 So, Christine and Ron Ling, you want to come forward please? So, every three years NHGRI is required to put together a report documenting and characterizing the populations and individuals that have been recruited to participate in the research projects that are both funded by the extramural research program, but also including the intramural investigators work as well. In the past, this report was done every two years. It's now the triennial report and it's actually required of all the institutes of NIH. So, Ron Ling and Christine Chang put this report together, but Christine will give the presentation. Can you guys hear me well? Okay, so, this triennial council report I am presenting on behalf of the NHGRI inclusion team, which includes myself and Ron Ling Li in extramural and Sarah Hall in intramural. So, I'm going to start with a background of inclusion reporting requirements and why inclusion is important and done. Then I'll provide a couple of important definitions. Then we'll take a look at the data and wrap up with a summary of where NHGRI is in inclusion and future. So, as background, NIH is mandated by the Public Health Service Act to ensure inclusion of women and minority groups that is appropriate to the scientific question under study. NIH has done this since 1994 and each IC, as Rudy mentioned, released vinyl reports that are publicly available at NIH report at the link shown there. But because of changes related to the 21st Century Cures Act, this is now being done triannually. So, the two definitions I want to point out is that inclusion is only done on clinical research and that is defined as research that is patient-oriented, meaning it's conducted with human subjects or with materials of human origin for which an investigator or his or her colleague directly interacts with human subjects. It also includes epidemiological and behavioral studies and outcomes in health services research. And then minority groups are defined here as all races except white and unknown race plus Hispanic ethnicity. And then lastly, sex gender is used interchangeably here. So, there are some studies that refer to biological sex and some studies suggest surveys that refer to gender identity. So, I'm sure you've all seen this in your grant application reports. This is the Inclusion Enrollment Report or IER. And this is used to collect planned enrollment data as well as cumulative or actual data. And you see going down on the left here are the racial categories and across the ethnic categories which are further subdivided by sex gender and unknown not reported in each category. So, now we're going to look at the data for FY16 through FY18. And you'll notice here that 2016 is listed twice the second time with an asterisk to indicate that a study that had over 1.2 million participants is excluded here. And the reason is for that, we're excluding is that the increased number of enrolled participants that year skews the data. So, we analyze the data with and without that study. And then you can see on average in extramural, the number of enrolled participants per study is larger than the average number of enrolled participants in intramural studies. Another thing I want to point out is that the data shown here, the IERs are only for prospective data. So, IERs with existing data are excluded with the exception of FY16 and FY17 intramural data. And that has to do with the capabilities of the IT tracking system then. It didn't have the flag to indicate existing data. So, therefore, this decrease that you see here from 109 to 9 IERs in intramural is really related to a change in the system that allowed us to correctly identify studies that used existing data as well as excluding studies that were no longer active. In this last fiscal year, 2018, and each year I had a change in and centralization of the intramural IT system to manage protocols that will greatly improve the quality and consistency of our inclusion data. So, looking at the data by sex gender, we can see that in general there was pretty even balance between sex gender categories proportions in the past three years with a slight decrease in the proportion of unknown sex gender. And then looking at the data by minorities, which, again, are defined as all races, except white and unknown, plus Hispanic ethnicity. You can see that after excluding that one outlier study, the proportion of minorities increase. And that has to do with that particular study having a low proportion of minority enrollment as well as a high proportion of unknown race. And between 2016 and 2018, minority enrollment made up between 25 to 35 percent of participants. And there is a little bit of a dip between 2016 and 2017, and I'll go into that in the next slide. So now we're going to dive into the data and look at by race, which are American Indian, Alaska Native, Asian, Black African American, Native Hawaiian Pacific Islander, white, more than one race or unknown not reported. And as you can see, some categories like the American Indian, Alaska Native and white and the Native Hawaiian Pacific Islander categories in yellow are very small. One thing I wanted to point out was that, again, you can see after you exclude that outlier study, the proportion of Black African American enrollment increased. But then you compare it to subsequent years, and it looks like it decreased going from FY16 to FY18. And that has to do with the conclusion of H3 Africa grants in 2016. So looking by ethnicity, we see that the Hispanic-Latino proportion has remained pretty low and steady during this reporting period. And then now these next few slides, we're going to look at data from more than just a few years, the past two by annual reports, so the report for 2011 to 2012 and the report for 2013 to 2014. We're also going to provide a reference to the U.S. Census 2018 population estimate, which we provide, but I want to caution people that the role of the NIH inclusion policy isn't to try to match the census proportions, but rather to support research that answers scientific questions appropriate for the study population and ultimately to the U.S. population. So going back to the data, we can see that over time the sectioner proportion has evened out. And now looking at major racial categories, so white, Black African American, Asian, unknown, not reported, we see that over time the Black and African American proportion has increased while the proportion of Asians has decreased. And then looking at the data by ethnic categories, we see that the proportion of Hispanic-Latino enrollment was highest in the 2000 to 2014 reporting period, and lowest in the most recent one at just 3.5%, and that in comparison to the previous slide, the proportion of Hispanic-Latino enrollment is much smaller than the U.S. Census estimate. So in summary, in comparison to the past two reporting periods, the proportion of Black-African American enrollment has increased while Asian and Hispanic-Latino enrollment has decreased. However, programs such as H3Africa, CSER, the clinical sequencing evidence-generating research program and the Ignite or implementing genomics and practice program focus on increasing minority enrollment through different ways, so H3Africa does this through large-scale population studies on the African population by African researchers, and then CSER and Ignite requested in their most recent iterations enhanced diversity applications. And then there are other efforts at NHGRI to increase diversity, and that includes the diversity supplements, and then the diversity action plans in training, which will increase the diversity in the biomedical and genomics research workforce. And then lastly, because of the change in the intramural IT system, in FY18, going forward, we feel that this will improve the quality of the data and our efforts to monitor inclusion in the future. Thank you, and Rongling and I are happy to take questions or comments. I assume that the changes and the definition of the clinical trial haven't yet affected the data you presented, right? Okay, and any rumors that somehow the CDC or other major organizations were going to change the definitions of the kinds of categories or that OMB would change them, and that's just rumors unsubstantiated, right? I haven't heard of anything. So, I'm not sure that it matters, right? I mean, this is every participant that's been recruited for any study, whether it's classified as a clinical trial or not, I don't think it's... Maybe I misunderstood. I thought that she said that this did was for clinical trials. No, so actually clinical research and clinical trials are different. Yeah, clinical research. So this would exclude clinical trials? No, it would include. It would include them, but you're just putting a different label on it, it's all going to end up in this report. No, it's not a different label. This is bigger than clinical trials. This includes outcomes. Yeah. That's Jim. Okay. I do think the Asian decreasing is interesting and important for grants that are talking about underrepresented groups. I believe Asians are not necessarily always included. And so, there may be some influence on that. I know, I noticed for the Emerge, Asians were included, but that may have some influence on where people are putting their recruitment efforts. Yeah, that's good point. Sharon, actually, we have to go with the OMB categories, and Asians are not included in it. I thought you had Asians on your slide today. I think it was Hawaiian Pacific Island. No, I looked actually. Was Asian there? Okay, I'll look. Because I was going to ask the question and then I saw it there. Okay. So, I do think that's an issue where they're being underrepresented in genomics research because they're not on that definition. The point is they're not being actively recruited because they're not considered underrepresented minorities. Right. Well, so for example, in the CSER, where you have to hit a certain proportion, you could not include Asians. And so, I think we're going to continue to underrepresent them if people can't make a concerted effort to enroll them. Yeah, it's a good point. I agree with you. Jeff? Yeah, I guess I'm wondering about the decision to include foreign nationals in the recruitment data and specifically the H3 Africa population. That seems to me to sort of skew the numbers of what we're looking for, some reasonable representativeness of the U.S. population. Yeah, Jeff, I think it's a good point. This data report generated results and they also separated the U.S. only and all of them include international samples. We have separate results there. Dr. Ease? If I could follow up on the question Jeff just asked, were you able to distinguish between the African, foreign national Africans versus the African Americans, since you already have that data, is there any distinction that you were able to identify, trends, interesting findings? I do agree, it's kind of unusual to put that together, but since you already have it, have you determined anything? Yeah, we can write another data, exclude the H3 Africa participants from Africa and the cities trend. It's most likely that the infractions in 2016, that's most of them by Africa from Africa, Africa from Africa. So we can exclude that and see what are the results. Anyway, we have that data. Thank you. Okay, if there are no other questions, then I neglected to tell you that we need to vote for this because this is a report that Council is blessing or accepting or approving of. So can I get a motion, rather, to approve the report? Accept the report. Okay. A second? All in favor? Any opposed? Any abstentions? Okay. Thank you very much. Thank you. Christine Rohnling, thank you.