el gato malo just released another analysis using table 5 which is monthly, separated by male and female and uses ONS data only which means it has the person year calculations. He also tried is best to look at <21 days post dose which is stymied by the "<3" deaths numbers for all but the older groups. A first read through, it makes a strong case although healthy or unhealthy user bias is not able to be addressed by any public data.
Yes, looks like his graphs match per-person-year, either the raw or the ONS-adjusted. Would have been great if he showed the raw numbers so I didn't have to dive into those sheets to verify. (*edit: I see it was briskly specified in the first bullet. It's still easier for me to see numbers for myself.)
This post once again reflects unfamiliarity with the work already done on the ONS data. It is like a greatest-hits compilation of mistakes others have already made many times.
He starts with the 1st / 2nd >21 days razzle-dazzle. This group has always had high death rates because people close to death stop getting more injections. The real number for 1st / 2nd 21 days is the 3rd dose >21 days group, because that is where most 1st/2nd dosers go in the end.
Then, by focusing exclusively on May, he razzle-dazzles the artifact in the 40 year-olds which is a product of fluke low unvaccinated deaths for that month (34 deaths instead of trend of 120 to 50) and the bad month in male vaccinated 80/90+ year-olds. It is only that last thing that is interesting, but we have no idea if it was the beginning of a trend (hence the data freeze) or just another fluke.
Lastly he razzle-dazzles with the real-time <21 day rates. Which for may are totally aberrant, don't reflect any earlier months. This is easily visible in the whole period results on Table 6.
The whole reason Fenton suspected a lag in this set was because at first they were extremely low. Ok, so now in May they are high. But when did most people get dosed (earlier months, when the <21 values were consistently low)? The results for May don't retroactively change what happened for all those earlier people. And who are these few thousand weird people that put off getting injected for so long? What is different about them from the earlier people? Did something change about getting an injection in May or, more likely, was it that different people were getting injections than most recipients.
So like I said in my post most people tackling the ONS sheets just get tripped up on the buckets which have weird data artifacts, no one can show lower acm when the buckets are combined. He's just repeating old mistakes.
Brian, I'm sorry, could you (or anyone) please explain a bit more about what exactly "combining the buckets" means? (I read the Panera article too, it's very interesting, but still not sure that I understand what exactly you mean by "combining the buckets")
For example, consider we have monthly data with the number of deaths, separately for unvaccinated, 1 dose, 2 doses, 3+ doses. We know the number of people in each category (in the ideal case, at least). The "conventional" way of calculating relative risk based on this data is obvious (separately for each vaccinated category, just from the number of deaths and population counts for each such category, compared with the same for unvaccinated).
Does "combining the buckets" apply to such data/approach? If so, how exactly would it change the calculations? How exactly should we combine the vaccinated categories? Or is it only applicable to some other kind of data (like person-years)?
Working from your second paragraph, what is important here is that people who have 2 doses also had 1 dose, and people who have 3 also had both 2 and 1. So for any case where you want to know the "real" 1 dose all-cause mortality rate ("how often are people who had at least 1 dose dying") you need to add in the 2 and 3 dose-havers. Same if you want to know the "real" 2 dose rates, you need to add in the 3 dose-havers.
It might seem like separating is a good idea to compare "performance," but what really happens is the people who stopped at just 1 or 2 doses will include lots of people who had a bad reaction to the shots or otherwise a serious health downturn that led to a change in previous medical behavior (getting shots). These are your die-ers. For the 1st dose >21 days group this effect will be particularly extreme.
And so is that itself still a big huge story? Well, it might be if the absolute death counts were very high in this group. But they aren't. By definition, since when you add those deaths back in to the "real" 1 dose >21 rate (combine the buckets) the rate is low.
In terms of harms I do think disabilities are likely to prove a much more telling statistic (than deaths) and not enough attention is being paid to them.
The reasons are obvious: the sheer numbers dwarf deaths, the vax status is (relatively) incorruptible, the person is alive for verification and further investigation and the person has motivation to tell the truth about their injury (welfare programs) and the likely cause (blame, possibly litigation in the future).
Obviously at present there isn’t much salience out there in respect of the vaccines being linked to disability, but I’m sure it will come. Notwithstanding that, just looking at the sheer numbers registering as disabled looks revealing.
What’s nice about death is that it’s objective, though only at the primary level. So the disability starts have indeed skyrocketed in UK and US but does this mean there are actually more disabled people, or just people in lists that say disabled? And there are multiple common elements beyond injections to explain the trend being international (ie incentivized unemployment). And I think that’s why no one does any work on it, either. But yes, both the nature of the rise and the involvement of vaccines could be clarified by actually following up with people.
You raise some good points, but (here in the UK at least) there is at least some gatekeeping around who gets classed as disabled since this is linked to welfare payments.
I computed vaccinated death rate/unvaccinated death rate using table 2 for May 2022 (the year that looked worst in egm's analysis. I got ~1.45 for 18-49, 0.9 for 50-59, 0.81 for 60-69, 0.84 for 70-79, 1.04 for 80-89 and 2.81 for 90+. I presume you would claim that I am seeing unhealthy user bias for those under 50 and healthy use bias for 50-79. But what is happening for 80-89 and 90+. They are nearly universally vaccinated and maybe the very small unvaccinated 90+ group consists of mostly the healthiest folks in that bracket. Afterall, those who weren't vaccinated because they were near end of life in early 2021 would have been long-since dead.
This does make some sense, but my question centers around the fact that the essential vaccination rate is higher in all age brackets for table 2, effectively increasing the vaxxed denominator and dereasing the unvaxxed denonimator, usually substantially as a percentage basis because it is already small.
Why should I trust one set of vaccination numbers over the other? Does table 2 represent a clear set of people who are in the country at the time and whose medical history including vaccination and death is clearly tracked even if it isn't everybody. Or are the unvaccinated undercounted for some reason. Perhaps to put it another way, does table 2 represent a clear set of individuals who were either vaccinated or not prior to that month (or who spent part of the month in each category) and who either died or did not?
"The data presented this week is the provisional proportion of living people resident in England who had received COVID-19 vaccinations. Individuals vaccinated in England who have a registered address outside of England or where their address, age, or sex is unknown have been excluded. Due to changes in GP practice lists, in order to include newly registered patients and remove those who are no longer resident, there will be slight variation to the figures to reflect those who are currently resident in England."
So it seems to be a relatively current data set based on the people currently residing in England.
So the true question becomes, "Which dataset contains a more accurate representation of the unvaccinated population?" Since the unvaccinated population is small, it is much more important to measure it accurately. I'm not convinced that the very high vaccination rates in table 2 are accurate. Although, if vaccination numbers do respresent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset.
Finally, the significant change in relative death rates from January to June adds even more complexity to the issue.
A good night's sleep hopefully will help me organize this better. Here are my points and questions:
1. As you point out, table 2 is the subset of the population that appear in all of several different data sets. I am still not convinced that it shows a consistent populations in which every person's vaccination status and death or lack thereof is known. Can you point me to information that might convince me one way or another?
2. Table 8 seems to include the entire population as the total monthly deaths seems to reasonably match the expected values for the entire country. I haven't taken the step to find independent monthly death data to verify, though. That's still on my list.
3. As I point out above, egm gets his vaccination status data from what seems to be a consistent, well-done population wide vaccination status database that accounts for those who reside outside England and checks against recent contact with the health care system. It shows lower vaccination rates than percentages computed from the person-years column in table 2. This is why his numbers suggest the vaccine is killing lots of people - larger denominator for the unvaxxed and smaller for the vaxxed while proportion of deaths in each category is more or less the same between the two tables.
4. When vaccination rate is high, which is true in all but the youngest age group and is especially true in the oldest groups, small differences in vaccination rate make a big difference in the computation.
5. You mentioned that table 8 suffers from not knowing about deaths of people who left the country, but the description of egm's vaccination data set suggests that at least the vax rate part of the computation does. Second, the deaths seem to be every or nearly every death in the country (I know, what if vaccination data includes people not in the country?). Third, the relative proportion of missed deaths in the unvaxxed group would have to be larger than the proportion missed in the vaxxed group for his numbers to be wrong in the direction they were wrong. I can't think of any reason a larger proportion of unvaxxed than vaxxed people would leave the country or otherwise hide from the death surveillance system before they died. If the proportions were similar, the results would not change in a meaningful way.
Numbers are identical in the most recent and June version of the sheet. So table 8 seems to reflect 90% of deaths. So you still probably don't want an outside denominator - you want to know who's actually being tracked by Table 8.
RE 3, the problem with the UKHSA unvaccinated rates is that they were also imputed into the "too good to be true" Covid reports from late 2021 to whenever they stopped. I stopped posting about UKHSA results in October 2021, it was too much of an outlier with reports from other places. Eventually, other places did match the UKHSA but they were also, always, highly vaccinated "islands" like the Iceland Dashboard or NY state databases. And so I named this suspicion of out-of-system "immortal unvaccinated people" the "Iceland Dashboard Bias" https://unglossed.substack.com/p/the-ny-kids-paper#footnote-4-49567294
This dovetails with 5. The issue is that if people are traveling, moved, died without being recorded, whatever, no events (vaccination or infection) can be recorded for them. They are immortal unvaccinated people. Having a vaccine record in the system (whatever the system in question is) is like saying "here" in attendance at the beginning of class. So when you compare rates of "getting test questions wrong" in the class, you have a problem where one of the groups didn't say "here" and you don't know how many are absent.
"using table 2 for May 2022 ... ~1.45 for 18-49 .... 2.81 for 90+"
The 18-39 group has always been an unfortunate batch, as your unvaccinated end up are a bit younger (though not to an extreme in the UK). Table 6 has five-year age groups (whole period) and doesn't replicate the signal except in the teens. Or didn't as of last year. To see if the real-time trend got worse in these groups you would have to collate different versions of the spreadsheet. Maybe it did - after all it's not at all implausible that vaccine deaths would start to show up in the "cleaner" younger age groups.
In may I get 15.5/17.8 for unvaccinated/any-dosed deaths per person-year for 90+ in table 2, May 2022. Maybe one of us has a number in the wrong place. So here, the unvaccinated are doing better, as you say there shouldn't be any "deathbed vaccine abstainers" left at this point. Of the ~6786 any-dose deaths, very few are listed as involving "Covid-19." So maybe this was a vaccine deaths bump.
It's certainly entirely possible that the ONS stopped updating because the threads were coming loose at exactly this point.
Regarding the UKHSA vaccine breakdown, that's good info about the process used to try to exclude people who leave England. I don't want to hinge my case entirely on the accuracy of that graph in reflecting the real-world, however. Instead it's just that we don't know if Table 8 is related to that set or not.
"Although, if vaccination numbers do represent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset." Right - as far as accuracy it doesn't matter if it's only a sample that doesn't reflect the real-world unvaccinated rate, the sampling insures accuracy (but also unhealthy user bias) in of itself.
Yes, I used the wrong box in computing the 90+ vaxxed death rate/unvaxxed death rate. It's 1.23, not 2.81.
I agree, 18-39 is somewhat age confounded because vax rates increase somewhat by age while typical death rates increase much more over that range.
"It's certainly entirely possible that the ONS stopped updating because the threads were coming loose at exactly this point." - We've certainly seen this before in other ONS data series! I also can't say that I totally trust ONS not to massage data at least a bit in favor of the narrative before releasing, although the good old USA is much, much more willing to do that.
Have you analyzed Igor Chudov's analysis of excess mortality by country and vaccination rate and by UK deprivation quintile and vaccination rate? Those weren't able to fully separate vaxxed from unvaxxed, but they do point to something, at least if there aren't some devilish confounders hiding somewhere.
Finally, my second cousin's 15-year old son collapsed and died on Monday. The first death in my mom's family under age 40 since her uncle died at age 13 in 1913 from misdiagnosed appendicitis more than 25 years before mom was born. Terribly sad and angering, but it might finally change the mind of my 5-dose brother, sister-in-law, aunt and uncle. The only death not much above 40 was her type-I diabetic first cousin.
I don't know, but I bet that sudden death in 15-year olds, especially boys, is an excellent predictor of injection status these days. I might know eventually.
Are you aware of this substack by Norman Fenton who is a maths professor from Queen Mary’s London specialising in risk assessment and statistics. He also has his own website normanfenton.com I remember him going on in the past that it’s very difficult to interpret the U.K. results as the ONS and NIMS have different numbers of population and unvaccinated. I can’t point you to a specific post but I’m sure you’d find all his analyses worthwhile.
I am sure Fenton is astronomically more skilled at math than me. But the ONS critique has always been logically flawed.
The thing about the ONS data set is that it just says "here's who's in our set," counts their days spent in whichever of the vax status buckets, counts their deaths (with bucket status at death), then shows you how the buckets look as far as days-spent and deaths. It's not that wobbly.
So imagine you have a mailing list with 30 people, and you track 1) days spent after you email them and 2) emails they send to you, at which point they stop counting for days. So you have a bucket for "<21 days after I send email one" and ">21 days days after I send email one (but no second email yet)."
Fenton thinks you are counting "<21 days after I send email one" replies in your "0 email" (ie counting just-dosed deaths in unvaccinated) bucket. But since the UK used a long interval for the initial injection roll-out, you should also see the same mis-count piling just-second-emailed replies into your ">21 days after I send email one." And then you show Fenton that that bucket, while it looks scary in a per-day email basis, has basically next-to-zero replies (deaths) in it. Like the ONS >21 days post-dose-1 category drops a few dozen deaths a month across all age groups. These are where lagged post-2nd dose deaths *would* drop. This means that if there is a lag, it would only shift a few dozen deaths between the unvaccinated and <21 days post-dose-1 group per month and wouldn't flip the latter to being less than the former.
I like your category (in the referenced post at the top), "Into the Weeds of Government Data". I used to venture into those weeds regularly, although thankfully that job ended nine years ago, never to return. It was federal and California public health data, but it seems that government data is government data. Lots of aggregated data, no de-identified raw (too sensitive to release for some reason). Having to guess the unique keys. Asking questions of the aggregate that can't be answered.
I didn't follow your analysis in great detail because it reminded me too much of working with government data, and my head started to hurt like it used to. But "Viola"? OK, the edited version says "voilà" or something close to that. But now I have MST3K to ponder. Making progress, I think. Something about "riffing"?
Oh my God. The MST3K bit was supposed to be dropped in my final revision of that footnote before publishing (after I found that a Google search doesn't pull a reference so it would confuse people who don't remember the scene off-hand). Substack is doing this thing where the 2nd-or-so oldest draft overwrites the version you publish, it is getting crazy!
OK, that's clearer now. :) I think. I didn't know about the overwrite issue, but I have seen peculiar things happening with it lately. I previously backed up my own blog posts elsewhere, and I was planning to resume that practice. Now I definitely will. Thank you!
As someone who wrote several posts on mortality, I must note something that we should all be aware of.
It is not directly related to this Brian's post but I thought I can express my feelings here.
ANALYZING MORTALITY IN-DEPTH IS VERY DIFFICULT.
They invented randomized trials partly to bypass these difficulties.
For example, let's say that a fat, triple boosted guy named Bob dies a month after having his third COVID.
Having this person in mortality statistics, and having his demographic characteristics, can we answer a simple question: what killed Bob? Fatness? Covid? Vaccines? Cholesterol? Or something else?
Some people allege a healthy user bias. Some people allege unhealthy user bias. etc etc
It is not very easy. It is also not very easy to do causality properly on the population level.
This is why most respectable nations have demographics institutes, full of statisticians, MDs etc etc.
I could, for example, do a regression of "deaths" by "boosters", note a positive relationship and write about "association". Yeah, there is an association.
But how much proof do we have? I tried hard to find "proof" beyond correlations and sometimes, I believe I found other convincing statistical things, like having similar slopes in UK deprivation quartiles and worldwide country level statistics. That increases our strong suspicions about vaccines.
But proof "beyond reasonable doubt" may remain elusive. So many confounders can be alleged.
We can only prove something by analyzing individual level data on millions of people, and hopefully doing some targeted interventions.
The data is not available, the authorities are ignoring excess mortality and discontinuing their reports.
This leaves us wondering, naturally, if something nefarious is going on.
And something nefarious IS going on because ignoring excess mortality IS nefarious by definition.
quite, https://doi.org/10.1093/ije/dyac204 . The authors conclude, 'We document substantial heterogeneity and uncertainty in estimates of excess mortality. All estimates should be taken with caution in their interpretation as they miss detailed account of demographics, such as changes in the age group populations over the study period.'
All very true. And I'm not even *against* people believing the data is all fake or might-as-well-be-fake. It doesn't bother me. This is a post-replication-crisis world, data isn't truth.
But math is still math and I can't even figure out what went on with this one...
I don't get good vibes from this one. Seems to be rebranding efforts to *reduce* "with not from" pneumonia death-tallies (and attribute deaths to underlying comorbidities) as evidence of efforts to *increase* "with not from" pneumonia death-tallies.
You’d need to read John Dee’s entire work not just the latest, he is a bit like Mathew Crawford in that regard, there is lots of it. I’ll see if I can find a good one that addresses all cause mortality (he has lot’s on that). He’s also in the process of analyzing Joel Smalley’s FOI data which should be interesting.
I agree John Dee does good work. He does not seem to be pushing any narrative other than the data has never shown an OMG hair-on-fire pandemic, so let's shut down society.
Apart from that, he just goes digging in the data looking for signals, correlations, etc.
Excluding unvaxxed is good as long as you also exclude their deaths. That's better than counting them when you can't see their deaths (because they died off-record a long time ago; because they moved or have been traveling; etc.). You want your "control" group to truly be in contact with the system. So that is why the ONS data is pretty good; but what it is good at is showing a very biased distribution of injection.
It's likely close to that. Again, this is good, as long as I don't count your injection or death (which by definition I will not, since you do not meet the qualification based on prior engagement). It will be more like a recruitment-based test-control trail then a true population monitoring system. So you have biases but you also have a very sturdy person-years denominator. I think egm doesn't get that the ONS data has these exclusions and that is why he didn't grasp that the person-years column is the denominator.
"The Office for National Statistics (ONS) vaccine mortality surveillance reports (the latest being are based on a subset of 39 million England residents that excludes all those not registered with a GP and not registered in the 2011 census."
I think you’ve remembered correctly, John Dee (and Fenton) try to take that all into account and make adjustments for it. That’s why they can’t say that they’re correct with a high degree of certainty because they don’t have access to all the available data they’d need to be more certain so can only speculate based on the data available.
This commentary is sadly above my pay grade:). But it concerns me (and at the same time makes me hopeful) that you are calling out EGM for confirming my biases which are most depressing...his reach seems huge...and if we’re repeating false info that’s concerning. For me it’s easy...NO MORE MANDATES EVER!! Have you reached out to him for clarification?
So I think he's just a content generator. "Here's a post with charts saying that what you readers already want to think is proven once again." Ok then...
I've learnt a great deal from EGM's posts but have also noticed a lack of willingness to address criticisms. My own example is the decline in births issue. While I am not saying I was correct (https://lostintranslations.substack.com/p/the-wedding-crasher), the way EGM completely ignored and continues to ignore the pandemic collapse in weddings was really puzzling to me.
Also, I am based in Europe and notice that sometimes the American substackers often jump to conclusions from data (often simply scraping from World in Data) when the subtelties of the regional data are much more complex.
Thanks again for your posts, Brian, they are really providing a valuable bulwark against the confirmation bias that plagues both camps.
I guess the reason I am not dead yet despite being unvaxxed is that I am not obese. After all, the fake President told me that as an unvaxxed person I was going to die last winter or and the latest this winter (although, this winter is not over yet.)
Maybe he skipped over the bit on his teleprompter where it said "you fat fucks are gonna die this winter if you are not vaxxed!"
Thanks for that link. I hope and pray he’s right. And he should do the debate with Steve or Brett. Ive come to consider myself a “crack pot anti-vaxxer” and I’d like to not be one...
That actually looks like a promising analysis. As for his "not knowing anyone who had a problem after injection" claim, call me skeptical. The disconnect here is that I think it's correct that the excess mortality in 2021 is being driven by the virus, not the vaccine. But also it's not normal for young adults to just drop dead every week.
And a lot of biological processes are exponential. I think we are on the verge of a ramp-up in the sudden deaths rate. Like a ball rolling toward the edge of a hill.
Out of interest, what makes you think the sudden death rate will be on the ramp-up? And by what mechanism do you think this is most likely to occur due to? Tolerance derived cardiac distraction? Subclinical myocarditis? Other?
Mostly it is just how I intuit the situation. It is a bit like how a car goes from looking new to looking old. The myocarditis is like a big dent that shortens the time before other smaller insults manifest in "old" ness. (But maybe immediately afterward the car still looks "new" overall.) Animals are big systems comprised of all these same cofactors, which is why they can't live forever. But hearts can't be replaced like bumpers. I mean they can but it doesn't make for quite the same solution to the problem.
Tolerance cardiac destruction is a question mark. If there is a wave of sudden deaths in the coming three months that then subsides, then that will look like tolerance. Of course it would be much better if someone just looked for spike and virus in autopsies. The recent German sudden deaths autopsies study dropped the ball on that aspect.
With regard to ‘hearts can’t be replaced’; we do now live in a post-mRNA-injectable world don’t forget. I’m sure the good folks at Pfizer could create a new product that instructs the body to produce new heart cells! We could call it a vaccine against SADS. YOU HEARD IT HERE FIRST
I'm sure you're aware that Ed Dowd looks at the actuary data from the insurance industry, and not government data, in his excess deaths analyses. I can't comment on the accuracy of his statistical analysis, but I thought insurance actuary data was probably more accurate than government excess death data. What do you think Brian?
As long as both use their prior rates as a benchmark, "accuracy" shouldn't matter too much, though maybe a little. Like how you could use jewelry store sales to measure a recession. But maybe using Walmart sales wouldn't show the same drop in spending because people still go to a Walmart. In this case the insurance data might be more like the jewelry store and not see what the government sees as far as off-the-grid people who die all the time. Either way both should have something to offer.
Darn, I was writing a long response and somehow just lost it :(. I guess basically I wanted to say that how can we track much from the UK when they used midazolam at such a high frequency and failed to give antibiotics for secondary pneumonia creating a crazy pull forward effect and their vaccination schedule started with AZ, then J and J, then Pfizer and finally Moderna leading to a jumble of information?
I am not per se advocating for trust in the ONS data, though I think it's pretty consistent with healthy user bias and so it looks legit. But primarily I am showing that the ONS data doesn't show a mortality impact even if there almost certainly is one (as described in my reply to Shelly S below).
So when readers see posts about the ONS data claiming it shows an impact, I would advise caution. Maybe if they ever release results from after May 2022 that will change.
I haven't written on the "failed to use antibiotics" subject yet but I don't think that one holds water. On the other hand I wouldn't dismiss other avenues of medically spiking the death counts.
I think one of the biggest valid criticisms of the UK data is that the the UK authorities simply don't know how many people are in the UK! As a result the unvaxxed population is clearly underestimated because the default calculation is Total Pop - Vaxxed Pop = Unvaxxed Pop.
Note: I think this must also apply to states like New York, California, Texas, Florida, etc. which also have huge immigrant populations not necessarily accurately reflected in official data.
Yes, yes, very true RE NY and CA. There's also lots of moving out from both states in any post-2020 context.
The thing about ONS however is that it uses layered contacts. The people that are "in" PHDA are in it (they have had contact with all three of the 2011 Census, the General Practice Extraction Service (GPES), and the Hospital Episode Statistics (HES)). This means that the final two sets, injections and deaths, shouldn't be missing a lot of events for the peole "in" all 3 of those other sets. Person-years and deaths should be pretty accurate and of course any false "ins" would make the unvaccinated look better, not worse.
I always appreciate your straightforward and honest analysis! I guess I’m primarily concerned about the new classifications of death through new codes in the US system. I’m not good enough with the data to crunch anything but any new classifications that are suddenly born during a pandemic make me raise an eyebrow...
I've also been meaning to give that line a look. I find the way Ethical Skeptic just assumes-back a bunch of cancer deaths off-putting, but maybe there's still something there.
I’m defense of EGM, and myself, we’ve spent months trying to get clean all-cause mortality data from the US and UK bureaucracies and neither are willing to share their taxpayer-financed info with taxpayers. I’d be curious if anyone has a theory as to why.
But the ONS data is clean. It just doesn't get acknowledged by "our side" because it doesn't support worse mortality. This is probably just due to healthy user bias but it is what it is.
I disbelieve you. In BC Canada ACM started increasing fairly dramatically since the onset of the vax. In April of 2022 for example according to the government 51% were triple vaxed. Yet 76% of COVID deaths were of triple vaxed. That’s COVID. That isn’t ACM. 14% were never vaxed. Yet 7% of COVID deaths were of never vaxed. Your chances of dying from COVID Were 3x greater if you had been triple vaxed than not vaxed. You say there isn’t yet a smoking gun!! They are all over the place. In every government data set that comes out. Let me ask you a question. I’m assuming you are vaxed. Knowing what you know now, would you do it again? Knowing what you know now, will you get any more boosters? Knowing what you know now would you fear COVID as you likely did? If You say Yes to any of those questions I doubt you are telling the truth.
Maybe your understanding of a smoking gun is different than mine. The smoking gun is a giant clue. It doesn’t prove the case. All Cause Mortality increases everywhere the vax is should be enough to stop it. The case has to be thoroughly researched. Which can’t happen provided we keep arguing/nit picking about whether all those athletes really means anything. Whether fertility rates dropping/all
Cause mortality surging/miscarriages up massively etc really can be definitively charged to the vax. It is absurd. OF COURSE there is enough to stop it. Then we spend three to five years proving it out.
I always used it as less than what it is! I stand corrected. Although I think the data is proof something is happening. IE a crime has been committed. What exactly and to what extent needs to be determined. .
Then 4 data points from the UK in May 2022 would help: number of unvaccinated, number of vaccinated, number of vaccinated deaths, number of unvaccinated deaths.
That's in there. You could reverse-calculate number of individuals from person-years by (I think) multiplying py's by 11.78, though this wouldn't account for individuals who transition from unvaccinated to vaccinated during the month.
For May, you do still end up with a flip for the 40-49 year-olds but that's mostly because the unvaccinated have a very light month (34 deaths as opposed to a trend of around 120 to 50).
Importantly, when it comes to the ONS data, converting it to rates and graphs can disguise just how small the counts are.
So in May, 40-49 year olds,
ever-injected: 365 deaths (counting <3 values as 2), 4.772 million individuals
unvaxxed: 34 deaths, .666 million individuals (woops)
Result is 50% higher mortality in the ever-injected but once again that's a reflection of the fluke light month in the unvaxxed. And you can do that for every group.
Only the very young show worse outcomes, likely because of prioritizing the "vulnerable" here and low overall uptake.
ONS also gives whole-period counts on Table 3. So if there were a smoking gun in there it wouldn't be very hard to show (nvm, this one isn't age-stratified).
Again, likely this is all due to healthy user bias hiding whatever deaths the injections are actually causing.
Ha - hence the instinctual need to inflate how many lives are theoretically saved. "10s of millions per year," lol. So intuitions of the net negative can't rise to the surface.
I confess that at face value you speak over my head, but I want to know the REAL facts, whether they support my bias or not. Belief doesn’t create truth, truth should shape belief. So, would you say that the data seem to say that the injected appear to be overall “doing better” than the non? And are the data credible? (i.e, statistics for dummies)
What I think is happening in the UK is that people who are near death don't get the Covid vaccines. This makes it impossible to "see" deaths caused by the Covid vaccines in the data - it still looks like the Covid-vaccinated are dying at less than the trend rate.
If you were to exaggerate this effect to the extreme, it would be as if every single all-cause death in the Covid-vaccinated is caused by the vaccine, because anyone who was going to die of natural causes didn't get the vaccine (everyone who got the vaccine should still be alive, and only died because of the vaccine). The reality is some sort of mix. So the Covid vaccine is killing people but it looks like natural deaths. This also holds true for things like myocarditis.
The Covid-vaccinated might have lower rates than the unvaccinated, but higher than what they should be having (because they are healthier people who were not likely to have myocarditis).
The UK guidance layered two biases here. "Clinically extremely vulnerable" were prioritized in all given age groups, but "those with health conditions that put them at very high risk of serious outcomes" were in the excluded from injection category. https://www.bmj.com/content/372/bmj.n421
This! I have also long suspected that a subgroup of the unvaccinated are THE MOST sick/immune compromised/old/frail/vulnerable/etc., ie. deaths are by default preprogrammed into the unvaxxed group (especially succeeding a dose rollout).
Eugyppius also addressed this quite a while back (in '21?). If I recall correclty it was possible to see in the public data that a very small slice of the population was responsible for the overwhelming number of hospitalisations, ICUs, and deaths - old/sick people - regardless of vax status! Thus population-wide vax campaigns were a waste of time and ressources when the authorities could have been focusing on comprehensively vaxxing a much narrower target group.
Overall there is just a weird mix of healthy/unhealthy bias going on. For example, with flu jab normally there is a healthy bias for those getting jabbed. But, the public health campaign to get everyone covid vaxxed was so great, I think many who normally forgo the flu jab did get the covid vax. So while the absolute most vulnerable remain in the "unvaxxed" pop, the less than average healthy may well be overrepresented in the "vaxxed" pop. As a result, the population groups are hopelessly confounded rendering them almost worthless when trying to draw conconclusions.
Of course the hypocrisy is that public health promoted the figures when it served their narrative and then play the confounders card when it no longer serves their narrative.
I was someone who thought there was a smoking gun in the public data but now accept even if it is there it is perhaps impossible to discern it.
I would guess that what happens when you construct a data-set based on health engagement, you get a bifurcation of ~95+% healthcare-seekers who get the experimental transfection and ~5% healthcare-avoiders who are only in the system at all because they are at the point in life where healthcare-avoiding is no longer sustainable - their legs are going from diabetes, lungs are failing etc. - and these do not get the experimental transfection. Because no systems in liberal countries are omnipotent you only see people on both extremes / biases clearly.
This is the best explanation of some of these stats that I have seen. Thank you Brian. I'm pretty good at math(engineer by trade) but I look at the statistics heavy stuff and I know something is wrong and it is hard to figure out what. Everybody is trying to tease a signal out of data that just isn't there. COVID statistics has become its own industry but it seems that the truth is that without better data all any of this is doing is keeping subscribers paying.(not so much you, more like the entertaining but statistically torturous cat)
That's probably correct, though I suspect that self-awareness / malicious intent isn't rampant on that front. If there is, then it's more about fueling the hype-burnout cycle. But I think most of these writers really believe they have "proven" negative efficacy. Then there are a few that I suspect are too smart for that.
I try not to be cynical about it. When having answers pays it is no fun saying 'I just don't have the data to get you the truth.' I mean at least at my 'job' I can go out and collect data. Having a data analysis cash cow where you are stuck with other people's data, other people who hate you and obstruct you at every turn, must be a real pain. I should be grateful that my writing doesn't make enough money to expose me to that.
Just as I wouldn't trust most statistical analysis supporting the mainstream / pharma position. It's a bit like statistics and science are largely religious magic shows, when the mainstream priests perform these powerful spells predicting why you need to take their products, people shop for priests that perform powerful spells predicting why the products are bad.
You're so right! Science has been turned into a religion, which destroys science and obscures true religion. Dammit. I went into physics because its dispassionate nature was so wonderful; it seemed to offer pure reason applied to the world. Even physics is now tainted, infested with climate ooga booga, gender posturing, and cults of personality around charismatic control freaks. I really don't trust "the Science™️.
I expected no better from him. He's turned physics into a vaudeville act. Nice though, that he was kind enough to show his arse with the cult of vaccines.
el gato malo just released another analysis using table 5 which is monthly, separated by male and female and uses ONS data only which means it has the person year calculations. He also tried is best to look at <21 days post dose which is stymied by the "<3" deaths numbers for all but the older groups. A first read through, it makes a strong case although healthy or unhealthy user bias is not able to be addressed by any public data.
https://boriquagato.substack.com/p/another-look-at-uk-all-cause-mortality
Yes, looks like his graphs match per-person-year, either the raw or the ONS-adjusted. Would have been great if he showed the raw numbers so I didn't have to dive into those sheets to verify. (*edit: I see it was briskly specified in the first bullet. It's still easier for me to see numbers for myself.)
This post once again reflects unfamiliarity with the work already done on the ONS data. It is like a greatest-hits compilation of mistakes others have already made many times.
He starts with the 1st / 2nd >21 days razzle-dazzle. This group has always had high death rates because people close to death stop getting more injections. The real number for 1st / 2nd 21 days is the 3rd dose >21 days group, because that is where most 1st/2nd dosers go in the end.
Then, by focusing exclusively on May, he razzle-dazzles the artifact in the 40 year-olds which is a product of fluke low unvaccinated deaths for that month (34 deaths instead of trend of 120 to 50) and the bad month in male vaccinated 80/90+ year-olds. It is only that last thing that is interesting, but we have no idea if it was the beginning of a trend (hence the data freeze) or just another fluke.
Lastly he razzle-dazzles with the real-time <21 day rates. Which for may are totally aberrant, don't reflect any earlier months. This is easily visible in the whole period results on Table 6.
The whole reason Fenton suspected a lag in this set was because at first they were extremely low. Ok, so now in May they are high. But when did most people get dosed (earlier months, when the <21 values were consistently low)? The results for May don't retroactively change what happened for all those earlier people. And who are these few thousand weird people that put off getting injected for so long? What is different about them from the earlier people? Did something change about getting an injection in May or, more likely, was it that different people were getting injections than most recipients.
So like I said in my post most people tackling the ONS sheets just get tripped up on the buckets which have weird data artifacts, no one can show lower acm when the buckets are combined. He's just repeating old mistakes.
Brian, I'm sorry, could you (or anyone) please explain a bit more about what exactly "combining the buckets" means? (I read the Panera article too, it's very interesting, but still not sure that I understand what exactly you mean by "combining the buckets")
For example, consider we have monthly data with the number of deaths, separately for unvaccinated, 1 dose, 2 doses, 3+ doses. We know the number of people in each category (in the ideal case, at least). The "conventional" way of calculating relative risk based on this data is obvious (separately for each vaccinated category, just from the number of deaths and population counts for each such category, compared with the same for unvaccinated).
Does "combining the buckets" apply to such data/approach? If so, how exactly would it change the calculations? How exactly should we combine the vaccinated categories? Or is it only applicable to some other kind of data (like person-years)?
Working from your second paragraph, what is important here is that people who have 2 doses also had 1 dose, and people who have 3 also had both 2 and 1. So for any case where you want to know the "real" 1 dose all-cause mortality rate ("how often are people who had at least 1 dose dying") you need to add in the 2 and 3 dose-havers. Same if you want to know the "real" 2 dose rates, you need to add in the 3 dose-havers.
It might seem like separating is a good idea to compare "performance," but what really happens is the people who stopped at just 1 or 2 doses will include lots of people who had a bad reaction to the shots or otherwise a serious health downturn that led to a change in previous medical behavior (getting shots). These are your die-ers. For the 1st dose >21 days group this effect will be particularly extreme.
And so is that itself still a big huge story? Well, it might be if the absolute death counts were very high in this group. But they aren't. By definition, since when you add those deaths back in to the "real" 1 dose >21 rate (combine the buckets) the rate is low.
All clear and makes sense. Thanks for the clarification!
In terms of harms I do think disabilities are likely to prove a much more telling statistic (than deaths) and not enough attention is being paid to them.
The reasons are obvious: the sheer numbers dwarf deaths, the vax status is (relatively) incorruptible, the person is alive for verification and further investigation and the person has motivation to tell the truth about their injury (welfare programs) and the likely cause (blame, possibly litigation in the future).
Obviously at present there isn’t much salience out there in respect of the vaccines being linked to disability, but I’m sure it will come. Notwithstanding that, just looking at the sheer numbers registering as disabled looks revealing.
http://www.phinancetechnologies.com/HumanityProjects/US%20Disabilities%20-%20Part1.htm
What’s nice about death is that it’s objective, though only at the primary level. So the disability starts have indeed skyrocketed in UK and US but does this mean there are actually more disabled people, or just people in lists that say disabled? And there are multiple common elements beyond injections to explain the trend being international (ie incentivized unemployment). And I think that’s why no one does any work on it, either. But yes, both the nature of the rise and the involvement of vaccines could be clarified by actually following up with people.
You raise some good points, but (here in the UK at least) there is at least some gatekeeping around who gets classed as disabled since this is linked to welfare payments.
I get that this may have become lax.
A good counter-argument.
I computed vaccinated death rate/unvaccinated death rate using table 2 for May 2022 (the year that looked worst in egm's analysis. I got ~1.45 for 18-49, 0.9 for 50-59, 0.81 for 60-69, 0.84 for 70-79, 1.04 for 80-89 and 2.81 for 90+. I presume you would claim that I am seeing unhealthy user bias for those under 50 and healthy use bias for 50-79. But what is happening for 80-89 and 90+. They are nearly universally vaccinated and maybe the very small unvaccinated 90+ group consists of mostly the healthiest folks in that bracket. Afterall, those who weren't vaccinated because they were near end of life in early 2021 would have been long-since dead.
This does make some sense, but my question centers around the fact that the essential vaccination rate is higher in all age brackets for table 2, effectively increasing the vaxxed denominator and dereasing the unvaxxed denonimator, usually substantially as a percentage basis because it is already small.
Why should I trust one set of vaccination numbers over the other? Does table 2 represent a clear set of people who are in the country at the time and whose medical history including vaccination and death is clearly tracked even if it isn't everybody. Or are the unvaccinated undercounted for some reason. Perhaps to put it another way, does table 2 represent a clear set of individuals who were either vaccinated or not prior to that month (or who spent part of the month in each category) and who either died or did not?
With table 8, there clearly are more deaths tracked, implying a larger group of people. But where do the vaccinated numbers used by EGM (which can be downloaded here: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines#vaccination-rates
Information on how the values are calculated can be found on page 80 of this report:
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1127554/Weekly_Flu_and_COVID-19_report_w1.pdf
Here is the nugget:
"The data presented this week is the provisional proportion of living people resident in England who had received COVID-19 vaccinations. Individuals vaccinated in England who have a registered address outside of England or where their address, age, or sex is unknown have been excluded. Due to changes in GP practice lists, in order to include newly registered patients and remove those who are no longer resident, there will be slight variation to the figures to reflect those who are currently resident in England."
So it seems to be a relatively current data set based on the people currently residing in England.
So the true question becomes, "Which dataset contains a more accurate representation of the unvaccinated population?" Since the unvaccinated population is small, it is much more important to measure it accurately. I'm not convinced that the very high vaccination rates in table 2 are accurate. Although, if vaccination numbers do respresent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset.
Finally, the significant change in relative death rates from January to June adds even more complexity to the issue.
A good night's sleep hopefully will help me organize this better. Here are my points and questions:
1. As you point out, table 2 is the subset of the population that appear in all of several different data sets. I am still not convinced that it shows a consistent populations in which every person's vaccination status and death or lack thereof is known. Can you point me to information that might convince me one way or another?
2. Table 8 seems to include the entire population as the total monthly deaths seems to reasonably match the expected values for the entire country. I haven't taken the step to find independent monthly death data to verify, though. That's still on my list.
3. As I point out above, egm gets his vaccination status data from what seems to be a consistent, well-done population wide vaccination status database that accounts for those who reside outside England and checks against recent contact with the health care system. It shows lower vaccination rates than percentages computed from the person-years column in table 2. This is why his numbers suggest the vaccine is killing lots of people - larger denominator for the unvaxxed and smaller for the vaxxed while proportion of deaths in each category is more or less the same between the two tables.
4. When vaccination rate is high, which is true in all but the youngest age group and is especially true in the oldest groups, small differences in vaccination rate make a big difference in the computation.
5. You mentioned that table 8 suffers from not knowing about deaths of people who left the country, but the description of egm's vaccination data set suggests that at least the vax rate part of the computation does. Second, the deaths seem to be every or nearly every death in the country (I know, what if vaccination data includes people not in the country?). Third, the relative proportion of missed deaths in the unvaxxed group would have to be larger than the proportion missed in the vaxxed group for his numbers to be wrong in the direction they were wrong. I can't think of any reason a larger proportion of unvaxxed than vaxxed people would leave the country or otherwise hide from the death surveillance system before they died. If the proportions were similar, the results would not change in a meaningful way.
Re 2, there's still discrepancy between listed total deaths.
For example, table 8 vs https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/monthlyfiguresondeathsregisteredbyareaofusualresidence/2022 for England
Feb: 39481 / 43,081
Mar: 42529 / 46,202
Numbers are identical in the most recent and June version of the sheet. So table 8 seems to reflect 90% of deaths. So you still probably don't want an outside denominator - you want to know who's actually being tracked by Table 8.
RE 3, the problem with the UKHSA unvaccinated rates is that they were also imputed into the "too good to be true" Covid reports from late 2021 to whenever they stopped. I stopped posting about UKHSA results in October 2021, it was too much of an outlier with reports from other places. Eventually, other places did match the UKHSA but they were also, always, highly vaccinated "islands" like the Iceland Dashboard or NY state databases. And so I named this suspicion of out-of-system "immortal unvaccinated people" the "Iceland Dashboard Bias" https://unglossed.substack.com/p/the-ny-kids-paper#footnote-4-49567294
This dovetails with 5. The issue is that if people are traveling, moved, died without being recorded, whatever, no events (vaccination or infection) can be recorded for them. They are immortal unvaccinated people. Having a vaccine record in the system (whatever the system in question is) is like saying "here" in attendance at the beginning of class. So when you compare rates of "getting test questions wrong" in the class, you have a problem where one of the groups didn't say "here" and you don't know how many are absent.
Thank you for taking on the project!
"using table 2 for May 2022 ... ~1.45 for 18-49 .... 2.81 for 90+"
The 18-39 group has always been an unfortunate batch, as your unvaccinated end up are a bit younger (though not to an extreme in the UK). Table 6 has five-year age groups (whole period) and doesn't replicate the signal except in the teens. Or didn't as of last year. To see if the real-time trend got worse in these groups you would have to collate different versions of the spreadsheet. Maybe it did - after all it's not at all implausible that vaccine deaths would start to show up in the "cleaner" younger age groups.
In may I get 15.5/17.8 for unvaccinated/any-dosed deaths per person-year for 90+ in table 2, May 2022. Maybe one of us has a number in the wrong place. So here, the unvaccinated are doing better, as you say there shouldn't be any "deathbed vaccine abstainers" left at this point. Of the ~6786 any-dose deaths, very few are listed as involving "Covid-19." So maybe this was a vaccine deaths bump.
It's certainly entirely possible that the ONS stopped updating because the threads were coming loose at exactly this point.
Regarding the UKHSA vaccine breakdown, that's good info about the process used to try to exclude people who leave England. I don't want to hinge my case entirely on the accuracy of that graph in reflecting the real-world, however. Instead it's just that we don't know if Table 8 is related to that set or not.
"Although, if vaccination numbers do represent the same population from which deaths are drawn, it is by definition and accurate number, although perhaps subject to it's own selection bias given it is a subset." Right - as far as accuracy it doesn't matter if it's only a sample that doesn't reflect the real-world unvaccinated rate, the sampling insures accuracy (but also unhealthy user bias) in of itself.
Yes, I used the wrong box in computing the 90+ vaxxed death rate/unvaxxed death rate. It's 1.23, not 2.81.
I agree, 18-39 is somewhat age confounded because vax rates increase somewhat by age while typical death rates increase much more over that range.
"It's certainly entirely possible that the ONS stopped updating because the threads were coming loose at exactly this point." - We've certainly seen this before in other ONS data series! I also can't say that I totally trust ONS not to massage data at least a bit in favor of the narrative before releasing, although the good old USA is much, much more willing to do that.
Have you analyzed Igor Chudov's analysis of excess mortality by country and vaccination rate and by UK deprivation quintile and vaccination rate? Those weren't able to fully separate vaxxed from unvaxxed, but they do point to something, at least if there aren't some devilish confounders hiding somewhere.
Finally, my second cousin's 15-year old son collapsed and died on Monday. The first death in my mom's family under age 40 since her uncle died at age 13 in 1913 from misdiagnosed appendicitis more than 25 years before mom was born. Terribly sad and angering, but it might finally change the mind of my 5-dose brother, sister-in-law, aunt and uncle. The only death not much above 40 was her type-I diabetic first cousin.
That's awful! I am sorry to hear that. And he had taken the experimental gene injection?
I link to Igor's work in yesterday's post, I do find it promising.
I don't know, but I bet that sudden death in 15-year olds, especially boys, is an excellent predictor of injection status these days. I might know eventually.
thought experiment:
https://woodhouse.substack.com/p/thought-experiments
https://wherearethenumbers.substack.com/archive?sort=new
Are you aware of this substack by Norman Fenton who is a maths professor from Queen Mary’s London specialising in risk assessment and statistics. He also has his own website normanfenton.com I remember him going on in the past that it’s very difficult to interpret the U.K. results as the ONS and NIMS have different numbers of population and unvaccinated. I can’t point you to a specific post but I’m sure you’d find all his analyses worthwhile.
I am sure Fenton is astronomically more skilled at math than me. But the ONS critique has always been logically flawed.
The thing about the ONS data set is that it just says "here's who's in our set," counts their days spent in whichever of the vax status buckets, counts their deaths (with bucket status at death), then shows you how the buckets look as far as days-spent and deaths. It's not that wobbly.
So imagine you have a mailing list with 30 people, and you track 1) days spent after you email them and 2) emails they send to you, at which point they stop counting for days. So you have a bucket for "<21 days after I send email one" and ">21 days days after I send email one (but no second email yet)."
Fenton thinks you are counting "<21 days after I send email one" replies in your "0 email" (ie counting just-dosed deaths in unvaccinated) bucket. But since the UK used a long interval for the initial injection roll-out, you should also see the same mis-count piling just-second-emailed replies into your ">21 days after I send email one." And then you show Fenton that that bucket, while it looks scary in a per-day email basis, has basically next-to-zero replies (deaths) in it. Like the ONS >21 days post-dose-1 category drops a few dozen deaths a month across all age groups. These are where lagged post-2nd dose deaths *would* drop. This means that if there is a lag, it would only shift a few dozen deaths between the unvaccinated and <21 days post-dose-1 group per month and wouldn't flip the latter to being less than the former.
I saw this in my email and said oh good, another substack discussion with Brian weighing in. Thanks, I always appreciate your perspective. 👍🏽💕
I like your category (in the referenced post at the top), "Into the Weeds of Government Data". I used to venture into those weeds regularly, although thankfully that job ended nine years ago, never to return. It was federal and California public health data, but it seems that government data is government data. Lots of aggregated data, no de-identified raw (too sensitive to release for some reason). Having to guess the unique keys. Asking questions of the aggregate that can't be answered.
I didn't follow your analysis in great detail because it reminded me too much of working with government data, and my head started to hurt like it used to. But "Viola"? OK, the edited version says "voilà" or something close to that. But now I have MST3K to ponder. Making progress, I think. Something about "riffing"?
Cello.
Oh my God. The MST3K bit was supposed to be dropped in my final revision of that footnote before publishing (after I found that a Google search doesn't pull a reference so it would confuse people who don't remember the scene off-hand). Substack is doing this thing where the 2nd-or-so oldest draft overwrites the version you publish, it is getting crazy!
OK, that's clearer now. :) I think. I didn't know about the overwrite issue, but I have seen peculiar things happening with it lately. I previously backed up my own blog posts elsewhere, and I was planning to resume that practice. Now I definitely will. Thank you!
As someone who wrote several posts on mortality, I must note something that we should all be aware of.
It is not directly related to this Brian's post but I thought I can express my feelings here.
ANALYZING MORTALITY IN-DEPTH IS VERY DIFFICULT.
They invented randomized trials partly to bypass these difficulties.
For example, let's say that a fat, triple boosted guy named Bob dies a month after having his third COVID.
Having this person in mortality statistics, and having his demographic characteristics, can we answer a simple question: what killed Bob? Fatness? Covid? Vaccines? Cholesterol? Or something else?
Some people allege a healthy user bias. Some people allege unhealthy user bias. etc etc
It is not very easy. It is also not very easy to do causality properly on the population level.
This is why most respectable nations have demographics institutes, full of statisticians, MDs etc etc.
I could, for example, do a regression of "deaths" by "boosters", note a positive relationship and write about "association". Yeah, there is an association.
But how much proof do we have? I tried hard to find "proof" beyond correlations and sometimes, I believe I found other convincing statistical things, like having similar slopes in UK deprivation quartiles and worldwide country level statistics. That increases our strong suspicions about vaccines.
But proof "beyond reasonable doubt" may remain elusive. So many confounders can be alleged.
We can only prove something by analyzing individual level data on millions of people, and hopefully doing some targeted interventions.
The data is not available, the authorities are ignoring excess mortality and discontinuing their reports.
This leaves us wondering, naturally, if something nefarious is going on.
And something nefarious IS going on because ignoring excess mortality IS nefarious by definition.
quite, https://doi.org/10.1093/ije/dyac204 . The authors conclude, 'We document substantial heterogeneity and uncertainty in estimates of excess mortality. All estimates should be taken with caution in their interpretation as they miss detailed account of demographics, such as changes in the age group populations over the study period.'
Yes and no, what we have 17% increase in excess compared to 2017-2019, it cannot be explained by changing age
All very true. And I'm not even *against* people believing the data is all fake or might-as-well-be-fake. It doesn't bother me. This is a post-replication-crisis world, data isn't truth.
But math is still math and I can't even figure out what went on with this one...
Brian, there is some excellent analysis of UK data by John Dee if your interested.
https://substack.com/profile/59616496-john-dee
If this guy can’t find a smoking gun, I don’t think anyone else can.
He also bakes a pretty good cake.
I don't get good vibes from this one. Seems to be rebranding efforts to *reduce* "with not from" pneumonia death-tallies (and attribute deaths to underlying comorbidities) as evidence of efforts to *increase* "with not from" pneumonia death-tallies.
You’d need to read John Dee’s entire work not just the latest, he is a bit like Mathew Crawford in that regard, there is lots of it. I’ll see if I can find a good one that addresses all cause mortality (he has lot’s on that). He’s also in the process of analyzing Joel Smalley’s FOI data which should be interesting.
https://jdee.substack.com/p/missing-deaths-exploration-part-1?utm_source=profile&utm_medium=reader2
I agree John Dee does good work. He does not seem to be pushing any narrative other than the data has never shown an OMG hair-on-fire pandemic, so let's shut down society.
Apart from that, he just goes digging in the data looking for signals, correlations, etc.
I agree, but I do think he may be on the payroll of Big Tea and Biscuits 😀
Excluding unvaxxed is good as long as you also exclude their deaths. That's better than counting them when you can't see their deaths (because they died off-record a long time ago; because they moved or have been traveling; etc.). You want your "control" group to truly be in contact with the system. So that is why the ONS data is pretty good; but what it is good at is showing a very biased distribution of injection.
It's likely close to that. Again, this is good, as long as I don't count your injection or death (which by definition I will not, since you do not meet the qualification based on prior engagement). It will be more like a recruitment-based test-control trail then a true population monitoring system. So you have biases but you also have a very sturdy person-years denominator. I think egm doesn't get that the ONS data has these exclusions and that is why he didn't grasp that the person-years column is the denominator.
"The Office for National Statistics (ONS) vaccine mortality surveillance reports (the latest being are based on a subset of 39 million England residents that excludes all those not registered with a GP and not registered in the 2011 census."
Norman Elliott Fenton
Queen Mary, University of London
https://www.researchgate.net/publication/364310694_Implications_of_the_Office_for_National_Statistics_estimates_of_Covid-19_vaccine_take_up_in_England_on_the_representativeness_of_its_sample_population?channel=doi&linkId=63459d419cb4fe44f31d90fd&showFulltext=true
I think you’ve remembered correctly, John Dee (and Fenton) try to take that all into account and make adjustments for it. That’s why they can’t say that they’re correct with a high degree of certainty because they don’t have access to all the available data they’d need to be more certain so can only speculate based on the data available.
This commentary is sadly above my pay grade:). But it concerns me (and at the same time makes me hopeful) that you are calling out EGM for confirming my biases which are most depressing...his reach seems huge...and if we’re repeating false info that’s concerning. For me it’s easy...NO MORE MANDATES EVER!! Have you reached out to him for clarification?
I don't think it would be productive. His prior errors (though once again I'm not 100% about having the correct take on this one) have been noticed and not corrected (https://unglossed.substack.com/p/norcal-pregnancy-study-etc#footnote-anchor-6-50778978).
So I think he's just a content generator. "Here's a post with charts saying that what you readers already want to think is proven once again." Ok then...
I've learnt a great deal from EGM's posts but have also noticed a lack of willingness to address criticisms. My own example is the decline in births issue. While I am not saying I was correct (https://lostintranslations.substack.com/p/the-wedding-crasher), the way EGM completely ignored and continues to ignore the pandemic collapse in weddings was really puzzling to me.
Also, I am based in Europe and notice that sometimes the American substackers often jump to conclusions from data (often simply scraping from World in Data) when the subtelties of the regional data are much more complex.
Thanks again for your posts, Brian, they are really providing a valuable bulwark against the confirmation bias that plagues both camps.
I dunno why you are wrestling with these probably faulty data-sets when Ron Unz tells us that the problem is Obesity!
https://www.unz.com/runz/obesity-and-the-end-of-the-vaxxing-debate/
I guess the reason I am not dead yet despite being unvaxxed is that I am not obese. After all, the fake President told me that as an unvaxxed person I was going to die last winter or and the latest this winter (although, this winter is not over yet.)
Maybe he skipped over the bit on his teleprompter where it said "you fat fucks are gonna die this winter if you are not vaxxed!"
It would be good to know.
Thanks for that link. I hope and pray he’s right. And he should do the debate with Steve or Brett. Ive come to consider myself a “crack pot anti-vaxxer” and I’d like to not be one...
🤣🤣
That actually looks like a promising analysis. As for his "not knowing anyone who had a problem after injection" claim, call me skeptical. The disconnect here is that I think it's correct that the excess mortality in 2021 is being driven by the virus, not the vaccine. But also it's not normal for young adults to just drop dead every week.
And a lot of biological processes are exponential. I think we are on the verge of a ramp-up in the sudden deaths rate. Like a ball rolling toward the edge of a hill.
Out of interest, what makes you think the sudden death rate will be on the ramp-up? And by what mechanism do you think this is most likely to occur due to? Tolerance derived cardiac distraction? Subclinical myocarditis? Other?
Asking for a friend…
Mostly it is just how I intuit the situation. It is a bit like how a car goes from looking new to looking old. The myocarditis is like a big dent that shortens the time before other smaller insults manifest in "old" ness. (But maybe immediately afterward the car still looks "new" overall.) Animals are big systems comprised of all these same cofactors, which is why they can't live forever. But hearts can't be replaced like bumpers. I mean they can but it doesn't make for quite the same solution to the problem.
Tolerance cardiac destruction is a question mark. If there is a wave of sudden deaths in the coming three months that then subsides, then that will look like tolerance. Of course it would be much better if someone just looked for spike and virus in autopsies. The recent German sudden deaths autopsies study dropped the ball on that aspect.
Thanks for sharing your thoughts.
With regard to ‘hearts can’t be replaced’; we do now live in a post-mRNA-injectable world don’t forget. I’m sure the good folks at Pfizer could create a new product that instructs the body to produce new heart cells! We could call it a vaccine against SADS. YOU HEARD IT HERE FIRST
I'm sure you're aware that Ed Dowd looks at the actuary data from the insurance industry, and not government data, in his excess deaths analyses. I can't comment on the accuracy of his statistical analysis, but I thought insurance actuary data was probably more accurate than government excess death data. What do you think Brian?
As long as both use their prior rates as a benchmark, "accuracy" shouldn't matter too much, though maybe a little. Like how you could use jewelry store sales to measure a recession. But maybe using Walmart sales wouldn't show the same drop in spending because people still go to a Walmart. In this case the insurance data might be more like the jewelry store and not see what the government sees as far as off-the-grid people who die all the time. Either way both should have something to offer.
Darn, I was writing a long response and somehow just lost it :(. I guess basically I wanted to say that how can we track much from the UK when they used midazolam at such a high frequency and failed to give antibiotics for secondary pneumonia creating a crazy pull forward effect and their vaccination schedule started with AZ, then J and J, then Pfizer and finally Moderna leading to a jumble of information?
I am not per se advocating for trust in the ONS data, though I think it's pretty consistent with healthy user bias and so it looks legit. But primarily I am showing that the ONS data doesn't show a mortality impact even if there almost certainly is one (as described in my reply to Shelly S below).
So when readers see posts about the ONS data claiming it shows an impact, I would advise caution. Maybe if they ever release results from after May 2022 that will change.
I haven't written on the "failed to use antibiotics" subject yet but I don't think that one holds water. On the other hand I wouldn't dismiss other avenues of medically spiking the death counts.
I think one of the biggest valid criticisms of the UK data is that the the UK authorities simply don't know how many people are in the UK! As a result the unvaxxed population is clearly underestimated because the default calculation is Total Pop - Vaxxed Pop = Unvaxxed Pop.
Note: I think this must also apply to states like New York, California, Texas, Florida, etc. which also have huge immigrant populations not necessarily accurately reflected in official data.
Yes, yes, very true RE NY and CA. There's also lots of moving out from both states in any post-2020 context.
The thing about ONS however is that it uses layered contacts. The people that are "in" PHDA are in it (they have had contact with all three of the 2011 Census, the General Practice Extraction Service (GPES), and the Hospital Episode Statistics (HES)). This means that the final two sets, injections and deaths, shouldn't be missing a lot of events for the peole "in" all 3 of those other sets. Person-years and deaths should be pretty accurate and of course any false "ins" would make the unvaccinated look better, not worse.
I always appreciate your straightforward and honest analysis! I guess I’m primarily concerned about the new classifications of death through new codes in the US system. I’m not good enough with the data to crunch anything but any new classifications that are suddenly born during a pandemic make me raise an eyebrow...
I've also been meaning to give that line a look. I find the way Ethical Skeptic just assumes-back a bunch of cancer deaths off-putting, but maybe there's still something there.
I’m defense of EGM, and myself, we’ve spent months trying to get clean all-cause mortality data from the US and UK bureaucracies and neither are willing to share their taxpayer-financed info with taxpayers. I’d be curious if anyone has a theory as to why.
But the ONS data is clean. It just doesn't get acknowledged by "our side" because it doesn't support worse mortality. This is probably just due to healthy user bias but it is what it is.
I disbelieve you. In BC Canada ACM started increasing fairly dramatically since the onset of the vax. In April of 2022 for example according to the government 51% were triple vaxed. Yet 76% of COVID deaths were of triple vaxed. That’s COVID. That isn’t ACM. 14% were never vaxed. Yet 7% of COVID deaths were of never vaxed. Your chances of dying from COVID Were 3x greater if you had been triple vaxed than not vaxed. You say there isn’t yet a smoking gun!! They are all over the place. In every government data set that comes out. Let me ask you a question. I’m assuming you are vaxed. Knowing what you know now, would you do it again? Knowing what you know now, will you get any more boosters? Knowing what you know now would you fear COVID as you likely did? If You say Yes to any of those questions I doubt you are telling the truth.
I'm not vaccinated. Being able to read ONS spreadsheets correctly shouldn't be a marker for taking the Covid vaccine.
Maybe your understanding of a smoking gun is different than mine. The smoking gun is a giant clue. It doesn’t prove the case. All Cause Mortality increases everywhere the vax is should be enough to stop it. The case has to be thoroughly researched. Which can’t happen provided we keep arguing/nit picking about whether all those athletes really means anything. Whether fertility rates dropping/all
Cause mortality surging/miscarriages up massively etc really can be definitively charged to the vax. It is absurd. OF COURSE there is enough to stop it. Then we spend three to five years proving it out.
I am not nitpicking about "whether X can be charged to the vax." I am showing that X in this case was totally miscalculated. Just math.
I see your point. I was actually answering Jon there. Re the smoking gun
I always used it as less than what it is! I stand corrected. Although I think the data is proof something is happening. IE a crime has been committed. What exactly and to what extent needs to be determined. .
Then 4 data points from the UK in May 2022 would help: number of unvaccinated, number of vaccinated, number of vaccinated deaths, number of unvaccinated deaths.
That's in there. You could reverse-calculate number of individuals from person-years by (I think) multiplying py's by 11.78, though this wouldn't account for individuals who transition from unvaccinated to vaccinated during the month.
For May, you do still end up with a flip for the 40-49 year-olds but that's mostly because the unvaccinated have a very light month (34 deaths as opposed to a trend of around 120 to 50).
Importantly, when it comes to the ONS data, converting it to rates and graphs can disguise just how small the counts are.
So in May, 40-49 year olds,
ever-injected: 365 deaths (counting <3 values as 2), 4.772 million individuals
unvaxxed: 34 deaths, .666 million individuals (woops)
Result is 50% higher mortality in the ever-injected but once again that's a reflection of the fluke light month in the unvaxxed. And you can do that for every group.
Only the very young show worse outcomes, likely because of prioritizing the "vulnerable" here and low overall uptake.
ONS also gives whole-period counts on Table 3. So if there were a smoking gun in there it wouldn't be very hard to show (nvm, this one isn't age-stratified).
Again, likely this is all due to healthy user bias hiding whatever deaths the injections are actually causing.
Ha - hence the instinctual need to inflate how many lives are theoretically saved. "10s of millions per year," lol. So intuitions of the net negative can't rise to the surface.
I confess that at face value you speak over my head, but I want to know the REAL facts, whether they support my bias or not. Belief doesn’t create truth, truth should shape belief. So, would you say that the data seem to say that the injected appear to be overall “doing better” than the non? And are the data credible? (i.e, statistics for dummies)
What I think is happening in the UK is that people who are near death don't get the Covid vaccines. This makes it impossible to "see" deaths caused by the Covid vaccines in the data - it still looks like the Covid-vaccinated are dying at less than the trend rate.
If you were to exaggerate this effect to the extreme, it would be as if every single all-cause death in the Covid-vaccinated is caused by the vaccine, because anyone who was going to die of natural causes didn't get the vaccine (everyone who got the vaccine should still be alive, and only died because of the vaccine). The reality is some sort of mix. So the Covid vaccine is killing people but it looks like natural deaths. This also holds true for things like myocarditis.
The Covid-vaccinated might have lower rates than the unvaccinated, but higher than what they should be having (because they are healthier people who were not likely to have myocarditis).
In Canada those nearing death do get the vax
The UK guidance layered two biases here. "Clinically extremely vulnerable" were prioritized in all given age groups, but "those with health conditions that put them at very high risk of serious outcomes" were in the excluded from injection category. https://www.bmj.com/content/372/bmj.n421
Bartram is local and seems to be in-the-know on actual healthcare practices and is the originator of this explanation for the ONS bias/artifact https://bartram.substack.com/p/the-healthy-vaccinee-effect-i-older
This! I have also long suspected that a subgroup of the unvaccinated are THE MOST sick/immune compromised/old/frail/vulnerable/etc., ie. deaths are by default preprogrammed into the unvaxxed group (especially succeeding a dose rollout).
Eugyppius also addressed this quite a while back (in '21?). If I recall correclty it was possible to see in the public data that a very small slice of the population was responsible for the overwhelming number of hospitalisations, ICUs, and deaths - old/sick people - regardless of vax status! Thus population-wide vax campaigns were a waste of time and ressources when the authorities could have been focusing on comprehensively vaxxing a much narrower target group.
Overall there is just a weird mix of healthy/unhealthy bias going on. For example, with flu jab normally there is a healthy bias for those getting jabbed. But, the public health campaign to get everyone covid vaxxed was so great, I think many who normally forgo the flu jab did get the covid vax. So while the absolute most vulnerable remain in the "unvaxxed" pop, the less than average healthy may well be overrepresented in the "vaxxed" pop. As a result, the population groups are hopelessly confounded rendering them almost worthless when trying to draw conconclusions.
Of course the hypocrisy is that public health promoted the figures when it served their narrative and then play the confounders card when it no longer serves their narrative.
I was someone who thought there was a smoking gun in the public data but now accept even if it is there it is perhaps impossible to discern it.
I would guess that what happens when you construct a data-set based on health engagement, you get a bifurcation of ~95+% healthcare-seekers who get the experimental transfection and ~5% healthcare-avoiders who are only in the system at all because they are at the point in life where healthcare-avoiding is no longer sustainable - their legs are going from diabetes, lungs are failing etc. - and these do not get the experimental transfection. Because no systems in liberal countries are omnipotent you only see people on both extremes / biases clearly.
This is the best explanation of some of these stats that I have seen. Thank you Brian. I'm pretty good at math(engineer by trade) but I look at the statistics heavy stuff and I know something is wrong and it is hard to figure out what. Everybody is trying to tease a signal out of data that just isn't there. COVID statistics has become its own industry but it seems that the truth is that without better data all any of this is doing is keeping subscribers paying.(not so much you, more like the entertaining but statistically torturous cat)
That's probably correct, though I suspect that self-awareness / malicious intent isn't rampant on that front. If there is, then it's more about fueling the hype-burnout cycle. But I think most of these writers really believe they have "proven" negative efficacy. Then there are a few that I suspect are too smart for that.
I try not to be cynical about it. When having answers pays it is no fun saying 'I just don't have the data to get you the truth.' I mean at least at my 'job' I can go out and collect data. Having a data analysis cash cow where you are stuck with other people's data, other people who hate you and obstruct you at every turn, must be a real pain. I should be grateful that my writing doesn't make enough money to expose me to that.
Excellent explanation thankyou
Just as I wouldn't trust most statistical analysis supporting the mainstream / pharma position. It's a bit like statistics and science are largely religious magic shows, when the mainstream priests perform these powerful spells predicting why you need to take their products, people shop for priests that perform powerful spells predicting why the products are bad.
You're so right! Science has been turned into a religion, which destroys science and obscures true religion. Dammit. I went into physics because its dispassionate nature was so wonderful; it seemed to offer pure reason applied to the world. Even physics is now tainted, infested with climate ooga booga, gender posturing, and cults of personality around charismatic control freaks. I really don't trust "the Science™️.
Tyson offers a timely example https://popularrationalism.substack.com/p/neil-degrasse-tyson-falls-prey-to
I expected no better from him. He's turned physics into a vaudeville act. Nice though, that he was kind enough to show his arse with the cult of vaccines.
Thanks for that link.