How Can You Ignore The Correlation Between Weight And Health?

This is one of the most common questions I get. The question goes something like: there is such a strong correlation between being higher weight and having health issues, how can you just dismiss that? Especially when so much of the research you point to is correlational in itself.

The thing is, tt’s not about just dismissing the correlational relationship between weight and health out of hand, it’s about examining the evidence around that correlation to test the strength of it.

Before I get too far into it, the relationship between correlation and causation is at the foundation of research methods (my first research methods teacher made us repeat “correlation never ever, never ever, never ever implies causation in every class!). If two things are correlated, it means that they happen at the same time. What it doesn’t mean is that one of those things causes the other. For example there is a strong correlation between cis male pattern baldness and cardiac incidents. If we assumed that baldness caused heart attacks that would be a faulty assumption. If we then assumed that making affected people grow hair would reduce cardiac incidents (creating a government sponsored “War on Baldness” blaming people for not growing hair, etc.) that would be another faulty assumption. In fact a third factor causes both the baldness, and the higher rates of cardiac incidents.

So, when we see a correlational relationship between weight and health, but without a causal mechanism, the first question we have to ask is – what is the quality of the evidence?

We have to examine the research that finds this correlation for quality, and when we do, we find it lacking in some of the most basic principles of research. For example, if fat people are tested early and often for a health condition and thin people are almost never tested unless they have advanced symptoms, it’s spurious to assume that the health condition occurs more often in fat people. In another example, since too-tight blood pressure cuffs give too-high readings, and often fat people’s blood pressure is tested using a too-tight cuff, we have to ask ourselves how accurate that correlation is.

The next question we have to ask is – could something else be causing this relationship?

In this case there are at least three major candidates – weight stigma (as examined in Muennig’s studies for example), weight cycling (for example, in their paper Weight Science: Evaluating the Evidence for a Paradigm Shift, Bacon and Aphramor found that the health impacts of weight cycling could explain all of the excess mortality that was attributed to “ob*sity” in both Framingham and the NHANES), and inequalities in healthcare (examined in Lee and Pausé’s Stigma in Practice: Barriers to Health for Fat Women for example.)

So again, it’s not about simply dismissing the correlation out of hand. It’s about the reality that until we can account for the possible impacts of the research issues and confounding variables, the correlation between weight and health has to be held in serious question.

Not to mention, the fallout from the extremely questionable acceptance of the correlation of weight and health as a causal relationship (and the follow up extremely questionable assumption that weight loss is the “solution”) drives massive additional harm (looking at you Weight Loss Industry.). That’s even more significant considering that studies like Wei et. al., Matheson et. al., etc. show that (understanding that health is not an obligation, barometer of worthiness, or entirely within our control) there are plenty of ways to support the health of people of any size that have nothing to do with body size manipulation (you can find diagnosis-specific weight neutral practice guides and a resource and research bank at www.HAESHealthSheets.com)

Finally, while I think it’s worth having these discussions since so much of fat people’s treatment, including in healthcare, is driven by this, we can never lose sight of the fact that fat people have the right to live without shame, stigma, bullying or oppression no matter why we are fat, no matter what the “health impacts” might be, and whether or not we could, or even want to, become thin. That includes the rights to equal accommodation, including in healthcare.

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3 thoughts on “How Can You Ignore The Correlation Between Weight And Health?

  1. Excellent article Ragen. We will keep working towards the day when people Don’t have their minds made up like steel traps against fatness and anything to do with it, faster than you can say correlation and causation. By the second syllable, most biases have already kicked in causing a literal white noise in the ears. When it comes out it sounds like this, “There may be studies, BUT, “I know some fat people blank, BUT, It might not be every time, BUT. As Pee Wee Herman told us, everyone’s got a Big But standing between them and doing, being and seeing anything but the track they’ve been playing since they became capable of cognition.
    For fat people, or “persons with fat”, we are surrounded by high walls with gun posts and angry scared people with trigger fingers just waiting to shoot down any diverging opinion in their “Everybody Knows” pantheon o’ hangups.
    Shoot back, run for cover. So many people NEED to believe the stupidest of things in this world, for the little bit of time we’re on it. It’s lucky any of us get out alive…Wait, we don’t get out alive, BUT fat people, or persons with fat, they get out way faster than the rest of it. And sicker, Everybody Knows That! Wash Rinse PISS OFF!

  2. That’s a bit of a straw man, though, isn’t it? We DON’T ignore the correlation between weight and health. The subject gets more ink and thought that just about any other. The point of disagreement is what that gap in health outcomes between thin people and fat people means. What we’re rejecting is the mainstream “common knowledge” that it means fat people do bad things to deserve bad health while thin people do good things to deserve good health… and a primary factor in that rejection is that the data does not and has never supported that *cough* “model,” even when unscrupulous people tried to make it.

    1. Excellent point. Probably in the “You can lead a horse to water… People can be SO determined to not see ANYTHING that doesn’t support their view, they physically can’t see it.

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