone to read this piece:
Le Polain de Waroux O, Maguire H, Moren A. The case-cohort design in outbreak investigations. Euro Surveill. 2012;17(25):pii=20202. Available online: http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=20202
sbpmebxu replied on 7/29/2015 7:35:38 PM: 1
sbpmebxu replied on 7/29/2015 8:10:37 PM: 1
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