1 00:00:01,000 --> 00:00:02,233 [ Clock ticking ] 2 00:00:02,266 --> 00:00:04,900 {\an1}-If you were born in 1900, 3 00:00:04,933 --> 00:00:07,066 {\an1}you could expect to live, on average, 4 00:00:07,100 --> 00:00:09,533 just 32 years. 5 00:00:09,566 --> 00:00:13,666 {\an1}Today, global life expectancy is more than twice that. 6 00:00:13,700 --> 00:00:17,166 {\an1}It's one of the greatest achievements in human history. 7 00:00:17,200 --> 00:00:19,433 ♪♪ 8 00:00:19,466 --> 00:00:22,100 {\an1}-This is the story of the ideas that have in the space 9 00:00:22,133 --> 00:00:27,433 {\an1}of just a century or two changed what it is to be us... 10 00:00:27,466 --> 00:00:29,100 {\an1}by transforming the kind 11 00:00:29,133 --> 00:00:32,766 {\an1}of lives we might live... 12 00:00:32,800 --> 00:00:36,100 {\an1}no longer short and under the shadow of disease, 13 00:00:36,133 --> 00:00:39,766 {\an1}but healthy and long. 14 00:00:39,800 --> 00:00:43,066 {\an1}I'm David Olusoga, a historian. 15 00:00:43,100 --> 00:00:45,433 {\an1}I have to say, it does look like the sort of laboratory 16 00:00:45,466 --> 00:00:48,433 you see in the "Frankenstein" movie. 17 00:00:48,466 --> 00:00:51,133 {\an1}-And I'm science writer Steven Johnson. 18 00:00:51,166 --> 00:00:52,733 {\an1}-The average Black American 19 00:00:52,766 --> 00:00:54,866 lives three and a half years less 20 00:00:54,900 --> 00:00:56,566 {\an1}than the average American. 21 00:00:56,600 --> 00:00:58,666 -That, to me, is just really shocking. 22 00:00:58,700 --> 00:01:00,000 {\an1}-We're investigating 23 00:01:00,033 --> 00:01:03,133 {\an1}the forgotten heroes of global health. 24 00:01:03,166 --> 00:01:04,966 {\an1}-The scientists, doctors, 25 00:01:05,000 --> 00:01:06,800 {\an1}researchers, and activists 26 00:01:06,833 --> 00:01:10,300 {\an1}who touched billions of lives. 27 00:01:10,333 --> 00:01:12,800 One of their most revolutionary ideas 28 00:01:12,833 --> 00:01:14,300 is using data -- 29 00:01:14,333 --> 00:01:16,300 {\an1}the daily tracking of numbers 30 00:01:16,333 --> 00:01:20,333 {\an1}to help stop epidemics in their tracks. 31 00:01:20,366 --> 00:01:22,333 {\an1}Today, as the world grapples 32 00:01:22,366 --> 00:01:24,333 {\an1}with another deadly pandemic, 33 00:01:24,366 --> 00:01:27,333 {\an1}we reveal how data became one 34 00:01:27,366 --> 00:01:29,500 {\an1}of the most powerful tools we have 35 00:01:29,533 --> 00:01:31,766 {\an1}in the fight for extra life. 36 00:01:31,800 --> 00:01:37,833 {\an8}♪♪ 37 00:01:39,666 --> 00:01:44,800 {\an8}♪♪ 38 00:01:44,833 --> 00:01:46,300 -I'm in the U.S., 39 00:01:46,333 --> 00:01:47,966 {\an1}and David's in the U.K., 40 00:01:48,000 --> 00:01:50,266 {\an1}and given what's going on in the world, 41 00:01:50,300 --> 00:01:51,600 {\an1}we're meeting online 42 00:01:51,633 --> 00:01:55,100 {\an1}to explore the story of data. 43 00:01:55,133 --> 00:01:56,433 There he is. 44 00:01:56,466 --> 00:01:57,600 -Hey, there. -Good morning, David. 45 00:01:57,633 --> 00:01:58,966 -How you doing? 46 00:01:59,000 --> 00:02:00,700 {\an1}-You're looking well. -Thank you. 47 00:02:00,733 --> 00:02:02,200 {\an1}If you cast your mind back 48 00:02:02,233 --> 00:02:06,500 {\an1}to the early weeks of the COVID-19 pandemic, 49 00:02:06,533 --> 00:02:08,833 {\an1}what you kept hearing from scientists 50 00:02:08,866 --> 00:02:11,433 {\an1}is how little we knew, that this was a novel virus. 51 00:02:11,466 --> 00:02:14,200 By its nature, we didn't have the information, 52 00:02:14,233 --> 00:02:16,200 and it was all about gathering data. 53 00:02:16,233 --> 00:02:18,033 {\an1}-There was no hope of a vaccine. 54 00:02:18,066 --> 00:02:20,400 {\an1}There were no therapeutics that could help us. 55 00:02:20,433 --> 00:02:22,233 {\an1}What we did have were numbers, right? 56 00:02:22,266 --> 00:02:24,400 {\an1}We had information about where the virus was, 57 00:02:24,433 --> 00:02:25,400 {\an1}where it was spreading. 58 00:02:25,433 --> 00:02:27,166 {\an1}-When we picked up a newspaper, 59 00:02:27,200 --> 00:02:28,500 we didn't look at the sports pages 60 00:02:28,533 --> 00:02:30,433 {\an1}or the stock market. 61 00:02:30,466 --> 00:02:33,266 {\an1}We just wanted to know data about this disease. 62 00:02:33,300 --> 00:02:34,733 {\an1}-And that gave us clues 63 00:02:34,766 --> 00:02:36,633 {\an1}about how to potentially protect ourselves. 64 00:02:36,666 --> 00:02:39,800 {\an1}-I was on a journey around the Caribbean and North America 65 00:02:39,833 --> 00:02:42,133 at the time, and we literally -- 66 00:02:42,166 --> 00:02:43,366 {\an1}You would leave one country, 67 00:02:43,400 --> 00:02:45,200 {\an1}and you would look in the airport on your phone 68 00:02:45,233 --> 00:02:46,866 {\an1}at what the infection rates were 69 00:02:46,900 --> 00:02:48,033 in the country you were landing at. 70 00:02:48,066 --> 00:02:50,266 {\an1}So data became the thing 71 00:02:50,300 --> 00:02:52,600 that underlied our daily actions. 72 00:02:52,633 --> 00:02:54,166 {\an1}-This kind of data analysis is 73 00:02:54,200 --> 00:02:56,833 {\an1}something that has been going on quietly behind the scenes 74 00:02:56,866 --> 00:02:58,666 {\an1}for more than a century, 75 00:02:58,700 --> 00:03:01,400 {\an1}looking for patterns of the spread of disease, 76 00:03:01,433 --> 00:03:02,533 calculating risk, 77 00:03:02,566 --> 00:03:04,033 {\an1}and using that data 78 00:03:04,066 --> 00:03:05,500 {\an1}to extend our lives. 79 00:03:05,533 --> 00:03:06,833 {\an1}-And those innovations, 80 00:03:06,866 --> 00:03:09,166 {\an1}just like vaccines and just like drugs, 81 00:03:09,200 --> 00:03:11,333 {\an1}they have inventors and pioneers 82 00:03:11,366 --> 00:03:13,800 that very often we've forgotten about. 83 00:03:13,833 --> 00:03:16,700 ♪♪ 84 00:03:16,733 --> 00:03:19,300 This use of data to fight disease 85 00:03:19,333 --> 00:03:21,300 {\an1}has its roots in the epidemics 86 00:03:21,333 --> 00:03:24,966 {\an1}that plagued Victorian Britain. 87 00:03:25,000 --> 00:03:28,166 {\an1}It's the year 1866. 88 00:03:30,033 --> 00:03:31,666 {\an1}Here in the East End of London, 89 00:03:31,700 --> 00:03:34,500 {\an1}in the neighborhoods surrounding the River Lea, 90 00:03:34,533 --> 00:03:37,333 there are reports of an outbreak of cholera, 91 00:03:37,366 --> 00:03:40,100 {\an1}and back in the middle of the 19th century, 92 00:03:40,133 --> 00:03:43,300 {\an1}this is one of the most polluted rivers in Britain. 93 00:03:46,500 --> 00:03:47,666 {\an1}And it runs through one 94 00:03:47,700 --> 00:03:49,933 of the most densely-populated neighborhoods 95 00:03:49,966 --> 00:03:53,766 in what was then the biggest city on earth. 96 00:03:53,800 --> 00:03:56,433 {\an8}♪♪ 97 00:03:56,466 --> 00:03:59,600 {\an8}News of cholera is making the headlines. 98 00:03:59,633 --> 00:04:01,333 {\an8}♪♪ 99 00:04:01,366 --> 00:04:04,166 {\an7}20 deaths by mid-July. 100 00:04:04,200 --> 00:04:06,166 {\an8}♪♪ 101 00:04:06,200 --> 00:04:09,333 {\an7}A week later, it's 308. 102 00:04:09,366 --> 00:04:10,833 {\an8}♪♪ 103 00:04:10,866 --> 00:04:13,000 {\an7}By August, the weekly death toll 104 00:04:13,033 --> 00:04:15,533 {\an7}has reached almost 1,000. 105 00:04:17,000 --> 00:04:18,800 {\an1}The city is under attack, 106 00:04:18,833 --> 00:04:20,800 {\an1}and people are terrified 107 00:04:20,833 --> 00:04:23,300 for their lives. 108 00:04:23,333 --> 00:04:25,700 {\an1}Cholera is given many nicknames, 109 00:04:25,733 --> 00:04:28,366 {\an1}but some people call it "the blue death," 110 00:04:28,400 --> 00:04:31,700 {\an1}and that's because it turns the skin of its victims 111 00:04:31,733 --> 00:04:34,033 {\an1}a gray blue color. 112 00:04:34,066 --> 00:04:37,333 {\an1}It begins just like a mild case of food poisoning. 113 00:04:37,366 --> 00:04:39,333 {\an1}But those affected soon experience 114 00:04:39,366 --> 00:04:42,500 {\an1}extreme bouts of vomiting and diarrhea, 115 00:04:42,533 --> 00:04:45,500 {\an1}and some die within 48 hours 116 00:04:45,533 --> 00:04:47,533 {\an1}of extreme dehydration. 117 00:04:51,066 --> 00:04:53,166 {\an1}The London of the mid 1800s 118 00:04:53,200 --> 00:04:54,666 {\an1}had already been through 119 00:04:54,700 --> 00:04:57,833 {\an1}three cholera epidemics. 120 00:04:57,866 --> 00:05:00,766 {\an1}And with so many people dying in childhood, 121 00:05:00,800 --> 00:05:02,933 {\an1}life expectancy is struggling 122 00:05:02,966 --> 00:05:05,766 {\an1}to get above 37 years. 123 00:05:05,800 --> 00:05:11,933 ♪♪ 124 00:05:11,966 --> 00:05:13,933 Watching as the mortality figures 125 00:05:13,966 --> 00:05:16,100 {\an1}go up and up with each week 126 00:05:16,133 --> 00:05:18,966 {\an1}is a doctor called William Farr, 127 00:05:19,000 --> 00:05:21,633 but he's not out treating patients. 128 00:05:21,666 --> 00:05:23,733 {\an1}He's a medical statistician. 129 00:05:23,766 --> 00:05:26,566 {\an1}Farr is responsible for keeping records 130 00:05:26,600 --> 00:05:30,366 {\an1}of births, marriages, and deaths in the United Kingdom. 131 00:05:30,400 --> 00:05:31,200 But he... 132 00:05:31,233 --> 00:05:33,100 {\an1}He has a much bigger agenda 133 00:05:33,133 --> 00:05:34,766 {\an1}because what William Farr understands 134 00:05:34,800 --> 00:05:36,400 {\an1}is the power of numbers 135 00:05:36,433 --> 00:05:39,900 {\an1}when presented in the right way to tell stories 136 00:05:39,933 --> 00:05:42,233 and even to solve 137 00:05:42,266 --> 00:05:44,266 {\an1}medical mysteries. 138 00:05:49,100 --> 00:05:52,233 These are William Farr's life tables. 139 00:05:52,266 --> 00:05:55,733 {\an1}They are raw data, numbers, arranged into columns. 140 00:05:55,766 --> 00:05:58,233 And while all of this information 141 00:05:58,266 --> 00:05:59,900 {\an1}can seem a bit mundane, 142 00:05:59,933 --> 00:06:01,633 {\an1}what it allowed Farr to do 143 00:06:01,666 --> 00:06:05,800 {\an1}was to see the patterns in the numbers. 144 00:06:05,833 --> 00:06:08,633 {\an1}He doesn't just want to know when someone died. 145 00:06:08,666 --> 00:06:10,966 He wants to know the cause of death. 146 00:06:11,000 --> 00:06:12,800 He wants to know the age of death. 147 00:06:12,833 --> 00:06:14,133 He wants to know their occupation. 148 00:06:14,166 --> 00:06:18,300 {\an1}And he wants to know where they lived. 149 00:06:18,333 --> 00:06:22,466 And he was able to transform these raw numbers 150 00:06:22,500 --> 00:06:25,800 {\an1}to diagrams like these. 151 00:06:25,833 --> 00:06:27,300 {\an1}What Farr was doing 152 00:06:27,333 --> 00:06:28,900 {\an1}was revolutionary. 153 00:06:28,933 --> 00:06:30,866 ♪♪ 154 00:06:30,900 --> 00:06:33,533 {\an1}No one had gathered and analyzed data 155 00:06:33,566 --> 00:06:36,033 {\an1}in such a sophisticated way, 156 00:06:36,066 --> 00:06:38,100 {\an1}and he hoped to find clues 157 00:06:38,133 --> 00:06:42,433 {\an1}to help him understand the latest cholera epidemic. 158 00:06:42,466 --> 00:06:45,033 {\an1}So, this one is an attempt 159 00:06:45,066 --> 00:06:46,700 {\an1}to plot all the deaths 160 00:06:46,733 --> 00:06:48,566 {\an1}in England from cholera 161 00:06:48,600 --> 00:06:50,500 {\an1}each day of the year. 162 00:06:50,533 --> 00:06:52,933 {\an1}And this this shape, this curve, 163 00:06:52,966 --> 00:06:55,266 {\an1}we're very familiar with that. 164 00:06:55,300 --> 00:06:57,433 {\an1}And it's called Farr's Curve, 165 00:06:57,466 --> 00:07:00,233 {\an1}because it was William Farr who discovered 166 00:07:00,266 --> 00:07:02,566 {\an1}that epidemics rise and fall 167 00:07:02,600 --> 00:07:04,000 {\an1}in a symmetrical pattern. 168 00:07:04,033 --> 00:07:07,666 {\an1}And so today, when we talk about the curve of an epidemic, 169 00:07:07,700 --> 00:07:09,500 {\an1}when we talk about flattening the curve, 170 00:07:09,533 --> 00:07:12,866 {\an1}we are harking back to the work that William did 171 00:07:12,900 --> 00:07:15,533 in the middle of the 19th century. 172 00:07:15,566 --> 00:07:20,200 ♪♪ 173 00:07:20,233 --> 00:07:22,433 {\an1}The strange thing about Farr's work, though, 174 00:07:22,466 --> 00:07:27,700 {\an1}is his great invention, the curve, is terrifying. 175 00:07:27,733 --> 00:07:30,500 {\an1}-The terror that comes from exponential growth, 176 00:07:30,533 --> 00:07:33,333 {\an1}that that upward curve of an outbreak 177 00:07:33,366 --> 00:07:35,033 {\an1}that Farr documented, 178 00:07:35,066 --> 00:07:39,233 {\an1}the speed with which an outbreak can go from two people sick 179 00:07:39,266 --> 00:07:41,900 {\an1}to 200 people sick to 2,000 people sick. 180 00:07:41,933 --> 00:07:43,900 -I think one of the particular horrors 181 00:07:43,933 --> 00:07:46,666 {\an1}of that moment in 1866 182 00:07:46,700 --> 00:07:49,500 {\an1}is that no one's got any delusions now 183 00:07:49,533 --> 00:07:52,500 {\an1}about just how awful this could be, 184 00:07:52,533 --> 00:07:54,233 {\an1}because this is the fourth 185 00:07:54,266 --> 00:07:56,933 {\an1}of the great Victorian cholera epidemics. 186 00:07:56,966 --> 00:07:59,833 {\an1}There's all sorts of doubt and questions 187 00:07:59,866 --> 00:08:01,333 about the nature of this disease, 188 00:08:01,366 --> 00:08:03,000 {\an1}but there's absolute certainty about one thing, 189 00:08:03,033 --> 00:08:04,533 {\an1}is it is a killer. 190 00:08:04,566 --> 00:08:05,900 -And in 1866, 191 00:08:05,933 --> 00:08:08,566 {\an1}he's doing a different kind of investigation here 192 00:08:08,600 --> 00:08:12,900 {\an8}in the sense that he's using data in real time 193 00:08:12,933 --> 00:08:15,666 {\an7}to investigate an outbreak as it is unfolding. 194 00:08:15,700 --> 00:08:16,666 {\an8}And in this way, 195 00:08:16,700 --> 00:08:18,666 {\an8}it's very similar to the use of data 196 00:08:18,700 --> 00:08:20,566 {\an7}in the COVID-19 pandemic. 197 00:08:20,600 --> 00:08:22,166 {\an7}He's looking for evidence 198 00:08:22,200 --> 00:08:24,666 that can show who was getting sick, 199 00:08:24,700 --> 00:08:26,166 who was dying, 200 00:08:26,200 --> 00:08:27,566 {\an1}and most importantly, 201 00:08:27,600 --> 00:08:31,566 {\an1}where is the disease coming from? 202 00:08:31,600 --> 00:08:35,733 {\an1}-The cholera epidemic of 1866 is a medical mystery, 203 00:08:35,766 --> 00:08:39,233 {\an1}and it comes as a shock because during the previous decade, 204 00:08:39,266 --> 00:08:42,233 {\an1}the disease had been largely dormant in London. 205 00:08:42,266 --> 00:08:45,666 {\an1}But it is also unexpected because during that decade, 206 00:08:45,700 --> 00:08:48,000 {\an1}enormous defenses had been built 207 00:08:48,033 --> 00:08:50,833 {\an1}to protect the city from disease. 208 00:08:50,866 --> 00:08:54,133 {\an8}♪♪ 209 00:08:54,166 --> 00:08:56,133 {\an7}Following the last cholera epidemic 210 00:08:56,166 --> 00:08:58,400 {\an8}a decade earlier, 211 00:08:58,433 --> 00:09:00,033 {\an1}a vast system of tunnels 212 00:09:00,066 --> 00:09:02,133 {\an1}had been built beneath the city 213 00:09:02,166 --> 00:09:04,633 to rid London of its foul-smelling, 214 00:09:04,666 --> 00:09:06,833 {\an1}disease-causing sewage. 215 00:09:06,866 --> 00:09:11,366 ♪♪ 216 00:09:11,400 --> 00:09:15,033 Those defenses consisted of machines 217 00:09:15,066 --> 00:09:17,866 {\an1}like this giant pumping engines, 218 00:09:17,900 --> 00:09:18,966 {\an1}and they were connected 219 00:09:19,000 --> 00:09:22,133 to 82 miles of underground sewers, 220 00:09:22,166 --> 00:09:24,633 {\an1}and building all of this was an epic feat 221 00:09:24,666 --> 00:09:26,466 {\an1}of sanitary engineering. 222 00:09:26,500 --> 00:09:28,300 {\an1}This was the biggest, 223 00:09:28,333 --> 00:09:32,000 {\an1}most sophisticated sewage system in the world. 224 00:09:33,833 --> 00:09:36,633 {\an7}Since the discovery that cholera could be caused 225 00:09:36,666 --> 00:09:38,966 {\an7}by drinking contaminated water, 226 00:09:39,000 --> 00:09:41,800 {\an7}it was thought that ridding London of its sewage 227 00:09:41,833 --> 00:09:45,066 {\an8}would also rid it of the disease. 228 00:09:45,100 --> 00:09:47,600 {\an1}There was real hope that this, 229 00:09:47,633 --> 00:09:50,266 all of this, surely that was enough 230 00:09:50,300 --> 00:09:52,433 to ensure that there would not be 231 00:09:52,466 --> 00:09:54,866 {\an1}another epidemic of cholera. 232 00:09:54,900 --> 00:09:57,866 ♪♪ 233 00:09:57,900 --> 00:10:02,200 {\an1}Farr was puzzled that the outbreak was still growing. 234 00:10:02,233 --> 00:10:04,433 ♪♪ 235 00:10:04,466 --> 00:10:07,033 {\an1}What Farr now knows for sure 236 00:10:07,066 --> 00:10:09,566 {\an1}is that the building of London's sewers 237 00:10:09,600 --> 00:10:12,900 {\an1}hasn't driven cholera out of the city. 238 00:10:12,933 --> 00:10:15,700 {\an1}What he doesn't know is why. 239 00:10:15,733 --> 00:10:25,200 ♪♪ 240 00:10:25,233 --> 00:10:28,700 {\an1}William Farr is determined to solve this mystery, 241 00:10:28,733 --> 00:10:31,033 {\an1}using the thing he loves most -- 242 00:10:31,066 --> 00:10:32,866 data. 243 00:10:32,900 --> 00:10:35,366 {\an1}Knowing that cholera is transmitted in water, 244 00:10:35,400 --> 00:10:38,200 {\an1}he does an unusual thing, 245 00:10:38,233 --> 00:10:41,700 {\an1}he adds a new category of information, 246 00:10:41,733 --> 00:10:43,866 {\an1}the water company that supplies 247 00:10:43,900 --> 00:10:47,700 {\an1}each victim's drinking water. 248 00:10:47,733 --> 00:10:50,733 {\an1}The data shows a clear pattern. 249 00:10:55,400 --> 00:10:57,166 Far works out 250 00:10:57,200 --> 00:10:59,766 {\an1}that the great majority of the cholera cases 251 00:10:59,800 --> 00:11:03,166 {\an1}are connected to the East London Waterworks Company, 252 00:11:03,200 --> 00:11:04,900 {\an1}and so immediately, 253 00:11:04,933 --> 00:11:08,133 {\an1}he has notices put up in the affected areas, 254 00:11:08,166 --> 00:11:10,466 warning people not to drink water 255 00:11:10,500 --> 00:11:13,166 {\an1}unless it's first been boiled. 256 00:11:16,500 --> 00:11:25,600 ♪♪ 257 00:11:25,633 --> 00:11:27,766 {\an1}Using the mortality reports 258 00:11:27,800 --> 00:11:29,633 {\an1}to work backwards in time... 259 00:11:32,033 --> 00:11:34,133 {\an1}...the data reveals the first deaths 260 00:11:34,166 --> 00:11:36,466 {\an1}took place in June, 261 00:11:36,500 --> 00:11:39,800 {\an1}weeks before the peak of the epidemic... 262 00:11:39,833 --> 00:11:43,966 ♪♪ 263 00:11:44,000 --> 00:11:48,466 {\an1}...helping to close in on the source of the outbreak. 264 00:11:48,500 --> 00:11:54,733 ♪♪ 265 00:11:54,766 --> 00:11:57,233 {\an1}A laborer named Hedges had been living in a house 266 00:11:57,266 --> 00:11:59,233 {\an1}on the edge of the river 267 00:11:59,266 --> 00:12:03,300 {\an1}in a part of London called Bromley-by-Bow. 268 00:12:03,333 --> 00:12:06,133 {\an1}A group of investigators are sent to examine 269 00:12:06,166 --> 00:12:09,566 {\an1}the sanitary conditions at the address. 270 00:12:09,600 --> 00:12:11,800 What they find is that the toilet 271 00:12:11,833 --> 00:12:16,633 {\an1}has been expelling wastewater directly into the river. 272 00:12:16,666 --> 00:12:19,100 Contaminating one of the water companies 273 00:12:19,133 --> 00:12:20,633 {\an1}nearby reservoirs, 274 00:12:20,666 --> 00:12:22,733 which hadn't been properly isolated 275 00:12:22,766 --> 00:12:25,233 from the river. 276 00:12:25,266 --> 00:12:27,233 {\an1}The result was the deaths 277 00:12:27,266 --> 00:12:31,066 {\an1}of over 5,500 people. 278 00:12:31,100 --> 00:12:39,066 ♪♪ 279 00:12:39,100 --> 00:12:42,233 {\an1}Within a few weeks of Farr's investigations, 280 00:12:42,266 --> 00:12:44,500 {\an1}the Waterworks are cleaned up 281 00:12:44,533 --> 00:12:46,166 {\an1}and the source of the outbreak 282 00:12:46,200 --> 00:12:49,166 {\an1}is stopped dead in its tracks. 283 00:12:49,200 --> 00:12:54,333 ♪♪ 284 00:12:54,366 --> 00:12:56,400 {\an1}William Farr's meticulous work 285 00:12:56,433 --> 00:12:59,900 led to something of enormous significance. 286 00:12:59,933 --> 00:13:02,500 {\an7}Cholera, King Cholera, the disease 287 00:13:02,533 --> 00:13:04,666 {\an7}the Victorians feared more than any other, 288 00:13:04,700 --> 00:13:06,833 {\an7}the disease that stalked their nightmares 289 00:13:06,866 --> 00:13:09,366 {\an1}was finally driven from the city of London, 290 00:13:09,400 --> 00:13:11,700 {\an1}not by a new vaccine, 291 00:13:11,733 --> 00:13:13,400 {\an1}not by a new medicine, 292 00:13:13,433 --> 00:13:16,400 but by data. 293 00:13:16,433 --> 00:13:20,233 {\an1}And it was never to return. 294 00:13:20,266 --> 00:13:24,733 {\an1}-The legacy of William Farr is around us everywhere 295 00:13:24,766 --> 00:13:26,766 {\an1}in the age of COVID-19. -Yeah. 296 00:13:26,800 --> 00:13:29,366 {\an1}-I remember watching the daily briefings, 297 00:13:29,400 --> 00:13:31,633 {\an1}and the centerpiece of those briefings 298 00:13:31,666 --> 00:13:32,933 was Farr's Curve. 299 00:13:32,966 --> 00:13:35,800 {\an1}-If you look at the curves of outbreaks -- 300 00:13:35,833 --> 00:13:37,966 {\an1}you know, they go big peaks, and they come down. 301 00:13:38,000 --> 00:13:41,033 {\an1}What we need to do is flatten that down. 302 00:13:41,066 --> 00:13:43,700 {\an1}That would ultimately have less deaths. 303 00:13:43,733 --> 00:13:48,166 {\an1}-The most important question in the world at that point was, 304 00:13:48,200 --> 00:13:50,633 {\an1}when was that curve gonna crest? 305 00:13:50,666 --> 00:13:52,200 And whether we could flattened it 306 00:13:52,233 --> 00:13:53,766 {\an1}by washing our hands 307 00:13:53,800 --> 00:13:55,433 {\an1}and social distancing and wearing masks 308 00:13:55,466 --> 00:13:57,166 {\an1}and all of the different interventions we made. 309 00:13:57,200 --> 00:14:01,433 {\an1}We were all chasing the shape of that outbreak. 310 00:14:01,466 --> 00:14:04,133 {\an1}-And we all learned something that those experts already knew, 311 00:14:04,166 --> 00:14:06,866 which is that there's a horrible time lag. 312 00:14:06,900 --> 00:14:09,200 And when we see infections rising, 313 00:14:09,233 --> 00:14:10,466 {\an1}we know that there's -- 314 00:14:10,500 --> 00:14:11,966 {\an1}nothing can stop the fact 315 00:14:12,000 --> 00:14:14,866 {\an1}that the death rates will be rising in the future. 316 00:14:14,900 --> 00:14:17,300 -By the time you are tracking the deaths, 317 00:14:17,333 --> 00:14:18,900 the outbreak has already been unleashed 318 00:14:18,933 --> 00:14:20,766 {\an1}in the society around it. 319 00:14:20,800 --> 00:14:22,200 {\an1}So the question is 320 00:14:22,233 --> 00:14:25,200 {\an1}whether we can use the same kind of approach, 321 00:14:25,233 --> 00:14:30,200 {\an1}but using new technology to get earlier in the cycle, 322 00:14:30,233 --> 00:14:31,400 {\an1}not just tracking deaths, 323 00:14:31,433 --> 00:14:33,366 not just tracking hospitalizations, 324 00:14:33,400 --> 00:14:36,066 {\an1}but actually tracking emergent infections 325 00:14:36,100 --> 00:14:37,833 {\an1}potentially before the community 326 00:14:37,866 --> 00:14:39,833 is even aware that an outbreak is happening. 327 00:14:39,866 --> 00:14:41,533 {\an1}Because if you can do that, 328 00:14:41,566 --> 00:14:44,366 {\an1}then you can create a kind of early warning system 329 00:14:44,400 --> 00:14:46,466 {\an1}that might actually prevent the outbreak 330 00:14:46,500 --> 00:14:48,100 {\an1}from happening at all. 331 00:14:48,133 --> 00:14:53,933 {\an8}♪♪ 332 00:14:53,966 --> 00:14:57,333 {\an1}Amazingly, one of the most promising sources of data 333 00:14:57,366 --> 00:14:59,533 {\an1}for detecting outbreaks early 334 00:14:59,566 --> 00:15:02,166 {\an1}is precisely the thing that used to kill us 335 00:15:02,200 --> 00:15:05,166 {\an1}in the days of cholera. 336 00:15:05,200 --> 00:15:07,333 {\an1}Public health officials and researchers 337 00:15:07,366 --> 00:15:09,900 are tracking the spread of COVID 338 00:15:09,933 --> 00:15:13,633 {\an1}by looking for signs of the virus in sewage. 339 00:15:13,666 --> 00:15:15,866 {\an1}It's a new approach that may actually help us 340 00:15:15,900 --> 00:15:19,033 get ahead of William Farr's epidemic curve 341 00:15:19,066 --> 00:15:21,566 {\an1}by detecting rising case loads 342 00:15:21,600 --> 00:15:24,733 {\an1}before outbreaks spiral out of control. 343 00:15:24,766 --> 00:15:29,433 ♪♪ 344 00:15:29,466 --> 00:15:31,166 {\an1}This is one of the largest 345 00:15:31,200 --> 00:15:34,500 {\an1}wastewater treatment plants in California... 346 00:15:34,533 --> 00:15:37,833 ♪♪ 347 00:15:37,866 --> 00:15:42,333 {\an1}...handling the sewage of over 740,000 people. 348 00:15:42,366 --> 00:15:46,000 ♪♪ 349 00:15:46,033 --> 00:15:49,266 {\an1}Eileen White is in charge of the facility, 350 00:15:49,300 --> 00:15:50,800 {\an1}and she's at the forefront 351 00:15:50,833 --> 00:15:52,966 {\an1}of trialing this new approach 352 00:15:53,000 --> 00:15:55,966 {\an1}to tackling COVID-19. 353 00:15:56,000 --> 00:15:58,833 {\an7}-When a person gets infected with the coronavirus, 354 00:15:58,866 --> 00:16:02,233 {\an7}they may not feel symptoms for several days or a week, 355 00:16:02,266 --> 00:16:03,433 {\an1}but when they're infected, 356 00:16:03,466 --> 00:16:05,933 {\an1}whether they're symptomatic or asymptomatic, 357 00:16:05,966 --> 00:16:09,433 {\an1}they're gonna shed the virus in their poop. 358 00:16:09,466 --> 00:16:12,033 {\an1}So when a person uses the bathroom at home, 359 00:16:12,066 --> 00:16:14,533 they flush, and all that sewage flows 360 00:16:14,566 --> 00:16:17,366 {\an1}to this main wastewater treatment plant. 361 00:16:17,400 --> 00:16:20,533 ♪♪ 362 00:16:20,566 --> 00:16:22,866 {\an1}So, that sample then comes into our lab 363 00:16:22,900 --> 00:16:24,866 {\an1}and then our scientists in the lab 364 00:16:24,900 --> 00:16:28,700 {\an1}put it into our PCR machine 365 00:16:28,733 --> 00:16:31,566 that can actually quantify the presence 366 00:16:31,600 --> 00:16:34,233 {\an1}of the coronavirus in the sewage. 367 00:16:34,266 --> 00:16:36,733 So this can give the public health professionals 368 00:16:36,766 --> 00:16:39,266 {\an1}a good week's advance notice 369 00:16:39,300 --> 00:16:43,166 {\an1}of the presence of coronavirus in a region. 370 00:16:43,200 --> 00:16:47,166 {\an1}-This is an incredible early-warning system 371 00:16:47,200 --> 00:16:49,866 {\an1}revealing hotspots of coronavirus 372 00:16:49,900 --> 00:16:54,000 before people even have symptoms. 373 00:16:54,033 --> 00:16:55,666 {\an1}We should pause here just for a second 374 00:16:55,700 --> 00:16:57,166 {\an1}to appreciate and understand 375 00:16:57,200 --> 00:16:59,366 the technology that makes this possible. 376 00:16:59,400 --> 00:17:02,200 {\an1}One of the relatively new technologies 377 00:17:02,233 --> 00:17:06,200 {\an1}that has been absolutely crucial in fighting COVID-19 378 00:17:06,233 --> 00:17:08,966 {\an1}and in collecting data about COVID-19 379 00:17:09,000 --> 00:17:12,100 {\an1}is something called PCR, or polymerase chain reaction, 380 00:17:12,133 --> 00:17:13,766 {\an1}-And that acronym, PCR, 381 00:17:13,800 --> 00:17:15,666 {\an1}along with flattening the curve, 382 00:17:15,700 --> 00:17:18,000 that became part of millions of people's lives 383 00:17:18,033 --> 00:17:19,166 {\an1}during the pandemic. 384 00:17:19,200 --> 00:17:21,666 {\an1}Because if you went for a COVID test, 385 00:17:21,700 --> 00:17:23,600 the chances are it was a PCR test. 386 00:17:23,633 --> 00:17:25,433 {\an1}But that same technology 387 00:17:25,466 --> 00:17:28,633 can also be used to analyze sewage. 388 00:17:28,666 --> 00:17:30,133 {\an1}-You can use it to seek out 389 00:17:30,166 --> 00:17:33,066 {\an1}tiny little snippets of genetic code, 390 00:17:33,100 --> 00:17:34,266 {\an1}and there would be otherwise, 391 00:17:34,300 --> 00:17:36,100 like, a needle in a vast haystack. 392 00:17:36,133 --> 00:17:37,800 {\an1}And what it does is, basically, 393 00:17:37,833 --> 00:17:41,866 {\an1}it makes copies of those little genetic snippets. 394 00:17:41,900 --> 00:17:45,000 {\an1}It's sometimes called a kind of genetic photocopier. 395 00:17:45,033 --> 00:17:47,466 {\an1}And by amplifying that code, 396 00:17:47,500 --> 00:17:49,000 {\an1}it brings it up to a signal 397 00:17:49,033 --> 00:17:53,166 {\an1}that's loud enough that it can be detected. 398 00:17:53,200 --> 00:17:56,433 Sewage sampling can identify coronavirus 399 00:17:56,466 --> 00:17:59,933 at levels as low as one case per 10,000 people 400 00:17:59,966 --> 00:18:02,500 {\an1}and even different variants. 401 00:18:02,533 --> 00:18:04,566 {\an1}-We're in the control room, 402 00:18:04,600 --> 00:18:09,900 {\an1}and so we have 1,600 miles of sewer pipe. 403 00:18:09,933 --> 00:18:13,166 {\an1}We can sample from any one of our large pipes, 404 00:18:13,200 --> 00:18:18,066 {\an1}and that will tell us what's happening in a particular area. 405 00:18:18,100 --> 00:18:20,066 {\an1}And you can see early on 406 00:18:20,100 --> 00:18:23,266 {\an1}where the case loads were pretty low. 407 00:18:23,300 --> 00:18:27,366 {\an1}You can see the peaks that we had over the summer 408 00:18:27,400 --> 00:18:29,433 {\an1}And definitely it shows the recent spike 409 00:18:29,466 --> 00:18:31,433 that we've seen in early December, 410 00:18:31,466 --> 00:18:35,433 {\an1}after the Thanksgiving holiday. 411 00:18:35,466 --> 00:18:38,633 {\an1}-The technique can also be used to sample sewage 412 00:18:38,666 --> 00:18:41,466 {\an1}from individual buildings like nursing homes, 413 00:18:41,500 --> 00:18:45,200 {\an1}schools, and student housing. 414 00:18:45,233 --> 00:18:47,166 -When you pick up the sewage signal, 415 00:18:47,200 --> 00:18:49,700 {\an1}that's an indicator to the public health professionals 416 00:18:49,733 --> 00:18:53,000 {\an1}that you want to go in, do individual testing, 417 00:18:53,033 --> 00:18:55,333 and then isolate and quarantine people. 418 00:18:55,366 --> 00:18:58,833 {\an1}And it'll be even more important as the vaccines are rolled out. 419 00:18:58,866 --> 00:19:01,266 We can be using the wastewater surveillance data 420 00:19:01,300 --> 00:19:03,100 to find out where are we still seeing 421 00:19:03,133 --> 00:19:05,566 {\an1}the presence of coronavirus in the community 422 00:19:05,600 --> 00:19:08,833 {\an1}and maybe those people aren't getting vaccinated 423 00:19:08,866 --> 00:19:11,500 {\an1}and maybe there needs to be a public outreach effort 424 00:19:11,533 --> 00:19:15,166 {\an1}to inform people of the vaccine. 425 00:19:15,200 --> 00:19:18,633 {\an1}The public health officers have really seen 426 00:19:18,666 --> 00:19:22,900 {\an1}how wastewater surveillance can be a cost effective tool 427 00:19:22,933 --> 00:19:25,400 in managing the global pandemic. 428 00:19:25,433 --> 00:19:29,833 ♪♪ 429 00:19:29,866 --> 00:19:32,133 {\an1}-Sewage surveillance is a great way 430 00:19:32,166 --> 00:19:34,966 {\an1}to identify infections in a community 431 00:19:35,000 --> 00:19:38,400 {\an1}before they show up in official figures. 432 00:19:38,433 --> 00:19:41,333 {\an1}But what about in ourselves? 433 00:19:41,366 --> 00:19:43,833 {\an1}Is there any way of using data to detect COVID 434 00:19:43,866 --> 00:19:48,033 {\an1}and other viral infections earlier in individuals? 435 00:19:51,766 --> 00:19:55,266 {\an1}I'm here in the hills above Stanford University. 436 00:19:58,333 --> 00:19:59,800 {\an1}-Extremely natural. 437 00:19:59,833 --> 00:20:01,800 [ Both laugh ] 438 00:20:01,833 --> 00:20:05,733 {\an1}Dr. Snyder's an expert in health and wearable tech. 439 00:20:05,766 --> 00:20:07,533 ♪♪ 440 00:20:07,566 --> 00:20:09,733 {\an1}I've been lucky enough to enroll in his study 441 00:20:09,766 --> 00:20:13,733 {\an1}that's trying to determine if smart devices can be used 442 00:20:13,766 --> 00:20:18,666 {\an1}to detect COVID infection before symptoms appear. 443 00:20:18,700 --> 00:20:21,200 {\an8}Well, Michael, you are on the cutting edge 444 00:20:21,233 --> 00:20:23,033 {\an7}of all of this technology. 445 00:20:23,066 --> 00:20:25,533 {\an7}I got to start just by asking 446 00:20:25,566 --> 00:20:28,133 {\an7}how many different devices are you wearing right now, 447 00:20:28,166 --> 00:20:30,500 {\an1}tracking your your body state? 448 00:20:30,533 --> 00:20:32,000 {\an1}-I think seven right now. 449 00:20:32,033 --> 00:20:33,000 -Seven?! -Yeah. 450 00:20:33,033 --> 00:20:34,800 {\an1}-I mean, I could see four. 451 00:20:34,833 --> 00:20:36,466 -Yeah. So, four is smartwatches. 452 00:20:36,500 --> 00:20:38,233 {\an1}This is actually an exposometer 453 00:20:38,266 --> 00:20:39,666 that will measure 454 00:20:39,700 --> 00:20:41,500 {\an1}the various things that I'm exposed to. 455 00:20:41,533 --> 00:20:45,666 {\an1}I have a continuous glucose monitor on my side here, 456 00:20:45,700 --> 00:20:47,733 my smartphone. 457 00:20:47,766 --> 00:20:49,366 {\an1}Well, that's six, right? 458 00:20:49,400 --> 00:20:50,833 {\an1}No, that's seven, isn't it? Yeah. 459 00:20:50,866 --> 00:20:53,466 {\an1}-So you have to budget like an extra half hour 460 00:20:53,500 --> 00:20:56,466 {\an1}to go through security at the airport. 461 00:20:56,500 --> 00:20:59,000 {\an1}What's so powerful about your COVID study 462 00:20:59,033 --> 00:21:02,333 is this idea that you're able to detect 463 00:21:02,366 --> 00:21:04,000 {\an1}the presence of an infection 464 00:21:04,033 --> 00:21:06,533 {\an1}before people actually experience symptoms. 465 00:21:06,566 --> 00:21:07,900 -Is that right? -That's right. Yeah. 466 00:21:07,933 --> 00:21:10,333 {\an1}So what happens is, when you get ill, 467 00:21:10,366 --> 00:21:12,666 {\an1}your resting heart rate will jump up typically 468 00:21:12,700 --> 00:21:14,333 {\an1}about seven to eight beats a minute. 469 00:21:14,366 --> 00:21:15,666 {\an1}It's true for COVID. 470 00:21:15,700 --> 00:21:18,400 {\an1}It's true for other kinds of viral infections, as well, 471 00:21:18,433 --> 00:21:20,433 {\an1}Your immune system becomes activated, 472 00:21:20,466 --> 00:21:23,666 {\an1}and that requires energy, elevated heart rate. 473 00:21:23,700 --> 00:21:25,600 {\an1}For a normal viral infection, 474 00:21:25,633 --> 00:21:27,900 it will jump up about 30 hours early 475 00:21:27,933 --> 00:21:30,300 prior to you feeling symptomatic. 476 00:21:30,333 --> 00:21:32,933 ♪♪ 477 00:21:32,966 --> 00:21:34,700 {\an1}So this is our very first participant 478 00:21:34,733 --> 00:21:37,166 {\an1}enrolled in the study. 479 00:21:37,200 --> 00:21:41,466 {\an1}That first red signal is when he felt symptomatic. 480 00:21:41,500 --> 00:21:43,466 {\an1}And the yellow signal is 481 00:21:43,500 --> 00:21:47,700 {\an1}when we first told him something was up. 482 00:21:47,733 --> 00:21:51,200 {\an1}It turns out, if you look at the purple day there, 483 00:21:51,233 --> 00:21:53,133 {\an1}that's actually when they're diagnosed with COVID. 484 00:21:53,166 --> 00:21:54,800 {\an1}The red day is the day before it, 485 00:21:54,833 --> 00:21:58,166 {\an1}when they first had symptoms. 486 00:21:58,200 --> 00:21:59,833 But if you look at the resting heart rate, 487 00:21:59,866 --> 00:22:02,233 {\an1}you'll see it jumps up nine and a half days 488 00:22:02,266 --> 00:22:04,133 {\an1}before they were symptomatic. 489 00:22:04,166 --> 00:22:06,500 {\an1}So, this person's been ill for nine and a half days 490 00:22:06,533 --> 00:22:07,666 {\an1}without knowing it, 491 00:22:07,700 --> 00:22:09,466 {\an1}spreading the virus, presumably, 492 00:22:09,500 --> 00:22:12,300 {\an1}when he could have been caught with a smartwatch. 493 00:22:12,333 --> 00:22:13,666 {\an1}-That's incredibly powerful. 494 00:22:13,700 --> 00:22:14,800 {\an1}Part of what you're picking up 495 00:22:14,833 --> 00:22:18,133 {\an1}is the long period of asymptomatic. 496 00:22:18,166 --> 00:22:19,166 -That's right. 497 00:22:19,200 --> 00:22:21,166 {\an1}-Which is so distinctive of this virus. 498 00:22:21,200 --> 00:22:22,600 -That's right. 499 00:22:22,633 --> 00:22:25,266 {\an1}So right now, 20% of the U.S. has a smartwatch -- 500 00:22:25,300 --> 00:22:26,666 {\an1}50 million people. 501 00:22:26,700 --> 00:22:28,633 {\an1}And all you have to do is download an app, 502 00:22:28,666 --> 00:22:29,800 and the signals 503 00:22:29,833 --> 00:22:32,900 {\an1}of elevated physiology can alert you. 504 00:22:32,933 --> 00:22:35,166 {\an1}So it can really reach, we think, 505 00:22:35,200 --> 00:22:36,766 {\an1}the entire world, in fact. 506 00:22:36,800 --> 00:22:38,866 ♪♪ 507 00:22:38,900 --> 00:22:42,200 {\an1}-This wearable tech can monitor heart rate and rhythm, 508 00:22:42,233 --> 00:22:43,733 {\an1}blood oxygen levels, 509 00:22:43,766 --> 00:22:47,233 {\an1}skin conductivity, and sleep patterns at night, 510 00:22:47,266 --> 00:22:51,100 {\an1}searching the data for anything out of the ordinary 511 00:22:51,133 --> 00:22:54,766 {\an1}and triggering an alert to go see a doctor. 512 00:22:54,800 --> 00:22:56,700 In the future, it's hoped that the tech 513 00:22:56,733 --> 00:22:57,900 {\an1}will be able to distinguish 514 00:22:57,933 --> 00:22:59,966 between different viral infections 515 00:23:00,000 --> 00:23:02,766 {\an1}such as COVID-19 and the flu, 516 00:23:02,800 --> 00:23:07,033 and even track how people are aging. 517 00:23:07,066 --> 00:23:08,533 {\an1}-And what we've discovered is actually, 518 00:23:08,566 --> 00:23:10,266 {\an1}by following people over time, 519 00:23:10,300 --> 00:23:12,166 that people are aging differently. 520 00:23:12,200 --> 00:23:13,366 It's like a car. 521 00:23:13,400 --> 00:23:16,033 {\an1}You know, your car gets older as it goes along, 522 00:23:16,066 --> 00:23:18,100 {\an1}but some parts break down first. 523 00:23:18,133 --> 00:23:20,800 {\an1}Maybe those are the ones you can pay most attention to. 524 00:23:20,833 --> 00:23:21,966 {\an1}You might exercise more. 525 00:23:22,000 --> 00:23:23,066 {\an1}You might get checkups 526 00:23:23,100 --> 00:23:25,200 {\an1}and maybe you have something going on 527 00:23:25,233 --> 00:23:26,633 where you would like to intervene, 528 00:23:26,666 --> 00:23:28,966 {\an1}possibly with drugs, to actually, you know, 529 00:23:29,000 --> 00:23:30,366 better improve your heart health. 530 00:23:30,400 --> 00:23:32,100 {\an1}I don't know if a wearable will tell you 531 00:23:32,133 --> 00:23:34,533 {\an1}if you have early cancer or not, but believe it or not, 532 00:23:34,566 --> 00:23:37,033 {\an1}we're gonna give that a try, too. 533 00:23:37,066 --> 00:23:40,233 ♪♪ 534 00:23:40,266 --> 00:23:42,466 {\an1}-Now, I don't want to get all Pollyannaish 535 00:23:42,500 --> 00:23:45,266 about the latest Silicon Valley gadgets. 536 00:23:45,300 --> 00:23:47,133 {\an1}I mean, the truth is that wearable tech 537 00:23:47,166 --> 00:23:49,633 {\an1}is not gonna be the same level of breakthrough 538 00:23:49,666 --> 00:23:51,800 {\an1}as, say, vaccines. 539 00:23:51,833 --> 00:23:55,200 {\an1}But if you think about it, conditions like cancer, 540 00:23:55,233 --> 00:23:56,933 {\an1}heart disease, and diabetes 541 00:23:56,966 --> 00:23:58,700 {\an1}are responsible for two thirds 542 00:23:58,733 --> 00:24:00,666 {\an1}of deaths in the United States. 543 00:24:00,700 --> 00:24:04,166 {\an1}And we know that early detection is key. 544 00:24:04,200 --> 00:24:07,066 {\an1}So if monitoring wearable data 545 00:24:07,100 --> 00:24:09,733 {\an1}can help us pick up these conditions earlier, 546 00:24:09,766 --> 00:24:12,566 {\an1}that could help us all live longer lives. 547 00:24:12,600 --> 00:24:17,900 ♪♪ 548 00:24:17,933 --> 00:24:20,300 {\an1}So, I'm curious where you come down on this, David, 549 00:24:20,333 --> 00:24:22,466 {\an1}I mean, there's this kind of spectrum in the debate 550 00:24:22,500 --> 00:24:25,500 between the folks who are willing to share 551 00:24:25,533 --> 00:24:28,366 {\an1}every intimate detail about their health. 552 00:24:28,400 --> 00:24:30,166 {\an1}On the other hand, you have a growing movement of people 553 00:24:30,200 --> 00:24:32,166 {\an1}who believe that sharing 554 00:24:32,200 --> 00:24:34,800 {\an1}that much information with big tech 555 00:24:34,833 --> 00:24:39,233 {\an1}is potentially an invasion of our personal liberty. 556 00:24:39,266 --> 00:24:42,900 {\an1}-I think what's been interesting is where people stood 557 00:24:42,933 --> 00:24:45,800 {\an1}on that position before and after the pandemic. 558 00:24:45,833 --> 00:24:49,500 {\an1}If the collection of data could be shown 559 00:24:49,533 --> 00:24:52,133 {\an1}to lower death rates and infection rates, 560 00:24:52,166 --> 00:24:54,700 {\an1}as arguably it has been in countries like South Korea, 561 00:24:54,733 --> 00:24:57,100 where they used mobile phone data 562 00:24:57,133 --> 00:24:58,700 {\an1}to enforce lockdowns, 563 00:24:58,733 --> 00:25:02,766 {\an1}are you willing to have that level of intrusion in your life 564 00:25:02,800 --> 00:25:04,600 {\an1}and in your digital life 565 00:25:04,633 --> 00:25:06,766 if it means that you can live in a country 566 00:25:06,800 --> 00:25:08,800 {\an1}where your other liberties, like the liberty of being able 567 00:25:08,833 --> 00:25:10,766 {\an1}to go and have dinner with your family 568 00:25:10,800 --> 00:25:12,966 {\an1}or get on the train or get on a plane, 569 00:25:13,000 --> 00:25:17,133 {\an1}whether that exchange of liberties is worthwhile. 570 00:25:17,166 --> 00:25:19,166 {\an1}-It's a great point, but it's also 571 00:25:19,200 --> 00:25:21,933 {\an1}about exposing places where the outcomes 572 00:25:21,966 --> 00:25:24,066 {\an1}and particularly in terms of life expectancy, 573 00:25:24,100 --> 00:25:27,200 {\an1}have been severe between one part of a community and another. 574 00:25:27,233 --> 00:25:29,033 {\an1}-We see these disparities in African-American 575 00:25:29,066 --> 00:25:32,000 {\an1}but also Hispanic communities. -Right. 576 00:25:32,033 --> 00:25:34,666 {\an1}-And those inequalities have been stark 577 00:25:34,700 --> 00:25:36,600 {\an1}when it comes to COVID-19. 578 00:25:36,633 --> 00:25:41,100 ♪♪ 579 00:25:41,133 --> 00:25:42,700 {\an1}-I'm Dr. Vanessa Apea. 580 00:25:42,733 --> 00:25:46,233 I'm a consultant, and I work in East London. 581 00:25:48,333 --> 00:25:52,033 {\an1}And a lot of the work that I do in terms of my research 582 00:25:52,066 --> 00:25:53,866 {\an1}is looking at our communities 583 00:25:53,900 --> 00:25:57,633 {\an1}and understanding how health inequalities play out. 584 00:25:57,666 --> 00:26:00,366 ♪♪ 585 00:26:00,400 --> 00:26:02,466 {\an7}So if I were going to paint a picture of East London, 586 00:26:02,500 --> 00:26:05,666 {\an7}what I would say is that it's beautiful and vibrant. 587 00:26:05,700 --> 00:26:07,066 {\an1}We've got Indian populations. 588 00:26:07,100 --> 00:26:09,066 We've got Bangladeshi populations. 589 00:26:09,100 --> 00:26:10,900 {\an1}We've got Pakistani populations. 590 00:26:10,933 --> 00:26:13,233 {\an1}We've got Black African, Black Caribbean. 591 00:26:13,266 --> 00:26:17,233 {\an1}So it's a real mix all living together. 592 00:26:17,266 --> 00:26:19,000 {\an1}But on top of that, 593 00:26:19,033 --> 00:26:21,666 {\an1}you've got pockets of definite affluence, 594 00:26:21,700 --> 00:26:24,633 {\an1}and then you've got pockets of definite deprivation, 595 00:26:24,666 --> 00:26:25,800 as well. 596 00:26:25,833 --> 00:26:28,166 And within that, you will see any walk of life 597 00:26:28,200 --> 00:26:30,966 coming through into the hospital. 598 00:26:31,000 --> 00:26:33,100 {\an1}-Dr. Apea was among the first 599 00:26:33,133 --> 00:26:35,900 {\an1}to study the impact of COVID-19 600 00:26:35,933 --> 00:26:37,866 {\an1}on different ethnicities. 601 00:26:37,900 --> 00:26:41,366 {\an1}She followed the outcomes of over 1,700 patients 602 00:26:41,400 --> 00:26:44,933 {\an1}admitted to hospitals in East London. 603 00:26:44,966 --> 00:26:47,200 {\an1}-So if we look at the top graph, 604 00:26:47,233 --> 00:26:50,366 {\an1}we're looking at days from hospital admission. 605 00:26:50,400 --> 00:26:53,300 {\an1}So we're looking up to 30 days. 606 00:26:53,333 --> 00:26:55,500 {\an1}And then when we look at the different curves, 607 00:26:55,533 --> 00:26:58,800 {\an1}what we're looking at is different ethnicities by color. 608 00:26:58,833 --> 00:27:00,800 So Asian is blue. 609 00:27:00,833 --> 00:27:02,300 Black is red. 610 00:27:02,333 --> 00:27:04,800 {\an1}And green is white. 611 00:27:04,833 --> 00:27:06,733 {\an1}If we were going to say 612 00:27:06,766 --> 00:27:10,000 {\an1}that all ethnicities had the same outcome, 613 00:27:10,033 --> 00:27:13,100 {\an1}you would expect all the lines to be on top of each other. 614 00:27:13,133 --> 00:27:17,133 {\an1}So they would all come together and follow the same trajectory. 615 00:27:20,333 --> 00:27:24,633 {\an1}But when you look at this, there is a difference. 616 00:27:24,666 --> 00:27:27,133 {\an1}there is a significant gap. 617 00:27:27,166 --> 00:27:31,300 ♪♪ 618 00:27:31,333 --> 00:27:34,866 {\an1}When we looked at the survival at 30 days, 619 00:27:34,900 --> 00:27:37,200 {\an1}we saw that for those of Black 620 00:27:37,233 --> 00:27:39,033 {\an1}and those of Asian ethnicity, 621 00:27:39,066 --> 00:27:41,866 {\an1}we saw an increased risk of death. 622 00:27:41,900 --> 00:27:47,700 ♪♪ 623 00:27:47,733 --> 00:27:51,700 {\an1}When we look at those of Black and Asian ethnicity, 624 00:27:51,733 --> 00:27:54,366 {\an1}they were younger and fitter 625 00:27:54,400 --> 00:27:57,700 {\an1}and were dying from COVID-19. 626 00:27:57,733 --> 00:28:01,566 And for me, it was extremely sobering. 627 00:28:01,600 --> 00:28:03,066 {\an1}One of the key questions is, 628 00:28:03,100 --> 00:28:07,200 {\an1}if we are seeing differences in outcomes of COVID-19 629 00:28:07,233 --> 00:28:10,666 {\an1}between white, Asian, and Black communities, 630 00:28:10,700 --> 00:28:12,966 why are we seeing these differences? 631 00:28:13,000 --> 00:28:15,733 {\an1}There's a natural, immediate tendency 632 00:28:15,766 --> 00:28:19,233 of many people to just focus on biology. 633 00:28:19,266 --> 00:28:21,500 {\an1}This is really reductionist. 634 00:28:21,533 --> 00:28:23,833 {\an1}It's not me being Black. 635 00:28:23,866 --> 00:28:26,066 It's the racism that I'm experiencing 636 00:28:26,100 --> 00:28:30,100 {\an1}as a result of being Black that affects my health outcomes. 637 00:28:36,900 --> 00:28:39,200 {\an1}-The idea that these varying outcomes 638 00:28:39,233 --> 00:28:42,600 {\an1}are due to genetic differences between ethnicities 639 00:28:42,633 --> 00:28:45,933 {\an1}is completely refuted by science. 640 00:28:45,966 --> 00:28:48,766 {\an1}-They grow the DNA and separate its parts 641 00:28:48,800 --> 00:28:51,666 {\an1}so a computer can read its chemical sequence. 642 00:28:51,700 --> 00:28:55,200 {\an1}It's the human genome, the genetic spelling of man. 643 00:28:55,233 --> 00:28:58,733 {\an1}-In particular by the work of the Human Genome Project, 644 00:28:58,766 --> 00:29:02,966 {\an1}led by Dr. Francis Collins, who in 2001, 645 00:29:03,000 --> 00:29:05,300 {\an1}published the first DNA map 646 00:29:05,333 --> 00:29:07,800 {\an1}of the human genetic code. 647 00:29:07,833 --> 00:29:11,333 {\an1}It's our instruction book for human biology, 648 00:29:11,366 --> 00:29:13,066 {\an1}and the notion that we could actually 649 00:29:13,100 --> 00:29:15,433 read that book in any of our lifetimes 650 00:29:15,466 --> 00:29:17,433 would have been considered unthinkable 651 00:29:17,466 --> 00:29:20,600 {\an1}20 or 30 years ago. 652 00:29:20,633 --> 00:29:22,600 {\an1}-I know you're saving the world from a pandemic. 653 00:29:22,633 --> 00:29:24,100 {\an8}And so... 654 00:29:24,133 --> 00:29:26,433 {\an7}-With a lot of help from a lot of people. 655 00:29:26,466 --> 00:29:28,500 {\an8}-Let's start with the Human Genome Project. 656 00:29:28,533 --> 00:29:31,133 {\an1}What has that revealed to us 657 00:29:31,166 --> 00:29:34,466 {\an1}about the connection between health outcomes, 658 00:29:34,500 --> 00:29:36,966 {\an1}genetics, and race? 659 00:29:37,000 --> 00:29:38,633 {\an1}-Well, it didn't become clear 660 00:29:38,666 --> 00:29:40,466 {\an1}with that first reference human genome, 661 00:29:40,500 --> 00:29:43,066 {\an1}because that was just one actual patchwork 662 00:29:43,100 --> 00:29:45,233 of four or five different people. 663 00:29:45,266 --> 00:29:47,666 {\an1}Now, gosh, there's hundreds of thousands of genomes. 664 00:29:47,700 --> 00:29:50,500 {\an1}And we've worked really hard to be sure that that's inclusive, 665 00:29:50,533 --> 00:29:52,466 {\an1}including now a lot of genomes from Africa, 666 00:29:52,500 --> 00:29:54,133 {\an1}which is the cradle of humanity. 667 00:29:54,166 --> 00:29:55,366 {\an1}So here's the bottom line. 668 00:29:55,400 --> 00:30:00,533 {\an1}We're all 99.9% the same. 669 00:30:00,566 --> 00:30:02,033 {\an1}We're incredibly similar. 670 00:30:02,066 --> 00:30:03,700 We're one family, 671 00:30:03,733 --> 00:30:06,233 {\an1}and there's more variation within a group, 672 00:30:06,266 --> 00:30:08,633 {\an1}let's say white Americans, 673 00:30:08,666 --> 00:30:11,633 {\an1}than between groups like a single white American 674 00:30:11,666 --> 00:30:13,133 {\an1}and a single African. 675 00:30:13,166 --> 00:30:14,933 {\an1}-So, given what we know then 676 00:30:14,966 --> 00:30:19,100 {\an1}about the lack of kind of pronounced genetic differences 677 00:30:19,133 --> 00:30:20,600 {\an1}between racial groups, 678 00:30:20,633 --> 00:30:24,100 {\an1}when we see pronounced differences in outcomes, 679 00:30:24,133 --> 00:30:25,566 {\an1}as in the COVID crisis, right. 680 00:30:25,600 --> 00:30:28,066 {\an1}Where we, you know, see this disproportionate impact on 681 00:30:28,100 --> 00:30:29,133 {\an1}the African-American community 682 00:30:29,166 --> 00:30:31,933 {\an1}in the United States, for instance, 683 00:30:31,966 --> 00:30:33,466 {\an1}how do we interpret that? 684 00:30:33,500 --> 00:30:36,433 {\an1}-Most of what we see in health disparities 685 00:30:36,466 --> 00:30:39,666 {\an1}is on the basis of something other than DNA variation. 686 00:30:39,700 --> 00:30:43,166 {\an1}This is primarily on the basis of environmental exposures, 687 00:30:43,200 --> 00:30:45,166 {\an1}and we can see that that's a reflection 688 00:30:45,200 --> 00:30:48,333 {\an1}of their social environment. 689 00:30:48,366 --> 00:30:50,500 {\an1}-So it's not genetic, 690 00:30:50,533 --> 00:30:54,500 {\an1}but what is driving the differences that we see? 691 00:30:54,533 --> 00:30:56,500 {\an1}-I believe that the key thing is 692 00:30:56,533 --> 00:30:59,933 {\an1}the social context in which people are living. 693 00:30:59,966 --> 00:31:02,500 Those from Black and Asian communities 694 00:31:02,533 --> 00:31:04,833 {\an1}are more likely to live 695 00:31:04,866 --> 00:31:08,633 {\an1}in a multigenerational household in a smaller space, 696 00:31:08,666 --> 00:31:13,100 {\an1}so your risk of transmission and vulnerability goes up. 697 00:31:13,133 --> 00:31:15,300 ♪♪ 698 00:31:15,333 --> 00:31:18,300 {\an1}And Black and Asian and minority ethnic communities, 699 00:31:18,333 --> 00:31:22,300 {\an1}we see that they are disproportionately more 700 00:31:22,333 --> 00:31:23,833 {\an1}in frontline roles, 701 00:31:23,866 --> 00:31:28,000 {\an1}in which people need direct, face-to-face contact. 702 00:31:28,033 --> 00:31:30,700 So it's about high risk of exposure, 703 00:31:30,733 --> 00:31:32,366 {\an1}higher vulnerability. 704 00:31:32,400 --> 00:31:36,266 {\an1}And then also that's translated into more severe disease 705 00:31:36,300 --> 00:31:39,433 {\an1}and higher rates of death, as well. 706 00:31:39,466 --> 00:31:44,000 {\an1}-What COVID-19 did was it illuminated, 707 00:31:44,033 --> 00:31:48,100 {\an1}focused their attention on disadvantages based on race 708 00:31:48,133 --> 00:31:50,200 {\an1}that were already there, 709 00:31:50,233 --> 00:31:52,433 {\an1}because the reason for those disparities 710 00:31:52,466 --> 00:31:53,566 {\an1}has got nothing to do 711 00:31:53,600 --> 00:31:55,900 with the biology of people of different races 712 00:31:55,933 --> 00:31:57,366 {\an1}from different parts of the world 713 00:31:57,400 --> 00:31:59,333 {\an1}and everything to do with racism. 714 00:31:59,366 --> 00:32:02,433 {\an1}This is about how racism and racial disadvantage 715 00:32:02,466 --> 00:32:04,966 {\an1}left certain population groups vulnerable, 716 00:32:05,000 --> 00:32:06,866 more vulnerable than other people. 717 00:32:06,900 --> 00:32:08,833 {\an1}-And now we're starting to get a sense of the impact of this 718 00:32:08,866 --> 00:32:11,000 {\an1}on overall life expectancy, right? 719 00:32:11,033 --> 00:32:14,000 {\an1}So in the first half of 2020, we believe now 720 00:32:14,033 --> 00:32:16,333 {\an1}that overall life expectancy in the United States 721 00:32:16,366 --> 00:32:19,833 {\an1}dropped by just under a year for the entire population. 722 00:32:19,866 --> 00:32:22,100 {\an1}But for African-American males, 723 00:32:22,133 --> 00:32:24,966 it dropped by nearly three years. 724 00:32:25,000 --> 00:32:26,600 {\an1}So, it's an extraordinary difference, 725 00:32:26,633 --> 00:32:29,233 {\an1}a real gap that you can see in the data. 726 00:32:29,266 --> 00:32:33,400 {\an1}-It's a horrible thing, and because those communities 727 00:32:33,433 --> 00:32:36,933 {\an1}have been suffering racial disadvantage for decades, 728 00:32:36,966 --> 00:32:40,600 {\an1}that's taken a toll long before the pandemic arrived. 729 00:32:40,633 --> 00:32:42,266 {\an1}So they're more likely 730 00:32:42,300 --> 00:32:44,233 to be suffering from preexisting conditions. 731 00:32:44,266 --> 00:32:46,166 {\an1}-That's such an important point 732 00:32:46,200 --> 00:32:50,533 {\an1}in thinking about the long-term story of human health, right. 733 00:32:50,566 --> 00:32:55,166 {\an1}Because so much of it is about seeing the invisible pathogens, 734 00:32:55,200 --> 00:32:57,066 {\an1}right, seeing the virus under the microscope 735 00:32:57,100 --> 00:32:58,566 or the bacterium under the microscope 736 00:32:58,600 --> 00:33:00,766 {\an1}and identifying it as the culprit 737 00:33:00,800 --> 00:33:03,100 {\an1}that is causing people to get sick. 738 00:33:03,133 --> 00:33:06,366 {\an1}But there's a whole other side to human health, 739 00:33:06,400 --> 00:33:08,566 {\an1}which is about the environment that people live in 740 00:33:08,600 --> 00:33:11,733 {\an1}and the conditions that shape their day-to-day lives. 741 00:33:11,766 --> 00:33:13,433 {\an1}And in fact, this is a part 742 00:33:13,466 --> 00:33:15,433 {\an1}of the detective work of data collection 743 00:33:15,466 --> 00:33:18,200 {\an1}that dates all the way back to the 19th century, as well. 744 00:33:18,233 --> 00:33:26,433 ♪♪ 745 00:33:26,466 --> 00:33:34,666 ♪♪ 746 00:33:34,700 --> 00:33:36,333 {\an1}So, it's a block that way. 747 00:33:39,566 --> 00:33:41,700 Here it is. 748 00:33:41,733 --> 00:33:45,166 {\an1}Almost every major city has multiple monuments 749 00:33:45,200 --> 00:33:48,733 to lives lost in military conflicts, 750 00:33:48,766 --> 00:33:53,000 {\an1}but you almost never see memorials to the lives saved 751 00:33:53,033 --> 00:33:56,300 thanks to medical or public health breakthroughs. 752 00:33:56,333 --> 00:34:01,200 {\an1}This little plaque commemorating the work of W.E.B Du Bois 753 00:34:01,233 --> 00:34:02,900 is the exception. 754 00:34:04,800 --> 00:34:06,933 {\an1}Now, Du Bois is famous today 755 00:34:06,966 --> 00:34:08,933 {\an1}for his work as a sociologist 756 00:34:08,966 --> 00:34:10,866 {\an1}and a political activist, 757 00:34:10,900 --> 00:34:14,033 but he was also an unappreciated pioneer 758 00:34:14,066 --> 00:34:16,166 {\an1}in the study of human health, 759 00:34:16,200 --> 00:34:20,333 {\an1}thanks to work that he did in the 1890s here, 760 00:34:20,366 --> 00:34:22,333 {\an1}in Philadelphia's 7th Ward. 761 00:34:22,366 --> 00:34:30,166 ♪♪ 762 00:34:30,200 --> 00:34:38,000 ♪♪ 763 00:34:38,033 --> 00:34:42,000 Du Bois was an aspiring sociologist. 764 00:34:42,033 --> 00:34:43,966 {\an1}And also something of a prodigy. 765 00:34:44,000 --> 00:34:45,366 He was the first African-American 766 00:34:45,400 --> 00:34:47,466 {\an1}to get a Ph.D. from Harvard, 767 00:34:47,500 --> 00:34:49,133 {\an1}and at the time, he was reading 768 00:34:49,166 --> 00:34:51,800 {\an1}this openly racist academic literature 769 00:34:51,833 --> 00:34:53,633 {\an1}that portrayed African-Americans 770 00:34:53,666 --> 00:34:57,466 {\an1}as intrinsically unhealthy or criminal. 771 00:34:57,500 --> 00:34:59,966 ♪♪ 772 00:35:00,000 --> 00:35:02,266 {\an1}But Du Bois was determined 773 00:35:02,300 --> 00:35:04,433 to delve deeper into the problem, 774 00:35:04,466 --> 00:35:06,600 using data. 775 00:35:06,633 --> 00:35:09,166 {\an1}The opportunity arose when he was invited 776 00:35:09,200 --> 00:35:12,866 to study Philadelphia's 7th Ward, 777 00:35:12,900 --> 00:35:17,000 {\an1}an area with a large African-American community, 778 00:35:17,033 --> 00:35:19,200 {\an1}ravaged by poverty, crime, 779 00:35:19,233 --> 00:35:21,533 {\an1}and high child mortality. 780 00:35:21,566 --> 00:35:24,033 ♪♪ 781 00:35:24,066 --> 00:35:25,533 {\an1}The white establishment framed 782 00:35:25,566 --> 00:35:29,266 {\an1}these very real problems in racist terms, 783 00:35:29,300 --> 00:35:34,433 {\an1}pointing to alleged deficiencies in African-Americans. 784 00:35:34,466 --> 00:35:36,766 {\an1}Du Bois moved to Philadelphia 785 00:35:36,800 --> 00:35:39,233 {\an1}and took on the task of documenting 786 00:35:39,266 --> 00:35:44,400 {\an1}the social and health problems of the 7th Ward. 787 00:35:44,433 --> 00:35:47,466 {\an1}One of the key questions he wanted to answer 788 00:35:47,500 --> 00:35:52,033 {\an1}was why life expectancy in the 7th Ward was so low. 789 00:35:52,066 --> 00:35:55,866 {\an1}He spent countless hours exploring this neighborhood, 790 00:35:55,900 --> 00:36:00,000 {\an1}visited over 2,000 homes, interviewing their occupants, 791 00:36:00,033 --> 00:36:01,866 surveying their living conditions, 792 00:36:01,900 --> 00:36:04,433 {\an1}poring over documents in the library, 793 00:36:04,466 --> 00:36:07,533 {\an1}and amassing a mountain of information. 794 00:36:07,566 --> 00:36:15,333 ♪♪ 795 00:36:15,366 --> 00:36:18,300 With this data, Du Bois created this 796 00:36:18,333 --> 00:36:21,966 {\an1}extraordinary color-coded map of the 7th Ward, 797 00:36:22,000 --> 00:36:23,866 {\an7}documenting all the homes 798 00:36:23,900 --> 00:36:27,200 {\an7}and dividing them up into different economic classes. 799 00:36:27,233 --> 00:36:29,566 {\an7}And what the data revealed 800 00:36:29,600 --> 00:36:31,533 {\an8}was very telling. 801 00:36:31,566 --> 00:36:38,200 {\an8}♪♪ 802 00:36:38,233 --> 00:36:41,533 {\an1}87% of residents had no access 803 00:36:41,566 --> 00:36:43,433 {\an1}to an indoor toilet. 804 00:36:43,466 --> 00:36:45,900 ♪♪ 805 00:36:45,933 --> 00:36:49,233 {\an1}Almost none had hot water. 806 00:36:49,266 --> 00:36:51,133 {\an1}And overcrowding was rife 807 00:36:51,166 --> 00:36:55,100 {\an1}with as many as 10 people living in a single room. 808 00:36:55,133 --> 00:36:57,600 {\an1}To make matters worse, 809 00:36:57,633 --> 00:36:59,600 landlords were charging higher rents 810 00:36:59,633 --> 00:37:01,333 {\an1}to Black residents, 811 00:37:01,366 --> 00:37:03,466 {\an1}exacerbating their poverty 812 00:37:03,500 --> 00:37:06,866 {\an1}along with their poor health. 813 00:37:06,900 --> 00:37:10,466 {\an1}It all combined to mean that, in Philadelphia, 814 00:37:10,500 --> 00:37:15,133 {\an1}the likelihood of a Black child dying before the age of 15 815 00:37:15,166 --> 00:37:18,066 {\an1}was twice that of a white child. 816 00:37:18,100 --> 00:37:22,900 ♪♪ 817 00:37:22,933 --> 00:37:25,500 Du Bois published that data and the map 818 00:37:25,533 --> 00:37:27,700 {\an1}in a groundbreaking book. 819 00:37:27,733 --> 00:37:29,600 {\an1}It painted a picture of what today 820 00:37:29,633 --> 00:37:32,266 {\an1}we would call systemic racism. 821 00:37:32,300 --> 00:37:34,466 {\an1}If the life expectancy of African-Americans 822 00:37:34,500 --> 00:37:35,966 {\an1}was going to improve, 823 00:37:36,000 --> 00:37:39,233 {\an1}the entire environment and economic system around them 824 00:37:39,266 --> 00:37:40,900 {\an1}was going to have to be changed. 825 00:37:40,933 --> 00:37:44,433 ♪♪ 826 00:37:44,466 --> 00:37:48,400 {\an1}Du Bois wrote, "The most difficult social problem is 827 00:37:48,433 --> 00:37:50,666 {\an1}the peculiar attitude of the nation 828 00:37:50,700 --> 00:37:54,600 {\an1}towards the well-being of the race. 829 00:37:54,633 --> 00:37:56,400 {\an1}There have been few other cases 830 00:37:56,433 --> 00:37:58,733 in the history of civilized people 831 00:37:58,766 --> 00:38:01,100 {\an1}where human suffering has been viewed 832 00:38:01,133 --> 00:38:03,933 {\an1}with such peculiar indifference." 833 00:38:06,200 --> 00:38:07,833 {\an1}Du Bois proved that 834 00:38:07,866 --> 00:38:10,233 {\an1}the health problems in the 7th Ward 835 00:38:10,266 --> 00:38:13,400 {\an1}weren't caused by some natural inferiority, 836 00:38:13,433 --> 00:38:16,700 but rather that the system itself was rigged 837 00:38:16,733 --> 00:38:19,366 {\an1}to shorten black lives. 838 00:38:19,400 --> 00:38:22,033 ♪♪ 839 00:38:22,066 --> 00:38:26,100 {\an1}Then in 1900, Du Bois took an exhibition of his work 840 00:38:26,133 --> 00:38:28,200 {\an1}to the World Expo in Paris, 841 00:38:28,233 --> 00:38:29,933 {\an1}an international showcase 842 00:38:29,966 --> 00:38:33,600 of cultural and scientific achievement, 843 00:38:33,633 --> 00:38:36,666 where he revealed startling visualizations 844 00:38:36,700 --> 00:38:38,666 {\an1}of the data he'd collected. 845 00:38:38,700 --> 00:38:44,400 ♪♪ 846 00:38:44,433 --> 00:38:50,133 ♪♪ 847 00:38:50,166 --> 00:38:53,800 {\an1}This towering achievement of brilliantly designed data 848 00:38:53,833 --> 00:38:58,366 {\an1}opened the world's eyes to the reality of Black lives. 849 00:38:58,400 --> 00:39:03,266 ♪♪ 850 00:39:03,300 --> 00:39:07,233 {\an1}When I look at these bold and colorful images, 851 00:39:07,266 --> 00:39:10,133 {\an1}it's almost like looking at modern art, 852 00:39:10,166 --> 00:39:13,633 {\an1}but it's art in the service of social progress, 853 00:39:13,666 --> 00:39:16,033 not so much speaking truth to power 854 00:39:16,066 --> 00:39:19,300 {\an1}as it is using data to reveal truths 855 00:39:19,333 --> 00:39:22,366 {\an1}to make the invisible visible. 856 00:39:22,400 --> 00:39:30,533 ♪♪ 857 00:39:30,566 --> 00:39:34,433 {\an1}Du Bois is such an inspiring figure on so many levels, 858 00:39:34,466 --> 00:39:37,233 {\an1}but in terms of looking at these environmental causes, 859 00:39:37,266 --> 00:39:40,066 {\an1}he was decades ahead of everyone else. 860 00:39:40,100 --> 00:39:42,900 I mean, it's just incredibly visionary work. 861 00:39:42,933 --> 00:39:45,733 {\an1}-He really believed that if America was forced 862 00:39:45,766 --> 00:39:48,766 {\an1}to confront these inequalities, 863 00:39:48,800 --> 00:39:50,000 the contribution 864 00:39:50,033 --> 00:39:52,100 and the suffering of African-Americans 865 00:39:52,133 --> 00:39:54,300 {\an1}could be brought to American and world attention, 866 00:39:54,333 --> 00:39:56,366 that it would bring about change. 867 00:39:56,400 --> 00:39:58,766 {\an7}Wouldn't it be wonderful if you could say 868 00:39:58,800 --> 00:40:01,833 {\an7}these data visualizations were the moment in which America 869 00:40:01,866 --> 00:40:03,933 {\an1}confronted the legacy of slavery and racism, 870 00:40:03,966 --> 00:40:05,933 {\an1}and there was a kind of parallel America 871 00:40:05,966 --> 00:40:07,600 {\an1}that never happened, that could have happened. 872 00:40:07,633 --> 00:40:09,333 {\an1}So it's kind of heartbreaking 873 00:40:09,366 --> 00:40:11,666 that his warnings weren't heeded. 874 00:40:11,700 --> 00:40:16,700 ♪♪ 875 00:40:16,733 --> 00:40:19,233 {\an1}-Linda Villarosa is a journalist 876 00:40:19,266 --> 00:40:23,800 {\an1}who investigates today's racial inequalities in health. 877 00:40:23,833 --> 00:40:27,666 {\an1}We've seen with the COVID crisis that Black Americans 878 00:40:27,700 --> 00:40:30,733 {\an1}are far more likely to suffer severe illness or die. 879 00:40:30,766 --> 00:40:33,500 {\an1}-African-Americans just simply have poorer health outcomes. 880 00:40:33,533 --> 00:40:35,800 {\an7}In all of the top-10 causes of death, 881 00:40:35,833 --> 00:40:39,066 {\an7}we die younger and more often. 882 00:40:39,100 --> 00:40:40,966 {\an1}Several months ago, before COVID, 883 00:40:41,000 --> 00:40:44,233 {\an1}I took my mother to Chicago, where she was born, 884 00:40:44,266 --> 00:40:45,966 and I had looked at the statistics. 885 00:40:46,000 --> 00:40:49,533 {\an1}Where she grew up in Englewood, a section of Chicago, 886 00:40:49,566 --> 00:40:51,300 {\an1}people lived to age 60. 887 00:40:51,333 --> 00:40:52,600 {\an1}That's the average age. 888 00:40:52,633 --> 00:40:53,966 {\an1}But 9 miles north, 889 00:40:54,000 --> 00:40:55,766 {\an1}in the wealthiest part of Chicago, 890 00:40:55,800 --> 00:40:57,200 {\an1}people lived to age 90. 891 00:40:57,233 --> 00:41:00,300 {\an1}So, there's a 30-year age gap. 892 00:41:00,333 --> 00:41:02,233 {\an1}-And that to me is just really shocking. 893 00:41:02,266 --> 00:41:05,933 {\an1}-This isn't about some inherent disability 894 00:41:05,966 --> 00:41:07,900 {\an1}or something wrong with your genetics. 895 00:41:07,933 --> 00:41:10,933 It's actually a problem of society. 896 00:41:10,966 --> 00:41:12,166 -I think one of the most important 897 00:41:12,200 --> 00:41:14,533 {\an1}new scientific breakthroughs 898 00:41:14,566 --> 00:41:16,700 {\an1}is this idea of weathering, 899 00:41:16,733 --> 00:41:19,366 {\an1}that there's kind of this long-term stress response 900 00:41:19,400 --> 00:41:21,900 {\an1}that leads to these health outcomes. 901 00:41:21,933 --> 00:41:23,300 {\an1}-Weathering is the idea 902 00:41:23,333 --> 00:41:26,166 {\an1}that your body is fighting against discrimination. 903 00:41:26,200 --> 00:41:27,933 {\an1}Whether it's discrimination by the police, 904 00:41:27,966 --> 00:41:29,900 {\an1}whether it's discrimination in housing, 905 00:41:29,933 --> 00:41:31,833 {\an1}whether it's discrimination in employment, 906 00:41:31,866 --> 00:41:36,033 {\an1}or everyday insults, your body physiology changes. 907 00:41:36,066 --> 00:41:39,266 {\an1}The fight or flight syndrome kicks in, which is healthy. 908 00:41:39,300 --> 00:41:42,100 {\an1}That's good, but it shouldn't kick in over and over. 909 00:41:42,133 --> 00:41:43,600 -Right. -So you shouldn't be having 910 00:41:43,633 --> 00:41:44,933 {\an1}your heart rate go up. 911 00:41:44,966 --> 00:41:46,933 {\an1}You shouldn't be having your blood pressure rise. 912 00:41:46,966 --> 00:41:48,666 {\an1}You shouldn't be having your stress hormones 913 00:41:48,700 --> 00:41:51,100 {\an1}kick in over and over again. 914 00:41:51,133 --> 00:41:54,500 {\an1}And so those wear away at the systems of the body 915 00:41:54,533 --> 00:41:58,333 {\an1}and make you prematurely age at a cellular level. 916 00:41:58,366 --> 00:42:00,166 -Right. -It's hard for a society 917 00:42:00,200 --> 00:42:02,866 {\an1}to admit that racism is harming the bodies of people 918 00:42:02,900 --> 00:42:04,233 {\an1}who are discriminated against. 919 00:42:04,266 --> 00:42:05,733 {\an1}-So add all those things up together, 920 00:42:05,766 --> 00:42:07,666 {\an1}and you get this really 921 00:42:07,700 --> 00:42:09,166 {\an1}disproportionate impact of the disease. 922 00:42:09,200 --> 00:42:12,066 {\an1}That's that's the crisis, what we need to be focusing on. 923 00:42:12,100 --> 00:42:16,000 {\an1}-And that is exactly what Du Bois was looking at in 1899. 924 00:42:16,033 --> 00:42:19,866 {\an1}So the fact that we're still looking at it is alarming, 925 00:42:19,900 --> 00:42:22,133 {\an1}but it's also promising that we understand it more. 926 00:42:22,166 --> 00:42:24,333 {\an1}We're acknowledging the problem more 927 00:42:24,366 --> 00:42:26,666 {\an1}and that the next generation of health care providers 928 00:42:26,700 --> 00:42:28,966 {\an1}is trying to push back against. 929 00:42:29,000 --> 00:42:34,033 ♪♪ 930 00:42:34,066 --> 00:42:36,733 {\an1}-I think we have to assume that in any given age, 931 00:42:36,766 --> 00:42:39,500 {\an1}even our own age, despite all our advanced knowledge, 932 00:42:39,533 --> 00:42:42,333 that we have our own blind spots, 933 00:42:42,366 --> 00:42:44,200 {\an1}and that in 10 or 20 years, we're gonna turn around 934 00:42:44,233 --> 00:42:47,233 {\an1}and say, oh, that should have been obvious to us. 935 00:42:47,266 --> 00:42:49,166 {\an1}And that was how I felt a little bit 936 00:42:49,200 --> 00:42:51,533 {\an1}when I first heard about the theory of weathering. 937 00:42:51,566 --> 00:42:52,733 {\an1}Something in the society, 938 00:42:52,766 --> 00:42:54,066 {\an1}something in our assumptions 939 00:42:54,100 --> 00:42:57,100 {\an1}about how health works kept us from seeing it. 940 00:42:57,133 --> 00:43:00,466 {\an1}-But those insights can only come out of data, 941 00:43:00,500 --> 00:43:03,100 {\an1}out of observation, and it's usually observation 942 00:43:03,133 --> 00:43:05,966 {\an1}that's taken place over decades. 943 00:43:06,000 --> 00:43:08,166 {\an1}And also these ideas emerge into worlds 944 00:43:08,200 --> 00:43:09,333 {\an1}in which they're contested. 945 00:43:09,366 --> 00:43:11,100 They don't stay in peer-reviewed journals 946 00:43:11,133 --> 00:43:12,266 {\an1}or in universities. 947 00:43:12,300 --> 00:43:14,733 They emerge into a political world. 948 00:43:14,766 --> 00:43:17,833 {\an1}And very often, these ideas have to fight for their existence. 949 00:43:17,866 --> 00:43:20,533 {\an1}They have to win people over. 950 00:43:27,266 --> 00:43:29,266 ♪♪ 951 00:43:29,300 --> 00:43:31,766 {\an1}-It's the late 1950s. 952 00:43:31,800 --> 00:43:34,500 {\an1}An aspiring young doctor, Herbert Needleman, 953 00:43:34,533 --> 00:43:38,100 {\an1}is working at Philadelphia's Children's Hospital, 954 00:43:38,133 --> 00:43:40,433 {\an1}Needleman begins seeing high numbers of kids 955 00:43:40,466 --> 00:43:42,633 {\an1}admitted with lead poisoning, 956 00:43:42,666 --> 00:43:45,866 {\an1}and he's completely mystified. 957 00:43:45,900 --> 00:43:48,633 {\an1}Because at the time, it was believed only exposure 958 00:43:48,666 --> 00:43:51,966 {\an1}to very high levels of lead could be harmful. 959 00:43:52,000 --> 00:43:53,666 {\an1}Needleman's office overlooked 960 00:43:53,700 --> 00:43:56,266 {\an1}a school playground here in Philadelphia, 961 00:43:56,300 --> 00:44:00,833 {\an1}and that vantage point gave him a hunch. 962 00:44:00,866 --> 00:44:03,700 {\an1}Perhaps even low levels of lead 963 00:44:03,733 --> 00:44:06,300 {\an1}could build up over time in the body, 964 00:44:06,333 --> 00:44:09,200 {\an1}creating hidden chronic health problems. 965 00:44:09,233 --> 00:44:12,133 ♪♪ 966 00:44:12,166 --> 00:44:15,266 {\an1}Needleman realized that if his theory was right, 967 00:44:15,300 --> 00:44:17,733 {\an1}a whole generation of kids growing up 968 00:44:17,766 --> 00:44:19,266 {\an1}in crumbling urban neighborhoods, 969 00:44:19,300 --> 00:44:21,800 like here in Philadelphia's 7th Ward, 970 00:44:21,833 --> 00:44:25,300 {\an1}were being exposed to a dangerous toxin, 971 00:44:25,333 --> 00:44:28,800 {\an1}most notoriously lead paint. 972 00:44:28,833 --> 00:44:31,633 ♪♪ 973 00:44:31,666 --> 00:44:33,366 {\an1}It was especially disturbing 974 00:44:33,400 --> 00:44:36,866 {\an1}that baby cribs were painted with lead paint. 975 00:44:36,900 --> 00:44:39,133 When it chipped or was chewed on, 976 00:44:39,166 --> 00:44:41,100 {\an1}the lead had a sweet taste, 977 00:44:41,133 --> 00:44:43,766 {\an1}making it particularly enticing. 978 00:44:43,800 --> 00:44:46,500 ♪♪ 979 00:44:46,533 --> 00:44:48,200 {\an1}If Needleman was right, 980 00:44:48,233 --> 00:44:52,600 {\an1}almost everyone was being exposed in some way. 981 00:44:52,633 --> 00:44:56,200 {\an1}Environments with lead paint or lead water pipes 982 00:44:56,233 --> 00:44:59,000 or lead pollution from gasoline cars 983 00:44:59,033 --> 00:45:03,066 {\an1}could be incredibly damaging for their inhabitants. 984 00:45:03,100 --> 00:45:05,600 {\an1}And so, like a good detective, 985 00:45:05,633 --> 00:45:08,066 {\an1}Needleman went looking for proof. 986 00:45:08,100 --> 00:45:11,100 {\an1}He needed data and lots of it. 987 00:45:11,133 --> 00:45:13,700 {\an1}And the first step was to find a measurement 988 00:45:13,733 --> 00:45:15,933 {\an1}hidden in the human body 989 00:45:15,966 --> 00:45:19,600 that could reveal long-term lead exposure. 990 00:45:19,633 --> 00:45:23,433 ♪♪ 991 00:45:23,466 --> 00:45:25,533 {\an1}Lead effects many organs in the body, 992 00:45:25,566 --> 00:45:29,700 {\an1}but it accumulates over time in the bones. 993 00:45:29,733 --> 00:45:31,666 {\an1}You can see the lead in the bones here 994 00:45:31,700 --> 00:45:33,766 {\an1}in these telltale white lines 995 00:45:33,800 --> 00:45:37,100 {\an1}of lead poisoning at the joints. 996 00:45:37,133 --> 00:45:39,533 {\an1}Which can then leach into the bloodstream 997 00:45:39,566 --> 00:45:41,733 {\an1}and be absorbed by the brain. 998 00:45:41,766 --> 00:45:43,600 ♪♪ 999 00:45:43,633 --> 00:45:47,933 {\an1}Shockingly, that led causes areas of the brain to shrink, 1000 00:45:47,966 --> 00:45:50,466 {\an1}impairing memory and learning. 1001 00:45:50,500 --> 00:45:52,500 ♪♪ 1002 00:45:52,533 --> 00:45:55,933 {\an1}And because their brains are developing so rapidly, 1003 00:45:55,966 --> 00:45:58,766 {\an1}children are particularly vulnerable. 1004 00:45:58,800 --> 00:46:02,200 ♪♪ 1005 00:46:02,233 --> 00:46:05,333 {\an1}To prove that lead was building up in children over time, 1006 00:46:05,366 --> 00:46:06,833 {\an1}causing long term harm, 1007 00:46:06,866 --> 00:46:08,833 {\an1}Needleman needed a way 1008 00:46:08,866 --> 00:46:10,766 {\an1}to measure the level of lead 1009 00:46:10,800 --> 00:46:13,433 in their bones. 1010 00:46:13,466 --> 00:46:16,500 {\an1}But there was a problem. 1011 00:46:16,533 --> 00:46:19,333 {\an1}Taking bone samples from a living person 1012 00:46:19,366 --> 00:46:23,000 is an invasive and painful procedure. 1013 00:46:23,033 --> 00:46:26,233 {\an1}Then he had an idea. 1014 00:46:26,266 --> 00:46:28,433 {\an8}Needleman's data came from something 1015 00:46:28,466 --> 00:46:31,900 {\an7}that kids leave under their pillows all the time -- 1016 00:46:31,933 --> 00:46:34,333 {\an8}baby teeth. 1017 00:46:34,366 --> 00:46:36,666 {\an1}He collected the teeth directly from the kids themselves 1018 00:46:36,700 --> 00:46:38,433 {\an1}and from dental clinics, 1019 00:46:38,466 --> 00:46:41,166 {\an1}ultimately amassing such a large supply 1020 00:46:41,200 --> 00:46:44,533 {\an1}that he became known locally as the Tooth Fairy. 1021 00:46:44,566 --> 00:46:48,066 ♪♪ 1022 00:46:48,100 --> 00:46:49,400 {\an1}It allowed him to track 1023 00:46:49,433 --> 00:46:53,733 {\an1}children's lead levels over many years -- 1024 00:46:53,766 --> 00:46:56,733 {\an1}something that had never been done before, 1025 00:46:56,766 --> 00:46:58,933 {\an8}The data, 1026 00:46:58,966 --> 00:47:00,766 {\an7}published in 1972, 1027 00:47:00,800 --> 00:47:03,766 {\an8}was shocking. 1028 00:47:03,800 --> 00:47:06,466 {\an1}Needleman discovered that inner-city kids 1029 00:47:06,500 --> 00:47:07,933 {\an1}living in older buildings 1030 00:47:07,966 --> 00:47:10,433 and exposed to greater traffic pollution 1031 00:47:10,466 --> 00:47:13,100 {\an1}had five times more lead in their bodies 1032 00:47:13,133 --> 00:47:15,466 {\an1}than kids from the suburbs. 1033 00:47:15,500 --> 00:47:19,800 {\an1}Now Needleman needed to prove that lead exposure caused harm. 1034 00:47:19,833 --> 00:47:21,066 {\an1}He analyzed the teeth 1035 00:47:21,100 --> 00:47:23,066 {\an1}of over 2,000 children 1036 00:47:23,100 --> 00:47:24,933 {\an1}looking at lead levels, 1037 00:47:24,966 --> 00:47:27,100 {\an1}and then he gave a battery of tests to the children 1038 00:47:27,133 --> 00:47:29,366 with the highest and the lowest levels -- 1039 00:47:29,400 --> 00:47:31,333 {\an1}IQ tests, verbal tests, 1040 00:47:31,366 --> 00:47:35,000 {\an1}surveys of their teachers about the kid's behavior. 1041 00:47:35,033 --> 00:47:37,233 {\an1}The data was clear. 1042 00:47:37,266 --> 00:47:40,066 {\an1}Children whose exposure to lead was highest 1043 00:47:40,100 --> 00:47:43,733 {\an1}scored 4 points lower in IQ tests. 1044 00:47:43,766 --> 00:47:48,066 ♪♪ 1045 00:47:48,100 --> 00:47:52,066 {\an1}And when he continued to study the children as they grew up, 1046 00:47:52,100 --> 00:47:54,533 he found greater high school dropout rates, 1047 00:47:54,566 --> 00:47:56,166 {\an1}poorer hand-eye coordination, 1048 00:47:56,200 --> 00:47:58,500 {\an1}and other persistent problems 1049 00:47:58,533 --> 00:48:01,700 {\an1}even a decade later. 1050 00:48:01,733 --> 00:48:05,433 {\an1}-The data showed conclusively that lead was harming 1051 00:48:05,466 --> 00:48:08,666 {\an1}the long-term health of children. 1052 00:48:08,700 --> 00:48:11,166 {\an1}So, this should be the tipping point, 1053 00:48:11,200 --> 00:48:13,966 {\an1}the moment the industry is forced to remove lead 1054 00:48:14,000 --> 00:48:16,600 {\an1}from household products and petroleum. 1055 00:48:16,633 --> 00:48:18,433 ♪♪ 1056 00:48:18,466 --> 00:48:21,633 {\an1}But rather than accept all this, 1057 00:48:21,666 --> 00:48:23,966 {\an1}that lead industry instead decided 1058 00:48:24,000 --> 00:48:28,300 {\an1}to try and discredit Dr. Needleman, 1059 00:48:28,333 --> 00:48:30,466 {\an1}and they did this by attacking 1060 00:48:30,500 --> 00:48:32,466 {\an1}his most important asset -- 1061 00:48:32,500 --> 00:48:34,300 his data. 1062 00:48:34,333 --> 00:48:37,433 ♪♪ 1063 00:48:37,466 --> 00:48:40,766 {\an1}Needleman testified that he had made a small mistake 1064 00:48:40,800 --> 00:48:43,266 {\an1}in one of his mathematical calculations. 1065 00:48:43,300 --> 00:48:46,100 {\an1}This was in the years before computers were used 1066 00:48:46,133 --> 00:48:48,433 {\an1}to process big data sets. 1067 00:48:48,466 --> 00:48:51,766 {\an1}But that mistake didn't change his findings. 1068 00:48:51,800 --> 00:48:54,533 {\an1}But then his own university 1069 00:48:54,566 --> 00:48:56,166 {\an1}launched an investigation. 1070 00:48:56,200 --> 00:48:57,733 {\an1}They locked him out of his files 1071 00:48:57,766 --> 00:49:01,633 and prevented him from accessing his own research. 1072 00:49:01,666 --> 00:49:08,000 ♪♪ 1073 00:49:08,033 --> 00:49:10,633 {\an7}-I'd like to now call forward to testify 1074 00:49:10,666 --> 00:49:12,900 {\an7}Dr. Herbert L. Needleman. 1075 00:49:12,933 --> 00:49:15,566 {\an7}-When I last testified here in 1979, 1076 00:49:15,600 --> 00:49:17,366 {\an7}there was considerable disagreement 1077 00:49:17,400 --> 00:49:21,433 {\an7}about the impact of lead at low dose on children. 1078 00:49:21,466 --> 00:49:23,366 {\an7}There's been an explosion of scientific knowledge 1079 00:49:23,400 --> 00:49:26,366 {\an7}from human studies and from the animal laboratories 1080 00:49:26,400 --> 00:49:27,933 {\an7}about the effects of lead. 1081 00:49:27,966 --> 00:49:30,466 {\an7}-Needleman's name was eventually cleared. 1082 00:49:30,500 --> 00:49:32,400 His research, along with all of the other 1083 00:49:32,433 --> 00:49:34,066 {\an1}studies that relied on it, 1084 00:49:34,100 --> 00:49:37,800 became accepted as scientific consensus. 1085 00:49:37,833 --> 00:49:40,166 {\an1}And we now know that there is 1086 00:49:40,200 --> 00:49:44,100 {\an1}no safe level of lead in the human body. 1087 00:49:44,133 --> 00:49:47,366 {\an1}His data ultimately resulted in the removal of lead 1088 00:49:47,400 --> 00:49:50,800 from paint and other household goods. 1089 00:49:50,833 --> 00:49:54,766 {\an7}-And there is a broad consensus on the part of everybody 1090 00:49:54,800 --> 00:49:56,266 {\an7}except the lead industry and its spokesmen, 1091 00:49:56,300 --> 00:50:00,266 {\an7}that lead is extremely toxic at extremely low doses. 1092 00:50:00,300 --> 00:50:03,600 {\an7}-Thank you so much. 1093 00:50:03,633 --> 00:50:06,466 {\an7}-But it took until 1996, 1094 00:50:06,500 --> 00:50:10,166 {\an7}over three decades after his research had begun, 1095 00:50:10,200 --> 00:50:12,333 {\an8}for the sale of leaded gasoline 1096 00:50:12,366 --> 00:50:16,166 {\an7}to finally be banned in the US. 1097 00:50:16,200 --> 00:50:18,533 {\an1}It was not banned in the U.K. 1098 00:50:18,566 --> 00:50:22,266 {\an1}until the year 2000. 1099 00:50:22,300 --> 00:50:25,433 {\an1}More than 400,000 people a year in America 1100 00:50:25,466 --> 00:50:27,066 {\an1}are dying because of lead 1101 00:50:27,100 --> 00:50:29,500 {\an1}that they ingested when they were children. 1102 00:50:29,533 --> 00:50:30,966 {\an1}Now, they wouldn't be dying 1103 00:50:31,000 --> 00:50:33,200 {\an1}if at the beginning of this journey, 1104 00:50:33,233 --> 00:50:34,900 {\an1}the industry had got behind Needleman, 1105 00:50:34,933 --> 00:50:37,066 {\an1}rather than refuting his claims 1106 00:50:37,100 --> 00:50:40,000 {\an1}or attacking his personality, they'd listened. 1107 00:50:40,033 --> 00:50:43,133 {\an1}The story of Needleman and the lead industry 1108 00:50:43,166 --> 00:50:45,000 {\an1}is in some ways very similar 1109 00:50:45,033 --> 00:50:46,666 to another story of the 20th century, 1110 00:50:46,700 --> 00:50:51,166 {\an1}which is the tobacco industry's long-delaying strategy, 1111 00:50:51,200 --> 00:50:52,833 {\an1}fighting against data, 1112 00:50:52,866 --> 00:50:54,900 fighting against medical research 1113 00:50:54,933 --> 00:50:57,900 {\an1}that showed first that cigarette smoking is unhealthy, 1114 00:50:57,933 --> 00:51:01,300 then showed strong links to cancer. 1115 00:51:01,333 --> 00:51:03,433 {\an1}-We saw very clearly 1116 00:51:03,466 --> 00:51:05,666 at the beginning of the COVID-19 pandemic. 1117 00:51:05,700 --> 00:51:07,900 {\an1}I mean, we had political leaders in the United States 1118 00:51:07,933 --> 00:51:10,700 {\an1}who did not want to test for the presence of the virus 1119 00:51:10,733 --> 00:51:12,566 because somehow 1120 00:51:12,600 --> 00:51:15,666 {\an1}the rising caseload would make us look bad. 1121 00:51:15,700 --> 00:51:19,200 {\an1}And that was just such a fundamental mistake, 1122 00:51:19,233 --> 00:51:21,900 {\an1}and and it betrayed the whole history 1123 00:51:21,933 --> 00:51:26,266 {\an1}of the role of data in extending our lives. 1124 00:51:26,300 --> 00:51:27,533 {\an1}-And what would be really worrying 1125 00:51:27,566 --> 00:51:29,500 if we see history repeating itself 1126 00:51:29,533 --> 00:51:33,633 {\an1}and data once again disguised or dismissed as opinion. 1127 00:51:33,666 --> 00:51:36,400 {\an1}We've got a battle about air pollution. 1128 00:51:36,433 --> 00:51:38,366 We've, of course, got a global fight 1129 00:51:38,400 --> 00:51:39,933 {\an1}against the climate crisis. 1130 00:51:39,966 --> 00:51:42,033 {\an1}Data is not our enemy. 1131 00:51:42,066 --> 00:51:46,300 {\an1}-The truth is, you can't begin to think about solving problems, 1132 00:51:46,333 --> 00:51:47,466 {\an1}whether those problems are coming 1133 00:51:47,500 --> 00:51:50,266 {\an1}from natural threats, like viruses, 1134 00:51:50,300 --> 00:51:54,600 {\an1}or manmade threats, like pollution or smoking. 1135 00:51:54,633 --> 00:51:58,100 {\an1}You can't begin to imagine a solution to the problem 1136 00:51:58,133 --> 00:52:00,233 {\an1}until you see the problem, 1137 00:52:00,266 --> 00:52:03,400 {\an1}and you can't see the problem 1138 00:52:03,433 --> 00:52:05,500 without data. 1139 00:52:06,833 --> 00:52:09,333 {\an7}-As the world grapples with another deadly pandemic, 1140 00:52:09,366 --> 00:52:11,833 {\an8}we reveal how changing our behavior 1141 00:52:11,866 --> 00:52:15,000 {\an7}became one of the most powerful tools that we have 1142 00:52:15,033 --> 00:52:17,666 {\an1}in the fight for extra life. 1143 00:52:17,700 --> 00:52:21,066 {\an1}-At the molecular level, soap breaks everything apart. 1144 00:52:21,100 --> 00:52:22,966 {\an1}At the level of society, 1145 00:52:23,000 --> 00:52:26,466 it helps us to hold everything together. 1146 00:52:26,500 --> 00:52:27,966 -"How To Have Sex In An Epidemic." 1147 00:52:28,000 --> 00:52:30,266 {\an1}-This is an historic document. -Yeah. 1148 00:52:30,300 --> 00:52:31,300 -This is arguably 1149 00:52:31,333 --> 00:52:33,100 the biggest story in human history, 1150 00:52:33,133 --> 00:52:35,500 how we doubled our life expectancy. 1151 00:52:36,833 --> 00:52:46,300 {\an8}♪♪ 1152 00:52:46,333 --> 00:52:55,866 {\an8}♪♪ 1153 00:52:55,900 --> 00:53:05,500 {\an8}♪♪