Correlation and Causation: Real World Problems

In yesterday’s post, I got a bit worked up over sloppy reporting on a study on dietary interventions in pregnancy. 

This led to an interesting comment from the Assistant Village Idiot regarding weight gain recommendations for pregnant women.  The current weight gain recommendation is 25 – 35 pounds for a normal BMI woman, but AVI commented that it used to be much lower, and that women were hospitalized to stop them from eating too much.
I didn’t actually know that, so I immediately decided to look it up.  
I stumbled on to a fascinating presentation put together by an OB at UCSF on the history of maternal weight gain recommendations (link goes to the PowerPoint slides).  It not only confirmed what AVI had mentioned, but also gave some of the reasoning….which turned out to be a very interesting example of people erroneously conflating correlation with causation.  
Apparently part of the reason why they (they being doctor’s circa 1930) were so nervous about weight gain in pregnancy was that they were trying to prevent preeclampsia.  Now preeclampsia is a life threatening condition if left untreated, and one of the warning signs is rapid weight gain.  Apparently some doctors actually thought that the symptom was the cause, and believed that all excessive weight gain was a sign the patient was about to become preeclamptic.  Thus, the theory went, limiting weight gain would prevent preeclampsia and aid in “figure preservation” to boot*.  
Sadly, this also led to higher infant mortality, disability, and mental retardation….which seems a pretty steep price to pay for what was really a data analysis error.  As I’ve said before, this is why statistics are so relevant in medicine….the cost for getting things wrong is too steep to not be careful.
*To note, it is actually true that preeclampsia is linked to higher weight/glucose/insulin production….but the way they went about addressing it did as much harm to the fetus as good.  Current weight gain recommendations are set to optimize outcomes for the babies, not the mothers.  

2 thoughts on “Correlation and Causation: Real World Problems

  1. Heartbreaking dialogue:

    Mother-daughter argument we were familiar with in the 1970's. Mother, a dermatologist's wife, was chastising her daughter for the (then, at 32 weeks) 30-lb weight gain.

    “Dr. Y threatened to put me in the hospital if I gained more than 14 lbs.”

    Brutal replay: “All five of your children were born with birth defects.”

    Which was entirely true, though the defects ranged from trivial to severe, and were not all necessarily attributable to weight.

    I believe a case could be made that doctors did not definitively do more good than harm until 1950.

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  2. Pingback: 6 Examples of Correlation/Causation Confusion | graph paper diaries

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