The Panera Kingdom Problem

RE endlessly misinterpreted ONS by-dose-status all cause mortality stats.

The Panera Kingdom Office of National Statistics (ONS) attempts to track whether the Panera Timers are protecting citizens from death by “Covid-19” or otherwise influencing non-Covid-19 and overall death rate. They especially want to measure and compare short-term and long-term death rates…

It turns out that the task the ONS wishes to accomplish is impossible. The data they compile ends up making no sense, for what seems like as many reasons as there are citizens in the Panera lobby (40+ million).

The following post is in the category, “Into the Weeds of Government Data.” Reader beware. Also note that I am working with cellular data only, so formatting and some links will be appended to the online version of this post after the email sends out (click the title to visit the current online version).

The Background

Panera Kingdom

Panera Kingdom, also known as the UK ONS data set, is a mythical and definitely not copyright-infringing kingdom in which the entire population meets the following four conditions:

• They were counted in the 2011 England census.

• In the Public Health Data Asset.

• In the GP Patient Register.

• The are literally sitting in a giant Panera lobby. Millions of people, in one giant Panera lobby that literally really exists.

For this reason, the number of people believed not be given a Panera Timer (“Un-Timered”) in Panera Kingdom is accurate. So, for example, if someone was in the census but not the Registrar, it is more likely that they can get a timer or die without those events being formally tallied by the ONS data set; but they aren’t technically in Panera Kingdom to begin with, so they can’t mess up the counts. For this reason, the tallying of outcomes for the Un-Timered in the ONS data set is more accurate than the UKHSA data, as well as numerous comparable dashboards around the world. Panera Kingdom is a magical utopia of accurate death counting by Timer-having status.

For this reason, the stats for Panera Kingdom are incredibly confusing and deceptive all the time. We’ll examine why below.

Steve Kirsch

Has written a from-scratch analysis of the Panera Kingdom data set. His analysis was published in roughly three different forms; a before-email, at-email, and after-email version. The first contained a misinterpretation which was pointed out in the comments by others who have looked at the Panera Kingdom data previously; the second removed that erroneous analysis to leave a bare-bones outline of the original post; the third added a new analysis and some follow-up, resulting in the sprawling document that was online as of yesterday.

I admire Steve Kirsch. However, in this case he has merely repeated two common misinterpretations of the Panera Kingdom data. I have not visited the post since yesterday, and have not downloaded his annotated versions of the Panera Kingdom data. And in fact I have only read the portion of the third version in which Kirsch offered a rebuttal to the “straggler” critique. It was this rebuttal which contained the second misinterpretation.

Unglossed

Is an online journal which seeks, inter alia (among other junk), to skewer the simplistic, deceptive, and often cultish version of science propagated by Science Journalists and the mainstream media, which in the present era implies a frequent focus on SARS-CoV-2 and the injections against the same.

But I also take an adversarial approach to any memes within the counter-narrative that I find easy to debunk. The reason I attack these memes is because if they are not refuted by “our side,” they will instead be refuted by the MSM. Here I do not speak of any random creative theory, like 5G poisoning or what have you, but those which are purported to be supported by official research or statistics. These memes take over what is considered to be the most “high-credibility argument” against the MSM, pro-vaccine narrative. Even if they are actually comical junk science (OAS).

Our side’s investing of credibility in comical junk science (OAS) or easy-to-make misinterpretations (today’s subject) will restore the credibility of the MSM. It will, as I have warned before, “Make Fact Checking Accurate Again.” Unglossed opposes this.

So, apologies that in this case it befalls this journal to critique Kirsch’s analysis on the Panera Kingdom data.

You

Are somehow still reading this. Well, bravo.

Background complete.

The Panera Kingdom Experiment

Inside the literal, giant Panera lobby that comprises Panera Kingdom, no one has been up to the counter to get their First and Second Timers yet. (The Panera Timers are little coasters that vibrate when it is time to go back up to the counter; unlike in regular Paneras, they always go off a set amount of time after being received, at which point the citizen is eligible to go get the next Timer.) During breakfast, permission to do so is then granted to the citizens in the lobby by age (oldest first) and “vulnerability” status (“vulnerable” first). No one has to come get a Timer; but the first two Timers prove to be very popular in Panera Kingdom.

After everyone has been permitted to get their First and Second Timers, and some more time has passed, lunch begins; permission to get Third Timers is issued with the same classifications as before. Citizens, of course, further have to wait until their Second Timer goes off, which is six hours after they receive it. The Third Timers prove to be less popular.

The following assemblage of citizen-Timer-experiences eventually results:

• Some people don’t get a Timer (rare in older ages, uncommon in younger).

• Some people only get the First Timer (very rare in all ages).

• Some people only get the First and Second Timer (uncommon in all ages).

• Some people get all Three Timers (common or fairly common in all ages).

The Panera Kingdom Office of National Statistics (ONS), a very modern bureaucratic affair which reflects the ever-increasing modernization of the fledgling Panera Kingdom, attempts to track whether the Timers are protecting citizens from death by “Covid-19” or otherwise influencing non-Covid-19 and overall death rate. They especially want to measure and compare short-term and long-term death rates. (You might think it’s weird to die in a Panera lobby, but you know what they say, “different strokes for different folks.”)

It turns out that the task the ONS wishes to accomplish is impossible. The data they compile ends up making no sense, for what seems like as many reasons as there are citizens in the Panera lobby (40+ million).

The Un-Timered / Just-First-Timered Problem

Even though the vulnerable are prioritized for Timers, it is found that the Just-First-Timered die much less than the normal background rate for their age, especially among the elderly.

The problem here is that when the elderly are dying, it is not usually sudden. At any moment, you can walk around the Panera lobby and predict the elderly that are about to die, because often enough, they are literally on their death-bed. These do not get up to go to the counter and get their First Timer. The result is that the just-First-Timered group has a much less-than-normal death rate, because the normal death rate is driven by individuals on their death bed at any given moment. These individuals are not motivated to get a Timer.

A wrinkle, here, is that the staff in Panera Kingdom are friendly. They will walk Timers out to those in the Care Ward of the Panera lobby. However, it at least seems that they also tend to select against walking Timers out to the visibly near-death; and there is language in the Panera Kingdom Timer-use guidelines that supports or at least doesn’t rule out this conclusion. (https://www.bmj.com/content/372/bmj.n421).

This problem is the one featured in the first (pre-email) version of Kirsch’s post. It has been discussed previously by myself as well as by “Bartram,” who writes at Bartram’s Folly.

Subproblem

This becomes less and less true in younger age sets. By the time the ONS is tracking individuals under ~45, for example, deaths are more random, and the “about-to-die” walk up to get their Timers, because they don’t know they are about to die.

But generally, in every age group but the under-30s, there is a “not-on-deathbed-bias” that makes just having gotten a First Timer appear to confer magic health benefits in the ONS data set!

Likewise, it appears that after a few minutes, the mere declaration of permission to get a First Timer “makes” the Un-Timered suddenly incredibly less healthy.

This bizarre result leads to observer suspicion that the ONS is not recording events in correct order. For example, it was proposed by Norman Fenton that what is occurring is that the Panera Kingdom ONS is recording deaths that occur just after citizens get their Timer as occurring when they are still Un-Timered. The result would be that deaths caused by the Timers, even those counted as “Covid-19” deaths, might explain the increase in Un-Timered Deaths in the ONS data. However, this is unlikely to be the case, as exactly the same delay should take place when the First Timer goes off, at 21 minutes (in younger group that primarily received mRNA Timers): Deaths that occur just after receipt of the Second-Timer should then be mis-counted as “First Timer <21 Minutes,” with the result that the First Timer <21 Minutes death rate in any given age group does not look lower-than-background; it should be only Second Timer < 21 Minutes that “collects” the benefit of the alleged time lag. Fenton and Co. have since amended their theory to allege that a time lag or “variety of factors” is resulting in the weird ONS data. Put simply, I think a “not-deathbed-bias” is far more likely.

Subproblem

If not distinguishing deaths by age, or if lumping the truly young with the middle-aged, this problem is overwritten by an entirely different problem, which produces variable results over time:

When permission to get First Timers is issued to the elderly, the overall death rate for Just-First-Timered in Panera Kingdom looks much higher than the overall death rate for the Un-Timered. This is because the first is elderly and vulnerable, the other is younger and healthier. So in the all-ages group, it looks like just having gotten a First Timer is incredibly detrimental to health when breakfast starts, and then either neutral or incredibly beneficial to health near the end of breakfast (when the younger begin to be Just-First-Timered, but are also still more likely to be Un-Timered, so it’s a bit of a wash). The first is totally opposite the apparent effect in the by-age view; the latter less so.

So the ONS all-age all-cause-deaths data just seems to jump around all over the place throughout the entire day!

Subproblem(s and subproblems and subproblems)

ONS-recorded “Covid-19” deaths, which are just deaths preceded by a positive test for the virus, recycle some of these biases and not others.

Because the measurement (the ONS data) doesn’t know if it is distorting Covid-19 deaths as well, we can’t truly know if the Timers protect against Covid-19 (the thing the data wants to measure).

All we know is that if we assume that the Timers are just incredibly, marvelously protective against dying from Covid-19, all sorts of examples of how the data is confusing the issue can be given. Maybe the on-the-deathbed are just coincidentally getting a false-positive test for the virus that causes Covid-19, rather than dying from Covid-19. Maybe not. But both are unlikely to walk up to get a First (or any) Timer! And once again, this is less true in younger age groups. It’s a profound, gigantic mess.

Subproblem(s and subproblems and subproblems)

Not only is protection from the Timers semi-impossible to measure, so are negative effects of the Timer on health. Again, we can assume that the Timers are incredibly, marvelously deadly, and still come up with more subproblems:

In the oldest groups, the ONS still observes a protective effect, so it would appear that the “not-deathbed-bias” is overriding our assumed Timer-deadliness.

In very youngest-group, Timers are not as popular. So we can’t be certain that the fact that the Just-First-Timered suddenly start to die more than the Un-Timered in these age groups (under 30) is capturing the effect of the Timers, or reflecting the prioritization of the vulnerable. After all, our assumption leaves open the question of how quickly the incredible, marvelous deadliness of the Timers results in death. What if most recipients die after the Timer goes off?

Can we use the ONS’s distinguishing of short and long term post-Timer death rates to figure this out?

Lol, no.

The Holding-a-Timer-That-Went-Off problem

The ONS separately tracks deaths for citizens who are still holding their Timer after it goes off. The “Just-First-Timered” group, regardless of when in the day they came up to get their timer, has only been holding their Timer for 21 minutes. Then (Pfizer Timers) or another hour later (Astra-Zeneca) the First Timer goes off, at which point they are meant to come up to the counter to get their Second Timer, so they can enter the Just-Second-Timered data set.

But, for some reason, not everyone who gets a First-Timer goes up to get a Second Timer when it goes off.

What do we know, from the first round, can cause Panera Kingdom citizens not to go up to get their timer?

Being on their death-bed.

This occurs whenever permission for any Timer is given for any group, or when Timers go off (at which point citizens may come up to the counter and get another Timer).

Thus, when the ONS compiles deaths for citizens whose First Timer has already or might have already gone off (First Timer > 21 Minutes) it looks totally different than the rates for Just-First-Timered (First Timer < 21 Minutes)! Instead it looks like First Timers confer massive health benefits for 21 Minutes, and then become profoundly deadly.

Likewise, it looks like Second Timers seem health-conferring for 21 Minutes, don’t seem to do much to change health after 21 Minutes, but all the sudden, at 6 Hours, become profoundly deadly.

What happens at 6 hours?

The Second Timer “goes off”: Citizens of Panera Kingdom who have received a Second Timer become eligible to come up and get their Third Timer. This all occurs, in general, after lunch is declared, and Third Timers are approved. The ONS data for “Second Timer > 6 Months” is always showing citizens who are eligible for a Third Timer but don’t come up to get it. At certain parts of the day (right after lunch is declared), the data for this group can reflect a preference to get Timers “early” relative to permission, but at later parts of the day it doesn’t really reflect this at all.

Mostly, being in the “Second Timer > 6 Months” group only reflects “having a Second Timer after it has gone off” (not going up to get the Third Timer); not so much how “early” one went up to get the Second Timer in the first place.

So, both the “First Timer >21 Minutes” and “Second Timer > 6 Hours” group are by definition stragglers.

Rebutting this critique of his analysis of the Second Timer > 6 Hours group is the mistake made in the third (after email) version of Kirsch’s post. This critique is valid.

(Not So Much of a) Subproblem

This critique remains valid even if we assume, once again, an incredible negative health impact from the Timers.

In examining the Just-First-Timered problem, we set aside the question of what, besides age, “vulnerability,” and “deathbed,” can lead a citizen of Panera Kingdom not to come up for the First Timer. As said, the Timers are very popular, but not mandatory. Citizens did not get a Timer if they didn’t want one. It might be true that the generally unhealthy (not just on deathbed), who may be less interested in “seeking health,” are less interested in the Timers (which are believed by Panera Kingdom citizens to be pro-health); but it may also be the case that the less interested to “seek health” are unlikely to be in Panera Kingdom to begin with, because they aren’t on the GP Patient Register. Or the opposite could be true. Or both. It’s a mess. The ONS data has no way of telling us how badly this mess distorts the death rates.

This mess doesn’t recycle to the Second and Third Timers. And so, the “stragglers” who don’t go up to get new Timers after their current Timers go off are people who “liked Timers” when they were first given permission.

What is something that can make someone stop liking Timers?

Having a massive adverse reaction to a Timer!

And so “stragglers,” in other words, might be all the citizens who are on their deathbeds because of the Timers, and thus do not go up to get a new Timer when the current ones go off.

So it could be that negative health impacts from the Timer are driving deaths in the ONS “Second Timer > 6 Hours” group; those impacts still wouldn’t be driving the deaths rate. The rate is a product of the denominator of “Person-Time spent holding a expired Second Timer;” and this denominator is primarily defined by the citizens who do get a Third Timer. However, as the Timers become less popular, this effect dwindles.

Given the above, it is more important to look at the absolute count of deaths in these groups. Do they seem high? If not, we can probably disregard the rate. If yes, then we need to find a way to appraise whether the “real rate” is high that doesn’t rely solely on the way the ONS classifies citizens. Explicitly:

We need to re-add the Third-Timered to the “Second Timer > 6 Hours” group, and both the Third-Timered and Second-Timered to the “First Timer > 21 Minutes” groups, in terms of both deaths and person-time.

Feel free to do so. The ONS is going to just keep updating their data after you. Panera Kingdom is just going to keep giving permission for new Timers. Sorting out the truth from the incredible and ever-evolving mess of the Panera Kingdom ONS statistics would be a labor of love.

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