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Aye, Christine. I haven't seen you in ages. Nice to see you. On a long time. Right.

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Christine, when did we start? Like, was it 2019? When 18? 18? Okay. I was trying to figure that out. Other than things like I knew, I know it was going in 19. So it's finally 18.

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Yeah, we had COVID in there.

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But we kept doing, we kept on. We just changed the volume.

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Hey, Christine. Hi, did you sell a send out a summary packet? I thought they had said that they would be supplying a PDF of the findings or the presentation. Did I miss that?

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I don't believe that's been seven out yet.

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Okay. In

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terms of time management, do we have a lot of time?

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because this is very important, but we do have other things to go through. So I would

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I'd like someone maybe you Christine to act as a timekeeper because we really need to get the

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conclusions done and spend time for questions if you see what I'm saying. They had said before you

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came on Charles, there was going to be 30 to 40 minutes and then questions after that. That's just

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introduction. That's a scene for the option.

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The entire meeting, the presentation of the health assessment environmental justice study.

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Okay. Fair enough. Thank you.

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We'll prioritize the presentation.

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And if we run out of time by 8 p.m.

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It will just move any other rest of the items for next meeting.

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If you go to 8 p.m. with questions.

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If John and the rest of the group is okay with that, we have to eat with this form.

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Yeah, if we need that much time, that's fine.

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If we can finish up and have time for the rest of our agenda, that would be better, but this is important.

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Do we don't do this very often?

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Do we have the other UCLA doctor or are we still waiting?

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I think Dr. Lou is still having a problem getting in.

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Do you see anything Edgar?

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Okay.

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Do you see it now?

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Okay.

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Yeah, I'm getting in.

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Let me see.

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But I can see your name here.

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Is he on my phone?

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Jason?

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Oh, don't you say that he's joining now?

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Oh, there is.

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There he is.

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Yep.

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Okay.

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All right.

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Okay.

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So you've got everyone that you need, Christine?

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I think so, yes.

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Okay, so let's get started. Welcome everyone to the, I don't know what month this is,

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October, I guess, meeting of the cap. Glad to have you all here and we're going to spend much

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of tonight's meeting talking about the health study by UCLA and Christine is going to start it off,

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Christine. Thank you. So welcome everybody and welcome to our UCLA team.

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We are tonight, this is the first public presentation of the results of the second Baldwin Hills Health Assessment and Environmental Justice study required by the settlement agreement from 2008.

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And I want to thank the UCLA team for their diligent work.

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They pulled out all the stops to make this study, everything it could possibly be.

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They squeezed every tiny bit of information out of it that they could.

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And really did a great job.

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I feel for this community, they took the community's concerns very, very seriously

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and addressed pretty much everyone and I also want to thank and acknowledge the chat members

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and these are the folks that were selected by this group to represent the Baldwin Hills community.

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This group of people, some of you are on here, you might want to raise your hand so people know who

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I see Liz, I see Jenny, I see Charles, I see Kelly, Erica, Frank, Dean participated, Melanie, and these folks gave up an hour and a half about once a month for the past few years to, you know, question the, the researchers and ask all their questions and, um,

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You know, let them know how important this study was to this community.

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And I feel like they deserve a lot of thanks for the amount of time they put into it and the care they gave and attention.

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And, you know, listening to all these very complicated results and sifting through them.

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And so I feel like the CHAP really represented the community very, very well and you will have my gratitude for a long, long time.

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So now let me turn it over to Dr. Henry Liu and Dr. Lara Cushing.

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And if one of you can, I don't know who's sharing slides, but go ahead and do your presentation.

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And if you can feel free also to introduce your team.

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Okay.

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Thank you.

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Chris, Christine.

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Good evening, everyone.

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I'm the Henry Liu, a professor and the Chair of the Public Public Health Program at the UCRA.

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And I honor to serve at Principal Investigator for this very important project.

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So, this evening with me is Dr. Kupche, who is a software professor,

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user, and environment health specialist. Dr. Jason Shen, who is a research

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faculty, and Lee Nui Zhou, who is a PhD candidate and analyst on the project.

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And the each of them will take a chance. They can introduce themselves more to

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sit time. So before our presentation, I'd like to say a few sentences we really like to thank.

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For the strong support we received from the community during the entire conduct of this project,

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including chat members, a number of them in this chat member as well, and even this large chat

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members and Los Angeles County project officers and the county agent members and particularly

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the committee members who participated in this study who contribute their experience

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and to share with their experience and contribute to this aggregated data.

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So Dr. Kushen, I will present and for us will answer questions.

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So just kind of quite a lot, but we will try to be as efficient as possible.

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Slide up.

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Great.

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Next slide please.

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Yeah, so the most important hypothesis for this study

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He is still looking at whether residents living near the oil field is actually have the

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association, you know, the near to the Ingu oil field, associated with a higher risk of adverse

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health outcome, which including a number of them here, pictures you can show the birth

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So,

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the population included in this particular study are those who live within 1.5 miles

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of the angle of all your field, or LF boundaries, you know, this diagram shows the circle inside

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those included conceptually and then outside it beyond and not included.

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Next slide please.

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So the wind direction is another important factor we are

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looking at. So for wind direction based on the prevailing wind direction we

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define the presence as downwind and upwind two groups within, of course, is 1.5 mile radius.

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Next slide please. And the two main goals of this study. First is we analyzed existing

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per record, basically across a long span of 20 years from 2000 to 2019, cover about 40,000

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light birth within this 1.5 miles radius. Then the second part of the analysis, including

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in fresh data collection is actually conduct survey and biometric data collection in the community.

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We successfully included more than 600 was the target we actually we put more than 600

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With a help, as mentioned earlier from the community, from July 2023 to June 2024, who live within that boundary.

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Next slide please.

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So the structure for presentation will be first go through the birth outcome, study and analysis results,

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and then the health survey biometric data collection,

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then we have overall conclusion limitations

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and then the implications take home message.

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So now I'm going to turn this party to you,

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Dr. Kushin, who will take lead on the birth outcome

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part of the presentation. Dr. Kushin.

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Thank you, Dr. Liu.

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and hello everyone. I'm Laura Cushing. So this first portion of the study, we used administrative

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birth records. So secondary data over a 20-year time frame. As Dr. Lou mentioned, to focus on two

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primary outcomes. These two outcomes can impact the survival and health of babies and they can lead to

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long-term respiratory, cognitive, and other health problems.

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One is preterm birth that's being born too soon before 37 completed weeks of pregnancy.

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And the second is called small for gestational age, which we're going to abbreviate as SGA

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throughout the presentation.

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And this is when a baby's born too small, meaning below the 10th percentile for their week

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of gestation.

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and it's a measure of restricted growth in utero.

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Next slide.

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So when we look at the rates of these two outcomes

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in the community living within one and a half miles

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of the oil field, we see that they're slightly higher

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than LA County overall over the same time period.

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And these P values here, so these differences

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We're statistically significant, meaning that they're unlikely to have been due to chance.

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So you'll see that as well throughout the presentation.

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We star with a little asterisk differences that where we had statistical significance

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or pretty good confidence that the differences we were seeing were not just due to random

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chance.

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Next slide.

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We also saw that rates of these two adverse birth outcomes did not vary substantially

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with distance to the oil field.

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So if we just look within that 1.5 mile radius and further sets up the population of babies

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born within half a mile, half a mile to one mile or one mile to one and a half miles,

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The rates of preterm birth in small-for-just-statial age are not that different from each other.

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It was the intermediate group that had the worst outcomes, so the highest rate of preterm

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births in small-for-just-stational age, but we could not rule out that those differences

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were not due to chance.

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Next slide.

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When we looked at downwind versus upwind of the oil field, we did see that preterm birth

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rates were higher downwind as compared to upwind and that the difference was

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But now, broken down further into downwind versus upwind. So the first two numbers, for example, for preterm birth, we see the preterm birth rate was 9.8% in the downwind population living closest to the oil field versus 6.4% in the upwind population living within half a mile. And those differences were statistically significant. Next slide.

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If we look by,

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By race or ethnicity, we also see disparities in the rates of pre-term birth.

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So overall, within this community within 1.5 miles of the Ingwood oil field, non-Hispanic

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white parents had the lowest rates of pre-term births.

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Rates of pre-term birth decreased among Asian and Latino parents with distance to the oil

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field. So if you look at the two rows labeled Asian-American and Hispanic or Latinx, you see the

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percentages go down as you move farther from the oil field. But at every distance racial disparities

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remain and they were largest when comparing black or African-American birth parents with white birth

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parents. So you see, you know, rates 9 to 10 percent among the black community versus

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closer to 5 or 6 percent among the white community. Next slide.

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So what could cause these higher

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rates of preterm birth downwind and closer to the oil field? Well, one thing is harmful exposures

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emanating from the oil field. We know, for example, that air pollution has been linked to preterm

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birth in other contexts. But it could also just be a coincidence, right? It could be

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that residents living downwind in these in this area have higher rates of other risk factors

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for pre-term births such as being older or not getting enough pre-natal care. Next slide.

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So what we do is we apply statistical modeling techniques that allow us to kind of control for

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these other risk factors and better isolate the possible effects of the oil field.

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And this helps rule out alternative explanations for any associations that we see

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between living near and down one of the oil field and pre-term births.

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So if you're a visual person, try to represent this in a schematic here.

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So, you know, we're really interested in the relationship between residents near the oil field

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in our measures of health, but there is these other risk factors in the background that we are

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controlling for through this statistical technique. Next slide.

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So when we do that, we still see

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that living down one of the oil fields is associated with a higher likelihood of pre-traffers.

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I'm going to walk through this graph because we're going to show a few of these. So what's shown on

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this graph in the vertical red dotted line is what's called a null or the value of one

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and the diamonds on the chart are our measures of association also known as odds ratios.

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So when those diamonds are to the right or above one it indicates an association between living

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downwind and the outcome of preterm births. If they're to the left of the red line, it indicates

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a negative association, but that's not shown on this particular chart. And the error bars,

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the horizontal lines, those give you a sense of our degree of certainty about this association.

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So we feel more confident that the association is real when those error bars do not cross the red

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dotted line. So the two highlighted effect estimates here, neither of those error bars

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crossed the dotted line, so we have more confidence that those associations are not

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due to chance. And in particular, the lower highlighted effect estimate, the way we

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interpret that, that's for the group living within half a mile, is that the odds of preterm

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birth was 56% higher for that group living within half a mile and downwind compared to those living

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within half a mile and upwind. So the same thing we saw at the table but now we're accounting for

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age whether it was the person's first baby or second baby whether they got prenatal care and how

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much their level of education etc. All the things we could measure about them are accounted for in

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this estimate. Next slide. I'm going to wave my arms because that's how I give my

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lights to turn back on in my office. That's not working. Okay, so in some communities

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living within one and a half miles in the oil field had slightly worse birth outcomes

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than in LA County as a whole. Number two, residents among residents living within half a mile

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of the oil field. We saw that living downwind was associated with a higher likelihood of preterm

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birth. And the association was unlikely to do the chance and not explained by the other risk

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factors we could measure like age and prenatal care or the amount of traffic near a person's home.

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And three, we saw no evidence that living near or downwind of the oil field was associated with

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fetal growth, that other measure that I showed you of small for gestational age. We did not see

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any associations there. So I'll turn it back to Dr. Liu.

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Next slide.

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Your mute is Henry.

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Sorry about that. Thanks Dr. Kuchen. Now let's move on to the resident

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health survey and biometric data collection and analysis. Next slide please.

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So for how we

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we recruit the recruit of the started present and we basically use two projects. Why is the so called address based random selection?

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That's origin design and by complex survey design, we identify certain potential addresses that may all the survey.

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Because there's some challenges going with base approach, the paces slower than with

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packet.

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So we also used so-called convenience sampling, which actually we, with booths on the ground

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and into the community and the setup recruitment like booths and then recruit on site in

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community centers, YMCA's, libraries across quite a number of them. Of course, when we select,

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we try to make this as uniformities reveal across the community as possible.

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And then who could participate in those who live in within 1.5 miles of the oil fence?

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And per household, we will only allow one member to participate to avoid dependence of the data to reduce the quality of the data.

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So what we measure is from biometric, we measure blood pressure and lung functions.

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And then for the survey, we recruit some background information and then the tier self reported health symptoms and chronic health conditions.

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Next slide.

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So here is the results of the sample we recruited compared with actually community statistics,

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the demographic distribution. You can see from here, this is the second column is from the survey,

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statistics, specifically for this 1.5 miles radius. And from days you compare those point

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as to make, you can see we saw a higher response rate from white and the college educated residents.

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00:24:11,710 --> 00:24:19,390
And you can see the difference. 44% if you look at the community that's only about 25.7%,

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in this area. And then from the lower side, we saw lower response from Latino residents.

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And also the non-college educated residents, particularly those with less than high school

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education at the very last low, you can see we only have 1.2%, but in the area we have about

225
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12.4% of residents with this education group. Next slide.

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What do blood pressure numbers

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mean? We measured systolic blood pressure and systolic blood pressure, and everybody knows that,

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So then there's specific definitions from CDC and for normal, it has to be less than 120

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and less than 80 for systolic, diastolic.

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And then elevated, define in the range that's the middle row and then hypertension is defined

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is 130 or higher for systolic, or for diastolic, 80 or higher. This is a very

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standard definition across the nation. Next slide, please.

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So, this is the

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00:26:04,390 --> 00:26:11,170
result for average blood pressure and hypertension rate. So, you can see we

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We have the high blood pressure that's defined in the previous slide, and then we have

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average blood pressure and then average systolic blood pressure.

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00:26:27,650 --> 00:26:35,230
You can see here the risk of high blood pressure will collect similar compared with our

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a county, which is the last column. Participants living in the middle radius, which is a third

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column, 0.5 to 1 miles, had somehow a lower hypertension rate. And then participants living near

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to all you feel. From 0 to 1 miles, which is the first two

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00:27:04,230 --> 00:27:08,990
readings, which is the second and third column, I just slightly lower

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diastolic blood pressure on average. So the trend is not that very clear in terms

243
00:27:21,050 --> 00:27:31,490
what one could intuitively expect. Later we'll see there's some reasons behind it and this is

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combined lump across all of this ethnicity group. Next slide please.

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00:27:40,830 --> 00:27:42,350
So this is the blood pressure

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00:27:42,350 --> 00:27:50,170
by wind direction. We analyze this similar way as the birth outcome, breakdown by the

247
00:27:50,170 --> 00:27:58,450
stratified by the three radius and if you look at this we can see for the

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00:27:58,450 --> 00:28:05,030
first radius which is close to the one and we can see the difference between

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downwind and upper one which impact from downwind direction for high blood

250
00:28:18,610 --> 00:28:22,090
pressure and also for average

251
00:28:22,630 --> 00:28:25,690
diastolic blood pressure, but not for

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systolic blood pressure.

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00:28:28,650 --> 00:28:31,330
The actress indicate

254
00:28:32,610 --> 00:28:36,070
the statistics is significant

255
00:28:36,310 --> 00:28:40,550
is not due to chance, but this

256
00:28:40,550 --> 00:28:44,510
is undergiastic analysis, meaning is just

257
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look at this observed value

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not taking into account the other factors yet. Next slide. So this is the blood pressure

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00:28:59,950 --> 00:29:08,730
result of the risk-ethnistic group. We showed the detail of this risk-out breakdown by risk-ethnicity.

260
00:29:08,730 --> 00:29:18,270
So, the, you know, we have rows, is the different width ethnic group, and the first, the second

261
00:29:18,270 --> 00:29:25,230
column is near the radius, and then so on and so forth. So, among African-American, Asian

262
00:29:25,230 --> 00:29:34,630
and Hispanic participants, the highest width of high blood pressure or observed closest to the

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00:29:34,630 --> 00:29:42,830
you can see that for this three Luis Adonis group and you can see

264
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Pakistan African American you can see 64.1% and then the highest among that role

265
00:29:51,170 --> 00:29:58,170
H American, same thing, and Hispanic vatine vatine.

266
00:30:00,210 --> 00:30:09,330
Those groups also have the same plan. However, for Caucasian participant, the read of the

267
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high blood pressure somehow actually is in other direction. So, this, you can see this, this

268
00:30:20,330 --> 00:30:31,570
could be the cause of the previous slide, why we see L is in terms of aggregated statistics,

269
00:30:31,970 --> 00:30:40,930
point estimate across the three radiates is inconsistent, you know, because by this

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as many groups, somehow, these different and in different directions.

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00:30:47,910 --> 00:30:48,470
Next slide.

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00:30:51,670 --> 00:31:01,390
So this is the adjusted analysis, meaning we're taking into account, we look at the blood

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00:31:01,390 --> 00:31:16,790
pressure, but compare, say, look at the between, you know, downwind upwind and then we also controlling

274
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for the other factors. The factor being controlled at the bottom of the slides, which include

275
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each gender with ethnicity, education, years leaving in the neighborhood et cetera.

276
00:31:31,070 --> 00:31:41,310
So from here, after we control it, I thought the cushion explained in detail the meanings

277
00:31:41,310 --> 00:31:52,890
interpretation of all the labels, we can see the overall the first bar on the

278
00:31:52,890 --> 00:32:06,050
very top that is slightly not overlap with the red color vertical bar. So this

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00:32:06,050 --> 00:32:14,110
indicate there is a difference there. So this hypertension is associated with a

280
00:32:14,110 --> 00:32:21,790
wind direction and this of course is for the increase likelihood of hypertension

281
00:32:21,790 --> 00:32:33,150
after the adjustment. However, this is overall is combined, not like a specific

282
00:32:33,150 --> 00:32:41,130
think about which read is. So how close someone lived within the IOF did not

283
00:32:41,130 --> 00:32:49,970
seem to influence the outcome. And is when you combine these three read is

284
00:32:49,970 --> 00:32:59,710
together, then you see this slightly impact of the downward. So regarding other

285
00:32:59,710 --> 00:33:08,090
factors we found that the man or age overweight participant and those with previous hypertension

286
00:33:08,090 --> 00:33:12,110
diagnosis were more likely to have high blood pressure.

287
00:33:13,250 --> 00:33:14,170
Next slide,

288
00:33:17,460 --> 00:33:18,540
lung function.

289
00:33:18,860 --> 00:33:27,400
This is another important part of the biometric data collection and we collect two key very

290
00:33:27,400 --> 00:33:37,240
very popular measures. One is called FED1, which is the volume of the breath, exhaled with

291
00:33:37,240 --> 00:33:47,360
effort in just one sec. FVC, on the other hand, is the full amount of air that is exhaled

292
00:33:47,360 --> 00:33:54,660
with an effort in a complete breath. So it's total how much you can exhaled.

293
00:33:54,660 --> 00:34:05,240
So, then we also have a ratio, which is the ratio between if you want at VC, which basically

294
00:34:05,240 --> 00:34:11,140
measures with a percent of the error can be exhaled within a first sec.

295
00:34:11,140 --> 00:34:40,060
So based on these three mirrors, we actually define the normal and abnormal based on predictive value by just by age, gender and height because we all know, you know, long function is very highly related with age with gender with height.

296
00:34:40,060 --> 00:34:55,920
So, the value to define as abnormal will be, and, oh, define normal will be, abnormal

297
00:34:55,920 --> 00:35:04,900
of course is the other way around. No more will be the ratio is greater than 0.7.

298
00:35:06,980 --> 00:35:18,100
All and both individuals have even one, seven percent actually, 17 percent. And then both

299
00:35:18,100 --> 00:35:29,720
f u1 and f uc is above 80% of the predict value. So for a given person, the abnormal or normal

300
00:35:29,720 --> 00:35:39,540
function is defined by combination of the ratio and then the individual value of f uc and f uv1.

301
00:35:40,740 --> 00:35:42,320
Next slide please.

302
00:35:44,720 --> 00:35:51,280
So here is the result of average normal and the normal on functions with.

303
00:35:52,320 --> 00:36:03,540
And this the rows we can see clearly is of normal on function and then the average the

304
00:36:03,540 --> 00:36:07,180
FV1, FVC, and the columns on the radius.

305
00:36:07,560 --> 00:36:10,800
Participant leaving nearest to the oil field

306
00:36:10,800 --> 00:36:14,960
had the highest rate of normal lung function.

307
00:36:15,860 --> 00:36:17,700
We can see that.

308
00:36:17,780 --> 00:36:27,200
But that's 66.9% compared with 62.862.7.

309
00:36:27,820 --> 00:36:31,880
Then the participant leaving near to the oil field

310
00:36:31,880 --> 00:36:43,120
at a lower average, if you want, if you see, you can see that is in a pretty clear direction

311
00:36:43,780 --> 00:36:55,600
from the nearest to the further east. We could not interrupt that this difference would

312
00:36:55,600 --> 00:37:00,420
due to chance, because all the p-value is greater than 0.05.

313
00:37:01,420 --> 00:37:01,920
Next slide.

314
00:37:05,340 --> 00:37:09,780
The average of normal long function by wind direction.

315
00:37:11,000 --> 00:37:13,740
This is a single analysis by the breakdown

316
00:37:13,740 --> 00:37:19,200
by stratified by the radius and then compare

317
00:37:19,200 --> 00:37:25,140
between upper and lower upper and down wind direction.

318
00:37:25,860 --> 00:37:34,540
And we can say first is the higher lead of normal long function was observed in the downwind

319
00:37:34,540 --> 00:37:43,640
part of the path, leaving from 0.5 to 1.5, but could not rule out this was due to chance

320
00:37:43,640 --> 00:37:45,400
because it's not significant.

321
00:37:46,440 --> 00:37:49,660
Let's see in the white color, you know, just regular white color.

322
00:37:49,660 --> 00:38:02,360
then for the brown color, orange color, the IVV1, IVVC would lower on average among

323
00:38:02,360 --> 00:38:13,540
downwind versus upwind participant, leaving from 0.5 to 1.5, which is the second to the

324
00:38:13,540 --> 00:38:24,140
third with this. But if we can see there's actually their meaning, we do see a significant difference,

325
00:38:25,040 --> 00:38:33,040
not, you know, due to chance, but this is unadjusted analysis. Next slide.

326
00:38:33,040 --> 00:38:43,800
So, looking at cross-risk ethnicity disparity impact of distance on abnormal long-function

327
00:38:43,800 --> 00:38:51,840
among different risk ethnic groups, please break down by risk ethnicities to, you know,

328
00:38:52,000 --> 00:39:01,800
by the radius. We see the consistent increase of abnormal long-function rate for African-American

329
00:39:01,800 --> 00:39:05,700
and individuals even further away from iOS.

330
00:39:07,200 --> 00:39:12,600
Yeah, you can see this somehow is in this direction.

331
00:39:15,860 --> 00:39:21,580
It's that's the first low 83 and then somehow increased

332
00:39:21,580 --> 00:39:24,240
86 and increased further.

333
00:39:24,360 --> 00:39:27,360
This seems to indicate at least for every market,

334
00:39:27,540 --> 00:39:31,420
we do not observe any impact of the distance.

335
00:39:31,800 --> 00:39:40,700
Then, Hispanic and Latinx and Asian American Asian-American group show very

336
00:39:40,700 --> 00:39:51,240
the response to increasing distance from I-O-F. Then, white individuals exceed the decreasing

337
00:39:51,240 --> 00:39:51,800
trend.

338
00:39:53,060 --> 00:39:59,460
So, that we can see for the Asian and Hispanic, you can see is that there's no

339
00:39:59,460 --> 00:40:08,160
simple clear direction pattern and for Caucasian and you can see a clear

340
00:40:12,060 --> 00:40:19,920
downtrend from closes to the further east, which seems intuitive, easy to

341
00:40:19,920 --> 00:40:22,340
interpret. Next slide.

342
00:40:25,800 --> 00:40:31,960
So this is taking all those previous slides without which

343
00:40:31,960 --> 00:40:40,560
is under just then now we look at the adjusted. And so this first left-hand size FEV1, and after we

344
00:40:40,560 --> 00:40:49,260
adjust a number of the covariates listed on the bottom, we do not see any difference in FEV1.

345
00:40:50,300 --> 00:41:00,780
And for IVC, we have done a similar analysis and compare the difference. And we see

346
00:41:01,960 --> 00:41:12,920
But this is compare of course between upper one and down one and we see somehow for the overall combine and we see a

347
00:41:12,920 --> 00:41:30,920
complicated protective effect on FWC, which is complicated and could do to some unmeasured

348
00:41:30,920 --> 00:41:39,480
factors and need to look into this further.

349
00:41:40,860 --> 00:41:41,520
Next slide.

350
00:41:42,400 --> 00:41:48,760
So the effect of demographic health and the environment factors on long function.

351
00:41:49,420 --> 00:41:53,200
First, demographics influences age and residence distance.

352
00:41:53,980 --> 00:41:57,920
A resident duration is linked to decreased long function,

353
00:41:57,920 --> 00:42:06,900
according to a possible long-term environment or aging effect. For health factors, we did not

354
00:42:06,900 --> 00:42:16,600
find some significant impact from quite a number of other health factors, such as smoking,

355
00:42:17,060 --> 00:42:27,120
as my recent cough, etc. For environment factors, the seasonal variation we do see a long function

356
00:42:27,120 --> 00:42:29,180
with words still in winter months.

357
00:42:30,260 --> 00:42:32,900
And for traffic and green space,

358
00:42:33,420 --> 00:42:38,000
measures that was not about clear effects

359
00:42:38,000 --> 00:42:40,480
on long function observed.

360
00:42:42,080 --> 00:42:42,880
Next slide.

361
00:42:45,200 --> 00:42:46,460
Self-report symptoms.

362
00:42:46,920 --> 00:42:49,940
We examined 23 symptoms.

363
00:42:50,780 --> 00:42:53,160
Participants might have experienced.

364
00:42:54,100 --> 00:42:56,180
And among the 23 most commonly,

365
00:42:56,180 --> 00:43:04,020
we report symptoms in a community including sneezing or running nose, fatigue, irritation of

366
00:43:04,020 --> 00:43:09,560
eyes, watery eyes, and headaches. Next slide.

367
00:43:11,770 --> 00:43:18,850
So these shows the three most observed reported

368
00:43:18,850 --> 00:43:31,110
symptoms which is sore throat headaches and a couple of hearing. For sore throat and headaches

369
00:43:31,110 --> 00:43:39,550
what lasts frequently reported among residents near to the old field. You can see that's the first two

370
00:43:39,550 --> 00:43:57,310
figures, and actually going the other way around. For the closer distance, you actually see less reported such two symptoms.

371
00:43:57,310 --> 00:44:06,470
And then for top of hearing, which of course is not significant across the three distance,

372
00:44:07,150 --> 00:44:12,590
was reported more frequently among residents living near the oil field.

373
00:44:13,010 --> 00:44:23,630
But the difference was not that it got significant, meaning we can rule out it is by chance along

374
00:44:23,630 --> 00:44:31,390
Next slide. So the summary of SERP report symptom. After judgment of the other

375
00:44:31,390 --> 00:44:36,850
factors there were no longer a statistical significant difference between the

376
00:44:36,850 --> 00:44:43,270
distance or field and the symptom to report it. Then or less likely to report any

377
00:44:43,270 --> 00:44:52,230
of those symptoms we exact. And the other, the or the participant or less likely to

378
00:44:52,230 --> 00:44:54,990
we poor soul throat or headaches.

379
00:44:56,630 --> 00:44:59,630
It's interesting we find that higher the BMI

380
00:44:59,630 --> 00:44:59,750
Bye.

381
00:45:00,000 --> 00:45:15,280
Social with a slight increase in likelihood of reporting each symptom, suggesting we have something to do with a symptom occurrence. Next slide.

382
00:45:20,370 --> 00:45:34,130
So, for the multivariate analysis, just in those covariates, we actually, you can see these figures. Every figure across the middle of across the right.

383
00:45:34,130 --> 00:45:42,890
vertical bar, so we do not actually see anything significant after controlling adjusting

384
00:45:42,890 --> 00:45:52,730
covariates. Next slide. So this is the last part of the outcome. It serves report health

385
00:45:52,730 --> 00:46:02,470
condition by distance. And multiple frequently reported health condition among participants, including

386
00:46:02,470 --> 00:46:10,710
high-class law, cancer, heart problem, miscarriage, allergies, chronic

387
00:46:10,710 --> 00:46:17,210
obstructive pulmonary disease, CODD, chronic bronchitis, and pneumonia.

388
00:46:19,550 --> 00:46:28,170
Among these reported, there's two, we can see some difference and before we

389
00:46:28,170 --> 00:46:35,830
adjust the covariance, which is high cholesterol level and

390
00:46:35,830 --> 00:46:43,090
cancer rate for more commonly among the resident living near to the oil field.

391
00:46:44,110 --> 00:46:51,270
And most commonly reported, the cancer types are two, one's breast cancer and one's

392
00:46:52,870 --> 00:47:04,250
of course this is just unadjusted. Next slide please. So this is again unadjusted results

393
00:47:04,250 --> 00:47:17,350
looking at these two, you know, conditions that see the difference by the distance.

394
00:47:17,350 --> 00:47:28,630
one is the high cholesterol and you can see when closer to the oil fence, the higher

395
00:47:28,630 --> 00:47:37,170
base and the same thing for cancer and the closer and then you can see a higher

396
00:47:38,170 --> 00:47:46,590
percent. However, we can see in the next class after we carefully conduct analysis

397
00:47:46,590 --> 00:47:50,570
is controlling the factors, these are actually all wind away.

398
00:47:50,770 --> 00:47:52,770
We do not see a difference anymore.

399
00:47:53,670 --> 00:47:54,650
So next slide, please.

400
00:47:57,280 --> 00:47:57,440
Yeah.

401
00:47:57,940 --> 00:48:03,940
So this is the results looking at both the high-class hall

402
00:48:03,940 --> 00:48:05,280
and the concert.

403
00:48:06,400 --> 00:48:09,000
So the solution was no longer observed

404
00:48:09,000 --> 00:48:11,180
after counting all the risk factors.

405
00:48:12,420 --> 00:48:15,660
You can see that the hand side high-class hall

406
00:48:15,660 --> 00:48:22,560
and after controlling factors we do not see the difference anymore. For cancer

407
00:48:22,560 --> 00:48:31,540
and you can see because the further is a radius as very small frequency. So

408
00:48:31,540 --> 00:48:34,820
statistically you have a combine with the next

409
00:48:34,820 --> 00:48:41,640
the statute, which is 0.5 to 1.

410
00:48:42,440 --> 00:48:52,440
So right now the comparison will be near the radius compared to the other two radius together,

411
00:48:53,020 --> 00:49:00,380
and we do not see difference anymore. So for this analysis, we adjust it for each gender

412
00:49:00,380 --> 00:49:13,220
with ethnicity, education, ever-smoker, BMI, and even the gas stove usage and other factors.

413
00:49:14,940 --> 00:49:24,480
Next slide. So some refining for biometric measures. So first, the orange color lines after

414
00:49:24,480 --> 00:49:32,960
adjusting adjustment, downwind was associated with an increased likelihood of the high blood pressure.

415
00:49:33,680 --> 00:49:42,140
This one is, you know, we saw from earlier that combined analysis. So the second

416
00:49:43,120 --> 00:49:47,700
point here, long-function with the lower amount of participant living, closes to all you feel

417
00:49:47,700 --> 00:49:53,340
and the lower in the downward direction.

418
00:49:54,560 --> 00:49:57,600
This difference just went away, yeah.

419
00:49:57,900 --> 00:50:00,540
So that's why it's a white color print.

420
00:50:01,340 --> 00:50:05,940
So the third point is collect protective finding.

421
00:50:06,300 --> 00:50:09,060
That's what we mentioned earlier after counting for other factors.

422
00:50:09,640 --> 00:50:16,640
Leaving downward of the oil field was associated with somehow better FVC.

423
00:50:16,640 --> 00:50:30,860
So, this is kind of a complicated finding and the within behind this is not clear to us.

424
00:50:31,820 --> 00:50:39,380
And that's going to be some underlying interaction or uncontrolled measures.

425
00:50:40,720 --> 00:50:41,160
Next slide.

426
00:50:41,160 --> 00:50:49,520
So some refining for health service is a two key point of controlling demographics, health

427
00:50:49,520 --> 00:50:50,840
and environment factors.

428
00:50:51,360 --> 00:51:02,360
The report of those symptoms of sore throat headaches was no longer a difference between

429
00:51:02,360 --> 00:51:05,860
in the participant, living at different distance.

430
00:51:07,100 --> 00:51:12,760
And the same thing for the conditions,

431
00:51:13,940 --> 00:51:17,460
and after the health conditions, after controlling

432
00:51:17,460 --> 00:51:20,700
demographic health environment factors,

433
00:51:21,460 --> 00:51:25,220
and reported high-class law and the rate of cancer

434
00:51:25,220 --> 00:51:27,880
were no longer different among participants,

435
00:51:28,220 --> 00:51:30,020
living both at different distance.

436
00:51:30,020 --> 00:51:40,620
Next slide. So, conclusion, for the birth outcome analysis, this

437
00:51:40,620 --> 00:51:46,740
analysis suggests that all you feel may have increased risk of return birth

438
00:51:46,740 --> 00:51:52,980
among residents living nearby and downwind. Of course, we cannot do all the

439
00:51:52,980 --> 00:51:59,380
possibility that overfinding away explained by some other factors, we were unable

440
00:51:59,380 --> 00:52:08,020
to measure because it's complicated. And then, however, of course, overfinding is actually

441
00:52:08,020 --> 00:52:14,520
consistent with three studies in California, one central valley, Pennsylvania, and then

442
00:52:14,520 --> 00:52:23,740
third ones from Texas. And overstudy was also unable to assess miscarriage, which may actually

443
00:52:23,740 --> 00:52:26,260
to under-ask made of the healthy impact.

444
00:52:27,400 --> 00:52:27,780
Next slide.

445
00:52:29,560 --> 00:52:31,720
We have limited evidence to suggest

446
00:52:32,620 --> 00:52:38,680
regarding the blood pressure and the lung function,

447
00:52:40,020 --> 00:52:43,580
the social issue, and the finding.

448
00:52:44,300 --> 00:52:47,220
And the blood pressure finding is consistent

449
00:52:47,220 --> 00:52:50,280
with a prior study in other Los Angeles

450
00:52:50,280 --> 00:52:57,380
neighborhood and near all your development. However, the long-functioning fighting is somehow

451
00:52:57,380 --> 00:53:08,850
inconsistent with prior study in Los Angeles and may be due to other limitations of our study.

452
00:53:09,570 --> 00:53:17,270
For example, such as the underrepresentation of a certain subpopulation.

453
00:53:17,270 --> 00:53:23,010
and study limitation, we have a number of them, you know, could be such as, you know,

454
00:53:23,250 --> 00:53:29,710
lack of information about participant, what they actually was, or years, on treatment

455
00:53:29,710 --> 00:53:38,610
of respiratory disease or others. And that may mean we did not detect and impact that

456
00:53:38,610 --> 00:53:41,570
actually does exist. Next slide.

457
00:53:41,570 --> 00:53:41,670
Yes.

458
00:53:43,490 --> 00:53:47,590
And we do not have sufficient evidence to suggest the

459
00:53:48,570 --> 00:53:52,310
redness near the oil field increase risk of cancer

460
00:53:52,310 --> 00:53:54,250
or high cholesterol.

461
00:53:55,070 --> 00:54:00,810
As we all know, the cancer development is complicated

462
00:54:00,810 --> 00:54:04,250
multi-factor driven and is a long term.

463
00:54:05,770 --> 00:54:15,190
And we actually, you know, this is a, it's, you know, for single-sided, you know, this

464
00:54:15,190 --> 00:54:25,830
is the part of this, it's hard to find this, you know, conclusive, you know, results.

465
00:54:27,630 --> 00:54:36,950
The prior studies in Colorado and Texas had linked the residence near the oil field and

466
00:54:36,950 --> 00:54:47,270
the gas development with higher incidence of children, cancer, but there's no prior study

467
00:54:47,270 --> 00:54:52,390
have assessed the population, you know, since it began in California.

468
00:54:53,310 --> 00:55:02,170
Overstudies rely on a self-reported health condition which not verified through a medical record.

469
00:55:02,470 --> 00:55:14,150
Yeah. Next slide. So these are the recommendations. First, given the high rate of adverse

470
00:55:14,150 --> 00:55:23,610
all the comms compared with Erwin Conkey as a whole, and also the suggestive evidence of an

471
00:55:23,610 --> 00:55:32,210
adverse effect of all you feel on pre-term birth. So the program to support pregnant people

472
00:55:32,610 --> 00:55:42,270
could be benefit community. The risk for developing cancer is really complicated

473
00:55:42,270 --> 00:55:51,770
and the cause of cancer in a single community really hard challenge to detect.

474
00:55:52,790 --> 00:56:01,710
Future researchers research could be conducted in, for example, large sample size with regions

475
00:56:01,710 --> 00:56:11,270
or even also look at the long-time span of the transfer registry data, which

476
00:56:11,270 --> 00:56:19,130
will support to capture every single case. And then we can look at that. For this

477
00:56:19,130 --> 00:56:28,690
specific area, 0.5 regions and then compare with others and over time. And future

478
00:56:28,690 --> 00:56:35,710
The first study is to measure contaminate in people bodies, so-called biomonitron, could

479
00:56:35,710 --> 00:56:49,390
actually help to identify a specific gluten-exposed for those who are actually living within the

480
00:56:51,970 --> 00:56:57,850
vicinity neighborhood of the oil field.

481
00:56:58,750 --> 00:56:59,710
Next slide.

482
00:57:01,270 --> 00:57:02,710
Yeah, that's all.

483
00:57:03,130 --> 00:57:03,890
Thank you.

484
00:57:03,890 --> 00:57:07,190
Sorry to take longer than expected.

485
00:57:07,850 --> 00:57:10,250
Trying to get this through quick.

486
00:57:11,470 --> 00:57:15,570
So now let's open the floor for question.

487
00:57:16,070 --> 00:57:18,450
Thank you very much, Dr. Lou and Dr. Cushing.

488
00:57:18,450 --> 00:57:20,550
why don't we just go back first to Christine.

489
00:57:22,910 --> 00:57:26,430
Thank you. I just wanted to let people know on the call

490
00:57:26,430 --> 00:57:32,810
that we have representatives from our health promotion bureau maternal child natalescent health

491
00:57:32,810 --> 00:57:39,150
our deputy director for health promotion dr. Priavatra and our director of maternal child natalescent

492
00:57:39,150 --> 00:57:46,370
health Melissa Franklin and some folks from the I believe with the African-American infant mortality

493
00:57:46,370 --> 00:57:51,870
project who are actually on the call tonight in case anybody has questions about, you know,

494
00:57:52,030 --> 00:57:57,590
programs to support pregnant people. Thank you very much, Dr. Lou and Dr. Cushion for your

495
00:57:57,590 --> 00:58:04,470
presentation. Thank you, Christine. Dr. Luke, can you get rid of the screenshot on the thank you

496
00:58:04,470 --> 00:58:11,050
shot so we can see everybody's faces again? Okay. Thank you. Charles. Thank you. Hi. Thank you.

497
00:58:11,050 --> 00:58:20,610
Thanks for the presentation. I have some questions and the first question is, are you

498
00:58:20,610 --> 00:58:24,750
implying, and this is probably directed more to Dr. Kushin, are you applying a

499
00:58:24,750 --> 00:58:29,270
heavy identifier at a cause and effect relationship with the ingot oil field

500
00:58:29,270 --> 00:58:34,970
and the outcomes that you've presented to us today?

501
00:58:38,680 --> 00:58:40,380
Well let me start just by

502
00:58:40,380 --> 00:58:50,140
saying the way kind of we think about causing effect in epidemiology is not through a single study.

503
00:58:50,560 --> 00:58:57,620
It's through the aggregation of evidence from multiple studies, each with their own strengths and weaknesses.

504
00:58:58,480 --> 00:59:04,100
We start to see the same thing over and over. We have more confidence that it's causal relationship.

505
00:59:05,500 --> 00:59:13,600
So, I wouldn't say that we can definitively say that living near the oil field caused

506
00:59:14,050 --> 00:59:21,580
any, caused elevated rates of pre-term birth, but I can say that when we look across studies

507
00:59:22,170 --> 00:59:30,980
across the country, including the one we conducted here, we repeatedly see an association between

508
00:59:30,980 --> 00:59:37,000
living near oil and gas development and pre-term birth. Not in every study, but in many studies.

509
00:59:39,120 --> 00:59:44,360
So I hope that answers your question. Well, it opens up on other series of questions.

510
00:59:44,620 --> 00:59:50,300
Correlation is not causation. We can definitively, by we I mean the cap, can definitively state

511
00:59:50,300 --> 00:59:55,340
that we know what's coming off the oil field. A lot of the presentation presupposes that there

512
00:59:55,340 --> 00:59:59,800
has it as chemicals coming off the oil feed but we

513
01:00:00,000 --> 01:00:29,200
There is nothing. So we have a situation where we, with high confidence, two places of decimal, can state categorically that we know what's coming off the oil field and it meets the protective requirements that we get from the professional organizations. So if some, and I think you may have found something here, if something's wrong, we need to, you know, dig a little deeper. Second question. Second question is, you're suggesting I made a note.

514
01:00:29,200 --> 01:00:38,100
56% higher impact? Is that normally distributed or is that possibly influenced by a pocket of

515
01:00:38,100 --> 01:00:47,960
very bad or very high outcomes? Because 56% seems like the variation between health outcomes

516
01:00:47,960 --> 01:00:56,100
in the United States and a third world country. I mean, 56% is, you know, 56%. That's high.

517
01:00:56,100 --> 01:00:59,220
Hey, someone would have noticed that I would think before.

518
01:01:00,200 --> 01:01:05,660
So is the data normally distributed within that group because you're citing percentages

519
01:01:05,660 --> 01:01:07,040
not frequency distributions?

520
01:01:08,580 --> 01:01:12,640
In other words, is it just a little bad or is it 56% bad?

521
01:01:14,910 --> 01:01:21,310
Well, it's not that 56% of babies were born pre-term, if that's what you're suggesting.

522
01:01:21,310 --> 01:01:31,330
is that the likelihood of preterm births was 56% higher among that nearby downwind population

523
01:01:31,330 --> 01:01:37,710
versus nearby upwind population. So it's a little bit against that the number 56 sounds big,

524
01:01:37,930 --> 01:01:45,170
you know, just to give you a sense roughly 10% of babies are born preterm. So we're not saying

525
01:01:45,170 --> 01:01:50,810
that 56% of babies were born preterm in that population. We're saying that the likelihood was

526
01:01:50,810 --> 01:01:55,470
slightly elevated in the one group versus the other, the downwind versus the

527
01:01:55,470 --> 01:02:05,070
upwind. Hopefully that... Yes, I understand that, but you're presenting it either

528
01:02:05,070 --> 01:02:09,810
or is it just a little bit? I mean, is it, I don't know what the units are, is it one

529
01:02:09,810 --> 01:02:17,470
day, is it two days, or is it, you know, a clinically significant time factor?

530
01:02:19,590 --> 01:02:20,030
Sorry,

531
01:02:22,510 --> 01:02:28,430
I'm trying to think of a good analogy, Dr. Lou, if you have any suggestions, feel free.

532
01:02:28,630 --> 01:02:34,550
It's like I don't want to like giving birth to rolling the dice because that's terrible

533
01:02:36,830 --> 01:02:46,020
having gone through it myself, but what am I trying to say? I'm trying to say it's a

534
01:02:48,100 --> 01:03:08,860
Yeah, let me chant you a little bit. Yeah, like the cushion motion, you know, this 56 percent, that's just the likelihood does not mean, you know, specifically. And then I also like to chant you a little bit patching a little bit about the coastal impact issue.

535
01:03:08,860 --> 01:03:20,460
Remember this, the study design and the data we have, this is not the repeal measure longitudinal design per se.

536
01:03:21,020 --> 01:03:31,800
Even for the birth outcome, we do have data over time, but that is not actually a repeal measure, typical repeal measure design.

537
01:03:31,800 --> 01:03:41,720
which we prepare a longitudinal design is purposely for identified causal impact, causal effect.

538
01:03:42,580 --> 01:03:50,700
For overall analysis, yes, clearly we do identify the significant association,

539
01:03:50,700 --> 01:03:59,500
So, which point to the direction that there is a potential, first of all, the strong

540
01:03:59,500 --> 01:04:08,120
association, there is a potential causal impact, effect relationship, underlying relationship.

541
01:04:08,660 --> 01:04:17,940
But with cross-sectional analysis, cross-sectional data, we are unable to see definitely whether

542
01:04:17,940 --> 01:04:21,060
whether it's a causal effect or not.

543
01:04:21,300 --> 01:04:21,760
Okay, all right.

544
01:04:22,080 --> 01:04:26,500
And finally, Dr. Could you put up your last slide, please?

545
01:04:26,720 --> 01:04:28,760
Could you build your last conclusion,

546
01:04:29,020 --> 01:04:30,460
aside with your last conclusions?

547
01:04:31,200 --> 01:04:31,720
Sure.

548
01:04:32,140 --> 01:04:34,200
Slide that contained your last conclusions.

549
01:04:37,000 --> 01:04:40,260
Li Yu, can you help to put very last one?

550
01:04:41,000 --> 01:04:44,920
Because I'm not the smartest person.

551
01:04:45,880 --> 01:04:48,240
I'm a dumb guy, but it seems to me

552
01:04:48,240 --> 01:04:55,200
that we have two ways of looking at things. We have scientific data that was collected over

553
01:04:55,200 --> 01:05:01,780
many years, calibrated instruments that speaks to a specific conclusion. What's going off

554
01:05:01,780 --> 01:05:07,580
the oil field, and that so far is green. It's not indicative of anything that's going to impact

555
01:05:07,580 --> 01:05:14,880
the community. And then we have studies that, well, they're statistically valid, but I don't want to

556
01:05:14,880 --> 01:05:20,160
have questions, but there's a lot of concern as to whether they actually have a cause and effect

557
01:05:20,160 --> 01:05:27,220
relationship. Anyway, so you're saying, you know, this is not the last one. You had to go to

558
01:05:27,220 --> 01:05:32,680
the next one. Yeah. Yeah. Okay. All right. So your recommendations, everyone here supports more

559
01:05:32,680 --> 01:05:37,640
support for pregnant people, whatever the cause. What I want to make sure is that we don't go chasing

560
01:05:37,640 --> 01:05:44,720
using a bogie that isn't there, we need to make sure that if the oil field is complicit

561
01:05:44,720 --> 01:05:50,600
in this situation, that we define that because we could say eliminating the oil field isn't

562
01:05:50,600 --> 01:05:56,260
going to necessarily make anything better. Secondly, you talk about future studies to measure

563
01:05:56,260 --> 01:06:01,900
contaminants in people, being exposed to specific pollutants associated with oil drilling.

564
01:06:02,000 --> 01:06:06,880
We're doing that, we're absolutely doing that, and the answer is there are no specific components

565
01:06:07,580 --> 01:06:10,680
associated with oil drilling that is escaping into the community.

566
01:06:10,880 --> 01:06:21,340
We know that so we can't keep mentioning that there may be these undetectable hazards

567
01:06:21,760 --> 01:06:23,680
that we know are not there.

568
01:06:23,940 --> 01:06:26,140
Anyway, that's all I have to say for now.

569
01:06:26,280 --> 01:06:26,880
But thank you.

570
01:06:27,020 --> 01:06:28,900
I think you guys did a great job on this study.

571
01:06:29,020 --> 01:06:29,820
Thank you.

572
01:06:29,980 --> 01:06:30,860
Thank you, Charles.

573
01:06:31,840 --> 01:06:33,940
Before we go to Jenny with the next question,

574
01:06:33,940 --> 01:06:37,480
and Erica asked if these slides could be shared.

575
01:06:37,960 --> 01:06:40,700
Can someone put these on the website?

576
01:06:40,920 --> 01:06:42,140
Can you do have them Edgar?

577
01:06:43,400 --> 01:06:43,480
Yeah.

578
01:06:44,260 --> 01:06:44,440
Okay.

579
01:06:45,040 --> 01:06:46,900
And again, if you can get rid of the slides,

580
01:06:46,900 --> 01:06:48,940
so we can see everybody again and Jenny.

581
01:06:49,360 --> 01:06:50,700
Oh, John, if I can just...

582
01:06:50,700 --> 01:06:51,260
Oh, go ahead.

583
01:06:51,280 --> 01:06:54,200
There's gonna be a final report, the slides

584
01:06:54,200 --> 01:06:55,440
and the final report.

585
01:06:55,660 --> 01:06:59,240
And there's some other reports from the study,

586
01:06:59,460 --> 01:07:02,580
like they recorded the input they got from the chap

587
01:07:02,580 --> 01:07:03,980
and how they responded to it.

588
01:07:04,380 --> 01:07:06,320
Those kinds of things will all end up being shared

589
01:07:06,320 --> 01:07:07,680
with Edgar to put on the website.

590
01:07:08,760 --> 01:07:10,360
And what's the timing on that?

591
01:07:12,100 --> 01:07:14,960
The end of the contract period is December.

592
01:07:15,420 --> 01:07:16,920
So by the end of December.

593
01:07:17,240 --> 01:07:18,840
So within a few, within a month or two.

594
01:07:18,880 --> 01:07:20,100
Yeah, thank you.

595
01:07:20,460 --> 01:07:20,640
Jenny.

596
01:07:21,340 --> 01:07:22,240
Yes, thank you.

597
01:07:23,140 --> 01:07:27,320
Dr. Liu, I actually think Dr. Cushing mentioned that

598
01:07:27,320 --> 01:07:30,840
you need to do multiple studies in order to get confidence

599
01:07:30,840 --> 01:07:37,260
in order to have a trend that you can feel confident and you suggest that the the bio monitoring would

600
01:07:37,260 --> 01:07:42,900
that have to be done among the same sample or at least a sample that X absolutely mirrors this

601
01:07:42,900 --> 01:07:43,360
study?

602
01:07:47,990 --> 01:07:55,670
Not necessarily and I was you know also speaking about not just multiple studies of this

603
01:07:55,670 --> 01:08:04,250
community in particular but just overall like if you want to figure out the answer to a question

604
01:08:04,250 --> 01:08:11,330
and studying it in multiple populations with differing study designs and methods can help

605
01:08:11,330 --> 01:08:16,730
you have more confidence. If you're seeing the same answer over and over again,

606
01:08:17,070 --> 01:08:21,290
then you start to have more confidence in those. But you do want it to relate to the

607
01:08:21,290 --> 01:08:29,310
angle would oil feel specifically. Yeah, I mean, I think a bio monitoring study here would be worth

608
01:08:29,310 --> 01:08:37,710
while it doesn't necessarily ideally it would if people who participated in this study it would

609
01:08:37,710 --> 01:08:42,890
be great if there were to be a biomedical study that would be the same people because we already

610
01:08:42,890 --> 01:08:49,170
have all this information about their lung function their blood pressure etc. that we could also

611
01:08:49,170 --> 01:08:56,610
and their lifestyle because we did ask lifestyle questions yes so that would be ideal and then really

612
01:08:56,610 --> 01:08:59,430
quickly. How is this going to be disseminated to the public?

613
01:09:04,870 --> 01:09:07,010
Sorry, I miss you last question. You repeat,

614
01:09:07,370 --> 01:09:09,570
Virginia. How is the public going to learn about

615
01:09:10,050 --> 01:09:12,430
the results of this study and what it means to them?

616
01:09:15,330 --> 01:09:19,390
You mean how the public will learn the result from this study?

617
01:09:21,940 --> 01:09:29,760
Yeah. Yeah, we will have, as Kristi mentioned, we have the final report,

618
01:09:29,760 --> 01:09:41,580
or summarize all the results and in language and we're actually in quite a detailed way,

619
01:09:41,640 --> 01:09:44,360
which were eventually be available to the public.

620
01:09:45,080 --> 01:09:45,460
How?

621
01:09:46,820 --> 01:09:48,480
How was somebody?

622
01:09:48,540 --> 01:09:54,820
How will my neighbors be able to learn what this study has told us?

623
01:09:54,820 --> 01:09:59,120
You know, we don't really have a dissemination plan for it.

624
01:10:00,580 --> 01:10:03,200
Really, we are sharing it with the community.

625
01:10:03,340 --> 01:10:04,680
It will be on the website.

626
01:10:05,300 --> 01:10:08,320
But we could talk about, you know, potential ways

627
01:10:08,320 --> 01:10:10,940
and maybe we can talk with some community partners

628
01:10:10,940 --> 01:10:13,320
about ways to get the word out about study.

629
01:10:14,060 --> 01:10:14,640
Great, thank you.

630
01:10:14,800 --> 01:10:15,660
Thank you, everyone.

631
01:10:16,020 --> 01:10:16,580
Thank you, Jenny.

632
01:10:17,020 --> 01:10:19,020
Yeah, I've got a number of questions actually.

633
01:10:19,740 --> 01:10:22,220
Well, I won't address them here, huh?

634
01:10:22,220 --> 01:10:24,940
Joe, hold on, we got two other people first.

635
01:10:25,260 --> 01:10:25,340
Can we?

636
01:10:25,540 --> 01:10:25,700
Okay.

637
01:10:26,160 --> 01:10:26,380
Okay.

638
01:10:26,880 --> 01:10:27,520
Jim Jones.

639
01:10:28,060 --> 01:10:28,360
We're

640
01:10:33,760 --> 01:10:35,860
eager to see so many familiar faces there.

641
01:10:37,300 --> 01:10:39,260
I'm sorry that in this, Dr. Cushings,

642
01:10:40,100 --> 01:10:42,460
second go round on the presentation.

643
01:10:42,980 --> 01:10:44,940
I'm sure it was fantastic as the first.

644
01:10:46,320 --> 01:10:48,300
One of the things that I was wondering,

645
01:10:48,700 --> 01:10:50,480
actually, do I have my camera on?

646
01:10:50,880 --> 01:10:51,120
Actually.

647
01:10:51,800 --> 01:10:52,000
Okay.

648
01:10:52,860 --> 01:10:53,580
I'm sorry.

649
01:10:53,580 --> 01:11:02,140
One of the major concerns was this, in listening to the presentation again, would it be fair

650
01:11:02,140 --> 01:11:10,020
to say that throughout all of the statistical information that was received and analyzed,

651
01:11:10,020 --> 01:11:24,120
that if downwind locations actually are areas in which there's a significant and statistical

652
01:11:24,120 --> 01:11:30,380
difference showing the effects where it should say deleteries effects of being a proximity

653
01:11:30,380 --> 01:11:36,120
to the oil field. And that basically goes between the preterm birth study and the other health

654
01:11:36,120 --> 01:11:38,600
assessment. Would that be fair to say?

655
01:11:41,380 --> 01:11:50,160
Yeah, I would say that, yeah, to a lot degree. Yes, because remember also if we look at those

656
01:11:50,160 --> 01:11:58,380
bi-variable results, when you look at the compare downwind versus the upperwind, those impact

657
01:11:58,380 --> 01:12:07,020
you see that when you actually stay, it'll leave closer to the oil field, which means, you know,

658
01:12:07,140 --> 01:12:16,540
like a double impact, right, from the wind direction and the direction and the distance. So overall,

659
01:12:16,980 --> 01:12:25,380
yes, the Darwin showed up in both birth outcomes and other health measures.

660
01:12:25,380 --> 01:12:37,440
Excellent. And would it be accurate to state that wind direction from West Southwest to East

661
01:12:37,440 --> 01:12:43,800
Northeast is wind blowing in the downwind direction? Would that be fair to say?

662
01:12:49,020 --> 01:12:52,680
Yes. Yeah. Okay.

663
01:12:53,580 --> 01:13:00,540
Would it be fair to say that we have an operator that is in our midst that considers

664
01:13:01,300 --> 01:13:08,840
wind direction like that in the middle of an oil spill to be considered good news?

665
01:13:10,140 --> 01:13:14,820
And if there's any question about that, I suggest that we look back to the

666
01:13:14,820 --> 01:13:25,360
CHAP study July 2024, CHAP meeting recording, minutes 44, minute zero, zero seconds to 51 minutes

667
01:13:25,360 --> 01:13:33,680
and 25 seconds. Basically my point is this, if we are being told that it is good news that

668
01:13:33,680 --> 01:13:40,580
we have wind direction blowing down wind and we know for a fact that there are deleterious

669
01:13:40,580 --> 01:13:47,080
effects from whatever the OCs are emanating from that from that field. I mean, as Charles

670
01:13:47,080 --> 01:13:53,700
pointed out, we may not know what they are, but we were provided with an explanation of

671
01:13:53,700 --> 01:14:02,960
all the oil is dispersant. If that's the case, doesn't mean that it doesn't evaporate.

672
01:14:03,260 --> 01:14:08,580
It turns into something that apparently may not be able to be measured or identified, and

673
01:14:08,580 --> 01:14:14,940
And it's obviously statistically being proven that it's causing a problem in South Los Angeles.

674
01:14:17,610 --> 01:14:21,170
So this is something that I think that we really need to take a look at.

675
01:14:27,290 --> 01:14:27,770
Thank you, Tim.

676
01:14:28,430 --> 01:14:31,430
Let's go at Joe and then Megan and then Liz and then Frank.

677
01:14:32,250 --> 01:14:34,890
Yeah, I mean, I have a, I have a number of questions.

678
01:14:35,070 --> 01:14:36,010
I'm not going to go through them all.

679
01:14:36,130 --> 01:14:37,510
Is this report?

680
01:14:37,810 --> 01:14:45,670
Is it deemed as final or is it something that we can comment on and so forth?

681
01:14:48,210 --> 01:14:55,870
was it just going to be flat out final? Has it been reviewed by anybody besides done internally?

682
01:15:00,000 --> 01:15:29,900
Yeah, this is not the report itself. This is the, you know, basically the, of course, the meat part of the report. This is the results. And I, I will say, you know, is, is open for comment. I, I will say, sure, open. This is, you know, open to public. And it can open for comment.

683
01:15:29,900 --> 01:15:48,400
That's for sure. Yeah. And in terms of the final report that eventually is, you know, a document. And whether we present it, they will be part of that document.

684
01:15:50,180 --> 01:15:53,360
I just see a number of things I'd like to look at.

685
01:15:53,460 --> 01:15:57,260
I mean, one is just the protocols that were followed for this.

686
01:15:57,500 --> 01:16:04,860
And two, did you do any upfront screenings like Cal E mod or Cal doubly E mod on the facilities here?

687
01:16:05,400 --> 01:16:09,560
Did you guys look at other sources other than the oil?

688
01:16:09,700 --> 01:16:17,020
Did you do a screening such as AQMD's mate studies to look at other toxic facilities?

689
01:16:17,020 --> 01:16:23,400
We're right, I think something like, I think it's 173 on the list and there's a lot of

690
01:16:23,400 --> 01:16:28,760
air toxic facilities right in this neck of the woods, too, in the England, well, or near

691
01:16:28,760 --> 01:16:29,800
the England, well, field.

692
01:16:30,360 --> 01:16:31,420
Did you look at any of those?

693
01:16:34,790 --> 01:16:42,430
Yeah, that's very good question, waiting, you know, this result does not point to any specific

694
01:16:42,430 --> 01:16:47,690
think the sources or something, this is only the distance.

695
01:16:48,550 --> 01:16:53,770
As you mentioned, there's other sources of contaminate

696
01:16:53,770 --> 01:16:59,110
and we do not have any conclusion or something

697
01:16:59,110 --> 01:17:00,790
on that specific level.

698
01:17:01,570 --> 01:17:04,190
And that's a very good point.

699
01:17:04,950 --> 01:17:08,910
That will be the other issues.

700
01:17:09,950 --> 01:17:16,410
you know, someone need to look into it and eventually the country build this, you know,

701
01:17:16,630 --> 01:17:20,070
eventually to the clip of the different sources.

702
01:17:22,400 --> 01:17:29,900
Yeah, I mean, there's other things as well, but I would like the opportunity to read and comment on this.

703
01:17:30,980 --> 01:17:38,200
Me being representing the well field, I just see a number of flaws right off the top here

704
01:17:38,200 --> 01:17:40,880
in terms of the conclusions that you're coming to.

705
01:17:41,640 --> 01:17:43,200
But you know, want to be objective

706
01:17:43,200 --> 01:17:44,540
and take a look at the report.

707
01:17:44,960 --> 01:17:45,560
That's for sure.

708
01:17:46,360 --> 01:17:46,440
So.

709
01:17:47,800 --> 01:17:48,320
Yeah.

710
01:17:48,680 --> 01:17:49,060
Sure.

711
01:17:49,540 --> 01:17:49,800
Yeah.

712
01:17:50,340 --> 01:17:53,200
We understand, Dr. Liu, you can submit comments

713
01:17:53,720 --> 01:17:57,100
and those will be considered by UCLA

714
01:17:57,100 --> 01:18:00,520
and put in and referenced in the final report.

715
01:18:00,640 --> 01:18:01,140
Is that correct?

716
01:18:02,540 --> 01:18:02,780
You know.

717
01:18:03,400 --> 01:18:05,960
I'm just going to jump in here real quick.

718
01:18:05,960 --> 01:18:08,480
We didn't include that in the scope.

719
01:18:09,340 --> 01:18:12,040
I think what we're going to do is have a final report,

720
01:18:12,040 --> 01:18:14,940
but certainly anybody can comment

721
01:18:14,940 --> 01:18:18,920
and those, you know, if the cap wants,

722
01:18:19,120 --> 01:18:21,500
those comments could also be uploaded as well.

723
01:18:22,840 --> 01:18:25,660
And Joe, with respect to what you're saying,

724
01:18:25,860 --> 01:18:29,900
basically this study mirrors the kind of methods

725
01:18:29,900 --> 01:18:32,500
that have been used around the country

726
01:18:32,500 --> 01:18:34,440
to look at this kind of an issue

727
01:18:34,440 --> 01:18:46,600
from an epidemiologic point of view and typically they don't take into consideration something like the mate's study and I will say that any study is going to have limitations certainly.

728
01:18:46,600 --> 01:19:00,420
So, and it was stated, you know, in the conclusions that, you know, it's possible that it was another factor, but the results are suggestive and consistent.

729
01:19:00,640 --> 01:19:06,640
So, we try to be careful with our language about how we talk about results of studies.

730
01:19:07,620 --> 01:19:10,920
And so, you know, we can't say that something has been proven.

731
01:19:11,720 --> 01:19:17,420
So, your points are well taken, but I do think we are going to have a final report.

732
01:19:17,680 --> 01:19:22,800
We will review it for clarity, and then we're going to let UCLA put out their report,

733
01:19:22,960 --> 01:19:29,120
and then I feel like other groups can do what they want with that and upload it as, you

734
01:19:29,120 --> 01:19:30,320
know, as the cap desires.

735
01:19:31,580 --> 01:19:32,060
Understood.

736
01:19:32,820 --> 01:19:40,040
You know, one thing I'll stop there is that, you know, our field is, we're different from

737
01:19:40,040 --> 01:19:43,840
From other fields around the country, particularly because we're in California, we're under full

738
01:19:43,840 --> 01:19:44,700
vapor recovery.

739
01:19:44,920 --> 01:19:49,940
We realize we get leaks that we have to stay on top of from time to time to time.

740
01:19:51,440 --> 01:19:56,800
But we're much different from those that operate, let's say in Colorado or Pennsylvania, where

741
01:19:56,800 --> 01:20:02,980
we've got just much more strict or permanent requirements and control requirements.

742
01:20:04,700 --> 01:20:05,340
That's it.

743
01:20:06,100 --> 01:20:06,580
Thank you, Jill.

744
01:20:07,220 --> 01:20:07,440
Megan.

745
01:20:08,420 --> 01:20:08,980
Yeah.

746
01:20:08,980 --> 01:20:17,440
Thank you, everybody, for those who worked on this city and those who were part of the committee giving input on it.

747
01:20:21,880 --> 01:20:38,280
I've heard a lot more anecdotal stories of folks with higher cancer rates in a lot of the neighborhoods that I've talked closely with.

748
01:20:38,280 --> 01:20:40,680
And so I'm.

749
01:20:47,160 --> 01:20:51,960
Are you still there? Yeah. Can you hear me? I couldn't for a second, but now I can.

750
01:20:52,480 --> 01:21:02,280
I apologize. I'll talk closer to my computer pardon me. When we look at the amount of oil drilling

751
01:21:02,280 --> 01:21:10,060
that happens in Los Angeles County, do you happen to know if there is a comparison, if the state has

752
01:21:10,060 --> 01:21:16,560
done similar type studies that cover regions, because I think it would be interesting, given

753
01:21:16,560 --> 01:21:22,900
the amount of oil drilling that is happening in this county, if there is a way of comparing,

754
01:21:23,760 --> 01:21:29,260
doing more regional comparisons as opposed to field-to-field, because there's a field here,

755
01:21:29,360 --> 01:21:36,820
but there's also fields in Long Beach, and refineries in Wilmington and Carson, and a lot

756
01:21:36,820 --> 01:21:46,520
of other oil drilling type industries that also have an impact on the surrounding health,

757
01:21:47,040 --> 01:21:53,020
and so I'm just wondering how that compares with, you know, say, folks in regions where there

758
01:21:53,020 --> 01:21:58,960
is no oil drilling in the state of California, if any of that is available information.

759
01:21:59,680 --> 01:22:06,960
Yeah, thanks, Megan. That's a good point. As far as based on over knowledge, there's no such

760
01:22:06,960 --> 01:22:17,580
prior study conducted. That's one of our recommendations. You can see that in the summary slide,

761
01:22:17,860 --> 01:22:23,760
the last slide. That's what we recommended. Yeah. I agree that would be a good,

762
01:22:25,380 --> 01:22:32,100
It's a robust way to look further, you know, this underlying potential relationship.

763
01:22:40,580 --> 01:22:41,060
Okay.

764
01:22:41,260 --> 01:22:42,000
Is that it, Megan?

765
01:22:44,480 --> 01:22:44,840
Yes.

766
01:22:45,040 --> 01:22:45,340
Thank you.

767
01:22:45,620 --> 01:22:45,980
Okay.

768
01:22:46,140 --> 01:22:46,560
Thank you.

769
01:22:46,660 --> 01:22:46,940
Liz.

770
01:22:53,240 --> 01:22:54,100
Can't hear you.

771
01:22:54,420 --> 01:22:54,660
Sorry.

772
01:22:54,900 --> 01:22:56,320
Couldn't get my thing to go on.

773
01:22:57,080 --> 01:23:02,000
What a start by saying thank you for being a part of this and for everybody else who took

774
01:23:02,000 --> 01:23:02,280
part.

775
01:23:02,420 --> 01:23:08,060
I think you see, you know, did any incredible job, but I think that from the very start, the

776
01:23:08,060 --> 01:23:14,300
concerns that we had or I had and I felt the group had from our first meetings

777
01:23:14,300 --> 01:23:21,760
were that we wanted to make sure that the comparison of what they were

778
01:23:21,760 --> 01:23:26,680
showing to the county showed it all times. I was very disappointed that that

779
01:23:26,680 --> 01:23:32,700
wasn't displayed in the document. So how is this ruling compared to

780
01:23:32,700 --> 01:23:38,760
Los Angeles County. That's a big one. The other thing, you know, Joe bought a great point,

781
01:23:39,000 --> 01:23:43,760
we aren't like any other oil field. We are more regulated than anywhere else in the country.

782
01:23:44,080 --> 01:23:49,720
And the other issue is you do contributing factors. When you go out to Pennsylvania and you

783
01:23:49,720 --> 01:23:55,680
look on global map and you look at these fields, they're in the middle of nowhere and they have nothing.

784
01:23:56,100 --> 01:24:01,720
And we have one of the largest airports in the United States. Ben, we have more freeways,

785
01:24:01,720 --> 01:24:10,360
more cars. We have documented how much pollution we have. And I don't feel that those factors will

786
01:24:10,360 --> 01:24:16,460
pull out. And if they were, I mean, I thought that was from the linear regression. And I think that

787
01:24:17,200 --> 01:24:24,080
Dr. Hans, the math fabulous, but I just don't know that the data was totally available.

788
01:24:24,080 --> 01:24:34,400
So, you know, that was a factor for me, but I, and also the issue of, you know, we held

789
01:24:34,400 --> 01:24:40,320
this health study and extended it for over a year so that the SNAP study could be a part

790
01:24:40,320 --> 01:24:40,700
of this.

791
01:24:40,840 --> 01:24:44,140
And yet, I don't see anywhere where the SNAP status applied.

792
01:24:44,640 --> 01:24:51,000
All of this information of them showing them exactly what chemicals are here and what

793
01:24:51,000 --> 01:24:58,740
chemicals related me to the study that you did. I think it should have been addressed

794
01:24:58,740 --> 01:25:07,020
and noted because you have a real time show of the chemicals all around us in the field.

795
01:25:07,740 --> 01:25:14,180
And then I know that I'm not a scientist, I'm just an average Joe who studies a lot of

796
01:25:14,180 --> 01:25:18,980
the stuff, I read a lot about it and I can't tell you how many hours I've gotten sucked

797
01:25:18,980 --> 01:25:27,620
in deleting. But health risk assessments are required by Weha, which is the mother of

798
01:25:27,620 --> 01:25:35,560
all health and safety for the state of California. And the Inglewood Wildfield and all other fields,

799
01:25:35,560 --> 01:25:41,760
I believe, have to complete those settings. And we have passed again, and our field has passed

800
01:25:41,760 --> 01:25:47,020
again and again with flying colors. And that's the only thing we should know. And this goes to what

801
01:25:47,020 --> 01:25:53,940
you know Charles was saying we do have this sad and I mean I was sad to see that the health

802
01:25:53,940 --> 01:26:00,900
studies, that the mate's study and these healthcare assessment studies and studies from our original

803
01:26:00,900 --> 01:26:07,700
EIR, they're not included in this and you know I was kind of like a ding dong just touting about this

804
01:26:07,700 --> 01:26:15,780
in meetings again and again and again and so I wish that in some way because you know to say some

805
01:26:15,780 --> 01:26:23,700
of these things where I, some of the data, it seems that, you know, this is, it's a utilization

806
01:26:23,700 --> 01:26:31,560
potentially of unadjusted data. And that's not fair and steady of like, that wouldn't pass

807
01:26:31,560 --> 01:26:37,940
re-haused requirements, and it wouldn't pass in a health risk assessment. So, and I'm not a scientist,

808
01:26:38,180 --> 01:26:43,880
so you can probably talk circles around my comments, and I don't deny it. But I'm just saying my biggest

809
01:26:43,880 --> 01:26:49,040
thing was that I wanted, and I know Charles and the rest of the people that were from the cap,

810
01:26:49,520 --> 01:26:58,000
we wanted people to present in a manner that the cap members could understand, not having sat

811
01:26:58,000 --> 01:27:04,100
through, you know, not devoting about a month of, you know, meeting every month since 2018,

812
01:27:04,520 --> 01:27:11,060
because we started with one and then we went on and went with UCLN. And I mean, I learned so much,

813
01:27:11,060 --> 01:27:24,640
But the issue of presentation was, you know, as we said, at the end of our group meeting before we hire before Christine, you know, put this together and they put it out to them.

814
01:27:25,020 --> 01:27:36,480
It was supposed to come to the cap with a definitive answer. The data we have shows this and I understand that they have some other potential conclusions.

815
01:27:36,480 --> 01:27:48,100
But I don't feel, and tell me if I'm wrong, are these facts from the shift to data, you know, and the fact that we didn't utilize a blind study.

816
01:27:48,520 --> 01:27:54,420
How much effect does that have on the results? And then I mean, bringing me the issue.

817
01:27:54,620 --> 01:28:00,340
I mean, I thought it was very odd to see you guys at it as a possible issue people using gas stoves.

818
01:28:00,340 --> 01:28:04,280
I mean, there are a lot of controversy, and you can say people have live in a cold

819
01:28:04,280 --> 01:28:06,520
sack and everyone has tons of chemicals under their skin.

820
01:28:07,100 --> 01:28:13,460
But the bigger factor to me instead of those items were what about La Sianica Boulevard?

821
01:28:13,760 --> 01:28:15,220
What about LAX?

822
01:28:15,700 --> 01:28:17,900
What about the average pollution rate?

823
01:28:18,080 --> 01:28:23,220
And as Christine has always said, all these years, and the person her predecessor before her,

824
01:28:23,540 --> 01:28:29,340
you know, when you get on the freeway, they have to have one at every single on-ramp saying

825
01:28:29,340 --> 01:28:34,340
that the chemicals found in Los Angeles are way above standard.

826
01:28:35,220 --> 01:28:41,060
So I just, I hope that a little more detail could be,

827
01:28:41,600 --> 01:28:44,260
you know, like an executive summary at the front

828
01:28:44,260 --> 01:28:48,720
and they're going into what this is instead of the conclusions

829
01:28:48,720 --> 01:28:52,940
at the end because I still don't know.

830
01:28:53,180 --> 01:28:57,420
I know I've looked at a lot of those not the slides before,

831
01:28:57,420 --> 01:29:04,500
But I can just say, there's a few of them I still don't understand and I'm not proud of it because I felt like, gosh, I should understand this.

832
01:29:04,620 --> 01:29:11,860
And I did look at forum online and kind of look around and we did, I know as a group, ask more questions during the process.

833
01:30:00,000 --> 01:30:29,740
I mean, if this is going to go through 43 slides to conclusions, it should be full of points in the front. And then I wish, and I guess I asked again to do. How are these contributing factors. I mean, I heard what you said, but we should say that it's a disclaimer that these contributing factors that include the airport that include and LAX, the wildest part of the study I found was that LAX.

834
01:30:29,740 --> 01:30:34,720
you think it's the planes, it's not the planes, it's all the trucks and people driving in it,

835
01:30:34,880 --> 01:30:39,120
millions of people driving in and out of the airport. Most people don't know that.

836
01:30:39,580 --> 01:30:43,080
They think that the planes are the big thing about the airport, it's the traffic,

837
01:30:43,540 --> 01:30:47,640
it's the diesel particulates, which we don't have, you know, we don't have that issue in the field

838
01:30:47,640 --> 01:30:53,740
and people think, oh yeah, there's fun in the diesel. That's an immense factor. And so, I mean,

839
01:30:53,740 --> 01:31:00,140
there should be maybe a little explanation of those factors including this because you guys

840
01:31:00,140 --> 01:31:05,380
are incredible scientists and when I ask these questions you do answer them for me and it's

841
01:31:05,380 --> 01:31:11,820
really helpful but I think that some of the data should be disseminated to the group to the average

842
01:31:12,460 --> 01:31:18,980
Joe in the public that'll be reading this because why read this I don't want it to sound as though

843
01:31:18,980 --> 01:31:26,980
So, you know, the health department and Lee Ha and all these other groups have not been

844
01:31:26,980 --> 01:31:27,660
doing their job.

845
01:31:28,000 --> 01:31:34,660
And then the incredible data that snaps provided us with, you know, I was very excited to have

846
01:31:34,660 --> 01:31:36,160
included, but it doesn't appear.

847
01:31:37,860 --> 01:31:38,180
So.

848
01:31:40,800 --> 01:31:41,360
Okay.

849
01:31:41,780 --> 01:31:43,220
Thanks, Liz.

850
01:31:43,840 --> 01:31:46,940
Yeah, you, you, thanks for all your comments.

851
01:31:46,940 --> 01:31:53,340
Yeah, I think you brought up a number of issues. Let me try to address a few. I think I

852
01:31:54,140 --> 01:32:02,040
maybe, you know, I'd like people to meet something and then others can tell me why is the very

853
01:32:02,040 --> 01:32:09,360
first one regarding our county comparison and we did try it. You can see for the birth outcome,

854
01:32:09,360 --> 01:32:16,360
we do have that statistic from county so we were able to compare. For other health outcomes,

855
01:32:16,360 --> 01:32:22,240
company has very limited data or like you could see that for a blood pressure we

856
01:32:22,240 --> 01:32:29,100
did identify the county estimate for high blood pressure. That's the only

857
01:32:29,100 --> 01:32:36,180
estimate we got but even that was somehow different compared with what we

858
01:32:36,180 --> 01:32:42,220
have. Their high blood pressure is first you know meet with that definition in a

859
01:32:42,220 --> 01:32:50,860
like above 130, you know, 6.0 and then, you know, that's only, it's 80. But they also have

860
01:32:50,860 --> 01:32:57,760
definition is if you are on medication, hyperlateral medication, you also automatically be classified

861
01:32:57,760 --> 01:33:05,840
a hyperlateral pressure. So in over data, we, we did not collect detailed information about whether,

862
01:33:05,840 --> 01:33:11,500
you know, people actually on medication, because that becomes very complicated. There's a lot of,

863
01:33:12,700 --> 01:33:20,640
conditions, it's very hard to collect that information. But we tried our best to compare

864
01:33:20,640 --> 01:33:26,780
with county, unfortunately there's limited data. And then later if anyone knows there's

865
01:33:26,780 --> 01:33:34,420
other data, county sources, we can get, let us know, we will definitely look into that.

866
01:33:34,420 --> 01:33:43,240
Then the second, you mentioned about the conditions, three ways, a lot of traffic in this neighborhood.

867
01:33:43,680 --> 01:33:45,660
We totally understand that.

868
01:33:45,720 --> 01:33:49,200
Yeah, there's multi-factors underlying.

869
01:33:49,560 --> 01:33:51,260
We tried to control some.

870
01:33:52,240 --> 01:33:57,780
We did look at the traffic impact, look at the green zone,

871
01:33:57,780 --> 01:34:07,860
the, you know, based on the common definition of that green zoom measures, those things

872
01:34:07,860 --> 01:34:14,480
did not actually, you know, shoot up much significant in the analysis.

873
01:34:15,400 --> 01:34:25,300
Yeah, regarding the SNAP data, yeah, you write that is something, you know, seasonal and

874
01:34:25,300 --> 01:34:30,320
and then it will collect the current information in the neighborhood.

875
01:34:31,020 --> 01:34:35,600
But unfortunately due to the delay of the data collection,

876
01:34:35,600 --> 01:34:41,840
although because we actually finished all of our data collection back in June,

877
01:34:42,320 --> 01:34:47,660
after when they fully running, there's very little windows overlap.

878
01:34:47,660 --> 01:34:54,400
We can actually get the data which be able to transfer

879
01:34:54,400 --> 01:35:01,040
for useable format and link with our analysis.

880
01:35:01,920 --> 01:35:05,640
And that could be down in the future,

881
01:35:05,900 --> 01:35:10,580
once this data is all fully available

882
01:35:10,580 --> 01:35:13,320
and then we can match with the windows

883
01:35:14,190 --> 01:35:17,680
and to see how much overlap we can have.

884
01:35:18,080 --> 01:35:20,440
But that window is very short

885
01:35:21,350 --> 01:35:23,160
because the timeline is you know,

886
01:35:23,160 --> 01:35:33,220
they have the same number of challenges eventually. And yeah, as far as the format you mentioned,

887
01:35:33,220 --> 01:35:39,460
you know, should have executive summary in front than instead of at the end. The other one,

888
01:35:39,660 --> 01:35:47,240
yes, we can easily address that because this is not a final report per se. This is just the

889
01:35:47,240 --> 01:35:53,160
presentation of the results, final report, yes, we will have a formal executive summary

890
01:35:53,160 --> 01:36:00,660
in front so that people can see that kind of high level summary start with and then

891
01:36:00,660 --> 01:36:07,680
run them with all the way to the end of the report. That's the few points I took notes here,

892
01:36:07,680 --> 01:36:09,340
I'm pretty sure I need to something.

893
01:36:09,860 --> 01:36:11,820
Um, is there any other?

894
01:36:12,240 --> 01:36:13,180
Uh, or anything?

895
01:36:13,520 --> 01:36:16,660
Okay, so I appreciate those things.

896
01:36:16,740 --> 01:36:18,480
And that's what I said, you always are thrown.

897
01:36:18,780 --> 01:36:20,460
So I would love that also.

898
01:36:20,820 --> 01:36:23,080
If an and thank you for saying it would be at the front.

899
01:36:23,200 --> 01:36:24,180
And I think that's great.

900
01:36:24,320 --> 01:36:25,360
But the other item.

901
01:36:25,360 --> 01:36:29,340
So I think it would be great if the disclaimer is included.

902
01:36:29,900 --> 01:36:30,240
That.

903
01:36:31,000 --> 01:36:32,480
One Los Angeles.

904
01:36:33,100 --> 01:36:37,000
In comparison with the evil oil field in comparison with.

905
01:36:37,000 --> 01:36:40,060
if numerous other oil fields that are studying regularly

906
01:36:40,060 --> 01:36:43,960
have far more close-by contributing factors,

907
01:36:43,960 --> 01:36:46,340
such as LAX and the roads.

908
01:36:46,920 --> 01:36:50,860
And then two, I think it would be great if you said,

909
01:36:51,060 --> 01:36:54,760
unfortunately, that the SNAP study doesn't coincide

910
01:36:54,760 --> 01:36:56,500
so that data couldn't be included.

911
01:36:56,720 --> 01:36:58,200
Because I mean, that's straight up, it's true.

912
01:36:58,600 --> 01:37:01,900
You guys wait, we kept going, trying to have them start,

913
01:37:02,040 --> 01:37:03,720
but then they got delayed again and again

914
01:37:03,720 --> 01:37:05,400
between COVID and other staffs.

915
01:37:05,400 --> 01:37:11,000
but I think that would be a common question. If I live near there and I read this, I'd go,

916
01:37:11,120 --> 01:37:15,500
well, where about the SAP setting? We've heard that it has all this data, but I understand why

917
01:37:15,500 --> 01:37:19,960
it can't be included. So that would be great if those disclaimers were provided.

918
01:37:21,020 --> 01:37:26,940
Thank you, Liz. Frank, I know I said you were Latinx, but let me have Priya come in because I

919
01:37:26,940 --> 01:37:31,100
suspect she may be talking on this point, and then you'll be after Priya.

920
01:37:31,680 --> 01:37:38,800
Hi, good evening. It's Priya. Actually, thank you for having me. Great to see so much interest in this topic.

921
01:37:41,740 --> 01:37:46,980
As Christine mentioned, I'm from the Department of Public Health. I'm Deputy Director of the Health Promotion Bureau, and I'm an OBGYN.

922
01:37:47,960 --> 01:37:59,900
I really appreciate this work. I guess one thing, a couple things I wanted to share for just future study is like a lot of these facts can be additive on pre-term births.

923
01:37:59,900 --> 01:38:00,900
So it's like multifactorial.

924
01:38:01,080 --> 01:38:04,180
So when we say that like perhaps like

925
01:38:05,060 --> 01:38:08,940
particulate matter, like volatile compounds cause inflammation,

926
01:38:09,240 --> 01:38:11,300
right, we don't understand clearly the pathway to preterm birth.

927
01:38:11,400 --> 01:38:13,500
But that's been suggested in other studies.

928
01:38:13,700 --> 01:38:16,600
Like that's one factor for maybe spontaneous preterm birth.

929
01:38:16,660 --> 01:38:18,440
But then when I also hear that like hypertension,

930
01:38:18,760 --> 01:38:20,620
rates of hypertension may be increased downwind.

931
01:38:20,760 --> 01:38:22,500
Like we know that individuals,

932
01:38:23,600 --> 01:38:25,980
people with reproductive capacity of high blood pressure,

933
01:38:26,320 --> 01:38:28,280
prior to pregnancy are increased risk

934
01:38:28,280 --> 01:38:33,240
for like pre-eclampsia and other complications, right, that also lead to pre-term birth often

935
01:38:33,240 --> 01:38:36,860
or very common causes, more common causes that we understand of pre-term birth. So just like,

936
01:38:37,260 --> 01:38:40,940
I think it's really interesting that a lot of this could be additive in the perinatal population,

937
01:38:40,940 --> 01:38:46,700
so just wanted to point that out. And then we do have a lot of colleagues, as Christine mentioned,

938
01:38:47,600 --> 01:38:52,520
attending today from our Department of Public Health and Maternal Child and Adolescent Health Division.

939
01:38:53,200 --> 01:39:00,880
And so there are already a lot of existing programs to try to mediate this stressor on pregnancy

940
01:39:00,880 --> 01:39:01,740
and others.

941
01:39:03,200 --> 01:39:07,340
And so I just wanted to shout out, we have a home visitation program, we have a dual

942
01:39:07,340 --> 01:39:11,480
program, we have a guaranteed income pilot, we have a lot of stuff that can help to sort

943
01:39:11,480 --> 01:39:15,840
of like protect these individuals who may be at increased risk of these outcomes and those

944
01:39:15,840 --> 01:39:16,480
already exist.

945
01:39:16,880 --> 01:39:21,640
So when it is time, Christine and others to like disseminate this, I think it would be great

946
01:39:21,640 --> 01:39:26,900
to pair it with some resources. So, folks who see this information feel like they're supported

947
01:39:26,900 --> 01:39:29,980
in other ways. Just wanted to add that. Thanks for the time.

948
01:39:30,800 --> 01:39:33,680
Thank you very much. Frank and then Kelly and then Melanie.

949
01:39:38,550 --> 01:39:49,030
Yeah, I had to. I was just curious if this study could have a side-by-side comparison to the

950
01:39:49,710 --> 01:39:55,190
previous city to find out if there's been a change for the good or for the better.

951
01:39:58,490 --> 01:40:05,310
That concern me that no reference has been made to the previous city.

952
01:40:06,430 --> 01:40:16,530
The other point I was going to bring up is that I know months ago they made reference

953
01:40:16,530 --> 01:40:27,730
to one of our meetings that we should not add an opinion in this to seeing bias.

954
01:40:28,190 --> 01:40:34,570
And I think if Joe is going to do that, then fine, let him, you know, oil company has

955
01:40:34,570 --> 01:40:36,290
that, they have that right to do that.

956
01:40:36,530 --> 01:40:44,910
But I truly feel that it should be standalone, should not be included in this study, if the

957
01:40:44,910 --> 01:40:56,970
a scientific study. I also questioned, uh, I did, I went back and I had a friend with

958
01:40:56,970 --> 01:41:04,730
the FAA. He dug for me and statistically we looked at the wind conditions. We went

959
01:41:04,730 --> 01:41:14,790
back 30 years of data. He looked at. And we came to the conclusion that the wind

960
01:41:14,790 --> 01:41:24,590
has actually increased the actual relative wind has increased. We also looked at

961
01:41:24,590 --> 01:41:33,330
what occurs at night and the requirement of the reverse direction of landing. At

962
01:41:33,330 --> 01:41:39,370
night we land on six and seven at LAX. So we're over the ocean because of the the

963
01:41:39,370 --> 01:41:47,650
The winds are changing and we talked about, this particular gentleman is well-qualified.

964
01:41:47,830 --> 01:41:59,150
We talked about how the heat has changed in our region and in the morning the winds change

965
01:41:59,150 --> 01:42:05,190
a different direction for a purpose and at night it goes back out to the sea so as the

966
01:42:05,190 --> 01:42:05,790
ground cools.

967
01:42:05,790 --> 01:42:17,550
So I guess you're paying, you're putting a lot of emphasis on the win, but do you truly understand

968
01:42:17,550 --> 01:42:25,250
what the win is truly doing? And that's where it leaves me with a question mark. So those are my

969
01:42:25,250 --> 01:42:31,370
two points. I think you did a great job doc, you know, considering everything that you went through.

970
01:42:31,370 --> 01:42:34,930
So, I tip my hand to the graduations.

971
01:42:35,770 --> 01:42:36,830
Thank you, Frank.

972
01:42:37,530 --> 01:42:39,410
Let me quickly address the first one.

973
01:42:39,630 --> 01:42:41,190
Maybe a doctor cushion.

974
01:42:41,330 --> 01:42:46,070
If you can address the second one in some way.

975
01:42:46,590 --> 01:42:49,770
Yeah, the first one we, yeah, that's a good point

976
01:42:49,770 --> 01:42:51,550
compared with a prior study.

977
01:42:51,710 --> 01:42:54,430
We did actually reference the prior study

978
01:42:54,430 --> 01:42:57,530
when we designed a FOXM other survey instrument.

979
01:42:58,050 --> 01:43:00,430
We did look at the prior study.

980
01:43:00,430 --> 01:43:07,390
the instrument that happened in these areas.

981
01:43:07,670 --> 01:43:11,270
We want trying to design a way eventually can compare.

982
01:43:11,950 --> 01:43:13,730
And then the second one, when we do analysis,

983
01:43:14,710 --> 01:43:17,750
we try our best to be objective.

984
01:43:18,590 --> 01:43:22,810
We just stay with the science and to our own analysis.

985
01:43:23,150 --> 01:43:25,450
We do not want to be influenced

986
01:43:25,450 --> 01:43:32,850
because we see the prior study results one way that our analysis sure in certain

987
01:43:32,850 --> 01:43:41,450
way so we try to be independent objective but your point is you know link the

988
01:43:41,450 --> 01:43:46,310
results eventually of our study with a player yeah that's a very good point

989
01:43:46,310 --> 01:43:52,750
I think that you know sure be done once this is complete you know we can link that

990
01:43:52,750 --> 01:44:05,210
But rather not, you know, before to study, then we say, oh, this is this direction, then we should, you know, pay attention or something because that shouldn't be, you know, in

991
01:44:06,390 --> 01:44:10,390
potentially being influenced in some way, yeah.

992
01:44:10,830 --> 01:44:11,610
Thanks for that.

993
01:44:11,610 --> 01:44:19,190
I just want to add, I'm sorry Doc, but you got to understand if, if at certain hours

994
01:44:19,190 --> 01:44:23,710
the, the, are we really truly talking about up and downwind?

995
01:44:24,690 --> 01:44:28,150
I mean, you're looking at the relative time of the day, but what happens at night?

996
01:44:28,590 --> 01:44:30,550
What happens at, at six o'clock?

997
01:44:31,170 --> 01:44:37,670
I'm telling you, winds are not at 240 at 10, they're not, they're not.

998
01:44:38,590 --> 01:44:45,790
I've got 32,000 flying hours. I've got a lot of time. And believe me coming in LAX, they're

999
01:44:45,790 --> 01:44:54,490
not 240 at 10. Sometimes there's 067 at 5. They're all, they're, they're very dynamic.

1000
01:44:57,270 --> 01:44:58,850
Yeah, I was going to be about that.

1001
01:44:58,890 --> 01:44:59,970
That's uh, that was your...

1002
01:45:00,000 --> 01:45:29,860
Yeah, no problem. Yeah, I agree. That's when direction is not always consistent in one way or the other. You know, it's a change varies from time to time. And we use the prevailing direction and of cushion, public comment, there'll be more on this. Yeah, what we use was actually the prevailing direction over all hours of the day, over five years. So,

1003
01:45:29,860 --> 01:45:36,880
So it's very much, you know, does not get into all the nuance that you're bringing up Frank from all of your experience.

1004
01:45:38,940 --> 01:45:58,740
So, you know, what we were attempting to do was just to, it's an imperfect measure, you know, where we don't have knew we're not differentiating between people that live within the, quote unquote,

1005
01:45:58,740 --> 01:46:04,140
downwind population, what we call downwind, you know, some are more downwind than

1006
01:46:04,140 --> 01:46:08,940
others, some are downwind at night, and maybe they're all upwinded in the day.

1007
01:46:09,120 --> 01:46:16,140
You know what I mean? Like we did not vary, we did not try to be more precise on

1008
01:46:16,140 --> 01:46:21,640
an individual level basis. I was kind of beyond the scope of what we could do, but

1009
01:46:21,640 --> 01:46:27,040
we're more trying to get out like over a lifetime, you know, if you live in this

1010
01:46:27,040 --> 01:46:34,820
home, would the wind more often be blowing towards you or away from you from that well field?

1011
01:46:34,820 --> 01:46:40,060
That's kind of what we were trying to get at because we don't have, you know,

1012
01:46:40,160 --> 01:46:44,620
it'd be different if we had, for example, repeated lung functions on the same person,

1013
01:46:44,840 --> 01:46:50,680
like a longitudinal study design, like Dr. Lou alluded to before, you know, then you could look

1014
01:46:50,680 --> 01:46:54,020
on this day when we took your long function measurement,

1015
01:46:54,320 --> 01:46:58,540
how was the wind that day versus the other day we took your measure.

1016
01:46:58,700 --> 01:46:59,320
We don't have that.

1017
01:46:59,440 --> 01:47:01,440
We only have a single snapshot in time.

1018
01:47:02,420 --> 01:47:05,120
So to us it made the most sense to kind of compare

1019
01:47:05,120 --> 01:47:07,580
to the prevailing wind direction,

1020
01:47:08,080 --> 01:47:12,680
knowing that it's imperfect and it simplifies a lot of things.

1021
01:47:13,920 --> 01:47:14,880
I don't know if that helps.

1022
01:47:15,740 --> 01:47:17,960
Okay. Thank you. Kelly.

1023
01:47:18,840 --> 01:47:27,920
Hi, thank you everyone for this presentation. It's really fascinating to see and I see all of the rigor that went into this science.

1024
01:47:28,620 --> 01:47:39,340
I do have two questions, but I want to just try to restate a couple of the things that I'd bird because I do feel like the presenters were quite clear about it.

1025
01:47:39,920 --> 01:47:54,020
One, yes, this is associated data, so these are correlations and, you know, Dr. Lee repeatedly stated, we can't rule other factors out, which that just seems like important to lift up because we've always known this whole time.

1026
01:47:54,120 --> 01:48:01,020
In fact, we've discussed it in the chat many times that we wish we could rule out LA based air pollution.

1027
01:48:01,020 --> 01:48:06,600
And that we, you know, I think I remember asking a question about I wish there was an

1028
01:48:06,600 --> 01:48:12,400
equivalent population in a valley just like ours with the same exposures to drilling

1029
01:48:12,400 --> 01:48:18,260
or refineries or blah, blah, blah without all the traffic, right? And there just is no such thing,

1030
01:48:18,380 --> 01:48:27,200
but that the downwind upwind factors did actually make it much stronger. So I just really appreciate

1031
01:48:27,200 --> 01:48:34,520
That particular aspect of the study, which in the beginning I don't think I understood was going to be an option.

1032
01:48:35,440 --> 01:48:40,980
I also just wanted to restate that you know that what I'm understanding is that these measures.

1033
01:48:41,520 --> 01:48:47,400
Of course the measures, but the stratifications in particular so you just spoke to a vector pushing in terms of the.

1034
01:48:47,740 --> 01:48:53,400
It's like a summative number. It's the best we can do in terms of the prevailing win.

1035
01:48:53,400 --> 01:48:59,100
but it's it's not just the best we can do. It's based on consensus from experts in the field,

1036
01:48:59,300 --> 01:49:04,860
you know, which is that is the nature of science and it's never static. It's always iterating.

1037
01:49:05,480 --> 01:49:11,340
And so it would hopefully be refined over time, but for this moment in time, it's considered an

1038
01:49:11,340 --> 01:49:21,380
accepted way to to make a delineation based on upwind and downwind based on the experts in the field.

1039
01:49:21,380 --> 01:49:27,400
I also appreciated Dr. Pushing talking about epidemiology results being strengthened when

1040
01:49:27,400 --> 01:49:32,720
we continue to study with similar questions, sometimes in the same population, sometimes

1041
01:49:32,720 --> 01:49:37,960
in similar populations, ideally in different populations with similar exposures, so you

1042
01:49:37,960 --> 01:49:44,040
can, you know, with enough repeated findings, you can be more and more certain.

1043
01:49:44,440 --> 01:49:50,540
Although you all may know that we continue to use the word theory in science way after the

1044
01:49:50,540 --> 01:49:53,520
time when we're pretty sure it's causational.

1045
01:49:55,080 --> 01:50:00,200
So, you know, and I'm just so grateful for these results, and I'm pretty confident that

1046
01:50:00,200 --> 01:50:05,480
no one here would advocate that we write up or ignore statistically significant findings

1047
01:50:05,480 --> 01:50:10,820
when I was thinking about, like, trying to correlate this return data set with the SNAPs,

1048
01:50:11,340 --> 01:50:15,660
data set, and, you know, my understanding is that rigorous and specifically statistically

1049
01:50:15,660 --> 01:50:21,400
with powerful findings really have to come with a priori design, which means you come with

1050
01:50:21,400 --> 01:50:27,140
a hypothesis, you make a guess, you make a claim, the hypothesis was presented at the beginning

1051
01:50:27,140 --> 01:50:33,640
of this presentation. So for us to tie it to the SNAP study, we would have had to have a hypothesis

1052
01:50:33,640 --> 01:50:41,120
at the start of the study, a priori, about specific compounds being studied in the SNAP study and

1053
01:50:41,120 --> 01:50:47,720
our prediction about their effect or correlation to outcomes for three-term

1054
01:50:47,720 --> 01:50:53,180
birth. We did not do that. So that study could still be done, but I just want to

1055
01:50:53,180 --> 01:50:58,440
make sure I point out that's my understanding of the, so I don't find that the

1056
01:50:58,440 --> 01:51:02,520
SNAPs contradicts necessarily this study because if you go back active or

1057
01:51:02,520 --> 01:51:07,840
fact, it's considered hedging your bets in the science world. And so my two

1058
01:51:07,840 --> 01:51:13,560
questions are will this vote appear a review which I think was something some other folks were

1059
01:51:13,560 --> 01:51:18,380
were getting at it's not clear to me that that's happening and then you referred to some other

1060
01:51:18,380 --> 01:51:25,220
studies but I didn't catch them Dr. Lou I thought you said maybe in Texas and maybe another state and

1061
01:51:25,220 --> 01:51:29,500
if you wouldn't mind putting them in the chat I'd like to look them up I appreciate it thank you all

1062
01:51:29,500 --> 01:51:33,670
so much okay thank you

1063
01:51:39,940 --> 01:51:45,660
regarding peer review I can talk with our department to see

1064
01:51:45,660 --> 01:51:53,620
if there's a way they'd like to do that and UCLA is also free to submit for publication.

1065
01:51:58,930 --> 01:52:05,190
Okay. Thank you. Melody in front of some very nice fall foliage.

1066
01:52:06,770 --> 01:52:11,230
Yes, hi everybody. Thank you. Yes, just bring in in the feeling of the fall.

1067
01:52:14,630 --> 01:52:19,970
Kelly, I wanted to thank you for your comments and I wanted to

1068
01:52:20,430 --> 01:52:26,770
build off that a little bit, but one of the things that Kelly kind of points out is that,

1069
01:52:26,930 --> 01:52:36,230
you know, this study is a little bit of a snapshot in time in terms of what it is and I think

1070
01:52:36,230 --> 01:52:48,470
we need to recognize that. I know that there's a lot of comments coming in tonight that, you know,

1071
01:52:48,470 --> 01:52:56,430
know, and to be incorporated into the study. But I question whether, whether that needs

1072
01:52:56,430 --> 01:53:02,310
to happen now or whether that's part of the subsequent work. And with that, I just want

1073
01:53:02,310 --> 01:53:08,290
to kind of go back a little bit to, you know, the whole purpose of doing the study is because

1074
01:53:08,290 --> 01:53:15,570
there's a requirement under the settlement agreement. And, you know, I'm looking at the

1075
01:53:15,570 --> 01:53:20,630
And technically, there's supposed to be a study done every five years.

1076
01:53:20,930 --> 01:53:27,950
So with the last study being produced in 2012, you know, we should right now be putting out the third study.

1077
01:53:28,090 --> 01:53:32,250
And we are just trying to get the second study out the door.

1078
01:53:33,050 --> 01:53:42,170
So the question then comes like, well, what becomes the timing of the third study, you know, are we five years from this?

1079
01:53:42,170 --> 01:53:48,050
should we already into that five-year cycle, you know, how do we do that? And I bring that up

1080
01:53:48,050 --> 01:53:53,570
because I think, you know, the CAP maybe needs to think about and provide some guidance on

1081
01:53:54,150 --> 01:53:59,910
what is the role of this particular study in terms of meeting the requirements of putting

1082
01:53:59,910 --> 01:54:09,450
information out the door and what should be, you know, now's the time to start talking about the

1083
01:54:09,450 --> 01:54:16,470
scope of the subsequent study, what is appropriate from this in terms of our comments that starts

1084
01:54:16,470 --> 01:54:22,550
to get rolled into the next step. So, you know, that's kind of a question or something to

1085
01:54:22,550 --> 01:54:32,610
think about. And going, you know, number of comments have been made about, you know, what are

1086
01:54:32,610 --> 01:54:39,830
the conclusions of the study, and it may be that, as has been pointed out, there are

1087
01:54:39,830 --> 01:54:45,750
no, you know, we can't say causality for sure at this point of time, and that is just something

1088
01:54:45,750 --> 01:54:54,110
that has to be built upon, but the purpose of the study is to put out information, and

1089
01:54:55,610 --> 01:55:00,850
people will use the study just like they've used the other studies, like the one that Jill

1090
01:55:00,850 --> 01:55:08,550
Johnston prepared, you know, I know there's a we reviewed that even as part of this group

1091
01:55:08,550 --> 01:55:15,470
thinking about the scope for this current study and, you know, there were potential aspects

1092
01:55:15,470 --> 01:55:20,170
of that that didn't apply to us and we picked and chooseed and the same thing with the studies

1093
01:55:20,170 --> 01:55:25,630
from Texas and Colorado. We, you know, we did consider those and we picked and chooseed and, you

1094
01:55:25,630 --> 01:55:29,810
know, some of the reasons like, for example, the Colorado studies didn't apply because they

1095
01:55:29,810 --> 01:55:36,570
were in a rural environment versus, you know, our study being in a very urban environment.

1096
01:55:39,330 --> 01:55:47,470
And related to that and not to downplay, Liz's very, you know, important point that it's

1097
01:55:49,070 --> 01:55:55,510
really important to try and figure out how we filter out what is a contributor potentially

1098
01:55:55,510 --> 01:56:02,810
of the oil field versus these other surrounding factors and, you know, Liz, you made a statement

1099
01:56:02,810 --> 01:56:09,530
that, you know, kind of maybe I misinterpreted it, but kind of implying that, you know, the

1100
01:56:09,530 --> 01:56:14,870
angle of oil field has all of these other factors that don't apply to other oil fields.

1101
01:56:15,070 --> 01:56:19,250
But, you know, other oil fields in LA County have other aspects.

1102
01:56:19,510 --> 01:56:23,790
So for example, when we start looking at health concerns in the Wilmington area, we're

1103
01:56:23,790 --> 01:56:32,610
looking at a highly industrialized area relative to Baldwin Hills and influences of the

1104
01:56:32,610 --> 01:56:32,930
port.

1105
01:56:32,930 --> 01:56:39,210
When we start looking at oil fields in the Rose Hills area, we're dealing with where some

1106
01:56:39,210 --> 01:56:41,990
of the, you know, landfill issues are.

1107
01:56:42,410 --> 01:56:48,950
So there's always going to be something and I don't know if that's, I don't know how we,

1108
01:56:48,950 --> 01:57:11,810
you know, correct that aspect of it, it might be kind of a lost leader path to start going down and trying to look at all the nuances of all the other oil fields and comparing them because I'm not sure again going back to the settlement agreement that that was the intention for that.

1109
01:57:11,810 --> 01:57:18,610
And looking at the settlement agreement, it does say that the county when preparing this

1110
01:57:18,610 --> 01:57:24,150
will review other agencies' reports regarding air quality water, seismic data, and where

1111
01:57:24,150 --> 01:57:27,770
feasible, where feasible in its assessment.

1112
01:57:28,050 --> 01:57:36,890
So, you know, the points that were raised about comparing to mates, trying to incorporate

1113
01:57:36,890 --> 01:57:44,530
great snaps if there's information, usable information available at this time, etc. would

1114
01:57:44,530 --> 01:57:49,350
be appropriate to incorporate into the report. And lastly, again, looking at the settlement

1115
01:57:49,350 --> 01:57:58,910
agreement, it says that once this report is presented, that the county shall consider

1116
01:57:58,910 --> 01:58:05,030
reasonable comments by the cap and the health working group. So I don't know what that means

1117
01:58:05,030 --> 01:58:12,630
in terms of, you know, a comment and response type of approach to this, but I just wanted

1118
01:58:12,630 --> 01:58:17,370
to bring those to everybody's attention and just kind of throw out there to the cap.

1119
01:58:17,690 --> 01:58:25,110
Like, what is our intention of the study, this information, as it going to be, I don't believe

1120
01:58:25,110 --> 01:58:30,270
that it's intended to be used for any rulemaking that's currently going on with the CSD.

1121
01:58:31,070 --> 01:58:36,670
It's just a piece of information that's out there for now, and the concerns that we're

1122
01:58:36,670 --> 01:58:43,310
raising, if they can't be addressed or aren't part of the scope of this current study,

1123
01:58:43,510 --> 01:58:51,910
then we ought to start making a tick list and a schedule for the third study, which technically

1124
01:58:51,910 --> 01:58:54,530
would have been due now, except for the delays.

1125
01:58:55,430 --> 01:58:57,490
So that's my long rambling implement.

1126
01:58:57,990 --> 01:58:58,590
Thank you, Melanie.

1127
01:58:58,590 --> 01:59:10,850
We've that's you've taken us to our eight o'clock ending time, but I would like to give Christine or either the doctors a chance to conclude if you have any brief concluding comments.

1128
01:59:12,670 --> 01:59:34,010
Yeah, very quick. Thanks everyone for you, stimulate and help for comments and feedback to our team. Yeah, we were, you know, taking into account all those comments saved and incorporate those we can in a final report process. Thank you, everyone.

1129
01:59:34,790 --> 01:59:47,470
Thank you. Christine, anything that you have to say, okay, well thank you. Your report is very impressive and the presentation was was very helpful and I think we all really appreciate you're being with us tonight.

1130
01:59:48,430 --> 01:59:59,970
Ed, you're I think we're going to pass on all the regional planning thing, but I do want to give Joe at least a brief chance to do whatever you want to do operators report that's fine, but I assume you should at least invite us.

1131
02:00:00,000 --> 02:00:27,820
We're off to the home list and so forth. So I want to thank all those who help participate in that. Our annual meeting and I'll say picnic is Saturday starts at 12 noon, ends at two at our office area 5640 South Fairfax Avenue. Everybody's invited starts at 12 noon, ends at two. There's going to be pumpkin pumpkins to give away as well. I call it street tacos, besides their real good stuff.

1132
02:00:30,140 --> 02:00:32,040
And I guess that's a death for now.

1133
02:00:32,200 --> 02:00:34,840
I can we can cover some of the other stuff in the next time around.

1134
02:00:35,720 --> 02:00:36,560
Thank you.

1135
02:00:36,940 --> 02:00:40,680
Get your anything that you want to say in before next month.

1136
02:00:42,020 --> 02:00:43,940
No, just the next meeting.

1137
02:00:44,140 --> 02:00:45,660
We have falls on on Thanksgiving.

1138
02:00:45,720 --> 02:00:49,040
So just keep in mind that we will now have a meeting in November.

1139
02:00:50,520 --> 02:00:51,520
So that's right.

1140
02:00:51,660 --> 02:00:54,560
So we need to have the date of the next meeting, which is.

1141
02:00:54,680 --> 02:00:55,460
Is it on here?

1142
02:00:55,800 --> 02:00:56,200
Yes.

1143
02:00:56,200 --> 02:01:04,780
Yes, it's usually the second Thursday. December 12th is our next meeting, so we get six weeks before we return to see each other again.

1144
02:01:05,720 --> 02:01:06,860
Paul's hand is raised.

1145
02:01:07,880 --> 02:01:08,420
I'm sorry, what?

1146
02:01:08,940 --> 02:01:10,180
Paul's hand is raised.

1147
02:01:11,380 --> 02:01:11,620
Paul.

1148
02:01:12,560 --> 02:01:13,420
Okay, Paul.

1149
02:01:15,820 --> 02:01:20,240
Yeah, I just wanted to follow up those questions with regard to the, I think it's T3.

1150
02:01:20,240 --> 02:01:26,940
We produce water tank that overflowed with regard to its age and or any other specific

1151
02:01:26,940 --> 02:01:30,060
events that led to releases.

1152
02:01:30,920 --> 02:01:31,200
Okay.

1153
02:01:31,340 --> 02:01:32,980
We're going to have to do that next meeting.

1154
02:01:33,520 --> 02:01:33,920
Why is that?

1155
02:01:34,080 --> 02:01:34,440
Joe's there.

1156
02:01:35,420 --> 02:01:37,660
Because it's past our ending time.

1157
02:01:38,860 --> 02:01:39,220
Really?

1158
02:01:40,080 --> 02:01:40,540
Yes.

1159
02:01:41,340 --> 02:01:43,160
That's what we said two hours ago.

1160
02:01:43,160 --> 02:01:45,280
That's probably the most ridiculous thing I've ever heard.

1161
02:01:45,460 --> 02:01:46,860
When we start, well, okay.

1162
02:01:46,900 --> 02:01:48,180
It's going to take them two or three minutes.

1163
02:01:48,620 --> 02:01:49,360
Thank you for that.

1164
02:01:49,360 --> 02:01:53,720
that. You're welcome. Thank you for ignoring my questions.

1165
02:01:56,490 --> 02:01:59,990
Okay. Okay. See hopefully many

1166
02:01:59,990 --> 02:02:08,050
of you at the Halloween bash at the oil field and see the rest of you on December 12th.

1167
02:02:08,130 --> 02:02:13,390
Thank you all and thank you again to UCLA and the Department of Health who really appreciate

1168
02:02:13,390 --> 02:02:16,510
the report. Thank you and everyone have a happy Thanksgiving.

1169
02:02:16,510 --> 02:02:19,190
Bye bye.