The Highwire with Del Bigtree - MATHEMATICIAN UNCOVERS DISTURBING MORTALITY DATA

Episode Date: December 24, 2021

British Mathematician & Professor of Risk Management, Norman Fenton, walks Del through UK data on the incredible peak of excess deaths that occurred in the U.K. at the same time as the covid vaccine rollout.#NormanFenton #CovidDeathAnomaliesBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-highwire-with-del-bigtree--3620606/support.

I want to talk about this issue that we keep dealing with, right? Every time you turn on the news, every time you turn on the view, they're saying the same ridiculous statement, pandemic of the unvaccinated. This is what that looks like. Doctors call it a pandemic among the unvaccinated. I'm worried about the way the winter's going to go and I'm worried about the people who are unvaccinated. Pretty much everyone who's unvaccinated is going to expect to get sick. The hospital says the majority of their COVID-related deaths are among patients who are unvaccinated. About two-thirds of the patients that are there, either on oxygen or an ICU, are actually the unvaccinated.

The real problem, I think, right now in this country, is that we need to vaccinate the unvaccinated. All right, you know, this is a, I mean, we've been showing you all of the information coming out of Israel, you know, coming out of Gibraltar, these places that are far more vaccinated than we are. and where you look at those places, they're saying things like 60 to 80% of the people in our hospitals and those that are dying are vaccinated. So it's curious why the UK and America

seem to have a different perspective on this. And we've been confused. You know, I've talked about we have different theories on what might be happening here, but it would be really great if someone could crunch the numbers and where would you find it? If they're hiding the data

or if they're accidentally somehow manipulating the data or missing data and putting, you know, the vaccinated death, in the wrong column, how would you find it? Well, as it turns out, we ran into a brilliant article that came out just over a week ago. This is the headline on that article. The latest statistics on England mortality data suggests systematic miscategorization of vaccine status and uncertain effectiveness of COVID-19 vaccination. This is Norman Fenton, who has been studying this data.

He is from the UK, a professor of risk information, but Norman Fenton has been putting out the data, looking at what we've talked about this before. We can look at COVID deaths and we can look at COVID infections and all of that we get PCR testing. It's very confusing, but certainly there'd be another way to look at it and that way would be through all cause mortality. I'm joined now by Professor Norman Fenton. All right. Hi. How are you doing today? I'm doing good. Thanks. All right. Thank you for joining us today. Now, you have a little bit of a history, right, of writing papers based on mortality data. You've been talking about this pandemic

in many different ways. So just tell me a little bit about your background and what interested you and what types of things you've been reporting. Well, because my research team specializes in data analytics and risk assessment, and we were also involved in a major collaborative project which was doing improved medical decision making,

risk decision making it was kind of inevitable we would get involved in the analysis of COVID data right from the start and we just looked at the publicly available data the stuff that was coming from different governments and initially the work we did was not controversial we were looking at in case rates infection rates and we were very well published you know how our material got published in prestigious journals it was widely distributed there was no problem with that but As soon as when around about September 2020, the entire COVID narrative was we saw it was being driven by this fundamentally flawed idea of equating a COVID case with a positive PCR test. Right. So when they started mass testing asymptomatic people, the case numbers were being driven up and the uh with this mass testing and because they were also

talking about because they were classifying hospitalizations and deaths associated with these PCR tests. So for example, you know, a COVID death was classified as anybody who died within 28 days of a positive PCR test. Whether they've had a heart attack or a brain aneurism or even a car accident, we've heard stories that because they'd had a positive COVID test 28 days earlier, then they, the death certificate or the death gets reported as a COVID- death. And we've been completely, just so you know, on the high wire, we've been discussing that here in the United States of America. We changed our death certificates here so that where it used to be an underlying cause, you died of cancer, you died of, you know, heart disease, complications

of those, but the complication was pneumonia or something brought on by the flu or COVID. Now COVID was the cause of death. We've been saying, certainly that is muddying up the data and changing the way we've looked at it since the beginning of time. Yeah, absolutely. And also hospitalisation. So in the UK, anybody who tests positive within 14 days before admission to hospital, for whatever reason, classified as a COVID hospitalization. And of course, they're tested on entry to hospital and then more or less, you know,

very frequently afterwards. And if they test positive at any time during that period, that's a COVID hospitalization. It's a bit like the Eagle Song, Hotel California. You can check in, check out only time you want. but you can never leave. So it's a problem. So anyway, so the point is that we then started to write papers, which were raising concerns,

which were identifying kind of like anomalies in the reporting, and really looking at these kinds of exaggerated figures, and we were looking at other, you know, a lot of things didn't look right when you compared the case numbers that the government was producing, and then compared that with the data on, for example, triages that people were actually, call an ambulance service for the COVID conditions.

They just didn't match up. Right, got it. And so as soon as we started doing that, we could no longer get our stuff published. We couldn't even get it. We couldn't even get it. We'd send it to the journals and it would be rejected without review. We couldn't even get it accepted onto pre-print service,

which accept anything because that's the whole point. It's stuff which you just put up there before review. So, you know, that's a, that's a sort of a separate issue. But we've plugged away. We're still publishing this stuff. We put it on research. It actually gets very, very well read there. So we're not that bothered about where this goes,

as long as our research has actually read. So that's what we've been doing. And that's culminated recently with the whole vaccination stuff. We weren't looking particularly at vaccinations at all. I didn't really have any strong view about them. I thought it was great when the vaccine came around. I thought this would be excellent. But as we dug into the data,

we started to look at the like the VAR colleague of mine, Scott McClatham, led, again, a very well-known paper now on analysis of the VERS data. Looking at that and then we decided that we were also very suspicious, very suspicious of these studies, all of the studies, both the randomized trials and also the observational studies, which were claiming the vaccine was very effective

at stopping transmissions. And when we looked at those, there were all kinds of problems. There were all kinds of problems with those studies. And we felt actually the best way to determine whether the vaccines were, well, basically safe and effective and was to look at all-cause mortality. So over a period of time, when you compare the vaccinated and the unvaccinated, we should clearly be seeing

fewer deaths amongst the vaccinated than the unvaccinated. So you just, we just said, we don't care where they're categorizing them. Let's just look at all cause, like all the causes of that that are out there. Exactly. Because look, if COVID is as deadly as claimed. Yep. And if the vaccine is as effective as claimed, then what you should be seeing is that for COVID deaths, there should be a significantly lower number of COVID deaths.

Right. Okay. So you've got some great graphs here. So take us through this. This is how you sort of explain it. in your work. Yes, so these are the COVID deaths. So if the vaccine is as effective, if COVID as

claimed that the vaccine is effective as claimed, then you're gonna see more, you're gonna see a lot more deaths amongst the unvaccinated than the vaccinated as far as COVID deaths are concerned. And similarly, but alternatively, if the vaccine is as safe as claimed, right, then okay, there's gonna be a few, a small number of adverse reactions, but you should see only a few more

deaths amongst the unvaccinated and vaccinated. So now what we're going to do is we're giving room for the fact that there might be a few injuries that to be expected from a small group of people. So it might be an uptick. Otherwise, this should be even. It could be even or just a little bit more in the vaccinated because we're talking about they're going to have the same amount of heart attacks, going to have a same amount of brain atoms and all the other reasons you die.

Just with COVID, if we're going to be specific about that, you would assume that the unvaccined are going to share the larger burden of COVID deaths. Okay, that makes sense. So when you now, we're going to add them together, we're going to just put them all together. So what we're looking now at is the all caused death. So this is our all caused mortality. We don't care whether these are, you know, we're lumping together, whether it's a COVID death, whether it's a non-COVID death.

So what you should be seeing, the net effect of that is that you should be seeing more deaths amongst the unvaccinated than vaccinated. And essentially, this should be in each, you know, for different age groups. Yeah. Okay. If it's different for different age groups, then you'll see, well, maybe there's, different maybe the vaccines has more benefit in some age groups than others so that's we should be doing this that's what we want to do this for every different age group okay and this completely

bypasses it completely bypassed all the problems with the other studies where you've got to worry about oh what's the definition of a case right and here we simply see a vaccinated person is anybody who's at least one jab basically okay all right great so because obviously in the first two weeks again as you i think i was picked up on your previous about in the first two weeks, they try and ignore, they try and say, well, okay, we won't count those people as vaccinated. You know, saying bad happens to them. They're unvaccinated. Well, no, because if adverse reactions happen, they actually happen very, at any vaccine, they're likely to happen, more likely to happen in that. Exactly. If we're going to be an injury

from a vaccine, as they say themselves, they admit themselves that they don't need to do long-term studies because the issues you see with vaccinations happen within the first couple of weeks. only we're going to put all those issues into the unvaccinated category. At least that's what they're doing here in the United States of America. But so, so you looked at this data. You actually decided to take a look at the data. So let's see what you found out. Yeah, before I want to come on to the data that we looked at,

I just want to bring up the slide of the of the data which published. Yeah. From a different part. There we go. That's the one. This is one. And then if what if this, so this was published by public health England. I think they're also called the United Kingdom Health and Security Authority now.

I keep changing the names of these things, right? But this was, it was a government publication, and this was at the same time as we looked at the mortality data, this is looking at effective, this is looking at vaccine effectiveness in terms of the infection rate of the vaccinated against the unvaccinated. What you can see here, what you can see here is that in the age, click the next slide, let's highlight it. There we go. So, so what I see is, I see rates amongst persons vaccinated with two doses.

compared to rates amongst persons not vaccinated. And from 30 to 39, it appears that, and this is per 100,000, so this is, they break this down, so you can't say it's a comparison based on populations. Per 100,000, there's 956 cases amongst the vaccinated and only 751 in the not vaccinated. And that continues the trend. Look at the next to them, 49. This is their own data. This is their own data showing that the vaccinated are getting more infections than the unvaccinated.

Yeah, and it's true, notice it's true for each age category because it's important, it's important that we have to do these things for each age category because otherwise you get this thing called age confounding. So where the problem is that older people are more likely to die or get infected or whatever with the death stuff particularly. Well, let's be clear because we get in, by the way, folks, this is what I want to call these, you know, segments in the weeds. We like to get in the weeds because we want to make sure you have the truth. You're not going to just get some headline here. we're going to get in the details. But when you talk about that issue with age, obviously the elderly are more at risk. Those were truly the ones that were risk. Almost everybody else, very, very low risk. But you had a high risk group. They were also the first ones vaccinated. So you could see that, you know, they're being the first ones vaccinated. They're also the ones that are the most frail. So they could be vaccinated. They die first. And then it looks like the younger people that haven't been vaccinated yet. They're the unvaccinated are doing better. But what you're really comparing is youth to the elderly versus vaccinated or unvaccinated.

and that would be a problem. That's why we have to do it by different age categories. Okay, got it. And incidentally, just going back to that side there, the point is that for every one of the, every one of the age categories above 30, yeah. The infection rate amongst the fully vaccinated

was a lot, was quite a lot higher than amongst the unvaccinated. Yeah. Now that's quite shocking given people think, well, okay, this is, this is, this is fix. Now, there was a lot of controversy about this because some ex-people were saying, oh, well, the government was using unreliable data.

They should have used the ONS data, not the NIMS data, but when we looked at, all the data was unreliable. You couldn't say one was more reliable than the other. Well, let's be clear. This is the data that they're reporting when they're saying on the news, well, the government, the CDC says and the NHS says that get your vaccine because it's clearly better to be vaccinated. Their own data thrown up on websites is showing the opposite to be true.

Okay. Exactly. And now so now we say, well, let's forget about vaccine effectiveness. Because they are there then saying, well, forget about that. Okay, they admit that it doesn't stop infection. of the vaccine, but it stops you from dying or it stops you from being hospitalized with a serious condition. Hence, yeah, we're going to look at the mortality data. And the Office for National Statistics in the UK now has published, and this is the report that we've focused on and done the

analysis as published, published last month the data on mortality rate by vaccination status and age category, because before they hadn't done it by age category. And so because you have had that age confound effect, we couldn't really do a proper analysis and a proper comparison. Now, what they do is if you now click on it, you'll see what they did, the age categories, although it was crude. So what you can see is that if you click now, that they didn't provide us with a proper age categorization. Obviously not as thorough as you would expect people to be in the middle of a pandemic that truly want to understand what's going on. Let's go age category 10 to 59. I mean, 18 and less is a zero death rate. So it's hard to imagine.

why you would group that in with someone 59 years old, unless you're still trying to make a mess of things. And remember, they only provided this because we put in a freedom of information request and also spoke to the authors of the previous version of the ship pool. But at least we've got that. So at least we've got something decent we can work on for the older age categories, right? So what we're going to do, we start to look at the data. Let's look at the 10 to 59 category. Okay. And there you have the all-cause mortality rate. So this is a plot each week of the number of deaths per 100,000 in the comparing the unvaccinated to the vaccinated. And this looks bad for the vaccination because you can see it's much higher. But no, this doesn't tell us anything,

right? And it's because it's too big an age group. You can't really make any conclusions. Again, because the people most in that age group, 10 to 59, the people most likely to die are the older people, you know, the 50s to 90s to 59s. Right. And they're also the ones more like. to be vaccinated. The younger ones at this stage throughout weren't, right? So this, we can't, I mean, actually, people have picked up on this and said, my God, this is evidence that the vaccine is really terrible. It's really killing. No, no, this graph doesn't, although it looks like it's saying that. You look, you're making this point. If you were, if you had an agenda, you could throw this out there and say, look, I've proved it. That's not your goal. We're not trying to manipulate it.

You want to get this right. You're saying, look, I can see the anomaly right there. And I can explain it. I can explain it by the fact of the unvaccinated. are healthier, they're younger, they're more likely to do well, no matter what you do, the vaccine are going to be older, sicker people. But you take it a step further, right? So let's get to that. Let's go to where you go to try and figure out what's really going on. Yeah, okay, so let's now start looking at the 60, each of the older case.

This is each of the older age categories. So 60 to 69 age group here. Here you see it's flipped. Here, the unvaccinated has a higher mortality rate than the vaccinated, which looks good for the vaccination. Right, but there's something wrong with this plot, which shows the data, there's something fundamentally wrong here, which is that you've got this peak, which happens to coincide,

that high mortality. Mount Everest I'm looking at there. I'd be asking myself, what happened? Did a volcano break loose in the middle of the UK and wipe these people out? What is that? Well, what we know about that peak, and we'll see it clearly in some of the latest stuff, is that that coincides with when the vaccine,

vaccine program rolled out at its peak to this particular age group. And you'll see a similar, similar pattern, but with a different peak in a different place for the other age group. So go on, go on to the next age group. Okay. The 70s and 79. There's the peak again. They were vaccinated earlier. They were vaccinated earlier that the peak. So that's where you see the peak of mortality. Again, coinciding with the peak when the vaccine roll out. Got it. And exactly the same. Go to the 80 to 80 plus. group. So they're all seeing this peak at the exact and at different times of the year,

so you can't blame something else. It's based on the age groups and when they were, their vaccine program started, they're all having this peak in the unvaccinated who didn't receive the vaccine. Exactly. It's crazy. Now people have said, people have said, well, hang on said, maybe maybe COVID mortality is causing these peaks. But if you see on the next graph, you'll see that's not the case. Okay. Because this is the, this is looking for the 60 to 60, group, all deaths which have got nothing to do with COVID. These are the non-COVID deaths. Right.

Okay. Non-COVID deaths. You've got this bizarre situation that somehow when the vaccine rollout happens at its peak, the unvaccinated are dying of something other than COVID. And they're dying in huge numbers. A much greater rate than the vaccinated. It's ridiculous. So let's bring it back here.

Just for people that I think, you know, obviously we've looked at graphs. I've been talking to Professor Fenton, but let's make this clear. If you're vaccinating, you know, a giant group of people, you would expect if there's some injuries from that vaccine for them to be, you know, seeing a little peak. But why would the group across the other side of the fence standing in the field having nothing to do with this vaccine? Why would they be having a peak of death at the moment that the people getting the vaccines are getting the vaccine? It makes no sense unless those people go ahead. We're misclassified. We're misclassified.

So now we want to show you just a hypothetical example because this really gets across the point of how very, very simple shifts in data or very simple misclassifications can produce What we know for sure is there's got to be a misclassification happening here. That's somehow. There's a, it's, well, actually in my hypothetical example, I, which I'll go to the hypothetical, let's do it. Okay. Yeah. In this example, right, I'm going to do something other than a misclassification, right?

And then you want to see it happens. So in this hypothetical example, what we're assuming is that there's a vaccine, a new vaccine has been rolled out to a population of 10 million people. Okay. But actually, it's not rid of vaccine, it's just a placebo. It has no effect whatsoever. It doesn't increase or decrease deaths. It just, and if we assume that the mortality rate in this population is 50 per 100,000,

then every week, right, we'd expect to see for every 100,000, we expect to see 50 people die. Got. Okay. And so we're going to ramp, we're going to put out this vaccination program, right? And ramp it up. Both groups are the same. Unvaccinated and vaccinated are the same. They both have exactly identical. They're getting the same thing, right? Accislation is exactly the same, right? But they just think that there's a vaccine here. So as you can see, you know, in the first week, we start off with very few people vaccinated. So I mean 100,000, there's only 50 deaths, right? Whereas a much larger number in the unvaccinated, it's a much bigger population.

But the rate is still the same. There's still 50 per 100,000 die because that's the mortality rate for this population. Got it. And as you go on each week, you're getting a higher proportion, increasingly higher number of people getting vaccinated. And therefore a higher proportion of those while the unvaccinated number that are unvaccinated. It says there's taking from the column. Once you're vaccinated, you're no longer in the unvaccinated column. So that's going down. Okay.

Right. But the mortality rate stays the same. Now, here's the little trick, because this is. All I'm going to do now is say, well, let's suppose that the deaths, there's a delay in the death reporting. Let's just suppose that that 50 in week one gets reported as number of deaths in week two and 100 in week two. And there you see, and look what happens to the mortality rates. I've done nothing other than shift the death reporting by one week. And what you see is somehow the vaccinated are suddenly, somehow, you know, less than half in,

the beginning less than half the mortality of the so-called unvaccinated, even those of these are all getting the same stuff. And look at the fact the unvaccinated mortality rate starts to really peak. You get this ridiculous peak there with the 179 to 148 at the time when the vaccine rollout has peaked. Wow. And you graph this out, right? You graph that out. What does that graph look like? When we graph out your hypothetical, just moving it one week, delay it one week. And there you are. you have a perfect peak that looks just like the peak that we are actually seeing in their data. And this apparently, you've got this placebo. We know it's a placebo.

It's a hypothetical example. We set it up like that. And yet, this has a, it's a miracle. It's a miracle. This placebo is a miracle vaccine. You know, it's saving people. Anyway, the problem now, the interesting thing about this, I should say that we don't think, we think that the death reporting and the O&S report wasn't delayed by a week.

But if you, if when someone dies shortly after a vaccination, like for example, if they die in the same week or a couple of weeks later, right? And if it's then not reported as being vaccinated, which we believe is likely, even though they say, oh, no, everybody, you know, they say if it's with, if it's, they'll classify someone as having a vaccination within 21 days. It means even if there's a day later, they'll still count count that as a person. Well, let's be clear. Let's be clear. I mean, and I don't know if it's the same in, in England, but. Here in America, our CDC tells us we are not going to consider you vaccinated until 14 days. Do we have that? Can we bring that up? This is from our own website when you've been fully vaccinated. And it says you are, here it is. If you don't meet these requirements regardless of age, you are not fully vaccinated.

You have to be two weeks after the second dose in a two-dose series. So both Pfizer and Moderna, so that first month after the first shot, you're considered unvaccinated if you get sick and die. and then after you get your second shot, 14 days, two full weeks, if you get sick and die, you're going to land categorized in the unvaccinated column. So, and Johnson Johnson, the single doses, they wait till two weeks after that first single dose shot. And so that right there, you're saying that could create this exact same anomaly too if you're just considering them. Okay. It would.

In fact, if you just did it by one, even by one minute, you get exactly the identical figures to what I showed you. And you can prove it's identical. and my paper or the blog does the proof, right? So it's exactly the same effect, right? Now, to be fair, I have to say that the ONS is telling us that no, that we don't do that 14 days, that even if it's less than 14 days, they are supposed to be, if they die, they're supposed to be recorded as vaccinated.

But the way things work in the, you know, in the hospitals and surgeries, if they die shortly after, the paperwork isn't going to all be submitted. It's, you know, it's actually, I think it's unlikely that all, of that information about whether they got vaccinated, it's going to be there. Especially in situations where a person tends to go to a clinic or to their personal doctor to get vaccinated, if they end up in the ER or dying, they're in the nearest hospital. They could be in a different system.

So many times what we're seeing is that your only list is having been vaccinated. If you're vaccinated in the same hospital you end up in for your critical care. So right there, you could see problems and anomalies. Okay. Yeah, exactly, exactly. So if we go on to the next. Next slide. Okay, here we go.

Yeah, so here's another. So now this is not the only reason to demonstrate, demonstrates that the data is totally unreliable and they're something wrong going on here. Okay. Because look at this is, these plots are three different classes of vaccinated people. Forget about the unvaccinated. These are people who are vaccinated. Okay. So, I mean, I'm colorblind, but the, the gray line, the orange line is two dose mortality.

The orange line is at least 21 days mortality with no COVID. And then the blue line is within. So under 21 days mortality, no COVID. Right. Now, these are non, these are all non, so these are non COVID. Yeah, these are non COVID deaths. Yeah. Yeah. Right. And these are different categories of vaccinated people. They should all have to say they should all be the same and there's a and I've got a line we've looked at the life mortality table. So if you just click you'll see that come up here. Okay. Click click on it. There we go. So we go. Oh, these are dead because this is the thing. So next one. Okay. Yeah. That's where they should all be. Sorry, go back. Go back to that gray line. There we go. That's what we should be.

You expect a little bit of fluctuation, obviously, right? But they should all be around there. And yet what you, what have you got here? That second, the first dose seems to be really deadly. Catastrophic. Right. Whereas you get the second dose and suddenly you become immortal. Suddenly this is a magic, this is a, this is a cure for all medical conditions. These people just don't die. Because remember folks, these are not COVID deaths.

These are just regular. Why is it should be affecting any of it? This is just where we're at. Your average person should be on that gray line. Why are we seeing these gigantic anomalies when we're just When we look at vaccine status, it shouldn't be affecting this category of other deaths, not COVID debts. All right. Amazing. So it's a real problem.

It's a problem. The problem, the data is, you know, we don't know why the data has got so screwed up, whether it's just incommendants. I don't know why. I mean, we've got some hypothesis, but anyway, but there are other things. It's not even the only thing. There's another, there are other problems. So Nick, I'll give you another really weird example of the data problem. So look at this. So this data, this data set is based on the 2011 census data in the UK. So it's all people who were registered in the 2011 census and were registered with a general practitioner in 2019. Okay. That's why it's only a subset. It's a very specific subset of the UK population, right? And the reason that is because you've got good records on these people because you've got the GP records, right?

So you should have good data, right? But the thing about it is, because you're it is because it includes no new people, no new people come into this, right? The death, the number, every time someone, the number of deaths in any one week, that number, the population of the next week should be exactly reduced by that amount. Exactly. They're now dead, they're not a part of the population any longer, but you have the deaths, you see total deaths, 14,537, you see the total population, but when you go down to the next number, it's like there's 10,000 extra people in the column that shouldn't

be there. They should be dead. Only in those first, so from April, whatever it is April, the 9th, you get it right. There's no error. You've got the zero. You see the zeros in the right hand column there, right? There are no errors. But there's something that they're adding, where are these, you know, every week at the beginning, they're adding 10,000 people. I mean, I'm not saying this train massively changes anyway. It just gives an indication of how thick, this whole thing. Why are they doing this? What's going on here? Is this? Is it malicious? Is it incompetence? Are they trying to portray things in a particular way? It just shows you how unreliable the data is.

That's the point. Okay. And so therefore, you know, what we've got to do is hypothesize what the reasons are for these anomalies in the data and how we can adjust them. That's what we're doing. Now, so that's what we're going to, that's what the, I think we're next look at the, kind of like suggested hype if you go to us. Yeah. Okay. So what we've done here. Yeah.

This is fine. So what we've done here is simply do the adjustment where we're making the assumption that the that there was that miscaterization. Unvaccinated, no COVID mortality on the orange line. The blue line is the adjusted vaccinated, no COVID mortality. But then you have two doses of vaccines in the gray dots and one dose of vaccines. And when you isolate them that way. Yeah, that's just showing when those dots are just showing when the when the vaccination programs are rolled out. So it reached its peak of the orange one. right so that's just showing you when so that's just showing you when the rollouts were for this group

right for the first dose and the second dose right so here you see with the first start as the first when the first dose was um was being rolled out right when we've done these when we've done these adjustments of the miscategorization and also adjusted this difference in the lifetime life table mortality figures right yeah they should again this should all be because this is yes non-cove mortality these should all be flat along the same line right but you and it you've got this peak at the beginning right now what is the explanation why why are the vaccines and now we're seeing the vaccinated people like dying at a higher at this higher rate at the period when the vaccination program is rolled out for that group now possible explanation possible explanation

these are in the UK the vaccination program they prioritized it in each age group for people who were the most at risk, right? The people with most comorbidities, particularly frail, right? Yeah. They decided because those people were genuinely most at risk of dying from COVID, it made sense to vaccinate those people first, right? That's why it was done, right? But of course, what we're seeing here is that it appears that those most frail people, what's happening, because you've got that quick hit immediately when the vaccination is rolled out. A possible explanation, We're not saying this for sure, but it seems like the most reasonable explanation is that those people were because of the problem with the immune system, whatever. Those are the people not only the most of the risk of the COVID, but they were most at risk of the effects of the vaccine.

Charging up their immune system. They were too frail. Exactly. Now, the probability is that those people, those people probably would have, those are the people who probably would have died within a few weeks anyway. Right. Okay. But it would just because you, but with the vaccine just kind of like pushed, you know, could have pushed them, push them over the edge now. Right. Now that there is no other, you know, we looked at all the possible other possible explanations. We looked at socioeconomic factors. We looked at the paper looks at a whole load of stuff. Yeah. Other people, you know, other people, we've had people try and discredit us on this, right? Because they offer a different explanation. And you know what their explanation is? Their explanation, they say actually the original graphs. are correct. It is the unvaccinated who are dying at the time when they're rolling out the vaccine. And they're saying the reason is, oh, it's because those people who are close to death are chosen not to have the vaccine. Now that we know that is not true in the UK. We know, right? We know

that the people close to death were getting the vaccine, right? They were doing it in the those who were close to death didn't get the vaccine and therefore they were dying. They were going to die. That was exactly here in America too. Those were the first people. If you got cancer, you got heart disease, you got to get this vaccine right away. All right. Let's go to your conclusions. You list them out so that people can read them. Let's go to this conclusion's page. Here we go. Here's your conclusions. Vaccine effectiveness studies are generally flawed because the reliance on determining COVID cases. The simplest and most objective way to determine overall risk benefit of the COVID vaccines is to

compare all-cause mortality between the vaccinated and unvaccinated latest ONS report on mortality by vaccine status should provide the necessary data, but is flawed in many ways as we've pointed out. The anomalies most easily explained by misclassification of some unvaccinated deaths as vaccinated. After adjusting for the misclassification appears that shortly after vaccination, people may be exposed to an increased mortality risk. All right. And whatever the explanation, the ONS data is both unreliable and misleading, absent any better explanation, Akam's razor would support our conclusions,

which is, you know, you have inconsistencies in this data at the very least. At the very least, they cannot be using this. this data to say, look how great our vaccine is and you unvaccinated are at high risk. We know it because of our data. This data, when it's properly looked at, shows that there's actually a problem, you know, that is not making sense and appears to be caused by the vaccine itself. Yeah. And what it definitely doesn't do is provide any data that the vaccine is particularly safe and effective. It certainly doesn't provide the evidence for that.

And in a sense, we shouldn't be surprising. One of the slides that we sort of missed, we skip through was that was from the Pfizer you know from the actual randomized let's go can we bring up the Pfizer because it's important to show that Pfizer knows here we go we'll bring that up hold on so incidentally this is my slide i took this from i picked this up and i couldn't find the person i couldn't wasn't i didn't know who to credit you do but so this is looking at the it's comparing the vaccine arm in the trial with the placebo arm so you have an equal number of people And of course this was looking to see how effective it was at stopping transmission. But also we can look over time at how many of those people died, whatever reason.

If you look at, so if you look at again, this isn't, and then click on it again, it'll expand. There we go. So what you actually see, first of all, actually in that of course these were not a lot, I mean it was a very biased trial because it was people, it was volunteers. These were not, you know, they didn't take on any sick people or anything like that. So you wouldn't expect to see many deaths. But again, if COVID is as bad as, as as as deadly as it was claimed, then the placebo arm should be seen more.

So do we add those numbers when we're looking at this? We're talking about deaths. I mean, we're talking about... All it is, it's just showing at that point. 21 to 15. There's just a few more deaths in the vaccine arm than the placebo arm. Right. Certainly not one.

It certainly flies in the face of the, the, face of the major statement they make, which is it reduces your, you know, serious cases. It didn't, even in Pfizer's own study, it didn't even achieve that. That was their own experimental trial. Of course, you know, they weren't looking for side. They were just trying to test. They were just trying to optimize it for effectiveness, you know, about whether it stopped transmissions. And, you know, as I've shown from the hypothetical example, it's easy to get that to be shown to be effective at the beginning. Right. And then it, of course, study kind of like Waynes. It's just, it's a statistical, you know, you can get that by,

as I say, by the placebo, just using a simple statistical trick of, of just, just shifting the reporting all week. You know what I love about doing interviews with guys like you, Professor Fenton, is how excited you get about a bunch of bar graphs and numbers. And, you know, you make me excited about it. And clearly, and I want to say this, I want to thank you for taking the time, not only to do this work, but to share it with me and our audience here. at the high wire, because this is what gives me hope. What gives me hope is that whether it's just totally, you know, stupidity, lack of talent, or it's nefarious in some way, what we do know is that, you know, people like you are out there. You've been doing this work in every way sideways

since your career began, and now suddenly you're deciding, well, let me take a peek at this other data and the biggest news story there is, and you're seeing anomalies that shouldn't be there. And it's so important right now because though these are just little numbers on a graph, clearly these are people's lives. These are decisions being made. We are forcing these products into healthier and healthier people that have no risk to the virus, but if there's a risk in this vaccine, we should know about it and we should have better data. Our countries, especially the UK and America, should be doing so much such a better job at this. There's no reason to have this type of data be so messy, except that we're seeing really the bias,

the desire for this vaccine to work from the moment they promoted it up to us before it ever even got through its safety trials. They were telling us how great it was going to be. That shows you that you're really looking at an endpoint and then designing the science prior to getting there. And so I want to thank you for your work, Professor Fenton. I hope you will keep us abreast as I know you're going to look deeper into this. And I only hope that mainstream media starts, you know, helping you get this information out there. Thanks. I'd just like to say there was a team of people who were listed at the, you know, listed on the paper and people can see those.

And at the first author of the paper with my colleague, Martin Nill, and he deserves a lot of credit, which I, you know, didn't give earlier. So thanks very much. There it is. Here it is. Let's go ahead and list them because they, too, are heroes putting their names and careers on the line, unfortunately, just to tell the truth. This was the paper. Latest Statistics on England mortality data suggests systematic miscategorization of vaccine status and uncertain effectiveness of COVID-19 vaccination.

Martin Neil, Norman, Fenton, Joel Smalley, Claire, Craig, Joshua, Goetzkow, Scott McLaughlin, Jonathan Engler, and Jessica Rose. Thank you all for your service to the world with this important paper. And of course, for all of you watching, go ahead. In the acknowledgments, you'll see that there were clinicians who were also involved in this. but they had to remain anonymous because otherwise, obviously, their career is threatened. I mean, we get a lot of flack as well, but it's not going to hopefully not terminate our careers. All right, Professor Fenton, thank you for your work.

Thank you for your time. I think it is clear that something stinks in Shinoa right now, and we hope we can clear it up. It's really important. People's lives are hanging the balance. Keep up your good work, and we look forward to having you on the high wire again soon. Great to be on. Thanks a lot. All right. Take care. and be sure to check out our live broadcast of the High Wire

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