Coronavirus thread

[45dotACP]

Apologies for the bevvy of questions.

When you contracted covid had it been 2 weeks since you got your first shot?

Did that complicate getting or scheduling your 2nd?

Is there any known mitigating impact on a vaccinated person getting "long covid" where people who don't have major symptoms initially wind up with hugely major problems down the line?

Personally, yes I contracted the virus almost two weeks exactly from getting the vaccine.

I self monitored my symptoms and entered them into my medical app that my HR department asked me to download, and they sent me a thermometer, a pulse oximeter, and a bunch of masks as well to accurately record symptoms daily.

Scheduling dose two was tricky because the logistics side of things. We were about to close vaccinations for employees and open up to the community, so if I didn't get dose two sooner, I would not be able to get it later.

Rather than wait the then-suggested 90 days to get dose two, I received the blessing of the head of our infectious disease department to get dose two about 10 days after I was asymptomatic. This was coincidental with when I was about suggested to get dose 2 anyways. As far as being asymptomatic, I had no fever, or runny nose and my sense of taste was more or less back. I still would get a twinge in my neck and start coughing if I tried to sing a high note.

The key, I was told, is not to get the doses too close together. You can get them further apart than 4 weeks.

All that said, the side effects of the second dose were brisk. I had a 102 degree fever and generalized soreness. A dose of ibuprofen or two and all was well. I mostly just sat around the house playing video games and watching YouTube and 48 hours later I could sing and dance and go back to work no problems.

I would say the same as what @Nephrology said above about long term effects and what the vaccine can or cannot for for people with them. We just don't know yet. Evidence in favor of the vaccine reducing long COVID side effects is mostly anecdotal at the moment and developing, funding and publishing a study on that could take a long time. Most people don't understand that the speed and the scrutiny under which these vaccines were developed and studied was extraordinary and a testament to the power of science and technology.

As for long term effects personally, I'm considerably more fatigued through the day lately, but not to the point where it negatively effects my life. I can still go to work, to the gym, spend time with the fiancee, teach classes to new grads etc...I do stutter a bit more when I'm trying to find the words for something but can still have a somewhat intelligent conversation.

But let's be real.. that's probably just because I'm 30 now, and officially "one of the olds" in the eyes of high school 45dotACP

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[willie]

I have a nit to pick with part of that - they say that the prevalence during the trial matters. To a first order effect, I don't think so.

Suppose you run a trial for BrandA involving 100k people. Out of that 100k people, 100 who got the vaccine get sick, vs 1000 who got the placebo - the efficacy is 90%.

Now you do the same for BrandB, but the pandemic is worse. Out of the 100k people, 200 who got the vaccine get sick, vs 2000 who got the placebo. The efficacy is also 90%.

I don't see why those aren't at least mostly comparable.

Analysis of variance statistics process these types of data to help give accurate interpretations. But first, the numbers entered into the program must represent data gathered in experiments that meet stringent criteria. For these reasons unless we know the sources of data and how they are treated, our discussion might be like gun oil arguments. I love to argue about gun oil.

[ccmdfd]

Interesting couple of patients this morning.


Both male, 60 and 45. Both had COVID last year, one in June, other in November.


Both had asthma in the very distant past, was well controlled prior to covid on little to no meds.


Now both are in severe persistent asthma stage with breathing tests 30% of where they should be. Failing the strongest asthma meds we have, and there’s no pneumonia or scarring or such on their CT’s.



For me, and others in my practice, this is a new thing. We haven’t been seeing asthma post covid, only scarring/fibrosis and such.

Now two on same day.


cc

[Nephrology]

For what it's worth, a new letter in JAMA suggests that the COVID vaccine only produces an antibody response in 17% of solid organ transplant patients after the first dose, vs 100% as reported in the Moderna/Pfizer vaccine data submitted to FDA.

This is a bit disappointing, but study limitations are significant and should be noted - not the least of which is the fact that they only measured antibody production after the first of the two dose series. It's also questionable how comparable the patients tested in this study are vs. those in the Pfizer/Moderna trials.

The authors of this paper say the tests they use to detect anti COVID antibodies have a sensitivity of ~85-86%. This means that if we tested 100 patients who truly have anti-COVID antibodies, we'd expect about 85-86 of them to have a positive test result, while the remaining 14-15 would be "false negatives." This makes it hard for me to understand how understand their results vs. Moderna/Pfizer data as they clearly must have used different tests.

Anyway, just the latest piece of interesting COVID news I've read...

[whomever]

Analysis of variance statistics process these types of data to help give accurate interpretations. But first, the numbers entered into the program must represent data gathered in experiments that meet stringent criteria. For these reasons unless we know the sources of data and how they are treated, our discussion might be like gun oil arguments. I love to argue about gun oil.

I was making a simple point about ratios; I'm not sure how ANOVA comes into play. If you want to explain it in more detail, my degree is in statistics, so I can probably follow a technical explanation.

[willie]

Then you are the expert. I confess that
I did not review the studies' prospecti nor their raw data nor their bibliographies. Plus unless it pertained to frogs and roads,
I might not have understood any of it.

[Nephrology]

I have a nit to pick with part of that - they say that the prevalence during the trial matters. To a first order effect, I don't think so.

Suppose you run a trial for BrandA involving 100k people. Out of that 100k people, 100 who got the vaccine get sick, vs 1000 who got the placebo - the efficacy is 90%.

Now you do the same for BrandB, but the pandemic is worse. Out of the 100k people, 200 who got the vaccine get sick, vs 2000 who got the placebo. The efficacy is also 90%.

I don't see why those aren't at least mostly comparable.

Late to this, but prevalence of disease does matter in the sense that a disease that is more prevalent will also be more commonly detected, which has important implications for the statistical power of required to accruately measure certain endpoints, especially if they are uncommon (e.g. patients who are vaccinated that get COVID).

For example, in the Pfizer/Moderna studies, if they were conducted during a time in which COVID was more prevalent, and the vaccine is highly (but not completely) protective, this means that you're going to need to enroll substantially more patients to detect enough vaccinated patients who get covid to robustly elicit your effect size and significance. This may be reflected by the fact that Janssen (J&J subsidiary) enrolled approx 44k participants in the trial, whereas pfizer/moderna enrolled 30k each IIRC.

I haven't dug into any of these results deeply enough to know if that is the case/to what degree/etc, but it's definitely conceivable that an increased prevalence of COVID-19 during one study vs another could alter your findings. Obviously Moderna/Pfizer did their power calculations well in advance and the people who did so know more about stats than I do, so I don't think Pfizer's study is bad, but definitely could be influenced by underlying disease prevalence.

[whomever]

Late to this, but prevalence of disease does matter in the sense that a disease that is more prevalent will also be more commonly detected, which has important implications for the statistical power of required to accruately measure certain endpoints, especially if they are uncommon (e.g. patients who are vaccinated that get COVID).

For example, in the Pfizer/Moderna studies, if they were conducted during a time in which COVID was more prevalent, and the vaccine is highly (but not completely) protective, this means that you're going to need to enroll substantially more patients to detect enough vaccinated patients who get covid to robustly elicit your effect size and significance. This may be reflected by the fact that Janssen (J&J subsidiary) enrolled approx 44k participants in the trial, whereas pfizer/moderna enrolled 30k each IIRC.

I haven't dug into any of these results deeply enough to know if that is the case/to what degree/etc, but it's definitely conceivable that an increased prevalence of COVID-19 during one study vs another could alter your findings. Obviously Moderna/Pfizer did their power calculations well in advance and the people who did so know more about stats than I do, so I don't think Pfizer's study is bad, but definitely could be influenced by underlying disease prevalence.

I'm still not grokking this.

I fully agree that sample sizes can affect the power of the results; if you don't have enough data you can't draw conclusions. For a limiting case, if the disease prevalence is so low that none of the trial participants, vaccine or control, get sick, then you can't draw any conclusions :-)

I also agree that different variants, etc, can make a difference.

I am making a narrow, nitpicky point here - if you have two studies:

Study1, 100k participants (50k each vacc/control) and 1k of the control and 500 of the vacc group got sick, and
Study2, 100k participants (50k each vacc/control) and 2k of the control and 1000 of the vacc group got sick

because the disease prevalence during Study2 was twice what it was during Study1, that is exactly what I think the math would predict if the efficacy was the same for both trials. I'm trying to find the math behind the assertion (that I think I hear from the Vox video) that those are not actually the expected values you would see for equal efficacy, for non-extreme values of disease prevalence (i.e., neither of the control groups had close to 0% or close to 100% get sick.

I really do get that the notion that you can have other kinds of differences between studies that can affect the results.

I realize this is a narrow, nitpicky, theoretical point that may not be of general interest. But I went to the trouble to get a degree in this, and I don't understand the math behind the assertion, and I'd like to understand it, if it wasn't a mistake.

[TiroFijo]

"The only way that you can effectively compare to two different medications or two different vaccines is if you have a randomized controlled trial where some people are getting one vaccine and some people are getting the other," Dr. Meg Sullivan, Mecklenburg County Public Health's Medical Director, said. "That didn't happen here."

In fact, doctors say the conditions of the Pfizer, Moderna, and Johnson and Johnson trials were vastly different.

"They were tested in different points in time. They were tested in different populations of people when there were different strains circulating in those different populations of people," Dr. Linda Bell, State Epidemiologist for South Carolina's Department of Health and Environmental Control, said.

https://www.wcnc.com/article/news/he...b-4998fecd8544

https://www.nytimes.com/interactive/...ronavirus.html

[MickAK]

I am making a narrow, nitpicky point here - if you have two studies:

Study1, 100k participants (50k each vacc/control) and 1k of the control and 500 of the vacc group got sick, and
Study2, 100k participants (50k each vacc/control) and 2k of the control and 1000 of the vacc group got sick

because the disease prevalence during Study2 was twice what it was during Study1, that is exactly what I think the math would predict if the efficacy was the same for both trials. I'm trying to find the math behind the assertion (that I think I hear from the Vox video) that those are not actually the expected values you would see for equal efficacy, for non-extreme values of disease prevalence (i.e., neither of the control groups had close to 0% or close to 100% get sick.

The more bouncy colored balls that are bouncing the more likely that they will bounce into a non-colored ball and make it change color. But, in order for the colored bouncy ball-ness to be included in the trial a model for how fast the balls are bouncing and how random their movements are has to be used and we don't have one yet, or at least an agreed upon one. So they just went with number of colored balls. Essentially. They probably said bouncy less.