1 00:00:00,200 --> 00:00:03,440 Speaker 1: Now here's a highlight from Coast to Coast AM on 2 00:00:03,560 --> 00:00:06,640 Speaker 1: iHeart Radio and welcome back to Coast to Coast George 3 00:00:06,720 --> 00:00:09,000 Speaker 1: nor with you our final segment with Alex barn sin 4 00:00:09,080 --> 00:00:11,559 Speaker 1: as we talk about the coronavirus. Alex, we were talking 5 00:00:11,600 --> 00:00:14,840 Speaker 1: about death statistics and you were getting into New York. 6 00:00:14,960 --> 00:00:18,760 Speaker 1: Let's let's get back into that. Sure, so, just very quickly, So, 7 00:00:18,800 --> 00:00:22,560 Speaker 1: the city has reported seventy five hundred confirmed death That 8 00:00:22,600 --> 00:00:25,439 Speaker 1: means there was a lab test showing the presence of 9 00:00:25,480 --> 00:00:28,960 Speaker 1: coronavirus and someone who died, and another four thousand quote 10 00:00:29,080 --> 00:00:32,400 Speaker 1: quote probable desk where they essentially the person had a 11 00:00:32,440 --> 00:00:35,560 Speaker 1: cough or had you know, a fever or some other 12 00:00:35,680 --> 00:00:39,760 Speaker 1: symptom that could have been coronavirus related and died. The 13 00:00:39,960 --> 00:00:43,240 Speaker 1: problem is that the city has also said that since 14 00:00:43,280 --> 00:00:46,960 Speaker 1: this began, you know, which was which was essentially mid March, 15 00:00:48,159 --> 00:00:51,159 Speaker 1: I think March eleventh was the first coronavirus death, there 16 00:00:51,159 --> 00:00:54,960 Speaker 1: have been about three thousand deaths, more than you would 17 00:00:55,000 --> 00:00:57,840 Speaker 1: have expected in New York City. Right in New York City, 18 00:00:57,880 --> 00:01:01,200 Speaker 1: every day people die, right exactly, you know, and so 19 00:01:01,200 --> 00:01:03,639 Speaker 1: so and you know, the city has a large population, 20 00:01:03,720 --> 00:01:06,400 Speaker 1: and it has a lot of elderly people. So, um, 21 00:01:06,600 --> 00:01:09,720 Speaker 1: depending on the season of the year, there could be two, 22 00:01:09,880 --> 00:01:11,800 Speaker 1: you know, two hundred deaths a day. There's going to 23 00:01:11,880 --> 00:01:13,400 Speaker 1: be fewer in the summer than be a few more 24 00:01:13,400 --> 00:01:16,080 Speaker 1: in the winter. There might be you know, seasonally, some 25 00:01:16,080 --> 00:01:17,959 Speaker 1: some winters are worse than others. There might be two 26 00:01:18,040 --> 00:01:20,160 Speaker 1: hundred and fifty deaths a day. You know, it just 27 00:01:20,280 --> 00:01:23,320 Speaker 1: it just varies right that day to day, year to year. 28 00:01:23,880 --> 00:01:28,280 Speaker 1: In any case, the city health commissioner said that the 29 00:01:28,319 --> 00:01:32,240 Speaker 1: city had seen three thousand additional deaths, but there have 30 00:01:32,280 --> 00:01:36,720 Speaker 1: been eleven thousand, five hundred deaths attributed to coronavirus. So 31 00:01:36,720 --> 00:01:38,959 Speaker 1: so it's it's pretty hard to look at that and 32 00:01:39,040 --> 00:01:42,520 Speaker 1: not realize that, Um, you know, some of these people 33 00:01:42,720 --> 00:01:46,360 Speaker 1: must have died with rather than of the coronavirus, and 34 00:01:46,360 --> 00:01:49,360 Speaker 1: others they were you know, they were extremely elderly, and 35 00:01:49,440 --> 00:01:53,440 Speaker 1: they had underlying conditions, and uh, you know, they became 36 00:01:53,560 --> 00:01:57,360 Speaker 1: infected because the infection spread very rapidly in the city 37 00:01:57,360 --> 00:02:00,680 Speaker 1: in the last month, and they die and then you know, 38 00:02:00,720 --> 00:02:03,120 Speaker 1: and they would have died anyway, and and then there's 39 00:02:03,160 --> 00:02:04,680 Speaker 1: going to be another group of people who are who 40 00:02:04,760 --> 00:02:09,560 Speaker 1: are quite sick, who the coronavirus probably pushed over the edge. Right. So, 41 00:02:09,560 --> 00:02:12,280 Speaker 1: so I have, you know, the classic example, I have, 42 00:02:12,360 --> 00:02:15,600 Speaker 1: you know, congestive heart failure. I have end stage renal disease. 43 00:02:15,680 --> 00:02:18,639 Speaker 1: I have you know, I'm on dialysis. I'm very sick, 44 00:02:18,680 --> 00:02:21,040 Speaker 1: and uh you know, and I'm gonna die. I'm gonna 45 00:02:21,080 --> 00:02:24,040 Speaker 1: die this year at some point, but who knows when. 46 00:02:24,160 --> 00:02:26,519 Speaker 1: And then I get the coronavirus, and it does lead 47 00:02:26,560 --> 00:02:29,080 Speaker 1: me to die, you know, more quickly than you would 48 00:02:29,120 --> 00:02:32,840 Speaker 1: have otherwise. So so so most of the people who 49 00:02:32,919 --> 00:02:36,240 Speaker 1: are dying, uh, you know, of coronavirus are quite you 50 00:02:36,240 --> 00:02:38,960 Speaker 1: know again, they're quite old, and they're quite sick, and 51 00:02:39,280 --> 00:02:42,880 Speaker 1: I think there's a legitimate argument around whether those death 52 00:02:42,919 --> 00:02:46,440 Speaker 1: should be classified as coronavirus death or as death from 53 00:02:46,440 --> 00:02:48,639 Speaker 1: the underlying condition. And people have this argument about the 54 00:02:48,639 --> 00:02:51,560 Speaker 1: flu too. There was actually a very good article, um 55 00:02:51,960 --> 00:02:56,400 Speaker 1: from Canada from the CBC in twenty twelve about flu 56 00:02:56,560 --> 00:02:59,840 Speaker 1: death estimates, raising this very point. So this is not 57 00:02:59,880 --> 00:03:03,000 Speaker 1: a new debate, you know, this debate of you know, 58 00:03:03,480 --> 00:03:07,080 Speaker 1: when people are quite sick and you know, a virus 59 00:03:07,120 --> 00:03:10,240 Speaker 1: pushes them over the edge, what the correct classification is. 60 00:03:10,280 --> 00:03:13,880 Speaker 1: It's it's very clear that for coronavirus. We're gonna we're 61 00:03:13,880 --> 00:03:17,760 Speaker 1: gonna classify as many deaths as possible as coronavirus deaths 62 00:03:18,080 --> 00:03:21,280 Speaker 1: as coronavirus related. And that's fine like that, as long 63 00:03:21,320 --> 00:03:24,440 Speaker 1: as it's disclosed, that's a that's an okay way to 64 00:03:24,480 --> 00:03:26,960 Speaker 1: do it. But people should be aware of what's going on, 65 00:03:27,080 --> 00:03:29,920 Speaker 1: and they should be aware that the all cause mortality 66 00:03:30,000 --> 00:03:33,840 Speaker 1: numbers in New York don't quite line up with the 67 00:03:33,919 --> 00:03:38,120 Speaker 1: top line coronavirus numbers. And what about the hysteria Alex. 68 00:03:38,200 --> 00:03:40,280 Speaker 1: I was in a supermarket a couple of days ago, 69 00:03:40,480 --> 00:03:42,960 Speaker 1: and I don't wear the mask. I just don't, and 70 00:03:43,480 --> 00:03:45,760 Speaker 1: a lot of people do, and if they want to, 71 00:03:45,880 --> 00:03:48,480 Speaker 1: that's their prerogative. There's a lady coming down to the 72 00:03:48,520 --> 00:03:52,160 Speaker 1: aisle with her cart wearing a mask, and I'm walking 73 00:03:52,200 --> 00:03:54,840 Speaker 1: down the aisle with a little basket without a mask. 74 00:03:55,240 --> 00:03:58,840 Speaker 1: You would think the grim Reaper was approaching her. She 75 00:03:59,000 --> 00:04:02,160 Speaker 1: turned around and like ran with their card to get 76 00:04:02,160 --> 00:04:04,120 Speaker 1: away from me because I didn't have a mask on. 77 00:04:04,520 --> 00:04:07,440 Speaker 1: So people are terrified of this and they have no 78 00:04:07,480 --> 00:04:09,840 Speaker 1: idea what the risks actually this is again, this is 79 00:04:09,840 --> 00:04:11,720 Speaker 1: the stuff that I that I want to focus on. 80 00:04:11,920 --> 00:04:13,760 Speaker 1: There's no idea what the risks actually ordered them, and 81 00:04:13,760 --> 00:04:17,279 Speaker 1: they have no idea what the transmission vectors actually are. Okay, 82 00:04:17,320 --> 00:04:20,039 Speaker 1: it is it is incredibly clear. Now we have data 83 00:04:20,120 --> 00:04:23,799 Speaker 1: from Germany and China, and you know, we have months 84 00:04:23,920 --> 00:04:29,960 Speaker 1: of of epidemiology on this most most in Italy. Most 85 00:04:30,400 --> 00:04:35,240 Speaker 1: transmission of this virus occurs in really three ways. One 86 00:04:35,560 --> 00:04:39,039 Speaker 1: in the home. Okay, it's this is not measles, It's 87 00:04:39,040 --> 00:04:41,280 Speaker 1: not you know, it's not like if I sneeze from 88 00:04:41,279 --> 00:04:43,840 Speaker 1: across the room, you're going to get this. There has 89 00:04:43,880 --> 00:04:47,840 Speaker 1: to be some generally prolonged contact, right, you know, not 90 00:04:47,839 --> 00:04:50,600 Speaker 1: not days, but but there's got to be some prolonged contact. 91 00:04:50,680 --> 00:04:53,159 Speaker 1: So so the first way is in the home. And 92 00:04:53,200 --> 00:04:55,200 Speaker 1: by the way, this is an argument that that that 93 00:04:55,600 --> 00:04:58,960 Speaker 1: sort of goes against lockdowns because if if, if, if 94 00:04:58,960 --> 00:05:02,920 Speaker 1: a lot of transmission is happening in the home, that's right, 95 00:05:03,040 --> 00:05:05,680 Speaker 1: and you may actually make things worse resan better in 96 00:05:05,680 --> 00:05:09,040 Speaker 1: the short run. Okay. The second primary mode of transmission 97 00:05:09,080 --> 00:05:11,920 Speaker 1: appears to be public transportation, which is another reason why 98 00:05:11,920 --> 00:05:14,560 Speaker 1: New York City has gotten hit so badly. If you're 99 00:05:14,600 --> 00:05:17,400 Speaker 1: on a subway and you're on the subway for an 100 00:05:17,400 --> 00:05:20,800 Speaker 1: hour with somebody who has this, you know, there's a 101 00:05:20,839 --> 00:05:23,120 Speaker 1: good chance that it's going to jump around to other 102 00:05:23,160 --> 00:05:26,240 Speaker 1: people in that subway car. Okay. And the third primary 103 00:05:26,360 --> 00:05:31,400 Speaker 1: way is what's called nosakom, which is essentially in hospitals 104 00:05:31,680 --> 00:05:34,279 Speaker 1: and in nursing homes if you're a medical setting. And 105 00:05:34,320 --> 00:05:37,920 Speaker 1: again that makes sense because because you know these are 106 00:05:39,320 --> 00:05:41,640 Speaker 1: the doctors. Essentially, the doctors and the nurses and the 107 00:05:41,680 --> 00:05:44,400 Speaker 1: staff can be a vector of transmission, or nursing homes 108 00:05:44,440 --> 00:05:47,440 Speaker 1: that may not be that unfortunately that clean, that can 109 00:05:47,480 --> 00:05:51,560 Speaker 1: be a vector of transmission. So those are the three 110 00:05:51,640 --> 00:05:55,200 Speaker 1: main ways. The Chinese. There's a Chinese paper that came 111 00:05:55,200 --> 00:05:57,480 Speaker 1: out about a week ago where a bunch of Chinese 112 00:05:57,520 --> 00:06:02,000 Speaker 1: researchers looked at no joke Sounds of cases seven thousand 113 00:06:02,760 --> 00:06:07,040 Speaker 1: infections and they found that two two of the seven 114 00:06:07,080 --> 00:06:11,760 Speaker 1: thousand had occurred through outdoor transmission. And when they looked 115 00:06:11,760 --> 00:06:14,520 Speaker 1: at they looked at yeah, I know, it's incredible. They 116 00:06:14,520 --> 00:06:18,159 Speaker 1: looked at three hundred clusters totaling about twelve hundred people. 117 00:06:18,400 --> 00:06:22,160 Speaker 1: None of those clusters were outdoor, Most of them are 118 00:06:22,200 --> 00:06:25,040 Speaker 1: in the home. Most of the rest were in public transportation. 119 00:06:25,320 --> 00:06:30,919 Speaker 1: And even if you looked at stores and offices and restaurants, 120 00:06:30,960 --> 00:06:34,160 Speaker 1: it was relatively well, this spreads in the home, and 121 00:06:34,200 --> 00:06:37,719 Speaker 1: it spreads on public transportation. And so what we've done, 122 00:06:38,000 --> 00:06:39,760 Speaker 1: I mean, you can all do a little bit about 123 00:06:39,839 --> 00:06:43,839 Speaker 1: restaurants and bars and stores. But what we've done, by 124 00:06:44,000 --> 00:06:47,160 Speaker 1: denying people the chance to be outside and denying people 125 00:06:47,160 --> 00:06:50,440 Speaker 1: the chance of children the chance to play, is just insane. 126 00:06:50,560 --> 00:06:54,039 Speaker 1: It's insane, and it's doubly insane because kids are not 127 00:06:54,120 --> 00:06:57,280 Speaker 1: at risk. It really it really upsets me. And I 128 00:06:57,279 --> 00:07:00,919 Speaker 1: wonder how many of us have the flu virus in 129 00:07:01,160 --> 00:07:04,279 Speaker 1: us but our immune system is kept it where it 130 00:07:04,320 --> 00:07:09,000 Speaker 1: doesn't even fester. Is that conceivable? I you know, see 131 00:07:09,000 --> 00:07:11,160 Speaker 1: that's a gonna thing. I don't know, okay, And and 132 00:07:11,200 --> 00:07:12,880 Speaker 1: so that's the kind of stuff I try not to 133 00:07:12,920 --> 00:07:17,040 Speaker 1: guess that because what I'm more interested in is is 134 00:07:17,080 --> 00:07:20,480 Speaker 1: the coronavirus? Right? And you know, look again, it's quite 135 00:07:20,480 --> 00:07:24,280 Speaker 1: clear the flu. You know, tens of millions of Americans 136 00:07:24,320 --> 00:07:27,560 Speaker 1: get it every year. In Italy, by the way, you know, 137 00:07:27,600 --> 00:07:30,520 Speaker 1: which has been quite badly hit by the coronavirus. Italy 138 00:07:30,560 --> 00:07:33,080 Speaker 1: actually tends to get very badly hit by the flu 139 00:07:33,240 --> 00:07:37,960 Speaker 1: on an annual basis, And in twenty sixteen, twenty seventeen. 140 00:07:38,360 --> 00:07:41,320 Speaker 1: The best estimates are that twenty five thousand people at 141 00:07:41,320 --> 00:07:44,640 Speaker 1: a minimum died of influenza. And if you look at 142 00:07:44,640 --> 00:07:47,560 Speaker 1: what they call influenza like illnesses in Italy, forty five 143 00:07:47,600 --> 00:07:51,119 Speaker 1: thousand died, which would be equivalent of about a quarter 144 00:07:51,160 --> 00:07:54,840 Speaker 1: million people in the US and and is about where 145 00:07:54,880 --> 00:07:58,280 Speaker 1: the coronavirus numbers are likely to come in for Italy. 146 00:07:58,440 --> 00:08:01,480 Speaker 1: So and that's a bad number, okay, but we didn't 147 00:08:01,680 --> 00:08:04,920 Speaker 1: blow up the world economy when that happens. I want 148 00:08:04,920 --> 00:08:07,000 Speaker 1: to spend some time talking about your book, telling your 149 00:08:07,080 --> 00:08:10,080 Speaker 1: children in the last ten minutes we've got remaining here, Alex. 150 00:08:10,320 --> 00:08:13,280 Speaker 1: But before we get into that, what would be your 151 00:08:13,400 --> 00:08:17,840 Speaker 1: best wish with this coronavirus situation in this country right 152 00:08:17,840 --> 00:08:21,120 Speaker 1: now that people would understand who's really at risk, and 153 00:08:21,120 --> 00:08:25,280 Speaker 1: that they would stop hiding their children away, and ideally 154 00:08:25,320 --> 00:08:28,440 Speaker 1: that we open the schools pretty soon, because well, one 155 00:08:28,440 --> 00:08:30,960 Speaker 1: of the other terrible things about this is, you know, 156 00:08:31,040 --> 00:08:33,079 Speaker 1: this puts a lot of stress on families right there, 157 00:08:33,160 --> 00:08:36,320 Speaker 1: Oh my god, rus and just just physically the fact 158 00:08:36,320 --> 00:08:37,960 Speaker 1: that people are on top of each other all the 159 00:08:38,040 --> 00:08:42,720 Speaker 1: time and children are being hurt in some cases, you know, 160 00:08:42,800 --> 00:08:44,719 Speaker 1: they and I don't want to exaggerate it, but there 161 00:08:44,840 --> 00:08:47,640 Speaker 1: is some evidence that child abuse is rising, and even that, 162 00:08:48,120 --> 00:08:51,040 Speaker 1: you know, fatal child abuse is rising. Suicides, you're going 163 00:08:51,080 --> 00:08:54,400 Speaker 1: ups has been talking about this, suicide, domestic violence, of 164 00:08:54,520 --> 00:09:00,040 Speaker 1: child abuse. It's all going the wrong way. So the 165 00:09:00,120 --> 00:09:02,760 Speaker 1: number one thing I would say to people is understand 166 00:09:03,360 --> 00:09:06,760 Speaker 1: your risk, and if you have children, your child's risk. 167 00:09:07,000 --> 00:09:10,360 Speaker 1: Please understand that. And let your kids get some air 168 00:09:10,440 --> 00:09:12,360 Speaker 1: and play with their friends. If you don't want them 169 00:09:12,360 --> 00:09:14,719 Speaker 1: to play inside with their friends, Okay, I mean it's 170 00:09:14,800 --> 00:09:19,000 Speaker 1: kind of crazy, but okay, let people have play dates outside. 171 00:09:19,400 --> 00:09:23,000 Speaker 1: You're just hurting your children. What about the eighteen year 172 00:09:23,000 --> 00:09:25,520 Speaker 1: olds who are going to graduate from high school and 173 00:09:25,640 --> 00:09:28,160 Speaker 1: they may not get that diploma because schools have been 174 00:09:28,200 --> 00:09:32,439 Speaker 1: closed and they were going to go to college in September? 175 00:09:32,520 --> 00:09:36,240 Speaker 1: What happened? Listen, If this is still going on in September, 176 00:09:36,240 --> 00:09:39,120 Speaker 1: there will be riots in the streets. Okay, all question. 177 00:09:39,240 --> 00:09:42,400 Speaker 1: Look what happened in Michigan just yesterday? You know that's right, 178 00:09:42,559 --> 00:09:44,840 Speaker 1: That's right. It's been a matter of weeks, and especially 179 00:09:44,920 --> 00:09:47,240 Speaker 1: if people start to understand what the numbers really are, 180 00:09:48,160 --> 00:09:50,400 Speaker 1: all right, I tell your children the truth about barijuana, 181 00:09:50,480 --> 00:09:53,680 Speaker 1: mental illness and violence. How did this come about? So 182 00:09:53,679 --> 00:09:56,280 Speaker 1: so you know, if this is actually in any ways, 183 00:09:56,440 --> 00:09:59,040 Speaker 1: it's very interesting because so tell your children. My wife 184 00:09:59,120 --> 00:10:02,120 Speaker 1: is a psychiatrist. She's a forensic psychiatrist, so she deals 185 00:10:02,120 --> 00:10:04,840 Speaker 1: with the criminine mentally Yale, Oh my god. And over 186 00:10:04,880 --> 00:10:07,640 Speaker 1: a period of years she was telling me, you know, 187 00:10:08,160 --> 00:10:10,680 Speaker 1: this person was high as a kite when they had 188 00:10:10,760 --> 00:10:13,560 Speaker 1: you committed this terrible crime. And you know this person, 189 00:10:14,240 --> 00:10:18,800 Speaker 1: you know, descended into schizophrenia or severe bipolar disorder after 190 00:10:19,040 --> 00:10:22,280 Speaker 1: years of smoking cannabis. And I honestly didn't believe her because, 191 00:10:22,360 --> 00:10:24,800 Speaker 1: like most you know, people who went to Yale and 192 00:10:24,840 --> 00:10:27,120 Speaker 1: worse the New York Times, I quote unquote knew that 193 00:10:27,160 --> 00:10:29,600 Speaker 1: marijuana was safe, even though I didn't really you know, 194 00:10:29,800 --> 00:10:31,719 Speaker 1: use it. And as you know, and the handful of 195 00:10:31,720 --> 00:10:33,120 Speaker 1: times in my life when I had you said, I 196 00:10:33,160 --> 00:10:36,920 Speaker 1: didn't particularly like it, but whatever, it didn't care. So 197 00:10:36,920 --> 00:10:40,920 Speaker 1: so she eventually convinced me to start looking at what 198 00:10:40,960 --> 00:10:44,120 Speaker 1: the medical data actually said. The data published in really 199 00:10:44,160 --> 00:10:46,880 Speaker 1: good peer review journals like The Lancet or the British 200 00:10:46,920 --> 00:10:49,559 Speaker 1: Medical Journal or the Journal of the American Medical Association, 201 00:10:49,960 --> 00:10:54,480 Speaker 1: and the data is unequivocal. Cannabis is a real risk 202 00:10:54,559 --> 00:10:59,720 Speaker 1: factor for severe mental illness, especially schizophrenia, and it unquestionably 203 00:10:59,720 --> 00:11:04,239 Speaker 1: caused this short term psychotic episodes. Uh, you know frequently 204 00:11:05,240 --> 00:11:09,840 Speaker 1: so so so and listen, you know this, this is 205 00:11:09,840 --> 00:11:12,520 Speaker 1: frequent enough that users will joke about it. Right. You 206 00:11:12,960 --> 00:11:15,880 Speaker 1: you really high and you wind up in your closet 207 00:11:16,000 --> 00:11:20,240 Speaker 1: for for the night because you're afraid your friends, you know, 208 00:11:20,400 --> 00:11:23,920 Speaker 1: are gonna are gonna steal all your money or whatever 209 00:11:23,960 --> 00:11:27,800 Speaker 1: it is. You you've had a minor psychotic episode. You know, 210 00:11:27,840 --> 00:11:30,080 Speaker 1: it's not it's not, that's not the kind that binds 211 00:11:30,120 --> 00:11:32,439 Speaker 1: you in the emergency room. But a step beyond that 212 00:11:32,520 --> 00:11:35,800 Speaker 1: will land you and you could do something to yourself 213 00:11:36,400 --> 00:11:40,160 Speaker 1: that's right, or to somebody else. So so, so here's 214 00:11:40,160 --> 00:11:41,920 Speaker 1: the thing. So I wrote this book. It came out 215 00:11:42,040 --> 00:11:45,560 Speaker 1: last jan Or it's You Tell Your Children and and 216 00:11:45,880 --> 00:11:48,040 Speaker 1: I was a reporter for The Times for ten years. 217 00:11:48,160 --> 00:11:52,240 Speaker 1: And the book is in terms of its analysis of 218 00:11:52,280 --> 00:11:56,360 Speaker 1: the data, it's it's airtight. Okay. I've I've presented it 219 00:11:56,400 --> 00:12:01,760 Speaker 1: to you know, psychiatry groups in you know, on multiple continents. Um, 220 00:12:01,920 --> 00:12:04,680 Speaker 1: it's all peer reviewed research. You know, there's a little 221 00:12:04,679 --> 00:12:08,280 Speaker 1: bit of original research in there, and and I'm happy 222 00:12:08,280 --> 00:12:11,880 Speaker 1: if people want to say, look, this risk made me real, 223 00:12:12,000 --> 00:12:14,920 Speaker 1: but we should still legalize cannabis. You know, alcohol, legal 224 00:12:15,000 --> 00:12:17,960 Speaker 1: alcohol kills lots of people cannabis, So what if there's 225 00:12:17,960 --> 00:12:20,400 Speaker 1: some risk, it should be legal and nobody should be 226 00:12:20,520 --> 00:12:24,000 Speaker 1: arrested for it. Okay, that's a totally that's a totally 227 00:12:24,040 --> 00:12:28,040 Speaker 1: plausible argument. But that's not how the book was received. Okay, 228 00:12:28,080 --> 00:12:34,239 Speaker 1: the book was attacked by pro cannabis advocates and who said, basically, 229 00:12:34,720 --> 00:12:37,680 Speaker 1: you know, the science is wrong, and Barrenson is cherry picking, 230 00:12:37,840 --> 00:12:40,400 Speaker 1: and you know I'm misreading the data and none of 231 00:12:40,440 --> 00:12:44,080 Speaker 1: that stuff was true. And in places like The Times, 232 00:12:44,120 --> 00:12:47,280 Speaker 1: at the Washington Post and a lot of cable outlets 233 00:12:47,600 --> 00:12:51,480 Speaker 1: essentially refused to cover the book. And so when this 234 00:12:51,640 --> 00:12:55,280 Speaker 1: happened with the coronavirus in the last month, this is 235 00:12:55,320 --> 00:12:58,760 Speaker 1: something I'm very used to now I understand how media 236 00:12:58,840 --> 00:13:03,320 Speaker 1: group think was because these people just refuse to engage 237 00:13:03,400 --> 00:13:06,079 Speaker 1: in a meaningful way with a book that a lot 238 00:13:06,120 --> 00:13:10,400 Speaker 1: of psychiatrists have endorsed and no is correct. And you know, 239 00:13:11,200 --> 00:13:13,160 Speaker 1: we most likely would have done a show with you 240 00:13:13,280 --> 00:13:15,360 Speaker 1: just on your book, by the way, just so you know, 241 00:13:15,480 --> 00:13:17,640 Speaker 1: you know, and maybe listen, we could do that lots 242 00:13:17,679 --> 00:13:19,080 Speaker 1: of time, but you've got to, you know, if you've 243 00:13:19,080 --> 00:13:21,640 Speaker 1: got to spare hour, right, But because it's a really 244 00:13:21,920 --> 00:13:24,559 Speaker 1: you know, people should know about this. But the States 245 00:13:24,720 --> 00:13:28,000 Speaker 1: and coronavirus are a hundred times it's high right, we're 246 00:13:28,080 --> 00:13:32,559 Speaker 1: literally putting our economy into depression on the basis of 247 00:13:33,440 --> 00:13:38,080 Speaker 1: unfortunately a lot of media hysteria that that as more 248 00:13:38,160 --> 00:13:42,360 Speaker 1: data rolls in, is not being updated and the media 249 00:13:42,559 --> 00:13:46,760 Speaker 1: is so busy yelling at itself that it won't engage 250 00:13:46,800 --> 00:13:50,360 Speaker 1: with what the science really says. And that is very 251 00:13:50,400 --> 00:13:54,280 Speaker 1: bad news, it really is. Well, you've done a remarkable 252 00:13:54,360 --> 00:13:56,280 Speaker 1: job there. How do people get that? Book? Tell Your 253 00:13:56,360 --> 00:14:00,160 Speaker 1: Children Children is available on Amazon and you know, it's 254 00:14:00,160 --> 00:14:02,719 Speaker 1: funny people seem to want to buy the hardcover. The 255 00:14:02,760 --> 00:14:05,599 Speaker 1: paperback actually has served an afterward that I where I 256 00:14:05,720 --> 00:14:07,880 Speaker 1: go into some of the you know, the media response 257 00:14:07,960 --> 00:14:10,280 Speaker 1: to book, which which you know, for people who are 258 00:14:10,320 --> 00:14:14,760 Speaker 1: interested in that topic, is directly relevant. Alex's website is 259 00:14:14,800 --> 00:14:17,640 Speaker 1: linked up at Coast to Coast am dot com. Alex, 260 00:14:17,720 --> 00:14:20,240 Speaker 1: why do you think the media back to coronavirus for 261 00:14:20,280 --> 00:14:23,040 Speaker 1: a moment? Why do you think the media seems to 262 00:14:23,080 --> 00:14:26,120 Speaker 1: be creating this hysteria. I mean, I've I've never seen 263 00:14:26,160 --> 00:14:30,720 Speaker 1: them hype something like they've hyped this, you know. Again, 264 00:14:30,840 --> 00:14:33,440 Speaker 1: I think I think people in New York were quite 265 00:14:33,480 --> 00:14:37,000 Speaker 1: scared for a while. Um, and that was real. And 266 00:14:37,080 --> 00:14:40,560 Speaker 1: I do think unfortunately that everything is seen through the 267 00:14:40,600 --> 00:14:43,480 Speaker 1: prism of Trump, uh, you know. And by the way 268 00:14:43,480 --> 00:14:45,960 Speaker 1: that that definitely hurt. Tell your children too, that you know, 269 00:14:46,000 --> 00:14:48,720 Speaker 1: somehow Trump was viewed as as an anti cannabis even 270 00:14:48,720 --> 00:14:50,960 Speaker 1: though there's not much evidence that he cares either way 271 00:14:50,960 --> 00:14:54,840 Speaker 1: about cannabis. And so so when the when when the 272 00:14:54,920 --> 00:14:57,520 Speaker 1: fact that the that that Trump is the president and 273 00:14:57,560 --> 00:15:03,000 Speaker 1: the response in February was in adequate led people to, 274 00:15:03,280 --> 00:15:05,520 Speaker 1: you know, to fight with him, literally to fight with him. 275 00:15:05,520 --> 00:15:09,040 Speaker 1: People like Jim Acosta at the at the uh you know, 276 00:15:09,080 --> 00:15:12,680 Speaker 1: Atman or Maggie Haberman at the Times. People people will 277 00:15:12,720 --> 00:15:15,440 Speaker 1: just will just attack him and he and he is 278 00:15:15,480 --> 00:15:18,640 Speaker 1: smart in that he knows that's actually good for his brand, 279 00:15:18,720 --> 00:15:21,680 Speaker 1: I think, and so he baits them and it becomes 280 00:15:21,720 --> 00:15:25,760 Speaker 1: about him rather than about the reality of the data. 281 00:15:25,920 --> 00:15:29,080 Speaker 1: How is your former newspaper, The New York Times handling 282 00:15:29,120 --> 00:15:33,720 Speaker 1: this coronavirus story? In your opinion, they're handling quite badly. Um, 283 00:15:34,280 --> 00:15:36,880 Speaker 1: you know, they look they there's been a lot of 284 00:15:37,040 --> 00:15:41,160 Speaker 1: problems in New York. They could have written and they 285 00:15:41,200 --> 00:15:43,520 Speaker 1: have written a lot of stories about those problems and 286 00:15:43,640 --> 00:15:47,000 Speaker 1: still said, what's happening in New York is not entirely 287 00:15:47,040 --> 00:15:49,240 Speaker 1: representative of what we're seeing the rest of the country. 288 00:15:49,520 --> 00:15:52,160 Speaker 1: In the rest of the country, hospitals are not chilling 289 00:15:52,280 --> 00:15:54,840 Speaker 1: up with coronavirus patients in the rest of the country. 290 00:15:54,880 --> 00:15:59,240 Speaker 1: In fact, hospitals are furlowing workers and a couple of 291 00:15:59,240 --> 00:16:02,320 Speaker 1: hospitals have And that's an astonishing fact. Right We're in 292 00:16:02,400 --> 00:16:03,720 Speaker 1: the middle of what it is supposed to be the 293 00:16:03,840 --> 00:16:07,200 Speaker 1: worst pandemics since the Spanish flue, and we have hospitals 294 00:16:07,280 --> 00:16:11,120 Speaker 1: closing and nurses being laid off, and doctors closing their 295 00:16:11,120 --> 00:16:15,480 Speaker 1: practices because they don't have patience right now, And so 296 00:16:15,680 --> 00:16:20,520 Speaker 1: the Times should have presented a much more accurate overall 297 00:16:20,560 --> 00:16:23,280 Speaker 1: picture and could have done that. We'll still talking about 298 00:16:23,320 --> 00:16:27,320 Speaker 1: the problems of the virus things. Yeah, and that's happening 299 00:16:27,480 --> 00:16:30,640 Speaker 1: all around the country. But when we start going back 300 00:16:30,680 --> 00:16:33,320 Speaker 1: to work, do you think that's going to change and 301 00:16:33,880 --> 00:16:39,160 Speaker 1: flip around again? I think I hope that over time, 302 00:16:39,400 --> 00:16:42,000 Speaker 1: you know, people who aren't too completely terrified to leave 303 00:16:42,040 --> 00:16:44,920 Speaker 1: their houses will see you know what, I don't actually 304 00:16:44,920 --> 00:16:47,480 Speaker 1: know anybody who died from this except maybe you know, 305 00:16:47,560 --> 00:16:51,280 Speaker 1: my eight year old, you know, grandmother's friend or something 306 00:16:51,320 --> 00:16:53,080 Speaker 1: like that. And I know people who got it and 307 00:16:53,120 --> 00:16:56,240 Speaker 1: recovered from it. And I hope people will see Okay, 308 00:16:56,280 --> 00:17:00,320 Speaker 1: you know, yes, this was bad for some people, but overall, 309 00:17:00,440 --> 00:17:03,720 Speaker 1: it didn't wreck society, and we really have to get 310 00:17:03,720 --> 00:17:06,760 Speaker 1: back to work. And again, more than anything else, I 311 00:17:06,800 --> 00:17:10,800 Speaker 1: hope people see my kids are fine, and yeah, and 312 00:17:11,119 --> 00:17:13,080 Speaker 1: I think they better get used to it coming back 313 00:17:13,119 --> 00:17:16,320 Speaker 1: every season. I don't think this is going to go away. No, 314 00:17:16,440 --> 00:17:19,280 Speaker 1: that's that's that's that's true. Maybe this or maybe you 315 00:17:19,320 --> 00:17:22,359 Speaker 1: know some cousin of it, right, but we don't shut 316 00:17:22,359 --> 00:17:25,440 Speaker 1: society down for the flu, and we can't shut society 317 00:17:25,480 --> 00:17:27,800 Speaker 1: down for this, not based on what the death numbers are. 318 00:17:27,960 --> 00:17:30,399 Speaker 1: If this we're killing you know, five or ten or 319 00:17:30,440 --> 00:17:33,160 Speaker 1: you know, God forbid twenty percent of people, I mean 320 00:17:33,200 --> 00:17:36,080 Speaker 1: we you know, then it would then we'd really have 321 00:17:36,200 --> 00:17:40,199 Speaker 1: to Basically, we work our society for a virus that 322 00:17:40,320 --> 00:17:44,879 Speaker 1: seems to at worst kill maybe one in three hundred 323 00:17:44,880 --> 00:17:47,320 Speaker 1: people who get it, and nearly all those people are 324 00:17:47,440 --> 00:17:51,240 Speaker 1: you know, are extremely old and sick, or maybe you know, 325 00:17:51,320 --> 00:17:53,760 Speaker 1: maybe it's one and two hundred, but but it's it's 326 00:17:53,840 --> 00:17:56,919 Speaker 1: not it's not a number that should destroy our society, 327 00:17:57,080 --> 00:18:00,439 Speaker 1: especially considering who's really being hurt by this. We're going 328 00:18:00,520 --> 00:18:02,199 Speaker 1: to have to live with it. It's not it's not 329 00:18:02,240 --> 00:18:05,240 Speaker 1: gonna be fun, you know, but but we live with things. 330 00:18:05,280 --> 00:18:08,280 Speaker 1: We live. We live with HIV, we live with tuberculosis, 331 00:18:08,359 --> 00:18:12,280 Speaker 1: you know. We we we live with forty thousand automobile 332 00:18:12,400 --> 00:18:16,679 Speaker 1: deaths a year. We can't shut society exactly. Listen to 333 00:18:16,800 --> 00:18:20,040 Speaker 1: more Coast to Coast AM every weeknight at one a m. 334 00:18:20,160 --> 00:18:23,159 Speaker 1: Eastern and go to Coast to Coast am dot com 335 00:18:23,200 --> 00:18:23,560 Speaker 1: for more