1 00:00:00,040 --> 00:00:01,080 Speaker 1: Posed a lot. 2 00:00:01,200 --> 00:00:05,680 Speaker 2: It exposed people, it exposed industries, it exposed the so 3 00:00:05,800 --> 00:00:09,800 Speaker 2: called experts, it exposed our government, and we were fortunate 4 00:00:10,000 --> 00:00:14,400 Speaker 2: throughout it all to have some brave truth tellers. And 5 00:00:14,440 --> 00:00:17,680 Speaker 2: one of those individuals is doctor Harvey Risch. He's a 6 00:00:17,760 --> 00:00:20,520 Speaker 2: senior scholar now at the Brownstone Institute. He's also a 7 00:00:20,560 --> 00:00:24,200 Speaker 2: physician and a professor Moritus of Epidemiology at Yale School 8 00:00:24,239 --> 00:00:27,520 Speaker 2: of Public Health and Yale School of Medicine. He's been 9 00:00:27,560 --> 00:00:31,040 Speaker 2: on the podcast before. He was fearlessly on all over 10 00:00:31,080 --> 00:00:33,560 Speaker 2: the news networks trying to bring us the truth about 11 00:00:33,600 --> 00:00:36,560 Speaker 2: everything during COVID, including vaccines. 12 00:00:37,400 --> 00:00:39,440 Speaker 1: So when I saw this study. 13 00:00:39,200 --> 00:00:42,320 Speaker 2: Come out by the Global Vaccine Data Network, I wanted 14 00:00:42,360 --> 00:00:44,040 Speaker 2: to have him on to walk. 15 00:00:43,880 --> 00:00:45,040 Speaker 1: Us through it. 16 00:00:45,040 --> 00:00:48,440 Speaker 2: It is being called the largest COVID vaccine study to date. 17 00:00:48,760 --> 00:00:51,520 Speaker 2: It analyzed ninety nine million people who received the COVID 18 00:00:51,640 --> 00:00:55,120 Speaker 2: vaccinations across eight countries and it found a correlation with 19 00:00:55,160 --> 00:01:00,440 Speaker 2: things like myercarditis, among other things. So what should we 20 00:01:00,480 --> 00:01:03,080 Speaker 2: know about this study, how was it done? And what 21 00:01:03,120 --> 00:01:07,679 Speaker 2: do we know today about the COVID vaccines. Also, as 22 00:01:07,959 --> 00:01:10,960 Speaker 2: more and more children and young people are encouraged to 23 00:01:10,959 --> 00:01:13,240 Speaker 2: get more vaccines than they were previously. 24 00:01:13,920 --> 00:01:17,080 Speaker 1: Is that needed? Is that further health or is it 25 00:01:17,160 --> 00:01:17,959 Speaker 1: profit driven? 26 00:01:18,360 --> 00:01:20,920 Speaker 2: We're going to dig into all of these issues and 27 00:01:21,000 --> 00:01:30,360 Speaker 2: more with a truth teller, a brave man, doctor, Harvey Rish. Well, doctor, 28 00:01:30,400 --> 00:01:33,800 Speaker 2: appreciate you taking the time. I saw this vaccine study 29 00:01:33,840 --> 00:01:36,200 Speaker 2: that came out and I really wanted to have you 30 00:01:36,240 --> 00:01:38,240 Speaker 2: on and have you kind of walk us through it. 31 00:01:38,280 --> 00:01:40,039 Speaker 1: So we appreciate you making the time. 32 00:01:40,280 --> 00:01:41,720 Speaker 3: Sure happy to we have. 33 00:01:41,880 --> 00:01:44,800 Speaker 2: I guess they're calling it the largest COVID vaccine study 34 00:01:45,080 --> 00:01:49,800 Speaker 2: to date, with a global vaccine data network analyze ninety 35 00:01:49,880 --> 00:01:53,280 Speaker 2: nine million people who received the COVID vaccines or vaccinations 36 00:01:53,280 --> 00:01:56,960 Speaker 2: across eight countries. I guess just for starter, you know, 37 00:01:57,040 --> 00:02:00,680 Speaker 2: what are your takeaways from that study, how it was 38 00:02:00,720 --> 00:02:02,360 Speaker 2: analyzed and the findings. 39 00:02:03,560 --> 00:02:08,560 Speaker 3: Well, my impression is a study is basically a good one. However, 40 00:02:09,160 --> 00:02:14,600 Speaker 3: the authors are all it looks like, all involved in 41 00:02:14,639 --> 00:02:18,799 Speaker 3: the public health establishments of their various countries, and so 42 00:02:18,840 --> 00:02:22,240 Speaker 3: they have more or less a vested interest to show 43 00:02:22,320 --> 00:02:25,520 Speaker 3: that the vaccines were good, not harmful, and so on, 44 00:02:26,440 --> 00:02:32,480 Speaker 3: as opposed to a disinterested, independent researcher who might have 45 00:02:32,680 --> 00:02:39,120 Speaker 3: done the study. That having been said, I felt the 46 00:02:39,760 --> 00:02:46,919 Speaker 3: way that the results were reported, because the analysis was 47 00:02:47,800 --> 00:02:52,040 Speaker 3: an average over all age groups and so on for 48 00:02:52,800 --> 00:02:55,960 Speaker 3: some of the outcomes that are much more important in 49 00:02:56,040 --> 00:03:02,160 Speaker 3: defined age groups. For example, the myocdis outcome, which they 50 00:03:02,320 --> 00:03:06,480 Speaker 3: reported to have a relative risk of something like sixfold 51 00:03:07,240 --> 00:03:12,240 Speaker 3: after the second dose, and that's a significant symptom, but 52 00:03:12,600 --> 00:03:15,800 Speaker 3: that's averaged across all age groups. We know that the 53 00:03:15,880 --> 00:03:20,240 Speaker 3: risk of milcarditis is much higher in fifteen to thirty 54 00:03:20,320 --> 00:03:22,640 Speaker 3: year old males and fifteen to thirty year old females 55 00:03:22,680 --> 00:03:28,120 Speaker 3: for that matter, So what was the increased risk the 56 00:03:28,160 --> 00:03:31,320 Speaker 3: relative risk in that age group that when you average 57 00:03:31,360 --> 00:03:34,520 Speaker 3: it out over the whole population comes to sixfold. It 58 00:03:34,600 --> 00:03:38,800 Speaker 3: might have been twentyfold in that age group, and they 59 00:03:38,880 --> 00:03:43,880 Speaker 3: hid that by not providing that specifically. The second thing 60 00:03:44,840 --> 00:03:55,720 Speaker 3: is that they did this analysis without providing any more granularity. 61 00:03:56,320 --> 00:03:59,520 Speaker 3: This is an extension of what I just said, by age, 62 00:03:59,600 --> 00:04:03,400 Speaker 3: by by sex, and so on, of these various factors, 63 00:04:03,760 --> 00:04:06,720 Speaker 3: and then they concluded that. Oh and the other thing 64 00:04:06,840 --> 00:04:11,440 Speaker 3: is that they only provided relative risks, not absolute risks. 65 00:04:11,720 --> 00:04:14,560 Speaker 3: So what we really want to know is not that 66 00:04:14,680 --> 00:04:19,159 Speaker 3: myocarditis has a six or twentyfold increased relative risk, but 67 00:04:19,240 --> 00:04:23,840 Speaker 3: we want to know in vaccinated males aged fifteen to thirty, 68 00:04:24,320 --> 00:04:28,000 Speaker 3: we want to know how many per thousand actually got myocarditis. 69 00:04:28,720 --> 00:04:31,880 Speaker 3: Was it one hundred per thousand, was it ten per thousand, 70 00:04:32,000 --> 00:04:34,680 Speaker 3: was it one per thousand, Even if that was increased 71 00:04:34,720 --> 00:04:38,800 Speaker 3: by five or ten or twenty fold, what matters is 72 00:04:39,200 --> 00:04:41,599 Speaker 3: the absolute risk, because if you're going to make a 73 00:04:41,640 --> 00:04:44,520 Speaker 3: decision to go and get these vaccines, you need to 74 00:04:44,560 --> 00:04:47,080 Speaker 3: know what the risk is for you, not whether it's elevated, 75 00:04:47,120 --> 00:04:49,359 Speaker 3: but what the risk is for you. So if the 76 00:04:49,440 --> 00:04:53,440 Speaker 3: risk is one percent, that's something really to consider carefully. 77 00:04:53,440 --> 00:04:56,120 Speaker 3: If the risk is one in a thousand, well maybe 78 00:04:56,200 --> 00:04:59,200 Speaker 3: it's not so bad, you understand so, and they hid 79 00:04:59,240 --> 00:04:59,640 Speaker 3: that also. 80 00:05:01,200 --> 00:05:03,360 Speaker 2: Isn't that kind of been the problem with all of 81 00:05:03,360 --> 00:05:06,359 Speaker 2: this from the beginning in the sense of like even 82 00:05:06,400 --> 00:05:10,159 Speaker 2: from the beginning with COVID, of like, Okay, yes, if 83 00:05:10,200 --> 00:05:13,160 Speaker 2: you were older or if you had comorbidities, you were 84 00:05:13,200 --> 00:05:15,400 Speaker 2: more at risk, but if you're young and healthy, you weren't. 85 00:05:15,520 --> 00:05:18,800 Speaker 2: Or with the vaccine, Okay, maybe if you're elderly you 86 00:05:18,880 --> 00:05:20,600 Speaker 2: might want to think about getting the vaccine, but if 87 00:05:20,600 --> 00:05:23,080 Speaker 2: you're younger and healthy, you know, like they haven't really 88 00:05:23,160 --> 00:05:28,200 Speaker 2: delineated this entire time between the specific risk profile of 89 00:05:28,240 --> 00:05:30,839 Speaker 2: each group and even you know, with you describing sort 90 00:05:30,839 --> 00:05:33,480 Speaker 2: of how the study was done, of not breaking down 91 00:05:33,480 --> 00:05:37,400 Speaker 2: those groups when that really matters greatly in terms of 92 00:05:37,520 --> 00:05:39,520 Speaker 2: you know, your risk from the vaccine or even back 93 00:05:39,520 --> 00:05:40,880 Speaker 2: in the day in the beginning of this, like your 94 00:05:40,960 --> 00:05:41,799 Speaker 2: risk from COVID. 95 00:05:44,680 --> 00:05:49,000 Speaker 3: I think that this is much more profound even and 96 00:05:49,120 --> 00:05:52,600 Speaker 3: that is that people who got COVID did not need 97 00:05:52,640 --> 00:05:58,520 Speaker 3: to be vaccinated. That it's that this was a manipulated 98 00:05:59,400 --> 00:06:04,360 Speaker 3: messaging that started off with the vaccines will provide ninety 99 00:06:04,400 --> 00:06:08,800 Speaker 3: five percent reduced risk of getting COVID and give you immunity. Well, 100 00:06:09,080 --> 00:06:11,320 Speaker 3: but they said nothing about whether you have a risk 101 00:06:11,360 --> 00:06:14,880 Speaker 3: of transmitting COVID to others, whether you might get COVID anyway, 102 00:06:15,520 --> 00:06:19,920 Speaker 3: and so on. And then after some six to twelve 103 00:06:19,920 --> 00:06:24,480 Speaker 3: months and it was became apparent that the vaccines were leaky, 104 00:06:24,600 --> 00:06:29,200 Speaker 3: that people were getting COVID even after vaccination, that the 105 00:06:29,839 --> 00:06:32,919 Speaker 3: messaging changed to be, oh, it'll keep you from getting 106 00:06:32,920 --> 00:06:35,680 Speaker 3: hospitalized or dying from COVID, had nothing to do with transmission, 107 00:06:35,680 --> 00:06:38,960 Speaker 3: which was the real issue, and so all of this 108 00:06:39,480 --> 00:06:46,599 Speaker 3: got manipulated to suit the benefit of someone some entity 109 00:06:47,640 --> 00:06:51,440 Speaker 3: against the public health interests of the general population. And 110 00:06:52,279 --> 00:06:56,880 Speaker 3: one ask address who was pushing this narrative that if 111 00:06:56,920 --> 00:07:00,000 Speaker 3: the idea is that for many of the vaccine mandates, 112 00:07:00,400 --> 00:07:04,520 Speaker 3: the rationale was you'll reduce risk of transmitting the infection 113 00:07:04,560 --> 00:07:07,800 Speaker 3: to others if you get vaccinated. Well, if you've already 114 00:07:07,839 --> 00:07:11,200 Speaker 3: had COVID, you reduce the risk of transmitting the virus 115 00:07:11,680 --> 00:07:14,080 Speaker 3: to others to the same or greater degree than if 116 00:07:14,080 --> 00:07:17,120 Speaker 3: you had been vaccinated. But people who had had COVID 117 00:07:17,320 --> 00:07:21,760 Speaker 3: were not exempted from getting vaccinated, and the pushback, the 118 00:07:21,760 --> 00:07:24,520 Speaker 3: messaging pushback on that was, well, you'll have even more 119 00:07:24,560 --> 00:07:27,120 Speaker 3: immunity if you get vaccinated, even after you've had COVID, 120 00:07:27,280 --> 00:07:31,200 Speaker 3: which was irrelevant, illogical, because the whole point was you 121 00:07:31,200 --> 00:07:34,640 Speaker 3: set a standard by the mandate for how much reduced 122 00:07:34,720 --> 00:07:38,720 Speaker 3: risk you might convey by getting vaccinated, and you meet 123 00:07:38,800 --> 00:07:43,120 Speaker 3: that standard by an alternative form by having already had COVID. 124 00:07:43,480 --> 00:07:46,160 Speaker 3: Not everybody has to map the standard wasn't everybody has 125 00:07:46,200 --> 00:07:49,240 Speaker 3: to maximize their immunity. The standard was the threshold that 126 00:07:49,360 --> 00:07:52,760 Speaker 3: vaccines were supposed to provide. And so this irrationality, this 127 00:07:52,880 --> 00:07:56,640 Speaker 3: lack of logic, was made everybody should have made everybody 128 00:07:57,000 --> 00:07:59,080 Speaker 3: sit up and say, wait a minute, this is a fraud. 129 00:07:59,400 --> 00:08:03,240 Speaker 3: If if my having had COVID providing me as much 130 00:08:03,280 --> 00:08:07,400 Speaker 3: immunity as the vaccines don't qualify for satisfying the vaccine mandate, 131 00:08:07,560 --> 00:08:11,400 Speaker 3: then there's something else going on, and this is a fraud. 132 00:08:12,200 --> 00:08:15,320 Speaker 1: Well, what do you think that something else is going on? 133 00:08:16,400 --> 00:08:17,240 Speaker 1: You know why? 134 00:08:17,640 --> 00:08:20,880 Speaker 2: I guess why do you think that this entire thing 135 00:08:20,920 --> 00:08:23,720 Speaker 2: has just been so illogical from the beginning. 136 00:08:24,400 --> 00:08:28,840 Speaker 3: Because I think there was a necessity to vaccine, vaccinate 137 00:08:28,880 --> 00:08:31,240 Speaker 3: the entire planet as much as the planet as could 138 00:08:31,280 --> 00:08:34,880 Speaker 3: be vaccinated, to show that a vaccine was the end 139 00:08:34,960 --> 00:08:39,720 Speaker 3: product of the whole pandemic. And what the reason that 140 00:08:39,840 --> 00:08:41,839 Speaker 3: I believed the vaccines had to be the end of 141 00:08:41,880 --> 00:08:48,800 Speaker 3: product of the pandemic is that this virus was bioengineered. 142 00:08:49,200 --> 00:08:52,800 Speaker 3: There's no question that it was bio engineered. That all 143 00:08:52,840 --> 00:08:59,559 Speaker 3: of the scientific evidence, all of the spy intelligence evidence, 144 00:09:00,080 --> 00:09:03,920 Speaker 3: everything points to it leaked from the Wuhan Institute of 145 00:09:03,960 --> 00:09:09,000 Speaker 3: Virology that it was made under bioengineering engineering techniques that 146 00:09:09,400 --> 00:09:13,199 Speaker 3: were developed by Ralph Barrack at the University of North 147 00:09:13,200 --> 00:09:17,840 Speaker 3: Carolina and taught to Chinese researchers who took it to 148 00:09:18,320 --> 00:09:21,600 Speaker 3: the wib In Muhana and developed it there and it 149 00:09:21,679 --> 00:09:27,199 Speaker 3: leaked from there. Now you have to realize that the 150 00:09:27,240 --> 00:09:34,200 Speaker 3: development of a bioengineered gain of function virus is essentially 151 00:09:34,240 --> 00:09:40,000 Speaker 3: a bioweapon. That gain of function research that makes what 152 00:09:40,280 --> 00:09:47,600 Speaker 3: are animal in natural viruses that exist in wildlife that 153 00:09:47,920 --> 00:09:52,040 Speaker 3: might spill over into humans. In general, those viruses are 154 00:09:52,040 --> 00:09:56,840 Speaker 3: not very severe in humans because they're not adapted to humans. 155 00:09:56,840 --> 00:10:01,040 Speaker 3: They haven't been propagated in humans for thousands of virus 156 00:10:01,080 --> 00:10:05,320 Speaker 3: generations to become adept at infecting humans. There are adept 157 00:10:05,400 --> 00:10:09,200 Speaker 3: at infecting animals, and animals have different cell receptors, different 158 00:10:09,240 --> 00:10:12,679 Speaker 3: molecules and so on, different immune receptors and all this 159 00:10:13,120 --> 00:10:19,240 Speaker 3: that makes each animal species basically unique as a lock 160 00:10:19,320 --> 00:10:23,000 Speaker 3: and key system for a virus and its species and 161 00:10:23,040 --> 00:10:27,800 Speaker 3: its animal species. So this virus was perfectly adapted to 162 00:10:27,880 --> 00:10:31,920 Speaker 3: humans when it was first released, which means that it 163 00:10:32,000 --> 00:10:35,679 Speaker 3: was engineered for that, which means that the development of this, 164 00:10:35,800 --> 00:10:38,680 Speaker 3: the research of this qualifies it to be a bioweapon 165 00:10:38,720 --> 00:10:41,600 Speaker 3: because of the nature of illness and death that it 166 00:10:41,679 --> 00:10:45,600 Speaker 3: caused in the population when it first was released. And 167 00:10:45,880 --> 00:10:49,080 Speaker 3: we have a Bioweapons Treaty that President Ford signed in 168 00:10:49,120 --> 00:10:53,120 Speaker 3: nineteen seventy five that said we are prohibited in the 169 00:10:53,160 --> 00:10:57,200 Speaker 3: whole every country that signed this is prohibited from developing 170 00:10:57,320 --> 00:11:01,360 Speaker 3: offensive bioweapons. It's against the law against that treaty. And 171 00:11:01,400 --> 00:11:05,679 Speaker 3: the only loophole in that is that small quantities of 172 00:11:05,720 --> 00:11:09,760 Speaker 3: bioweapons could be developed for the purposes of making vaccines. 173 00:11:10,800 --> 00:11:18,520 Speaker 3: So that means that translate to twenty nineteen, this virus 174 00:11:19,120 --> 00:11:22,960 Speaker 3: is released, and it has to be justified. All this 175 00:11:23,000 --> 00:11:26,280 Speaker 3: work has to be justified because the end result was 176 00:11:26,320 --> 00:11:30,880 Speaker 3: the idea of making a vaccine against it, and nobody 177 00:11:31,000 --> 00:11:34,040 Speaker 3: was prepared to make that vaccine because it took a 178 00:11:34,120 --> 00:11:38,079 Speaker 3: year for the vaccine to actually to be made and 179 00:11:38,559 --> 00:11:44,760 Speaker 3: rolled out. And that means, and this parallels the fact 180 00:11:44,840 --> 00:11:50,400 Speaker 3: that there's been this gain of function bioweapons research going 181 00:11:50,440 --> 00:11:54,640 Speaker 3: on all over the world even after the Bioweapons Treaty, 182 00:11:55,200 --> 00:11:58,840 Speaker 3: all of it claiming to be what's called dual use research, 183 00:11:59,280 --> 00:12:01,680 Speaker 3: that it's for the purpose of making vaccines. Yet there's 184 00:12:01,720 --> 00:12:05,640 Speaker 3: never been any commercial vaccines for any of these bioweapon viruses. Sure, 185 00:12:05,679 --> 00:12:08,880 Speaker 3: we have vaccine research for other pathogens that we know 186 00:12:08,960 --> 00:12:12,880 Speaker 3: about that exists, but that are developed as bioweapons. These 187 00:12:12,880 --> 00:12:16,880 Speaker 3: are gain of function viruses that the only rationale that 188 00:12:16,920 --> 00:12:19,719 Speaker 3: were allowed to have for their development is that we're 189 00:12:19,720 --> 00:12:23,079 Speaker 3: making a vaccine. So the vaccine had to come out 190 00:12:23,440 --> 00:12:27,680 Speaker 3: to supply the rationale, the evidence that this virus development, 191 00:12:28,760 --> 00:12:32,560 Speaker 3: this bioweapons virus development, was for the purpose of making 192 00:12:32,600 --> 00:12:35,880 Speaker 3: a vaccine. If there was no vaccine, then this would 193 00:12:35,920 --> 00:12:39,160 Speaker 3: have been offensive bioweapon development would have been illegal against 194 00:12:39,160 --> 00:12:43,120 Speaker 3: the treaty and the population. The general population would rightfully 195 00:12:43,400 --> 00:12:46,760 Speaker 3: have called for a complete end to all bioweapons research 196 00:12:47,280 --> 00:12:50,520 Speaker 3: because it has no benefit for It has no military 197 00:12:50,520 --> 00:12:53,520 Speaker 3: benefit for US, has no defense benefit for US. It 198 00:12:53,559 --> 00:12:56,640 Speaker 3: has only risk because the only benefit would be vaccines. 199 00:12:56,679 --> 00:12:59,480 Speaker 3: But if there's no vaccine, then there's no benefit at all. 200 00:13:00,280 --> 00:13:05,559 Speaker 3: The vaccine was a charade to justify this offensive bioweapons 201 00:13:05,559 --> 00:13:08,679 Speaker 3: research that's been going on for decades and decades. The 202 00:13:08,880 --> 00:13:12,440 Speaker 3: biometry industry was required to put this out in order 203 00:13:12,480 --> 00:13:15,640 Speaker 3: to justify itself, to keep the general population from being 204 00:13:15,679 --> 00:13:17,560 Speaker 3: outraged and shutting this industry down. 205 00:13:18,480 --> 00:13:20,760 Speaker 1: So why do countries engage in it? Then? 206 00:13:22,360 --> 00:13:25,679 Speaker 3: Because they are vested interests who make money off of it. 207 00:13:25,920 --> 00:13:29,120 Speaker 3: There are careers that you know, all of these scientists 208 00:13:29,200 --> 00:13:32,679 Speaker 3: who claim to be making vaccines for bioweapons, and all 209 00:13:32,720 --> 00:13:36,720 Speaker 3: they're basically doing is developing the bioweapons as a preliminary 210 00:13:36,720 --> 00:13:40,760 Speaker 3: step to making the vaccines, and their grand applications all 211 00:13:40,920 --> 00:13:43,160 Speaker 3: justify it with saying they're going to make a vaccine 212 00:13:43,200 --> 00:13:45,280 Speaker 3: for it, but somehow they never get around to making 213 00:13:45,360 --> 00:13:49,720 Speaker 3: the vaccines because all the work is spent doing bioweapons research. 214 00:13:50,200 --> 00:13:52,040 Speaker 2: We're going to take a quick break more with doctor 215 00:13:52,040 --> 00:13:59,559 Speaker 2: Harvey Rish. Do we over vaccinate or society? I mean, 216 00:13:59,600 --> 00:14:01,280 Speaker 2: you know, and if you look at the vaccines that 217 00:14:01,320 --> 00:14:04,280 Speaker 2: are recommended to you know, children, to you know, babies, 218 00:14:04,280 --> 00:14:07,719 Speaker 2: to young people, they've obviously increased over the years. I mean, 219 00:14:07,840 --> 00:14:10,480 Speaker 2: is it like I'm not against all vaccines, right, Like 220 00:14:10,559 --> 00:14:12,839 Speaker 2: you look at something like polio. My understanding is that 221 00:14:13,320 --> 00:14:16,040 Speaker 2: it has like a fifteen to thirty percent fatality rate 222 00:14:16,120 --> 00:14:18,360 Speaker 2: or something like that. That's pretty significant, right, Like polio 223 00:14:18,400 --> 00:14:19,320 Speaker 2: has been around for forever. 224 00:14:19,440 --> 00:14:22,400 Speaker 1: So like I'm okay with some but it does seem like. 225 00:14:23,240 --> 00:14:25,360 Speaker 2: Are we being vaccinated to the point we are for 226 00:14:25,480 --> 00:14:29,000 Speaker 2: financial reasons or because it's actually in or vested interests 227 00:14:29,040 --> 00:14:31,440 Speaker 2: as a people in a population and a country. 228 00:14:32,240 --> 00:14:34,440 Speaker 3: I don't think we can answer that question. This is 229 00:14:34,720 --> 00:14:39,000 Speaker 3: controversial because some of the mandated vaccines offer things that 230 00:14:39,200 --> 00:14:42,920 Speaker 3: do not have human to human transmission. For example, while 231 00:14:42,960 --> 00:14:45,840 Speaker 3: I think tenness is something that people should vaccinate for, 232 00:14:46,360 --> 00:14:49,680 Speaker 3: there should not be a mandate for it because tedness 233 00:14:49,720 --> 00:14:52,680 Speaker 3: is not transmitted from human to human. So there's no reason. 234 00:14:52,800 --> 00:14:58,160 Speaker 3: See the whole idea of vaccine mandates are to prevent transmissions, 235 00:14:58,240 --> 00:15:00,920 Speaker 3: not to keep people out of the hospital for dying. 236 00:15:01,000 --> 00:15:03,520 Speaker 3: That is their own medical choice. We think that prudent 237 00:15:03,520 --> 00:15:07,080 Speaker 3: people would do that, but it's their fundamental freedom of 238 00:15:07,160 --> 00:15:07,800 Speaker 3: choice to do that. 239 00:15:08,320 --> 00:15:12,800 Speaker 2: So looking at the COVID vaccine, you know, specifically, I 240 00:15:12,800 --> 00:15:16,000 Speaker 2: guess because the media really the way they covered the 241 00:15:16,240 --> 00:15:20,680 Speaker 2: story and the study in general, was oh, Okay, you 242 00:15:20,680 --> 00:15:23,880 Speaker 2: know there's some impact, right, Like you could have myer 243 00:15:23,960 --> 00:15:24,520 Speaker 2: card itis. 244 00:15:24,640 --> 00:15:26,160 Speaker 1: You know, you could have. 245 00:15:27,480 --> 00:15:30,280 Speaker 2: You know, blood clots, et cetera, like these things could 246 00:15:30,280 --> 00:15:32,560 Speaker 2: have but it's small. It's you know, it's not a 247 00:15:32,600 --> 00:15:37,000 Speaker 2: big deal. It's like they really like downplaying it. I 248 00:15:37,000 --> 00:15:41,360 Speaker 2: guess what to what extent are these COVID vaccines. 249 00:15:42,800 --> 00:15:47,200 Speaker 1: Like safe? Right? Like what what what is? You know? 250 00:15:47,960 --> 00:15:52,400 Speaker 2: I guess how dangerous? How safe are these COVID vaccines. 251 00:15:53,480 --> 00:15:57,120 Speaker 3: Well, that's a relative standard because we've pulled vaccines from 252 00:15:57,200 --> 00:16:01,920 Speaker 3: the marketplace with way way fewer at serious adverse events. 253 00:16:02,000 --> 00:16:04,800 Speaker 3: In the past, the standard has been at the level 254 00:16:04,880 --> 00:16:07,760 Speaker 3: of a few hundred serious adverse events the vaccine is pulled. 255 00:16:08,440 --> 00:16:12,160 Speaker 3: And we know that for the COVID vaccines as of now, 256 00:16:12,800 --> 00:16:18,760 Speaker 3: deaths reported to the Various database via ers deaths reported 257 00:16:18,760 --> 00:16:20,800 Speaker 3: on day zero one or two. Day zero is when 258 00:16:20,840 --> 00:16:23,160 Speaker 3: the day you get the vaccine, day zero one or two. 259 00:16:24,680 --> 00:16:27,200 Speaker 3: In that is, twelve thousand people have died on day 260 00:16:27,280 --> 00:16:32,120 Speaker 3: zero one or two of getting this vaccine. Now, I 261 00:16:32,120 --> 00:16:33,960 Speaker 3: don't know what you think the background rate of people 262 00:16:33,960 --> 00:16:36,840 Speaker 3: should have been dug, but it's in the maybe tens 263 00:16:37,720 --> 00:16:42,400 Speaker 3: or twenties, not twelve thousand. So we know there's a 264 00:16:42,440 --> 00:16:46,120 Speaker 3: major signal there that the number of deaths in the 265 00:16:46,240 --> 00:16:49,160 Speaker 3: various reported is some of my thirty seven thousand now, 266 00:16:49,640 --> 00:16:53,640 Speaker 3: but going out to longer time stretch after vaccination, and 267 00:16:53,720 --> 00:16:59,800 Speaker 3: we've seen all this other data on excess mortality excess 268 00:17:01,120 --> 00:17:06,760 Speaker 3: disability that started in twenty twenty one in national US 269 00:17:06,840 --> 00:17:13,040 Speaker 3: and UK surveys that we know that these vaccines are 270 00:17:13,560 --> 00:17:19,240 Speaker 3: not safe. That it's so what matters is the risk 271 00:17:19,400 --> 00:17:24,240 Speaker 3: benefit quantitative relationship, and that was never provided to the 272 00:17:24,280 --> 00:17:29,199 Speaker 3: population ever, So that means nobody was even able to 273 00:17:29,240 --> 00:17:31,960 Speaker 3: get informed consent if they had even been told, which 274 00:17:31,960 --> 00:17:33,679 Speaker 3: they hadn't been, but if they had been told that 275 00:17:33,720 --> 00:17:36,800 Speaker 3: there's some risk of serious adverse events like mile chroditis 276 00:17:36,800 --> 00:17:39,040 Speaker 3: and other things, they were never told how big that 277 00:17:39,160 --> 00:17:42,359 Speaker 3: risk is. It was always gas lit. Oh, it's negligible, 278 00:17:42,359 --> 00:17:46,159 Speaker 3: it's minor. The problem is that if you're going to 279 00:17:46,240 --> 00:17:51,199 Speaker 3: vaccinate three hundred million people in the US, then something 280 00:17:51,240 --> 00:17:55,000 Speaker 3: that's even one in ten thousand becomes in the thousands 281 00:17:55,040 --> 00:17:58,080 Speaker 3: of people affected or tens of thousands of people affected, 282 00:17:58,640 --> 00:18:01,919 Speaker 3: and that that becomes serious that you know, we can't 283 00:18:01,960 --> 00:18:06,200 Speaker 3: have fifty thousand or one hundred thousand or more people 284 00:18:06,640 --> 00:18:12,800 Speaker 3: severely injured, neurological diseases, clotting diseases, cancer, and other things 285 00:18:13,160 --> 00:18:17,000 Speaker 3: from a vaccine, of which the virus infection that they 286 00:18:17,080 --> 00:18:21,399 Speaker 3: would have gotten if the vaccine actually worked, the virus 287 00:18:21,440 --> 00:18:25,320 Speaker 3: infactor were gotten, would not have anywhere near the magnitude 288 00:18:24,880 --> 00:18:29,800 Speaker 3: of risk that what they experienced from the vaccine. It's 289 00:18:29,840 --> 00:18:32,760 Speaker 3: a matter of risk benefit. So in the age groups 290 00:18:33,440 --> 00:18:37,760 Speaker 3: that had essentially zero risk of mortality from this, which 291 00:18:37,800 --> 00:18:42,040 Speaker 3: is children, young adults. There should never have been any vaccination, 292 00:18:42,160 --> 00:18:47,040 Speaker 3: let alone mandated vaccination, because those people, at least the 293 00:18:47,040 --> 00:18:50,600 Speaker 3: healthy ones want people who don't have chronic conditions, have 294 00:18:50,960 --> 00:18:53,919 Speaker 3: had essentially statistically zero risk of dying from this virus. 295 00:18:54,800 --> 00:18:58,040 Speaker 3: So there was no cause. There's no risk benefit benefit 296 00:18:58,119 --> 00:19:01,280 Speaker 3: for them, only risk, and that's not appropriate. 297 00:19:02,240 --> 00:19:04,080 Speaker 1: Well, you know, that's why I never got it. 298 00:19:04,119 --> 00:19:06,600 Speaker 2: And also just you know, COVID was just never risk 299 00:19:06,600 --> 00:19:08,200 Speaker 2: statistically to my life. 300 00:19:08,240 --> 00:19:10,240 Speaker 1: And then obviously, once you know, they kind of. 301 00:19:10,160 --> 00:19:12,639 Speaker 2: Started trying to coerce people and they getting it, I 302 00:19:12,880 --> 00:19:15,840 Speaker 2: found that really suspicious. And then also as well, when 303 00:19:15,840 --> 00:19:18,399 Speaker 2: we saw it wasn't stopping the spread of COVID, it 304 00:19:18,440 --> 00:19:20,640 Speaker 2: didn't really make sense to get it period. I guess 305 00:19:20,640 --> 00:19:23,160 Speaker 2: one thing I found interesting is that the study also 306 00:19:23,280 --> 00:19:27,199 Speaker 2: found it wasn't just the mRNA vaccine that had led to, 307 00:19:27,640 --> 00:19:30,280 Speaker 2: you know, some of these adverse reactions. They also found 308 00:19:30,280 --> 00:19:33,639 Speaker 2: that the viral vector vaccines were linked to higher blood 309 00:19:33,640 --> 00:19:37,720 Speaker 2: clods as well as increased likelihood of Gillian Barr syndrome, 310 00:19:38,440 --> 00:19:43,320 Speaker 2: neurological or you know, And so I guess our mRNA 311 00:19:43,480 --> 00:19:47,520 Speaker 2: vaccines more inherently dangerous than the viral vector vaccines or 312 00:19:47,680 --> 00:19:51,159 Speaker 2: were it's just these vaccines in general. I guess like 313 00:19:51,200 --> 00:19:52,959 Speaker 2: that was something I found that was a little bit 314 00:19:53,000 --> 00:19:56,520 Speaker 2: interesting because I've always kind of had this, I guess, 315 00:19:56,640 --> 00:19:59,160 Speaker 2: negative viewpoint on the m RNA vaccine just because it's 316 00:19:59,160 --> 00:20:01,719 Speaker 2: so new. But we've done viral vector of vaccines for 317 00:20:01,920 --> 00:20:04,359 Speaker 2: you know, a long time, right, So I don't know, 318 00:20:04,400 --> 00:20:06,879 Speaker 2: did that surprise you or what do you kind of like, 319 00:20:06,920 --> 00:20:08,359 Speaker 2: what do you what do you derive from that. 320 00:20:09,960 --> 00:20:16,120 Speaker 3: Today? Nothing surprises me. I think that there's different components 321 00:20:16,160 --> 00:20:22,480 Speaker 3: of hazard in this. I think that the nanolipelparticle envelope 322 00:20:22,520 --> 00:20:26,720 Speaker 3: itself has a hazard. I think that the spike protein 323 00:20:27,480 --> 00:20:32,480 Speaker 3: has a hazard. And I think that so the viral 324 00:20:32,560 --> 00:20:36,439 Speaker 3: vector and the novavax vaccines, for example, that are not 325 00:20:36,720 --> 00:20:43,400 Speaker 3: mRNA per se, those are those have spike toxicity potential problems. 326 00:20:43,640 --> 00:20:49,920 Speaker 3: And then nano lipolparticle that has both its own lipid 327 00:20:50,200 --> 00:20:56,720 Speaker 3: envelope problem as well as the spike protein problem. 328 00:20:57,040 --> 00:20:59,720 Speaker 2: We've done because am I'm correct, viral vector vaccines are 329 00:20:59,720 --> 00:21:02,280 Speaker 2: typical how vaccines are done? Correct or am I I 330 00:21:02,320 --> 00:21:04,399 Speaker 2: just want to make sure I got that point correct. 331 00:21:04,280 --> 00:21:09,760 Speaker 3: Well, you're talking about killed viruses basically attenuated viruses. Yes, 332 00:21:09,880 --> 00:21:12,600 Speaker 3: that's a classical method of vaccines. 333 00:21:12,840 --> 00:21:14,359 Speaker 1: Okay, all right, I just wanted to make sure I 334 00:21:14,359 --> 00:21:18,600 Speaker 1: didn't mess that up for the audience. You know, do 335 00:21:19,160 --> 00:21:19,520 Speaker 1: we know? 336 00:21:19,640 --> 00:21:22,520 Speaker 2: You know, one concern I had had about the vaccine 337 00:21:22,520 --> 00:21:24,239 Speaker 2: and why I didn't get it too, is just like 338 00:21:24,440 --> 00:21:26,520 Speaker 2: questions about like what it could do to a woman's 339 00:21:26,520 --> 00:21:29,200 Speaker 2: fertility or even a man's. Do we have any research 340 00:21:29,400 --> 00:21:32,840 Speaker 2: on concerns about fertility or is that kind of something 341 00:21:32,880 --> 00:21:34,800 Speaker 2: that's still unstudied. 342 00:21:35,760 --> 00:21:40,840 Speaker 3: That is being studied. The CDC people have put out 343 00:21:41,280 --> 00:21:45,720 Speaker 3: a few papers on that purporting to claim no hazard 344 00:21:46,200 --> 00:21:50,919 Speaker 3: to fertility, whereas there have been large numbers of anecdotal 345 00:21:51,000 --> 00:21:56,480 Speaker 3: reports in their early rollout of the vaccines among healthcare workers, 346 00:21:56,480 --> 00:22:04,120 Speaker 3: for example, women having various really altered menstrual patterns, excess 347 00:22:04,160 --> 00:22:09,399 Speaker 3: amount of flow, days of flow, menstruating on days that 348 00:22:09,560 --> 00:22:15,040 Speaker 3: weren't expected to be from their periods, menopausal women having periods, 349 00:22:15,280 --> 00:22:19,399 Speaker 3: all sorts of things like that that are symptomatic of 350 00:22:19,480 --> 00:22:24,919 Speaker 3: something altered in their reproductive regulation. But we don't know 351 00:22:25,080 --> 00:22:28,080 Speaker 3: about whether that matters for fertility or not. There have 352 00:22:28,280 --> 00:22:30,800 Speaker 3: been there, I believe there is some evidence to suggest 353 00:22:31,560 --> 00:22:37,480 Speaker 3: that birth rates have declined and you know, after the lockdown. 354 00:22:37,520 --> 00:22:39,760 Speaker 3: I joke that I would have expected birth rates to 355 00:22:39,800 --> 00:22:42,960 Speaker 3: start increasing nine months after the lockdowns, but I don't 356 00:22:42,960 --> 00:22:46,080 Speaker 3: think much has been seen, and if anything, it's been 357 00:22:46,200 --> 00:22:49,280 Speaker 3: decreased numbers of births. But this is going to require 358 00:22:49,359 --> 00:22:52,160 Speaker 3: some much more serious study. And one of the main 359 00:22:52,200 --> 00:22:57,119 Speaker 3: problems about the whole pandemic from the beginning is that 360 00:22:57,680 --> 00:23:00,840 Speaker 3: the organizations that are tasked are public health organizations that 361 00:23:00,920 --> 00:23:05,400 Speaker 3: are tasked with studying treatment, adverice effects, everything about it, 362 00:23:05,800 --> 00:23:10,240 Speaker 3: have decidedly chosen not to look at things that might 363 00:23:10,359 --> 00:23:15,760 Speaker 3: show damage or harm. They basically make claims of safety 364 00:23:15,960 --> 00:23:20,280 Speaker 3: without demonstrating data to prove the safety and the I think, 365 00:23:20,520 --> 00:23:22,680 Speaker 3: and they don't do the studies that could show harm. 366 00:23:23,160 --> 00:23:27,800 Speaker 3: And that's been the problem, whether that was early, you know, 367 00:23:28,400 --> 00:23:36,320 Speaker 3: outpatient treatment of repurposed drugs or these reproductive harms. The 368 00:23:36,400 --> 00:23:43,680 Speaker 3: CDC has data on it has clinical chart information on 369 00:23:43,880 --> 00:23:47,520 Speaker 3: some two hundred and twenty five I think million Americans. 370 00:23:48,359 --> 00:23:52,080 Speaker 3: It has not made any of those data transparent. It 371 00:23:52,119 --> 00:23:58,159 Speaker 3: has data on three hundred million Americans insurance claims. So 372 00:23:58,240 --> 00:24:01,399 Speaker 3: this is the treatment data that gets filtered into the 373 00:24:01,680 --> 00:24:08,679 Speaker 3: insurance payment system and Medicare and Medicaid, and it's not 374 00:24:08,760 --> 00:24:11,159 Speaker 3: made any of that public. It's not analyzed any of 375 00:24:11,200 --> 00:24:14,760 Speaker 3: that and made that public. So we know that these 376 00:24:14,800 --> 00:24:17,040 Speaker 3: agencies have the data to do these studies and they're 377 00:24:17,040 --> 00:24:18,080 Speaker 3: not revealing what they know. 378 00:24:19,600 --> 00:24:21,240 Speaker 1: Really starting to believe. 379 00:24:21,240 --> 00:24:23,040 Speaker 2: Also, I kind of laughed myself for a second, but 380 00:24:23,080 --> 00:24:26,879 Speaker 2: the population rates just because I like, basically stayed with 381 00:24:26,880 --> 00:24:28,159 Speaker 2: my parents for a while and I was like in 382 00:24:28,200 --> 00:24:30,840 Speaker 2: their basement alone drinking wine. So I think maybe it's 383 00:24:31,000 --> 00:24:34,520 Speaker 2: just too many people like me in that situation. But 384 00:24:34,720 --> 00:24:38,439 Speaker 2: you know, I'm really starting to believe that, like the 385 00:24:38,480 --> 00:24:42,440 Speaker 2: medical industry, including you know, pharmaceutical and all of it, 386 00:24:42,480 --> 00:24:47,200 Speaker 2: is the purpose is more to make money than it 387 00:24:47,240 --> 00:24:50,040 Speaker 2: is to bring about health in society or to keep 388 00:24:50,119 --> 00:24:50,640 Speaker 2: us healthy. 389 00:24:51,760 --> 00:24:53,200 Speaker 1: Well is that a fair assessment? 390 00:24:53,760 --> 00:25:01,920 Speaker 3: Yes, I mean think about that. The this started way 391 00:25:02,160 --> 00:25:08,280 Speaker 3: earlier than COVID. In nineteen ninety one, some investigators, some scientists, 392 00:25:08,359 --> 00:25:12,600 Speaker 3: medical scientists, created a discipline they called evidence based medicine. 393 00:25:13,000 --> 00:25:17,240 Speaker 3: I thought this was obnoxious, as if medicine before them 394 00:25:17,600 --> 00:25:19,879 Speaker 3: was cargo cult science, that there was no science to 395 00:25:19,920 --> 00:25:22,840 Speaker 3: medicine before that, which is absurd. So anyway, and what 396 00:25:22,880 --> 00:25:27,360 Speaker 3: they did is they claimed that randomized control trials were 397 00:25:27,440 --> 00:25:32,119 Speaker 3: the gold standard of evidence. Over time, that statement got 398 00:25:32,880 --> 00:25:36,800 Speaker 3: perverted to being randomized control trials are the only acceptable 399 00:25:36,840 --> 00:25:41,080 Speaker 3: form of evidence. And what that means is that because 400 00:25:41,119 --> 00:25:44,480 Speaker 3: the cost of a randomized control trial is somewhere between 401 00:25:44,920 --> 00:25:48,400 Speaker 3: five and one hundred million dollars, that only where there's 402 00:25:48,400 --> 00:25:53,560 Speaker 3: a profit motive for testing something, where there's basically a 403 00:25:53,600 --> 00:25:58,840 Speaker 3: patent product that will make money large amounts of money 404 00:25:58,840 --> 00:26:00,960 Speaker 3: more than the five or hundred men million dollars in 405 00:26:01,000 --> 00:26:05,320 Speaker 3: the trial, that that is the only kind of product 406 00:26:05,560 --> 00:26:09,679 Speaker 3: that will ever get into the marketplace because of this 407 00:26:09,720 --> 00:26:13,800 Speaker 3: corrupted system. Now, I've written a long essay at the 408 00:26:13,880 --> 00:26:18,000 Speaker 3: Radstone Institute and readers can look there for it listeners 409 00:26:18,359 --> 00:26:24,400 Speaker 3: that talks about this fraud that randomized control trials are 410 00:26:24,400 --> 00:26:28,800 Speaker 3: not the gold standard evidence because they're not done in 411 00:26:28,840 --> 00:26:31,240 Speaker 3: a way that would make them that, because they need 412 00:26:31,320 --> 00:26:35,000 Speaker 3: to have large numbers of outcome events. So, for example, 413 00:26:35,160 --> 00:26:40,119 Speaker 3: in the original Pfizer vaccine trial, while there were twenty 414 00:26:40,119 --> 00:26:42,400 Speaker 3: two thousand people who got the vaccine, in twenty two 415 00:26:42,400 --> 00:26:47,480 Speaker 3: thousand controls who didn't, the number of infections in the 416 00:26:47,560 --> 00:26:51,359 Speaker 3: vaccine group was eight. Eight is not a randomized number. 417 00:26:51,720 --> 00:26:53,399 Speaker 3: You know, if you flip a coin ten times, you 418 00:26:53,440 --> 00:26:55,600 Speaker 3: could get seven heads and three tails or vice versa 419 00:26:56,160 --> 00:26:59,120 Speaker 3: very easily happens a third of the time, and so 420 00:26:59,400 --> 00:27:02,600 Speaker 3: that means that the randomization didn't work in that study. 421 00:27:02,720 --> 00:27:05,719 Speaker 3: That eight is just not randomized, and so biases that 422 00:27:05,720 --> 00:27:09,280 Speaker 3: the trial is supposed to remove by being randomized were 423 00:27:09,320 --> 00:27:13,000 Speaker 3: not removed because the numbers of the outcomes weren't big enough. 424 00:27:13,240 --> 00:27:16,640 Speaker 3: And this happens all over these randomized trials. And at 425 00:27:16,680 --> 00:27:20,439 Speaker 3: the same time, the quality of non randomized but control 426 00:27:20,520 --> 00:27:24,120 Speaker 3: trials has improved from nineteen ninety one, when the evidence 427 00:27:24,119 --> 00:27:29,000 Speaker 3: based medicine people claimed that observational studies non randomized trials 428 00:27:29,240 --> 00:27:34,200 Speaker 3: were biased, until today when investigators like myself and others 429 00:27:34,640 --> 00:27:37,359 Speaker 3: we know huge amounts about all of the diseases that 430 00:27:37,400 --> 00:27:39,760 Speaker 3: we study, and we know what the risk factors are, 431 00:27:40,000 --> 00:27:42,000 Speaker 3: and we measure them in the studies and we adjust 432 00:27:42,080 --> 00:27:45,040 Speaker 3: for them, and so we clean up all that potential 433 00:27:45,080 --> 00:27:49,640 Speaker 3: bias and make our observational non randomized studies very high quality. 434 00:27:49,760 --> 00:27:53,600 Speaker 3: Then this has been shown empirically to that non randomized 435 00:27:53,600 --> 00:27:57,360 Speaker 3: but controlled studies are evidentially just as good as randomized 436 00:27:57,400 --> 00:28:03,880 Speaker 3: trials today, but the the medical industry has convinced the 437 00:28:03,960 --> 00:28:07,560 Speaker 3: FDA that only randomized trials count. And what this does 438 00:28:07,680 --> 00:28:13,000 Speaker 3: is it lucks in the ability to sell prescription medications 439 00:28:13,040 --> 00:28:16,479 Speaker 3: only for things that our patent, that there's a patent interest, 440 00:28:16,720 --> 00:28:19,160 Speaker 3: and therefore the ability to get large amounts of money 441 00:28:19,320 --> 00:28:22,720 Speaker 3: by charging hundreds of thousands of dollars per pill or 442 00:28:22,800 --> 00:28:25,719 Speaker 3: whatever to sell this stuff in the open market. And 443 00:28:25,760 --> 00:28:29,040 Speaker 3: this is why repurposed drugs will never get approved for 444 00:28:29,160 --> 00:28:35,200 Speaker 3: anything that have large marketplaces because they don't make enough 445 00:28:35,280 --> 00:28:40,280 Speaker 3: money and they haven't got the force of randomized trials 446 00:28:40,640 --> 00:28:44,080 Speaker 3: to apply to the corrected regulatory system. 447 00:28:44,320 --> 00:28:46,120 Speaker 1: We're going to take a quick break more with doctor 448 00:28:46,160 --> 00:28:51,560 Speaker 1: Harvey Rish. What's wild to me too? 449 00:28:51,640 --> 00:28:53,760 Speaker 2: And like what I've learned through all of this and 450 00:28:53,800 --> 00:28:56,000 Speaker 2: talking to so many people like you and just so 451 00:28:56,040 --> 00:28:59,600 Speaker 2: many senators like raand Paul, and like, what's wild is 452 00:28:59,680 --> 00:29:02,400 Speaker 2: that you know, as you or you were pointing out, 453 00:29:02,440 --> 00:29:04,400 Speaker 2: we've got a lot of these studies where you know, 454 00:29:04,440 --> 00:29:07,600 Speaker 2: the people doing them have an interest in making money 455 00:29:07,680 --> 00:29:10,160 Speaker 2: and profiting. And then like we're not gonna like who 456 00:29:10,160 --> 00:29:12,400 Speaker 2: can trust a study done by like Pfizer about the 457 00:29:12,480 --> 00:29:15,240 Speaker 2: vaccine they want to inject and everyone's arms and make 458 00:29:15,360 --> 00:29:17,600 Speaker 2: you know, tons and tons of money off of. But 459 00:29:17,640 --> 00:29:21,440 Speaker 2: then also to the extent that government controls studies as well, 460 00:29:21,520 --> 00:29:25,640 Speaker 2: in stifles like independent studies and like through grant money. 461 00:29:25,440 --> 00:29:26,200 Speaker 1: And things like that. 462 00:29:26,280 --> 00:29:29,239 Speaker 2: Like I I think it's kind of wild just how 463 00:29:29,320 --> 00:29:32,160 Speaker 2: much of an impact the NIH has and the kind 464 00:29:32,160 --> 00:29:34,080 Speaker 2: of research that's out there. So it's like you can't 465 00:29:34,080 --> 00:29:36,360 Speaker 2: really it's like very hard outside of people like you 466 00:29:36,400 --> 00:29:38,600 Speaker 2: bringing truth to light. It's very hard to get the 467 00:29:38,600 --> 00:29:41,000 Speaker 2: truth when you've got you know, studies being done by 468 00:29:41,120 --> 00:29:44,400 Speaker 2: privateers and then the government coming in and also sort 469 00:29:44,400 --> 00:29:47,600 Speaker 2: of dictating what truth gets to light as well. 470 00:29:47,680 --> 00:29:49,400 Speaker 1: So it's like very hard to get the truth. 471 00:29:49,840 --> 00:29:52,840 Speaker 3: Well, the regulatory agencies have been corrupted, they've been captured. 472 00:29:52,880 --> 00:29:57,280 Speaker 3: The FDA and CDC have both been captured by industry. 473 00:29:56,920 --> 00:30:00,560 Speaker 3: They they are part of this is the There are 474 00:30:00,640 --> 00:30:04,560 Speaker 3: user fees for FDA. Sixty percent of the FDA's budget 475 00:30:04,680 --> 00:30:08,960 Speaker 3: is paid for by pharma, and as well, there are 476 00:30:09,040 --> 00:30:13,000 Speaker 3: private charitable foundations sitting above both the FDA and CDC 477 00:30:13,440 --> 00:30:16,120 Speaker 3: that allow people like Bill Gates to send three hundred 478 00:30:16,120 --> 00:30:19,200 Speaker 3: million dollars to the CDC and the CDC relies on 479 00:30:19,240 --> 00:30:21,960 Speaker 3: that money and is controlled by that money, and this 480 00:30:22,000 --> 00:30:24,600 Speaker 3: should never have been allowed. We don't allow people to 481 00:30:24,680 --> 00:30:28,760 Speaker 3: pay for government interests to corrupt the government because of 482 00:30:28,800 --> 00:30:32,040 Speaker 3: their interests, So this should have been illegal from the start, 483 00:30:32,240 --> 00:30:38,240 Speaker 3: and it's still happening today. Also, pharma has a financial 484 00:30:38,280 --> 00:30:46,160 Speaker 3: interest in making people halfway better from their treatments. Okay, 485 00:30:46,440 --> 00:30:48,840 Speaker 3: if you make somebody halfway better, it looks like your 486 00:30:48,840 --> 00:30:52,400 Speaker 3: treatment does something, so it has a place in the marketplace. 487 00:30:52,760 --> 00:30:55,600 Speaker 3: On the other hand, if you cure somebody of something, 488 00:30:56,280 --> 00:30:59,720 Speaker 3: then you can't keep selling them the medication, so you 489 00:30:59,760 --> 00:31:03,040 Speaker 3: make them halfway better, and that means number one, they 490 00:31:03,040 --> 00:31:07,200 Speaker 3: stay on your medication for forever. Number two, that the 491 00:31:07,680 --> 00:31:11,360 Speaker 3: side effects of your medication generate more money for pharma 492 00:31:11,360 --> 00:31:15,000 Speaker 3: to treat that. And so there's an interest in generating 493 00:31:15,080 --> 00:31:19,000 Speaker 3: medications that are imperfect and that have their own adverse 494 00:31:19,240 --> 00:31:22,720 Speaker 3: effects rather than things that are curative from the beginning. 495 00:31:24,000 --> 00:31:25,800 Speaker 2: You know one thing that's been on my mind since 496 00:31:25,840 --> 00:31:29,040 Speaker 2: you had mentioned it earlier. With COVID being a bioweapon, 497 00:31:29,720 --> 00:31:32,440 Speaker 2: does that mean the intention of the bioweapon was to 498 00:31:32,560 --> 00:31:35,040 Speaker 2: kill as it did kill a lot of elderly people 499 00:31:35,720 --> 00:31:40,280 Speaker 2: and people with comorbidities, or are there like consequences that 500 00:31:40,320 --> 00:31:42,720 Speaker 2: we're unaware of that we'll be dealing with from having 501 00:31:42,760 --> 00:31:46,200 Speaker 2: gotten COVID in the future, because obviously if it was manipulated, 502 00:31:46,320 --> 00:31:49,880 Speaker 2: then you know, I would think that, you know, there 503 00:31:49,880 --> 00:31:52,520 Speaker 2: would be question, you know, like, so what does that 504 00:31:52,560 --> 00:31:54,080 Speaker 2: do to our bodies in the long term? 505 00:31:54,120 --> 00:31:57,640 Speaker 3: I guess well, I can't speak to the intention of 506 00:31:57,680 --> 00:32:01,640 Speaker 3: all the scientists who I know who this bioweapons research. 507 00:32:02,240 --> 00:32:04,600 Speaker 3: I think they're basically sociopaths. I think they have no 508 00:32:04,720 --> 00:32:08,960 Speaker 3: consciences that they think this is just scientific, scientifically interesting 509 00:32:09,120 --> 00:32:11,120 Speaker 3: and I can get grants and support myself and make 510 00:32:11,160 --> 00:32:13,480 Speaker 3: a career out of this, and that's as far as 511 00:32:13,480 --> 00:32:18,000 Speaker 3: they're thinking goes. And I think they are just totally 512 00:32:18,320 --> 00:32:21,440 Speaker 3: it's totally irrelevant to them that any of these things 513 00:32:21,760 --> 00:32:24,640 Speaker 3: that have got out and damaged tens or hundreds of 514 00:32:24,680 --> 00:32:28,360 Speaker 3: millions of people would be well, that's life, you know that. 515 00:32:28,560 --> 00:32:32,560 Speaker 3: I think they just don't care. They live in a theoretical, 516 00:32:32,640 --> 00:32:35,840 Speaker 3: intellectual world with no common sense. And that's been a 517 00:32:35,880 --> 00:32:39,400 Speaker 3: big problem that we've observed in more general society in 518 00:32:39,680 --> 00:32:45,880 Speaker 3: the last few decades, but in particular, these virus researchers 519 00:32:46,600 --> 00:32:51,120 Speaker 3: making these gain of function viruses. They are basically thinking, Wow, 520 00:32:51,120 --> 00:32:53,520 Speaker 3: could we do this? How interesting would this be to 521 00:32:53,560 --> 00:32:55,959 Speaker 3: do this? This is this is a scientific challenge, and 522 00:32:56,000 --> 00:32:58,560 Speaker 3: we can solve this problem. Actually do it. Wow, wouldn't 523 00:32:58,560 --> 00:33:01,040 Speaker 3: that be interesting? And that's as far as the thinking goes. 524 00:33:01,840 --> 00:33:03,720 Speaker 2: It's like they're trying to play god or something to 525 00:33:04,600 --> 00:33:10,480 Speaker 2: a degree before we go in. You know, looking back 526 00:33:10,600 --> 00:33:13,600 Speaker 2: on all of this with COVID and obviously you know, 527 00:33:14,040 --> 00:33:16,040 Speaker 2: you kind of dug into a lot of the problems 528 00:33:16,080 --> 00:33:19,480 Speaker 2: that the alphabet agencies have just you know, government medicine 529 00:33:19,520 --> 00:33:23,400 Speaker 2: in general. Like, what are some changes do you think 530 00:33:23,480 --> 00:33:26,480 Speaker 2: that we could enact as a country that would lead 531 00:33:26,600 --> 00:33:31,959 Speaker 2: to just a healthier, more truthful society where you know, 532 00:33:32,040 --> 00:33:35,320 Speaker 2: people are being treated in the manner in which is 533 00:33:35,960 --> 00:33:39,800 Speaker 2: positive to their health and longevity. I guess you know 534 00:33:39,800 --> 00:33:43,040 Speaker 2: what kind of changes could we make? Should we make? 535 00:33:44,920 --> 00:33:47,680 Speaker 3: We need to do two major things and a lot 536 00:33:47,680 --> 00:33:50,000 Speaker 3: of minor things. The major things are, we have to 537 00:33:50,040 --> 00:33:54,560 Speaker 3: remove all of the We have to remove all of 538 00:33:54,560 --> 00:34:00,000 Speaker 3: the pharma advertising from television and public media. That correct 539 00:34:00,440 --> 00:34:05,080 Speaker 3: the media that forces the media to spout pharma messaging 540 00:34:05,240 --> 00:34:08,319 Speaker 3: because they become addicted to the advertising revenue. So that's 541 00:34:08,360 --> 00:34:11,200 Speaker 3: the first thing. No other country in the world allows 542 00:34:11,320 --> 00:34:15,120 Speaker 3: pharma advertising that way. The second thing is we have 543 00:34:15,239 --> 00:34:18,160 Speaker 3: to end the charitable foundations at the top of the 544 00:34:18,200 --> 00:34:22,120 Speaker 3: FDA and the CDC. We have to change the people 545 00:34:22,239 --> 00:34:25,279 Speaker 3: who are in charge of the FDA and CDC the 546 00:34:25,360 --> 00:34:29,880 Speaker 3: top scientific echelon to people who are not going to 547 00:34:29,960 --> 00:34:35,560 Speaker 3: go and become heads or on advisory panels of drug 548 00:34:35,600 --> 00:34:40,480 Speaker 3: companies the minute they leave those government regulatory agencies. Basically, 549 00:34:40,480 --> 00:34:46,839 Speaker 3: we have to reduce the regulatory corruption from pharma into 550 00:34:46,880 --> 00:34:49,239 Speaker 3: those agencies and make the agencies back to just doing 551 00:34:49,280 --> 00:34:53,520 Speaker 3: their regulatory jobs based on explicit standards that they have 552 00:34:53,600 --> 00:34:56,880 Speaker 3: to use. Those are the major things that have to 553 00:34:56,880 --> 00:35:01,040 Speaker 3: be done in order to decouple the regul natory agencies 554 00:35:01,080 --> 00:35:06,960 Speaker 3: from pharma itself. Now we have to interfere with the 555 00:35:07,120 --> 00:35:12,560 Speaker 3: process whereby regulatory agency scientists, the people who do the 556 00:35:12,640 --> 00:35:16,279 Speaker 3: scientific work in FDA and CDC, then the regulatory work 557 00:35:16,320 --> 00:35:20,440 Speaker 3: and so on, don't see that their only career improvement 558 00:35:20,520 --> 00:35:22,399 Speaker 3: is to go to work in pharma, because then they're 559 00:35:22,400 --> 00:35:25,680 Speaker 3: not going to make any adverse judgments against pharma products 560 00:35:25,680 --> 00:35:27,719 Speaker 3: because they won't get a job in pharma, so that 561 00:35:27,880 --> 00:35:33,640 Speaker 3: has to be removed. And I think that the last 562 00:35:33,680 --> 00:35:39,960 Speaker 3: thing is that even though we have monopoly laws on 563 00:35:40,040 --> 00:35:43,279 Speaker 3: the books and pharma, there are enough pharma companies so 564 00:35:43,320 --> 00:35:46,200 Speaker 3: that not a single one has a monopoly. They are 565 00:35:46,239 --> 00:35:49,960 Speaker 3: so large in the amount of money and resources that 566 00:35:50,000 --> 00:35:52,760 Speaker 3: they have under their control that they've become de facto 567 00:35:52,840 --> 00:35:59,319 Speaker 3: monopolies that they can control large volumes of medical advertising, 568 00:36:00,040 --> 00:36:05,240 Speaker 3: paying for advertising in medical journals and so on, medical 569 00:36:05,320 --> 00:36:09,239 Speaker 3: journal editors and reviews and all of that that corrupts 570 00:36:09,440 --> 00:36:12,640 Speaker 3: the medical journals that they pay for grants and speakers 571 00:36:12,920 --> 00:36:15,920 Speaker 3: and docs. They've correct that corrupted almost all of academic 572 00:36:16,000 --> 00:36:21,480 Speaker 3: medicine by paying for doctors to give you know, basically 573 00:36:21,480 --> 00:36:24,800 Speaker 3: money to shut them up to anything except the pharma messaging. 574 00:36:25,000 --> 00:36:27,719 Speaker 3: And you have to realize that when you develop a 575 00:36:27,760 --> 00:36:30,920 Speaker 3: successful medication, it costs a billion dollars to develop a 576 00:36:30,920 --> 00:36:33,600 Speaker 3: successful medication, because ninety nine out of one hundred fail 577 00:36:33,640 --> 00:36:35,279 Speaker 3: and you have to go through all one hundred to 578 00:36:35,320 --> 00:36:37,840 Speaker 3: get the one that works, and so a billion dollars 579 00:36:37,840 --> 00:36:40,080 Speaker 3: for the scientific work, and then there's two billion dollars 580 00:36:40,120 --> 00:36:44,320 Speaker 3: you have to spend to corrupt the marketplace by paying 581 00:36:44,320 --> 00:36:48,120 Speaker 3: off doctors and academic doctors all over the world, and grants, 582 00:36:48,160 --> 00:36:52,000 Speaker 3: speaking fees, teaching fees, all this stuff to make doctors 583 00:36:52,040 --> 00:36:55,480 Speaker 3: aligned with your messaging. That has to end, That has 584 00:36:55,520 --> 00:37:00,480 Speaker 3: to largely end. And it's all of this gigantic corrupting 585 00:37:00,520 --> 00:37:04,319 Speaker 3: influence that is the facto monopoly that that is what 586 00:37:04,480 --> 00:37:07,600 Speaker 3: has to be changed in order to make the whole 587 00:37:08,360 --> 00:37:12,040 Speaker 3: regulatory and marketplace system more objective. 588 00:37:12,880 --> 00:37:15,960 Speaker 2: Sorry, if you don't mind, I'd just like to ad so. 589 00:37:16,160 --> 00:37:21,160 Speaker 2: I know that you specialize in cancer as well. We're saying, 590 00:37:21,320 --> 00:37:24,520 Speaker 2: you know, rates going up in younger adults. Why do 591 00:37:24,560 --> 00:37:25,440 Speaker 2: you think that is? 592 00:37:26,880 --> 00:37:29,279 Speaker 3: I think that we don't have a good handle on 593 00:37:29,480 --> 00:37:34,960 Speaker 3: rates yet we do have a lot of reports of 594 00:37:35,960 --> 00:37:40,960 Speaker 3: individuals who have had unusual cancers at younger ages. And 595 00:37:41,920 --> 00:37:45,279 Speaker 3: one thing that's very good in the US is we 596 00:37:45,360 --> 00:37:49,359 Speaker 3: have a very large cancer reporting system and this is 597 00:37:49,400 --> 00:37:55,319 Speaker 3: the SEER system Seer Surveillance, Epidemiology and Results that were 598 00:37:55,360 --> 00:37:59,080 Speaker 3: set up I think in the nineteen seventies to collect 599 00:37:59,080 --> 00:38:03,600 Speaker 3: information on as a reportable disease in most states, so 600 00:38:03,680 --> 00:38:08,240 Speaker 3: this comes from twenty some states and large metropolitan areas 601 00:38:08,600 --> 00:38:11,839 Speaker 3: to collect information about cancer cases as they occur. It 602 00:38:11,840 --> 00:38:13,879 Speaker 3: takes two to three years to get all of these 603 00:38:13,960 --> 00:38:18,200 Speaker 3: data harmonized and cleaned up and available, and it's online 604 00:38:19,600 --> 00:38:25,920 Speaker 3: on the NCI websites that you can probe these cancer 605 00:38:27,120 --> 00:38:30,279 Speaker 3: rates by cancer type, by age group, by year, by 606 00:38:31,200 --> 00:38:35,399 Speaker 3: metropolitan area, by race, sex, and so on and look 607 00:38:35,440 --> 00:38:38,440 Speaker 3: at the rates. And so we don't have it yet 608 00:38:38,920 --> 00:38:42,279 Speaker 3: from twenty twenty one and twenty twenty two. It's just 609 00:38:42,600 --> 00:38:44,280 Speaker 3: it's going to be a while before it comes online. 610 00:38:44,280 --> 00:38:47,600 Speaker 3: The second thing is that cancer takes some years to develop. 611 00:38:48,400 --> 00:38:50,520 Speaker 3: What you see at the very beginning if there's an 612 00:38:50,560 --> 00:38:54,879 Speaker 3: increased risk of cancer, are the blood cancers, leukemia's lymphomas, 613 00:38:55,120 --> 00:38:57,680 Speaker 3: You see those after two to three years because they're 614 00:38:57,719 --> 00:39:01,080 Speaker 3: shorter latency. Lung cancer is five years. Solid cancers are 615 00:39:01,080 --> 00:39:04,120 Speaker 3: ten to twenty to thirty years. It takes. You'll also 616 00:39:04,200 --> 00:39:08,160 Speaker 3: see cancers that have gone into remission that come out 617 00:39:08,160 --> 00:39:11,520 Speaker 3: of remission, so for example, breast cancer that has been 618 00:39:11,560 --> 00:39:13,880 Speaker 3: in a remission for five or ten years and then 619 00:39:13,920 --> 00:39:16,560 Speaker 3: after a vaccination suddenly comes out of remission. So we've 620 00:39:16,560 --> 00:39:19,400 Speaker 3: seen anecdotal reports of some of these things, but we 621 00:39:19,440 --> 00:39:24,040 Speaker 3: don't have a quantitative estimate yet on these. The generation 622 00:39:24,160 --> 00:39:28,160 Speaker 3: of new cancers based on the vaccine exposures. 623 00:39:29,239 --> 00:39:34,320 Speaker 2: Okay, Doctor Harvey Rish, fascinating. Really appreciate you being so brave. 624 00:39:34,400 --> 00:39:36,440 Speaker 2: I know it's not easy, and I know a lot 625 00:39:36,520 --> 00:39:38,360 Speaker 2: of you guys who spoke the truth have had to, 626 00:39:38,719 --> 00:39:41,600 Speaker 2: you know, deal with a lot of crap to put 627 00:39:41,600 --> 00:39:44,440 Speaker 2: it lightly. Appreciate you being a truth teller and for 628 00:39:44,560 --> 00:39:46,040 Speaker 2: taking so much time to come on the show. 629 00:39:46,120 --> 00:39:47,440 Speaker 1: Really do it's my pleasure. 630 00:39:47,480 --> 00:39:48,200 Speaker 3: Thanks for covering this. 631 00:39:48,520 --> 00:39:49,759 Speaker 1: It was doctor Harvey Rish. 632 00:39:49,920 --> 00:39:52,560 Speaker 2: Appreciate him taking the time to come on the show. 633 00:39:52,600 --> 00:39:54,320 Speaker 1: I mean that was powerful. 634 00:39:54,440 --> 00:39:56,439 Speaker 2: Like I don't know about you guys listening at home, 635 00:39:56,480 --> 00:39:59,240 Speaker 2: but I was in awe of what he was saying. 636 00:39:59,400 --> 00:40:02,880 Speaker 2: So appreciate his bravery, Appreciate his truth telling. Appreciate you 637 00:40:02,880 --> 00:40:05,560 Speaker 2: guys for listening. Also with one of the things, John Casting, 638 00:40:05,600 --> 00:40:08,720 Speaker 2: my producer, for putting the show together every Monday and Thursday, 639 00:40:08,760 --> 00:40:10,759 Speaker 2: but you can listen throughout the week until next time.