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What Exactly Does “Risk” Mean?

by Scott MacStravic

I have noticed a disturbing inconsistency in the use of the word “risk” when describing the occurrence of certain “risk factors” and the nasty things for which they are reported to be a risk.  One meaning of the term, and one that seems to be the most popular meaning in the popular press, involves reports that when a given risk factor is found, it is often accompanied by the nasty thing.  So when it is found that both obesity and diabetes have grown at alarming levels in recent years, for example, and that people who are obese are more likely to have diabetes, it is reported that obesity is a “risk factor” for diabetes.

The trouble with any such conclusion reached through “cross-sectional” analysis, i.e. a simultaneous look at two factors in a given population at one time, it is literally impossible to tell whether one tends to cause the other, or vice versa, or if there is no causal connection at all.  For example, if it is found that people who drink a lot of sodas, whether sugary or diet, tend to be more likely to be overweight and obese than those who refrain from sodas, relatively speaking, what does that show?

The answer is – absolutely nothing, except that they seem to occur together.  It could be that some people who drink lots of sugary sodas are obese because their intake greatly increases their daily calorie intake, and they naturally gain weight.  At the same time, other people who are already obese may have shifted to diet sodas in a vain hope to lose weight, while continuing to eat high-calorie foods and engage in little exercise, both of which tend to preserve rather than correct obesity.  Or it could be that both are signs of a “sweet tooth” preference for foods that naturally tend to be high in calories, and this general preference, rather than the ingestion of diet sodas is what causes obesity.  Or there might be no causal connection, whatsoever.

The point is that the only way to arrive at a logical guess as to whether there is any causal relationship between two factors, and which causes the other, is to perform a “longitudinal” analysis, which follows populations to see if introduction of that factor into one segment of that population, where no other significant differences exist between the two segments, is then followed by a difference in the occurrence of some nasty thing.  All other definitions and applications of risk identification can mislead as often as they help focus health improvement attention.

The fact that the illogical and limited definition and application of “risk” in the purely statistical artifact sense is by far the most common choice in popular media is simply another reason for people to treat media stories with some skepticism.  It is unfortunate that mass media, which could be a major and meaningful ally in efforts to protect and improve the health of populations, is instead a major source of doubtful or misleading information.  And the fact that third-party payors do not even compensate healthcare providers for countering such misleading information is another indictment of the insurance industry.

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