1 00:00:12,012 --> 00:00:14,348 My talk today is titled. 2 00:00:14,348 --> 00:00:17,351 And when our health inequalities unfair. 3 00:00:17,351 --> 00:00:19,615 I will, I will explain how this connects 4 00:00:19,615 --> 00:00:20,521 to this course. 5 00:00:21,255 --> 00:00:23,590 So it's a usual disclaimer. 6 00:00:23,590 --> 00:00:26,960 I just to represent my view and then no one else 7 00:00:28,495 --> 00:00:30,163 answer this talk. 8 00:00:30,163 --> 00:00:33,189 The focus is equity considerations 9 00:00:33,189 --> 00:00:34,434 in the design 10 00:00:34,768 --> 00:00:37,638 and conduct of clinical research. 11 00:00:37,638 --> 00:00:40,376 So here when I say clinical research 12 00:00:40,376 --> 00:00:42,809 I use that term very broad way. 13 00:00:43,210 --> 00:00:46,213 So anything to do with any human health. 14 00:00:46,713 --> 00:00:50,217 And then I give a lot of example of the intervention 15 00:00:50,217 --> 00:00:53,453 studies and then clinical trials. 16 00:00:53,854 --> 00:00:56,690 But what I talk about today 17 00:00:56,690 --> 00:01:00,227 really applies to any other kind of like 18 00:01:00,227 --> 00:01:03,563 observational studies and the other designs too. 19 00:01:04,231 --> 00:01:07,534 And then it's not just that medicine person. 20 00:01:08,001 --> 00:01:11,038 It could be you can extend this discussion 21 00:01:11,038 --> 00:01:14,141 to social behavioral research to. 22 00:01:15,709 --> 00:01:16,610 And then I want to 23 00:01:16,610 --> 00:01:20,147 make two points, in this talk. 24 00:01:20,547 --> 00:01:24,565 First is that the equity considerations are not a 25 00:01:24,565 --> 00:01:25,385 checkbox. 26 00:01:25,953 --> 00:01:28,956 They're woven into principles for ethical, 27 00:01:30,090 --> 00:01:31,591 clinical research. 28 00:01:31,591 --> 00:01:36,964 So I suppose you're, quite familiar with, 29 00:01:36,964 --> 00:01:41,134 these eight principles for ethical clinical research. 30 00:01:41,735 --> 00:01:45,572 So when I say equity, it's not like 31 00:01:45,572 --> 00:01:49,532 I want to add one more consideration to the 32 00:01:49,532 --> 00:01:50,177 eight. 33 00:01:50,777 --> 00:01:55,916 It's more like for every thing, every one of them, 34 00:01:56,516 --> 00:01:59,519 you have ethical considerations. 35 00:01:59,786 --> 00:02:02,489 And then the interesting thing is that 36 00:02:02,489 --> 00:02:05,492 then it's not like I when I say it's not checkbox, 37 00:02:06,393 --> 00:02:08,673 which means that then it's not like okay, 38 00:02:08,673 --> 00:02:09,396 for this one 39 00:02:09,396 --> 00:02:12,010 principle there's then the ethics consideration 40 00:02:12,010 --> 00:02:12,399 check. 41 00:02:12,799 --> 00:02:16,136 Another one check. It's not like that. 42 00:02:16,136 --> 00:02:17,270 It's not that simple. 43 00:02:17,270 --> 00:02:21,308 So if you think about ethical considerations in one, 44 00:02:21,608 --> 00:02:24,645 that might lead to the other principle. 45 00:02:24,911 --> 00:02:28,415 So it's kind of woven into and today 46 00:02:28,415 --> 00:02:30,687 because of that time considerations 47 00:02:30,687 --> 00:02:33,153 and that I just focus on these three. 48 00:02:36,189 --> 00:02:36,857 And then the 49 00:02:36,857 --> 00:02:39,860 second point I want to make is the bottom one 50 00:02:40,193 --> 00:02:44,598 to make equity constellations explicit and meaningful. 51 00:02:45,165 --> 00:02:47,601 It is important to think carefully 52 00:02:47,601 --> 00:02:51,171 about when health inequalities are unfair. 53 00:02:51,605 --> 00:02:55,008 So probably kind of what what do I mean? 54 00:02:55,008 --> 00:02:58,258 You know, I hope this becomes clear as I 55 00:02:58,258 --> 00:02:58,745 talk. 56 00:03:01,081 --> 00:03:02,282 So today 57 00:03:02,282 --> 00:03:05,252 this talk is guided by three questions. 58 00:03:05,686 --> 00:03:08,822 First, who should be in the study and why? 59 00:03:09,556 --> 00:03:13,260 Second, which health inequalities are unfair and why? 60 00:03:13,794 --> 00:03:17,984 And then finally, how do equity considerations influence this 61 00:03:17,984 --> 00:03:18,465 study? 62 00:03:18,465 --> 00:03:21,701 Design, analysis, plan and reporting. 63 00:03:22,736 --> 00:03:26,006 And then this is not the easy question. 64 00:03:26,006 --> 00:03:29,009 It's like I mean we have a clear answer. 65 00:03:29,709 --> 00:03:33,276 And you can memorize it and sometimes answers your 66 00:03:33,276 --> 00:03:33,847 meaning 67 00:03:34,214 --> 00:03:35,749 and you'll have to pick. 68 00:03:35,749 --> 00:03:37,350 So it's you know, 69 00:03:37,350 --> 00:03:39,200 it's not like an easy but and I hope 70 00:03:39,200 --> 00:03:41,254 that then that by the end of this talk, 71 00:03:41,254 --> 00:03:45,895 I can convince you, Nathan, it's important to examine these 72 00:03:45,895 --> 00:03:46,760 questions. 73 00:03:47,761 --> 00:03:49,529 So let's start. 74 00:03:49,529 --> 00:03:52,532 Who should be in a study and then why 75 00:03:53,767 --> 00:03:55,068 and then why? 76 00:03:55,068 --> 00:03:57,604 The reason I think that there are three. 77 00:03:57,604 --> 00:04:00,474 We can think about participation, 78 00:04:00,474 --> 00:04:03,376 biology and societal concern. 79 00:04:03,376 --> 00:04:06,379 And then I'll go one by one. 80 00:04:06,746 --> 00:04:08,448 The first it's participation. 81 00:04:08,448 --> 00:04:12,552 Probably familiar to you from previous lectures. 82 00:04:13,053 --> 00:04:15,419 So consideration here is the fair 83 00:04:15,419 --> 00:04:18,358 distribution of benefits and the burden. 84 00:04:18,992 --> 00:04:21,862 So as you probably already heard, 85 00:04:21,862 --> 00:04:25,265 what it means to participate in studies has changed 86 00:04:25,265 --> 00:04:29,069 from bearing the burden to accessing to innovations. 87 00:04:29,336 --> 00:04:33,907 So it used to be that, the participation 88 00:04:35,542 --> 00:04:37,310 in the clinical trials 89 00:04:37,310 --> 00:04:40,313 is the special thing that you needed. 90 00:04:40,747 --> 00:04:43,750 The justification and then study 91 00:04:43,750 --> 00:04:47,521 participants are usually very similar homogeneous people. 92 00:04:48,054 --> 00:04:52,125 And now it's shifted that then that the default is 93 00:04:52,125 --> 00:04:55,262 that then everybody should be able to participate. 94 00:04:55,262 --> 00:04:59,199 And if you exclude them you need justification. 95 00:04:59,199 --> 00:05:02,747 So I think that this in itself is in the quite interesting 96 00:05:02,747 --> 00:05:03,236 history 97 00:05:03,236 --> 00:05:06,373 of how our views changed. 98 00:05:06,773 --> 00:05:10,310 But here, if, that could be one reason 99 00:05:10,310 --> 00:05:13,313 that then the why you want to, involve, 100 00:05:13,713 --> 00:05:15,982 diverse population in your study 101 00:05:15,982 --> 00:05:17,880 or because, you know, you care about fair 102 00:05:17,880 --> 00:05:19,686 distribution of benefits and a burden. 103 00:05:20,821 --> 00:05:22,856 But that's not the only reason 104 00:05:22,856 --> 00:05:24,582 why you might want to think about 105 00:05:24,582 --> 00:05:26,726 different kinds of people in your study. 106 00:05:27,360 --> 00:05:30,836 And then here I give you biology and societal 107 00:05:30,836 --> 00:05:31,531 concern. 108 00:05:32,399 --> 00:05:34,534 So for biology, 109 00:05:34,534 --> 00:05:38,154 the thinking is that, that you are interested in in 110 00:05:38,154 --> 00:05:39,005 your study, 111 00:05:39,306 --> 00:05:44,077 the some causal, relationship x causes Y. 112 00:05:44,644 --> 00:05:47,188 And you might believe for whatever reason 113 00:05:47,188 --> 00:05:48,181 that then that, 114 00:05:49,216 --> 00:05:52,953 causality may work differently in different groups 115 00:05:52,953 --> 00:05:55,496 of human beings because of biological 116 00:05:55,496 --> 00:05:56,389 differences. 117 00:05:56,790 --> 00:05:59,637 So here I want to give an example of the 118 00:05:59,637 --> 00:05:59,993 dual 119 00:05:59,993 --> 00:06:02,996 vascular disease. 120 00:06:04,097 --> 00:06:06,867 This is kind of infographics, 121 00:06:06,867 --> 00:06:09,836 from the American Heart Association. 122 00:06:09,836 --> 00:06:12,639 So heart attack symptoms, 123 00:06:12,639 --> 00:06:14,874 between men and women 124 00:06:14,874 --> 00:06:17,877 probably this is now, you know, they quite, 125 00:06:19,446 --> 00:06:22,382 pushed out a lot so that some people know 126 00:06:22,382 --> 00:06:25,253 that men and women have a quite different 127 00:06:25,253 --> 00:06:27,354 symptoms of the heart attack. 128 00:06:28,221 --> 00:06:31,176 You can see I don't go over, details, 129 00:06:31,176 --> 00:06:33,093 but just looking at it. 130 00:06:33,093 --> 00:06:35,862 Women have a longer list than the men. 131 00:06:35,862 --> 00:06:37,631 So you can imagine that. 132 00:06:37,631 --> 00:06:40,200 Then you just study men. 133 00:06:40,200 --> 00:06:43,203 You miss what's happening in women. 134 00:06:43,503 --> 00:06:46,106 And it's not just the symptoms. 135 00:06:46,106 --> 00:06:48,508 That's different. 136 00:06:48,508 --> 00:06:50,681 And then it's underlying, like, a 137 00:06:50,681 --> 00:06:53,446 biological mechanisms could be different. 138 00:06:53,446 --> 00:06:56,650 So this is on the, one of the recent studies. 139 00:06:56,650 --> 00:06:59,335 Again, I'm not gonna go into the details 140 00:06:59,335 --> 00:07:02,088 because this is not a medical talk, but, 141 00:07:03,123 --> 00:07:04,157 it shows that 142 00:07:04,157 --> 00:07:07,560 in that probably there's some important hormonal 143 00:07:07,994 --> 00:07:11,753 and the molecular mechanisms difference between men and 144 00:07:11,753 --> 00:07:12,232 women, 145 00:07:12,599 --> 00:07:15,869 although we are talking about exactly the same disease. 146 00:07:16,169 --> 00:07:18,465 So how that manifests in the body 147 00:07:18,465 --> 00:07:20,273 is biologically different 148 00:07:20,273 --> 00:07:23,276 between men and women. Women. 149 00:07:23,977 --> 00:07:27,580 So that is a good reason to suspect that in a well, 150 00:07:27,580 --> 00:07:31,551 we cannot just study men, which we traditionally did. 151 00:07:31,918 --> 00:07:33,887 We might miss a lot of things. 152 00:07:33,887 --> 00:07:38,194 And we cannot say that causal relationship, generalize 153 00:07:38,194 --> 00:07:38,992 to women. 154 00:07:39,392 --> 00:07:41,661 So there's a good reason that then that 155 00:07:41,661 --> 00:07:43,930 you should study both of these groups. 156 00:07:44,130 --> 00:07:47,224 So it's just in the one of the examples 157 00:07:47,224 --> 00:07:48,968 of the biology drives 158 00:07:48,968 --> 00:07:52,906 your interest to have different kind of people in your study. 159 00:07:54,741 --> 00:07:57,744 Another one is the societal concern. 160 00:07:58,011 --> 00:08:00,581 So here again, we might be interested 161 00:08:00,581 --> 00:08:03,083 in some of the causal relationship. 162 00:08:03,850 --> 00:08:06,453 And you might expect that 163 00:08:06,453 --> 00:08:09,456 that relationship works differently 164 00:08:09,956 --> 00:08:15,121 for different kinds of people because of how we organize our 165 00:08:15,121 --> 00:08:15,895 society. 166 00:08:17,364 --> 00:08:18,932 So one way 167 00:08:18,932 --> 00:08:22,494 to think about, think about this is that there might be 168 00:08:22,494 --> 00:08:23,336 differential 169 00:08:23,336 --> 00:08:27,374 intervention effects by social group characteristics. 170 00:08:27,807 --> 00:08:31,578 So here example is that, the effects of women's 171 00:08:31,578 --> 00:08:34,314 group participation on neonatal mortality 172 00:08:34,314 --> 00:08:35,982 rate by marginalization. 173 00:08:35,982 --> 00:08:38,852 So let me explain a bit more. 174 00:08:38,852 --> 00:08:42,122 So this is taken from the meta analysis. 175 00:08:42,122 --> 00:08:44,134 So there are many different studies 176 00:08:44,134 --> 00:08:45,859 countries they focus on that. 177 00:08:45,859 --> 00:08:48,155 And then I'm just focusing on one country 178 00:08:48,155 --> 00:08:48,995 without Nepal. 179 00:08:49,596 --> 00:08:52,781 So intervention is that an older some pregnant 180 00:08:52,781 --> 00:08:53,266 women, 181 00:08:53,900 --> 00:08:57,804 participating in a, some women's group intervention while they're 182 00:08:57,804 --> 00:08:58,405 pregnant. 183 00:08:58,872 --> 00:09:01,441 And then, the comparison 184 00:09:01,441 --> 00:09:04,873 is an a waste intervention and then result and then 185 00:09:04,873 --> 00:09:05,478 outcome. 186 00:09:05,478 --> 00:09:08,136 We are interested in interested in 187 00:09:08,136 --> 00:09:11,184 is that the neonatal mortality of the, 188 00:09:12,085 --> 00:09:14,392 the babies of the women, if we participate 189 00:09:14,392 --> 00:09:16,589 or not participate in the intervention. 190 00:09:17,924 --> 00:09:18,358 And then 191 00:09:18,358 --> 00:09:21,479 here the investigators thought that maybe this 192 00:09:21,479 --> 00:09:22,429 intervention, 193 00:09:23,463 --> 00:09:26,666 have a different effect depending on the, 194 00:09:26,966 --> 00:09:30,136 degree of marginalization here. 195 00:09:30,136 --> 00:09:33,373 In this study, marginalization is the, 196 00:09:34,441 --> 00:09:37,043 define lowest literacy. 197 00:09:37,043 --> 00:09:39,512 So most marginalized group, is 198 00:09:39,512 --> 00:09:43,016 the women who were little bit and poor, 199 00:09:43,616 --> 00:09:47,043 and the less marginalized is the less, the rest of the 200 00:09:47,043 --> 00:09:47,487 women. 201 00:09:48,121 --> 00:09:51,721 So the right hand side, you see, you know, 202 00:09:51,721 --> 00:09:53,093 I can't use it. 203 00:09:53,093 --> 00:09:56,539 So you see that the dotted line is the control 204 00:09:56,539 --> 00:09:57,063 group, 205 00:09:57,730 --> 00:10:00,900 and the only line is the intervention. 206 00:10:00,900 --> 00:10:03,899 And then you see on the vertical line 207 00:10:03,899 --> 00:10:06,573 is the, neonatal mortality rate. 208 00:10:06,573 --> 00:10:08,591 So going up higher is then the high 209 00:10:08,591 --> 00:10:10,610 mortality rate, which is not good. 210 00:10:10,610 --> 00:10:14,380 So you want to be lower and you see year one. 211 00:10:14,814 --> 00:10:16,816 And then you okay. 212 00:10:16,816 --> 00:10:20,553 So here we see that then that for the control group 213 00:10:21,120 --> 00:10:24,003 the intervention works for the year 214 00:10:24,003 --> 00:10:24,991 one and two 215 00:10:24,991 --> 00:10:28,495 because they decline is lower than the dotted line. 216 00:10:29,028 --> 00:10:32,541 But interestingly for the marginalized group, if 217 00:10:32,541 --> 00:10:33,199 you just 218 00:10:33,199 --> 00:10:35,650 look at the first year there doesn't seem 219 00:10:35,650 --> 00:10:37,504 to be any intervention effect. 220 00:10:37,937 --> 00:10:40,940 But year two is a big, big difference. 221 00:10:41,474 --> 00:10:45,881 So if you don't suspect that, then the maybe this intervene in 222 00:10:45,881 --> 00:10:46,379 effect 223 00:10:46,379 --> 00:10:49,496 is different depending on the different kinds of 224 00:10:49,496 --> 00:10:50,016 people. 225 00:10:50,283 --> 00:10:53,419 You can design this study and you would totally miss that. 226 00:10:54,020 --> 00:10:57,348 So that's one way to kind of think, okay, 227 00:10:57,348 --> 00:10:59,459 when I, design the study, 228 00:10:59,726 --> 00:11:01,748 maybe I need to include different 229 00:11:01,748 --> 00:11:02,729 kinds of women, 230 00:11:03,329 --> 00:11:06,799 based on the marginalization, because I expect this. 231 00:11:06,799 --> 00:11:08,401 Right. So that's one way. 232 00:11:09,869 --> 00:11:11,604 The other is the bottom one. 233 00:11:11,604 --> 00:11:15,498 So you might expect that, differential intervention 234 00:11:15,498 --> 00:11:16,109 effects 235 00:11:16,109 --> 00:11:19,646 within the traditionally excluded groups. 236 00:11:20,246 --> 00:11:22,975 So here I want to give you an example of 237 00:11:22,975 --> 00:11:23,316 the, 238 00:11:23,616 --> 00:11:27,153 evidence of a high intensity lifestyle based program 239 00:11:27,453 --> 00:11:29,973 for obesity treatment in primary care 240 00:11:29,973 --> 00:11:32,492 clinics in low income neighborhoods. 241 00:11:32,892 --> 00:11:37,230 So now we are just focusing on traditionally excluded group, 242 00:11:37,230 --> 00:11:40,233 which is that low income neighborhood. 243 00:11:40,633 --> 00:11:42,268 So here is the study. 244 00:11:42,268 --> 00:11:45,338 So population is the primary care 245 00:11:45,338 --> 00:11:48,341 patients living in low income. 246 00:11:48,541 --> 00:11:52,412 Neighborhood intervention is a high intensity 247 00:11:52,712 --> 00:11:55,327 lifestyle based program for obesity 248 00:11:55,327 --> 00:11:56,149 treatment. 249 00:11:56,149 --> 00:11:59,819 Usually these things, include like a, you know, diet 250 00:12:00,053 --> 00:12:04,222 and nutrition advice and then the physical exercise 251 00:12:04,222 --> 00:12:04,958 programs 252 00:12:04,958 --> 00:12:07,868 and all those kind of things, or combine 253 00:12:07,868 --> 00:12:10,997 comparison and then this intensive program 254 00:12:10,997 --> 00:12:14,968 and the usual care, usual care could be just doctors advice. 255 00:12:15,301 --> 00:12:17,971 They're not in the intervention program. 256 00:12:17,971 --> 00:12:19,818 Outcome is that on the percentage 257 00:12:19,818 --> 00:12:21,441 change of their body weight. 258 00:12:22,241 --> 00:12:24,574 And the major findings is that in 259 00:12:24,574 --> 00:12:27,614 in two years, how much body weight change? 260 00:12:27,914 --> 00:12:31,718 So here you see that the income intensive lifestyle 261 00:12:31,718 --> 00:12:35,583 group had the, the biggest amount of weight 262 00:12:35,583 --> 00:12:36,122 loss. 263 00:12:36,556 --> 00:12:40,026 So it seems to be working and, and it's good 264 00:12:40,026 --> 00:12:42,735 that the navigation in this study, 265 00:12:42,735 --> 00:12:45,365 the study population is entirely 266 00:12:45,665 --> 00:12:48,122 those people who were traditionally 267 00:12:48,122 --> 00:12:51,070 not included, you know, excluded in that, 268 00:12:52,639 --> 00:12:54,947 the studies and the we don't pay attention 269 00:12:54,947 --> 00:12:55,441 so much. 270 00:12:55,441 --> 00:12:57,977 So this is a good results. 271 00:12:57,977 --> 00:13:01,014 And then you would imagine that the, this, 272 00:13:02,148 --> 00:13:04,751 low income neighborhoods, 273 00:13:04,751 --> 00:13:07,787 they share one characteristic which is low income. 274 00:13:08,187 --> 00:13:09,621 But in the they are different kinds of 275 00:13:09,621 --> 00:13:09,922 people. 276 00:13:09,922 --> 00:13:13,714 So there might be some different intervention effects within 277 00:13:13,714 --> 00:13:14,093 them. 278 00:13:14,427 --> 00:13:18,331 So this study authors did just that. 279 00:13:18,331 --> 00:13:20,166 So how about by race? 280 00:13:20,166 --> 00:13:25,322 So in this neighborhood, their black, people, non-black 281 00:13:25,322 --> 00:13:26,072 people. 282 00:13:26,372 --> 00:13:28,608 And then are there some differences? 283 00:13:28,608 --> 00:13:30,910 And yes, they found the difference. 284 00:13:30,910 --> 00:13:36,282 So here you see the, the, solid line is in this graph 285 00:13:36,282 --> 00:13:40,320 is the usual care dotted line is the intervention group. 286 00:13:40,753 --> 00:13:43,823 So for most intervention groups it works. 287 00:13:44,357 --> 00:13:47,794 But interestingly a nice a bit unfortunately 288 00:13:48,061 --> 00:13:51,631 the effect of the intervention is a bit less 289 00:13:52,198 --> 00:13:56,436 for black population than the other race. 290 00:13:57,336 --> 00:13:57,637 Right. 291 00:13:57,637 --> 00:14:03,142 So if you design this way you could kind of tease out the 292 00:14:04,110 --> 00:14:07,113 this and different, intervention effect. 293 00:14:08,614 --> 00:14:10,850 So so 294 00:14:10,850 --> 00:14:14,053 I introduce you like a for societal concern. 295 00:14:14,053 --> 00:14:15,918 One could be that then the differential 296 00:14:15,918 --> 00:14:16,923 intervention effect. 297 00:14:16,923 --> 00:14:20,593 But social group characteristic that some people study. 298 00:14:21,094 --> 00:14:24,203 The other is that that you can exclusively focus 299 00:14:24,203 --> 00:14:24,397 on 300 00:14:24,397 --> 00:14:27,075 the traditionally excluded group, 301 00:14:27,075 --> 00:14:30,403 which is the obesity intervention study. 302 00:14:31,070 --> 00:14:34,073 So primary interest here is that on the 303 00:14:35,742 --> 00:14:38,745 why we're interested in this and societal concern 304 00:14:38,978 --> 00:14:41,922 is that, it's driven from the concern 305 00:14:41,922 --> 00:14:44,150 for unfairness or inequity. 306 00:14:44,550 --> 00:14:46,821 So these groups, when we think about them, 307 00:14:46,821 --> 00:14:47,253 there's 308 00:14:47,253 --> 00:14:50,289 something wrong about it, ethically and morally. 309 00:14:50,757 --> 00:14:53,926 That should be the reason why 310 00:14:53,926 --> 00:14:57,029 we want to study. 311 00:14:57,029 --> 00:14:59,265 So here's a good, 312 00:14:59,265 --> 00:15:01,734 time to talk about terminology. 313 00:15:01,734 --> 00:15:05,098 So I've been saying the equity inequality, 314 00:15:05,098 --> 00:15:07,340 I saw many different terms. 315 00:15:07,340 --> 00:15:11,365 And it's super confusing because they have many similar 316 00:15:11,365 --> 00:15:11,878 terms. 317 00:15:11,878 --> 00:15:15,815 And unfortunately researchers don't use consistently. 318 00:15:16,182 --> 00:15:19,385 And then also when you talk to philosophers, 319 00:15:19,652 --> 00:15:22,655 public health people, they use differently. 320 00:15:22,955 --> 00:15:25,362 So this is I would claim that and this is 321 00:15:25,362 --> 00:15:27,593 the way, you know, people should use. 322 00:15:27,593 --> 00:15:30,229 But this is how I make sense of it. 323 00:15:30,229 --> 00:15:32,932 And I hope it makes sense to you. 324 00:15:32,932 --> 00:15:36,004 And then in my advice is the to understand 325 00:15:36,004 --> 00:15:36,736 this now. 326 00:15:37,103 --> 00:15:39,050 And when you talk to people about equity, 327 00:15:39,050 --> 00:15:40,807 you you have a conversation that the 328 00:15:40,807 --> 00:15:42,675 what do you mean equity. 329 00:15:42,675 --> 00:15:44,610 And in addition, to make it clear 330 00:15:44,610 --> 00:15:46,434 that they know what you're talking about, 331 00:15:46,434 --> 00:15:48,214 it's just super confusing in the field. 332 00:15:48,481 --> 00:15:51,331 I've been working in this field for 20 years and it's 333 00:15:51,331 --> 00:15:52,084 unfortunately 334 00:15:52,084 --> 00:15:53,352 still like that. 335 00:15:53,352 --> 00:15:55,525 So the way I, it makes sense to me 336 00:15:55,525 --> 00:15:56,355 is that the, 337 00:15:56,889 --> 00:16:01,561 health inequalities, these are differences 338 00:16:02,295 --> 00:16:05,898 between groups or individuals, just differences. 339 00:16:06,666 --> 00:16:09,302 If you don't have any anything, it's just differences. 340 00:16:10,636 --> 00:16:11,470 And then 341 00:16:11,470 --> 00:16:15,208 among them, their health inequities. 342 00:16:15,942 --> 00:16:18,386 And when I say health inequities, 343 00:16:18,386 --> 00:16:19,645 which is unfair, 344 00:16:19,645 --> 00:16:22,214 how differences between groups or across 345 00:16:22,214 --> 00:16:23,049 individuals. 346 00:16:24,650 --> 00:16:27,220 As you can imagine, unfair. 347 00:16:27,220 --> 00:16:29,522 You can define differently 348 00:16:29,522 --> 00:16:32,291 and maybe depends on how you feel. 349 00:16:32,291 --> 00:16:34,569 But the difference here is that in health, 350 00:16:34,569 --> 00:16:36,629 inequalities are not ethically moral. 351 00:16:36,629 --> 00:16:38,883 It's just a gigantic category of 352 00:16:38,883 --> 00:16:39,799 differences. 353 00:16:39,799 --> 00:16:43,436 And when you start to think about unfairness 354 00:16:43,736 --> 00:16:47,173 ethically problematic, then it's inequity. 355 00:16:48,140 --> 00:16:52,345 And then often people use health disparities. 356 00:16:52,345 --> 00:16:55,411 And that's how, can use a lot in the United 357 00:16:55,411 --> 00:16:55,982 States. 358 00:16:56,482 --> 00:16:58,117 And this is how I understand it. 359 00:16:58,117 --> 00:17:01,754 So it's one way to define what is unfair. 360 00:17:02,521 --> 00:17:06,806 So disparities I have differences associated 361 00:17:06,806 --> 00:17:07,293 with 362 00:17:07,293 --> 00:17:10,396 some socially meaningful characteristics 363 00:17:10,396 --> 00:17:13,399 often indicative of social disadvantage. 364 00:17:13,599 --> 00:17:16,221 So for example health disparities 365 00:17:16,221 --> 00:17:18,604 or health differences by race 366 00:17:19,472 --> 00:17:24,076 income education disability status. 367 00:17:24,343 --> 00:17:27,235 And those grades kind of indicate social 368 00:17:27,235 --> 00:17:28,247 disadvantage. 369 00:17:29,916 --> 00:17:32,418 And then how is equity 370 00:17:32,418 --> 00:17:35,146 which I use as the absence of health 371 00:17:35,146 --> 00:17:36,055 inequities. 372 00:17:37,690 --> 00:17:39,258 If you forget everything 373 00:17:39,258 --> 00:17:43,170 about what I say today, I hope this is the one that then 374 00:17:43,170 --> 00:17:43,729 to you, 375 00:17:44,163 --> 00:17:47,166 you know, in your dream it shows up sometime. 376 00:17:47,400 --> 00:17:51,304 So I kind of made a big thing about the non societal concern. 377 00:17:51,304 --> 00:17:52,872 And then we talk about, 378 00:17:52,872 --> 00:17:55,860 you know, inequity disparities difference 379 00:17:55,860 --> 00:17:57,610 and so I carry on that. 380 00:17:57,944 --> 00:18:01,047 And now it comes to the second guiding 381 00:18:01,047 --> 00:18:04,050 question of which these are unfair. 382 00:18:04,050 --> 00:18:05,151 And why. 383 00:18:05,151 --> 00:18:05,618 Because in that 384 00:18:05,618 --> 00:18:08,269 I give you the kind of distinction between 385 00:18:08,269 --> 00:18:10,289 how is inequality and inequity. 386 00:18:10,623 --> 00:18:14,293 And inequity is the unfair inequalities. 387 00:18:14,293 --> 00:18:16,896 But what do you mean by unfair? 388 00:18:16,896 --> 00:18:20,399 So, this stepping back, 389 00:18:22,268 --> 00:18:25,132 including diverse study subjects for societal 390 00:18:25,132 --> 00:18:25,705 concern, 391 00:18:26,072 --> 00:18:30,596 that kind of study often is operationalized to include or 392 00:18:30,596 --> 00:18:31,310 focus on 393 00:18:31,644 --> 00:18:34,714 historically overlooked social groups. 394 00:18:35,314 --> 00:18:38,327 So, as I already mentioned in the Health 395 00:18:38,327 --> 00:18:40,286 Disparities group select, 396 00:18:40,286 --> 00:18:43,766 this signifies social disadvantage or power 397 00:18:43,766 --> 00:18:44,657 imbalance. 398 00:18:45,024 --> 00:18:45,825 So in sample. 399 00:18:45,825 --> 00:18:48,728 So what are those groups I already kind of behind it. 400 00:18:50,262 --> 00:18:52,665 You know, NIH guidelines, 401 00:18:52,665 --> 00:18:55,609 says that the minority minority group 402 00:18:55,609 --> 00:18:56,802 is the readily 403 00:18:56,802 --> 00:19:00,121 identifiable subset of the US population, 404 00:19:00,121 --> 00:19:00,606 which 405 00:19:00,606 --> 00:19:05,223 which is distinguished by either racial, ethnic, and or cultural 406 00:19:05,223 --> 00:19:05,945 heritage. 407 00:19:06,212 --> 00:19:08,414 So that's the definition. 408 00:19:08,414 --> 00:19:11,312 And then also there's this thing called a progress 409 00:19:11,312 --> 00:19:11,717 class. 410 00:19:12,652 --> 00:19:14,053 This is just acronym. 411 00:19:14,053 --> 00:19:16,688 So you can see what the progress stands for 412 00:19:16,688 --> 00:19:17,056 here. 413 00:19:17,356 --> 00:19:19,892 So this is some kind developed 414 00:19:19,892 --> 00:19:24,363 by some researchers 2003 and Cochrane, 415 00:19:24,764 --> 00:19:28,100 a Campbell collaboration, which is they are 416 00:19:28,434 --> 00:19:31,303 establishing method for the, 417 00:19:31,303 --> 00:19:34,173 the systematic reviews and a meta analysis 418 00:19:34,173 --> 00:19:36,263 so that there are many different, 419 00:19:36,263 --> 00:19:37,276 single studies. 420 00:19:38,044 --> 00:19:39,512 But we need to look 421 00:19:39,512 --> 00:19:43,323 all those evidence together and what we can say and how to 422 00:19:43,323 --> 00:19:43,783 do it. 423 00:19:43,783 --> 00:19:46,352 So that's the kind of groups needed. 424 00:19:46,352 --> 00:19:49,130 And then they took on to, maybe equity 425 00:19:49,130 --> 00:19:51,323 considerations are important. 426 00:19:51,323 --> 00:19:54,060 And they promoted this a progress class. 427 00:19:54,060 --> 00:19:56,535 So if we are interested in certain groups 428 00:19:56,535 --> 00:19:57,863 for societal concern, 429 00:19:57,863 --> 00:19:59,732 these are the groups. So you can look at them 430 00:20:01,434 --> 00:20:01,834 and then you 431 00:20:01,834 --> 00:20:04,937 hear whatever way you look in the literature. 432 00:20:04,937 --> 00:20:08,374 The common thread is what I call binary approach. 433 00:20:08,908 --> 00:20:10,810 So this is how we think. 434 00:20:10,810 --> 00:20:14,546 So they say health, you know, whatever health outcome you look 435 00:20:14,546 --> 00:20:14,847 for, 436 00:20:14,847 --> 00:20:18,350 it could be life expectancy, mortality, 437 00:20:18,617 --> 00:20:21,620 health equity, quality of life, 438 00:20:22,054 --> 00:20:24,123 cancer prevalence, whatever. 439 00:20:24,123 --> 00:20:25,591 You can take a look. 440 00:20:25,591 --> 00:20:27,326 It's the health you can measure. 441 00:20:27,326 --> 00:20:29,295 And going up there's a healthy going down. 442 00:20:29,295 --> 00:20:30,963 There's a less healthy. 443 00:20:30,963 --> 00:20:32,909 And then you have these social groups 444 00:20:32,909 --> 00:20:34,066 you're interested in. 445 00:20:34,066 --> 00:20:36,971 And then I quote attribute by attribute 446 00:20:36,971 --> 00:20:39,205 like income, race, ethnicity, 447 00:20:39,205 --> 00:20:41,355 sexual orientation, disability status, 448 00:20:41,355 --> 00:20:43,109 whatever you're interested in. 449 00:20:43,342 --> 00:20:44,977 You can have groups. 450 00:20:44,977 --> 00:20:48,547 So this is how people typically think about it. 451 00:20:50,216 --> 00:20:50,916 It's okay. 452 00:20:50,916 --> 00:20:53,822 But in it, I think there's some challenges 453 00:20:53,822 --> 00:20:55,621 of this way of centering. 454 00:20:55,821 --> 00:20:58,357 And you know, I just list it for you. 455 00:20:58,357 --> 00:21:00,693 So, now 456 00:21:00,693 --> 00:21:04,128 these are kind of groups that indicate social 457 00:21:04,128 --> 00:21:05,197 disadvantage. 458 00:21:05,197 --> 00:21:08,701 That list is growing longer and longer every year, 459 00:21:09,668 --> 00:21:12,538 which in itself is not not a bad thing 460 00:21:12,538 --> 00:21:15,180 because I think we are more sensitive 461 00:21:15,180 --> 00:21:16,609 about those issues. 462 00:21:16,842 --> 00:21:18,420 But in them list is getting longer, 463 00:21:18,420 --> 00:21:20,312 meaning that, there are many, many groups 464 00:21:20,513 --> 00:21:22,414 every year. 465 00:21:22,414 --> 00:21:24,517 And then also in that, 466 00:21:24,517 --> 00:21:26,584 the picture I show you, you know, 467 00:21:26,584 --> 00:21:27,586 it looks as if, 468 00:21:27,987 --> 00:21:30,262 if you belong to that group, your health 469 00:21:30,262 --> 00:21:32,424 is exactly the same within the group. 470 00:21:32,424 --> 00:21:34,527 And of course it's not the case. Right. 471 00:21:34,527 --> 00:21:38,397 So the earlier I showed you that the mapping that the 472 00:21:38,397 --> 00:21:39,565 obesity, study, 473 00:21:40,266 --> 00:21:43,936 you went to the lower, income neighborhood, but in the 474 00:21:43,936 --> 00:21:45,504 there the racial difference. 475 00:21:45,504 --> 00:21:47,973 So some there's some differences between them. 476 00:21:47,973 --> 00:21:50,783 But it's like a this way of looking at 477 00:21:50,783 --> 00:21:51,744 is that then 478 00:21:51,744 --> 00:21:54,747 the intersectionality are often overlooked. 479 00:21:55,181 --> 00:21:56,916 And then if you start to pay attention 480 00:21:56,916 --> 00:21:59,882 to intersectionality, for example, the income and in 481 00:21:59,882 --> 00:22:00,452 the race, 482 00:22:00,920 --> 00:22:03,684 then you have more number of people 483 00:22:03,684 --> 00:22:04,790 to look into. 484 00:22:04,790 --> 00:22:07,560 So that group is getting more and more. 485 00:22:07,560 --> 00:22:10,896 And then also interestingly, the group definition itself 486 00:22:10,896 --> 00:22:11,730 could change. 487 00:22:12,064 --> 00:22:15,000 So I can think about sex, you know, 488 00:22:15,000 --> 00:22:17,147 when I go into graduate school, like, 489 00:22:17,147 --> 00:22:19,004 yeah, sex is a binary variable. 490 00:22:19,438 --> 00:22:22,041 And then now is like a low maybe not. 491 00:22:22,041 --> 00:22:23,275 It's a continuous. 492 00:22:23,275 --> 00:22:25,277 And you have to measure differently. 493 00:22:25,277 --> 00:22:29,181 So that group definition itself could change. 494 00:22:29,682 --> 00:22:32,983 And then often those reasons why the selected group 495 00:22:32,983 --> 00:22:34,019 characteristics 496 00:22:34,019 --> 00:22:36,891 signifies concerns for unfairness 497 00:22:36,891 --> 00:22:40,459 and inequity, are often just intriguing. 498 00:22:40,459 --> 00:22:43,128 Well, it's a social use advantage. 499 00:22:43,128 --> 00:22:46,232 I mean, you don't go anything beyond. 500 00:22:46,232 --> 00:22:49,459 So then what's happening is that there's so many 501 00:22:49,459 --> 00:22:50,669 different groups. 502 00:22:50,669 --> 00:22:52,004 We think it's important. 503 00:22:52,004 --> 00:22:55,174 And then we describe inequality this way 504 00:22:55,407 --> 00:22:58,203 and the multiple way and then bits of lots of 505 00:22:58,203 --> 00:22:59,011 information. 506 00:22:59,345 --> 00:23:02,514 And then a simple question like are we making any progress 507 00:23:02,848 --> 00:23:04,950 to reduce those inequalities. 508 00:23:04,950 --> 00:23:07,219 We don't know because there's so many. 509 00:23:07,219 --> 00:23:11,257 And then how can we make a kind of synthesize account? 510 00:23:11,457 --> 00:23:14,126 How can we synthesize those results. 511 00:23:14,126 --> 00:23:14,793 We don't know. 512 00:23:15,995 --> 00:23:19,644 So I think that then a deeper philosophical 513 00:23:19,644 --> 00:23:20,833 discussion is 514 00:23:20,833 --> 00:23:24,377 we need to include a diverse set of subjects for societal 515 00:23:24,377 --> 00:23:24,937 concern. 516 00:23:25,404 --> 00:23:29,687 So as I already mentioned in some examples, for social 517 00:23:29,687 --> 00:23:30,242 groups 518 00:23:30,242 --> 00:23:32,993 of concern, racial, ethnic minorities, 519 00:23:32,993 --> 00:23:34,947 sexual gender, minorities, 520 00:23:35,247 --> 00:23:37,253 people with low income, low education 521 00:23:37,253 --> 00:23:39,585 and disabilities, these are just examples. 522 00:23:39,952 --> 00:23:43,122 But then you have to ask which groups to choose. 523 00:23:43,122 --> 00:23:46,401 And then and right now researchers 524 00:23:46,401 --> 00:23:50,162 just pick up four very intricate sets. 525 00:23:50,162 --> 00:23:52,148 But I think that and then maybe we can do 526 00:23:52,148 --> 00:23:53,165 a little bit deeper. 527 00:23:53,165 --> 00:23:56,702 We say, you know, philosophy for X and Y. 528 00:23:57,336 --> 00:23:59,571 One is concerned about these social groups. 529 00:23:59,571 --> 00:24:04,810 So here I got the one example of the Eric lectures work. 530 00:24:05,210 --> 00:24:09,615 So maybe you're concerned about recognition of people 531 00:24:09,982 --> 00:24:12,785 then it's a good reason to pick 532 00:24:12,785 --> 00:24:15,788 racial, ethnic or asexual or gender minorities. 533 00:24:15,788 --> 00:24:18,570 So something to do with and discrimination 534 00:24:18,570 --> 00:24:20,292 that you might pick does. 535 00:24:20,693 --> 00:24:23,092 Or maybe you're concerned about resources, 536 00:24:23,092 --> 00:24:24,863 how resources are distributed. 537 00:24:25,297 --> 00:24:27,997 So you might go to more class based groups 538 00:24:27,997 --> 00:24:30,569 like a people with low income education 539 00:24:31,503 --> 00:24:32,404 or maybe 540 00:24:32,404 --> 00:24:36,241 concerns for representation, like a political representation. 541 00:24:36,241 --> 00:24:37,343 That's your reasons. 542 00:24:37,343 --> 00:24:38,877 And people with disability. 543 00:24:38,877 --> 00:24:42,247 It's just an example. But you can see that. 544 00:24:42,247 --> 00:24:44,316 Then you go some deeper than that 545 00:24:44,316 --> 00:24:47,720 might give you some reason that why you pick those groups 546 00:24:48,187 --> 00:24:50,636 or I add concerns for the current 547 00:24:50,636 --> 00:24:52,491 or historical situation. 548 00:24:52,491 --> 00:24:55,103 So, I'm from Canada, so I feel more 549 00:24:55,103 --> 00:24:58,163 comfortable to give you an example here. 550 00:24:58,530 --> 00:25:01,533 Canadian example, which is indigenous population 551 00:25:01,834 --> 00:25:04,261 in Canada past ten years, indigenous 552 00:25:04,261 --> 00:25:05,137 populations. 553 00:25:05,137 --> 00:25:08,198 And what Canada did to them became 554 00:25:08,198 --> 00:25:09,908 just so important. 555 00:25:09,908 --> 00:25:13,011 And and now it's like, well, they're 556 00:25:13,011 --> 00:25:16,882 rich indigenous people, highly educated, politically, 557 00:25:17,349 --> 00:25:20,185 quite strongly influential indigenous people. 558 00:25:20,185 --> 00:25:24,156 But it doesn't matter if you have indigenous heritage. 559 00:25:24,390 --> 00:25:25,891 We have to be worried about 560 00:25:25,891 --> 00:25:28,160 because of that historical reason, 561 00:25:28,160 --> 00:25:29,895 what we did as a country. 562 00:25:30,295 --> 00:25:33,899 So that's just no question other than then you have to 563 00:25:33,899 --> 00:25:34,500 measure. 564 00:25:34,833 --> 00:25:36,935 So that could be one good reason. 565 00:25:36,935 --> 00:25:40,105 So I just want to have that kind of conversation going 566 00:25:40,105 --> 00:25:42,562 a little bit for the empirical researchers 567 00:25:42,562 --> 00:25:44,376 to show these kind of results. 568 00:25:44,376 --> 00:25:47,186 So that's my hope that then, people 569 00:25:47,186 --> 00:25:49,515 start to talk about it more. 570 00:25:50,215 --> 00:25:52,818 So the final, the guiding question. 571 00:25:52,818 --> 00:25:55,821 So now I hope you kind of have us some, 572 00:25:56,121 --> 00:25:59,758 you know, ideas of what this equity considerations are. 573 00:26:00,159 --> 00:26:02,389 But then how do equity considerations 574 00:26:02,389 --> 00:26:03,595 influence the study 575 00:26:03,595 --> 00:26:05,731 design analysis plan and reporting. 576 00:26:07,132 --> 00:26:08,233 So again, 577 00:26:08,233 --> 00:26:10,622 you have to go back to the reason 578 00:26:10,622 --> 00:26:12,504 why you wanted to include 579 00:26:12,504 --> 00:26:14,889 diverse population and participation 580 00:26:14,889 --> 00:26:16,942 biology and societal concerns. 581 00:26:17,643 --> 00:26:19,411 And then I want to walk you through. 582 00:26:19,411 --> 00:26:21,914 But here is that just a stepping back. 583 00:26:21,914 --> 00:26:24,983 The traditional study is that you have a population 584 00:26:24,983 --> 00:26:27,541 in your study subject intervention group, 585 00:26:27,541 --> 00:26:29,288 the non intervention group. 586 00:26:29,588 --> 00:26:31,196 And then you want to see the outcome 587 00:26:31,196 --> 00:26:32,357 and how that's different. 588 00:26:32,357 --> 00:26:36,069 So this is typical way to do and I'm sorry 589 00:26:36,069 --> 00:26:37,129 the example 590 00:26:37,129 --> 00:26:40,682 hypothetical example I assigned a reading Cookson's 591 00:26:40,682 --> 00:26:41,099 work. 592 00:26:41,533 --> 00:26:45,637 So I want to walk through you with this example. 593 00:26:45,904 --> 00:26:48,824 Populations that low income country 594 00:26:48,824 --> 00:26:51,910 intervention is an antenatal dietary 595 00:26:51,910 --> 00:26:54,085 medication and supplementation program, 596 00:26:54,085 --> 00:26:56,148 much like, you know, pregnant women. 597 00:26:57,049 --> 00:26:58,917 A the 598 00:26:58,917 --> 00:27:01,920 the intervention study that we talked about earlier, 599 00:27:02,154 --> 00:27:05,714 comparisons are babies whose mothers received the 600 00:27:05,714 --> 00:27:06,658 intervention 601 00:27:06,859 --> 00:27:09,183 and the babies whose mothers did not receive the 602 00:27:09,183 --> 00:27:09,862 intervention. 603 00:27:10,128 --> 00:27:11,763 And then we want to know that birth weight, 604 00:27:13,765 --> 00:27:14,833 so traditional 605 00:27:14,833 --> 00:27:18,361 study is and usually we look at an average treatment 606 00:27:18,361 --> 00:27:18,904 effect. 607 00:27:19,137 --> 00:27:21,874 So here you see that that's the density. 608 00:27:21,874 --> 00:27:25,277 Like a what's the, the, birth weight. 609 00:27:25,744 --> 00:27:29,882 And then here's the worst way going that way. 610 00:27:30,182 --> 00:27:33,352 And then it's the, what's the distribution of it 611 00:27:33,719 --> 00:27:37,291 and then a control and then untreated it's a 612 00:27:37,291 --> 00:27:38,590 different color 613 00:27:39,725 --> 00:27:42,628 and then their different, distribution 614 00:27:42,628 --> 00:27:45,631 and then and that dotted line is the average. 615 00:27:45,898 --> 00:27:50,435 So you analyze the difference between this, that means 616 00:27:50,836 --> 00:27:53,978 and you report what's the difference between 617 00:27:53,978 --> 00:27:54,406 them. 618 00:27:54,873 --> 00:27:56,475 So that's the traditional way 619 00:27:56,475 --> 00:27:58,786 that the, the all the typical intervention 620 00:27:58,786 --> 00:27:59,611 studies we do. 621 00:28:00,212 --> 00:28:02,654 So how does it look if you have equity 622 00:28:02,654 --> 00:28:03,682 concentrations. 623 00:28:04,082 --> 00:28:06,852 So the reason why you're interested 624 00:28:06,852 --> 00:28:09,392 in encouraging diverse population is that 625 00:28:09,392 --> 00:28:10,322 participation. 626 00:28:10,656 --> 00:28:13,392 We already talked about this right then. 627 00:28:16,695 --> 00:28:17,863 And then if 628 00:28:17,863 --> 00:28:20,908 fair subject selection is a primary concern 629 00:28:20,908 --> 00:28:21,333 here, 630 00:28:21,333 --> 00:28:24,021 and then if there's no reason to suspect 631 00:28:24,021 --> 00:28:26,104 or the interest in potentially 632 00:28:26,104 --> 00:28:28,195 differential intervention effects 633 00:28:28,195 --> 00:28:30,475 due to biology or societal concern, 634 00:28:31,076 --> 00:28:33,645 no additional considerations for this study. 635 00:28:33,645 --> 00:28:36,648 Design analysis on reporting, maybe necessary. 636 00:28:36,882 --> 00:28:38,852 All you have to worry about is that the 637 00:28:38,852 --> 00:28:40,519 you go to the diverse population 638 00:28:40,852 --> 00:28:44,076 and you make sure that the study subjects, 639 00:28:44,076 --> 00:28:45,457 selection is fair 640 00:28:45,924 --> 00:28:48,927 and then you just do business as usual. 641 00:28:49,361 --> 00:28:51,663 But if it is biology, remember that 642 00:28:51,663 --> 00:28:53,487 in a cardiovascular disease, maybe men 643 00:28:53,487 --> 00:28:55,167 and the women there are different, 644 00:28:56,335 --> 00:28:58,170 then it has a big implication 645 00:28:58,170 --> 00:29:01,013 for study design, analysis plan and 646 00:29:01,013 --> 00:29:01,907 reporting. 647 00:29:02,307 --> 00:29:04,879 So you need to make sure you include 648 00:29:04,879 --> 00:29:06,878 both groups, men and women. 649 00:29:06,878 --> 00:29:10,015 In that example you have to run the study 650 00:29:10,015 --> 00:29:13,018 like among them intervention works. 651 00:29:13,485 --> 00:29:15,522 And then you have to do the separate 652 00:29:15,522 --> 00:29:16,088 analysis. 653 00:29:16,088 --> 00:29:18,052 If you are a quantitative person, 654 00:29:18,052 --> 00:29:19,124 it's a stratified 655 00:29:19,124 --> 00:29:22,204 analysis, we say, and you have to report 656 00:29:22,204 --> 00:29:23,128 separately. 657 00:29:23,128 --> 00:29:26,666 Otherwise you don't know if men and women have a 658 00:29:26,666 --> 00:29:28,066 different, effect, 659 00:29:29,334 --> 00:29:31,844 societal concern, which is the equity, 660 00:29:31,844 --> 00:29:32,504 then yes, 661 00:29:32,504 --> 00:29:34,640 you have to do a bit differently. 662 00:29:34,640 --> 00:29:36,842 And there's an equity consideration. 663 00:29:36,842 --> 00:29:39,154 People start to use different terms, 664 00:29:39,154 --> 00:29:41,146 like in health equity relevant 665 00:29:41,380 --> 00:29:45,622 randomized trials or equity informed informative 666 00:29:45,622 --> 00:29:46,418 methods. 667 00:29:46,952 --> 00:29:49,021 And they're two different approaches. 668 00:29:49,021 --> 00:29:51,873 One is focusing on the traditionally excluded 669 00:29:51,873 --> 00:29:52,190 good 670 00:29:52,658 --> 00:29:55,133 and the other is in assessing differences 671 00:29:55,133 --> 00:29:56,461 across social groups. 672 00:29:57,195 --> 00:30:00,966 So focusing on traditionally escalate group, 673 00:30:01,233 --> 00:30:05,237 which is not that different in terms of the study design. 674 00:30:05,504 --> 00:30:07,639 It's just me. You have to go to those. 675 00:30:07,639 --> 00:30:09,726 And that traditionally excluded groups 676 00:30:09,726 --> 00:30:11,977 like all the example is that's in there, 677 00:30:12,244 --> 00:30:14,461 the researchers who went to the low income 678 00:30:14,461 --> 00:30:15,147 neighborhood 679 00:30:15,147 --> 00:30:19,618 and then they selected people, and then the rest is business, 680 00:30:20,652 --> 00:30:21,787 as usual. Right. 681 00:30:21,787 --> 00:30:24,790 So now these distributions, 682 00:30:25,691 --> 00:30:28,026 came from that particular a group. 683 00:30:28,026 --> 00:30:30,337 And then you see intervention effect, 684 00:30:30,337 --> 00:30:32,397 just like the traditional study. 685 00:30:32,831 --> 00:30:34,966 It's just a population is different here, 686 00:30:36,768 --> 00:30:38,737 but a bit more complex. 687 00:30:38,737 --> 00:30:41,740 This and assessing differences across social groups. 688 00:30:42,007 --> 00:30:44,910 Now you have to go to the population. 689 00:30:44,910 --> 00:30:47,854 Well representing that social characteristic of 690 00:30:47,854 --> 00:30:48,480 interest. 691 00:30:48,714 --> 00:30:50,583 So if we are interested in income 692 00:30:50,583 --> 00:30:51,717 you'll have to have 693 00:30:51,717 --> 00:30:53,351 a very low income people. 694 00:30:53,351 --> 00:30:55,568 And in high income people you cannot 695 00:30:55,568 --> 00:30:57,723 just have a certain income people. 696 00:30:57,723 --> 00:30:58,156 Right. 697 00:30:58,156 --> 00:31:00,707 So you do that and then when you run 698 00:31:00,707 --> 00:31:03,328 the study, make sure you measure it. 699 00:31:03,662 --> 00:31:06,031 You have to measure income. 700 00:31:06,031 --> 00:31:08,500 Every income is your interest race. 701 00:31:08,500 --> 00:31:11,661 If race is the interest and the analysis 702 00:31:11,661 --> 00:31:14,506 the examine the intervention effect 703 00:31:14,506 --> 00:31:17,509 across those characteristics in a way that. 704 00:31:17,809 --> 00:31:20,078 So the study would look like this. 705 00:31:20,078 --> 00:31:23,815 So this is the again that cookson's study. 706 00:31:23,815 --> 00:31:28,787 But the null income rank is added. 707 00:31:29,187 --> 00:31:32,724 So the each day low income to high income. 708 00:31:33,425 --> 00:31:36,061 Right. And then what's the birth weight. 709 00:31:37,562 --> 00:31:38,029 And then 710 00:31:38,029 --> 00:31:40,701 here you can see for that low birth weight 711 00:31:40,701 --> 00:31:42,100 what's the difference 712 00:31:42,634 --> 00:31:45,637 between intervention and non intervention group. 713 00:31:46,004 --> 00:31:47,139 And then a middle. 714 00:31:47,139 --> 00:31:49,274 And in a high income high. 715 00:31:49,274 --> 00:31:52,377 So you can at the different 716 00:31:52,377 --> 00:31:55,726 that level of income how the intervention effect is 717 00:31:55,726 --> 00:31:56,448 different. 718 00:31:56,848 --> 00:31:58,683 And you can report that. 719 00:31:58,683 --> 00:32:02,487 So for this hypothetical study it's a wonderful study because. 720 00:32:02,487 --> 00:32:06,558 And then the lower the income is the greater the intervention 721 00:32:06,558 --> 00:32:07,292 effect is. 722 00:32:08,493 --> 00:32:12,297 If you data measure income you can't do this right. 723 00:32:12,631 --> 00:32:15,116 And so you need to plan in advance 724 00:32:15,116 --> 00:32:17,235 of what you want to look for 725 00:32:17,569 --> 00:32:20,839 and how to analyze it and how to report. 726 00:32:23,341 --> 00:32:25,310 So there's some challenges 727 00:32:25,310 --> 00:32:27,699 for equity relevant and informative 728 00:32:27,699 --> 00:32:28,313 studies. 729 00:32:28,580 --> 00:32:33,064 One is sample size because on that those are the 730 00:32:33,064 --> 00:32:33,718 groups 731 00:32:33,718 --> 00:32:35,557 indicating the social disadvantage 732 00:32:35,557 --> 00:32:36,855 are usually very small. 733 00:32:37,422 --> 00:32:40,991 So large sample size is needed for typically 734 00:32:40,991 --> 00:32:42,127 small groups. 735 00:32:42,494 --> 00:32:43,662 We are interested in 736 00:32:44,763 --> 00:32:47,005 minority groups and the racial minority 737 00:32:47,005 --> 00:32:47,465 groups. 738 00:32:47,465 --> 00:32:49,801 And by definition minority is a small group. 739 00:32:49,801 --> 00:32:52,804 So you have to increase the number. 740 00:32:53,238 --> 00:32:55,768 But as long as diverse socioeconomic 741 00:32:55,768 --> 00:32:57,876 characteristics are reported, 742 00:32:57,876 --> 00:33:02,280 studies can be equity relevant, informative, post hoc, 743 00:33:02,647 --> 00:33:05,166 meaning that only if you report it 744 00:33:05,166 --> 00:33:06,351 and then if you 745 00:33:07,118 --> 00:33:11,056 if you measure if you report it and if you publish the study 746 00:33:11,289 --> 00:33:14,359 later, people can go back and then check it again. 747 00:33:14,826 --> 00:33:16,561 And that's not there. 748 00:33:16,561 --> 00:33:19,407 Many quite exciting quantitative methods 749 00:33:19,407 --> 00:33:20,332 how to do it 750 00:33:20,332 --> 00:33:23,001 so that combining data from many studies 751 00:33:23,001 --> 00:33:24,603 does the meta analysis. 752 00:33:25,003 --> 00:33:27,096 So your study might be very small, 753 00:33:27,096 --> 00:33:29,374 but it makes you collect and combine 754 00:33:29,374 --> 00:33:30,508 and later. 755 00:33:30,508 --> 00:33:33,678 And then also there's a method called the Bayesian methods. 756 00:33:33,678 --> 00:33:37,716 And that overcome the small sample size issues. 757 00:33:38,149 --> 00:33:41,953 So at least it's wonderful if you can design 758 00:33:41,953 --> 00:33:45,090 a very large study. 759 00:33:45,090 --> 00:33:48,927 That's enough, power to detect those. 760 00:33:48,927 --> 00:33:52,530 And small differences that small groups, if you cannot 761 00:33:53,798 --> 00:33:56,101 still measure those characteristics, 762 00:33:56,101 --> 00:33:59,905 then you can help, other researchers 763 00:33:59,905 --> 00:34:02,941 to kind of combining the power later 764 00:34:04,409 --> 00:34:06,845 cost could be the issue because and 765 00:34:06,845 --> 00:34:09,567 and now you have to go to those groups 766 00:34:09,567 --> 00:34:12,217 that then traditionally not invited. 767 00:34:12,217 --> 00:34:15,175 Therefore they may not be very willing 768 00:34:15,175 --> 00:34:16,421 to participate. 769 00:34:16,922 --> 00:34:18,818 But in interestingly NIH guidelines, 770 00:34:18,818 --> 00:34:20,926 it doesn't a cost cannot be the reason. 771 00:34:21,660 --> 00:34:23,786 And so what happens is that, researchers 772 00:34:23,786 --> 00:34:24,796 write their grants 773 00:34:25,130 --> 00:34:28,133 and then they justify, you know, we want to do this study. 774 00:34:28,133 --> 00:34:30,499 And if it is more expensive equation 775 00:34:30,499 --> 00:34:32,537 and they want to go into that, 776 00:34:32,537 --> 00:34:34,372 those difficult to reach population 777 00:34:34,372 --> 00:34:35,840 for their societal concern. 778 00:34:36,174 --> 00:34:37,742 And I 779 00:34:37,742 --> 00:34:40,745 sure will do it because it's important. 780 00:34:40,745 --> 00:34:44,783 So cost should not be the reason I think that's ideal. 781 00:34:46,051 --> 00:34:49,874 And in practice maybe that state consideration 782 00:34:49,874 --> 00:34:50,622 to make. 783 00:34:50,622 --> 00:34:52,989 But in that I think it's a good thing to 784 00:34:52,989 --> 00:34:53,758 think about. 785 00:34:53,758 --> 00:34:56,198 And then, as you could see in my little 786 00:34:56,198 --> 00:34:56,761 diagram, 787 00:34:57,228 --> 00:34:59,764 this equity relevance study is again, 788 00:34:59,764 --> 00:35:02,667 fairly complex in how you design it. 789 00:35:04,436 --> 00:35:07,439 So the summary, 790 00:35:07,839 --> 00:35:09,575 we asked in three guiding questions 791 00:35:09,575 --> 00:35:11,609 who should be in the study and, and why. 792 00:35:12,077 --> 00:35:14,294 They're different reasons and which health 793 00:35:14,294 --> 00:35:16,247 inequalities are unfair and and why. 794 00:35:17,115 --> 00:35:19,006 And then how do equity considerations 795 00:35:19,006 --> 00:35:19,517 influence 796 00:35:19,517 --> 00:35:22,520 the study design analysis plan and reporting. 797 00:35:23,121 --> 00:35:25,558 And then I hope the the two key points 798 00:35:25,558 --> 00:35:28,059 I wanted to make are now kind of clear 799 00:35:28,493 --> 00:35:30,567 equity considerations are not the 800 00:35:30,567 --> 00:35:31,196 checkbox. 801 00:35:31,196 --> 00:35:33,732 And they are woven into the principles 802 00:35:33,732 --> 00:35:35,734 for ethical clinical research 803 00:35:36,167 --> 00:35:38,408 and to make equity considerations 804 00:35:38,408 --> 00:35:40,105 explicit and meaningful. 805 00:35:40,372 --> 00:35:43,141 It is important to think carefully about 806 00:35:43,141 --> 00:35:46,044 when inequalities around their. 807 00:35:46,044 --> 00:35:47,345 Okay. Thank you very much.