I’ve mentioned before on this blog that I get annoyed when people link A to B and B to C and then proceed to assume that the relationship between A and C is just the average/sum/etc of the first two. In pure mathematics, the technical term for this is transitivity and it tends to be pretty valid.
I learned recently that there is actually a term for this when applied to epidemiology research: teleoanalysis. Developed in the realm of public health, it’s defined as
the synthesis of different categories of evidence to obtain a quantitative general summary of (a) the relation between a cause of a disease and the risk of the disease and (b) the extent to which the disease can be prevented.
It has also been criticized, in large part because it was invented to help support pre-existing assumptions. Both papers I linked to reference the “does cutting back on saturated fat actually prevent heart disease” controversy as an example.
I was thinking of this recently when reader Dubbahdee sent me this article about bicycle helmet laws. The issue follows the same formula as above:
A. Bicycle helmets protect cyclists
B. Mandatory helmet laws increase the number of cyclists who wear helmets
C. Bicycle helmet laws save lives
What’s interesting is it appears this is not the case. The paper’s authors suggest that increased helmet laws decrease bike ridership, and apparently having lots of bicyclists in an area makes it safer for cyclists in general. Also, helmet laws seem to potentially inoculate lawmakers against making any bigger changes…the sort that actually help cyclist safety (infrastructure building, etc).
I thought this was interesting because it’s absolutely proven that you as an individual should wear a helmet, but the conclusions drawn from that weren’t valid. Someone out there guaranteed that these helmet laws would save x number of lives, and they were wrong.