Serious adverse events

well welcome to this talk and a really warm welcome to Dr Joseph Freeman all the way from Louisiana so Dr Freeman is an Emergency Physician and also a author Dr Freeman welcome and thank you so much for coming on oh it's my pleasure to be here so you're a busy doctor you're an Emergency Physician in Louisiana working in different areas you work shifts and somehow you've found time to do this excellent re-analysis of the original publications of the safety data from the Pfizer and the moderna vaccines what stimulated you to look into this and to do this re-analysis in the first place well initially we you know I was prior to covet also I was doing research and then with covid coming on of course that became the topic everything else got thrown to the Wayside and when the vaccine was released initially I we I had gone over the full FDA briefing for both Pfizer and moderna published in a physician website basically a summary of of what we had of what they had discussed in the in the large briefing and initially I didn't have any specific safety concerns based off of that data uh it was more I had some general concern for a novel a novel uh platform that had never that had limited safety testing but it was very non-specific of what what harms there could be just the knowledge that sometimes we've learned about harms frequently we learn about harms with time that we've had from when we approve a drug and that's for drugs that have gone through you know many years of safety data testing so it was just a general concern that I hadn't then in April April 2020 which is about four or five months after the vaccine was authorized came this two large studies that together sort of confirmed that the spike protein was was toxic and that it was causing all the all these weird things that we'd been learning about for the last year year and a half I guess at that point about what covid can cause all these strange things heart attacks strokes The Diarrhea um you know the stomach issues uh all sorts of different issues and I was sort of shocked by that finding because we've made a vaccine that makes our body produce Spike protein and uh that is was really bad luck in my mind if if we had known that the spike protein was toxic when they were building this vaccine I doubt they would have used that as the go-to protein so that and was my initial concern then the follow-up concern was the public response to this discovery which was uh saying things such as oh the spike protein in the vaccine is different than the spike protein in the virus and my thought was well that's not good either because if it's different then it won't work it has to be the same that's how this whole thing sort of works and then there's this idea that it just stays in your arm and at the time there wasn't good biodistribution data it was and in general as a as a physician I mean if you inject something into an arm it's pretty unusual for it to stay in the arm and we're just boosting it over and over are we just is that all just staying in your you know you're right bicep just keeps getting bigger and bigger from the amount of injections it didn't make sense uh we later learned it was being by there was biodistribution and uh and then at the same time there was also a CDC myth website where I would say that saying that the covid vaccines the spike protein the that the vaccine produces is is toxic is a myth so I I felt like the response to learning that the spike protein was toxic should have been a you know you know a moment like you know put your seats up you know you know the plane we're coming in for a crash landing this is a problem we need to figure this out really quickly um but instead it seemed like it was just dismissed so initially my uh you know I put together this idea of a study to look to create a list of Spike protein harms and see if the vaccine is inducing Spike protein Harms in the in the trial um has um given it was called a myth by the CDC at the time we felt that it was not going to be looked kindly upon to have a spike protein list and so but we found is a this group uh called the Brighton collaboration yeah yeah that had a list of Adverse Events of special interest and the list included we had created an actual list for the coveted Spike protein and uh it was most of them were contained within this AEI aesi list and it's because of how they created they look at all the things that covet had been responsible for in addition to that they are looking for possible harms of the of the lipid nanoparticle and just what we'd expect from various other vaccines in the past they create a list of things that are potentially caused by the vaccine we don't know they are it's done before the clinical trial they're supposed to be used within the clinical trial to look for if this vaccine is causing harm and uh so we took that list it's endorsed by The Who to to reanalyze these clinical trials to examine for the possibility that they could be causing harm so aesi Adverse Events of special interest and that who actually are the Brighton collaboration who are the individuals involved here and and where do they get their Authority from oh they've been they've been creating these adverse event lists Adverse Events of special interest for some period of time and they're essentially experts in vaccinology they they have a pretty good track record for identifying uh harms of vaccines that prior to prior to the clinical trials and uh so they've been pretty reliable it's why the who has has endorsed them for using that list for evaluating vaccines yes an established group of experts isn't it now you conducted a a secondary analysis on the original trials that were conducted by Pfizer and moderna these phase three randomized clinical trials in adults and these of course were famously infamously uh both published in that definitive text the New England Journal of Medicine um can you tell us just roughly what what sort of Trials were done what is a placebo-controlled phase three trial and and what is a secondary analysis was there something wrong with the primary analysis or something oh so um a clinical one you know a double-blind randomized trial you know you compare a medical intervention and then you have versus a placebo group where everyone's supposed to be blinded and not know and then ideally if you know for the FDA they're they advise that they should be designed to have the outcome be clinically meaningful and uh and while they're doing it they're also looking at various other things so there's lots of data that they produce and uh secondary analysis takes that data and applies a different question or applies it in a slightly different way to see if the results would be the same as the initial trial and so there this is It's kind of it's a common practice that's been done for for um clinical trials for for as long as I I know of uh so that's the general idea of it yeah now you identified uh excess risk in the vaccinated group as opposed to the non-vaccinated group in terms of Adverse Events Adverse Events of special interest um what do we mean by excess risk surely there's a risk with anything oh excess risk means uh so we're looking at serious Adverse Events in our that was our specific analysis serious Adverse Events are defined by Pfizer and moderna very much the same it's leads to death or life-threatening it's hospitalization permanent disability it's these things are serious and it's their definition serious but they are they are all serious things that you wouldn't want to happen to you um so so when we uh so when you look at the clinical trial when you look at the we just simply collect that data from on serious Adverse Events we count them we pull them all from from the data set and uh that's and then we have the list we count them basically in both of the charts and then essentially we do our the secondary analysis of it which would be the aesi list of looking at which ones would fit of those serious ones would fit into this aesi category and what I mean by what we mean by excess risk is is there so you'd expect serious Adverse Events for example heart attacks they're just going to happen their study has 40 000 people yep do more happen in the vaccine group is what an excess risk is because both groups will have heart attacks no matter what but are there more heart attacks are there more Strokes are there more you know is there more anything that is a serious adverse event in the vaccine group is what an excess risk would be I mean I do find it a picture that this wasn't done and presented in the original papers in the New England Journal of Medicine it's a Pity that independent academics such as you had to do this um were you asked to do this by the pharmaceutical industry or were you funded or sponsored in any way to do this no no there's no funding at all this is all every clinical there are seven of us on seven scientists working on this no one was no one was paid in a single dime it was all done with our free with our free time so and it's just moving that that experts such as yourself do take the time to do this for the benefit of all of us so we certainly appreciate what you've done and uh while being sad that it it's necessary for people like you to do this so it's a great thing to do so if you if you went into science you know there's you know that's it's a calling to to do science so so uh I you know finding people this it wasn't a problem finding there were people who were concerned and interested in this project and and they all volunteered and I I that part doesn't really surprise me that that there are people volunteering especially with the concerns that I was Raising before that there felt to be a lack of an investigation so there was people felt the need to put their you know skills that they've trained with their whole life to to the for the benefit of of everyone else yeah no it's it's uh it's it's it's it's just still a noble thing to do it's it's a great thing to do go going on results is Joseph um fires of vaccine excess risk of serious Adverse Events so this is how much more likely there was to be a serious adverse event of special interest in the vaccine group versus the placebo group now um it's 10.1 per 10 000 excess Adverse Events higher than the placebo Baseline where it was 17.6 so it's it's 10.1 higher now I've kind of just done a simple sum I'm not a statistician really but uh you know 10.1 for 10 000 is it fair to say this transposes into one serious adverse event per 990 uh vaccinations with the Pfizer um yeah that is a um definitely about the estimate of what that would be uh I think it's for all of these increases in excess excess risk I think it's best to almost think of the that one in 900 that year that you're saying there it's these are estimates and it's if you did the trial again it would be slightly different so it's the there's a range of possibilities it could be it could be one in 800 it could be one in a thousand in there but and that's for Adverse Events of special interest the thing that was actually the most shocking finding of our res of our entire study to me and I think to the rest of my team was uh when we just calculated when we just added up all the serious Adverse Events in the trial there was a higher incidence of of serious Adverse Events not Adverse Events of special interests just they didn't have to do any analysis they just needed to count if they just counted the number of events in both groups in the Pfizer trial you see that there's an increase of about it's 18 and 10 000 and uh relative risk or risk ratio of 1.36 yeah and so that's a 36 percent increase um our confidence intervals are are all above one which you know suggests with the 95 arbitrary like the threshold of is 95 that we use and it means that in the Pfizer trial that the people in the vaccine group were more likely there was a more serious Adverse Events in the vaccine group than there were in the placebo group and uh I the reason that that was missed is uh because they the surprising thing is if you read the New England Journal it says that the incidence of serious Adverse Events in both groups was similar I believe that's the term they used it was similar or they say or they say balanced but that's a very strange way to describe something that's higher in the vaccine group and uh it's a very strange way to describe it indeed yeah so we actually wrote the New England Journal about that and asked them to to correct this because the the term they say the incidence of serious Adverse Events is balance or similar and uh we asked them to change it to say that it's higher in the vaccine group because that patiently it is from your re-analysis oh it's it's this one's not even a re-analysis as much as just counting the numbers yeah there's there's not much we just count we just did the math for them that was there it's not yeah not much reanalysis needs to happen uh and and you know it's I believe that the reason they maybe stated it as such was because they were it's an error in that they were trying to say the incidence of participants who experience a serious adverse event is similar because if you look at the the way you can measure these things or we just counted the number of events they counted the number of participants who experienced one and um so there's you know a 27 increase in the number of participants who experience one but it's it's it you know it's confidence interval which you know would be the statistically significant term it wouldn't cross it it would be you know it would cross it would cross that confidence interval so maybe that's why they felt they can say it but either way it's stated incorrectly in the New England Journal as as of today you're a very generous man and I can see that um in your the way you evaluate that the the moderna vaccines risk of side serious uh Adverse Events uh 15.1 per 10 000. and and that works out at uh one per 662 vaccine doses given yep even higher combining the combining the two it works out at 12.5 per 100 000 but so basically One Chance in 800 of a serious adverse event um is this sort of risk commonplace in medicine is it you take these risks on a daily basis as you prescribe for your patients or would you think well just a minute well it depends on the on the drug uh so you know for a chemotherapeutic I would imagine the rate for a serious Adverse Events is much much higher than that but for a vaccine uh that at least the CDC reports that the rate for most vaccines is about one to two per million so this is a a multi-fold difference in in serious Adverse Events rate wow so we've gone from one to two per million to one to eight hundred which a quick sum in my head is like 1200 times more common okay it's definitely it's it is way worse it's it's definitely a lot more yeah it's like a thousand times worse by those criteria yes it is wow if you want to do the math yep a thousand times worse yeah yeah don't take my mental arithmetic for that but it's an awful lot little um so you also gave a re-analysis you said in terms of um risk ratio just briefly what what is risk ratio please uh it's essentially a relative risk how you would report relative risk so for the way you would interpret that would be one point it's if you're in the vaccine group for the uh with the Pfizer vaccina or we could do the combined one which was uh off top my head what was it one point the the the fight the fire says 1.36 1.36 yes yes the modernas are 1.06 and the combined is 1.16 so combined it's 16 more likely to have an adverse yeah so reaction 16 more likely yes exactly yeah 1.36 times 1.16 yes the way you interpret a relative risk is you take that number and you say it's 1.16 times the risk of yeah yeah but it's still fair to say that the combined figure is a 16 higher risk of getting an address it did and there's reason to believe that the within the moderna trial that there's we are concerned that there are serious Adverse Events that are hidden within it in the mostly in the placebo group uh that were from covid-19 caused by kova 19 and typically for a safety analysis you are supposed to remove the efficacy outcome or you create a what's caused called an all-cause outcome like an all-cause hospitalization and it's not that all cause outcomes are are aren't useful they are useful but when you're doing a safety analysis you just want to look at the the Harms and not so you are supposed to remove efficacy covid-19 being an efficacy outcome serious covid-19 being an efficacy outcome Pfizer did remove that and moderna did not we attempted to remove we attempted to remove those covid-19 cases from moderna however the problem is that with covid-19 serious outcomes you could end up with multiple serious Adverse Events from covid-19 that would be still littered within there that we wouldn't be able we weren't able to identify because we didn't have the we didn't have the data to identify it so when the modern uh data is showing six percent greater risk of serious Adverse Events in the vaccine group and the Pfizer is showing 36 percent that's comparing apples with oranges then it's a different methodology of collecting the data it is and we've we've been speaking with the FDA and also with moderna first first with you know with modernity to try to get this data so that we can actually look at it with that efficacy outcome removed and also ask the FDA why they allowed um moderna to run their safety analysis in such a in such a manner uh differently from the Pfizer because I would and without any mention that it was run differently they they should have at least mentioned that it was different I'm not sure if it went was unnoticed or I'm not 100 sure why uh Pfizer and moderna analyze their safety data slightly differently well for the amount of money and amount of expertise that the FDA it's quite an oversight yeah I would have said I mean they were they were they were quite rushed and um it's the kind of thing that uh if it took us several months to identify that that issue yeah so uh they had to approve this and just uh a month or two or so that wouldn't surprise me if it just didn't get it identified because of the the you know how quickly the vaccine was was authorized yeah but it's their full-time job and it was your part-time job so uh and you picked it up and they didn't as the point so Fair yeah um so you suggest a formal um harm benefit analysis what what is a harm benefit analysis and why are you suggesting that this is done oh so a harm benefit analysis simply as you you try to take the harms that are that you know that are any interventions causing you look at the benefits and then you compare them uh an issue there's lots of issues always with harm benefit analysis and uh because you know for a drug that prevents heart attacks for example right it just so it turns out usually that whatever harms that that drug causes it's probably not increasing heart attacks too so you can't just compare heart attacks and heart attacks because that would that's just the benefit and so then there becomes a lot of subjectivity in terms of how do you compare and there's been lots of different methodologies to do it uh one one way would have been great if the vaccine trial had this would have just been to look at all cause hospitalizations who got hospitalized who got hospitalized the least which group obviously but they yeah yeah it's in fact for paxilavid uh pfizers paxilvid trial that was actually their primary outcome was all cause hospitalization and in the vaccine trial they actually still we still don't know the all-cause hospitalization rate um from that trial it's never been made public so that would be one way that would be useful if we could just get that data released that would be useful to to understand if the if the vaccine were hospitalized way less than the vaccine group oh that's you know the you know that's great if the vaccine was reducing all-cause hospitalizations it's working but if the vaccine group has has higher rates of hospitalization then that would be pretty concerning um or if it's or if it's just too small of a trial to identify it and it's a null result that wouldn't that would not be useful either way but either either way we should have I think the data should be public well we've got reason to believe that fires around moderna will be watching this video so as soon as they hear this no doubt the data will be in the post to you and you can carry out that uh analysis I would hope I would hope but for the harm benefit analysis that that we did uh we we took the Adverse Events of special interest yep and we compare the the act the excess risk that we're identifying because these are all things that are considered serious life-threatening yes life-threatening hospitalization permanent disability I presume that the large majority of them were due to hospitalization and uh then we compared it to covid-19 hospitalizations and and you know people critique like these are not the same because some of the hospitalizations for one thing won't be as serious as a covid-19 hospitalization but in reality there'll be there's some minor covet hospitalizations in fact probably the majority are and then there's some ICU level intubations and people intubated in the ICU and the same would be true for the serious Adverse Events that some people had a stroke and that's pretty devastating and some people just had you know diarrhea that was so severe they needed to come in the hospital to get IV to prevent them from dying from dehydration yeah and uh they probably walk out without much but in that harm benefit analysis what we identified from within the trial is that the rate of serious Adverse Events the excess risk um in for special of special interest group they're higher than the reduction in in uh covid-19 hospitalization so within the trial it looks like you were more likely to have an adverse event of special interest in the vaccine group than you were to have have a covid-19 hospitalization reduced now when I talk about that that harm benefit analysis it's important to understand their limitations to how we can interpret it I think and it's important to mention those because you know let's say the trial had gone on for six months maybe then the that we would have seen the hospitalization reduction increase maybe there if there was a covid-19 surge the hospitalization rate would have been and it would have balanced it out the other way or maybe if they just did older people maybe you know older nursing home patients maybe the hospitalization reduction would have been increased um for you know and it would have made tilted it towards the vaccine being beneficial however had the trial been done in younger or healthier patients with less hospitalizations to reduce or during Omicron When there's less hospitalizations to reduce or Omicron where the vaccine is known to be less effective and uh in that situation you would actually the harm benefit analysis would tilt in the other direction towards more harm to benefit and I think that when you consider that you know there's different ways that this can go I think that the harm benefit analysis we put forward simply demonstrates the fragility of this harm to benefit ratio and how just the slightest you know differences can can alter it in one way or the other and that that in itself should be concerning the odd patient being excluded could make a huge difference to the overall data so to adjudicate whether the risk benefit analysis is affecting a particular age group or a particular sex you would need to have that information you would need this participant level data has that now been released no no the participant level data which means knowing the serious adverse event that in was that each person had um their their age their sex any sort of demographical information we have on them uh no we don't have that that hasn't been made public uh we've actually asked we we sent a letter to an open letter to the CEOs of Pfizer and moderna it was published in the in the bmj uh asking for this data it's would be necessary for us to help us to do some subgroup analysis because you know maybe maybe these adverse events are happening in that elderly at-risk group and maybe the benefits are outweighing the harm in that in that specific group maybe they're spread through if they're spread throughout then maybe you know it's a problematic and problematic in the younger people but you know it's difficult we can't make much uh of any sort of subgroup analysis to without that patient level data and that's why it would be important to to make it public and yeah that's the drug companies have this data they just don't release that data to Independent researchers such as yourself yes the the drug companies and and the FDA have it I am I believe for some reason the the European uh EMA does not have it they do not have it uh we had asked them for it as well uh they have to put in a special request for it they said to us but yeah FDA has it as well and they also have not released it to the public well FDA if you're watching get Dr Fryman this data ASAP please so we can analyze it then we'll all know because that might mean that a particular intervention is less appropriate in a particular age group but unless we have the age group data we don't know or it could mean it's appropriate for everyone it we don't we don't know maybe it's great I mean I don't know exactly what the data will show it's I don't know we don't have it what we'd have to figure out what it you know would definitely determine what it shows after after we obtain it yeah I don't like the secrecy but there you go that's what we have now now the results of the initial trial were to do with um the likelihood of getting infected with the virus and they expressed this in terms of a relative risk vaccinated versus unvaccinated um do you think it would have been a good idea if they're given some indication to Absolute risk as well as relative risk and could you maybe just sort of pick those two apart a little bit for us because first it was it was a reduction in symptomatic infection not right infection yeah so it's so yeah essentially it was uh like coveted like cold like symptoms essentially with a positive test they didn't do just weekly testing which would have been a superior in my opinion a superior way of of judging efficacy or it wasn't reduction in hospitalization which also I think would have been a better way of running that trial it was symptomatic infection which has some subjectivity in it and uh but the relative risk versus absolute risk uh a distinction there is a you know they said 95 reduction in symptomatic infection it was relative to the placebo group so I think in the vaccine and you know it was about for Pfizer I think it was like 162 versus seven I might have my number slightly off there but it's about that so their the relative risk was 95 less now it an absolute risk would have been looking at okay what was the risk of infection in the placebo group and the vaccine group and and so there were twenty thousand twenty thousand or so people in each group so it was you know point point something percent versus a smaller point it was a decimal it was a fraction of a percent of infections that it prevented uh and that you know when you talk about it as absolute risk this is a long-held debate within you know very very nerdy confines of medical statistics and medical communication but uh it the reality is both relative risk and absolute risk have value um they should both be discussed to get a better understanding of what you're dealing with I I think with the covid vaccine there was maybe a reasonableness to talk about the relative risk to emphasize it more because I think at the time they believed that this vaccine would be to and done and that we would have the the vaccine efficacy for the rest of our lives like most other vaccines and so if you say 95 effective it's that actually is a better way of determining that if they were correct with their hope if their Hope was correct that the vaccine would just last for you know for 20 30 years then you'd say oh that look over the next 20 30 years you get this 95 benefit which you know you don't do a trial for 20 30 years um however like if it's for example if the trial had gone on for two to three years and um I think that absolute absolute risk then wouldn't be that would be I worry that to do it with the two month did it could be a little misleading if yeah it would be way below one percent efficacy yeah but let's say the travel years it could have been higher yeah yes exactly if it's two well I think a a great thought experiment hypothetical is if the trial had gone on for two to three years and we were looking at uh all-cause hospitalization the thing the point that they still haven't released yet now it looks to me that the vaccine does increase some hospitalizations likely from as we see in our study at least for certain it looks like that in the Pfizer trial now if we looked at that over a two to three year period and uh presuming that the vaccinated group would get boosters whenever the CDC decided it was a good idea for them to get them because or maybe they could have actually let's say they even figured out the correct timing for for boosters with with trial data that would have been useful but imagine such a trial was done and it was done perfectly over two to three years would there be a reduction in an all-cause hospitalization because that's the question that actually people want to know the answer to Is is if I take this vaccine and I take it how I'm supposed to do I go to the hospital less you work at hospital more and uh at two to three years I believe that uh the problem with that is well first off is that we don't know the answer and um I don't think anyone can be certain on the answer to that especially given that we know the massive rapid waning of these of the vaccines and uh then you compare that with uh the fact that uh people post infection have prolonged prolonged immunity much longer than the than the vaccine then Omicron comes in and we the vaccine has reduced immunity we really once we had this whole infection it's not reducing infection anymore it's just reducing hospitalization and death which was based off of observational data which is not a good way to determine the efficacy of a drug and we've that has gotten us into complete mistakes so many times in the past so if that trial had been done the big thing is we don't know the answer and um but it would have been use it would be useful I wish it had been done so that we have the answer we would know that we're giving someone a medication that is is to their benefit that would be yeah that would be great so the initial trial was preventing symptomatic infection then you could say that the goal posts were moved to say it prevented hospitalization but we don't have the all-course hospitalization data so well also the thing that concerns me about this is if you look at the infection mortality rate I guess you can say or infection hospitalization rate in the clinical trial in the vaccinated group uh it their infection mortality rate and their infection uh hospitalization rate is not lower it's not lower than the placebo group it looks to me like in the clinical trial that the way that the vaccine reduced hospitalization and well it didn't we didn't see death and mortality Improvement but the way it reduced hospitalization was probably by preventing infection initially so if it stops preventing against infection I don't biologically I just don't see how it then gained a special ability that it didn't have in the clinical trial to to reduce hospitalization and I I would be concerned that in an observational study looking at hospitalization that you result in something called a you could have something called a healthy user bias where uh you know as was shown in in even to be occurring with the covet vaccine in a New England Journal response from vinay Prasad and Tracy Beth Hogue where they were showing that this healthy vaccine user bias could account for the entire entirety uh possibly of the benefit so that's people who are healthier anyway elected to be vaccinated at an earlier stage sort of sort of uh is you know it's a healthier people elected to but also people who are in hospice who are about to die don't don't really get it or if you're in the ICU right now you wouldn't necessarily get it um so you see a strong healthy user bias especially probably in the first six months after after a vaccine is going to be distributed and then it's going to decreases with time which is sort of exactly how this hospitalization benefit appears to be occurring and uh I don't know if if it's you know it's possible that the vaccine have is having great hospitalization benefits that um but we don't know how to how to adjust for this health healthy vaccine user bias uh so I would be I think people should be concerned that we are unaware we're uncertain if if the vaccine is reducing hospitalization and death in the manner that it's being I think I I feel it's sort of being reported in that this with this certainty this level of confidence of we know we know it's doing this and we don't usually speak like that or shouldn't speak like that in the medical community about the efficacy of any medical intervention that we're basing it on observational data we've just we just keep getting that wrong uh oh we've gotten that over and over again wrong and often those interventions when we get it wrong when we look at it we find out it's doing the exact opposite it's harming people when we were you know for hormone replacement therapy being one of the most famous examples we thought it was reducing heart attack strokes and breast cancer did a randomized trial whoops it was increasing heart attack strokes and breast cancer yeah indeed so it so medical history is useful for knowing how you need to learn the mystery that is for sure yeah yeah you're very honest in your paper about the the limitations uh limitations to the follow-up time but that wasn't your fault that was the way the studies were done um serious Adverse Events um who who adjudicated what was a serious Adverse Events it seemed to be mostly The Regulators you didn't have the raw clinical date he didn't have the blood results so you could adjudicate yourself you just had to take the regulator's word for what was a serious adverse event I yeah it was Regulators but I I actually don't I believe that it was actually the clinicians on site of who were working with uh you know the companies hired by Pfizer and moderna would would be the ones who would would educate them but they they were supposed to have been blinded to to these results so they shouldn't have known if they're in the vaccine or it's possible that there was some unblinding and they may have may have known a little bit but it turns out that the participants had a pretty good idea it seems like which which group they were in based on some side effects yeah and of course what is a serious you know what is a serious event is is subjective to the individual is that old joke in medicine that a minor procedures any procedure carried out on someone else it's easy to say it's minor when it's someone else rather than yourself it's um yeah um this is not this is not that uh a person thinking that a serious uh Siri um an adverse event that occurred to them was Sirius is not relevant to what we um is called a serious adverse event it has to be called a serious adverse event by a clinician got it so it's but yes you're correct in that and that people will people have things that are that are serious to them yeah sure that that wouldn't make it into this into this uh grouping yeah yeah yeah and we've already talked about the lack of participant level data the difficulty with full transparency and uh the lack of detailed clinical data so all those things that were limitations for the study I think we're completely out of your control yes how long the study was that is one of the critiques of our harm benefit analysis yeah there was you know you would see someone who has a blog or such or tweeting that you can't possibly do a harm benefit analysis based on two months of of data and I I I I would I'm concerned if they believe that then why would you uh authorize the population to take a drug if you can't do a proper harm benefit analysis it seems so I so I I suggest I mean does I don't know if that individual thinks that the trial was too short to have been authorized and we should have waited for for more data before authorizing but uh that was their critique of our harm that is a common critique of our harm benefit analysis yeah just as a condition as a clinician um Joseph he's seeing many patients admitted with covid now in in your sort of day-to-day practice no uh back you know in the first couple of surges uh it was my entire icus were filled with people uh very sick with covid-19 in the classical syndrome of cover 19. I was responding to two to three of these rapid responses a day when I work at night I'm the only physician in the hospital so after any time something goes wrong so I have a very good finger on the pulse of of what's going on in terms of you know people getting real sick people dying right I have to pronounce every one of them and first couple of surges every I would see two to three a shift and typically it would be two it would be you know one every one every three or four shifts this is a massive massive increase in in you know death and Devastation I guess but since about uh since the Omicron uh variant uh the interesting thing was Prior also go I'd watch the news increase in infections walk into work if it had been a couple days off like oh yeah it's infections are increasing I see it hospitalization's increasing oh yeah that's true hospitalizations are increasing deaths are increasing yep it was like it was like the weather it's like when you walk out of your house you look at your phone oh 70 degrees oh that does feel like 70. you know I can't say that the numbers are 100 accurate um but the the direction yeah you saw things increasing and decreasing and now however during Omicron when they said infections are increasing that was true then then they said hospitalizations were increasing not I didn't see hospitalizations from Omicron increasing I saw people being admitted who just randomly had covid-19 who maybe had a psychosis episode or they had missed their dialysis and but I I think that our system had a little bit of trouble distinguishing between you know people who were being admitted for totally random things and had kova 19. this is people with covid-19 as opposed to foreign and I I don't think that was a serious problem in 2020 or through much of 2021 and maybe till the end of 2021 uh but by February 2022 I would say that they were by far the majority of covid-19 hospitalizations in fact for me since February of 2022 I have not admitted a single covid-19 patient so that's about a year and a half since I've admitted one and uh I I you know when I speak to my colleagues about this it's that's quite similar for them maybe you know one reports you know a couple handful would report oh I've admitted one you know I admitted one two or three months ago I don't and they're like I don't know what Ally this man must have crawled down to get this old covid but it's quite clear to the clinicians uh you know who are treating this that the disease has fundamentally changed yeah uh with with Omicron it's not causing hospitalizations at the same rate and this is not just my own personal experience uh there's a study I believe out of Denmark that was showing that something about 75 percent of their covid-19 deaths were were not related not related to kova 19 there's a talk from a the CEO of uh I wanna some Los Angeles Hospital who's saying that he they they were saying that ninety percent of their patients in the ICU who are coveted 19 positive are not there for covid-19 there has been some besides that published article and and the CEO's talk there has been some mention of it but it's it isn't it hasn't been that publicly discussed I feel like there's a still a great fear that the disease is causing hospitalizations at these great rates but I I personally I don't see that and I don't know anyone who any clinician who has been seeing that yeah so you're not seeing the side to kind of storms you're not seeing the Soggy lungs with the cute respiratory distress syndrome you're not seeing the ground glass opacity but you are seeing people who have got comorbidities being admitted with those comorbidities but not for covid-19 yeah for you know they had something else that happened to them yeah could I could it be that I mean you would need to do a lot more research but it could it be that kova 19 is increasing these other things as we know the old one did and maybe that's but but it's not it's not the classical syndrome like we were seeing before and and that's another concern right now for the covid vaccine of talking about for boosters and such uh because if we're saying it's not stopping infection even the claim that I questioned if that's even true uh if it's only purpose is stopping hospitalizations yeah if it's not causing hospitalizations then I I I am curious to know then what benefit it would offer uh if it's not you know if it's if that's the if that's the case so uh I I still don't know actually and I I would like to see a trial that that demonstrated that that you know the coveted vaccine boosters are reducing hospitalization and death as we you know public as publicly said by much of our many of our governments yeah we're being protected against something that's essentially no risk at all and even then we're not really being protected and we've got the Adverse Events it's a very bizarre situation we seem to be in at the moment yes briefly Joseph just just to just to finish if you don't mind the FDA you've done an update uh release about the fda's thinking on this um now that they're using Neil Neo real-time surveillance or so the claim for vaccine safety um is this working are you getting are we getting vaccine safety data in near real time so yeah the near real-time surveillance that the FDA is published on it's it was it was designed actually by uh by Martin koldorf he's one of the main authors on the original paper of that did real near real-time surveillance purposes to identify serious harms by this vaccine rapidly while it's being administered to basically stop essentially stop it if necessary to withdraw it if necessary they published the first the first publication of this was 21 months after the vaccine was authorized so the majority of 20 majority of most countries have already taken the vaccine at that point and and the uh many have taken a booster I believe it was probably the fourth or fifth booster was being recommended at that at the point of publication of this near near real-time surveillance so so by near real-time surveillance the FDA mean a 21-month delay yeah to be fair they put it in quotes it means if it wasn't so serious this is laughable yeah it's just it's it's not it's not ideal no but um yeah it's not a deal no no no no absolutely is there risk of false positives in the FDA data that something could be thought of being an adverse event which actually really wasn't or is there a risk of false negatives well they very oh they discuss it very in detail in their paper about that they designed their surveillance to avoid false positives and they made multiple different decisions along the way to avoid false positives and you know there's various things they could do and how they do their statistical testing they had a they also said that uh you know if you're they had a group of anonymous experts who judged each adverse event of special interest that they were looking at and said okay if it has less than uh you know a relative risk of 1.25 so a 25 percent increase if it has less than that it's not clinically significant they they didn't explain how they determined that but you know things like heart attack and stroke so if you have like a 24 increase in heart attacks or Strokes they determine that to be not clinically significant which I find strange because you know we have drugs like such as uh I also find that strange yeah we have drugs like statins for which we know uh in people who've had heart attacks they reduce their chances of Heart Attack by about you know one close to one percent and uh so why are we why are we even using a drug that reduces heart attacks by one percent if it's not clinically significant to do so it's a cons it's a relative inconsistency I see between those two things and so the false positives that they are trying to reduce I understand because if you find a false positive for a harm yep then you can probably look worse than it actually was yes yeah and you could maybe even withdraw a product unnecessarily that has all this benefit and that's definitely their concern and uh but the the problem is for surveillance studies a false positive is actually not that bad because what you do with a false positive is you just study it again and you determine if it's a false positive because if it's a false positive when you study it again it will go away and you will realize that it's a false positive um however with false negatives which it just so happens if you design a study to avoid false positives it's like a dial essentially where you could either avoid false and negatives or avoid false positives but you're gonna whatever you dial it one way it makes the other one more likely yeah so they dialed it all the way towards avoiding false positives which means that there's a possibility for false negatives the problem of a surveillance study is usually missing missing significant event missing real harm missing real harm but you don't know it's there and if you do that with surveillance the problem is that if you miss a false negative you don't do an additional study to identify that false to confirm that false negative because you don't even know that it happened you don't know what you don't know yes you don't see it so with a surveillance study typically you would actually want to turn the dial towards avoiding false negatives at the cost of of false positives and so I uh I find it concerning actually we found that my me and my co-authors for that we we wrote It's a peer-reviewed uh critique of the of the surveillance system that that uh is highly concerning that they they in fact they didn't even ident in the younger age group they failed to identify that uh moderna was causing um that the moderna vaccine caused myocarditis and when we know that that that is is occurring um it shows that that your system is simply not adequate for for doing this if they can't identify something that is increasing we know it's an obviously Associated it's not even a debated thing it's increasing the relative risk so high that everyone who studies it identifies it uh so the fact that it didn't identify it didn't signal it that that is should raise everyone's red flag that this this system is not adequate for for surveillance for for our population some may talk about difficulties inadequacy Others May say a complete waste of time in a racket but yeah people will interpret that differently there's different ways to interpret it yeah um the FDA seems to be defining what is minimally clinically significant things that are minimally clinically clinically significant if there's enough of them would be immensely clinically significant to me um well yes I think that's actually what their numbers are based on uh they're they're saying if if you know if you have a 26 percent increase in in heart attacks that would be clinically significant because it would affect so many people but if you had 10 or 15 or 20 increase in heart attacks that's it's too small of a number of heart attacks that maybe it's not clear I presume they don't explain it very well they they how they how these experts determined these these numbers and and the fact that most of them are just 1.25 down the down the road suggests that they didn't use any sort of mathematical equation for determining the background rate of saying okay this many more of these is bad um so I mean I don't know how they came up with it but they said that they did say they said that they were experts and are they collecting data from all 50 states no the the data for this for the near real-time surveillance is based on uh Insurance data uh from seven states uh so they so there are no missing out 43 States they are missing out in 43 States but uh you know I don't know I don't know if they don't have the data for the other 43 States my the the biggest concern from uh you know any sort of epidemiological evaluation is that uh they didn't say how they choose chose those seven states so they can choose them how they how they please and and when you can choose things how you please in in epidemiological studies you can find results that are there that that you could find results that aren't there or you could or you could avoid finding results that that are there and uh it's hot you know for example the the CDC they did a masking study I know where they showed that masking I think it's cool I want to say it was school masking or mask mandates were effective when you looked at this six week or two month period this period and then another group did the study and they expanded it to six months and the effect went away so it's like when you're just choosing random numbers and you're not explaining when you're just choosing random data sets and not explaining where that data is coming or why it was chosen uh it leads that up to you know the readers for their imagination they can come up with any reason that these data sets were chosen um 43 states don't worry about it just yeah it's also possible that states because they have different different demographics of course you can you can presume it's possible that the vaccine's harmful in certain demographics that some states may not have so you would really want to make sure that you're looking at it you know lots of subgroup analyzes and such but uh if States just don't have a high percentage of something then you just won't see it if it's specific to that to that disease State you know that comority might have more older people or again you might have less older people exactly yeah exactly yeah or even Alaska you have more males than females I know and like I don't know you know that would be you know just there's so there are there's just different demographics between the states that uh different different occupation groups lots of differences I would just want to see a reason a reason why sure I think there needs to at least be a rationale and they probably have one but they didn't publish it and they didn't respond to our our critique yeah so all the different places the FDA are getting data from is this all pulled into a central spreadsheet for pulled centralized analysis that's actually one of the worst parts of that near a real-time surveillance uh just so they have three Insurance data data sets and they typically you would take them all together add them all together to increase your power because that actually what that does is that increases that that will decrease your chance of false negatives and decrease your chance of false positives so if they're still concerned about false positives why they to to runs to run it three different times that's a that's a disaster that's bad for false positives in a sense but um it's it's also bad it's it's also bad for false negatives so and typically you would pool those three the all the data sets that you have and it's like they did not I'm like a meta-analysis yeah you know I mean my thing reminds me my first year students know that the more people you have in the study the more likely you are to get valid data out of it this is pretty basic stuff uh it's you yes and no I mean it gives you more power but if there's if there's confounders within the data set already that just gives you more more likelihood to identify the confounder that that exists so it's possible more isn't always better it's you want it to be as well controlled as possible and such but uh but yeah you're more likely to find the difference between groups that's that's for certain you yeah and is is all this data from the FDA published for independent peer reviewing analysis uh no no no that that they have it published in their Journal article that the individual data is not is not available for for re-analysis from those seven states or if their data exists from the other 43 it's not it's not been made public I also would point out this pooling thing it I don't know the reason why the FDA didn't pool theirs but they you know how they have it in three different cutting it up into three different data sets it it just it reminds me of this of this technique that used that I think it's still probably done by uh companies where they're getting when there's a lawsuit for uh maybe you know a toxin that they put into the water or something like that they'll they'll they'll go to the trial with like five or six different data sets saying look our our toxins not doesn't cause cancer when you look at it we we looked at it in six different different ways and it didn't find cancer in any way but if someone takes those six trials together and you put you put them together then lo and behold the toxin will cause cancer but they keep them separate to to win a lawsuit and I you know I don't know that I I can't imagine that the FDA would do something in the same manner as as a as a company trying to avoid uh you know a lawsuit however it it is I just find it strange that they would do something that that is very similar so I and I like I said I don't they they have not explained their rationale for why they've done it in such a fashion but I would love for them to explain that that would be excellent for us to know quite a few strange things really yeah it's fascinating that research is is science and a bit of an art form isn't it really that the whole topic of research and that it can be used for immense good but it can also be used to present a particular perspective in the right hands double-edged sword oh yes that's it is very much the case I mean there's yes there it is very it's something that's always bothered me in science when you read through it and you you read um like activism activist science in a sense of you know people who are convinced of their result and they go around designing a study in such a fashion to to obtain that result and you see it with Financial interests and you can also see it with people who just have you know for whatever reason uh uh a bias towards one result being the case but cleverly designed studies can can often lead people to to the wrong conclusion it's and it's it's why it's so important that uh any for any study uh that your public that people publish that that they release their their transparent and that they release their data for other people to analyze and uh that's definitely true for anyone for anything with financial interest but I I think it's true for even non-financial interests like our group for example we we've been fully transparent with our our data and uh and we would we would ask that the that the FDA be transparent with the coveted vaccine data and Pfizer and moderna be transparent I there really is not a good reason that we shouldn't be if we're all here have the same goal of of trying to identify the truths of what we're doing here with our with our clinical medical interventions like that should that's our goal that should be the goal of everyone involved but uh and because I understand that if our study is incorrect all right that that I want people to take our data replicate our study and show us that we're wrong and uh where you know I would love for someone to do replication studies and you know show that our studies write or show that we got some things wrong and that would be very useful that's that's the right way to do science is that we you know keep it transparent let it be repeated let's figure out what's true what's not true but yeah things are a little harder right now with that all we want to do is discern the nature of reality right work out what's efficacious work out what the dangers are for the benefit of everyone which is what your work has done so on behalf of um well the entire population is really of of our countries thank you for everything you've done and um we wish we wish you well in in all your endeavors it's uh it's an impressive piece of work and uh also impressive answers from the interview room if you've got to the end of it well done so thank you for watching thank you Dr Freeman from all the way from Louisiana so uh great to talk to you thank you it has been a pleasure maybe we could do it again sometime I hope so yeah thank you thank you

Channel: Dr. John Campbell

Views: 1,390,178

Keywords: physiology, nursing, NCLEX, health, disease, biology, medicine, nurse education, medical education, pathophysiology, campbell, human biology, human body

Id: vsh5xNjc1Fs

Length: 63min 8sec (3788 seconds)

Published: Mon Sep 11 2023