American military veterans have a suicide problem.
Some have theorized the reason is deployment-related trauma.
Leveraging the random assignment of new soldiers to units with different deployment cycles, Bruhn et al. found that was wrong.
Deployment did not increase suicides.
Looking only at violent deployments (ones with peer casualties), there aren't noncombat mortality effects either.
What explains veteran suicide rates?
The reason seems to be that the proposition is wrong: veterans do not have increased suicide risk.
This may seem surprising, but it's not. Their suicide rates are elevated over the general population because most of them are young White men. That group has a suicide issue.
There are good and bad parts to this observation.
On the one hand, it means that there is not selection of suicidal people into the military.
On the other, demographic selection makes this problem into one that agencies like the VA will probably not be able to fix on their own
because it's not a soldier problem, it's a young White male problem.
I don't know how this can be fixed, but presumably tackling opiate use would help.
Soliman (2022) found that DEA crackdowns on overprescribing pharmacies resulted in fewer local suicide deaths.
Soliman also found that sanctioning specific doctors affected opioid-related mortality more generally without impacting suicide rates. Effects were generally larger for males than females and they were larger for people aged 30-49 than those aged 15-29 or 85+. No race data.
Kennedy-Hendricks et al. found that Florida's pill mill crackdown reduced opioid overdose mortality considerably.
Their supplement contained details on the characteristics of the people who died from opioid overdoses, but I wasn't able to access it.
This analysis has several advantages compared to earlier ones.
The most obvious is the whole-genome data combined with a large sample size. All earlier whole-genome heritability estimates have been made using smaller samples, and thus had far greater uncertainty.
The next big thing is that the SNP and pedigree heritability estimates came from the same sample.
This can matter a lot.
If one sample has a heritability of 0.5 for a trait and another has a heritability of 0.4, it'd be a mistake to chalk the difference up to the method.
The original source for the Medline p-values explicitly compared the distributions in the abstracts and full-texts.
They found that there was a kink such that positive results had excess lower-bounds above 1 and negative results had excess upper-bounds below 1.
They then explicitly compared the distributional kinkiness from Medline to the distributions from an earlier paper that was similar to a specification curve analysis.
That meant comparing Medline to a result that was definitely not subject to p-hacking or publication bias.
I got blocked for this meager bit of pushback on an obviously wrong idea lol.
Seriously:
Anyone claiming that von Neumann was tutored into being a genius is high on crack. He could recite the lines from any page of any book he ever read. That's not education!
'So, what's your theory on how von Neumann could tell you the exact weights and dimensions of objects without measuring tape or a scale?'
'Ah, it was the education that was provided to him, much like the education provided to his brothers and cousins.'
'How could his teachers have set him up to connect totally disparate fields in unique ways, especially given that every teacher who ever talked about him noted that he was much smarter than them and they found it hard to teach him?'
This study also provides more to differentiate viral myocarditis from vaccine """myocarditis""", which again, is mild, resolves quickly, etc., unlike real myocarditis.
To see what it is, first look at this plot, showing COVID infection risks by time since diagnosis:
Now look at risks since injection.
See the difference?
The risks related to infection hold up for a year or more. The risks related to injection, by contrast, are short-term.