Why don’t you address the actual comments I made to you on twitter and on your substack (linked below) instead of the critiques you somehow attribute to me even though I never made those comments to you or voiced those critiques?
Here I'll link to the twitter comments for your convenience for you to address (or you could respond to my cri…
Why don’t you address the actual comments I made to you on twitter and on your substack (linked below) instead of the critiques you somehow attribute to me even though I never made those comments to you or voiced those critiques?
Here I'll link to the twitter comments for your convenience for you to address (or you could respond to my critiques I actually posted on this substack this morning that you have not addressed)
And in case it is too much trouble to go click on the links to my other tweets, here I'll paraphrase the critiques I actually made:
1. The two example facilities you focused on were facilities where Covid infected individuals were sent when released from the hospital, not long term residential facilities, which you could have easily seen had you just googled the name of the facilities from the database you analyzed.
This explains the anomalies in the other thread that you claim are clear evidence of vaccine deaths, including low case counts, high death counts, and a rapid turnover weekly with dead residents quickly replaced with new residents.
It would be honest for you to admit it was a mistake for you to highlight those two examples, and an oversight for you not to google the facility since you had their name to see what type of facility they were.
How many other facilities in your analyses are like these, which would of course have numerous Covid deaths out of proportion to Covid cases given many whose positive tests were at some other place not captured in this database but their deaths captured in this data base?
Btw it is not only facilities with more deaths than cases affected by this blatant bias but any facility for which people are transferred with severe Covid that have many Covid deaths for which the cases were not counted.
2. The other major flaw I pointed out was your choice of "IFR" as endpoint, which cannot be validly computed from this data base given its limitations and characteristics.
No legitimate scientific paper would estimate IFR like you do here because of the data limitations and issues pointed out above -- since you clearly are not getting measures of cases and deaths on the same cohort of people — only the subset of cases actually obtained at the facility were captured in the data set, and the covid deaths were not limited to this subset of measured cases but includes covid deaths from cases occurring outside the facilities.
It is simply not possible to get a reasonable estimate of IFR (or more precisely you should really call it CFR) from the data in these database.
If a paper used the endpoint IFR defined as you did, computed from a database with these limitations, any peer review process would reject the paper outright because of the fact that you cannot get a valid measurement of that endpoint from data like these.
And this bias is not constant over time -- it is especially evident during times of massive covid surges in which many severe cases moved into the nursing homes -- precisely as happened in January 2021 at many locations in the USA.
3. There are many other flaws in your analyses I could critique, but I have not mentioned or explained them to you
You’re completely ignoring the comparison data among the same same facilities and then you went down rabbit holes that have nothing to do with the conclusion. Why not take the time to research the data yourself? If you find it doesn’t match Steve’s conclusion perhaps he will discuss it with you.
Even if what he says is true... they brought infected dying patients into nursing homes while the healthy population had to be locked down? This is the argument? Incredible
Why don’t you address the actual comments I made to you on twitter and on your substack (linked below) instead of the critiques you somehow attribute to me even though I never made those comments to you or voiced those critiques?
Here I'll link to the twitter comments for your convenience for you to address (or you could respond to my critiques I actually posted on this substack this morning that you have not addressed)
https://twitter.com/jsm2334/status/1693604309013872719?s=20
https://twitter.com/jsm2334/status/1693605539266105813?s=20
https://twitter.com/jsm2334/status/1693723767216701878?s=20
And in case it is too much trouble to go click on the links to my other tweets, here I'll paraphrase the critiques I actually made:
1. The two example facilities you focused on were facilities where Covid infected individuals were sent when released from the hospital, not long term residential facilities, which you could have easily seen had you just googled the name of the facilities from the database you analyzed.
This explains the anomalies in the other thread that you claim are clear evidence of vaccine deaths, including low case counts, high death counts, and a rapid turnover weekly with dead residents quickly replaced with new residents.
It would be honest for you to admit it was a mistake for you to highlight those two examples, and an oversight for you not to google the facility since you had their name to see what type of facility they were.
How many other facilities in your analyses are like these, which would of course have numerous Covid deaths out of proportion to Covid cases given many whose positive tests were at some other place not captured in this database but their deaths captured in this data base?
Btw it is not only facilities with more deaths than cases affected by this blatant bias but any facility for which people are transferred with severe Covid that have many Covid deaths for which the cases were not counted.
2. The other major flaw I pointed out was your choice of "IFR" as endpoint, which cannot be validly computed from this data base given its limitations and characteristics.
No legitimate scientific paper would estimate IFR like you do here because of the data limitations and issues pointed out above -- since you clearly are not getting measures of cases and deaths on the same cohort of people — only the subset of cases actually obtained at the facility were captured in the data set, and the covid deaths were not limited to this subset of measured cases but includes covid deaths from cases occurring outside the facilities.
It is simply not possible to get a reasonable estimate of IFR (or more precisely you should really call it CFR) from the data in these database.
If a paper used the endpoint IFR defined as you did, computed from a database with these limitations, any peer review process would reject the paper outright because of the fact that you cannot get a valid measurement of that endpoint from data like these.
And this bias is not constant over time -- it is especially evident during times of massive covid surges in which many severe cases moved into the nursing homes -- precisely as happened in January 2021 at many locations in the USA.
3. There are many other flaws in your analyses I could critique, but I have not mentioned or explained them to you
You’re completely ignoring the comparison data among the same same facilities and then you went down rabbit holes that have nothing to do with the conclusion. Why not take the time to research the data yourself? If you find it doesn’t match Steve’s conclusion perhaps he will discuss it with you.
Even if what he says is true... they brought infected dying patients into nursing homes while the healthy population had to be locked down? This is the argument? Incredible