I have a pretty major update for one of my articles.
It has to do with Justice Jackson's comment that when Black newborns are delivered by Black doctors, they're much more likely to survive, justifying racially discriminatory admissions.
We now know she was wrong🧵
So if you don't recall, here's how Justice Jackson described the original study's findings.
She was wrong to describe it this way, because she mixed up percentage points with percentages, and she's referring to the uncontrolled rather than the fully-controlled effect.
After I saw her mention this, I looked into the study and found that its results all seemed to have p-values between 0.10 and 0.01.
Or in other words, the study was p-hacked.
If you look across all of the paper's models, you see that all the results are borderline significant at best, and usually just-nonsignificant, which is a sign of methodological tomfoolery and results that are likely fragile.
With all that said, I recommended ignoring the paper.
Today, a reanalysis has come out, and it doesn't tell us why the coefficients are all at best marginally significant, but instead, why they're all in the same direction.
The reason has to do with baby birthweights.
So, first thing:
(A) At very low birthweights, babies have higher mortality rates, and they're similar across baby races;
(B) At very low birthweights, babies have higher mortality rates, and they're similar across physician races.
Second thing: Black infants tend to have lower birthweights.
MIxed infants tend to birthweights in-between Blacks and Whites, and there's a mother effect, such that Black mothers have smaller mixed babies than White mothers (selection is still possible)
(A) Black babies with high birthweights disproportionately go to Black doctors;
(B) The Black babies sent to White doctors disproportionately have very low birthweights.
If you control for birthweight when running the original authors' models, two things happen.
For one, they fit a lot better.
For two, the apparently beneficial effect of patient-doctor racial concordance for Black babies disappears:
At this point, we have to ask ourselves why the original study didn't control for birthweight. One sentence in the original paper suggests the authors knew it was a potential issue, but they still failed to control for it.
PNAS also played an important role in keeping the public misinformed because they didn't mandate that the paper include its specification, so no one could see if birthweight was controlled. If we had known the full model details, surely someone would have called this out earlier.
Ultimately, we have ourselves yet another case of PNAS publishing highly popular rubbish and it taking far too long to get it corrected.
Let me preregister something else:
The original paper will continue to be cited more than the correction with the birthweight control.
The public will continue to be misled by the original, bad result. PNAS should probably retract it for the good of the public, but if I had to bet, they won't.
So people like Justice Jackson will continue to cite it to support their case for racial discrimination.
They'll continue doing that even though they're wrong.
Relationships between class and fertility and IQ and fertility used to routinely be negative in the not-so-distant past.
But across the developed world, they're increasingly positive, albeit only slightly. In this Swedish birth cohort (1951-67), the transition came early:
In this example, there's also some interesting confounding: between families, IQ isn't monotonically associated with fertility, but within families, it is.
Something seems to suppress the IQ-fertility relationship between families!
Sweden's positive IQ-fertility gradient (which, above, is just for males, since it's draftee data), has been around for quite a while (but has varied, too), whereas in countries like France, Japan, and the U.S., the gradient shift towards being slightly positive is more recent.
This is a really strong claim based on really scant evidence.
Add in a control for family history or use Bonferroni instead of Benjamini-Hochberg and 5-aminovaleric acid betaine goes nonsignificant. Add in polygenic risk scores too and Cyclo(Leu-Pro) goes nonsignificant.
Using a small number of the total tests (multiple comparison correction was too lax), the model with both metabolites in it alone led to p-values of 0.3512 for 5-AVAB and 0.0188 for Cyclo(Leu-Pro) and that's from a model without family history or genetic risk.
I don't see any good reason why, but the authors preferred to make inferences from a model missing important controls they had available
But to make matters worse, 5-AVAB wasn't measured super well, and the analyses with cLP were not quantitative at all, as most data was missing
I have just put out an article dealing with numerous misconceptions about this topic, and a complete explanation of why autism diagnoses have become more common.
It starts with acknowledging that more kids are diagnosed than in the past:
But this is misleading for a few reasons.
One has to do with how this data was sourced. We didn't have a DSM with autism in it before 1980, so all the oldest people in this cohort were diagnosed as adults.
Adults are underdiagnosed. Go out of your way to diagnose? Same rates.
So something is off about this graph.
A major issue is that the older diagnoses here were done under a more arbitrary criteria: Autism has only been a described thing since Kanner's studies in 1943 and mass diagnosis kicked off in 1980.