a)In 1964, the Public Health Service of the United States studied the effects of

Question

a)In 1964, the Public Health Service of the United States studied the effects of smoking on health in a sample of 42,00 households. For men and for women in each age group, they found that those who had never smoked were on average somewhat healthier than the current smokers, but the current smokers were on average much healthier than the former smokers. Why did they study men and women and the different age groups separately?.

b)The lesson seems to be that you shouldn’t start smoking, but once you’ve started, don’t stop. Find some plausible explanations for this surprising relationship between quitting smoking and health. Find at least one plausible confounding factor and one plausible mediating factor that might account for part of the relationship.

c) Explain how a confounding factor provides an explanation that is an alternative to the conclusion that quitting causes bad health while a mediating factor provides an explanation that is consistent with causality.

Explanation / Answer

a) The basic principle being followed here is to minimize “nuisance” variation by putting subjects into homogeneous blocks. “Age” is quite possibly a confounder, because health deteriorates with age and also older people are more likely to have smoked longer. “Controlling” for age then accounts for this. As for “gender”, since we know that genders react biologically differently, and also are subject to different societal pressures, we need to be prepared for the possibility that different genders will smoke differently and have different health effects.

b) Even if you want to agree, you should admit that this study is not evidence in support of following this prescription. First, this was an observational study (how could it be otherwise?) and therefore you should never rush to conclude causation. The causal claim you’re being asked to evaluate is this: does quitting smoking negatively affect health?

The data seem to say “yes”, but the experimental design doesn’t support this conclusion. There is at least one fairly obvious confounding factor: people in poor health are likely to quit smoking, either because the doctor tells them to, or because they simply are physically not capable of smoking any longer. (At least one of you pointed out that if you included those who died from smoking, the quitters would seem to be in even worse health than the smokers.)

Remember that the definition of a confounding factor is that it has to affect both assignment to treatment group and the response variable. Here, “poor health” determines which group they belong to (smoking or quitting) and the response variable (quality of health.)

Many people got distracted by the “recently” in the problem statement. There is an argument to be made that quitting smoking might cause short-term health problems. But if so, then you are agreeing with the premise of the argument that quitting does cause poor health (even if for the short term). And a problem with this is that it is hard to see how, in every age group, even among the elderly, quitting made health problems worse. Some people pointed out that there are, in fact, known side effects to quitting (most noticably a weight gain). But realistically speaking, the phrase “ current smokers were on average much healthier than those who had recently stopped smoking” must be interpreted to mean more than the fact that the quitters were slightly heavier than the smokers.

This answer (that the time period was too short to see the positive health effects of quitting) does a good job of explaining the results, but is not as convincing and full an explanation as describing a confounding factor that provides a more plausible explanation.

c) Not every factor that is associated with both the exposure and the disease is aconfounding variable. Such a factor could be a MEDIATING VARIABLE. A mediator is also associated with both the independent and dependent variables, but is part of the causal chain between the independent and dependent variables.

Thus, mediating factor provides an explanation that is consistent with causality. while the  confounding factor provides an explanation that is an alternative to the conclusion that quitting causes bad health

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