Dr. Chris Masterjohn – Big Questions and Holes in the Data on COVID

Content By: Ari Whitten & Dr. Chris Masterjohn

In this episode, I am speaking with Dr. Chris Masterjohn – who has a Ph.D. in nutritional sciences and who is widely regarded as one of the top nutritional biochemistry experts in the world – about the big holes in the data on COVID and vaccines.

Table of Contents

In this podcast, Dr. Masterjohn and I discuss: 

  • The BIGGEST misconceptions the general public have on C19
  • The vaccine trials and the way the data was reported (and why this matters!)
  • Are all COVID patients in the hospital admitted because of the disease?
  • The best data to use to assess how dangerous COVID is

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Ari: Hey, everyone. This is Ari. Welcome back to the Energy Blueprint podcast. I am very excited for today’s guest. It’s at least the third, if not the fourth podcast we’ve done together over the last several years. He is one of the world’s foremost experts on nutritional biochemistry. Someone I personally follow and admire greatly for the work that he does in his field. He’s really one of the world’s top in that area. Over the last two years, like me, he has developed a very deep obsession with COVID data. He brings a different angle and a different layer of expertise that I bring to it.

He’s been fixated on elements of this story that are very, very fascinating that I have, personally, not delved very deeply into. He’s uncovered some really fascinating stuff and in some cases some pretty shocking stuff. He’s also someone that from very early on in the COVID pandemic developed a guide. He was focused on practical solutions and he developed a guide to the best natural treatments that, at first, using logical speculation and then as more and more research came out adding that to his guide and keep updating the addition every month or two.

To talk about all the latest evidence that’s emerging around various treatments. From things like vitamin A and Zinc, vitamin D, vitamin C, melatonin, iodine nasal rinses, and all kinds of things like that. He’s really been someone that has been committed to scientific truth and uncovering the truth, which is not always easy to do, especially, the last two years. Also, someone providing practical solutions to help people minimize their risks of COVID, and minimize their risks of getting severe COVID. With all of that said, I’m sure that’s not sufficient in some ways of an intro, but hopefully, sufficient for now. Welcome back to the show, Chris, such a pleasure to have you.

Chris: Thanks, Ari. It’s great to be back. Appreciate it.

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The biggest misconceptions people have about COVID

Ari: First of all, I think a good place to start, where I will allow you to guide where you want to go first, because there’s so much territory we could cover. Is a broad question of what do you think are some of the biggest misconceptions and misunderstandings that people have in the general public about COVID, about treatments for COVID, about any aspect of the last two years. What do you think are maybe the top? You don’t have to list them out, but some of the top three, four biggest misconceptions that people have.

Chris: Unfortunately I think one of the biggest misconceptions is twofold. One is that the PCR test means what we think it does, and the other is that the PCR test doesn’t mean anything. You have two different camps out there. Some people who think that we can just throw the test out, and then the other camp is the one where we get all the COVID data from. Where we have data all over the world all of which we think is supposed to mean on the surface and, apparently, doesn’t.

The problem specifically, is that, on the one hand, while there may indeed be confounders with the PCR test, it was tested out at the very beginning and shown to be specific for whatever COVID is, as opposed to most other respiratory viruses. It definitely means something. However, one of the things that emerged early in the vaccine trials was that the vaccines were doing something to make people who had gotten sick, who thought they had COVID. Whose doctors thought they might have gotten COVID, get a negative PCR test despite being sick.

One of the questions that emerged was what is this suspected COVID-19 or COVID-like illness? It was Peter Doshi and the BMJ who had, initially, drawn our attention to this in December 2020, when the EUA came out for the Pfizer vaccine. The reason we knew about this was not that it was published in any peer-reviewed papers, but rather that the advisory board of the FDA had voted to give the EUA to the Pfizer vaccine. Because they met and voted, they produced a briefing document from their meeting. In the briefing document, we got a sense of some of the data that Pfizer had submitted to the FDA for that purpose, that never, ever, ever appeared in their peer-reviewed trial reports.

What that shows is that there were 3,580 cases of suspected COVID-19, or what I would broadly categorize with some of the other data that I’m calling COVID-like illness. Actually, I should back up. The model of all the vaccine trials was you personally decided you thought you might have had COVID. You told them centrally, you told someone about that involved at the trial. Then they scheduled a telehealth visit that, optimally, according to the protocol occurred within three days of you getting symptoms.

The doctor talked to you, decided whether they thought you had COVID. If they agreed with you, that they also thought you might have had COVID, they would tell you to self-swab your nose, send it in the mail, and then they would get the results. If you were a positive test you got counted as asymptomatic case. If you were a negative test you got ignored, you never showed up in the peer-reviewed trial report.

When they came out with the first results saying it was 95% effective, what that meant was that if all the negative tests that came out of those telehealth visits were ignored. You just counted up the people who were sick and got a positive test then the vaccine suppressed you become– Excuse me. If you were sick and you got a positive test you became asymptomatic case. If you were sick and you got a negative test you were ignored.

Because so few of the people who were sick got positive tests the vaccine was said– Excuse me. Because so few of the people who were vaccinated got positive tests the vaccines were said to be 95% effective against being asymptomatic case. In the trial reports, you never saw the people who they had COVID, and you never found out what happened to them. They had disappeared. To say it was 95% effective meant that once you thought you were sick you had a 95% reduced chance of getting a positive test.

All right. What we hear is it’s 95% effective against COVID. What we know is it’s 95% effective against getting a positive PCR test when you are sick such that you and the doctor both think you might have COVID. There’s no sense of that in the trial reports, there’s just 95% effective. What Peter Doshi draws our attention to in December 2020, coming out of this FDA briefing, is that over 95% of the people who thought they had COVID, whose doctors thought they might also have COVID got negative PCR tests. If we look at the vaccine efficacy against getting sick it’s 9%. That is it reduced the chance of getting sick. By the way, this is over the two-month results, when the vaccine is at peak efficacy.

Ari: To be clear, you’re talking about the initial trial data that the emergency use authorization was based on. You’re not talking about the current vaccine efficacy estimates. You’re talking about when they [crosstalk].

Chris: I’ll get to that in a minute. I’m setting the stage for what we knew in December 2020. In December 2020, the background is what everyone thinks we know is a vaccine is 95% effective against COVID. What we actually know, and only because it was leaked in the FDA briefing document, is that it’s 95% effective against getting a positive test once you’re sick. If, and this is what I said about this misconception, the PCR test means what we think it does, that is it distinguishes between you have COVID or you don’t. In fact, the mainstream interpretation of the trial reports is correct, because getting 95% efficacy against a positive test once you’re sick means 95% effective against COVID.

There’s two lingering questions that we want to come to an answer about here.

The big questions to ask about the vaccine efficacy

Ari: Chris, do you mind if I interject just a moment. I want to make sure that people have followed everything you said because there’s-

Chris: Yes, sure. Go ahead.

Ari: -some layers to this that if people haven’t paid a lot of attention to all the nature of PCR testing and these kinds of things, it might have been hard to follow. I’ll try to rephrase, summarize, succinctly you tell me if it needs any editing. In the initial vaccine trial, of the total pool of COVID-like illness, of people who suspected they were sick and they thought they had COVID. Over 90%, I think you said 95% of people. I remember reading this article from Peter Doshi and the BMJ said, “Over 95% of those people who suspected they had COVID, did not test positive for COVID.”

If you take the measurement of vaccine efficacy for the subset of people, the 5% or so of people who did test positive for COVID, then you arrive at a 95% efficacy of the vaccine and preventing COVID. If on the other hand, you do a vaccine efficacy estimate, not based on PCR test, negative or positive, but based on the total pool of people who reported COVID-like illness. You arrive at something approximating 0 to 10%, let’s say, vaccine efficacy, almost zero. Is that accurate?

Chris: Yes, that’s exactly the case. There’s two lingering questions. One is, is this 95% efficacy against the test actually telling us that it’s 95% effective against COVID? That’s one question. The second question is, does it matter? Because what was the other sickness if it wasn’t COVID? There’s two questions. One point of view might be that the PCR test is completely accurate in distinguishing genuine COVID from other sickness, and COVID is so much worse than other sickness.

That it’s hugely important that you getting sick was not COVID, because that means you’re not going to wind up with serious illness, wind up in the ICU and die. The second question though is, are we actually just missing actual COVID by getting a negative test? Those are two questions. Let’s try to answer the point about whether it matters at the December 2020 mark, at the very beginning of this. At the December, 2020 mark, when the UA was given for the Pfizer vaccine and we had this essentially leaked data from the trial, going to the FDA. What we know at that point is that there were six cases of serious illness.

Three were in the vaccine group, three were in the placebo group, 0% efficacy, but the vaccine made two out of those three people get negative tests. It looks like it’s 67% effective against serious illness. When you read the trial report, in fact it’s 0% effective against serious illness. When you look at the total six people split between those two groups. Four of the people were hospitalized, two in the vaccine group, two in the placebo group, 0% efficacy, but both of the hospitalized vaccinated people tested negative. 100% efficacy against being hospitalized when you look at the positive test.

The overall picture that we get here is, we still don’t know if we’re distinguishing between real COVID or not, but at the point of December, 2020, it doesn’t matter. There’s no effect on hospitalizations for COVID-like illness. There’s no effect on serious COVID-like illness at all, at that point. Now, follow up to halfway through last year, we have six month results coming out for Pfizer. We have six month results coming out for Moderna, we the results from J&J. We have some results from AstraZeneca. In that one of those cases, do we have the data on COVID-like illness? We have gotten nowhere in discovering that at the end of the vaccine trials, nowhere.

Ari: Just new one answer, I know this was maybe mentioned briefly a minute ago. I think it was the case that the vaccine trial data that Pfizer reported did not contain any mention of all the COVID-like illness that was PCR test negative. It was leaked in another document, a CDC, FDA document. What were the details of–?

Chris: What happened was, none of the trial reports, even at the two-month mark, let alone the six-month mark. Absolutely, none of the trial reports anyone who is PCR negative, who was sick, none of them. However, because the FDA’s vaccine advisory board met in to vote on the EUA for the Pfizer vaccine and produced a briefing document that released the numbers that they privately reviewed. That’s why we have them for the two-month mark.

Currently, there was a Aaron [unintelligible 00:15:54] Group who’s a lawyer leading, I forgot the name of them. It’s phmpt.org is their website. This is the famous Pfizer documents that the FDA wanted until 2076 to release. That’s being dumped heavily this year because the judge did not allow them to take 55 years to dump it. I assume, somewhere in that series of data dumps, that’s coming out over the next six months. We are going to find the suspected COVID-19 or COVID-like illness from the six-month results of the Pfizer data.

It’s not what’s been released so far. I went through the documents that came out in the latest 150,000 pages. I didn’t read them straight through, but I keyboard searched through it and it’s not there yet. It’s in there somewhere. We just don’t have it. This is the backdrop of the vaccine trials, where those of us who were paying attention to what Peter Doshi had drawn our attention to in December 2020 are sitting there waiting and saying, “Does it really matter that we are ignoring 95% of the sick people in the trials, and that we know nothing about them? What does that mean? That’s what we’re wondering.”

Ari: Does it matter that the vaccine had almost no efficacy in preventing all of that COVID-like illness that didn’t test positive?

Chris: We are stuck with something similar in the observational data, because everything that we hear about COVID-19 data ignores anyone who tests negative. When you hear what happened to COVID-19 cases, serious cases, COVID-19 hospitalizations, and COVID 19 mortality. Every single study that comes out is doing, literally, the exact same thing by ignoring everyone who tests negative, even when they come out and say something like, “98% of the COVID-19 patients in our ICU are unvaccinated.”

You still have to say, “What about the other people in the ICU? Is that 98% of the people in the ICU? Or is it 5% of the people in the ICU?” They’re just not telling you about the people that tested negative. This is a repeating thing. We get the first hint of this, in the end of October of 2021, when the CDC comes out with a headline that says, “The vaccines are five times more effective than natural immunity at preventing infection.” This headline it gets perpetuated through all the media. There’s all this debate, especially around the vaccine mandates, not allowing natural immunity exemptions.

I go to this paper, and one of the things that emerges from this paper is that in the hospitals, what they did was the CDC has a network of hospitals across 10 states called the Vision Network. These hospitals pool together all the electronic health records for data to be analyzed by CDC scientists. They had 200,000 hospitalizations for COVID-like illness. The way they defined it here was, they looked at ICD codes, which are codes that doctors use to describe symptoms and reasons someone’s in the hospital. They looked at the codes for respiratory failure, pneumonia, dyspnea, which is trouble breathing, cough, vomiting, fever, and diarrhea.

They put this together, and they said, “This is COVID-19-like illness or COVID-like illness.” This is a different way of looking at it, then the Pfizer trial or the other vaccine trials, but it’s essentially the same thing. It’s people thought they had COVID, the doctor thought they had COVID, something like that. Although in this case, it’s the CDC scientists who are, retrospectively, going back and saying, “These people look like they came in with something that looked like COVID.”

Ari: Based on their ICD codes.

Chris: Right. Keep in mind that this is serious stuff, because A, all these people are hospitalized, so it’s somewhat serious, but also although there’s diarrhea and fever in here, there’s also respiratory failure, and COVID-like pneumonia in here. This is including the span of moderate to very severe disease that someone would be hospitalized for. They don’t tell us what the data looks like for the 200,000 hospitalizations. They say that they’re there, but then they say, “What we want to know about is,” because keep in mind, this headline is about the efficacy of natural immunity versus vaccination to prevent you from getting COVID again.

This is saying, “Does the vaccine protect you from future COVID better than having gotten COVID already protect you.” What they say is, “We’re going to take these 200,000 hospitalizations, we’re going to narrow down to a subset of people that had at least two PCR tests, that were really at least three months apart.” If it’s within three months, they’re like, “Well, what if you kept testing positive for the first time?” If it’s three months apart, at least, then we can say that’s two separate cases of COVID, that’s the idea. We want to PCR tests at least three months apart, two weeks after your second dose, are fully vaccinated and never infected, or who were previously infected, but never vaccinated.

When they put all those criteria on it, they get a pool of 7,000 people out of 200,000 hospitalizations. They never tell us who tests positive or negative in the big thing of 200,000, but in the 7,000, we see two things. Just like in the Pfizer trial, 95% of these people test negative. That makes us wonder, in the big set of data, are 95% of people hospitalized for COVID-like illness testing negative? Because if that’s the case, then the big story here has nothing to do with natural immunity versus the vaccine, it has to do with every single headline we see in the news. All of it means nothing, because it’s all about COVID in the hospitals, and it’s ignoring 95% of the people with COVID-like illness hospital.

That should be the big headline. To not answer the question for the 200,000 people is crazy, but the other thing we find out is that 85% of them are fully vaccinated. We find out that fully vaccinated people outnumber naturally immune never vaccinated people in hospital six to one. They come out, and they say, “Here’s the thing, because the fully vaccinated people were 95% less likely to test positive on the PCR test, the vaccine is 95% effective.” The numbers were a little bit different, it was not that impressive, but then they did adjustments to it.

They wound up coming out with the headline that the vaccine was 5.5 times more effective at protecting you against COVID-19 associate hospitalization. Which sounds like it’s keeping you out of the hospital, but what it means is it’s giving you a negative test. This is the point of late October, early November last year, I’m just completely obsessed with trying to know, does the big sample of 200,000 look like the little sample of 7,000? If it does, it looks just like the leaked documents from the Pfizer trial, where we’re just completely focusing on the wrong thing because most of the people hospitalized for COVID-like illness test negative and are vaccinated.

We fast-forward to recently, just a couple months ago, finally, the CDC comes out with data, and this time they’re trying to tell us that the booster shots for Pfizer and Moderna so booster mRNA shots are 90 to 94% effective against COVID-19 hospitalizations. They say, “This 94% is from the Delta dominant period and the 90% is from the Omicron dominant period.” What they’re trying to tell us is that even though the vaccines don’t seem to be that great against getting sick with Omicron, they’re still protecting you from hospitalization.

We know that because efficacy against COVID-19 hospitalizations only dropped from 94% to 90% for the people who had booster shots. If you hear that the vaccine is 90% effective against COVID-19 hospitalization, could you rephrase that to me and tell me what you think that means?

Ari: The way that most people would interpret that is, “Oh, my gosh, it’s almost complete protection on there’s no way I’m going to be hospitalized or die if I have 95% protection from some bad outcome.”

Chris: Right. There’s no way that I would be hospitalized if I get COVID. If they say 90% effective against COVID-19 associated hospitalizations, in anyone’s mind, that means if I get COVID, then I don’t get hospitalized. First of all, these studies are called tested negative case control designs. What they do is they take people who are equally sick or equally hospitalized, and they calculate vaccine efficacy based on testing negative for the pathogen of interest. In this study, first of all, finding one that was not in the headline, 79% of the people hospitalized for COVID-like illness in the participating hospitals of the Vision Network.

Across 89,000 hospitalizations between late August of 2021, and early January of 2022, 79% tested negative for COVID. The first thing is, every time you hear numbers like blah, blah, blah, happened to COVID patients in the hospital, you have to ask, what about the 80% of people not included in your data who are hospitalized for COVID-like illness? Why are you not telling me about 80% of the people hospitalized with respiratory failure? That’s the first thing.

Ari: Chris, I’ll let you complete the second thing, there’s something I want to interject, but go ahead.

Chris: Well, the second thing is that 57% of them are vaccinated. When they say, “90% effective against COVID-19 associated hospitalizations,” they mean that if you are hospitalized with respiratory failure, pneumonia, trouble breathing, cough, fever, vomiting, and diarrhea, the vaccine is 90% effective at making you test negative for COVID. That’s not what anyone thinks what that means when they’re 90% effective against COVID-19 associated hospitalizations. Just to tie it all together, we now know that what’s happening in the real world looks very similar to what we were worried it might look like in December 2020, when Peter Doshi raised our concerns about the PCR negative COVID-like illness. Pfizer trial, real-life, same problem [crosstalk] you’re going to say?

Ari: Just to wrap that thought up and say it in a different way. This is again, the difference between depending on how you count your COVID-like illness and calculate the vaccine efficacy, it’s the difference between something approximating 100% efficacy versus something approximating 0% efficacy. It’s a pretty significant deal, you’re right.

Chris: That’s right. That’s exactly right.

Ari: What I was going to say is there’s one thing I struggle with in my interactions with some people who know nothing about this topic, except what they watch on media on TV or in the newspaper. There is a level of disbelief that either the data fraud or the data manipulation or the level of incompetence could possibly be this grand. You could possibly have this level of misrepresentation of the data. I just want to add one other related layer to this that has been widely talked about, and actually even in peer-reviewed science.

To give people context, because what Chris is talking about here is something that he’s personally digging into the data and uncovering that really very few people have talked about since Peter Doshi’s original article in the BMJ. Here’s something that people have talked about is widely known and not even controversial though it’s crazy. When they count COVID hospitalizations in hospitals, there’s been studies, at least one in children, and at least one in adults. Where they found that 50% or more of COVID hospitalizations aren’t even hospitalized from COVID, they’re there for other reasons.

They’re there because they have a broken arm or they’re there because they’re in there for heart disease or for diabetes treatment or cancer treatment. They test them and find they test PCR positive. “Oh, now you’re a COVID hospitalization.” There’s been an article in the Atlantic about this. Many articles written in mainstream publications. This is no conspiracy theory. Again, this is peer-reviewed data that has talked about this, showing that 50% plus of “COVID hospitalizations are not actual COVID hospitalizations.” There’s something very important to take away from that. Again, either the level of incompetence is so extreme that they can’t even count.

They can’t even discern between somebody who’s in there for heart disease treatment with a PCR test positive but has no COVID symptoms, versus somebody who has severe COVID and is on the verge of dying from COVID. They’re not distinguishing those two people. Either extreme incompetence or outright intentional fraud, data fraud. Those are the only two explanations for that, and for people listening, you have to choose which one of those two things you think is more plausible. Either they’re extraordinarily incompetent or they’re deliberately misrepresenting the data.

Chris: It gets so much worse than either of us are saying, because what you are saying is a completely independent, but intersecting dataset from what I’m saying. Your problem actually multiplies by mind to give us the real problem. 80% of people who are in the hospital with COVID-like illness don’t have COVID, so are not COVID-19 associated hospitalizations. 60% of people who test positive in the hospital don’t have COVID-like illness. The percentage of people that are in the hospital with COVID-like illness and test positive is very few people in the hospital.

One thing is clear is that the hospitals can never be full of COVID patients because they’re full of people where 60% of the people that are testing positive for COVID don’t have COVID-like illness, and 80% of people who have COVID-like illness are testing negative for COVID. There’s no way the hospitalists could be full of people who have COVID-like illness that test positive for it. There’s lingering questions where we don’t- so this PCR negative COVID-like illness, what is it? There was a study that came out just a couple weeks ago, and it takes forever to publish a peer-reviewed paper. This is using a pre-vaccine rollout data set, and it’s a small study, but it gives us a hint.

What they did was they said, and actually the title of the study, I was floored when I saw this, because I’ve been writing about the pandemic of PCR negative COVID-like illness. Then I see this study in PubMed, that’s called RTPCR negative COVID-19. I’m like, “Wow,” so I look at this study and pre-vaccine, what they did was they took people who had COVID-like illness. They looked at positive or negative testing on the PCR, but then they gave them antibody tests. The thing with an antibody test is an IgG, a positive result on an IgG antibody test will generally tell you about any past infection. Whereas a positive result on IgM tells you about a past recent infection.

If you’re in the hospital for COVID-like illness and you test positive for IgM antibodies, you are probably hospitalized for a case of COVID. Now, it’s conceivable that you had COVID got over it then a week later got COVID-like illness that was not COVID, put you in the hospital, but that’s not the case. One way to look at this is if you test negative on the PCR, but positive with IgM, while you’re hospitalized with the symptoms of COVID, then you are probably a real COVID case. What that data looks like is if there were compelling alternative explanations for the COVID-like illness, such as an exacerbation of COPD, Chronic Obstructive Pulmonary Disease, an exacerbation of heart failure or bacterial pneumonia.

Generally speaking, those people did not have an increased level of antibody suggesting that they had COVID. If they didn’t have a compelling, alternative explanation, about 70% of the PCR-negative people are actually COVID cases. Probably, this PCR negative COVID-like illness, that’s 80% of the people hospitalized for COVID-like illness. Probably that 80% some of it’s COPD exacerbation, some of it’s heart failure exacerbation, some of it’s bacterial pneumonia. Some very large percent of it is actually COVID, and they’re just getting a negative PCR result.

Ari: Yes. Sorry, complete the thought then I’ll ask.

Chris: Well, so we have some hints of what it is, and my personal suspicion is that that was pre-vaccine rollout. Post-vaccine rollout, I think it’s that, plus it’s some degree of vaccine side effects, vaccine generated autoimmunity, vaccine generated spike protein toxicity combined with those other explanations. The big question, we know the CDC has the answer to this, but they have not released is what’s the ICU rate and the mortality rate for PCR negative COVID-like illness?

I feel like if it were validating to share that information, they would share it because they have it. I really think we need legal action to get that from them because if it’s the case as it turned out in the Pfizer trial, that the people with PCR-negative COVID-like illness are just as likely to go into the ICU and die. Then the vaccines they’re doing nothing at all. We need to know that.

Why we are mis-informed about COVID

Ari: Right, and to that point, just so people, again, don’t understand this, don’t think this is, “Oh, this is some crazy talk. This is just a conspiracy theorist.” There was an article in the New York Times recently, talking about the fact that the CDC is not releasing huge swaths of data they have on COVID. Including they have the data, which is, in my opinion, probably the most important data to share. They have the data looking at All-cause mortality and hospital hospitalization rates, but All-cause mortality, that’s risk of dying from any cause, broken down by status, comparing vaccinated versus unvaccinated people.

They have that data, and they are not sharing it. We can only, in my opinion, there’s only one plausible explanation for why they wouldn’t want to share that data, and it’s because it’s not flattering to the narratives that they’ve been promoting. If it was supportive of the narratives, of course, they would share it. If it was true that vaccinated people had a much lower risk of dying from one of the major causes of death, that would be apparent in the data. They would be all over sharing that. Of course, that information would be everywhere, but they’re not sharing that data, and that is extraordinarily suspicious, to say the least.

Chris: I completely agree with that, and I think it’s slightly worse than that because the closest thing that the CDC did to looking at All-cause mortality by vaccination status. To hit this point home, all this controversy over whether someone died with COVID or because of COVID. Or whether someone got a side effect because of the vaccine or after the vaccine, all of these things are completely controlled for when you’re looking at All-cause mortality. All the controversy and all the possibility of manipulation or poor interpretation comes at assessing cause and effect between particular things that happened and why and died. There’s no controversy over whether someone died.

Someone’s either dead or not. We can get past all these arguments, and just look at the simplest way to summarize risk-reward with respect to mortality. In one statistic that completely avoids all these questions about manipulation or poor interpretation is to look at All-cause mortality. It should be the central thing that we want to see. The closest that the CDC had gotten to doing that was a paper where they looked at non-COVID mortality. In this paper, they initially- actually, I want to back up for a second. When you publish data, if you make adjustments to the data, the right thing to do is to show the raw data set that your adjustments came from. Explain what adjustment you made and why, and then show the effects of making that adjustment.

You should always be suspicious of anyone, no matter what the topic is, who is presenting you highly adjusted data and not showing you where it came from. This is a red flag that they are trying to hide what they don’t- it doesn’t matter whether it’s politically or emotionally charged. If it’s something does pomegranate juice reduce blood sugar? You would expect them to show the actual blood sugar results before and after the pomegranate juice, in the pomegranate group and the control group.

If they present you a graph that shows the percent change from baseline of the pomegranate and doesn’t show you the control data or something like that, you’re like, “Well, wait a second. What was their blood sugar measurement? You’re trying to scam me.”

Ari: Hence the famous Mark Twain, “There’s lies, there’s damned lies, and then there’s statistics.”

Chris: Okay. In this study where I’m hoping that the CDC looks at All-cause mortality, here’s what they do. They, first of all, they say, “The vaccines would be expected to reduce COVID mortality. Therefore, we looked at non-COVID mortality.” The first red flag, is, wait a second. Why didn’t you say, “As expected and as shown in table one, the vaccines reduced COVID mortality?” They didn’t show the COVID mortality in the paper at all. That’s weird. We chose to look at non-COVID mortality, rather than total mortality. In the non-COVID mortality.

They then say, “We wanted to control for healthcare-seeking behavior. We didn’t want to control vaccinated people just to the total pool of unvaccinated people, because we didn’t want to pool in these health-conscious people who got vaccinated with lazy people who don’t go to the doctor,” or whatever. The control group who we picked was instead of just straight up unvaccinated people, it was people who did not get the COVID vaccine, but got at least one flu vaccine over the last two years.

Ari: That’s a bizarre criteria to choose.

Chris: Right. Well, it gets worse than that because remember the reason they chose this criteria is they want to control for healthcare-seeking behavior, and, essentially, health consciousness.

Ari: Which, of course, is the extent to which you’ve gotten flu vaccines. It has nothing to do with nutrition and lifestyle.

Chris: Yes, first of all, there’s something deeply idiosyncratic about this group of people that got at least one flu vaccine the last two years, but spent all of the last year resisting every social convention and government mandate and employee mandate. Everything was pushing you, that you must get the vaccine, and they really remained obstinate through this entire time. There’s something idiosyncratic about those people behaviorally. They do not seem to me to be the same as the people who got the COVID vaccine. Anyway, put that aside because we can’t know what was going on in their minds, but we can know what was in the CDC report.

Remember the purpose here, the purpose is to control for healthcare seeking behavior. What they find is that the COVID vaccines cut non-COVID mortality in half, by making that comparison. Now, do they think that the COVID vaccines actually cut non-COVID mortality in half? No, not at all. They never even suggest that. Their conclusion is that the data were confounded by healthy vaccinee effect, which is a subtype of a healthy user bias. Which means that the people who had gotten the COVID vaccine had more health-conscious and, therefore, had other better health consciousness-related behaviors than their control group.

They explain their primary finding. Their primary explanation for their primary finding is that the data were confounded by a healthy user bias, but remember the reason they picked that control group is because they were trying to control for that exact bias. The normal thing to do here would’ve been to be like, “My control group didn’t work. Let me go back to the original data set and find it different way of adjusting the data.” They don’t do that. That’s the end of it.

Ari: Right. I think I remember reading that paper. I remember reading one bizarre finding. You probably will remember this, but I think they even showed that the vaccinated group was something three times less likely to die in car accidents or something like that. Obviously, there’s no plausible reason why getting the COVID vaccine would make you less likely to die in car accidents. It has to be some issue with the groups that you’re comparing.

Chris: Yes, I’m not sure if that was in the same paper, but yes. I think the big point with this paper is they, essentially, got to the end of it and were like, “We can’t conclude anything useful from this,” and yet they have the data comparing the total mortality rate between vaccinated, unvaccinated people. They still haven’t released it. Even by the end of this paper, they get to the end of it. They, essentially, dismiss their own finding is like, “Well, this was an exercise in complete futility,” but they don’t show us the actual data.

The fact that they went so out of their way to publish something on the edge of the total mortality topic, and went through this crazy adjustment on its surface doesn’t make any sense, and then conclude that it didn’t work. Then still at the end of it aren’t showing us the All-cause mortality difference. I think very much supports your suspicion that they don’t like what the All-cause mortality looks like.

Ari: Part of my suspicion is not just speculation, but this is a topic I’ve spent a lot of time on the All-cause mortality data from around the world. I’ll just briefly state a couple things of importance. Just logically, this is science 101, how you would design an experiment and expect to find data supporting your hypothesis. Let’s say you’ve got some new cause of death in the population. It’s killing huge numbers of people. It’s a major cause of death, killing tons more people than would normally be dying, excess mortality. You have a treatment for that. That dramatically reduces is, let’s say, 95% effective in preventing death from that major cause of death.

Well, what would happen if you compare levels of how many people are dying before anybody got that 95% effective treatment. Versus how many people are dying after most of the population has gotten that treatment. That’s so wonderfully effective at decreasing your likelihood of dying from that major cause of death. Basic logic means that you’re going to see a huge reduction in the total number of people dying. If you don’t, then the data doesn’t support your hypothesis, that that treatment is preventing large numbers of people dying.

The US data is such that if and anybody can look this up freely, there’s plenty of articles online. If you want to fact-check me, please do. If you compare 2020 All-cause mortality to 2021, it’s higher 2021, the year when most of the population got vaccinated, we had higher All-cause mortality in the United States. If you want to control for the fact that okay, at the beginning of the year, not that many people were vaccinated. You can also look at just quarter three and quarter four of the year when a majority of the population was vaccinated. It’s still higher for quarter three and quarter four in the United States. You can also look at the same data from all over Europe, from many countries around the world.

You can find in most places, the same exact findings as high or higher All-cause mortality, post most of the population getting the vaccine compared to before anybody got it. That is simply, totally, inconsistent with the hypothesis that the vaccine is preventing huge numbers of people from dying that would otherwise be dying. You cannot reconcile those two things because the benefit, the claims around this vaccine, aren’t showing up in the all-cause mortality data.

Chris: Yes.

Ari: Have you looked into that data? Do you have anything to add on that?

Chris: No. I’ve heard that, but I haven’t done the deep dive like you have into it. I think the best counterargument you could possibly make to that would be that most of those deaths occurred in unvaccinated people. If you could show that, and that I don’t think we do have for the United States.

Ari: Well, what you do is you look at countries where they vaccinated nearly the entire population. There’s many ways you can break that down. What’s the mortality rate pre-vaccine versus post-vaccine in countries like, let’s say Israel, where they vaccinated everybody. There’s many other countries that are examples of that. You find the same thing there. In fact, when you look at younger age groups, you find that there’s much greater elevations in All-cause mortality post-vaccine than pre-vaccine. You can also break it down in terms of All-cause mortality in specific demographics in the age group that we know is highly susceptible to COVID.

Like 65-plus-year-olds, where the vaccination rate, even in the United States, it isn’t just 60 or 70%. It’s over 95%, almost everybody’s vaccinated. You can look at quarter three and quarter four, All-cause mortality in that specific demographic, and see that there is no apparent reduction in All-cause mortality. That could be consistent with the idea that these vaccines are preventing huge numbers of people dying from a major cause of death. That’s totally not apparent in the data.

Chris: Well, if it turns out that we’re getting very close to a complete picture. We’re just missing a couple, like missing links in the middle. If you take that, then what I’ve been saying is like working up from ground zero. We are hiding the fact that the vaccine what it’s doing might be limited to giving people a negative test once they’re sick. The data that’s missing is do the people with negative test who are hospitalized for COVID-like illness, do these people worsen and get disabled or die, at the same or worse rate than the people with the positive tests. That’s the blank thing that we don’t know.

On the other end, we see no apparent mortality reduction in the overall population as a result of the vaccine rollout. If you just like the math would seem to if you just manipulate the equation and get the unknown to one side. It seems like the middle has to tell you that the PCR-negative COVID-like illness in the hospital, is just as fatal as the PCR-positive COVID-like illness. If it is then that’s the clincher that tells you actually, the vaccine’s not doing anything clinical [crosstalk].

Ari: All right. It’s a small subset of the total pool of people dying that it’s insignificant in terms of the total numbers.

Chris: Yes. It’s very possible too that the vaccine is reducing COVID mortality by some much smaller number than we thought, like 30% or something like that. It’s making up for it by even causing some of the inflammation that might be associated with COPD exacerbations, heart failure exacerbations, or something like that.

The challenges with the PCR tests

Ari: Got it. Okay. I want to ask something about the PCR test, some of the details that you said, I didn’t want to interrupt earlier. I think this is an important layer to ask about. I spent a lot of time learning about and listening to researchers talk out the PCR test and the nature of it, and learning about [unintelligible 00:55:26], who was the Nobel Prize-winning scientist, who invented that technology. Who, unfortunately, died just a few minutes before the few months before the pandemic started. Who was a vocal voice for not using that test, and the way that it has been used all over the world for the last two years?

My understanding of the test is that, if anything, its major flaw is that it’s overly sensitive and likely to result in false positives rather than false negatives. How do you reconcile that statement? Which many scientists have made with the notion that somehow 70% of COVID or 70% or more of COVID could be PCR-negative, but still be COVID?

Chris: Yes, I see what you’re saying. You’re saying if it’s overly sensitive, then you would expect it to be catching all the COVID, and then a lot more that’s not COVID. First of all, I think it’s possible for it to be both in a certain sense, because remember we do have this dual-pronged data problem in the hospitals where the majority of people that are testing positive, don’t have COVID. The vast majority of people who seem to have COVID don’t test positive.

We actually might have both of those things going on. Here’s how I would say. I actually have a very in-depth attempt to explain what I call the hospitalization paradox. If you go to chrismasterjohnphd.com/paradox, you’ll get my written explanation of that. I think what’s going on is the vaccines, I believe, are helping pull a negative test forward in time. If you get sick with COVID, you don’t necessarily get a positive test, but very many people get COVID get positive tests, and then it takes them a while to be able to get rid of that positive test. You are testing in the nose, that represents is that your immune system is mounting response.

That’s pressing the presumably the viral replication in your nose, which is then reducing the amount of RNA in your nose that can get detected by the PCR test. I believe the vaccinations are helping the immune system generate a negative test in the nose faster, in a way that does not necessarily protect against systemic inflammation of the viral infection and may actually make it worse. The basic gist of this is that whether you’re vaccinated or not your natural immune system, your first response, if you get in your nose, is that you have a local immune system in your nose that is not dependent on your systemic immune system. That will produce unique antibodies called secretory IGA.

The secretory IGA is intrinsically noninflammatory. It plays a role of mopping things up. It’s very much like a physical binder system to clean up without initiating any systemic inflammatory response. Whether you’re vaccinated or not, you have this possibility, but it takes some time. Let’s say, the first- well it doesn’t always take some time. Let’s say you’re a little kid, and you’re getting sick all the time. Little kids have extremely high mucosal immunity, and, of course, COVID antibodies, there’s some cross-reactivity with cold viruses and stuff like that. Even if you’ve been exposed to colds a lot, and for that reason you have those antibodies in your nose. If you have a super strong mucosal immune system, you might stop that in its tracks and nothing ever happens. It got in your nose, but nothing happened.

Let’s take someone who is 30 years old and their mucosal immunity is moderate and they get the virus and they do get some level of sickness. What’s happening is they’re taking days to mount an effective mucosal IgA response in their nose. By the time they mount such a response that they can generate a negative test, they already have a systemic inflammatory response because to some degree, the virus already got passed the mucosal IgA system. Now the next time they get the virus, they might not get sick at all because now they have prepped their mucosal IgA to respond much more quickly, but the first time they’re going to get sick.

Now, the vaccinated person, they also can take that time to develop the same noninflammatory mucosal IgA response in their nose, but they have an additional asset, so to speak. They got an injection in their arm, and that generated systemic, meaning traveling through their blood, IgG antibodies. If they get any trigger of an additional infection, those systemic antibodies can go through the roof and they’ll spill over into the nose, and because they’re primed to do so, they can react like that. They can spill over into the nose and generate a negative test in the nose.

`The problem is unlike IgA antibodies, IgG antibodies are inflammatory. Not only that, but they correlate with– just to generate them in the first place, you generate a big systemic inflammatory response. Just after the second dose of the Pfizer vaccine, for example, IFN gamma, interferon gamma, goes up 20 fold, and it correlates with the IgG antibodies. That reflects the fact that you are producing an inflammatory response to get the IgG antibodies. Then those IgG antibodies, when they rise in response to the illness, they are inflammatory themselves. So you have two cycles of inflammation.

Not only that, but now we know that the spike protein from the vaccine persists for at least 4 months after vaccination in the body, it’s possible that enduring spike protein in the body is also part of what drives a better antibody response 6 months out. If that’s the case, then we might also have correlations with your ability to generate that response with cumulative spike protein toxicity, or other effects of the spike protein, such as autoimmunity, or just general inflammation.

I believe what’s happening is that the vaccine is causing people to very quickly squash the thing in their nose, but at the expense of having much more inflammation as a result. I think that’s why we’re– remember that when you get a serious illness of COVID-19, the main thing that’s making it serious is the inflammatory response to it. If you are getting a faster negative test in your nose at the expense of a greater level of systemic inflammation, then I think that’s why we see a lot of people that might actually be a genuine COVID case winding up in the hospital with COVID-like illness, but testing negative and being vaccinated.

Ari: Wow. It’s a crazy thing to think about. To everybody listening, I think it’s maybe important to clarify that what Chris is saying is his own speculation to make sense and reconcile large amounts of different layers of data kind of pointing in this direction. He’s attempting to create a hypothesis to explain what is otherwise unexplainable. Just keep that context in mind, this is not a claim that this must be the truth. Is that accurate? Do you want to say anything on that, Chris?

Chris: It’s my hypothesis to explain the data that has no explanation, but there is data supporting it. Some of what I said are just facts. It is a fact that IgA antibodies are noninflammatory and that what I outlined about the postal response and how it’s noninflammatory is a fact. What I said about the vaccine, the Pfizer vaccine 20fold increasing interferon gamma after the second dose is a fact, the dependence on systemic IgG antibodies is a fact. The point where my speculation enters is that I’m suggesting that these facts are why someone can get a negative test and yet have COVID. Even at the point where we, where I said that we know some of the COVID-like illness that tests negative is genuine COVID in the hospitals, even that is– no one really has the facts because it’s hard to say what the genuine COVID case is.

If your positive IgM is your benchmark, it’s a real COVID case. If PCR is your benchmark, it’s not a real COVID case. There’s a lot of lack of clarity on what do we mean when we say it’s a true COVID case. This is my hypothesis, but there are certain central facts which are, 79% of the people in the hospital with COVID-like illness are testing negative. We don’t know what their fate is, we have no idea, and there are differences in natural and vaccine-induced immune responses that do make the vaccine response faster and do make it more inflammatory.

VAERS data should we trust it?

Chris: I’m inclined to think that the problem is as it looks in VAERS, but 15 to 50 times worse than that.

Ari: Right. It’s not just you that’s saying that, for people listening, there’s many researchers who have expertise in this area who are– the estimates that I’ve seen are between, I think the lowest one is 5 fold, and the highest one is I think at least 50, if not greater than that.

Chris: Yes. Where I would go with VAERS analysis is Jessica Rose, she has an excellent substack. It’s jessicar.substack.com where she is specializing in analyzing the VAERS data, but she did one and Steve Kirsch did another one and a number of other people have used different methods to try to come up with an underreporting factor. Not even in the CDC reports have they tried to come out and say that VAERS is overreported. That’s not a serious contention. Anyway.

Looking at the VAERS reports is one way to look at that. I’m much more focused on– you had mentioned my COVID guide. I’m trying to make it so the eighth edition that comes out will address what can be done to protect against vaccine side effects. Because I know that many people in my audience have gotten vaccinated who either did it out of choice and then are now worried about whether that has long term side effects or felt pressured into it. I’m studying it more from a mechanistic standpoint now to try to understand what the mechanisms of toxicity are, more than I’m deep-diving into the rates of various problems.

The wild card though is just, it’s now more reasonable than ever to say that six months wasn’t long enough to study it. When people were defending not having biodistribution data of the spike protein and not needing to study the vaccines for longer than six months, they were also saying that it stays in the shoulder, and it’ll be gone within a few days.

Ari: Meaning their track record of predictions is not looking great.

Chris: Also those specific claims were needed to justify why it didn’t matter that we didn’t have biodistribution data and why it didn’t matter that we didn’t have data longer than six months. Now we know that no one has ever looked for the spike protein at any point in time after the vaccination and not found it. Even in the early reports, the first report that came out said that it was staying in the blood for up to 28 days but in most people, after a couple of weeks, it starts going away.

The paper that came out in November showing that it persists for two months in the lymph nodes of the armpits also showed that the antibodies in the blood interfere with the assay for its detection. Even those initial results that suggested a lot of people in the blood it’s dropping off at the two-week mark, those are now very questionable because we know that that’s when the antibodies start rising.

If you’re testing for something that is trying to bind to the spike protein and elicit some detection response so that you know it’s there, the antibodies aren’t necessarily protecting you from it just because you can’t measure it. They’re just gloaming onto it and letting go, and gloaming onto it and letting go. They can just obstruct the assay without actually doing anything to clear it from your body.

One paper that came out end of last year showed that the spike protein is found in circulating exosomes four months after vaccination. When they looked two months, they found it, when they looked four months, they found it. There’s just no point in time where it’s not found. Now you want to know two things. First of all, wherever they look for it, they find it. They looked in the lymph nodes of the armpits, but it’s not like they looked in biopsies of ovaries and didn’t find it. They were just reporting on the lymph nodes of the armpits.

It’s in all these places that they said it would never go, and it’s in for months after they said it would disappear. Not only that, but now we’re saying, “We need fourth doses.” They didn’t ever study that. If you’re trying to say something about the rate of a certain side effect from the current data set, and yet we know that it takes at least four months– that four months is not long enough to clear the spike protein from your system, and we are going to now say every six months you need a new dose. Why put any trust in the current rate of side effects anyway? Because we have no idea what they’re going to multiply to two to three years out if we’re going to persist in the endless dosing schedule.

I think it’s just a huge question mark. I don’t know. I feel like at this point, two years out, we now have way fewer answers than we ever thought we had before. I don’t feel confident in rating a risk-reward at all. One thing that’s very interesting from– I’m not a toxicologist, my PhD is nutrition sciences, and I assume that toxicology would always use the similar principles I’m about to describe, but I can tell you that in nutrition science because nutrients have toxicity profiles, I can tell you how we do it in nutrition science.

For example, what used to be called the Institute of Medicine is now the National Academy of Medicine, the principle when they set the upper limit for a nutrient is that the worse your data, the more conservative you are. The better your data, the more liberal you are. If we know that 500 milligrams per day of pyridoxine hydrochloride, which is a form of B6, is the lowest reported dose in published case reports of neurological toxicity, but we have no idea if that’s actually the lowest dose that caused the negative effect. When they set the upper limit for B6, they applied a safety factor of 5 fold to that., so to go from 500 to 100, because they said we have this number of 500 but we don’t trust it very much, so we’re going to apply this massive safety uncertainty window.

I would think that if your data is terrible– for example, people will dismiss VAERS as junk data. If your safety data is junk, then you should have a very, very, very large margin of safety. If people are not in agreement that the underreporting is 48 fold in that system, and some people think it’s 5 fold and other people think it’s 30 fold, You don’t average those out. You say, “We are so uncertain of the underreporting factor that we’re going to multiply the highest observed underreporting factor by five.” That’s the reasonable thing to do.

When they set the upper limit for B6, they didn’t say, “Well, we’re going to average the dosing that was used in all the different case reports of neurological toxicity and set that as the upper limit.”` They said, “We’re going to take the lowest one, and then multiply an uncertainty factor of five.” If you were doing something similar with the VAERS data, because it’s junk data, you would say, “Okay, the greatest observed underreporting factor in the current literature is 48. We’re going to declare it 200 when we’re doing our risk-reward.” What we see is the total opposite, it is, “We are uncertain of this data, therefore we ignore it.”

Ari: No matter how much of this concerning data accumulates, because there’s a bit of uncertainty, we’re just going to brush it off completely. No matter what level of signal is apparent in that data, we’re just going to ignore it because the data– we’re going to decide it’s not good enough.

Chris: Right. That strikes me as the complete opposite of a rational approach to risk-reward.

Chris Masterjohn’s best tips for preventing COVID infection

Ari: Absolutely. That’s what the purpose of that system is there for, it’s to show you a safety signal. If you’re going to ignore whatever safety signal shows up there, then there’s no reason for that data to be collected at all. My last question to you, I know you have to go. I know this is a question you could probably answer in half an hour, but I’ll let you do a very succinct version of it.

I’m a talker as well, so I get it, and you like to go into a very deep level of detail in your answers. If you were going to give people just a few practical recommendations, everything you’ve learned from doing eight editions of this guide, what would be your top three or five recommendations? Then after you do that, let people know where they can get the guide from you and follow your work as you release more articles related to this topic.

Chris: For COVID prevention?

Ari: Yes.

Chris: Number one would be, keep your vitamin D status between 50 and 60 milligrams per milliliter. Number two would be, have a cabinet full of sickness prevention items that you use for COVID prevention or for cold or flu or whatever it is, I think there’s a lot of overlap there. It’s good to have a set of antiseptics that are used for when you have high confidence that you are exposed, and you want to make sure that it doesn’t go any further.

You can set what you want to use, depending on how worried you are, you can set what constitutes an exposure however you want. Some of the things that are most valuable in such a set as that would be would be zinc acetate lozenges, which when sucked on and not chewed and swallowed, are a very good way to deliver the antiviral effects of zinc to the nose and throat.

Some type of iodine-based antiseptic, if you don’t have any problems with iodine, like a 0.5% or 1% povidone-iodine solution would be the one that has the clearest literature behind it. Some people use more natural variants of that like Lugol’s and a Neti-Pot or something like that. I particularly like Betadine MSDS Cold Defense Spray which is an iota-carrageenan nasal spray, just as a travel version of that because you can keep it in your backpack or your glove compartment in your car or something like that. It’s convenient if you are traveling and all of a sudden you start sneezing and you’re around someone who ‘s sick.

Ari: Did you see Enovid’s nitric oxide product? Have you seen that?

Chris: I have not.

Ari: Oh, interesting. Okay, I’ll send it to you after. It’s some Israeli company that created a nasal spray of two, I think, pretty benign compounds that when you spray it, the two compounds which are held in different compartments mix together, and then they dramatically boost nitric oxide levels. They’ve showed it can sterilize I think 99% of COVID virus in your nose.

Chris: Very cool. I didn’t know about that. That’d be great if you send that to me.

Ari: I’ve tried the iodine nasal rinses and found it horribly unpleasant, so I prefer the Enovid.

Chris: I believe that and I’d like to try it. They are super effective. Iodine will kill anything in your nose. At certain percentage it burns a little bit. I think 0.5% is the efficacy, comfort balance. You can pretty much pick your antiseptic of choice. Any kind of antiseptic rinse through your mucosal membranes when you’re exposed is going to be extremely helpful.

There’s a collection of things if and when you do get sick, I think extra fat-soluble vitamins, especially vitamin D is the top one there. There’s a handful of other black seed oil, melatonin at night, Quercetin Phytosome, which is a particular product that’s much more bioavailable than regular Corsten, or Corsten mixed with Bromley and vitamin C. Then I really think for people who get respiratory stress, David Brownstein’s nebulized hydrogen peroxide and iodine protocol, and supplemental arginine are the two most important things.

Ari: Awesome. Chris, where can people get your guide? What’s it called? Where can they get it? Then, where can they follow your work more broadly?

Chris: The guide is at chrismasterjohnphd.com/covidguide. The short name for it is the COVID Guide and the long name is the Food and Supplement Guide for the Coronavirus. It’s currently in its seventh edition. When you purchase it you will always get free updates. The eighth edition is on its way once I finish my spike toxicity research. To generally follow me out follow me on Substack at chrismasterjohnphd.substack.com. You get the COVID Guide for free if you become a paid subscriber to my Substack newsletter.

Ari: Awesome. Chris, thank you so much for this episode. Thank you for all the extra time going almost two hours, I really appreciate it and more broadly from that, thank you for all the work that you’ve been doing on this topic for the last few years. You have a very unique brain, a very unique mind, and the expertise, and just the way your brain works and analyzes problems I think is very, very special. Thank you for devoting so much of your time to this topic. You’ve been a very important and unique voice that the world needs. Thank you so much. I appreciate it.

Chris: Thank you, Ari. It’s great to be here. I really appreciate that.


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