[0:09] Aye, Christine. I haven't seen you in ages. Nice to see you. On a long time. Right.

[0:24] 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.

[0:41] Yeah, we had COVID in there.

[0:45] But we kept doing, we kept on. We just changed the volume.

[0:55] 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?

[1:11] I don't believe that's been seven out yet.

[1:13] Okay. In

[1:17] terms of time management, do we have a lot of time?

[1:23] because this is very important, but we do have other things to go through. So I would

[1:29] I'd like someone maybe you Christine to act as a timekeeper because we really need to get the

[1:33] conclusions done and spend time for questions if you see what I'm saying. They had said before you

[1:40] came on Charles, there was going to be 30 to 40 minutes and then questions after that. That's just

[1:46] introduction. That's a scene for the option.

[1:49] The entire meeting, the presentation of the health assessment environmental justice study.

[2:00] Okay. Fair enough. Thank you.

[2:03] We'll prioritize the presentation.

[2:06] And if we run out of time by 8 p.m.

[2:08] It will just move any other rest of the items for next meeting.

[2:13] If you go to 8 p.m. with questions.

[2:18] If John and the rest of the group is okay with that, we have to eat with this form.

[2:26] Yeah, if we need that much time, that's fine.

[2:29] If we can finish up and have time for the rest of our agenda, that would be better, but this is important.

[2:35] Do we don't do this very often?

[2:37] Do we have the other UCLA doctor or are we still waiting?

[2:43] I think Dr. Lou is still having a problem getting in.

[2:48] Do you see anything Edgar?

[2:51] Okay.

[2:51] Do you see it now?

[2:52] Okay.

[2:53] Yeah, I'm getting in.

[2:53] Let me see.

[2:54] But I can see your name here.

[2:57] Is he on my phone?

[2:59] Jason?

[3:00] Oh, don't you say that he's joining now?

[3:03] Oh, there is.

[3:04] There he is.

[3:05] Yep.

[3:06] Okay.

[3:06] All right.

[3:07] Okay.

[3:08] So you've got everyone that you need, Christine?

[3:11] I think so, yes.

[3:12] Okay, so let's get started. Welcome everyone to the, I don't know what month this is,

[3:18] October, I guess, meeting of the cap. Glad to have you all here and we're going to spend much

[3:25] of tonight's meeting talking about the health study by UCLA and Christine is going to start it off,

[3:32] Christine. Thank you. So welcome everybody and welcome to our UCLA team.

[3:43] 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.

[4:00] And I want to thank the UCLA team for their diligent work.

[4:08] They pulled out all the stops to make this study, everything it could possibly be.

[4:13] They squeezed every tiny bit of information out of it that they could.

[4:19] And really did a great job.

[4:22] I feel for this community, they took the community's concerns very, very seriously

[4:27] and addressed pretty much everyone and I also want to thank and acknowledge the chat members

[4:37] and these are the folks that were selected by this group to represent the Baldwin Hills community.

[4:45] This group of people, some of you are on here, you might want to raise your hand so people know who

[4:52] 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,

[5:21] You know, let them know how important this study was to this community.

[5:26] 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.

[5:36] And, you know, listening to all these very complicated results and sifting through them.

[5:41] 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.

[5:53] So now let me turn it over to Dr. Henry Liu and Dr. Lara Cushing.

[6:00] And if one of you can, I don't know who's sharing slides, but go ahead and do your presentation.

[6:09] And if you can feel free also to introduce your team.

[6:13] Okay.

[6:13] Thank you.

[6:14] Chris, Christine.

[6:16] Good evening, everyone.

[6:18] I'm the Henry Liu, a professor and the Chair of the Public Public Health Program at the UCRA.

[6:26] And I honor to serve at Principal Investigator for this very important project.

[6:31] So, this evening with me is Dr. Kupche, who is a software professor,

[6:38] user, and environment health specialist. Dr. Jason Shen, who is a research

[6:46] faculty, and Lee Nui Zhou, who is a PhD candidate and analyst on the project.

[6:52] And the each of them will take a chance. They can introduce themselves more to

[6:56] sit time. So before our presentation, I'd like to say a few sentences we really like to thank.

[7:05] For the strong support we received from the community during the entire conduct of this project,

[7:13] including chat members, a number of them in this chat member as well, and even this large chat

[7:22] members and Los Angeles County project officers and the county agent members and particularly

[7:32] the committee members who participated in this study who contribute their experience

[7:39] and to share with their experience and contribute to this aggregated data.

[7:47] So Dr. Kushen, I will present and for us will answer questions.

[7:56] So just kind of quite a lot, but we will try to be as efficient as possible.

[8:02] Slide up.

[8:08] Great.

[8:10] Next slide please.

[8:14] Yeah, so the most important hypothesis for this study

[8:21] He is still looking at whether residents living near the oil field is actually have the

[8:31] association, you know, the near to the Ingu oil field, associated with a higher risk of adverse

[8:40] health outcome, which including a number of them here, pictures you can show the birth

[8:53] So,

[8:56] the population included in this particular study are those who live within 1.5 miles

[9:04] of the angle of all your field, or LF boundaries, you know, this diagram shows the circle inside

[9:13] those included conceptually and then outside it beyond and not included.

[9:21] Next slide please.

[9:25] So the wind direction is another important factor we are

[9:29] looking at. So for wind direction based on the prevailing wind direction we

[9:35] define the presence as downwind and upwind two groups within, of course, is 1.5 mile radius.

[9:51] Next slide please. And the two main goals of this study. First is we analyzed existing

[10:01] per record, basically across a long span of 20 years from 2000 to 2019, cover about 40,000

[10:13] light birth within this 1.5 miles radius. Then the second part of the analysis, including

[10:24] in fresh data collection is actually conduct survey and biometric data collection in the community.

[10:34] We successfully included more than 600 was the target we actually we put more than 600

[10:45] With a help, as mentioned earlier from the community, from July 2023 to June 2024, who live within that boundary.

[10:59] Next slide please.

[11:02] So the structure for presentation will be first go through the birth outcome, study and analysis results,

[11:12] and then the health survey biometric data collection,

[11:15] then we have overall conclusion limitations

[11:18] and then the implications take home message.

[11:23] So now I'm going to turn this party to you,

[11:28] Dr. Kushin, who will take lead on the birth outcome

[11:34] part of the presentation. Dr. Kushin.

[11:38] Thank you, Dr. Liu.

[11:39] and hello everyone. I'm Laura Cushing. So this first portion of the study, we used administrative

[11:50] birth records. So secondary data over a 20-year time frame. As Dr. Lou mentioned, to focus on two

[11:57] primary outcomes. These two outcomes can impact the survival and health of babies and they can lead to

[12:05] long-term respiratory, cognitive, and other health problems.

[12:09] One is preterm birth that's being born too soon before 37 completed weeks of pregnancy.

[12:18] And the second is called small for gestational age, which we're going to abbreviate as SGA

[12:23] throughout the presentation.

[12:25] And this is when a baby's born too small, meaning below the 10th percentile for their week

[12:33] of gestation.

[12:34] and it's a measure of restricted growth in utero.

[12:38] Next slide.

[12:41] So when we look at the rates of these two outcomes

[12:44] in the community living within one and a half miles

[12:47] of the oil field, we see that they're slightly higher

[12:50] than LA County overall over the same time period.

[12:56] And these P values here, so these differences

[13:00] We're statistically significant, meaning that they're unlikely to have been due to chance.

[13:09] So you'll see that as well throughout the presentation.

[13:12] We star with a little asterisk differences that where we had statistical significance

[13:20] or pretty good confidence that the differences we were seeing were not just due to random

[13:27] chance.

[13:28] Next slide.

[13:31] We also saw that rates of these two adverse birth outcomes did not vary substantially

[13:37] with distance to the oil field.

[13:39] So if we just look within that 1.5 mile radius and further sets up the population of babies

[13:46] born within half a mile, half a mile to one mile or one mile to one and a half miles,

[13:55] The rates of preterm birth in small-for-just-statial age are not that different from each other.

[14:02] It was the intermediate group that had the worst outcomes, so the highest rate of preterm

[14:07] births in small-for-just-stational age, but we could not rule out that those differences

[14:12] were not due to chance.

[14:14] Next slide.

[14:17] When we looked at downwind versus upwind of the oil field, we did see that preterm birth

[14:23] rates were higher downwind as compared to upwind and that the difference was

[15:00] 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.

[15:27] If we look by,

[15:31] By race or ethnicity, we also see disparities in the rates of pre-term birth.

[15:37] So overall, within this community within 1.5 miles of the Ingwood oil field, non-Hispanic

[15:45] white parents had the lowest rates of pre-term births.

[15:48] Rates of pre-term birth decreased among Asian and Latino parents with distance to the oil

[15:55] field. So if you look at the two rows labeled Asian-American and Hispanic or Latinx, you see the

[16:02] percentages go down as you move farther from the oil field. But at every distance racial disparities

[16:10] remain and they were largest when comparing black or African-American birth parents with white birth

[16:18] parents. So you see, you know, rates 9 to 10 percent among the black community versus

[16:24] closer to 5 or 6 percent among the white community. Next slide.

[16:31] So what could cause these higher

[16:33] rates of preterm birth downwind and closer to the oil field? Well, one thing is harmful exposures

[16:39] emanating from the oil field. We know, for example, that air pollution has been linked to preterm

[16:45] birth in other contexts. But it could also just be a coincidence, right? It could be

[16:51] that residents living downwind in these in this area have higher rates of other risk factors

[16:57] for pre-term births such as being older or not getting enough pre-natal care. Next slide.

[17:05] So what we do is we apply statistical modeling techniques that allow us to kind of control for

[17:11] these other risk factors and better isolate the possible effects of the oil field.

[17:17] And this helps rule out alternative explanations for any associations that we see

[17:22] between living near and down one of the oil field and pre-term births.

[17:27] So if you're a visual person, try to represent this in a schematic here.

[17:33] So, you know, we're really interested in the relationship between residents near the oil field

[17:39] in our measures of health, but there is these other risk factors in the background that we are

[17:44] controlling for through this statistical technique. Next slide.

[17:52] So when we do that, we still see

[17:55] that living down one of the oil fields is associated with a higher likelihood of pre-traffers.

[18:00] I'm going to walk through this graph because we're going to show a few of these. So what's shown on

[18:06] this graph in the vertical red dotted line is what's called a null or the value of one

[18:17] and the diamonds on the chart are our measures of association also known as odds ratios.

[18:25] So when those diamonds are to the right or above one it indicates an association between living

[18:34] downwind and the outcome of preterm births. If they're to the left of the red line, it indicates

[18:40] a negative association, but that's not shown on this particular chart. And the error bars,

[18:48] the horizontal lines, those give you a sense of our degree of certainty about this association.

[18:56] So we feel more confident that the association is real when those error bars do not cross the red

[19:03] dotted line. So the two highlighted effect estimates here, neither of those error bars

[19:11] crossed the dotted line, so we have more confidence that those associations are not

[19:15] due to chance. And in particular, the lower highlighted effect estimate, the way we

[19:23] interpret that, that's for the group living within half a mile, is that the odds of preterm

[19:29] birth was 56% higher for that group living within half a mile and downwind compared to those living

[19:36] within half a mile and upwind. So the same thing we saw at the table but now we're accounting for

[19:44] age whether it was the person's first baby or second baby whether they got prenatal care and how

[19:51] much their level of education etc. All the things we could measure about them are accounted for in

[19:59] this estimate. Next slide. I'm going to wave my arms because that's how I give my

[20:06] lights to turn back on in my office. That's not working. Okay, so in some communities

[20:13] living within one and a half miles in the oil field had slightly worse birth outcomes

[20:18] than in LA County as a whole. Number two, residents among residents living within half a mile

[20:25] of the oil field. We saw that living downwind was associated with a higher likelihood of preterm

[20:30] birth. And the association was unlikely to do the chance and not explained by the other risk

[20:37] factors we could measure like age and prenatal care or the amount of traffic near a person's home.

[20:43] And three, we saw no evidence that living near or downwind of the oil field was associated with

[20:48] fetal growth, that other measure that I showed you of small for gestational age. We did not see

[20:55] any associations there. So I'll turn it back to Dr. Liu.

[21:02] Next slide.

[21:11] Your mute is Henry.

[21:15] Sorry about that. Thanks Dr. Kuchen. Now let's move on to the resident

[21:21] health survey and biometric data collection and analysis. Next slide please.

[21:29] So for how we

[21:31] we recruit the recruit of the started present and we basically use two projects. Why is the so called address based random selection?

[21:44] That's origin design and by complex survey design, we identify certain potential addresses that may all the survey.

[21:55] Because there's some challenges going with base approach, the paces slower than with

[22:03] packet.

[22:04] So we also used so-called convenience sampling, which actually we, with booths on the ground

[22:11] and into the community and the setup recruitment like booths and then recruit on site in

[22:23] community centers, YMCA's, libraries across quite a number of them. Of course, when we select,

[22:32] we try to make this as uniformities reveal across the community as possible.

[22:40] And then who could participate in those who live in within 1.5 miles of the oil fence?

[22:48] And per household, we will only allow one member to participate to avoid dependence of the data to reduce the quality of the data.

[22:59] So what we measure is from biometric, we measure blood pressure and lung functions.

[23:06] And then for the survey, we recruit some background information and then the tier self reported health symptoms and chronic health conditions.

[23:20] Next slide.

[23:21] So here is the results of the sample we recruited compared with actually community statistics,

[23:37] the demographic distribution. You can see from here, this is the second column is from the survey,

[23:52] statistics, specifically for this 1.5 miles radius. And from days you compare those point

[24:02] as to make, you can see we saw a higher response rate from white and the college educated residents.

[24:11] And you can see the difference. 44% if you look at the community that's only about 25.7%,

[24:20] in this area. And then from the lower side, we saw lower response from Latino residents.

[24:37] And also the non-college educated residents, particularly those with less than high school

[24:47] education at the very last low, you can see we only have 1.2%, but in the area we have about

[24:55] 12.4% of residents with this education group. Next slide.

[25:05] What do blood pressure numbers

[25:09] mean? We measured systolic blood pressure and systolic blood pressure, and everybody knows that,

[25:17] So then there's specific definitions from CDC and for normal, it has to be less than 120

[25:27] and less than 80 for systolic, diastolic.

[25:33] And then elevated, define in the range that's the middle row and then hypertension is defined

[25:43] is 130 or higher for systolic, or for diastolic, 80 or higher. This is a very

[25:56] standard definition across the nation. Next slide, please.

[26:03] So, this is the

[26:04] result for average blood pressure and hypertension rate. So, you can see we

[26:11] We have the high blood pressure that's defined in the previous slide, and then we have

[26:18] average blood pressure and then average systolic blood pressure.

[26:27] You can see here the risk of high blood pressure will collect similar compared with our

[26:35] a county, which is the last column. Participants living in the middle radius, which is a third

[26:44] column, 0.5 to 1 miles, had somehow a lower hypertension rate. And then participants living near

[26:57] to all you feel. From 0 to 1 miles, which is the first two

[27:04] readings, which is the second and third column, I just slightly lower

[27:08] diastolic blood pressure on average. So the trend is not that very clear in terms

[27:21] what one could intuitively expect. Later we'll see there's some reasons behind it and this is

[27:31] combined lump across all of this ethnicity group. Next slide please.

[27:40] So this is the blood pressure

[27:42] by wind direction. We analyze this similar way as the birth outcome, breakdown by the

[27:50] stratified by the three radius and if you look at this we can see for the

[27:58] first radius which is close to the one and we can see the difference between

[28:05] downwind and upper one which impact from downwind direction for high blood

[28:18] pressure and also for average

[28:22] diastolic blood pressure, but not for

[28:25] systolic blood pressure.

[28:28] The actress indicate

[28:32] the statistics is significant

[28:36] is not due to chance, but this

[28:40] is undergiastic analysis, meaning is just

[28:44] look at this observed value

[28:46] not taking into account the other factors yet. Next slide. So this is the blood pressure

[28:59] result of the risk-ethnistic group. We showed the detail of this risk-out breakdown by risk-ethnicity.

[29:08] So, the, you know, we have rows, is the different width ethnic group, and the first, the second

[29:18] column is near the radius, and then so on and so forth. So, among African-American, Asian

[29:25] and Hispanic participants, the highest width of high blood pressure or observed closest to the

[29:34] you can see that for this three Luis Adonis group and you can see

[29:42] Pakistan African American you can see 64.1% and then the highest among that role

[29:51] H American, same thing, and Hispanic vatine vatine.

[30:00] Those groups also have the same plan. However, for Caucasian participant, the read of the

[30:09] high blood pressure somehow actually is in other direction. So, this, you can see this, this

[30:20] could be the cause of the previous slide, why we see L is in terms of aggregated statistics,

[30:31] point estimate across the three radiates is inconsistent, you know, because by this

[30:40] as many groups, somehow, these different and in different directions.

[30:47] Next slide.

[30:51] So this is the adjusted analysis, meaning we're taking into account, we look at the blood

[31:01] pressure, but compare, say, look at the between, you know, downwind upwind and then we also controlling

[31:16] for the other factors. The factor being controlled at the bottom of the slides, which include

[31:23] each gender with ethnicity, education, years leaving in the neighborhood et cetera.

[31:31] So from here, after we control it, I thought the cushion explained in detail the meanings

[31:41] interpretation of all the labels, we can see the overall the first bar on the

[31:52] very top that is slightly not overlap with the red color vertical bar. So this

[32:06] indicate there is a difference there. So this hypertension is associated with a

[32:14] wind direction and this of course is for the increase likelihood of hypertension

[32:21] after the adjustment. However, this is overall is combined, not like a specific

[32:33] think about which read is. So how close someone lived within the IOF did not

[32:41] seem to influence the outcome. And is when you combine these three read is

[32:49] together, then you see this slightly impact of the downward. So regarding other

[32:59] factors we found that the man or age overweight participant and those with previous hypertension

[33:08] diagnosis were more likely to have high blood pressure.

[33:13] Next slide,

[33:17] lung function.

[33:18] This is another important part of the biometric data collection and we collect two key very

[33:27] very popular measures. One is called FED1, which is the volume of the breath, exhaled with

[33:37] effort in just one sec. FVC, on the other hand, is the full amount of air that is exhaled

[33:47] with an effort in a complete breath. So it's total how much you can exhaled.

[33:54] So, then we also have a ratio, which is the ratio between if you want at VC, which basically

[34:05] measures with a percent of the error can be exhaled within a first sec.

[34:11] 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.

[34:40] So, the value to define as abnormal will be, and, oh, define normal will be, abnormal

[34:55] of course is the other way around. No more will be the ratio is greater than 0.7.

[35:06] All and both individuals have even one, seven percent actually, 17 percent. And then both

[35:18] f u1 and f uc is above 80% of the predict value. So for a given person, the abnormal or normal

[35:29] function is defined by combination of the ratio and then the individual value of f uc and f uv1.

[35:40] Next slide please.

[35:44] So here is the result of average normal and the normal on functions with.

[35:52] And this the rows we can see clearly is of normal on function and then the average the

[36:03] FV1, FVC, and the columns on the radius.

[36:07] Participant leaving nearest to the oil field

[36:10] had the highest rate of normal lung function.

[36:15] We can see that.

[36:17] But that's 66.9% compared with 62.862.7.

[36:27] Then the participant leaving near to the oil field

[36:31] at a lower average, if you want, if you see, you can see that is in a pretty clear direction

[36:43] from the nearest to the further east. We could not interrupt that this difference would

[36:55] due to chance, because all the p-value is greater than 0.05.

[37:01] Next slide.

[37:05] The average of normal long function by wind direction.

[37:11] This is a single analysis by the breakdown

[37:13] by stratified by the radius and then compare

[37:19] between upper and lower upper and down wind direction.

[37:25] And we can say first is the higher lead of normal long function was observed in the downwind

[37:34] part of the path, leaving from 0.5 to 1.5, but could not rule out this was due to chance

[37:43] because it's not significant.

[37:46] Let's see in the white color, you know, just regular white color.

[37:49] then for the brown color, orange color, the IVV1, IVVC would lower on average among

[38:02] downwind versus upwind participant, leaving from 0.5 to 1.5, which is the second to the

[38:13] third with this. But if we can see there's actually their meaning, we do see a significant difference,

[38:25] not, you know, due to chance, but this is unadjusted analysis. Next slide.

[38:33] So, looking at cross-risk ethnicity disparity impact of distance on abnormal long-function

[38:43] among different risk ethnic groups, please break down by risk ethnicities to, you know,

[38:52] by the radius. We see the consistent increase of abnormal long-function rate for African-American

[39:01] and individuals even further away from iOS.

[39:07] Yeah, you can see this somehow is in this direction.

[39:15] It's that's the first low 83 and then somehow increased

[39:21] 86 and increased further.

[39:24] This seems to indicate at least for every market,

[39:27] we do not observe any impact of the distance.

[39:31] Then, Hispanic and Latinx and Asian American Asian-American group show very

[39:40] the response to increasing distance from I-O-F. Then, white individuals exceed the decreasing

[39:51] trend.

[39:53] So, that we can see for the Asian and Hispanic, you can see is that there's no

[39:59] simple clear direction pattern and for Caucasian and you can see a clear

[40:12] downtrend from closes to the further east, which seems intuitive, easy to

[40:19] interpret. Next slide.

[40:25] So this is taking all those previous slides without which

[40:31] is under just then now we look at the adjusted. And so this first left-hand size FEV1, and after we

[40:40] adjust a number of the covariates listed on the bottom, we do not see any difference in FEV1.

[40:50] And for IVC, we have done a similar analysis and compare the difference. And we see

[41:01] But this is compare of course between upper one and down one and we see somehow for the overall combine and we see a

[41:12] complicated protective effect on FWC, which is complicated and could do to some unmeasured

[41:30] factors and need to look into this further.

[41:40] Next slide.

[41:42] So the effect of demographic health and the environment factors on long function.

[41:49] First, demographics influences age and residence distance.

[41:53] A resident duration is linked to decreased long function,

[41:57] according to a possible long-term environment or aging effect. For health factors, we did not

[42:06] find some significant impact from quite a number of other health factors, such as smoking,

[42:17] as my recent cough, etc. For environment factors, the seasonal variation we do see a long function

[42:27] with words still in winter months.

[42:30] And for traffic and green space,

[42:33] measures that was not about clear effects

[42:38] on long function observed.

[42:42] Next slide.

[42:45] Self-report symptoms.

[42:46] We examined 23 symptoms.

[42:50] Participants might have experienced.

[42:54] And among the 23 most commonly,

[42:56] we report symptoms in a community including sneezing or running nose, fatigue, irritation of

[43:04] eyes, watery eyes, and headaches. Next slide.

[43:11] So these shows the three most observed reported

[43:18] symptoms which is sore throat headaches and a couple of hearing. For sore throat and headaches

[43:31] what lasts frequently reported among residents near to the old field. You can see that's the first two

[43:39] figures, and actually going the other way around. For the closer distance, you actually see less reported such two symptoms.

[43:57] And then for top of hearing, which of course is not significant across the three distance,

[44:07] was reported more frequently among residents living near the oil field.

[44:13] But the difference was not that it got significant, meaning we can rule out it is by chance along

[44:23] Next slide. So the summary of SERP report symptom. After judgment of the other

[44:31] factors there were no longer a statistical significant difference between the

[44:36] distance or field and the symptom to report it. Then or less likely to report any

[44:43] of those symptoms we exact. And the other, the or the participant or less likely to

[44:52] we poor soul throat or headaches.

[44:56] It's interesting we find that higher the BMI

[44:59] Bye.

[45:00] Social with a slight increase in likelihood of reporting each symptom, suggesting we have something to do with a symptom occurrence. Next slide.

[45:20] 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.

[45:34] vertical bar, so we do not actually see anything significant after controlling adjusting

[45:42] covariates. Next slide. So this is the last part of the outcome. It serves report health

[45:52] condition by distance. And multiple frequently reported health condition among participants, including

[46:02] high-class law, cancer, heart problem, miscarriage, allergies, chronic

[46:10] obstructive pulmonary disease, CODD, chronic bronchitis, and pneumonia.

[46:19] Among these reported, there's two, we can see some difference and before we

[46:28] adjust the covariance, which is high cholesterol level and

[46:35] cancer rate for more commonly among the resident living near to the oil field.

[46:44] And most commonly reported, the cancer types are two, one's breast cancer and one's

[46:52] of course this is just unadjusted. Next slide please. So this is again unadjusted results

[47:04] looking at these two, you know, conditions that see the difference by the distance.

[47:17] one is the high cholesterol and you can see when closer to the oil fence, the higher

[47:28] base and the same thing for cancer and the closer and then you can see a higher

[47:38] percent. However, we can see in the next class after we carefully conduct analysis

[47:46] is controlling the factors, these are actually all wind away.

[47:50] We do not see a difference anymore.

[47:53] So next slide, please.

[47:57] Yeah.

[47:57] So this is the results looking at both the high-class hall

[48:03] and the concert.

[48:06] So the solution was no longer observed

[48:09] after counting all the risk factors.

[48:12] You can see that the hand side high-class hall

[48:15] and after controlling factors we do not see the difference anymore. For cancer

[48:22] and you can see because the further is a radius as very small frequency. So

[48:31] statistically you have a combine with the next

[48:34] the statute, which is 0.5 to 1.

[48:42] So right now the comparison will be near the radius compared to the other two radius together,

[48:53] and we do not see difference anymore. So for this analysis, we adjust it for each gender

[49:00] with ethnicity, education, ever-smoker, BMI, and even the gas stove usage and other factors.

[49:14] Next slide. So some refining for biometric measures. So first, the orange color lines after

[49:24] adjusting adjustment, downwind was associated with an increased likelihood of the high blood pressure.

[49:33] This one is, you know, we saw from earlier that combined analysis. So the second

[49:43] point here, long-function with the lower amount of participant living, closes to all you feel

[49:47] and the lower in the downward direction.

[49:54] This difference just went away, yeah.

[49:57] So that's why it's a white color print.

[50:01] So the third point is collect protective finding.

[50:06] That's what we mentioned earlier after counting for other factors.

[50:09] Leaving downward of the oil field was associated with somehow better FVC.

[50:16] So, this is kind of a complicated finding and the within behind this is not clear to us.

[50:31] And that's going to be some underlying interaction or uncontrolled measures.

[50:40] Next slide.

[50:41] So some refining for health service is a two key point of controlling demographics, health

[50:49] and environment factors.

[50:51] The report of those symptoms of sore throat headaches was no longer a difference between

[51:02] in the participant, living at different distance.

[51:07] And the same thing for the conditions,

[51:13] and after the health conditions, after controlling

[51:17] demographic health environment factors,

[51:21] and reported high-class law and the rate of cancer

[51:25] were no longer different among participants,

[51:28] living both at different distance.

[51:30] Next slide. So, conclusion, for the birth outcome analysis, this

[51:40] analysis suggests that all you feel may have increased risk of return birth

[51:46] among residents living nearby and downwind. Of course, we cannot do all the

[51:52] possibility that overfinding away explained by some other factors, we were unable

[51:59] to measure because it's complicated. And then, however, of course, overfinding is actually

[52:08] consistent with three studies in California, one central valley, Pennsylvania, and then

[52:14] third ones from Texas. And overstudy was also unable to assess miscarriage, which may actually

[52:23] to under-ask made of the healthy impact.

[52:27] Next slide.

[52:29] We have limited evidence to suggest

[52:32] regarding the blood pressure and the lung function,

[52:40] the social issue, and the finding.

[52:44] And the blood pressure finding is consistent

[52:47] with a prior study in other Los Angeles

[52:50] neighborhood and near all your development. However, the long-functioning fighting is somehow

[52:57] inconsistent with prior study in Los Angeles and may be due to other limitations of our study.

[53:09] For example, such as the underrepresentation of a certain subpopulation.

[53:17] and study limitation, we have a number of them, you know, could be such as, you know,

[53:23] lack of information about participant, what they actually was, or years, on treatment

[53:29] of respiratory disease or others. And that may mean we did not detect and impact that

[53:38] actually does exist. Next slide.

[53:41] Yes.

[53:43] And we do not have sufficient evidence to suggest the

[53:48] redness near the oil field increase risk of cancer

[53:52] or high cholesterol.

[53:55] As we all know, the cancer development is complicated

[54:00] multi-factor driven and is a long term.

[54:05] And we actually, you know, this is a, it's, you know, for single-sided, you know, this

[54:15] is the part of this, it's hard to find this, you know, conclusive, you know, results.

[54:27] The prior studies in Colorado and Texas had linked the residence near the oil field and

[54:36] the gas development with higher incidence of children, cancer, but there's no prior study

[54:47] have assessed the population, you know, since it began in California.

[54:53] Overstudies rely on a self-reported health condition which not verified through a medical record.

[55:02] Yeah. Next slide. So these are the recommendations. First, given the high rate of adverse

[55:14] all the comms compared with Erwin Conkey as a whole, and also the suggestive evidence of an

[55:23] adverse effect of all you feel on pre-term birth. So the program to support pregnant people

[55:32] could be benefit community. The risk for developing cancer is really complicated

[55:42] and the cause of cancer in a single community really hard challenge to detect.

[55:52] Future researchers research could be conducted in, for example, large sample size with regions

[56:01] or even also look at the long-time span of the transfer registry data, which

[56:11] will support to capture every single case. And then we can look at that. For this

[56:19] specific area, 0.5 regions and then compare with others and over time. And future

[56:28] The first study is to measure contaminate in people bodies, so-called biomonitron, could

[56:35] actually help to identify a specific gluten-exposed for those who are actually living within the

[56:51] vicinity neighborhood of the oil field.

[56:58] Next slide.

[57:01] Yeah, that's all.

[57:03] Thank you.

[57:03] Sorry to take longer than expected.

[57:07] Trying to get this through quick.

[57:11] So now let's open the floor for question.

[57:16] Thank you very much, Dr. Lou and Dr. Cushing.

[57:18] why don't we just go back first to Christine.

[57:22] Thank you. I just wanted to let people know on the call

[57:26] that we have representatives from our health promotion bureau maternal child natalescent health

[57:32] our deputy director for health promotion dr. Priavatra and our director of maternal child natalescent

[57:39] health Melissa Franklin and some folks from the I believe with the African-American infant mortality

[57:46] project who are actually on the call tonight in case anybody has questions about, you know,

[57:52] programs to support pregnant people. Thank you very much, Dr. Lou and Dr. Cushion for your

[57:57] presentation. Thank you, Christine. Dr. Luke, can you get rid of the screenshot on the thank you

[58:04] shot so we can see everybody's faces again? Okay. Thank you. Charles. Thank you. Hi. Thank you.

[58:11] Thanks for the presentation. I have some questions and the first question is, are you

[58:20] implying, and this is probably directed more to Dr. Kushin, are you applying a

[58:24] heavy identifier at a cause and effect relationship with the ingot oil field

[58:29] and the outcomes that you've presented to us today?

[58:38] Well let me start just by

[58:40] saying the way kind of we think about causing effect in epidemiology is not through a single study.

[58:50] It's through the aggregation of evidence from multiple studies, each with their own strengths and weaknesses.

[58:58] We start to see the same thing over and over. We have more confidence that it's causal relationship.

[59:05] So, I wouldn't say that we can definitively say that living near the oil field caused

[59:14] any, caused elevated rates of pre-term birth, but I can say that when we look across studies

[59:22] across the country, including the one we conducted here, we repeatedly see an association between

[59:30] living near oil and gas development and pre-term birth. Not in every study, but in many studies.

[59:39] So I hope that answers your question. Well, it opens up on other series of questions.

[59:44] Correlation is not causation. We can definitively, by we I mean the cap, can definitively state

[59:50] that we know what's coming off the oil field. A lot of the presentation presupposes that there

[59:55] has it as chemicals coming off the oil feed but we

[1:00:00] 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.

[1:00:29] 56% higher impact? Is that normally distributed or is that possibly influenced by a pocket of

[1:00:38] very bad or very high outcomes? Because 56% seems like the variation between health outcomes

[1:00:47] in the United States and a third world country. I mean, 56% is, you know, 56%. That's high.

[1:00:56] Hey, someone would have noticed that I would think before.

[1:01:00] So is the data normally distributed within that group because you're citing percentages

[1:01:05] not frequency distributions?

[1:01:08] In other words, is it just a little bad or is it 56% bad?

[1:01:14] Well, it's not that 56% of babies were born pre-term, if that's what you're suggesting.

[1:01:21] is that the likelihood of preterm births was 56% higher among that nearby downwind population

[1:01:31] versus nearby upwind population. So it's a little bit against that the number 56 sounds big,

[1:01:37] you know, just to give you a sense roughly 10% of babies are born preterm. So we're not saying

[1:01:45] that 56% of babies were born preterm in that population. We're saying that the likelihood was

[1:01:50] slightly elevated in the one group versus the other, the downwind versus the

[1:01:55] upwind. Hopefully that... Yes, I understand that, but you're presenting it either

[1:02:05] or is it just a little bit? I mean, is it, I don't know what the units are, is it one

[1:02:09] day, is it two days, or is it, you know, a clinically significant time factor?

[1:02:19] Sorry,

[1:02:22] I'm trying to think of a good analogy, Dr. Lou, if you have any suggestions, feel free.

[1:02:28] It's like I don't want to like giving birth to rolling the dice because that's terrible

[1:02:36] having gone through it myself, but what am I trying to say? I'm trying to say it's a

[1:02:48] 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.

[1:03:08] Remember this, the study design and the data we have, this is not the repeal measure longitudinal design per se.

[1:03:21] Even for the birth outcome, we do have data over time, but that is not actually a repeal measure, typical repeal measure design.

[1:03:31] which we prepare a longitudinal design is purposely for identified causal impact, causal effect.

[1:03:42] For overall analysis, yes, clearly we do identify the significant association,

[1:03:50] So, which point to the direction that there is a potential, first of all, the strong

[1:03:59] association, there is a potential causal impact, effect relationship, underlying relationship.

[1:04:08] But with cross-sectional analysis, cross-sectional data, we are unable to see definitely whether

[1:04:17] whether it's a causal effect or not.

[1:04:21] Okay, all right.

[1:04:22] And finally, Dr. Could you put up your last slide, please?

[1:04:26] Could you build your last conclusion,

[1:04:29] aside with your last conclusions?

[1:04:31] Sure.

[1:04:32] Slide that contained your last conclusions.

[1:04:37] Li Yu, can you help to put very last one?

[1:04:41] Because I'm not the smartest person.

[1:04:45] I'm a dumb guy, but it seems to me

[1:04:48] that we have two ways of looking at things. We have scientific data that was collected over

[1:04:55] many years, calibrated instruments that speaks to a specific conclusion. What's going off

[1:05:01] the oil field, and that so far is green. It's not indicative of anything that's going to impact

[1:05:07] the community. And then we have studies that, well, they're statistically valid, but I don't want to

[1:05:14] have questions, but there's a lot of concern as to whether they actually have a cause and effect

[1:05:20] relationship. Anyway, so you're saying, you know, this is not the last one. You had to go to

[1:05:27] the next one. Yeah. Yeah. Okay. All right. So your recommendations, everyone here supports more

[1:05:32] support for pregnant people, whatever the cause. What I want to make sure is that we don't go chasing

[1:05:37] using a bogie that isn't there, we need to make sure that if the oil field is complicit

[1:05:44] in this situation, that we define that because we could say eliminating the oil field isn't

[1:05:50] going to necessarily make anything better. Secondly, you talk about future studies to measure

[1:05:56] contaminants in people, being exposed to specific pollutants associated with oil drilling.

[1:06:02] We're doing that, we're absolutely doing that, and the answer is there are no specific components

[1:06:07] associated with oil drilling that is escaping into the community.

[1:06:10] We know that so we can't keep mentioning that there may be these undetectable hazards

[1:06:21] that we know are not there.

[1:06:23] Anyway, that's all I have to say for now.

[1:06:26] But thank you.

[1:06:27] I think you guys did a great job on this study.

[1:06:29] Thank you.

[1:06:29] Thank you, Charles.

[1:06:31] Before we go to Jenny with the next question,

[1:06:33] and Erica asked if these slides could be shared.

[1:06:37] Can someone put these on the website?

[1:06:40] Can you do have them Edgar?

[1:06:43] Yeah.

[1:06:44] Okay.

[1:06:45] And again, if you can get rid of the slides,

[1:06:46] so we can see everybody again and Jenny.

[1:06:49] Oh, John, if I can just...

[1:06:50] Oh, go ahead.

[1:06:51] There's gonna be a final report, the slides

[1:06:54] and the final report.

[1:06:55] And there's some other reports from the study,

[1:06:59] like they recorded the input they got from the chap

[1:07:02] and how they responded to it.

[1:07:04] Those kinds of things will all end up being shared

[1:07:06] with Edgar to put on the website.

[1:07:08] And what's the timing on that?

[1:07:12] The end of the contract period is December.

[1:07:15] So by the end of December.

[1:07:17] So within a few, within a month or two.

[1:07:18] Yeah, thank you.

[1:07:20] Jenny.

[1:07:21] Yes, thank you.

[1:07:23] Dr. Liu, I actually think Dr. Cushing mentioned that

[1:07:27] you need to do multiple studies in order to get confidence

[1:07:30] in order to have a trend that you can feel confident and you suggest that the the bio monitoring would

[1:07:37] that have to be done among the same sample or at least a sample that X absolutely mirrors this

[1:07:42] study?

[1:07:47] Not necessarily and I was you know also speaking about not just multiple studies of this

[1:07:55] community in particular but just overall like if you want to figure out the answer to a question

[1:08:04] and studying it in multiple populations with differing study designs and methods can help

[1:08:11] you have more confidence. If you're seeing the same answer over and over again,

[1:08:17] then you start to have more confidence in those. But you do want it to relate to the

[1:08:21] angle would oil feel specifically. Yeah, I mean, I think a bio monitoring study here would be worth

[1:08:29] while it doesn't necessarily ideally it would if people who participated in this study it would

[1:08:37] be great if there were to be a biomedical study that would be the same people because we already

[1:08:42] have all this information about their lung function their blood pressure etc. that we could also

[1:08:49] and their lifestyle because we did ask lifestyle questions yes so that would be ideal and then really

[1:08:56] quickly. How is this going to be disseminated to the public?

[1:09:04] Sorry, I miss you last question. You repeat,

[1:09:07] Virginia. How is the public going to learn about

[1:09:10] the results of this study and what it means to them?

[1:09:15] You mean how the public will learn the result from this study?

[1:09:21] Yeah. Yeah, we will have, as Kristi mentioned, we have the final report,

[1:09:29] or summarize all the results and in language and we're actually in quite a detailed way,

[1:09:41] which were eventually be available to the public.

[1:09:45] How?

[1:09:46] How was somebody?

[1:09:48] How will my neighbors be able to learn what this study has told us?

[1:09:54] You know, we don't really have a dissemination plan for it.

[1:10:00] Really, we are sharing it with the community.

[1:10:03] It will be on the website.

[1:10:05] But we could talk about, you know, potential ways

[1:10:08] and maybe we can talk with some community partners

[1:10:10] about ways to get the word out about study.

[1:10:14] Great, thank you.

[1:10:14] Thank you, everyone.

[1:10:16] Thank you, Jenny.

[1:10:17] Yeah, I've got a number of questions actually.

[1:10:19] Well, I won't address them here, huh?

[1:10:22] Joe, hold on, we got two other people first.

[1:10:25] Can we?

[1:10:25] Okay.

[1:10:26] Okay.

[1:10:26] Jim Jones.

[1:10:28] We're

[1:10:33] eager to see so many familiar faces there.

[1:10:37] I'm sorry that in this, Dr. Cushings,

[1:10:40] second go round on the presentation.

[1:10:42] I'm sure it was fantastic as the first.

[1:10:46] One of the things that I was wondering,

[1:10:48] actually, do I have my camera on?

[1:10:50] Actually.

[1:10:51] Okay.

[1:10:52] I'm sorry.

[1:10:53] One of the major concerns was this, in listening to the presentation again, would it be fair

[1:11:02] to say that throughout all of the statistical information that was received and analyzed,

[1:11:10] that if downwind locations actually are areas in which there's a significant and statistical

[1:11:24] difference showing the effects where it should say deleteries effects of being a proximity

[1:11:30] to the oil field. And that basically goes between the preterm birth study and the other health

[1:11:36] assessment. Would that be fair to say?

[1:11:41] Yeah, I would say that, yeah, to a lot degree. Yes, because remember also if we look at those

[1:11:50] bi-variable results, when you look at the compare downwind versus the upperwind, those impact

[1:11:58] you see that when you actually stay, it'll leave closer to the oil field, which means, you know,

[1:12:07] like a double impact, right, from the wind direction and the direction and the distance. So overall,

[1:12:16] yes, the Darwin showed up in both birth outcomes and other health measures.

[1:12:25] Excellent. And would it be accurate to state that wind direction from West Southwest to East

[1:12:37] Northeast is wind blowing in the downwind direction? Would that be fair to say?

[1:12:49] Yes. Yeah. Okay.

[1:12:53] Would it be fair to say that we have an operator that is in our midst that considers

[1:13:01] wind direction like that in the middle of an oil spill to be considered good news?

[1:13:10] And if there's any question about that, I suggest that we look back to the

[1:13:14] CHAP study July 2024, CHAP meeting recording, minutes 44, minute zero, zero seconds to 51 minutes

[1:13:25] and 25 seconds. Basically my point is this, if we are being told that it is good news that

[1:13:33] we have wind direction blowing down wind and we know for a fact that there are deleterious

[1:13:40] effects from whatever the OCs are emanating from that from that field. I mean, as Charles

[1:13:47] pointed out, we may not know what they are, but we were provided with an explanation of

[1:13:53] all the oil is dispersant. If that's the case, doesn't mean that it doesn't evaporate.

[1:14:03] It turns into something that apparently may not be able to be measured or identified, and

[1:14:08] And it's obviously statistically being proven that it's causing a problem in South Los Angeles.

[1:14:17] So this is something that I think that we really need to take a look at.

[1:14:27] Thank you, Tim.

[1:14:28] Let's go at Joe and then Megan and then Liz and then Frank.

[1:14:32] Yeah, I mean, I have a, I have a number of questions.

[1:14:35] I'm not going to go through them all.

[1:14:36] Is this report?

[1:14:37] Is it deemed as final or is it something that we can comment on and so forth?

[1:14:48] was it just going to be flat out final? Has it been reviewed by anybody besides done internally?

[1:15:00] 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.

[1:15:29] 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.

[1:15:50] I just see a number of things I'd like to look at.

[1:15:53] I mean, one is just the protocols that were followed for this.

[1:15:57] And two, did you do any upfront screenings like Cal E mod or Cal doubly E mod on the facilities here?

[1:16:05] Did you guys look at other sources other than the oil?

[1:16:09] Did you do a screening such as AQMD's mate studies to look at other toxic facilities?

[1:16:17] We're right, I think something like, I think it's 173 on the list and there's a lot of

[1:16:23] air toxic facilities right in this neck of the woods, too, in the England, well, or near

[1:16:28] the England, well, field.

[1:16:30] Did you look at any of those?

[1:16:34] Yeah, that's very good question, waiting, you know, this result does not point to any specific

[1:16:42] think the sources or something, this is only the distance.

[1:16:48] As you mentioned, there's other sources of contaminate

[1:16:53] and we do not have any conclusion or something

[1:16:59] on that specific level.

[1:17:01] And that's a very good point.

[1:17:04] That will be the other issues.

[1:17:09] you know, someone need to look into it and eventually the country build this, you know,

[1:17:16] eventually to the clip of the different sources.

[1:17:22] Yeah, I mean, there's other things as well, but I would like the opportunity to read and comment on this.

[1:17:30] Me being representing the well field, I just see a number of flaws right off the top here

[1:17:38] in terms of the conclusions that you're coming to.

[1:17:41] But you know, want to be objective

[1:17:43] and take a look at the report.

[1:17:44] That's for sure.

[1:17:46] So.

[1:17:47] Yeah.

[1:17:48] Sure.

[1:17:49] Yeah.

[1:17:50] We understand, Dr. Liu, you can submit comments

[1:17:53] and those will be considered by UCLA

[1:17:57] and put in and referenced in the final report.

[1:18:00] Is that correct?

[1:18:02] You know.

[1:18:03] I'm just going to jump in here real quick.

[1:18:05] We didn't include that in the scope.

[1:18:09] I think what we're going to do is have a final report,

[1:18:12] but certainly anybody can comment

[1:18:14] and those, you know, if the cap wants,

[1:18:19] those comments could also be uploaded as well.

[1:18:22] And Joe, with respect to what you're saying,

[1:18:25] basically this study mirrors the kind of methods

[1:18:29] that have been used around the country

[1:18:32] to look at this kind of an issue

[1:18:34] 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.

[1:18:46] 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.

[1:19:00] So, we try to be careful with our language about how we talk about results of studies.

[1:19:07] And so, you know, we can't say that something has been proven.

[1:19:11] So, your points are well taken, but I do think we are going to have a final report.

[1:19:17] We will review it for clarity, and then we're going to let UCLA put out their report,

[1:19:22] and then I feel like other groups can do what they want with that and upload it as, you

[1:19:29] know, as the cap desires.

[1:19:31] Understood.

[1:19:32] You know, one thing I'll stop there is that, you know, our field is, we're different from

[1:19:40] From other fields around the country, particularly because we're in California, we're under full

[1:19:43] vapor recovery.

[1:19:44] We realize we get leaks that we have to stay on top of from time to time to time.

[1:19:51] But we're much different from those that operate, let's say in Colorado or Pennsylvania, where

[1:19:56] we've got just much more strict or permanent requirements and control requirements.

[1:20:04] That's it.

[1:20:06] Thank you, Jill.

[1:20:07] Megan.

[1:20:08] Yeah.

[1:20:08] Thank you, everybody, for those who worked on this city and those who were part of the committee giving input on it.

[1:20:21] 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.

[1:20:38] And so I'm.

[1:20:47] Are you still there? Yeah. Can you hear me? I couldn't for a second, but now I can.

[1:20:52] I apologize. I'll talk closer to my computer pardon me. When we look at the amount of oil drilling

[1:21:02] that happens in Los Angeles County, do you happen to know if there is a comparison, if the state has

[1:21:10] done similar type studies that cover regions, because I think it would be interesting, given

[1:21:16] the amount of oil drilling that is happening in this county, if there is a way of comparing,

[1:21:23] doing more regional comparisons as opposed to field-to-field, because there's a field here,

[1:21:29] but there's also fields in Long Beach, and refineries in Wilmington and Carson, and a lot

[1:21:36] of other oil drilling type industries that also have an impact on the surrounding health,

[1:21:47] and so I'm just wondering how that compares with, you know, say, folks in regions where there

[1:21:53] is no oil drilling in the state of California, if any of that is available information.

[1:21:59] Yeah, thanks, Megan. That's a good point. As far as based on over knowledge, there's no such

[1:22:06] prior study conducted. That's one of our recommendations. You can see that in the summary slide,

[1:22:17] the last slide. That's what we recommended. Yeah. I agree that would be a good,

[1:22:25] It's a robust way to look further, you know, this underlying potential relationship.

[1:22:40] Okay.

[1:22:41] Is that it, Megan?

[1:22:44] Yes.

[1:22:45] Thank you.

[1:22:45] Okay.

[1:22:46] Thank you.

[1:22:46] Liz.

[1:22:53] Can't hear you.

[1:22:54] Sorry.

[1:22:54] Couldn't get my thing to go on.

[1:22:57] What a start by saying thank you for being a part of this and for everybody else who took

[1:23:02] part.

[1:23:02] I think you see, you know, did any incredible job, but I think that from the very start, the

[1:23:08] concerns that we had or I had and I felt the group had from our first meetings

[1:23:14] were that we wanted to make sure that the comparison of what they were

[1:23:21] showing to the county showed it all times. I was very disappointed that that

[1:23:26] wasn't displayed in the document. So how is this ruling compared to

[1:23:32] Los Angeles County. That's a big one. The other thing, you know, Joe bought a great point,

[1:23:39] we aren't like any other oil field. We are more regulated than anywhere else in the country.

[1:23:44] And the other issue is you do contributing factors. When you go out to Pennsylvania and you

[1:23:49] look on global map and you look at these fields, they're in the middle of nowhere and they have nothing.

[1:23:56] And we have one of the largest airports in the United States. Ben, we have more freeways,

[1:24:01] more cars. We have documented how much pollution we have. And I don't feel that those factors will

[1:24:10] pull out. And if they were, I mean, I thought that was from the linear regression. And I think that

[1:24:17] Dr. Hans, the math fabulous, but I just don't know that the data was totally available.

[1:24:24] So, you know, that was a factor for me, but I, and also the issue of, you know, we held

[1:24:34] this health study and extended it for over a year so that the SNAP study could be a part

[1:24:40] of this.

[1:24:40] And yet, I don't see anywhere where the SNAP status applied.

[1:24:44] All of this information of them showing them exactly what chemicals are here and what

[1:24:51] chemicals related me to the study that you did. I think it should have been addressed

[1:24:58] and noted because you have a real time show of the chemicals all around us in the field.

[1:25:07] And then I know that I'm not a scientist, I'm just an average Joe who studies a lot of

[1:25:14] the stuff, I read a lot about it and I can't tell you how many hours I've gotten sucked

[1:25:18] in deleting. But health risk assessments are required by Weha, which is the mother of

[1:25:27] all health and safety for the state of California. And the Inglewood Wildfield and all other fields,

[1:25:35] I believe, have to complete those settings. And we have passed again, and our field has passed

[1:25:41] again and again with flying colors. And that's the only thing we should know. And this goes to what

[1:25:47] you know Charles was saying we do have this sad and I mean I was sad to see that the health

[1:25:53] studies, that the mate's study and these healthcare assessment studies and studies from our original

[1:26:00] EIR, they're not included in this and you know I was kind of like a ding dong just touting about this

[1:26:07] in meetings again and again and again and so I wish that in some way because you know to say some

[1:26:15] of these things where I, some of the data, it seems that, you know, this is, it's a utilization

[1:26:23] potentially of unadjusted data. And that's not fair and steady of like, that wouldn't pass

[1:26:31] re-haused requirements, and it wouldn't pass in a health risk assessment. So, and I'm not a scientist,

[1:26:38] so you can probably talk circles around my comments, and I don't deny it. But I'm just saying my biggest

[1:26:43] thing was that I wanted, and I know Charles and the rest of the people that were from the cap,

[1:26:49] we wanted people to present in a manner that the cap members could understand, not having sat

[1:26:58] through, you know, not devoting about a month of, you know, meeting every month since 2018,

[1:27:04] because we started with one and then we went on and went with UCLN. And I mean, I learned so much,

[1:27:11] 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.

[1:27:25] 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.

[1:27:36] 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.

[1:27:48] How much effect does that have on the results? And then I mean, bringing me the issue.

[1:27:54] I mean, I thought it was very odd to see you guys at it as a possible issue people using gas stoves.

[1:28:00] I mean, there are a lot of controversy, and you can say people have live in a cold

[1:28:04] sack and everyone has tons of chemicals under their skin.

[1:28:07] But the bigger factor to me instead of those items were what about La Sianica Boulevard?

[1:28:13] What about LAX?

[1:28:15] What about the average pollution rate?

[1:28:18] And as Christine has always said, all these years, and the person her predecessor before her,

[1:28:23] you know, when you get on the freeway, they have to have one at every single on-ramp saying

[1:28:29] that the chemicals found in Los Angeles are way above standard.

[1:28:35] So I just, I hope that a little more detail could be,

[1:28:41] you know, like an executive summary at the front

[1:28:44] and they're going into what this is instead of the conclusions

[1:28:48] at the end because I still don't know.

[1:28:53] I know I've looked at a lot of those not the slides before,

[1:28:57] 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.

[1:29:04] 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.

[1:30:00] 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.

[1:30:29] you think it's the planes, it's not the planes, it's all the trucks and people driving in it,

[1:30:34] millions of people driving in and out of the airport. Most people don't know that.

[1:30:39] They think that the planes are the big thing about the airport, it's the traffic,

[1:30:43] it's the diesel particulates, which we don't have, you know, we don't have that issue in the field

[1:30:47] and people think, oh yeah, there's fun in the diesel. That's an immense factor. And so, I mean,

[1:30:53] there should be maybe a little explanation of those factors including this because you guys

[1:31:00] are incredible scientists and when I ask these questions you do answer them for me and it's

[1:31:05] really helpful but I think that some of the data should be disseminated to the group to the average

[1:31:12] Joe in the public that'll be reading this because why read this I don't want it to sound as though

[1:31:18] So, you know, the health department and Lee Ha and all these other groups have not been

[1:31:26] doing their job.

[1:31:28] And then the incredible data that snaps provided us with, you know, I was very excited to have

[1:31:34] included, but it doesn't appear.

[1:31:37] So.

[1:31:40] Okay.

[1:31:41] Thanks, Liz.

[1:31:43] Yeah, you, you, thanks for all your comments.

[1:31:46] Yeah, I think you brought up a number of issues. Let me try to address a few. I think I

[1:31:54] maybe, you know, I'd like people to meet something and then others can tell me why is the very

[1:32:02] first one regarding our county comparison and we did try it. You can see for the birth outcome,

[1:32:09] we do have that statistic from county so we were able to compare. For other health outcomes,

[1:32:16] company has very limited data or like you could see that for a blood pressure we

[1:32:22] did identify the county estimate for high blood pressure. That's the only

[1:32:29] estimate we got but even that was somehow different compared with what we

[1:32:36] have. Their high blood pressure is first you know meet with that definition in a

[1:32:42] like above 130, you know, 6.0 and then, you know, that's only, it's 80. But they also have

[1:32:50] definition is if you are on medication, hyperlateral medication, you also automatically be classified

[1:32:57] a hyperlateral pressure. So in over data, we, we did not collect detailed information about whether,

[1:33:05] you know, people actually on medication, because that becomes very complicated. There's a lot of,

[1:33:12] conditions, it's very hard to collect that information. But we tried our best to compare

[1:33:20] with county, unfortunately there's limited data. And then later if anyone knows there's

[1:33:26] other data, county sources, we can get, let us know, we will definitely look into that.

[1:33:34] Then the second, you mentioned about the conditions, three ways, a lot of traffic in this neighborhood.

[1:33:43] We totally understand that.

[1:33:45] Yeah, there's multi-factors underlying.

[1:33:49] We tried to control some.

[1:33:52] We did look at the traffic impact, look at the green zone,

[1:33:57] the, you know, based on the common definition of that green zoom measures, those things

[1:34:07] did not actually, you know, shoot up much significant in the analysis.

[1:34:15] Yeah, regarding the SNAP data, yeah, you write that is something, you know, seasonal and

[1:34:25] and then it will collect the current information in the neighborhood.

[1:34:31] But unfortunately due to the delay of the data collection,

[1:34:35] although because we actually finished all of our data collection back in June,

[1:34:42] after when they fully running, there's very little windows overlap.

[1:34:47] We can actually get the data which be able to transfer

[1:34:54] for useable format and link with our analysis.

[1:35:01] And that could be down in the future,

[1:35:05] once this data is all fully available

[1:35:10] and then we can match with the windows

[1:35:14] and to see how much overlap we can have.

[1:35:18] But that window is very short

[1:35:21] because the timeline is you know,

[1:35:23] they have the same number of challenges eventually. And yeah, as far as the format you mentioned,

[1:35:33] you know, should have executive summary in front than instead of at the end. The other one,

[1:35:39] yes, we can easily address that because this is not a final report per se. This is just the

[1:35:47] presentation of the results, final report, yes, we will have a formal executive summary

[1:35:53] in front so that people can see that kind of high level summary start with and then

[1:36:00] run them with all the way to the end of the report. That's the few points I took notes here,

[1:36:07] I'm pretty sure I need to something.

[1:36:09] Um, is there any other?

[1:36:12] Uh, or anything?

[1:36:13] Okay, so I appreciate those things.

[1:36:16] And that's what I said, you always are thrown.

[1:36:18] So I would love that also.

[1:36:20] If an and thank you for saying it would be at the front.

[1:36:23] And I think that's great.

[1:36:24] But the other item.

[1:36:25] So I think it would be great if the disclaimer is included.

[1:36:29] That.

[1:36:31] One Los Angeles.

[1:36:33] In comparison with the evil oil field in comparison with.

[1:36:37] if numerous other oil fields that are studying regularly

[1:36:40] have far more close-by contributing factors,

[1:36:43] such as LAX and the roads.

[1:36:46] And then two, I think it would be great if you said,

[1:36:51] unfortunately, that the SNAP study doesn't coincide

[1:36:54] so that data couldn't be included.

[1:36:56] Because I mean, that's straight up, it's true.

[1:36:58] You guys wait, we kept going, trying to have them start,

[1:37:02] but then they got delayed again and again

[1:37:03] between COVID and other staffs.

[1:37:05] but I think that would be a common question. If I live near there and I read this, I'd go,

[1:37:11] well, where about the SAP setting? We've heard that it has all this data, but I understand why

[1:37:15] it can't be included. So that would be great if those disclaimers were provided.

[1:37:21] Thank you, Liz. Frank, I know I said you were Latinx, but let me have Priya come in because I

[1:37:26] suspect she may be talking on this point, and then you'll be after Priya.

[1:37:31] Hi, good evening. It's Priya. Actually, thank you for having me. Great to see so much interest in this topic.

[1:37:41] 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.

[1:37:47] 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.

[1:37:59] So it's like multifactorial.

[1:38:01] So when we say that like perhaps like

[1:38:05] particulate matter, like volatile compounds cause inflammation,

[1:38:09] right, we don't understand clearly the pathway to preterm birth.

[1:38:11] But that's been suggested in other studies.

[1:38:13] Like that's one factor for maybe spontaneous preterm birth.

[1:38:16] But then when I also hear that like hypertension,

[1:38:18] rates of hypertension may be increased downwind.

[1:38:20] Like we know that individuals,

[1:38:23] people with reproductive capacity of high blood pressure,

[1:38:26] prior to pregnancy are increased risk

[1:38:28] for like pre-eclampsia and other complications, right, that also lead to pre-term birth often

[1:38:33] or very common causes, more common causes that we understand of pre-term birth. So just like,

[1:38:37] I think it's really interesting that a lot of this could be additive in the perinatal population,

[1:38:40] so just wanted to point that out. And then we do have a lot of colleagues, as Christine mentioned,

[1:38:47] attending today from our Department of Public Health and Maternal Child and Adolescent Health Division.

[1:38:53] And so there are already a lot of existing programs to try to mediate this stressor on pregnancy

[1:39:00] and others.

[1:39:03] And so I just wanted to shout out, we have a home visitation program, we have a dual

[1:39:07] program, we have a guaranteed income pilot, we have a lot of stuff that can help to sort

[1:39:11] of like protect these individuals who may be at increased risk of these outcomes and those

[1:39:15] already exist.

[1:39:16] So when it is time, Christine and others to like disseminate this, I think it would be great

[1:39:21] to pair it with some resources. So, folks who see this information feel like they're supported

[1:39:26] in other ways. Just wanted to add that. Thanks for the time.

[1:39:30] Thank you very much. Frank and then Kelly and then Melanie.

[1:39:38] Yeah, I had to. I was just curious if this study could have a side-by-side comparison to the

[1:39:49] previous city to find out if there's been a change for the good or for the better.

[1:39:58] That concern me that no reference has been made to the previous city.

[1:40:06] The other point I was going to bring up is that I know months ago they made reference

[1:40:16] to one of our meetings that we should not add an opinion in this to seeing bias.

[1:40:28] And I think if Joe is going to do that, then fine, let him, you know, oil company has

[1:40:34] that, they have that right to do that.

[1:40:36] But I truly feel that it should be standalone, should not be included in this study, if the

[1:40:44] a scientific study. I also questioned, uh, I did, I went back and I had a friend with

[1:40:56] the FAA. He dug for me and statistically we looked at the wind conditions. We went

[1:41:04] back 30 years of data. He looked at. And we came to the conclusion that the wind

[1:41:14] has actually increased the actual relative wind has increased. We also looked at

[1:41:24] what occurs at night and the requirement of the reverse direction of landing. At

[1:41:33] night we land on six and seven at LAX. So we're over the ocean because of the the

[1:41:39] The winds are changing and we talked about, this particular gentleman is well-qualified.

[1:41:47] We talked about how the heat has changed in our region and in the morning the winds change

[1:41:59] a different direction for a purpose and at night it goes back out to the sea so as the

[1:42:05] ground cools.

[1:42:05] So I guess you're paying, you're putting a lot of emphasis on the win, but do you truly understand

[1:42:17] what the win is truly doing? And that's where it leaves me with a question mark. So those are my

[1:42:25] two points. I think you did a great job doc, you know, considering everything that you went through.

[1:42:31] So, I tip my hand to the graduations.

[1:42:35] Thank you, Frank.

[1:42:37] Let me quickly address the first one.

[1:42:39] Maybe a doctor cushion.

[1:42:41] If you can address the second one in some way.

[1:42:46] Yeah, the first one we, yeah, that's a good point

[1:42:49] compared with a prior study.

[1:42:51] We did actually reference the prior study

[1:42:54] when we designed a FOXM other survey instrument.

[1:42:58] We did look at the prior study.

[1:43:00] the instrument that happened in these areas.

[1:43:07] We want trying to design a way eventually can compare.

[1:43:11] And then the second one, when we do analysis,

[1:43:14] we try our best to be objective.

[1:43:18] We just stay with the science and to our own analysis.

[1:43:23] We do not want to be influenced

[1:43:25] because we see the prior study results one way that our analysis sure in certain

[1:43:32] way so we try to be independent objective but your point is you know link the

[1:43:41] results eventually of our study with a player yeah that's a very good point

[1:43:46] I think that you know sure be done once this is complete you know we can link that

[1:43:52] 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

[1:44:06] potentially being influenced in some way, yeah.

[1:44:10] Thanks for that.

[1:44:11] I just want to add, I'm sorry Doc, but you got to understand if, if at certain hours

[1:44:19] the, the, are we really truly talking about up and downwind?

[1:44:24] I mean, you're looking at the relative time of the day, but what happens at night?

[1:44:28] What happens at, at six o'clock?

[1:44:31] I'm telling you, winds are not at 240 at 10, they're not, they're not.

[1:44:38] I've got 32,000 flying hours. I've got a lot of time. And believe me coming in LAX, they're

[1:44:45] not 240 at 10. Sometimes there's 067 at 5. They're all, they're, they're very dynamic.

[1:44:57] Yeah, I was going to be about that.

[1:44:58] That's uh, that was your...

[1:45:00] 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,

[1:45:29] 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.

[1:45:38] 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,

[1:45:58] downwind population, what we call downwind, you know, some are more downwind than

[1:46:04] others, some are downwind at night, and maybe they're all upwinded in the day.

[1:46:09] You know what I mean? Like we did not vary, we did not try to be more precise on

[1:46:16] an individual level basis. I was kind of beyond the scope of what we could do, but

[1:46:21] we're more trying to get out like over a lifetime, you know, if you live in this

[1:46:27] home, would the wind more often be blowing towards you or away from you from that well field?

[1:46:34] That's kind of what we were trying to get at because we don't have, you know,

[1:46:40] it'd be different if we had, for example, repeated lung functions on the same person,

[1:46:44] like a longitudinal study design, like Dr. Lou alluded to before, you know, then you could look

[1:46:50] on this day when we took your long function measurement,

[1:46:54] how was the wind that day versus the other day we took your measure.

[1:46:58] We don't have that.

[1:46:59] We only have a single snapshot in time.

[1:47:02] So to us it made the most sense to kind of compare

[1:47:05] to the prevailing wind direction,

[1:47:08] knowing that it's imperfect and it simplifies a lot of things.

[1:47:13] I don't know if that helps.

[1:47:15] Okay. Thank you. Kelly.

[1:47:18] 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.

[1:47:28] 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.

[1:47:39] 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.

[1:47:54] In fact, we've discussed it in the chat many times that we wish we could rule out LA based air pollution.

[1:48:01] And that we, you know, I think I remember asking a question about I wish there was an

[1:48:06] equivalent population in a valley just like ours with the same exposures to drilling

[1:48:12] or refineries or blah, blah, blah without all the traffic, right? And there just is no such thing,

[1:48:18] but that the downwind upwind factors did actually make it much stronger. So I just really appreciate

[1:48:27] That particular aspect of the study, which in the beginning I don't think I understood was going to be an option.

[1:48:35] I also just wanted to restate that you know that what I'm understanding is that these measures.

[1:48:41] Of course the measures, but the stratifications in particular so you just spoke to a vector pushing in terms of the.

[1:48:47] It's like a summative number. It's the best we can do in terms of the prevailing win.

[1:48:53] but it's it's not just the best we can do. It's based on consensus from experts in the field,

[1:48:59] you know, which is that is the nature of science and it's never static. It's always iterating.

[1:49:05] And so it would hopefully be refined over time, but for this moment in time, it's considered an

[1:49:11] accepted way to to make a delineation based on upwind and downwind based on the experts in the field.

[1:49:21] I also appreciated Dr. Pushing talking about epidemiology results being strengthened when

[1:49:27] we continue to study with similar questions, sometimes in the same population, sometimes

[1:49:32] in similar populations, ideally in different populations with similar exposures, so you

[1:49:37] can, you know, with enough repeated findings, you can be more and more certain.

[1:49:44] Although you all may know that we continue to use the word theory in science way after the

[1:49:50] time when we're pretty sure it's causational.

[1:49:55] So, you know, and I'm just so grateful for these results, and I'm pretty confident that

[1:50:00] no one here would advocate that we write up or ignore statistically significant findings

[1:50:05] when I was thinking about, like, trying to correlate this return data set with the SNAPs,

[1:50:11] data set, and, you know, my understanding is that rigorous and specifically statistically

[1:50:15] with powerful findings really have to come with a priori design, which means you come with

[1:50:21] a hypothesis, you make a guess, you make a claim, the hypothesis was presented at the beginning

[1:50:27] of this presentation. So for us to tie it to the SNAP study, we would have had to have a hypothesis

[1:50:33] at the start of the study, a priori, about specific compounds being studied in the SNAP study and

[1:50:41] our prediction about their effect or correlation to outcomes for three-term

[1:50:47] birth. We did not do that. So that study could still be done, but I just want to

[1:50:53] make sure I point out that's my understanding of the, so I don't find that the

[1:50:58] SNAPs contradicts necessarily this study because if you go back active or

[1:51:02] fact, it's considered hedging your bets in the science world. And so my two

[1:51:07] questions are will this vote appear a review which I think was something some other folks were

[1:51:13] were getting at it's not clear to me that that's happening and then you referred to some other

[1:51:18] studies but I didn't catch them Dr. Lou I thought you said maybe in Texas and maybe another state and

[1:51:25] if you wouldn't mind putting them in the chat I'd like to look them up I appreciate it thank you all

[1:51:29] so much okay thank you

[1:51:39] regarding peer review I can talk with our department to see

[1:51:45] if there's a way they'd like to do that and UCLA is also free to submit for publication.

[1:51:58] Okay. Thank you. Melody in front of some very nice fall foliage.

[1:52:06] Yes, hi everybody. Thank you. Yes, just bring in in the feeling of the fall.

[1:52:14] Kelly, I wanted to thank you for your comments and I wanted to

[1:52:20] build off that a little bit, but one of the things that Kelly kind of points out is that,

[1:52:26] you know, this study is a little bit of a snapshot in time in terms of what it is and I think

[1:52:36] we need to recognize that. I know that there's a lot of comments coming in tonight that, you know,

[1:52:48] know, and to be incorporated into the study. But I question whether, whether that needs

[1:52:56] to happen now or whether that's part of the subsequent work. And with that, I just want

[1:53:02] to kind of go back a little bit to, you know, the whole purpose of doing the study is because

[1:53:08] there's a requirement under the settlement agreement. And, you know, I'm looking at the

[1:53:15] And technically, there's supposed to be a study done every five years.

[1:53:20] So with the last study being produced in 2012, you know, we should right now be putting out the third study.

[1:53:28] And we are just trying to get the second study out the door.

[1:53:33] So the question then comes like, well, what becomes the timing of the third study, you know, are we five years from this?

[1:53:42] should we already into that five-year cycle, you know, how do we do that? And I bring that up

[1:53:48] because I think, you know, the CAP maybe needs to think about and provide some guidance on

[1:53:54] what is the role of this particular study in terms of meeting the requirements of putting

[1:53:59] information out the door and what should be, you know, now's the time to start talking about the

[1:54:09] scope of the subsequent study, what is appropriate from this in terms of our comments that starts

[1:54:16] to get rolled into the next step. So, you know, that's kind of a question or something to

[1:54:22] think about. And going, you know, number of comments have been made about, you know, what are

[1:54:32] the conclusions of the study, and it may be that, as has been pointed out, there are

[1:54:39] no, you know, we can't say causality for sure at this point of time, and that is just something

[1:54:45] that has to be built upon, but the purpose of the study is to put out information, and

[1:54:55] people will use the study just like they've used the other studies, like the one that Jill

[1:55:00] Johnston prepared, you know, I know there's a we reviewed that even as part of this group

[1:55:08] thinking about the scope for this current study and, you know, there were potential aspects

[1:55:15] of that that didn't apply to us and we picked and chooseed and the same thing with the studies

[1:55:20] from Texas and Colorado. We, you know, we did consider those and we picked and chooseed and, you

[1:55:25] know, some of the reasons like, for example, the Colorado studies didn't apply because they

[1:55:29] were in a rural environment versus, you know, our study being in a very urban environment.

[1:55:39] And related to that and not to downplay, Liz's very, you know, important point that it's

[1:55:49] really important to try and figure out how we filter out what is a contributor potentially

[1:55:55] of the oil field versus these other surrounding factors and, you know, Liz, you made a statement

[1:56:02] that, you know, kind of maybe I misinterpreted it, but kind of implying that, you know, the

[1:56:09] angle of oil field has all of these other factors that don't apply to other oil fields.

[1:56:15] But, you know, other oil fields in LA County have other aspects.

[1:56:19] So for example, when we start looking at health concerns in the Wilmington area, we're

[1:56:23] looking at a highly industrialized area relative to Baldwin Hills and influences of the

[1:56:32] port.

[1:56:32] When we start looking at oil fields in the Rose Hills area, we're dealing with where some

[1:56:39] of the, you know, landfill issues are.

[1:56:42] So there's always going to be something and I don't know if that's, I don't know how we,

[1:56:48] 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.

[1:57:11] And looking at the settlement agreement, it does say that the county when preparing this

[1:57:18] will review other agencies' reports regarding air quality water, seismic data, and where

[1:57:24] feasible, where feasible in its assessment.

[1:57:28] So, you know, the points that were raised about comparing to mates, trying to incorporate

[1:57:36] great snaps if there's information, usable information available at this time, etc. would

[1:57:44] be appropriate to incorporate into the report. And lastly, again, looking at the settlement

[1:57:49] agreement, it says that once this report is presented, that the county shall consider

[1:57:58] reasonable comments by the cap and the health working group. So I don't know what that means

[1:58:05] in terms of, you know, a comment and response type of approach to this, but I just wanted

[1:58:12] to bring those to everybody's attention and just kind of throw out there to the cap.

[1:58:17] Like, what is our intention of the study, this information, as it going to be, I don't believe

[1:58:25] that it's intended to be used for any rulemaking that's currently going on with the CSD.

[1:58:31] It's just a piece of information that's out there for now, and the concerns that we're

[1:58:36] raising, if they can't be addressed or aren't part of the scope of this current study,

[1:58:43] then we ought to start making a tick list and a schedule for the third study, which technically

[1:58:51] would have been due now, except for the delays.

[1:58:55] So that's my long rambling implement.

[1:58:57] Thank you, Melanie.

[1:58:58] 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.

[1:59:12] 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.

[1:59:34] 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.

[1:59:48] 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.

[2:00:00] 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.

[2:00:30] And I guess that's a death for now.

[2:00:32] I can we can cover some of the other stuff in the next time around.

[2:00:35] Thank you.

[2:00:36] Get your anything that you want to say in before next month.

[2:00:42] No, just the next meeting.

[2:00:44] We have falls on on Thanksgiving.

[2:00:45] So just keep in mind that we will now have a meeting in November.

[2:00:50] So that's right.

[2:00:51] So we need to have the date of the next meeting, which is.

[2:00:54] Is it on here?

[2:00:55] Yes.

[2:00:56] 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.

[2:01:05] Paul's hand is raised.

[2:01:07] I'm sorry, what?

[2:01:08] Paul's hand is raised.

[2:01:11] Paul.

[2:01:12] Okay, Paul.

[2:01:15] Yeah, I just wanted to follow up those questions with regard to the, I think it's T3.

[2:01:20] We produce water tank that overflowed with regard to its age and or any other specific

[2:01:26] events that led to releases.

[2:01:30] Okay.

[2:01:31] We're going to have to do that next meeting.

[2:01:33] Why is that?

[2:01:34] Joe's there.

[2:01:35] Because it's past our ending time.

[2:01:38] Really?

[2:01:40] Yes.

[2:01:41] That's what we said two hours ago.

[2:01:43] That's probably the most ridiculous thing I've ever heard.

[2:01:45] When we start, well, okay.

[2:01:46] It's going to take them two or three minutes.

[2:01:48] Thank you for that.

[2:01:49] that. You're welcome. Thank you for ignoring my questions.

[2:01:56] Okay. Okay. See hopefully many

[2:01:59] of you at the Halloween bash at the oil field and see the rest of you on December 12th.

[2:02:08] Thank you all and thank you again to UCLA and the Department of Health who really appreciate

[2:02:13] the report. Thank you and everyone have a happy Thanksgiving.

[2:02:16] Bye bye.

October 2024 Baldwin Hills Committee CAP Meeting

Agenda

Transcript