This Long Covid study's reliance on serology derails its claim to have a control goup. What's more, it treats beliefs as *inventors of reality* while overlooking that typically beliefs are *reflections of* reality.
Laypeople call this "gaslighting".🧵 jamanetwork.com/journals/jamai…
1. An Out of Control Control Group
In this study, participants are given serology tests.
Participants are notified of the results and asked if they believe they had Covid, a question that just as well could be phrased as: Do you believe the test results?
Not everyone does. The study found 1,091 positives. Somewhat more than ½ the positives (638) did not believe the results. A fair assumption is they were asymptomatic.
The 453 remaining positives (“positive believers”) agreed with the results. Amongst the negatives, most agreed with the results but 461 (the “negative believers") believed they had Covid anyway.
The 461 negative believers are the study’s control group and the persistence of their symptoms is compared with those of the positives.
Since persistent symptoms are most associated with believers (some of who were -), the authors conclude persistent symptoms have more to do with beliving one had Covid than with Covid infection itself.
Unsurprisingly, the study has been welcomed by LC skeptics & drawn the ire of LC sufferers & activists. But the study relies on contrasting sero+s with (sero) negative believers.
However, serology testing’s propensity for false –‘s, suggests some negative believers were right about having had Covid. One issue is, not everyone infected seroconverts (produces antibodies).
What’s more, producing low levels of antibodies that fade to undetectable levels can be, not evidence of infection’s absence, but of Long Covid’s presence.
Still, this only suggests that there are actual Covid cases amongst the believers who were negative. We don’t know how many, though we have clues: prior to the study, 656 believers had their Covid confirmed.
This is 203 more than the 453 + believers the study found. Where did these 203 believers wind up? Apparently, in with the 461 negative believers which, as it happens, is the not just the CONTROL GROUP, but somewhat less than half of it.
That some formerly +s are, months later (at the time of the study’s testing period) sero-negative is predictable.
If you’re like me, you are wondering why, given this predictability & the prior confirmation, the authors are at all surprised believers have a lot in common. Most conspicuously of all, what they have in common is: having had Covid.
2. Conceptual Issues and a Conclusion that Isn’t
The implication that symptoms are most associated with belief is not lost on anyone with the study being reported as showing LC is “in the head” or that belief is causing symptoms.
But the study shows no such thing. First, an association between belief and symptoms does not itself establish causality and, even if the association were causal, which is the direction of the causal arrow?
The data itself is indifferent between belief causing symptoms & symptoms causing belief. Yet the authors, tipping their hands by putting LC in quotes, ignore the chicken or the egg problem by inviting the conclusion that belief drives symptoms.
But how would it work for belief to drive symptoms?
Are we to suppose that, one fine day, for no reason whatsoever, based on no symptoms, someone comes to believe they had Covid?
If beliefs are so dislodged from reason in this way, why don’t more of us believe we can fly?
The authors’ view of belief as symptom creators is made more curious because not all the believers even attributed their symptoms to Covid.
In grad school, I had a friend who insisted he was having a heart attack. He also insisted he had chest pain & that the pain was attributable to the heart attack. Yet, in the authors’ telling, the scenario is even more unhinged than that.
In their scenario, there is a free-floating belief in illness (not caused by symptoms) that causes symptoms which the believer may or may not attribute to the believed illness they don’t have.
What if we tried assuming, instead, that people aren’t crazy?
We could begin w/this chart of symptoms & the differences btw the antibody +s & the -s (which, recall, include actual Covid). In fact, we might notice that, whether + or -, whether believers or doubters, a lot of people feel persistently unwell.
Is it all in everyone’s head?
LC minimizers have a strategy. Shades of it are in this paper. The strategy starts with the observation that symptoms of LC can be found in the general population. The conclusion is LC doesn’t define a unique condition or “isn’t real”.
If you aren’t seeing LC, the problem might be you lack a key question like: To what extent does this or that symptom interfere with your daily activities?
That would be my question, were I truly interested in finding the LC sufferers.
End
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Having built a haystack around school opening data, @ProfEmilyOster challenges readers to find the difference remote or in-person makes to community case rates. You can if you search, but first let's look at the haystack in which the difference is buried🧵 nature.com/articles/s4159…
The authors’ describe the study:
“The aim of this national, retrospective cohort study was to evaluate the impact of school mode and opening to in-person education on subsequent changes in community incidence of SARS-CoV-2.”
The school modes the authors consider are: traditional (in-person), virtual and hybrid. The authors conclude that learning modes make no statistically significant difference to incidence of Covid in a community.
It’s déjà vu all over again as @TracyBethHoeg does the same dumpster dive for Ontario’s “Adverse Events Following Immunization” (AEFI) reporting system that she did for VAERS. Naturally, there are problems. 🧵
As a brief reminder, Hoeg used the raw data of VAERS, despite its numerous disclaimers against doing so, allegedly to extract cases of myocarditis case investigators might have missed. It doesn’t go well. sciencebasedmedicine.org/peer-review-of…
Turning her sights on Ontario, Hoeg doubles down on the mistake; this time not even doing the slightest investigation into Ontario’s AEFI reports (and by “slightest investigation” I mean reading the report) but instead using its raw, unadjudicated data to establish case rates.
1. So the VE of 95% is in terms of hospitalizations only which is the only outcome he considers. Probably in terms of hospitalized or not, boosters might not move the needle much *if* boosters waned at a similar rate.
2. But not everyone thinks of the 3rd shot as a booster but more as the final shot in a 3-dose regimen. If that’s right, there wouldn’t be the same waning after 3 that we see after 2.
3. If the 3rd shot increased durability, then it reduces hospitalizations over whatever the time-frame to a actual booster (shot #4) would be. That adds up.
Faust also assumes myocarditis rates for 3rd shot = rates for 2nd shots which likely isn't true.
If you’re like me, you raise a skeptical eye when @apsmunro is happy. So I took a look at this study and I’m not embarrassed to say: this myth is far from busted. 🧵
The study compares symptoms recorded by an app from periods of Alpha dominance to the period of Delta’s dominance. It purports to show Delta is no worse than Alpha when it comes to kids: either in terms disease burden (number of symptoms) or duration. medrxiv.org/content/10.110…
Describing the symptoms tracked, the authors tell us they “do not include some common paediatric co-morbidities (e.g., neurological or neuro-disability disorders).”
Opening by calling quarantine “age old” as if, like leeching, it was largely quackery, @MonicaGandhi takes readers on a path of omissions & oversights before arriving, as if clandestinely, at a conclusion so sane, one suspects she is embarrassed by it.🧵 washingtonpost.com/outlook/2021/1…
Despite its age, much like leeching, quarantine has a modern-day place. Gandhi concedes as much in a paragraph mixed with a variety of platitudes about costs and benefits.
Though Gandhi is correct that “in many cases quarantines are probably doing more harm than good”, “probably” carries a lot of weight given that cases where quarantine would do more harm than good are indistinguishable from those where quarantine wouldn’t.