Ok. So. There appears to be a misunderstanding here between the lay idea of an association, and the more complex way it is used in context in an epidemiological settinghttps://twitter.com/WeDietitians/status/1037561280175857664 …
Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him
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Health Nerd Retweeted We Dietitians
Ok. So. There appears to be a misunderstanding here between the lay idea of an association, and the more complex way it is used in context in an epidemiological settinghttps://twitter.com/WeDietitians/status/1037561280175857664 …
Health Nerd added,
There are many different levels of association. You point to the example of the number of dietitians increasing as obesity rates increase, so let's talk about that
This is what's known as a simple correlation. I can think of several simple explanations for this fact: 1. dietitians cause obesity 2. more obese people = more demand for dietitians 3. external confounder (i.e. population growth)
Other examples of simple correlations include that ice-cream is associated with drownings, and that the age of Miss America is associated with murders by steam, hot vapours, and hot objects in the US
Now, this is VERY DIFFERENT from what the dietary guidelines mean when they say that there is "an association" between adherence to the guidelines and reduced morbidity/mortalitypic.twitter.com/e6msl2lGq5
In this case, it is basically impossible - for obvious ethical and practical reasons - to run large RCTs on adherence to dietary guidelines This means that is not possible to generate conclusive causal evidence
What that means is that THE BEST EVIDENCE POSSIBLE is Grade C, or from observational trials In this case, 5 large, well-controlled analyses that generate significant evidence for a point Very different to a simple correlation
So, here, "an association between" means "very likely causally linked, but it is impossible to entirely eliminate the possibility of confounding"
While it is true to say that these results are not definitively causal - hence the cautioning statement - it is extremely likely that guidelines improve health This is the opposite of our previous example, which as I identified was almost certainly down other factors
This is the challenge with interpreting epidemiological evidence, that often you have to infer causation from evidence that can never entirely eliminate the possibility that it is a simple correlation
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