The title of today’s post is culled from the peer-reviewed paper “Is young fatherhood causally related to midlife mortality? A sibling fixed-effect study in Finland” by Elina Einiö and two others in the Journal of Epidemiology and Community Health. The question the title poses is answered “Yes.”
What is it with these academic attacks on marriage? Last week having a kid was said to be worse than death. “Having A Kid Worse Than Divorce Or Death? Wee P-values Say Yes.” Yesterday another academic asked, “Two thirds of married people admit to or desire an affair. Is it time to rethink sexual morality?” She says yes. (At least she didn’t abuse any p-values.) And everything about this making-the-rounds “Tinder-hookup culture” article is depressing.
What is striking in today’s academic foray is the word “causally” in the title. Wee p-values are being used to claim a causal relationship, which is exactly the wrong thing to do (video). It might be true that having kids young kills men, but proving it via wee p-values cannot be done.
It also sounds preposterous to claim that young men fathering children kills those men. That’s, after all, what “mortality” means. Kills. Rubs out. Knocks on the head. Sends to the Great Beyond. Being “at risk” for mortality because of having kids early means that the act of fathering is somehow killing some men.
Now this study looked at a sample of Finns. The authors followed men from age 45 to death or age 54, at which time they were “censored” (in the usual statistical way). All-cause mortality or censoring was the end point.
That’s a common approach, but it’s a silly one. Bus runs over a man, which is a cause of death (no p-value needed to confirm). If that man fathered a child young, in this database it was counted against him as a death not being caused by the bus but because he was a young father. In order for this to be true, it has to be that this young father walked (or was pushed? or slipped? it’s Finland, after all) in the bus’s path because he had a kid before gray hair set in. That sound plausible?
No. It doesn’t sound impossible, of course. But it is implausible. Especially when you consider the same thing can and must be said for every other “mode” of death. And listen, since it is obvious that the authors are wrong and that young fatherhood in and of itself isn’t killing men, we’re not after direct causes, but causes of the cause of the death. A cause of the cause of death in the bus example is that a young father was forced to take a bus to work because he was a young father.
How could this happen? Well, I don’t know, but that is, of course, no proof that it cannot.
Here’s the conclusion: “Men who had their first child before the age of 22 or at ages 22–24 had higher mortality as compared with their brothers who had their first child at the median or mean age of 25–26.” Smells like an arbitrary cut-off, no? Like maybe, just perhaps—I make no accusation—that ages were played with until a wee p-value from the model came forth.
But this is ungenerous. Nobody really hunts for wee p-values, right? The real story is in their Table 1, which is reproduced at the top of this post.
More (but not all) fathers under 22 had only “basic” education, more were unmarried, more were divorced than older fathers. This suggests it’s not so much fatherhood which killed the 6.6% of the young men, but other activities. What might these be?
We have no idea, at least, no idea from this data set. For, you (don’t) see, the authors never examined the stated or measured cause of death in any case. They should have—but that’s too much work!
This is a very important point, which we must repeat. Something caused each young and each old father to die (of those who died, naturally). If we say it is young fatherhood that is killing some young men, then it must be something else that killed the old fathers. What was that or what were those causes? Why and how did they differ?
The problem with classical statistical analysis is that it substitutes formulaic manipulation for hard work and hard thinking. And it’s a lousy substitute.
What is needed is (A) proper understanding that statistics can’t prove cause, and more importantly than anything else (B) a new (old)—dare we say a third?—way of doing analyses.
Thanks to KA Rogers for alerting us to this article.