Most of the adverse events considered by the committee have multifactorial etiologies. Nonetheless, many people with one or more of these risk factors do not develop CHD, and some cases of CHD occur in people without any of the risk factors. It has been amply demonstrated that smoking, high blood pressure, lack of exercise, and high serum cholesterol levels are all causally related to the development of CHD. In other words, most health outcomes of interest have multifactorial etiologies.Ī good example is coronary heart disease (CHD). Although the idea that a "proper" cause must be both necessary and sufficient underlies Koch's postulates of causality (see Glossary in Appendix c), it is now generally recognized that for most exposure-outcome relations, exposure (i.e., the putative cause) is neither necessary nor sufficient to cause the outcome (third interpretation above). Vaccine x is a necessary cause of GBS if the disease occurs only among vaccine x recipients (second interpretation above). The first interpretation corresponds to the notion of a sufficient cause vaccine x is a sufficient cause of GBS if all vaccine x recipients develop the disease. Does the statement '' Vaccine x causes GBS'' mean that (1) all persons immunized with vaccine x will develop GBS, (2) all cases of GBS are caused by exposure to vaccine x, or (3) there is at least one person whose GBS was caused or will be caused by vaccine x? Consider, for example, the relation between vaccine x and Guillain-Barré syndrome ( GBS). Despite its importance, however, causality is not a concept that is easy to define or understand (Kramer and Lane, 1992). It also lies at the heart of this committee's charge: to make causal inferences about the relation between vaccines routinely administered to children in the United States and several specific adverse health outcomes. To conclude one way or the other, you need to assume that the reverse does not exist.The concept of causality is of cardinal importance in health research, clinical practice, and public health policy. X and Y are correlated, but is X causing Y or is Y causing X? We can see that negation of option D breaks the argument - hence, it is the right answer.Įssentially, when you observe some correlation between 2 events X and Y, check for the presence and direction of a cause-effect relationship. What does this say about the experiment? It is not low ISA that causes poor mental health (as manifested through low scores on the tests) on the other hand, the people already had mental health problems, which, in turn, lowered their immune system activity.
Let's try negating option D: Mental illness causes people's immune system activity to decrease. The problem with option C is that it doesn't talk about people with normal immune system activity! Only about those with high ISA. On what basis/evidence is this conclusion made? On the observation that people who have low levels of immune system activity tend to score lower on tests of mental health than do people with normal or high immune system activity. Here, the conclusion is that the immune system protects against mental illness. (D) Mental illness does not cause people's immune-system activity to decrease. (C) People with high immune-system activity cannot develop mental illnessÂ
The researcher's conclusion depends on which of the following assumptions? I am posting only the answer choices relevant to this discussion on reverse causation.Ī researcher discovered that people who have low levels of immune' system activity tend to score much lower on tests of mental health than do people with normal or high immune' system activity．The researcher concluded from this experiment that the immune system protects against mental illness as well as against physical disease．
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