In other words, this case study is a relatively ordinary result in line with what we know: that the major COVID-19 vaccines greatly reduce the chance of severe illness but leave nontrivial chance of infection and transmission.
All of the major symptoms including high fever, cough, severe fatigue, loss of taste/smell, and shortness of breath still count as mild under their definition. This is very much a disease you don’t want to get, even the so called mild version that happens in vaccinated people.
1. https://www.covid19treatmentguidelines.nih.gov/overview/clin...
Isn't the point that levels below severe are not generally fatal and don't require hospitalization. Nobody thinks it's good to get sick, but it generally ceases to be a threat to public health with vaccination.
The severity of mild covid is still potentially long lasting and I'm willing to guess elderly aren't going to handle it well.
Letting covid become endemic could reduce our life expecty considerably.
I'm confused by this comment. I do think they are a problem, that's why I am encouraged by a vaccine that reduces covid to something that is not life threatening and that we can stop freaking out about.
I am relatively certain this is the reason they are banned from youtube... i have seen a (now removed) video from a crank on youtube who suggested saturation bombing of high incidence areas might be the solution...
edit: curious how hn readers feel they will be able to correct misguided beliefs if they don't even know what those beliefs are.
It's a problem, but we can manage it better than overflowing morgues. Frequent tests, rapid antigen testing (let's say at the entrance of restaurants, clubs, festivals), encouraging people who feel ill to stay the fuck away from others, and so on.
- Long COVID post-vaccine. >10% of people have brain damage visible on MRIs, including mild cases.
- Future mutations. People have been overly optimistic for 15 months now, and believed bad things couldn't happen. They do. There's no reason to believe Delta or Gamma are the end, or anywhere close.
can you point to that MRI imaging? curious to see.
https://www.medrxiv.org/content/10.1101/2021.06.11.21258690v...
This one scared me since it was a large, relatively unbiased sample, with before-and-after imaging. It also showed brain damage even in mild cases of COVID19 in >10% of cases.
There are a lot of supporting smaller-scale studies too, replicating the same general result. E.g.
https://www.khou.com/article/news/health/coronavirus/covid-1... https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066611/
If you Google, you'll find dozens of other small-scale studies.
It's not surprising that covid19 can cause brain damage (and heart damage, and death) to unvaccinated people.
I'm now realizing formatting / phrasing was unclear.
1. >10% of mild / asymptomatic cases have brain damage.
2. The million dollar question is whether this includes vaccinated cases. Vaccines reduce hospitalizations and deaths by far more than they do mild and asymptomatic cases.
It shows no such thing. It's an analysis of MRIs where the authors infer loss of gray matter in specific regions of the brain. This is in no way "brain damage", and representing it this way is leaping to wild conclusions.
Lest you not believe me, here is a randomized controlled trial, showing that "excessive online video gaming" reduces orbitofrontal gray matter:
https://pubmed.ncbi.nlm.nih.gov/29057579/
(...so your Mom was right: gaming is turning your brain to mush!)
Here is a review that shows that similar losses in gray matter are associated with anxiety and sleep loss (two problems that I'm sure didn't affect anyone in 2020):
https://pubmed.ncbi.nlm.nih.gov/29244642/
Similarly: "Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder"
https://pubmed.ncbi.nlm.nih.gov/31341158/
Just for fun: here's a paper that shows that "tooth loss was a causal factor for volume reduction in brain areas related to memory, learning and cognition"
https://pubmed.ncbi.nlm.nih.gov/29475808/
(bonus points: can you spot the missing correlate?)
The fact is, you can find research literature associating "loss of gray matter" with pretty much anything. And if a reliable trend does exist across this literature, it seems to be that gray matter changes are often seen in...wait for it: depressed people and the aged.
But I'm sure that Covid has done nothing to depress people or affect the aged, so we can probably safely ignore that little detail.
Concussions ?
(To be fair to the researchers of this paper, they do discuss some of this, but they focus on a causal relationship between tooth loss and the other factors. They never really consider that the relationship between these factors and tooth loss could be reversed.)
On the other hand, a handful of studies, one correlational with large n, a few causal, a good theoretical basis, etc. do move us up that pipeline quite a bit.
Mild and asymptomatic cases of COVID19 due seem to cause brain damage leading to brain fog.
- Reports of brain fog in isolation? Psychosomatic.
- Correlational studies? Correlation is not causation.
- Case studies? Anecdotal.
- Extrapolation from olfactory symptoms? Theoretical.
And so on.
Put together, though, it's a pretty strong case. It's not airtight, but it's well into the well-supported theory range.
Define "brain fog". Tell me what the diagnostic criteria are, and how one might make an objective measurement of its presence and magnitude.
Bonus question: tell me how your stated criteria differs from the pre-established diagnostic criteria for depression.
One can survey a random sample of the population, ask them if they have ever "felt the presence of God", and find a strong signal confirming this. It does not make God a diagnostic factor in a medical study.
If the person lacks dysphoria or anhedonia, would that satisfy the question? I understand your angle (I think), but for comparison, the last time I had a serious flu I found that even after I felt better, it was extremely difficult to focus at work. For about 3 days, gradually improving each. I suspect that is what people refer to as "brain fog", and I could distinguish it from depression by (among other things) a lack of dysphoria / anhedonia (and generally speaking, other depression signs).
I remember in medical school that when we were interviewing patients receiving chemo they would have us do a neuro exam, and very distinctly remember when one guy got angry at me when he couldn't answer some of my questions. He didn't seem to have depression and nobody told me I was doing it wrong when I said he had "chemo brain". So its certainly a real thing in the general sense, and can certainly be caused by a variety of medical conditions.
I guess a more constructive question would be -- assuming a long term cognitive impact, what (practical) research should these researchers be doing instead? Or what if when they asked about brain fog they _also_ asked about depressive symptoms?
No. That is certainly more specific than ~all of what you hear in the media surrounding "brain fog", but you can't define something by what it isn't.
Example: I have the wiggles. I'm not itchy though, and my muscles don't hurt.
OK, great. What are the wiggles?
> the last time I had a serious flu I found that even after I felt better, it was extremely difficult to focus at work. For about 3 days, gradually improving each. I suspect that is what people refer to as "brain fog"
Could be! The problem is, until there's a definition (and ideally some kind of objective measure), all of these self-reports are blind people describing different parts of an elephant.
> I guess a more constructive question would be -- assuming a long term cognitive impact, what (practical) research should these researchers be doing instead? Or what if when they asked about brain fog they _also_ asked about depressive symptoms?
We could start by simply using established terminology and testing. What percentage of patients reporting "brain fog" show up as depressed using a standard screen?
It's literally the easiest thing in the world to do...why isn't it done?
Why does the size of the study matter so much if the endpoint of the study is absurd, the gathering process was a fishing expedition, and the whole thing is subject to confirmation bias?
Even if you believe that these researchers are finding real signals in these MRI scans (which I don't automatically grant; even they admit that some of the "pathologies" they've identified aren't significant, and they didn't pre-declare the endpoints anyway, so you can't rely on conventional statistical significance thresholds), the fact that they know the outcome for each subject hopelessly poisons the data.
> Denial is quite a river these days.
People have a habit of inventing fictions they believe wholeheartedly in order to ignore a truth they cannot accept.
Yes, there are studies which show virtually everything, but in this case, we have:
- >10% of mild cases reporting long COVID brain fog (without MRIs)
- Visible correlations on MRIs with large n (cited study)
- Lots of small-scale studies / looking at specific cases
- Some understanding of a relevant mechanism-of-action (see: olfactory loss)
Together, that's about as strong evidence as you'd expect after 15 months. We have effect, we have correlation, we have case studies, and we understand why it's plausible.
The big question is whether it strikes vaccinated mild / asymptomatic cases. We don't know. There are a lot of cases like this.
"Brain fog" is not a diagnosis. It has no definition. It has no test. Literally anyone could say they have it, and not be wrong.
It also overlaps substantially with "fatigue"...which we all know comes along with a lot of other common issues. Such as depression.
> Visible correlations on MRIs with large n (cited study)
The size of n doesn't matter if the thing you're reporting is not a meaningful metric. Here, we have a paper that has gone on a fishing expedition for a quasi-subjective metric with unknown levels of noise, which is widely "shown" to be associated with many common and uncommon issues across the research literature.
This is a low-quality data set. But yes, it is a larger low-quality data set.
> Lots of small-scale studies / looking at specific cases
Collections of anecdotes are not data.
> Some understanding of a relevant mechanism-of-action (see: olfactory loss)
...for a single symptom (loss of smell). But no, we don't know why that happens, and to the extent we do, the current best hypothesis has nothing to do with neurons, but rather, the scaffolding around those neurons.
> Together, that's about as strong evidence as you'd expect after 15 months.
Nonsense. We've been debating this "long covid" for more than a year now. There are apparently many sufferers. We could have easily conducted randomized, longitudinal, controlled trials. We have not.
The total evidence for "long covid" continues to be anecdotes and self-reported "symptoms", of indeterminate duration, amongst populations that are mostly self-selected for having "long covid". I believe that we'll eventually find out that some of these things are real, but right now, this is just hysteria.
Can you please propose a "randomized, longitudinal, controlled trials" one might conduct to figure that out?
Preferably, one which would pass an IRB review. We can't randomly infect 10,000 ethnic minorities with COVID19 anymore, which I think what you're suggesting. The Tuskegee Syphilis Study and the Nuremberg Trials took care of that for us.
Short of something like that, we work from mixed methods evidence.
As a footnote, a year isn't a long time in the world of research. That's sometimes quite literally how long it takes from when you apply for a grant to when funding lands in your account. And you're asking about a phenomenon which often occurs months later.
This is not a herculean problem. It's essentially the definition of any halfway decent medical study:
* pick a set of measurable endpoints from the pantheon of "long covid" symptoms that are likely to be real. Objectively measurable endpoints should be mixed in with subjective ones (e.g. "fatigue", "loss of smell", "reduced lung capacity", "heart inflammation").
* pre-register these endpoints, so that you can't go back on a fishing expedition later, when your first choices don't pan out.
* pick a set of participants at random (balancing for demographics of interest: age, co-morbidities, weight, gender, etc.)
* measure those endpoints at the start of the study so that you have a pre-trial baseline.
* follow those people over time for the endpoints of interest.
* some percentage will get infected with SARS-CoV2. verify this via testing.
* at the end of the study, compare the group that caught SARS-CoV2 with the ones who did not along the endpoints of interest. compare both groups with their own pre-trial baselines.
This is not the ONLY way of doing such a study, but it would be vastly better than any data currently reported. The biggest challenge is that it has to be done before the pandemic passes, and the number of cases drops too low to get a significant result in a reasonable period of time. The window on this is rapidly closing.
> As a footnote, a year isn't a long time in the world of research. That's sometimes quite literally how long it takes from when you apply for a grant to when funding lands in your account. And you're asking about a phenomenon which often occurs months later.
There have been multiple RCTs conducted during the pandemic, despite the bureaucratic inertia of the academy, and "long covid" is one of the biggest remaining controversies. There's no universe in which you couldn't get funding and approval for such a study in short order.
> This is not a herculean problem. It's essentially the definition of any halfway decent medical study:
You're running in circles. That's not a randomized control trial. You'll get biases since the set of people infected with COVID19 isn't random.
This is not much better than existing studies. They're not preregistered, but that's the only upside of your methodology.
> There have been multiple RCTs conducted during the pandemic, despite the bureaucratic inertia of the academy, and "long covid" is one of the biggest remaining controversies. There's no universe in which you couldn't get funding and approval for such a study in short order.
IRBs are set up to prevent subject harm. An RCT, in this case, would involve randomly infecting people with COVID19 to eliminate the bias above. That will never fly.
If I’m sick in bed for two weeks I can’t work or take care of my family so those mild symptoms very much would be a danger, not to mention the dangers from long COVID which hasn’t been ruled out in vaccinated folks and the dangers of exposing people who can’t be vaccinated.
I traveled by bus all the way from Southern Italy to Sweden just before the virus hit the news, and fell sick with the same symptoms for about a week from the day I arrived.
The fever was nasty, but the one thing I remember most is lying there alone in the middle of the night feeling like I wasn't getting enough oxygen and wondering what the hell was going on.
When my fever broke, I went about my life like normal. Still I'd hate to imagine what it would've been like in a not so vaccinated era like the early 1900s. I probably would've died.
This is not the case for new strains. There are reports from the 1918 influenza strain that it killed young people over night: Folks went to bed with fever and didn't wake up the next day.
If you had the flu or know somebody who had, it's no joke.
Ever been to a high altitude city? I was short of breath for my first week in Mexico City, and my lungs hurt after exercise. It’s very disconcerting, but also just a slight inconvenience.
Sure, try not to get sick. Understand that there is some risk of symptoms. But getting COVID once you've had the vaccine shouldn't really be a concern worthy of altering any behaviors to avoid for healthy people.
> In other words, this case study is a relatively ordinary result in line with what we know: that the major COVID-19 vaccines greatly reduce the chance of severe illness but leave nontrivial chance of infection and transmission.
I dont think this particular case is a proof of your claim at all.
Also from the study:
> Such a low vaccine efficiency against infection by the Gamma variant was not expected because in vitro studies have shown a similar reduction of neutralization for Beta or Gamma variants by BNT162b2-elicited antibodies (5) and a conserved CD4+ T-cell response against spike proteins from the Beta variant (6).
So, no, this was not "business as usual", in fact the results are so shocking that the investigators wrote:
> Given the surprisingly high attack rate, we hypothesized potential dysfunctions of conservation or administration of vaccines, but the absence of traceable cold-chain interruption and the use of different batches seemed to refute this hypothesis.
So the study so far suggests that the Pfizer vaccines may well have an actual low effectivenes rate against this virus variant.
They were not. They were obese and hypertensive. That is unhealty no matter what vaccines you take.
https://en.m.wikipedia.org/wiki/List_of_countries_by_obesity...
In other words, natural immunity from previous Covid case was the only protection and the vaccinated miners were not statistically different than the unvaccinated (sample size of 25 vaccinated at 60% attack rate vs sample size of 4 at 75% attack rate is inconclusive).
I think mining is exceptionally poor for your health, esp re respiratory disease, lack of vitamin D, and also usually highly correlated with near poverty (and the health issues that’s associated with)