1 00:00:00,240 --> 00:00:04,680 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,760 --> 00:00:09,080 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,200 --> 00:00:25,760 Speaker 1: production of I Heart Brading. Hello, welcome back to the show. 5 00:00:25,840 --> 00:00:28,280 Speaker 1: My name is Matt, my name is Noel. They called 6 00:00:28,320 --> 00:00:30,800 Speaker 1: me Ben. We are joined as always with our super 7 00:00:30,840 --> 00:00:35,280 Speaker 1: producer Paul Mission Control Decade. Most importantly, you are you. 8 00:00:35,280 --> 00:00:39,240 Speaker 1: You are here, and that makes this stuff they don't 9 00:00:39,280 --> 00:00:43,080 Speaker 1: want you to know. It's your favorite Quentin Quarantineos coming 10 00:00:43,080 --> 00:00:48,559 Speaker 1: back again, live in direct or digital full effect. We 11 00:00:48,760 --> 00:00:52,280 Speaker 1: are We hope this episode finds you happy and healthy 12 00:00:52,520 --> 00:00:56,640 Speaker 1: as can be. We're doing We're doing pretty well. We've 13 00:00:56,640 --> 00:01:00,280 Speaker 1: got our own personal and professional milestones coming up. But 14 00:01:00,520 --> 00:01:04,600 Speaker 1: as always, we are here to keep the podcast lights 15 00:01:04,680 --> 00:01:08,040 Speaker 1: on until we get black bagged or until we go 16 00:01:08,240 --> 00:01:13,479 Speaker 1: full mad Max. We've been getting a ton of correspondence 17 00:01:13,520 --> 00:01:18,040 Speaker 1: for which we're grateful, uh, in email form, on social media, 18 00:01:18,520 --> 00:01:26,160 Speaker 1: and in voicemail. There's a particular voicemail that inspired today's episode. Um, 19 00:01:26,200 --> 00:01:28,399 Speaker 1: and Matt, you have a you have a version of 20 00:01:28,440 --> 00:01:31,720 Speaker 1: this week cleaned up for public consumption, right? Yes, this 21 00:01:31,720 --> 00:01:34,800 Speaker 1: this person called in you know the way a lot 22 00:01:34,800 --> 00:01:39,240 Speaker 1: of people do, and gave their information and everything and 23 00:01:39,280 --> 00:01:42,120 Speaker 1: just told us about an issue, a problem, And in 24 00:01:42,120 --> 00:01:47,319 Speaker 1: this case it was someone who you know works in 25 00:01:47,360 --> 00:01:50,920 Speaker 1: a capacity related to this that we wanted to remove 26 00:01:50,960 --> 00:01:55,480 Speaker 1: the name of this person. UM, and you'll see why 27 00:01:55,560 --> 00:01:59,920 Speaker 1: perhaps as we listen to it. UM. This person called 28 00:02:00,200 --> 00:02:04,280 Speaker 1: in February and just had some pretty alarming information for us. 29 00:02:04,280 --> 00:02:05,600 Speaker 1: So I think we should just go ahead and play 30 00:02:05,640 --> 00:02:09,640 Speaker 1: it and then we'll talk about it. Hi, guys, I 31 00:02:09,639 --> 00:02:11,520 Speaker 1: think I'm calling him to them. Sitting in my office 32 00:02:11,560 --> 00:02:15,400 Speaker 1: a little frustrated right now. I just listened to your 33 00:02:15,440 --> 00:02:20,080 Speaker 1: show on COVID nineteen and you were talking about conspiracies. 34 00:02:20,840 --> 00:02:23,240 Speaker 1: I worked for them, the VAT of Indian Commission. What 35 00:02:23,360 --> 00:02:26,960 Speaker 1: I'm doing today is what's like what the Massachusetts governor 36 00:02:27,040 --> 00:02:29,400 Speaker 1: did that you mentioned. We have to work with the 37 00:02:29,520 --> 00:02:36,480 Speaker 1: National Guard to get tribes ppees. So Helping Human Services 38 00:02:36,919 --> 00:02:39,359 Speaker 1: HF is supposed to work with tribes and help them 39 00:02:39,360 --> 00:02:42,920 Speaker 1: get these the tribe for making their orders, I HF 40 00:02:43,080 --> 00:02:46,680 Speaker 1: is taking those orders and never getting to them. So 41 00:02:46,760 --> 00:02:49,440 Speaker 1: we believe that what you're doing is using the paperwork 42 00:02:49,919 --> 00:02:52,959 Speaker 1: to show that they're getting the supplies that the federal 43 00:02:53,080 --> 00:02:57,720 Speaker 1: governments formed them. So our national Guard is now getting 44 00:02:57,720 --> 00:03:01,120 Speaker 1: our list. They're going to grab the supplied and they're 45 00:03:01,160 --> 00:03:05,560 Speaker 1: going to get into the tribe. Probably think somehow Summer 46 00:03:05,880 --> 00:03:09,399 Speaker 1: Company is making money off it. It's not a conspiracy. 47 00:03:09,520 --> 00:03:11,800 Speaker 1: I mean, this is stuff I worked with every day 48 00:03:12,440 --> 00:03:15,480 Speaker 1: as a long, long time listener. I really appreciate you, 49 00:03:15,680 --> 00:03:18,959 Speaker 1: and I hope that you all think thank you so much. 50 00:03:19,840 --> 00:03:23,600 Speaker 1: Sober and stuff. Yeah, and again, as as Matt said 51 00:03:23,680 --> 00:03:28,160 Speaker 1: that that occurred in February, several months ago. Now, uh, 52 00:03:28,160 --> 00:03:31,359 Speaker 1: and it is painting a picture. First off, we want 53 00:03:31,360 --> 00:03:34,680 Speaker 1: to say when we heard this message, we were both 54 00:03:34,720 --> 00:03:37,640 Speaker 1: grateful to you caller, and we hope you're listening now. 55 00:03:39,040 --> 00:03:43,000 Speaker 1: We're also horrified to learn the extent to which this 56 00:03:43,080 --> 00:03:47,560 Speaker 1: crisis has grown. The well, you know, it's something that 57 00:03:47,840 --> 00:03:50,680 Speaker 1: we kind of predicted a while back in a COVID 58 00:03:50,720 --> 00:03:55,440 Speaker 1: episode where if the federal leadership is not cohesive and 59 00:03:55,560 --> 00:03:59,440 Speaker 1: governors feel it is endangering their constituents, they will band together. 60 00:03:59,560 --> 00:04:03,600 Speaker 1: So we've seeing not one, but three regional alliances of 61 00:04:03,680 --> 00:04:07,440 Speaker 1: different governors using national Guard, which is what the national 62 00:04:07,480 --> 00:04:10,800 Speaker 1: Guard is for, by the way, uh, to get those supplies. 63 00:04:11,800 --> 00:04:14,920 Speaker 1: But this is affecting the native population in a way 64 00:04:14,960 --> 00:04:18,400 Speaker 1: that's not really getting into the headlines, you know, and this, 65 00:04:18,920 --> 00:04:22,200 Speaker 1: you know, there's something interesting about the timeline here, Holler. 66 00:04:22,360 --> 00:04:26,040 Speaker 1: When you had called us, we had we had been 67 00:04:26,120 --> 00:04:30,760 Speaker 1: researching but had not yet recorded nor published our earlier 68 00:04:30,839 --> 00:04:35,960 Speaker 1: episode on forced assimilation of Native people's. Uh. We want 69 00:04:36,000 --> 00:04:39,720 Speaker 1: to thank everyone from both the US and abroad who 70 00:04:39,760 --> 00:04:43,080 Speaker 1: responded to that recent episode. We had a lot of 71 00:04:43,080 --> 00:04:45,280 Speaker 1: people right in from Canada, a lot of people right 72 00:04:45,360 --> 00:04:48,440 Speaker 1: in from Australia. And one thing that I I don't 73 00:04:48,440 --> 00:04:50,160 Speaker 1: know about you guys, but one thing that really struck 74 00:04:50,200 --> 00:04:54,799 Speaker 1: me about that is that the response internationally, we found 75 00:04:54,800 --> 00:04:58,120 Speaker 1: a lot of people unfortunately saying, you know, you can 76 00:04:58,279 --> 00:05:02,240 Speaker 1: mad lib the entire episode own on Native assimilation and 77 00:05:02,320 --> 00:05:06,599 Speaker 1: just replace Native American with you know, uh, indigenous people 78 00:05:06,680 --> 00:05:11,400 Speaker 1: or Aboriginal people of Australia or with First nations. Yeah, 79 00:05:11,440 --> 00:05:17,039 Speaker 1: it's certainly a fairly common thing that we we've seen 80 00:05:17,279 --> 00:05:20,000 Speaker 1: in a lot of places where the indigenous people is 81 00:05:20,040 --> 00:05:24,800 Speaker 1: just it's the view I guess from the conquering people 82 00:05:24,960 --> 00:05:28,840 Speaker 1: is that you just have to assimilate and make them 83 00:05:28,839 --> 00:05:31,440 Speaker 1: a part of your own or or eradicate. And that's 84 00:05:31,480 --> 00:05:34,960 Speaker 1: the thing This is not some kind of problem that's 85 00:05:34,960 --> 00:05:39,840 Speaker 1: been solved, some kind of relic you know from history. UM. 86 00:05:39,920 --> 00:05:46,160 Speaker 1: It is absolutely ongoing, and there is a new developing chapter. 87 00:05:46,760 --> 00:05:50,159 Speaker 1: UM and what could probably best be described as, um 88 00:05:50,240 --> 00:05:52,919 Speaker 1: A a horror story that I think was set up 89 00:05:52,960 --> 00:05:55,640 Speaker 1: perfectly by our caller. And I think we all know 90 00:05:55,720 --> 00:06:00,000 Speaker 1: that the title of this chapter is COVID. Yeah, yeah, 91 00:06:00,200 --> 00:06:04,240 Speaker 1: I do think it is appropriate to describe it that way. 92 00:06:04,279 --> 00:06:07,159 Speaker 1: This is not some long forgotten crisis from a dusty 93 00:06:07,279 --> 00:06:10,680 Speaker 1: history book, and it's far from brand new problem. It's 94 00:06:10,720 --> 00:06:13,400 Speaker 1: a new iteration and I do like to think of 95 00:06:13,440 --> 00:06:17,000 Speaker 1: it as a long novel, a horror story in which 96 00:06:17,080 --> 00:06:19,920 Speaker 1: this is just the newest chapter. And if this is 97 00:06:20,320 --> 00:06:23,640 Speaker 1: if that analogy holds, which I'm pretty happy with, then 98 00:06:23,680 --> 00:06:27,760 Speaker 1: the title of this chapter would be COVID. So here 99 00:06:27,800 --> 00:06:30,600 Speaker 1: are the facts to start off, We're not going to 100 00:06:30,680 --> 00:06:35,080 Speaker 1: waste time today telling you what coronavirus is, what COVID 101 00:06:35,160 --> 00:06:37,600 Speaker 1: nineteen is. We have plenty of episodes in the back 102 00:06:37,720 --> 00:06:41,839 Speaker 1: that do a pretty good job we think of giving 103 00:06:41,839 --> 00:06:44,240 Speaker 1: you the lay of land there, so check those out 104 00:06:44,240 --> 00:06:47,119 Speaker 1: for more information. Right now, we're going to do something 105 00:06:47,160 --> 00:06:49,719 Speaker 1: we do every episode, which is this we'll give you 106 00:06:49,760 --> 00:06:54,200 Speaker 1: the current statistics of COVID nineteen in the United States. Again, 107 00:06:54,320 --> 00:06:56,760 Speaker 1: we still do not have accurate numbers, so we'll have 108 00:06:56,839 --> 00:07:00,080 Speaker 1: to give you a range. Uh the c d C, 109 00:07:00,560 --> 00:07:04,359 Speaker 1: UH and various other institutions disagree. But everybody's kind of 110 00:07:04,400 --> 00:07:06,880 Speaker 1: in the same ballpark right now, which is either a 111 00:07:06,920 --> 00:07:09,400 Speaker 1: good sign or if you're someone who thinks this is 112 00:07:09,880 --> 00:07:13,600 Speaker 1: intentionally being misreported one way or the other, this is 113 00:07:13,640 --> 00:07:18,040 Speaker 1: a sign of increased collaboration. Confirmed cases are, according to 114 00:07:18,080 --> 00:07:22,280 Speaker 1: the CDC, one million, fivefty one thousand and nine five 115 00:07:22,840 --> 00:07:27,640 Speaker 1: as of May twenty uh New York Times rounded that 116 00:07:27,680 --> 00:07:31,400 Speaker 1: down to one point five million, and the deaths continue 117 00:07:32,080 --> 00:07:36,760 Speaker 1: to come um. Again, these numbers may be pretty low, 118 00:07:37,000 --> 00:07:40,000 Speaker 1: just depending on how the reporting is actually coming through. 119 00:07:40,600 --> 00:07:43,120 Speaker 1: According to the CDC in the United States, there have 120 00:07:43,160 --> 00:07:47,440 Speaker 1: been ninety three thousand and sixty one deaths, and the 121 00:07:47,480 --> 00:07:49,640 Speaker 1: New York Times has it a little bit higher, around 122 00:07:49,720 --> 00:07:54,200 Speaker 1: ninety four thousand, seven hundred and seventeen deaths. And as 123 00:07:54,520 --> 00:07:59,239 Speaker 1: we established earlier uh IM previous episodes, the thing about 124 00:07:59,280 --> 00:08:02,080 Speaker 1: those numbers is that they have a lot of problems 125 00:08:02,160 --> 00:08:05,880 Speaker 1: with methodology, with accuracy. One of the big questions is 126 00:08:05,880 --> 00:08:09,440 Speaker 1: whether that is through incompetence, whether it is through simply 127 00:08:09,480 --> 00:08:14,280 Speaker 1: being overwhelmed or whether it is somehow through design. But 128 00:08:14,720 --> 00:08:17,880 Speaker 1: in our previous episodes, we didn't talk about something that's 129 00:08:17,920 --> 00:08:21,120 Speaker 1: extremely important and it's it's a huge part of today's 130 00:08:21,120 --> 00:08:24,680 Speaker 1: show as well, and that is the demographics of people 131 00:08:24,960 --> 00:08:29,360 Speaker 1: being infected. You see, COVID is not an equal opportunity 132 00:08:29,520 --> 00:08:33,280 Speaker 1: killer in the United States. Certain demographics have been hit 133 00:08:33,559 --> 00:08:39,320 Speaker 1: much much more heavily. That's right, according to preliminary data. Uh, sex, age, 134 00:08:39,440 --> 00:08:43,920 Speaker 1: and ethnicity all seem to play roles. Scientists are still 135 00:08:43,960 --> 00:08:46,680 Speaker 1: working to get a better sense of what all of 136 00:08:46,720 --> 00:08:49,920 Speaker 1: this truly means. But here's kind of the lay of 137 00:08:49,920 --> 00:08:52,600 Speaker 1: the land as we understand it. When it comes to age, 138 00:08:52,920 --> 00:08:57,320 Speaker 1: the evidence points to the idea that infected patients risk 139 00:08:57,440 --> 00:09:02,800 Speaker 1: of dying from the coronavirus increases with every additional decade UM. 140 00:09:02,840 --> 00:09:06,680 Speaker 1: In one study in particular, published in The Lancet in March, 141 00:09:07,520 --> 00:09:11,520 Speaker 1: researchers um compiled data from thirty eight different countries that 142 00:09:11,600 --> 00:09:14,679 Speaker 1: suggest the virus kills up to thirteen point four percent 143 00:09:15,120 --> 00:09:19,240 Speaker 1: of patients eighty years UH and older, compared with an 144 00:09:19,240 --> 00:09:24,400 Speaker 1: overall estimated case fatality rate of one point three eight percent. 145 00:09:24,520 --> 00:09:27,000 Speaker 1: All of this checks out um with just kind of 146 00:09:27,040 --> 00:09:30,360 Speaker 1: the empirical data that we have just from watching the 147 00:09:30,400 --> 00:09:34,240 Speaker 1: news and and following the story. The elderly are absolutely 148 00:09:34,400 --> 00:09:40,160 Speaker 1: the most at risk, largely because of reasons of immunity. Yeah, 149 00:09:40,160 --> 00:09:43,040 Speaker 1: this is just something that is a reality. As your 150 00:09:43,080 --> 00:09:47,800 Speaker 1: body gets older, Uh, your immune system just doesn't function 151 00:09:47,840 --> 00:09:50,120 Speaker 1: as well as it used to. And that's just the 152 00:09:50,200 --> 00:09:53,480 Speaker 1: reality that we all face, at least for now, until 153 00:09:53,720 --> 00:09:58,120 Speaker 1: some magical technology comes along that stops that from happening. 154 00:09:58,600 --> 00:10:02,360 Speaker 1: You know, you within your body, things like T cells, macrophages, 155 00:10:02,679 --> 00:10:05,560 Speaker 1: they all slow down their processes. They don't again, they 156 00:10:05,600 --> 00:10:09,560 Speaker 1: don't function as as um swiftly as they once did. 157 00:10:09,679 --> 00:10:13,320 Speaker 1: And your body also slows down the production of lymphis 158 00:10:13,320 --> 00:10:16,680 Speaker 1: sites um or at least it produces, you know, fewer 159 00:10:16,760 --> 00:10:20,840 Speaker 1: of them. It's it's a real problem because with COVID, 160 00:10:21,160 --> 00:10:24,240 Speaker 1: with you know, when you're infected by coronavirus, if you've 161 00:10:24,280 --> 00:10:27,240 Speaker 1: gotten a weak immune system, it's way harder for your 162 00:10:27,240 --> 00:10:30,959 Speaker 1: body to then defend against this new outsider that's coming through, 163 00:10:31,000 --> 00:10:33,960 Speaker 1: this invader that's in your system, you know, and that 164 00:10:34,000 --> 00:10:38,280 Speaker 1: could be anything. And that's why over the past several decades, 165 00:10:38,280 --> 00:10:41,080 Speaker 1: we've seen pretty alarming numbers with the common cold like 166 00:10:41,160 --> 00:10:45,080 Speaker 1: flew when you know, in older populations, especially in nursing 167 00:10:45,120 --> 00:10:48,200 Speaker 1: homes and other facilities like that. And this is even 168 00:10:48,240 --> 00:10:51,760 Speaker 1: worse if you've got some other previously existing conditions, like 169 00:10:51,800 --> 00:10:55,040 Speaker 1: if your heart or your kidneys, or your liver, your 170 00:10:55,160 --> 00:10:58,320 Speaker 1: lungs maybe just aren't working as well as they once 171 00:10:58,400 --> 00:11:01,280 Speaker 1: did because you've had a disease in the past, the 172 00:11:01,400 --> 00:11:05,400 Speaker 1: ravages of time. Yeah, and I you know, I think 173 00:11:05,480 --> 00:11:08,800 Speaker 1: we need to hit on a very important sidebar here, 174 00:11:08,840 --> 00:11:13,680 Speaker 1: which is pre existing conditions. The term pre existing conditions 175 00:11:13,760 --> 00:11:15,880 Speaker 1: is something that a lot of people in the US 176 00:11:16,040 --> 00:11:20,720 Speaker 1: learned from a private insurance company, often the hard way, 177 00:11:20,760 --> 00:11:25,199 Speaker 1: but it plays a big role in COVID nineteen as well. UH. 178 00:11:25,240 --> 00:11:29,000 Speaker 1: The Chinese government has their own Center for Disease Control 179 00:11:29,040 --> 00:11:33,600 Speaker 1: and Prevention, and they issued a study that found people 180 00:11:33,760 --> 00:11:38,560 Speaker 1: with no underlying chronic medical conditions had a fatality rate 181 00:11:38,640 --> 00:11:42,080 Speaker 1: in general around point nine percent, But for people with 182 00:11:42,120 --> 00:11:47,120 Speaker 1: cardiovascular disease, just that one variable increase the fatality rate 183 00:11:47,200 --> 00:11:52,360 Speaker 1: to ten point five percent. Respiratory disease ups your likelihood 184 00:11:52,800 --> 00:11:56,679 Speaker 1: to six point three percent. UH. Even people with hypertension. 185 00:11:56,960 --> 00:12:00,120 Speaker 1: Hypertension is incredibly common. They're a little there, like three 186 00:12:00,200 --> 00:12:02,800 Speaker 1: hundred twenty three million or so people who live in 187 00:12:02,840 --> 00:12:07,480 Speaker 1: the United States officially, uh, and of those around a 188 00:12:07,559 --> 00:12:11,520 Speaker 1: hundred million have hypertension. It's so common. It means that 189 00:12:11,600 --> 00:12:14,280 Speaker 1: you know, well, less than one out of three people 190 00:12:14,320 --> 00:12:17,119 Speaker 1: have some form of this, and that takes your fatality 191 00:12:17,200 --> 00:12:21,800 Speaker 1: rate from point nine percent to six percent. There may also, 192 00:12:22,000 --> 00:12:25,800 Speaker 1: it must be said, be some sort of genetic factor here, 193 00:12:26,240 --> 00:12:30,360 Speaker 1: but honestly, I dug into it in the published science, 194 00:12:30,559 --> 00:12:34,079 Speaker 1: just isn't there yet. We can we can say there's 195 00:12:34,160 --> 00:12:37,240 Speaker 1: there's maybe something, but we don't know the mechanisms, and 196 00:12:37,240 --> 00:12:41,760 Speaker 1: it would be irresponsible, uh for anyone other than a 197 00:12:41,800 --> 00:12:46,720 Speaker 1: genetic scientists or an epidemiologists to claim otherwise. We do 198 00:12:46,800 --> 00:12:52,320 Speaker 1: know in terms of biology and genetics that biological sex, 199 00:12:52,760 --> 00:12:56,840 Speaker 1: not gender, not the not the social constructing, but biological 200 00:12:56,920 --> 00:13:02,160 Speaker 1: sex does also play a crucial role. And I've I've 201 00:13:02,200 --> 00:13:06,280 Speaker 1: got some bad news for us guys, not the winners 202 00:13:06,320 --> 00:13:09,440 Speaker 1: in this one. So yeah, it's true. Uh. While men 203 00:13:09,600 --> 00:13:14,679 Speaker 1: and women UM have roughly equal rates of COVID nineteen infection, 204 00:13:15,160 --> 00:13:18,720 Speaker 1: UM more men are dying from the disease in every 205 00:13:18,720 --> 00:13:22,680 Speaker 1: single country. In China, for example, where the virus originated, 206 00:13:23,360 --> 00:13:30,920 Speaker 1: six of deaths were men. In Italy, sixty of coronavirus 207 00:13:30,960 --> 00:13:35,720 Speaker 1: related deaths have been men um And as we've discussed, 208 00:13:35,840 --> 00:13:39,360 Speaker 1: you know, this data is not perfect, but based on 209 00:13:39,400 --> 00:13:42,880 Speaker 1: the data that we have gathered in New York City, 210 00:13:42,920 --> 00:13:46,840 Speaker 1: in particular, the epicenter of the North American outbreak, men 211 00:13:47,240 --> 00:13:52,559 Speaker 1: made up of known COVID nineteen hospitalizations but sixty two 212 00:13:53,480 --> 00:13:57,000 Speaker 1: of fatalities as early as April. And and the thing 213 00:13:57,120 --> 00:14:01,160 Speaker 1: is we don't really know why, which means it's likely 214 00:14:01,360 --> 00:14:04,680 Speaker 1: a mix of factors. Um Men are more more likely 215 00:14:04,720 --> 00:14:07,880 Speaker 1: to be smokers, for example. And uh, yeah, I know 216 00:14:07,920 --> 00:14:12,760 Speaker 1: it's gross, but practice less good hygiene. I guess, um, 217 00:14:13,280 --> 00:14:15,880 Speaker 1: I know, Ben, I think you've got some stats around 218 00:14:15,880 --> 00:14:19,560 Speaker 1: this that are a little bit eye opening. Yeah, yeah, 219 00:14:19,600 --> 00:14:23,920 Speaker 1: I've pepper this pepper Today's story with some I guess 220 00:14:24,480 --> 00:14:28,800 Speaker 1: moments of levity. This one is is kind of fart joky, uh, 221 00:14:28,880 --> 00:14:32,280 Speaker 1: but but it's true all for all the dudes listening 222 00:14:32,320 --> 00:14:35,080 Speaker 1: to this. Uh, you know, even if you identify as 223 00:14:35,160 --> 00:14:40,800 Speaker 1: dude or whatever. Uh, we're in general grocer, in in 224 00:14:40,960 --> 00:14:46,320 Speaker 1: the restroom, and in the kitchen. Uh. One in twenty 225 00:14:46,560 --> 00:14:50,000 Speaker 1: men say that they barely ever washed their hands with 226 00:14:50,080 --> 00:14:52,960 Speaker 1: soap after going to the bathroom at home, for instance. 227 00:14:53,440 --> 00:14:57,360 Speaker 1: And there's a there's a pretty cool well I opening site, 228 00:14:58,000 --> 00:15:03,520 Speaker 1: uh on pull this for you gov dot com where 229 00:15:03,560 --> 00:15:07,160 Speaker 1: where uh already four in ten Americans don't always wash 230 00:15:07,240 --> 00:15:09,280 Speaker 1: our hands with soap when we go to the bathroom. 231 00:15:10,080 --> 00:15:12,600 Speaker 1: But men are Men are by far the worst, which 232 00:15:12,600 --> 00:15:17,400 Speaker 1: means we're just giving opportunities for things like bacteria to 233 00:15:17,920 --> 00:15:22,720 Speaker 1: just spread. You know, men are indeed the worst. Just 234 00:15:22,760 --> 00:15:28,520 Speaker 1: putting that out there real quick as a father. Oh yeah, totally, totally, totally. 235 00:15:28,960 --> 00:15:32,880 Speaker 1: But as gross as we are, it doesn't quite explain 236 00:15:33,200 --> 00:15:37,560 Speaker 1: this disparity that we've been seeing. Right if you're just 237 00:15:37,600 --> 00:15:42,280 Speaker 1: thinking about New York where of the hospitalizations are male, 238 00:15:42,360 --> 00:15:48,440 Speaker 1: but then somehow of the fatalities are male. Um, it 239 00:15:48,520 --> 00:15:51,640 Speaker 1: just doesn't. That doesn't compute. I mean we are talking 240 00:15:51,640 --> 00:15:54,600 Speaker 1: about five what what is it like five percent or 241 00:15:54,680 --> 00:15:59,240 Speaker 1: something of of men, so like you're almost getting there, um, 242 00:15:59,280 --> 00:16:03,080 Speaker 1: just to add that little extra grossness, uh, to explain it. 243 00:16:03,120 --> 00:16:07,240 Speaker 1: But no, no, no, it doesn't. They are also generally 244 00:16:07,360 --> 00:16:10,800 Speaker 1: less likely to see a doctor, even correcting from all 245 00:16:10,880 --> 00:16:16,280 Speaker 1: other factors socioeconomic, etcetera. But you're right, Matt, this does 246 00:16:16,360 --> 00:16:22,240 Speaker 1: not explain the gap here. So this is just spitballing. 247 00:16:22,280 --> 00:16:25,760 Speaker 1: Maybe there's a little bit ted talky, but if we're 248 00:16:25,760 --> 00:16:29,000 Speaker 1: being absolutely honest, and if you you know, if you 249 00:16:29,040 --> 00:16:34,320 Speaker 1: read enough about the human species in any field of study, 250 00:16:34,680 --> 00:16:37,800 Speaker 1: it's apparent in the grand scheme of things, this is 251 00:16:37,840 --> 00:16:40,280 Speaker 1: more bad news for the dudes in the audience. If 252 00:16:40,320 --> 00:16:44,080 Speaker 1: you're a dude, you are not as biologically important as 253 00:16:44,120 --> 00:16:47,480 Speaker 1: a woman, full stop. I mean, you know, women are 254 00:16:47,520 --> 00:16:52,040 Speaker 1: horrifically repressed in societies throughout the world, but in terms 255 00:16:52,200 --> 00:16:57,720 Speaker 1: of the actual beyond politics species, women are way more important. 256 00:16:58,040 --> 00:17:00,680 Speaker 1: Women are the only ones who can rely doubly carry 257 00:17:00,720 --> 00:17:03,720 Speaker 1: a human child to term, rear it to adulthood, and 258 00:17:03,760 --> 00:17:08,000 Speaker 1: that's all that our species cares about from the biological level. 259 00:17:08,640 --> 00:17:12,960 Speaker 1: So it makes sense then that if the human species 260 00:17:13,119 --> 00:17:16,000 Speaker 1: was forced to sacrifice some of its own, it's gonna 261 00:17:16,040 --> 00:17:20,280 Speaker 1: go for the elderly first, and then the males. I mean, 262 00:17:20,400 --> 00:17:22,880 Speaker 1: not for nothing are so many young men sent off 263 00:17:22,920 --> 00:17:25,679 Speaker 1: to die in war. They're just not as necessary to 264 00:17:25,760 --> 00:17:30,199 Speaker 1: a functioning society as women, which is super cold, and 265 00:17:30,359 --> 00:17:32,639 Speaker 1: I hope that's not a surprise to anyone. But it 266 00:17:32,680 --> 00:17:36,720 Speaker 1: comes in playing pandemics too. We know, like the this 267 00:17:36,840 --> 00:17:41,160 Speaker 1: is not this is not something that uh, the your 268 00:17:41,240 --> 00:17:45,440 Speaker 1: world leader sat around and decided in a smokey back room, 269 00:17:45,480 --> 00:17:48,240 Speaker 1: even if they weren't there, even if no government existed. 270 00:17:48,840 --> 00:17:53,760 Speaker 1: We are hardwired to sacrifice dudes. First. You can see 271 00:17:53,760 --> 00:17:57,520 Speaker 1: it in the sex based differences of the immune system, 272 00:17:57,560 --> 00:18:00,680 Speaker 1: Like take the X chromosome. Everybody has an X chromosome. 273 00:18:00,720 --> 00:18:03,760 Speaker 1: You know, everybody has at least what it stores genes 274 00:18:03,760 --> 00:18:07,240 Speaker 1: that are incredibly important to your body's immune system as 275 00:18:07,240 --> 00:18:10,560 Speaker 1: well as micro RNA. And women if your call, have 276 00:18:10,720 --> 00:18:14,720 Speaker 1: two X chromosomes. Uh, they also produce more estrogen that's 277 00:18:14,760 --> 00:18:18,359 Speaker 1: proven to help with immune cell activity. Uh. Testosterone, by 278 00:18:18,359 --> 00:18:22,199 Speaker 1: the way, helps with inflammation a k A injuries. So 279 00:18:22,400 --> 00:18:27,119 Speaker 1: send them to war and uh. This this means that women, 280 00:18:27,680 --> 00:18:31,800 Speaker 1: again the biological term, can respond more quickly and efficiently 281 00:18:31,840 --> 00:18:35,040 Speaker 1: to infection in general. In fact, early work in China 282 00:18:35,119 --> 00:18:39,080 Speaker 1: this hasn't been published yet, but it's been publicly you know, propagated. 283 00:18:39,359 --> 00:18:44,159 Speaker 1: It indicates that Chinese women with COVID nineteen have in 284 00:18:44,240 --> 00:18:47,120 Speaker 1: general higher level of antibodies than men. And it goes 285 00:18:47,160 --> 00:18:50,679 Speaker 1: across the board. And there is a disparity also between 286 00:18:50,720 --> 00:18:54,800 Speaker 1: the life expectancy of men and women within the United States. 287 00:18:55,040 --> 00:18:58,080 Speaker 1: Just to put this out there, as of seventeen, life 288 00:18:58,080 --> 00:19:01,239 Speaker 1: expectancy for a male in this is just completely in 289 00:19:01,359 --> 00:19:08,879 Speaker 1: general was seventies six point one and for females it 290 00:19:09,040 --> 00:19:13,240 Speaker 1: was eighty one point one. So already, uh, there, there 291 00:19:13,280 --> 00:19:15,720 Speaker 1: there's a bit of difference in how long one is 292 00:19:15,760 --> 00:19:20,440 Speaker 1: expected to live just depending on your gender. Yeah, they 293 00:19:20,440 --> 00:19:23,120 Speaker 1: did the math, right, they did the terrible scary math. 294 00:19:23,240 --> 00:19:26,200 Speaker 1: We should also point out even in the seven so 295 00:19:26,280 --> 00:19:29,359 Speaker 1: called blue zones or the human population that's where you 296 00:19:29,440 --> 00:19:32,200 Speaker 1: have the highest likelihood of living past a hundred years old, 297 00:19:32,600 --> 00:19:37,520 Speaker 1: the life expectancy disparities still exists. It's smaller, but but 298 00:19:37,600 --> 00:19:41,639 Speaker 1: it still exists. Your chances of living longer as a 299 00:19:41,720 --> 00:19:46,680 Speaker 1: dude are just not super good. But but some people 300 00:19:46,720 --> 00:19:49,400 Speaker 1: may be wondering, some of our fellow listeners may be wondering, 301 00:19:49,640 --> 00:19:53,480 Speaker 1: why are you pulling so many stats from China? Uh? 302 00:19:53,680 --> 00:19:57,040 Speaker 1: One reason, first off, should be a parent that's where 303 00:19:57,240 --> 00:20:01,080 Speaker 1: the infection has been around the longest, so there's more 304 00:20:01,200 --> 00:20:05,320 Speaker 1: data to pool. Uh. But you know, you might say, well, 305 00:20:05,359 --> 00:20:09,240 Speaker 1: what if there's something about ethnicity that's different. Because I 306 00:20:09,280 --> 00:20:12,560 Speaker 1: have heard you might be saying, and rightly so, that 307 00:20:12,680 --> 00:20:16,960 Speaker 1: ethnicity does play a role in whether or not someone 308 00:20:17,040 --> 00:20:20,320 Speaker 1: will contract COVID and whether or not they will die 309 00:20:20,400 --> 00:20:23,280 Speaker 1: from it or be seriously injured. This is a huge, huge, 310 00:20:23,359 --> 00:20:26,919 Speaker 1: messy one in the US, which is still considered a 311 00:20:26,960 --> 00:20:34,760 Speaker 1: developed nation. Uh. Racial minorities are disproportionately affected by COVID nineteen. 312 00:20:35,160 --> 00:20:38,560 Speaker 1: If you are Latino or you are Black, and you 313 00:20:38,600 --> 00:20:41,720 Speaker 1: are living in New York City, for instance, this disease 314 00:20:42,040 --> 00:20:46,439 Speaker 1: will be literally twice as deadly for you than it 315 00:20:46,440 --> 00:20:49,680 Speaker 1: would be for like European Americans. Yeah, it's insane. And 316 00:20:49,840 --> 00:20:53,919 Speaker 1: the black population in Michigan is only about fourteen percent 317 00:20:54,280 --> 00:20:58,600 Speaker 1: of the total population but accounts for forty percent of 318 00:20:58,640 --> 00:21:02,320 Speaker 1: the deaths. Uh. Um. And and this is a whole 319 00:21:02,400 --> 00:21:06,760 Speaker 1: different issue than those disparities between age and biological sex. 320 00:21:07,320 --> 00:21:11,240 Speaker 1: This has absolutely nothing to do with with biology. Um. 321 00:21:11,280 --> 00:21:15,720 Speaker 1: A journalist named Ibram X kendy Um put it perfectly 322 00:21:16,119 --> 00:21:18,320 Speaker 1: uh in a quote from an article he wrote for 323 00:21:18,359 --> 00:21:21,520 Speaker 1: The Atlantic, saying, quote to explain the disparities in the 324 00:21:21,560 --> 00:21:25,879 Speaker 1: mortality rate, too many politicians and commentators are noting that 325 00:21:25,920 --> 00:21:30,840 Speaker 1: black people have more underlying medical conditions, but crucially they're 326 00:21:30,880 --> 00:21:35,080 Speaker 1: not explaining why, or they blame the choices made by 327 00:21:35,119 --> 00:21:40,240 Speaker 1: black people or poverty or obesity, but not racism there. 328 00:21:40,320 --> 00:21:45,119 Speaker 1: It is there. It is no ding ding ding racism. Yeah, 329 00:21:45,160 --> 00:21:47,920 Speaker 1: these pre existing conditions that are faced by so many 330 00:21:48,040 --> 00:21:54,439 Speaker 1: minority communities, including like, these medical conditions are let's not 331 00:21:54,600 --> 00:21:58,360 Speaker 1: victim blame here. Those are the results of systematic racism. 332 00:21:58,840 --> 00:22:02,880 Speaker 1: It affects health and profound ways. Like, for instance, there's 333 00:22:02,880 --> 00:22:06,320 Speaker 1: the old stereotype where you'll hear somebody say, you know, 334 00:22:06,640 --> 00:22:10,320 Speaker 1: my um like we you know, we're the u S. 335 00:22:10,359 --> 00:22:11,919 Speaker 1: We have a lot of we have a lot of 336 00:22:11,920 --> 00:22:16,520 Speaker 1: friends and loved ones whose parents maybe have immigrated right 337 00:22:16,560 --> 00:22:19,439 Speaker 1: from a different country. And there's this old stereotype here 338 00:22:19,480 --> 00:22:21,440 Speaker 1: all the time where it's like, oh, my parents don't 339 00:22:21,440 --> 00:22:24,360 Speaker 1: trust doctors, they won't go to doctors. Well, there are 340 00:22:24,400 --> 00:22:28,399 Speaker 1: studies that prove they're not just being you know, recalcitrant 341 00:22:28,520 --> 00:22:32,840 Speaker 1: or something. There's they have probably gone to a doctor 342 00:22:33,240 --> 00:22:37,600 Speaker 1: and had the experience where the doctor takes their description 343 00:22:37,640 --> 00:22:41,240 Speaker 1: of their pain or their condition much less. Seriously, multiple 344 00:22:41,280 --> 00:22:45,560 Speaker 1: studies confirm, specifically with black people, that doctors tend not 345 00:22:45,640 --> 00:22:48,840 Speaker 1: to believe them. They tend not to believe their patients 346 00:22:48,880 --> 00:22:52,520 Speaker 1: based on their skin color. That's messed up. And and 347 00:22:52,640 --> 00:22:56,600 Speaker 1: that's not even like the studies without getting two in 348 00:22:56,600 --> 00:22:59,080 Speaker 1: the weeds. The studies are pretty solid. They they ad 349 00:22:59,119 --> 00:23:04,440 Speaker 1: just for things. Is um like uh self aware bias, 350 00:23:04,800 --> 00:23:06,600 Speaker 1: you know what I mean. I'm not saying that doctors 351 00:23:06,800 --> 00:23:11,479 Speaker 1: are consciously being uh real pills about this. They just 352 00:23:11,640 --> 00:23:16,480 Speaker 1: inherently have been sort of programmed by society to doubt 353 00:23:16,560 --> 00:23:20,280 Speaker 1: people based on their skin color. It's true. And and 354 00:23:20,359 --> 00:23:22,920 Speaker 1: you know what, um, Ben dropped a word in there 355 00:23:23,040 --> 00:23:27,200 Speaker 1: that perhaps you like, I just had to look up recalcitrant. 356 00:23:27,280 --> 00:23:29,480 Speaker 1: It's our word of the day, and it has exactly 357 00:23:29,840 --> 00:23:33,119 Speaker 1: it exactly describes what Ben is talking about here. It 358 00:23:33,200 --> 00:23:37,920 Speaker 1: is defined as having an obstinately uncooperative attitude towards authority 359 00:23:38,040 --> 00:23:43,000 Speaker 1: or discipline. Yes, yeah, oh man, I'm so glad that's 360 00:23:43,000 --> 00:23:47,280 Speaker 1: the actual definition. Good vocabulary corner on stuff they don't 361 00:23:47,280 --> 00:23:50,080 Speaker 1: want you to know. I love it. Always something every 362 00:23:50,160 --> 00:23:53,119 Speaker 1: day Ben and Ben will throw in a word like that. 363 00:23:53,359 --> 00:23:56,800 Speaker 1: I would say every other episode, maybe maybe every episode 364 00:23:56,920 --> 00:23:58,840 Speaker 1: where I have to go, let me find out what 365 00:23:58,960 --> 00:24:02,000 Speaker 1: that word means bust me if I get it wrong. 366 00:24:02,320 --> 00:24:06,320 Speaker 1: You know, I think we all we all can um. Well, 367 00:24:06,359 --> 00:24:09,240 Speaker 1: we all are paid to talk, so sometimes when we 368 00:24:09,280 --> 00:24:11,440 Speaker 1: get carried away there there are some words that I've 369 00:24:11,560 --> 00:24:15,080 Speaker 1: used incorrectly in the past. You know. UM, the way 370 00:24:15,160 --> 00:24:17,720 Speaker 1: I see it, the way I see its vocabulary goals, 371 00:24:17,960 --> 00:24:19,520 Speaker 1: you know what I mean, Like this is an instagram 372 00:24:19,600 --> 00:24:23,600 Speaker 1: able moment. I completely agree. But the takeaway of vocabulary, 373 00:24:23,640 --> 00:24:27,240 Speaker 1: aside from all of this is that we're still kind 374 00:24:27,280 --> 00:24:31,000 Speaker 1: of unpacking all of this data. Obviously, and since there's 375 00:24:31,000 --> 00:24:35,400 Speaker 1: really no federal level plan for addressing this pandemic and 376 00:24:35,440 --> 00:24:39,720 Speaker 1: it's largely been kind of up to the states. UM, 377 00:24:39,760 --> 00:24:44,480 Speaker 1: there's really no unified accountability that scienceists have to lean on, 378 00:24:44,560 --> 00:24:47,640 Speaker 1: and they need that. UM. But what we are learning 379 00:24:48,240 --> 00:24:50,879 Speaker 1: and what we are able to start to unpack UH 380 00:24:51,040 --> 00:24:54,600 Speaker 1: in this data is really really disturbing, and it's the 381 00:24:54,600 --> 00:24:59,080 Speaker 1: fact that certain marginalized people are absolutely being left behind. 382 00:25:00,200 --> 00:25:03,320 Speaker 1: And there's there's more to this story. There's a story 383 00:25:03,440 --> 00:25:06,560 Speaker 1: that's not being told, UH, and we we get a 384 00:25:06,680 --> 00:25:11,000 Speaker 1: snapshot of it from UH. From you caller who are 385 00:25:11,080 --> 00:25:14,600 Speaker 1: keeping anonymous for your safety. UH. You may have seen 386 00:25:15,440 --> 00:25:19,560 Speaker 1: a statistic if you've been reading about COVID nineteen as 387 00:25:19,680 --> 00:25:21,400 Speaker 1: you know, as an old said that data is still 388 00:25:21,400 --> 00:25:23,920 Speaker 1: coming in. You may have seen this statistic, but you've 389 00:25:23,960 --> 00:25:26,199 Speaker 1: seen it relegated to a line or two in a 390 00:25:26,200 --> 00:25:30,520 Speaker 1: lot of recent news stories and the think pieces and 391 00:25:30,560 --> 00:25:35,200 Speaker 1: the you know, uh, the high faluting articles, and it's 392 00:25:35,240 --> 00:25:37,320 Speaker 1: usually something like this, this is what it's been in 393 00:25:37,400 --> 00:25:41,159 Speaker 1: recent months. Well, some of the highest infection rates in 394 00:25:41,160 --> 00:25:46,119 Speaker 1: the country have been on Native American land, peplo's reservations 395 00:25:46,160 --> 00:25:49,760 Speaker 1: and so on. If that's true, and it most assuredly is, 396 00:25:50,200 --> 00:25:54,159 Speaker 1: that is an insane thing to leave as a throwaway aside. 397 00:25:54,440 --> 00:25:59,120 Speaker 1: You know what I mean. It's it's like, um, it's 398 00:25:59,119 --> 00:26:02,080 Speaker 1: like saying it's like saying, hey, here's uh, here's a 399 00:26:02,160 --> 00:26:07,040 Speaker 1: dry report on the statistics of airplanes. Um. You know 400 00:26:07,280 --> 00:26:10,280 Speaker 1: it is true that recently, uh, two people have been 401 00:26:10,320 --> 00:26:13,439 Speaker 1: documented being able to traverse the bounds of gravity and 402 00:26:13,480 --> 00:26:17,560 Speaker 1: fly unaided. But the Boeing seven seven has a wind 403 00:26:17,560 --> 00:26:20,440 Speaker 1: speed of X. Like, why are we why are we 404 00:26:20,480 --> 00:26:25,160 Speaker 1: throwing that line away? What is going on? We'll tell 405 00:26:25,200 --> 00:26:34,520 Speaker 1: you after a word from our sponsor. Here's where it 406 00:26:34,560 --> 00:26:41,040 Speaker 1: gets crazy. Yeah, I mean, there seems to be uh, 407 00:26:41,080 --> 00:26:46,480 Speaker 1: a like emergency within the emergency um brewing right now 408 00:26:46,600 --> 00:26:49,920 Speaker 1: in one of the country's most historically vulnerable populations. In 409 00:26:49,960 --> 00:26:53,400 Speaker 1: the United States, there are five and seventy four recognized 410 00:26:53,520 --> 00:26:57,600 Speaker 1: federally recognized Native American tribes. UM, but they're often being 411 00:26:57,720 --> 00:27:03,640 Speaker 1: under reported, misreported, or are ignored entirely, UM, being relegated 412 00:27:03,720 --> 00:27:08,000 Speaker 1: to the that you know checkbox on you know documents 413 00:27:08,000 --> 00:27:11,680 Speaker 1: you see just called other. And that's the the very 414 00:27:11,720 --> 00:27:16,080 Speaker 1: definition of mothering right there. Ye well it is. In 415 00:27:16,400 --> 00:27:21,560 Speaker 1: The Guardian, a UK newspaper news organization, reported on this, 416 00:27:21,960 --> 00:27:25,080 Speaker 1: and we've got to quote from them here and I'm 417 00:27:25,080 --> 00:27:28,240 Speaker 1: just gonna read it. Quote of state health departments have 418 00:27:28,320 --> 00:27:32,639 Speaker 1: released some racial demographic data which has already revealed stark 419 00:27:32,920 --> 00:27:35,840 Speaker 1: disparities in the impact of COVID nineteen in black and 420 00:27:35,960 --> 00:27:39,600 Speaker 1: Latin X communities. They're using Latin X here. We know 421 00:27:39,640 --> 00:27:43,680 Speaker 1: that's a term that is uh not perfect. Um, let's 422 00:27:43,680 --> 00:27:46,920 Speaker 1: just continue on. But of those states, almost half did 423 00:27:46,960 --> 00:27:50,880 Speaker 1: not explicitly include Native Americans in their breakdowns and instead 424 00:27:50,960 --> 00:27:59,879 Speaker 1: categorize them under the label other miscellaneous. Basically. Yeah. Abby 425 00:28:00,040 --> 00:28:03,560 Speaker 1: Gale Echo Hawk, who is the director of the Urban 426 00:28:03,680 --> 00:28:06,960 Speaker 1: Indian Health Board and also the chief research officer of 427 00:28:07,000 --> 00:28:12,280 Speaker 1: the Seattle Indian Health Board. UH responded by I wouldn't 428 00:28:12,320 --> 00:28:14,880 Speaker 1: hear what you all think of this quote. I feel 429 00:28:14,880 --> 00:28:18,440 Speaker 1: like she's implying that there's some cooking of the books. 430 00:28:18,480 --> 00:28:21,960 Speaker 1: She says by including us in the other category, it 431 00:28:22,040 --> 00:28:28,120 Speaker 1: effectively eliminates us in the data, and there's I feel 432 00:28:28,160 --> 00:28:32,239 Speaker 1: like that's pretty valid there. There's something else that a 433 00:28:32,280 --> 00:28:35,600 Speaker 1: lot of people outside of that community probably don't know. 434 00:28:35,880 --> 00:28:39,120 Speaker 1: It's something that, um, you know, to to be fully transparent, 435 00:28:39,400 --> 00:28:42,479 Speaker 1: I was not aware of until we were doing some 436 00:28:42,560 --> 00:28:46,640 Speaker 1: research on the force dissimilation Native Americans. Did you know 437 00:28:47,120 --> 00:28:50,880 Speaker 1: that the Native American population in the US is the 438 00:28:50,960 --> 00:28:54,480 Speaker 1: only segment of the US that is born with a 439 00:28:54,600 --> 00:28:58,600 Speaker 1: legal right to healthcare? Now everybody else in the world. 440 00:28:58,920 --> 00:29:02,560 Speaker 1: That's gonna be really weird to hear, because if you 441 00:29:02,640 --> 00:29:04,959 Speaker 1: don't live in the US, the odds are that, at 442 00:29:05,040 --> 00:29:08,240 Speaker 1: least on paper, you are born with a legal right 443 00:29:08,280 --> 00:29:11,560 Speaker 1: to healthcare. We are not. We are absolutely not. Only 444 00:29:11,600 --> 00:29:16,160 Speaker 1: the Native American population is. But still it's it's on paper, 445 00:29:16,200 --> 00:29:19,440 Speaker 1: it's in theory. But ben aren't we entitled to life, liberty, 446 00:29:19,480 --> 00:29:23,280 Speaker 1: and the pursuit of happiness? Right? Right? Yeah? Yeah, I'm 447 00:29:23,320 --> 00:29:26,920 Speaker 1: sorry that that pursuit really encompasses a lot of aspirational 448 00:29:26,960 --> 00:29:29,880 Speaker 1: stuff that we are not in fact entitled to at all. No, 449 00:29:30,080 --> 00:29:32,000 Speaker 1: it's true, And I mean, like, what what would be 450 00:29:32,040 --> 00:29:34,760 Speaker 1: some other others by the way, are we talking about 451 00:29:34,800 --> 00:29:38,760 Speaker 1: like American Samoans, Like what would be some other things 452 00:29:38,800 --> 00:29:42,120 Speaker 1: that would be put into that other category just just 453 00:29:42,160 --> 00:29:45,400 Speaker 1: out of curiosity. Yeah, that's a great question. Depends on 454 00:29:45,640 --> 00:29:49,520 Speaker 1: the organization asking and how they want to, you know, 455 00:29:49,640 --> 00:29:53,000 Speaker 1: Bernet's level frame it, so you might you might see 456 00:29:53,000 --> 00:29:55,960 Speaker 1: things that like so the US is still grappling with 457 00:29:56,040 --> 00:30:01,320 Speaker 1: how to classify what they think of as someone someone's 458 00:30:01,360 --> 00:30:06,440 Speaker 1: specific identification in the Latin diaspora for instance, Right, Uh, 459 00:30:06,480 --> 00:30:08,800 Speaker 1: they might say, like you, you probably used to see 460 00:30:08,840 --> 00:30:12,840 Speaker 1: those things where it's like white white Latino, non white Latino. 461 00:30:13,000 --> 00:30:16,520 Speaker 1: Things like that. Other can include things like um like 462 00:30:16,640 --> 00:30:22,120 Speaker 1: malungeon for instance. Uh, a lot of smaller ethnicities that 463 00:30:22,400 --> 00:30:27,440 Speaker 1: don't have a large enough population to really show up 464 00:30:27,440 --> 00:30:30,600 Speaker 1: on a PIH graph essentially, so you might see like 465 00:30:30,720 --> 00:30:35,280 Speaker 1: maybe romani um. And it's also it gives people an 466 00:30:35,280 --> 00:30:38,240 Speaker 1: opportunity to say, you know, I'm not on this list, 467 00:30:38,640 --> 00:30:41,120 Speaker 1: but I don't think I fit in any of these 468 00:30:41,120 --> 00:30:45,400 Speaker 1: other categories you've listed. So I'm you know, I'm Romani, 469 00:30:45,600 --> 00:30:49,800 Speaker 1: I'm I'm a lungeon, I'm etcetera. You know, it's also 470 00:30:49,840 --> 00:30:52,960 Speaker 1: an opportunity for a lot of people to be uh 471 00:30:53,440 --> 00:30:56,360 Speaker 1: to try out there there there stand up chops. I'm 472 00:30:56,400 --> 00:30:58,200 Speaker 1: sure there are a lot of crazy things entered on 473 00:30:58,280 --> 00:31:01,360 Speaker 1: other like I'm a robo America and I wasn't. I 474 00:31:01,400 --> 00:31:04,040 Speaker 1: wasn't trying to be flipping or dismissive and saying American Samoa. 475 00:31:04,080 --> 00:31:07,200 Speaker 1: I know that's a category known as Pacific islander. Um. 476 00:31:07,240 --> 00:31:09,160 Speaker 1: You know that would be a box. But what you 477 00:31:09,440 --> 00:31:12,920 Speaker 1: this is not, say the census that there is intention 478 00:31:13,120 --> 00:31:17,120 Speaker 1: well I don't know to me implied intention in squashing 479 00:31:17,160 --> 00:31:21,560 Speaker 1: down these groups into single check boxes as opposed to 480 00:31:21,560 --> 00:31:24,360 Speaker 1: having a more broad categorization and and to to the 481 00:31:24,400 --> 00:31:27,960 Speaker 1: point of of the of the person that we referenced earlier, 482 00:31:28,360 --> 00:31:32,360 Speaker 1: it does feel like a way of just kind of 483 00:31:32,400 --> 00:31:37,240 Speaker 1: hodgepodging all of these inconvenient groups together into one box 484 00:31:37,320 --> 00:31:41,080 Speaker 1: so they don't really have to address any specifics around it. 485 00:31:41,760 --> 00:31:45,360 Speaker 1: That's I think you've hit it on the head there, Knull, 486 00:31:45,520 --> 00:31:48,400 Speaker 1: Because there there are two things here. The first one 487 00:31:48,680 --> 00:31:52,120 Speaker 1: is that you know, we talked about how Native Americans 488 00:31:52,160 --> 00:31:56,000 Speaker 1: in this country, indigenous people's are born with the right 489 00:31:56,040 --> 00:31:58,640 Speaker 1: to healthcare, but what does that really mean? How does 490 00:31:58,680 --> 00:32:02,440 Speaker 1: that translate right? And also why do they have that? 491 00:32:03,800 --> 00:32:07,240 Speaker 1: Isn't it because of the treaties that were signed back 492 00:32:07,240 --> 00:32:09,440 Speaker 1: in the day where all of the land and the 493 00:32:09,480 --> 00:32:13,320 Speaker 1: resources on that land was just taken again perhaps their 494 00:32:13,440 --> 00:32:16,000 Speaker 1: land is an incorrect way to describe it, but the 495 00:32:16,080 --> 00:32:20,240 Speaker 1: areas in which they lived were taken um and the 496 00:32:20,240 --> 00:32:24,720 Speaker 1: the concept here was that as part of the treaty, 497 00:32:24,840 --> 00:32:29,920 Speaker 1: one of the things you get our services like healthcare, housing, education, 498 00:32:30,480 --> 00:32:34,480 Speaker 1: all of these things that were then provided by the colonists, 499 00:32:34,480 --> 00:32:40,760 Speaker 1: the colonizers us really um from the past, and that's 500 00:32:40,760 --> 00:32:45,080 Speaker 1: why there are things that exist called the Bureau of 501 00:32:45,080 --> 00:32:49,240 Speaker 1: Indian Affairs or the Indian Health Service, or the Indian 502 00:32:49,280 --> 00:32:53,840 Speaker 1: Health Board or the Indian the Nevada Indian Commission. But 503 00:32:55,000 --> 00:32:59,120 Speaker 1: that agreement that was entered into a long long time ago, 504 00:32:59,280 --> 00:33:02,800 Speaker 1: or at least that concept of an agreement, it doesn't 505 00:33:02,960 --> 00:33:08,840 Speaker 1: seem to be translating over all these years to actual health, 506 00:33:09,560 --> 00:33:13,960 Speaker 1: especially health and human services, to to assistance uh in 507 00:33:13,960 --> 00:33:19,200 Speaker 1: in these populations. Yeah, exactly, so how bad is it? 508 00:33:19,640 --> 00:33:23,960 Speaker 1: Think about this, If the Navajo Nation were a state, 509 00:33:24,040 --> 00:33:26,720 Speaker 1: if there were fifty one states and the Navo Nation 510 00:33:26,840 --> 00:33:29,440 Speaker 1: was one, then just a few weeks ago it would 511 00:33:29,440 --> 00:33:32,760 Speaker 1: have ranked third in the country for COVID nineteen infection 512 00:33:32,800 --> 00:33:36,400 Speaker 1: cases per one thousand people per capita. That would put 513 00:33:36,400 --> 00:33:39,840 Speaker 1: it just behind New York and New Jersey. To be fair, 514 00:33:40,240 --> 00:33:43,800 Speaker 1: the nation is trying to test at a much higher 515 00:33:43,880 --> 00:33:46,800 Speaker 1: rate than a lot of states, but the problem is 516 00:33:46,840 --> 00:33:49,640 Speaker 1: growing because that would have been just a few weeks 517 00:33:49,680 --> 00:33:52,920 Speaker 1: ago as number three. If it were state this like 518 00:33:53,080 --> 00:33:56,440 Speaker 1: last week, it would be the highest per capita rate 519 00:33:56,520 --> 00:34:02,800 Speaker 1: of COVID nineteen infection in the US entire The population 520 00:34:03,360 --> 00:34:09,560 Speaker 1: UH is being disproportionately affected by and by COVID nineteen. Yeah, 521 00:34:09,560 --> 00:34:12,040 Speaker 1: and the problem doesn't seem to be going anywhere. In fact, 522 00:34:12,080 --> 00:34:15,520 Speaker 1: it seems to be growing. In Arizona, UH, Native Americans 523 00:34:15,520 --> 00:34:18,240 Speaker 1: make up six percent of the population but sixteen percent 524 00:34:18,280 --> 00:34:22,080 Speaker 1: of the COVID cases. In New Mexico, they're less than 525 00:34:22,200 --> 00:34:25,399 Speaker 1: ten percent of the population but one third of all 526 00:34:25,640 --> 00:34:29,759 Speaker 1: coronavirus cases. And again, nothing to do with biology here 527 00:34:30,080 --> 00:34:33,720 Speaker 1: other than sex and age. There's absolutely no genetic reason 528 00:34:34,120 --> 00:34:37,839 Speaker 1: for these populations to to be seeing such a disparity 529 00:34:38,239 --> 00:34:41,919 Speaker 1: um in the number of cases uh in that they're 530 00:34:41,960 --> 00:34:46,280 Speaker 1: acquiring and dying from COVID nineteen at such a higher 531 00:34:46,360 --> 00:34:50,479 Speaker 1: rate than literally everyone else. Um. It really does come 532 00:34:50,520 --> 00:34:54,040 Speaker 1: down to that othering that we talked about and that 533 00:34:54,080 --> 00:35:00,160 Speaker 1: discriminatory attitude towards this marginalized population in particular. And we're 534 00:35:00,160 --> 00:35:03,480 Speaker 1: gonna get more into why that might be after a 535 00:35:03,560 --> 00:35:14,920 Speaker 1: quick word from our sponsor. So, like other populations in 536 00:35:15,080 --> 00:35:19,480 Speaker 1: the world and in the US, this this population, the 537 00:35:19,600 --> 00:35:23,520 Speaker 1: Native American population in the United States, was uh not 538 00:35:23,719 --> 00:35:29,040 Speaker 1: receiving adequate services or you know, like health care services 539 00:35:29,560 --> 00:35:32,600 Speaker 1: before the pandemic. And you know what we're eventually going 540 00:35:32,640 --> 00:35:35,400 Speaker 1: to be calling like the days before, you know what 541 00:35:35,440 --> 00:35:41,120 Speaker 1: I mean, the old normal things still frankly sucked in 542 00:35:41,239 --> 00:35:44,359 Speaker 1: terms of healthcare. Uh. The c d C based here 543 00:35:44,360 --> 00:35:46,799 Speaker 1: in Atlanta, by the way, Centers for Disease Control and 544 00:35:46,800 --> 00:35:51,879 Speaker 1: Prevention had already done some studies indicating that this population 545 00:35:52,000 --> 00:35:56,080 Speaker 1: experienced diabetes three times more than any other racial or 546 00:35:56,080 --> 00:35:59,400 Speaker 1: ethnic group in the US, had the highest rates of asthma, 547 00:35:59,800 --> 00:36:05,040 Speaker 1: and again in the before four days pre pandemic, the 548 00:36:05,120 --> 00:36:10,120 Speaker 1: federal health system that was serving the Native American population 549 00:36:10,440 --> 00:36:14,280 Speaker 1: was already by its own admission and by outside studies, 550 00:36:14,600 --> 00:36:19,400 Speaker 1: confirmed to be chronically underfunded. So so like what's in 551 00:36:19,440 --> 00:36:22,480 Speaker 1: a report to the earlier question about other Right, we 552 00:36:22,560 --> 00:36:26,640 Speaker 1: talked about the census earlier in a previous episode and 553 00:36:26,680 --> 00:36:31,120 Speaker 1: how it asked some questions that people find invasive at times. Right. Uh, 554 00:36:31,200 --> 00:36:33,600 Speaker 1: you know, you're asked to disclose things like your race, 555 00:36:33,680 --> 00:36:37,360 Speaker 1: your ethnicity, your age, or income in the midst of 556 00:36:37,400 --> 00:36:43,080 Speaker 1: a pandemic. This data, when used ethically, helps us understand 557 00:36:43,160 --> 00:36:47,920 Speaker 1: the underlying problems about how an infection takes hold in 558 00:36:47,960 --> 00:36:51,399 Speaker 1: a given community. So, so let's look a little bit 559 00:36:51,400 --> 00:36:54,480 Speaker 1: into what the census had to say. Yeah, according to 560 00:36:54,680 --> 00:36:59,080 Speaker 1: that census, half of all American, Indian, and Alaska Natives 561 00:36:59,200 --> 00:37:02,400 Speaker 1: live in ten different states within the United States. So 562 00:37:02,480 --> 00:37:06,680 Speaker 1: ten out of fifty as of April, all ten had 563 00:37:06,719 --> 00:37:10,759 Speaker 1: published some kind of racial demographic data, and six of 564 00:37:10,800 --> 00:37:15,240 Speaker 1: those included a breakdown for Native Americans within their states. However, 565 00:37:15,360 --> 00:37:19,600 Speaker 1: four did not Texas, Florida, New York, and Michigan, and 566 00:37:19,719 --> 00:37:22,719 Speaker 1: a spokesperson for the Texas Department of State Health Services 567 00:37:22,719 --> 00:37:27,480 Speaker 1: said that Native Americans were categorized as quote other because 568 00:37:27,520 --> 00:37:30,799 Speaker 1: their case numbers were simply small compared to the other 569 00:37:30,840 --> 00:37:34,319 Speaker 1: groups that they were counting within within this data. And 570 00:37:34,360 --> 00:37:39,520 Speaker 1: then Abigail Echo Hawk, who we mentioned earlier, responded, uh. 571 00:37:39,560 --> 00:37:45,240 Speaker 1: And this response, I I feel was honest and blunt 572 00:37:45,320 --> 00:37:48,960 Speaker 1: at a time when honesty and bluntness are needed. Uh. 573 00:37:49,080 --> 00:37:52,319 Speaker 1: Echo Hawks said, quote, we are a small population of 574 00:37:52,360 --> 00:37:56,759 Speaker 1: people because of genocide, no other reason. If you eliminate 575 00:37:56,840 --> 00:37:59,960 Speaker 1: us in the data, we don't exist. We don't exist 576 00:38:00,239 --> 00:38:04,600 Speaker 1: for the allocation of resources. You know. Uh, I think 577 00:38:04,640 --> 00:38:08,759 Speaker 1: that's a powerful statement. And and you know it alludes 578 00:38:08,800 --> 00:38:13,359 Speaker 1: to some of the previous episodes or previous chapters when 579 00:38:13,360 --> 00:38:17,879 Speaker 1: I'm calling that horror story. Uh. Leaving native populations out 580 00:38:17,880 --> 00:38:21,440 Speaker 1: of public health information is not a new thing in 581 00:38:21,440 --> 00:38:25,240 Speaker 1: this country at all. And it's not an historically distant 582 00:38:25,280 --> 00:38:30,080 Speaker 1: thing either. It's a lot closer to a routine pattern 583 00:38:30,320 --> 00:38:33,640 Speaker 1: of uh negligence and abuse. Yeah. I mean it's a 584 00:38:33,640 --> 00:38:36,759 Speaker 1: form of like cultural erasure. Really. I mean, when the 585 00:38:36,800 --> 00:38:39,719 Speaker 1: CDC released its most comprehensive study to date on race 586 00:38:39,800 --> 00:38:44,120 Speaker 1: and maternal mortality back in January, UH, it did not 587 00:38:44,160 --> 00:38:47,839 Speaker 1: include Native populations. Even though the Urban Indian Health Institute 588 00:38:48,080 --> 00:38:50,520 Speaker 1: in its own studies found that Native women living in 589 00:38:50,560 --> 00:38:54,040 Speaker 1: cities were more than four and a half times more 590 00:38:54,120 --> 00:38:57,800 Speaker 1: likely to die during pregnancy and childbirth than white women. 591 00:38:58,280 --> 00:39:01,000 Speaker 1: So then let's look to the Indian Health Services or 592 00:39:01,040 --> 00:39:04,080 Speaker 1: i h S, which serves two point five million tribal 593 00:39:04,120 --> 00:39:08,520 Speaker 1: citizens across thirty seven states within the US. UM they've 594 00:39:08,560 --> 00:39:12,320 Speaker 1: been they've been publishing daily reports about the outbreak about 595 00:39:12,320 --> 00:39:15,400 Speaker 1: the coronavirus and how it's affecting, you know, the people 596 00:39:15,400 --> 00:39:18,400 Speaker 1: that they serve. Here's the here's the problem though, i 597 00:39:18,680 --> 00:39:22,720 Speaker 1: h S can't track hospitalization or mortality data for most 598 00:39:22,920 --> 00:39:24,840 Speaker 1: of the people that it's trying to serve, most of 599 00:39:24,840 --> 00:39:28,239 Speaker 1: the patients because the majority of the facilities that they're 600 00:39:28,280 --> 00:39:31,439 Speaker 1: actually trying to track or that these these citizens get 601 00:39:31,480 --> 00:39:35,400 Speaker 1: to go to, they don't provide intensive care. So critically 602 00:39:35,400 --> 00:39:37,959 Speaker 1: ill patients, like when you know, when you actually have 603 00:39:38,640 --> 00:39:43,040 Speaker 1: COVID nineteen UM, they're they're transferred to non i h 604 00:39:43,160 --> 00:39:46,640 Speaker 1: S hospitals. So then the data that you know that 605 00:39:46,719 --> 00:39:49,360 Speaker 1: they would be recording with your the the i h 606 00:39:49,480 --> 00:39:52,520 Speaker 1: S would be recording is going somewhere else to some 607 00:39:52,640 --> 00:39:56,080 Speaker 1: other group to try and you know, hopefully get put 608 00:39:56,120 --> 00:39:59,160 Speaker 1: into the data set. Somewhere else. But then guess what 609 00:39:59,640 --> 00:40:04,680 Speaker 1: it hits that other category. It's crazy. The only public 610 00:40:05,520 --> 00:40:11,640 Speaker 1: national coronavirus database that is also reporting uh, you know, 611 00:40:11,719 --> 00:40:17,280 Speaker 1: specifically tribal affiliation is coming out of Indian Country today. 612 00:40:17,320 --> 00:40:21,439 Speaker 1: That's an independent native run newspaper and as a team 613 00:40:21,480 --> 00:40:24,960 Speaker 1: of sixteen people, and they have been just sixteen and 614 00:40:25,000 --> 00:40:29,600 Speaker 1: they've been working around the clock adding positive COVID cases 615 00:40:29,640 --> 00:40:32,400 Speaker 1: and deaths that they can verify to a daily report 616 00:40:32,800 --> 00:40:36,399 Speaker 1: you can find on on their website. And we want 617 00:40:36,440 --> 00:40:40,319 Speaker 1: to mention again, um that while this is happening, you know, 618 00:40:40,600 --> 00:40:46,840 Speaker 1: people are finding themselves in a situation where they feel 619 00:40:47,280 --> 00:40:50,920 Speaker 1: rightly often that they cannot trust the federal government because 620 00:40:50,920 --> 00:40:54,600 Speaker 1: they're not receiving the ppe they were promised. I love 621 00:40:54,640 --> 00:40:58,719 Speaker 1: the caller pointed out the opportunity for bad actors, corruption 622 00:40:58,760 --> 00:41:03,840 Speaker 1: and graft here. It's it's certainly happening. We haven't we 623 00:41:03,880 --> 00:41:07,400 Speaker 1: haven't been able to find out where that uh financial 624 00:41:07,480 --> 00:41:10,400 Speaker 1: leak is who's getting a big but I am I 625 00:41:10,440 --> 00:41:13,600 Speaker 1: am convinced this out there. You can't. You can't have 626 00:41:14,360 --> 00:41:20,000 Speaker 1: government private cooperation at this extent without someone taking a 627 00:41:20,040 --> 00:41:22,279 Speaker 1: little taste, you know. Yeah, yeah, And we've even seen 628 00:41:22,520 --> 00:41:28,319 Speaker 1: cases of UM shipments destined for hospitals being intercepted, you know, 629 00:41:28,520 --> 00:41:32,240 Speaker 1: by the government and distributed to God knows who, God 630 00:41:32,239 --> 00:41:34,399 Speaker 1: knows where or for what ends, you know, because there's 631 00:41:34,440 --> 00:41:38,239 Speaker 1: just no transparency there. This seems like a more even 632 00:41:38,280 --> 00:41:41,560 Speaker 1: more insidious version of that long story short people are 633 00:41:41,600 --> 00:41:46,200 Speaker 1: falling through the cracks, and the big question here is 634 00:41:46,280 --> 00:41:52,080 Speaker 1: whether that is through UM federal and state level incompetence, 635 00:41:52,680 --> 00:41:57,440 Speaker 1: whether it's through negligence, or whether it is somehow through design, 636 00:41:57,680 --> 00:42:01,920 Speaker 1: which I know might sound alarmists to a lot of 637 00:42:02,000 --> 00:42:04,680 Speaker 1: us in the audience today, but we have to remember 638 00:42:05,080 --> 00:42:09,040 Speaker 1: the precedent set by history, the earlier chapters in this book, 639 00:42:09,280 --> 00:42:12,680 Speaker 1: when these sorts of things happened very much by design. 640 00:42:13,120 --> 00:42:19,799 Speaker 1: So unlike states and counties, tribal systems can't rely on 641 00:42:19,880 --> 00:42:23,160 Speaker 1: these health care systems to tell them when their citizens 642 00:42:23,200 --> 00:42:27,680 Speaker 1: are sick or dying. They have to collect this information themselves. 643 00:42:28,040 --> 00:42:32,720 Speaker 1: And that's incredibly dangerous because that means that you're flying blind. 644 00:42:33,200 --> 00:42:38,280 Speaker 1: It's a tough situation to find yourself in. Uh According 645 00:42:38,320 --> 00:42:41,680 Speaker 1: to the National Indian Health Board, only half of the 646 00:42:41,719 --> 00:42:45,960 Speaker 1: tribal government surveyed said they received COVID nineteen related information 647 00:42:46,040 --> 00:42:50,080 Speaker 1: from the federal or state governments, places where you know 648 00:42:50,200 --> 00:42:53,680 Speaker 1: a lot of other counties and local governments are getting 649 00:42:53,719 --> 00:42:59,160 Speaker 1: information from and fewer than one five had received money 650 00:42:59,480 --> 00:43:03,960 Speaker 1: technical assistants or supplies. And specifically we're talking about that 651 00:43:04,120 --> 00:43:06,960 Speaker 1: ppe there, the ventilators, things that are going to be 652 00:43:06,960 --> 00:43:12,920 Speaker 1: absolutely necessary. We talked about the problems with the hospitals 653 00:43:12,920 --> 00:43:15,160 Speaker 1: where a lot of the native populations can actually go to. 654 00:43:15,480 --> 00:43:19,759 Speaker 1: This is just really a dire situation. And you know, 655 00:43:21,680 --> 00:43:23,560 Speaker 1: then you look to the the thing that a lot 656 00:43:23,600 --> 00:43:26,240 Speaker 1: of people have been talking about in politics, the Cares Act, 657 00:43:26,800 --> 00:43:32,200 Speaker 1: the bailout. Essentially, there was eight billion dollars that was 658 00:43:32,239 --> 00:43:35,960 Speaker 1: allocated to tribes at the end of March. However, the 659 00:43:36,000 --> 00:43:38,839 Speaker 1: payments didn't start rolling out until a week after the 660 00:43:38,880 --> 00:43:44,919 Speaker 1: April deadline that was imposed by Congress. Um. That's that's 661 00:43:44,920 --> 00:43:48,799 Speaker 1: a problem. Yeah, And there's just to just to put 662 00:43:48,840 --> 00:43:53,880 Speaker 1: this in perspective, um, Yeah, to to put it in 663 00:43:54,000 --> 00:43:58,040 Speaker 1: like a stark, bleeding red meat headline. Here, think about 664 00:43:58,080 --> 00:44:02,560 Speaker 1: this due to the neglect on the part of your 665 00:44:02,680 --> 00:44:06,040 Speaker 1: federal government. If you live in this country, the conditions 666 00:44:06,080 --> 00:44:09,319 Speaker 1: on reservations right now as we record this are so 667 00:44:09,440 --> 00:44:14,560 Speaker 1: bad that Doctors Without Borders, the Doctors Without Borders has 668 00:44:14,600 --> 00:44:18,840 Speaker 1: deployed inside the territory of the US for the first 669 00:44:18,920 --> 00:44:23,480 Speaker 1: time in history. Doctors without Borders had to step in. Yeah, 670 00:44:23,520 --> 00:44:26,840 Speaker 1: this is a group that goes to war torn areas 671 00:44:27,040 --> 00:44:30,440 Speaker 1: in the world that are dangerous due to places where 672 00:44:30,640 --> 00:44:34,960 Speaker 1: as there's If there's not help, then people will be 673 00:44:35,080 --> 00:44:40,000 Speaker 1: dying all over and and consistently. And they're here now. 674 00:44:41,000 --> 00:44:45,160 Speaker 1: And this is only a snapshot of this gigantic problem 675 00:44:45,280 --> 00:44:50,600 Speaker 1: that is affecting multiple communities in the United States, communities 676 00:44:50,640 --> 00:44:56,840 Speaker 1: that have been historically brushed aside, UH provably, demonstrably conspired against, 677 00:44:57,080 --> 00:45:00,720 Speaker 1: ignored by the larger power structure. E. Then in times 678 00:45:00,760 --> 00:45:04,799 Speaker 1: when things are like relatively normal, you know, it is 679 00:45:04,840 --> 00:45:07,960 Speaker 1: not hyperbolic to say that right now, lives are literally 680 00:45:08,120 --> 00:45:12,600 Speaker 1: on the line. Earlier in April, it's a little while ago, 681 00:45:13,120 --> 00:45:19,799 Speaker 1: UH Senators Senator Elizabeth Warren and Congresswoman Ayanna Pressley had 682 00:45:19,920 --> 00:45:23,959 Speaker 1: joined forces with some other colleagues to introduce a bill 683 00:45:24,040 --> 00:45:28,680 Speaker 1: that was specifically calling for consultation with tribal governments on 684 00:45:28,719 --> 00:45:32,880 Speaker 1: this federal level info collection, and they wanted to allocate 685 00:45:32,920 --> 00:45:37,080 Speaker 1: an additional three million dollars in funding to h I 686 00:45:37,360 --> 00:45:39,920 Speaker 1: h S. Yeah. Warren went on to say, quote, you 687 00:45:39,960 --> 00:45:42,360 Speaker 1: can't fix what you can't see. If we want to 688 00:45:42,400 --> 00:45:44,600 Speaker 1: slow the spread of the virus and ensure our response 689 00:45:44,719 --> 00:45:48,600 Speaker 1: is robust and equitable, we need comprehensive data on who's 690 00:45:48,600 --> 00:45:53,160 Speaker 1: getting tested, who's getting treatment, and who is dying. UM 691 00:45:53,600 --> 00:45:57,239 Speaker 1: very much supporting this notion of breaking out that other 692 00:45:57,360 --> 00:46:01,600 Speaker 1: category and getting more granular detail about this stuff, because 693 00:46:01,719 --> 00:46:03,600 Speaker 1: you can't do anything about it if you don't know 694 00:46:03,640 --> 00:46:06,759 Speaker 1: what's happening. And at one quick point there UM for 695 00:46:06,800 --> 00:46:09,360 Speaker 1: the for the war end quote. I know this. This 696 00:46:09,440 --> 00:46:13,280 Speaker 1: is interesting because many of us may remember that during 697 00:46:13,640 --> 00:46:18,360 Speaker 1: the earlier UM primaries, right when the d n C 698 00:46:18,560 --> 00:46:22,279 Speaker 1: was figuring out who they would select to be them 699 00:46:22,920 --> 00:46:26,799 Speaker 1: the Democratic candidate, and then in the next election a 700 00:46:26,840 --> 00:46:32,000 Speaker 1: few months from now, maybe then Warren took some heat 701 00:46:32,239 --> 00:46:36,680 Speaker 1: for claiming to have Native American ancestry, when in fact 702 00:46:37,480 --> 00:46:40,319 Speaker 1: it later came out that was that was not that 703 00:46:40,440 --> 00:46:43,439 Speaker 1: was not an accurate statement. So I want to acknowledge that, 704 00:46:44,000 --> 00:46:46,439 Speaker 1: but we also want to acknowledge there is a problem here. 705 00:46:46,560 --> 00:46:51,200 Speaker 1: And the American Medical Association stepped in as well. They said, 706 00:46:51,239 --> 00:46:56,719 Speaker 1: you know, look, telemedicine might help. Telemedicine, of course, is 707 00:46:57,000 --> 00:46:59,640 Speaker 1: kind of like what we're doing when we record podcast. 708 00:47:00,080 --> 00:47:02,800 Speaker 1: There's a doctor on the other side, you describe your symptoms. 709 00:47:02,800 --> 00:47:06,960 Speaker 1: They're able to observe you somewhat, you know. Uh, So 710 00:47:07,000 --> 00:47:09,600 Speaker 1: they say, maybe that could help mitigate the outbreak. They 711 00:47:09,760 --> 00:47:14,839 Speaker 1: urge the current presidential administration to start collecting and publishing 712 00:47:15,320 --> 00:47:21,360 Speaker 1: mortality data for coronavirus by race and ethnicity. This scares people, 713 00:47:21,800 --> 00:47:26,000 Speaker 1: of course, whenever something is collected by so called race 714 00:47:26,080 --> 00:47:30,280 Speaker 1: or ethnicity, you know, it's it's always, uh, there's always 715 00:47:30,320 --> 00:47:32,560 Speaker 1: the thought in the back of people's heads, which is 716 00:47:32,640 --> 00:47:36,200 Speaker 1: unfortunately valid, that will this later be used against me. 717 00:47:36,520 --> 00:47:40,120 Speaker 1: So it's understandable that people would be afraid of that. Uh. 718 00:47:40,120 --> 00:47:44,960 Speaker 1: But their idea, they m a idea, is that this 719 00:47:45,040 --> 00:47:50,040 Speaker 1: would expand our system overall. It would expand our ability 720 00:47:50,160 --> 00:47:55,960 Speaker 1: to combat misinformation. It would help us uh prioritize testing 721 00:47:56,040 --> 00:47:58,280 Speaker 1: and treatment, and it would make sure that the people 722 00:47:58,280 --> 00:48:03,120 Speaker 1: who need help get the help they need. But is 723 00:48:03,160 --> 00:48:05,360 Speaker 1: that going to be enough, you know, as the pandemic 724 00:48:05,360 --> 00:48:09,320 Speaker 1: pigeon already flow in the coupe. Yeah, you know, I 725 00:48:09,320 --> 00:48:11,040 Speaker 1: I would I just like to jump back quickly to 726 00:48:11,080 --> 00:48:13,960 Speaker 1: that Cares Act. The eight million dollars that's been allocated 727 00:48:13,960 --> 00:48:18,080 Speaker 1: to tribes, um, eight million dollars seems like a lot 728 00:48:18,120 --> 00:48:19,960 Speaker 1: of money. It sounds like a lot of money. Then 729 00:48:20,000 --> 00:48:23,000 Speaker 1: you think about that that's gonna have to be distributed 730 00:48:23,600 --> 00:48:28,080 Speaker 1: amongst thirty seven states, with all of these human beings, 731 00:48:28,080 --> 00:48:34,000 Speaker 1: with all of these medical facilities. It's almost ludicrous to 732 00:48:34,000 --> 00:48:37,000 Speaker 1: to think about such such a low number for that 733 00:48:37,120 --> 00:48:39,040 Speaker 1: number of people in facility. I mean, it's truly a 734 00:48:39,120 --> 00:48:41,240 Speaker 1: drop in the bucket when you think of how costly 735 00:48:41,280 --> 00:48:45,000 Speaker 1: it is to uh set up these kinds of you know, 736 00:48:45,160 --> 00:48:49,000 Speaker 1: healthcare infrastructures. I mean it's the equipment is expensive, the 737 00:48:49,000 --> 00:48:53,279 Speaker 1: construction is expensive, the expertise involved required. I mean, it's 738 00:48:53,320 --> 00:48:57,000 Speaker 1: just it's it's it's almost insulting. It is insulting. Yeah, 739 00:48:57,239 --> 00:49:01,320 Speaker 1: well said. And this is again, this is an ongoing 740 00:49:02,160 --> 00:49:06,160 Speaker 1: development and it's one that has it has some reporting. 741 00:49:06,520 --> 00:49:10,120 Speaker 1: But you know, we we mentioned the Guardian for example, 742 00:49:10,239 --> 00:49:14,919 Speaker 1: because uh, the UK has has been doing some good 743 00:49:14,960 --> 00:49:19,760 Speaker 1: reporting on this. Also shout out to the Atlantic doing 744 00:49:19,800 --> 00:49:24,560 Speaker 1: some excellent journalism. But this is this is definitely something 745 00:49:24,600 --> 00:49:27,920 Speaker 1: that we need to be aware of, and you know, 746 00:49:27,960 --> 00:49:32,319 Speaker 1: at various levels beyond politics at various levels in the 747 00:49:32,480 --> 00:49:35,799 Speaker 1: in the halls of government. This does seem to be 748 00:49:35,920 --> 00:49:41,280 Speaker 1: something that people don't want you to know, uh, because naturally, 749 00:49:41,800 --> 00:49:44,440 Speaker 1: how on earth would the public agreed to this situation? 750 00:49:45,640 --> 00:49:53,160 Speaker 1: What's being done to help many other populations in similar dangers? Right, 751 00:49:54,040 --> 00:49:56,520 Speaker 1: let us let us know what you think, let us 752 00:49:56,520 --> 00:49:59,680 Speaker 1: know what your experiences like on the grounds you know, 753 00:50:00,080 --> 00:50:04,279 Speaker 1: we record this. Uh. Many states, including our own, or 754 00:50:04,440 --> 00:50:08,839 Speaker 1: practicing what has been called or branded as phased reopening, 755 00:50:09,000 --> 00:50:11,839 Speaker 1: I believe is the term. As a matter of fact, 756 00:50:11,920 --> 00:50:14,279 Speaker 1: peek behind the curtain. I don't know if you guys 757 00:50:14,320 --> 00:50:17,000 Speaker 1: have been keeping track, but uh, I've been looking at 758 00:50:17,040 --> 00:50:21,120 Speaker 1: whether or not our the building where our office is based, 759 00:50:21,280 --> 00:50:23,759 Speaker 1: is going to open. I think the rooftop is open now, 760 00:50:23,880 --> 00:50:25,600 Speaker 1: but not the rest of it. I just hoped that 761 00:50:25,680 --> 00:50:29,040 Speaker 1: the patio at our neighborhood bar hangout the local opens 762 00:50:29,120 --> 00:50:33,279 Speaker 1: up soon. Kidding, but you know, it could well be. 763 00:50:33,320 --> 00:50:38,600 Speaker 1: In the cards, I wrote to those guys checking like, seriously, 764 00:50:38,600 --> 00:50:41,600 Speaker 1: they're the best chicken wings in Atlanta, I wrote. I 765 00:50:41,640 --> 00:50:43,960 Speaker 1: wrote to those guys because we all go there so 766 00:50:44,040 --> 00:50:46,960 Speaker 1: much that we're pretty much friends with them. Uh. I 767 00:50:46,960 --> 00:50:49,439 Speaker 1: wrote to those folks at the local and I was like, hey, 768 00:50:49,560 --> 00:50:53,239 Speaker 1: you know, let's what's going on with the chicken wings. 769 00:50:53,280 --> 00:50:55,279 Speaker 1: I felt like an attic. I felt I felt like 770 00:50:55,280 --> 00:50:58,360 Speaker 1: they could picture me scratching the inside of my elbow 771 00:50:58,440 --> 00:51:03,840 Speaker 1: or something. Uh, And they are thinking maybe best case scenario, um, 772 00:51:05,120 --> 00:51:09,520 Speaker 1: maybe like the mid June into June. But I don't know. Everybody. 773 00:51:09,560 --> 00:51:12,800 Speaker 1: Everybody in this country is waiting for the other shoe 774 00:51:12,840 --> 00:51:17,040 Speaker 1: to drop. Yep. Yeah, And you know, we all have 775 00:51:17,200 --> 00:51:20,479 Speaker 1: to decide that we're ready to go back out there, 776 00:51:20,880 --> 00:51:24,040 Speaker 1: you know, not just the places opening, but the people us. 777 00:51:24,600 --> 00:51:27,359 Speaker 1: We have to decide we're okay with hanging out at him. 778 00:51:27,600 --> 00:51:31,000 Speaker 1: I mean, we've got basically a collective case of PTSD 779 00:51:31,600 --> 00:51:34,279 Speaker 1: that we're probably all gonna be struggling with for some 780 00:51:34,360 --> 00:51:35,960 Speaker 1: time in terms of like how this is going to 781 00:51:36,040 --> 00:51:39,360 Speaker 1: affect our behavior and what might trigger us not to 782 00:51:39,400 --> 00:51:42,600 Speaker 1: mention if you actually either had the disease yourself, or 783 00:51:42,640 --> 00:51:44,600 Speaker 1: if you know someone that had it, or god forbid, 784 00:51:44,600 --> 00:51:47,920 Speaker 1: you lost a loved one or someone very close to you, 785 00:51:47,920 --> 00:51:50,960 Speaker 1: you know, from from this disease. So uh, it's it's 786 00:51:51,000 --> 00:51:54,360 Speaker 1: it's not just enough to say okay. Governor says everything's good, 787 00:51:54,440 --> 00:51:58,319 Speaker 1: game on. You know, that's not what it's gonna take 788 00:51:58,320 --> 00:52:01,480 Speaker 1: more than that. Although not to soapbox at all here, 789 00:52:01,680 --> 00:52:04,239 Speaker 1: it has been a little troubling to see here in 790 00:52:04,280 --> 00:52:07,640 Speaker 1: Georgia in particular, how a lot of folks have taken 791 00:52:07,680 --> 00:52:10,919 Speaker 1: that announcement of quote unquote opening back up the state 792 00:52:11,040 --> 00:52:14,040 Speaker 1: as an all clear and are just flooding back out 793 00:52:14,080 --> 00:52:17,080 Speaker 1: into the parks and droves with no masks, and you 794 00:52:17,080 --> 00:52:19,759 Speaker 1: know it's it's happening in Italy to um, the Prime 795 00:52:19,840 --> 00:52:22,279 Speaker 1: Minister of Italy made a statement about how, hey, if 796 00:52:22,280 --> 00:52:24,759 Speaker 1: you kids don't shape up, we're gonna silicone you into 797 00:52:24,800 --> 00:52:27,880 Speaker 1: your homes during this next wave or whatever. So like 798 00:52:27,960 --> 00:52:32,480 Speaker 1: you said, shoe droppage and just come on, be a 799 00:52:32,520 --> 00:52:35,400 Speaker 1: good steward folks, don't be a jerk. I've got to 800 00:52:35,480 --> 00:52:38,120 Speaker 1: mention one thing too, because it's a complex problem and 801 00:52:38,239 --> 00:52:41,120 Speaker 1: somebody that implies across across the country. Now, you know, 802 00:52:41,200 --> 00:52:45,319 Speaker 1: there's there's so many people who started their business right 803 00:52:45,360 --> 00:52:48,200 Speaker 1: when this was going down, right, there's so many there's 804 00:52:48,239 --> 00:52:51,440 Speaker 1: so many people who have to because they have no 805 00:52:51,600 --> 00:52:57,560 Speaker 1: support other than like a fairly poultry um financial net 806 00:52:57,560 --> 00:52:59,880 Speaker 1: from the government. There's so many people who are like 807 00:53:00,160 --> 00:53:02,600 Speaker 1: I have to go back to work. I get it. 808 00:53:02,640 --> 00:53:06,480 Speaker 1: I don't want to, but I also, you know, I 809 00:53:06,520 --> 00:53:09,400 Speaker 1: like it when my kids can eat call me crazy 810 00:53:09,440 --> 00:53:13,400 Speaker 1: like maybe maybe feedom like on a daily basis. I 811 00:53:13,440 --> 00:53:16,120 Speaker 1: feel like I have to do this. It's it's uh, 812 00:53:16,160 --> 00:53:18,440 Speaker 1: it's a rock and a hard place, uh for a 813 00:53:18,440 --> 00:53:23,359 Speaker 1: lot of people. And this this situation, Um, you know, 814 00:53:23,560 --> 00:53:25,920 Speaker 1: like we have to be careful in the soapbox, but 815 00:53:26,440 --> 00:53:29,560 Speaker 1: I wanna I wanna be I have to play the 816 00:53:29,719 --> 00:53:33,680 Speaker 1: part of that character at all those fairytale tropes who 817 00:53:33,680 --> 00:53:36,920 Speaker 1: shows up and it's like I have one more gift. Uh, 818 00:53:37,040 --> 00:53:40,000 Speaker 1: so with one more gift for you. Uh. The things 819 00:53:40,040 --> 00:53:43,640 Speaker 1: that are happening now behind the scenes, the opportunism that 820 00:53:43,760 --> 00:53:47,520 Speaker 1: is occurring because of this pandemic. We're not going going 821 00:53:47,560 --> 00:53:50,600 Speaker 1: to learn the full extent of it until several years 822 00:53:50,640 --> 00:53:55,440 Speaker 1: from now. Bad stuff is going down, very bad things 823 00:53:55,480 --> 00:53:59,279 Speaker 1: on corporate level, on governmental level. Again, if you if 824 00:53:59,320 --> 00:54:01,520 Speaker 1: you think about it from that perspective, if you want 825 00:54:01,560 --> 00:54:04,600 Speaker 1: to be Mackavellian, now is the perfect time to do 826 00:54:04,640 --> 00:54:07,440 Speaker 1: a lot of stuff that the public would ordinarily not 827 00:54:07,560 --> 00:54:10,040 Speaker 1: allow you to do. And I think that's a danger 828 00:54:10,360 --> 00:54:14,759 Speaker 1: of COVID nineteen that's going to last for a for 829 00:54:14,880 --> 00:54:18,280 Speaker 1: a long time. It's gonna affect later generations the changes 830 00:54:18,320 --> 00:54:21,240 Speaker 1: they are happening now, the laws they're going to be enacted. 831 00:54:21,400 --> 00:54:24,560 Speaker 1: Watch out, as Noel said, be good stewards. This is 832 00:54:25,360 --> 00:54:27,439 Speaker 1: this is going to take a lot of people having 833 00:54:27,480 --> 00:54:30,080 Speaker 1: their eyes on it. But you know, that's maybe a 834 00:54:30,080 --> 00:54:33,480 Speaker 1: story for a different day. Again, we hope this finds 835 00:54:33,480 --> 00:54:36,040 Speaker 1: you happy and healthy. We'd love to hear from you. 836 00:54:36,040 --> 00:54:38,600 Speaker 1: You can find us all over the internet where on Facebook, 837 00:54:38,600 --> 00:54:41,960 Speaker 1: We're on Twitter, we're on Instagram. Uh. You know, you 838 00:54:42,040 --> 00:54:45,480 Speaker 1: can also take a note from our anonymous caller and 839 00:54:45,719 --> 00:54:48,200 Speaker 1: give us a ring directly. Our number is one eight 840 00:54:48,360 --> 00:54:52,319 Speaker 1: three three st d w y t K. Call in, 841 00:54:52,360 --> 00:54:54,120 Speaker 1: tell us what you think about this episode, give us 842 00:54:54,120 --> 00:54:57,160 Speaker 1: an idea for another one. Just talk to us for 843 00:54:57,160 --> 00:55:00,239 Speaker 1: a while. We will listen, we promise you. If you 844 00:55:00,280 --> 00:55:02,440 Speaker 1: do call, we can see your number, We may call 845 00:55:02,480 --> 00:55:06,280 Speaker 1: you back, and we may use your uh your recorded 846 00:55:06,320 --> 00:55:09,680 Speaker 1: voicemail on the air. So let us know if you 847 00:55:09,680 --> 00:55:11,480 Speaker 1: don't want us to share certain things, or you don't 848 00:55:11,480 --> 00:55:13,279 Speaker 1: want certain things on, or if you're just calling us 849 00:55:13,280 --> 00:55:14,960 Speaker 1: to call us and you don't want it played at all. 850 00:55:15,400 --> 00:55:19,000 Speaker 1: Um I would just say to the person who called 851 00:55:19,040 --> 00:55:21,759 Speaker 1: and let us know about what was going on there 852 00:55:21,760 --> 00:55:24,680 Speaker 1: in the Nevada Indian Commission. Please give us a call 853 00:55:24,719 --> 00:55:28,799 Speaker 1: back with an update. Uh, if you can, we we 854 00:55:28,800 --> 00:55:31,680 Speaker 1: would love to know just what's going on now. You 855 00:55:31,680 --> 00:55:34,680 Speaker 1: can also reach us as individuals if you wish. Um 856 00:55:34,719 --> 00:55:38,640 Speaker 1: I am on Instagram at how Now Noel Brown. You 857 00:55:38,680 --> 00:55:44,799 Speaker 1: can find me at Matt Frederick Underscore, I Heeart, I Think, 858 00:55:45,320 --> 00:55:48,520 Speaker 1: and you can find me as always in a burst 859 00:55:48,520 --> 00:55:52,040 Speaker 1: of creativity at Ben Bullen on Instagram. You can find 860 00:55:52,080 --> 00:55:56,160 Speaker 1: me at Ben Bullen h s W on Twitter, or 861 00:55:56,400 --> 00:55:59,680 Speaker 1: you know, Crossroads at midnight, Mirror in a dark room, 862 00:55:59,719 --> 00:56:02,600 Speaker 1: all all the hits you know, I'm I'm, I'm around 863 00:56:02,600 --> 00:56:06,279 Speaker 1: on them. If you don't, if you don't cotton to 864 00:56:06,360 --> 00:56:10,360 Speaker 1: the occult, if you don't care for the new occult 865 00:56:10,520 --> 00:56:13,600 Speaker 1: social media, and if you're terrified of phones, which I 866 00:56:13,680 --> 00:56:17,080 Speaker 1: of all people get, there's always one way to contact 867 00:56:17,120 --> 00:56:21,120 Speaker 1: us seven every day of the year, soul CiCe, equinox 868 00:56:21,239 --> 00:56:23,680 Speaker 1: doesn't matter. You can shoot us a note at our 869 00:56:23,760 --> 00:56:26,920 Speaker 1: good old fashioned email address where we all conspiracy at 870 00:56:26,920 --> 00:56:48,360 Speaker 1: iHeart radio dot com. Stuff they don't want you to 871 00:56:48,400 --> 00:56:51,160 Speaker 1: know is a production of i heart Radio. 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