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In other words, when some factor is necessary to cause an effect, it is impossible to have the effect without the cause. [3] X would instead be a sufficient cause of y when the occurrence of x implies that y must then occur. [2] in other words, when some factor is sufficient to cause an effect, the presence of the cause guarantees the ...
In the field of epidemiology, the causal mechanisms responsible for diseases can be understood using the causal pie model.This conceptual model was introduced by Ken Rothman to communicate how constellations of component causes can lead to a sufficient cause to lead to a condition of interest and that reflection on these sets could improve epidemiological study design.
Necessary condition analysis follows a step-by-step approach to identify necessary conditions. The key steps involved in conducting NCA are as follows: Formulation of a necessity hypothesis: The first step in NCA is to clearly define the theoretical expectation specifying the condition(s) that may be necessary for the outcome of interest.
A component cause is a factor that, along with other component causes, forms a sufficient cause for a disease. A sufficient cause is a complete combination of component causes necessary for the disease to manifest. Diseases result from a chain of causally related events, starting from an initial event to the clinical appearance of the disease.
A condition can be both necessary and sufficient. For example, at present, "today is the Fourth of July" is a necessary and sufficient condition for "today is Independence Day in the United States". Similarly, a necessary and sufficient condition for invertibility of a matrix M is that M has a nonzero determinant.
Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between exposures to molds and infant pulmonary hemorrhage, [14] ultraviolet B radiation, vitamin D and cancer, [15] [16] vitamin D and pregnancy and neonatal outcomes, [17] alcohol and cardiovascular ...
Epidemiological (and other observational) studies typically highlight associations between exposures and outcomes, rather than causation. While some consider this a limitation of observational research, epidemiological models of causation (e.g. Bradford Hill criteria) [7] contend that an entire body of evidence is needed before determining if an association is truly causal. [8]
Causes may sometimes be distinguished into two types: necessary and sufficient. [19] A third type of causation, which requires neither necessity nor sufficiency, but which contributes to the effect, is called a "contributory cause". Necessary causes If x is a necessary cause of y, then the presence of y necessarily implies the prior occurrence ...