Episode Details

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Part one of this was published on December 13, but this piece was pre-empted when the USPSTF put forth dangerous dieting recommendations for children (remember that the public comment period ends January 16.) In the past I’ve written pieces specifically about issues and mistakes that are made with terminology that is used…let’s call it differently in weight science as well as common statistics mistakes and mishaps. In part 1 I offered some additional terminology troubles today in part two we’re discussing statistics shenanigans.

Using a percentage that seems high without proper context:

In one example of this, early in the COVID-19 pandemic I saw a news report claiming that so-called “ob*sity”* was a risk factor for severe COVID because, in a particular city, 25% of severe negative outcomes were in people who are classified as “ob*se.”  At first, that might seem like a large number, but that doesn’t justify calling being higher-weight a risk factor. In order to even begin to be able to draw conclusions from this, we have to at least know the total number of so-called ob*se people who live in the city - otherwise we have no way to know if 25% is higher or lower than the total percentage of this population. I looked it up and that number was 38%.

Several things are issues here.

First, if I were trying to draw conclusions from this (and I wouldn’t, more on that in a moment) I would conclude that being higher-weight is protective, since 38% of the community is higher-weight, but only 25% of the people with severe outcomes were. (Said another way, people who weren’t “ob*se” were 62% of the overall population but 75% of the severe outcomes.) That’s the main statistical issue here. You can’t use a percentage like this without contextualizing it.

Moreover, I wouldn’t draw conclusions from this at all. First, because “ob*sity” is simply a ratio of weight and height. Making assumptions that since a group of people have some physical characteristic in common (like, in this case, height-weight ratio) then that physical characteristic is the reason for the difference in outcomes is on extremely shaky ground, scientifically speaking. In this example, since there are many other factors that can impact this result (including the fact that higher-weight people are at the mercy of a healthcare system in which practitioner weight bias is rampant and, even if that’s not an issue, the tools, best practices, pharmacotherapies and more, that are used are typically developed for thin bodies/excluding fat bodies) we don’t know what number of those severe outcomes were due to healthcare inequalities or other factors.

Relative vs Absolute Risk

Novo Nordisk recently used this one in their manipulative press release about the possible cardiovascular benefits of Wegovy.

Relative Risk Reduction is the percentage decrease of risk in the group who received an intervention vs the group that didn’t receive the intervention. This number can be helpful to determine differences in outcomes between groups, but it’s not that helpful in determ