1 00:00:05,760 --> 00:00:10,520 Speaker 1: Didn't want anybody. This astounding and unprecedented story continues to evolve. 2 00:00:10,720 --> 00:00:13,520 Speaker 1: We are dealing with a challenge and a crisis that 3 00:00:13,560 --> 00:00:16,200 Speaker 1: we have never seen in our lifetimes. We know the 4 00:00:16,280 --> 00:00:19,240 Speaker 1: hospital surge is coming and it has only just begun. 5 00:00:19,600 --> 00:00:26,280 Speaker 1: COVID nineteen can beat as a pandemic. Hi everyone, I'm 6 00:00:26,360 --> 00:00:30,840 Speaker 1: Katie Kuric, and this is next question. This week, we're 7 00:00:30,880 --> 00:00:37,200 Speaker 1: recognizing a sobering anniversary the coronavirus pandemic one year later. 8 00:00:38,000 --> 00:00:43,280 Speaker 1: One year ago mid March, I actually genuinely felt almost 9 00:00:43,320 --> 00:00:46,360 Speaker 1: almost panic, almost real genuine worry, which for an e 10 00:00:46,520 --> 00:00:51,040 Speaker 1: R doctor like me is extremely unusual. I could recognize 11 00:00:51,120 --> 00:00:54,320 Speaker 1: right away that a US epademic and a global pandemic 12 00:00:54,720 --> 00:00:56,720 Speaker 1: was just going to be in all hands on deck 13 00:00:56,840 --> 00:00:59,040 Speaker 1: kind of a thing. For the first time, I really 14 00:00:59,080 --> 00:01:01,760 Speaker 1: felt like I was entering ad no idea would come next. 15 00:01:02,440 --> 00:01:05,560 Speaker 1: In retrospect, I feel sort of lucky that I got 16 00:01:05,560 --> 00:01:07,040 Speaker 1: sick when I did. I think I would have been 17 00:01:07,120 --> 00:01:09,880 Speaker 1: much more distraught if I knew then what I knew now. 18 00:01:12,240 --> 00:01:17,600 Speaker 1: On March eleven, the World Health Organization declared the coronavirus 19 00:01:17,640 --> 00:01:23,640 Speaker 1: a pandemic after watching the slow tidal wave of infections, deaths, 20 00:01:24,120 --> 00:01:28,560 Speaker 1: and fear consume most of Asia and Europe. Americans finally 21 00:01:28,640 --> 00:01:33,959 Speaker 1: felt COVID's impact at home. This totally unknown novel virus 22 00:01:34,480 --> 00:01:39,920 Speaker 1: took root and completely up ended our lives. Remember when 23 00:01:39,959 --> 00:01:43,559 Speaker 1: we were wiping down groceries like milk and even bags 24 00:01:43,560 --> 00:01:48,560 Speaker 1: of potato chips and leaving packages outside for forty eight hours. 25 00:01:49,160 --> 00:01:52,640 Speaker 1: We've come a long way from those panics stricken early months. 26 00:01:53,280 --> 00:01:56,560 Speaker 1: So on this episode, we're exploring all that we've learned 27 00:01:56,560 --> 00:02:01,720 Speaker 1: about COVID nineteen, about our healthcare system, about science, and 28 00:02:01,840 --> 00:02:05,880 Speaker 1: maybe even about ourselves. I got sick with COVID pretty 29 00:02:06,040 --> 00:02:08,799 Speaker 1: early on. In kind of the trajectory of the pandemic 30 00:02:08,840 --> 00:02:12,000 Speaker 1: hitting the United States, we'll be hearing firsthand accounts from 31 00:02:12,080 --> 00:02:17,600 Speaker 1: three Americans intimately involved with COVID nineteen. An emergency room doctor, 32 00:02:17,880 --> 00:02:23,000 Speaker 1: an epidemiologist, but first a patient. My name is Fiona 33 00:02:23,080 --> 00:02:27,600 Speaker 1: Lowenstein and I'm an independent journalist, UM speaker, and consultant 34 00:02:27,639 --> 00:02:30,799 Speaker 1: based in New York City. A friend of mine came 35 00:02:30,800 --> 00:02:34,600 Speaker 1: over for dinner on March tenth. She got sick before 36 00:02:34,680 --> 00:02:37,880 Speaker 1: my eyes, like literally got pale, said I don't feel well. 37 00:02:38,240 --> 00:02:41,520 Speaker 1: Of course, we were both like, is this COVID. I mean, 38 00:02:41,520 --> 00:02:43,359 Speaker 1: it was so new at that point, nothing had even 39 00:02:43,360 --> 00:02:46,000 Speaker 1: shut down in the city. Um. She went home right away, 40 00:02:46,040 --> 00:02:48,680 Speaker 1: and then three days later, UM, I developed a fever 41 00:02:48,760 --> 00:02:52,000 Speaker 1: and a headache. You know, I'm young. I was twenty 42 00:02:52,000 --> 00:02:53,519 Speaker 1: six at the time I got sick, and I don't 43 00:02:53,520 --> 00:02:57,799 Speaker 1: have any pre existing conditions. Um, I'm very healthy otherwise, 44 00:02:57,840 --> 00:02:59,679 Speaker 1: I like exercised six times a week. I used to 45 00:02:59,680 --> 00:03:03,000 Speaker 1: teach UGA classes. UM. So I assumed that, you know, 46 00:03:03,080 --> 00:03:05,480 Speaker 1: if it was the worst case scenario and it was COVID, 47 00:03:05,919 --> 00:03:08,880 Speaker 1: I would get better relatively quickly. I would be able 48 00:03:08,919 --> 00:03:10,760 Speaker 1: to write it out at home. That was very much 49 00:03:10,840 --> 00:03:12,919 Speaker 1: kind of the public health messaging that we were getting 50 00:03:12,960 --> 00:03:16,440 Speaker 1: at the time. But by day five, Fiona started having 51 00:03:16,480 --> 00:03:20,200 Speaker 1: trouble breathing. It started as kind of like, oh, I feel, 52 00:03:20,360 --> 00:03:22,400 Speaker 1: you know, winded, or I'm having trouble catching my breath 53 00:03:22,400 --> 00:03:24,040 Speaker 1: when I get up to go to the bathroom. And 54 00:03:24,080 --> 00:03:25,480 Speaker 1: then by the end of the day it was like 55 00:03:26,000 --> 00:03:29,640 Speaker 1: I couldn't talk, I couldn't eat even really because just 56 00:03:29,720 --> 00:03:32,959 Speaker 1: the exertion was was winding me. I could barely walk 57 00:03:33,000 --> 00:03:35,600 Speaker 1: to the bathroom. I was communicating with my partner like 58 00:03:35,680 --> 00:03:39,160 Speaker 1: writing on a right on white off board and my 59 00:03:39,240 --> 00:03:41,160 Speaker 1: partner actually had to call the e R on my 60 00:03:41,280 --> 00:03:43,720 Speaker 1: behalf and explain my symptoms and they were like, you 61 00:03:43,760 --> 00:03:47,040 Speaker 1: have to come in right away. Fiona went to the 62 00:03:47,120 --> 00:03:49,800 Speaker 1: e R and after a night of treatment and oxygen, 63 00:03:50,120 --> 00:03:53,280 Speaker 1: she was admitted to the hospital. They wheeled me, you know, 64 00:03:53,520 --> 00:03:55,640 Speaker 1: from the ear into the into the hospital. I was 65 00:03:55,680 --> 00:03:59,280 Speaker 1: just sobbing the entire way there um and the nurse 66 00:03:59,560 --> 00:04:02,839 Speaker 1: said to me, Oh, now I'm gonna get emotional when 67 00:04:02,840 --> 00:04:04,680 Speaker 1: I When I got there, the nurse said to me, 68 00:04:05,360 --> 00:04:07,720 Speaker 1: you've been through a lot, and I know it's really 69 00:04:07,800 --> 00:04:11,520 Speaker 1: scary and it's gonna be okay, Like you're with us 70 00:04:11,520 --> 00:04:13,320 Speaker 1: now and we're going to take care of you. And 71 00:04:14,040 --> 00:04:16,920 Speaker 1: just the validation that, like what had happened actually was 72 00:04:17,080 --> 00:04:20,479 Speaker 1: very scary, and also that you know, in the e 73 00:04:20,600 --> 00:04:23,120 Speaker 1: ER everyone was very stressed out and they were very helpful, 74 00:04:23,120 --> 00:04:24,600 Speaker 1: but they were also like, we have no idea what's 75 00:04:24,600 --> 00:04:25,840 Speaker 1: going on, and we don't know if you'll be able 76 00:04:25,880 --> 00:04:28,440 Speaker 1: to get tested and that sort of thing. But to 77 00:04:28,520 --> 00:04:32,080 Speaker 1: have someone really likes affirmed that I was going to 78 00:04:32,200 --> 00:04:33,920 Speaker 1: be taken care of and going to be looked after, 79 00:04:34,120 --> 00:04:36,279 Speaker 1: it calmed me down and it made me feel better, 80 00:04:36,320 --> 00:04:38,840 Speaker 1: and I feel I mean, those people who took care 81 00:04:38,880 --> 00:04:40,960 Speaker 1: of me while I was there, I feel so indebted 82 00:04:41,000 --> 00:04:45,840 Speaker 1: to them. Luckily, Fiona's hospital stay was brief, and after 83 00:04:46,000 --> 00:04:48,760 Speaker 1: one more night and some tests, she was able to 84 00:04:48,760 --> 00:04:51,360 Speaker 1: go home. When I was discharged from the hospital, they 85 00:04:51,400 --> 00:04:54,880 Speaker 1: all cheered for me and clapped and we're like, yeah, Fiona, 86 00:04:55,080 --> 00:04:57,760 Speaker 1: like you can do it. You're gonna get better. Um, 87 00:04:57,839 --> 00:05:05,600 Speaker 1: you know, And unfortunately wasn't that simple. But yeah, one 88 00:05:05,680 --> 00:05:08,039 Speaker 1: year ago was the strangest time in my life. The 89 00:05:08,120 --> 00:05:12,960 Speaker 1: outbreak of COVID nineteen wasn't only scary for patients. Doctors 90 00:05:13,040 --> 00:05:16,000 Speaker 1: were scrambling to make sense of it too. We were 91 00:05:16,080 --> 00:05:19,080 Speaker 1: just waiting to find out what was happening in New 92 00:05:19,160 --> 00:05:21,920 Speaker 1: York and it was sort of like, Okay, boss's next, right, 93 00:05:21,960 --> 00:05:24,360 Speaker 1: it's coming to Boston. So it was sort of this 94 00:05:24,440 --> 00:05:28,400 Speaker 1: moment of Okay, here we go. Are we ready? We'll 95 00:05:28,520 --> 00:05:41,200 Speaker 1: hear from Dr Jeremy fast right after this. Dr Jeremy 96 00:05:41,240 --> 00:05:44,880 Speaker 1: Faust is an emergency physician at Brigham and Women's Hospital 97 00:05:44,920 --> 00:05:49,440 Speaker 1: in Boston. He is for the most part unflappable an 98 00:05:49,440 --> 00:05:54,000 Speaker 1: occupational hazard, but this time last year, he felt a 99 00:05:54,400 --> 00:05:59,479 Speaker 1: very uncomfortable feeling panic. I'm working an overnight shift in 100 00:05:59,520 --> 00:06:03,080 Speaker 1: my e R and I have a patient with pneumonia 101 00:06:03,360 --> 00:06:05,200 Speaker 1: and I look at the X ray and I just 102 00:06:05,279 --> 00:06:08,720 Speaker 1: my eyes go, WHOA, that's a nasty pneumonia. That's all 103 00:06:08,800 --> 00:06:11,680 Speaker 1: over the lungs. That's wow, Okay, that's kind of an 104 00:06:11,720 --> 00:06:14,520 Speaker 1: older person. And so I occasionally will see that, but 105 00:06:14,520 --> 00:06:19,600 Speaker 1: pretty unusual. But I was pretty impressed. Two hours later, pneumonia, 106 00:06:19,760 --> 00:06:25,520 Speaker 1: younger patient, middle aged, same X ray. Oh my god, Like, 107 00:06:25,600 --> 00:06:28,040 Speaker 1: look at that X ray, like that is just nasty. 108 00:06:28,320 --> 00:06:30,200 Speaker 1: I was just like, check this out to my colleague, like, 109 00:06:30,240 --> 00:06:34,400 Speaker 1: look at that thing. Third patient overnight in one night, 110 00:06:34,680 --> 00:06:37,400 Speaker 1: same X ray, Like, nasty pneumonia. Now it's like, now 111 00:06:37,400 --> 00:06:39,360 Speaker 1: we would call this classic COVID pneumonia. Like now I 112 00:06:39,400 --> 00:06:41,440 Speaker 1: could like, look at that x raam be like COVID. 113 00:06:41,800 --> 00:06:44,120 Speaker 1: But at the time I just never seen it. So 114 00:06:44,800 --> 00:06:47,400 Speaker 1: I said to my colleagues or whatever, I said, we 115 00:06:47,440 --> 00:06:50,840 Speaker 1: need to test these patients for coronavirus. And we got 116 00:06:50,880 --> 00:06:52,760 Speaker 1: the little list out and it said, well, do they 117 00:06:52,880 --> 00:06:57,599 Speaker 1: have the criteria that the testing requirements? No, no they don't. 118 00:06:58,920 --> 00:07:00,359 Speaker 1: I was like, well I don't care. Look at this, 119 00:07:00,440 --> 00:07:02,159 Speaker 1: look at this, Look at these X rays. There's three 120 00:07:02,200 --> 00:07:05,000 Speaker 1: of them, you know, one night, we need to test everybody, 121 00:07:05,040 --> 00:07:07,039 Speaker 1: so and we didn't. We didn't have the tests. We 122 00:07:07,040 --> 00:07:09,600 Speaker 1: weren't able to do it. And so I had this 123 00:07:09,720 --> 00:07:12,080 Speaker 1: that panic where I was where I was thinking, oh gosh, 124 00:07:12,360 --> 00:07:15,680 Speaker 1: these people are everywhere, They're going to be everywhere, and 125 00:07:15,720 --> 00:07:17,880 Speaker 1: we're not even able to detect it. And so until 126 00:07:17,880 --> 00:07:19,640 Speaker 1: it's too late, until they have X rays that looked 127 00:07:19,680 --> 00:07:23,440 Speaker 1: like this, And when when that happened, I just completely 128 00:07:23,520 --> 00:07:26,240 Speaker 1: realized like, yeah, Houston, we have a problem. This is 129 00:07:26,560 --> 00:07:29,760 Speaker 1: we have a major crisis right here, and we don't 130 00:07:29,800 --> 00:07:31,480 Speaker 1: even know it yet. We don't even we can't even 131 00:07:31,480 --> 00:07:35,640 Speaker 1: detect it. You couldn't test people for quite a while. 132 00:07:36,320 --> 00:07:39,000 Speaker 1: Why did it take so long to be able to 133 00:07:39,040 --> 00:07:43,480 Speaker 1: test these people? The tests weren't available. You just simply 134 00:07:43,520 --> 00:07:49,720 Speaker 1: didn't have the tests. The CDC had a major fiasco 135 00:07:49,880 --> 00:07:52,360 Speaker 1: about this. They didn't develop a test and time, they 136 00:07:52,400 --> 00:07:56,160 Speaker 1: had quality problems on the inside. It's one of the great, um, 137 00:07:56,200 --> 00:07:59,680 Speaker 1: you know, mistakes of how that was managed. Um you know, 138 00:07:59,680 --> 00:08:01,160 Speaker 1: when you think about that what do you need to 139 00:08:01,200 --> 00:08:05,160 Speaker 1: make a test? You need to understand the genetics of 140 00:08:05,200 --> 00:08:07,800 Speaker 1: the virus or the bacteria you need to understand some 141 00:08:07,920 --> 00:08:10,120 Speaker 1: you need to have something, some molecular understanding of what's 142 00:08:10,160 --> 00:08:14,440 Speaker 1: tests for. We had that information in January. When you 143 00:08:14,480 --> 00:08:17,600 Speaker 1: look at the people who made the vaccine, they had 144 00:08:17,600 --> 00:08:21,680 Speaker 1: this thing sequenced in days. In a matter of weeks, 145 00:08:21,680 --> 00:08:25,400 Speaker 1: the protein structures were available. So we actually, interestingly enough, 146 00:08:25,800 --> 00:08:30,920 Speaker 1: the the prototypes for the vaccines were already being developed 147 00:08:30,920 --> 00:08:33,320 Speaker 1: in February, and we didn't have a test that was 148 00:08:33,320 --> 00:08:36,400 Speaker 1: functional in the United States. So it just took time 149 00:08:36,440 --> 00:08:39,240 Speaker 1: to ramp up and catch up. So to me, it's 150 00:08:39,240 --> 00:08:43,640 Speaker 1: like some of that infrastructure exists right now for you know, 151 00:08:43,800 --> 00:08:48,000 Speaker 1: quote unquote cod like make the swabs, make the viral media, 152 00:08:48,080 --> 00:08:49,600 Speaker 1: make sure you have the system set up, and then 153 00:08:49,760 --> 00:08:54,440 Speaker 1: the last second swapping whatever molecule you needed to be. 154 00:08:54,960 --> 00:09:02,480 Speaker 1: But we didn't do any of that. What was that 155 00:09:02,600 --> 00:09:06,840 Speaker 1: like as a physician who is trained and is passionate 156 00:09:06,880 --> 00:09:10,959 Speaker 1: about taking care of other people to have to do it, 157 00:09:11,200 --> 00:09:15,720 Speaker 1: uh at arm's length or more. Yeah, it's really hard, 158 00:09:16,000 --> 00:09:20,040 Speaker 1: um to connect with people through a shield and an 159 00:09:20,040 --> 00:09:23,080 Speaker 1: eyemask and if you know, a ninety five mask and 160 00:09:23,120 --> 00:09:28,000 Speaker 1: a big gown, because you just look, it doesn't it 161 00:09:28,040 --> 00:09:29,960 Speaker 1: doesn't matter how you look. But you look like yourself, 162 00:09:30,240 --> 00:09:32,240 Speaker 1: and so when someone can't see that you are you, 163 00:09:33,400 --> 00:09:36,920 Speaker 1: it's just really hard to connect with them. And what 164 00:09:37,040 --> 00:09:39,280 Speaker 1: it does is it sort of made the medicine I 165 00:09:39,360 --> 00:09:43,520 Speaker 1: feel really impersonal, which maybe was an okay thing, sort 166 00:09:43,559 --> 00:09:45,839 Speaker 1: of almost like a defense mechanism, like a distance thing. 167 00:09:47,120 --> 00:09:49,040 Speaker 1: I don't think we spent nearly as much time in 168 00:09:49,040 --> 00:09:51,880 Speaker 1: those patient rooms as UM we usually do. I know 169 00:09:51,960 --> 00:09:54,360 Speaker 1: we didn't. We went in less often. I was trying 170 00:09:54,360 --> 00:09:56,200 Speaker 1: to minimize trips, so if we could go in and 171 00:09:56,200 --> 00:09:57,840 Speaker 1: do something for the nurses, or they could go and 172 00:09:57,880 --> 00:09:59,560 Speaker 1: do something for us, like you know, we're trying not 173 00:09:59,640 --> 00:10:02,760 Speaker 1: to you know, go into too much. I think the 174 00:10:03,320 --> 00:10:08,560 Speaker 1: harder piece UM actually was trying to talk patients through it, 175 00:10:09,400 --> 00:10:14,439 Speaker 1: to reassure them without downplaying, as physicians were so used 176 00:10:14,480 --> 00:10:17,160 Speaker 1: to being able to say to our patients, Okay, I've 177 00:10:17,240 --> 00:10:19,520 Speaker 1: seen this before, here's what's going, here's what here's let 178 00:10:19,559 --> 00:10:21,360 Speaker 1: me tell you what's gonna happen, or let me let 179 00:10:21,400 --> 00:10:23,920 Speaker 1: me give you a range of possibilities based on your condition. 180 00:10:24,400 --> 00:10:26,240 Speaker 1: And so we give our patients. I like to give 181 00:10:26,240 --> 00:10:28,360 Speaker 1: my patients like a really frank and honest assessment of 182 00:10:28,360 --> 00:10:30,679 Speaker 1: where they're at so I don't sugarcoat, but I don't 183 00:10:30,880 --> 00:10:33,720 Speaker 1: I'm not a doomsday or either. I say, look, here's 184 00:10:33,840 --> 00:10:35,360 Speaker 1: some things that could that could go down, and I 185 00:10:35,360 --> 00:10:37,480 Speaker 1: want you to understand that, so you, you know, just 186 00:10:37,520 --> 00:10:40,600 Speaker 1: know what to expect with. What I found so difficult 187 00:10:40,640 --> 00:10:43,720 Speaker 1: with this disease was we didn't know. So how can 188 00:10:43,760 --> 00:10:45,360 Speaker 1: I look at someone and say, oh, yeah, I've seen 189 00:10:45,360 --> 00:10:47,600 Speaker 1: this tons of times and you know, here's how long 190 00:10:47,600 --> 00:10:49,800 Speaker 1: it's gonna take you to feel better. I didn't know 191 00:10:49,840 --> 00:10:52,360 Speaker 1: any of that. So it was it felt like you 192 00:10:52,400 --> 00:10:55,880 Speaker 1: were sort of, um, you know, driving blind in a way. 193 00:10:56,320 --> 00:10:58,320 Speaker 1: We and we also had very little to offer patients 194 00:10:58,320 --> 00:11:01,280 Speaker 1: other than oxygen, other than intubation if they needed to 195 00:11:01,360 --> 00:11:04,400 Speaker 1: want to ventilator, and eventually we started giving steroids and 196 00:11:04,440 --> 00:11:07,200 Speaker 1: all those others, a few other things that may help 197 00:11:07,240 --> 00:11:09,760 Speaker 1: a little. But that was the hard part was the 198 00:11:09,800 --> 00:11:12,520 Speaker 1: sense of not just powerlessness, but a sense of I 199 00:11:12,559 --> 00:11:15,800 Speaker 1: can't even tell you what I think really because we 200 00:11:15,840 --> 00:11:23,400 Speaker 1: are an uncharted territory. We watch this in real time, 201 00:11:23,760 --> 00:11:27,720 Speaker 1: and doctors and nurses had to learn like almost just 202 00:11:27,840 --> 00:11:31,200 Speaker 1: trying it, you know, wing it in some ways. So 203 00:11:31,679 --> 00:11:34,640 Speaker 1: what do we now know? What is the standard of care? 204 00:11:35,200 --> 00:11:42,040 Speaker 1: For COVID patients. Okay, so it really depends on your 205 00:11:42,080 --> 00:11:46,400 Speaker 1: severity of disease. And what I will say is that 206 00:11:46,600 --> 00:11:50,040 Speaker 1: if you do not have what we call hypoxy a 207 00:11:50,160 --> 00:11:55,440 Speaker 1: low oxygen, hypoxy just literally means auctions too low. If 208 00:11:55,520 --> 00:11:58,880 Speaker 1: if your oction levels are normal, there's really not a 209 00:11:58,880 --> 00:12:01,520 Speaker 1: ton that I think makes a huge difference. I think 210 00:12:01,559 --> 00:12:04,200 Speaker 1: that you know, some of these monoclonal antibodies have been 211 00:12:04,240 --> 00:12:08,280 Speaker 1: talked about the there's a very narrow of people who 212 00:12:08,320 --> 00:12:11,560 Speaker 1: that might help. Um. But for the most part, if 213 00:12:11,600 --> 00:12:15,360 Speaker 1: you have normal oxygen, in my mind, you don't. There's 214 00:12:15,360 --> 00:12:17,880 Speaker 1: not much we can offer you at this time. If 215 00:12:18,000 --> 00:12:22,480 Speaker 1: you do have hypoxeall oxygen, then the things that we 216 00:12:22,559 --> 00:12:24,600 Speaker 1: know to give you our oxygen and we don't know 217 00:12:24,640 --> 00:12:27,480 Speaker 1: if that saves your life or anything. But the theory 218 00:12:27,600 --> 00:12:31,320 Speaker 1: is that you get your muscles just get less tired sooner, 219 00:12:31,360 --> 00:12:34,120 Speaker 1: you crap out soon, and your body has more energy 220 00:12:34,160 --> 00:12:37,800 Speaker 1: to fight the virus. Right. Yeah, Ostensibly that everything is 221 00:12:37,840 --> 00:12:40,960 Speaker 1: better when you're oxygen needd right. So we'll never be 222 00:12:40,960 --> 00:12:42,640 Speaker 1: able to test that because it's just we give the 223 00:12:42,640 --> 00:12:47,199 Speaker 1: oxygen okay um, and then the steroids, the dex and 224 00:12:47,240 --> 00:12:52,200 Speaker 1: methos on steroid has really been shown to have a 225 00:12:52,600 --> 00:12:56,240 Speaker 1: what we call immortality benefit. It saves lives of people 226 00:12:56,240 --> 00:12:58,680 Speaker 1: who need oxygen, a little bit among people who just 227 00:12:58,720 --> 00:13:02,280 Speaker 1: need any kind of oxygen, and a ton uh tenor 228 00:13:03,240 --> 00:13:06,640 Speaker 1: among people who need to be on ventilators. And when 229 00:13:06,640 --> 00:13:09,200 Speaker 1: I saw that data, my eyes just bugged out because 230 00:13:09,280 --> 00:13:11,360 Speaker 1: it was almost too good to be true. But it's 231 00:13:11,480 --> 00:13:13,480 Speaker 1: it's it's so far, you know, because I think it's 232 00:13:13,840 --> 00:13:16,400 Speaker 1: probably mostly true. In other words, I think that it's 233 00:13:16,480 --> 00:13:18,959 Speaker 1: the ballpark. You know, we'll never really know. But um 234 00:13:19,000 --> 00:13:21,200 Speaker 1: so that's a huge, huge thing, is that we give 235 00:13:21,240 --> 00:13:24,319 Speaker 1: steroids to people who have low oxygen and that has 236 00:13:24,360 --> 00:13:29,200 Speaker 1: a mortality benefit. This virus has really laid bare the 237 00:13:29,280 --> 00:13:33,000 Speaker 1: health disparities that exist in this country. And I know 238 00:13:33,200 --> 00:13:37,079 Speaker 1: that you realized it almost immediately when you saw some 239 00:13:37,120 --> 00:13:41,480 Speaker 1: patients in the Brigham e Er, didn't you. Oh yeah, 240 00:13:41,520 --> 00:13:45,360 Speaker 1: I mean it was just uncanny. And I give credit 241 00:13:45,440 --> 00:13:50,400 Speaker 1: to my colleagues, um black positions, persons of color in 242 00:13:50,400 --> 00:13:55,240 Speaker 1: the medical community who I work with, who pointed this out, 243 00:13:55,600 --> 00:13:57,600 Speaker 1: you know, you know, they say, look, have you noticed 244 00:13:57,600 --> 00:14:02,679 Speaker 1: something here? And um, I always understood. I thought I 245 00:14:02,760 --> 00:14:05,760 Speaker 1: understood this before I really thought I did right, Um, 246 00:14:05,800 --> 00:14:08,120 Speaker 1: But I didn't. I did. I did not. Um. I 247 00:14:08,160 --> 00:14:10,920 Speaker 1: hate to admit it, like I just never really never 248 00:14:11,000 --> 00:14:14,480 Speaker 1: really landed as much as it landed this year. It's 249 00:14:14,520 --> 00:14:18,920 Speaker 1: not just that black and Hispanic people were just proportionately 250 00:14:18,960 --> 00:14:24,520 Speaker 1: affected by coronavirus among adults. Four is that black and 251 00:14:24,560 --> 00:14:29,320 Speaker 1: Hispanic people were the majority of deaths among a mathematical 252 00:14:29,400 --> 00:14:32,920 Speaker 1: majority among death in this country of coronavirus and young adults. 253 00:14:33,440 --> 00:14:38,120 Speaker 1: And that to me is just unbelievable. Um. It's it's 254 00:14:38,120 --> 00:14:44,040 Speaker 1: an unbelievable um indictment of the system failing people, and 255 00:14:44,080 --> 00:14:46,080 Speaker 1: that we need to really shake it up and and 256 00:14:46,080 --> 00:14:49,400 Speaker 1: and rebuild. I'll say one thing with a little bit 257 00:14:49,400 --> 00:14:52,080 Speaker 1: of like sort of um. One piece of good news 258 00:14:52,200 --> 00:14:55,960 Speaker 1: is about the time that when adjusted for disease severity 259 00:14:56,000 --> 00:14:59,480 Speaker 1: and and everything else, once the patients are in the hospital, 260 00:14:59,760 --> 00:15:03,600 Speaker 1: the outcomes were appropriately sort of distribute distributed. So in 261 00:15:03,680 --> 00:15:05,920 Speaker 1: other words, the hospital care has been has been equal 262 00:15:06,120 --> 00:15:08,120 Speaker 1: um in terms of outcomes. That made that was that 263 00:15:08,160 --> 00:15:10,040 Speaker 1: gave me a side of relief to see that. But 264 00:15:10,200 --> 00:15:13,120 Speaker 1: what what we have not seen we have seen, I 265 00:15:13,120 --> 00:15:16,520 Speaker 1: should say, is that the disproportionate numbers who show up 266 00:15:16,520 --> 00:15:19,160 Speaker 1: on our doorstep, so and and so you have to 267 00:15:19,200 --> 00:15:21,760 Speaker 1: reach the community because if you do have a patient 268 00:15:21,880 --> 00:15:27,840 Speaker 1: who is showing up far sicker than you know, white populations, 269 00:15:27,840 --> 00:15:31,320 Speaker 1: for example, we need to understand why. We want to understand, 270 00:15:31,320 --> 00:15:34,040 Speaker 1: like why where is the messaging that we can reach them? 271 00:15:34,120 --> 00:15:36,360 Speaker 1: Why where? How are we failing? How are we not 272 00:15:36,520 --> 00:15:39,480 Speaker 1: able to do that m messaging and outreach and care 273 00:15:39,880 --> 00:15:42,160 Speaker 1: so that by the time people come to the hospital, 274 00:15:42,400 --> 00:15:44,440 Speaker 1: the disparities are already playing out in front of my eyes. 275 00:15:45,440 --> 00:15:49,520 Speaker 1: What's interesting is that we have seen in the black population, 276 00:15:50,080 --> 00:15:52,560 Speaker 1: UM a little bit of a comeback story there. The 277 00:15:52,960 --> 00:15:57,920 Speaker 1: early on the black population just devastating numbers. I mean again, 278 00:15:58,000 --> 00:15:59,400 Speaker 1: as I said, like I thought I got it, but 279 00:15:59,440 --> 00:16:01,040 Speaker 1: I didn't get it until I saw it, you know. 280 00:16:01,640 --> 00:16:04,760 Speaker 1: And but then over the summer and number the fall, 281 00:16:04,800 --> 00:16:07,920 Speaker 1: the numbers fall and fall, and at this point later 282 00:16:07,960 --> 00:16:11,240 Speaker 1: in the in the crisis, UM, there still is access 283 00:16:11,280 --> 00:16:14,640 Speaker 1: mortality among lack Americans, but it's actually pretty similar to 284 00:16:14,680 --> 00:16:18,040 Speaker 1: white Americans, which is really interesting. I think that some 285 00:16:18,120 --> 00:16:20,800 Speaker 1: of my colleagues who have been out there making the 286 00:16:20,800 --> 00:16:25,560 Speaker 1: case about about access and disparities have actually had measurable 287 00:16:25,640 --> 00:16:29,560 Speaker 1: success and they're saving lives, but we haven't seen the 288 00:16:29,640 --> 00:16:33,000 Speaker 1: drops we want to see in every every ethnicity and race, 289 00:16:33,360 --> 00:16:35,040 Speaker 1: and so we still have a lot of work to do. 290 00:16:36,400 --> 00:16:41,960 Speaker 1: So it's unclear whether it's you know, physiological considerations you 291 00:16:42,000 --> 00:16:47,960 Speaker 1: know that are making certain populations uh more likely to 292 00:16:47,960 --> 00:16:52,440 Speaker 1: to get sicker and die, or its access to care 293 00:16:52,800 --> 00:16:57,480 Speaker 1: you know, basically income inequality uh that results in people 294 00:16:57,520 --> 00:17:02,760 Speaker 1: living in cramped quarters, people not having healthy diets, uh, 295 00:17:02,880 --> 00:17:06,160 Speaker 1: you know, all the things that go hand in hand 296 00:17:06,200 --> 00:17:10,399 Speaker 1: with poverty in this country. So yeah, I would actually 297 00:17:10,400 --> 00:17:12,280 Speaker 1: I would not even put it as an either or 298 00:17:12,480 --> 00:17:14,479 Speaker 1: So what I would say is, I don't think that 299 00:17:14,560 --> 00:17:18,399 Speaker 1: these massive disparities have anything to do with genetics. So 300 00:17:18,440 --> 00:17:21,920 Speaker 1: in other words, that the disparities in terms of access, 301 00:17:22,480 --> 00:17:24,760 Speaker 1: it really has to do with whether a patient or 302 00:17:24,800 --> 00:17:29,960 Speaker 1: a person arrives at the moment of infection with a 303 00:17:30,000 --> 00:17:34,000 Speaker 1: series of conditions that are preventable that we're preventable um, 304 00:17:34,080 --> 00:17:38,360 Speaker 1: that then render their risk factors like off the charts, right, 305 00:17:38,400 --> 00:17:42,439 Speaker 1: So that to me is baked into that these social 306 00:17:42,440 --> 00:17:47,560 Speaker 1: determinants of health, these these stomach factors of inequality racism 307 00:17:47,600 --> 00:17:51,560 Speaker 1: that play out in a sort of magnified way. Um. Suddenly, 308 00:17:51,760 --> 00:17:54,560 Speaker 1: so it's not that, um, you know, one community or 309 00:17:54,600 --> 00:17:57,240 Speaker 1: another has genetics that's hurting them. That's not the situation 310 00:17:57,280 --> 00:18:00,040 Speaker 1: at all. It's that the diabetes and the hypertension and 311 00:18:00,040 --> 00:18:03,280 Speaker 1: the Kindian disease and all these other things that make 312 00:18:03,320 --> 00:18:06,000 Speaker 1: a different smoking Actually even is that that's something that 313 00:18:06,240 --> 00:18:09,480 Speaker 1: makes a difference we learned which is not equally distributed 314 00:18:09,520 --> 00:18:13,680 Speaker 1: across race and income. All these things when you arrive 315 00:18:13,720 --> 00:18:18,720 Speaker 1: at the moment of infection UM have have tremendous implications 316 00:18:18,760 --> 00:18:23,320 Speaker 1: for your outcomes. So the social determinants of health really 317 00:18:23,359 --> 00:18:28,280 Speaker 1: have an impact on the physical determinants of health. That's right. 318 00:18:28,320 --> 00:18:33,200 Speaker 1: So social, the social factors are what deny people access 319 00:18:33,359 --> 00:18:38,199 Speaker 1: to preventative care or to modulate diseases that all of 320 00:18:38,280 --> 00:18:41,920 Speaker 1: us would get if it weren't for the correct medical interventions. 321 00:18:42,280 --> 00:18:44,119 Speaker 1: So some of us are able to avoid it because 322 00:18:44,200 --> 00:18:46,919 Speaker 1: we're privileged and we're plugged in, and others of us 323 00:18:46,960 --> 00:18:49,560 Speaker 1: are not. And so then at the moment that you're 324 00:18:49,560 --> 00:18:53,320 Speaker 1: infectively coronavirus, you know, you're punished or whatever because of 325 00:18:53,359 --> 00:19:02,159 Speaker 1: society's choices. Are are are unfortunate? UM structure, do you 326 00:19:02,160 --> 00:19:05,119 Speaker 1: think in five years will have a much better understanding 327 00:19:05,560 --> 00:19:08,240 Speaker 1: of this virus, how and why it behaves the way 328 00:19:08,280 --> 00:19:12,600 Speaker 1: it does, and what has happened in the last year plus. Yes, 329 00:19:13,040 --> 00:19:14,840 Speaker 1: I think there are three things that we're gonna learn 330 00:19:14,960 --> 00:19:17,359 Speaker 1: that are going to save lives going forward. So we've 331 00:19:17,400 --> 00:19:21,720 Speaker 1: lost lives in this country and millions of over the 332 00:19:21,760 --> 00:19:25,040 Speaker 1: world across. And one of the only things that like 333 00:19:25,280 --> 00:19:28,680 Speaker 1: makes me like not just like collapse when I hear 334 00:19:28,720 --> 00:19:32,959 Speaker 1: that number is to think, Um, Okay, maybe we can 335 00:19:33,040 --> 00:19:35,880 Speaker 1: learn so much from this that in the long run, 336 00:19:36,400 --> 00:19:38,720 Speaker 1: years from now, we will save lives in the aggregate. 337 00:19:39,200 --> 00:19:43,040 Speaker 1: And when we cross that that threshold depends on two things. 338 00:19:43,200 --> 00:19:46,240 Speaker 1: How much we learn and how many lives you save today. Right, 339 00:19:46,240 --> 00:19:47,880 Speaker 1: So if we can keep that number low and our 340 00:19:47,920 --> 00:19:51,040 Speaker 1: knowledge increasing, then we can get there sooner. And so 341 00:19:51,119 --> 00:19:53,040 Speaker 1: one thing I think we're going to learn from this 342 00:19:53,119 --> 00:19:57,320 Speaker 1: virus is about transmission dynamics of lots of viruses. We're 343 00:19:57,320 --> 00:20:00,280 Speaker 1: gonna learn all kinds of things about transmission and mix 344 00:20:00,800 --> 00:20:03,640 Speaker 1: and so we're gonna understand how better to control disease. 345 00:20:04,200 --> 00:20:05,920 Speaker 1: The second thing I think we're gonna understand a lot 346 00:20:05,960 --> 00:20:10,560 Speaker 1: better is how to leverage the MR and a vaccine technology. 347 00:20:10,800 --> 00:20:16,040 Speaker 1: This technology is truly impressive. It didn't happen overnight. It 348 00:20:16,119 --> 00:20:18,040 Speaker 1: was people said, oh, how do we get a vaccine 349 00:20:18,080 --> 00:20:20,320 Speaker 1: ino one year? And the answer is we didn't. This 350 00:20:20,400 --> 00:20:24,400 Speaker 1: vaccine um was the rubber met the road in one year. 351 00:20:24,800 --> 00:20:27,600 Speaker 1: But this vaccine took twenty years to develop. It took 352 00:20:27,640 --> 00:20:30,959 Speaker 1: two years to develop in some ways because of our understanding. 353 00:20:31,359 --> 00:20:33,280 Speaker 1: And now people say, look, what are the things we 354 00:20:33,280 --> 00:20:35,679 Speaker 1: can do now that we know this technology actually works? 355 00:20:36,320 --> 00:20:40,040 Speaker 1: Um and I I think that the implications are huge. 356 00:20:40,119 --> 00:20:42,920 Speaker 1: Might help people with cancer in some cases, might help 357 00:20:43,240 --> 00:20:45,920 Speaker 1: malaria vaccine. Can there begin a bowl of vaccine? I 358 00:20:45,920 --> 00:20:47,960 Speaker 1: don't know the answers to that, but I think that this, 359 00:20:48,520 --> 00:20:52,639 Speaker 1: this success story is just huge. And also, um, what 360 00:20:52,840 --> 00:20:56,720 Speaker 1: good can happen when we do trials correctly, when there's 361 00:20:56,760 --> 00:21:00,480 Speaker 1: good regulation and there's good UM buy in. The last 362 00:21:00,520 --> 00:21:03,320 Speaker 1: thing I think we might learn from this virus that 363 00:21:03,400 --> 00:21:06,080 Speaker 1: could be applicable to not just this virus, but many 364 00:21:06,119 --> 00:21:10,480 Speaker 1: other conditions is the long term consequences, the long code 365 00:21:10,560 --> 00:21:12,440 Speaker 1: or long haul. I have no idea what we're gonna 366 00:21:12,440 --> 00:21:14,879 Speaker 1: call this. There's gonna be different terminologies. UM. So I 367 00:21:14,880 --> 00:21:16,640 Speaker 1: want to watch the way we say it. But we're 368 00:21:16,680 --> 00:21:20,159 Speaker 1: just beginning to study this and there are people who 369 00:21:20,240 --> 00:21:23,520 Speaker 1: have acute diseases like things that come and go right, 370 00:21:23,880 --> 00:21:28,320 Speaker 1: like coronavirus, and they have long term effects. And it's 371 00:21:28,400 --> 00:21:34,040 Speaker 1: really hard to study that for most diseases because and 372 00:21:34,040 --> 00:21:36,480 Speaker 1: then I'm always diagnosed with the right disease, or there's 373 00:21:36,480 --> 00:21:39,159 Speaker 1: just there's just a few of them. Now we have 374 00:21:39,200 --> 00:21:42,359 Speaker 1: a cohort of people, unfortunately, who we can really look 375 00:21:42,400 --> 00:21:46,160 Speaker 1: at and work with together to learn about what happens 376 00:21:46,200 --> 00:21:48,639 Speaker 1: to the body when it responds to a major, major 377 00:21:48,760 --> 00:21:52,679 Speaker 1: insult like this virus is. And my guess is that 378 00:21:52,720 --> 00:21:55,760 Speaker 1: the sort of long haul, long term COVID syndrome that 379 00:21:55,760 --> 00:22:00,760 Speaker 1: we're seeing is not particular to coronavirus it's self, but 380 00:22:00,840 --> 00:22:03,879 Speaker 1: as much more something that could happen as rules of 381 00:22:03,960 --> 00:22:06,639 Speaker 1: many many infections diseases, and if we can start to 382 00:22:06,720 --> 00:22:11,240 Speaker 1: untangle how that is occurring and why and target that, 383 00:22:11,520 --> 00:22:14,600 Speaker 1: it could be that we could help people avoid long 384 00:22:14,680 --> 00:22:17,720 Speaker 1: term suffering from a variety of diseases. So I think 385 00:22:17,720 --> 00:22:21,679 Speaker 1: that this is why studying long term symptoms of COVID 386 00:22:21,800 --> 00:22:24,480 Speaker 1: is extremely important. I mean, patients really in a way 387 00:22:24,520 --> 00:22:27,440 Speaker 1: discovered this. Doctors did not discovered this, so patients talking 388 00:22:27,480 --> 00:22:29,920 Speaker 1: about it, and but I think that we're receptive to that, 389 00:22:29,960 --> 00:22:32,440 Speaker 1: so we should study that because we can actually learn 390 00:22:32,480 --> 00:22:37,280 Speaker 1: from this. When I was discharged from the hospital, they 391 00:22:37,280 --> 00:22:40,760 Speaker 1: all cheered for me and collapsed and we're like, yeah, Fiona, 392 00:22:41,000 --> 00:22:43,640 Speaker 1: like you can do it. You're gonna get better, um, 393 00:22:43,760 --> 00:22:49,440 Speaker 1: you know, And unfortunately wasn't that simple. But yeah, Fiona Lowenstein, 394 00:22:49,520 --> 00:22:52,159 Speaker 1: you might recall, is the twenty six year old New 395 00:22:52,240 --> 00:22:56,080 Speaker 1: Yorker who was hospitalized last March for COVID. But after 396 00:22:56,119 --> 00:22:58,320 Speaker 1: I got home, I remember that Wednesday night, I was 397 00:22:58,359 --> 00:23:00,080 Speaker 1: like kind of trying to clean up my room a 398 00:23:00,080 --> 00:23:02,720 Speaker 1: little bit and make it a nicer space, and I 399 00:23:02,800 --> 00:23:06,920 Speaker 1: opened a bottle of essential oil, like a lavender essential oil, 400 00:23:06,920 --> 00:23:09,040 Speaker 1: because I was like, oh, well, you know, diffuse it 401 00:23:09,040 --> 00:23:11,119 Speaker 1: in the room and it'll feel good. Um. And I 402 00:23:11,160 --> 00:23:14,320 Speaker 1: couldn't smell it, like I I literally thought someone had 403 00:23:14,320 --> 00:23:18,000 Speaker 1: replaced the oil with water. So then there was this 404 00:23:18,080 --> 00:23:20,720 Speaker 1: period of a few weeks where I was still quite 405 00:23:20,760 --> 00:23:26,440 Speaker 1: sick um and developing different seemingly unrelated symptoms every day. 406 00:23:26,480 --> 00:23:28,720 Speaker 1: I mean it was like, Okay, I can't smell, and 407 00:23:28,760 --> 00:23:31,119 Speaker 1: now I'm having g I issues. And then I was 408 00:23:31,200 --> 00:23:34,720 Speaker 1: having like these really intense headaches and these strange new 409 00:23:34,800 --> 00:23:37,280 Speaker 1: symptoms was like having I paint and I was very 410 00:23:37,359 --> 00:23:43,720 Speaker 1: light sensitive, lingered hives and rashes, um extreme sensitivity to temperature. 411 00:23:44,160 --> 00:23:46,880 Speaker 1: Eventually it became clear, though she didn't know it at 412 00:23:46,880 --> 00:23:53,359 Speaker 1: the time, Fiona was a COVID long hauler at that time, 413 00:23:53,359 --> 00:23:56,600 Speaker 1: like there weren't many stories of young people dying, and 414 00:23:56,640 --> 00:23:59,680 Speaker 1: there weren't any stories of long COVID or long haul 415 00:23:59,720 --> 00:24:03,480 Speaker 1: COVID it, so I certainly wasn't thinking about that. Since 416 00:24:03,520 --> 00:24:06,760 Speaker 1: her positive test in the hospital, Fiona had been sharing 417 00:24:06,800 --> 00:24:10,480 Speaker 1: her COVID journey on Instagram and she was becoming a 418 00:24:10,520 --> 00:24:16,160 Speaker 1: magnet for other patients desperate for information and guidance. As 419 00:24:16,200 --> 00:24:19,080 Speaker 1: people were reaching out to me online, I was hearing 420 00:24:19,119 --> 00:24:22,639 Speaker 1: these exact same symptoms. But what was more striking was 421 00:24:22,760 --> 00:24:24,560 Speaker 1: that a lot of the people that I was connecting 422 00:24:24,600 --> 00:24:27,600 Speaker 1: with were my age, and they had had a milder 423 00:24:27,640 --> 00:24:29,840 Speaker 1: case than I had, but had gotten sick, you know, 424 00:24:30,040 --> 00:24:33,200 Speaker 1: first second, third week of March, and they still weren't 425 00:24:33,200 --> 00:24:36,800 Speaker 1: getting better. And that was what kind of was the 426 00:24:36,840 --> 00:24:39,040 Speaker 1: red flag, because for me, I was like, Okay, my 427 00:24:39,080 --> 00:24:41,520 Speaker 1: case was pretty bad. I was hospitalized, so you know, 428 00:24:41,560 --> 00:24:43,399 Speaker 1: maybe it's going to take three to four weeks for 429 00:24:43,440 --> 00:24:45,320 Speaker 1: me to feel like my normal self. But these people 430 00:24:45,359 --> 00:24:47,879 Speaker 1: who like just had a mild grade fever of like 431 00:24:48,160 --> 00:24:51,960 Speaker 1: a hundred degrees, why are they still feeling so sick? 432 00:24:52,040 --> 00:24:55,560 Speaker 1: You know? Three or four weeks down the line. In 433 00:24:55,680 --> 00:24:58,560 Speaker 1: late March, Fiona wrote an opted for The New York Times, 434 00:24:59,000 --> 00:25:02,800 Speaker 1: a warning for young people to take this virus seriously. 435 00:25:03,400 --> 00:25:07,840 Speaker 1: It was called I'm twenty six. Coronavirus sent me to 436 00:25:07,920 --> 00:25:11,480 Speaker 1: the hospital. So that also helped connect me to a 437 00:25:11,480 --> 00:25:13,680 Speaker 1: lot of other COVID patients because people saw the news 438 00:25:13,760 --> 00:25:15,359 Speaker 1: and they and they kind of found me on email 439 00:25:15,400 --> 00:25:17,840 Speaker 1: or social media. And it was really helpful to talk 440 00:25:17,880 --> 00:25:20,520 Speaker 1: to these people because you know, they validated by experience 441 00:25:20,520 --> 00:25:22,760 Speaker 1: and vice versa. But it was also very dreaming because 442 00:25:22,760 --> 00:25:25,320 Speaker 1: I was communicating with each of them individually, and so 443 00:25:25,359 --> 00:25:26,840 Speaker 1: I would wake up in the morning and be like, 444 00:25:26,920 --> 00:25:29,240 Speaker 1: oh my gosh, I have like all of these dm 445 00:25:29,320 --> 00:25:30,920 Speaker 1: s I have to respond to, and this person is 446 00:25:30,920 --> 00:25:33,159 Speaker 1: in California and their boyfriend is on a ventilator, and 447 00:25:33,160 --> 00:25:35,639 Speaker 1: this person is in Paris, And I realized I should 448 00:25:35,640 --> 00:25:37,520 Speaker 1: just put them all in a chat together so that 449 00:25:37,560 --> 00:25:39,399 Speaker 1: we can all talk to each other. And that was 450 00:25:39,440 --> 00:25:41,880 Speaker 1: something my friend Sabrina and I had talked about as well. 451 00:25:41,960 --> 00:25:44,919 Speaker 1: Was just there's no resources, there's no place to go 452 00:25:45,040 --> 00:25:47,040 Speaker 1: to get information on this as a patient, and our 453 00:25:47,160 --> 00:25:49,600 Speaker 1: doctors are so overwhelmed that they can't even you know, 454 00:25:49,640 --> 00:25:53,520 Speaker 1: answer our emails or our calls. So we created this 455 00:25:53,560 --> 00:25:57,640 Speaker 1: little mini support group. It was just in like an 456 00:25:57,680 --> 00:26:01,200 Speaker 1: Instagram DM. It had maybe like what five thirty people 457 00:26:01,280 --> 00:26:04,120 Speaker 1: on their UM and people were just sharing updates about 458 00:26:04,160 --> 00:26:07,679 Speaker 1: their lives UM but also sharing like very tangible needs 459 00:26:07,760 --> 00:26:10,480 Speaker 1: and questions like you know, what did they do for 460 00:26:10,560 --> 00:26:14,840 Speaker 1: you when you were hospitalized? In Apriliana wrote another op 461 00:26:15,000 --> 00:26:18,720 Speaker 1: ed for The New York Times called Coronavirus Recovery Isn't 462 00:26:18,760 --> 00:26:21,560 Speaker 1: so quick or simple. In it, she linked to the 463 00:26:21,600 --> 00:26:27,000 Speaker 1: ad hoc support group she'd started. Overnight, two thousand people joined. 464 00:26:27,359 --> 00:26:30,960 Speaker 1: It was astounding and of course, like I felt both like, 465 00:26:31,080 --> 00:26:34,040 Speaker 1: oh my god, I'm not alone, because people were writing 466 00:26:34,080 --> 00:26:35,600 Speaker 1: in their sign up from like, oh my gosh, I've 467 00:26:35,640 --> 00:26:36,920 Speaker 1: been sick for a month and I don't know why 468 00:26:36,960 --> 00:26:38,879 Speaker 1: and I can't get better, and this is like the 469 00:26:38,880 --> 00:26:41,119 Speaker 1: first that I've heard that this is happening to other people. 470 00:26:41,480 --> 00:26:43,760 Speaker 1: But it was also very overwhelming because I was thinking, 471 00:26:43,760 --> 00:26:46,840 Speaker 1: how am I going to support these people like it's 472 00:26:47,000 --> 00:26:49,679 Speaker 1: it's it was me and you know my friend running 473 00:26:49,680 --> 00:26:51,720 Speaker 1: this and we were doing it through Body Politics, which 474 00:26:51,800 --> 00:26:54,600 Speaker 1: is a group that we ran prior to COVID that 475 00:26:54,680 --> 00:26:56,320 Speaker 1: you know, did events in New York City and kind 476 00:26:56,320 --> 00:26:58,880 Speaker 1: of focus on the intersections of health and social justice, 477 00:26:59,040 --> 00:27:03,119 Speaker 1: but had a very small volunteer team. We had exceeded 478 00:27:03,400 --> 00:27:06,199 Speaker 1: you know, Instagram's chat limit, we moved to WhatsApp, then 479 00:27:06,200 --> 00:27:09,919 Speaker 1: we exceeded WhatsApps chat limit, and eventually we got on 480 00:27:10,000 --> 00:27:13,480 Speaker 1: slack um and that's where we are today. This community 481 00:27:13,600 --> 00:27:17,840 Speaker 1: is called the Body Politic COVID nineteen support group. More 482 00:27:17,880 --> 00:27:21,600 Speaker 1: than twenty people have signed up since it started in April, 483 00:27:21,960 --> 00:27:25,400 Speaker 1: with more than ten thousand active members today, it has 484 00:27:25,400 --> 00:27:29,240 Speaker 1: a team of thirty to forty volunteers who moderate this 485 00:27:29,440 --> 00:27:34,040 Speaker 1: virtual city of support. We have I think about seventy 486 00:27:34,080 --> 00:27:37,880 Speaker 1: different channels on Slack and these channels are like little 487 00:27:37,960 --> 00:27:42,920 Speaker 1: sub groups for different discussions based on either topic or community. 488 00:27:42,960 --> 00:27:45,760 Speaker 1: So we have channels for almost every system of the body. 489 00:27:46,080 --> 00:27:48,800 Speaker 1: We have UM, a couple of private channels that you 490 00:27:48,960 --> 00:27:51,840 Speaker 1: join by messaging you know, the administrators of the group 491 00:27:51,960 --> 00:27:55,639 Speaker 1: UM and that's the lgbt Q plus channel, the BIPOC channel, 492 00:27:55,760 --> 00:27:59,000 Speaker 1: and the Medical Professionals Channel. We also have channels for 493 00:27:59,200 --> 00:28:02,399 Speaker 1: people in you know, South America. We have channels for 494 00:28:02,480 --> 00:28:04,359 Speaker 1: people in Europe. We have channels for people in New 495 00:28:04,440 --> 00:28:07,080 Speaker 1: York City. You can go into the Victory's channel and 496 00:28:07,119 --> 00:28:09,040 Speaker 1: just see the good dues, or you can go into 497 00:28:09,160 --> 00:28:11,159 Speaker 1: you know, the mental health channel, or that we have 498 00:28:11,200 --> 00:28:14,600 Speaker 1: a need to Vent channel, right, because sometimes we need that, 499 00:28:14,640 --> 00:28:16,280 Speaker 1: but we don't all need to see it all the time. 500 00:28:19,200 --> 00:28:21,119 Speaker 1: And another initiative I should mention that grew out of 501 00:28:21,119 --> 00:28:24,359 Speaker 1: the group is UM, the Patient led Research Collaborative, which 502 00:28:24,440 --> 00:28:28,080 Speaker 1: started in Body politic Um in April. There were some 503 00:28:28,119 --> 00:28:31,720 Speaker 1: patients in the group who were scientists who worked in medicine, 504 00:28:31,800 --> 00:28:35,360 Speaker 1: who had backgrounds in you know, survey design and research, 505 00:28:35,480 --> 00:28:38,280 Speaker 1: and basically said, okay, we're seeing a lot of anecdotal 506 00:28:38,480 --> 00:28:42,440 Speaker 1: evidence that is very contradictory to what we're seeing, you know, 507 00:28:42,440 --> 00:28:45,000 Speaker 1: in the mainstream media and on the CDC's website. How 508 00:28:45,040 --> 00:28:47,680 Speaker 1: can we actually find data to support what we think 509 00:28:47,720 --> 00:28:49,240 Speaker 1: is going on here? And of course they were talking 510 00:28:49,280 --> 00:28:51,640 Speaker 1: about you know, the wide variety of symptoms and the 511 00:28:51,760 --> 00:28:54,600 Speaker 1: long term symptoms. UM. So they did their first survey 512 00:28:54,680 --> 00:28:59,560 Speaker 1: in April, UM and just put out a second preprint 513 00:28:59,640 --> 00:29:03,240 Speaker 1: on their i CANT survey, which focuses on some more 514 00:29:03,280 --> 00:29:06,680 Speaker 1: issues facing long COVID patients and COVID patients, like sleep 515 00:29:06,720 --> 00:29:09,440 Speaker 1: issues and mental health and some of the lesser known things. 516 00:29:09,760 --> 00:29:13,120 Speaker 1: They've been hugely instrumental, I think UM in in helping 517 00:29:13,120 --> 00:29:16,440 Speaker 1: people understand that long COVID is real, and I think 518 00:29:16,440 --> 00:29:19,280 Speaker 1: they're also doing something really important in terms of helping 519 00:29:19,280 --> 00:29:23,480 Speaker 1: people understand how communities that have been impacted by illnesses 520 00:29:23,520 --> 00:29:27,280 Speaker 1: can be involved in the research processes to find treatments 521 00:29:27,280 --> 00:29:34,120 Speaker 1: and cures for those illnesses. What's been most astounding has 522 00:29:34,160 --> 00:29:36,640 Speaker 1: just been the way that everything that happened in those 523 00:29:36,640 --> 00:29:41,040 Speaker 1: early months has affected such amazing change. Congress just announced 524 00:29:41,080 --> 00:29:43,560 Speaker 1: that they were allocating I think this was in December January, 525 00:29:43,600 --> 00:29:46,440 Speaker 1: that they're allocating one point one five billion dollars to 526 00:29:46,480 --> 00:29:50,120 Speaker 1: the NIH to study long COVID and related post COVID 527 00:29:50,120 --> 00:29:53,719 Speaker 1: sequel i UM and that's incredible, Like that is, you know, 528 00:29:54,040 --> 00:29:58,440 Speaker 1: everything that we would have wanted months ago. Like so 529 00:29:58,520 --> 00:30:02,880 Speaker 1: many other aspects of COVID, Supporting COVID patients and recovering 530 00:30:02,960 --> 00:30:05,760 Speaker 1: from the virus has been something Fiona has had to 531 00:30:05,880 --> 00:30:08,800 Speaker 1: learn on the job, but she says there are a 532 00:30:08,880 --> 00:30:13,280 Speaker 1: lot of key takeaways that can extend well beyond this pandemic. 533 00:30:14,080 --> 00:30:17,040 Speaker 1: I think when you're running a COVID support group, or 534 00:30:17,080 --> 00:30:21,600 Speaker 1: really any patient support group, there's a few very important 535 00:30:21,960 --> 00:30:25,000 Speaker 1: guiding lights to keep in mind, and the first is 536 00:30:25,200 --> 00:30:28,720 Speaker 1: keeping it patient centered. The second is always providing context 537 00:30:28,800 --> 00:30:32,560 Speaker 1: on recommendations and advice um, and the third is always acknowledging, 538 00:30:33,000 --> 00:30:35,880 Speaker 1: you know, other aspects of politics and people's identities that 539 00:30:35,960 --> 00:30:39,440 Speaker 1: might be intersecting with their experience of being sick. I 540 00:30:39,480 --> 00:30:41,800 Speaker 1: didn't realize that at the time, but the thing that 541 00:30:41,920 --> 00:30:44,800 Speaker 1: those nurses gave me that I needed so badly was 542 00:30:44,840 --> 00:30:49,160 Speaker 1: just affirmation and validation. That was incredibly helpful. And so 543 00:30:49,760 --> 00:30:51,640 Speaker 1: I think that's what we really lad with in the 544 00:30:51,680 --> 00:30:55,360 Speaker 1: support group was You're not alone. What you're experiencing is 545 00:30:55,400 --> 00:30:58,160 Speaker 1: not in your head, and you know there are thousands 546 00:30:58,200 --> 00:31:00,920 Speaker 1: of other people who are here too, just walk with 547 00:31:01,000 --> 00:31:05,840 Speaker 1: you through it. I've mostly recovered from COVID, I think. 548 00:31:05,880 --> 00:31:07,880 Speaker 1: I say recovered. That doesn't mean I'm exactly the same 549 00:31:07,880 --> 00:31:10,440 Speaker 1: person I was before, but I I've mostly recovered from 550 00:31:10,480 --> 00:31:14,280 Speaker 1: COVID UM and now I really am passionate about trying 551 00:31:14,280 --> 00:31:17,080 Speaker 1: to get other people who have survived COVID or who 552 00:31:17,120 --> 00:31:19,280 Speaker 1: were part of the long COVID community, but have you know, 553 00:31:19,360 --> 00:31:23,240 Speaker 1: recovered to still stay engaged with these issues because I 554 00:31:23,240 --> 00:31:25,040 Speaker 1: don't know that this is the last pandemic we're going 555 00:31:25,080 --> 00:31:28,280 Speaker 1: to see in our lifetime, and I know that the 556 00:31:28,320 --> 00:31:31,000 Speaker 1: pandemic is not going to be over the day that 557 00:31:31,040 --> 00:31:35,400 Speaker 1: everyone gets vaccinated. Coming up. What COVID has taught us 558 00:31:35,440 --> 00:31:53,600 Speaker 1: about science, that's right after this. I started studying COVID 559 00:31:53,800 --> 00:31:57,040 Speaker 1: when I realized that I had something to contribute as 560 00:31:57,080 --> 00:31:59,640 Speaker 1: a person who has studied each TV for a long 561 00:31:59,680 --> 00:32:02,760 Speaker 1: time and how people live with HIV for a long time, 562 00:32:03,280 --> 00:32:05,920 Speaker 1: and so this infectious disease was very fascinating to me 563 00:32:06,000 --> 00:32:09,320 Speaker 1: when coronavirus emerged, so I began studying it as soon 564 00:32:09,360 --> 00:32:12,640 Speaker 1: as I could. Dr carry all Top is an associate 565 00:32:12,680 --> 00:32:17,200 Speaker 1: professor of epidemiology at the Johns Hopkins School of Public Health. 566 00:32:18,040 --> 00:32:21,800 Speaker 1: What we've learned is that we can do science faster 567 00:32:22,040 --> 00:32:26,480 Speaker 1: with the right resources in place. And we've also learned 568 00:32:26,560 --> 00:32:30,480 Speaker 1: that even with the right resources in place, science is 569 00:32:30,520 --> 00:32:34,959 Speaker 1: still really hard and we and we've known that it is. 570 00:32:35,120 --> 00:32:37,920 Speaker 1: It is baby step by baby step, one piece of 571 00:32:37,960 --> 00:32:41,520 Speaker 1: evidence on top of the next in order to really 572 00:32:41,560 --> 00:32:44,480 Speaker 1: make progress. And we've known that for a long time. 573 00:32:44,640 --> 00:32:47,920 Speaker 1: I mean, every quote breakthrough that we have is built 574 00:32:48,000 --> 00:32:51,360 Speaker 1: on a mountain of baby steps that we took in 575 00:32:51,480 --> 00:32:54,120 Speaker 1: order to get to that to that peak where we 576 00:32:54,200 --> 00:32:58,240 Speaker 1: have what we consider something to be a breakthrough. There 577 00:32:58,240 --> 00:33:02,240 Speaker 1: are some amazing virologists out there that really pushed forward, 578 00:33:02,840 --> 00:33:05,720 Speaker 1: you know, our understanding of MR and A vaccines and 579 00:33:05,960 --> 00:33:10,320 Speaker 1: went from you know, a decade of research in in 580 00:33:10,560 --> 00:33:13,080 Speaker 1: phase one two in three trials and then boom, here 581 00:33:13,080 --> 00:33:16,400 Speaker 1: we go, brand new virus on the scene, and and 582 00:33:16,520 --> 00:33:20,800 Speaker 1: they can create these vaccines and get them tested in 583 00:33:21,040 --> 00:33:25,520 Speaker 1: large numbers of people safely and quickly. So I think 584 00:33:25,560 --> 00:33:31,120 Speaker 1: we we definitely have learned a lot. We've learned a 585 00:33:31,120 --> 00:33:34,680 Speaker 1: lot about hospital capacity, We've learned a lot about where 586 00:33:34,720 --> 00:33:38,320 Speaker 1: to meet people because we understand and in public health, 587 00:33:38,360 --> 00:33:42,000 Speaker 1: we always knew that your plan is as good as 588 00:33:42,120 --> 00:33:45,280 Speaker 1: what you can get people to go along with, right, 589 00:33:45,920 --> 00:33:48,600 Speaker 1: So if you have a community that takes a hard 590 00:33:48,640 --> 00:33:52,640 Speaker 1: stand against masks, your plan can't just be masks for 591 00:33:52,680 --> 00:33:57,080 Speaker 1: that community. So it is about that implementation of what 592 00:33:57,160 --> 00:34:00,480 Speaker 1: we know from our scientific knowledge and how we roll 593 00:34:00,560 --> 00:34:05,200 Speaker 1: that out, how we communicate, how we present the information 594 00:34:05,800 --> 00:34:10,279 Speaker 1: so that people are ready to listen and accept or 595 00:34:10,520 --> 00:34:14,600 Speaker 1: or question and ask great questions. And we build that 596 00:34:14,680 --> 00:34:19,239 Speaker 1: partnership with all eyes on scientists and the research they're 597 00:34:19,280 --> 00:34:23,080 Speaker 1: doing in real time about the virus. Sometimes the information 598 00:34:23,120 --> 00:34:27,160 Speaker 1: we're so hungry for isn't actually ready for public consumption. 599 00:34:27,840 --> 00:34:30,799 Speaker 1: The scientific information that's come out in the last year, 600 00:34:31,239 --> 00:34:34,759 Speaker 1: it is most definitely drinking from a scientific fire hose. 601 00:34:35,160 --> 00:34:38,239 Speaker 1: There is just so much information that has come out. 602 00:34:38,640 --> 00:34:42,560 Speaker 1: We're thinking about science um in in faster terms, and 603 00:34:42,640 --> 00:34:45,840 Speaker 1: putting our information out there more quickly, even if it 604 00:34:45,920 --> 00:34:48,319 Speaker 1: isn't fully peer reviewed, and how you have to be 605 00:34:48,400 --> 00:34:51,320 Speaker 1: careful of that um But you know, there's this balance 606 00:34:51,440 --> 00:34:57,640 Speaker 1: of just needing information as a pandemic rages on, and 607 00:34:57,680 --> 00:35:01,600 Speaker 1: then of course we can't talk about science without acknowledging 608 00:35:01,760 --> 00:35:07,120 Speaker 1: it's strange bedfellow the federal government. Dr Altov says, what 609 00:35:07,200 --> 00:35:12,000 Speaker 1: we've learned about that may prepare us for whatever comes next. 610 00:35:13,040 --> 00:35:16,239 Speaker 1: One of my favorite lines is that public health is 611 00:35:16,320 --> 00:35:19,880 Speaker 1: best working when you don't notice it, and and science 612 00:35:19,960 --> 00:35:22,360 Speaker 1: is a little bit in that way too. Write when 613 00:35:22,719 --> 00:35:27,320 Speaker 1: when we are progressing in science, then we have medications 614 00:35:27,440 --> 00:35:32,319 Speaker 1: for your illness. And when public health is working then 615 00:35:32,600 --> 00:35:35,720 Speaker 1: you don't notice. But your drinking water is safe and 616 00:35:36,280 --> 00:35:39,440 Speaker 1: your road trip is safe, and all of those pieces 617 00:35:39,440 --> 00:35:42,879 Speaker 1: come together and it just becomes you know, the air 618 00:35:42,960 --> 00:35:46,640 Speaker 1: we breathe. And so I think what we've also learned 619 00:35:46,680 --> 00:35:51,680 Speaker 1: through this pandemic is that science and public health are ongoing, 620 00:35:51,880 --> 00:35:56,239 Speaker 1: and when they're not properly invested in, it only takes 621 00:35:56,520 --> 00:36:00,120 Speaker 1: a pandemic to show show where the cracks are. And 622 00:36:00,239 --> 00:36:04,160 Speaker 1: you know, our public health infrastructure has been a place 623 00:36:04,160 --> 00:36:07,320 Speaker 1: where there has been under investment for for a while, 624 00:36:07,520 --> 00:36:13,000 Speaker 1: and specifically in pandemic preparedness. But now we see different 625 00:36:13,000 --> 00:36:17,799 Speaker 1: federal agencies looking at the reports that academics have put 626 00:36:17,840 --> 00:36:22,240 Speaker 1: together about pandemic preparedness and they're picking them up and saying, 627 00:36:22,400 --> 00:36:25,960 Speaker 1: we need to make some policy based on what this 628 00:36:25,960 --> 00:36:30,360 Speaker 1: this science tells us. And that to me is so thrilling. 629 00:36:30,600 --> 00:36:34,879 Speaker 1: It's it's just like this bright rainbow after you know, 630 00:36:35,000 --> 00:36:42,600 Speaker 1: what has been a very lengthy storm. I think it's 631 00:36:42,600 --> 00:36:46,360 Speaker 1: important to remember that pandemics end They always do. That 632 00:36:46,520 --> 00:36:49,440 Speaker 1: this virus will become what we call endemic, so we 633 00:36:49,480 --> 00:36:52,719 Speaker 1: will live with it and control it hopefully the way 634 00:36:52,719 --> 00:36:56,799 Speaker 1: that we control measles for example. Um, what we will 635 00:36:56,840 --> 00:36:59,520 Speaker 1: need to do in order to kind of get to 636 00:36:59,600 --> 00:37:03,480 Speaker 1: that is we will need to have enough immuo logic 637 00:37:03,640 --> 00:37:07,680 Speaker 1: control in the population so that we do not see 638 00:37:08,160 --> 00:37:11,239 Speaker 1: vast numbers of people getting sick. And so what does 639 00:37:11,280 --> 00:37:15,400 Speaker 1: that mean. It's vaccination. Really, That's that's where we're headed. 640 00:37:16,080 --> 00:37:19,200 Speaker 1: We're not there yet, but I do think we will 641 00:37:19,239 --> 00:37:24,880 Speaker 1: get there. And the most important lesson we are all connected. 642 00:37:26,400 --> 00:37:30,160 Speaker 1: I mean, it's just that simple. We are all connected. 643 00:37:30,800 --> 00:37:33,200 Speaker 1: And I don't know if we all had an awareness 644 00:37:33,440 --> 00:37:36,840 Speaker 1: of how connected we are until you have something like 645 00:37:36,880 --> 00:37:41,960 Speaker 1: an infectious disease that doesn't care who you are, it 646 00:37:42,040 --> 00:37:45,239 Speaker 1: will infect you if given the chance. So I think 647 00:37:45,280 --> 00:37:47,960 Speaker 1: that is a really important thing for us all to 648 00:37:48,080 --> 00:37:52,160 Speaker 1: remember that we are connected and we need to help 649 00:37:52,200 --> 00:37:59,000 Speaker 1: take care of each other. How wong it go? I 650 00:37:59,239 --> 00:38:04,120 Speaker 1: love you and if Powhip, and a huge thank you 651 00:38:04,200 --> 00:38:08,840 Speaker 1: to Dr Carrie all Top, Fiona Lowenstein and Dr Jeremy Faust, 652 00:38:09,200 --> 00:38:12,200 Speaker 1: not just for peen on our podcast, but for the 653 00:38:12,280 --> 00:38:16,479 Speaker 1: extraordinary work they have all done over this last year. 654 00:38:16,719 --> 00:38:21,520 Speaker 1: Let's get away, Let's make a change. I want to 655 00:38:21,640 --> 00:38:25,080 Speaker 1: see all of the Kurds, I've been looking at grave. 656 00:38:25,120 --> 00:38:27,840 Speaker 1: If It's soon. Next Question with Katie Curic is a 657 00:38:27,880 --> 00:38:31,000 Speaker 1: production of I Heart Media and Katie Kurk Media. The 658 00:38:31,040 --> 00:38:35,880 Speaker 1: executive producers Army, Katie Curic, and Courtney Litz. The supervising 659 00:38:35,880 --> 00:38:40,960 Speaker 1: producer is Lauren Hansen. Associate producers Derek Clements, Adrianna Fasio, 660 00:38:41,280 --> 00:38:44,920 Speaker 1: and Emily Pinto. The show is edited and mixed by 661 00:38:45,000 --> 00:38:48,719 Speaker 1: Derrek Clements. For more information about today's episode, or to 662 00:38:48,760 --> 00:38:51,360 Speaker 1: sign up for my morning newsletter, wake Up Call, go 663 00:38:51,440 --> 00:38:54,080 Speaker 1: to Katie correct dot com. You can also find me 664 00:38:54,120 --> 00:38:57,840 Speaker 1: at Katie Curic on Instagram and all my social media channels. 665 00:38:58,239 --> 00:39:01,080 Speaker 1: For more podcasts from my Heart Ray video, visit the 666 00:39:01,120 --> 00:39:04,800 Speaker 1: I Heart Radio app, Apple Podcast, or wherever you listen 667 00:39:04,840 --> 00:39:10,360 Speaker 1: to your favorite shows. I want to go some lovely