1 00:00:01,800 --> 00:00:03,170 Dr. Kelvin Choi: Good morning, everyone. 2 00:00:03,170 --> 00:00:05,340 This is Kelvin Choi, senior investigator 3 00:00:05,340 --> 00:00:06,830 from the National Institute of Minority Health 4 00:00:06,830 --> 00:00:09,610 and Health Disparities. Welcome to the workshop. 5 00:00:09,610 --> 00:00:11,870 This is the first NIH Health Disparities 6 00:00:11,870 --> 00:00:15,330 Interest Group workshop titled Integrating Social Determinants 7 00:00:15,330 --> 00:00:19,120 and Structural Influences Measures in Biomedical Research. 8 00:00:19,930 --> 00:00:22,440 We have overwhelming responses from you all, 9 00:00:22,440 --> 00:00:24,070 and we have a lot of people register. 10 00:00:24,070 --> 00:00:27,410 So I'm glad that even though it's very early 11 00:00:27,410 --> 00:00:30,200 on the West Coast, you're able to join us, 12 00:00:30,200 --> 00:00:32,630 and for the East Coast people, I'm glad that you have time 13 00:00:32,630 --> 00:00:34,580 to get a cup of coffee before we start. 14 00:00:36,140 --> 00:00:38,010 Before I go further into the workshop, 15 00:00:38,010 --> 00:00:42,210 let me also thank the workshop 16 00:00:42,210 --> 00:00:45,420 planning committee, this has been a lot of work 17 00:00:45,420 --> 00:00:47,870 in the making to make this workshop happen. 18 00:00:47,870 --> 00:00:50,050 We have a fantastic lineup of speakers 19 00:00:50,050 --> 00:00:53,620 because of these individuals who have worked very hard 20 00:00:53,620 --> 00:00:55,780 in putting this workshop together. 21 00:00:55,780 --> 00:00:57,750 You will find their names and their bios 22 00:00:57,750 --> 00:01:01,560 in the speaker bio page on our website. 23 00:01:03,240 --> 00:01:05,340 Special thanks to the National Institute of Minority Health 24 00:01:05,340 --> 00:01:06,580 and Health Disparities 25 00:01:06,580 --> 00:01:10,170 for also sponsoring financially for the workshop, 26 00:01:10,170 --> 00:01:15,420 providing the resources needed to support the logistics 27 00:01:15,420 --> 00:01:18,680 and other means so that we can put the workshop together. 28 00:01:18,680 --> 00:01:22,800 I want to remind you all to please make sure 29 00:01:22,800 --> 00:01:25,900 you mute yourself during the whole workshop 30 00:01:25,900 --> 00:01:28,920 other than the breakout sessions later in the afternoon. 31 00:01:28,920 --> 00:01:31,120 And please ask question using Slido, 32 00:01:31,120 --> 00:01:34,180 which we will share with you the information about Slido 33 00:01:34,180 --> 00:01:35,780 on the chat very soon. 34 00:01:36,530 --> 00:01:38,080 You can ask questions during the presentation 35 00:01:38,080 --> 00:01:39,640 or after the presentation. 36 00:01:39,640 --> 00:01:41,790 Most of the sessions is following, 37 00:01:41,790 --> 00:01:43,200 we'll have a Q&A session. 38 00:01:43,200 --> 00:01:45,160 So you can post questions in Slido 39 00:01:45,160 --> 00:01:47,160 and the moderator will pick up those questions, 40 00:01:47,160 --> 00:01:53,400 and ask to the speakers. This is the agenda of the day, 41 00:01:53,400 --> 00:01:55,260 we're going to have introduction and welcome, 42 00:01:55,260 --> 00:01:56,760 and then we'll have a short overview 43 00:01:56,760 --> 00:01:59,610 of the workshop following two lectures. 44 00:01:59,610 --> 00:02:00,880 And then we have a lunch break, 45 00:02:00,880 --> 00:02:03,250 and after that we have two case study sessions 46 00:02:03,250 --> 00:02:05,410 and a breakout session afterwards. 47 00:02:05,410 --> 00:02:07,750 And then we'll have a closing remarks at 4:30. 48 00:02:07,750 --> 00:02:10,440 So it's a very packed day and lineup 49 00:02:10,440 --> 00:02:11,880 with very fantastic speakers. 50 00:02:11,880 --> 00:02:13,529 I hope you'll enjoy the workshop. 51 00:02:15,950 --> 00:02:18,760 Now, let me turn to two very significant 52 00:02:18,760 --> 00:02:21,700 and fantastic speakers to open the workshop for us. 53 00:02:21,700 --> 00:02:23,990 The first speaker is Dr. Shannon Zenk, 54 00:02:23,990 --> 00:02:27,070 the Director of the National Institute of Nursing Research. 55 00:02:27,690 --> 00:02:30,450 She joined NINR in September of 2020, 56 00:02:30,450 --> 00:02:33,590 following a 14-year career as a faculty member 57 00:02:33,590 --> 00:02:35,020 at the University of Illinois, 58 00:02:35,020 --> 00:02:37,650 Chicago College of Nursing and Institute 59 00:02:37,650 --> 00:02:39,250 for Health Research and Policy. 60 00:02:39,940 --> 00:02:42,260 She's also the co-chair of the NIH 61 00:02:42,260 --> 00:02:46,840 Social Determinants of Health Research Coordinating Committee 62 00:02:46,840 --> 00:02:50,790 together with NIMHD Director Dr. Pérez-Stable. 63 00:02:52,070 --> 00:02:56,050 Following Dr. Zenk, we have Dr. Monica Webb Hooper, 64 00:02:56,050 --> 00:02:58,230 who is the deputy director of National Institute 65 00:02:58,230 --> 00:03:00,390 on Minority Health and Health Disparities. 66 00:03:00,390 --> 00:03:03,180 She works closely with Dr. Pérez-Stable, 67 00:03:03,180 --> 00:03:05,180 the director and leadership, 68 00:03:05,180 --> 00:03:07,240 to oversee all aspects of the institute 69 00:03:07,240 --> 00:03:10,080 and to advance the mission of promoting 70 00:03:10,080 --> 00:03:12,540 the health of populations with health disparity 71 00:03:12,540 --> 00:03:14,740 and health equity. Without further ado, 72 00:03:14,740 --> 00:03:16,740 I will hand it over to Dr. Shannon Zenk. 73 00:03:20,260 --> 00:03:22,710 Dr. Shannon Zenk: Hi, good morning, everyone. 74 00:03:22,710 --> 00:03:26,480 Thank you, Dr. Choi, and it's a pleasure to be with you. 75 00:03:26,480 --> 00:03:30,570 Thank you for inviting me to join today's workshop. 76 00:03:30,570 --> 00:03:33,070 So the theme today focused on social 77 00:03:33,070 --> 00:03:35,030 and structural determinants of health 78 00:03:35,030 --> 00:03:37,980 is a distinct interest to me, 79 00:03:37,980 --> 00:03:40,380 the National Institute of Nursing Research 80 00:03:40,380 --> 00:03:42,660 and across the NIH. 81 00:03:43,190 --> 00:03:46,680 At NINR, we recognize the importance of structural 82 00:03:46,680 --> 00:03:50,440 and social factors to individual and community health, 83 00:03:50,440 --> 00:03:51,700 and acknowledge 84 00:03:51,700 --> 00:03:54,880 that they are major drivers of health and equity. 85 00:03:55,950 --> 00:03:58,080 So I am honored to open the meeting 86 00:03:58,080 --> 00:04:02,350 along with my NIMHD colleague, Dr. Monica Webb Hooper. 87 00:04:05,390 --> 00:04:08,320 So while there are multiple conceptualizations 88 00:04:08,320 --> 00:04:10,810 of our literature, let me share one perspective 89 00:04:10,810 --> 00:04:13,600 on social and structural determinants of health. 90 00:04:14,170 --> 00:04:15,750 The World Health Organization 91 00:04:15,750 --> 00:04:18,240 defines social determinants of health 92 00:04:18,240 --> 00:04:21,740 as the conditions in which people are born, 93 00:04:21,740 --> 00:04:24,130 grow, live, work and age. 94 00:04:24,670 --> 00:04:28,160 These conditions are shaped by structural factors 95 00:04:28,160 --> 00:04:33,700 that comprise economic, legal, political and cultural forces 96 00:04:33,700 --> 00:04:38,520 and systems such as public and institutional policies 97 00:04:38,520 --> 00:04:40,660 that shape the distribution of power 98 00:04:40,660 --> 00:04:43,170 and resources across society 99 00:04:43,170 --> 00:04:45,930 and people's social position within it. 100 00:04:46,730 --> 00:04:50,340 Social Determinants include community living conditions, 101 00:04:50,340 --> 00:04:55,450 and individual family and social and economic circumstances 102 00:04:55,450 --> 00:04:57,510 such as educational attainment, 103 00:04:57,510 --> 00:05:00,780 social isolation, and nutrition security 104 00:05:00,780 --> 00:05:04,260 are sometimes also considered social determinants of health. 105 00:05:05,050 --> 00:05:07,380 Social and structural factors shape health 106 00:05:07,380 --> 00:05:09,420 for better or for worse. 107 00:05:10,630 --> 00:05:13,010 So that brings me to a related term 108 00:05:13,010 --> 00:05:15,510 that is increasingly seen in the literature, 109 00:05:15,510 --> 00:05:19,360 particularly related to health care, and that's social needs. 110 00:05:19,360 --> 00:05:22,240 Some use the term social needs to refer to 111 00:05:22,240 --> 00:05:27,230 an adverse individual or family social determinant of health, 112 00:05:27,230 --> 00:05:29,930 while others think of social needs as downstream 113 00:05:29,930 --> 00:05:34,040 manifestations of the impact of social determinants of health. 114 00:05:34,770 --> 00:05:37,530 Now, regardless of whether they're under the umbrella 115 00:05:37,530 --> 00:05:40,480 of social determinants or a separate concept, 116 00:05:40,480 --> 00:05:44,260 it's important to note that solutions to health inequities 117 00:05:44,260 --> 00:05:48,980 must include addressing upstream structural and social factors. 118 00:05:48,980 --> 00:05:52,010 And we need research to identify solutions 119 00:05:52,010 --> 00:05:55,700 upstream to downstream. And NIH is doing just that. 120 00:05:55,700 --> 00:05:58,360 So let me share just a couple of examples. 121 00:05:59,510 --> 00:06:03,270 So at NIH, we recognize the need to enhance coordination 122 00:06:03,270 --> 00:06:06,260 and collaboration to accelerate research 123 00:06:06,260 --> 00:06:07,909 on social determinants of health. 124 00:06:08,530 --> 00:06:10,430 In support of this collaboration, 125 00:06:10,430 --> 00:06:14,160 an NIH-wide Social Determinants of Health Research 126 00:06:14,160 --> 00:06:16,970 Coordinating Committee was recently formed. 127 00:06:17,650 --> 00:06:20,620 The RCC, which is a co-host of today's meeting, 128 00:06:20,620 --> 00:06:24,170 has been embraced across much of NIH, 129 00:06:24,170 --> 00:06:27,710 with 18 NIH institutes, centers and offices 130 00:06:27,710 --> 00:06:30,060 serving on the executive committee. 131 00:06:31,520 --> 00:06:34,760 Another NIH-wide initiative is the common funds 132 00:06:34,760 --> 00:06:38,330 ComPASS program. The program aims to develop 133 00:06:38,330 --> 00:06:42,280 or assess community-led structural interventions 134 00:06:42,280 --> 00:06:45,620 that leverage multi-sectoral partnerships 135 00:06:45,620 --> 00:06:48,280 to improve health outcomes of communities, 136 00:06:48,280 --> 00:06:52,160 to reduce health disparities, and to change systems, 137 00:06:52,160 --> 00:06:56,760 policies and practices to achieve optimal health for all. 138 00:06:56,760 --> 00:07:00,270 So I encourage you to check out the website shown here 139 00:07:00,270 --> 00:07:02,840 to learn more about the funding opportunities 140 00:07:02,840 --> 00:07:04,440 through this program. 141 00:07:05,550 --> 00:07:07,320 In fact, social determinants of health 142 00:07:07,320 --> 00:07:09,860 are being integrated into a wide variety 143 00:07:09,860 --> 00:07:13,330 of NIH-wide initiatives and programs. 144 00:07:13,330 --> 00:07:16,270 These include new initiatives such as the climate change 145 00:07:16,270 --> 00:07:17,720 and health initiative, 146 00:07:17,720 --> 00:07:20,640 and IMPROVE, which is focused on maternal health. 147 00:07:22,510 --> 00:07:25,220 So now, let me tell you a little bit about NINR 148 00:07:25,220 --> 00:07:28,200 and our commitment to eliminating health disparities 149 00:07:28,200 --> 00:07:32,040 and advancing health equity through rigorous research, 150 00:07:32,040 --> 00:07:35,520 a commitment that we share with everyone here today. 151 00:07:36,290 --> 00:07:40,020 Now, what sets NINR apart from other NIH institutes 152 00:07:40,020 --> 00:07:41,560 is that our research, 153 00:07:41,560 --> 00:07:44,570 with nursing's perspective at its core, 154 00:07:44,570 --> 00:07:47,340 is focused on health solutions for people 155 00:07:47,340 --> 00:07:50,360 in the context of their lives and living conditions 156 00:07:50,360 --> 00:07:53,520 and across the many settings where nurses practice. 157 00:07:54,780 --> 00:07:56,950 We are committed to leaving nursing science 158 00:07:56,950 --> 00:07:59,370 to solve pressing health challenges 159 00:07:59,370 --> 00:08:02,960 and inform practice and policy, optimizing health 160 00:08:02,960 --> 00:08:06,010 and advancing health equity into the future. 161 00:08:06,690 --> 00:08:10,150 Our mission statement reflects our belief that nursing research 162 00:08:10,150 --> 00:08:14,450 is the key to unlocking the power and potential of nursing. 163 00:08:14,450 --> 00:08:17,310 And our strategic plan describes how we want to leverage 164 00:08:17,310 --> 00:08:20,690 the strengths and unique knowledge and perspectives 165 00:08:20,690 --> 00:08:24,600 inherent to the discipline to the benefit of all people. 166 00:08:25,910 --> 00:08:27,360 Within our strategic plan, 167 00:08:27,360 --> 00:08:29,550 we include these guiding principles 168 00:08:29,550 --> 00:08:33,350 that all NINR supported work should incorporate. 169 00:08:34,280 --> 00:08:36,980 And you can see that among these is a commitment 170 00:08:36,980 --> 00:08:39,930 to research that advances equity, 171 00:08:39,930 --> 00:08:43,330 diversity, inclusion, and accessibility. 172 00:08:44,680 --> 00:08:47,220 Moreover, one of our five research lenses 173 00:08:47,220 --> 00:08:49,230 that is perspectives through 174 00:08:49,230 --> 00:08:51,400 which we examine health challenges 175 00:08:51,400 --> 00:08:55,010 is that of health equity. From NINR's perspective, 176 00:08:55,010 --> 00:08:58,270 health inequities are rooted in structural factors, 177 00:08:58,270 --> 00:09:00,140 including structural racism. 178 00:09:00,680 --> 00:09:02,690 By supporting research through this lens, 179 00:09:02,690 --> 00:09:05,630 we aim to reduce and ultimately eliminate 180 00:09:05,630 --> 00:09:08,430 the systemic and structural inequities 181 00:09:08,430 --> 00:09:11,940 that place disadvantaged groups at an unfair, 182 00:09:11,940 --> 00:09:14,350 unjust, and avoidable disadvantage 183 00:09:14,350 --> 00:09:16,870 in attaining their full health potential. 184 00:09:16,870 --> 00:09:19,800 So we're interested in supporting nursing research 185 00:09:19,800 --> 00:09:21,840 that examines structural interventions 186 00:09:21,840 --> 00:09:25,730 to remove obstacles to help an increase the availability 187 00:09:25,730 --> 00:09:27,730 and accessibility of resources. 188 00:09:28,920 --> 00:09:30,900 And another of our research lenses 189 00:09:30,900 --> 00:09:33,530 focuses on social determinants of health. 190 00:09:34,100 --> 00:09:37,800 Through this lens, we aim to identify effective interventions 191 00:09:37,800 --> 00:09:40,670 to improve health and quality of life 192 00:09:40,670 --> 00:09:43,180 by addressing the conditions of daily life. 193 00:09:43,930 --> 00:09:47,590 We want our investigators to address upstream and midstream 194 00:09:47,590 --> 00:09:52,220 causes of health outcomes, identify how to limit exposure 195 00:09:52,220 --> 00:09:55,510 to adverse social and economic conditions, 196 00:09:55,510 --> 00:09:58,250 and how to limit susceptibility 197 00:09:58,250 --> 00:10:01,630 to the biological embedding of social determinants. 198 00:10:02,940 --> 00:10:05,740 All of our research lenses, which you can see here, 199 00:10:05,740 --> 00:10:08,390 are complementary and synergistic. 200 00:10:08,390 --> 00:10:10,620 However, we encourage researchers 201 00:10:10,620 --> 00:10:12,010 to view the health equity 202 00:10:12,010 --> 00:10:14,540 and social determinants of health lenses 203 00:10:14,540 --> 00:10:18,760 as primary foci through which to consider the other lenses. 204 00:10:20,190 --> 00:10:22,890 Our lenses reflect NINR's investment 205 00:10:22,890 --> 00:10:25,940 in long-standing interest in health equity 206 00:10:25,940 --> 00:10:28,040 and social determinants of health. 207 00:10:28,040 --> 00:10:30,800 For example, a third of NINR's budget 208 00:10:30,800 --> 00:10:34,570 already focuses on research to eliminate health disparities, 209 00:10:34,570 --> 00:10:38,980 second only to NIMHD, and 26% of our budget 210 00:10:38,980 --> 00:10:42,170 already focuses on social determinants of health. 211 00:10:43,890 --> 00:10:46,290 So this year, we're hosting a series of webinars 212 00:10:46,290 --> 00:10:49,390 led by science practice and policy experts 213 00:10:49,390 --> 00:10:53,690 focus on each of NINR's Strategic Plan research lenses. 214 00:10:53,690 --> 00:10:55,560 Our first lecture held in July 215 00:10:55,560 --> 00:10:59,080 highlighted the social determinants of health lens, 216 00:10:59,080 --> 00:11:02,050 and you can view that lecture still on YouTube 217 00:11:02,050 --> 00:11:05,770 to learn more about nursing research's perspective 218 00:11:05,770 --> 00:11:07,419 on social determinants of health. 219 00:11:08,670 --> 00:11:12,100 And in October, we're holding our next director's lecture. 220 00:11:12,100 --> 00:11:14,150 So I hope you'll consider joining us 221 00:11:14,150 --> 00:11:15,880 for this virtual event, 222 00:11:15,880 --> 00:11:18,360 where we'll discuss research priorities 223 00:11:18,360 --> 00:11:22,440 and the practice and policy implications of nursing research 224 00:11:22,440 --> 00:11:24,200 through the health equity lens. 225 00:11:25,530 --> 00:11:28,300 So thank you again for allowing me to join you 226 00:11:28,300 --> 00:11:30,280 and share NINR's perspective 227 00:11:30,280 --> 00:11:33,190 on Social Determinants of Health and Health Equity. 228 00:11:33,190 --> 00:11:36,760 It is truly a pleasure to welcome you to today's meeting. 229 00:11:36,760 --> 00:11:39,350 And I'm looking forward to the rest of the day. 230 00:11:39,350 --> 00:11:41,910 I'll turn it over to Dr. Monica Webb Hooper. 231 00:11:42,690 --> 00:11:44,990 Dr. Monica Webb Hooper: Well, thank you very, very much. 232 00:11:44,990 --> 00:11:47,960 I appreciate the opportunity to be here. 233 00:11:47,960 --> 00:11:51,150 And it's great to see such a robust audience. 234 00:11:51,150 --> 00:11:53,600 And of course, I'm delighted to help open the meeting 235 00:11:53,600 --> 00:11:55,490 with the fabulous Dr. Zenk. 236 00:11:55,490 --> 00:11:58,050 So again, thank you for the invitation. 237 00:11:59,080 --> 00:12:02,190 So I want to offer a few additional points. 238 00:12:02,190 --> 00:12:04,050 I mean, Dr. Zenk has already described 239 00:12:04,050 --> 00:12:06,350 what we mean by social determinants of health. 240 00:12:07,090 --> 00:12:09,940 And I will add that social determinants of health 241 00:12:09,940 --> 00:12:12,260 are not negative, per se, 242 00:12:12,260 --> 00:12:14,700 and they are distinct from health disparities, 243 00:12:14,700 --> 00:12:16,710 although sometimes this is how they sort of 244 00:12:16,710 --> 00:12:18,650 are discussed in the literature. 245 00:12:18,650 --> 00:12:20,160 So health disparities, as we know, 246 00:12:20,160 --> 00:12:24,210 refer to preventable differences in the burden of disease, 247 00:12:24,210 --> 00:12:26,090 injury, violence and opportunities 248 00:12:26,090 --> 00:12:29,020 to achieve optimal health experienced 249 00:12:29,020 --> 00:12:32,850 by socially disadvantaged groups who often face discrimination. 250 00:12:33,510 --> 00:12:37,140 And importantly, both the social determinants of health 251 00:12:37,140 --> 00:12:41,390 and health disparities, by definition, are modifiable. 252 00:12:41,390 --> 00:12:44,570 This means they do not have to exist they can be modified, 253 00:12:44,570 --> 00:12:47,220 and we have thus opportunities for change. 254 00:12:47,220 --> 00:12:50,170 So stated another way, social determinants of health 255 00:12:50,170 --> 00:12:53,860 can operate to drive effective health promotion 256 00:12:53,860 --> 00:12:56,390 and overall well-being in advantaged groups 257 00:12:56,390 --> 00:13:01,310 such as clean air, water, great school systems, beautiful parks, 258 00:13:01,310 --> 00:13:03,230 you know, appropriate social support 259 00:13:03,230 --> 00:13:06,370 while driving health problems an undue burden, 260 00:13:06,370 --> 00:13:08,160 or what we know as health disparities 261 00:13:08,160 --> 00:13:09,500 among disadvantaged groups. 262 00:13:09,500 --> 00:13:12,640 So I called the factors that drive health disparities 263 00:13:12,640 --> 00:13:14,860 adverse social determinants of health. 264 00:13:14,860 --> 00:13:18,980 And the goal is for everyone to have positive and protective 265 00:13:18,980 --> 00:13:21,610 social determinants in their lives consistently. 266 00:13:22,490 --> 00:13:26,330 Healthy People 2030 describes five broad domains 267 00:13:26,330 --> 00:13:29,640 of social determinants of health and each of these broad domains 268 00:13:29,640 --> 00:13:32,590 are affected by structural racism, 269 00:13:32,590 --> 00:13:35,000 which also had a major hand 270 00:13:35,000 --> 00:13:39,290 in shaping the distribution of money, power and resources 271 00:13:39,290 --> 00:13:42,230 for the benefit of white persons in the United States 272 00:13:42,230 --> 00:13:44,790 and to the disadvantage of African-American 273 00:13:44,790 --> 00:13:47,990 or foundational black Americans and other groups. 274 00:13:49,240 --> 00:13:51,500 The conceptualization that we at NIMHD 275 00:13:51,500 --> 00:13:53,830 have about social determinants of health 276 00:13:53,830 --> 00:13:55,620 is that they include both structural 277 00:13:55,620 --> 00:13:57,940 as well as individual factors. 278 00:13:57,940 --> 00:14:00,710 This table is a non-exhaustive list, 279 00:14:00,710 --> 00:14:03,860 but it offers examples of structural factors, 280 00:14:03,860 --> 00:14:06,460 such as environmental exposures, 281 00:14:06,460 --> 00:14:10,280 access to not only healthcare but high quality 282 00:14:10,280 --> 00:14:12,760 and culturally appropriate healthcare, 283 00:14:12,760 --> 00:14:14,480 not only access to food, 284 00:14:14,480 --> 00:14:17,660 but access to high quality nutritious foods, 285 00:14:17,660 --> 00:14:21,370 green spaces in neighborhoods and technology access. 286 00:14:21,370 --> 00:14:24,140 And among the individual factors are cultural beliefs, 287 00:14:24,140 --> 00:14:28,930 social support, exposure to violence, digital literacy, 288 00:14:28,930 --> 00:14:32,170 and trust or distrust, which I think in many cases 289 00:14:32,170 --> 00:14:35,070 are underappreciated social determinants of health. 290 00:14:35,890 --> 00:14:38,950 I want to focus in a little bit more on structural racism 291 00:14:38,950 --> 00:14:41,460 and specifically racial segregation, 292 00:14:41,460 --> 00:14:44,450 which is a key social factor determinant of health. 293 00:14:44,980 --> 00:14:49,110 There is significant evidence that segregation worsens health. 294 00:14:49,110 --> 00:14:51,620 Segregation through redlining, 295 00:14:51,620 --> 00:14:55,240 created communities of concentrated poverty 296 00:14:55,240 --> 00:14:58,010 with high levels of neighborhood disadvantage, 297 00:14:58,010 --> 00:15:00,770 low quality housing and economic divestment. 298 00:15:01,450 --> 00:15:04,450 Segregated neighborhoods with primarily racial 299 00:15:04,450 --> 00:15:06,700 and ethnic minoritized residents 300 00:15:06,700 --> 00:15:09,160 are more likely to have less resource 301 00:15:09,160 --> 00:15:11,760 healthcare settings and school systems. 302 00:15:11,760 --> 00:15:15,180 Environmental injustice in these locations 303 00:15:15,180 --> 00:15:18,230 leave residents subject to elevated exposures 304 00:15:18,230 --> 00:15:23,230 to physical and chemical hazards that adversely impact health. 305 00:15:23,230 --> 00:15:26,560 So in turn, the physical conditions and neighborhood 306 00:15:26,560 --> 00:15:31,180 environments that characterize many segregated geographic areas 307 00:15:31,180 --> 00:15:34,220 make it more difficult to practice the healthy behaviors 308 00:15:34,220 --> 00:15:35,820 we'd all like to see. 309 00:15:36,330 --> 00:15:39,970 You may be familiar with the NIMHD research framework, 310 00:15:39,970 --> 00:15:43,840 which reflects an evolving conceptualization of factors 311 00:15:43,840 --> 00:15:46,340 that affect racial and ethnic minority health 312 00:15:46,340 --> 00:15:49,060 and health disparities. The model is grounded 313 00:15:49,060 --> 00:15:51,470 in the well-known socio-ecological model 314 00:15:51,470 --> 00:15:53,150 and attends to social determinants 315 00:15:53,150 --> 00:15:55,449 or what I often called contributors of health. 316 00:15:56,220 --> 00:15:58,180 Much of the research has focused 317 00:15:58,180 --> 00:16:00,840 on individual level biological mechanisms, 318 00:16:00,840 --> 00:16:04,930 so the purple box or an individual health behavior 319 00:16:04,930 --> 00:16:08,750 in the green box as explanations for poor health 320 00:16:08,750 --> 00:16:11,520 that we often observe among minoritized groups 321 00:16:11,520 --> 00:16:13,260 and for disparities. 322 00:16:13,260 --> 00:16:17,430 As you can see, a singular focus on biology or genetics, 323 00:16:17,430 --> 00:16:22,300 or individual behavior, misses the great complexity 324 00:16:22,300 --> 00:16:25,040 of how we understand and address health disparities, 325 00:16:25,040 --> 00:16:28,160 and does not span the other domains of influence 326 00:16:28,160 --> 00:16:30,430 such as the physical or built environment, 327 00:16:30,430 --> 00:16:33,150 socio-cultural environment or healthcare system, 328 00:16:33,150 --> 00:16:35,060 or the other levels of influence, 329 00:16:35,060 --> 00:16:39,280 the interpersonal, communities, societal, within these domains. 330 00:16:39,280 --> 00:16:41,980 So consideration of social contributors 331 00:16:41,980 --> 00:16:45,160 will lead us to think more holistically 332 00:16:45,160 --> 00:16:46,760 and to study the same way. 333 00:16:47,790 --> 00:16:51,140 Another point to note about the NIMHD research framework, 334 00:16:51,140 --> 00:16:54,560 the model centers racism and discrimination 335 00:16:54,560 --> 00:16:57,110 as notable social factors that affect 336 00:16:57,110 --> 00:16:59,260 Minority Health and Health Disparities. 337 00:16:59,260 --> 00:17:00,909 Indeed, if you look at the model, 338 00:17:01,540 --> 00:17:04,370 discrimination is the only factor apparent 339 00:17:04,370 --> 00:17:06,730 across all levels of influence 340 00:17:06,730 --> 00:17:08,790 within the socio-cultural environment. 341 00:17:09,780 --> 00:17:11,730 So how can you get started incorporating 342 00:17:11,730 --> 00:17:16,130 social determinants of health into your own research? 343 00:17:16,130 --> 00:17:18,330 I borrowed these slides from Dr. Deb Duran. 344 00:17:18,330 --> 00:17:19,690 So thank you, Deb. 345 00:17:19,690 --> 00:17:22,350 I wanted to point you to a collection of resources 346 00:17:22,350 --> 00:17:25,000 pulled together and vetted by NIMHD. 347 00:17:25,000 --> 00:17:28,600 We released a special collection in the PhenX toolkit, 348 00:17:28,600 --> 00:17:31,050 and PhenX stands for Phenotypes and Exposures, 349 00:17:31,050 --> 00:17:33,070 focus on Social Determinants of Health 350 00:17:33,070 --> 00:17:36,210 with 19 instruments in a core collection 351 00:17:36,210 --> 00:17:38,180 and two specialty collection 352 00:17:38,180 --> 00:17:42,050 that we hope encourages standardized data collection, 353 00:17:42,050 --> 00:17:45,290 quality and consistency of the data collected, 354 00:17:45,290 --> 00:17:46,910 and the ability to share 355 00:17:46,910 --> 00:17:49,180 and combine data from multiple studies, 356 00:17:49,180 --> 00:17:51,180 as well as translational research 357 00:17:51,180 --> 00:17:54,040 and effective interventions to reduce health disparities. 358 00:17:54,040 --> 00:17:56,370 And we currently have a working group of experts 359 00:17:56,370 --> 00:17:59,260 reviewing measures to expand the collection 360 00:17:59,260 --> 00:18:03,220 with specialty collections and new measures to the toolkit. 361 00:18:03,820 --> 00:18:05,440 This table provides more detail 362 00:18:05,440 --> 00:18:08,350 on the measures included in the current Core Collection, 363 00:18:08,350 --> 00:18:11,360 which includes 16 measures, basic demographics, 364 00:18:11,360 --> 00:18:12,980 which are not social determinants, 365 00:18:12,980 --> 00:18:16,560 but important that we collect and standardized ways, 366 00:18:16,560 --> 00:18:19,550 access to health services, food insecurity, 367 00:18:19,550 --> 00:18:22,320 health literacy, occupational prestige. 368 00:18:22,320 --> 00:18:25,360 These are the two specialty collections currently available. 369 00:18:25,360 --> 00:18:27,220 The individual specialty collection 370 00:18:27,220 --> 00:18:29,030 includes measures for use and research 371 00:18:29,030 --> 00:18:31,360 where the information is self-reported 372 00:18:31,360 --> 00:18:34,250 or answered by family or caregivers. 373 00:18:34,250 --> 00:18:36,290 The structural specialty collection 374 00:18:36,290 --> 00:18:38,620 includes measurement and data sources, 375 00:18:38,620 --> 00:18:41,090 often from publicly available datasets 376 00:18:41,090 --> 00:18:43,380 at the structural or community level. 377 00:18:44,120 --> 00:18:47,340 I'll close with just a few other observations about this area, 378 00:18:47,340 --> 00:18:49,230 there is a need for increased research 379 00:18:49,230 --> 00:18:50,890 to enhance our understanding 380 00:18:50,890 --> 00:18:54,640 specifically about how social contributors interact 381 00:18:54,640 --> 00:18:56,800 to affect health and health disparities. 382 00:18:56,800 --> 00:19:01,130 We can make progress if we explicitly incorporate 383 00:19:01,130 --> 00:19:02,830 social determinants of health, 384 00:19:02,830 --> 00:19:04,920 which can inform our research questions, 385 00:19:04,920 --> 00:19:08,440 our procedures, and how we interpret findings. 386 00:19:08,440 --> 00:19:12,100 Longitudinal studies are very useful to help identify leaks 387 00:19:12,100 --> 00:19:15,420 with adverse social determinants of health among youth, 388 00:19:15,420 --> 00:19:17,460 and how they impact adult health, 389 00:19:17,460 --> 00:19:21,080 of course use established measures where possible. 390 00:19:21,080 --> 00:19:23,350 And as we talk more and more about health equity, 391 00:19:23,350 --> 00:19:26,260 keep in mind that addressing relevant 392 00:19:26,260 --> 00:19:29,760 social determinants of health is a part of equity work, 393 00:19:29,760 --> 00:19:32,290 or client principles of justice, fairness, 394 00:19:32,290 --> 00:19:36,800 and opportunities for optimal health in research and practice. 395 00:19:36,800 --> 00:19:40,970 We know that collaborations are hugely important in this space, 396 00:19:40,970 --> 00:19:43,700 and including context experts, 397 00:19:43,700 --> 00:19:46,090 in addition to those with content or science, 398 00:19:46,090 --> 00:19:47,950 methodology expertise. 399 00:19:47,950 --> 00:19:52,440 And finally, a true focus on adverse social contributors 400 00:19:52,440 --> 00:19:55,330 to help moves from a biomedical approach 401 00:19:55,330 --> 00:19:58,980 to a biopsychosocial one or more of a whole person approach 402 00:19:58,980 --> 00:20:01,320 and helps us make meaningful improvements 403 00:20:01,320 --> 00:20:03,200 in health disparities as the goal. 404 00:20:03,880 --> 00:20:06,750 So I want to thank you again very much for the opportunity 405 00:20:06,750 --> 00:20:09,590 to help you start the day I hope that you enjoy the meeting, 406 00:20:09,590 --> 00:20:12,560 and of course, I invite you to connect with NIMHD. 407 00:20:12,560 --> 00:20:14,160 Thank you. 408 00:20:15,250 --> 00:20:16,490 Dr. Monica Webb Hooper: Thank you for 409 00:20:16,490 --> 00:20:18,770 your important insights, Dr. Zenk and Hooper. 410 00:20:19,320 --> 00:20:21,670 Good morning, everyone. I am Constanza Camargo. 411 00:20:21,670 --> 00:20:23,430 I am an intermodal investigator 412 00:20:23,430 --> 00:20:25,079 at the National Cancer Institute. 413 00:20:25,680 --> 00:20:28,479 We are fortunate to have a fine group of speakers today. 414 00:20:29,490 --> 00:20:32,480 The full bios are available on the website. 415 00:20:34,680 --> 00:20:36,390 The workshop is being recorded 416 00:20:36,390 --> 00:20:39,160 and will be available for viewing in about three days. 417 00:20:40,250 --> 00:20:42,850 All participants will be notified by email. 418 00:20:43,910 --> 00:20:47,720 You can ask questions using the App, Slido, 419 00:20:47,720 --> 00:20:50,860 you can scan here the QR code 420 00:20:50,860 --> 00:20:54,320 or you can find the link in the chat. 421 00:20:55,010 --> 00:20:58,500 You can ask questions during or after the presentations. 422 00:20:59,930 --> 00:21:02,130 We are going to have two breakout sessions 423 00:21:02,130 --> 00:21:03,410 in the afternoon. 424 00:21:03,410 --> 00:21:05,360 We encourage your active participation. 425 00:21:05,900 --> 00:21:09,140 And we are hoping that in sharing our ideas today, 426 00:21:09,140 --> 00:21:12,040 we can make some progress in this important field. 427 00:21:12,040 --> 00:21:16,050 Now, we are going to move to the first session led by Dr. Choi. 428 00:21:16,050 --> 00:21:17,520 Thank you. 429 00:21:17,520 --> 00:21:19,280 Dr. Kelvin Choi: Hello again, everyone. 430 00:21:19,280 --> 00:21:21,780 So we are a little ahead of schedule, 431 00:21:21,780 --> 00:21:23,620 which is good because we have a packed day. 432 00:21:23,620 --> 00:21:28,030 So we're going to go ahead and start our first sessions, 433 00:21:28,030 --> 00:21:31,470 which is exactly led by a speaker 434 00:21:31,470 --> 00:21:33,119 who doesn't need no introduction. 435 00:21:34,200 --> 00:21:36,400 And if I'm going to read her bio, 436 00:21:36,400 --> 00:21:37,980 it's going to be like 30 minutes 437 00:21:37,980 --> 00:21:39,380 of just reading her accomplishments. 438 00:21:39,380 --> 00:21:43,120 So I'm not going to do that, as Costanza said, 439 00:21:43,120 --> 00:21:45,040 the full bio of the speaker is on the website, 440 00:21:45,040 --> 00:21:48,280 so you can actually look at all the contributions 441 00:21:48,280 --> 00:21:52,550 and accomplishment that our next speaker have in her career. 442 00:21:54,600 --> 00:21:58,690 Anyway, I'm still going to introduce her as a courtesy. 443 00:21:58,690 --> 00:22:03,600 So we have Dr. Paula Braveman as our keynote speaker today. 444 00:22:03,600 --> 00:22:05,250 Dr. Paula Braveman is a professor 445 00:22:05,250 --> 00:22:07,750 of Family and Community Medicine, 446 00:22:07,750 --> 00:22:10,740 and the founding director of the Center for Health Equity 447 00:22:10,740 --> 00:22:13,130 at the University of California, San Francisco. 448 00:22:14,010 --> 00:22:16,720 Dr. Braveman has studied and published 449 00:22:16,720 --> 00:22:20,880 extensively on health equity, health disparity, racism, 450 00:22:20,880 --> 00:22:23,240 and other social determinants of health. 451 00:22:23,240 --> 00:22:25,760 Throughout her career, she has collaborated 452 00:22:25,760 --> 00:22:30,370 with local, state, national and international health agencies 453 00:22:30,370 --> 00:22:33,600 to see and conduct rigorous research 454 00:22:33,600 --> 00:22:35,590 translated into practice- 455 00:22:36,910 --> 00:22:39,910 Dr. Paula Braveman: Thank you, and good morning to everyone. 456 00:22:40,650 --> 00:22:44,290 It's a real pleasure to be participating in this workshop. 457 00:22:45,150 --> 00:22:48,720 I'm going to talk about why we should measure social 458 00:22:48,720 --> 00:22:51,760 and structural factors in health research. 459 00:22:51,760 --> 00:22:58,990 And I feel that my work has been made much easier 460 00:22:58,990 --> 00:23:04,050 by the great presentations by Dr. Hooper and Dr. Zenk. 461 00:23:07,680 --> 00:23:09,280 Next slide, please. 462 00:23:11,230 --> 00:23:14,400 So there have been tremendous scientific advances 463 00:23:14,400 --> 00:23:18,340 in the past 20 to 30 years that have shed light 464 00:23:18,340 --> 00:23:21,040 on how social factors influence health. 465 00:23:21,660 --> 00:23:23,900 And of course, there's so much that we don't know, 466 00:23:23,900 --> 00:23:26,740 and that's usually what we're focused on, 467 00:23:26,740 --> 00:23:28,590 but we do know a lot. 468 00:23:29,430 --> 00:23:34,100 And I would say that the question is no longer 469 00:23:34,100 --> 00:23:36,830 whether social factors influence health, 470 00:23:37,640 --> 00:23:40,410 but the pathways and the mechanisms through 471 00:23:40,410 --> 00:23:46,280 which they do that in relation to specific health outcomes, 472 00:23:46,280 --> 00:23:48,450 and how best to intervene. 473 00:23:49,050 --> 00:23:51,670 So this slide just lists some areas 474 00:23:52,240 --> 00:23:55,210 in which I think that science has advanced 475 00:23:55,210 --> 00:23:59,120 tremendously, generally over the past 20 or 30 years, 476 00:23:59,120 --> 00:24:01,570 the big leaps have been taken, 477 00:24:01,570 --> 00:24:04,190 that put us in a different situation 478 00:24:04,700 --> 00:24:07,820 now than people interested 479 00:24:07,820 --> 00:24:11,960 in the social determinants of health were in 20 years ago. 480 00:24:14,130 --> 00:24:15,730 Next slide, please. 481 00:24:16,550 --> 00:24:19,170 One of the areas where we have a lot of knowledge 482 00:24:19,170 --> 00:24:22,860 is about how economic resources can affect health. 483 00:24:23,750 --> 00:24:27,260 Now, by economic resources, I don't mean just income, 484 00:24:27,260 --> 00:24:31,580 but also wealth, which is short for accumulated wealth, 485 00:24:31,580 --> 00:24:36,090 the assets that people accumulate across lifetimes 486 00:24:36,090 --> 00:24:37,690 and across generations, 487 00:24:37,690 --> 00:24:39,950 and wealth is what you have to fall back on 488 00:24:40,940 --> 00:24:44,810 when you have a time of temporarily low-income. 489 00:24:45,660 --> 00:24:47,520 We have a lot of knowledge 490 00:24:47,520 --> 00:24:51,170 about how income and wealth influence health, 491 00:24:51,790 --> 00:24:53,600 but it is often measured very, 492 00:24:53,600 --> 00:24:57,150 very inadequately in health research. 493 00:24:58,170 --> 00:25:01,980 This slide just tries to give you a little whirlwind tour 494 00:25:02,530 --> 00:25:07,310 through what we know about income or wealth. 495 00:25:07,310 --> 00:25:10,950 So some of the things that we know are rather obvious, 496 00:25:10,950 --> 00:25:14,760 although it's been important to have a literature 497 00:25:14,760 --> 00:25:18,600 created that demonstrates their role. 498 00:25:19,160 --> 00:25:23,420 But it's rather obvious that the more income and wealth you have, 499 00:25:23,420 --> 00:25:29,050 the more access to medical care that you have, a nutritious diet 500 00:25:29,050 --> 00:25:32,860 costs a lot more than a diet that isn't nutritious. 501 00:25:32,860 --> 00:25:35,580 You have many more options for physical activity 502 00:25:36,110 --> 00:25:40,490 when you have adequate income and wealth. 503 00:25:41,480 --> 00:25:44,150 You have more options for the kind of housing 504 00:25:44,150 --> 00:25:46,800 and neighborhood conditions that you can live in 505 00:25:46,800 --> 00:25:52,240 and provide to your children when you have income and wealth, 506 00:25:52,970 --> 00:25:59,220 and you are able to obtain lots of services that someone can't, 507 00:25:59,860 --> 00:26:04,240 who does not have adequate income or wealth. 508 00:26:04,240 --> 00:26:09,450 So those are what I would call kind of obvious ways 509 00:26:09,450 --> 00:26:11,620 in which income or wealth can affect health. 510 00:26:11,620 --> 00:26:15,890 But I think we think less often about some other ways. 511 00:26:15,890 --> 00:26:20,820 So for example, how all of those factors that I just mentioned, 512 00:26:20,820 --> 00:26:24,410 including the services, how they can affect stress. 513 00:26:25,790 --> 00:26:30,280 If you're having trouble with your childcare situation, 514 00:26:30,280 --> 00:26:34,260 or with your transportation, if you have an adequate income, 515 00:26:34,260 --> 00:26:37,640 and you have wealth to fall back on, 516 00:26:38,250 --> 00:26:42,010 it's much easier for you to make alternative arrangements 517 00:26:42,830 --> 00:26:45,320 for childcare, for transportation. 518 00:26:45,320 --> 00:26:48,730 So if you don't have adequate economic resources, 519 00:26:48,730 --> 00:26:51,410 then it's stressful. That's another level 520 00:26:52,400 --> 00:26:57,560 on which the economic resources can operate on health. 521 00:26:58,190 --> 00:27:05,070 And we know that stress can increase instability in a family 522 00:27:05,070 --> 00:27:06,890 and then family instability 523 00:27:06,890 --> 00:27:10,950 becomes in turn another stressor in itself, 524 00:27:10,950 --> 00:27:14,210 and we have some vicious cycles that get created. 525 00:27:14,210 --> 00:27:17,730 I think another way in which literature 526 00:27:17,730 --> 00:27:20,930 tells us that income and wealth matter for health 527 00:27:21,780 --> 00:27:27,170 is not at all obvious, and that's in how the income 528 00:27:27,170 --> 00:27:33,070 and the wealth of one's parents shape one's education. 529 00:27:34,080 --> 00:27:38,510 And then education, in turn is generally 530 00:27:39,560 --> 00:27:41,810 the most important factor in determining 531 00:27:41,810 --> 00:27:47,000 the kind of occupation that we have access to, 532 00:27:47,000 --> 00:27:52,210 and occupation, in turn is the strongest determinant 533 00:27:52,210 --> 00:27:56,210 of income and wealth. 534 00:27:56,210 --> 00:27:57,810 Next slide, please. 535 00:28:01,710 --> 00:28:03,880 We know that features of neighborhoods 536 00:28:03,880 --> 00:28:05,270 can influence health, 537 00:28:05,270 --> 00:28:08,790 and we know that income and wealth and racism 538 00:28:08,790 --> 00:28:11,700 powerfully determine the neighborhood conditions 539 00:28:11,700 --> 00:28:16,100 that inhibit income, wealth, and education and stress, 540 00:28:16,100 --> 00:28:18,750 we know that income and wealth and education 541 00:28:18,750 --> 00:28:20,650 and stress and racism 542 00:28:20,650 --> 00:28:23,600 are powerfully shaped by policies, 543 00:28:23,600 --> 00:28:26,680 by laws and by entrenched practices. 544 00:28:27,400 --> 00:28:30,180 Throughout my talk, to make it more concrete, 545 00:28:30,180 --> 00:28:33,920 I will often use examples related to how racism 546 00:28:33,920 --> 00:28:36,070 in particular influences health, 547 00:28:36,630 --> 00:28:39,890 but the relevance extends beyond that more broadly 548 00:28:39,890 --> 00:28:44,770 to how a range of social factors influence many health outcomes. 549 00:28:45,740 --> 00:28:49,430 So for example, let's consider racial residential segregation. 550 00:28:50,340 --> 00:28:53,260 Racial segregation was deliberately created 551 00:28:53,260 --> 00:28:58,010 by explicit Jim Crow laws after slavery became illegal. 552 00:28:59,290 --> 00:29:02,410 Discrimination in housing is no longer legal, 553 00:29:02,410 --> 00:29:04,350 but the legacy of Jim Crow 554 00:29:04,350 --> 00:29:08,150 endures in pervasive racial segregation 555 00:29:08,150 --> 00:29:10,960 that is perpetuated by many discriminatory 556 00:29:10,960 --> 00:29:14,820 but generally unwritten policies and practices. 557 00:29:15,430 --> 00:29:18,530 Segregation tracks many black people into neighborhoods 558 00:29:18,530 --> 00:29:21,040 that are less healthy in many ways, 559 00:29:21,040 --> 00:29:24,960 including exposure to pollution, and unhealthy foods, 560 00:29:24,960 --> 00:29:29,910 and concentrated poverty. This affects not only poor 561 00:29:29,910 --> 00:29:33,300 but also many middle-class African Americans. 562 00:29:33,300 --> 00:29:36,350 Segregation cuts black people off from services, 563 00:29:36,350 --> 00:29:39,270 from good schools, from decent jobs, 564 00:29:39,270 --> 00:29:40,940 and from economic opportunity. 565 00:29:41,840 --> 00:29:47,100 All of these then perpetuate low socio-economic status, 566 00:29:47,100 --> 00:29:50,640 SES abbreviated. 567 00:29:50,640 --> 00:29:55,070 So all of these perpetuate low SES and poor health, 568 00:29:55,760 --> 00:29:59,550 not only across a lifetime, but across generations. 569 00:29:59,550 --> 00:30:03,630 And this can happen even without any individual consciously 570 00:30:03,630 --> 00:30:08,070 intending to discriminate, because it is structural racism, 571 00:30:08,070 --> 00:30:11,120 which is built into systems and structures, 572 00:30:11,640 --> 00:30:13,390 structures such as policies, 573 00:30:13,390 --> 00:30:16,540 established practices, laws and beliefs. 574 00:30:17,170 --> 00:30:21,090 Most health studies do not measure neighborhood conditions 575 00:30:21,090 --> 00:30:22,800 or the factors that shape them. 576 00:30:23,940 --> 00:30:25,540 Next slide, please. 577 00:30:27,960 --> 00:30:31,610 And next, yes, thanks. Let's do the animation there. 578 00:30:31,610 --> 00:30:33,750 I would just- yeah, thank you very much. 579 00:30:33,750 --> 00:30:36,920 So we know that education can powerfully influence 580 00:30:36,920 --> 00:30:38,540 health in many ways. 581 00:30:39,180 --> 00:30:41,060 So one way and probably the way 582 00:30:41,060 --> 00:30:44,120 that is most familiar to most people 583 00:30:44,120 --> 00:30:47,760 is by how education determines knowledge 584 00:30:47,760 --> 00:30:49,330 and coping skills, 585 00:30:49,330 --> 00:30:52,610 which in turn influence health related behaviors. 586 00:30:53,760 --> 00:30:57,330 The health effects of this type of causal pathway 587 00:30:57,330 --> 00:31:00,010 will generally not be immediate, 588 00:31:00,010 --> 00:31:04,170 they will often only manifest over decades or generations. 589 00:31:04,930 --> 00:31:06,530 Next slide, please. 590 00:31:08,650 --> 00:31:12,360 And please do the animation as well. 591 00:31:12,360 --> 00:31:13,940 Thank you very much. 592 00:31:13,940 --> 00:31:17,280 So we also know that education can influence health 593 00:31:17,280 --> 00:31:21,110 because of its powerful effects on the kinds of occupations 594 00:31:21,110 --> 00:31:24,300 that one could have, which determine one's income, 595 00:31:24,930 --> 00:31:28,500 and which also determine the education 596 00:31:28,500 --> 00:31:29,790 of the next generation. 597 00:31:29,790 --> 00:31:36,120 And this slide is a, of course, very- like the previous one, 598 00:31:36,120 --> 00:31:40,200 a very simplified attempt to call attention 599 00:31:40,200 --> 00:31:43,420 to just some of the many pathways 600 00:31:43,420 --> 00:31:46,780 through which educational attainment can influence health, 601 00:31:46,780 --> 00:31:51,500 and this is through how education can influence 602 00:31:52,640 --> 00:31:55,770 health indirectly by shaping our work, 603 00:31:56,330 --> 00:32:00,310 and then how the work determines the income level 604 00:32:00,310 --> 00:32:03,970 and to some extent the ability to accumulate wealth, 605 00:32:05,280 --> 00:32:10,050 and work also determines a whole range of work-related resources, 606 00:32:10,050 --> 00:32:12,540 of which health insurance is only one. 607 00:32:13,670 --> 00:32:16,220 And then the work also determines 608 00:32:16,220 --> 00:32:18,330 the working conditions 609 00:32:18,330 --> 00:32:22,310 and the exposure to hazardous conditions, 610 00:32:22,310 --> 00:32:24,650 and also the level of stress. 611 00:32:25,360 --> 00:32:30,400 So next slide, please. And please do the animation. 612 00:32:30,400 --> 00:32:32,000 Thank you very much. 613 00:32:32,960 --> 00:32:37,370 And another sizable body of research 614 00:32:37,370 --> 00:32:40,600 suggests that education also can influence health 615 00:32:40,600 --> 00:32:42,790 through a number of pathways 616 00:32:42,790 --> 00:32:45,380 that we can refer to as psychosocial. 617 00:32:46,140 --> 00:32:49,750 So as with the other pathways, in many cases, 618 00:32:49,750 --> 00:32:51,670 the effects are not immediate, 619 00:32:51,670 --> 00:32:55,490 they only play out over decades or generations. 620 00:32:55,490 --> 00:32:59,400 So if you're doing a study, it's in the usual timeframe 621 00:32:59,400 --> 00:33:01,640 in which we get to do our research, 622 00:33:02,250 --> 00:33:08,730 which is generally not more than a few years, 623 00:33:09,300 --> 00:33:13,290 you're going to miss the health implications, 624 00:33:13,820 --> 00:33:18,310 and tier these- pathways are again, 625 00:33:18,310 --> 00:33:22,080 a very simplified depiction of them, 626 00:33:22,080 --> 00:33:24,620 but once they have been featured in the literature, 627 00:33:24,620 --> 00:33:29,500 include our social standing or subjective social status, 628 00:33:30,170 --> 00:33:33,250 which can shape the social and the economic resources 629 00:33:33,250 --> 00:33:37,510 to which we have access to shape our perceived status, 630 00:33:38,070 --> 00:33:43,570 and in virtue of their effects on all of those factors, 631 00:33:43,570 --> 00:33:47,660 to shape the level of stress to which were exposed. 632 00:33:48,300 --> 00:33:51,910 And another important psychosocial factor 633 00:33:51,910 --> 00:33:55,230 featured in the research literature 634 00:33:55,230 --> 00:34:00,850 is how social networks operate to determine levels 635 00:34:00,850 --> 00:34:02,710 of social and economic resources, 636 00:34:02,710 --> 00:34:05,720 but also the norms to which we're exposed, 637 00:34:05,720 --> 00:34:11,790 the social support that we have access to, and stress as well. 638 00:34:12,820 --> 00:34:16,060 And then I think more familiar to more people 639 00:34:17,930 --> 00:34:25,810 is health can be influenced by how educational attainment 640 00:34:25,810 --> 00:34:29,450 can affect our control beliefs or beliefs 641 00:34:29,450 --> 00:34:33,150 about our powerlessness or power, 642 00:34:33,150 --> 00:34:37,110 our sense of control, fatalism, mastery, 643 00:34:37,110 --> 00:34:39,540 and how that shapes- there's a big literature showing 644 00:34:39,540 --> 00:34:45,400 how that shapes our responses to stressors and our coping. 645 00:34:46,110 --> 00:34:48,260 And then, of course, a very big literature 646 00:34:48,850 --> 00:34:54,260 relating response to stressors and coping with health. 647 00:34:55,730 --> 00:34:57,340 Next slide, please. 648 00:34:57,340 --> 00:35:02,060 So, many studies measured years or levels of education, 649 00:35:02,060 --> 00:35:05,600 but educational quality is rarely measured. 650 00:35:07,940 --> 00:35:10,200 Very substantial evidence has revealed 651 00:35:10,200 --> 00:35:13,360 that childhood social conditions shape health 652 00:35:13,360 --> 00:35:15,460 across the entire life course, 653 00:35:15,990 --> 00:35:21,590 generally not manifesting until mid or late adulthood. 654 00:35:22,700 --> 00:35:26,810 Health Studies rarely measure childhood conditions and this, 655 00:35:27,330 --> 00:35:29,930 and I want to underscore that this is one of the areas 656 00:35:29,930 --> 00:35:33,450 I think where the literature is most robust 657 00:35:33,450 --> 00:35:36,810 in showing how childhood socio-economic as well 658 00:35:36,810 --> 00:35:42,660 as other social conditions shape our health across our lifetimes, 659 00:35:42,660 --> 00:35:47,220 including in adulthood. Next slide, please. 660 00:35:50,330 --> 00:35:53,920 Neuroscience has revealed that chronic, 661 00:35:53,920 --> 00:35:56,720 and by chronic I mean persistent over time, 662 00:35:56,720 --> 00:35:58,610 that chronic stress, 663 00:35:58,610 --> 00:36:01,970 particularly if it occurs in childhood, 664 00:36:01,970 --> 00:36:04,860 can lead to chronic disease in adulthood. 665 00:36:05,640 --> 00:36:09,420 Neuroscientists have identified physiologic pathways 666 00:36:09,420 --> 00:36:12,960 leading from chronic stress to health damage, 667 00:36:12,960 --> 00:36:17,290 including through effects on neuro endocrine pathways 668 00:36:17,290 --> 00:36:19,910 and on inflammation and immune system 669 00:36:19,910 --> 00:36:23,750 functioning that are known to be involved in chronic disease. 670 00:36:24,440 --> 00:36:27,050 Very few studies measured chronic stress 671 00:36:27,050 --> 00:36:29,240 over a person's life course, 672 00:36:29,240 --> 00:36:32,020 and this is another potentially very important 673 00:36:32,020 --> 00:36:35,790 unmeasured social factor in health research. 674 00:36:36,540 --> 00:36:38,140 Next slide, please. 675 00:36:40,530 --> 00:36:44,130 Knowledge of the physiologic effects of chronic stress 676 00:36:44,130 --> 00:36:47,470 has shed light on how experiences of discrimination 677 00:36:47,470 --> 00:36:50,720 can lead more directly to ill health 678 00:36:50,720 --> 00:36:53,650 than the pathways that I've been talking about up to now. 679 00:36:54,220 --> 00:36:57,410 So let's consider discrimination based on race. 680 00:36:58,340 --> 00:36:59,740 And there is discrimination 681 00:36:59,740 --> 00:37:03,670 based on many more characteristics as well, 682 00:37:03,670 --> 00:37:07,280 but let's focus on race-based discrimination. 683 00:37:07,280 --> 00:37:10,280 Research has shown that race-based discrimination 684 00:37:10,280 --> 00:37:11,880 can damage health, 685 00:37:12,720 --> 00:37:17,740 it is likely not only over insults, threats, or violence, 686 00:37:17,740 --> 00:37:19,980 although these continue to occur. 687 00:37:21,010 --> 00:37:24,870 Some studies have shown that racism also can affect health 688 00:37:24,870 --> 00:37:29,480 through the direct psychological effects of experiencing 689 00:37:29,480 --> 00:37:34,430 and probably also anticipating or fearing unfair treatment. 690 00:37:35,200 --> 00:37:36,960 All of these can be stressful. 691 00:37:37,880 --> 00:37:40,510 And it's not just dramatic incidents. 692 00:37:40,510 --> 00:37:44,440 It's the cumulative effects of daily experiences 693 00:37:44,440 --> 00:37:47,180 that may be ambiguous or somewhat subtle. 694 00:37:48,710 --> 00:37:51,510 It's having to be constantly vigilant 695 00:37:51,510 --> 00:37:53,480 for a slight or an insult. 696 00:37:53,480 --> 00:37:56,460 There's a lot of literature looking at vigilance. 697 00:37:56,460 --> 00:37:59,550 Whether those slights or insults were intended or not, 698 00:38:00,150 --> 00:38:02,380 and perhaps the cumulative effects 699 00:38:02,380 --> 00:38:05,560 of countless little assaults on your self-esteem, 700 00:38:06,640 --> 00:38:09,130 or maybe for a woman, 701 00:38:09,130 --> 00:38:12,570 the effects of learning that yet another unarmed black man 702 00:38:12,570 --> 00:38:15,640 has been killed by police and constantly wondering 703 00:38:15,640 --> 00:38:18,770 whether your husband or your son is next. 704 00:38:19,510 --> 00:38:21,230 So how many health studies measure 705 00:38:21,230 --> 00:38:24,760 people's experiences of race-based discrimination 706 00:38:24,760 --> 00:38:27,360 as a chronic stressor? Very few. 707 00:38:28,110 --> 00:38:32,500 This is potentially another very important unmeasured difference. 708 00:38:33,300 --> 00:38:38,280 Next slide, please. There also is increasing awareness 709 00:38:38,280 --> 00:38:41,000 of how structural racism damages health 710 00:38:41,000 --> 00:38:45,250 by transmitting social disadvantage in multiple domains 711 00:38:45,250 --> 00:38:48,990 and across generations. By structural racism, 712 00:38:48,990 --> 00:38:53,950 I mean racism that is so deeply embedded in systems, 713 00:38:53,950 --> 00:38:57,960 laws, policies, entrenched practices and beliefs, 714 00:38:57,960 --> 00:39:00,110 that it may not be easy to see, 715 00:39:00,810 --> 00:39:04,240 at least not for those who are not its victims. 716 00:39:05,300 --> 00:39:09,510 Examples of structural racism include residential segregation. 717 00:39:10,150 --> 00:39:14,140 They also include voter suppression and gerrymandering, 718 00:39:14,140 --> 00:39:17,050 mass incarceration of black men and boys, 719 00:39:17,830 --> 00:39:22,190 biased lending practices that are obstacles to buying a home 720 00:39:22,190 --> 00:39:24,620 or starting or expanding a business, 721 00:39:25,260 --> 00:39:28,210 biased hiring or job promotions, 722 00:39:29,130 --> 00:39:31,660 biased selection for elite schools, 723 00:39:32,350 --> 00:39:36,900 and environmental injustice. So structural racism 724 00:39:36,900 --> 00:39:40,160 doesn't need any particular individual consciously 725 00:39:40,160 --> 00:39:43,270 intending to discriminate, it functions 726 00:39:43,270 --> 00:39:48,060 and it is maintained by faceless systems and structures. 727 00:39:49,090 --> 00:39:50,690 Next slide, please. 728 00:39:52,820 --> 00:39:55,190 Now, I want to show you this diagram. 729 00:39:56,070 --> 00:40:01,070 And I need to acknowledge that this diagram comes from a book 730 00:40:01,070 --> 00:40:03,920 that I've written on the social determinants of health, 731 00:40:03,920 --> 00:40:07,930 it's now in press at Oxford University Press. 732 00:40:08,560 --> 00:40:11,380 I know that the font is rather small 733 00:40:12,360 --> 00:40:16,160 in the contents of the boxes that you see in this slide, 734 00:40:16,160 --> 00:40:19,600 but I want to show it to you now to give you an overview, 735 00:40:19,600 --> 00:40:20,810 and in a minute, 736 00:40:20,810 --> 00:40:24,050 I'll show it to you in pieces that will be easier to read. 737 00:40:25,580 --> 00:40:29,180 All such conceptual diagrams and frameworks, 738 00:40:29,180 --> 00:40:31,380 this one has many limitations, 739 00:40:31,380 --> 00:40:35,920 and it is certainly not the only way of framing these issues, 740 00:40:35,920 --> 00:40:38,760 but I hope that it makes a few key points. 741 00:40:39,480 --> 00:40:42,480 First, that health and health disparities 742 00:40:42,480 --> 00:40:48,350 represent the outcomes of long and often complex causal chains. 743 00:40:50,110 --> 00:40:52,900 Earlier, I talked about how income and wealth, 744 00:40:52,900 --> 00:40:55,770 how neighborhood and childhood conditions, 745 00:40:55,770 --> 00:40:58,880 education, and racism affect health, 746 00:40:59,840 --> 00:41:02,380 but what determines those factors? 747 00:41:03,000 --> 00:41:06,630 We need to ask what influences the influences? 748 00:41:07,520 --> 00:41:10,310 What is upstream and hard to see 749 00:41:10,310 --> 00:41:12,970 that triggers the downstream causes 750 00:41:12,970 --> 00:41:15,210 that are more easily observable? 751 00:41:16,470 --> 00:41:18,740 So the diagram attempts to show 752 00:41:18,740 --> 00:41:22,910 that the farthest upstream factors at the source, 753 00:41:22,910 --> 00:41:25,540 the beginning of the causal chain are power, 754 00:41:26,080 --> 00:41:27,680 and social values. 755 00:41:28,270 --> 00:41:31,450 Power and social values shape systems, 756 00:41:31,450 --> 00:41:33,520 for example, political systems, 757 00:41:33,520 --> 00:41:36,060 voting systems, economic systems, 758 00:41:36,060 --> 00:41:39,190 educational systems, bank lending, 759 00:41:39,190 --> 00:41:42,870 and structures such as laws and policies. 760 00:41:43,910 --> 00:41:46,300 Power and social values influence 761 00:41:46,300 --> 00:41:50,010 one's access to resources and opportunities, 762 00:41:50,010 --> 00:41:53,390 which in turn influence the downstream exposures 763 00:41:53,390 --> 00:41:57,150 and experiences that harm health or promote it, 764 00:41:57,740 --> 00:42:02,050 which in turn directly influence the biological mechanisms 765 00:42:02,050 --> 00:42:05,860 that directly produce health and health disparities. 766 00:42:06,830 --> 00:42:09,410 And perhaps I should take a few seconds 767 00:42:09,410 --> 00:42:11,840 to define what I mean by social values. 768 00:42:11,840 --> 00:42:15,840 This will mean the prevailing values in a society 769 00:42:15,840 --> 00:42:21,140 about compassion, about justice, about inclusion. 770 00:42:23,190 --> 00:42:28,840 I hope that the diagram suggests at least 771 00:42:28,840 --> 00:42:32,760 that these long and complex causal chains 772 00:42:32,760 --> 00:42:36,940 often play out not over the space of a couple of years, 773 00:42:36,940 --> 00:42:41,750 but over decades or generations, so most studies will not observe 774 00:42:41,750 --> 00:42:45,520 the health consequences of these upstream factors 775 00:42:45,520 --> 00:42:48,060 because they cannot follow populations 776 00:42:48,060 --> 00:42:50,420 for decades or generations. 777 00:42:51,040 --> 00:42:53,010 So we often study- because of this, 778 00:42:53,010 --> 00:42:56,040 we often study just a tiny fragment 779 00:42:56,040 --> 00:42:58,460 of a causal chain that's of interest, 780 00:42:58,460 --> 00:43:02,830 but we need to think about and measure to the extent 781 00:43:02,830 --> 00:43:06,890 possible and acknowledge the factors that are upstream 782 00:43:06,890 --> 00:43:09,050 from whatever we are able to study. 783 00:43:09,630 --> 00:43:11,080 We need to acknowledge 784 00:43:11,080 --> 00:43:15,270 potentially important unmeasured upstream factors, 785 00:43:15,270 --> 00:43:17,370 which can make a very important difference, 786 00:43:17,370 --> 00:43:19,770 the acknowledgement of what was unmeasured 787 00:43:19,770 --> 00:43:21,450 can make very important difference 788 00:43:21,450 --> 00:43:25,310 in how the knowledge that we produce with our research, 789 00:43:25,310 --> 00:43:27,760 how that knowledge is used. 790 00:43:29,100 --> 00:43:30,700 Next slide, please. 791 00:43:31,530 --> 00:43:35,040 Now let's look at the diagram piece by piece. 792 00:43:35,040 --> 00:43:36,240 And as I just noted, 793 00:43:36,240 --> 00:43:40,270 the farthest upstream factors are power and social values 794 00:43:40,270 --> 00:43:43,270 which shape the systems and the structures, 795 00:43:43,270 --> 00:43:46,870 the laws, the policies, the entrenched practices. 796 00:43:47,490 --> 00:43:51,190 There is a wonderful book by Professor Daniel Dawes 797 00:43:51,190 --> 00:43:54,640 called The Political Determinants of Health. 798 00:43:55,400 --> 00:43:58,980 So he went a step further than talking 799 00:43:58,980 --> 00:44:01,370 about the social determinants of health 800 00:44:01,370 --> 00:44:03,930 to talk specifically about a subset 801 00:44:03,930 --> 00:44:07,370 of the social determinants which are political, 802 00:44:07,370 --> 00:44:09,990 or have to do with political power. 803 00:44:11,470 --> 00:44:16,170 This book shines a spotlight on the most upstream factors 804 00:44:16,170 --> 00:44:19,150 that set in motion multiple causal pathways 805 00:44:19,150 --> 00:44:21,470 leading to good or ill health. 806 00:44:22,170 --> 00:44:26,640 The systems and structures often determine an individual's access 807 00:44:26,640 --> 00:44:30,870 to resources and opportunities- that's the box on the right- 808 00:44:30,870 --> 00:44:34,200 such as educational and employment opportunities, 809 00:44:34,200 --> 00:44:36,820 access to earning a decent income 810 00:44:36,820 --> 00:44:38,640 and building sufficient wealth, 811 00:44:39,200 --> 00:44:43,000 to have economic security and to educate your children. 812 00:44:44,180 --> 00:44:46,710 So let's consider structural racism 813 00:44:46,710 --> 00:44:49,770 as an example of an upstream social factor. 814 00:44:50,790 --> 00:44:54,530 Structural racism has systematically constrained 815 00:44:54,530 --> 00:44:57,350 African Americans' economic opportunities, 816 00:44:57,350 --> 00:45:00,940 their access to resources and opportunities, 817 00:45:00,940 --> 00:45:04,230 resulting in less favorable education, 818 00:45:04,230 --> 00:45:08,270 employment, income, wealth and neighborhood conditions. 819 00:45:08,270 --> 00:45:12,450 So for example, well documented discrimination in bank 820 00:45:12,450 --> 00:45:15,620 lending has made it more difficult for African Americans 821 00:45:15,620 --> 00:45:18,540 to accumulate wealth in the form of home 822 00:45:18,540 --> 00:45:20,950 ownership and thriving businesses. 823 00:45:21,630 --> 00:45:23,890 Less home ownership in a community 824 00:45:23,890 --> 00:45:26,690 means less revenue from property taxes, 825 00:45:26,690 --> 00:45:30,500 which are very important source of funding for schools. 826 00:45:30,500 --> 00:45:35,170 So schools in segregated areas are often under-resourced, 827 00:45:35,170 --> 00:45:37,340 compromising educational quality. 828 00:45:38,870 --> 00:45:42,130 Voter suppression deprives people of the opportunity 829 00:45:42,130 --> 00:45:44,960 to influence policies affecting them. 830 00:45:44,960 --> 00:45:46,580 It's another structural, 831 00:45:46,580 --> 00:45:49,070 another manifestation of structural racism. 832 00:45:49,710 --> 00:45:52,020 Gerrymandering is a system 833 00:45:52,020 --> 00:45:55,700 that often makes the votes of people of color count less, 834 00:45:56,250 --> 00:45:58,680 which deprives them of equal opportunity 835 00:45:58,680 --> 00:46:00,840 to shape laws and policies 836 00:46:01,440 --> 00:46:04,460 that in turn distribute the resources 837 00:46:04,460 --> 00:46:07,030 and the opportunities that shape health. 838 00:46:08,190 --> 00:46:12,550 Structural racism produces mass incarceration of black men, 839 00:46:13,160 --> 00:46:16,290 condemning them and their families to poverty 840 00:46:16,290 --> 00:46:19,370 and economic insecurity throughout life, 841 00:46:19,370 --> 00:46:22,890 because the stigma of incarceration follows them 842 00:46:22,890 --> 00:46:26,030 to their graves, denying them good jobs. 843 00:46:27,670 --> 00:46:29,270 Next slide, please. 844 00:46:33,010 --> 00:46:35,910 So this slide focuses in 845 00:46:35,910 --> 00:46:41,030 on how the access to resources and opportunities 846 00:46:42,230 --> 00:46:46,030 lead to exposures and experiences 847 00:46:46,030 --> 00:46:49,130 that can either harm health or promote health. 848 00:46:49,990 --> 00:46:52,330 So in turn, the lack of access 849 00:46:52,330 --> 00:46:55,280 to the resources and opportunities often determines 850 00:46:55,280 --> 00:46:59,560 whether someone is exposed to downstream conditions 851 00:46:59,560 --> 00:47:02,150 that harm health, or promote good health. 852 00:47:02,990 --> 00:47:05,850 And here, I've listed just a number. 853 00:47:05,850 --> 00:47:09,590 And as with other parts of this, this diagram, 854 00:47:09,590 --> 00:47:13,590 non-exhaustive list of relevant factors 855 00:47:13,590 --> 00:47:17,260 that include chronic stress, environmental hazards, 856 00:47:17,260 --> 00:47:21,510 school quality, housing, the quality of food, 857 00:47:22,080 --> 00:47:25,560 food security, food and exercise environment, 858 00:47:25,560 --> 00:47:28,710 and what do I mean by the food environment? 859 00:47:30,420 --> 00:47:33,890 I think most people have heard of the concept of a food desert. 860 00:47:33,890 --> 00:47:36,900 Actually, the literature is somewhat inconsistent 861 00:47:37,440 --> 00:47:40,510 on the role of food desert, 862 00:47:40,510 --> 00:47:42,800 meaning the lack of full-service grocery stores, 863 00:47:42,800 --> 00:47:47,870 the role that that plays in health disparities, 864 00:47:47,870 --> 00:47:50,490 but where the literature is very consistent 865 00:47:50,490 --> 00:47:52,970 is in the role of food swamps. 866 00:47:52,970 --> 00:47:57,140 In other words, food swamp would be a community 867 00:47:58,010 --> 00:48:02,980 that is full of convenience stores, liquor stores, 868 00:48:02,980 --> 00:48:05,160 and fast-food places. 869 00:48:05,160 --> 00:48:07,920 And there the literature is really pretty consistent 870 00:48:07,920 --> 00:48:10,400 about that as a factor that affects health. 871 00:48:12,730 --> 00:48:15,360 Other health-harming or health-promoting factors 872 00:48:15,360 --> 00:48:17,750 certainly include access to medical care, 873 00:48:17,750 --> 00:48:20,730 but also the quality of medical care, 874 00:48:21,570 --> 00:48:26,550 our social networks and our health-related behaviors. 875 00:48:26,550 --> 00:48:28,150 Next slide, please. 876 00:48:32,640 --> 00:48:39,530 So, this then depicts how exposure 877 00:48:39,530 --> 00:48:41,340 to the health-harming 878 00:48:41,340 --> 00:48:44,640 or the non-health promoting conditions 879 00:48:44,640 --> 00:48:47,890 in turn triggers the biological mechanisms 880 00:48:47,890 --> 00:48:51,580 that directly produce good or poor health. 881 00:48:52,910 --> 00:48:55,730 These mechanisms include neuro endocrine, 882 00:48:56,660 --> 00:48:59,870 immune and inflammatory processes, 883 00:49:01,590 --> 00:49:04,010 and other processes. 884 00:49:06,760 --> 00:49:08,360 So next slide, please. 885 00:49:09,880 --> 00:49:14,190 Now, I want to take a quick look again at the whole diagram. 886 00:49:15,030 --> 00:49:19,880 So most biomedical research focuses exclusively 887 00:49:19,880 --> 00:49:23,880 on the biological mechanisms and their health effects. 888 00:49:23,880 --> 00:49:27,520 Some public health studies look at the role of access 889 00:49:27,520 --> 00:49:30,890 to resources and opportunities that shape health. 890 00:49:31,460 --> 00:49:33,040 A lot of public health research 891 00:49:33,040 --> 00:49:36,410 focuses on how downstream health-harming 892 00:49:36,410 --> 00:49:39,460 or health-promoting factors damage health, 893 00:49:40,020 --> 00:49:42,390 but it is very rare for a health study 894 00:49:42,390 --> 00:49:46,190 to go all the way upstream and examine 895 00:49:46,190 --> 00:49:50,240 or at least acknowledge the roles of power 896 00:49:50,240 --> 00:49:52,040 and social values, 897 00:49:52,040 --> 00:49:54,630 and what Professor Dawes has called 898 00:49:54,630 --> 00:49:56,990 the political determinants of health, 899 00:49:56,990 --> 00:50:00,650 which drive the systems, the power policies, 900 00:50:00,650 --> 00:50:04,880 the laws and the pervasive and entrenched practices. 901 00:50:06,340 --> 00:50:07,940 Next slide, please. 902 00:50:09,130 --> 00:50:12,290 So what happens when we don't consider 903 00:50:13,290 --> 00:50:16,650 the influences on the influences? 904 00:50:16,650 --> 00:50:19,970 How often do you read scientific papers 905 00:50:19,970 --> 00:50:23,190 concluding that an observed racial difference in health 906 00:50:23,190 --> 00:50:27,130 must reflect underlying biological differences 907 00:50:27,130 --> 00:50:28,640 or behaviors, 908 00:50:28,640 --> 00:50:31,730 because the researchers saw a racial difference, 909 00:50:31,730 --> 00:50:36,100 even after they controlled for SES? 910 00:50:36,880 --> 00:50:40,730 It is not possible to control for SES, 911 00:50:40,730 --> 00:50:43,130 SES is too multifaceted, 912 00:50:43,130 --> 00:50:47,510 it is not just your income or your education, 913 00:50:47,510 --> 00:50:51,030 but both and your accumulated wealth. 914 00:50:51,030 --> 00:50:54,570 It's also the quality of your education, 915 00:50:54,570 --> 00:50:57,190 your neighborhood's socio-economic conditions, 916 00:50:57,190 --> 00:51:00,830 and your parents' education, income and wealth 917 00:51:00,830 --> 00:51:02,430 when you were a child. 918 00:51:03,070 --> 00:51:07,740 It is all of these factors and more throughout a person's life, 919 00:51:07,740 --> 00:51:09,780 and none of us can capture all of it. 920 00:51:10,900 --> 00:51:15,020 Because of racism and largely because of structural racism 921 00:51:15,020 --> 00:51:18,200 at the same level of education, black and Latino 922 00:51:18,200 --> 00:51:22,140 people have far less income than white people do, 923 00:51:22,140 --> 00:51:25,850 and at the same income level, black and Latino 924 00:51:25,850 --> 00:51:29,320 people live in unhealthier neighborhoods, 925 00:51:29,320 --> 00:51:32,540 and have a fraction of the accumulated wealth. 926 00:51:33,650 --> 00:51:35,990 All of these differences can affect health, 927 00:51:36,590 --> 00:51:39,150 yet these factors are rarely measured, 928 00:51:39,150 --> 00:51:41,870 but studies often conclude that racial difference 929 00:51:41,870 --> 00:51:44,090 is genetic or behavioral 930 00:51:44,090 --> 00:51:48,020 if it persists after control for SES. 931 00:51:48,740 --> 00:51:51,260 So race, the race variable, 932 00:51:51,260 --> 00:51:54,430 which is often seen in health studies, 933 00:51:54,430 --> 00:52:00,120 that variable is often capturing unmeasured socio-economic 934 00:52:00,120 --> 00:52:02,720 and other social factors, 935 00:52:02,720 --> 00:52:06,010 including important structural factors. 936 00:52:06,920 --> 00:52:08,520 Next slide, please. 937 00:52:10,750 --> 00:52:15,940 So, when we fail to consider the upstream factors, 938 00:52:15,940 --> 00:52:18,860 our results can mislead policymakers, 939 00:52:19,890 --> 00:52:23,230 which can result in wasted resources 940 00:52:24,520 --> 00:52:26,560 and in needless suffering. 941 00:52:26,560 --> 00:52:29,500 So when we don't consider the upstream factors, 942 00:52:29,500 --> 00:52:34,230 we contribute to the continuation of policies 943 00:52:34,230 --> 00:52:38,850 that persist in focusing on downstream solutions 944 00:52:38,850 --> 00:52:40,070 and mechanisms 945 00:52:40,070 --> 00:52:43,890 while upstream forces and factors are untouched. 946 00:52:43,890 --> 00:52:47,150 So the upstream factors continue to generate 947 00:52:47,150 --> 00:52:49,090 the downstream conditions 948 00:52:49,090 --> 00:52:52,170 that trigger the mechanisms that harm health, 949 00:52:52,170 --> 00:52:56,890 or that fail to promote it. I also want to note 950 00:52:56,890 --> 00:53:01,070 that when there are important unmeasured social differences, 951 00:53:01,690 --> 00:53:03,830 including structural differences, 952 00:53:03,830 --> 00:53:08,470 our results also may unintentionally feed biases, 953 00:53:08,470 --> 00:53:11,840 including unfounded biases about the basis 954 00:53:11,840 --> 00:53:14,930 in racial disparities in health. 955 00:53:16,350 --> 00:53:19,730 Next slide, please. So what can we do about it? 956 00:53:21,290 --> 00:53:23,100 The fact is that none of us 957 00:53:23,100 --> 00:53:27,940 can study an entire causal pathway for many reasons. 958 00:53:27,940 --> 00:53:35,570 One is how long they are, you know, playing out over time, 959 00:53:37,620 --> 00:53:39,220 but also their complexity. 960 00:53:40,160 --> 00:53:44,010 And the- and I would like to note here 961 00:53:44,010 --> 00:53:47,630 that in a lot of the diagrams that I've shown you, 962 00:53:48,650 --> 00:53:53,940 these are gross simplifications of the causal pathways 963 00:53:53,940 --> 00:53:56,660 that are involved in the effect of social 964 00:53:56,660 --> 00:53:59,410 and structural factors on health, 965 00:53:59,410 --> 00:54:04,690 none of my diagrams showed the interactions 966 00:54:04,690 --> 00:54:09,120 between different factors that were listed on the diagram, 967 00:54:10,510 --> 00:54:14,280 or the interactions with factors 968 00:54:14,280 --> 00:54:16,710 that weren't listed on the diagram. 969 00:54:17,640 --> 00:54:20,040 So it's very complex. 970 00:54:21,210 --> 00:54:25,810 But although no one can study an entire causal pathway, 971 00:54:25,810 --> 00:54:29,180 we can at least consider what's in it, 972 00:54:29,180 --> 00:54:32,590 and we can frame both our research questions 973 00:54:32,590 --> 00:54:37,230 and our conclusions accordingly, connecting the dots 974 00:54:37,230 --> 00:54:40,620 and acknowledging the limitations that are there. 975 00:54:40,620 --> 00:54:42,930 So for example, we can acknowledge 976 00:54:42,930 --> 00:54:44,340 that the race variable 977 00:54:44,340 --> 00:54:49,300 is very likely capturing unmeasured childhood experiences 978 00:54:49,300 --> 00:54:53,100 and direct and indirect experiences of racism. 979 00:54:54,300 --> 00:54:56,260 So I want to appeal to you that 980 00:54:56,260 --> 00:54:59,710 when you read or you conduct studies 981 00:54:59,710 --> 00:55:03,500 including variables representing race, 982 00:55:04,460 --> 00:55:07,760 you need to be aware that regardless 983 00:55:07,760 --> 00:55:10,130 of what else it may represent, 984 00:55:10,130 --> 00:55:13,020 the race variable is always picking up 985 00:55:13,570 --> 00:55:17,250 the totality of unmeasured experiences 986 00:55:17,250 --> 00:55:20,390 and exposures that a person of that race 987 00:55:20,390 --> 00:55:23,360 may have had throughout their life pores, 988 00:55:24,100 --> 00:55:26,290 which could have had health effects. 989 00:55:26,290 --> 00:55:30,130 And even the smartest researcher could have measured at best 990 00:55:30,130 --> 00:55:34,840 only a tiny fraction of those experiences and exposures. 991 00:55:34,840 --> 00:55:38,530 So I want to urge you to ask always, are you looking at race? 992 00:55:39,300 --> 00:55:43,220 Or are you looking at racism, including structural racism? 993 00:55:44,510 --> 00:55:48,850 I think that another thing that we can do is to collaborate, 994 00:55:48,850 --> 00:55:52,790 those of us in the health field or training in the health field, 995 00:55:53,700 --> 00:55:56,590 we can collaborate with social scientists 996 00:55:56,590 --> 00:55:58,660 who can help us to be aware, 997 00:55:58,660 --> 00:56:01,390 more aware of unmeasured differences 998 00:56:01,390 --> 00:56:05,470 and the implications of those unmeasured differences 999 00:56:05,470 --> 00:56:09,440 for the conclusions that are reasonable to make from a study. 1000 00:56:11,460 --> 00:56:16,430 Next slide, please. So as I noted earlier, 1001 00:56:16,430 --> 00:56:20,590 the question is no longer do social factors, 1002 00:56:20,590 --> 00:56:22,960 including structural factors affect health, 1003 00:56:23,590 --> 00:56:26,290 but how do they operate in relation 1004 00:56:26,290 --> 00:56:29,290 to different specific health outcomes? 1005 00:56:29,290 --> 00:56:32,810 And how best can we intervene equitably, 1006 00:56:33,790 --> 00:56:35,870 effectively, and efficiently? 1007 00:56:36,400 --> 00:56:40,920 So I do think that the paradigm in health research has shifted, 1008 00:56:41,480 --> 00:56:45,530 giving more attention to social and structural factors. 1009 00:56:46,260 --> 00:56:49,420 I'm showing you this cartoon, and I like this cartoon, 1010 00:56:49,420 --> 00:56:51,950 because it captures the notion 1011 00:56:51,950 --> 00:56:55,700 that although we have made progress in understanding 1012 00:56:55,700 --> 00:56:58,500 how social and structural factors shape health, 1013 00:56:59,010 --> 00:57:03,180 we are still, unfortunately, a very long way from seeing 1014 00:57:04,950 --> 00:57:08,510 that understanding translated into better health, 1015 00:57:09,200 --> 00:57:12,290 and particularly into greater health equity. 1016 00:57:13,190 --> 00:57:15,130 So thank you for your attention. 1017 00:57:15,130 --> 00:57:18,940 And I'm very much looking forward to the Q&A. 1018 00:57:20,460 --> 00:57:21,800 Dr. Kelvin Choi: Thank you very much, Dr. Braveman, 1019 00:57:21,800 --> 00:57:24,570 a very- of course very insightful, 1020 00:57:26,120 --> 00:57:31,660 and a broad lecture and kind of gave us an overall sense 1021 00:57:31,660 --> 00:57:34,830 of how we address the issue and how we actually 1022 00:57:34,830 --> 00:57:37,120 examine the effect of social determinants 1023 00:57:37,670 --> 00:57:40,080 and structural influences in our research, 1024 00:57:40,080 --> 00:57:42,910 you know, from basic science to clinical science 1025 00:57:42,910 --> 00:57:45,900 to publishing science and even in interventions. 1026 00:57:45,900 --> 00:57:48,330 I mean, one of the key message that you point out there 1027 00:57:48,330 --> 00:57:51,990 is really be very precise about what we measure, 1028 00:57:51,990 --> 00:57:53,460 don't use some of the- 1029 00:57:53,460 --> 00:57:56,030 some of the measures we have used in the past 1030 00:57:56,030 --> 00:57:57,270 may pick up a lot of things 1031 00:57:57,270 --> 00:57:58,840 that we don't even know what they are. 1032 00:57:58,840 --> 00:58:01,140 And being precise is very important, 1033 00:58:01,140 --> 00:58:04,310 so that we know, are we actually measuring race, 1034 00:58:04,820 --> 00:58:06,590 or are we actually measuring racism? 1035 00:58:06,590 --> 00:58:08,680 Which one are we actually studying? 1036 00:58:08,680 --> 00:58:10,820 One of the questions came from the audience, 1037 00:58:10,820 --> 00:58:13,570 which is an interesting one, and I'll pose this to you. 1038 00:58:14,700 --> 00:58:17,910 The question worded as, do we still need more research 1039 00:58:17,910 --> 00:58:20,550 showing how racism affects chronic health? 1040 00:58:20,550 --> 00:58:24,010 Or do we need new policy or structural changes? 1041 00:58:24,010 --> 00:58:27,060 And I think the question to kind of rephrase 1042 00:58:27,060 --> 00:58:30,060 it a little bit is, well, do we know enough 1043 00:58:30,060 --> 00:58:33,000 or do we still need to know more in terms of research? 1044 00:58:33,650 --> 00:58:37,800 Or, and at the same time, how do we conduct research 1045 00:58:37,800 --> 00:58:39,720 related to policy and structural changes 1046 00:58:39,720 --> 00:58:41,870 that impact health disparity? 1047 00:58:44,670 --> 00:58:46,270 Dr. Paula Braveman: That is a very interesting 1048 00:58:46,870 --> 00:58:48,530 and important question. 1049 00:58:48,530 --> 00:58:51,710 And my answer would be that we need both. 1050 00:58:53,950 --> 00:58:58,970 One of the reasons that we need to continue 1051 00:58:58,970 --> 00:59:03,560 to demonstrate the toll that racism takes on health 1052 00:59:04,200 --> 00:59:10,100 is because the public has very short attention span 1053 00:59:10,830 --> 00:59:16,580 and needs to be reminded by fresh results 1054 00:59:17,280 --> 00:59:25,160 constantly about what the associations are, 1055 00:59:25,160 --> 00:59:30,280 so it's not enough to just cite past research. 1056 00:59:32,240 --> 00:59:40,230 But of course, we also need the more upstream research 1057 00:59:40,230 --> 00:59:49,100 and to show- to shed some light on what will be necessary 1058 00:59:49,100 --> 00:59:52,930 in terms of policy changes to do something about it. 1059 00:59:55,210 --> 01:00:01,290 I think that's always there, that question is often there 1060 01:00:01,290 --> 01:00:04,710 for people who do this kind of research: 1061 01:00:04,710 --> 01:00:07,490 Why do I need to be demonstrating this again? 1062 01:00:07,490 --> 01:00:12,940 And isn't the moral arguments against racism enough? 1063 01:00:13,990 --> 01:00:20,290 But unfortunately, in this society, 1064 01:00:20,290 --> 01:00:21,770 it isn't enough, 1065 01:00:21,770 --> 01:00:25,780 and demonstrating and continuing to demonstrate the toll 1066 01:00:27,190 --> 01:00:33,210 in terms of health that it takes is important, 1067 01:00:33,210 --> 01:00:36,520 and speaking to what the implications 1068 01:00:36,520 --> 01:00:41,870 are in terms of the waste of human resources. 1069 01:00:45,050 --> 01:00:48,590 Dr. Kelvin Choi: Yeah, that is a continuous struggle in terms 1070 01:00:48,590 --> 01:00:51,260 of continuing to provide the evidence to showcase 1071 01:00:51,260 --> 01:00:55,960 to how, in fact, racism for what mechanism impact health 1072 01:00:55,960 --> 01:00:57,190 and without evidence, 1073 01:00:57,190 --> 01:01:00,970 we may be able to move the policy agenda a little further. 1074 01:01:01,530 --> 01:01:04,390 And other questions came in asking about 1075 01:01:04,390 --> 01:01:07,150 what additional measure would you suggest to use 1076 01:01:07,150 --> 01:01:11,030 to measure racism when also measuring race? 1077 01:01:12,930 --> 01:01:14,660 So let me repeat the question, 1078 01:01:15,500 --> 01:01:17,350 what additional measure would you suggest 1079 01:01:17,350 --> 01:01:21,210 to be used to measure racism when also measuring race? 1080 01:01:23,490 --> 01:01:25,310 Dr. Paula Braveman: Well, that's another 1081 01:01:26,650 --> 01:01:32,390 very important question. So there are some validated 1082 01:01:32,390 --> 01:01:37,140 measures of experiences of racism. 1083 01:01:37,140 --> 01:01:42,080 So it depends upon what you mean by measuring racism, there's- 1084 01:01:42,080 --> 01:01:50,050 we can measure the extent to which a society is racist, 1085 01:01:51,340 --> 01:01:53,670 and that's different from measuring the extent 1086 01:01:53,670 --> 01:01:59,450 to which an individual has experienced racism 1087 01:01:59,450 --> 01:02:02,120 in different forms. 1088 01:02:03,690 --> 01:02:15,700 I think that legal people and policy experts, 1089 01:02:15,700 --> 01:02:19,410 you know, collaborating with them can be very helpful 1090 01:02:19,410 --> 01:02:26,280 for us in sort of figuring out what the, you know, 1091 01:02:26,280 --> 01:02:31,780 the extent to which a given system 1092 01:02:31,780 --> 01:02:37,070 or structure is racist. 1093 01:02:37,070 --> 01:02:41,560 And we always need to think of the difference 1094 01:02:41,560 --> 01:02:49,230 between de facto racism and de jure racism, 1095 01:02:49,230 --> 01:02:52,580 so racism that's there in the laws, 1096 01:02:52,580 --> 01:02:56,290 you know, that you can point to in the laws versus racism 1097 01:02:56,290 --> 01:02:59,010 that it doesn't appear in any law, 1098 01:02:59,010 --> 01:03:04,860 it's just deeply embedded in historical structures 1099 01:03:05,780 --> 01:03:09,290 and has tremendous power in that way. 1100 01:03:09,290 --> 01:03:11,690 So we need to measure both of those, 1101 01:03:11,690 --> 01:03:16,750 the de facto and de jure racism in a society. 1102 01:03:18,060 --> 01:03:20,150 In terms of measuring the extent 1103 01:03:20,150 --> 01:03:24,470 to which an individual has experienced racism, 1104 01:03:25,030 --> 01:03:30,140 there are some well validated instruments, 1105 01:03:30,140 --> 01:03:35,780 and I think David William's measure of everyday racism 1106 01:03:35,780 --> 01:03:41,990 is one of the most utilized and it's one of the best. 1107 01:03:41,990 --> 01:03:46,440 I think Nancy Krieger has also done some great work 1108 01:03:47,760 --> 01:03:49,520 to measure racism. 1109 01:03:49,520 --> 01:03:53,300 The limitation with those, though, I must say 1110 01:03:53,300 --> 01:03:56,800 is that they're very long, their instruments are very long. 1111 01:03:56,800 --> 01:04:02,840 And most of us actually cannot use an instrument 1112 01:04:02,840 --> 01:04:07,210 that long to measure racism, which is one of many variables 1113 01:04:07,210 --> 01:04:09,810 that we need to measure in our society. 1114 01:04:09,810 --> 01:04:15,690 So there are also a number of more brief measures 1115 01:04:16,460 --> 01:04:18,060 that get used, 1116 01:04:21,350 --> 01:04:26,550 and you can find those in the literature. 1117 01:04:26,550 --> 01:04:33,030 I mean, what about- you know, the rest of your question was, 1118 01:04:33,030 --> 01:04:36,780 well, as I understood it, how do we measure racism, 1119 01:04:36,780 --> 01:04:39,020 but how do we measure race? 1120 01:04:39,540 --> 01:04:44,400 And I think the most important thing about measuring race 1121 01:04:44,400 --> 01:04:49,360 is realizing that it is a social construct, 1122 01:04:49,360 --> 01:04:54,660 not a biological construct, and that in measuring race, 1123 01:04:54,660 --> 01:04:59,510 we are measuring the kinds of experiences and exposures 1124 01:04:59,510 --> 01:05:06,740 that someone of a given race is likely to have had, 1125 01:05:06,740 --> 01:05:09,260 and too often there's an assumption 1126 01:05:09,260 --> 01:05:10,770 in biomedical research 1127 01:05:10,770 --> 01:05:16,750 that racism is like age, race- age, race, sex, 1128 01:05:16,750 --> 01:05:21,900 or maybe age, race, gender, you know, that those are just- 1129 01:05:21,900 --> 01:05:23,480 you always need to measure those, 1130 01:05:23,480 --> 01:05:27,850 and it's kind of a knee jerk response 1131 01:05:27,850 --> 01:05:31,370 without thinking about why you need to measure those 1132 01:05:31,370 --> 01:05:36,560 and without thinking about how grouping race with age 1133 01:05:37,630 --> 01:05:46,270 and gender or sex reinforces the unfounded notion 1134 01:05:47,560 --> 01:05:50,210 that race is biological. 1135 01:05:51,720 --> 01:05:56,000 I think that a lot of us think that when we're measuring race, 1136 01:05:57,770 --> 01:06:00,480 what we're doing is incredibly crude, 1137 01:06:01,980 --> 01:06:12,050 and that is, we are measuring the large geographic area, 1138 01:06:12,050 --> 01:06:15,550 generally a continent, but not only a continent, 1139 01:06:18,520 --> 01:06:22,120 that a person's ancestors are from, 1140 01:06:26,960 --> 01:06:33,080 and not that we're measuring something biological. 1141 01:06:33,080 --> 01:06:35,790 There are secondary- superficial, 1142 01:06:35,790 --> 01:06:39,580 secondary physical characteristics 1143 01:06:39,580 --> 01:06:43,160 that map out to some extent 1144 01:06:43,160 --> 01:06:47,730 according to your geographic area of ancestry, 1145 01:06:48,650 --> 01:06:52,540 but no one has demonstrated 1146 01:06:52,540 --> 01:06:59,460 that those secondary physical characteristics are associated 1147 01:06:59,460 --> 01:07:04,350 with important underlying biological differences. 1148 01:07:07,300 --> 01:07:08,710 Dr. Kelvin Choi: So Dr. Braveman, 1149 01:07:08,710 --> 01:07:10,520 you have- in your response, 1150 01:07:10,520 --> 01:07:14,560 you point to an interesting, and a related question, 1151 01:07:15,340 --> 01:07:18,210 which is the idea of intersectionality. 1152 01:07:18,210 --> 01:07:19,970 So how do we study? 1153 01:07:19,970 --> 01:07:21,730 So when we talk about measuring race, 1154 01:07:21,730 --> 01:07:24,730 but there's also other factors that intersect with race 1155 01:07:24,730 --> 01:07:31,180 when we think about race and how racial identity intersect 1156 01:07:31,180 --> 01:07:35,030 with sexual identity and intersect with class, 1157 01:07:36,690 --> 01:07:38,290 with income and education. 1158 01:07:39,020 --> 01:07:41,980 Can you talk more about how we can better understand, 1159 01:07:42,540 --> 01:07:44,880 you know, these different aspects 1160 01:07:44,880 --> 01:07:46,880 and different identity influence health? 1161 01:07:46,880 --> 01:07:51,600 And how do we actually measure and study them? 1162 01:07:54,000 --> 01:07:55,530 Dr. Paula Braveman: With that kind of question, 1163 01:07:55,530 --> 01:07:58,340 you're going to really make people afraid to tread 1164 01:07:58,340 --> 01:08:03,370 on this territory, because it's so complicated. 1165 01:08:04,960 --> 01:08:07,910 But the fact is, we do know enough to say 1166 01:08:07,910 --> 01:08:14,500 that it's not just your race, and it's not just your gender, 1167 01:08:14,500 --> 01:08:17,570 and it's not just your sexual orientation, 1168 01:08:17,570 --> 01:08:19,890 or your gender identity, 1169 01:08:20,670 --> 01:08:26,210 but all of the above and that they interact with each other. 1170 01:08:26,210 --> 01:08:29,060 I think this term intersectionality has- 1171 01:08:31,380 --> 01:08:37,610 well, it's a code word for referring to the interactions, 1172 01:08:38,780 --> 01:08:46,750 and the interactions among the social advantage 1173 01:08:46,750 --> 01:08:53,170 or the social disadvantage that one may have in virtue 1174 01:08:53,170 --> 01:08:58,300 of the different aspects of one's identity. 1175 01:08:58,940 --> 01:09:03,020 And when we don't take that into consideration, 1176 01:09:03,020 --> 01:09:05,860 we can be way off the mark. 1177 01:09:07,030 --> 01:09:16,620 I think, here too, as with looking at racial disparities, 1178 01:09:19,870 --> 01:09:27,250 it's not just measuring these different aspects 1179 01:09:27,250 --> 01:09:32,160 of a person's experience and identity, 1180 01:09:33,050 --> 01:09:37,600 but it's also because we won't be able to measure all of it, 1181 01:09:38,240 --> 01:09:42,610 it's acknowledging that we haven't measured 1182 01:09:42,610 --> 01:09:45,800 this which could be very, very important. 1183 01:09:47,300 --> 01:09:51,090 So I think it would improve our research so much 1184 01:09:53,430 --> 01:10:00,020 if we were more conscientious in thinking 1185 01:10:00,020 --> 01:10:06,170 about the multiple and interacting upstream factors 1186 01:10:06,170 --> 01:10:08,900 that can affect a person's health, 1187 01:10:08,900 --> 01:10:13,400 but also, so not just trying to measure 1188 01:10:13,400 --> 01:10:16,600 as much as we can of that, which we should, 1189 01:10:17,810 --> 01:10:21,310 but also acknowledging what we can't measure. 1190 01:10:22,780 --> 01:10:29,200 And I want to underscore, I think it was Dr. Hooper 1191 01:10:29,200 --> 01:10:33,520 who mentioned the PhenX social determinants measures, 1192 01:10:33,520 --> 01:10:42,250 this is a very important resource for investigators 1193 01:10:42,250 --> 01:10:46,640 who want to take these issues seriously, 1194 01:10:46,640 --> 01:10:50,900 and I think NIMHD deserves a real pat on its back 1195 01:10:50,900 --> 01:10:56,780 for having sponsored that. 1196 01:10:56,780 --> 01:11:00,100 But if you're looking for ways 1197 01:11:00,100 --> 01:11:06,620 to measure different social variables, 1198 01:11:09,330 --> 01:11:14,430 you've got the results of a lot of thinking 1199 01:11:14,430 --> 01:11:17,970 by subject area experts, 1200 01:11:20,110 --> 01:11:24,530 and it can save you a lot of time 1201 01:11:24,530 --> 01:11:26,310 and a lot of wasted effort. 1202 01:11:26,310 --> 01:11:32,550 So if you Googled PhenX, P-H-E-X 1203 01:11:32,550 --> 01:11:38,710 and then X, PhenX SDOH for social determinants of health 1204 01:11:38,710 --> 01:11:42,550 or you just Google PhenX social determinants, 1205 01:11:42,550 --> 01:11:44,930 you'll come to the toolkit, 1206 01:11:45,670 --> 01:11:48,700 which is the collection of measures. 1207 01:11:50,450 --> 01:11:52,220 Dr. Kelvin Choi: Thank you, Dr. Braveman, 1208 01:11:52,220 --> 01:11:53,450 for your wonderful talk 1209 01:11:53,450 --> 01:11:56,540 and also for your endorsement of the PhenX toolkit. 1210 01:11:56,540 --> 01:12:00,890 It is definitely a lot of work by the colleagues at NIMHD. 1211 01:12:01,920 --> 01:12:03,790 We are unfortunately running out of time, 1212 01:12:03,790 --> 01:12:05,560 I know that we continue to have Q&A, 1213 01:12:05,560 --> 01:12:07,130 this can last a whole workshop, 1214 01:12:07,130 --> 01:12:11,180 but we do have other interesting sessions lineup 1215 01:12:12,030 --> 01:12:15,430 later this afternoon. So thanks again, Dr. Braveman, 1216 01:12:15,430 --> 01:12:19,910 and we will make the information about the PhenX 1217 01:12:19,910 --> 01:12:22,830 toolkit available on the workshop website, 1218 01:12:22,830 --> 01:12:25,179 so people can go there and look for it as well. 1219 01:12:25,740 --> 01:12:27,440 Once again, thank you, Dr. Braveman. 1220 01:12:27,440 --> 01:12:29,789 Thanks for the wonderful talk and your insight. 1221 01:12:30,670 --> 01:12:32,270 Dr. Paula Braveman: Thank you. 1222 01:12:32,830 --> 01:12:36,120 Dr. Kelvin Choi: Now we turn over to Dr. Norton, 1223 01:12:36,120 --> 01:12:38,150 who will be chairing the next session. 1224 01:12:40,920 --> 01:12:42,180 Dr. Jenna Norton: Thanks, Dr. Choi 1225 01:12:42,180 --> 01:12:43,410 and Dr. Braveman 1226 01:12:43,410 --> 01:12:49,270 for that excellent opening to our workshop and overview. 1227 01:12:49,270 --> 01:12:52,530 Great talk as always. I am pleased to be here. 1228 01:12:53,240 --> 01:12:55,540 As Kelvin said, my name is Jenna Norton. 1229 01:12:55,540 --> 01:12:57,580 I am with the National Institute of Diabetes, 1230 01:12:57,580 --> 01:12:58,870 Digestive and Kidney Diseases. 1231 01:12:58,870 --> 01:13:02,340 And I'm pleased to be here to chair a panel, 1232 01:13:02,340 --> 01:13:04,670 which I think we had a great segue 1233 01:13:04,670 --> 01:13:08,450 with this discussion into on the measures and methods 1234 01:13:08,450 --> 01:13:10,310 on integrating social determinants of health 1235 01:13:10,310 --> 01:13:12,240 in biomedical research. 1236 01:13:12,240 --> 01:13:14,810 So I think that discussion 1237 01:13:14,810 --> 01:13:16,790 queued this panel up very nicely. 1238 01:13:17,510 --> 01:13:20,500 So I'm pleased to introduce our three speakers. 1239 01:13:20,500 --> 01:13:22,380 First, we have Evelyn Gallego, 1240 01:13:22,380 --> 01:13:25,630 who is the CEO and founder of EMI Advisors, 1241 01:13:25,630 --> 01:13:29,400 and the program manager for the HL7 Gravity Project. 1242 01:13:29,400 --> 01:13:33,930 She'll be sharing her work and the work of many others 1243 01:13:33,930 --> 01:13:36,490 on developing data standards for integrating 1244 01:13:36,490 --> 01:13:39,590 social determinants of health into electronic health records, 1245 01:13:39,590 --> 01:13:43,060 which may be another potential future source of data 1246 01:13:43,060 --> 01:13:44,990 for this area. 1247 01:13:44,990 --> 01:13:48,810 We will also hear from Dr. Patricia Keenan, 1248 01:13:48,810 --> 01:13:50,820 who is a senior researcher at the Agency 1249 01:13:50,820 --> 01:13:53,150 for Healthcare Research and Quality. 1250 01:13:53,150 --> 01:13:57,270 And she's here to tell us about AHRQ's small area 1251 01:13:57,270 --> 01:14:00,020 database for measures on social determinants of health. 1252 01:14:00,580 --> 01:14:03,800 And finally, we'll hear from Dr. Scarlett Lin Gomez, 1253 01:14:03,800 --> 01:14:07,020 who is a Professor and Vice Chair for faculty development 1254 01:14:07,020 --> 01:14:09,880 in the Department of Epidemiology and Biostatistics, 1255 01:14:09,880 --> 01:14:11,560 and a member of the Helen Diller Family 1256 01:14:11,560 --> 01:14:14,580 Comprehensive Cancer Center at the University of California, 1257 01:14:14,580 --> 01:14:15,880 San Francisco, 1258 01:14:15,880 --> 01:14:17,850 and she'll be telling us about some of the key methods 1259 01:14:17,850 --> 01:14:21,000 she's been using to integrate social determinants of health 1260 01:14:21,000 --> 01:14:24,380 into her research. So Evelyn, please take it away. 1261 01:14:30,800 --> 01:14:32,110 Evelyn Gallego: Thank you- 1262 01:14:32,110 --> 01:14:33,320 Dr. Jenna Norton: Evelyn, you're on mute. 1263 01:14:33,320 --> 01:14:35,760 Evelyn Gallego: Yes. Thank you so much. 1264 01:14:35,760 --> 01:14:37,520 And good morning, everyone. 1265 01:14:37,520 --> 01:14:40,630 Thank you so much for the opportunity to speak today 1266 01:14:40,630 --> 01:14:42,600 about the Gravity Project, 1267 01:14:42,600 --> 01:14:46,740 very timely and aligned with the presentations we've had to date. 1268 01:14:46,740 --> 01:14:51,660 So just quickly, I know we have limited time, a quick agenda, 1269 01:14:51,660 --> 01:14:54,360 I will go over the Gravity Project 1270 01:14:54,360 --> 01:14:56,950 for those not familiar with what it is, 1271 01:14:57,550 --> 01:14:59,800 talk through our accomplishments to date, 1272 01:14:59,800 --> 01:15:02,140 our success factors for scalability 1273 01:15:02,140 --> 01:15:05,750 and how our work aligns to the Department of Health 1274 01:15:05,750 --> 01:15:09,400 and Human Services or HHS SDOH strategic approach. 1275 01:15:11,290 --> 01:15:14,910 So it was very nice to hear from our presenters earlier 1276 01:15:14,910 --> 01:15:19,820 that did a terrific job going through all these terms. 1277 01:15:19,820 --> 01:15:23,820 And we like to, when we kick off the Gravity Project 1278 01:15:23,820 --> 01:15:26,610 is ground on these definitions and terms, 1279 01:15:26,610 --> 01:15:30,480 because we do recognize as a project, as you'll learn, 1280 01:15:30,480 --> 01:15:34,500 that we are working on creating a common language 1281 01:15:35,340 --> 01:15:38,980 so that the terms can be captured electronically. 1282 01:15:38,980 --> 01:15:41,840 And we've noted in the past two years, 1283 01:15:42,490 --> 01:15:45,550 that more of these terms are being used interchangeably. 1284 01:15:45,550 --> 01:15:49,390 Again, perhaps this is not happening on the research side, 1285 01:15:49,390 --> 01:15:52,220 but we are seeing it as we work on data standards 1286 01:15:52,220 --> 01:15:56,180 that we have these terms now being integrated, 1287 01:15:56,180 --> 01:15:57,780 or called in we have a growing- 1288 01:15:57,780 --> 01:16:01,500 So we do want to recognize that there is a- 1289 01:16:01,500 --> 01:16:03,260 you know, and as presented earlier, 1290 01:16:03,260 --> 01:16:06,180 there are specific definitions for these terms, 1291 01:16:07,100 --> 01:16:09,210 and we'd like to define them at a very high level. 1292 01:16:09,210 --> 01:16:10,930 So I won't go through all of them, 1293 01:16:10,930 --> 01:16:15,900 but acknowledge that when we refer to health equity, 1294 01:16:15,900 --> 01:16:17,880 it's really at a systems level, right? 1295 01:16:17,880 --> 01:16:21,810 So we talk about health equity, meaning when every person 1296 01:16:21,810 --> 01:16:25,220 has the opportunity to attain his or her full health. 1297 01:16:25,220 --> 01:16:28,330 But when we talk about health equity at a community level, 1298 01:16:28,330 --> 01:16:30,580 then we speak of social determinants of health, 1299 01:16:30,580 --> 01:16:33,440 which we all know are defined by the WHO 1300 01:16:33,440 --> 01:16:36,660 and the CDC as the conditions in which people are born, 1301 01:16:36,660 --> 01:16:38,570 grow, live, work and age. 1302 01:16:38,570 --> 01:16:41,890 But then SDOH is further defined at an individual level 1303 01:16:41,890 --> 01:16:45,170 as protective factors, social risks and social needs, 1304 01:16:45,170 --> 01:16:47,970 again, lots of discussion on what these terms mean. 1305 01:16:48,810 --> 01:16:50,370 What I do want to acknowledge here, 1306 01:16:50,370 --> 01:16:53,340 and again, we've heard this is that health equity is advanced 1307 01:16:53,340 --> 01:16:57,040 not only by addressing the social determinants of health, 1308 01:16:57,040 --> 01:17:00,110 but also how we improve access and quality of care. 1309 01:17:00,110 --> 01:17:03,470 And again, this ties very much what we just heard 1310 01:17:03,470 --> 01:17:06,850 from Dr. Braveman, Dr. Zenk, and Dr. Hooper. 1311 01:17:08,110 --> 01:17:09,630 So I won't spend too much time here, 1312 01:17:09,630 --> 01:17:14,420 I think all of us joining today are really clear on 1313 01:17:14,940 --> 01:17:19,100 why addressing or why SDOH are important. 1314 01:17:19,100 --> 01:17:22,930 You may already be familiar with the visual on the right. 1315 01:17:23,720 --> 01:17:25,720 I'm here more to acknowledge that again, 1316 01:17:26,920 --> 01:17:30,140 the interest in health systems, right, and addressing 1317 01:17:30,140 --> 01:17:33,850 SDOH has even, you know, occurred before COVID. 1318 01:17:33,850 --> 01:17:35,250 And if you go through the history 1319 01:17:35,250 --> 01:17:38,990 of what the Gravity Project is really very much grounded 1320 01:17:38,990 --> 01:17:41,420 on the interest of health systems 1321 01:17:41,420 --> 01:17:44,920 to address the individual social needs 1322 01:17:44,920 --> 01:17:46,160 as they started to shift 1323 01:17:46,160 --> 01:17:49,310 from fee-for-service models to value-based payment. 1324 01:17:50,020 --> 01:17:52,640 And of course, we have a lot of growing evidence 1325 01:17:52,640 --> 01:17:55,520 around how unmet social needs 1326 01:17:55,520 --> 01:17:57,840 negatively impact health outcomes. 1327 01:17:58,570 --> 01:18:00,260 So again, won't spend too much time here. 1328 01:18:00,260 --> 01:18:01,990 I think we spent a lot of time. 1329 01:18:01,990 --> 01:18:03,470 What I do want to spend time on 1330 01:18:03,470 --> 01:18:04,900 and the purpose of this discussion 1331 01:18:04,900 --> 01:18:08,530 is that even though we say there is this business case, 1332 01:18:08,530 --> 01:18:11,610 it is very clear why capturing this information 1333 01:18:11,610 --> 01:18:14,110 in electronic systems is important, 1334 01:18:14,110 --> 01:18:19,260 challenges remain on the ability for existing electronic systems 1335 01:18:19,260 --> 01:18:21,400 to capture this type of information 1336 01:18:21,400 --> 01:18:25,360 and exchange it across with other systems. 1337 01:18:25,360 --> 01:18:27,190 And so this slide is really to acknowledge 1338 01:18:27,190 --> 01:18:29,910 the two challenges that the Gravity Project 1339 01:18:30,800 --> 01:18:32,080 was set up to address 1340 01:18:32,080 --> 01:18:33,910 and it's around the standardization 1341 01:18:33,910 --> 01:18:36,210 of the data collection and storage 1342 01:18:36,210 --> 01:18:39,660 and the sharing of this data across ecosystem parties. 1343 01:18:39,660 --> 01:18:41,150 We're not grounded on addressing 1344 01:18:41,150 --> 01:18:44,430 all these other challenges as listed here. 1345 01:18:44,430 --> 01:18:48,210 It's really to say that there is- 1346 01:18:48,210 --> 01:18:49,810 it's important for industry 1347 01:18:50,430 --> 01:18:53,060 as a whole to be able to continue to work 1348 01:18:53,060 --> 01:18:56,880 on addressing these challenges to get us to ultimately 1349 01:18:56,880 --> 01:19:00,040 to a place where we can use this data 1350 01:19:00,040 --> 01:19:03,610 not only for patient care, but also for research. 1351 01:19:04,340 --> 01:19:07,080 So it brings us to what the Gravity Project is. 1352 01:19:07,660 --> 01:19:09,110 Here's our mission statement, 1353 01:19:09,110 --> 01:19:11,680 it is to advance and promote equitable health 1354 01:19:11,680 --> 01:19:15,300 and social care by leading the development and validation 1355 01:19:15,300 --> 01:19:19,000 of consensus-driven interoperability standards 1356 01:19:19,000 --> 01:19:20,870 on social determinants of health. 1357 01:19:22,200 --> 01:19:26,450 Our project was initiated in 2019, May 2019. 1358 01:19:26,450 --> 01:19:29,740 It came from- and not listed on this slide, 1359 01:19:29,740 --> 01:19:34,410 it was work from evidence gathered from Dr. Laura Gottlieb 1360 01:19:34,410 --> 01:19:36,540 at SIREN, Social Interventions Research 1361 01:19:36,540 --> 01:19:39,070 and Evaluation Network at University of California 1362 01:19:39,070 --> 01:19:41,980 in San Francisco, and Dr. Caroline Fichtenberg. 1363 01:19:41,980 --> 01:19:44,200 They had identified through their work 1364 01:19:44,200 --> 01:19:46,630 the lack of codes available or data standards 1365 01:19:46,630 --> 01:19:49,870 to represent this data. And with that reason, 1366 01:19:49,870 --> 01:19:51,890 and as we go through with initial funding 1367 01:19:51,890 --> 01:19:53,689 from Robert Wood Johnson Foundation, 1368 01:19:54,280 --> 01:19:57,600 they came to my team and I at EMI Advisors 1369 01:19:57,600 --> 01:20:00,010 to help them solve this problem. Right? 1370 01:20:00,010 --> 01:20:02,070 So we say our scope of our project 1371 01:20:02,070 --> 01:20:05,990 came to be around data standards and data standards to represent 1372 01:20:05,990 --> 01:20:09,660 and exchange patient level or individual level data 1373 01:20:09,660 --> 01:20:13,780 documented across the four clinical activities of screening 1374 01:20:13,780 --> 01:20:18,110 for social risk, assessment, diagnoses, goal setting, 1375 01:20:18,110 --> 01:20:20,330 and treatment or interventions. 1376 01:20:20,330 --> 01:20:23,040 Our scope is also to test and validate 1377 01:20:23,040 --> 01:20:26,860 this type of data for use not only in patient care, 1378 01:20:26,860 --> 01:20:29,190 but to support care coordination between health 1379 01:20:29,190 --> 01:20:32,720 and human services sectors, population health management, 1380 01:20:32,720 --> 01:20:34,910 public health, value-based payment, 1381 01:20:34,910 --> 01:20:37,160 and of course, clinical research. 1382 01:20:37,160 --> 01:20:40,490 To date, we have completed data concepts 1383 01:20:40,490 --> 01:20:43,750 for 17 social determinants of health domains, 1384 01:20:43,750 --> 01:20:47,410 they're those small little icons you see on the right-hand side. 1385 01:20:48,390 --> 01:20:51,940 These domains initially are grounded by those identified 1386 01:20:51,940 --> 01:20:55,870 in the 2014 National Academy of Science and Engineering 1387 01:20:55,870 --> 01:20:57,730 and Medicine, or NASEM, 1388 01:20:57,730 --> 01:20:59,370 capturing social and behavioral domains 1389 01:20:59,370 --> 01:21:01,350 in electronic health records 1390 01:21:01,350 --> 01:21:05,730 that helped inform the standards referenced in the Office 1391 01:21:05,730 --> 01:21:08,660 of the National Coordinator certification program. 1392 01:21:08,660 --> 01:21:11,710 To date, we've also been working on incorporating the domains 1393 01:21:11,710 --> 01:21:15,130 as defined by Healthy People 2030. 1394 01:21:17,610 --> 01:21:19,600 And here's our conceptual framework. 1395 01:21:20,290 --> 01:21:24,810 We talk about this in all our discussion. 1396 01:21:24,810 --> 01:21:27,730 So our framework is centered around the concepts 1397 01:21:27,730 --> 01:21:29,440 that can be documented 1398 01:21:29,440 --> 01:21:31,460 and shared across those four activities. 1399 01:21:31,460 --> 01:21:34,830 I mentioned screening, diagnoses, goal setting, 1400 01:21:34,830 --> 01:21:36,430 and interventions. 1401 01:21:37,310 --> 01:21:40,840 And this is regardless of the input system. 1402 01:21:41,630 --> 01:21:44,590 So it could be an individual or patient's digital app, 1403 01:21:45,250 --> 01:21:47,910 clinical providers electronic health record, 1404 01:21:47,910 --> 01:21:50,550 a social service provider's IT system 1405 01:21:50,550 --> 01:21:53,020 or community referral platform. 1406 01:21:53,020 --> 01:21:56,080 So we say we are agnostic to the systems and tools 1407 01:21:56,080 --> 01:21:57,900 being used in the field, 1408 01:21:57,900 --> 01:22:01,440 and the importance is capturing this data in a standardized way. 1409 01:22:01,440 --> 01:22:05,960 And as I mentioned, we do this to support 1410 01:22:05,960 --> 01:22:09,340 this limits exchange of data following. 1411 01:22:09,340 --> 01:22:11,920 And we talk about our work in terms 1412 01:22:11,920 --> 01:22:14,880 of supporting three overarching use cases. 1413 01:22:15,470 --> 01:22:19,010 The first one listed here is gathering the SDOH data 1414 01:22:19,010 --> 01:22:21,330 in conjunction with the patient encounter. 1415 01:22:21,330 --> 01:22:23,270 The second use case is documenting 1416 01:22:23,270 --> 01:22:25,420 and tracking the SDOH 1417 01:22:25,420 --> 01:22:29,560 or social needs-related interventions to completion. 1418 01:22:29,560 --> 01:22:33,500 This would include, for example, a referral from a health system 1419 01:22:33,500 --> 01:22:35,700 to a community-based organization. 1420 01:22:35,700 --> 01:22:38,090 And last use case is the ability to gather 1421 01:22:38,090 --> 01:22:41,750 and aggregate the SDOH data for uses beyond point of care 1422 01:22:41,750 --> 01:22:43,870 to include clinical research, 1423 01:22:43,870 --> 01:22:45,810 public health, population health. 1424 01:22:45,810 --> 01:22:47,830 So we're grounded around these activities, 1425 01:22:47,830 --> 01:22:51,720 and we just completed for example, 1426 01:22:51,720 --> 01:22:53,970 although this is very clinically grounded, 1427 01:22:54,670 --> 01:22:56,240 we just completed public health. 1428 01:22:56,240 --> 01:22:58,240 So an extension of the third use case, 1429 01:22:58,240 --> 01:23:01,780 we worked with the CDC on creating respective 1430 01:23:01,780 --> 01:23:05,760 SDOH public health use cases to support public health. 1431 01:23:06,500 --> 01:23:09,790 And we'll talk about how we can do that for research. 1432 01:23:09,790 --> 01:23:11,650 So we execute our work 1433 01:23:11,650 --> 01:23:14,770 within three work streams as listed here. 1434 01:23:14,770 --> 01:23:17,040 We talk about- the first work stream 1435 01:23:17,040 --> 01:23:20,670 is the terminology work stream where we focus on defining, 1436 01:23:20,670 --> 01:23:22,440 creating these data definitions, 1437 01:23:22,440 --> 01:23:25,990 identifying codes, submitting for the new codes, 1438 01:23:25,990 --> 01:23:27,639 working with the coding stewards, 1439 01:23:28,430 --> 01:23:30,910 and the coding systems are the medical terminology 1440 01:23:30,910 --> 01:23:34,020 SNOMED CT, ICD-10 and LOINC, 1441 01:23:34,020 --> 01:23:35,820 and then publishing in the National Library 1442 01:23:35,820 --> 01:23:37,990 of Medicine Value Set Authority Center. 1443 01:23:38,540 --> 01:23:40,630 The other work stream is our technical work stream 1444 01:23:40,630 --> 01:23:43,900 where we primarily as an HL7 FHIR accelerators, 1445 01:23:43,900 --> 01:23:46,880 so we became a FHIR accelerator in August of 2019. 1446 01:23:47,600 --> 01:23:49,720 So we are focused on the technical standard 1447 01:23:49,720 --> 01:23:55,630 creating HL7 FHIR specifications following the HL7 process. 1448 01:23:55,630 --> 01:23:58,190 Our third workstream are activities around testing, 1449 01:23:58,190 --> 01:24:01,030 as I mentioned, our scope is to validate these standards 1450 01:24:01,030 --> 01:24:02,880 are the right standards in the field, 1451 01:24:03,980 --> 01:24:05,740 and this is our pilot workstream. 1452 01:24:07,470 --> 01:24:12,290 So we do all this collaboration through these three workgroups. 1453 01:24:12,290 --> 01:24:18,550 To date, we have convened over 2,500 individuals 1454 01:24:18,550 --> 01:24:21,850 from across the health and human services ecosystem. 1455 01:24:21,850 --> 01:24:24,390 And when I talk about this, it's not just the health 1456 01:24:24,390 --> 01:24:26,660 IT vendors or the health systems, 1457 01:24:26,660 --> 01:24:29,010 this is community-based organization, 1458 01:24:29,010 --> 01:24:31,320 these are researchers, these are state agencies, 1459 01:24:31,320 --> 01:24:36,090 these are health plans, these are payers, these are vendors. 1460 01:24:36,090 --> 01:24:38,480 Again, many of them joining us who didn't even know 1461 01:24:38,480 --> 01:24:40,760 what a standard was or a coded terminology. 1462 01:24:41,830 --> 01:24:44,500 We've convened them for the last now three years. 1463 01:24:45,230 --> 01:24:47,380 We convene through the terminology workstream, 1464 01:24:47,380 --> 01:24:50,220 which we meet bi-weekly through public collaborative, 1465 01:24:50,220 --> 01:24:51,970 this is currently on hold. 1466 01:24:51,970 --> 01:24:55,150 So you see that in red here because we are fundraising. 1467 01:24:55,150 --> 01:24:58,070 So I'll talk through what that means. 1468 01:24:58,070 --> 01:25:00,710 We also have a technical work stream facilitated 1469 01:25:00,710 --> 01:25:03,840 through weekly calls, managed through HL7. 1470 01:25:03,840 --> 01:25:06,080 And I will announce later on that 1471 01:25:06,080 --> 01:25:07,480 we have a pilot's workstream 1472 01:25:07,480 --> 01:25:10,500 that we meet monthly starting this week. 1473 01:25:10,500 --> 01:25:13,280 So everyone is welcome to participate in these. 1474 01:25:13,280 --> 01:25:17,780 These are not siloed or closed meetings. 1475 01:25:17,780 --> 01:25:20,120 Everything is recorded and made available. 1476 01:25:20,120 --> 01:25:23,450 One of our core values is having open transparency, 1477 01:25:23,450 --> 01:25:26,870 public calls, that anyone can leverage our materials. 1478 01:25:27,830 --> 01:25:31,870 So, our project, we are a project, so we're not an entity. 1479 01:25:32,450 --> 01:25:36,830 We are funded through in-kind and sponsorship. 1480 01:25:36,830 --> 01:25:38,690 I mentioned we initially were funded 1481 01:25:38,690 --> 01:25:40,900 for the Robert Wood Johnson Foundation. 1482 01:25:40,900 --> 01:25:44,080 To date, we receive support from multiple stakeholder groups 1483 01:25:44,080 --> 01:25:49,080 as listed here, health plans, provider, groups, associations, 1484 01:25:49,080 --> 01:25:50,970 payers, technology vendors, 1485 01:25:50,970 --> 01:25:52,520 and of course, the federal government. 1486 01:25:52,520 --> 01:25:57,750 So you'll see a list of HHS agencies on the right-hand side. 1487 01:25:59,430 --> 01:26:02,340 Here's our roadmap for 2022. 1488 01:26:02,340 --> 01:26:04,630 So I mentioned those three work streams. 1489 01:26:05,450 --> 01:26:08,220 On a terminology workstream, this year, 1490 01:26:08,220 --> 01:26:10,830 we completed three new domains, health literacy, 1491 01:26:10,830 --> 01:26:12,770 health insurance coverage status- 1492 01:26:12,770 --> 01:26:16,610 oops! sorry- medical costs burden, 1493 01:26:16,610 --> 01:26:18,820 and we spent the summer months building. 1494 01:26:19,400 --> 01:26:21,960 So what we say is when we meet, convene the public, 1495 01:26:21,960 --> 01:26:25,170 we bring them then together to help us define the concepts, 1496 01:26:25,170 --> 01:26:27,940 we bring subject matter experts. And then in the background, 1497 01:26:27,940 --> 01:26:30,380 we then start working with our coding stewards 1498 01:26:30,380 --> 01:26:32,840 on what new codes need to be developed, 1499 01:26:32,840 --> 01:26:36,190 and submitting them for publication. 1500 01:26:36,190 --> 01:26:40,010 We are currently on hold as we look to launch the next domain, 1501 01:26:40,010 --> 01:26:42,690 which is digital, and equity, you see it pending, 1502 01:26:42,690 --> 01:26:44,730 because we are in the process of fundraising 1503 01:26:44,730 --> 01:26:46,450 to completely launch that. 1504 01:26:46,450 --> 01:26:50,020 And I'll go through what other domains are in our pipeline. 1505 01:26:50,020 --> 01:26:51,770 On the technical work stream, 1506 01:26:51,770 --> 01:26:56,100 we have completed publication of the HL7 1507 01:26:56,100 --> 01:26:59,620 SDOH clinical care FHIR implementation guide, 1508 01:26:59,620 --> 01:27:02,130 it was published as a standard for trial use 1509 01:27:02,130 --> 01:27:05,090 Version One in August of last year. 1510 01:27:05,800 --> 01:27:09,130 We are in the process of publishing the second version 1511 01:27:09,130 --> 01:27:12,230 of that FHIR implementation guide for this fall. 1512 01:27:12,230 --> 01:27:15,430 And then we are also very eager to start- 1513 01:27:15,430 --> 01:27:18,140 kickoff our pilots workgroup this week. 1514 01:27:20,040 --> 01:27:23,960 Okay. So on our terminology side, 1515 01:27:23,960 --> 01:27:27,750 I want to acknowledge that we do have a rigorous process 1516 01:27:27,750 --> 01:27:29,710 for developing coded concepts. 1517 01:27:30,260 --> 01:27:32,360 The visual you see on the right-hand side 1518 01:27:32,360 --> 01:27:35,110 illustrates how this applies to the clinical workflow 1519 01:27:35,110 --> 01:27:37,060 as I presented in our framework. 1520 01:27:38,090 --> 01:27:39,980 This means that as we look across, 1521 01:27:39,980 --> 01:27:41,670 screening concepts are coded in 1522 01:27:41,670 --> 01:27:44,740 LOINC, as we go through diagnoses, 1523 01:27:44,740 --> 01:27:47,180 they're coded in SNOMED and ICD-10, 1524 01:27:47,180 --> 01:27:50,630 particularly ICD-10 Z-codes, goals are coded in 1525 01:27:50,630 --> 01:27:54,670 SNOMED CT, and interventions are SNOMED-CT. 1526 01:27:57,720 --> 01:28:00,040 So, to date, as I mentioned, 1527 01:28:00,040 --> 01:28:01,990 what you see on the left-hand side, 1528 01:28:01,990 --> 01:28:06,170 we've completed data definitions for 17 domains, 1529 01:28:06,170 --> 01:28:08,980 we have 16 LOINC screener codes available, 1530 01:28:08,980 --> 01:28:11,140 14 ICD-10, Z-codes, 1531 01:28:12,310 --> 01:28:15,680 and eight were included in the fiscal year 1532 01:28:15,680 --> 01:28:19,510 2023 CMS IPPS rule, SNOMED 1533 01:28:19,510 --> 01:28:22,970 CT, we have 16 intervention codes available, 1534 01:28:23,480 --> 01:28:26,980 over 140 value sets in the National Library of Medicine, 1535 01:28:26,980 --> 01:28:29,520 and very exciting that our data class was included 1536 01:28:29,520 --> 01:28:31,400 in the Office of the National Coordinator, 1537 01:28:31,400 --> 01:28:33,710 US corps for data interoperability version 1538 01:28:33,710 --> 01:28:35,170 two and carries over- 1539 01:28:35,170 --> 01:28:38,090 right now they're getting ready for V3. 1540 01:28:38,090 --> 01:28:41,380 So ultimately, for us today is really to acknowledge 1541 01:28:41,380 --> 01:28:43,380 that there are coded concepts available, 1542 01:28:44,020 --> 01:28:46,520 they're published, they're ready for use, 1543 01:28:46,520 --> 01:28:53,560 and they can be incorporated in data collection and exchange. 1544 01:28:54,270 --> 01:28:57,080 One of the key activities our team did as they worked with 1545 01:28:57,080 --> 01:28:59,760 SNOMED CT on interventions 1546 01:28:59,760 --> 01:29:02,920 was thinking of how best to group these type of activities. 1547 01:29:02,920 --> 01:29:06,310 So the Gravity Project came up with nine 1548 01:29:06,310 --> 01:29:09,200 or eight intervention types as listed here. 1549 01:29:09,200 --> 01:29:13,380 This is to acknowledge that referral being one of, 1550 01:29:13,380 --> 01:29:16,580 I would say the most common intervention type 1551 01:29:16,580 --> 01:29:19,310 as referrals are being carried out from the health system 1552 01:29:19,310 --> 01:29:23,180 to a social service provider, community-based organization. 1553 01:29:23,780 --> 01:29:26,360 But there are other intervention types. 1554 01:29:26,360 --> 01:29:28,420 And again, these are all coded by 1555 01:29:29,270 --> 01:29:32,660 SNOMED CT codes, and they have specific definitions. 1556 01:29:32,660 --> 01:29:35,830 So when we have coded concepts and we publish them, 1557 01:29:35,830 --> 01:29:37,570 they are grouped in this way. 1558 01:29:38,810 --> 01:29:41,190 So what does our next steps look like? 1559 01:29:41,190 --> 01:29:44,860 So this is to acknowledge that as we fundraise to kick off 1560 01:29:44,860 --> 01:29:46,540 the digital and equity domain, 1561 01:29:46,540 --> 01:29:49,560 we've also been working with our governance committees 1562 01:29:49,560 --> 01:29:52,240 on what are the other domains in the pipeline. 1563 01:29:52,890 --> 01:29:55,430 So what you see here on the left-hand side, 1564 01:29:55,430 --> 01:29:57,450 and the green is, you know, upcoming, 1565 01:29:57,450 --> 01:30:00,760 so very timely to be talking about racism, 1566 01:30:00,760 --> 01:30:02,290 bias and discrimination, 1567 01:30:02,290 --> 01:30:04,590 that is a domain that we have in our pipeline. 1568 01:30:04,590 --> 01:30:06,190 I know it's of strong interest 1569 01:30:06,190 --> 01:30:10,800 to even one of our sponsors, RWJF. 1570 01:30:10,800 --> 01:30:12,560 We've also been looking at green space 1571 01:30:12,560 --> 01:30:14,790 and adverse childhood experiences. 1572 01:30:14,790 --> 01:30:16,870 Other ones that are being proposed 1573 01:30:16,870 --> 01:30:18,850 have been expanded material hardship, 1574 01:30:18,850 --> 01:30:20,510 incarceration, migrant laborers, 1575 01:30:20,510 --> 01:30:23,210 again, very much tied to Healthy People 2030. 1576 01:30:23,800 --> 01:30:25,920 Activities that are proposed moving forward 1577 01:30:25,920 --> 01:30:30,720 is to collaborate on shared ontologies and open taxonomies. 1578 01:30:30,720 --> 01:30:33,610 So not everyone is using SNOMED 1579 01:30:33,610 --> 01:30:36,530 CT, ICD-10, the medical terminologies, 1580 01:30:36,530 --> 01:30:39,380 we acknowledge other taxonomies, such as the 211 1581 01:30:39,380 --> 01:30:42,500 LA taxonomy that's being currently in use 1582 01:30:42,500 --> 01:30:45,720 right now by many community-based organizations, 1583 01:30:45,720 --> 01:30:46,990 and it needs to be readily 1584 01:30:46,990 --> 01:30:49,190 mapped to the medical terminologies. 1585 01:30:49,190 --> 01:30:50,800 We've also been looking at developing 1586 01:30:50,800 --> 01:30:52,210 supplemental terminology 1587 01:30:52,210 --> 01:30:55,500 deliverables to address sharing information 1588 01:30:55,500 --> 01:30:58,690 on social care program eligibility and enrollment. 1589 01:30:58,690 --> 01:31:00,500 That's currently not in scope for gravity, 1590 01:31:00,500 --> 01:31:02,530 but it's of interest to our community. 1591 01:31:02,530 --> 01:31:04,210 And lastly, we want to participate 1592 01:31:04,210 --> 01:31:06,530 in quality measure development to lend coding, 1593 01:31:06,530 --> 01:31:08,630 content and measurement insight. 1594 01:31:08,630 --> 01:31:11,140 Our terminology director, Dr. Sarah DeSilvey 1595 01:31:11,140 --> 01:31:13,970 does participate in some measurement discussions, 1596 01:31:13,970 --> 01:31:16,390 but we've not brought it into our community 1597 01:31:16,390 --> 01:31:19,300 because our scope has been the data standards 1598 01:31:19,300 --> 01:31:24,110 to support individual level documentation 1599 01:31:24,110 --> 01:31:25,710 as part of a clinical encounter. 1600 01:31:26,890 --> 01:31:28,850 So I'll move on to our technical work stream 1601 01:31:28,850 --> 01:31:32,420 acknowledging that our work is supported by NewWave Saffron 1602 01:31:32,420 --> 01:31:34,540 and the American Medical Association. 1603 01:31:35,630 --> 01:31:38,400 Our implementation guide just here is to acknowledge 1604 01:31:38,400 --> 01:31:42,800 we do have a current standard published through HL7, 1605 01:31:42,800 --> 01:31:45,580 these are open standards for implementers to use. 1606 01:31:46,250 --> 01:31:49,760 The IG supports all the clinical activities 1607 01:31:49,760 --> 01:31:52,750 I mentioned, assessments, diagnoses, goals, 1608 01:31:53,400 --> 01:31:55,410 but it also incorporates consent, 1609 01:31:56,140 --> 01:31:58,100 tying into other national standards 1610 01:31:58,100 --> 01:31:59,880 for capturing individual consent. 1611 01:32:00,770 --> 01:32:03,920 And it also has draft specifications 1612 01:32:03,920 --> 01:32:06,830 for capturing race and ethnicity data. 1613 01:32:06,830 --> 01:32:08,910 So again, not race and ethnicity, 1614 01:32:08,910 --> 01:32:11,300 completely agree with the comments made earlier, 1615 01:32:11,300 --> 01:32:12,910 they are not social determinants of health, 1616 01:32:12,910 --> 01:32:15,160 but there is data that needs to be captured 1617 01:32:15,160 --> 01:32:18,200 as part of the record 1618 01:32:18,200 --> 01:32:21,420 and the information that is exchanged about an individual. 1619 01:32:21,420 --> 01:32:23,290 We look to the research community 1620 01:32:23,290 --> 01:32:26,560 to help better define what those concepts need to be. 1621 01:32:26,560 --> 01:32:29,360 I think there are standards that exist, 1622 01:32:29,360 --> 01:32:32,900 but they're not commonly used across systems. 1623 01:32:32,900 --> 01:32:37,980 And we're in the process of targeting a publication 1624 01:32:37,980 --> 01:32:39,690 for fall of this year. 1625 01:32:39,690 --> 01:32:43,530 This is just a high level, the testing interactions. 1626 01:32:43,530 --> 01:32:47,020 So it's to acknowledge that the implementation guide 1627 01:32:47,020 --> 01:32:50,760 has guidance around how a FHIR-enabled entity 1628 01:32:50,760 --> 01:32:53,250 can exchange with another FHIR-enabled entity, 1629 01:32:53,250 --> 01:32:55,280 for example, a clinical site 1630 01:32:56,530 --> 01:32:58,910 sharing with a coordination platform 1631 01:32:58,910 --> 01:33:01,170 or another clinical site or a payer. 1632 01:33:01,170 --> 01:33:04,480 But it also acknowledges that many of the interactions 1633 01:33:04,480 --> 01:33:07,640 will be between a FHIR-enabled system, 1634 01:33:07,640 --> 01:33:09,170 and when that is not FHIR-enabled, 1635 01:33:09,170 --> 01:33:10,800 like a community-based organization 1636 01:33:10,800 --> 01:33:14,010 that will never adopt the FHIR standard, 1637 01:33:14,010 --> 01:33:17,160 because they have no need to base on their population groups, 1638 01:33:17,160 --> 01:33:20,480 and policy around what standards they can use, 1639 01:33:20,480 --> 01:33:22,780 but the FHIR IG has some guidance 1640 01:33:22,780 --> 01:33:25,220 on how to support those data transactions. 1641 01:33:26,340 --> 01:33:29,010 Here are technical considerations or activities 1642 01:33:29,010 --> 01:33:31,060 under consideration for the technical workstream. 1643 01:33:31,060 --> 01:33:32,340 So we want to do more, 1644 01:33:32,340 --> 01:33:36,480 we propose to identify methods of anonymization of data 1645 01:33:36,480 --> 01:33:38,790 to support reporting for public health. 1646 01:33:38,790 --> 01:33:42,720 We want to develop new use cases and requirements for aggregation 1647 01:33:42,720 --> 01:33:45,340 and de-identification of SDOH information 1648 01:33:45,340 --> 01:33:47,170 for population health and research. 1649 01:33:47,880 --> 01:33:49,980 We want to develop and incorporate new use cases 1650 01:33:49,980 --> 01:33:51,940 that support existing FHIR IG. 1651 01:33:51,940 --> 01:33:55,830 So our FHIR IG, again, grounded on the clinical encounter, 1652 01:33:56,600 --> 01:34:00,160 clinical to social care provider, 1653 01:34:00,160 --> 01:34:02,900 but we have a strong interest in the field 1654 01:34:02,900 --> 01:34:04,240 about supporting data 1655 01:34:04,240 --> 01:34:06,680 exchange between one community-based organization 1656 01:34:06,680 --> 01:34:08,500 and another community-based organization 1657 01:34:08,500 --> 01:34:10,380 never touching the health system, 1658 01:34:10,380 --> 01:34:13,380 as well as a CBO exchanging to a state agency 1659 01:34:13,380 --> 01:34:14,850 that is very common. 1660 01:34:14,850 --> 01:34:16,570 And particularly for one of the use cases, 1661 01:34:16,570 --> 01:34:19,950 one of our partners is working on foster care for children. 1662 01:34:21,230 --> 01:34:23,330 So our pilots work stream, 1663 01:34:23,330 --> 01:34:24,940 so we launched officially this week. 1664 01:34:24,940 --> 01:34:27,260 So this is an alert to all of you 1665 01:34:27,260 --> 01:34:29,700 if you want to follow testing in the field, 1666 01:34:29,700 --> 01:34:32,980 we launched this week. Our pilots affinity workgroups 1667 01:34:32,980 --> 01:34:35,850 are as a peer-to-peer learning forum for entities 1668 01:34:35,850 --> 01:34:39,290 participating in the real-world testing of the standards. 1669 01:34:40,100 --> 01:34:42,590 It's also established to foster a collaborative 1670 01:34:42,590 --> 01:34:44,920 learning environment for pilot participants. 1671 01:34:44,920 --> 01:34:47,610 So we've not had this opportunity in the past, 1672 01:34:47,610 --> 01:34:50,140 a lot of the standards are developed and published 1673 01:34:50,140 --> 01:34:53,600 by the standard development organization like HL7, 1674 01:34:53,600 --> 01:34:56,730 and you know, there's forums for learning but it's not broad, 1675 01:34:56,730 --> 01:35:01,080 and again, it's mainly targeted to the implementers themselves, 1676 01:35:01,080 --> 01:35:02,420 but this will be open. 1677 01:35:02,420 --> 01:35:04,260 And I do want to acknowledge, again, 1678 01:35:04,260 --> 01:35:07,200 the support of the Office of the National Coordinator, 1679 01:35:07,200 --> 01:35:09,550 the Administration for Community Living, 1680 01:35:09,550 --> 01:35:14,110 IHE USA and AARP, in supporting this work. 1681 01:35:15,190 --> 01:35:18,140 Levels of participation, again, open, 1682 01:35:18,720 --> 01:35:21,110 and individuals can join as an observer 1683 01:35:21,110 --> 01:35:24,390 and just follow along and hear from pilot sites 1684 01:35:24,390 --> 01:35:27,100 on how they're testing and implementing the standards. 1685 01:35:27,100 --> 01:35:29,510 And then we have the actual pilot participants 1686 01:35:30,400 --> 01:35:32,860 that will be testing in the field. 1687 01:35:32,860 --> 01:35:36,150 And again, all are invited and the link is here at the bottom. 1688 01:35:36,150 --> 01:35:38,949 I think these slides will be shared with you afterwards. 1689 01:35:40,260 --> 01:35:43,830 This is what our tiered piloting approach looks like. 1690 01:35:43,830 --> 01:35:46,360 We've developed a tiered approach really with the premise 1691 01:35:46,360 --> 01:35:48,880 that we want to meet entities that are testing 1692 01:35:48,880 --> 01:35:51,740 and implementing the standards where they're at. Right? 1693 01:35:52,250 --> 01:35:56,100 We know FHIR is required as a standard for data 1694 01:35:56,100 --> 01:35:58,740 exchange in the US, 1695 01:35:59,530 --> 01:36:02,170 but you know, not everyone is there, 1696 01:36:02,170 --> 01:36:04,170 people are starting to update their systems. 1697 01:36:04,170 --> 01:36:06,970 So this is really to allow an incremental way 1698 01:36:06,970 --> 01:36:09,050 for entities to test through, 1699 01:36:09,870 --> 01:36:11,700 you know, just testing the terminology. 1700 01:36:11,700 --> 01:36:14,530 This is what tier one is, this is really, you know, 1701 01:36:14,530 --> 01:36:16,730 how you define the concepts in your system, 1702 01:36:17,410 --> 01:36:20,970 are you all using, you know, the same language, 1703 01:36:20,970 --> 01:36:23,860 we say, and your system can understand and read it? 1704 01:36:23,860 --> 01:36:25,680 The second tier is really to support 1705 01:36:25,680 --> 01:36:27,510 existing data infrastructure. 1706 01:36:27,510 --> 01:36:30,410 So, for example, you know, we have recognized standards 1707 01:36:30,410 --> 01:36:35,090 like the HL7 Clinical Document Architecture, CDA, 1708 01:36:35,090 --> 01:36:38,190 we have HL7 V2s that are commonly used right now, 1709 01:36:38,190 --> 01:36:40,370 particularly for ADT feeds. 1710 01:36:40,370 --> 01:36:42,310 And we have the direct transport. 1711 01:36:42,310 --> 01:36:45,260 The third tier is the full FHIR model. 1712 01:36:46,440 --> 01:36:48,370 So what are success factors to date? 1713 01:36:49,130 --> 01:36:50,930 We say that, you know, it's great, 1714 01:36:50,930 --> 01:36:52,270 we've published the standards, 1715 01:36:52,270 --> 01:36:54,130 we have a lot of standards available, 1716 01:36:54,130 --> 01:36:56,880 but their value really comes into the integration 1717 01:36:56,880 --> 01:36:58,220 into several areas. 1718 01:36:58,220 --> 01:37:01,130 So we talked about integration at the policy level, 1719 01:37:01,130 --> 01:37:03,340 I mentioned USCDI already, 1720 01:37:03,340 --> 01:37:05,740 within payment models, within programs. 1721 01:37:05,740 --> 01:37:08,020 Again, we need these levers to promote 1722 01:37:08,020 --> 01:37:10,260 and encourage the use of the standards. 1723 01:37:10,260 --> 01:37:13,180 Also the standards to be called out in other standards, 1724 01:37:13,180 --> 01:37:14,880 Gravity is a FHIR accelerator. 1725 01:37:14,880 --> 01:37:18,920 There are other five HL7 FHIR accelerators. 1726 01:37:19,750 --> 01:37:21,399 And so we work closely with them. 1727 01:37:22,510 --> 01:37:24,520 Inclusion of the standards in grant programs. 1728 01:37:24,520 --> 01:37:25,720 So we do have examples. 1729 01:37:25,720 --> 01:37:28,540 I'll talk in the next slide, how they've been included. 1730 01:37:28,540 --> 01:37:31,320 Where we need to do more work is at the practice level, 1731 01:37:31,320 --> 01:37:33,770 so having repeatable process for adopting, 1732 01:37:33,770 --> 01:37:37,480 implementing and using the standards and this type of data, 1733 01:37:37,480 --> 01:37:40,850 and how we encourage or influence innovation 1734 01:37:40,850 --> 01:37:42,940 in the market around creating new tools 1735 01:37:42,940 --> 01:37:45,060 for capturing aggregation of the data. 1736 01:37:47,180 --> 01:37:49,700 So this is just quickly how they've been included 1737 01:37:49,700 --> 01:37:51,420 in policy programs and grants. 1738 01:37:51,420 --> 01:37:55,220 So I already mentioned USCDI data classes. 1739 01:37:55,220 --> 01:37:56,820 So they're there. 1740 01:37:57,600 --> 01:38:00,930 We have talked through about CMS 1741 01:38:00,930 --> 01:38:03,600 having it in their Inpatient Prospective system, 1742 01:38:03,600 --> 01:38:05,900 so they point to USCDI. 1743 01:38:07,880 --> 01:38:09,650 CMS also had their Medicare Advantage 1744 01:38:09,650 --> 01:38:13,200 and Part D final rule that requires special needs plan 1745 01:38:13,200 --> 01:38:14,770 to use the standardized questions, 1746 01:38:14,770 --> 01:38:17,560 again, pointing to USCDI version two. 1747 01:38:17,560 --> 01:38:18,940 And then the Gravity standards 1748 01:38:18,940 --> 01:38:21,690 have three active grant programs right now, 1749 01:38:21,690 --> 01:38:24,170 or I would say challenge programs. 1750 01:38:24,170 --> 01:38:25,980 The Administration for Community Living 1751 01:38:25,980 --> 01:38:27,790 has the social care challenge grant, 1752 01:38:29,130 --> 01:38:32,840 as a requirement for the grant, they had to adopt the standards. 1753 01:38:32,840 --> 01:38:35,870 ONC has the Leading Edge Accelerator projects, 1754 01:38:35,870 --> 01:38:38,190 they have requirements to adopt the standards, 1755 01:38:38,190 --> 01:38:40,160 and the Administration for Children and Families 1756 01:38:40,160 --> 01:38:42,190 has an innovations grant right now 1757 01:38:42,190 --> 01:38:43,890 that they also have the requirement 1758 01:38:43,890 --> 01:38:46,770 to adopt the Gravity standards. 1759 01:38:47,370 --> 01:38:50,410 We also like to talk through of how our work 1760 01:38:50,410 --> 01:38:55,010 to date aligns with national. 1761 01:38:55,010 --> 01:38:58,610 So this year, HHS published the HHS 1762 01:38:58,610 --> 01:39:03,130 strategic approach to address social determinants of health, 1763 01:39:03,130 --> 01:39:04,960 where they acknowledge that addressing 1764 01:39:04,960 --> 01:39:08,960 SDOH involves coordination across the sectors. 1765 01:39:08,960 --> 01:39:13,210 So I'd like to demonstrate this. We saw this visual earlier on 1766 01:39:13,210 --> 01:39:14,560 but I really talked through about it 1767 01:39:14,560 --> 01:39:17,490 in terms where Gravity Project aligns. 1768 01:39:17,490 --> 01:39:21,020 And we really say we are supporting the midstream level, 1769 01:39:21,820 --> 01:39:25,310 where we are developing data standards to support health 1770 01:39:25,310 --> 01:39:29,750 and human services integration and creating that language 1771 01:39:29,750 --> 01:39:32,730 needed to support interoperability. 1772 01:39:32,730 --> 01:39:36,380 And then here is just a snapshot of how we align to goal one, 1773 01:39:36,380 --> 01:39:38,530 we're directly supporting a robust 1774 01:39:38,530 --> 01:39:40,870 and interconnected data infrastructure 1775 01:39:40,870 --> 01:39:45,520 to support care coordination and evidence-based policymaking. 1776 01:39:45,520 --> 01:39:47,560 I think that concludes my presentation. 1777 01:39:48,150 --> 01:39:50,480 Thank you all and I'll wait for Q&A later on. 1778 01:39:51,480 --> 01:39:52,840 Dr. Jenna Norton: Yes, I think we'll have time 1779 01:39:52,840 --> 01:39:54,050 for Q&A at the end. 1780 01:39:54,050 --> 01:39:57,160 So next up, I would like to invite Dr. Patricia Keenan, 1781 01:39:57,160 --> 01:39:58,760 to speak, please. 1782 01:40:01,600 --> 01:40:02,890 Dr. Patricia Keenan: Thank you, Dr. Norton. 1783 01:40:02,890 --> 01:40:04,850 And thank you so much to the NIH 1784 01:40:04,850 --> 01:40:06,370 health disparities interest group 1785 01:40:06,370 --> 01:40:08,720 and the NIMHD for the opportunity 1786 01:40:08,720 --> 01:40:13,220 to talk about our work on the AHRQ SDOH database. 1787 01:40:13,220 --> 01:40:16,080 Social determinants of health is an important focus area 1788 01:40:16,080 --> 01:40:19,179 across the Agency for Healthcare Research and Quality or AHRQ. 1789 01:40:19,690 --> 01:40:22,830 And SDOH contributes to AHRQ's mission, 1790 01:40:22,830 --> 01:40:26,340 which is to produce evidence to make healthcare safer, 1791 01:40:27,210 --> 01:40:31,180 higher quality, more equitable, accessible and affordable. 1792 01:40:31,180 --> 01:40:32,780 Next slide, please. 1793 01:40:33,770 --> 01:40:36,930 So just briefly, I'll go through some context 1794 01:40:36,930 --> 01:40:38,770 around social determinants of health. 1795 01:40:38,770 --> 01:40:42,160 And I'll move quickly through a series of slides 1796 01:40:42,160 --> 01:40:45,870 that describe contents of the SDOH database, 1797 01:40:45,870 --> 01:40:49,960 and then talk at the end about some examples of data uses. 1798 01:40:49,960 --> 01:40:54,050 Next slide, please. Great. 1799 01:40:55,260 --> 01:40:58,600 So if you look at the map on the right side of the chart, 1800 01:40:58,600 --> 01:41:00,260 this is showing the relation 1801 01:41:00,260 --> 01:41:04,550 between premature mortality deaths below age 75 1802 01:41:05,070 --> 01:41:08,150 and social vulnerability across counties in the US, 1803 01:41:08,150 --> 01:41:12,880 this is using the 2018 CDC social vulnerability index. 1804 01:41:12,880 --> 01:41:14,200 And what you can see here 1805 01:41:14,200 --> 01:41:17,530 is that the areas that are shaded in dark purple 1806 01:41:18,530 --> 01:41:22,030 are the areas with both the highest premature mortality, 1807 01:41:22,030 --> 01:41:24,430 as well as the highest social vulnerability. 1808 01:41:25,070 --> 01:41:27,410 And I suspect that with this audience, 1809 01:41:27,410 --> 01:41:29,110 these long-standing differences 1810 01:41:29,110 --> 01:41:31,260 and the role of social determinants of health 1811 01:41:31,260 --> 01:41:33,700 and contributing to health outcomes, 1812 01:41:33,700 --> 01:41:37,440 as well as the geographic concentration of that impact 1813 01:41:37,440 --> 01:41:40,530 is all, you know, familiar to this group. 1814 01:41:41,660 --> 01:41:44,870 But what I would pose is that even with a growing momentum 1815 01:41:44,870 --> 01:41:47,470 around social determinants of health, 1816 01:41:47,470 --> 01:41:50,160 there's still substantial opportunity 1817 01:41:50,160 --> 01:41:53,650 remaining to expand the use of community level's 1818 01:41:53,650 --> 01:41:55,400 social determinants of health data, 1819 01:41:55,920 --> 01:41:58,650 to generate evidence on how best to actually address 1820 01:41:58,650 --> 01:42:02,220 these differences, and improve health. 1821 01:42:03,390 --> 01:42:06,930 And that's really the focus of SDOH database 1822 01:42:06,930 --> 01:42:11,930 is to create a standardized community level resource 1823 01:42:11,930 --> 01:42:14,670 pulling data from multiple public sources, 1824 01:42:15,210 --> 01:42:18,450 and spanning the SDOH domains of social context, 1825 01:42:18,450 --> 01:42:19,710 economic contexts, 1826 01:42:19,710 --> 01:42:21,610 and et cetera, shown on the slide. 1827 01:42:22,330 --> 01:42:26,750 So the purpose really is to make SDOH data easier to use, 1828 01:42:27,720 --> 01:42:32,260 both so that community level data can be included in studies 1829 01:42:32,260 --> 01:42:36,300 to account for contextual differences across areas, 1830 01:42:36,810 --> 01:42:40,180 to improve the robustness of study findings, 1831 01:42:41,140 --> 01:42:44,290 as well as to contribute to studies 1832 01:42:44,290 --> 01:42:48,100 that are working to identify effective interventions, 1833 01:42:48,100 --> 01:42:51,880 and ultimately inform efforts to improve health outcomes 1834 01:42:51,880 --> 01:42:53,480 and health equity. 1835 01:42:54,050 --> 01:42:56,260 A key aspect of the SDOH database 1836 01:42:56,260 --> 01:42:58,530 is that it's linkable by geography. 1837 01:42:59,370 --> 01:43:02,870 Currently, data are available at a county level, 1838 01:43:02,870 --> 01:43:05,660 zip code level, and census tract level. 1839 01:43:06,990 --> 01:43:09,970 So this is my sort of SDOH database in one slide, 1840 01:43:09,970 --> 01:43:12,810 and I'll go into more detail, but as I noted, 1841 01:43:12,810 --> 01:43:16,390 move through relatively quickly, and the slides will be available 1842 01:43:17,120 --> 01:43:21,550 after the workshop for reference, for more information. 1843 01:43:21,550 --> 01:43:23,150 Next slide, please. 1844 01:43:25,440 --> 01:43:29,020 So the basic approach we've taken thus far, 1845 01:43:29,020 --> 01:43:31,870 this is a relatively newer project at AHRQ, 1846 01:43:31,870 --> 01:43:34,570 and so we've worked to develop the database, 1847 01:43:34,570 --> 01:43:36,160 make it publicly available 1848 01:43:36,160 --> 01:43:38,950 and iteratively improve it over time. 1849 01:43:38,950 --> 01:43:41,010 We began with an environmental scan, 1850 01:43:41,010 --> 01:43:44,590 we released initial data files in 2020, 1851 01:43:44,590 --> 01:43:47,580 and just put out a revision this past summer. 1852 01:43:49,250 --> 01:43:52,220 And I think I covered the approach on the prior slide. 1853 01:43:52,930 --> 01:43:54,530 Next slide, please. 1854 01:43:55,900 --> 01:43:57,500 So just to, you know, show again, 1855 01:43:57,500 --> 01:44:01,990 the geographic levels, county, zip code, census tract level, 1856 01:44:01,990 --> 01:44:04,320 we've expanded the geography over time, 1857 01:44:04,320 --> 01:44:09,950 and we're looking to get still more granular in future work. 1858 01:44:09,950 --> 01:44:11,550 Next slide, please. 1859 01:44:14,620 --> 01:44:16,740 So this slide is showing more detail 1860 01:44:16,740 --> 01:44:19,750 underneath the social determinants domains. 1861 01:44:20,940 --> 01:44:24,390 We have a series of sub topics 1862 01:44:24,390 --> 01:44:27,280 and these are coded into the code books, 1863 01:44:27,280 --> 01:44:30,820 so that you can actually look for subsets 1864 01:44:30,820 --> 01:44:33,540 of variables on specific topics. I won't read through this, 1865 01:44:33,540 --> 01:44:37,080 but this gives, you know, a flavor of what's available. 1866 01:44:37,080 --> 01:44:38,680 Next slide, please. 1867 01:44:40,620 --> 01:44:43,640 Here, we're showing the number of variables 1868 01:44:43,640 --> 01:44:47,010 available across the different geographic levels. 1869 01:44:47,010 --> 01:44:49,520 I suspect it will not come as a surprise 1870 01:44:49,520 --> 01:44:51,210 to you all that there are- 1871 01:44:51,210 --> 01:44:54,230 is the greatest number of variables at the county level, 1872 01:44:54,230 --> 01:44:59,070 and the availability is lower at the zip code 1873 01:44:59,070 --> 01:45:03,310 and census tract levels. Next slide, please. 1874 01:45:06,340 --> 01:45:09,490 Here we're showing a sampling of data sources 1875 01:45:09,490 --> 01:45:12,130 within the SDOH database. 1876 01:45:12,130 --> 01:45:14,270 This is ranging from a number of different 1877 01:45:14,880 --> 01:45:17,090 executive branch departments, 1878 01:45:17,090 --> 01:45:19,310 as well as some external sources. 1879 01:45:21,190 --> 01:45:26,140 Next slide, please. To provide a little more detail, 1880 01:45:26,140 --> 01:45:29,780 you can read through this slide at your leisure, 1881 01:45:29,780 --> 01:45:34,280 I'll just note the first row, which is showing 1882 01:45:34,280 --> 01:45:37,730 some of the different socio-economic indices 1883 01:45:37,730 --> 01:45:40,450 that are available within the SDOH database, 1884 01:45:40,450 --> 01:45:42,700 from the Census Bureau as well as CDC. 1885 01:45:43,220 --> 01:45:46,330 And I'll use the social vulnerability index again 1886 01:45:46,330 --> 01:45:49,710 in a couple more slides later in the presentation. 1887 01:45:51,350 --> 01:45:52,950 Next slide, please. 1888 01:45:54,580 --> 01:45:57,990 So here, we're expanding on additional examples 1889 01:45:57,990 --> 01:45:59,980 focusing on health care context. 1890 01:46:00,540 --> 01:46:02,840 And they will know that in addition 1891 01:46:02,840 --> 01:46:05,730 to aspects of the healthcare delivery system, 1892 01:46:06,430 --> 01:46:10,720 we also have included information on health behaviors 1893 01:46:10,720 --> 01:46:13,310 and health outcomes, health outcomes, you know, 1894 01:46:13,310 --> 01:46:14,790 in case you're thinking yourself 1895 01:46:14,790 --> 01:46:18,440 that's not a social determinant, that is, of course, correct. 1896 01:46:18,440 --> 01:46:21,880 We did include some aggregated information 1897 01:46:21,880 --> 01:46:25,300 on health outcomes and health conditions 1898 01:46:25,300 --> 01:46:27,600 because we thought it would be helpful for people 1899 01:46:27,600 --> 01:46:30,110 to be able to look even just within the database, 1900 01:46:31,610 --> 01:46:34,590 to see some relationships between social determinants 1901 01:46:34,590 --> 01:46:36,570 and health status and outcomes. 1902 01:46:37,800 --> 01:46:39,400 Next slide, please. 1903 01:46:40,970 --> 01:46:42,710 For the data source documentation, 1904 01:46:42,710 --> 01:46:45,120 we have a couple of different files. 1905 01:46:45,630 --> 01:46:48,060 One is a PDF that we refer 1906 01:46:48,060 --> 01:46:50,380 to as the data source documentation file. 1907 01:46:51,000 --> 01:46:54,360 It contains information source by source 1908 01:46:54,360 --> 01:46:56,430 on the different variables included 1909 01:46:56,430 --> 01:46:57,710 and, you know, sort of caveats 1910 01:46:57,710 --> 01:47:00,950 and things to be aware of with each of the sources. 1911 01:47:02,240 --> 01:47:04,550 We also have codebooks available by year, 1912 01:47:05,120 --> 01:47:07,570 and those can be filtered, as I mentioned earlier, 1913 01:47:07,570 --> 01:47:10,070 by SDOH domains and subtopics. 1914 01:47:11,600 --> 01:47:15,600 Next slide, please. So I won't talk through this, 1915 01:47:15,600 --> 01:47:19,750 this is more of a, you know, sort of user reference for, 1916 01:47:19,750 --> 01:47:22,760 you know, ways to find different types of information 1917 01:47:22,760 --> 01:47:26,350 within the documentation and codebook files. 1918 01:47:26,970 --> 01:47:28,570 Next slide, please. 1919 01:47:30,170 --> 01:47:34,810 So as we worked to assemble the database, 1920 01:47:35,770 --> 01:47:37,400 we thought about characteristics 1921 01:47:37,400 --> 01:47:40,470 to make the data readily available. 1922 01:47:40,470 --> 01:47:43,480 And so there are a couple of aspects of standardization 1923 01:47:43,480 --> 01:47:45,080 that we've implemented. 1924 01:47:45,860 --> 01:47:47,570 One is that the naming conventions 1925 01:47:47,570 --> 01:47:50,090 are standardized across all of the datasets, 1926 01:47:51,420 --> 01:47:55,220 and that makes it easier to look across the different variables 1927 01:47:55,220 --> 01:47:57,790 and see what regional data source they came from, 1928 01:47:57,790 --> 01:48:01,020 as well as have interpretable name, 1929 01:48:01,680 --> 01:48:03,980 that is, you know, reflective of the concept 1930 01:48:03,980 --> 01:48:05,600 underneath the variable. 1931 01:48:07,730 --> 01:48:10,580 Another aspect of standardization 1932 01:48:10,580 --> 01:48:13,680 is the geographic level. 1933 01:48:13,680 --> 01:48:18,180 So in some cases, that required geographic transformation 1934 01:48:18,180 --> 01:48:21,940 into the geographic levels of the database. 1935 01:48:22,880 --> 01:48:25,540 And one other piece that I'll mention 1936 01:48:25,540 --> 01:48:29,580 that's not listed on the slide is the data years, 1937 01:48:30,100 --> 01:48:32,700 some data sources are released as of a given year, 1938 01:48:32,700 --> 01:48:36,520 but the underlying sources can vary by year. 1939 01:48:37,940 --> 01:48:41,460 And so the way we've assembled the data is all, 1940 01:48:42,140 --> 01:48:44,030 you know, sort of consistent 1941 01:48:44,030 --> 01:48:46,600 with the calendar year of the data source, 1942 01:48:46,600 --> 01:48:48,750 the underlying data reflect that data year. 1943 01:48:49,710 --> 01:48:51,310 Next slide, please. 1944 01:48:52,790 --> 01:48:55,950 So we'll turn to just a couple of brief examples of variables 1945 01:48:55,950 --> 01:48:57,840 within the database 1946 01:48:57,840 --> 01:49:01,830 and talk a little bit about potential for data linkages. 1947 01:49:02,350 --> 01:49:03,950 Next slide, please. 1948 01:49:04,980 --> 01:49:12,990 So this first slide is showing the- sorry, 1949 01:49:12,990 --> 01:49:17,560 a little block on my screen- is showing broadband access 1950 01:49:18,350 --> 01:49:22,330 and the poverty rate across counties in the US. 1951 01:49:22,330 --> 01:49:24,150 And so basically, what this is showing 1952 01:49:24,150 --> 01:49:27,060 is in the darker areas on the screen, 1953 01:49:28,060 --> 01:49:32,870 areas that have higher poverty and lower broadband use. 1954 01:49:33,490 --> 01:49:39,190 And you can also see from the sort of purple shaded areas, 1955 01:49:40,130 --> 01:49:43,350 places that are lower broadband, but are not- 1956 01:49:44,150 --> 01:49:46,070 are lower poverty areas. 1957 01:49:46,070 --> 01:49:51,850 And so, this kind of a map can show geographic, 1958 01:49:52,660 --> 01:49:55,020 you know, sort of access to broadband 1959 01:49:55,020 --> 01:49:58,350 reflected through different sources of potential, 1960 01:49:58,350 --> 01:49:59,830 you know, sort of characteristics, 1961 01:49:59,830 --> 01:50:01,270 like number of ride, 1962 01:50:01,270 --> 01:50:04,430 that can create barriers to broadband access, 1963 01:50:04,430 --> 01:50:06,980 which, as I think we've seen through the pandemic 1964 01:50:06,980 --> 01:50:09,500 is an important contributor 1965 01:50:09,500 --> 01:50:12,080 to people's well-being in terms of access to, 1966 01:50:13,020 --> 01:50:16,470 you know, education, healthcare, et cetera. 1967 01:50:17,600 --> 01:50:19,200 Next slide, please. 1968 01:50:21,010 --> 01:50:25,340 This next chart just briefly is showing track level annual 1969 01:50:25,340 --> 01:50:29,180 means of air particulate matter concentration across the US. 1970 01:50:29,860 --> 01:50:34,120 And we here can see just, you know, 1971 01:50:34,120 --> 01:50:38,120 sort of quite starkly the higher particulate matter 1972 01:50:38,120 --> 01:50:42,240 in the western part of the US reflecting wildfires. 1973 01:50:42,240 --> 01:50:45,070 And you can also see some darker shaded areas 1974 01:50:45,070 --> 01:50:49,540 across the US highlighting locations of cities. 1975 01:50:49,540 --> 01:50:51,140 Next slide, please. 1976 01:50:53,320 --> 01:50:58,690 So this chart is just an example to show that, 1977 01:50:59,230 --> 01:51:02,860 as we've heard from, you know, many stakeholders 1978 01:51:02,860 --> 01:51:05,030 that when thinking about community level data, 1979 01:51:05,030 --> 01:51:08,990 that having more granular levels of geography is important, 1980 01:51:08,990 --> 01:51:11,920 because there's a lot of variation, 1981 01:51:11,920 --> 01:51:14,650 you know, for example, within counties, 1982 01:51:14,650 --> 01:51:18,890 and so this is showing the social vulnerability index 1983 01:51:18,890 --> 01:51:20,200 at the tract level, 1984 01:51:20,200 --> 01:51:22,900 and, you know, the sort of ranges within 1985 01:51:22,900 --> 01:51:25,480 and across counties in Maryland. 1986 01:51:26,780 --> 01:51:30,250 And so again, this is the 2018 social vulnerability index, 1987 01:51:30,870 --> 01:51:33,590 which is the most recent version of the index 1988 01:51:33,590 --> 01:51:35,190 that's currently available. 1989 01:51:36,810 --> 01:51:38,410 Next slide, please. 1990 01:51:40,370 --> 01:51:43,870 So here, we're providing an example of a linkage 1991 01:51:43,870 --> 01:51:45,470 to another dataset, 1992 01:51:45,470 --> 01:51:49,630 which is the healthcare cost and utilization project data. 1993 01:51:50,470 --> 01:51:52,200 And this is showing that people 1994 01:51:52,200 --> 01:51:54,450 from higher social vulnerability areas 1995 01:51:54,450 --> 01:51:56,110 are more likely to have an ED 1996 01:51:56,110 --> 01:51:59,220 visit than those in lower social vulnerability areas. 1997 01:51:59,220 --> 01:52:02,640 So the Y axis is showing emergency department 1998 01:52:02,640 --> 01:52:03,970 visit rates. 1999 01:52:03,970 --> 01:52:07,640 And if you just look at the left side of the screen, 2000 01:52:07,640 --> 01:52:10,500 the overall social vulnerability index quartiles, 2001 01:52:10,500 --> 01:52:14,520 you can see that the ED visit rate increases 2002 01:52:14,520 --> 01:52:18,290 as the quartile of social vulnerability, 2003 01:52:18,290 --> 01:52:22,370 you know, increases from lower to higher social vulnerability. 2004 01:52:22,370 --> 01:52:26,750 And you see this pattern across the SVI subdomains as well, 2005 01:52:26,750 --> 01:52:29,880 except for the minority status sub domain. 2006 01:52:30,870 --> 01:52:32,470 Next slide, please. 2007 01:52:34,590 --> 01:52:37,290 So I won't talk through this chart, 2008 01:52:37,290 --> 01:52:40,050 but this just really is emphasizing 2009 01:52:40,050 --> 01:52:43,120 that a key role of the SDOH database has- 2010 01:52:43,780 --> 01:52:45,850 its envision in terms of its use 2011 01:52:45,850 --> 01:52:49,000 is to be linked with other data sources. 2012 01:52:49,000 --> 01:52:53,560 And so this chart has examples of a number of different sources 2013 01:52:54,130 --> 01:52:55,330 that are, you know, 2014 01:52:55,330 --> 01:52:58,560 available for linkage at different levels of geography, 2015 01:52:59,250 --> 01:53:03,090 which is, you know, sort of readily possible, 2016 01:53:03,090 --> 01:53:06,640 given the geographic structure of the SDOH database. 2017 01:53:08,170 --> 01:53:09,770 Next slide, please. 2018 01:53:12,340 --> 01:53:14,780 And so last, this slide highlights 2019 01:53:14,780 --> 01:53:17,200 some examples of potential analyses 2020 01:53:17,200 --> 01:53:19,180 using the SDOH database. 2021 01:53:20,220 --> 01:53:26,320 We're in preliminary stages of trying to address 2022 01:53:26,320 --> 01:53:29,790 the first couple of bullets on the slide, 2023 01:53:30,680 --> 01:53:33,320 with HCUP and Medicare claims data. 2024 01:53:34,320 --> 01:53:37,760 And beyond this, I just want to note and, you know, again, 2025 01:53:37,760 --> 01:53:39,670 it's wonderful to have the opportunity 2026 01:53:39,670 --> 01:53:41,270 to be part of this panel, 2027 01:53:42,170 --> 01:53:49,280 the tremendous promise of using these different efforts 2028 01:53:49,810 --> 01:53:54,150 to collect both social needs and social determinants data, 2029 01:53:55,040 --> 01:53:56,560 in analyses jointly. 2030 01:53:56,560 --> 01:53:59,050 And so, you know, one of the areas 2031 01:53:59,050 --> 01:54:01,420 where I think there's a lot of promise, 2032 01:54:01,420 --> 01:54:03,100 but also a lot of, you know, 2033 01:54:03,100 --> 01:54:05,910 potential for sort of methodological advances 2034 01:54:05,910 --> 01:54:10,210 is thinking through what is the sort of useful 2035 01:54:10,840 --> 01:54:15,960 and most impactful role of using community data in context 2036 01:54:15,960 --> 01:54:19,750 when the individual level data either does have, 2037 01:54:19,750 --> 01:54:22,350 you know, sort of relatively complete social 2038 01:54:22,350 --> 01:54:23,570 needs information, 2039 01:54:23,570 --> 01:54:26,950 or in case, you know, ranging from that to studies 2040 01:54:26,950 --> 01:54:29,300 where there's relatively limited 2041 01:54:29,300 --> 01:54:32,960 or no social needs kinds of information, 2042 01:54:32,960 --> 01:54:35,470 and, you know, sort of how does one interpret 2043 01:54:35,470 --> 01:54:38,180 and best use community level's social determinants 2044 01:54:38,180 --> 01:54:41,140 of health data across those different, 2045 01:54:41,860 --> 01:54:43,230 you know, sort of use cases, 2046 01:54:43,230 --> 01:54:45,630 which I think we are definitely still seeing, 2047 01:54:46,400 --> 01:54:50,050 you know, in the space of research in this area. 2048 01:54:51,260 --> 01:54:55,220 And so I'll turn to the next slide and basically stop there 2049 01:54:55,220 --> 01:54:58,790 and say that we really welcome feedback on the SDOH database. 2050 01:54:58,790 --> 01:55:03,250 We're continuing to think about ways to improve it over time. 2051 01:55:03,250 --> 01:55:05,900 And I look forward to the questions. Thanks. 2052 01:55:09,030 --> 01:55:10,390 Dr. Jenna Norton: Thanks very much, Dr. Keenan, 2053 01:55:10,390 --> 01:55:12,340 for that great presentation. 2054 01:55:12,340 --> 01:55:14,260 And last but certainly not least, 2055 01:55:14,260 --> 01:55:17,080 we have Dr. Scarlett Gomez. Please go ahead. 2056 01:55:18,870 --> 01:55:20,130 Dr. Scarlett Lin Gomez: Thank you, Jenna. 2057 01:55:20,130 --> 01:55:24,110 I'm going to go ahead and start my slides here. 2058 01:55:27,290 --> 01:55:31,650 So, the previous speakers have just set such a nice stage 2059 01:55:31,650 --> 01:55:33,250 for what I was going to be talking about, 2060 01:55:33,250 --> 01:55:35,599 and I've been following the questions on Slido. 2061 01:55:38,370 --> 01:55:40,880 We'll talk a little bit to focus a little bit more 2062 01:55:41,730 --> 01:55:43,720 on the direct application of- 2063 01:55:46,260 --> 01:55:48,210 can you guys still hear me? I just got- 2064 01:55:50,310 --> 01:55:53,220 Dr. Jenna Norton: Your audio went out for a second there. 2065 01:55:53,910 --> 01:55:56,320 Maybe try turning off your video just in case- 2066 01:55:57,490 --> 01:55:58,740 Dr. Scarlett Lin Gomez: Okay. 2067 01:55:58,740 --> 01:56:00,770 Dr. Jenna Norton: -in case it's taking up too much bandwidth. 2068 01:56:00,770 --> 01:56:02,370 Thank you. 2069 01:56:03,820 --> 01:56:06,120 Dr. Scarlett Lin Gomez: Okay, sorry about that. 2070 01:56:10,080 --> 01:56:11,729 How's that? Can you guys hear me? 2071 01:56:12,410 --> 01:56:13,630 Dr. Jenna Norton: So far so good. 2072 01:56:13,630 --> 01:56:15,820 Dr. Scarlett Lin Gomez: Okay. We'll keep our fingers crossed. 2073 01:56:16,370 --> 01:56:18,910 All right. Let me in the interest of time- 2074 01:56:19,800 --> 01:56:23,110 So you've seen a number of really great frameworks 2075 01:56:23,110 --> 01:56:27,080 for how we should sort of start to sort of move upstream 2076 01:56:27,080 --> 01:56:29,740 in terms of our thinking about structural 2077 01:56:29,740 --> 01:56:32,370 and social determinants of health. 2078 01:56:32,370 --> 01:56:34,480 I really like this particular graphic, 2079 01:56:34,480 --> 01:56:37,820 which was put out by the Benton Franklin Health District. 2080 01:56:37,820 --> 01:56:41,440 It's really kind of a different way to frame the nice- 2081 01:56:41,440 --> 01:56:43,239 Dr. Jenna Norton: Scarlett, I'm so sorry to interrupt. 2082 01:56:44,000 --> 01:56:45,700 We're not seeing your slides move. 2083 01:56:46,700 --> 01:56:49,470 Dr. Scarlett Lin Gomez: Oh, no. You're not seeing this? 2084 01:56:50,080 --> 01:56:52,140 Dr. Jenna Norton: Yeah, maybe if you could unshare 2085 01:56:52,140 --> 01:56:53,340 and just reshare again, 2086 01:56:53,340 --> 01:56:54,550 sometimes, I have to see this glitch. 2087 01:56:54,550 --> 01:56:55,840 Dr. Scarlett Lin Gomez: Okay. Dr. Jenna Norton: Thank you. 2088 01:56:55,840 --> 01:57:04,080 Dr. Scarlett Lin Gomez: Are you seeing this? 2089 01:57:06,620 --> 01:57:07,910 Dr. Jenna Norton: Yes, now we can, perfect. 2090 01:57:07,910 --> 01:57:09,300 Thank you. 2091 01:57:09,300 --> 01:57:10,540 Dr. Scarlett Lin Gomez: Okay. Oh, my goodness. 2092 01:57:10,540 --> 01:57:14,400 Okay. All right. So we've seen- 2093 01:57:14,400 --> 01:57:18,450 we've heard reiterated several times the definition 2094 01:57:18,450 --> 01:57:21,060 of social determinants of health from the WHO, 2095 01:57:22,010 --> 01:57:25,290 this really- framing this and situating this 2096 01:57:25,290 --> 01:57:28,390 within this graphic illustrates the social determinants 2097 01:57:28,390 --> 01:57:30,520 of health actually being midstream 2098 01:57:30,520 --> 01:57:33,430 in terms of the conditions in which we are born, 2099 01:57:33,430 --> 01:57:35,200 or work, live and age. 2100 01:57:35,200 --> 01:57:37,470 Where I would like to try to focus this talk 2101 01:57:37,470 --> 01:57:39,720 is moving upstream as Dr. Braveman 2102 01:57:39,720 --> 01:57:44,570 has nicely started to talk about what are these upstream factors, 2103 01:57:44,570 --> 01:57:48,210 what I did was I parsed out the WHO definition 2104 01:57:48,210 --> 01:57:50,140 to focus on the part of the definition 2105 01:57:50,140 --> 01:57:52,490 that talks about the social determinants of health 2106 01:57:52,490 --> 01:57:55,180 as to conditions in which we live in, 2107 01:57:55,180 --> 01:57:57,360 and then the structural determinants of health 2108 01:57:57,360 --> 01:57:59,800 which are the wider set of forces and systems 2109 01:57:59,800 --> 01:58:02,670 that are shaping the conditions of daily life. 2110 01:58:03,280 --> 01:58:05,260 I won't focus too much on this because again, 2111 01:58:05,260 --> 01:58:07,290 I think that doctors Hooper and Braveman 2112 01:58:07,290 --> 01:58:11,630 did just a wonderful job talking about this. 2113 01:58:11,630 --> 01:58:13,250 They're really- these are the isms, 2114 01:58:13,250 --> 01:58:16,090 the isms according to race, according to class, 2115 01:58:16,090 --> 01:58:19,640 immigration status, gender, language, sexual orientation, 2116 01:58:20,560 --> 01:58:24,370 these isms that determine the inequitable distribution 2117 01:58:24,370 --> 01:58:26,040 of resources 2118 01:58:26,040 --> 01:58:28,570 as determined by our institutions. 2119 01:58:28,570 --> 01:58:30,360 And so these refer to the policies, 2120 01:58:30,360 --> 01:58:33,610 programs and practices within our institutions, 2121 01:58:33,610 --> 01:58:35,630 specifically our age, governments, 2122 01:58:35,630 --> 01:58:38,620 schools, laws, regulations and businesses. 2123 01:58:38,620 --> 01:58:40,340 And these are the upstream factors 2124 01:58:40,340 --> 01:58:41,990 that shape the living conditions, 2125 01:58:41,990 --> 01:58:43,690 the social determinants of health. 2126 01:58:44,320 --> 01:58:47,700 So let's get down to business. How are we measuring this? 2127 01:58:47,700 --> 01:58:50,280 Firstly, the social determinants of health, 2128 01:58:50,280 --> 01:58:52,940 I want to- we've talked about the PhenX, 2129 01:58:52,940 --> 01:58:54,700 social determinants of health toolkit, 2130 01:58:54,700 --> 01:58:58,190 I think that is a great start in terms of 2131 01:58:58,190 --> 01:59:00,190 if you're kind of just getting started, 2132 01:59:00,190 --> 01:59:04,310 a really nice vetted set of off-the-shelf measures to use 2133 01:59:04,310 --> 01:59:08,390 for measuring the survey measures at the individual level 2134 01:59:08,390 --> 01:59:10,790 and geospatial measures at the contextual level. 2135 01:59:11,890 --> 01:59:13,970 I want to now talk about some of the approaches 2136 01:59:13,970 --> 01:59:17,730 that our group at UCSF have been taking in terms of measuring 2137 01:59:17,730 --> 01:59:20,280 social and structural determinants of health. 2138 01:59:21,130 --> 01:59:25,010 Several years ago now, this was before great toolkits 2139 01:59:25,010 --> 01:59:27,680 like the AHRQ measures have become available, 2140 01:59:27,680 --> 01:59:31,080 before the social- the PhenX toolkit became available, 2141 01:59:31,080 --> 01:59:33,120 we became really interested in trying to see, 2142 01:59:33,120 --> 01:59:36,390 you know, given the wealth of geospatial data 2143 01:59:36,390 --> 01:59:37,990 that are available out there, 2144 01:59:37,990 --> 01:59:42,300 can we harness any of that data to better measure social 2145 01:59:42,300 --> 01:59:44,280 and built environment measures? 2146 01:59:44,280 --> 01:59:47,450 So with that, we developed what we called 2147 01:59:47,450 --> 01:59:49,930 at the time California Neighborhoods Data System, 2148 01:59:49,930 --> 01:59:52,460 which through funding on several grants 2149 01:59:52,460 --> 01:59:55,090 have been since expanded to multiple states. 2150 01:59:55,680 --> 01:59:58,150 This is basically a set of curated data, 2151 01:59:59,040 --> 02:00:01,160 pulling from existing geospatial sources 2152 02:00:01,160 --> 02:00:03,590 for characterizing contextual factors, 2153 02:00:04,540 --> 02:00:06,370 these, we focus on date measures 2154 02:00:06,370 --> 02:00:08,560 that are available at the level of states. 2155 02:00:08,560 --> 02:00:11,370 So these are the states that we've since expanded to. 2156 02:00:12,390 --> 02:00:14,190 And we really wanted importantly, 2157 02:00:14,190 --> 02:00:17,680 to focus on a small of a geography as possible, 2158 02:00:17,680 --> 02:00:21,200 block group level, if possible, if not, then census tract level, 2159 02:00:21,200 --> 02:00:22,680 because these are the geographies 2160 02:00:22,680 --> 02:00:26,480 that really best represent how individual residents 2161 02:00:26,480 --> 02:00:28,430 conceptualize their neighborhood units. 2162 02:00:29,460 --> 02:00:34,120 We also have a historical data dating back to since 1990, 2163 02:00:34,120 --> 02:00:36,240 and are curating collecting these data 2164 02:00:36,240 --> 02:00:38,280 as they become available. 2165 02:00:38,280 --> 02:00:41,280 The specifics or the sort of broad domains of measures 2166 02:00:41,280 --> 02:00:44,230 that are included in our neighborhood data system, 2167 02:00:44,230 --> 02:00:46,490 are measures of social environments, 2168 02:00:46,490 --> 02:00:48,470 built environment, physical environment, 2169 02:00:48,470 --> 02:00:50,760 and also healthcare contexts. 2170 02:00:53,510 --> 02:00:55,930 We've pulled from various data sources 2171 02:00:55,930 --> 02:00:59,290 as they are of relevance to the studies that we have funded. 2172 02:00:59,290 --> 02:01:01,000 These include, of course, census data, 2173 02:01:01,000 --> 02:01:04,270 American Community Survey data, business data, 2174 02:01:04,270 --> 02:01:08,150 data from Esri GIS systems, their monitoring data, 2175 02:01:09,780 --> 02:01:13,030 street file data for calculating street connectivity, 2176 02:01:14,170 --> 02:01:15,630 departments of transportation, 2177 02:01:15,630 --> 02:01:17,510 departments of food and agriculture, 2178 02:01:17,510 --> 02:01:22,310 and then in California, we have a really unique data resource 2179 02:01:22,310 --> 02:01:24,960 that's supported by the Department of Public Health 2180 02:01:24,960 --> 02:01:26,230 called Cal EnviroScreen, 2181 02:01:26,230 --> 02:01:29,390 which in itself is a curated collection of multiple 2182 02:01:29,390 --> 02:01:31,020 physical environmental measures. 2183 02:01:33,170 --> 02:01:35,430 I want to talk about another project 2184 02:01:35,430 --> 02:01:37,080 that our group was involved 2185 02:01:37,080 --> 02:01:40,540 in as a sort of different way to both collect, curate 2186 02:01:40,540 --> 02:01:43,130 and disseminate a social determinants of health data 2187 02:01:43,130 --> 02:01:45,100 at the sister UCSF health Atlas. 2188 02:01:45,100 --> 02:01:47,350 And the URL is here if you'd like to go 2189 02:01:47,350 --> 02:01:49,230 and check it out yourself. 2190 02:01:49,230 --> 02:01:52,350 The health Atlas is basically an interactive mapping tool 2191 02:01:52,350 --> 02:01:56,260 that includes over 100 variables to explore neighborhood levels 2192 02:01:56,260 --> 02:01:57,860 social determinants of data, 2193 02:01:59,390 --> 02:02:02,840 characteristics to see how they relate to sub selected health 2194 02:02:02,840 --> 02:02:07,240 outcomes at a population level. These are the broad, 2195 02:02:07,240 --> 02:02:10,180 five broad domains of data measures that we include. 2196 02:02:10,180 --> 02:02:12,820 And so one can- the idea for this 2197 02:02:12,820 --> 02:02:16,060 was really to be able to make the data accessible 2198 02:02:16,060 --> 02:02:18,290 to a broad range of stakeholders, 2199 02:02:18,290 --> 02:02:20,160 from researchers, 2200 02:02:20,160 --> 02:02:24,520 as well as to community groups and advocacy groups. 2201 02:02:25,050 --> 02:02:27,810 And so one can go on and map different domains 2202 02:02:27,810 --> 02:02:29,050 that they're interested in, 2203 02:02:29,050 --> 02:02:30,890 they can map two domains at a time 2204 02:02:30,890 --> 02:02:34,660 to see how they're correlated. You can also download the data. 2205 02:02:34,660 --> 02:02:36,350 And many of these measures are available 2206 02:02:36,350 --> 02:02:40,120 as small as the census tract area level. 2207 02:02:42,000 --> 02:02:43,970 And now I want to shift focus and talk 2208 02:02:43,970 --> 02:02:46,540 about the structural determinants of health. 2209 02:02:46,540 --> 02:02:48,490 These are again the social inequities, 2210 02:02:48,490 --> 02:02:52,620 these isms, according to the minoritized social statuses, 2211 02:02:53,160 --> 02:02:56,720 and how they shape institutional inequities. 2212 02:02:59,640 --> 02:03:02,520 There have been of late just over the past couple of years 2213 02:03:02,520 --> 02:03:05,340 some great papers published and I saw a lot of comments 2214 02:03:05,340 --> 02:03:09,540 and questions in the slideshow pertaining to how do we measure, 2215 02:03:09,540 --> 02:03:11,510 how does one go about measuring 2216 02:03:11,510 --> 02:03:13,840 these upstream factors of structural racism? 2217 02:03:14,710 --> 02:03:16,590 This was one great paper from Alson 2218 02:03:16,590 --> 02:03:18,290 et al published last year 2219 02:03:19,040 --> 02:03:21,980 for suggested measures of structural racism. 2220 02:03:23,290 --> 02:03:25,050 I won't go through this in detail, 2221 02:03:25,050 --> 02:03:26,720 but I'm going to talk a bit more now 2222 02:03:26,720 --> 02:03:29,790 about approaches that our team has used, 2223 02:03:29,790 --> 02:03:33,060 this is in collaboration with Dr. Kirsten Beyer 2224 02:03:33,060 --> 02:03:35,340 from the Medical College of Wisconsin, 2225 02:03:35,340 --> 02:03:37,980 who has developed a rolling novel approach 2226 02:03:37,980 --> 02:03:43,740 of using data from the Home Mortgage Disclosure Act, HMDA, 2227 02:03:45,150 --> 02:03:50,300 which is mandatory reporting of mortgage application data. 2228 02:03:51,100 --> 02:03:53,290 So she's taken these data and she's developed 2229 02:03:53,290 --> 02:03:55,350 two measures of structural racism. 2230 02:03:55,900 --> 02:03:57,610 First is a measure of redlining. 2231 02:03:58,170 --> 02:04:01,400 This is the odds ratio of mortgage loan denial 2232 02:04:01,400 --> 02:04:02,710 within a census tract 2233 02:04:02,710 --> 02:04:05,120 compared to those outside of the census tract 2234 02:04:05,120 --> 02:04:08,940 but within an MSA, and because these are models, 2235 02:04:08,940 --> 02:04:12,230 she is able to adjust for a number of characteristics 2236 02:04:12,230 --> 02:04:15,090 including sex and the loan to income ratio. 2237 02:04:16,080 --> 02:04:19,460 So these are odds ratios. If the OR is greater than one, 2238 02:04:19,460 --> 02:04:21,060 it indicates that that neighborhood 2239 02:04:21,060 --> 02:04:22,300 or that census tract 2240 02:04:22,300 --> 02:04:26,280 is more likely to be denied mortgage applications 2241 02:04:26,280 --> 02:04:28,100 compared to its surrounding area. 2242 02:04:30,380 --> 02:04:32,180 The second measure that she's developed 2243 02:04:32,180 --> 02:04:34,450 using the Home Mortgage Disclosure Act data 2244 02:04:34,450 --> 02:04:36,860 is called racial bias in mortgage lending. 2245 02:04:37,570 --> 02:04:40,680 This is the odds ratio for denial of a mortgage application 2246 02:04:40,680 --> 02:04:44,380 from a black applicant compared to denial of a white applicant. 2247 02:04:44,380 --> 02:04:45,980 Again, this is based on model data, 2248 02:04:45,980 --> 02:04:48,630 so she, in her model, she has adjusted for sex 2249 02:04:48,630 --> 02:04:50,930 and the ratio of loan to income ratio. 2250 02:04:51,950 --> 02:04:54,260 Other racial groups are also coded, 2251 02:04:54,260 --> 02:04:56,720 so it's possible to calculate racial bias 2252 02:04:56,720 --> 02:05:00,810 for any two combination of groups, for example, 2253 02:05:00,810 --> 02:05:04,860 Hispanic or Latino applicants compared to white applicants. 2254 02:05:05,470 --> 02:05:08,200 And here, an odd ratio greater than one indicates 2255 02:05:08,200 --> 02:05:10,500 that that particular census tract or neighborhood 2256 02:05:10,500 --> 02:05:13,900 has a greater denial for the minoritized group 2257 02:05:13,900 --> 02:05:15,590 compared to white applicants. 2258 02:05:17,960 --> 02:05:20,160 Other measures of segregation then, 2259 02:05:20,160 --> 02:05:24,320 probably one the folks have seen the most often, 2260 02:05:24,320 --> 02:05:27,200 the traditionally used measures of segregation 2261 02:05:27,200 --> 02:05:31,480 from the conceptualized and developed by Dun and Massey 2262 02:05:31,480 --> 02:05:33,530 based on concentrations of racial or ethnic groups 2263 02:05:33,530 --> 02:05:34,830 within a smaller unit, 2264 02:05:34,830 --> 02:05:36,930 such as a block group distributed 2265 02:05:36,930 --> 02:05:39,010 across the larger units such as an MSA. 2266 02:05:39,670 --> 02:05:42,620 So some of these most commonly used measures of segregation 2267 02:05:42,620 --> 02:05:44,550 are evenness and dissimilarity, 2268 02:05:44,550 --> 02:05:46,700 but there's a number of different measures. 2269 02:05:47,360 --> 02:05:48,980 A slightly newer measure, 2270 02:05:49,880 --> 02:05:53,710 which has been operationalized by Nancy Krieger 2271 02:05:53,710 --> 02:05:55,930 and others is called the index of concentration 2272 02:05:55,930 --> 02:05:57,310 at the extremes. 2273 02:05:57,310 --> 02:06:00,220 These are at smaller area level measures, 2274 02:06:00,220 --> 02:06:03,380 so really measuring localized segregation. 2275 02:06:03,990 --> 02:06:07,430 And these compare proportions of two different subgroup 2276 02:06:07,430 --> 02:06:10,000 of populations, minoritized populations, 2277 02:06:10,000 --> 02:06:12,260 for example, black versus white residents, 2278 02:06:12,260 --> 02:06:15,410 high versus low-income, or combinations of these. 2279 02:06:15,410 --> 02:06:17,750 We can calculate these for race alone, 2280 02:06:17,750 --> 02:06:20,370 SES alone, and race by SES. 2281 02:06:24,400 --> 02:06:26,540 And finally, there are different, you know, 2282 02:06:26,540 --> 02:06:27,940 all of the measures we've talked about 2283 02:06:27,940 --> 02:06:30,630 are static at cross-sectional one point in time, 2284 02:06:30,630 --> 02:06:32,870 we can also, of course, conceptualize, 2285 02:06:33,730 --> 02:06:37,500 and measure structural racism across over time. 2286 02:06:38,960 --> 02:06:44,410 The 1930s Home Owners Lending Corporation 2287 02:06:44,410 --> 02:06:47,670 redlining maps were recently released for the US. 2288 02:06:48,190 --> 02:06:50,540 And so there have been research, so these are- 2289 02:06:50,540 --> 02:06:53,230 we've also included these in our health Atlas. 2290 02:06:53,920 --> 02:06:56,490 And research studies have begun to incorporate 2291 02:06:56,490 --> 02:07:00,240 these into assessing historical redlining 2292 02:07:00,240 --> 02:07:02,950 as they relate to current health inequities. 2293 02:07:03,610 --> 02:07:06,020 Gentrification is another way of looking 2294 02:07:06,020 --> 02:07:08,050 at changing neighborhoods over time. 2295 02:07:09,490 --> 02:07:11,430 And CI has released- 2296 02:07:11,430 --> 02:07:17,000 worked with the US Food and Drug Administration 2297 02:07:17,000 --> 02:07:21,900 to develop and release measures of persistent poverty 2298 02:07:21,900 --> 02:07:23,950 at county and census tract levels. 2299 02:07:23,950 --> 02:07:27,760 This is the census tracts that have been in poverty 2300 02:07:27,760 --> 02:07:31,130 over a sustained period of decades. 2301 02:07:33,010 --> 02:07:34,760 Nancy Krieger and others have looked 2302 02:07:34,760 --> 02:07:37,350 at the effects of resonance in Jim Crow 2303 02:07:37,350 --> 02:07:40,370 states as they relate to cancer health and mortality. 2304 02:07:41,280 --> 02:07:43,630 And then I want to just point to this concept 2305 02:07:43,630 --> 02:07:45,880 called spatial justice, which has been- 2306 02:07:46,390 --> 02:07:49,620 Baciu et al characterize 2307 02:07:49,620 --> 02:07:51,860 this as the relationship between people and places 2308 02:07:51,860 --> 02:07:54,120 as mediated by historical, current values, 2309 02:07:54,120 --> 02:07:57,370 assumptions, beliefs, policies, investments and practices. 2310 02:07:58,830 --> 02:08:01,730 So some of these historical measures 2311 02:08:01,730 --> 02:08:03,870 that I've described here 2312 02:08:03,870 --> 02:08:06,530 relate to the concept of spatial justice. 2313 02:08:08,050 --> 02:08:11,080 And I wanted to spend just a couple last minutes 2314 02:08:11,080 --> 02:08:15,000 talking about how we've applied these measures here 2315 02:08:15,000 --> 02:08:18,440 into one specific study called the RESPOND Study, 2316 02:08:18,440 --> 02:08:20,100 which is looking at prostate cancer 2317 02:08:20,100 --> 02:08:22,430 among black African American men. 2318 02:08:23,160 --> 02:08:26,830 RESPOND is jointly funded by NCI, NIMHD 2319 02:08:26,830 --> 02:08:29,070 and the Prostate Cancer Foundation. 2320 02:08:29,070 --> 02:08:32,880 It's led by Chris Heyman at USC. It's funded as a U19, 2321 02:08:32,880 --> 02:08:34,870 we proposed it as a program project, 2322 02:08:34,870 --> 02:08:37,910 so it comprises four projects. 2323 02:08:37,910 --> 02:08:42,250 The first project is led by myself and colleagues at UCSF. 2324 02:08:43,330 --> 02:08:45,710 RESPOND really is an integrated set of studies 2325 02:08:45,710 --> 02:08:47,470 that's focused on a common theme 2326 02:08:47,470 --> 02:08:49,940 of identifying the multi-level determinants 2327 02:08:49,940 --> 02:08:52,620 and characteristics of aggressive prostate cancer 2328 02:08:52,620 --> 02:08:54,470 among Black and African American men. 2329 02:08:55,700 --> 02:08:57,630 This is our conceptual framework, 2330 02:08:57,630 --> 02:09:00,920 we find that it helps us 2331 02:09:00,920 --> 02:09:02,870 to think about how the different projects 2332 02:09:02,870 --> 02:09:05,840 and how the different measures we are collecting 2333 02:09:05,840 --> 02:09:09,170 or we should be collecting relate together. 2334 02:09:09,170 --> 02:09:13,620 So, our conceptual framework is at the very top upstream, 2335 02:09:13,620 --> 02:09:15,220 we have structural racism, 2336 02:09:15,220 --> 02:09:18,620 which impacts upon life course social stressors 2337 02:09:18,620 --> 02:09:21,750 at the contextual level and at the individual level. 2338 02:09:22,550 --> 02:09:26,520 These together over time, over the life course produce stress, 2339 02:09:26,520 --> 02:09:33,540 which is embodied in impacts upon biological systems, 2340 02:09:34,520 --> 02:09:37,620 which in turn impact upon prostate cancer aggressiveness 2341 02:09:37,620 --> 02:09:40,280 and mortality, which is ultimately the outcome 2342 02:09:40,280 --> 02:09:42,060 of interest for the RESPOND study. 2343 02:09:42,690 --> 02:09:46,030 And then down here, you have, you see an arrow here 2344 02:09:46,030 --> 02:09:48,520 pertaining to a germline risk. 2345 02:09:48,520 --> 02:09:53,590 So the idea of this is that this pathway here can be moderated 2346 02:09:53,590 --> 02:09:54,920 by germline risk, 2347 02:09:54,920 --> 02:09:57,820 genetic ancestry and prostate cancer risk loci. 2348 02:09:59,010 --> 02:10:01,660 This is how the different projects within RESPOND 2349 02:10:03,190 --> 02:10:08,910 are collecting data pertaining to each of the different aspects 2350 02:10:08,910 --> 02:10:10,960 of this pathway of this framework 2351 02:10:10,960 --> 02:10:13,090 and then how they all relate together. 2352 02:10:13,090 --> 02:10:15,670 So I want to focus a bit more now on project one, 2353 02:10:15,670 --> 02:10:18,070 which is the project that we're leading at UCSF. 2354 02:10:20,840 --> 02:10:22,190 We, within RESPOND, 2355 02:10:22,190 --> 02:10:25,370 we are collecting data from multiple sources. 2356 02:10:25,370 --> 02:10:29,440 Firstly, we are identifying and recruiting men 2357 02:10:29,440 --> 02:10:31,900 based on state cancer registry. 2358 02:10:31,900 --> 02:10:34,310 So these are African American or black men 2359 02:10:34,310 --> 02:10:36,330 recently diagnosed with prostate cancer, 2360 02:10:37,030 --> 02:10:39,680 we reach out to them, ask them to participate in the study, 2361 02:10:39,680 --> 02:10:42,110 and they are asked to fill out a survey 2362 02:10:42,780 --> 02:10:47,840 which assesses the factors that you see here. 2363 02:10:48,910 --> 02:10:51,380 We included measures of life course 2364 02:10:51,380 --> 02:10:53,110 multi-level social stressors, 2365 02:10:53,110 --> 02:10:58,730 and so we really try to make a point here to adapt questions, 2366 02:10:58,730 --> 02:11:00,550 survey instruments that have already- 2367 02:11:00,550 --> 02:11:03,520 that are validated and or widely used, 2368 02:11:03,520 --> 02:11:06,150 so that we recognize that our- I should have mentioned, 2369 02:11:06,150 --> 02:11:08,920 our goal is to recruit 10,000 African American men 2370 02:11:08,920 --> 02:11:10,630 with prostate cancer nationwide, 2371 02:11:11,480 --> 02:11:14,290 we're recruiting from six state cancer registries. 2372 02:11:14,990 --> 02:11:19,010 So recognizing that this cohort of 10,000 African American men 2373 02:11:19,010 --> 02:11:22,330 with prostate cancer can really serve as a valuable resource 2374 02:11:22,330 --> 02:11:26,700 for comparison to- for additional research, 2375 02:11:26,700 --> 02:11:28,710 we really wanted to be sure to collect data 2376 02:11:28,710 --> 02:11:30,660 using measures that are well validated. 2377 02:11:31,170 --> 02:11:33,410 So we've adapted the Williams lifetime 2378 02:11:33,410 --> 02:11:34,820 everyday discrimination scale 2379 02:11:34,820 --> 02:11:38,740 just as an example to ask about current experiences 2380 02:11:38,740 --> 02:11:45,490 with discrimination as well as several other life periods. 2381 02:11:46,170 --> 02:11:47,450 We also, for example, 2382 02:11:47,450 --> 02:11:51,350 adapted the CDC adverse childhood experiences scale 2383 02:11:51,350 --> 02:11:53,990 to ask about adverse life experiences 2384 02:11:53,990 --> 02:11:55,590 over different life periods. 2385 02:11:58,770 --> 02:12:03,070 We've included Dr. Beyer's structural racism measures, 2386 02:12:03,070 --> 02:12:06,630 so through geocoding both participants' addresses 2387 02:12:06,630 --> 02:12:08,270 at time of diagnosis, 2388 02:12:08,270 --> 02:12:10,800 as well as ascertaining a residential history, 2389 02:12:10,800 --> 02:12:14,820 were able to access and ascertain these contextual 2390 02:12:14,820 --> 02:12:17,950 structural social determinants measures that you see here, 2391 02:12:18,550 --> 02:12:20,220 not only at the time of diagnosis, 2392 02:12:20,220 --> 02:12:23,740 but over their life course. And so the- you see the measures 2393 02:12:23,740 --> 02:12:26,820 that we're looking at racial ethnic segregation, composition, 2394 02:12:27,440 --> 02:12:29,470 social inbuilt environmental factors. 2395 02:12:31,610 --> 02:12:33,180 So we have a whole lot of data 2396 02:12:33,180 --> 02:12:35,520 that we are collecting pertaining to structural 2397 02:12:35,520 --> 02:12:36,860 and social determinants of health, 2398 02:12:36,860 --> 02:12:38,310 within this RESPOND study, 2399 02:12:38,310 --> 02:12:40,850 we have data across the life course, 2400 02:12:40,850 --> 02:12:42,790 we have data at multiple levels, 2401 02:12:42,790 --> 02:12:44,690 and we have data from multiple dimensions, 2402 02:12:44,690 --> 02:12:47,690 from self report, as well as from geospatial sources. 2403 02:12:49,540 --> 02:12:53,310 Our approach or plan is supply mixture modeling 2404 02:12:53,820 --> 02:12:57,130 to be able to combine these different domains 2405 02:12:57,130 --> 02:12:59,340 and dimensions together, 2406 02:12:59,340 --> 02:13:01,920 and then to capture to be able to have- 2407 02:13:03,110 --> 02:13:07,610 measure latent measures that capture each life period 2408 02:13:07,610 --> 02:13:09,190 for contextual level stressors 2409 02:13:09,190 --> 02:13:11,770 and individual level social stressors, 2410 02:13:11,770 --> 02:13:13,790 and then to develop trajectory models 2411 02:13:13,790 --> 02:13:16,770 so that we can measure how these stressors 2412 02:13:16,770 --> 02:13:18,950 at the contextual level and individual level 2413 02:13:18,950 --> 02:13:21,450 change over the life course. 2414 02:13:24,070 --> 02:13:27,850 So I want to emphasize and with the slide that, 2415 02:13:27,850 --> 02:13:30,890 as I'm sure you've appreciated from the talks 2416 02:13:30,890 --> 02:13:34,640 that we've heard so far is that these approaches 2417 02:13:34,640 --> 02:13:37,100 really requires a team science approach. 2418 02:13:37,100 --> 02:13:39,880 And I think, foremost, and most importantly, 2419 02:13:39,880 --> 02:13:42,580 we really need to include collaborators 2420 02:13:42,580 --> 02:13:44,750 who bring expertise in social sciences 2421 02:13:44,750 --> 02:13:46,560 as well as social epidemiology. 2422 02:13:47,610 --> 02:13:50,880 I think increasingly understanding the extent 2423 02:13:50,880 --> 02:13:55,060 to which these upstream factors impact upon biology, 2424 02:13:55,060 --> 02:13:57,580 we need to be able to work together 2425 02:13:57,580 --> 02:14:00,740 with genetic and molecular epidemiologists. 2426 02:14:00,740 --> 02:14:02,380 Obviously, we're talking- 2427 02:14:02,380 --> 02:14:04,500 oftentimes, we're talking about big data, 2428 02:14:04,500 --> 02:14:07,510 data sciences and bioinformatics become very important. 2429 02:14:08,420 --> 02:14:11,730 Biostatisticians, and to the extent that we also 2430 02:14:11,730 --> 02:14:14,820 are able to leverage existing geospatial data 2431 02:14:14,820 --> 02:14:16,870 and being able to work with environmental 2432 02:14:16,870 --> 02:14:19,670 and GIS epidemiologists, demographers, 2433 02:14:20,470 --> 02:14:24,830 it's also critically important. And most importantly, I think, 2434 02:14:24,830 --> 02:14:29,110 is working with communities and understanding it's nice 2435 02:14:29,110 --> 02:14:30,700 and all we have all of this data, 2436 02:14:30,700 --> 02:14:32,080 we as epidemiologists 2437 02:14:32,080 --> 02:14:37,240 and scientists can put our hats on and propose hypotheses, 2438 02:14:37,240 --> 02:14:41,060 but really listening to the life experiences 2439 02:14:41,060 --> 02:14:42,510 and stories of communities 2440 02:14:42,510 --> 02:14:45,140 to really understand the relevance 2441 02:14:45,140 --> 02:14:49,590 of what we should be studying, and what then helped us to study 2442 02:14:49,590 --> 02:14:52,560 is a really key part of this team science approach. 2443 02:14:53,090 --> 02:14:54,690 Thank you. 2444 02:14:56,120 --> 02:14:57,360 Dr. Jenna Norton: Thank you, Dr. Gomez. 2445 02:14:57,360 --> 02:14:58,640 That was an excellent talk. 2446 02:14:58,640 --> 02:15:00,770 I can only assume you have a crystal ball, 2447 02:15:02,370 --> 02:15:05,460 since your talk nicely addressed some of the questions 2448 02:15:05,460 --> 02:15:08,600 that came up in the chat during the prior discussions. 2449 02:15:09,270 --> 02:15:10,870 So thank you. 2450 02:15:12,110 --> 02:15:16,660 So I'd like to start off our discussion 2451 02:15:16,660 --> 02:15:19,660 with a question or sort of two comments and questions 2452 02:15:19,660 --> 02:15:21,830 that I think get at the heart of this panel. 2453 02:15:21,830 --> 02:15:25,010 So one comment pointing out the distinction 2454 02:15:25,010 --> 02:15:27,830 between population level social determinants of health, 2455 02:15:27,830 --> 02:15:29,320 and how they have different uses 2456 02:15:29,320 --> 02:15:32,750 than patient reported data at the individual level. 2457 02:15:33,760 --> 02:15:37,040 And a question that was addressed to Dr. Keenan, 2458 02:15:37,040 --> 02:15:40,570 but I think the panel all may wish to comment on 2459 02:15:40,570 --> 02:15:43,000 is sort of since the AHRQ database 2460 02:15:43,000 --> 02:15:45,220 and some of these other area level measures 2461 02:15:45,220 --> 02:15:47,370 were developed for the whole community, 2462 02:15:47,370 --> 02:15:49,590 to what extent can they be used as a proxy 2463 02:15:49,590 --> 02:15:51,670 when you're studying subpopulations 2464 02:15:51,670 --> 02:15:53,210 within the community, for example, 2465 02:15:53,210 --> 02:15:58,110 people with a specific disease, or other specific concern? 2466 02:16:06,180 --> 02:16:07,780 Dr. Patricia Keenan: I'm happy to start. 2467 02:16:08,300 --> 02:16:11,260 What I would say is that the community level 2468 02:16:11,260 --> 02:16:14,260 measures of SDOH, including the indices, 2469 02:16:15,570 --> 02:16:19,340 I think of them as capturing the community context. 2470 02:16:19,340 --> 02:16:23,890 And so, you know, for patients of a particular population, 2471 02:16:23,890 --> 02:16:27,750 if it's possible to link the community level data 2472 02:16:27,750 --> 02:16:34,730 to patient level information, then it would tell you about, 2473 02:16:34,730 --> 02:16:37,870 you know, characteristics of the communities in which, 2474 02:16:37,870 --> 02:16:40,430 you know, the population of interest lives. 2475 02:16:41,020 --> 02:16:42,320 I think that is different, 2476 02:16:42,320 --> 02:16:45,710 and as also pointed out in the comments, 2477 02:16:46,310 --> 02:16:49,100 that that's not the same as telling you, 2478 02:16:49,100 --> 02:16:52,340 you know, what the social needs circumstances 2479 02:16:52,340 --> 02:16:56,060 at a personal level are of a given population. 2480 02:16:56,870 --> 02:16:58,760 And I think that is a really important distinction. 2481 02:16:58,760 --> 02:17:01,500 And I was trying to sort of allude to that 2482 02:17:01,500 --> 02:17:03,690 in my concluding remarks, 2483 02:17:03,690 --> 02:17:08,630 because I think there is still a lot of more work 2484 02:17:08,630 --> 02:17:11,040 that could be done to help think through 2485 02:17:11,040 --> 02:17:14,550 and, you know, sort of have more, you know, 2486 02:17:14,550 --> 02:17:18,790 sort of a larger body of work, talking about, you know, 2487 02:17:18,790 --> 02:17:21,750 sort of how to use community level SDOH data, 2488 02:17:22,420 --> 02:17:24,100 and interpret it when you do 2489 02:17:24,100 --> 02:17:28,290 and don't have the individual level social needs information. 2490 02:17:29,200 --> 02:17:32,150 But again, I would, just as in sort of overall point, 2491 02:17:32,790 --> 02:17:35,740 emphasize, as was noted in some of the comments, 2492 02:17:37,150 --> 02:17:39,740 that community level and personal level information 2493 02:17:39,740 --> 02:17:42,680 definitely are not measuring exactly the same thing. 2494 02:17:43,750 --> 02:17:46,310 And so, you know, when used with 2495 02:17:46,310 --> 02:17:48,700 or without patient level social needs, 2496 02:17:48,700 --> 02:17:50,830 it also might be important to think 2497 02:17:50,830 --> 02:17:52,430 through how to interpret then, 2498 02:17:53,550 --> 02:17:58,150 you know, sort of the meaning of the community level of factors. 2499 02:18:02,980 --> 02:18:04,230 Dr. Jenna Norton: Dr. Gomez, 2500 02:18:04,230 --> 02:18:06,129 anything to add from your perspective? 2501 02:18:07,110 --> 02:18:08,350 Dr. Scarlett Lin Gomez: I don't. 2502 02:18:08,350 --> 02:18:10,740 I think that was just an excellent response, 2503 02:18:10,740 --> 02:18:12,590 I don't have anything further to add. 2504 02:18:13,780 --> 02:18:15,040 Dr. Jenna Norton: Wonderful. All right, 2505 02:18:15,040 --> 02:18:18,620 so another theme that I've seen in a couple questions here 2506 02:18:18,620 --> 02:18:26,320 is this idea of ethical concerns or other challenges, 2507 02:18:27,820 --> 02:18:30,200 you know, particularly if you're collecting information 2508 02:18:30,200 --> 02:18:33,360 on something sensitive like experiences of racism, 2509 02:18:34,010 --> 02:18:36,840 and integrating that into the electronic health record 2510 02:18:36,840 --> 02:18:38,870 and how that might be stigmatizing. 2511 02:18:38,870 --> 02:18:41,430 So Evelyn, I saw you already put a link into the chat 2512 02:18:41,430 --> 02:18:43,170 to begin to address that question, 2513 02:18:43,170 --> 02:18:44,380 but if you have further comments, 2514 02:18:44,380 --> 02:18:47,220 or Dr. Keenan or Dr. Gomez, if you have comments, 2515 02:18:47,220 --> 02:18:49,430 please feel free to add those as well. 2516 02:18:50,960 --> 02:18:52,820 Evelyn Gallego: Sure, I just- Yes, I put a link 2517 02:18:52,820 --> 02:18:55,880 because this comes up often in our discussions 2518 02:18:55,880 --> 02:18:57,840 when we first started the project, 2519 02:18:57,840 --> 02:19:02,480 it was always around, you know, how is this going to address 2520 02:19:02,480 --> 02:19:06,810 furthering health disparities, right, and discrimination? 2521 02:19:06,810 --> 02:19:08,600 And I do think we- 2522 02:19:08,600 --> 02:19:10,410 so we developed the data principles, 2523 02:19:10,410 --> 02:19:13,090 but knowing that, you know, there's still more work, 2524 02:19:13,090 --> 02:19:16,380 I think all what you are doing and the interest here is, 2525 02:19:16,380 --> 02:19:19,410 we need to be able to collect this type of data, 2526 02:19:19,410 --> 02:19:22,420 acknowledging that there are what I'd like- 2527 02:19:22,420 --> 02:19:24,980 you know, refer to unintended consequences, 2528 02:19:24,980 --> 02:19:28,550 but, you know, how can we do better with guiding principles? 2529 02:19:29,380 --> 02:19:33,300 I think more evidence is needed, but it's a risk overall. 2530 02:19:33,300 --> 02:19:35,640 I also do think the Gravity- 2531 02:19:35,640 --> 02:19:37,360 I will acknowledge the Gravity Project, 2532 02:19:37,360 --> 02:19:41,880 we are not directly working on standards related to consent, 2533 02:19:43,700 --> 02:19:47,080 that are being developed by other entities. 2534 02:19:47,080 --> 02:19:51,110 But I think that's also an area where we ensure that we are 2535 02:19:51,110 --> 02:19:53,440 gathering the individuals' consent to capture, 2536 02:19:53,440 --> 02:19:57,860 share and exchange the data. And it becomes truly important 2537 02:19:57,860 --> 02:20:00,210 when you have sensitive information 2538 02:20:00,210 --> 02:20:04,510 as we look at domains around violence and axes 2539 02:20:05,260 --> 02:20:08,660 where, you know, that data wants to be- 2540 02:20:08,660 --> 02:20:10,720 the individual wants to hold it close. 2541 02:20:15,440 --> 02:20:17,640 And if the other panelists want to chime in- 2542 02:20:19,390 --> 02:20:20,590 Female Speaker: I just want to add 2543 02:20:20,590 --> 02:20:23,960 that I think this is, you know, as epidemiologists, 2544 02:20:23,960 --> 02:20:26,690 we always want more data, better data and more data. 2545 02:20:28,030 --> 02:20:32,050 And I've been pushing a long time for standardized 2546 02:20:32,050 --> 02:20:35,970 and collection of social determinants of health 2547 02:20:35,970 --> 02:20:39,860 across healthcare systems, because we are increasingly 2548 02:20:39,860 --> 02:20:43,490 really relying a lot on that data for research, 2549 02:20:43,490 --> 02:20:46,980 but also, of course, for healthcare systems improvements. 2550 02:20:47,860 --> 02:20:51,880 But at the same time, I think, to Dr. Gallego's point, 2551 02:20:51,880 --> 02:20:54,570 we do have to be careful. And I know, for example, 2552 02:20:54,570 --> 02:20:57,890 in some California public institutions, 2553 02:20:57,890 --> 02:21:01,030 we explicitly do not collect data on, 2554 02:21:01,030 --> 02:21:03,870 for example, immigration status, because we- 2555 02:21:03,870 --> 02:21:06,850 even though we ourselves know that, 2556 02:21:07,370 --> 02:21:10,890 you know, we will not use it for wrong, 2557 02:21:10,890 --> 02:21:12,180 but that doesn't- 2558 02:21:12,180 --> 02:21:16,630 having there and putting patients in the position 2559 02:21:16,630 --> 02:21:19,110 of having to disclose that is not something 2560 02:21:19,110 --> 02:21:22,130 we as a healthcare institution 2561 02:21:22,130 --> 02:21:24,000 want to be in a position of doing. 2562 02:21:24,000 --> 02:21:26,860 So I think that there is definitely a balance 2563 02:21:26,860 --> 02:21:28,460 and a trade-off. 2564 02:21:31,140 --> 02:21:32,740 Dr. Jenna Norton: Excellent, thank you. 2565 02:21:33,280 --> 02:21:35,300 So another question. 2566 02:21:35,300 --> 02:21:37,490 So I'm going to combine two sort of different 2567 02:21:37,490 --> 02:21:39,090 but similar questions. 2568 02:21:39,910 --> 02:21:44,110 So one is this idea of, you know, 2569 02:21:44,110 --> 02:21:47,440 how do you collect information on these issues 2570 02:21:47,440 --> 02:21:50,460 for people who have stigmatized diseases, 2571 02:21:50,460 --> 02:21:53,510 who may not feel comfortable providing, 2572 02:21:53,510 --> 02:21:55,609 you know, all of their previous addresses, 2573 02:21:57,080 --> 02:21:59,800 or who might not want to answer extensive surveys 2574 02:21:59,800 --> 02:22:02,160 about discrimination or childhood experiences? 2575 02:22:02,980 --> 02:22:05,510 And then even for folks who may feel comfortable 2576 02:22:05,510 --> 02:22:10,430 providing these data, their addresses change over time, 2577 02:22:10,430 --> 02:22:12,870 and expecting people to recall all of these data over time 2578 02:22:12,870 --> 02:22:14,080 can be a challenge. 2579 02:22:14,080 --> 02:22:16,820 And so how do we deal with some of those issues? 2580 02:22:20,690 --> 02:22:22,290 Evelyn Gallego: That is a hard one, Jenna. 2581 02:22:24,890 --> 02:22:27,850 Even I think it's not only recollection, 2582 02:22:27,850 --> 02:22:30,790 we have- we see this even with clinical data, 2583 02:22:30,790 --> 02:22:32,920 you know, it's just even how do you, 2584 02:22:33,450 --> 02:22:36,170 like, data provenance, you ensure that that individual 2585 02:22:36,170 --> 02:22:39,260 is giving the right data, identity management, 2586 02:22:39,260 --> 02:22:41,630 all the things that need to carry 2587 02:22:41,630 --> 02:22:46,560 even before we get into we say demographic type information, 2588 02:22:46,560 --> 02:22:50,120 I think it's hard to track, I do see value 2589 02:22:50,120 --> 02:22:54,710 in a lot of the Health Information Exchange systems, 2590 02:22:56,050 --> 02:22:59,590 that can be the ones that help health systems 2591 02:22:59,590 --> 02:23:01,400 match for patient matching, 2592 02:23:01,400 --> 02:23:04,250 I think there's a significant opportunity there. 2593 02:23:04,250 --> 02:23:07,770 But, you know, as a patient and consumer myself, 2594 02:23:07,770 --> 02:23:09,600 you know, you do get frustrated 2595 02:23:09,600 --> 02:23:11,420 answering the same question all the time, 2596 02:23:11,420 --> 02:23:13,220 and you wish that there was a way 2597 02:23:13,220 --> 02:23:15,810 that it could auto populate with existing data. 2598 02:23:16,660 --> 02:23:20,840 But I do think that this is a common problem. Overall. 2599 02:23:24,560 --> 02:23:25,920 Female Speaker: I can speak to that a little bit. 2600 02:23:25,920 --> 02:23:28,140 I think there were two questions there. 2601 02:23:28,140 --> 02:23:35,880 First is challenge of asking some of these very sensitive 2602 02:23:36,670 --> 02:23:38,960 and potentially stigmatizing questions, 2603 02:23:38,960 --> 02:23:42,060 and we've definitely faced and we still continue to face that. 2604 02:23:42,060 --> 02:23:45,440 And I think one approach that really the Adverse Childhood 2605 02:23:45,440 --> 02:23:48,770 Experiences scale, for example, that we're using does have some 2606 02:23:48,770 --> 02:23:51,810 very, very sensitive items within it. 2607 02:23:53,060 --> 02:23:55,180 One approach that we have found 2608 02:23:55,180 --> 02:23:58,140 is making sure that our recruiters, 2609 02:23:58,140 --> 02:24:02,080 those who are interfacing directly with the patients 2610 02:24:02,080 --> 02:24:05,040 and the community members that they're recruiting, 2611 02:24:07,730 --> 02:24:11,230 can identify with those communities and patients 2612 02:24:11,230 --> 02:24:15,860 and having them be able to speak to I get it, 2613 02:24:15,860 --> 02:24:17,300 I know where you're coming from. 2614 02:24:17,300 --> 02:24:20,390 I know your concern. This is, you know- 2615 02:24:20,390 --> 02:24:23,870 I think this is why these questions are important, 2616 02:24:23,870 --> 02:24:25,340 and as researchers, 2617 02:24:25,340 --> 02:24:28,040 we've talked a lot too directly to these recruiters, 2618 02:24:28,040 --> 02:24:31,660 we've written a lot of FAQs for them to use, 2619 02:24:31,660 --> 02:24:33,460 that they have available at their tooltips 2620 02:24:33,460 --> 02:24:35,590 to be able to address these questions as they come up. 2621 02:24:35,590 --> 02:24:37,770 But I think that's really critical. 2622 02:24:39,500 --> 02:24:41,720 The second question was regarding recall 2623 02:24:41,720 --> 02:24:43,280 of residential history, 2624 02:24:43,280 --> 02:24:46,470 and yes, there's an "I can't even recall 2625 02:24:46,470 --> 02:24:49,570 were the last three addresses where I've lived at." 2626 02:24:51,160 --> 02:24:54,280 The approach we're using is actually through linkage 2627 02:24:54,280 --> 02:24:56,670 to public records databases, 2628 02:24:56,670 --> 02:25:00,620 to be able to ascertain residential history. 2629 02:25:00,620 --> 02:25:05,540 This is a method that was tested by Westat 2630 02:25:05,540 --> 02:25:07,260 in collaboration with NCI, 2631 02:25:07,260 --> 02:25:09,940 and there's a white paper that's been written about it. 2632 02:25:09,940 --> 02:25:12,970 So that's the approach we're using, 2633 02:25:12,970 --> 02:25:16,000 because we recognize that it is nearly impossible 2634 02:25:16,000 --> 02:25:18,090 to have people recall 2635 02:25:18,090 --> 02:25:20,210 their exact residential history over time. 2636 02:25:22,660 --> 02:25:23,980 Dr. Jenna Norton: Thank you. All right. 2637 02:25:23,980 --> 02:25:26,400 So in our last minute, I'm going to ask one last question. 2638 02:25:26,400 --> 02:25:27,920 There are so many good questions in the chat, 2639 02:25:27,920 --> 02:25:31,010 I'm sorry, I won't be able to raise them all to the panel. 2640 02:25:31,010 --> 02:25:34,290 But, so I wanted to bring up- 2641 02:25:34,290 --> 02:25:37,740 so there's a trend to use population level data 2642 02:25:37,740 --> 02:25:40,620 to target or triage person level interventions. 2643 02:25:40,620 --> 02:25:42,340 I'm wondering if the panel can give their thoughts 2644 02:25:42,340 --> 02:25:45,440 on this trend and its likelihood of reinforcing inequities. 2645 02:25:52,290 --> 02:25:54,300 Sorry, it's a little bit of a tough one, 2646 02:25:54,300 --> 02:25:56,570 and I welcome any comments or thoughts 2647 02:25:56,570 --> 02:25:58,540 from the panel on this one. 2648 02:25:58,540 --> 02:25:59,820 Dr. Patricia Keenan: Very briefly, 2649 02:25:59,820 --> 02:26:01,890 we've talked with some health systems 2650 02:26:01,890 --> 02:26:04,140 about how they use social determinants 2651 02:26:04,140 --> 02:26:05,530 and social needs data. 2652 02:26:05,530 --> 02:26:09,900 And we have heard of instances of systems 2653 02:26:09,900 --> 02:26:12,820 using a combination of social needs and community levels' 2654 02:26:12,820 --> 02:26:16,620 social determinants data to kind of do predictive modeling 2655 02:26:16,620 --> 02:26:19,760 to try to identify patients who, you know, might be at risk 2656 02:26:19,760 --> 02:26:21,730 for an adverse outcome down the road, 2657 02:26:22,700 --> 02:26:25,790 and then intervene. And so that's an example 2658 02:26:25,790 --> 02:26:29,280 of a use of the SDOH community level, 2659 02:26:29,280 --> 02:26:30,880 as well as social needs data. 2660 02:26:33,380 --> 02:26:36,810 The likelihood of, you know, sort of reinforcing inequities, 2661 02:26:36,810 --> 02:26:41,020 I might see if other panelists have comments on that, 2662 02:26:41,750 --> 02:26:44,930 you know, I think there's, you know, 2663 02:26:44,930 --> 02:26:49,330 can be possibilities of using data, 2664 02:26:49,330 --> 02:26:52,280 you know, kind of in ways that are intended and unintended. 2665 02:26:52,810 --> 02:26:54,650 And this is certainly, you know, 2666 02:26:54,650 --> 02:26:56,840 I'm sure not immune to that risk. 2667 02:26:59,840 --> 02:27:01,110 Evelyn Gallego: Thank you, Patricia, 2668 02:27:01,110 --> 02:27:03,810 I would just add, you know, our work is just starting. 2669 02:27:03,810 --> 02:27:06,800 So I- you know, for our intent is the use, you know, 2670 02:27:06,800 --> 02:27:09,380 our mission is really around using this type of data. 2671 02:27:09,380 --> 02:27:12,130 So that's, we're excited about the pilots, 2672 02:27:12,130 --> 02:27:13,630 where we can learn from entities 2673 02:27:13,630 --> 02:27:15,840 that are gathering individual level data 2674 02:27:15,840 --> 02:27:17,070 and seeing how they use it. 2675 02:27:17,070 --> 02:27:20,220 But follow Patricia's point around 2676 02:27:20,220 --> 02:27:22,150 that we've, you know, the health systems 2677 02:27:22,150 --> 02:27:25,970 and the payers are using a combination of this data, 2678 02:27:25,970 --> 02:27:27,970 they're most excited about the standards, 2679 02:27:27,970 --> 02:27:29,620 because they have to do the mapping, 2680 02:27:29,620 --> 02:27:31,910 you know, it's not all the same level of data, 2681 02:27:31,910 --> 02:27:35,880 and they just want to be able to have structure and standards 2682 02:27:35,880 --> 02:27:38,090 on how the data is represented 2683 02:27:38,090 --> 02:27:41,240 across different settings of care. 2684 02:27:41,240 --> 02:27:42,760 So I think there's an opportunity, 2685 02:27:42,760 --> 02:27:45,290 but again, there is that risk, too, 2686 02:27:45,290 --> 02:27:48,470 is we don't want to use the data to further health disparities. 2687 02:27:49,740 --> 02:27:52,350 And we're really at the start, right? 2688 02:27:53,580 --> 02:27:55,990 I always say to everyone, the standards is like we created 2689 02:27:55,990 --> 02:27:58,870 the dictionary with definitions, 2690 02:27:58,870 --> 02:28:01,419 and now everyone has to write and tell their story, 2691 02:28:02,310 --> 02:28:04,570 and we need to see what comes back. 2692 02:28:07,370 --> 02:28:08,570 Dr. Jenna Norton: All right, thank you. 2693 02:28:08,570 --> 02:28:10,210 So we're a little bit over time, 2694 02:28:10,210 --> 02:28:12,300 but we've cut into people's lunch, 2695 02:28:12,300 --> 02:28:13,630 so I apologize for that. 2696 02:28:13,630 --> 02:28:19,520 So we will be starting lunch from 1:00 till 1:45. 2697 02:28:19,520 --> 02:28:23,640 So please be back at 1:45 for the continuation of the meeting. 2698 02:28:24,410 --> 02:28:26,460 Thank you to our panelists again, 2699 02:28:26,460 --> 02:28:29,430 for great presentations and thoughtful responses 2700 02:28:29,430 --> 02:28:30,930 to these questions. 2701 02:28:30,930 --> 02:28:33,940 And, you know, there are many questions 2702 02:28:33,940 --> 02:28:36,580 we didn't get to in the chat and I think we will do our best 2703 02:28:36,580 --> 02:28:39,760 to try to find other ways of addressing those as possible. 2704 02:28:40,280 --> 02:28:42,579 Thanks, everybody, and enjoy your lunch break. 2705 02:28:57,550 --> 02:28:59,150 Dr. Sharon Jackson: Good afternoon, everyone. 2706 02:29:01,700 --> 02:29:03,300 Can everyone see me? 2707 02:29:05,160 --> 02:29:06,440 Dr. Jenna Norton: Yes. 2708 02:29:06,440 --> 02:29:08,780 Dr. Sharon Jackson: Great. Good afternoon, everyone. 2709 02:29:08,780 --> 02:29:10,280 Thanks for returning after lunch. 2710 02:29:10,280 --> 02:29:12,470 Hopefully you had a good quick break. 2711 02:29:12,470 --> 02:29:16,940 I'd like to introduce myself. My name is Dr. Sharon Jackson. 2712 02:29:16,940 --> 02:29:18,900 I'm with the National Institute on Minority Health 2713 02:29:18,900 --> 02:29:20,140 and Health Disparities. 2714 02:29:20,140 --> 02:29:21,990 And we're very excited this afternoon 2715 02:29:21,990 --> 02:29:24,490 to start our case studies sessions. 2716 02:29:24,490 --> 02:29:26,390 We're going to start this afternoon 2717 02:29:26,390 --> 02:29:29,870 with our basic science and clinical science case studies. 2718 02:29:29,870 --> 02:29:33,120 And we have the privilege of having two great panelists: 2719 02:29:33,120 --> 02:29:37,120 Dr. James Collins from Northwestern University. 2720 02:29:37,120 --> 02:29:39,060 He is a professor of pediatrics 2721 02:29:40,040 --> 02:29:43,680 at Northwestern University Feinberg School of Medicine, 2722 02:29:43,680 --> 02:29:46,810 and a medical director of the neonatal intensive care unit, 2723 02:29:46,810 --> 02:29:51,480 and the associate director of the pediatric residency program 2724 02:29:52,020 --> 02:29:56,720 at Ann & Robert H. Lurie Children's Hospital of Chicago. 2725 02:29:57,330 --> 02:30:01,600 And our panelists after Dr. Collins 2726 02:30:01,600 --> 02:30:05,260 will speak on basic science, and that's Dr. Jenny Tung. 2727 02:30:05,260 --> 02:30:08,630 Dr. Tung is a professor of evolutionary anthropology 2728 02:30:08,630 --> 02:30:10,450 and biology at Duke University, 2729 02:30:10,990 --> 02:30:12,980 and the director of the Department 2730 02:30:12,980 --> 02:30:15,600 of primate behavior and evolution 2731 02:30:15,600 --> 02:30:19,940 at the Max Planck Institute for Evolutionary Anthropology. 2732 02:30:20,530 --> 02:30:22,000 She is also an affiliate 2733 02:30:22,000 --> 02:30:25,250 of the Duke Population Research Institute, 2734 02:30:25,250 --> 02:30:27,830 and the Duke Center for the Study of Aging 2735 02:30:27,830 --> 02:30:29,650 and Human Development. 2736 02:30:29,650 --> 02:30:34,080 So with those introductions, Dr. Collins, take it away. 2737 02:30:34,080 --> 02:30:35,680 And thank you. 2738 02:30:38,610 --> 02:30:40,030 Dr. James Collins: Here we go. 2739 02:30:40,030 --> 02:30:42,829 Good afternoon, everybody. I trust that you can hear me. 2740 02:30:43,470 --> 02:30:47,300 I appreciate the opportunity to share with you my perception 2741 02:30:47,300 --> 02:30:50,110 on a long-standing issue in this country, 2742 02:30:50,110 --> 02:30:52,980 namely the racial disparity and adverse birth outcome. 2743 02:30:53,610 --> 02:30:55,250 I'm going to try to set things up 2744 02:30:55,250 --> 02:30:58,510 by really putting the spotlight on structural racism, 2745 02:30:58,510 --> 02:31:00,960 and show you how the social determinant of health 2746 02:31:01,540 --> 02:31:04,980 is an important contributor to our adverse birth outcomes. 2747 02:31:06,410 --> 02:31:09,320 But first, I'd like to set the stage- 2748 02:31:09,320 --> 02:31:13,880 see here, advance the slide. 2749 02:31:20,850 --> 02:31:25,000 Looks like my slides are frozen. Got to love technology. 2750 02:31:25,000 --> 02:31:28,380 Let's try this here. Here we go. 2751 02:31:29,570 --> 02:31:32,470 Set the stage by just kind of letting you know 2752 02:31:32,470 --> 02:31:36,580 how we do overall with respect to infant mortality rates, 2753 02:31:36,580 --> 02:31:38,320 which are defined as the number of deaths 2754 02:31:38,320 --> 02:31:41,180 which occur in the first year of life. 2755 02:31:41,180 --> 02:31:43,730 A high rate signifies unmet health needs. 2756 02:31:44,500 --> 02:31:46,790 As a country, we ranked neither first, 2757 02:31:46,790 --> 02:31:49,200 fifth, 10th, 15th, or 20th. 2758 02:31:49,720 --> 02:31:52,569 Actually, we're 27th in the world infant mortality rates, 2759 02:31:53,190 --> 02:31:56,020 and our position has deteriorated since 1960. 2760 02:31:57,060 --> 02:31:58,980 This slide shows infant mortality rates 2761 02:31:58,980 --> 02:32:00,500 by race in this country. 2762 02:32:00,500 --> 02:32:03,310 And we see that African American rates are very high, 2763 02:32:03,310 --> 02:32:06,110 essentially twice that of non-LatinX whites. 2764 02:32:07,670 --> 02:32:10,340 This is largely because birth weight and gestational age 2765 02:32:10,340 --> 02:32:12,900 are the primary determinants of birth outcome, 2766 02:32:12,900 --> 02:32:14,630 and African Americans have a 50 2767 02:32:14,630 --> 02:32:19,240 to 60% higher rate of being born prematurely at 37 weeks. 2768 02:32:20,960 --> 02:32:23,270 This slide shows this disparity exists 2769 02:32:23,270 --> 02:32:25,680 among women who have 12 or more years education. 2770 02:32:26,340 --> 02:32:29,230 The X axis is maternal education in years, 2771 02:32:29,230 --> 02:32:31,570 the Y axis is preterm birth rates. 2772 02:32:31,570 --> 02:32:34,520 Whites are in red, African Americans in white. 2773 02:32:34,520 --> 02:32:36,110 And we see for both races, 2774 02:32:36,110 --> 02:32:39,250 as education or something closely related to it improves, 2775 02:32:40,420 --> 02:32:42,160 preterm birth rates decline. 2776 02:32:42,160 --> 02:32:44,230 But even among those who are college educated, 2777 02:32:44,230 --> 02:32:46,570 we see that the disparity persists. 2778 02:32:48,610 --> 02:32:51,500 Transgenerational factors are defined as those factors, 2779 02:32:51,500 --> 02:32:54,510 conditions, environments expressed by one generation 2780 02:32:54,510 --> 02:32:57,510 that relate to the pregnancy outcome of the next generation. 2781 02:32:59,190 --> 02:33:01,180 We see that a greater percentage of African American 2782 02:33:01,180 --> 02:33:04,600 compared to white women were themselves born preterm. 2783 02:33:04,600 --> 02:33:08,410 We see African Americans in light blue, whites in red, 2784 02:33:08,410 --> 02:33:11,680 have a higher rate of themselves being born less than 30 weeks, 2785 02:33:11,680 --> 02:33:13,260 30 to 33 weeks, 2786 02:33:13,260 --> 02:33:17,560 and late preterm at 34 to 36 weeks. 2787 02:33:18,740 --> 02:33:20,330 This is important because we know 2788 02:33:20,330 --> 02:33:22,210 that maternal gestational age 2789 02:33:22,210 --> 02:33:24,690 is also associated with infant gestational age. 2790 02:33:25,220 --> 02:33:28,060 The X axis is maternal gestational age, 2791 02:33:28,060 --> 02:33:35,620 as it increases, the rate of preterm birth decreases. 2792 02:33:35,620 --> 02:33:37,740 But we see that the disparity persists 2793 02:33:37,740 --> 02:33:40,010 even among those women who born at term. 2794 02:33:42,200 --> 02:33:44,940 Slavery is the ultimate transgenerational process 2795 02:33:44,940 --> 02:33:46,560 for African Americans. 2796 02:33:46,560 --> 02:33:49,030 We know that approximately 400 years African Americans 2797 02:33:49,030 --> 02:33:50,420 have been in this country, 2798 02:33:50,420 --> 02:33:53,180 approximately two thirds was spent in slavery. 2799 02:33:53,780 --> 02:33:55,579 This created a slave health deficit. 2800 02:33:56,140 --> 02:33:57,620 The next 100 years, 2801 02:33:57,620 --> 02:34:01,400 essentially no citizenship rights were received. 2802 02:34:01,400 --> 02:34:03,999 Therefore, the slave health deficit was uncorrected. 2803 02:34:04,740 --> 02:34:06,590 It's really only been in my lifetime 2804 02:34:06,590 --> 02:34:09,270 that we received citizenship rights, 2805 02:34:09,270 --> 02:34:11,130 but clearly, it's been a struggle. 2806 02:34:12,980 --> 02:34:14,270 One of the most interesting things 2807 02:34:14,270 --> 02:34:16,480 is that women who themselves were born in Africa 2808 02:34:16,480 --> 02:34:19,240 or the Caribbean, who come to United States, 2809 02:34:19,240 --> 02:34:21,030 actually have birth outcomes very similar 2810 02:34:21,030 --> 02:34:22,630 to the general white population. 2811 02:34:23,480 --> 02:34:26,200 However, their daughters who grew up in this country, 2812 02:34:26,200 --> 02:34:29,550 one generation later, their birth outcome plummets 2813 02:34:29,550 --> 02:34:32,680 and actually approximates that of US-born black women. 2814 02:34:34,680 --> 02:34:39,060 So we conclude that race is not a biological construct 2815 02:34:39,060 --> 02:34:40,690 that reflects innate differences, 2816 02:34:40,690 --> 02:34:42,470 but a social construct 2817 02:34:42,470 --> 02:34:45,060 that precisely captures the impacts of racism. 2818 02:34:47,030 --> 02:34:49,670 This social construct also exists 2819 02:34:49,670 --> 02:34:51,660 among the LatinX population. 2820 02:34:51,660 --> 02:34:54,810 This slide shows infant mortality rate of LatinX infants 2821 02:34:54,810 --> 02:34:57,260 born in this country over a two-year period. 2822 02:34:57,260 --> 02:35:01,070 And we see for those women who define as black, 2823 02:35:01,070 --> 02:35:03,850 we see that they have a higher infant mortality rate 2824 02:35:03,850 --> 02:35:07,490 than those who are white, but only among the US-born, 2825 02:35:07,490 --> 02:35:09,620 only among those who have a lifelong residency 2826 02:35:09,620 --> 02:35:12,850 in the United States. Among foreign-born black women, 2827 02:35:12,850 --> 02:35:15,280 we do not see a disparity based on race. 2828 02:35:17,320 --> 02:35:19,080 So thus understanding the US-born 2829 02:35:19,080 --> 02:35:20,900 black women's pregnancy disadvantage 2830 02:35:20,900 --> 02:35:23,120 requires an analysis of the effects of race 2831 02:35:23,120 --> 02:35:24,970 as a social construct, 2832 02:35:24,970 --> 02:35:27,780 thus addressing racism in its various forms. 2833 02:35:29,720 --> 02:35:32,800 Hatred has affected the heart of America, 2834 02:35:32,800 --> 02:35:34,720 this heart is spreading into our intellect, 2835 02:35:34,720 --> 02:35:38,010 our economics, our politics, the result of this disease: 2836 02:35:38,010 --> 02:35:40,600 the death of George Floyd in Minnesota, 2837 02:35:40,600 --> 02:35:43,420 the shooting death of an unarmed jogger in Georgia, 2838 02:35:43,420 --> 02:35:46,170 the false accusation of a birdwatcher in New York City. 2839 02:35:46,840 --> 02:35:50,060 This was written by a columnist in Atlantic News 2840 02:35:50,060 --> 02:35:55,430 about two years ago. We've thought to ask a question, 2841 02:35:56,610 --> 02:35:59,140 do states with more killings of unarmed black people 2842 02:35:59,140 --> 02:36:01,670 have larger racial disparities and preterm birth? 2843 02:36:04,310 --> 02:36:07,050 We identified police killings from unarmed black people 2844 02:36:07,050 --> 02:36:12,350 from the 2013 to 2018 Mapping Police Violence database. 2845 02:36:12,350 --> 02:36:16,680 It included over 308 killings of unarmed black people. 2846 02:36:17,630 --> 02:36:20,370 The lowest quartile was states that had no killings, 2847 02:36:20,370 --> 02:36:23,469 remaining states were divided into quartiles one through four. 2848 02:36:23,990 --> 02:36:25,950 We extracted data from the CDC 2849 02:36:25,950 --> 02:36:29,840 with respect to birth records from non-LatinX white, 2850 02:36:29,840 --> 02:36:31,780 non-LatinX African American women. 2851 02:36:33,400 --> 02:36:35,900 This slide requires a little bit of concentration. 2852 02:36:36,670 --> 02:36:40,260 For this dataset, which included 2018 births, 2853 02:36:40,260 --> 02:36:44,670 the median preterm birth weight disparity was 1.5. 2854 02:36:45,630 --> 02:36:48,380 We see that states in the zero quartile, 2855 02:36:48,380 --> 02:36:50,700 those in light green or green, 2856 02:36:50,700 --> 02:36:54,940 tend to be to the left of this, or less than 1.5. 2857 02:36:54,940 --> 02:36:57,410 For those in the first quartile, the orange's group, 2858 02:36:57,410 --> 02:36:59,510 were also less than the median. 2859 02:37:01,210 --> 02:37:04,840 If we compare those groups who are in quartiles two 2860 02:37:04,840 --> 02:37:09,280 through four, this group here, these rates actually do exceed 2861 02:37:09,280 --> 02:37:11,280 these rates here statistically speaking. 2862 02:37:12,620 --> 02:37:14,960 From a statistical point of view, 2863 02:37:14,960 --> 02:37:19,900 it shows that states with more killings of unarmed black people 2864 02:37:19,900 --> 02:37:23,140 do indeed have larger black-white disparities 2865 02:37:23,140 --> 02:37:25,090 in preterm birth. 2866 02:37:25,090 --> 02:37:27,200 We speculated that the discriminatory behavior 2867 02:37:27,200 --> 02:37:28,430 of police officers 2868 02:37:28,430 --> 02:37:30,680 reflects structural racism and state culture, 2869 02:37:31,260 --> 02:37:33,830 which serve as upstream causes of health inequities 2870 02:37:33,830 --> 02:37:36,830 due to differential access to health promoting environments. 2871 02:37:38,400 --> 02:37:41,780 Redlining. Redlining is the practice of denying 2872 02:37:41,780 --> 02:37:44,250 or charging more for services such as insurance, 2873 02:37:44,250 --> 02:37:47,150 banking, access to healthcare or employment to residents 2874 02:37:47,150 --> 02:37:48,960 in often racially-determined areas. 2875 02:37:49,540 --> 02:37:52,930 The term refers to the practice of actually taking a red line 2876 02:37:52,930 --> 02:37:54,840 and making it by a map, 2877 02:37:54,840 --> 02:37:57,340 to just delineate where banks would not invest. 2878 02:37:59,930 --> 02:38:02,370 This is not something that happened back in the day, 2879 02:38:02,370 --> 02:38:04,690 we looked at data from Chicago, 2880 02:38:04,690 --> 02:38:07,340 looking at health, mortgage discrimination rates 2881 02:38:07,340 --> 02:38:10,460 and found that a redlining index here on the X axis 2882 02:38:10,460 --> 02:38:12,240 was associated with preterm birth rates 2883 02:38:12,240 --> 02:38:13,840 among African Americans. 2884 02:38:17,560 --> 02:38:21,990 Spatial social polarization, over the last several decades, 2885 02:38:21,990 --> 02:38:24,060 American neighborhoods have undergone a phenomenon 2886 02:38:24,060 --> 02:38:26,260 called spatial social polarization. 2887 02:38:27,080 --> 02:38:29,570 It is defined as the segregation within a society 2888 02:38:29,570 --> 02:38:32,800 that emerges from income inequality displacements, 2889 02:38:32,800 --> 02:38:35,780 resulting in differential groups from high to low. 2890 02:38:36,500 --> 02:38:39,000 Neighborhoods of extreme wealth and extreme poverty 2891 02:38:39,000 --> 02:38:40,460 have grown exponentially, 2892 02:38:40,460 --> 02:38:43,360 kind of squeezing the size of middle-income neighborhoods. 2893 02:38:43,950 --> 02:38:46,280 Not surprisingly, this change has not been distributed 2894 02:38:46,280 --> 02:38:47,880 evenly by race. 2895 02:38:48,550 --> 02:38:51,690 This slide here kind of explains this for Chicago, 2896 02:38:52,210 --> 02:38:55,580 we see in 1970, very high-income neighborhoods 2897 02:38:55,580 --> 02:38:57,929 were pretty neutral right around the lakeshore. 2898 02:38:58,670 --> 02:39:00,020 Very low-income neighborhoods 2899 02:39:00,020 --> 02:39:03,540 were concentrated South Side and near west side. 2900 02:39:04,130 --> 02:39:06,490 The vast majority of Chicago was middle-income. 2901 02:39:07,150 --> 02:39:10,010 However, we see over the next preceding 50 years 2902 02:39:10,010 --> 02:39:13,880 or so that the red has grown exponentially as we said, 2903 02:39:13,880 --> 02:39:16,770 as has the blue and that middle-income group 2904 02:39:16,770 --> 02:39:19,600 has been squeezed. What does this mean? 2905 02:39:20,490 --> 02:39:22,520 Is that the lifetime neighborhood experiences 2906 02:39:22,520 --> 02:39:24,990 in Chicago differ based on race. 2907 02:39:25,880 --> 02:39:29,790 We see that most, 84% of whites have a lifelong residence 2908 02:39:29,790 --> 02:39:32,010 in high-income neighborhoods. 2909 02:39:33,420 --> 02:39:35,170 Only about 2% of African Americans 2910 02:39:35,170 --> 02:39:36,540 live in such favorable neighborhoods 2911 02:39:36,540 --> 02:39:38,400 throughout their life course. 2912 02:39:38,400 --> 02:39:41,550 In stark contrast, nearly 80% of African Americans 2913 02:39:41,550 --> 02:39:44,380 have lifelong residence in low-income neighborhoods. 2914 02:39:44,380 --> 02:39:47,990 Only 2% of whites live in those unfavorable neighborhoods. 2915 02:39:49,180 --> 02:39:51,420 Not surprising, lifelong residents 2916 02:39:51,420 --> 02:39:53,090 of low-income neighborhoods in white 2917 02:39:53,090 --> 02:39:54,600 is associated with the increased risk 2918 02:39:54,600 --> 02:39:57,330 of low-birth-weight compared to lifelong residents 2919 02:39:57,330 --> 02:39:58,770 in high-income neighborhoods. 2920 02:39:58,770 --> 02:40:00,730 And this association tends to be the same 2921 02:40:00,730 --> 02:40:02,660 among African Americans and whites, 2922 02:40:02,660 --> 02:40:04,830 maybe even a little stronger among whites. 2923 02:40:06,790 --> 02:40:08,600 The population attributable risk 2924 02:40:08,600 --> 02:40:11,140 takes into account the relative risk, 2925 02:40:11,140 --> 02:40:13,290 but also takes into account the prevalence. 2926 02:40:13,810 --> 02:40:16,150 Because the prevalence of low-income neighborhoods 2927 02:40:16,150 --> 02:40:18,170 is so much higher for African Americans, 2928 02:40:18,170 --> 02:40:20,320 the population attributable risk of neighborhood poverty 2929 02:40:20,320 --> 02:40:22,560 is so much greater for African Americans. 2930 02:40:22,560 --> 02:40:24,410 If we were to get rid of poverty, 2931 02:40:24,410 --> 02:40:26,120 we would get rid of less than 5% 2932 02:40:26,120 --> 02:40:28,110 of low-birth-weight white infants, 2933 02:40:28,110 --> 02:40:34,010 less than 2% of preterm infants and less than 3% of SGA infants. 2934 02:40:34,550 --> 02:40:37,190 In contrast, if we got rid of poverty for African Americans, 2935 02:40:37,190 --> 02:40:40,380 we would get rid of 25% of low-birth-weight infants, 2936 02:40:40,380 --> 02:40:42,740 about 12% of preterm infants, 2937 02:40:42,740 --> 02:40:45,570 about 26% of those with a birth weight 2938 02:40:45,570 --> 02:40:48,020 less than 10 percentile for gestational age. 2939 02:40:50,280 --> 02:40:52,490 We ask the question, to what extent 2940 02:40:52,490 --> 02:40:55,520 is African American women's upward economic mobility 2941 02:40:55,520 --> 02:40:58,450 across the life-course associated with birth outcome? 2942 02:40:59,680 --> 02:41:02,030 We found that upward economic mobility 2943 02:41:02,030 --> 02:41:04,630 from early life residents in poor neighborhoods 2944 02:41:04,630 --> 02:41:07,120 is associated with a lower preterm birth rate 2945 02:41:07,120 --> 02:41:09,320 for African American in Cook County. 2946 02:41:10,570 --> 02:41:12,860 This Y axis is preterm birth weight. 2947 02:41:12,860 --> 02:41:15,250 We see for African Americans lifelong residents 2948 02:41:15,250 --> 02:41:16,850 in low-income neighborhoods, 2949 02:41:16,850 --> 02:41:20,030 that's less than the- in the first quartile, 2950 02:41:20,030 --> 02:41:22,400 meaningful income less than 20 grand a year, 2951 02:41:23,010 --> 02:41:25,059 that preterm birth rate is very elevated. 2952 02:41:25,580 --> 02:41:27,260 For those who went from the first quartile 2953 02:41:27,260 --> 02:41:28,890 to the second quartile, 2954 02:41:28,890 --> 02:41:30,890 preterm birth rates came down a little more. 2955 02:41:30,890 --> 02:41:32,730 For those who went from the first to the third, 2956 02:41:32,730 --> 02:41:34,290 things came down even more. 2957 02:41:34,290 --> 02:41:36,270 For those who had some significant upward mobility 2958 02:41:36,270 --> 02:41:39,380 from the first quartile to the fourth quartile, 2959 02:41:39,380 --> 02:41:42,430 this is meaningful income in excess of six figures, 2960 02:41:42,430 --> 02:41:44,980 you see that preterm birth rates were quite lower. 2961 02:41:46,410 --> 02:41:50,700 However, we found that moms are themselves low-birth-weight. 2962 02:41:51,240 --> 02:41:53,390 In this case, low-birth-weight is in white, 2963 02:41:53,910 --> 02:41:55,210 those who are not low-birth-weight 2964 02:41:55,210 --> 02:41:59,140 were in red, maternal economic mobility is on the X axis. 2965 02:41:59,140 --> 02:42:01,080 We see that in the red group, 2966 02:42:01,080 --> 02:42:04,550 preterm birth rates decline as upward mobility occurs. 2967 02:42:05,130 --> 02:42:08,970 For those who moms who were born poor and born small, 2968 02:42:08,970 --> 02:42:12,400 these birth rates hovered in the elevated category, 2969 02:42:12,400 --> 02:42:14,720 and that did not decline with upward mobility. 2970 02:42:16,510 --> 02:42:19,230 We did some fancy statistical modeling, 2971 02:42:19,230 --> 02:42:22,220 and we found that for moms who are aged 20 to 30 2972 02:42:22,220 --> 02:42:26,590 and not low-birth-weight, low, modest and high upward mobility 2973 02:42:26,590 --> 02:42:29,110 is associated with a decrease odds 2974 02:42:29,110 --> 02:42:30,930 of delivering a preterm infant. 2975 02:42:31,880 --> 02:42:34,640 However, for those moms who were born small themselves, 2976 02:42:35,220 --> 02:42:37,520 these odds ratios approximate at one, 2977 02:42:37,520 --> 02:42:38,760 and these [INAUDIBLE] 2978 02:42:38,760 --> 02:42:41,870 also included unity, showing that upward mobility 2979 02:42:41,870 --> 02:42:44,870 was not associated with decreased risk of preterm birth. 2980 02:42:46,370 --> 02:42:48,150 This slide we looked at another outcome measure, 2981 02:42:48,150 --> 02:42:50,670 this time small for gestational age, 2982 02:42:50,670 --> 02:42:52,710 birth weight less than a 10th percentile, 2983 02:42:52,710 --> 02:42:54,560 and we see a very similar trend. 2984 02:42:54,560 --> 02:42:57,180 Rates are very high for those of no upward mobility, 2985 02:42:57,180 --> 02:42:59,279 and things come down with upward mobility. 2986 02:43:00,290 --> 02:43:02,690 We do our same fancy modeling again. 2987 02:43:02,690 --> 02:43:07,030 And for those who had some low, modest or high upward mobility, 2988 02:43:07,030 --> 02:43:08,780 these odds ratios were significant. 2989 02:43:09,560 --> 02:43:12,030 But for those who were themselves born small, 2990 02:43:12,030 --> 02:43:13,980 these odds ratios were not significant. 2991 02:43:17,160 --> 02:43:20,590 We hypothesize that this reflects fetal programming. 2992 02:43:21,870 --> 02:43:23,950 Fetal programming acts at the level of DNA 2993 02:43:23,950 --> 02:43:25,650 in a phenomena called epigenetics. 2994 02:43:26,330 --> 02:43:27,940 Researchers have used low birth weight 2995 02:43:27,940 --> 02:43:30,700 as the major marker of aberrant fetal programming. 2996 02:43:31,490 --> 02:43:34,460 We suspect that aspects of mother's social environment 2997 02:43:34,460 --> 02:43:36,900 subjects her to influences during fetal life 2998 02:43:36,900 --> 02:43:39,590 that results in her slowed growth in utero, 2999 02:43:39,590 --> 02:43:41,950 but also programs her to have a preterm 3000 02:43:41,950 --> 02:43:43,800 or SGA infant as an adult. 3001 02:43:45,090 --> 02:43:46,890 Clearly, more research needs to be done 3002 02:43:46,890 --> 02:43:48,590 to focus on this hypothesis. 3003 02:43:50,040 --> 02:43:52,610 Still looking upstream, structural racism, 3004 02:43:53,190 --> 02:43:55,270 the socio-economic and cultural landscape 3005 02:43:55,270 --> 02:43:56,940 of African American fatherhood 3006 02:43:56,940 --> 02:43:59,500 suggests that the role of father involvement 3007 02:44:00,040 --> 02:44:03,270 and socioeconomic position may be particularly salient 3008 02:44:03,270 --> 02:44:05,470 to the racially disparity and birth outcome. 3009 02:44:06,340 --> 02:44:08,900 We know the college graduation rates for African American men 3010 02:44:08,900 --> 02:44:11,500 are very low compared to white men. 3011 02:44:11,500 --> 02:44:13,820 We know the unemployment rates are sky high, 3012 02:44:13,820 --> 02:44:16,869 we know that incarceration rates are also extremely elevated. 3013 02:44:18,380 --> 02:44:19,590 This is another side that requires 3014 02:44:19,590 --> 02:44:21,980 a little bit of concentration and I apologize. 3015 02:44:22,630 --> 02:44:24,790 But if we look here on the X axis, 3016 02:44:24,790 --> 02:44:27,190 this looks at the disparity with a difference 3017 02:44:27,190 --> 02:44:28,440 in preterm birth rates 3018 02:44:28,440 --> 02:44:31,370 between US-born in foreign-born women, 3019 02:44:31,370 --> 02:44:33,720 and we see that that disparity was 3.3. 3020 02:44:34,320 --> 02:44:38,200 The gray represents what is unexplained. 3021 02:44:38,200 --> 02:44:40,850 This is in one year worth of birth rate records. 3022 02:44:40,850 --> 02:44:43,700 Clearly structural racism isn't measured on the birth record. 3023 02:44:43,700 --> 02:44:45,090 So we can't really- 3024 02:44:45,090 --> 02:44:47,440 it's not surprising that this is unexplained. 3025 02:44:47,440 --> 02:44:49,450 There are other variables we couldn't measure. 3026 02:44:49,450 --> 02:44:52,500 We see that's light blue, prenatal care 3027 02:44:52,500 --> 02:44:53,810 and to pregnancy integral, 3028 02:44:53,810 --> 02:44:55,580 that light blue was palpable group. 3029 02:44:56,530 --> 02:44:59,160 The red group is the maternal education. 3030 02:44:59,160 --> 02:45:01,880 That's the explained, that maternal education 3031 02:45:01,880 --> 02:45:03,740 explained to disparity. 3032 02:45:03,740 --> 02:45:06,990 This darker blue is paternal involvement, 3033 02:45:06,990 --> 02:45:09,320 which is very weakly defined as being listed 3034 02:45:09,320 --> 02:45:10,950 on the birth certificate. 3035 02:45:10,950 --> 02:45:12,470 It seems to explain a greater part 3036 02:45:12,470 --> 02:45:14,410 of the disparity than maternal education. 3037 02:45:14,410 --> 02:45:17,560 If we look at the difference in preterm birth 3038 02:45:17,560 --> 02:45:20,110 between US-born and foreign-born white women 3039 02:45:20,110 --> 02:45:23,370 with preterm delivery, we see a very similar trend. 3040 02:45:24,250 --> 02:45:27,400 Namely, we know some groups are explained, 3041 02:45:27,400 --> 02:45:29,420 something is explained by prenatal care, 3042 02:45:29,420 --> 02:45:31,230 cigarette smoke, et cetera. 3043 02:45:31,230 --> 02:45:34,340 We know that maternal education explains the palpable group, 3044 02:45:34,340 --> 02:45:38,320 but what surprised us again, paternal involved involvement 3045 02:45:38,320 --> 02:45:39,640 seems to be explaining 3046 02:45:39,640 --> 02:45:42,040 such a significant proportion of this disparity. 3047 02:45:44,670 --> 02:45:46,830 This slide looks at the excess preterm birth rate 3048 02:45:46,830 --> 02:45:49,520 among US-born compared to foreign-born women, 3049 02:45:49,520 --> 02:45:51,670 those who have the role of dad's education. 3050 02:45:52,530 --> 02:45:55,480 The X axis is Dad's education this time, 3051 02:45:55,480 --> 02:45:57,830 Y axis continues to be preterm birth. 3052 02:45:57,830 --> 02:46:01,720 We see among US-born black women in blue 3053 02:46:01,720 --> 02:46:03,880 and foreign-born black women in gray, 3054 02:46:03,880 --> 02:46:06,380 we see that both groups' rates decline 3055 02:46:06,380 --> 02:46:08,560 as dad's education improves. 3056 02:46:10,890 --> 02:46:12,670 Using Oaxaca analysis, 3057 02:46:12,670 --> 02:46:15,650 which takes into account a contribution of a factor 3058 02:46:15,650 --> 02:46:19,610 with respect to other factors, we found that dad's education 3059 02:46:19,610 --> 02:46:21,960 as reported by mom on the birth certificate 3060 02:46:21,960 --> 02:46:24,100 explained a greater proportion of the disparity 3061 02:46:24,100 --> 02:46:27,970 between US-born and foreign-born black women in mom's education. 3062 02:46:29,590 --> 02:46:30,980 One of the weaknesses of this study 3063 02:46:30,980 --> 02:46:33,430 is that dad's education is reported by mom. 3064 02:46:35,230 --> 02:46:38,490 So we did a study looking at something more objective, 3065 02:46:38,490 --> 02:46:40,450 namely dad's neighborhood income 3066 02:46:40,450 --> 02:46:43,100 when he was born and dad's neighborhood income 3067 02:46:43,100 --> 02:46:47,590 when he delivered his child, when the baby was delivered. 3068 02:46:48,360 --> 02:46:49,760 And we see that for dads 3069 02:46:49,760 --> 02:46:52,620 that have lifelong low socioeconomic position here, 3070 02:46:54,400 --> 02:46:56,760 first year mortality was very elevated. 3071 02:46:56,760 --> 02:46:58,500 Probably as dad's socio-economic position 3072 02:46:58,500 --> 02:47:03,080 improves, rates decline, since the dads who were- 3073 02:47:03,080 --> 02:47:05,440 infants who are born to dads with a lifetime high 3074 02:47:05,440 --> 02:47:08,860 had a low infant mortality rate of 4.9 per 1,000. 3075 02:47:10,830 --> 02:47:12,800 This slide kind of summarizes 3076 02:47:12,800 --> 02:47:14,880 looking at early preterm birth rates 3077 02:47:14,880 --> 02:47:18,040 by a paternal acknowledgement and socioeconomic position. 3078 02:47:18,850 --> 02:47:21,780 And we see among infants born to African American moms, 3079 02:47:22,320 --> 02:47:24,170 those who had not acknowledged fathers 3080 02:47:24,170 --> 02:47:27,690 had a high rate of really preterm birth. 3081 02:47:28,570 --> 02:47:30,470 For those who had acknowledged fathers, 3082 02:47:30,470 --> 02:47:32,870 for those fathers of low socioeconomic position, 3083 02:47:33,440 --> 02:47:37,740 things came down a little bit more, 4.3 per 1,000%. 3084 02:47:39,370 --> 02:47:41,570 But for those who had a lifelong- 3085 02:47:41,570 --> 02:47:44,290 who were born to acknowledge dads for lifetime high, 3086 02:47:44,860 --> 02:47:46,970 that rate was down to 3.29. 3087 02:47:48,190 --> 02:47:52,210 Interesting, if you look at the ends for the city of Chicago, 3088 02:47:52,210 --> 02:47:54,250 which is the vast majority of Cook County, 3089 02:47:54,250 --> 02:47:56,180 we see that very small percentage 3090 02:47:56,180 --> 02:47:59,080 actually had acknowledged dads of life long high. 3091 02:48:00,170 --> 02:48:05,080 A same trend occurred among non-LatinX white mothers, 3092 02:48:05,080 --> 02:48:07,570 namely, for those who are not acknowledged, 3093 02:48:07,570 --> 02:48:09,690 early preterm birth rates were really elevated, 3094 02:48:09,690 --> 02:48:11,640 and then things marched their way down. 3095 02:48:12,900 --> 02:48:16,330 But also notice that a vast majority of these women 3096 02:48:17,390 --> 02:48:20,970 actually had dads who had high acknowledged, 3097 02:48:20,970 --> 02:48:23,670 were acknowledged and had high socioeconomic position. 3098 02:48:24,180 --> 02:48:28,080 Again, this suggests a role for dads that's independent of moms, 3099 02:48:28,080 --> 02:48:30,790 but clearly, this is an area of further research, 3100 02:48:30,790 --> 02:48:32,689 which would be important to delineate. 3101 02:48:34,150 --> 02:48:37,030 Another form of racism is personally-mediated, 3102 02:48:37,030 --> 02:48:39,430 a little bit easier to measure in some respect. 3103 02:48:41,550 --> 02:48:44,140 And we performed a study where we actually interviewed moms 3104 02:48:44,140 --> 02:48:46,330 who delivered preterm infants, 3105 02:48:46,330 --> 02:48:48,300 and we interviewed another group of moms 3106 02:48:48,300 --> 02:48:50,130 who delivered term infants, 3107 02:48:50,130 --> 02:48:52,160 these women were African American. 3108 02:48:52,160 --> 02:48:54,640 And we found that a greater percentage of those 3109 02:48:54,640 --> 02:48:56,350 who delivered preterm infants 3110 02:48:56,350 --> 02:48:59,580 were exposure to racism in three or more domains: 3111 02:48:59,580 --> 02:49:03,890 at work, getting work, in the social, in the real world, 3112 02:49:04,980 --> 02:49:06,770 compared to African American women 3113 02:49:06,770 --> 02:49:08,370 who delivered term infants. 3114 02:49:08,960 --> 02:49:12,270 This study was relatively small by EPI standards, 3115 02:49:12,270 --> 02:49:15,020 and it was retrospective. Since this study, 3116 02:49:15,020 --> 02:49:16,880 there have probably been over two dozen studies 3117 02:49:16,880 --> 02:49:19,660 which have expanded this and confirmed these findings. 3118 02:49:20,760 --> 02:49:22,140 The strongest is actually I believe, 3119 02:49:22,140 --> 02:49:25,080 by Paula Braveman et al and her group in California, 3120 02:49:25,080 --> 02:49:27,220 where they actually administered surveys 3121 02:49:27,220 --> 02:49:29,160 to over 2,000 African American women, 3122 02:49:29,670 --> 02:49:32,950 over 8,000 non-LatinX white women 3123 02:49:32,950 --> 02:49:34,330 who just have singleton term births 3124 02:49:34,330 --> 02:49:39,400 between 2011 and 2014. And in their study sample, 3125 02:49:39,400 --> 02:49:41,710 approximately 37% of African Americans 3126 02:49:41,710 --> 02:49:43,000 and 6% of white women 3127 02:49:43,000 --> 02:49:45,850 reported chronic worry about racial discrimination. 3128 02:49:47,090 --> 02:49:48,900 The investigators found that chronic worry 3129 02:49:48,900 --> 02:49:50,130 about racial discrimination 3130 02:49:50,130 --> 02:49:52,020 was associated with preterm birth 3131 02:49:52,020 --> 02:49:55,290 among African Americans, with the relative risk of 2.0. 3132 02:49:56,950 --> 02:49:59,930 Very interesting, the racial disparity in preterm birth 3133 02:49:59,930 --> 02:50:03,250 was attenuated and actually it became non-significant, 3134 02:50:03,250 --> 02:50:05,410 when adjusted for chronic worry, 3135 02:50:05,410 --> 02:50:09,700 relative risk went from 1.6 to 1.3, again, 3136 02:50:09,700 --> 02:50:12,940 suggesting that chronic worry for racial discrimination 3137 02:50:12,940 --> 02:50:15,840 is an important contributor to the racial disparity 3138 02:50:15,840 --> 02:50:17,440 and adverse birth outcome. 3139 02:50:19,290 --> 02:50:20,880 So I've given you a lot of information 3140 02:50:20,880 --> 02:50:22,480 in a short period of time. 3141 02:50:23,280 --> 02:50:26,210 And I want us to put on a public health path to say, 3142 02:50:26,210 --> 02:50:29,720 well, what challenges do we face in eliminating disparities? 3143 02:50:31,500 --> 02:50:34,600 One, I think, is a challenge- is that historical insults 3144 02:50:34,600 --> 02:50:37,060 contribute to current disparities. 3145 02:50:37,060 --> 02:50:40,620 Until the effects of past historical ills are undone, 3146 02:50:40,620 --> 02:50:42,490 disparities will not be eliminated. 3147 02:50:44,810 --> 02:50:46,690 Two, social factors 3148 02:50:46,690 --> 02:50:48,680 are the largest contributor to disparities, 3149 02:50:48,680 --> 02:50:52,530 but often ignored, medical, molecular and biological factors 3150 02:50:52,530 --> 02:50:54,700 get the most focus and funding. 3151 02:50:56,490 --> 02:50:58,850 Two, we do not consistently distinguish 3152 02:50:58,850 --> 02:51:02,170 between healthcare disparity and health disparity. 3153 02:51:02,170 --> 02:51:05,080 Differences in medical care is only one of many contributors 3154 02:51:05,080 --> 02:51:07,030 to the overall health disparities. 3155 02:51:09,020 --> 02:51:11,800 So if we're going to improve pregnancy and birth outcomes, 3156 02:51:11,800 --> 02:51:14,940 I think we have to go big, we have to go ecological, 3157 02:51:14,940 --> 02:51:16,170 we have to begin to address 3158 02:51:16,170 --> 02:51:18,410 the social and economic inequalities 3159 02:51:18,410 --> 02:51:21,180 that are the root causes of health disparities. 3160 02:51:24,080 --> 02:51:27,190 Our task should include putting structural racism 3161 02:51:27,190 --> 02:51:31,010 on the agenda, namely, naming it as a force determining 3162 02:51:31,010 --> 02:51:35,130 the distribution of other social determinants of health, 3163 02:51:35,880 --> 02:51:38,930 but also to routinely monitor for differential exposures, 3164 02:51:38,930 --> 02:51:41,400 opportunities and outcomes by race. 3165 02:51:43,590 --> 02:51:45,680 Thank you very much for your attention. 3166 02:51:45,680 --> 02:51:48,020 I'd like to hand the baton back. 3167 02:51:48,020 --> 02:51:49,550 And then hopefully, we'll have some time 3168 02:51:49,550 --> 02:51:52,810 possibly for some questions before the session ends. 3169 02:51:53,910 --> 02:51:56,380 Dr. Sharon Jackson: Absolutely. Thank you so much, Dr. Collins. 3170 02:51:56,380 --> 02:52:00,210 That was a fantastic talk. Quite thought-provoking. 3171 02:52:00,780 --> 02:52:04,540 And thank you for also making the segue to Dr. Tung 3172 02:52:04,540 --> 02:52:07,440 and we will have time at the end for a few questions 3173 02:52:07,440 --> 02:52:10,080 as well as in the breakout session later on. 3174 02:52:10,080 --> 02:52:12,080 So with that, Dr. Tung, if you're ready, 3175 02:52:13,280 --> 02:52:15,790 we look forward to your presentation. 3176 02:52:18,270 --> 02:52:20,890 Dr. Jenny Tung: Okay. Can you guys hear me all right? 3177 02:52:22,560 --> 02:52:23,860 Dr. Sharon Jackson: Yes. 3178 02:52:23,860 --> 02:52:25,460 Dr. Jenny Tung: Good. Okay. 3179 02:52:26,030 --> 02:52:29,230 Thank you very much to all of you for involving me 3180 02:52:29,230 --> 02:52:31,029 in this really fascinating workshop. 3181 02:52:32,410 --> 02:52:34,300 I'm going to switch gears a little bit 3182 02:52:34,300 --> 02:52:35,900 and talk not about humans, 3183 02:52:36,730 --> 02:52:39,970 but about our close relatives, the non-human primates. 3184 02:52:40,700 --> 02:52:44,740 Because I know, that's probably a minority interest 3185 02:52:44,740 --> 02:52:46,230 of the folks involved here, 3186 02:52:46,230 --> 02:52:48,860 I'll go ahead and start by just giving you an idea 3187 02:52:48,860 --> 02:52:51,950 about what one aspect of sociality 3188 02:52:51,950 --> 02:52:53,190 and social relationships 3189 02:52:53,190 --> 02:52:56,290 can look like in the nonhuman primates that we study. 3190 02:52:56,290 --> 02:53:00,600 So what I'm showing you here is a picture of two wild baboons. 3191 02:53:01,370 --> 02:53:04,740 This is a female named Rwanda, who my collaborators 3192 02:53:04,740 --> 02:53:08,930 and I have studied continuously from her birth in 2002, 3193 02:53:08,930 --> 02:53:10,650 to the present. And in this picture, 3194 02:53:10,650 --> 02:53:12,880 she happens to be engaging in affiliative social behavior, 3195 02:53:12,880 --> 02:53:16,020 the type of behavior that creates 3196 02:53:16,020 --> 02:53:18,330 and maintains social relationships 3197 02:53:18,330 --> 02:53:20,590 with her then adolescent daughter, Rodeo. 3198 02:53:21,520 --> 02:53:23,510 I'm showing this picture of Rwanda in part 3199 02:53:23,510 --> 02:53:26,160 because she is an example 3200 02:53:26,160 --> 02:53:29,580 of a particularly privileged member of baboon society. 3201 02:53:29,580 --> 02:53:32,990 In the social hierarchies that define baboon society, 3202 02:53:32,990 --> 02:53:37,290 she has been at the top or near the top through her entire life. 3203 02:53:38,760 --> 02:53:41,450 This has likely paid some dividends for her 3204 02:53:41,450 --> 02:53:45,660 in terms of measures of Darwinian fitness 3205 02:53:45,660 --> 02:53:49,520 or Darwinian success. She's had 10 live births so far, 3206 02:53:50,460 --> 02:53:53,080 three fetal losses, three miscarriages, 3207 02:53:53,080 --> 02:53:57,820 with her most recent birth a son last year in 2021. 3208 02:53:58,360 --> 02:54:00,480 I mentioned that this may have something to do 3209 02:54:00,480 --> 02:54:02,400 with her relative social privilege. 3210 02:54:02,400 --> 02:54:05,700 We know that high-ranking high social status mothers 3211 02:54:05,700 --> 02:54:08,510 in the society that I'm showing you here, 3212 02:54:08,510 --> 02:54:10,870 tend to reach maturation earlier, 3213 02:54:10,870 --> 02:54:13,520 tend to start their reproductive careers a little bit earlier, 3214 02:54:13,520 --> 02:54:16,430 and probably because they have better access to food 3215 02:54:16,430 --> 02:54:18,100 and other types of resources, 3216 02:54:18,100 --> 02:54:20,380 they also have shorter inner birth intervals. 3217 02:54:20,380 --> 02:54:22,780 They recover from postpartum amenorrhea earlier. 3218 02:54:23,800 --> 02:54:28,060 A number of Rwanda's offspring have been females like Rodeo, 3219 02:54:28,060 --> 02:54:30,780 and this likely confers another benefit to her. 3220 02:54:31,600 --> 02:54:35,800 Female kin are female baboon's closest social partners. 3221 02:54:35,800 --> 02:54:37,070 And we know that females 3222 02:54:37,070 --> 02:54:39,720 who are the most integrated into their groups 3223 02:54:39,720 --> 02:54:42,390 versus the most isolated in their groups experience 3224 02:54:42,390 --> 02:54:45,870 quite a large difference in their predicted adult survival. 3225 02:54:45,870 --> 02:54:47,990 So here I'm showing you the top quartile 3226 02:54:47,990 --> 02:54:50,580 versus the bottom quartile of the most socially integrated 3227 02:54:50,580 --> 02:54:52,990 versus socially isolated baboon females 3228 02:54:52,990 --> 02:54:55,030 and the difference is in life- 3229 02:54:55,030 --> 02:54:58,510 a difference in life expectancy of about two to three years. 3230 02:54:58,510 --> 02:54:59,730 Daughters like Rodeo 3231 02:54:59,730 --> 02:55:02,410 also experience relative social advantage. 3232 02:55:02,410 --> 02:55:04,670 And this is even in even larger effect sizes, 3233 02:55:04,670 --> 02:55:08,280 it's actually the largest effect size we know of on survival 3234 02:55:08,280 --> 02:55:10,650 in this particular population of primates. 3235 02:55:10,650 --> 02:55:13,820 What this picture is showing you is the difference in expected, 3236 02:55:13,820 --> 02:55:18,150 again, adult life expectancy for those individuals like rodeo 3237 02:55:18,150 --> 02:55:21,110 who were born to high social status mothers, 3238 02:55:21,110 --> 02:55:24,830 to socially connected mothers, to mothers that manage to live 3239 02:55:24,830 --> 02:55:27,640 throughout their entire juvenile periods. 3240 02:55:27,640 --> 02:55:30,060 And those are the sort of silver spoon females 3241 02:55:30,060 --> 02:55:31,650 that we show in dark blue, 3242 02:55:31,650 --> 02:55:34,340 who are expected to live a whopping 10 years, 3243 02:55:34,340 --> 02:55:37,490 about a decade longer than those females 3244 02:55:37,490 --> 02:55:38,690 who may live in the same group, 3245 02:55:38,690 --> 02:55:41,190 but experience three or more major sources 3246 02:55:41,190 --> 02:55:43,450 of social disadvantage early in life, 3247 02:55:43,450 --> 02:55:46,470 pointing again to the very large importance 3248 02:55:46,470 --> 02:55:50,020 of early-life experience in the lives of these animals, 3249 02:55:50,020 --> 02:55:52,880 similar to what's been described in human societies. 3250 02:55:54,040 --> 02:55:56,760 So I showed you a very granular example here, 3251 02:55:56,760 --> 02:55:59,580 but I want to point out the increasing amounts of work 3252 02:56:00,100 --> 02:56:03,660 in not only humans, but in other social mammals, 3253 02:56:03,660 --> 02:56:05,550 show that aspects of the social environment, 3254 02:56:05,550 --> 02:56:07,940 particularly things like social connectedness, 3255 02:56:08,800 --> 02:56:10,950 versus social isolation and social support, 3256 02:56:12,220 --> 02:56:16,970 have quite substantial effects on how long individuals live, 3257 02:56:16,970 --> 02:56:18,900 on their health and their survival. 3258 02:56:18,900 --> 02:56:20,660 So this is from a review some colleagues 3259 02:56:20,660 --> 02:56:22,980 and I wrote a few years ago, 3260 02:56:22,980 --> 02:56:25,160 looking at the data available at that time 3261 02:56:25,160 --> 02:56:28,280 on all cause mortality in natural populations, 3262 02:56:28,280 --> 02:56:32,760 that is populations outside captivity, of social mammals. 3263 02:56:32,760 --> 02:56:36,480 And what you can see is, humans are not alone. 3264 02:56:36,480 --> 02:56:38,590 Individuals who are more socially integrated 3265 02:56:38,590 --> 02:56:41,400 into their societies have more social support, 3266 02:56:41,400 --> 02:56:43,550 live longer natural lifespans. 3267 02:56:44,340 --> 02:56:47,940 And so this seems to be a general phenomenon 3268 02:56:47,940 --> 02:56:50,220 that is a major driver of the type of work 3269 02:56:50,220 --> 02:56:52,950 that my group does, which seeks to understand 3270 02:56:52,950 --> 02:56:56,670 why social relationships both in early life and adulthood 3271 02:56:56,670 --> 02:57:01,370 have such remarkably large effects on life outcomes. 3272 02:57:02,140 --> 02:57:05,310 These are the four species that my lab 3273 02:57:05,310 --> 02:57:08,290 has studied in the last year, a few years. 3274 02:57:08,290 --> 02:57:11,240 I'll focus today on the non-human primates 3275 02:57:11,240 --> 02:57:13,230 and talk briefly about the reasons 3276 02:57:13,230 --> 02:57:16,920 why studying these non-human animal models 3277 02:57:16,920 --> 02:57:19,060 can be useful in understanding 3278 02:57:19,060 --> 02:57:21,210 the social determinants of health in humans. 3279 02:57:21,210 --> 02:57:22,420 The first, of course, 3280 02:57:22,420 --> 02:57:24,860 is the relative simplicity of their society. 3281 02:57:24,860 --> 02:57:27,730 That's what makes animal models useful 3282 02:57:27,730 --> 02:57:31,340 in any aspect of the Biomedical Sciences. 3283 02:57:32,170 --> 02:57:35,270 Unlike in humans, it's relatively straightforward 3284 02:57:35,270 --> 02:57:39,720 to measure social bond strength and social status, 3285 02:57:39,720 --> 02:57:41,850 because all the social relationships 3286 02:57:41,850 --> 02:57:44,140 that these animals experience are constrained 3287 02:57:44,140 --> 02:57:46,150 to the social groups in which they live. 3288 02:57:46,150 --> 02:57:48,110 This actually makes them a somewhat poor model 3289 02:57:48,110 --> 02:57:52,240 for some of the larger scale structural sources of inequity 3290 02:57:52,240 --> 02:57:54,480 that have been talked about in this workshop, 3291 02:57:54,480 --> 02:57:58,880 but relatively good for trying to isolate the effects 3292 02:57:58,880 --> 02:58:03,060 of immediate individual differences in social support, 3293 02:58:03,060 --> 02:58:04,790 for instance, or in social status, 3294 02:58:04,790 --> 02:58:06,560 because social status tends to be pretty one 3295 02:58:06,560 --> 02:58:09,080 dimensional in these groups. 3296 02:58:09,080 --> 02:58:12,390 Additionally, while non-human primates live a long time 3297 02:58:12,390 --> 02:58:14,360 compared to classical animal models, 3298 02:58:14,360 --> 02:58:17,570 like fruit flies, or mice, or C. 3299 02:58:17,570 --> 02:58:22,220 elegans, they live shorter lifespans than humans do. 3300 02:58:22,220 --> 02:58:25,220 And a common method of collecting behavioral 3301 02:58:25,220 --> 02:58:28,120 and social data on them is to actually collect data 3302 02:58:28,120 --> 02:58:30,210 in real-time through direct observations, 3303 02:58:30,210 --> 02:58:33,600 which we can do in some cases on a near daily basis 3304 02:58:33,600 --> 02:58:35,040 from cradle to grave. 3305 02:58:35,040 --> 02:58:37,010 So what I'm showing you here, for example, 3306 02:58:37,010 --> 02:58:39,810 is from that baboon population I talked about earlier, 3307 02:58:39,810 --> 02:58:42,510 in which observations began in 1971. 3308 02:58:42,510 --> 02:58:44,340 This shows pedigree relationships, 3309 02:58:45,360 --> 02:58:47,800 from the first generation until where we are today, 3310 02:58:47,800 --> 02:58:50,180 which now is just cracking the ninth generation 3311 02:58:50,180 --> 02:58:51,870 of continuous data observation 3312 02:58:52,460 --> 02:58:54,270 on full lifespans of these animals. 3313 02:58:55,030 --> 02:58:57,830 And of course, in some settings, and this is a setting 3314 02:58:57,830 --> 02:59:00,840 I'll focus on for the rest of my presentation today, 3315 02:59:00,840 --> 02:59:03,130 it's possible to have some experimental control 3316 02:59:03,130 --> 02:59:07,180 over the social environment in a way that is both logistically 3317 02:59:07,180 --> 02:59:09,860 and ethically unfeasible in humans. 3318 02:59:09,860 --> 02:59:12,990 So I'll focus here, particularly on social status, 3319 02:59:12,990 --> 02:59:15,390 social rank in nonhuman primates, 3320 02:59:15,390 --> 02:59:17,700 which Robert Sapolsky and others have argued 3321 02:59:17,700 --> 02:59:20,850 is the closest approximation that we might be able 3322 02:59:20,850 --> 02:59:24,710 to make to differences in socio-economic status in humans, 3323 02:59:24,710 --> 02:59:27,880 because like socio-economic status, 3324 02:59:27,880 --> 02:59:30,740 differences in social rank control access 3325 02:59:30,740 --> 02:59:33,090 to resources in a systematic fashion 3326 02:59:33,090 --> 02:59:35,450 that can be quite stable over time, 3327 02:59:36,090 --> 02:59:40,780 and can be regularly enforced through harassment. 3328 02:59:40,780 --> 02:59:42,960 So here, I'll focus on the rhesus macaques, 3329 02:59:42,960 --> 02:59:45,110 that's the species shown here on the right, 3330 02:59:45,660 --> 02:59:46,950 because in rhesus macaques, 3331 02:59:46,950 --> 02:59:48,870 that is, in fact, the case in females, 3332 02:59:49,700 --> 02:59:52,240 that high status females regularly enforce 3333 02:59:52,240 --> 02:59:58,320 their rank through harassment and sort of ritualized behavior 3334 02:59:58,320 --> 03:00:02,780 that reinforces differences in the hierarchy, 3335 03:00:03,430 --> 03:00:04,970 and that low status individuals 3336 03:00:04,970 --> 03:00:07,000 may experience throughout their lifespans. 3337 03:00:07,000 --> 03:00:11,400 Crucially, most of this behavior is not by a physical contact, 3338 03:00:11,400 --> 03:00:15,670 but rather by displacement behavior, 3339 03:00:15,670 --> 03:00:17,390 by threat behavior, and so on. 3340 03:00:18,010 --> 03:00:20,670 I said that it's possible to study social status 3341 03:00:20,670 --> 03:00:22,150 and the long-term effects of differences 3342 03:00:22,150 --> 03:00:24,590 in social status experimentally in this system. 3343 03:00:24,590 --> 03:00:26,610 And that's because in female Rhesus, 3344 03:00:28,180 --> 03:00:31,470 we can manipulate social status over the long-term by 3345 03:00:31,470 --> 03:00:34,120 altering how females are housed together. 3346 03:00:34,120 --> 03:00:36,410 It turns out that when you introduce females 3347 03:00:36,410 --> 03:00:38,830 who had no prior social history with each other 3348 03:00:38,830 --> 03:00:40,470 into a new social group, 3349 03:00:40,470 --> 03:00:42,250 females who are introduced earlier 3350 03:00:42,250 --> 03:00:45,010 become high status in those groups. 3351 03:00:45,010 --> 03:00:47,130 And as you sequentially introduce females 3352 03:00:47,130 --> 03:00:50,320 into the group afterwards, they tend to occupy the space 3353 03:00:50,320 --> 03:00:52,970 in the hierarchy directly below the females 3354 03:00:52,970 --> 03:00:54,760 who already exist in those groups. 3355 03:00:54,760 --> 03:00:57,520 So we can randomize order of introduction, 3356 03:00:57,520 --> 03:00:58,730 and in doing so, 3357 03:00:58,730 --> 03:01:01,510 we can effectively randomize social status, 3358 03:01:01,510 --> 03:01:03,020 and those social status hierarchies 3359 03:01:03,020 --> 03:01:04,870 can remain stable for years. 3360 03:01:05,520 --> 03:01:09,340 So we've formed these five-member social groups. 3361 03:01:09,340 --> 03:01:12,200 And then we can replicate that group formation 3362 03:01:12,200 --> 03:01:13,710 across replicate social groups, 3363 03:01:13,710 --> 03:01:15,260 we typically work with about nine 3364 03:01:15,260 --> 03:01:16,860 or 10 different social groups. 3365 03:01:17,370 --> 03:01:19,890 To really pin down causal relationships 3366 03:01:19,890 --> 03:01:22,670 between the stress of social subordination 3367 03:01:22,670 --> 03:01:24,200 and downstream outcomes, 3368 03:01:24,200 --> 03:01:27,370 we can then do a secondary manipulation, 3369 03:01:27,370 --> 03:01:29,000 we usually do so about a year later, 3370 03:01:29,000 --> 03:01:30,480 where we take the females 3371 03:01:30,480 --> 03:01:32,290 who are top-ranking in those groups 3372 03:01:32,290 --> 03:01:34,680 and put them together into a new social group, 3373 03:01:34,680 --> 03:01:37,990 we take second-ranking females and put them together, 3374 03:01:37,990 --> 03:01:40,210 third-ranking, and so on down the hierarchy. 3375 03:01:41,140 --> 03:01:43,460 Because these females naturally will reform 3376 03:01:43,460 --> 03:01:45,770 the social hierarchy, they rapidly do so, 3377 03:01:45,770 --> 03:01:48,260 allowing us to watch what happens to females 3378 03:01:48,260 --> 03:01:50,640 who move from the highest level in their society 3379 03:01:50,640 --> 03:01:51,850 to the lowest level, 3380 03:01:51,850 --> 03:01:53,540 from the lowest level to the highest, 3381 03:01:53,540 --> 03:01:57,150 or who maintain high versus low social status 3382 03:01:57,150 --> 03:01:59,199 throughout the course of our experiments. 3383 03:01:59,860 --> 03:02:02,610 This is a picture that gives you an idea of how this works. 3384 03:02:02,610 --> 03:02:06,460 On the Y axis is a quantitative measure of social status, 3385 03:02:06,460 --> 03:02:09,820 high-high on the Y axis, low-low on the Y axis. 3386 03:02:09,820 --> 03:02:12,640 And this shows the process of group formation 3387 03:02:12,640 --> 03:02:14,870 for one of our social groups that we worked 3388 03:02:14,870 --> 03:02:17,840 with quite some time ago now, about a decade ago now. 3389 03:02:18,720 --> 03:02:20,500 Each line shows a different female, 3390 03:02:20,500 --> 03:02:23,010 and you can see sequential order of introduction 3391 03:02:23,010 --> 03:02:27,580 when they appear in the group, we introduce them into the group 3392 03:02:27,580 --> 03:02:31,220 and they very rapidly form a clearly delineated social status 3393 03:02:31,220 --> 03:02:33,570 hierarchy that again remain stable 3394 03:02:33,570 --> 03:02:35,570 throughout the course of the experiment. 3395 03:02:36,570 --> 03:02:38,730 So we've used this experimental paradigm 3396 03:02:38,730 --> 03:02:41,540 now to ask a lot of different questions 3397 03:02:41,540 --> 03:02:45,010 about what happens across the two phases of these studies, 3398 03:02:45,010 --> 03:02:46,940 the females, followed by rank rearrangement, 3399 03:02:46,940 --> 03:02:49,310 and then the same females later on 3400 03:02:49,310 --> 03:02:50,970 when they occupy different places 3401 03:02:50,970 --> 03:02:52,840 in the social status hierarchy. 3402 03:02:52,840 --> 03:02:57,010 We know now that we can manipulate social status, 3403 03:02:57,700 --> 03:03:00,310 and those manipulations provide evidence 3404 03:03:00,310 --> 03:03:02,410 that the chronic stress of subordination, 3405 03:03:03,170 --> 03:03:04,630 and again, I should say, 3406 03:03:04,630 --> 03:03:07,560 controlling for healthcare access, 3407 03:03:07,560 --> 03:03:10,590 controlling for access to food, controlling for shelter, 3408 03:03:10,590 --> 03:03:12,150 controlling for demographic structure, 3409 03:03:12,150 --> 03:03:13,700 and so on, all of those are the same 3410 03:03:13,700 --> 03:03:15,610 for all of these individuals, 3411 03:03:15,610 --> 03:03:17,850 has strong effects on behavioral traits, 3412 03:03:17,850 --> 03:03:22,390 including ones associated with relative baldness, 3413 03:03:22,390 --> 03:03:25,410 or relative anxiety in these groups, 3414 03:03:25,410 --> 03:03:28,480 that it influences physiology through influencing processes 3415 03:03:28,480 --> 03:03:31,380 associated with glucocorticoid negative feedback, 3416 03:03:31,380 --> 03:03:34,740 that it changes aspects of the contents of cells 3417 03:03:34,740 --> 03:03:37,220 through affecting mitochondrial DNA copy number. 3418 03:03:37,970 --> 03:03:42,440 And then it affects how the genome is actually regulated, 3419 03:03:42,440 --> 03:03:45,420 how much, when and where of a gene product 3420 03:03:45,420 --> 03:03:47,290 is made through influencing gene 3421 03:03:47,290 --> 03:03:49,560 expression in a cell-type dependent manner. 3422 03:03:50,440 --> 03:03:54,180 Additionally, we have some evidence that differences 3423 03:03:54,180 --> 03:03:56,350 in the social environment itself 3424 03:03:56,350 --> 03:03:59,390 can affect how the physical structure of the DNA 3425 03:03:59,390 --> 03:04:01,420 folds within the cell. 3426 03:04:01,420 --> 03:04:04,160 And I'll focus here particularly on some of our data, 3427 03:04:04,160 --> 03:04:07,480 showing that differences in social status influence 3428 03:04:07,480 --> 03:04:12,310 the ability of immune cells to respond to models of infection. 3429 03:04:12,310 --> 03:04:15,130 In this case, when we culture blood 3430 03:04:15,130 --> 03:04:17,310 from each of these individuals in tubes 3431 03:04:17,310 --> 03:04:20,020 that contain cell culture media only, basically, 3432 03:04:20,020 --> 03:04:23,040 the food that cells need to survive outside the body 3433 03:04:23,040 --> 03:04:25,690 for some time, or cell culture media, 3434 03:04:25,690 --> 03:04:29,100 in parallel, cell culture media plus another additive, 3435 03:04:29,100 --> 03:04:31,240 in this case, Lipopolysaccharide, 3436 03:04:31,240 --> 03:04:35,200 which is a component of the gram negative 3437 03:04:35,200 --> 03:04:39,220 bacterial cell wall and models infection 3438 03:04:39,220 --> 03:04:41,750 by a gram negative bacteria. 3439 03:04:42,640 --> 03:04:45,160 Okay. What I'll show you here is work 3440 03:04:45,160 --> 03:04:47,360 that was led by a former postdoc of mine, 3441 03:04:47,360 --> 03:04:52,030 Noah Snyder Mackler, and a postdoc of my collaborator, 3442 03:04:52,030 --> 03:04:54,030 Luis Pereira, Joaquín Sanz. 3443 03:04:54,030 --> 03:04:56,850 What they did was look at the levels of gene activity, 3444 03:04:56,850 --> 03:04:59,620 the levels of gene expression in two samples 3445 03:04:59,620 --> 03:05:02,100 collected from each of 45 individuals 3446 03:05:02,100 --> 03:05:04,640 that we had randomized into high versus middle 3447 03:05:04,640 --> 03:05:06,590 versus low social status. 3448 03:05:07,220 --> 03:05:10,650 So here we measure the activity of about 9,000 genes 3449 03:05:10,650 --> 03:05:13,990 in the genome for two samples for each of those individuals. 3450 03:05:13,990 --> 03:05:16,480 And I'm just sort of squishing down all of that data 3451 03:05:16,480 --> 03:05:19,820 into two dimensions here using a principal components analysis. 3452 03:05:19,820 --> 03:05:22,290 So each dot here represents a different sample. 3453 03:05:23,440 --> 03:05:26,240 The first principal component of variation 3454 03:05:26,240 --> 03:05:28,360 in the overall sample maps on 3455 03:05:28,360 --> 03:05:31,960 to whether that sample was unstimulated, 3456 03:05:31,960 --> 03:05:33,820 was a control sample in blue, 3457 03:05:33,820 --> 03:05:36,140 versus whether it was exposed to LPS. 3458 03:05:36,140 --> 03:05:38,430 And you can see a big separation between samples 3459 03:05:38,430 --> 03:05:40,910 that think they're being attacked by bacteria 3460 03:05:40,910 --> 03:05:43,270 versus those that aren't, that's exactly what we'd expect. 3461 03:05:43,270 --> 03:05:45,670 And it's what we would expect in our own cells 3462 03:05:45,670 --> 03:05:47,530 if we repeated this experiment, for example, 3463 03:05:47,530 --> 03:05:49,150 by drawing blood from humans. 3464 03:05:49,680 --> 03:05:51,600 What's perhaps more interesting and more relevant 3465 03:05:51,600 --> 03:05:53,160 to this audience, though, 3466 03:05:53,160 --> 03:05:56,600 is the second major access of variation in the sample, 3467 03:05:56,600 --> 03:05:59,410 the second principal component is very, very strongly 3468 03:06:00,100 --> 03:06:03,590 correlated with the social status of the individual 3469 03:06:03,590 --> 03:06:05,630 who donated that sample. 3470 03:06:05,630 --> 03:06:08,230 We can look at this in an even more granular fashion. 3471 03:06:08,230 --> 03:06:10,710 So now, I've switched from each dot in this plot, 3472 03:06:11,270 --> 03:06:14,950 and a plot being a sample, to each dot being a single gene 3473 03:06:14,950 --> 03:06:18,600 that we were able to measure in this experiment. 3474 03:06:18,600 --> 03:06:21,850 And what I'm showing you is the effect of dominance 3475 03:06:21,850 --> 03:06:23,110 rank of social status 3476 03:06:23,110 --> 03:06:26,970 on gene expression in the control condition on the X axis, 3477 03:06:26,970 --> 03:06:30,050 versus the effect of dominance rank social status 3478 03:06:30,050 --> 03:06:33,220 on gene expression in the same individuals 3479 03:06:33,220 --> 03:06:34,930 when their cells are stimulated 3480 03:06:35,790 --> 03:06:37,450 by this model of bacterial infection. 3481 03:06:37,450 --> 03:06:41,020 And what you can see is an overall positive correlation. 3482 03:06:41,020 --> 03:06:43,240 So in other words, if social status predicts 3483 03:06:43,240 --> 03:06:46,720 higher activity of a gene in the control condition, 3484 03:06:46,720 --> 03:06:50,180 it also tends to do so in the LPS stimulated condition. 3485 03:06:50,830 --> 03:06:53,480 However, we also observe this sort of cluster 3486 03:06:53,480 --> 03:06:54,880 of genes down here, 3487 03:06:54,880 --> 03:06:56,760 and what these represent are cases 3488 03:06:56,760 --> 03:06:59,570 where we have a dominance rank by condition, 3489 03:06:59,570 --> 03:07:01,490 by stimulation interaction, 3490 03:07:01,490 --> 03:07:04,490 and the direction of effect is where individuals 3491 03:07:04,490 --> 03:07:08,430 who are low status respond in an exaggerated way 3492 03:07:08,430 --> 03:07:11,550 to this LPS stimulation. 3493 03:07:11,550 --> 03:07:14,270 And that's another way of seeing that is shown here, 3494 03:07:14,270 --> 03:07:17,450 where the absolute value of the effect of stimulation 3495 03:07:17,450 --> 03:07:20,190 is shifted rightwards for low status individuals 3496 03:07:20,190 --> 03:07:23,030 in blue versus high status individuals in purple. 3497 03:07:24,110 --> 03:07:26,100 If we will look at what those genes are, 3498 03:07:26,100 --> 03:07:28,060 it turns out that they're highly enriched for genes 3499 03:07:28,060 --> 03:07:29,260 that are involved 3500 03:07:29,260 --> 03:07:31,360 in inflammation and innate immunity, 3501 03:07:31,360 --> 03:07:33,700 particularly the inflammatory response, 3502 03:07:33,700 --> 03:07:35,990 whereas genes that respond more strongly 3503 03:07:35,990 --> 03:07:37,270 in high status individuals 3504 03:07:37,270 --> 03:07:40,260 are also enriched for innate immune signaling, 3505 03:07:40,260 --> 03:07:42,430 but more so in association with the type 3506 03:07:42,430 --> 03:07:44,460 one interferon signaling pathway, 3507 03:07:44,460 --> 03:07:45,860 which is canonically associated 3508 03:07:45,860 --> 03:07:47,710 with the response to viral stimuli. 3509 03:07:48,460 --> 03:07:52,190 So what this tells us when we actually look at the biology 3510 03:07:52,190 --> 03:07:55,730 of the pathways that are sort of first line responders 3511 03:07:55,730 --> 03:07:59,270 to Lipopolysaccharide in a stimulation 3512 03:07:59,270 --> 03:08:02,300 is that we actually see effects of social status 3513 03:08:02,300 --> 03:08:06,130 at a very, very granular sub cellular level, 3514 03:08:06,130 --> 03:08:09,040 where cells from low-ranking individuals 3515 03:08:09,040 --> 03:08:11,220 actually sort of shunt the response down 3516 03:08:11,220 --> 03:08:14,770 this left-hand pathway, this MYD88-dependent pathway. 3517 03:08:14,770 --> 03:08:17,090 And cells from high-ranking individuals 3518 03:08:17,090 --> 03:08:18,960 tend to use an alternative pathway, 3519 03:08:18,960 --> 03:08:21,440 this sort of right hand TRIF-dependent pathway 3520 03:08:21,440 --> 03:08:23,950 at a higher rate. What does this mean? 3521 03:08:23,950 --> 03:08:26,220 It actually means that master regulators, 3522 03:08:26,220 --> 03:08:28,910 these transcription factors that control downstream 3523 03:08:28,910 --> 03:08:30,130 gene activity and control 3524 03:08:30,130 --> 03:08:33,430 much of the downstream response to infection, 3525 03:08:33,430 --> 03:08:35,160 are actually differentially regulated 3526 03:08:35,160 --> 03:08:37,670 in low-ranking and high-ranking individuals. 3527 03:08:37,670 --> 03:08:39,750 So this is a change that influences 3528 03:08:39,750 --> 03:08:43,420 not only overall gene activity, but gene activity in these very, 3529 03:08:43,420 --> 03:08:45,590 very targeted pathway dependent ways. 3530 03:08:46,650 --> 03:08:49,720 I told you, too, that we're able to watch the same individuals 3531 03:08:49,720 --> 03:08:52,260 when they're low status in the first part of our study, 3532 03:08:52,260 --> 03:08:54,660 and then high status in the second part of their study, 3533 03:08:54,660 --> 03:08:57,380 or high status to low status, and so on. 3534 03:08:57,380 --> 03:08:59,070 This is a measure, or measure of social status, 3535 03:08:59,070 --> 03:09:00,310 again, on the left-hand side, 3536 03:09:00,310 --> 03:09:03,440 and you can see that kind of change here. 3537 03:09:03,440 --> 03:09:06,730 So there is absolutely no correlation in our models 3538 03:09:06,730 --> 03:09:08,470 between the social status of females 3539 03:09:08,470 --> 03:09:09,750 in the beginning of the study, 3540 03:09:09,750 --> 03:09:13,080 and social status in the assumed females at the end of the study. 3541 03:09:13,080 --> 03:09:14,650 This means that we can also ask 3542 03:09:14,650 --> 03:09:16,780 about the long-term effects of social history, 3543 03:09:16,780 --> 03:09:19,900 about how an animal's past social experience 3544 03:09:19,900 --> 03:09:22,850 might continue to affect her down the road. 3545 03:09:23,630 --> 03:09:26,020 Okay. So here, I'm going to show you a little bit of data 3546 03:09:26,020 --> 03:09:27,530 that was collected one year 3547 03:09:27,530 --> 03:09:31,400 after we did that sort of mid-study rank rearrangement. 3548 03:09:31,400 --> 03:09:34,930 What we find again is that the rank of those individuals, 3549 03:09:34,930 --> 03:09:36,830 the social status of those individuals at the time 3550 03:09:36,830 --> 03:09:39,420 we actually sample in that second phase of the study, 3551 03:09:39,420 --> 03:09:41,590 influences the activity of lots of genes. 3552 03:09:41,590 --> 03:09:43,950 That's basically what I've already told you. 3553 03:09:43,950 --> 03:09:46,130 What we were somewhat surprised to find 3554 03:09:46,130 --> 03:09:49,350 is that the previous rank, the rank of those individuals, 3555 03:09:49,350 --> 03:09:52,200 the social status of those individuals over a year earlier, 3556 03:09:52,200 --> 03:09:54,080 which again is completely uncorrelated 3557 03:09:54,080 --> 03:09:56,750 with social statuses at the time of sampling, 3558 03:09:56,750 --> 03:09:59,710 also is significantly associated 3559 03:09:59,710 --> 03:10:02,140 with the activity of thousands of genes 3560 03:10:02,140 --> 03:10:04,050 in these circulating immune cells. 3561 03:10:05,030 --> 03:10:09,220 Even more remarkably, about 1,000 genes show evidence 3562 03:10:09,220 --> 03:10:13,910 of an interaction effect between past and current social status, 3563 03:10:13,910 --> 03:10:16,880 a social history effect that is strongly polarized 3564 03:10:16,880 --> 03:10:18,590 towards a greater memory, 3565 03:10:18,590 --> 03:10:21,880 if those females were socially disadvantaged in the past. 3566 03:10:21,880 --> 03:10:24,730 So I'm just showing you one gene here as an example, 3567 03:10:24,730 --> 03:10:29,740 this is current social status. And I've stratified the sample 3568 03:10:29,740 --> 03:10:32,120 by whether the sample comes from a female 3569 03:10:32,120 --> 03:10:33,640 who used to be low status, 3570 03:10:33,640 --> 03:10:36,660 who used to be medium status, or used to be high status. 3571 03:10:36,660 --> 03:10:39,590 Females who used to be socially advantaged high status 3572 03:10:39,590 --> 03:10:41,350 in their sort of former lives 3573 03:10:41,350 --> 03:10:45,600 are very sensitive to current social conditions, 3574 03:10:45,600 --> 03:10:47,880 where those who were socially disadvantaged 3575 03:10:48,460 --> 03:10:49,660 in the first phase of our study 3576 03:10:49,660 --> 03:10:53,920 are basically insensitive to their current conditions, 3577 03:10:53,920 --> 03:10:59,140 even if they're relatively high status at the time of sampling. 3578 03:10:59,140 --> 03:11:00,340 And this is a graph 3579 03:11:00,340 --> 03:11:02,780 that shows you a very similar kind of pattern, 3580 03:11:02,780 --> 03:11:06,390 but just aggregated across all 1,000 genes, right? 3581 03:11:06,390 --> 03:11:10,400 Females who were high past rank 3582 03:11:10,400 --> 03:11:12,710 are very sensitive to current rank, 3583 03:11:12,710 --> 03:11:15,220 females who were low past rank aren't. 3584 03:11:15,220 --> 03:11:17,520 And so what this is telling you, in other words, 3585 03:11:17,520 --> 03:11:19,410 is that it's those females who again, 3586 03:11:19,410 --> 03:11:21,350 were socially disadvantaged in the past, 3587 03:11:21,350 --> 03:11:23,930 who seemed to carry this baggage of social history forward 3588 03:11:23,930 --> 03:11:25,650 into the future. 3589 03:11:25,650 --> 03:11:28,190 So what we find overall through a series of studies 3590 03:11:28,190 --> 03:11:31,440 in these rhesus macaques is that differences 3591 03:11:31,440 --> 03:11:33,540 in the social environment, and what we interpret again 3592 03:11:33,540 --> 03:11:35,839 is the chronic stress of social subordination, 3593 03:11:36,730 --> 03:11:39,060 has what we interpret as a causal effect 3594 03:11:39,060 --> 03:11:42,330 on thousands of genes in circulating immune cells, 3595 03:11:42,330 --> 03:11:45,180 including the response to other types of challenge, 3596 03:11:45,180 --> 03:11:47,230 like an immune stimulation. 3597 03:11:47,230 --> 03:11:48,560 We believe these changes are linked 3598 03:11:48,560 --> 03:11:51,950 in part to transcription factor binding of DNA 3599 03:11:51,950 --> 03:11:55,910 to these master regulators of gene regulation, 3600 03:11:55,910 --> 03:11:59,090 that in turn change the actual polarization 3601 03:11:59,090 --> 03:12:00,560 of how different signaling pathways, 3602 03:12:00,560 --> 03:12:02,660 even in response to the same stimulus, 3603 03:12:02,660 --> 03:12:04,580 are used within the cell, 3604 03:12:04,580 --> 03:12:06,940 and that at least part of these effects 3605 03:12:06,940 --> 03:12:08,850 can be embedded into biological memory. 3606 03:12:08,850 --> 03:12:12,720 We don't know for how long but we can infer from our results 3607 03:12:12,720 --> 03:12:15,720 that this lasts at least a year, even in immune cells, 3608 03:12:15,720 --> 03:12:18,480 most of which are much younger than a year, 3609 03:12:18,480 --> 03:12:20,750 and even in adults who are not necessarily 3610 03:12:20,750 --> 03:12:23,700 going through these sort of critical periods of experience. 3611 03:12:24,270 --> 03:12:26,010 I just want to mention very briefly one 3612 03:12:26,010 --> 03:12:28,630 piece of unpublished data, 3613 03:12:28,630 --> 03:12:32,320 which extends some of this work to responses to vaccination, 3614 03:12:32,320 --> 03:12:33,900 and the adaptive immune system, 3615 03:12:33,900 --> 03:12:36,540 which we did by administering pediatric flu vaccine 3616 03:12:36,540 --> 03:12:37,780 to our animals 3617 03:12:37,780 --> 03:12:42,750 and looking at vaccine-specific antibody responses. 3618 03:12:42,750 --> 03:12:46,430 Here's a picture of an antibody response in our female macaques, 3619 03:12:46,430 --> 03:12:49,440 and then again, in a phase two of this sort of study, 3620 03:12:49,440 --> 03:12:52,920 and we find remarkably here is that measures of social status, 3621 03:12:52,920 --> 03:12:55,970 and even more strongly, behavioral measures 3622 03:12:55,970 --> 03:12:58,880 of the frequency of received harassment, 3623 03:12:58,880 --> 03:13:01,640 predict how strongly these animals 3624 03:13:01,640 --> 03:13:04,860 are able to respond to those vaccines 3625 03:13:05,640 --> 03:13:09,680 and mount an antibody response, which is something 3626 03:13:09,680 --> 03:13:12,140 that has perhaps particular resonance for us, 3627 03:13:12,140 --> 03:13:15,840 given our current environment as humans. 3628 03:13:16,480 --> 03:13:18,730 So in sum, I just want to say 3629 03:13:18,730 --> 03:13:20,820 that I think the emerging evidence, 3630 03:13:20,820 --> 03:13:23,080 especially across species evidence, 3631 03:13:23,080 --> 03:13:25,570 suggests that our dependence, the human dependence 3632 03:13:25,570 --> 03:13:27,730 on social relationships, and here again, 3633 03:13:27,730 --> 03:13:30,330 I'm talking about things like social isolation, 3634 03:13:30,330 --> 03:13:34,130 and social connectedness or relative social status 3635 03:13:34,130 --> 03:13:38,150 advantages or disadvantages in these sorts of small societies, 3636 03:13:38,150 --> 03:13:40,560 have much deeper roots in our evolutionary history. 3637 03:13:40,560 --> 03:13:43,590 These aren't things that humans have invented, 3638 03:13:43,590 --> 03:13:46,770 but rather we see some parallels in other species. 3639 03:13:47,540 --> 03:13:50,140 And that means that basic science in animal models, 3640 03:13:50,140 --> 03:13:52,000 just as it's been used to understand 3641 03:13:53,330 --> 03:13:55,780 the etiology of cancer or Alzheimer's disease, 3642 03:13:55,780 --> 03:13:58,930 or cardiovascular disease, may also help us untangle 3643 03:13:58,930 --> 03:14:01,130 some of the causal biological pathways 3644 03:14:01,130 --> 03:14:03,060 and mediating mechanisms that contribute 3645 03:14:03,060 --> 03:14:05,409 to the social determinants of health in humans. 3646 03:14:06,070 --> 03:14:07,980 And with that, I just want to thank the people 3647 03:14:07,980 --> 03:14:09,200 involved in this work, 3648 03:14:09,200 --> 03:14:12,370 particularly the trainees who led this work, 3649 03:14:12,370 --> 03:14:15,660 Joaquín Sanz, Paul Maurizio, and Joao Batista, 3650 03:14:15,660 --> 03:14:17,560 Noah Snyder Mackler and Ryan Campbell. 3651 03:14:17,560 --> 03:14:19,280 And just let you know that if you're interested 3652 03:14:19,280 --> 03:14:21,870 in this kind of work as it pertains to animal models, 3653 03:14:21,870 --> 03:14:25,700 I co-run a high-priority research network that is funded 3654 03:14:25,700 --> 03:14:27,770 by the National Institutes of Health 3655 03:14:27,770 --> 03:14:30,980 with Kathie Millen Harris at UNC and Alessandro Bartolomucci 3656 03:14:30,980 --> 03:14:33,890 at University of Minnesota, and we're very interested 3657 03:14:33,890 --> 03:14:36,120 in funding pilot projects and fellowships 3658 03:14:36,120 --> 03:14:38,340 for comparative studies, and so on. 3659 03:14:39,430 --> 03:14:41,450 So with that, I'll stop the share 3660 03:14:41,450 --> 03:14:46,100 and turn it back over to the whole group. 3661 03:14:47,450 --> 03:14:49,130 Dr. Sharon Jackson: All right, thank you very much, 3662 03:14:49,130 --> 03:14:51,850 Dr. Tung. Wonderful presentation. 3663 03:14:52,380 --> 03:14:55,770 Very exciting and we really appreciate 3664 03:14:55,770 --> 03:14:58,540 that last plug at the end for collaboration. 3665 03:14:58,540 --> 03:15:01,540 I'm sure that our participants will be very excited 3666 03:15:01,540 --> 03:15:05,300 about that opportunity as well. So I'd like to remind people 3667 03:15:05,300 --> 03:15:07,490 that you can post questions in Slido. 3668 03:15:08,040 --> 03:15:10,270 And just to get us started off, Dr. Tung, 3669 03:15:10,270 --> 03:15:14,670 I have a question and follow-up to your presentation just now. 3670 03:15:14,670 --> 03:15:17,890 And we will do questions for both participants. 3671 03:15:18,830 --> 03:15:22,690 One of the things that I found fascinating was, 3672 03:15:24,300 --> 03:15:28,270 what is the ability to change your social status 3673 03:15:28,270 --> 03:15:29,490 in these models? 3674 03:15:29,490 --> 03:15:33,190 I mean, it's clear that you've got a high social status 3675 03:15:33,190 --> 03:15:34,670 and a lower social status, 3676 03:15:34,670 --> 03:15:38,060 but is- it's a two-part question. 3677 03:15:38,060 --> 03:15:40,760 Is there an attempt to change social status? 3678 03:15:41,270 --> 03:15:44,580 And is that a possibility in these models? 3679 03:15:45,890 --> 03:15:47,160 Dr. Jenny Tung: Yeah, so I mean, here, 3680 03:15:47,160 --> 03:15:48,370 we did it experimentally. 3681 03:15:48,370 --> 03:15:51,610 And so experimentally, it's certainly very possible 3682 03:15:51,610 --> 03:15:56,220 to do targeted manipulations of the social hierarchy. 3683 03:15:56,870 --> 03:15:59,950 And that's been done not only in the species 3684 03:15:59,950 --> 03:16:01,820 I talked about, the rhesus macaques, 3685 03:16:01,820 --> 03:16:06,360 but it's also been done in mouse models of social hierarchies 3686 03:16:06,360 --> 03:16:10,300 and social defeat, as well. And I think that, you know, 3687 03:16:10,300 --> 03:16:12,300 offers a lot of opportunities to understand 3688 03:16:12,300 --> 03:16:14,450 the effects of social mobility itself, 3689 03:16:15,430 --> 03:16:19,210 and sort of resilience to social disadvantage 3690 03:16:19,210 --> 03:16:21,750 as it's represented in these models. 3691 03:16:22,840 --> 03:16:27,630 Sort of in nature, it depends a lot on the species, 3692 03:16:27,630 --> 03:16:29,720 and sometimes there are strong sex differences 3693 03:16:29,720 --> 03:16:31,740 within the species as well. 3694 03:16:31,740 --> 03:16:35,230 So for female rhesus macaques, and for female bathrooms, 3695 03:16:35,230 --> 03:16:37,290 which were the case I talked about here, 3696 03:16:38,140 --> 03:16:40,980 social hierarchies are nepotistic. 3697 03:16:42,080 --> 03:16:48,850 Females non-genetically inherit their status from their mothers 3698 03:16:48,850 --> 03:16:52,200 in a way that makes it quite stable over time. 3699 03:16:52,200 --> 03:16:55,570 Sometimes you see females who just, you know, 3700 03:16:55,570 --> 03:16:57,200 for whatever reason decided 3701 03:16:57,200 --> 03:16:59,600 that is not what they want to do. 3702 03:16:59,600 --> 03:17:01,760 And you'll see more social mobility than that. 3703 03:17:01,760 --> 03:17:04,420 But that's relatively rare, and because of its rareness, 3704 03:17:04,420 --> 03:17:06,090 it makes it sometimes hard 3705 03:17:06,090 --> 03:17:09,250 to get this sort of variation in sample sizes, 3706 03:17:09,250 --> 03:17:12,400 that we would like to be able to analyze that in greater depth. 3707 03:17:13,090 --> 03:17:14,960 Dr. Sharon Jackson: Great. Thank you very much. 3708 03:17:15,510 --> 03:17:17,310 I do have another question for you. 3709 03:17:18,740 --> 03:17:20,760 Would you comment on how animal models 3710 03:17:20,760 --> 03:17:24,990 may be used to study intergenerational health effects 3711 03:17:24,990 --> 03:17:27,060 of social determinants, 3712 03:17:27,060 --> 03:17:32,920 and which take much longer and time span 3713 03:17:32,920 --> 03:17:34,760 to observe in humans? 3714 03:17:36,730 --> 03:17:38,390 Dr. Jenny Tung: Sure, I'd love to comment on that. 3715 03:17:38,390 --> 03:17:40,370 Because the baboon study that I work on 3716 03:17:40,370 --> 03:17:43,530 is a long-term study that is multi-generational, 3717 03:17:43,530 --> 03:17:46,010 and I didn't focus on it today, 3718 03:17:46,010 --> 03:17:48,500 but we actually have started doing some work 3719 03:17:48,500 --> 03:17:52,010 on intergenerational effects, for example, 3720 03:17:52,550 --> 03:17:53,940 and it has some power, right? 3721 03:17:53,940 --> 03:17:56,850 Because one of the things- it sort of expands 3722 03:17:56,850 --> 03:18:00,230 more early-life affect work where we see- you sort of, 3723 03:18:00,230 --> 03:18:02,770 if you have multi-dimensional social disadvantage, 3724 03:18:02,770 --> 03:18:04,540 or multi-dimensional disadvantage, 3725 03:18:04,540 --> 03:18:07,910 in general, it has very, very powerful predictive effects 3726 03:18:07,910 --> 03:18:13,040 on health and lifespan. And what's very nice about this 3727 03:18:13,040 --> 03:18:15,990 is that those different dimensions of disadvantage 3728 03:18:15,990 --> 03:18:17,560 tend to be relatively uncorrelated 3729 03:18:17,560 --> 03:18:21,290 from each other in the baboons, allowing us to sort of identify 3730 03:18:21,290 --> 03:18:24,900 the things that may or may not have the largest effects. 3731 03:18:24,900 --> 03:18:26,680 And then intergenerationally, what we see 3732 03:18:26,680 --> 03:18:29,050 is that mothers who experienced 3733 03:18:29,050 --> 03:18:31,340 a lot of social disadvantage early in their lives, 3734 03:18:31,340 --> 03:18:34,139 particularly loss of their mothers when they were young, 3735 03:18:34,870 --> 03:18:39,130 also exhibit reduced ability to raise their own kids, 3736 03:18:39,130 --> 03:18:41,460 this sort of very generally 3737 03:18:41,460 --> 03:18:43,870 has some parallels to Dr. Collins's work, 3738 03:18:43,870 --> 03:18:47,750 their kids, you know, and this can be decades later, 3739 03:18:47,750 --> 03:18:51,470 are less likely to survive, to reach adulthood, 3740 03:18:51,470 --> 03:18:54,740 and that's controlling for their kids' immediate exposure 3741 03:18:54,740 --> 03:18:56,980 to social disadvantage early in life. 3742 03:18:56,980 --> 03:18:59,250 And so of course, we are very interested 3743 03:18:59,250 --> 03:19:03,750 in the mechanisms through which those relationships might occur. 3744 03:19:03,750 --> 03:19:05,340 We also see some long-term effects 3745 03:19:05,340 --> 03:19:08,620 of early-life on differences in DNA methylation, 3746 03:19:08,620 --> 03:19:10,110 and our animals measured in adulthood, 3747 03:19:10,110 --> 03:19:11,390 which again, is something 3748 03:19:11,390 --> 03:19:13,790 that I think Dr. Collins alluded to in his talk. 3749 03:19:14,830 --> 03:19:18,160 Dr. Sharon Jackson: Fascinating, and that's a perfect segue 3750 03:19:18,160 --> 03:19:21,210 to go back to Dr. Collins to ask some questions. 3751 03:19:21,210 --> 03:19:24,950 So I will pose a question for Dr. Collins now, 3752 03:19:25,820 --> 03:19:28,490 and then possibly, depending on the time, 3753 03:19:28,490 --> 03:19:30,360 we may have time for one more question, 3754 03:19:30,360 --> 03:19:32,120 but certainly in the breakout session, 3755 03:19:32,120 --> 03:19:35,340 we will have far more discussion on this. 3756 03:19:35,340 --> 03:19:39,700 So Dr. Collins, fantastic talk as well. 3757 03:19:39,700 --> 03:19:42,340 And do you have examples of how interventions 3758 03:19:42,340 --> 03:19:46,660 on social determinants of health affect birth outcomes? 3759 03:19:48,890 --> 03:19:50,490 Dr. James Collins: So interventions, 3760 03:19:54,610 --> 03:19:56,390 that's a good question. 3761 03:20:00,480 --> 03:20:03,020 I myself haven't looked at interventions 3762 03:20:03,020 --> 03:20:05,570 trying to address the social determinants. 3763 03:20:08,760 --> 03:20:11,810 What we have is just we've taken advantage 3764 03:20:11,810 --> 03:20:14,950 of natural experiments that have occurred. 3765 03:20:16,780 --> 03:20:19,240 I talked briefly about the upward mobility, 3766 03:20:20,380 --> 03:20:23,380 something similar in a different context 3767 03:20:23,380 --> 03:20:26,280 would be looking at affluent white women 3768 03:20:26,280 --> 03:20:30,160 who have downward mobility due to something happening 3769 03:20:30,160 --> 03:20:32,960 and their birth outcomes deteriorate 3770 03:20:32,960 --> 03:20:34,850 despite them being poor and affluent, 3771 03:20:35,580 --> 03:20:38,370 if they have downward mobility, things don't do well. 3772 03:20:38,370 --> 03:20:41,740 But in terms of interventions on the social determinants, 3773 03:20:42,990 --> 03:20:45,780 there was a study that looked at the stress 3774 03:20:45,780 --> 03:20:49,330 of having to work in a job 3775 03:20:49,330 --> 03:20:53,850 where you have little control over your workplace, 3776 03:20:53,850 --> 03:20:55,450 and this is looking in Europe, 3777 03:20:56,360 --> 03:20:59,530 and found that women who worked in high stress jobs 3778 03:20:59,530 --> 03:21:01,590 in terms of having no control 3779 03:21:01,590 --> 03:21:04,180 had more increased risk of preterm birth. 3780 03:21:04,180 --> 03:21:06,570 And the intervention was they actually look at women 3781 03:21:06,570 --> 03:21:09,760 who started to have some control over their workplace, 3782 03:21:10,780 --> 03:21:12,380 their birth outcomes improved. 3783 03:21:14,060 --> 03:21:17,420 But that was a European study. My brain is thinking, 3784 03:21:17,420 --> 03:21:18,670 but I'm not coming up with something 3785 03:21:18,670 --> 03:21:20,569 that explicitly answers your question. 3786 03:21:21,450 --> 03:21:23,900 Dr. Sharon Jackson: I think that's a foundational answer, 3787 03:21:23,900 --> 03:21:27,000 because, you know, I think that the shift to intervention 3788 03:21:27,000 --> 03:21:32,950 and, you know, dissemination of those interventional strategies 3789 03:21:33,770 --> 03:21:35,180 is always lagging. 3790 03:21:35,180 --> 03:21:36,440 Dr. James Collins: Yes, correct. 3791 03:21:36,440 --> 03:21:38,000 Dr. Sharon Jackson: And as you demonstrated, 3792 03:21:38,000 --> 03:21:40,420 there are studies that are taking this issue on 3793 03:21:40,420 --> 03:21:43,850 and combine them with what we've just heard from Dr. Tung, 3794 03:21:43,850 --> 03:21:48,170 and the ability to study these pathways 3795 03:21:48,950 --> 03:21:51,570 faster in the primate models, 3796 03:21:51,570 --> 03:21:53,660 will allow for some of these questions 3797 03:21:53,660 --> 03:21:57,580 and possible interventions to be integrated into, 3798 03:21:58,240 --> 03:22:02,960 you know, the disseminated reports on this phenomenon. 3799 03:22:02,960 --> 03:22:04,560 Dr. James Collins: Yeah, agree. Yep. 3800 03:22:05,070 --> 03:22:07,560 Dr. Sharon Jackson: Okay, and I do have one last question. 3801 03:22:07,560 --> 03:22:09,580 No, we may have time for another question. 3802 03:22:09,580 --> 03:22:11,730 I do have another question for Dr. Collins. 3803 03:22:15,330 --> 03:22:17,650 What's your response to Dr. Tung's work? 3804 03:22:17,650 --> 03:22:19,910 Do you see parallels and similarities? 3805 03:22:19,910 --> 03:22:21,790 And I guess we should throw this out for both of you. 3806 03:22:21,790 --> 03:22:23,000 Dr. James Collins: Sure. 3807 03:22:23,000 --> 03:22:24,660 Dr. Sharon Jackson: We'll start with you, Dr. Collins. 3808 03:22:24,660 --> 03:22:26,390 Dr. James Collins: Yeah, we both see parallels. 3809 03:22:26,390 --> 03:22:28,420 And I- you know, I wish we had communicated sooner, 3810 03:22:28,420 --> 03:22:32,610 though we try to. Obviously, there are parallels 3811 03:22:32,610 --> 03:22:35,450 in terms of the whole what happens early in life 3812 03:22:35,450 --> 03:22:37,730 can affect what happens later on. 3813 03:22:37,730 --> 03:22:41,110 And it's socially driven, and that the impact of, 3814 03:22:41,900 --> 03:22:44,710 in my work, looking at impoverishment early, 3815 03:22:44,710 --> 03:22:47,460 in her work, looking at social deprivation early, 3816 03:22:47,460 --> 03:22:51,010 those impacts are pretty, pretty, pretty tenuous. 3817 03:22:52,690 --> 03:22:56,260 Definitely see parallels. And we're in the process 3818 03:22:56,260 --> 03:22:58,290 of trying to address generational 3819 03:22:58,290 --> 03:23:01,350 across generations of humans, which takes a long time, 3820 03:23:01,350 --> 03:23:03,240 but we've been somewhat successful 3821 03:23:03,240 --> 03:23:05,000 going backwards to look forward. 3822 03:23:05,000 --> 03:23:09,020 So we have a dataset of women who were born in 1950s and 60s, 3823 03:23:09,020 --> 03:23:11,690 and their kids were born in the late 80s early 90s. 3824 03:23:12,610 --> 03:23:15,060 And now those children are about 30 years of age, 3825 03:23:15,790 --> 03:23:17,230 we want to look at their birth records, 3826 03:23:17,230 --> 03:23:19,120 and hopefully over the next few years, 3827 03:23:19,120 --> 03:23:21,040 we can actually start looking at some of this 3828 03:23:21,040 --> 03:23:24,570 across three generations, not just two generations. 3829 03:23:24,570 --> 03:23:28,170 But the whole concept of using a primate model 3830 03:23:28,170 --> 03:23:30,120 that has a little bit shorter lifespan, 3831 03:23:30,940 --> 03:23:33,930 to look at generations, is just very, very, very, 3832 03:23:33,930 --> 03:23:36,190 very intellectually stimulating. Yeah. 3833 03:23:39,350 --> 03:23:41,760 Dr. Jenny Tung: Yeah, you know, I see a lot of parallels. 3834 03:23:41,760 --> 03:23:45,700 I also see a lot of inspiration, things that work, 3835 03:23:46,950 --> 03:23:52,800 especially with sort of health in older age and lifespan 3836 03:23:52,800 --> 03:23:55,440 as outcomes, including linking to early life 3837 03:23:55,440 --> 03:23:58,180 in non-human primate animal models, 3838 03:23:58,920 --> 03:24:03,900 is so in its infancy relative to the long history of work 3839 03:24:03,900 --> 03:24:07,720 on the social determinants of health in human populations. 3840 03:24:07,720 --> 03:24:11,260 And so when we think about any kinds 3841 03:24:11,260 --> 03:24:13,700 of theoretical frameworks for our work, 3842 03:24:13,700 --> 03:24:18,560 or we're looking for, again, inspiration for models to test, 3843 03:24:19,150 --> 03:24:23,860 by talk to colleagues of mine who study human populations, 3844 03:24:23,860 --> 03:24:26,950 and especially, I have to say social scientists, 3845 03:24:26,950 --> 03:24:29,830 who, again, have been thinking about this longer and deeper 3846 03:24:29,830 --> 03:24:33,740 than those of us who work on non-human animal models. 3847 03:24:33,740 --> 03:24:34,970 So it was actually- 3848 03:24:34,970 --> 03:24:37,930 yeah, it was delightful to see Dr. Collins's presentation. 3849 03:24:41,140 --> 03:24:42,470 Dr. Sharon Jackson: Great, thank you. 3850 03:24:42,470 --> 03:24:45,180 And I think we have time for one last question for this session, 3851 03:24:45,180 --> 03:24:49,630 and then we'll move on to the second case studies. 3852 03:24:49,630 --> 03:24:52,060 And this is for Dr. Tung. 3853 03:24:52,590 --> 03:24:56,960 Does your work suggest social hierarchy is inevitable, 3854 03:24:56,960 --> 03:25:00,760 and should the goal be to decrease distance 3855 03:25:00,760 --> 03:25:02,880 from those at the top to the bottom 3856 03:25:03,540 --> 03:25:06,500 in the models that you're examining? 3857 03:25:08,280 --> 03:25:12,910 Dr. Jenny Tung: And so I think that if one takes 3858 03:25:12,910 --> 03:25:14,900 an evolutionary and comparative perspective, 3859 03:25:14,900 --> 03:25:18,160 and looked at sort of social hierarchy and social status, 3860 03:25:18,160 --> 03:25:19,990 you would actually very rapidly come to conclusion 3861 03:25:19,990 --> 03:25:23,120 that it's not inevitable. And that's because the models 3862 03:25:23,120 --> 03:25:24,700 that I talked to you about today, 3863 03:25:24,700 --> 03:25:26,510 the species that are in sex combinations 3864 03:25:26,510 --> 03:25:28,110 that I talked to you about today 3865 03:25:29,060 --> 03:25:32,180 tend to be pretty hierarchical species. 3866 03:25:32,180 --> 03:25:35,240 And that's where they make a good model 3867 03:25:35,240 --> 03:25:36,550 for what I was trying to study. 3868 03:25:36,550 --> 03:25:38,850 But there are actually a number of other 3869 03:25:38,850 --> 03:25:42,360 primates where hierarchies are their group living primates 3870 03:25:42,360 --> 03:25:46,360 are basically undetectable, or very, very flat. 3871 03:25:46,980 --> 03:25:50,360 And even within the species that I study, 3872 03:25:50,360 --> 03:25:53,600 which, again, they're pretty hierarchical species 3873 03:25:53,600 --> 03:25:55,960 that actually a term of art in primatology 3874 03:25:55,960 --> 03:25:57,530 for the rhesus macaques I had told you about 3875 03:25:57,530 --> 03:25:59,340 is that they are despotic species, 3876 03:25:59,900 --> 03:26:02,030 is that if you look social group by social group, 3877 03:26:02,030 --> 03:26:06,200 they're not all the same, right? We can draw like sort of lines 3878 03:26:06,200 --> 03:26:08,790 that describe how steep these hierarchies are, 3879 03:26:08,790 --> 03:26:10,260 and some are flatter than others. 3880 03:26:10,260 --> 03:26:11,730 It's just much harder to study that 3881 03:26:11,730 --> 03:26:14,330 because then you go from the space of N 3882 03:26:14,330 --> 03:26:16,130 equals the number of individuals to the space of N 3883 03:26:16,130 --> 03:26:17,370 equals the number of groups. 3884 03:26:17,370 --> 03:26:19,170 I think it's a fascinating question, 3885 03:26:19,170 --> 03:26:22,080 and understanding what mediates those differences 3886 03:26:22,080 --> 03:26:24,530 is definitely an area of interest for the future. 3887 03:26:26,650 --> 03:26:28,640 Dr. Sharon Jackson: Absolutely, thank you so much, 3888 03:26:28,640 --> 03:26:33,640 Dr. Collins and Dr. Tung, for your seminars here. 3889 03:26:33,640 --> 03:26:36,140 And everyone, we will have a breakout session 3890 03:26:36,140 --> 03:26:38,240 in just about an hour. 3891 03:26:38,240 --> 03:26:40,890 And we invite everyone to participate 3892 03:26:40,890 --> 03:26:43,620 in either this session or the breakout session 3893 03:26:43,620 --> 03:26:47,320 for our next set of speakers. 3894 03:26:47,320 --> 03:26:49,570 We're going to make a transition now 3895 03:26:49,570 --> 03:26:51,170 to the next breakout session. 3896 03:26:52,750 --> 03:26:54,100 Dr. Miya Whitaker: Good afternoon. 3897 03:26:54,100 --> 03:26:55,470 I am Dr. Miya Whitaker. 3898 03:26:55,470 --> 03:26:57,390 I'm a program director within the NIH 3899 03:26:57,390 --> 03:26:59,260 Office of Research on Women's Health, 3900 03:26:59,260 --> 03:27:00,550 and it is my distinct pleasure 3901 03:27:00,550 --> 03:27:02,860 to welcome you to today's case study session. 3902 03:27:03,390 --> 03:27:06,570 This case study session is focused on population science 3903 03:27:06,570 --> 03:27:08,130 and intervention science. 3904 03:27:08,130 --> 03:27:11,340 Before we get started, there are a few housekeeping items. 3905 03:27:11,340 --> 03:27:12,630 I just want to remind you 3906 03:27:12,630 --> 03:27:15,810 that there is live captioning available if needed. 3907 03:27:15,810 --> 03:27:17,260 If you have a question or questions 3908 03:27:17,260 --> 03:27:18,480 for either of our presenters, 3909 03:27:18,480 --> 03:27:20,860 please submit them using the chat function. 3910 03:27:20,860 --> 03:27:23,400 Feel free to indicate to which presenter 3911 03:27:23,400 --> 03:27:25,230 you'd like your question directed, 3912 03:27:25,230 --> 03:27:28,800 and I'd be happy to moderate those questions for you shortly. 3913 03:27:28,800 --> 03:27:31,540 We will now transition into our panelists' presentations 3914 03:27:31,540 --> 03:27:34,640 after which I will moderate a Q&A session. 3915 03:27:34,640 --> 03:27:36,970 And so we'll begin our case study session, 3916 03:27:36,970 --> 03:27:40,210 session two with Dr. Rachel Gold. 3917 03:27:40,210 --> 03:27:42,150 I want to welcome Doctors Rachel Gold 3918 03:27:42,150 --> 03:27:44,090 and Doctors Daphne Martschenko. 3919 03:27:44,090 --> 03:27:47,440 Dr. Gold is an epidemiologist and health services researcher 3920 03:27:47,440 --> 03:27:50,920 at the Kaiser Permanente Center for Health Research, in OCHIN. 3921 03:27:51,430 --> 03:27:54,170 Dr. Martschenko is a postdoctoral research scientist 3922 03:27:54,170 --> 03:27:56,680 at the Stanford Center for Biomedical Ethics. 3923 03:27:56,680 --> 03:28:02,210 And she is a co-organizer of the International Race Empire 3924 03:28:02,210 --> 03:28:04,580 and Education Research Collective. 3925 03:28:04,580 --> 03:28:07,310 I turn the virtual podium over to Dr. Gold. 3926 03:28:07,310 --> 03:28:09,670 The podium is yours, Dr. Gold, and thank you. 3927 03:28:10,560 --> 03:28:12,000 Dr. Rachel Gold: My pleasure. Thank you for having me today. 3928 03:28:12,000 --> 03:28:13,799 Can you hear me? Is everything good? 3929 03:28:15,460 --> 03:28:16,800 If I could get a verbal response. 3930 03:28:16,800 --> 03:28:18,080 Male Speaker: Yes, we do. 3931 03:28:18,080 --> 03:28:19,300 Dr. Rachel Gold: Okay, thank you very much. 3932 03:28:19,300 --> 03:28:20,970 I got my slides up and I can't see you. 3933 03:28:20,970 --> 03:28:22,940 Okay, terrific. Well, thanks so much for having me. 3934 03:28:22,940 --> 03:28:24,970 I want to just say to Doctors Tung and Collins, 3935 03:28:24,970 --> 03:28:27,640 that was an amazing set of presentations. 3936 03:28:27,640 --> 03:28:30,530 Dr. Tung, your work seems really fun. 3937 03:28:31,660 --> 03:28:34,080 I wish I had pictures of primates 3938 03:28:34,080 --> 03:28:35,680 to show you guys but I do not. 3939 03:28:36,870 --> 03:28:38,270 What I really like about the order 3940 03:28:38,270 --> 03:28:39,500 of how things have gone today 3941 03:28:39,500 --> 03:28:42,130 is we've talked about the structural drivers 3942 03:28:42,130 --> 03:28:47,250 of health inequities and social determinants of social risks, 3943 03:28:47,840 --> 03:28:51,510 and we've talked about some of the biological processes 3944 03:28:51,510 --> 03:28:54,330 that are involved. And I'm going to talk now about, 3945 03:28:54,330 --> 03:28:56,750 well, what can we do from a primary care perspective? 3946 03:28:56,750 --> 03:28:58,430 Like, how can we intervene? 3947 03:28:59,130 --> 03:29:00,470 And I'm going to talk about some work 3948 03:29:00,470 --> 03:29:02,750 that I've been doing in that space. 3949 03:29:04,200 --> 03:29:06,570 I think it'd be really nice transition. 3950 03:29:07,170 --> 03:29:11,090 So just to get started, I know this group all knows this, 3951 03:29:11,090 --> 03:29:13,410 but my team just recently published a paper 3952 03:29:13,410 --> 03:29:14,830 in the Journal of Preventive Medicine 3953 03:29:14,830 --> 03:29:18,880 showing that among community health center patients, 3954 03:29:18,880 --> 03:29:24,380 that's CHCs, with diabetes, who had a number of different 3955 03:29:24,380 --> 03:29:28,030 social risks, housing or transportation needs, 3956 03:29:28,760 --> 03:29:30,980 essentially no less likely than those 3957 03:29:30,980 --> 03:29:32,980 who didn't report having those risks 3958 03:29:32,980 --> 03:29:35,670 to be up-to-date on their diabetes kick, right? 3959 03:29:35,670 --> 03:29:37,400 They're getting the same quality of care, 3960 03:29:37,400 --> 03:29:39,260 regardless of whether they had a social risk, 3961 03:29:39,260 --> 03:29:42,090 up-to-date on A1C, screenings, et cetera. 3962 03:29:42,090 --> 03:29:45,150 But even among those who were up-to-date on care, 3963 03:29:46,410 --> 03:29:50,200 those with food insecurity had lower rates of controlled A1C, 3964 03:29:50,200 --> 03:29:51,910 those with transportation insecurity 3965 03:29:51,910 --> 03:29:55,290 had lower rates of controlled A1C, blood pressure and LDL. 3966 03:29:55,290 --> 03:29:57,440 And surprisingly to me, those with housing insecurity 3967 03:29:57,440 --> 03:30:00,380 did not show differences in those measures, 3968 03:30:00,380 --> 03:30:01,770 but clearly, they were associated 3969 03:30:01,770 --> 03:30:04,020 with food and transportation. And our take-home here, 3970 03:30:04,020 --> 03:30:07,460 which again, won't be a surprise to anyone in this audience, 3971 03:30:07,460 --> 03:30:09,940 is that just because someone guideline can coordinate care 3972 03:30:09,940 --> 03:30:12,160 isn't enough, because social risks 3973 03:30:12,160 --> 03:30:15,600 affect health outcomes way beyond access to care. 3974 03:30:16,920 --> 03:30:18,470 I'm going to try to go to the next slide. 3975 03:30:18,470 --> 03:30:19,690 Correct. Terrific. 3976 03:30:19,690 --> 03:30:23,220 So the National Academies in 2019 put out a report 3977 03:30:23,220 --> 03:30:25,230 on integrating social care and delivery of healthcare, 3978 03:30:25,230 --> 03:30:27,690 and they came up with what they call the five 3979 03:30:27,690 --> 03:30:29,650 As of social care integration, which is again, 3980 03:30:29,650 --> 03:30:32,160 how do we just use social risk information 3981 03:30:32,770 --> 03:30:34,030 in a clinical setting? 3982 03:30:34,030 --> 03:30:35,940 And I'm going to talk to you about a few of those today, 3983 03:30:35,940 --> 03:30:37,830 but we're focusing on adjustment. 3984 03:30:37,830 --> 03:30:40,090 The activities that are focused on healthcare delivery 3985 03:30:40,090 --> 03:30:41,690 fall into three categories. 3986 03:30:42,210 --> 03:30:45,330 I'll start with awareness, which is simply do we know, 3987 03:30:45,330 --> 03:30:47,580 does the care team know that there are factors 3988 03:30:47,580 --> 03:30:50,890 affecting a patient's ability perhaps to act on the care plan, 3989 03:30:50,890 --> 03:30:52,740 that they have social risks that are affecting their health? 3990 03:30:52,740 --> 03:30:55,360 Do they- assistance is really what we mean, 3991 03:30:55,360 --> 03:30:57,190 we talk about basically making referrals 3992 03:30:57,190 --> 03:30:59,240 to social service agencies, foodbank, 3993 03:30:59,240 --> 03:31:00,840 housing support, transportation, 3994 03:31:02,310 --> 03:31:04,840 or providing those services directly from the clinic. 3995 03:31:04,840 --> 03:31:08,000 Adjustment, which is where our team is focusing is, 3996 03:31:08,000 --> 03:31:10,020 can you adjust the care plan 3997 03:31:10,020 --> 03:31:13,380 so the patient who has perhaps some kind of social risk 3998 03:31:13,380 --> 03:31:15,360 is more able to act on the care plan? 3999 03:31:15,360 --> 03:31:17,770 And I'm going to talk to you about this quite a bit. 4000 03:31:17,770 --> 03:31:19,970 So what might adjustment activities involve? 4001 03:31:20,810 --> 03:31:23,280 For example, they might involve making sure medications 4002 03:31:23,280 --> 03:31:25,020 are affordable, making sure the patient 4003 03:31:25,020 --> 03:31:26,600 is prescribed a generic prescription 4004 03:31:26,600 --> 03:31:29,020 or a polypill where there's less of a copay. 4005 03:31:29,020 --> 03:31:30,730 Can you mail the prescription to their home 4006 03:31:30,730 --> 03:31:33,290 if they've got transportation insecurity? 4007 03:31:33,290 --> 03:31:34,660 Can you make follow-up care? 4008 03:31:34,660 --> 03:31:36,070 Can you space out the visits more 4009 03:31:36,070 --> 03:31:38,330 so there's not as much of a transportation barrier? 4010 03:31:38,330 --> 03:31:41,790 Can you provide telehealth? If someone is houseless, 4011 03:31:41,790 --> 03:31:44,020 can you avoid refrigerated medications? 4012 03:31:44,020 --> 03:31:46,340 If someone is food insecure and has diabetes, 4013 03:31:46,340 --> 03:31:49,030 do you need to think about modifying the insulin dose 4014 03:31:49,030 --> 03:31:51,980 for when during the month their food benefits run out? 4015 03:31:51,980 --> 03:31:53,879 These are just a few examples of many. 4016 03:31:54,580 --> 03:31:55,890 There is some beginning evidence 4017 03:31:55,890 --> 03:31:57,810 that shows you do get better clinical outcomes 4018 03:31:57,810 --> 03:31:59,460 when these adjustments are made. 4019 03:31:59,460 --> 03:32:02,390 But there's also evidence primarily 4020 03:32:02,390 --> 03:32:04,050 from my colleague Saul Wiener, 4021 03:32:04,050 --> 03:32:07,230 that adjustments only occur less than a fourth of the time 4022 03:32:07,230 --> 03:32:08,690 in many different care settings, 4023 03:32:08,690 --> 03:32:11,950 even when there's information presented about them. 4024 03:32:11,950 --> 03:32:14,250 So what is the best way to present social risk information 4025 03:32:14,250 --> 03:32:15,510 in the clinical setting- 4026 03:32:15,510 --> 03:32:18,260 and again, I'm mostly focused on primary care- 4027 03:32:18,260 --> 03:32:20,800 to help increase the provision of adjustments? 4028 03:32:20,800 --> 03:32:23,390 Might tools in the electronic health record help, 4029 03:32:23,390 --> 03:32:25,750 reminders, summaries, recommendations? 4030 03:32:25,750 --> 03:32:28,000 So we've got a study right now, I'm co-PI 4031 03:32:28,000 --> 03:32:32,230 with Dr. Laura Gottlieb at UCSF. It's funded by NIMHD. 4032 03:32:32,230 --> 03:32:33,520 Thank you, we really appreciate it. 4033 03:32:33,520 --> 03:32:34,890 And we believe it is the first study 4034 03:32:34,890 --> 03:32:37,040 to develop clinical decision support 4035 03:32:37,040 --> 03:32:39,850 targeting the provision of social risk informed care, 4036 03:32:39,850 --> 03:32:42,520 which is another way we talk about adjustments. 4037 03:32:42,520 --> 03:32:44,120 And the study is called CO-HERE. 4038 03:32:45,150 --> 03:32:48,150 Here's where we are, we spent about a couple of years 4039 03:32:48,150 --> 03:32:49,450 developing a set of tools, 4040 03:32:49,450 --> 03:32:53,050 working with CHC staff, diverse staff. 4041 03:32:53,910 --> 03:32:56,610 We looked at how those tools work and some pilot clinics, 4042 03:32:56,610 --> 03:32:58,080 we are now revising the tools 4043 03:32:58,080 --> 03:33:00,510 based on what we learned from the pilot process, 4044 03:33:00,510 --> 03:33:04,350 and we are about to start testing of the tools' impact on- 4045 03:33:04,350 --> 03:33:06,699 and we're looking at hypertension and diabetes. 4046 03:33:07,920 --> 03:33:09,200 We're right in the middle. 4047 03:33:09,200 --> 03:33:10,640 We are in the revision process right now. 4048 03:33:10,640 --> 03:33:12,010 And I'm going to talk to you a lot 4049 03:33:12,010 --> 03:33:13,250 about the content of the tools, 4050 03:33:13,250 --> 03:33:15,020 I think you'll think it's interesting. 4051 03:33:15,020 --> 03:33:16,920 Just in general, the COHERE tools, 4052 03:33:16,920 --> 03:33:20,250 this is the pilot version that we initially tested, again, 4053 03:33:20,250 --> 03:33:21,690 it's targeting care plan adjustment 4054 03:33:21,690 --> 03:33:23,290 in hypertension and diabetes, 4055 03:33:24,080 --> 03:33:27,080 and also helping to document those adjustments. 4056 03:33:27,080 --> 03:33:30,190 So the in brief, though, this is the way the tools work, 4057 03:33:30,190 --> 03:33:33,000 if the patient as we've been calling a clinical red flag, 4058 03:33:33,000 --> 03:33:35,280 uncontrolled hypertension, or diabetes, 4059 03:33:35,280 --> 03:33:38,560 or a high no-show rate for visits in the last year, 4060 03:33:38,560 --> 03:33:40,720 and if we know that they have social risk information, 4061 03:33:40,720 --> 03:33:42,110 or if that information isn't there, 4062 03:33:42,110 --> 03:33:43,400 and I'll show you that in a minute, 4063 03:33:43,400 --> 03:33:44,780 that is what activates the tool. 4064 03:33:44,780 --> 03:33:46,820 So those are the patients for whom these tools 4065 03:33:46,820 --> 03:33:48,420 become available in the EHR. 4066 03:33:49,950 --> 03:33:51,670 I'm not going to make you look at all of this text, 4067 03:33:51,670 --> 03:33:54,010 I want to just draw your attention to the first category- 4068 03:33:54,010 --> 03:33:55,270 excuse me, the first column, 4069 03:33:55,270 --> 03:33:58,360 to just note that there are three broad categories 4070 03:33:58,360 --> 03:34:00,780 that the pilot version of the tools fell into. 4071 03:34:00,780 --> 03:34:03,210 One was around awareness, not actually adjustment, 4072 03:34:03,210 --> 03:34:05,200 awareness has to precede adjustment. 4073 03:34:05,200 --> 03:34:07,090 But can you increase- how can we use these tools 4074 03:34:07,090 --> 03:34:08,990 to increase social risk awareness, 4075 03:34:08,990 --> 03:34:10,730 increase social risk screening, 4076 03:34:10,730 --> 03:34:13,200 and increase documentation in the EHR? 4077 03:34:14,130 --> 03:34:16,230 The second category is can you facilitate 4078 03:34:16,230 --> 03:34:19,460 and document patient provider discussions about social risks? 4079 03:34:20,520 --> 03:34:22,400 And can we also recommend 4080 03:34:22,400 --> 03:34:24,780 and also document care plan changes? 4081 03:34:26,160 --> 03:34:28,120 And again, a lot of the examples are quite like the ones 4082 03:34:28,120 --> 03:34:29,690 that I talked to you about before, 4083 03:34:29,690 --> 03:34:31,010 you'll be able to see these slides, 4084 03:34:31,010 --> 03:34:32,260 I know, on the website. 4085 03:34:32,260 --> 03:34:33,720 And I'll get into the stuff in more detail. 4086 03:34:33,720 --> 03:34:36,319 So I don't want to linger too long on all this text. 4087 03:34:37,520 --> 03:34:40,180 But the pilot clinics, here's what they like thought 4088 03:34:40,180 --> 03:34:42,210 about those pilot tools, the pilot version of the tools, 4089 03:34:42,210 --> 03:34:45,450 which I thought these responses were totally fascinating. 4090 03:34:45,450 --> 03:34:48,040 The most profound thing we heard was, 4091 03:34:48,740 --> 03:34:50,540 these are called Community Health Center staff, 4092 03:34:50,540 --> 03:34:52,300 and they said, "We already do this. 4093 03:34:52,300 --> 03:34:54,480 We don't need your tools to suggest 4094 03:34:54,480 --> 03:34:56,170 that we make care planning adjustments. 4095 03:34:56,170 --> 03:34:57,670 We do that. That's our job. 4096 03:34:57,670 --> 03:34:59,700 Don't tell us how to do our job." 4097 03:34:59,700 --> 03:35:02,860 They didn't like that. They didn't like recommendations 4098 03:35:02,860 --> 03:35:05,400 but they did like the documentation aspects 4099 03:35:05,400 --> 03:35:07,280 that helps our team communicate better 4100 03:35:07,280 --> 03:35:09,250 about the adjustments we're making. 4101 03:35:09,250 --> 03:35:11,210 They love the reminder to do the screening. 4102 03:35:11,210 --> 03:35:14,000 They love the reminder to document patients' 4103 03:35:14,000 --> 03:35:16,860 social risks in a standardized way using Z-codes. 4104 03:35:18,410 --> 03:35:20,960 They wanted fewer clicks, they wanted less text. 4105 03:35:21,480 --> 03:35:23,070 They wanted a one-click tool 4106 03:35:23,070 --> 03:35:25,860 to document provided adjustments. 4107 03:35:25,860 --> 03:35:28,940 And one suggestion that came up that we ran with was, 4108 03:35:28,940 --> 03:35:31,670 could they work like a well-baby visit checklist? 4109 03:35:31,670 --> 03:35:33,270 We discussed the following. 4110 03:35:34,750 --> 03:35:37,140 So here's how we revised those tools. 4111 03:35:37,140 --> 03:35:39,060 We kept the reminder alerts, 4112 03:35:39,060 --> 03:35:41,290 "Hey, this patient is due for social risk screening, 4113 03:35:41,290 --> 03:35:43,620 hey, do you want to document this recently- 4114 03:35:43,620 --> 03:35:44,870 this patient's social risk 4115 03:35:44,870 --> 03:35:47,280 as in the problem list with a Z-code?" 4116 03:35:47,280 --> 03:35:49,840 We got rid of tools that were in Smart-Sets, 4117 03:35:49,840 --> 03:35:51,210 which were kind of like order sets, 4118 03:35:51,210 --> 03:35:53,010 these recommended actions and preset orders. 4119 03:35:53,010 --> 03:35:54,370 They didn't like it. 4120 03:35:54,370 --> 03:35:56,650 They said, "You're telling me how to do my job." 4121 03:35:56,650 --> 03:35:58,600 We did add a documentation checklist. 4122 03:35:58,600 --> 03:36:00,340 So I'm going to show that to you in a minute. 4123 03:36:00,340 --> 03:36:02,500 With a list of potential adjustments 4124 03:36:02,500 --> 03:36:03,780 and the way we framed it, 4125 03:36:03,780 --> 03:36:05,690 as you'll see, it basically says, 4126 03:36:06,710 --> 03:36:09,020 the patient and I talked about the following. 4127 03:36:09,020 --> 03:36:11,810 So without actually suggesting folks do stuff, 4128 03:36:11,810 --> 03:36:14,330 we do have a list there that kind of serves two purposes. 4129 03:36:14,330 --> 03:36:16,740 Right? One is to be able to let folks do the documentation: 4130 03:36:16,740 --> 03:36:19,360 Yeah, I took care of this. We talked about the following. 4131 03:36:19,360 --> 03:36:21,580 And then you put it and add it to the note or the problem list, 4132 03:36:21,580 --> 03:36:24,250 but it also by being there inherently might, 4133 03:36:24,250 --> 03:36:26,030 you know, trigger some: 4134 03:36:26,030 --> 03:36:27,900 Oh, yeah, I should talk to that patient about that. 4135 03:36:27,900 --> 03:36:30,760 It might it, you know, just in case they forgot. 4136 03:36:30,760 --> 03:36:33,980 And in general, the tools look at managing medication costs, 4137 03:36:33,980 --> 03:36:37,710 this is driven by our stakeholders' interest, 4138 03:36:37,710 --> 03:36:40,000 titrating insulin based on food availability, 4139 03:36:40,000 --> 03:36:42,710 changing follow-up encounter modality and timing just again 4140 03:36:42,710 --> 03:36:44,810 to make sure you can get to that follow-up care, 4141 03:36:44,810 --> 03:36:46,800 and adding relevant information about all of that 4142 03:36:46,800 --> 03:36:48,070 to the after-visit summary 4143 03:36:48,070 --> 03:36:50,470 so the patient has a record of it as well. 4144 03:36:50,470 --> 03:36:52,050 One thing that is new in the tools 4145 03:36:52,050 --> 03:36:53,380 that we're going to take to trial 4146 03:36:53,380 --> 03:36:54,990 is that there are some choices that study clinics 4147 03:36:54,990 --> 03:36:56,190 are going to have to make. 4148 03:36:56,190 --> 03:36:57,920 One is, do you want to put this checklist- 4149 03:36:57,920 --> 03:36:59,450 which again, I'm going to show you soon- 4150 03:36:59,450 --> 03:37:00,910 in an existing note template? 4151 03:37:00,910 --> 03:37:02,420 So if you've got a visit note template, 4152 03:37:02,420 --> 03:37:05,200 it'd be pretty easy to just stick this checklist into that, 4153 03:37:05,200 --> 03:37:07,180 but not all clinics use that. 4154 03:37:07,180 --> 03:37:08,640 Have a rooming staff start a note 4155 03:37:08,640 --> 03:37:11,090 for the provider using a smart phrase, 4156 03:37:12,050 --> 03:37:16,510 or which is a text shortcut. Or have- or by having the tools 4157 03:37:17,620 --> 03:37:19,600 just right there in the problem list 4158 03:37:19,600 --> 03:37:23,290 or in the- oh, excuse me, 4159 03:37:23,290 --> 03:37:25,480 allowing the support staff to start the note 4160 03:37:25,480 --> 03:37:27,550 or allowing the support staff to do this documentation 4161 03:37:27,550 --> 03:37:29,960 because every CHC has different approaches 4162 03:37:29,960 --> 03:37:32,040 to this kind of which staff roles 4163 03:37:32,040 --> 03:37:33,930 are allowed to do which kind of documentation. 4164 03:37:33,930 --> 03:37:36,729 So in some, the tools that we are going to take to trial 4165 03:37:37,740 --> 03:37:39,010 are designed to, 4166 03:37:39,010 --> 03:37:41,610 one, identify the social risk screening is due, 4167 03:37:41,610 --> 03:37:43,220 and it's basically every year, 4168 03:37:43,220 --> 03:37:44,570 which sort of needs to be refreshed, 4169 03:37:44,570 --> 03:37:46,260 with a one-click link to conduct 4170 03:37:46,260 --> 03:37:48,280 that screening documentation in the EHR. 4171 03:37:49,010 --> 03:37:51,600 Another is an alert that says, "Hey, do you want to add 4172 03:37:51,600 --> 03:37:54,350 this Z-code to the problem list or the visit diagnosis? 4173 03:37:55,520 --> 03:37:57,500 Documenting the discussions about the social needs 4174 03:37:57,500 --> 03:38:00,350 and the related adjustments so everyone's aware of them. 4175 03:38:00,350 --> 03:38:03,020 Documenting socio-economic reasons, 4176 03:38:03,020 --> 03:38:05,700 costs specifically for medication non-adherence, 4177 03:38:05,700 --> 03:38:08,120 and also reviewing external formulary information. 4178 03:38:08,120 --> 03:38:10,000 Again, these are all steps to try 4179 03:38:10,000 --> 03:38:12,550 and ensure that medications are affordable. 4180 03:38:12,550 --> 03:38:14,010 And we're focused on medications, 4181 03:38:14,010 --> 03:38:15,580 and again in diabetes and hypertension 4182 03:38:15,580 --> 03:38:19,650 based on our CHC staff who helped design these tools. 4183 03:38:19,650 --> 03:38:22,320 We were going under their direction. 4184 03:38:22,320 --> 03:38:23,590 So I'm going to talk to you 4185 03:38:23,590 --> 03:38:25,030 a little bit about the specific tools. 4186 03:38:25,030 --> 03:38:28,780 One is- and BPA stands for Best Practice Advisory, 4187 03:38:28,780 --> 03:38:30,380 it's an alert in the EHR. 4188 03:38:30,930 --> 03:38:33,550 This one appears to usually the rooming staff, 4189 03:38:33,550 --> 03:38:35,010 but it's really at any encounter 4190 03:38:35,010 --> 03:38:36,370 where social risk screening is done. 4191 03:38:36,370 --> 03:38:37,610 So that might be a community health worker, 4192 03:38:37,610 --> 03:38:39,010 might be a social worker. 4193 03:38:39,010 --> 03:38:41,900 But in an encounter where that kind of screening is done, 4194 03:38:41,900 --> 03:38:43,530 that's where that shows up. 4195 03:38:43,530 --> 03:38:45,420 It pops up if the last social screening 4196 03:38:45,420 --> 03:38:46,650 is more than a year old. 4197 03:38:46,650 --> 03:38:48,470 And we focused on financial housing, 4198 03:38:48,470 --> 03:38:50,870 food, transportation and utilities and security. 4199 03:38:50,870 --> 03:38:53,050 It says social determinants screening is out of date, 4200 03:38:53,050 --> 03:38:54,940 it shows the results from those recent screening 4201 03:38:54,940 --> 03:38:56,200 and the last date screened 4202 03:38:56,200 --> 03:38:58,350 and gives you a quick link to complete the screening. 4203 03:38:58,350 --> 03:38:59,950 Pretty straightforward. 4204 03:39:00,870 --> 03:39:04,640 And then there's the Add Z-code to problem list alert. 4205 03:39:04,640 --> 03:39:09,890 And so the patient has a newly documented social risk status, 4206 03:39:09,890 --> 03:39:12,180 and that could mean they newly reported a social risk 4207 03:39:12,180 --> 03:39:13,930 or they reported it was resolved: 4208 03:39:13,930 --> 03:39:15,570 so I used to have housing security, 4209 03:39:15,570 --> 03:39:18,140 but now it's resolved. That shows up either way. 4210 03:39:19,710 --> 03:39:21,330 It's on the day of the screening. 4211 03:39:21,330 --> 03:39:22,610 It's generally for rooming staff, 4212 03:39:22,610 --> 03:39:23,860 when screening is entered. 4213 03:39:23,860 --> 03:39:26,950 If there's a medical red flag present, as I described before, 4214 03:39:26,950 --> 03:39:30,120 that appears for the provider until either the red flag 4215 03:39:30,120 --> 03:39:31,550 or the social need is resolved. 4216 03:39:31,550 --> 03:39:32,970 And we are going to see how people like that 4217 03:39:32,970 --> 03:39:35,380 because we know people don't like seeing alerts continually, 4218 03:39:35,380 --> 03:39:38,650 but we determined that that was important. 4219 03:39:38,650 --> 03:39:39,920 If the screening is not addressed, 4220 03:39:39,920 --> 03:39:41,719 if the patient doesn't get screened, 4221 03:39:42,320 --> 03:39:43,810 both the provider and the rooming staff, 4222 03:39:43,810 --> 03:39:47,090 again, will get a reminder about that as well. 4223 03:39:47,770 --> 03:39:50,470 The alert says "add this to the visit diagnosis." 4224 03:39:50,470 --> 03:39:53,200 If it's a newly resolved risk, as I said, it says, 4225 03:39:53,200 --> 03:39:55,800 "Do you want to resolve this on the problem list?" 4226 03:39:56,750 --> 03:40:00,820 And then it suggests going to the checklist to document the- 4227 03:40:00,820 --> 03:40:03,440 or using the Smart Phrase, Smart Text, 4228 03:40:03,440 --> 03:40:06,070 to document the adjustments that you have made 4229 03:40:06,070 --> 03:40:07,770 or and discussed with the patient. 4230 03:40:08,530 --> 03:40:11,510 And again, there's a link to the assessment and plan note, 4231 03:40:11,510 --> 03:40:13,210 which is part of the problem list. 4232 03:40:14,920 --> 03:40:17,200 You can also enter Z-code of the visit diagnosis 4233 03:40:17,200 --> 03:40:19,720 or to the problem list if it's not already there. 4234 03:40:19,720 --> 03:40:21,710 I'm going to show you- Oh, it's coming right up. 4235 03:40:21,710 --> 03:40:23,790 So once again, where is this checklist 4236 03:40:23,790 --> 03:40:25,390 that I which I speak so much? 4237 03:40:26,100 --> 03:40:27,920 Again, it can be accessed through note template, 4238 03:40:27,920 --> 03:40:30,090 it can be accessed in the problem list. 4239 03:40:30,090 --> 03:40:32,940 Once the Z-code is entered, it just shows up, here's- 4240 03:40:32,940 --> 03:40:36,470 and it's customized given to a specific patient's data. 4241 03:40:37,500 --> 03:40:39,400 It can also be added manually. 4242 03:40:39,400 --> 03:40:41,010 And here's what it says at the beginning, 4243 03:40:41,010 --> 03:40:44,710 patient has recent whichever red flags triggered this, 4244 03:40:44,710 --> 03:40:46,950 HA1C is too high, blood pressure is not controlled, 4245 03:40:46,950 --> 03:40:48,200 and or history of no shows, 4246 03:40:48,200 --> 03:40:50,180 that will be customized to the patient, 4247 03:40:50,180 --> 03:40:52,779 and socioeconomic barriers as of the following date. 4248 03:40:53,530 --> 03:40:55,660 Based on the social risks documented in the chart, 4249 03:40:55,660 --> 03:40:58,970 we discussed dot-dot-dot. And here's what it looks like. 4250 03:40:59,800 --> 03:41:02,220 I know the language there isn't exactly what I just said, 4251 03:41:02,220 --> 03:41:03,930 you were still dialing in the actual- 4252 03:41:03,930 --> 03:41:05,480 the exact language it's going to say here, 4253 03:41:05,480 --> 03:41:07,380 but you can see what this looks like here, right? 4254 03:41:07,380 --> 03:41:10,620 Here, first, it's- you can add social risk education 4255 03:41:10,620 --> 03:41:11,960 to the after-visit summary. 4256 03:41:11,960 --> 03:41:13,890 There's a quick text link for that. 4257 03:41:14,420 --> 03:41:16,550 But here's what I really want to show you is this dark blue, 4258 03:41:16,550 --> 03:41:19,080 based on the social risks documented in the chart 4259 03:41:19,080 --> 03:41:21,370 to facilitate care plan adherence, we discussed, 4260 03:41:21,370 --> 03:41:23,370 and this is just a part of what's on the list. 4261 03:41:23,370 --> 03:41:25,610 And again, what's on the list would be also customized 4262 03:41:25,610 --> 03:41:27,360 to a given patient's circumstances, 4263 03:41:27,360 --> 03:41:29,630 but they could select talking to the pharmacist 4264 03:41:29,630 --> 03:41:31,650 about help lowering medication costs, 4265 03:41:31,650 --> 03:41:34,420 home delivery, 30 versus 90-day prescriptions, 4266 03:41:34,420 --> 03:41:37,950 again, for a less of a copay, a longer and it will follow up, 4267 03:41:37,950 --> 03:41:39,580 following up via telemedicine et cetera. 4268 03:41:39,580 --> 03:41:42,050 And there's also an- and I'll show you the details of that, 4269 03:41:42,050 --> 03:41:44,700 but I want you to see what it looked like in the EHR. 4270 03:41:46,230 --> 03:41:51,160 So here on the leftmost column is I think the complete list 4271 03:41:51,160 --> 03:41:52,810 of what you might see in that Smart-list text, 4272 03:41:52,810 --> 03:41:54,640 we discussed titrating insulin, 4273 03:41:55,280 --> 03:41:57,310 possible generic or formulary substitutions. 4274 03:41:57,310 --> 03:42:00,570 And this is what would show in the user, the clinical side, 4275 03:42:00,570 --> 03:42:02,700 and added to the note. And then on the patient text, 4276 03:42:02,700 --> 03:42:04,390 if you want to add it to the after-visit summary, 4277 03:42:04,390 --> 03:42:06,220 you would say you and your provider talked about 4278 03:42:06,220 --> 03:42:09,120 how to adjust your insulin dose based on your food intake. 4279 03:42:10,900 --> 03:42:13,480 Okay, so possible generic or formulary substitutions, 4280 03:42:13,480 --> 03:42:15,070 using GoodRX discounts 4281 03:42:15,070 --> 03:42:17,390 is a way to find a lower cost prescription. 4282 03:42:17,390 --> 03:42:19,700 Hey, the patient- the discount code from some 4283 03:42:19,700 --> 03:42:22,010 GoodRX may help, you can use this. 4284 03:42:22,010 --> 03:42:24,150 We discussed patient preference around prescriptions 4285 03:42:24,150 --> 03:42:26,430 in a role follow up, following for telemedicine, 4286 03:42:26,430 --> 03:42:28,550 the three- the ellipse here 4287 03:42:28,550 --> 03:42:30,810 means that there's an ability to add free text. 4288 03:42:30,810 --> 03:42:32,200 So other barriers to the patient's 4289 03:42:32,200 --> 03:42:33,750 taking medications as prescribed, 4290 03:42:33,750 --> 03:42:38,500 and then they can fill that in. And we discussed the patient 4291 03:42:38,500 --> 03:42:39,990 talking to the pharmacist about opportunities 4292 03:42:39,990 --> 03:42:43,220 to lower medication costs. And in the AVS, it says, 4293 03:42:43,220 --> 03:42:45,870 "Ask your pharmacist about ways to lower your costs." 4294 03:42:46,670 --> 03:42:48,270 Yeah, so let me move on. 4295 03:42:48,880 --> 03:42:52,970 Okay, and a couple other tools in this suite of tools we built, 4296 03:42:52,970 --> 03:42:55,019 I'm finished talking about the checklist. 4297 03:42:55,830 --> 03:42:58,800 There's a BPA that's within medication adherence, 4298 03:42:58,800 --> 03:43:00,760 and medication adherence is a very standard part 4299 03:43:00,760 --> 03:43:03,080 of rooming in primary care, I'm sure you know that. 4300 03:43:03,080 --> 03:43:05,120 But if a patient's got a medical red flag, 4301 03:43:05,120 --> 03:43:07,580 and any outpatient prescription, it shows up 4302 03:43:07,580 --> 03:43:13,200 when in the rooming best practice section of the EHR. 4303 03:43:13,200 --> 03:43:14,770 And if the patient's medication adherence 4304 03:43:14,770 --> 03:43:18,050 has not been documented, then they see this alert saying, 4305 03:43:18,050 --> 03:43:20,260 "Hey, medication documentation- 4306 03:43:20,260 --> 03:43:22,410 med compliance documentation is not complete." 4307 03:43:22,410 --> 03:43:24,090 And then there are reasons that are given; 4308 03:43:24,090 --> 03:43:26,300 taking, not taking, taking differently. 4309 03:43:27,460 --> 03:43:29,820 And there's an option which includes cost. 4310 03:43:29,820 --> 03:43:31,360 What we did that it's- 4311 03:43:31,360 --> 03:43:32,840 most of this was already in the EHR, 4312 03:43:32,840 --> 03:43:34,320 but what we added was that now 4313 03:43:34,320 --> 03:43:36,060 when they go into medications and orders, 4314 03:43:36,060 --> 03:43:40,180 it highlights the reason the patient is not taking it, 4315 03:43:40,180 --> 03:43:42,660 and the date it's reported, and that was to really try 4316 03:43:42,660 --> 03:43:44,260 and make that front and center. 4317 03:43:45,550 --> 03:43:48,380 And then when other medications are being ordered, 4318 03:43:48,380 --> 03:43:50,320 not in the medication compliance, 4319 03:43:50,320 --> 03:43:52,120 but just during med orders, 4320 03:43:52,120 --> 03:43:54,110 when there's a related order entered in the EHR, 4321 03:43:54,110 --> 03:43:57,209 and again, the patient's got a red flag in any of these risks, 4322 03:43:57,740 --> 03:44:00,620 there might be alerts related to titrating insulin again, 4323 03:44:00,620 --> 03:44:03,100 this medication is not available as a generic 4324 03:44:03,100 --> 03:44:04,899 or you're not prescribing a generic, 4325 03:44:05,540 --> 03:44:07,970 prompt about barriers to taking medications, 4326 03:44:09,080 --> 03:44:11,520 and consider patient preferences around dispense again. 4327 03:44:11,520 --> 03:44:13,510 And then there's also ability to add it with one click 4328 03:44:13,510 --> 03:44:17,760 and add a note to the pharmacy to all of these points. 4329 03:44:18,500 --> 03:44:21,100 Okay. Look at that. Great. 4330 03:44:22,280 --> 03:44:25,080 And we are about to recruit clinics for our main trial, 4331 03:44:25,080 --> 03:44:28,360 and that's what we're doing. 4332 03:44:28,360 --> 03:44:30,360 So I'm not aware of anyone 4333 03:44:30,360 --> 03:44:32,450 who's tried to do anything quite like this before. 4334 03:44:32,450 --> 03:44:34,590 I'd love to hear about it if folks are aware of that. 4335 03:44:34,590 --> 03:44:36,350 And I managed to get my talk in 15 minutes. 4336 03:44:36,350 --> 03:44:38,110 I'm going to stop sharing my slide 4337 03:44:38,110 --> 03:44:39,710 and turn it over to my colleagues, 4338 03:44:39,710 --> 03:44:41,860 and look forward to hearing your questions. 4339 03:44:44,040 --> 03:44:45,300 Dr. Miya Whitaker: Thank you, Dr. Gold. 4340 03:44:45,300 --> 03:44:47,460 This is an amazing presentation. 4341 03:44:47,460 --> 03:44:49,470 We're so grateful to have you here 4342 03:44:49,470 --> 03:44:51,010 to talk about this wonderful tool 4343 03:44:51,010 --> 03:44:53,540 and it definitely represents a great first step 4344 03:44:53,540 --> 03:44:57,480 in our enhanced ability to meet people's social needs. 4345 03:44:57,480 --> 03:44:59,510 So this is wonderful. 4346 03:44:59,510 --> 03:45:00,760 Dr. Rachel Gold: We'll find out if it does. 4347 03:45:00,760 --> 03:45:01,960 Dr. Miya Whitaker: Yeah, agreed. 4348 03:45:01,960 --> 03:45:03,190 Dr. Rachel Gold: I hope so. 4349 03:45:03,190 --> 03:45:04,430 Dr. Miya Whitaker: I hope so too. 4350 03:45:04,430 --> 03:45:06,190 And it definitely seems like it has a lot of potential. 4351 03:45:06,190 --> 03:45:08,760 So thank you again for sharing with us today. 4352 03:45:08,760 --> 03:45:12,330 So I now turn the virtual podium over to Dr. Martschenko. 4353 03:45:12,330 --> 03:45:14,030 Dr. Martschenko? 4354 03:45:14,030 --> 03:45:15,260 Dr. Daphne Martschenko: Thank you so much. 4355 03:45:15,260 --> 03:45:16,480 Thank you, Miya. 4356 03:45:16,480 --> 03:45:18,850 Thank you everyone who's played a role 4357 03:45:18,850 --> 03:45:21,300 in putting together what a wonderful workshop 4358 03:45:21,300 --> 03:45:22,900 it's been so far today. 4359 03:45:23,740 --> 03:45:26,470 I just want to check and make sure that Annamaria is here 4360 03:45:26,470 --> 03:45:29,110 and that you're able to unmute when it's your time, 4361 03:45:29,730 --> 03:45:30,960 and I will screenshare. 4362 03:45:30,960 --> 03:45:34,220 Okay, great. Awesome. Good to see you. All right. 4363 03:45:35,020 --> 03:45:37,750 So, thanks so much, again for having me. 4364 03:45:37,750 --> 03:45:39,120 My name is Daphne Martschenko. 4365 03:45:39,120 --> 03:45:42,220 I'm at the Stanford Center for Biomedical Ethics. 4366 03:45:42,220 --> 03:45:44,920 Hopefully, folks are seeing the right screen, 4367 03:45:44,920 --> 03:45:47,020 but please unmute if you are not. 4368 03:45:48,100 --> 03:45:50,920 What I want to talk through today is first, 4369 03:45:50,920 --> 03:45:53,020 introducing community engagement. 4370 03:45:53,590 --> 03:45:55,010 Some of the presentations we've heard today 4371 03:45:55,010 --> 03:45:56,500 have touched on elements of this, 4372 03:45:56,500 --> 03:45:59,150 but I'm going to do a deep dive into it a little bit. 4373 03:45:59,940 --> 03:46:01,580 And then I'm going to discuss the problem 4374 03:46:01,580 --> 03:46:03,180 of shallow engagement 4375 03:46:04,100 --> 03:46:07,460 and the need for community empowerment. 4376 03:46:07,460 --> 03:46:10,340 And we'll go through a case example, 4377 03:46:10,340 --> 03:46:11,940 a co-design process 4378 03:46:12,490 --> 03:46:15,100 that works to achieve community empowerment. 4379 03:46:15,720 --> 03:46:19,640 My presentation today is going to draw on two manuscripts, 4380 03:46:19,640 --> 03:46:21,210 one of which is forthcoming 4381 03:46:21,210 --> 03:46:23,240 and the other one which is under review. 4382 03:46:24,230 --> 03:46:27,130 And I will make sure that we can share the link 4383 03:46:27,130 --> 03:46:28,730 to the preprint of that bottom 4384 03:46:28,730 --> 03:46:32,740 one sometime during today's presentation. 4385 03:46:32,740 --> 03:46:35,790 But first, what is community engagement? 4386 03:46:36,610 --> 03:46:40,600 So community engagement is about garnering public recognition 4387 03:46:40,600 --> 03:46:44,710 as a social value of research and clinical services. 4388 03:46:44,710 --> 03:46:47,590 It is about respecting participant 4389 03:46:47,590 --> 03:46:49,190 and community values, 4390 03:46:49,870 --> 03:46:52,720 building trust, and also empowering those 4391 03:46:52,720 --> 03:46:56,500 who are traditionally left out of decision making processes. 4392 03:46:58,360 --> 03:46:59,790 That's a high level overview. 4393 03:46:59,790 --> 03:47:02,150 But there are also individual level and system 4394 03:47:02,150 --> 03:47:04,670 level objectives to community engaged research, 4395 03:47:04,670 --> 03:47:05,950 community engagement. 4396 03:47:05,950 --> 03:47:08,910 So some of the individual level objectives here are: 4397 03:47:08,910 --> 03:47:11,890 one, to build rapport between researchers, 4398 03:47:11,890 --> 03:47:14,660 clinicians, participants and communities. 4399 03:47:15,750 --> 03:47:17,560 Another individual level objective 4400 03:47:17,560 --> 03:47:20,970 is to enhance researchers' or clinicians' cultural humility, 4401 03:47:22,430 --> 03:47:24,890 and then also to increase researchers' 4402 03:47:24,890 --> 03:47:28,040 and clinicians' awareness of unconscious bias 4403 03:47:28,040 --> 03:47:31,310 of social political contexts, power structures, 4404 03:47:31,310 --> 03:47:33,100 and social determinants of health. 4405 03:47:34,560 --> 03:47:37,620 System level's objectives for community engagement 4406 03:47:37,620 --> 03:47:39,880 include understanding stakeholder's real life 4407 03:47:39,880 --> 03:47:41,910 needs and constraints, 4408 03:47:41,910 --> 03:47:43,560 empowering underserved communities 4409 03:47:43,560 --> 03:47:47,060 in health research agenda settings and processes, 4410 03:47:47,650 --> 03:47:50,680 and increasing the trustworthiness of research 4411 03:47:50,680 --> 03:47:52,690 in clinical institutions. 4412 03:47:53,910 --> 03:47:58,160 This approach to doing research and clinical translation 4413 03:47:58,160 --> 03:48:00,860 prioritizes co-learning with power sharing, 4414 03:48:01,460 --> 03:48:05,290 it also prioritizes local rather than global knowledge. 4415 03:48:05,800 --> 03:48:08,660 And as I've said already, community empowerment, 4416 03:48:08,660 --> 03:48:11,190 and social and responsive justice. 4417 03:48:11,190 --> 03:48:15,120 And one of the critical elements to community engagement 4418 03:48:15,120 --> 03:48:18,440 is power, benefit, and responsibility sharing, 4419 03:48:18,440 --> 03:48:20,720 which I'll get more into in a moment. 4420 03:48:23,230 --> 03:48:26,120 Other points to consider, so the data that is generated, 4421 03:48:26,120 --> 03:48:29,990 that is collected through community engaged work is local, 4422 03:48:29,990 --> 03:48:34,430 and it requires participants' knowledge of all uses. 4423 03:48:35,260 --> 03:48:39,200 It is also acquired via long- standing trusting relationships 4424 03:48:39,200 --> 03:48:42,300 between researchers, clinicians, and community members, 4425 03:48:42,920 --> 03:48:44,150 and informed consent, 4426 03:48:44,150 --> 03:48:48,180 which often gets mentioned is a living document. 4427 03:48:48,180 --> 03:48:50,190 So it's a formal form, 4428 03:48:50,190 --> 03:48:52,640 but it exists alongside informal agreements 4429 03:48:52,640 --> 03:48:53,930 that develop over time. 4430 03:48:53,930 --> 03:48:56,290 So I think one of the key take-home messages here 4431 03:48:56,290 --> 03:49:00,680 is that the simple informed consent documentation 4432 03:49:00,680 --> 03:49:03,240 that we have our study participants sign 4433 03:49:03,240 --> 03:49:05,450 is not the end all be all, 4434 03:49:05,450 --> 03:49:08,240 when it comes to community engaged research, 4435 03:49:08,240 --> 03:49:12,640 there is a series of informal contractual 4436 03:49:12,640 --> 03:49:15,310 re-consenting processes in the dialog 4437 03:49:15,310 --> 03:49:17,170 that is formed between researchers 4438 03:49:17,170 --> 03:49:19,240 and between the community members. 4439 03:49:20,590 --> 03:49:22,480 So as a kind of summary here, 4440 03:49:22,480 --> 03:49:26,600 community engagement really prioritizes power, benefit, 4441 03:49:26,600 --> 03:49:28,350 and responsibility sharing, 4442 03:49:28,350 --> 03:49:31,690 it is not prioritizing data sharing to the same extent 4443 03:49:31,690 --> 03:49:35,460 that we might see in, for example, secondary research. 4444 03:49:35,460 --> 03:49:38,010 And so I want to emphasize here 4445 03:49:38,010 --> 03:49:41,590 that it's really more than just listening to context experts 4446 03:49:41,590 --> 03:49:43,290 as was mentioned, 4447 03:49:43,290 --> 03:49:45,920 you know, earlier today in the introduction, 4448 03:49:45,920 --> 03:49:47,630 there was a discussion about the importance 4449 03:49:47,630 --> 03:49:50,170 of hearing the perspectives of context experts. 4450 03:49:51,440 --> 03:49:54,500 And in addition to listening and hearing those perspectives, 4451 03:49:54,500 --> 03:49:57,000 it's also about in community engagement work, 4452 03:49:57,000 --> 03:49:59,060 responding and taking action, 4453 03:49:59,670 --> 03:50:02,420 such that decision-making power is shared. 4454 03:50:02,420 --> 03:50:03,930 So that's what I mean by power sharing, 4455 03:50:03,930 --> 03:50:05,560 decision-making power is shared. 4456 03:50:06,070 --> 03:50:08,600 The benefits of doing the research 4457 03:50:08,600 --> 03:50:11,540 or implementing the clinical program are also shared, 4458 03:50:12,370 --> 03:50:13,980 and researchers and clinicians 4459 03:50:13,980 --> 03:50:16,170 are compelled to share the responsibility 4460 03:50:16,170 --> 03:50:18,950 to act in the interests of community members. 4461 03:50:19,660 --> 03:50:21,510 And so including context experts, 4462 03:50:21,510 --> 03:50:24,560 it's critical to ethically and socially 4463 03:50:24,560 --> 03:50:27,240 responsible research and research translation. 4464 03:50:27,780 --> 03:50:30,220 And if we're to take seriously the role 4465 03:50:30,220 --> 03:50:31,960 of social determinants in health, 4466 03:50:31,960 --> 03:50:34,460 then we need to enhance these social aspects 4467 03:50:34,460 --> 03:50:36,280 of research and clinical care. 4468 03:50:36,280 --> 03:50:38,680 And by that, I mean that us as researchers, 4469 03:50:38,680 --> 03:50:42,040 as clinicians, we need to listen, respond to, 4470 03:50:42,040 --> 03:50:45,350 and integrate the perspectives, values, 4471 03:50:45,350 --> 03:50:47,520 and needs of those that we're intending to help. 4472 03:50:47,520 --> 03:50:50,440 And we need to do so early, and we need to do so often. 4473 03:50:51,640 --> 03:50:53,950 The problem however, is that we don't always do this, 4474 03:50:53,950 --> 03:50:57,040 and that's what is called sometimes shallow engagement. 4475 03:50:57,640 --> 03:51:00,770 In shallow engagement, researchers, clinicians, 4476 03:51:00,770 --> 03:51:03,230 make assumptions upfront about communities 4477 03:51:03,230 --> 03:51:05,110 and then use engagement tools 4478 03:51:05,110 --> 03:51:07,510 as a way to validate those assumptions. 4479 03:51:08,270 --> 03:51:09,980 In shallow engagement, communities 4480 03:51:09,980 --> 03:51:12,070 are also brought in after research 4481 03:51:12,070 --> 03:51:14,770 has already been conceptualized or even conducted. 4482 03:51:15,310 --> 03:51:17,160 And communities are asked to comment 4483 03:51:17,160 --> 03:51:20,730 on already prepared materials rather than to be involved 4484 03:51:20,730 --> 03:51:22,600 in the very creation of those materials. 4485 03:51:22,600 --> 03:51:24,350 So this is one thing that I really appreciated 4486 03:51:24,350 --> 03:51:26,250 about Dr. Gold's presentation 4487 03:51:26,250 --> 03:51:29,100 is working with the actual stakeholders 4488 03:51:29,100 --> 03:51:32,980 to try and understand what is their reactions to the materials 4489 03:51:32,980 --> 03:51:35,779 that are being produced before they're fully rolled out. 4490 03:51:36,880 --> 03:51:40,310 And so lack of power sharing between communities and research 4491 03:51:40,310 --> 03:51:41,620 is one of the outputs, 4492 03:51:41,620 --> 03:51:44,070 one of the byproducts of shallow engagement. 4493 03:51:44,940 --> 03:51:47,140 So another way to frame this would be that 4494 03:51:47,140 --> 03:51:48,430 in shallow engagement, 4495 03:51:48,430 --> 03:51:51,990 the focus can be more so on data collection and data 4496 03:51:51,990 --> 03:51:55,360 sharing than on power, benefit, and responsibility sharing. 4497 03:51:55,360 --> 03:51:58,720 So thinking of community engagement as a means to an end 4498 03:51:58,720 --> 03:52:03,170 as a way to recruit people for inclusion in our research, 4499 03:52:03,170 --> 03:52:04,770 while that is important, 4500 03:52:05,300 --> 03:52:09,430 there is another model with community empowerment 4501 03:52:09,430 --> 03:52:12,610 that offers perhaps a bit deeper form of engagement. 4502 03:52:14,030 --> 03:52:16,780 So the need here is for community empowerment. 4503 03:52:16,780 --> 03:52:19,590 This is a shift from shallow to deep engagement. 4504 03:52:20,250 --> 03:52:23,600 Emphasis is placed on creating respectful relationships 4505 03:52:23,600 --> 03:52:25,950 with communities, and community members 4506 03:52:25,950 --> 03:52:30,240 are enabled to name the problems and solutions for themselves. 4507 03:52:30,240 --> 03:52:33,050 So it really is emphasizing that power, benefit, 4508 03:52:33,050 --> 03:52:35,649 and responsibility sharing that I mentioned earlier. 4509 03:52:36,490 --> 03:52:38,620 Priorities for community empowerment 4510 03:52:38,620 --> 03:52:40,870 are to build reciprocal relationships 4511 03:52:40,870 --> 03:52:42,270 and true partnership. 4512 03:52:42,270 --> 03:52:44,810 So this requires true power sharing built 4513 03:52:44,810 --> 03:52:49,850 on mutual respect and benefit. It also prioritizes co-learning, 4514 03:52:49,850 --> 03:52:51,440 so it acknowledges the importance 4515 03:52:51,440 --> 03:52:54,640 of everyone's contributions, individual expertise, 4516 03:52:54,640 --> 03:52:56,410 it's working to try and disrupt 4517 03:52:56,410 --> 03:52:59,610 the dichotomy between the expert and the public. 4518 03:53:01,440 --> 03:53:04,220 And finally, it also prioritizes transparent 4519 03:53:04,220 --> 03:53:05,430 and shared decision making. 4520 03:53:05,430 --> 03:53:07,930 So it heightens the accountability of researchers. 4521 03:53:08,740 --> 03:53:12,870 And a key message that is important in community 4522 03:53:12,870 --> 03:53:14,590 empowerment is being able to document 4523 03:53:14,590 --> 03:53:18,570 and demonstrating the impact of community participation, 4524 03:53:18,570 --> 03:53:21,360 community perspectives, values and needs in decisions 4525 03:53:21,360 --> 03:53:23,390 that are then made about research 4526 03:53:23,390 --> 03:53:24,990 or about clinical program. 4527 03:53:25,670 --> 03:53:28,240 So why community empowerment? 4528 03:53:29,020 --> 03:53:31,810 Community members, as has already been mentioned today, 4529 03:53:31,810 --> 03:53:34,970 are experts in community and healthcare experiences 4530 03:53:34,970 --> 03:53:38,330 in public interest. They are context experts. 4531 03:53:39,100 --> 03:53:41,720 And local communities, patients and their families 4532 03:53:41,720 --> 03:53:44,480 are the ones most likely to shoulder the harms 4533 03:53:44,480 --> 03:53:47,060 from the inadequate implementation 4534 03:53:47,060 --> 03:53:50,640 of new technologies or clinical services. 4535 03:53:50,640 --> 03:53:53,800 So in situations where uncertainty is high, 4536 03:53:54,350 --> 03:53:57,440 shared decision-making with community collaborators 4537 03:53:57,440 --> 03:54:01,230 is a means for arbitrating on hypothetical benefits 4538 03:54:01,230 --> 03:54:04,980 and risks while also ensuring that there is alignment 4539 03:54:04,980 --> 03:54:06,860 between healthcare systems, 4540 03:54:06,860 --> 03:54:09,720 and community values, interests and resources. 4541 03:54:10,710 --> 03:54:15,910 And so as I've mentioned, you know, these parties, 4542 03:54:15,910 --> 03:54:17,640 these communities, these patients, their families, 4543 03:54:17,640 --> 03:54:19,890 they're the ones most likely to shoulder the harms, 4544 03:54:19,890 --> 03:54:23,780 the burdens of uncertainties, and so their voices 4545 03:54:23,780 --> 03:54:26,560 should be a driving force of clinical translation. 4546 03:54:27,900 --> 03:54:33,060 And we see uncertainty as an opportunity 4547 03:54:33,060 --> 03:54:35,530 to evaluate community interest and preferences 4548 03:54:35,530 --> 03:54:38,150 proactively rather than reactively. 4549 03:54:38,150 --> 03:54:40,240 So right from the very start. 4550 03:54:40,240 --> 03:54:42,060 And so that brings me to the core of today, 4551 03:54:42,060 --> 03:54:46,650 which is an example of a community empowerment program 4552 03:54:46,650 --> 03:54:48,430 process that I've been a part of, 4553 03:54:48,430 --> 03:54:51,510 and we have a number of our community members 4554 03:54:51,510 --> 03:54:54,230 and my co-facilitator joining us here today, 4555 03:54:54,230 --> 03:54:58,230 and I'll be handing it over to one of our community members, 4556 03:54:58,230 --> 03:55:00,620 Annamaria, to speak in just a moment. 4557 03:55:01,160 --> 03:55:05,740 But to provide some background, some context for this project, 4558 03:55:05,740 --> 03:55:08,360 part of the motivation is the diversity problem 4559 03:55:08,360 --> 03:55:09,800 that is in genomics. 4560 03:55:09,800 --> 03:55:13,000 I'm sure many people here already are aware 4561 03:55:13,000 --> 03:55:14,870 that there are Eurocentric biases 4562 03:55:14,870 --> 03:55:19,180 in genome-wide association studies, one of the emerging 4563 03:55:19,700 --> 03:55:23,530 and increasingly used methods of genomic analyses. 4564 03:55:23,530 --> 03:55:28,260 And these biases in who is included which dataset, 4565 03:55:28,260 --> 03:55:33,040 which individuals are recruited for research fails 4566 03:55:33,040 --> 03:55:35,480 to make the benefits of genomics accessible, 4567 03:55:36,190 --> 03:55:40,090 and it also risks exacerbating health disparities 4568 03:55:40,090 --> 03:55:43,770 if and when these genomic technologies 4569 03:55:43,770 --> 03:55:46,990 are actually translated into clinical settings. 4570 03:55:48,440 --> 03:55:51,470 And so there is a high level of uncertainty 4571 03:55:51,470 --> 03:55:54,650 around the utility and validity of polygenic scores, 4572 03:55:54,650 --> 03:55:58,080 which are a aggregate composition 4573 03:55:58,080 --> 03:55:59,730 of the genetic effects 4574 03:55:59,730 --> 03:56:03,060 that are identified through genome-wide association studies. 4575 03:56:04,230 --> 03:56:07,380 And given the high level of uncertainty and utility, 4576 03:56:07,380 --> 03:56:09,450 high level of uncertainty around the utility 4577 03:56:09,450 --> 03:56:11,500 and validity of polygenic scores, 4578 03:56:11,500 --> 03:56:15,440 there is a fear that focusing on polygenic scores 4579 03:56:15,440 --> 03:56:16,720 will risk overly 4580 03:56:16,720 --> 03:56:20,020 emphasizing genetics and ignore social determinants of health, 4581 03:56:20,530 --> 03:56:24,000 and also risk placing responsibility on the individual 4582 03:56:24,000 --> 03:56:25,540 in a way that allows society 4583 03:56:25,540 --> 03:56:27,960 to absolve itself of responsibility. 4584 03:56:29,050 --> 03:56:30,350 Finally, as I've mentioned, 4585 03:56:30,350 --> 03:56:31,880 and I will continue to emphasize, 4586 03:56:31,880 --> 03:56:33,800 local communities, patients and their families 4587 03:56:33,800 --> 03:56:35,530 are most likely to shoulder the harms 4588 03:56:35,530 --> 03:56:39,080 from inadequate implementation of new technologies, 4589 03:56:39,080 --> 03:56:41,850 in this case, polygenic scores being one of them. 4590 03:56:41,850 --> 03:56:43,800 And so this is kind of the background context 4591 03:56:43,800 --> 03:56:46,220 that led to our community co-design, 4592 03:56:46,220 --> 03:56:48,520 the problem of diversity and genomics, 4593 03:56:48,520 --> 03:56:50,800 the high level of uncertainty around the utility 4594 03:56:50,800 --> 03:56:53,250 and validity of polygenic scores, 4595 03:56:53,250 --> 03:56:55,160 but the practical realities of the fact 4596 03:56:55,160 --> 03:56:57,660 that we are already seeing instances 4597 03:56:57,660 --> 03:57:00,940 in which individual researcher's clinical programs 4598 03:57:00,940 --> 03:57:03,400 are trying to incorporate polygenic scores 4599 03:57:03,400 --> 03:57:05,560 into the provision of clinical services. 4600 03:57:06,940 --> 03:57:08,380 So this community co-design, 4601 03:57:08,380 --> 03:57:11,100 I'm just highlighting for individuals in particular here, 4602 03:57:11,100 --> 03:57:15,520 although there's a much larger team of us behind this effort, 4603 03:57:15,520 --> 03:57:18,200 Hannah Wand was my co-facilitator on this project. 4604 03:57:18,810 --> 03:57:21,380 And then we had a number of community members, 4605 03:57:21,380 --> 03:57:24,010 but I particularly want to highlight Ting Pun, 4606 03:57:24,010 --> 03:57:26,620 Sheryl Michelson, and Annamaria Smitherman, 4607 03:57:26,620 --> 03:57:30,890 who worked with Hannah and I to write a manuscript 4608 03:57:30,890 --> 03:57:34,380 documenting what this process was like for all of us. 4609 03:57:35,450 --> 03:57:38,030 So in the community co-design, some of our goals here 4610 03:57:38,030 --> 03:57:40,840 were to support the precision public health services 4611 03:57:40,840 --> 03:57:43,220 and improve the prevention of chronic disease, 4612 03:57:43,960 --> 03:57:45,730 and to design a clinical program 4613 03:57:45,730 --> 03:57:48,740 in a way that is responsive to an inclusive 4614 03:57:48,740 --> 03:57:51,050 and respectful of the local community. 4615 03:57:51,050 --> 03:57:55,110 So in this case, this is the Stanford healthcare system, 4616 03:57:55,110 --> 03:57:57,930 the patients, the caregivers within Stanford healthcare. 4617 03:57:59,180 --> 03:58:03,100 And also, we wanted to decide alongside these community 4618 03:58:03,100 --> 03:58:06,020 members whether and how to translate polygenic scores 4619 03:58:06,020 --> 03:58:07,360 to clinical care. 4620 03:58:07,360 --> 03:58:11,260 So critically, we weren't coming in with the decision already 4621 03:58:11,260 --> 03:58:14,450 that there was going to be a preventative genomics program 4622 03:58:14,450 --> 03:58:16,890 in which polygenic scores were going to be utilized. 4623 03:58:16,890 --> 03:58:19,260 We wanted to first take it to the people 4624 03:58:19,260 --> 03:58:24,340 who would be the "end users," or the ones most impacted 4625 03:58:24,340 --> 03:58:27,890 and the ones that researchers are keeping in mind 4626 03:58:27,890 --> 03:58:32,890 when they're doing their work, the communities that would be 4627 03:58:32,890 --> 03:58:36,450 utilizing this technology in a practical setting. 4628 03:58:37,810 --> 03:58:40,720 So we had eight community members and two facilitators, 4629 03:58:40,720 --> 03:58:43,440 myself and Hannah, we met bi-weekly 4630 03:58:43,440 --> 03:58:46,630 from May through August of 2021 on Zoom. 4631 03:58:47,590 --> 03:58:49,770 Each meeting was approximately 90 minutes 4632 03:58:49,770 --> 03:58:53,060 except for our first session, which was two hours. 4633 03:58:53,060 --> 03:58:54,640 And at Stanford, we have a Patient 4634 03:58:54,640 --> 03:58:58,510 and Family Partner Program, or PFAC as it's abbreviated, 4635 03:58:58,510 --> 03:59:00,680 which is comprised of individuals 4636 03:59:00,680 --> 03:59:03,700 who are both patients within Stanford healthcare, 4637 03:59:03,700 --> 03:59:05,780 as well as caregivers to patients 4638 03:59:05,780 --> 03:59:09,580 within Stanford healthcare, who volunteer their time 4639 03:59:09,580 --> 03:59:12,940 to being part of initiatives such as this. 4640 03:59:14,350 --> 03:59:17,020 Our first meeting focused on introductions 4641 03:59:17,020 --> 03:59:19,950 and setting group expectations and norms, 4642 03:59:19,950 --> 03:59:22,830 and we then had three didactic sessions. 4643 03:59:22,830 --> 03:59:26,730 The first is on preventative health and risk stratification. 4644 03:59:26,730 --> 03:59:30,390 The second was on polygenic scores for risk prediction. 4645 03:59:30,390 --> 03:59:33,590 And the third was on data privacy of genetic information. 4646 03:59:33,590 --> 03:59:37,160 So these were meant to help everyone start 4647 03:59:37,160 --> 03:59:40,020 on a level playing field in terms of the knowledge about, 4648 03:59:41,140 --> 03:59:43,560 you know, polygenic scores, preventative health, 4649 03:59:43,560 --> 03:59:45,160 how it currently operates, 4650 03:59:45,830 --> 03:59:49,510 and how genetic information is protected 4651 03:59:49,510 --> 03:59:51,110 from a data privacy perspective. 4652 03:59:52,550 --> 03:59:55,220 We then had three open-ended sessions. 4653 03:59:55,220 --> 03:59:58,520 One was on informed consent for the utilization 4654 03:59:58,520 --> 04:00:00,870 of polygenic scores and clinical settings. 4655 04:00:01,450 --> 04:00:02,730 The second was on equity 4656 04:00:02,730 --> 04:00:06,060 and access to polygenic scores and genetic services. 4657 04:00:06,060 --> 04:00:09,270 And the third was polygenic results and long-term care. 4658 04:00:10,040 --> 04:00:12,880 And then we had a final session with all the community members 4659 04:00:12,880 --> 04:00:14,810 in which we collaboratively summarized 4660 04:00:14,810 --> 04:00:16,200 the community feedback, 4661 04:00:16,200 --> 04:00:18,620 including whether community members saw value 4662 04:00:18,620 --> 04:00:21,360 in implementing a polygenic score program, 4663 04:00:21,360 --> 04:00:23,400 and how community feedback 4664 04:00:23,400 --> 04:00:25,080 should be integrated into its design. 4665 04:00:25,080 --> 04:00:26,390 So as I mentioned earlier, 4666 04:00:26,390 --> 04:00:29,480 a key component of community empowerment 4667 04:00:29,480 --> 04:00:31,690 is not only allowing community members 4668 04:00:31,690 --> 04:00:34,460 to name the problems and solutions for themselves, 4669 04:00:34,460 --> 04:00:39,530 but also demonstrating how their participation in the research 4670 04:00:39,530 --> 04:00:41,150 or the design of the clinical program 4671 04:00:41,150 --> 04:00:43,280 is actually resulting in changes, 4672 04:00:43,280 --> 04:00:46,730 so really emphasizing the shared power 4673 04:00:46,730 --> 04:00:49,040 sharing in the decision-making process. 4674 04:00:49,720 --> 04:00:52,190 I now want to hand it over to Annamaria 4675 04:00:52,190 --> 04:00:56,740 to kind of talk through what the experience was like for her. 4676 04:00:56,740 --> 04:00:59,520 And so to do that, I'm going to stop sharing my screen 4677 04:00:59,520 --> 04:01:01,319 so that we can make sure to see her. 4678 04:01:03,190 --> 04:01:04,790 Annamaria Smitherman: Thank you, Daphne. 4679 04:01:05,600 --> 04:01:06,820 Yeah, I was really delighted 4680 04:01:06,820 --> 04:01:09,030 to be a part of this research group. 4681 04:01:09,030 --> 04:01:17,240 I learned, you know, so much, and as a teacher, 4682 04:01:17,240 --> 04:01:18,840 that's always exciting to me. 4683 04:01:19,590 --> 04:01:21,740 I think that the reason that this group really worked for me 4684 04:01:21,740 --> 04:01:25,320 came down to three parts. 4685 04:01:27,260 --> 04:01:29,900 Daphne and Hannah, and we used first names 4686 04:01:29,900 --> 04:01:33,050 as part of that equality and power sharing issue 4687 04:01:33,050 --> 04:01:34,650 that is subtle, but important. 4688 04:01:35,240 --> 04:01:38,170 Daphne and Hannah were very intentional 4689 04:01:38,170 --> 04:01:40,170 in everything that they did. 4690 04:01:42,050 --> 04:01:46,090 And it felt like not a moment was wasted. 4691 04:01:46,090 --> 04:01:48,560 There wasn't dithering, there was thinking 4692 04:01:48,560 --> 04:01:49,920 and there was reflection, 4693 04:01:49,920 --> 04:01:55,630 and there were moments of quiet pausing 4694 04:01:55,630 --> 04:01:58,980 while considerations were made, 4695 04:01:59,800 --> 04:02:03,840 but the work was intentional and intentionally inclusive 4696 04:02:03,840 --> 04:02:10,260 in the sense that everybody in the group was clearly heard 4697 04:02:11,270 --> 04:02:13,380 and given opportunities to be heard, 4698 04:02:13,380 --> 04:02:15,030 even folks who were sometimes quiet, 4699 04:02:15,030 --> 04:02:20,740 space was made for them to be heard and connected. 4700 04:02:22,360 --> 04:02:26,560 And that was really important. So the intention was there. 4701 04:02:26,560 --> 04:02:28,430 The second part that was really important to me 4702 04:02:28,430 --> 04:02:29,700 as a member of the community 4703 04:02:29,700 --> 04:02:32,560 was that we revisited issues on a regular basis. 4704 04:02:32,560 --> 04:02:34,700 So we were given new information, 4705 04:02:34,700 --> 04:02:39,060 Daphne just talked about the three informational meetings, 4706 04:02:39,060 --> 04:02:41,660 it wasn't done then, you know, we could come back to that. 4707 04:02:41,660 --> 04:02:44,910 And we came back to issues over and over and over again, 4708 04:02:44,910 --> 04:02:46,350 and looked at them in new ways 4709 04:02:46,350 --> 04:02:48,050 or having thought about something. 4710 04:02:50,420 --> 04:02:53,420 We wanted to offer more input that was allowed 4711 04:02:53,420 --> 04:02:56,220 and encouraged and welcomed. 4712 04:02:56,220 --> 04:02:58,320 So the intentionality, the revisiting, 4713 04:02:58,320 --> 04:03:01,150 and then finally, the educational part 4714 04:03:01,150 --> 04:03:02,600 was really crucial 4715 04:03:02,600 --> 04:03:06,600 to teach us everything that we needed to know 4716 04:03:06,600 --> 04:03:13,440 in order to make confident and thoughtful recommendations 4717 04:03:13,440 --> 04:03:16,540 about this potential program for Stanford. 4718 04:03:18,310 --> 04:03:20,100 So, I don't know, Daphne, 4719 04:03:20,100 --> 04:03:23,180 is there anything else you want to hear or like to know? 4720 04:03:23,180 --> 04:03:24,440 Dr. Daphne Martschenko: That's great, Annamaria. 4721 04:03:24,440 --> 04:03:25,650 And I hope that, you know, 4722 04:03:25,650 --> 04:03:27,610 when we get to the question and answer portion, 4723 04:03:27,610 --> 04:03:29,740 people could feel free to also direct questions 4724 04:03:29,740 --> 04:03:32,420 directly to Annamaria, as well. 4725 04:03:33,170 --> 04:03:34,650 Annamaria Smitherman: That would be great. 4726 04:03:34,650 --> 04:03:35,890 Dr. Daphne Martschenko: Thank you. 4727 04:03:35,890 --> 04:03:38,300 Okay, so I know we don't have too much time left, 4728 04:03:38,300 --> 04:03:40,030 and I'm eager to get to the Q&A. 4729 04:03:40,030 --> 04:03:42,750 So I just have a couple more slides. 4730 04:03:42,750 --> 04:03:45,790 I wanted to talk through some of the findings 4731 04:03:45,790 --> 04:03:49,770 or results from this experience. And as Annamaria said, you know, 4732 04:03:49,770 --> 04:03:52,100 the education really has been ongoing. 4733 04:03:52,100 --> 04:03:55,790 And we continue to work with these community partners, 4734 04:03:55,790 --> 04:03:57,240 even though we're not having these kinds 4735 04:03:57,240 --> 04:03:59,270 of formal regular Zoom meetings anymore. 4736 04:04:00,470 --> 04:04:03,170 So there were some initial considerations 4737 04:04:03,170 --> 04:04:07,960 that the individuals involved in the design 4738 04:04:07,960 --> 04:04:09,360 and conceptualization 4739 04:04:09,360 --> 04:04:12,030 of preventative genomics program had coming in. 4740 04:04:12,890 --> 04:04:16,250 One was that there was knowledge from the literature, 4741 04:04:16,250 --> 04:04:17,510 as I've already mentioned, 4742 04:04:17,510 --> 04:04:20,030 that polygenic scores have a high level of uncertainty 4743 04:04:20,030 --> 04:04:23,380 and there's debate over their validity and utility. 4744 04:04:24,390 --> 04:04:29,510 But as we have seen, increasing examples of polygenic scores 4745 04:04:29,510 --> 04:04:31,480 are currently being utilized 4746 04:04:31,480 --> 04:04:33,890 or starting to be utilized in clinical settings. 4747 04:04:33,890 --> 04:04:37,330 And so some of the review of the literature 4748 04:04:37,330 --> 04:04:40,840 suggested to us that polygenic scores may be scaled out 4749 04:04:40,840 --> 04:04:43,290 and supported through population health services, 4750 04:04:43,890 --> 04:04:46,110 but specific provider training needs 4751 04:04:46,110 --> 04:04:48,640 and infrastructural supports for this are unclear. 4752 04:04:49,490 --> 04:04:53,050 PCPs seem to find risk conveyed by polygenic scores 4753 04:04:53,050 --> 04:04:55,480 intuitive with current risk prediction 4754 04:04:55,480 --> 04:04:57,800 and multifactorial disease models, 4755 04:04:57,800 --> 04:04:59,940 but it's also a numerical value 4756 04:04:59,940 --> 04:05:02,650 that is grounded in a population distribution 4757 04:05:02,650 --> 04:05:05,100 and can be incorporated into existing 4758 04:05:05,100 --> 04:05:08,540 clinical risk calculators used in preventative screening. 4759 04:05:08,540 --> 04:05:11,960 And so the underlying genomic methodologies and limitations 4760 04:05:11,960 --> 04:05:14,200 are less familiar to providers 4761 04:05:14,200 --> 04:05:15,930 and should be the focus of future training. 4762 04:05:15,930 --> 04:05:18,670 So this is what some of the literature was suggesting. 4763 04:05:18,670 --> 04:05:21,580 But then we took it to community partners 4764 04:05:21,580 --> 04:05:25,250 and wanted really to incorporate their perspectives 4765 04:05:25,250 --> 04:05:27,380 in deciding who should be trained 4766 04:05:27,380 --> 04:05:30,290 to provide this information if it is deemed useful. 4767 04:05:31,370 --> 04:05:35,130 We heard that patients are more likely to engage with new 4768 04:05:35,130 --> 04:05:36,850 and possibly sensitive information 4769 04:05:36,850 --> 04:05:38,780 if they trust the healthcare system, 4770 04:05:38,780 --> 04:05:41,940 which I'm sure is not going to be a surprise to anybody here. 4771 04:05:41,940 --> 04:05:45,200 But the current uncertainties around polygenic scores, 4772 04:05:45,200 --> 04:05:47,300 their limitations and clinical validity 4773 04:05:47,300 --> 04:05:51,230 require attention to informed choices about polygenic testing, 4774 04:05:51,230 --> 04:05:52,910 it can't simply be offered 4775 04:05:52,910 --> 04:05:55,440 across a healthcare system by anyone. 4776 04:05:56,360 --> 04:06:00,480 And so people felt that genetic information polygenic scores, 4777 04:06:00,480 --> 04:06:02,120 even though it's a numerical value, 4778 04:06:02,120 --> 04:06:04,910 it's still grounded in genetics, it feels sensitive, 4779 04:06:04,910 --> 04:06:07,590 it feels emotionally latent to our group, 4780 04:06:07,590 --> 04:06:10,490 and so provider capacity to support polygenic score 4781 04:06:10,490 --> 04:06:13,180 testing decisions should require skills 4782 04:06:13,180 --> 04:06:15,770 and assessing personal values and personal utility. 4783 04:06:16,300 --> 04:06:18,610 And providers with proper training 4784 04:06:18,610 --> 04:06:21,300 are not helpful if they're not available in a reasonable time. 4785 04:06:21,300 --> 04:06:24,740 And so access is an important dimension of quality care. 4786 04:06:24,740 --> 04:06:26,440 It's not only who do people trust, 4787 04:06:26,440 --> 04:06:28,790 but how available are those people and equipped 4788 04:06:28,790 --> 04:06:31,240 to provide the information in an appropriate way? 4789 04:06:31,930 --> 04:06:33,210 And so this translated 4790 04:06:33,210 --> 04:06:36,320 into a couple of programmatic decisions. 4791 04:06:36,320 --> 04:06:39,230 And one of the big lessons from this co-design process 4792 04:06:39,230 --> 04:06:42,700 was that service delivery is an opportunity for building trust 4793 04:06:42,700 --> 04:06:46,050 and decision should not solely be driven by logistics, 4794 04:06:46,050 --> 04:06:49,050 by what's going to be the most logistically simple approach. 4795 04:06:49,850 --> 04:06:53,030 Provide suggested questions and educational materials 4796 04:06:53,030 --> 04:06:55,610 for the patient pre-visit to prepare patients 4797 04:06:55,610 --> 04:06:58,120 to think about their values in advance 4798 04:06:58,120 --> 04:07:01,550 and to maximize provider-patient discussion during the visit. 4799 04:07:02,210 --> 04:07:05,430 And also make sure that provider training 4800 04:07:05,430 --> 04:07:07,070 is not just about education, 4801 04:07:07,070 --> 04:07:09,440 you know, education about what polygenic scores 4802 04:07:09,440 --> 04:07:11,610 are and what information they capture 4803 04:07:11,610 --> 04:07:14,670 and how they might be used, but also in skills development, 4804 04:07:15,430 --> 04:07:17,000 thinking about how to interface 4805 04:07:17,000 --> 04:07:18,870 with patients to understand their values, 4806 04:07:18,870 --> 04:07:20,919 their needs when communicating with them. 4807 04:07:21,750 --> 04:07:24,240 Patient advocates can help providers to understand 4808 04:07:24,240 --> 04:07:27,300 the importance of personal utility and test decisions, 4809 04:07:27,300 --> 04:07:30,600 as well as the emotional nature of genetic information. 4810 04:07:30,600 --> 04:07:32,410 And finally, care should be coordinated 4811 04:07:32,410 --> 04:07:35,550 through team-based care and provider handoffs, 4812 04:07:35,550 --> 04:07:38,280 if different specialists are involved, 4813 04:07:39,060 --> 04:07:41,600 this provides stability that is needed 4814 04:07:41,600 --> 04:07:43,940 for long-term preventative health discussions. 4815 04:07:44,730 --> 04:07:46,150 Finally, I just want to acknowledge 4816 04:07:46,150 --> 04:07:49,220 that there are some limitations to what we've done here. 4817 04:07:49,220 --> 04:07:51,050 One is that the co-design process 4818 04:07:51,050 --> 04:07:53,120 like other forms of community engagement 4819 04:07:53,120 --> 04:07:58,470 is not immune to the traditional approaches to compensation, 4820 04:07:58,470 --> 04:08:01,650 the limitations of traditional approaches to compensation. 4821 04:08:02,320 --> 04:08:06,560 Community members are not full-time researchers, 4822 04:08:06,560 --> 04:08:07,880 not full-time clinicians, 4823 04:08:07,880 --> 04:08:09,600 they are really volunteering their time 4824 04:08:09,600 --> 04:08:13,590 to be a part of this process. And I think as a system, 4825 04:08:13,590 --> 04:08:15,270 we still need to do a much better job 4826 04:08:15,270 --> 04:08:17,250 of offering the appropriate compensation 4827 04:08:17,250 --> 04:08:18,870 to allow as many individuals 4828 04:08:18,870 --> 04:08:21,990 who wish to participate in this process be able to do so. 4829 04:08:22,670 --> 04:08:25,160 And also there are challenges with community 4830 04:08:25,160 --> 04:08:28,420 engaged initiatives that more broadly occur 4831 04:08:28,420 --> 04:08:31,250 in terms of diverse and inclusive recruitment 4832 04:08:31,250 --> 04:08:33,210 and bringing on community members 4833 04:08:33,210 --> 04:08:35,200 who have historically been exploited 4834 04:08:35,200 --> 04:08:36,800 by scientific research. 4835 04:08:38,180 --> 04:08:40,730 So in summary, there are very real uncertainties 4836 04:08:40,730 --> 04:08:42,920 and concerns about balancing the benefits 4837 04:08:42,920 --> 04:08:45,380 and risks in population implementation. 4838 04:08:46,070 --> 04:08:49,600 And communities and patients as the intended beneficiaries 4839 04:08:49,600 --> 04:08:52,450 are the ones that are most likely to shoulder uncertainties 4840 04:08:52,450 --> 04:08:54,749 and so therefore should drive decision-making. 4841 04:08:55,730 --> 04:08:59,060 Community empowerment requires a commitment to partnership 4842 04:08:59,060 --> 04:09:02,500 and adapting to shifting group dynamics and rapport. 4843 04:09:02,500 --> 04:09:04,770 And it also requires institutional support 4844 04:09:04,770 --> 04:09:06,020 facilitator training, 4845 04:09:06,020 --> 04:09:09,370 community relationship building, power sharing and time. 4846 04:09:09,370 --> 04:09:13,690 Time is another key thing that, you know, in academia, 4847 04:09:13,690 --> 04:09:16,160 we often want to work as quickly as we can 4848 04:09:16,160 --> 04:09:18,770 to get the next paper out to secure the next grant. 4849 04:09:18,770 --> 04:09:21,720 These kinds of relationships really do take time. 4850 04:09:23,180 --> 04:09:25,280 Finally, investment of resources 4851 04:09:25,280 --> 04:09:27,620 and time to community empowered co-design. 4852 04:09:27,620 --> 04:09:30,710 We believe that it is aligned with the stated long-term goals 4853 04:09:30,710 --> 04:09:32,080 of most healthcare systems 4854 04:09:32,080 --> 04:09:35,550 to support person-centered and value based care. 4855 04:09:36,310 --> 04:09:37,660 And I'm really looking forward 4856 04:09:37,660 --> 04:09:40,050 to having a discussion here as a group 4857 04:09:40,050 --> 04:09:42,590 and to the breakout sessions following on from this 4858 04:09:42,590 --> 04:09:44,850 so that we have more time to talk through these things. 4859 04:09:44,850 --> 04:09:46,960 But thank you so much for having me 4860 04:09:46,960 --> 04:09:49,640 and for having Annamaria, and Hannah and Sheryl, 4861 04:09:49,640 --> 04:09:52,290 who I know are all here in the Zoom room today. 4862 04:09:52,290 --> 04:09:53,600 Dr. Miya Whitaker: Thank you so much 4863 04:09:53,600 --> 04:09:56,660 for your great presentation. We so appreciate it. 4864 04:09:57,960 --> 04:10:00,120 You and Dr. Gold have been absolutely amazing 4865 04:10:00,120 --> 04:10:03,230 So first, we want to start with a question to Dr. Gold. 4866 04:10:03,230 --> 04:10:04,810 As you know, social risk research 4867 04:10:04,810 --> 04:10:07,700 incorporates an equity conscious approaches, 4868 04:10:07,700 --> 04:10:09,680 and it's intentional about engaging community partners 4869 04:10:09,680 --> 04:10:10,970 to offer 4870 04:10:10,970 --> 04:10:14,250 an increased opportunity to address unmet needs, 4871 04:10:14,840 --> 04:10:18,580 given disparities and burden of health-related social risks, 4872 04:10:18,580 --> 04:10:21,720 really highlight an important area of focus for investment 4873 04:10:21,720 --> 04:10:23,730 and new programming and policies. 4874 04:10:23,730 --> 04:10:25,040 So with that in mind, 4875 04:10:25,040 --> 04:10:28,870 are there opportunities within the COHERE tool 4876 04:10:29,610 --> 04:10:33,470 to sort of link community members to services 4877 04:10:33,470 --> 04:10:36,400 which leverage community partnerships for OCHIN 4878 04:10:36,400 --> 04:10:38,370 and Kaiser Permanente? 4879 04:10:38,370 --> 04:10:39,650 Dr. Rachel Gold: So you're talking about 4880 04:10:39,650 --> 04:10:41,090 assistance strategies 4881 04:10:41,090 --> 04:10:44,700 linking patients to community services. 4882 04:10:45,420 --> 04:10:46,870 You know, I think that's probably 4883 04:10:46,870 --> 04:10:48,160 the next step in the research. 4884 04:10:48,160 --> 04:10:49,640 And there is work going on around 4885 04:10:49,640 --> 04:10:52,490 how to do assistance strategies. 4886 04:10:52,490 --> 04:10:54,890 I think something that we need to be clear about 4887 04:10:54,890 --> 04:10:58,560 is there's only a very beginning body of evidence 4888 04:10:58,560 --> 04:11:01,080 that these referrals actually make a difference 4889 04:11:01,730 --> 04:11:03,570 in terms of health impacts. 4890 04:11:03,570 --> 04:11:06,150 The emerging body of evidence says it does, 4891 04:11:06,150 --> 04:11:07,370 but it's still emergent. 4892 04:11:07,370 --> 04:11:09,220 I would point you to Dr. Gottlieb had 4893 04:11:09,870 --> 04:11:11,340 an evidence review a couple years ago 4894 04:11:11,340 --> 04:11:12,760 that basically says we don't really know 4895 04:11:12,760 --> 04:11:13,960 this makes a difference, 4896 04:11:13,960 --> 04:11:15,810 but intuitively, one thinks it would. 4897 04:11:17,130 --> 04:11:18,330 So I guess I would just say 4898 04:11:18,330 --> 04:11:20,080 that's not what this project is focused on. 4899 04:11:20,080 --> 04:11:22,629 We're focused on adjustment, care plan adjustments. 4900 04:11:23,210 --> 04:11:25,200 We did think that we had to- it took a while for us 4901 04:11:25,200 --> 04:11:27,360 when we were working with our CHC staff 4902 04:11:27,990 --> 04:11:29,720 and clinicians in developing the tools 4903 04:11:29,720 --> 04:11:31,450 to kind of get folks' heads around that. 4904 04:11:31,450 --> 04:11:34,460 We're not talking about making referrals in this project. 4905 04:11:34,460 --> 04:11:37,280 We're talking about adjusting care plans and documenting that. 4906 04:11:37,280 --> 04:11:38,670 But of course, that's part of it. 4907 04:11:38,670 --> 04:11:39,910 Right? That goes back to the five 4908 04:11:39,910 --> 04:11:42,160 As of integrating social care in the medical care, right? 4909 04:11:42,160 --> 04:11:43,380 One of the five 4910 04:11:43,380 --> 04:11:45,330 As is assistance, can we mitigate? 4911 04:11:45,330 --> 04:11:46,930 Can we get rid of the social risk 4912 04:11:46,930 --> 04:11:49,450 to begin with by getting folks connected to services? 4913 04:11:49,450 --> 04:11:50,900 But this is another one, 4914 04:11:50,900 --> 04:11:53,150 which is can we then adjust the care plan? 4915 04:11:53,150 --> 04:11:54,830 Because you can't always mitigate. 4916 04:11:54,830 --> 04:11:57,880 So can you then do something else 4917 04:11:57,880 --> 04:12:00,150 that would make a difference, that makes sure at least 4918 04:12:00,150 --> 04:12:01,890 that the patient get the med into the body, 4919 04:12:01,890 --> 04:12:03,680 get the follow-up care that they need 4920 04:12:03,680 --> 04:12:06,950 while we try and fix these larger social problems, 4921 04:12:06,950 --> 04:12:10,470 or at least get folks, you know, referred to services? 4922 04:12:10,470 --> 04:12:12,410 I mean, I would say I'm very, very cognizant 4923 04:12:12,410 --> 04:12:14,560 that the work we're doing is a downstream solution 4924 04:12:14,560 --> 04:12:19,680 to an upstream problem, but I am, you know, some- 4925 04:12:19,680 --> 04:12:21,950 we're trying to do it that way, and while hopefully others 4926 04:12:21,950 --> 04:12:24,570 are working on the upstream source as well. 4927 04:12:24,570 --> 04:12:25,850 Dr. Miya Whitaker: So in relation 4928 04:12:25,850 --> 04:12:29,570 to what you just mentioned, did you encounter any resistance 4929 04:12:29,570 --> 04:12:32,990 to tool implementation or tool use among the clinic staff? 4930 04:12:32,990 --> 04:12:35,310 And was there anything that you had to do 4931 04:12:35,310 --> 04:12:37,100 to sort of increase adoption? 4932 04:12:37,900 --> 04:12:39,830 Dr. Rachel Gold: Yeah, that's a great question. 4933 04:12:39,830 --> 04:12:41,410 I'm an implementation scientist, 4934 04:12:41,410 --> 04:12:43,150 as well as a disparities researcher, 4935 04:12:43,150 --> 04:12:45,280 and I can tell you that building tools 4936 04:12:45,280 --> 04:12:46,880 does not mean tools get used. 4937 04:12:48,170 --> 04:12:50,340 Sometimes the way that we do work on my team 4938 04:12:50,340 --> 04:12:51,590 is first we try and see 4939 04:12:51,590 --> 04:12:54,860 if there is an effectiveness when a tool is used 4940 04:12:54,860 --> 04:12:58,130 before you try and really focus on further implementation. 4941 04:12:58,130 --> 04:12:59,690 Because if it doesn't help, 4942 04:12:59,690 --> 04:13:02,430 there's not really a need to focus on, you know- 4943 04:13:02,430 --> 04:13:04,400 I mean, I'd like to make sure we got a tool that's really 4944 04:13:04,400 --> 04:13:06,700 effective before I work on trying to get people to use it. 4945 04:13:06,700 --> 04:13:08,040 But it becomes quite a bit chicken 4946 04:13:08,040 --> 04:13:10,570 and egg thing in the research that my team does, 4947 04:13:10,570 --> 04:13:13,120 because you can't not have people use the tool, 4948 04:13:13,120 --> 04:13:14,370 then it's hard to get the, you know- 4949 04:13:14,370 --> 04:13:15,720 it's hard to test that effectiveness. 4950 04:13:15,720 --> 04:13:17,210 So the short answer to your question is, 4951 04:13:17,210 --> 04:13:18,450 in the pilot clinics, 4952 04:13:18,450 --> 04:13:21,360 because we haven't gone to trial yet, in the pilot clinics, 4953 04:13:21,360 --> 04:13:23,770 we provided a little bit of implementation support, 4954 04:13:23,770 --> 04:13:27,220 we saw pretty varied uptake of the tools, 4955 04:13:27,220 --> 04:13:30,070 but people did respond to the tools around- 4956 04:13:32,240 --> 04:13:34,660 There was fairly high uptake of like the tools 4957 04:13:34,660 --> 04:13:36,260 to support documentation. 4958 04:13:36,770 --> 04:13:39,030 And then what we had, as I said, again, was a lot of pushback 4959 04:13:39,030 --> 04:13:41,560 on the way the tools around adjustment were structured. 4960 04:13:41,560 --> 04:13:44,310 So we have modified them, and we'll see what happens 4961 04:13:44,310 --> 04:13:46,220 in the next set in the trial clinics, 4962 04:13:46,220 --> 04:13:48,960 we're going to provide a little bit more implementation support 4963 04:13:48,960 --> 04:13:50,600 to some of the trial clinics, 4964 04:13:51,400 --> 04:13:52,840 but we weren't really set up to do that. 4965 04:13:52,840 --> 04:13:54,190 It's always a tension in this work, right? 4966 04:13:54,190 --> 04:13:55,410 Because you want to see 4967 04:13:55,410 --> 04:13:56,630 how do something work in the real world, 4968 04:13:56,630 --> 04:13:58,870 like just turn the tool on and see what happens. 4969 04:13:58,870 --> 04:14:01,590 Ideally, if it's a good enough tool, people will use it. 4970 04:14:01,590 --> 04:14:05,320 In reality, that is far more complex. 4971 04:14:05,320 --> 04:14:07,200 My guess is that what we'll do with this study 4972 04:14:07,200 --> 04:14:09,730 is demonstrate effectiveness when used, 4973 04:14:09,730 --> 04:14:13,130 I mean, that's the hypothesis. And if so, then we would- 4974 04:14:13,130 --> 04:14:16,020 our next study would be how to enhance adoption. 4975 04:14:17,110 --> 04:14:18,620 Dr. Miya Whitaker: Thank you. Great response. 4976 04:14:18,620 --> 04:14:21,460 So helpful. Daphne, we have a question for you. 4977 04:14:22,060 --> 04:14:24,720 So in terms of genetics and genomics research, 4978 04:14:24,720 --> 04:14:27,280 we know that it's like undergoing exponential growth, 4979 04:14:27,280 --> 04:14:29,420 and the community engagement really does 4980 04:14:29,420 --> 04:14:32,550 play a pivotal role in that because it sort of enables us 4981 04:14:32,550 --> 04:14:35,020 to access previously underreached communities, 4982 04:14:35,580 --> 04:14:37,630 particularly in terms of facilitating 4983 04:14:37,630 --> 04:14:40,160 that bi-directional bio log to build trust, 4984 04:14:40,160 --> 04:14:42,340 provide education about health risks, 4985 04:14:42,340 --> 04:14:44,730 and health in general and also empower communities 4986 04:14:44,730 --> 04:14:47,210 to pursue both screening and care 4987 04:14:47,210 --> 04:14:49,980 and also sort of enter into the engagement 4988 04:14:49,980 --> 04:14:51,760 with health-related research. 4989 04:14:51,760 --> 04:14:55,290 Given this type of research involves biospecimen collection, 4990 04:14:56,530 --> 04:15:00,280 which can have some specific cultural significance, 4991 04:15:00,280 --> 04:15:04,540 are there tools that your team uses to sort of help respond 4992 04:15:04,540 --> 04:15:07,700 to questions and concerns about exploitation, 4993 04:15:07,700 --> 04:15:10,280 benefit-sharing, responsiveness to research- 4994 04:15:11,200 --> 04:15:14,410 researchers' responsiveness to community needs, excuse me, 4995 04:15:14,410 --> 04:15:17,210 and now there are nuances for you that are different 4996 04:15:17,210 --> 04:15:19,780 when you think about genetics and genomic research 4997 04:15:19,780 --> 04:15:21,100 versus other types of research. 4998 04:15:21,100 --> 04:15:22,750 And Dr. Gold, if you have some thoughts on that, 4999 04:15:22,750 --> 04:15:24,350 feel free to share as well. 5000 04:15:25,270 --> 04:15:26,660 Dr. Daphne Martschenko: That's such a good question. 5001 04:15:26,660 --> 04:15:27,890 Thank you for asking it. 5002 04:15:27,890 --> 04:15:31,450 I think that was one of the key reasons that we spent 5003 04:15:31,450 --> 04:15:33,680 and devoted a lot of time to talking through 5004 04:15:33,680 --> 04:15:37,290 whether people felt that polygenic scores 5005 04:15:37,930 --> 04:15:42,890 felt materially different to our community members than, 5006 04:15:42,890 --> 04:15:47,600 you know, having their genetic data 5007 04:15:50,340 --> 04:15:52,290 communicated to them through like a genetic counselor 5008 04:15:52,290 --> 04:15:54,120 if they did carrier screening or something like that. 5009 04:15:54,120 --> 04:15:57,140 So we wanted to understand if the numeric value 5010 04:15:57,140 --> 04:15:58,570 that is a polygenic score, 5011 04:15:58,570 --> 04:15:59,930 even though it's a numeric value, 5012 04:15:59,930 --> 04:16:02,700 if that felt different to community members 5013 04:16:02,700 --> 04:16:06,050 than other sources of genetic information, 5014 04:16:06,050 --> 04:16:07,260 or other sorts of information 5015 04:16:07,260 --> 04:16:10,290 that are derived from genetic data. 5016 04:16:10,290 --> 04:16:13,830 And, you know, I can have Annamaria weigh in here, 5017 04:16:13,830 --> 04:16:17,170 but I think community members did feel like polygenic scores 5018 04:16:17,170 --> 04:16:21,080 are sensitive information, it feels very personal to them. 5019 04:16:21,080 --> 04:16:27,250 And it's one of the reasons that I have been working 5020 04:16:27,250 --> 04:16:30,220 with a couple of authors to comment 5021 04:16:30,220 --> 04:16:32,230 on a piece around data sharing, 5022 04:16:32,230 --> 04:16:34,630 secondary data sharing, and community engaged research 5023 04:16:34,630 --> 04:16:38,220 and the tensions that are generated 5024 04:16:38,220 --> 04:16:42,060 between the goals of data sharing 5025 04:16:42,060 --> 04:16:44,010 and the goals of community engaged research 5026 04:16:44,010 --> 04:16:45,210 is part and parcel, 5027 04:16:45,210 --> 04:16:49,980 because when we think of secondary data analysis, 5028 04:16:49,980 --> 04:16:53,490 there's a level of separation between the researcher 5029 04:16:53,490 --> 04:16:55,330 who's doing the secondary data analysis 5030 04:16:55,330 --> 04:16:58,210 and the individuals who first gave that data. 5031 04:16:59,050 --> 04:17:01,210 And in particular, if that data was gathered 5032 04:17:01,210 --> 04:17:02,980 through community engaged research, 5033 04:17:02,980 --> 04:17:05,380 where trust building is so crucial, 5034 04:17:05,380 --> 04:17:07,100 you know, trust can't be sold, 5035 04:17:07,100 --> 04:17:09,780 it can't be bought, it can't be transferred. 5036 04:17:09,780 --> 04:17:15,200 And so there is a lack of responsibility 5037 04:17:15,200 --> 04:17:17,310 sharing that is expected of researchers 5038 04:17:17,310 --> 04:17:20,620 doing secondary data analysis and polygenic scores 5039 04:17:20,620 --> 04:17:25,990 are generated through secondary data analysis often. 5040 04:17:25,990 --> 04:17:28,350 So there's definitely I think, 5041 04:17:28,350 --> 04:17:32,040 this pointed out to us a huge problem 5042 04:17:32,040 --> 04:17:36,220 in terms of the sensitivity of this information, 5043 04:17:36,220 --> 04:17:38,430 but from the research side of things, 5044 04:17:38,430 --> 04:17:42,680 the lack of responsibility or accountability 5045 04:17:42,680 --> 04:17:45,300 that is expected of those researchers 5046 04:17:45,300 --> 04:17:47,210 who are generating those scores. 5047 04:17:47,210 --> 04:17:49,760 So I'll stop there and see if Annamaria has something 5048 04:17:49,760 --> 04:17:51,910 that she would like to add in, 5049 04:17:51,910 --> 04:17:53,860 and then I'll maybe say one more thing. 5050 04:17:55,120 --> 04:17:58,600 Annamaria Smitherman: Well, as most 5051 04:17:58,600 --> 04:18:01,830 [INAUDIBLE], there's a lot of that I didn't understand, 5052 04:18:02,350 --> 04:18:05,430 but I will say that this was something that our group 5053 04:18:05,430 --> 04:18:08,180 was really worried about for a variety of reasons. 5054 04:18:08,780 --> 04:18:12,630 So what worked or what I think shifted mindsets 5055 04:18:13,730 --> 04:18:17,170 or created an opening was, again, 5056 04:18:17,170 --> 04:18:19,170 the conversation with an ethicist 5057 04:18:19,170 --> 04:18:21,360 that was part of the educational part, 5058 04:18:21,360 --> 04:18:27,690 and then the time needed to process all the information, 5059 04:18:27,690 --> 04:18:30,600 to ask our questions, to have the conversations. 5060 04:18:30,600 --> 04:18:33,400 So education and time were really crucial 5061 04:18:33,400 --> 04:18:36,520 to opening up possibilities 5062 04:18:36,520 --> 04:18:38,670 for the members of this community, I think. 5063 04:18:40,090 --> 04:18:41,470 Dr. Daphne Martschenko: And the last thing 5064 04:18:41,470 --> 04:18:42,750 that I would just say is, 5065 04:18:42,750 --> 04:18:46,990 I don't think by any means we are presenting our process as, 5066 04:18:46,990 --> 04:18:50,550 you know, the perfect process. You know, there are limitations 5067 04:18:50,550 --> 04:18:54,850 that I think are part of a response 5068 04:18:54,850 --> 04:18:57,010 that is needed at the system's level 5069 04:18:57,010 --> 04:18:59,650 in terms of how we engage communities 5070 04:18:59,650 --> 04:19:02,100 and how we work to build the trust 5071 04:19:02,100 --> 04:19:07,500 that has in many instances been either not there from the start 5072 04:19:07,500 --> 04:19:09,980 or are irreparably damaged 5073 04:19:09,980 --> 04:19:13,380 because of historical exploitation of communities, 5074 04:19:13,380 --> 04:19:15,920 predominantly communities of color, communities 5075 04:19:15,920 --> 04:19:21,760 who are in lower socio-economic status brackets. 5076 04:19:21,760 --> 04:19:24,180 So I'll just add that point there. 5077 04:19:25,940 --> 04:19:27,230 Dr. Miya Whitaker: Thank you. 5078 04:19:27,230 --> 04:19:29,250 We appreciate, your response is so helpful. 5079 04:19:29,250 --> 04:19:31,440 And actually, it relates to one of the questions 5080 04:19:31,440 --> 04:19:34,460 that we received from an audience member 5081 04:19:35,070 --> 04:19:38,220 who ironically was asking about trust. 5082 04:19:38,220 --> 04:19:41,040 How do you build trust through community engagement? 5083 04:19:41,040 --> 04:19:44,700 Again, trust in some communities has been destroyed 5084 04:19:44,700 --> 04:19:46,240 by historical research. 5085 04:19:46,240 --> 04:19:49,670 And there's often communities who do not trust researchers, 5086 04:19:49,670 --> 04:19:51,200 or even physicians sometimes, 5087 04:19:51,200 --> 04:19:53,830 and so they might refuse to either participate 5088 04:19:53,830 --> 04:19:58,200 or refuse to share data. And so how do you fix trust? 5089 04:19:58,200 --> 04:20:01,840 And so before we sort of get into the nuances 5090 04:20:01,840 --> 04:20:04,590 of a community engaged research projects like yours, 5091 04:20:04,590 --> 04:20:06,870 Daphne, I thought, Dr. Gold, 5092 04:20:06,870 --> 04:20:09,940 when we think about community and community engagement 5093 04:20:09,940 --> 04:20:11,190 for your process, 5094 04:20:11,190 --> 04:20:13,870 there is community with the providers 5095 04:20:13,870 --> 04:20:17,080 that you were engaging with and associated with your project. 5096 04:20:17,080 --> 04:20:20,740 So how did you build trust with the partners 5097 04:20:20,740 --> 04:20:23,310 who were going to be using the COHERE tool? 5098 04:20:23,970 --> 04:20:26,500 And were there ever instances where trust was eroded 5099 04:20:26,500 --> 04:20:27,770 and you had to build it back? 5100 04:20:27,770 --> 04:20:29,490 And then we'll flip back over to Daphne 5101 04:20:29,490 --> 04:20:31,750 to kind of talk about her community engaged projects 5102 04:20:31,750 --> 04:20:33,000 and the differences there, 5103 04:20:33,000 --> 04:20:34,910 because I think it's so important to have 5104 04:20:34,910 --> 04:20:36,510 that both be in the space. 5105 04:20:37,430 --> 04:20:38,700 Dr. Rachel Gold: Yeah, sure, of course, 5106 04:20:38,700 --> 04:20:41,610 as I said, we went through a year-long process 5107 04:20:41,610 --> 04:20:44,720 working with a diverse set of CHC staff. 5108 04:20:44,720 --> 04:20:46,980 Now, these are staff-facing tools. 5109 04:20:46,980 --> 04:20:49,910 So, and again, because of budget limitations, 5110 04:20:50,800 --> 04:20:52,460 we were limited to working with CHC staff 5111 04:20:52,460 --> 04:20:54,080 rather than patients. Obviously, that's something 5112 04:20:54,080 --> 04:20:55,560 we'd want to think about in the future, 5113 04:20:55,560 --> 04:20:57,420 but these are tools that are in the EHR, 5114 04:20:57,420 --> 04:20:59,170 and so they're being seen by staff. 5115 04:21:00,200 --> 04:21:01,800 You know, I don't think so. 5116 04:21:02,710 --> 04:21:05,350 I don't feel like there was an issue of trust, you know, 5117 04:21:05,350 --> 04:21:06,630 because the way we set up our work 5118 04:21:06,630 --> 04:21:10,750 was we looked at hypertension diabetes guidelines 5119 04:21:10,750 --> 04:21:13,070 and said, well, here are the kinds of adjustments 5120 04:21:13,070 --> 04:21:16,170 that are sort of recommended in these guidelines. 5121 04:21:16,170 --> 04:21:18,310 Which of these would you want to see in the EHR? 5122 04:21:18,310 --> 04:21:19,950 So we thought about the content, and then we said, 5123 04:21:19,950 --> 04:21:21,270 is there anything that's missing? 5124 04:21:21,270 --> 04:21:22,560 And then we said, what would you like? 5125 04:21:22,560 --> 04:21:25,220 And then when would you like these tools to show up? 5126 04:21:25,220 --> 04:21:27,890 Which stage in a primary care workflow? 5127 04:21:27,890 --> 04:21:30,760 And we really just- they totally drove what we did, 5128 04:21:31,380 --> 04:21:32,720 where I felt like there was- 5129 04:21:32,720 --> 04:21:34,350 I mean, it wasn't exactly tension, 5130 04:21:34,350 --> 04:21:35,980 but something that was interesting for us 5131 04:21:35,980 --> 04:21:37,760 was sometimes they had different points of view. 5132 04:21:37,760 --> 04:21:39,000 Like, we had some folks- 5133 04:21:39,000 --> 04:21:40,660 and some was because they had different clinics, 5134 04:21:40,660 --> 04:21:44,040 so for example, once someone on our committee said, 5135 04:21:44,040 --> 04:21:46,310 "Well, I'd really like for you to recommend 5136 04:21:46,310 --> 04:21:50,470 that they have the tools, recommend a certain- 5137 04:21:51,030 --> 04:21:53,230 that we go to, you know, a certain pharmacy where we know 5138 04:21:53,230 --> 04:21:55,630 they can provide home delivery." But other folks that said, 5139 04:21:55,630 --> 04:21:58,150 "Well, wait a minute, no, no, our clinic has a pharmacy, 5140 04:21:58,150 --> 04:22:00,630 and we make some of our income on that pharmacy, 5141 04:22:00,630 --> 04:22:03,180 and we have to have our scripts go to our pharmacy, 5142 04:22:03,180 --> 04:22:05,480 because that would affect our clinic's income 5143 04:22:05,480 --> 04:22:07,340 if you send it to an outside pharmacy." 5144 04:22:07,340 --> 04:22:09,170 So we had to figure out how to make that work. 5145 04:22:09,170 --> 04:22:11,120 And sometimes what we came up with was, 5146 04:22:11,820 --> 04:22:13,520 okay, well, we'll let- you know, when we work 5147 04:22:13,520 --> 04:22:14,780 [INAUDIBLE], we let them choose. 5148 04:22:14,780 --> 04:22:16,170 You know, we'd say if we can't come up 5149 04:22:16,170 --> 04:22:17,720 with something that's happy for everybody, 5150 04:22:17,720 --> 04:22:19,200 maybe part of what happens in the tools 5151 04:22:19,200 --> 04:22:20,700 is there are three parts that you opt into. 5152 04:22:20,700 --> 04:22:22,250 And that's why in one of my slides, 5153 04:22:22,250 --> 04:22:24,230 I showed where there were several elements of the tools 5154 04:22:24,230 --> 04:22:26,570 were going to trial, that provide an option, 5155 04:22:26,570 --> 04:22:28,160 because we know that different clinics 5156 04:22:28,160 --> 04:22:29,770 have different workflows, 5157 04:22:29,770 --> 04:22:32,690 different, you know, just rules. And so we need to- 5158 04:22:32,690 --> 04:22:34,910 But I did not feel like there was an issue of losing trust, 5159 04:22:34,910 --> 04:22:36,110 we listened to what they said, 5160 04:22:36,110 --> 04:22:37,700 and then we would come back to them a couple months later 5161 04:22:37,700 --> 04:22:39,110 and say, "Is this what you meant?" 5162 04:22:39,110 --> 04:22:40,370 And they would either say yes or no. 5163 04:22:40,370 --> 04:22:41,910 And then we come back until they said, 5164 04:22:41,910 --> 04:22:43,390 "Yeah, that's what we wanted you to do." 5165 04:22:43,390 --> 04:22:44,730 Now, what was interesting was that 5166 04:22:44,730 --> 04:22:46,810 the exact same folks who developed- 5167 04:22:46,810 --> 04:22:48,230 who were in the development committee 5168 04:22:48,230 --> 04:22:50,740 as in the pilot clinics, but there was some overlap. 5169 04:22:50,740 --> 04:22:53,830 And what was interesting was that sometimes folks who told us 5170 04:22:53,830 --> 04:22:55,910 they wanted stuff in the tools, 5171 04:22:55,910 --> 04:22:58,240 when they trialed them didn't use them, 5172 04:22:58,240 --> 04:22:59,670 even though it's exactly what they said. 5173 04:22:59,670 --> 04:23:01,390 So, you know, that's doing real-world 5174 04:23:01,390 --> 04:23:02,630 health services research. 5175 04:23:02,630 --> 04:23:06,010 And we weren't that surprised, just kind of bemused by it. 5176 04:23:06,010 --> 04:23:08,670 But the trust, I think it's a different dynamic when you- 5177 04:23:08,670 --> 04:23:11,140 and I'm sure, you know, Dr. Martschenko, 5178 04:23:11,140 --> 04:23:15,110 you would obviously, you know, speak far better to this than I. 5179 04:23:15,110 --> 04:23:17,620 But when you're working with clinic staff as researchers, 5180 04:23:17,620 --> 04:23:19,190 it's the different dynamic than patients. 5181 04:23:19,190 --> 04:23:21,700 You know, the power differential is different. 5182 04:23:21,700 --> 04:23:24,070 So I would turn it to you, Dr. Martschenko. 5183 04:23:25,770 --> 04:23:27,630 Dr. Daphne Martschenko: Thank you so much, Dr. Gold. 5184 04:23:27,630 --> 04:23:29,950 Yeah, I mean, I think that one of the first things 5185 04:23:29,950 --> 04:23:31,170 that I would say 5186 04:23:31,170 --> 04:23:34,580 is I think acknowledging the legitimacy of the mistrust 5187 04:23:34,580 --> 04:23:37,860 that communities have is an important starting point. 5188 04:23:38,470 --> 04:23:39,750 The other thing that I would say 5189 04:23:39,750 --> 04:23:41,960 is that with community engaged research, 5190 04:23:42,630 --> 04:23:44,890 this centers attention on the community 5191 04:23:44,890 --> 04:23:47,260 rather than individual research participants 5192 04:23:47,260 --> 04:23:49,170 as is the common practice of, 5193 04:23:49,170 --> 04:23:51,730 you know, IRBs, institutional review boards, 5194 04:23:51,730 --> 04:23:54,390 and community engaged research is focusing on the harms 5195 04:23:54,390 --> 04:23:57,390 caused and potential benefits to a specific community. 5196 04:23:58,580 --> 04:24:03,700 So as someone who's in the world of bioethics, 5197 04:24:03,700 --> 04:24:05,210 one of the things we talked about 5198 04:24:05,210 --> 04:24:08,820 is the limitations of informed consent processes, 5199 04:24:08,820 --> 04:24:12,390 the limitations of institutional review boards, 5200 04:24:12,390 --> 04:24:14,890 but how often because of the regulatory landscape 5201 04:24:14,890 --> 04:24:16,170 that we have, 5202 04:24:16,170 --> 04:24:20,100 we see those mechanisms as the kind of safeguards 5203 04:24:20,100 --> 04:24:23,310 that are going to catch everything. 5204 04:24:23,310 --> 04:24:25,590 And, you know, IRBs, informed consent, 5205 04:24:25,590 --> 04:24:27,690 those are not what are going to play a role 5206 04:24:27,690 --> 04:24:31,500 in building trust between communities and researchers, 5207 04:24:31,500 --> 04:24:34,760 between communities and clinicians. 5208 04:24:34,760 --> 04:24:37,230 And so I think it's really important to recognize 5209 04:24:37,230 --> 04:24:40,030 that we're not going to be able to rely on 5210 04:24:40,030 --> 04:24:42,120 and we shouldn't rely on IRBs, 5211 04:24:42,120 --> 04:24:45,170 we shouldn't rely on the informed consent document 5212 04:24:45,170 --> 04:24:47,310 as the mechanism through which 5213 04:24:47,310 --> 04:24:49,040 we're going to build trust with communities 5214 04:24:49,040 --> 04:24:51,960 or we're going to ensure that our research is socially 5215 04:24:52,570 --> 04:24:54,880 and ethically responsible. 5216 04:24:54,880 --> 04:24:57,110 So I think, one, recognizing, you know, 5217 04:24:57,110 --> 04:25:00,590 that mistrust is legitimate, that mistrust is real. 5218 04:25:00,590 --> 04:25:02,520 And I was seeing, you know, in the Slido 5219 04:25:02,520 --> 04:25:04,410 that people were also mentioning 5220 04:25:04,410 --> 04:25:06,720 the great work that community health workers do, 5221 04:25:06,720 --> 04:25:07,920 the great work that people 5222 04:25:07,920 --> 04:25:10,150 who come from the same cultural background and language 5223 04:25:10,150 --> 04:25:12,680 that also understand the community struggle play, 5224 04:25:12,680 --> 04:25:15,470 and I think that that's absolutely right on the mark. 5225 04:25:15,470 --> 04:25:17,070 It's so, so important. 5226 04:25:17,760 --> 04:25:19,500 And then the final thing that I would say is, 5227 04:25:19,500 --> 04:25:21,450 I think we need to recognize the way 5228 04:25:21,450 --> 04:25:24,660 that the research enterprise has been structured to, 5229 04:25:24,660 --> 04:25:29,080 one, focus on the harms to research participants 5230 04:25:29,080 --> 04:25:31,570 rather than the broader social harms 5231 04:25:31,570 --> 04:25:35,720 that might be a byproduct of research that's done. 5232 04:25:35,720 --> 04:25:39,460 And so there's also a need for us to recognize 5233 04:25:39,460 --> 04:25:43,590 at a more systems level the ways in which we perpetuate mistrust 5234 04:25:43,590 --> 04:25:46,370 by kind of ignoring the broader downstream 5235 04:25:46,370 --> 04:25:48,520 social implications of the work that we do. 5236 04:25:50,550 --> 04:25:51,750 Dr. Miya Whitaker: Thank you. 5237 04:25:51,750 --> 04:25:57,070 Well said. We have a question from another attendee, 5238 04:25:57,070 --> 04:26:00,370 who states American Indian and Alaska Native data 5239 04:26:00,370 --> 04:26:05,750 is often scarce in our public health surveillance systems, 5240 04:26:05,750 --> 04:26:09,260 and with respect to community engagement and empowerment, 5241 04:26:09,260 --> 04:26:11,340 how do you dismantle structural racism 5242 04:26:11,340 --> 04:26:13,110 embedded in any of the data systems 5243 04:26:13,110 --> 04:26:17,250 that can lead to the erasure of indigenous communities' data 5244 04:26:17,250 --> 04:26:19,120 collection analysis and reporting? 5245 04:26:19,690 --> 04:26:21,430 Any thoughts from either of you on that? 5246 04:26:21,430 --> 04:26:23,790 And I think it's an interesting question to think about 5247 04:26:23,790 --> 04:26:27,450 with respect to development of EHR tools or whatever, 5248 04:26:27,450 --> 04:26:31,450 are there cultural tailoring processes 5249 04:26:31,450 --> 04:26:33,670 that need to be thought of or conceptualized 5250 04:26:33,670 --> 04:26:35,960 when we go about collecting that data 5251 04:26:35,960 --> 04:26:38,540 with that particular population? So, thoughts? 5252 04:26:40,530 --> 04:26:41,760 Dr. Rachel Gold: I'm just going to own I do 5253 04:26:41,760 --> 04:26:43,360 not have expertise in this area. 5254 04:26:43,960 --> 04:26:45,609 It's a very challenging question. 5255 04:26:46,140 --> 04:26:48,040 My only thought would be, you know, again, 5256 04:26:48,040 --> 04:26:51,810 and I'm working again on care team facing interventions, 5257 04:26:51,810 --> 04:26:54,630 not patient facing directly, supporting the care team. 5258 04:26:54,630 --> 04:26:56,900 But if we were going to be developing these tools 5259 04:26:56,900 --> 04:27:01,460 for a setting that was primarily serving indigenous 5260 04:27:01,460 --> 04:27:03,550 or native populations, I would want to talk- 5261 04:27:03,550 --> 04:27:05,780 I would want their teams to tell us 5262 04:27:05,780 --> 04:27:07,870 how to fix them to make them a better fit. 5263 04:27:07,870 --> 04:27:09,410 I mean, that's a bit kind of obvious, 5264 04:27:09,410 --> 04:27:12,200 but I mean, that's the best I got for you. 5265 04:27:12,200 --> 04:27:14,630 I'm sure Dr. Martschenko has a better answer 5266 04:27:14,630 --> 04:27:16,100 Dr. Miya Whitaker: And great points, Dr. Gold. 5267 04:27:16,100 --> 04:27:18,700 It seems like there's the potential for a partnership 5268 04:27:18,700 --> 04:27:22,920 with you and Indian health service facilities 5269 04:27:22,920 --> 04:27:25,430 to kind of begin to think about how to develop 5270 04:27:25,430 --> 04:27:28,150 or an application for the COHERE tool. 5271 04:27:28,970 --> 04:27:30,680 And Dr. Martschenko, 5272 04:27:30,680 --> 04:27:34,400 did you want to give us a last comment on this piece 5273 04:27:34,400 --> 04:27:37,040 before we transition into the breakout session? 5274 04:27:37,040 --> 04:27:39,100 Dr. Daphne Martschenko: Well, I would just echo 5275 04:27:39,100 --> 04:27:42,090 Dr. Gold's point and emphasizing that, you know, 5276 04:27:42,090 --> 04:27:44,350 I do not personally work with tribal communities. 5277 04:27:44,350 --> 04:27:45,590 I'm not an expert in there. 5278 04:27:45,590 --> 04:27:47,790 I would want to point people in the direction 5279 04:27:47,790 --> 04:27:49,640 of The Native BioData Consortium. 5280 04:27:49,640 --> 04:27:52,480 I think they're doing really outstanding work 5281 04:27:52,480 --> 04:27:55,350 to ensure that tribal communities have ownership 5282 04:27:55,350 --> 04:27:57,910 and say in how their specimens, their samples, 5283 04:27:57,910 --> 04:27:59,990 are used and for what purposes, 5284 04:27:59,990 --> 04:28:01,820 and I really think it kind of upsets 5285 04:28:01,820 --> 04:28:04,140 the existing dynamic in which, you know, 5286 04:28:04,140 --> 04:28:06,340 the kind of traditional models for researchers 5287 04:28:06,340 --> 04:28:08,230 to try and recruit people for their studies 5288 04:28:08,230 --> 04:28:11,290 and to have already come in with their idea of what it is 5289 04:28:11,290 --> 04:28:14,600 that they want to do, and it's very one directional, 5290 04:28:14,600 --> 04:28:17,570 The Native BioData Consortium kind of flips that model, 5291 04:28:17,570 --> 04:28:19,760 and that researchers have to come to the community members 5292 04:28:19,760 --> 04:28:21,560 and propose the research that they want to do 5293 04:28:21,560 --> 04:28:22,790 and be in dialogue with them. 5294 04:28:22,790 --> 04:28:25,110 So I would just refer people to that resource. 5295 04:28:26,640 --> 04:28:28,410 Dr. Miya Whitaker: Thank you so much to you both, 5296 04:28:28,410 --> 04:28:31,490 you have taken us through some really amazing content 5297 04:28:31,490 --> 04:28:35,000 and provided lots of food for thought for our next session. 5298 04:28:35,000 --> 04:28:37,890 We are now going to transition into our breakout sessions. 5299 04:28:37,890 --> 04:28:40,190 So we invite all of our attendees 5300 04:28:40,190 --> 04:28:42,190 to at the bottom of the screen, 5301 04:28:42,190 --> 04:28:45,100 select which breakout session you'd like to attend. 5302 04:28:45,100 --> 04:28:48,830 And we will join you momentarily to begin additional dialogue 5303 04:28:48,830 --> 04:28:52,230 on the content from today. And thank you so much again 5304 04:28:52,230 --> 04:28:54,300 to our panelists' important presentations, 5305 04:28:54,300 --> 04:28:56,550 we so appreciate you sharing your work today. 5306 04:29:16,460 --> 04:29:20,060 We as a community have experienced such a fantastic day 5307 04:29:20,060 --> 04:29:22,120 of sharing of knowledge and perspectives, 5308 04:29:22,680 --> 04:29:24,850 and we've witnessed case studies and discussed 5309 04:29:24,850 --> 04:29:28,700 how one might apply lessons learned via breakout sessions. 5310 04:29:28,700 --> 04:29:30,810 We encourage you all to continue efforts 5311 04:29:30,810 --> 04:29:33,170 to integrate social and structural determinants 5312 04:29:33,170 --> 04:29:34,870 of health measures into your work. 5313 04:29:35,390 --> 04:29:38,360 Now, to conclude what was a truly rich event, 5314 04:29:38,880 --> 04:29:41,260 we will have closing remarks from doctors 5315 04:29:41,260 --> 04:29:43,490 Janine Austin Clayton and Karen Parker. 5316 04:29:44,130 --> 04:29:46,200 Dr. Clayton is the Associate Director 5317 04:29:46,200 --> 04:29:48,060 for Research on Women's Health 5318 04:29:48,060 --> 04:29:50,040 and the Director of the Office of Research 5319 04:29:50,040 --> 04:29:52,260 on Women's Health here at NIH. 5320 04:29:52,260 --> 04:29:55,010 Dr. Clayton is the architect of the NIH policy 5321 04:29:55,010 --> 04:29:57,590 requiring scientists to consider sex 5322 04:29:57,590 --> 04:30:01,100 as a biological variable across the research spectrum, 5323 04:30:01,100 --> 04:30:04,670 part of NIH's initiative to enhance reproducibility 5324 04:30:04,670 --> 04:30:06,580 through rigor and transparency. 5325 04:30:06,580 --> 04:30:08,870 As the co-chair of the NIH Working Group 5326 04:30:08,870 --> 04:30:10,940 on Women in Biomedical Careers, Dr. 5327 04:30:10,940 --> 04:30:13,600 Clayton also leads NIH's efforts 5328 04:30:13,600 --> 04:30:15,790 to advance women in science careers. 5329 04:30:16,440 --> 04:30:19,310 Dr. Karen Parker is the Director of the Sexual 5330 04:30:19,310 --> 04:30:22,140 and Gender Minority Research Office here at the NIH. 5331 04:30:22,650 --> 04:30:25,710 Dr. Parker serves as the co-chair of the Trans NIH 5332 04:30:25,710 --> 04:30:28,870 Sexual and Gender Minority Research Coordinating Committee, 5333 04:30:28,870 --> 04:30:31,290 the NIH SGM Research Working Group 5334 04:30:31,290 --> 04:30:33,870 of the Council of Councils, and the NIH 5335 04:30:33,870 --> 04:30:36,150 Office of the Director Equity Council. 5336 04:30:36,150 --> 04:30:39,020 She also serves as the executive director 5337 04:30:39,020 --> 04:30:42,810 of the Department of Health and Human Services, LGBTQIA+ 5338 04:30:43,470 --> 04:30:45,070 Coordinating Committee. 5339 04:30:45,070 --> 04:30:46,560 Thank you, Doctors Clayton and Parker 5340 04:30:46,560 --> 04:30:48,760 for your contributions to NIH. 5341 04:30:48,760 --> 04:30:51,410 I now turn the virtual podium over to Dr. Clayton. 5342 04:30:52,200 --> 04:30:53,890 Dr. Janine Austin Clayton: Thank you so much, Dr. Whitaker. 5343 04:30:53,890 --> 04:30:56,820 You're hearing me okay? Awesome. 5344 04:30:56,820 --> 04:30:59,300 If somebody could bring up my slides, that would be great. 5345 04:30:59,300 --> 04:31:02,340 So we are coming down the homestretch. 5346 04:31:02,340 --> 04:31:04,410 This is an amazing workshop. 5347 04:31:04,410 --> 04:31:06,570 I want to acknowledge all the organizers 5348 04:31:06,570 --> 04:31:09,050 and the speakers and the session chairs, 5349 04:31:09,050 --> 04:31:11,840 the organizing committee in particular, 5350 04:31:11,840 --> 04:31:15,370 and of course NIMHD for leading these efforts. 5351 04:31:15,370 --> 04:31:20,140 And we were thrilled at ORWH to be part of this workshop. 5352 04:31:20,140 --> 04:31:21,670 And we're coming down to the homestretch, 5353 04:31:21,670 --> 04:31:23,630 and my closing remarks, 5354 04:31:23,630 --> 04:31:26,760 I'm asking you to think about asking more questions 5355 04:31:26,760 --> 04:31:28,800 to initiate change. Next slide. 5356 04:31:30,520 --> 04:31:33,170 So I'm going to highlight a couple of disparities 5357 04:31:33,170 --> 04:31:34,900 in the context of the health of women. 5358 04:31:34,900 --> 04:31:37,840 And if you click next, you're going to see disparities 5359 04:31:37,840 --> 04:31:40,290 here on this slide in terms of maternal health. 5360 04:31:40,290 --> 04:31:43,050 And you don't have to be able to read those small print 5361 04:31:43,050 --> 04:31:45,290 on those slides to see that across the United States, 5362 04:31:45,290 --> 04:31:48,500 we see varying situations by state. 5363 04:31:48,500 --> 04:31:50,940 And in the middle there is a security index, 5364 04:31:50,940 --> 04:31:56,350 and that's an index that looks at economic security, safety, 5365 04:31:56,950 --> 04:32:00,000 numbers of police reports, for example, food insecurity, 5366 04:32:00,770 --> 04:32:02,890 opportunities for economic growth, 5367 04:32:02,890 --> 04:32:05,540 and you can see variations across the United States. 5368 04:32:05,540 --> 04:32:07,490 And I'll just give you a couple of stats. 5369 04:32:07,490 --> 04:32:10,130 Over half of mothers to be in the United States 5370 04:32:10,130 --> 04:32:12,020 are experiencing food insecurity. 5371 04:32:12,020 --> 04:32:15,520 Over half of deliveries are occurring under Medicaid, 5372 04:32:15,520 --> 04:32:17,670 and over half of newborns- 5373 04:32:17,670 --> 04:32:21,210 about half of newborns are using WIC. 5374 04:32:21,210 --> 04:32:23,780 And at the bottom you can see maternal mortality. 5375 04:32:23,780 --> 04:32:25,350 Again, you don't need to be able 5376 04:32:25,350 --> 04:32:26,860 to read those states at the bottom. 5377 04:32:26,860 --> 04:32:29,670 The national average is in that horizontal line. 5378 04:32:29,670 --> 04:32:33,240 The blue dots are maternal mortality for white women. 5379 04:32:33,240 --> 04:32:35,820 The black dots- I mean, the red dots are for black women. 5380 04:32:35,820 --> 04:32:39,030 And you can see in nearly every state there's a disparity. 5381 04:32:39,030 --> 04:32:40,880 And click again for me, if you would. 5382 04:32:41,500 --> 04:32:44,870 We also see here in the healthcare affordability 5383 04:32:44,870 --> 04:32:48,360 is the more affordable the healthcare is, 5384 04:32:48,360 --> 04:32:52,010 the lower the maternal mortality rate by state. 5385 04:32:52,010 --> 04:32:55,030 And so here, just some of the differences across the US. 5386 04:32:55,030 --> 04:32:57,060 Next. 5387 04:32:57,060 --> 04:33:00,070 Here at ORWH, our mission is to expand women's 5388 04:33:00,070 --> 04:33:01,620 health research across NIH. 5389 04:33:01,620 --> 04:33:04,520 We work with all 27 institutes and centers to do that. 5390 04:33:04,520 --> 04:33:06,670 We want to make sure women in underrepresented racial 5391 04:33:06,670 --> 04:33:09,090 and ethnic groups are included in NIH support, 5392 04:33:09,090 --> 04:33:10,650 the clinical research and advance women 5393 04:33:10,650 --> 04:33:12,890 in biomedical careers. Click next. 5394 04:33:13,590 --> 04:33:17,090 And we imagine a world where sex and gender are integrated 5395 04:33:17,090 --> 04:33:18,330 across the biomedical research 5396 04:33:18,330 --> 04:33:20,510 continuum from those laboratory studies, 5397 04:33:20,510 --> 04:33:23,090 those animal models of human disease 5398 04:33:23,090 --> 04:33:24,870 through translation, clinical disease, 5399 04:33:24,870 --> 04:33:27,310 applied research, policy as well. 5400 04:33:27,970 --> 04:33:30,540 And we want to make sure that every woman receives 5401 04:33:30,540 --> 04:33:33,180 evidence-based diagnostics, treatment and care, 5402 04:33:33,180 --> 04:33:36,230 tailored to her own circumstances, goals, and needs, 5403 04:33:36,230 --> 04:33:38,580 and a world where all women in science careers 5404 04:33:38,580 --> 04:33:40,360 reach their full potential. 5405 04:33:40,360 --> 04:33:42,160 Next slide, please. 5406 04:33:42,160 --> 04:33:44,740 The way that we do that is through several frameworks. 5407 04:33:44,740 --> 04:33:47,330 The multidimensional framework that we put forward 5408 04:33:47,330 --> 04:33:51,060 on behalf of NIH in the current NIH-wide strategic plan 5409 04:33:51,060 --> 04:33:53,890 for women's health research emphasizes the fact 5410 04:33:53,890 --> 04:33:56,710 that we need to consider internal factors like sex 5411 04:33:56,710 --> 04:34:00,610 as a biological variable, genetic factors as well, 5412 04:34:01,180 --> 04:34:04,550 and external factors like social context, 5413 04:34:04,550 --> 04:34:06,150 gender as a social construct, 5414 04:34:06,830 --> 04:34:09,610 policies, environmental exposures, 5415 04:34:09,610 --> 04:34:11,550 environmental toxicants, 5416 04:34:12,740 --> 04:34:15,260 violence that someone may experience in environment, 5417 04:34:15,260 --> 04:34:19,060 and also all of that in the context of the life course. 5418 04:34:19,060 --> 04:34:21,580 The life course approach is germane to women's health. 5419 04:34:21,580 --> 04:34:23,240 It's absolutely critical to understanding 5420 04:34:23,240 --> 04:34:25,500 the health of women, realizing what has happened 5421 04:34:25,500 --> 04:34:27,530 before affects what happens in the future. 5422 04:34:27,530 --> 04:34:30,650 And I really enjoy Dr. Collins's presentation 5423 04:34:30,650 --> 04:34:32,520 and his discussion of epigenetics 5424 04:34:32,520 --> 04:34:36,510 and how profound it is to affect the health of women in that 5425 04:34:36,510 --> 04:34:39,610 you will affect the health of every person in society 5426 04:34:39,610 --> 04:34:40,950 by improving the health of women. 5427 04:34:40,950 --> 04:34:43,820 And I wish that we could be able to move that forward 5428 04:34:43,820 --> 04:34:45,110 in a more definitive way 5429 04:34:45,110 --> 04:34:47,610 so that we could accelerate progress for everyone. 5430 04:34:48,160 --> 04:34:49,770 One of the ways we hope to do that 5431 04:34:49,770 --> 04:34:51,670 is through the next NIH strategic plan 5432 04:34:51,670 --> 04:34:55,040 for women's health research. We have an RFI out right now. 5433 04:34:55,040 --> 04:34:56,930 You should have your voice heard, speak up, 5434 04:34:56,930 --> 04:34:58,920 let us know what's important to you 5435 04:34:58,920 --> 04:35:02,710 so that it can be included in the next NIH strategic plan. 5436 04:35:03,220 --> 04:35:05,260 Next slide, please. 5437 04:35:05,260 --> 04:35:08,700 So we could think about the old way of doing things 5438 04:35:08,700 --> 04:35:10,600 or the new way of doing things, 5439 04:35:10,600 --> 04:35:13,780 and I would put forward to you this ask, 5440 04:35:13,780 --> 04:35:17,150 how do we ask the right questions for change? 5441 04:35:17,150 --> 04:35:19,570 Because so much of what we end up doing 5442 04:35:19,570 --> 04:35:23,220 is in response to a question that's being asked, 5443 04:35:23,220 --> 04:35:25,980 and that question needs to be asked by us as researchers, 5444 04:35:25,980 --> 04:35:28,660 and we need to be listening to those communities 5445 04:35:28,660 --> 04:35:31,300 most affected by the diseases under study 5446 04:35:31,300 --> 04:35:33,120 and hear from them from the outset. 5447 04:35:33,120 --> 04:35:35,710 And we heard those concepts loud and clear 5448 04:35:35,710 --> 04:35:38,710 in many presentations today. Next slide please. 5449 04:35:39,240 --> 04:35:41,480 I do want to share with you the fact that we have, 5450 04:35:41,480 --> 04:35:43,520 for the first time ever in this country, 5451 04:35:43,520 --> 04:35:47,010 a national strategy on gender equity and equality. 5452 04:35:47,010 --> 04:35:50,100 This administration has put this front and center. 5453 04:35:50,980 --> 04:35:53,250 "It is imperative to encourage new generations 5454 04:35:53,250 --> 04:35:55,660 of scientists and clinicians to take women's health 5455 04:35:55,660 --> 04:35:58,620 seriously by stressing the need to step up 5456 04:35:58,620 --> 04:36:00,560 and make the world a healthier, 5457 04:36:00,560 --> 04:36:02,580 more equitable place for everyone." 5458 04:36:02,580 --> 04:36:04,970 And this is through policies and programs 5459 04:36:04,970 --> 04:36:07,250 that the federal government has established. 5460 04:36:07,760 --> 04:36:09,260 ORWH is leading a sex 5461 04:36:09,260 --> 04:36:12,670 and gender and intersectionality innovations collaborative 5462 04:36:13,200 --> 04:36:16,160 that is well-aligned with the national strategy 5463 04:36:16,160 --> 04:36:18,060 on gender equity and equality. 5464 04:36:18,060 --> 04:36:20,200 And it's summarized in that publication here. 5465 04:36:20,200 --> 04:36:22,210 Next slide, please. 5466 04:36:22,210 --> 04:36:24,370 Folks in Congress are also very concerned 5467 04:36:24,370 --> 04:36:27,010 about the health of women and asked us to, 5468 04:36:27,010 --> 04:36:29,090 on behalf of NIH, 5469 04:36:29,090 --> 04:36:32,720 host a women's health conference and look at several key issues. 5470 04:36:32,720 --> 04:36:34,410 And I will just cut to the chase 5471 04:36:34,410 --> 04:36:35,900 because we are at the end of the day. 5472 04:36:35,900 --> 04:36:38,920 And the URL is provided here for the meeting proceedings 5473 04:36:38,920 --> 04:36:40,380 and the full report, 5474 04:36:40,380 --> 04:36:42,930 "Perspectives on Advancing NIH Research to Inform 5475 04:36:42,930 --> 04:36:44,490 and Improve the Health of Women." 5476 04:36:44,490 --> 04:36:47,020 Some of the key cross-cutting themes that we're- 5477 04:36:47,020 --> 04:36:53,270 this group, our advisory council led the charge here, 5478 04:36:53,270 --> 04:36:56,360 were urgently needed our implementing best practices 5479 04:36:56,360 --> 04:36:57,740 so that their evidence-based 5480 04:36:57,740 --> 04:37:00,420 and evidence from people like the individuals 5481 04:37:00,420 --> 04:37:02,550 to whom the evidence is being applied 5482 04:37:02,550 --> 04:37:05,770 and a more holistic person-centered approach, 5483 04:37:06,580 --> 04:37:09,130 the importance of addressing care inequities, 5484 04:37:09,130 --> 04:37:10,910 especially among populations of women 5485 04:37:10,910 --> 04:37:12,820 with overlapping identities, 5486 04:37:12,820 --> 04:37:16,690 highlighting the disparities and pregnancy-related 5487 04:37:16,690 --> 04:37:19,580 and pregnancy-associated deaths in the United States, 5488 04:37:19,580 --> 04:37:21,190 which are far higher in black, 5489 04:37:21,190 --> 04:37:24,170 Alaskan native and American Indian individuals 5490 04:37:24,170 --> 04:37:26,210 compared to other pregnant persons. 5491 04:37:26,210 --> 04:37:29,010 Lower socioeconomic status and education 5492 04:37:29,010 --> 04:37:31,390 are also risk factors for multimorbidity 5493 04:37:31,390 --> 04:37:33,750 for women across their life course. 5494 04:37:33,750 --> 04:37:36,240 And that factor was also included 5495 04:37:36,240 --> 04:37:37,780 in several presentations today 5496 04:37:37,780 --> 04:37:39,940 when there was an effective education 5497 04:37:39,940 --> 04:37:42,610 and where there wasn't an effective education. 5498 04:37:42,610 --> 04:37:44,800 And an approach that is more intentional 5499 04:37:44,800 --> 04:37:46,500 where we focus on the health of women 5500 04:37:46,500 --> 04:37:48,450 using a life course perspective 5501 04:37:48,450 --> 04:37:51,460 and consider female-specific topics 5502 04:37:51,460 --> 04:37:53,940 such as recognizing that pregnancy 5503 04:37:53,940 --> 04:37:56,950 is a stress test that portends future health 5504 04:37:56,950 --> 04:38:00,020 of that woman and future generations. 5505 04:38:00,020 --> 04:38:03,080 Our over reliance on the past, on male animal models, 5506 04:38:03,080 --> 04:38:06,180 men in clinical trials, have left knowledge gaps, 5507 04:38:06,180 --> 04:38:08,590 and despite the sex as a biological variable policy, 5508 04:38:08,590 --> 04:38:11,130 we know there are knowledge gaps in basic science 5509 04:38:11,130 --> 04:38:12,900 and that informed translation. 5510 04:38:12,900 --> 04:38:14,320 Next slide please. 5511 04:38:14,320 --> 04:38:16,470 So what did we do at ORWH 5512 04:38:16,470 --> 04:38:19,930 in terms of trying to break down these structural barriers? 5513 04:38:19,930 --> 04:38:22,890 And next? Next. 5514 04:38:22,890 --> 04:38:25,430 And so here are just two programs 5515 04:38:25,430 --> 04:38:27,390 that I just want to highlight for you. 5516 04:38:27,390 --> 04:38:30,640 It's our privilege to lead the understudied, 5517 04:38:30,640 --> 04:38:33,350 underreported and underrepresented populations, 5518 04:38:33,350 --> 04:38:35,550 or U3 populations of women, 5519 04:38:35,550 --> 04:38:37,540 funding opportunity announcement and collaboration 5520 04:38:37,540 --> 04:38:39,620 with multiple institutes and centers. 5521 04:38:39,620 --> 04:38:41,550 We're looking at the influence of sex 5522 04:38:41,550 --> 04:38:43,510 and or gender at the intersection of sex, 5523 04:38:43,510 --> 04:38:44,790 race, and ethnicity 5524 04:38:44,790 --> 04:38:47,350 and other social determinants of health and disease 5525 04:38:47,350 --> 04:38:51,270 to fund research specifically focused on these populations. 5526 04:38:51,270 --> 04:38:54,600 And in fact, this is NIH's only funding opportunity announcement 5527 04:38:54,600 --> 04:38:58,130 that focuses on this particular set of populations. 5528 04:38:58,130 --> 04:39:01,770 You can take a look at our lecture series at the URL there 5529 04:39:01,770 --> 04:39:04,770 and just a few of the topics that are currently under study. 5530 04:39:05,720 --> 04:39:07,740 In addition, I'm privileged to co-chair along 5531 04:39:07,740 --> 04:39:09,430 with four other ICO directors, 5532 04:39:09,430 --> 04:39:12,200 the ComPASS Initiative and NIH Common Fund Program. 5533 04:39:12,930 --> 04:39:14,640 ComPASS stands for Community Partnerships 5534 04:39:14,640 --> 04:39:16,520 to Advance Science for Society, 5535 04:39:17,060 --> 04:39:19,950 and ComPASS is looking at enabling communities 5536 04:39:19,950 --> 04:39:23,060 and researchers to actually co-create research 5537 04:39:23,060 --> 04:39:24,900 together as partners, 5538 04:39:24,900 --> 04:39:28,120 true partners in all phases of the research process 5539 04:39:28,120 --> 04:39:30,340 to enhance the quality of interventions 5540 04:39:30,340 --> 04:39:31,950 so that they're community driven, 5541 04:39:31,950 --> 04:39:33,600 advancing health equity. 5542 04:39:34,160 --> 04:39:38,750 And there's information available 5543 04:39:38,750 --> 04:39:40,360 now and letters of intent 5544 04:39:40,360 --> 04:39:44,050 are due on November 18th from community organizations. 5545 04:39:44,050 --> 04:39:47,710 So please help us get the word out about this program 5546 04:39:47,710 --> 04:39:49,500 where the overall goals are to develop, 5547 04:39:49,500 --> 04:39:51,500 share, and evaluate community-led 5548 04:39:51,500 --> 04:39:54,070 health equity structural interventions 5549 04:39:54,070 --> 04:39:56,620 that leverage partnerships across multiple sectors 5550 04:39:56,620 --> 04:39:58,220 to reduce health disparities 5551 04:39:58,900 --> 04:40:02,120 and to help develop a new health equity research model 5552 04:40:02,120 --> 04:40:05,510 for community-led multi-sector structural interventions 5553 04:40:05,510 --> 04:40:08,610 across NIH, including other federal agencies. 5554 04:40:08,610 --> 04:40:10,420 So we've got technical assistance webinars 5555 04:40:10,420 --> 04:40:13,740 coming up in October. Please join us for those. 5556 04:40:13,740 --> 04:40:15,300 Next. 5557 04:40:15,300 --> 04:40:18,200 We're also pleased to be partnering with several ICOs 5558 04:40:18,200 --> 04:40:21,280 on a workshop that we are leading called Gender 5559 04:40:21,280 --> 04:40:23,380 and Health Impacts of Structural Sexism, 5560 04:40:23,380 --> 04:40:25,610 Gender Norms, Relational Power Dynamics, 5561 04:40:25,610 --> 04:40:26,910 and Gender Inequities, 5562 04:40:26,910 --> 04:40:30,320 that will be happening on Wednesday, October 26th. 5563 04:40:30,320 --> 04:40:35,240 So please join us for that. And my last slide, next please. 5564 04:40:35,240 --> 04:40:38,060 I'm just going to highlight ways to follow ORWH, 5565 04:40:38,770 --> 04:40:40,570 to sign up for a quarterly publication, 5566 04:40:40,570 --> 04:40:43,940 follow us on social media, get information about our events 5567 04:40:44,730 --> 04:40:47,280 and take a look at our interprofessional health 5568 04:40:47,280 --> 04:40:48,960 education page, our e-learning page, 5569 04:40:48,960 --> 04:40:51,700 where we have lots of courses available. 5570 04:40:51,700 --> 04:40:54,550 And I want to end by again, thanking the meeting organizers, 5571 04:40:54,550 --> 04:40:57,860 recognizing all the speakers from today, the session chairs, 5572 04:40:57,860 --> 04:41:00,870 and all of you who've taken time to attend today 5573 04:41:00,870 --> 04:41:02,140 and those behind the scenes 5574 04:41:02,140 --> 04:41:05,160 making everything work for us out in front. 5575 04:41:05,160 --> 04:41:06,660 Thank you so much, it's been my privilege 5576 04:41:06,660 --> 04:41:07,880 to be here with you today. 5577 04:41:07,880 --> 04:41:10,420 And I'll turn it back over to the meeting organizers 5578 04:41:10,420 --> 04:41:12,020 and Dr. Karen Parker. 5579 04:41:14,120 --> 04:41:15,380 Dr. Miya Whitaker: Thank you, Dr. Clayton. 5580 04:41:15,380 --> 04:41:17,220 We so appreciate your closing remarks. 5581 04:41:17,220 --> 04:41:21,520 And now Dr. Parker, the virtual podium is now yours. 5582 04:41:21,520 --> 04:41:24,069 Please deliver your closing remarks, and thank you. 5583 04:41:24,870 --> 04:41:26,470 Dr. Karen Parker: Thank you so much, Dr. Whitaker. 5584 04:41:27,140 --> 04:41:29,050 As was mentioned, my name is Karen Parker 5585 04:41:29,050 --> 04:41:31,230 and my pronouns are she and her, 5586 04:41:31,230 --> 04:41:32,960 and I serve as Director of the NIH 5587 04:41:32,960 --> 04:41:35,480 Sexual and Gender Minority Research Office. 5588 04:41:35,480 --> 04:41:37,460 And I also serve on the Social Determinants 5589 04:41:37,460 --> 04:41:40,740 of Health Executive Committee here at NIH. 5590 04:41:40,740 --> 04:41:43,370 I'd like to begin by thanking the organizers of this event 5591 04:41:43,370 --> 04:41:46,060 for inviting me to provide closing remarks today. 5592 04:41:46,060 --> 04:41:47,360 And I'm so happy to be here 5593 04:41:47,360 --> 04:41:49,920 among so many esteemed colleagues and participants. 5594 04:41:50,800 --> 04:41:52,360 First, I hope that you leave here today 5595 04:41:52,360 --> 04:41:55,160 with an understanding of the high level of enthusiasm 5596 04:41:55,160 --> 04:41:56,630 for and commitment 5597 04:41:56,630 --> 04:42:00,170 to social determinants of health research here at NIH. 5598 04:42:00,680 --> 04:42:02,710 As I listened throughout the day, 5599 04:42:02,710 --> 04:42:04,550 four themes really stood out to me. 5600 04:42:05,110 --> 04:42:07,580 And of course, I love alliteration, 5601 04:42:07,580 --> 04:42:11,370 so the first theme was collaboration and community. 5602 04:42:11,370 --> 04:42:13,600 The second is commitment. 5603 04:42:13,600 --> 04:42:17,380 The third is complexity and complication, 5604 04:42:17,380 --> 04:42:19,750 and the fourth is consequences. 5605 04:42:19,750 --> 04:42:22,170 So I'm just going to give a very broad overview 5606 04:42:22,170 --> 04:42:24,350 of some of the key points that I heard today 5607 04:42:24,350 --> 04:42:26,150 that I would like to lift up 5608 04:42:26,150 --> 04:42:30,530 as we move beyond this excellent event 5609 04:42:30,530 --> 04:42:34,480 back into our normal work lives. So first, I'm going to talk 5610 04:42:34,480 --> 04:42:36,410 about collaboration and community. 5611 04:42:36,410 --> 04:42:39,550 And we know that collaborations and connections with communities 5612 04:42:39,550 --> 04:42:41,540 are the key to broadening our understanding 5613 04:42:41,540 --> 04:42:43,189 of social determinants of health. 5614 04:42:43,860 --> 04:42:46,030 We've heard about the need for more team science 5615 04:42:46,030 --> 04:42:49,400 and how critical it is to reach beyond our professional silos 5616 04:42:49,400 --> 04:42:50,960 to those in other fields, 5617 04:42:50,960 --> 04:42:53,270 particularly those in the social sciences, 5618 04:42:53,270 --> 04:42:55,530 to ensure that we are considering all aspects 5619 04:42:55,530 --> 04:42:57,170 of social determinants of health 5620 04:42:57,170 --> 04:42:59,930 that are impacting the outcomes of our studies. 5621 04:42:59,930 --> 04:43:01,630 Go beyond your field to collaborate 5622 04:43:01,630 --> 04:43:04,120 with others who have different expertise. 5623 04:43:04,120 --> 04:43:07,200 We heard about even with more control for the environment, 5624 04:43:07,200 --> 04:43:10,010 we can learn from research in non-human primates 5625 04:43:10,010 --> 04:43:12,190 to better understand social isolation, 5626 04:43:12,190 --> 04:43:14,890 social status, and social advantage in humans. 5627 04:43:15,660 --> 04:43:18,610 The importance of including in a meaningful 5628 04:43:18,610 --> 04:43:21,960 and not perfunctory way community members in this work 5629 04:43:21,960 --> 04:43:23,280 was stressed. 5630 04:43:23,280 --> 04:43:26,310 Going beyond shallow engagement to community empowerment 5631 04:43:26,310 --> 04:43:27,550 was highlighted. 5632 04:43:27,550 --> 04:43:29,980 This includes respecting values of participants, 5633 04:43:29,980 --> 04:43:33,670 building trust and empowering those traditionally left 5634 04:43:33,670 --> 04:43:35,560 out of decision-making processes. 5635 04:43:36,160 --> 04:43:38,880 We were reminded that data is local 5636 04:43:38,880 --> 04:43:40,100 and that it requires 5637 04:43:40,100 --> 04:43:42,740 participants' knowledge of all uses. 5638 04:43:42,740 --> 04:43:44,400 In addition, the need for engagement 5639 04:43:44,400 --> 04:43:48,010 at the very beginning of any research process is critical. 5640 04:43:49,110 --> 04:43:51,610 We heard about projects working to develop data standards 5641 04:43:51,610 --> 04:43:53,590 and to test and validate standardized 5642 04:43:55,060 --> 04:43:57,740 social determinants of health data for clinical use 5643 04:43:57,740 --> 04:44:01,190 and how collaboration around this standardization is key. 5644 04:44:01,190 --> 04:44:03,130 We also learned about new collaborations 5645 04:44:03,130 --> 04:44:05,700 that aim to provide standardized community level 5646 04:44:05,700 --> 04:44:07,420 social determinants of health data 5647 04:44:07,420 --> 04:44:09,020 from multiple public sources. 5648 04:44:09,540 --> 04:44:11,580 We were reminded that words are powerful 5649 04:44:11,580 --> 04:44:13,730 and that collaborating around taxonomies 5650 04:44:13,730 --> 04:44:15,770 and language is critical. 5651 04:44:15,770 --> 04:44:18,390 This can range from how we talk about health equity 5652 04:44:18,390 --> 04:44:21,290 to the distinct differences between sex and gender. 5653 04:44:21,960 --> 04:44:23,800 We then learned more specifically about 5654 04:44:23,800 --> 04:44:25,290 how the integration of structural 5655 04:44:25,290 --> 04:44:27,130 and social determinants of health 5656 04:44:27,130 --> 04:44:30,070 at the neighborhood level and with tools such as GIS 5657 04:44:30,070 --> 04:44:32,490 can help us measure structural discrimination. 5658 04:44:33,990 --> 04:44:36,530 Next, I'd like to talk about commitment. 5659 04:44:36,530 --> 04:44:38,150 We know that this work will take commitment 5660 04:44:38,150 --> 04:44:41,970 from people across all sectors for very long periods of time 5661 04:44:41,970 --> 04:44:44,310 in order to really make a difference. 5662 04:44:44,310 --> 04:44:46,100 We heard about the need to measure neighborhood 5663 04:44:46,100 --> 04:44:48,320 and policy factors that impact health 5664 04:44:48,320 --> 04:44:51,190 and how influencers of health, such as education, 5665 04:44:51,190 --> 04:44:54,290 can take decades or generations to see change. 5666 04:44:54,290 --> 04:44:56,000 This takes commitment to the work 5667 04:44:56,000 --> 04:44:58,140 and to think about what can be done now 5668 04:44:58,140 --> 04:45:00,240 with what we currently know. 5669 04:45:00,240 --> 04:45:02,650 We were reminded that we must go upstream 5670 04:45:02,650 --> 04:45:04,650 to look at power and social values. 5671 04:45:05,220 --> 04:45:07,770 We must consider our systems, laws, policies, 5672 04:45:07,770 --> 04:45:10,770 norms, practices, and beliefs that shape our society. 5673 04:45:11,320 --> 04:45:13,690 This can include things like entrenched discrimination 5674 04:45:13,690 --> 04:45:14,940 in education, 5675 04:45:14,940 --> 04:45:17,330 housing and lending, to highlight just a few. 5676 04:45:18,190 --> 04:45:20,220 Research-examining laws and policies 5677 04:45:20,220 --> 04:45:22,340 that aim to erase the visibility of sexual 5678 04:45:22,340 --> 04:45:24,520 and gender minorities and political power 5679 04:45:24,520 --> 04:45:26,450 that reinforces disenfranchisement 5680 04:45:26,450 --> 04:45:28,170 of many different populations 5681 04:45:28,170 --> 04:45:31,600 must not fall to the side due to the immense commitment 5682 04:45:31,600 --> 04:45:33,900 it takes to do longitudinal studies 5683 04:45:33,900 --> 04:45:36,440 that examine these upstream contexts 5684 04:45:36,440 --> 04:45:38,040 across many generations. 5685 04:45:38,890 --> 04:45:41,200 We also heard about commitment to people, 5686 04:45:41,200 --> 04:45:44,050 including community members. We heard about mentoring 5687 04:45:44,050 --> 04:45:46,960 the next generation of researchers and providers 5688 04:45:46,960 --> 04:45:49,570 and the need to further fund more training. 5689 04:45:51,390 --> 04:45:54,990 Next, I'll talk about complexity and complication. 5690 04:45:55,540 --> 04:45:58,700 We know that this work is both complex and complicated. 5691 04:45:59,280 --> 04:46:01,740 We heard about the complicated and important process 5692 04:46:01,740 --> 04:46:04,880 of communicating social risk in order to increase adjustments 5693 04:46:04,880 --> 04:46:06,540 in the clinical setting. 5694 04:46:06,540 --> 04:46:09,240 Speakers discussed the issues surrounding data privacy, 5695 04:46:09,240 --> 04:46:10,460 genetic information, 5696 04:46:10,460 --> 04:46:13,110 integrating sensitive questions into care models, 5697 04:46:13,110 --> 04:46:15,310 and the ethics of data use in sharing, 5698 04:46:15,310 --> 04:46:18,050 shallow engagement, and the risks of inaction. 5699 04:46:18,840 --> 04:46:21,870 We were encouraged to rethink how we gain consent 5700 04:46:21,870 --> 04:46:23,800 and to shift our understanding of consent 5701 04:46:23,800 --> 04:46:25,600 to that of a living document 5702 04:46:25,600 --> 04:46:27,850 and embrace the concept of a re-consent 5703 04:46:27,850 --> 04:46:31,000 as a part of a dynamic and not static process. 5704 04:46:32,020 --> 04:46:34,820 We heard about challenges related to data and measurement. 5705 04:46:34,820 --> 04:46:37,000 This includes data related to American Indian 5706 04:46:37,000 --> 04:46:40,520 and Alaska native populations, those living in rural settings, 5707 04:46:40,520 --> 04:46:43,030 and members of the LGBTQI+ community. 5708 04:46:43,740 --> 04:46:47,290 Socioeconomic status commonly measured is multifaceted 5709 04:46:47,290 --> 04:46:49,350 and goes well beyond income. 5710 04:46:49,350 --> 04:46:54,000 Figuring out how to best measure concepts such as SES, 5711 04:46:54,000 --> 04:46:55,650 social capital, wealth, 5712 04:46:55,650 --> 04:46:59,170 and other related concepts is complex and complicated work. 5713 04:47:01,090 --> 04:47:02,970 And finally, consequences. 5714 04:47:02,970 --> 04:47:06,340 So what are the consequences of not doing this work? 5715 04:47:06,960 --> 04:47:09,420 The most urgent consequence is needless suffering 5716 04:47:09,420 --> 04:47:11,630 due to our inability to truly understand 5717 04:47:11,630 --> 04:47:14,370 the upstream social determinants of health. 5718 04:47:14,370 --> 04:47:17,160 However, we were reminded by our keynote speaker 5719 04:47:17,160 --> 04:47:19,930 that no one can study an entire causal pathway 5720 04:47:19,930 --> 04:47:22,400 but that it is important to measure what we can 5721 04:47:22,400 --> 04:47:24,660 and to always ask what we might be missing. 5722 04:47:25,280 --> 04:47:27,850 What is missing needs to be communicated in discussions 5723 04:47:27,850 --> 04:47:29,949 about our limitations and our conclusions. 5724 04:47:30,960 --> 04:47:32,220 In our opening remarks, 5725 04:47:32,220 --> 04:47:35,520 you heard from both NINR and NIMHD leadership 5726 04:47:35,520 --> 04:47:38,240 about some of the amazing work happening at NIH 5727 04:47:38,240 --> 04:47:40,660 focused on social determinants of health. 5728 04:47:40,660 --> 04:47:42,080 I'd like to wrap up my remarks 5729 04:47:42,080 --> 04:47:44,990 by highlighting one more example of work at the agency 5730 04:47:44,990 --> 04:47:47,300 focused on better understanding populations 5731 04:47:47,300 --> 04:47:48,900 experiencing health disparities. 5732 04:47:49,430 --> 04:47:51,360 We know that data is critical and that valid 5733 04:47:51,360 --> 04:47:54,370 and reliable measures are needed to illuminate disparities 5734 04:47:54,370 --> 04:47:56,520 in order to develop interventions and programs 5735 04:47:56,520 --> 04:47:58,200 to improve health. 5736 04:47:58,200 --> 04:48:00,390 The sexual and gender minority research office, 5737 04:48:00,390 --> 04:48:03,050 along with 18 other components of NIH, 5738 04:48:03,050 --> 04:48:05,290 recently commissioned the National Academies of Science, 5739 04:48:05,290 --> 04:48:06,790 Engineering and Medicine 5740 04:48:06,790 --> 04:48:09,580 to develop a consensus study on measuring sex, 5741 04:48:09,580 --> 04:48:11,780 gender identity, and sexual orientation. 5742 04:48:12,500 --> 04:48:15,480 This report released in March is the most comprehensive review 5743 04:48:15,480 --> 04:48:17,830 to date of measures of sex, gender identity, 5744 04:48:17,830 --> 04:48:19,470 and sexual orientation. 5745 04:48:19,470 --> 04:48:21,460 And it summarizes the current evidence-based 5746 04:48:21,460 --> 04:48:24,080 surrounding these measures for those working to advance 5747 04:48:24,080 --> 04:48:26,780 sexual and gender minority health and wellbeing. 5748 04:48:26,780 --> 04:48:29,650 It highlights key opportunities that the SGM research community 5749 04:48:29,650 --> 04:48:32,020 can leverage to advance representation 5750 04:48:32,020 --> 04:48:34,700 and further our understanding of SGM communities. 5751 04:48:35,360 --> 04:48:37,950 This report will serve as an integral foundation 5752 04:48:37,950 --> 04:48:39,660 for increasing the appropriate usage 5753 04:48:39,660 --> 04:48:42,250 of sexual orientation and gender identity measures 5754 04:48:42,250 --> 04:48:45,580 among other basic demographic questions in research, 5755 04:48:45,580 --> 04:48:48,160 clinical settings, and in administrative forms, 5756 04:48:48,160 --> 04:48:49,940 as well as enhancing our knowledge base 5757 04:48:49,940 --> 04:48:52,590 in variations and sex characteristics. 5758 04:48:52,590 --> 04:48:55,290 I encourage you to check out the report to learn more. 5759 04:48:56,750 --> 04:48:59,300 As a reminder, the four themes that I am taking with me 5760 04:48:59,300 --> 04:49:03,970 from today are: collaboration and community, commitment, 5761 04:49:03,970 --> 04:49:07,500 complexity and complication, and consequences. 5762 04:49:08,400 --> 04:49:11,430 Participating in today's event has been such a treat for me. 5763 04:49:11,430 --> 04:49:14,200 I have learned so much and am reinvigorated in my commitment 5764 04:49:14,200 --> 04:49:15,640 to increasing social determinants 5765 04:49:15,640 --> 04:49:17,410 of health research at NIH. 5766 04:49:18,000 --> 04:49:20,150 I hope that you too can return to your work 5767 04:49:20,150 --> 04:49:22,470 with a new sense of commitment and purpose. 5768 04:49:23,730 --> 04:49:25,680 As a reminder, this event was recorded 5769 04:49:25,680 --> 04:49:28,630 and will be publicly available in the coming weeks. 5770 04:49:28,630 --> 04:49:30,540 An email will be sent to all registrants 5771 04:49:30,540 --> 04:49:32,140 when the recording is posted. 5772 04:49:32,900 --> 04:49:35,020 I'd like to thank you again for attending today. 5773 04:49:35,020 --> 04:49:37,620 Your active participation and thoughtful questions 5774 04:49:37,620 --> 04:49:40,520 helped make this inaugural event a huge success. 5775 04:49:41,140 --> 04:49:42,740 Enjoy the rest of your day.