UVC: Deanna McLeod: More Harm Than Good: Attributing Vaccine Causation
The inaugural Understanding Vaccine Causation Conference, convened by World Council for Health Steering Committee Member, Shabnam Palesa Mohamed, took place on Feb. 5, 2022. The WCH Law and Activism Committee brought together legal practitioners, doctors, scientists, and jab victim data and advocacy groups to explore a key question: How are jab adverse events proved?
Deanna McLeod: More Harm Than Good: Attributing Vaccine Causation
[00:00:06] Shabnam Palesa Mohamed: Finishing off the inaugural conference with a Canadian, and that’s going to be Deanna McLeod, who also presented previously at the World Council for Health General Assembly, which takes place every Monday. Deanna is presenting on the topic More Harm Than Good: Attributing Vaccine Causation.
[00:00:24] Deanna, if you can hear me, please introduce yourself, tell us a few words and why you believe this conference is so important. And then lodge straight into your presentation, you’ve got about 15 minutes. Hopefully we will squeeze in some Q&A and then close.
[00:00:38] Deanna McLeod: My name is Deanna MacLeod and I’m principal and founder of a medical research firm called Kaleidoscope Strategic. Our firm specializes in preparing evidence-based guidelines and treatment reviews in oncology. And I am coming from a different point of view in the sense that unlike many of you I’ve come through industry, so the pharmaceutical world.
[00:01:02] And I’ve spent about 10 years in medical marketing and sales, working a lot with oncologists. And of course there, we, we consider safety very, very carefully. Our firm basically began looking at all of the data with respect to the vaccines early in 2021, when they were being rolled out. And immediately having been sensitized to what troublesome side effects profiles look like in phase three trials, we were immediately alarmed that this would be considered something that is safe or that it would be given to healthy people because the side effects profile was such, I mean, there was grade three side effects. Grade three adverse events. And you know, they were intending to give this to healthy people who weren’t really at risk.
[00:01:51] So the reason why I’m thankful that I’m part of this conference, is that I’d love to tell the story of safety from a pharmaceutical point of view. And I’m not going to color all pharmaceutical companies with the same brush.
[00:02:03] But one thing that I did see when I was in industry was a propensity to maximize benefit and minimize safety. And there are very specific tricks that they use when conducting their phase three trials that allow that to happen. And so what I’d like to do is I’d like to walk you through one trial specifically, which is the phase three Pfizer trial for their Covid-19 vaccine.
[00:02:27] As we’re going through it, I’d like to present to you how we, as a group, my medical research firm would prepare an evidence-based argument for a lack of safety in these particular jabs. And I’d like to highlight a few of the things that were done in these phase three trials that basically made it so that the safety profiles looked much more favorable than they actually were.
[00:02:48] Okay. So we’ve just gone through who I am. So I won’t spend too much time on that. One of the things that we absolutely want to do at least at Health Canada is to absolutely be sure that they’re proven safe before use. And I think even before we begin any complex discussion or any detailed look at the actual phase three trial that Pfizer use to approve their vaccine, I think that one of the things that we need to do is to think about what it means to prove that something is safe. In our world, that means that you have a phase three randomized controlled trial. So causation, is always limited to something that you can prove in a randomized context. I do acknowledge that observational trials are indeed very important, especially when you can’t do randomized clinical trials. But when you do have one, looking at it very carefully to see what it says, I think is an absolutely important element.
[00:03:35] So I’m just going to per sake of time, zip along here. And again, what we’re going to be looking at is level one evidence, which is from a randomized controlled trial versus level three or four evidence, which is where a lot of the conversation is we’re having for the very reason that we decided to, you know, complete these trials and then roll it out to the population outside of a clinical context.
[00:03:57] And so that’s the best available data that we have for the general population. And I definitely recognize and appreciate the importance of looking at that. However, one of the things that we want to do at least in our firm is to stay very clearly in the level one realm as long as we can. Because from a pharmaceutical point of view, they’re very, very experienced in the moniker that association does not mean causation. So what they’ll do, in court is they’ll say, if you can’t prove that there’s causation, an association between two things doesn’t prove causation. And so what they do is they basically try and keep everything into the real world.
[00:04:32] I’m going to show you some of the data and how they looked at their randomized controlled trial to get it out into the real world as fast as possible so that they can indeed say association isn’t causation and therefore not own up to or not be liable for a lot of the safety issues.
[00:04:49] I’m going to spend a little bit of time here, for me this looks like a very familiar chart. It’s the design of the trial. Some of you who are lawyers probably don’t recognize this at all but what it basically shows is what the trial design is. And from that trial design, you can actually determine what you can and cannot prove from the trial.
[00:05:06] And I think it’s really important to spend some time here and just look at that carefully because, if you were to make an evidence-based argument in court, you’ll need to know what this trial was designed to prove or not to prove.
[00:05:18] And so right out of the gate, one of the things I want to bring your attention to over here is the fact that this is short form of a patient group that was included in the trial. And so they were people who are older than 16 years, no prior Covid-19.
[00:05:33] So I’m just going to pause right there and highlight the fact that based on these phase three trials, there is no data to support use of these jabs in anybody that is Covid recovered. This was specifically run in patients who had no history of Covid-19, they had healthy immune systems, and for the most part, were at no risk of severe disease. So if we just pause for a moment and consider the population that was at greatest risk to Covid-19, they were the elderly and those with comorbidities, and those are shockingly absent from these trials.
[00:06:06] And so what our health officials did is they turned around with data in this population and said that this vaccine works in patients who are at high risk. And then of course they went on to recommend it for people who were Covid-19 recovered as well. However, in terms of proof, there’s absolutely no proof based on this phase three trial that the Covid-19 jabs work in those populations.
[00:06:30] The other thing I want to talk about here is the design of the trial. So you had a vaccine arm and then you have a placebo arm. So it’s a randomized placebo controlled trial, which is very attractive, and it’s what we look for. However, one of the things that we want to do when we’re building an evidence-based argument is say that you have to have a randomized control trial against the standard of care.
[00:06:50] And the standard of care here is not placebo. The standard of care up until this point has been natural immunity. So the fact that this trial did not compare the vaccine to natural immunity means that they have absolutely no basis for making any claims of the vaccine induced immunity versus natural immunity. And I think that that’s very important. And it also says that that puts both natural immunity and vaccine induced immunity on level playing fields.
[00:07:18] And finally, one of the things that I really want to highlight, that’s really important when you’re looking at this particular trial specifically is that usually for instance, in cancer, we would have different types of endpoints. So an endpoint for cancer trial would be for instance, efficacy endpoint would be shrinking the tumor. And then of course the side effects would be nausea, vomiting, hair loss, would be the adverse events.
[00:07:37] But one of the things that’s really quite intriguing and interesting about this particular trial, is that positive PCR test is that the primary efficacy endpoint, and what that is, it’s actually symptoms or adverse events plus a positive PCR test. So they also record symptoms in the absence of a PCR test, but they call them adverse events. So whenever it’s an adverse event, plus a positive PCR test, and most of the adverse events are Covid-like symptoms, they call it an efficacy endpoint. But whenever they look at Covid positivity without any symptoms they call it, or without a PCR test, they call it an adverse event. And so the adverse event for this was a positive PCR test, but they did selective testing and they didn’t use the, the standard of care, which is a virological assay, which is what they should have done to prove infectivity. And the PCR test was never meant as a diagnostic tool.
[00:08:33] One of the things that you may or may not know about the PCR test is that you can actually increase the level of false positives with a PCR test based on whatever cycle threshold you use for that particular, you know, when you’re running the test. And one of the things that was not indicated in any of the documentation for this particular study was what threshold they use. So we don’t really know whether the threshold changed by labs. We don’t know how consistent it was.
[00:08:59] So basically to us, when we look at this data, it basically tells us that, that’s an unreliable endpoint and that what we really need to do is really focus in on the adverse events to know exactly how effective this jab is.
[00:09:12] A couple last points. This particular trial did not tell us the benefit of Covid-19 vaccines in the vulnerable population. It didn’t tell us about hospitalizations or deaths because that wasn’t an endpoint. It doesn’t tell us about the duration of the response, because it wasn’t designed to do that. It doesn’t tell us about the benefit in Covid-19 recovered, and it’s not designed to assess the spread of the disease. So based on the actual available evidence, we don’t have sufficient evidence to support a lot of the policy that’s ongoing.
[00:09:44] And so if I were to be presenting an evidence-based argument, I would basically say that you cannot prove that the Covid-19 jab can reduce hospitalizations, death, you know, have benefit the elderly, benefit Covid-19 recovered, et cetera. And so that could be a very compelling argument if we want to be basing it on, what can we prove from this trial.
[00:10:05] The other thing that I think is particularly interesting, and I’ve highlighted this in red, is that the solicited adverse events were actually a primary endpoint for this trial. And the reason why that’s really important is that if in all the study reporting and everything that we hear from the media and from our health policy makers is that the vaccine was very safe, very effective. But what they don’t report on is the fact that safety was also a primary endpoint. And in fact, the study wasn’t powered to measure safety. And when we do measure safety, what we see is that the adverse events in the people who are vaccinated are actually higher than the people who aren’t vaccinated.
[00:10:41] Now, just to be sensitive to time, I’m just going to move along here, because I know that we have a little bit of time. I’m just going to pause on this slide here.
[00:10:51] Now we talked about the fact that there’s going to be a desire to minimize safety. And so there’s a few tricks that they, that I’ve seen in the literature whereby they minimize safety and one is minimal monitoring.
[00:11:04] And I think that’s really important in this particular trial, because when we’re looking at the efficacy end point, which was Covid-19 symptoms for the most part plus a positive PCR test. They monitored that continually through the whole period of the trial. However, when they were looking at just Covid like symptoms and calling it adverse events, they only measured it for seven days. So in the efficacy side of things, you’re getting these Covid leg symptoms measured for the duration of the trial. And here you’re only looking at symptoms for seven days.
[00:11:34] And then they have something called unsolicited, severe adverse events. So those would be adverse events that required hospitalization had injury. They’re, they’re quite severe. They’re something more than grade three or four severe adverse events. And they only measured those for one month. And for the unsolicited serious adverse events, which are prolonged hospitalization, permanent injury, or death, they only measured those for six months.
[00:12:00] So for the efficacy endpoint, they’re measuring for the duration and for these they’re just grabbing little windows of measuring it. And I know that I heard previously that, there were talking about adverse events and, are we capturing them? So just from a Canadian perspective, if they would report, say for instance, something like heart palpitations after getting your Covid-19 jab and it continued on. In Canada would take me two weeks to get a doctor’s appointment. The doctor might see me. Then he would refer me to a specialist. Two to three, maybe four weeks later I might get a specialist appointment who then might send me for testing and then bring me back. And only about six months later, I don’t know, maybe that’s an exaggeration, but a number of months later, would I find out that I actually had a heart injury, myocarditis or something related to this. However, they’re only capturing severe adverse events for one a month. So that’s basically under-reporting any of the adverse events that are associated with this. And so I think that that’s really something to emphasize when we’re talking about these particular things.
[00:12:59] And I think the other thing too, is knowing that the mode of action of this particular agent and the fact that it is delivering the spike protein, which we know to be pathogenic, and we know what type of pathogenesis it causes. It is shocking that this study did not look at subclinical endpoints. For instance, D dimer levels are C reactive proteins. Things that would identify cardiac damage for instance, because these would be things that we would know and expect in a phase three trial. And the fact that they’re absent and the fact that they didn’t look at them again, is another indicator for wanting to minimize safety.
[00:13:31] Another common tactic for minimizing safety is to declare something to be effective as quickly as possible. And so for instance, with this particular trial, they declared it to be effective within about two months. Based on preliminary data. And then what they do is they unblind the trial and they cross over, or they invite all of the placebo participants to cross over to the inoculation arm. And so in this particular trial, about 89% of the people crossed over. And again as the previous speaker commented, you know, you wonder if they know that you need to have a control arm to be able to find long-term safety issues. And indeed, by having everybody crossover, what they’re doing is they’re erasing any trail that we can prove that there’s any type of long-term safety problems with this.
[00:14:17] So again, we’re coming back to this idea that you want to emphasize, or make the benefits look as favorable as possible and minimize safety. So here, one of the tactics that they took when they were doing their efficacy is they combined the reporting of the adults and the adolescents. And by combining the reporting, what they do is we know that the vaccine immunity wanes over time. And so by combining it with the adolescent cohort, which only has two months follow-up in combining that with the adult cohort that has six months follow up we actually enhance the outcomes for the adult cohort. So it makes it seem as though it’s more effective than it is.
[00:14:53] And even with all of that, I’m just going to show you the outcomes of this particular trial. So again, when we’re looking at symptomatic cases on the right, in the inoculation arm that were 77 symptomatic cases, and then on the placebo arm there were 850, and of course there’s the 91% effective. It reduces the relative risk by 91%.
[00:15:14] But one of the things that it doesn’t emphasize was this relative risk change. However, when you look at the absolute risk change at four months, it’s only minus 4%, meaning that it’s really not benefiting many of the people in the population. In terms of severe cases, and I’m just going to emphasize it, this is a secondary endpoint, there was a difference in about 22 cases. There’s not enough events there to actually, you know, say that that’s actually a significant or not, or that that’s clinically meaningful, but again, they touted the 96% relative risk change.
[00:15:43] However, the benefit for that, the absolute risk change was really only minus 0.1%. So again, one of the ways that they were able to emphasize or enhance the benefits was by using this item called relative risk change. But if you actually look at absolute risk change, which is the difference between the placebo and the inoculation arm and they count it factors in how many people actually benefited, because most of them actually didn’t even get Covid or, or weren’t susceptible to it. The benefit is actually quite minimal.
[00:16:12] But let’s just step now into the treatment related adverse effects. So again, this is a primary endpoint of this trial and they only measured it from one month post the second dose. Whereas for the symptomatic cases, they measured it ongoing. And I want you to recall that for symptomatic cases, it was Covid-like symptoms plus a positive PCR test. And now we’re just looking at adverse events. So here we see that there’s 5,241 people on the inoculation arm that actually got an adverse event, right. Compared to placebo, which was 1,311. So that’s a 300% increase and almost a net increase of 4,000 people who got adverse events. And if you look at the reactor genicity profile, you can see that most of those were Covid-like symptoms. And that impacts 18% of the population.
[00:17:03] So if the reporting of this trial had been honest, what we would have seen is both end points, primary end points as symptomatic cases, and the treatment-related adverse events reported side-by-side and it would have been obvious that these jabs cause more harm. 300% versus 91%, 18% versus 4%, then good. Again, you know, we’re looking at the severe cases and this is a, a differences about 22 cases here. But if you actually look at any severe adverse event, you’ve got a difference of about a hundred more in the inoculation arm than not, which is an increase of 75% and an impact of 0.5%.
[00:17:43] So we’re reducing by 0.1% here, but we’re increasing by 0.5% here. And again, if we look at serious adverse events, there’s a numerical increase in the inoculation arm compared to the placebo arm. So that’s, you know, a 10% relative risk increase and a 0.05%. Now, I don’t think this is clinically significant, but the fact that it’s actually higher in the inoculation arm than the placebo arm is a little bit concerning.
[00:18:09] And I think that we heard discussion of all cause mortality. And so we’re looking at deaths here and, you know, in the reporting of this particular trial, there was a great emphasis between 15 and 14%. This was the unblinded and you can see it. It was highlighted in this particular table in the supplements. Of course it didn’t make it into the main paper that it was 15 and 14. And again, we’re noting that there’s a numerical increase in the inoculation arm versus the placebo arm. But buried in the text of the paper was this statement that after crossover you know, there were additional five people who were inoculated that actually died, for a total of 20 deaths on the vaccine arm and 14 deaths on the placebo arm.
[00:18:50] When we published this video, the video that we did in our report, we got a little bit of pushback because they duly noted that there was after crossover considerably more people on the vaccine arm than the placebo arm and therefore it was more reasonable to think that you would have additional deaths based on, you know, the sheer number of people during this unblinded period, this open label period. However, that said, I just want to emphasize the fact that the people who were enrolled into this trial were healthy individuals who were older than 16 years old.
[00:19:21] They were people who, if they did have any type of secondary conditions that they were controlled, these were not high risk patients. And to think that in six months, we see, you know, a total of 20 deaths on this side and 14 deaths on this side is a little bit concerning for this particular population. Now we know that the people in the placebo arm had more instances of Covid-19 and therefore we know that that’s associated with death. And so that it’s very possible that those deaths, you know, we would expect to see higher deaths in the placebo arm, but then you look at the inoculation arm and you ask yourself, okay, they haven’t been exposed to Covid-19 to any large degree compared to the placebo arm, what is killing these healthy individuals? And I think that this is a very important thing to drill down and to look at because it’s within the context of a phase three trial and you see a numerical trend in the wrong direction.
[00:20:12] And just on the note of death, I want to bring you down to this particular chart here, which talks about total Covid-19 related deaths. So we’ve inoculated now, 40 people we’ve caused adverse events, 40,000 people, excuse me, in this particular trial that there were enrolled 40,000, we’ve been inoculated 20. Of all of those people that were inoculated and enrolled into the trial, we really only prevented net one Covid related death So 40,000 people enrolled in this trial, many of them exposed to an experimental agent and we’ve reduced it by one death.
[00:20:48] Now, you know, I think at this point, at least for us, it begs to question, what are we doing and why are we doing this? If we can’t prevent deaths or there’s only a net difference of death of one, why are we doing this? And this is after six months.
[00:21:02] And then if you actually look at the number of deaths related to cardiovascular events, you can actually see that there’s a numerical trend or an increase in the number of deaths. And this is for the unblinded period, so it’s not confounded by crossover, that the number of people in the inoculation arm are actually dying of cardiovascular deaths more than the placebo and that the net is actually greater. The net increase is actually greater than the net reduction in Covid-19 deaths.
[00:21:31] So in terms of building evidence-based arguments, in terms of developing causation, I don’t think we can say that the vaccine causes death at this point because the numbers are too low and this wasn’t an endpoint of the trial, but going back to this trial, we can absolutely claim because this was a primary endpoint of the trial that the vaccine is causing more adverse events than the placebo arm. And I think that that’s a very sure footing in terms of making any type of an argument.
[00:22:01] I’m just going to fast forward, because I know we’re sensitive to time, to our children’s trials for these Pfizer vaccines. One thing I really want to emphasize in the design of these trials, and I’m not a hundred percent sure everybody knows this, but the primary endpoint of these trials was not a clinical endpoint. This was an immuno bridging trial that basically looked at immunogenicity – or neutralizing antibody titers in children. And there was no, the primary end point had no compare placebo comparitor. It was basically 190 children who were 12 to 15 years of age and they gave them the inoculation and measured their antibody titers.
[00:22:40] And they compare them to another cohort of vaccinated, 16 to 25 year olds looked at their antibody titers and compared them. And if, as long as this cohort here had similar antibody titers to that one, then they decided that it was non-inferior and therefore efficacious. Now that is not clinical efficacy. We’re not looking at clinical, you know, symptomatic PCR tests like we did for the adults. Although they did enroll 2000 patients and they did do an inoculation versus a placebo arm, this entire thing is descriptive, meaning that you can’t actually conclude anything from this. The only thing that we can conclude from this trial is that it was non-inferior to vaccination in a older cohort. And the number of patients in that is very, very low. I think too, if we’re going to be going after, um, causation or, you know, what can we prove? They actually can’t prove that it is clinically effective. It is basically an extrapolation from neutralizing antibody titers that is clinically effective. So they, they actually cannot prove that with this particular trial.
[00:23:49] So again, if we look at the number of adverse events in the inoculation arm, it’s higher than the placebo arm. And again, remember that these are the ones that actually got Covid-19. They have no severe adverse events, isn’t able to prevent any severe adverse event because there weren’t any, so they’re not at risk of severe disease. And yet here, what you see is this numerical trend, again, towards higher adverse events in that inoculation arm versus the placebo arm.
[00:24:16] And the here, this one is particularly concerning for me. You know, if you have a total of nine events, seven of them are in seven of the severe adverse events are in the inoculation arm. That’s a 249% increase relative risk increase, if we’re gonna play their numbers or play their game. And in terms of serious adverse events, four versus one. So again, why are we vaccinated children who aren’t at risk of severe disease? And why are we allowing something that goes on, you know, to produce this.
[00:24:48] Now again, I think that adverse events in the primary trial were a primary endpoint. Now in these immuno bridging or these secondary trials, I don’t think that they are, but again, we’re seeing the same troublesome trend. And the thing that’s particularly worrisome in these adolescents is that the strong trend towards increased adverse events were really only apparent, especially for those severe and serious adverse events were really only apparent after six months. And so this is looking at two months of data, and I’m super concerned to know what’s going to happen in six months from now to our, our adolescents who maybe are suffering from a dose dependent situation where there are adverse events are showing up, their safety signals are showing up much earlier.
[00:25:31] Just moving on to the five to 11 year old. Again, a very little difference between symptomatic cases and the two of them, it was an immuno bridging trial. So very little the clinical benefit evidence. And in two months, no severe cases whatsoever, an increase in adverse events in the inoculation arm of plus 42%. And again no great benefit in terms of preventing severe and serious adverse events.
[00:26:00] So right across the board, I think that it’s clear that the phase three trials show that it’s clear that these inoculations are causing more harm than good. I think the thing that is fantastic about this is that it’s level one evidence, specifically that safety was a primary end point, so we can’t assume causality. There were definitely a sufficient number of adverse events to do that. And I think that it’s fantastic that this group is together because I think this, uh, you know, we need to hold our governments to account that they actually went ahead with these experimental agents, despite these safety issues and rolled it out to a population.
[00:26:39] And one of the things that’s really concerning is if the adverse events look so unfavorable in this very healthy population how are these agents going to affect people who are frail and elderly people who have co-morbidities? We have absolutely no idea. And I’m very thankful for the observational data that we can look at for that. But I think it’s very concerning that we allowed these inoculations to actually be delivered to the general population given this data. And now that we have the six month adult data, I think that we absolutely have to stop the inoculation of children as soon as possible.
[00:27:16] So I think I went over my time. Maybe what I’ll do is I’ll just end here, uh, and I’ll pass it back over to our moderator
[00:27:25] Shabnam Palesa Mohamed: Deanna, what an outstanding presentation. I wish we’d heard you a little earlier on. Both you and Rob actually been really outstanding and I have so many questions while watching the presentation. It occurs to me that this is one of those presentations that every legal professional who intends entering the arena needs to watch because you, you know, formally from the inside, you understand how it works.
[00:27:50] And you’re pointing of the data to us in a very clear way that even laypeople people would be able to easily understand. One of the questions I wanted to ask is about the early unblinding and why that was done. But of course, if it’s pretty obvious why that was done and that’s to cover up the adverse effects that people were experiencing and to reduce them And similarly with the mixed. cohort a tactic that was being used.
[00:28:15] Can I ask if there’s any legal cases in Canada that are utilizing your work as evidence or your affidavit as witness
[00:28:25] Deanna McLeod: I think that there’s been multiple requests to incorporate this, and I’ve yet to get organized enough to prepare an affidavit. But I definitely think, you know, I’m not a lawyer, um, at all, but I do make evidence-based arguments all the time and we do know how to build one and how not to build one. And I do know exactly what’s going to stand up in terms of proof with, uh, pharmaceutical companies. I would be very much willing to prepare something that that could be used in that. But you know, you need to tread fairly carefully because you need to build it in just such a way that their lawyers won’t have any recourse. I guess we’re not really going to be engaging with their lawyers, but from a legal perspective, like you need to make sure that you’re looking at the right ones, that you don’t overstate your claims. Um, but I really feel that for this particular trial, because they tried to minimize safety and yet the safety signals still shine through so clearly, that it is absolutely what we need to do in order to prove causation and to, to be able to win in the courts. Otherwise they’ll always go back to association does not mean causation. But here we have them with level one evidence showing that they’re actually causing more harm than they are benefiting people.
[00:29:43] Shabnam Palesa Mohamed: On point. Rob Verkerk Do you have a question or a comment for Deanna McLeod I would be interested in hearing it.
[00:29:51] Rob Verkerk: Deanna, I’m fascinated by the battle around jab induced immunity versus natural immunity. And the fact that we have a kind of moving feast as well in different countries, as different proportions in different age groups start to acquire a combination of jab induced and naturally acquired immunity.
[00:30:11] In the UK at the moment, the antibody serology is giving us data that’s upwards of 98% of the population. But of course of the adult population, you’ve got close to 90% who are, who are jabbed. You got outright failure in terms of transmission, for any evidence that any of the existing jobs work on Omicron. So how do you factor that complicated picture in terms of the overall – I know you’re looking probably more trials, but I’m now looking to the real world because it’s a really important scientific question that needs to be answered.
[00:30:50] Deanna McLeod: You’re much more brave than I am. I look at real world data and I run away. [laughter] It’s just so complex and no matter how hard an argument we ever try to build with it, you know, it seems to always get shot down. I think powerful tools, like, you know, meta analysis and controlling for baseline factors and sensitivity analysis can go a long way that way. But I’m very motivated to stop these inoculations and therefore I’m inclined to head towards the level one data. And in terms of is there any data to support use in Covid-19 recovered people? There’s zero. So they basically are in the position where they should have to prove that it works. And if they come up with some observational trial to tell us that it works, then I would say, association, doesn’t prove causation. That’s not proof. Right? So you can turn it back. Whenever they come to us and say, oh yes, it works in high risk populations, look at my observational data. I would turn around and say, well, you didn’t prove that it worked because there’s too many confounding variables. So therefore you have no proof that it should work, so you should withdraw.
[00:31:52] So I don’t know whether that’s, uh, you know, to overly simplify it or not, but that tends to be where we go when we’re building evidence-based arguments.
[00:32:01] Rob Verkerk: Yeah. Absolutely. Fascinating.
[00:32:04] Shabnam Palesa Mohamed: Thank you very much. Deanna McLeod finishing our science in action session brilliantly.
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