This is about more than denying issues. It's about trying to get the whole picture. Ignoring any anecdotes you don't agree with doesn't help you get the whole picture.
I think you misunderstood. The point was that it’s not about picking anecdotes that support your conclusion.
You have to look at broader data collection and studies, not just whatever small bits of anecdata are conveniently nearby.
Moreover, those who get better nevertheless contribute to the stresses that are stretching health care system and economy to its limits. These stresses cause second order harms to those having to go without care as resources are mobilized to support those who "will be fine."
>A study looking at interventions in 11 European countries found that lockdowns and social distancing in the first wave helped lower the reproductive number (R) to below 1 by early May 2020[2]. R measures how many people one infected person will pass the virus on to. When R is above 1, the number of infections can rise exponentially. When R is below 1, the number of infections drops.
>In this study, the scientists developed a model to predict how many people would have died in these 11 countries, if no lockdown measures had been introduced. Comparing the actual number of deaths counted with the number of deaths predicted by their model, in the absence of any interventions, they found that around 3.1 million deaths were averted across these 11 countries from the beginning of the epidemic up until 4 May 2020. Scientific evidence shows that COVID-19 restrictions reduced virus transmission and, by extension, saved lives
0: https://healthfeedback.org/claimreview/evidence-shows-that-l...
Regardless of what you think of Makary's original opinion, there's no universe in which a prediction of the future from a medical expert should be labeled misinformation. If that's the bar, then ~all of the statistical models used to make scary predictions about Covid are also "misinformation", as are 95% of the things Anthony Fauci says on cable news.
"Politicians are holding children hostage in the basement of a pizza place" => clearly incorrect factual statement.
"I think that Covid infections will be far below their current numbers by summer" => opinion about the future.
(Regarding the particular claim advanced by health feedback here: it's a "fact check" based on a single paper, written by Neil Ferugson's group, that attempts to validate the predictions made by...Neil Ferguson's group. There have now been many others that say the opposite [1]. At the very least, you'd hope that a reputable "fact-checking" site would discuss these to present a balanced picture of the debate. Health feedback doesn't bother.)
[1] https://www.aier.org/article/lockdowns-do-not-control-the-co...
So I'm really not even sure where to start. Perhaps I should start with the fact that, under the best possible interpretation, you're treating the conceptual nuance of prediction vs. statement of fact as if it completely discredits the ability if Health Feedback to accurately parse COVID research. That is totally out of proportion to what would actually be proved by that argument.
It's also, I believe a distinction that is basically without merit as a commentary on what does or doesn't count as misinformation. Predictions most definitely do contain misinformation, serve to spread misinformation, can be based on bad reasoning that it is appropriate to criticize well ahead of the events they are predicting. In this case, the WSJ prediction turned out to be false and it was perfectly appropriate to call it out for the misinformation it was, and seeking cover by litigating whether the misinformation was retrospective or forward looking without defending the accuracy or underlying reasoning is asinine.
>Regarding the particular claim advanced by health feedback here: it's a "fact check" based on a single paper, written by Neil Ferugson's group, that attempts to validate the predictions made by...Neil Ferguson's group.
And this is so unhinged I almost don't even know what to say. There's nothing untoward about doing research that weighs on other research you have done; it's in collaboration with other partners and peer reviewed. If something was actually wrong with the study you could come out and say so instead of making captain of the JV debate team insinuations about hypothetical possibility of impropriety.
None of this is true. Frankly, I'm not even sure what the middle part means...herd immunity is not a "strategy"; it's just a fact. It's like claiming that "gravity" is a "strategy" for landing an airplane.
> So I'm really not even sure where to start
It's always good to start with something that isn't a fabrication and an ad hominem.
Their "Herd Immunity Strategy": Keep the elder parts of the population locked up and let children and younger parts get it without vaccination: https://gbdeclaration.org/
"In October 2020, AIER sponsored the “Great Barrington Declaration,” a controversial statement advocating for a herd immunity strategy in response to the COVID-19 pandemic. The World Health Organization and other health experts have suggested a herd immunity strategy would be “unethical,” dangerous, and deadly."
More info: https://www.desmog.com/american-institute-economic-research/
Again, herd immunity is not a "strategy". It is a fact. Global vaccination is also a "herd immunity strategy", in that it tries to achieve herd immunity in the population.
But now that I've cleared that up, you should also know that's not what the GBD says. Maybe you should actually read it, instead of repeating what other people say about it?
This willingness to rely on hearsay explains a lot why you're eager to dismiss a page full of links to studies (which is honestly the only reason I used the AIER page), instead of, you know, considering that there might be a debate here.
Obviously, it's much easier to pretend that you're right when you impugn any source that disagrees with your opinion, for disagreeing with your opinion.
https://www.nature.com/articles/d41586-021-00728-2
if you are right, fix the Wikipedia entry ;)
Did you check who co-signed it? Mr Banana Rama, Dr Johnny Bananas, Dr Johnny Fartpants, Dr Person Fakename, Harold Shipman, Professor Notaf Uckingclue, and Prof Cominic Dummings.
Also the study your sit you are in good company ...
And does not address any articles published in that year that showed effects. Here's one: https://www.nature.com/articles/s41586-020-2405-7
You can find way more than the "sources" your article cites. AIER is partisan and does not give you "the evidence."
But the model probably relies heavily on extrapolation. It is like an election forecast. That can have merit, but doesn't really lead to a definitive result.
Suppose 990/1,000 people that took a pill where fine and the rest died. Clearly it’s a serious risk even if you’re almost guaranteed to be fine. Looking at anecdotal evidence would show “most people where fine”/“10 cases of people dying!” But let’s change the numbers 999,999,990/1,000,000,000 took a pill and 10 died, now is it a serious risk or roughly as dangerous as driving to work? You still have “10 cases of people dying!” but it’s nowhere near the same thing.
The vaccine that was pulled very much came down to anecdotes vs numbers of injections in a cost benefit analysis. Presumably it would have still been administered to the elderly if their where no alternative vaccines. But as their where alternatives it wasn’t worth putting people at even such low risks.
You can download the data and crunch it... https://vaers.hhs.gov/data.html though note:
> Reports may include incomplete, inaccurate, coincidental and unverified information.
> The number of reports alone cannot be interpreted or used to reach conclusions about the existence, severity, frequency, or rates of problems associated with vaccines.
I love basing my opinions off of shitty data.
I would not be surprised that the anti-vaxxers know more about VAERS than the general population.
E.g. if 100 people walked out the door and had a heart attack the next day, you need to understand the base rate of heart attacks to know whether 100 is high, low, or normal. Then you need to put that number in the context of statistical significance, e.g. p < 0.05 means an up to 1 in 20 probability that your data is there by random chance, and if you look through all the AEFI data you'll analyse much more than 20 types of adverse event, so you'll most likely find an adverse event or two that looks statistically significant but fails to replicate in any studies.
> A report to VAERS generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after vaccine was given. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.
(crunching some data... Death... Death of pet is listed as one of them? well... ok)
And just looking at that data its... self reported. For example, running it for the current vaccines there's one death reported for a person under 6 months old which is rather surprising since it's not authorized for that age range.
Anyways, the thing is that doing the "simple" query on this shows that 2,694 age 80+ died after receiving the vaccine. That needs to be calibrated against the question of "how many people aged 80+ would die in that time range without either covid or the vaccine being present?"
And that is exactly the problem that you're describing.
The key thing is its there and if people want to approach the data using the proper statistical rigor... the data is there.
The relevant pipeline blog post: https://www.science.org/content/blog-post/get-ready-false-si...
> Bob Wachter of UCSF had a very good thread on Twitter about vaccine rollouts the other day, and one of the good points he made was this one. We're talking about treating very, very large populations, which means that you're going to see the usual run of mortality and morbidity that you see across large samples. Specifically, if you take 10 million people and just wave your hand back and forth over their upper arms, in the next two months you would expect to see about 4,000 heart attacks. About 4,000 strokes. Over 9,000 new diagnoses of cancer. And about 14,000 of that ten million will die, out of usual all-causes mortality. No one would notice. That's how many people die and get sick anyway.
> But if you took those ten million people and gave them a new vaccine instead, there's a real danger that those heart attacks, cancer diagnoses, and deaths will be attributed to the vaccine. I mean, if you reach a large enough population, you are literally going to have cases where someone gets the vaccine and drops dead the next day (just as they would have if they didn't get the vaccine). It could prove difficult to convince that person's friends and relatives of that lack of connection, though. Post hoc ergo propter hoc is one of the most powerful fallacies of human logic, and we're not going to get rid of it any time soon. Especially when it comes to vaccines. The best we can do, I think, is to try to get the word out in advance. Let people know that such things are going to happen, because people get sick and die constantly in this world. The key will be whether they are getting sick or dying at a noticeably higher rate once they have been vaccinated.
The referenced tweet is: https://twitter.com/Bob_Wachter/status/1333966348972539904?s...
Do you think it is worth getting the vaccine if you’ve already had covid and it wasn’t a big deal?
As in if you have firsthand experience that covid didn’t affect you much, wouldn’t that maybe be a valid reason to just say I’m not going to get vaccinated even though the risks of adverse short term effects are small (still don’t know about long term but probably small is my guess)
That said, quite a few people that had COVID before end up suffering far more the second time. As long as we are talking about individual cases, the first person do die of omicron was in their 50’s and had COVID before but never got vaccinated. But again even if we are talking about a 25 year old athlete you only get ~70 * 365 days, why spend more of them sick than you need to?
No reason to undermine a response that clearly was comparing one anecdote with another. Considering how children have been amazingly spared the effects of sars-cov-2 it is hard to see that they seem to have held the lions share of the mandates to slow the inevitable spread. And still do as the vaccined did not provide the high immuinty they initially promised.
The vaccines do an amazing job reducing hospitalizations among the vulnerable, it is not clear they have much benefit for the toddlers, and may even be harmful. Lets move on.
Externalities are real, and as such the discussion is about acceptable risk. So yes, in the most ridiculous and obvious sense it includes your kids, but you're assuming a lot too, and I question whether you're willing to reason critically - rather than emotionally - about school policy.
Compare [2] to an estimated 216 deaths ages 0-4 and an additional 156 ages 5-17 from the flu in 2018-2019.
Those aren't perfectly comparable -- we took serious additional measures against covid that we don't take against the flu. On the other hand, how much actual masking of the 4 and under set is going on in preschool or kindergarten?
[1] https://data.cdc.gov/NCHS/Provisional-COVID-19-Deaths-Focus-...
I dunno what the right measures are to take for kids that young, but the difference we're seeing between the flu measures we (mostly don't) take and some anti covid measures for young children is hard to explain.
And any time someone says, um, the data appears to show that covid is not actually that dangerous to young children, you get people (who I'm 99% sure don't lock their kids in the house for flu season) having a fit that you're risking their lives.
Not just kids learning verbal skills and to speak with masks on, but non-verbal-receptive kids, who rely more on facial expressions and visual cues.
For them, it's a double-whammy, because you're delaying their speaking and language/ listening skills further, but also depriving them of the alternative forms of communication that they rely on to cope with the primary deficits.
And that's before getting into whatever effects decreased socialization overall will have.
[1]https://www.usatoday.com/story/news/nation/2020/03/24/covid-...
Many many many people are getting omicron and having relatively mild infections. It is not the same variant. That doesn't mean it's 100% safe, obviously, but it certainly appears to be safeer.
Nobody says that if they know what they’re talking about.
The phrase is actually “The plural of anecdote is not evidence”. You can Google it if you don’t believe me.
You can’t collect a couple of anecdote and pretend it’s data.
I always thought the phrase was "The plural of anecdote is not data" but it looks like that is a misquote:
Nelson W. Polsby PS, Vol. 17, No. 4. (Autumn, 1984), pp. 778-781. Pg. 779: Raymond Wolfinger’s brilliant aphorism “the plural of anecdote is data” never inspired a better or more skilled researcher.
I e-mailed Wolfinger last year and got the following response from him:
“I said ‘The plural of anecdote is data’ some time in the 1969-70 academic year while teaching a graduate seminar at Stanford. The occasion was a student’s dismissal of a simple factual statement–by another student or me–as a mere anecdote. The quotation was my rejoinder. Since then I have missed few opportunities to quote myself. The only appearance in print that I can remember is Nelson Polsby’s accurate quotation and attribution in an article in PS: Political Science and Politics in 1993; I believe it was in the first issue of the year.”
To add another link to the one Bumby has http://blog.danwin.com/don-t-forget-the-plural-of-anecdote-i...
which actually it makes a lot more sense to me this quote than the misquote because >You can’t collect a couple of anecdote and pretend it’s data.
sure but you can collect 10000 anecdotes, put them in a spreadsheet with some information about the people who said it, and suddenly you got data.
I mean that is basically what I thought to myself every time somebody said the plural of anecdote is not data but bit my tongue because not wanting to get into a war over it, and now I find out the original was actually exactly what I thought it should be.
Thanks munificent! Your name certainly applies for me.
"The plural of anecdote is not data."
The methodology of collecting anecdotes matters immensely. Merely collecting anecdotes based on whoever manages to comment in a random thread opens your collection process up to massive selection bias. In fact we even have a name for this process: a filter bubble. Every filter bubble in existence is the result of people assuming that not only is the population of their bubble representative of the average person (it's not), but also that people on either side of an issue will be equally likely to offer their anecdote (they're not).
Without rigorous collection methodology, anecdotes do not sum to data.
(Note that this comment says nothing about the broader topic of omicron severity; I don't have data, listen to people who do.)
But that's not as pithy.
> you can collect 10000 anecdotes, put them in a spreadsheet with some information about the people who said it, and suddenly you got data.
You can, but that's not a controlled study. The anecdotes can't be verified. So that data is not worth citing. I wish it were, which would make science a lot easier.
It's clearly a very systemic disease affecting almost all organs and long term effects are impossible to tell right now (as opposed to the vaccine btw which cannot by definition create random effects some years later).
This hasn't been shown at all, that study can only show that diabetes and Covid are correlated. There's no reason to think the causation doesn't run in the opposite direction.
> What are the implications for public health practice?
> The increased diabetes risk among persons aged <18 years following COVID-19 highlights the importance of COVID-19 prevention strategies in this age group, including vaccination for all eligible persons and chronic disease prevention and treatment.
• The only <18's they can track is those who get tested
• You're more likely to get tested if in the hospital or showing severe symptoms
• <18's rarely have serious symptoms unless they have comorbidities (e.g. obesity)
• Therefore, most <18's with COVID that they can track were already unhealthy prior to the disease and already more likely to be diagnosed with diabetes or another chronic health condition.
That study doesn't show Covid increases risk of diabetes, it shows that children hospitalized with Covid are more likely to develop diabetes. Two very different things.
Edit: Fixed formatting and spelling from posting on mobile
[1] https://vinayprasadmdmph.substack.com/p/does-covid19-cause-d...
> "Third, the present analyses lacked information on covariates that could have affected the association between COVID-19 and incident diabetes, including prediabetes, race/ethnicity, and obesity status."
It is also important to note that correlation is not causation and this study only observes a correlation in the data set which may very well disappear after controlling for the aforementioned factors.
In New York, hospitalizations among kids quadrupled.
In Washington DC, children’s hospital admissions have roughly doubled.
In Texas, children’s hospitalizations were described as “staggering”.
In Alabama, cases were “like a rocket ship”.
In Louisiana, one doctor said: “We’ve never seen anything like it.”
In Ohio, one associate professor of internal medicine and pediatrics critical care recently told ABC news: “We’re on fire.”
Source: https://www.theguardian.com/us-news/2022/jan/05/covid-hospit...
>> of course hospitalizations are going up
It's not the anecdotes that are scaring people, it's the number of kids in hospitals that's scaring people.
In New York, hospitalizations among everyone more than quadrupled. So if kids quadrupled, they are doing better than average:
https://coronavirus.health.ny.gov/daily-hospitalization-summ...
Quotes about staggering, flaming rocket ships aside, if you don't bother to normalize your data to a meaningful baseline rate, you're either not capable of objectively analyzing the situation, or you're trying to mislead.
(It's also worth pointing out that in NY, something like 40-50% of "Covid hospitalizations" are unrelated to Covid, per the state's own statistics [1])
[1] https://gothamist.com/news/new-preliminary-state-data-shows-...
https://www.aap.org/en/pages/2019-novel-coronavirus-covid-19...
Roll a 100-sided die enough times, and eventually you will get a LOT of ones.
https://coronavirus.jhu.edu/data/hospitalization-7-day-trend
It's an average, some are 55% some are 146%