So, today's webinar, again, the impact of the COVID-19 pandemic on the mental health of older adults. A longitudinal analysis from the CLSA is being presented by Dr. Perman de Reina. Dr. Reina is the lead principal investigator of the CLSA, the scientific director of the McMaster Institute for Research on Aging. And a professor in the Department of Health Research Methods, Evidence and Impact at McMaster. He holds a tier 1, Canada research chair in gyro science and the Raymond and Margaret Labarge chair in research and knowledge application for optimal aging. He specializes in the epidemiology of aging with emphasis on developing the interdisciplinary field of gyro science to better understand aging from cell to society. So, without further ado, I will pass it on to off to Perman de Reina. Great. Thanks, Jennifer. Charlie, do I have the control now? Yeah, you're good to go. Thank you. Well, thank you everyone for joining us this afternoon to hear what I have to say. And as Jennifer introduced that, I'm going to be talking about some of the data we have been analyzing from the CLSA COVID questionnaire data. And I'm here on behalf of the main team members who actually designed and helped launch the CLSA COVID-19 questionnaire study plus who are also members of the Canadian longitudinal study on aging. And so I'm speaking on behalf of them and I couldn't say more or enough about our participants and we have incredible group of CLSA participants who have contributed to all aspects of the CLSA and especially that the CLSA COVID-19 study. And how quickly we launched this study and what type of responses we got is really something to be admired and we are very thankful to their participation. And none of this would have been done without our staff across the country through these very difficult challenging times working from home, dealing with parents and dealing with their children at the same time. So our full thank you and appreciation goes to all of our staff across the country. So, and I couldn't have done or what I'm presenting here without the right and left shoulders that sort of Susan and Tina sort of work with me and we sort of collectively take care of the CLSA. And they were also the main architects of the design of the CLSA COVID study, along with many of these investigators were part of the CLSA team. And I have specifically highlighted some of the people who spent a lot of time with us in designing, developing and solving problems as they emerged with the CLSA COVID questionnaire. And these people are highlighted in an in bold here on this slide. So, before I talk about the CLSA COVID, I think some of you might not, you might be on the call who don't know the CLSA platform and how this study itself was embedded within the CLSA platform. It's important for you to have a sense of what we are doing with core CLSA. So the core CLSA is developed as a research platform to generate data and evidence that informs not only. Not only at the public health policy, but policies at all level and advances the science of aging in this country and around the globe. Just a quick overview. It is a cohort at the baseline was 50,000 people between the ages of 45 and 85 and 20,000 of these were providing data through questionnaires only and they were a random sample of the Canadian population in 10 provinces. And 30,000 is what we call a comprehensive. This is where we do much more in depth investigations. We go to the homes of the people we collect blood samples do we do physical assessments at data collection. And sites and they were all, they are all established from 25 to 50 kilometer of 11 sites across seven provinces. So I won't spend a whole lot of time on the CLSA CLSA design core, but what I'm going to be talking later on, it will become relevant why I needed to show this slide to you. And, and just to put it in the context, they both are comprehensive and the CLSA that they roughly started in 2011 2012, and we finished the first baseline data collection and recruitment by 2015 from 2015 to 2018 we completed our follow up one. And 2018 we started our follow up to, and when we were in 2020 March, we actually suspended our data collection because of the, because of the pandemic, it was right at the beginning, even the lockdowns in most provinces started at on March 16. We had suspended the face to face data collection of the follow up to CLSA on March 8. We pivoted and rather than just collecting no data, we set up all our staff in their homes to collect as much data as we could through telephone interviews. And this is what actually was happening when we were in the middle of the follow up to this is a, it just gives you a sense of what was going on in Canada in early 2020 in March, February to March to April. And that's the first peak, and that's where many of the public health measures, lockdowns, different strategies were being implemented across the country. And a lot of that that was being done not knowing what was coming in the coming months. And then the summer months looked like they were peaks were going down and some of the areas across the country started to relax some of the lockdowns or other public health measures. And then again, as the fall started to emerge more closer to October, November, we started to see the peak for the pandemic again happen and again. All of the restrictions, all of the public health measures, people were staying indoors in homes and it had disproportionately affected at least for the first and the beginning of the second wave. For the elderly people, especially in the long term care, but also in the community also. So that sort of, as this was happening, and we had suspended the CLSA core face to face data collection. Basically, within a week of suspending the face to face data collection we started to discuss whether we should be launching a CLSA COVID-19 questionnaire study. And we were fortunate to get funding from Public Health Agency of Canada. They were, they are one of the major funders along with Jovinsky Research Institute for McMaster. McMaster Provost and Vice President Research Fund and McMaster Institute for Research and Aging. So they are the main funders of this CLSA COVID-19 questionnaire study. But we also got additional funding for the Nova Scotia component from the province of Nova Scotia, Susan Kirkland had applied for these funds and we were able to receive some funds to do some activities in that province. So we launched this study on April 15th to 2020. We actually, if I recall, first time we talked about doing something like this was, I was looking at my notes, it was actually on March 28th. So within roughly two weeks we had driven our staff crazy to develop software, develop questionnaires, program questionnaires and get it out in the field because it was important to capture people's perception. And originally we started this questionnaire to understand the epidemiology of the infection and the psychosocial and other behavioral aspects that people might experience. And so how did we recruit people? We have the core CLSA sample size is little over 51,000 and over from baseline to follow-up one and in the middle of the follow-up two. We've had some 8,638 people who were excluded because of either they had disease, they require a proxy, either they have withdrawn or for some other administrative reasons we were not able to contact them. To make the long story short, our eligible sample for the COVID-19 questionnaire study was roughly around 42,000 people. And we got a response rate of close to 67%, 28,559 responded to our baseline COVID questionnaire. And this is what the design of the study looked like. So large proportion of people in the CLSA were able to complete the questionnaires on the website that the web based interviews. But there were around 20% or 30% of the participants who didn't have access to internet, we collected the same data using phones. So the both sides had the baseline interviews, for logistic reasons, the phone based interviews were then repeated questionnaires were, data were collected by weekly. And in the baseline originally we started with weekly data collection but quickly we realized that it was becoming quite burdensome for participants. And then we also shifted that to bi-weekly for some period of time. And after that we were contacting our participant monthly. And then we conducted a exit questionnaire which started in early November and finished at the end of December 2020. So the data that I'm going to be presenting today in relation to this analysis I'm talking is only looking at the baseline and the exit questionnaire data. So the two waves of data during the pandemic that I will be talking about. So generally these were the things that the CLSA COVID-19 questionnaire included. These questionnaires are all on our CLSA website. I don't have time to go through each and every question we have. But generally these were the constructs that we focused on. We looked at COVID symptoms. We looked at COVID status, whether they were tested positive. We looked at risk factors. We looked at the healthcare use, health behaviors, what type of public health, how they were sort of engaging or using different public health measures, social factors, depression and anxiety. Just to be clear depression was, anxiety was not asked at, no depression and anxiety were asked both at the baseline and exit questionnaire. They were not asked in between for the weekly and bi-weekly or early monthly questionnaires. We also asked them questions related to economic consequences and in the exit questionnaire that was done in November to December, we also added additional questions related to function and mobility. So as part of this, this particular team of researchers, we worked very closely I mentioned earlier, we started to look at analyzing different aspects of the data that are emerging. And to be honest, we are still trying to understand the data and the nuances of the data. But we did focus on looking at some of the issues related to mental health, specifically the depression. So the reason we focused on mental health or depression in this context was that the, all of this was happening against the issues of pandemic restrictions and people being isolated in their homes or restricted to their own homes, not meeting with their family members or other social connection was happening with the backdrop of existing physical mental health morbidities, social isolation that existed prior to pandemic loneliness issues. And some other people who had caregivers or family or, or, or formal caregivers losing connection with that. So the question was all of this that is happening in, in, in the context of what people already had, and that these new emerging situations that were coming from the pandemic itself, how that is impacting the mental health specifically depression and anxiety in older people in the CLSA. I'm only going to be talking about depression today, anxiety, maybe some other times. But that's what I'm focusing on for this presentation. So we sort of started to look at some of the research questions that we will like to address. Obviously, we'll like to look at much more nuanced research questions that the data that we collected during the pandemic are limited because we didn't want to burden a lot of our participants. Nonetheless, some interesting findings have emerged, nothing unexpected, but still interest, interesting nonetheless. So the first question that we were interested in looking at to examine the relationship between social determinants of health and health related factors with changes in the prevalence of different depression and then we wanted to see are these are these association modified by time and time here I mean the pre pandemic versus during the pandemic. And the second question, which we have been working on for the last little while is looking at how the pandemic related stressors, the data we collected during the pandemic, experienced by the older adults are associated with the severity and trajectory of the depression. So I'm going to be talking about these two main questions today. Just to say we use the, the conceptualization of depression is that we use the 10 item center for epidemiological study short depression scale this is the same scale that we have used in the core CLSA. And many of you probably know each item includes four response categories ranging from zero to three, rarely or never less than one day some of the time one to two days, occasionally three to four days, and all of the time five to seven days. Total score ranges from zero to 30 with higher scores indicating greater number of depressive symptoms for our analysis. We dichotomize based on the recommendation of the scale developers that if you have a score of 10 or more. You are indicated to have your screening for depression and that you more likely most likely you have depression. So this is how for the purposes of this study we have classified. Depression when people have score of 10 or more. Obviously, we did not collect all the data as part of the COVID-19 questionnaire study. Some of the variables that I'm going to be using in my analysis was brought from our baseline and follow up one. Cycles of the core CLSA and these are the core areas that the variables that we looked at age, sex, ethnicity, household income, dwelling type, living area. How many people live in your household social participation, alcohol consumption, smoking status, physical activity, number of chronic condition, loneliness. And in some situations we looked at COVID-19 status and I'll come back to that later later. And to examine the first objective, which was to look at the relationship between social determinants and health related factors. We obviously did some descriptive analysis and I will provide some overarching picture what the data actually looks like. And then we also did a way to generalize estimating equation analysis, which is basically a logistic regression modeling that accounts the longitudinal data. And specifically, and it allows modeling for change in prevalence of depression, depression over time. And it's important to note that this does not look at individual level change, it only looks at population level change in the depression. The reason we used weighted GE that it allows one to handle missing data when it is missing at random in a much easier fashion as long as the missing data are monotonic. What I mean by monotonic is if we had a missing data at baseline of the CSA core, but we had data from individuals from follow-up one and during the COVID, this model can't handle that type of a missing data. For every person we have to have a baseline value present. And the flexibility that the GE framework allows that you can use wide variety of link function. And in this case, we use the logistic link function. And this analysis was performed on 39,604 individuals. And these are the people who had the monotonic pattern of missing data. And then we define time period as a pre COVID 19 period as data from the CLSA baseline and follow up and COVID 19 baseline and COVID 19 exit question. So there are in this particular analysis for three time points that we consider. And age was considered as a time varying courier covariate in the analysis. And then we also looked at interactions that that sort of answers the questions related to the interaction between period and some of the demographic or social and health related factors. And this gives you just a bit of an overarching picture of the data and this is CLSA baseline CLSA follow up CLSA COVID 19 baseline and CLSA COVID 19 exit question. And you can see the sample sizes across the top. And it sort of tells you a little bit about the distribution by age, by sex, by household income and the, and what the number of people who screen positive or negative for depression on the CSD 10 scale. I just to keep in mind in the, in the CLSA COVID 19 baseline or COVID exit question that we did not ask the income household income question. So the numbers you see are from using the data from the follow up one, the 3.84 and 3.80 that is based on the, on the follow up one income data. So first looking at some of the, of the, the longitudinal descriptive analysis, the, this is a change in prevalence of depression by period of the top part top chart and the bottom one is for sex. And here you can see that at the baseline that the, that the positive depression was around 13%. It was roughly the same at follow up one, but then it jumped to almost 20% at the CLSA COVID in 19 baseline and a little bit more, but roughly the same at the COVID 19 exit questionnaire. And if you look at the by sex, that the relative increase in female was much higher than male, but both sexes had an increase in the, in the depression. And if we look at by age group, and here we see that again, you see the CLSA baseline, which is the blue bar orange is the follow up one gray is the baseline COVID and and the green is COVID exit. You see actually the relative increase in, in depression scores or depression screen positive was much, much higher in the younger age groups than it was in the older age groups, but it, it, it happened across all age groups in our data. And this is the one of the interesting slides that even though it's a descriptive it's also holds true when we do a multivariate analysis, changing prevalence in depression by period and household income. If you see the less than 20,000 group of people, they had high depression proportions, even prior to the COVID 19, they went up a little bit, but they were already at the high end of it. So the relative increase there was not that big. However, if you look at the higher side of the income, which is 150,000 or over 100,000, you basically see that the jump was much higher, but still much less than the low income people on the whole. So it affected all sorts of people, but it if it that the pandemic affected the mental health of low income groups substantially because they already started at a higher level. And then we sort of started to look at other social participation and living status. You can see the depression rates were already higher with people with low social participation prior to the pandemic and went up substantially during the pandemic. And it also affected people who had high social participation, but not as much as it affected the low social participation group of people. And here you can see the people who were much more impacted by the pandemic in relation to depression were living alone versus not living alone. And if we look at the marker of health we used here was the number of chronic conditions, the notion of the multi morbidity. Again, substantive impact as the number of chronic conditions that people reported having and this is all coming from the baseline. Those group of people had a higher impact in relation to the mental health issues, but it was again happening across all all groups on this slide. So, after this descriptive analysis we decided to look at the, that the way to GE analysis where we wanted to see some of the adjusted relationships because what I showed you before, but we're not adjusted for anything those are mostly descriptive. And here the first, these are the five stars I picked for the discussion purposes here that you can see that in comparison to the baseline co, the comparison between the covert 19 baseline depression score in relation to the pre covert depression score. The odds were roughly around 1.8 and from coven 19 exit to pre covert 19 was almost two. So basically what it is saying that the people went there, odds of getting depression did go up in comparison to the pre pandemic time period. Over the eight month period, the 10 month period when we were collecting data, it didn't go up. It actually stayed pretty high. If anything, it went up a little bit. It will be interesting. If we are able to collect additional data in the coming months to see what happens with the current pandemic wave that we are seeing to the to the people's mental health. Here again, as I mentioned before, the younger age groups had a higher odds, low income groups. Again, three or more chronic condition twice as likely to report having depression and loneliness, which I didn't show you the descriptive was one of the stronger risk factor for for experiencing symptoms during the pandemic. And then we started to look at some of the interactions that I mentioned earlier on. Here you see that again, if you look at the time period, that is the covert 19 baseline versus the pre covert 19. And we look at the interaction between the time period and the income variable, let's say here, that's the second bar, the chart at the bottom of this slide. You can see the people in this that the, the lowest income group in comparison to the highest income group had odds ratios of four so that it disproportionately impacted people in in low income groups, even though it was also higher in higher income groups, but it had a disproportional impact in the low income groups. And this pattern was seen with all sorts of different. You see that up on the top slide, male versus female. Again, female experience much higher odds of experiencing depressive symptoms. And you see this pattern with multitudes of factors, but not as strong as what we saw in relation to income. Again, low versus high social participation, two and a half to almost 2.8 odds ratio living alone versus not living around to and and some of these things either went up with time or sustain themselves at the same level. Here, whether you are looking living in a household standalone house, or you're living in apartment or some sort of congregate setting type of a living arrangement, your odds ratio for experiencing depression was much higher in comparison to people who were living in detached homes. Again, here you see the differential impact of chronic conditions over time in in relation to the pre pandemic to during pandemic and and as the time went on, the impact of the chronic condition actually start started to be get a little stronger. Here we have odds ratio, I think 4.5 versus four. So that was the data related to the first research question. And, and the second question that we were interested was looking at how the pandemic related stressors experienced by older adults associated with severity and trajectory of the depression. So this is a slightly different take on the same question. So the, what is the, when I'm talking about the, the stressors that people experienced during pandemic. There was a series of questions that we asked in our baseline as well as in the exit questionnaire, related to different forms of stressors that people might have experienced health stressors whether they were themselves ill, someone close to them was ill or somebody died. It was difficult to be the accessing resources lots of income, unable to access necessary supplies of food unable to access usual health care or get prescription medicine medications. Questions about conflict whether there was a verbal or physical conflict separation from family and caregiver responsibility. So we sort of. This was not categorized like this in the questions that we asked, but these are the labels that for our analysis purposes we have given calling them health stressors, difficult accessing resources conflict separation from family and caregiving responsibilities. And, and as the bottom of the slide, we say that these experience were grouped as a yes if the participants endorse at least one experience in this specific category, and if they said no, then they were classified as a that no was not endorsing any category in that group of stressors that we have defined here. And here we actually looked at a slightly different type of analysis. This was a latent class growth modeling. It is a group based modeling strategy, which identifies distinct classes of individuals who follow a similar pattern of depression in this particular analysis. Over the four time periods that is CLSA baseline CLSA. First follow up COVID-19 baseline and COVID-19 exit. These trajectories were modeled using a proct fredge procedure in the in the SAS. And COVID-19 stressor experience of loneliness and all of the covariance, which we wanted to a desk for that we saw in previous analysis were added to the model. Model selection involves a bit of iterative estimation and some qualitative judgment. And but mostly, we use some of these fit fitting model criteria to determine which is a good fitting model and which is the model that we want to pursue. One of the advantages of advantages of this growth modeling. This latent class growth modeling is that when we have multiple groups, depending on what the shape of the curve might look like. You can use a linear term quadratic term or a cubic to fit the best fit fitting model in our case. It was actually came down to linear or a quadratic term term depending on what particular group we were looking at. This is where the latent class, it looks at the data it defined. It tells us that the there are three underlying categories of groups of people that experience depression. And based on the best fitting model, we actually defined this into three categories, the three groups of trajectories. The bottom one is the red one is the low and consistent. They were fairly low, but didn't change much over time. The green one is a moderate and increasing, and then the high is the blue one is high and increasing substantially. Just to remind you, based on the CSD, anyone who has a score of 10 and over is classified as having depressive symptom. And you see the high and increasing all have scores higher than 10. And the dotted lines are 95% confidence intervals around each one of these trajectories. Again, just to keep in mind that this is not defining an individual level trajectory. This analysis also looks at group of people who belong to that particular trajectory, the interpretation of the result is at a group level, not at the individual level. So, focusing on the, these are the all other variables that we adjusted for. And this was quite interesting majority of the health raters stressors did have impact on on individuals and here, what we are doing is the first set of columns I had we are taking the this green line moderate comparing it with the red one, and then comparing the blue one with the red one that's why we're the comparison happen so here after the groups of trajectories have been defined. We basically ended up running a multinomial logistic regression. And here comparing the moderate increasing group to the low increasing group, but these are the odds ratios linked to different categories of stressors. And here, the interesting part is that the conflict stressor was almost four times of in the moderate in comparison to the low, but that that effect size or odd ratio goes to almost eight so substantive impact on the mental health of the people who are actually going through a conflict whether it is in the same household, or outside the household we were not able to differentiate but it shows that there is a substantive impact. And then the again, loneliness issue is a substantive risk factor, especially for people who already had a higher level of depressive symptoms prior to experiencing the pandemic and going from five from the moderate to low to almost 15 other issues of 15 for that group. So this analysis gives you a bit of a different perspective that sort of looks at the severity of the depression and how that depression sort of impacts over time through these trajectories when related to these specific variables. So that's all I had to say today, basically in conclusion, COVID-19 pandemic is at a significant impact on the mental health of older people and I think it's important to note that especially the young old the 55 to 64 year old actually had higher proportion of depressive symptoms. Our results show that the prevalence of depression increase during the pandemic, as compared to the pre pandemic. And the increase and it sustained itself through the eight or month follow up that we did during the pandemic increase in depression is not evenly distributed disproportionately impacts some of the socially disadvantaged or people who have health issues. And the group of individuals who experienced pandemic related stresses such as conflict had much higher odds of being in a group that had the worst trajectory or depression. So, I will stop here and hopefully we can. I wanted to leave some time for question answers if there are some interest from the group. But I gather that there is some CLSA propaganda material here as well. Here is a on the CLSA website. We have a COVID-19 dashboard where you can go and see other variables descriptive data on our COVID questionnaires. And we will be adding additional follow up question. I think this is still based on the baseline COVID, but others are coming. And secondly, there's, we have decided to release the CLSA COVID questionnaire data to research community and some of you have probably applied as part of the current deadline that just recently closed. So they are available to other researchers to access for future research questions. And the CLSA core is funded by CHR and CFI and many of the provinces and universities across the country. Thank you. Thank you for Mender for the excellent presentation. As always, I'd like to now open it up to questions. Just reminder muting will remain on, but you can enter your questions into the chat box in the bottom right corner of the WebEx window. I didn't see any questions yet, but I'm sure there will. Here they come. They're starting. So we have a question from Teodora. What was the number of females and males in the study? And then also maybe you can comment if the difficulties because of language barriers and not knowing the system. So here, this slide shows what the distribution by sex is. So you can see the CLSA core itself have roughly 51% female. And that is similar to that in follow up one in the COVID baseline, we had around 52 and COVID 1953. So that's the male to female ratio in the CLSA and during the COVID questionnaire. What was the other question was please repeat what conflict refers to this was a single question we had in the CLSA. That asked people about about whether during this pandemic period. They experienced an increased verbal or physical conflict. So that's what I labeled that that construct as a conflict. It might not be the right label. That that's what we ended up labeling. So it is based on a single question. That we sort of looking at that data. Do you remember what the question was? There's a question. What, how was the conflict question worded? And when was that question asked? Pardon me. It was asked at the CLSA COVID baseline and then repeated at the CLSA COVID. Exit and the question was the, which of the following have you experienced during the COVID 19 pandemic? And then it asked these experiences that I've listed in on this slide and the question was, have you experienced increased verbal or physical conflict? It doesn't ask inside the family or outside the family. It just simply phrases the question in that fashion. Another question we have is understanding that data analysis continues. What ideas have emerged about how to mitigate depression and anxiety across these age groups? Well, we are barely scratching the data. And so right now we are looking at. I think obviously some of the mitigation strategies that will have to be targeted are going to be how it differentially have impacted young old people in low income groups. What kind of support structures they need in order to manage some of the stressors that they have experienced. I haven't really thought about what would be the interventions or strategy that would be needed to mitigate our goal up to now has been thinking about where are these risk factors and who is being impacted more than others. The people who work in the area of mitigation might have to look at these data and say, these might be the strategies that might happen. Obviously, if we look at the caregiving issues in this particular slide that is on the screen, it has impacted substantially older people because they are not able to see their grandchildren, their children, or their other loved ones. So the question is if we have to go through this type of crisis situation again, I bet we can and should be able to come up with plans that the caregivers, especially the family caregivers become part of the care system of the older people who might be living alone or might be living in the institutional setting. Mind you, in our data we have not looked at the institutional setting because these are all community development people, but nonetheless the issue remains the same. Low income side, I guess people have to deal with multiple issues in relation to not having resources, maybe living in small spaces, maybe living in multi-generational households. There might be multitudes of factors that might be driving their mental health issues in relation to the pandemic. So this is just scratching the surface, there are lots more to be done, whether CLC can answer all those questions remains to be seen. But those are some of my thoughts at this point in time. And I think that touches on one question that we had about what the findings may recommend for agencies working with seniors in the community and also prevention. Anything else related to that that you might. I think what it indicates is that the seniors who are in the low income households require special attention. So there needs to be some targeting done there. Other one is the seniors who were lonely. I think that again emerges as a important factor. We do have to make sure that we community organizations or public health agency of Canada or provincial agency think about that the loneliness that existed prior to the pandemic. How that actually increased the risk of some of the mental health issues and how do we think about providing that support to those people in these very, very difficult times. So I think you can pick multiple areas where story is that people need support and how do you provide the support that supports them through this time becomes critical. It's also, and I know some of our colleagues are looking at analysis. It's also interesting that the mental health issues are much more prominent in younger people. Maybe we need to do some additional analysis and understand the resiliency of older people here that their depression rates didn't go up as much as some of the younger people. And I think there are other things to be explored here. Yeah. Okay, switching gears to some of the, the, the data you presented just a question about the odds ratios and whether they were significant because they didn't see a P values. Well, I will for say I'm not a big fan of P values. That's why we provided confidence interval, but to give you a sense that if you are interested in P values, all of them were significant. Except for one that change after we adjusted for the loneliness and that was the living alone variable that became non significant, but everything else was pretty much associated statistically with the outcome. And what about the impact of the cognitive status of participants. We haven't looked at that. Yeah, that's what I thought. Okay. I'm working for there would be some analysis that we might look at in the future. The, the previous cognitive history, but we didn't collect cognitive data. As part of the COVID questionnaire, but as you know, we are still collecting data as part of the follow up to and there's close to 8,000 or so people or 10,000 people. On whom we have collected questionnaire data during pandemic, including telephone batteries of COVID. So we probably have opportunity once the follow up to data become available to address some of those questions as well. So I'm going to read this next one. It's rather lengthy. Do you know if any of the other international longitudinal studies on aging were set in locations that chose to adopt different public health measures at the outset of the pandemic. They add that it is distressing if not surprising to see the extent to which mental health was eroded and so important moving forward to be better prepared to respond to future disasters. Comparative analysis with other settings could be helpful. Any comment on that. Well, it is, first of all, it's really hard to figure out what strategies, what different jurisdictions or countries used. We couldn't even figure that part out easily in Canada across provinces. It's very hard thing to get data to determine what happened. One of the things that other studies, there are other studies that have come out and have shown the impact on the mental health of older individuals in different countries. But majority of the study, maybe there's one or two other studies that I have looked at have looked at or collected data just during the pandemic. I didn't have the data related to the pre-pandemic period that we have the advantage in relation to the CLSA. So the comparative analysis between pre-pandemic and during pandemic gives a special, I guess, interpretation of the CLSA data. But the data that have been collected longitudinally over time during pandemic are showing pretty much the same pattern in different parts of the world. So it is not just that there is something in Canada that is happening. This is the same pattern people are seeing in other countries as well. Even in countries like Australia, there's a paper that has come out of Australia which did not have as stringent public health measures for a longer period of time as we did here in other countries. But during the lockdown, they experienced the same, same impact. I haven't seen data during the opening, after the opening up of the restrictions, but the earlier on the patterns were the same. Another question here, you have a higher proportion of over 75 people for COVID baseline and exit compared with earlier waves. Could that contribute to higher percent of depression for COVID baseline and exit? So there's two things happening and that's a good point that's the question that remember we are picking people from the core CLSA and CLSA is aging. So we have taken that into account so that the higher proportion of people in older age groups as a function of the design of the CLSA. That's what I was trying to say. The age increase, we actually adjusted for age as a time varying core covariate when we are looking at some of the relationship with loneliness or income and other factors that is adjusted for age as a time covariate factor. So I personally don't think that is the reason that we would have seen higher proportion. First of all, I don't think we see a higher proportion of depression in the oldest age group. We see it in the youngest. But I whatever we see in that age group is not just function because we have more people in the 75 age group. At least that's how I am answering that question right now. I will have to think about it a bit more. We have another question about separation from families and that she says, I'm surprised that separation from families did not figure more prominently into your results during COVID lockdown. Any thoughts as to why? Well, we have this is the analysis I presented and separation for family showed a fairly moderate impact on the trajectories of worsening mental health. Do we have a much more elaborate data in the CLSA? No, simple question that people responded. I think some of these questions might be better answered with the, when we have CLSA follow up to data. As the questions are about the families, families, separation or caregiving a bit more nuanced in our data collection than they are in this one. But even with this single question, at least in the community developing at least the participants that are contributing to this data had increased odds ratio, but not as high as let's say conflict or loneliness. I just wanted to say if I remove the loneliness variable from the map model, the separation from family does go a little higher in its effect sizes. So loneliness probably explains the majority of it. What about data on perceived stress levels? Do you have data about perceived stress levels? I'm trying to remember. I can't. I don't think so. I don't think we collected that as part of the COVID questionnaires. But I don't think it was part of COVID, but maybe main wave. No, we don't have a perceived stress in the main ways we have. We can create a perceived stress variable in the previous from the previous waves, but there is no single questionnaire that has the perceived stress per se. We're getting towards the end of the hour and there's just a few questions left. If I just wanted to note, if we didn't, if we don't get to your question. We can follow up with you via email with some further comments about it. A question from Larry Chambers, which I'm not surprised he's asking this question. What actions are, what actions of government are informed by these results? If any. And we have there been any work to charge on at this question already earlier on. I think what this data identifies that the mental health of older people or the people over the age of 50 people over the age of 50 in the CLSA is not affecting people equally. There is a population heterogeneity. There is a different social determinants of health that the different social factors impact people differently. So I think these data probably not necessarily identify anything new, but reinforced that the pandemic we know from the infection point of view it has impacted population very differentially. And the same thing is happening in the context of the issue like mental health. It affects people very differently. People might be writing the same storm, but they're all in different boats in context of mental health. And I think that's what these data be a firm to some extent it identifies some of the, some of the new things for example that which to me was surprising that even after the first lockdown when we had the easing of the lockdown, the depression, the proportions of people with depression remain pretty high. It didn't go down. Question is how long it will take before people start to feel back to their original mental health or is it something they're going to have this for a long period of time we have some PTSD measures in the CLSA that will be interesting to look in the future how it has what impact traumatic impact it has on people's lives but I think that these data at least to me it emphasizes that the lockdown or public health measure which are important to have to prevent infection has had a differential impact on different groups of people and the mitigation strategies have to be targeted at those groups of people and mental health issues depression issues are are important. We are doing some other analyses in relation to persistence of symptoms. You see that game same population heterogeneity and differential impact on on on groups of people from low income males to females, people living alone versus not living alone. So it is a pattern. That is the social disadvantage groups are really have a experienced cost of them heavily, not only from infection but from other consequences the pandemic including mental health so maybe some of the strategies have to target those individuals. Okay, well, it's 101. I just want to also remind everyone before people start to leave if you can please complete the poll before you leave. I think a good way to end parminder is two last questions one that was asked initially. I do. I'm sorry. I have a call with the minister so I do have to go. When will the results be published. That's what I was going to ask. Well, the first paper the that the gene analysis is under review right now we'll see if anybody accepts it and the other second objective is being written up. Okay, great. Well, thank you very much and to everyone who we didn't answer your questions we will follow up with you. A few closing remarks before people start to leave. I'd like to remind everyone that CLSA data access request applications are ongoing. The next deadline is September 8th. If you can visit our website under data access to review the available data in including any of the COVID-19 questionnaire study data as well as additional details about the application process. I already reminded you to complete your survey. The upcoming webinar for May is entitled a long term cognitive impairment following concussion findings from the CLSA. It will be presented by Dr. Mark Bedard who recently defended his PhD thesis in clinical psychology at the University of Ottawa. You can visit our website to register for that if you're interested. And remember the CLSA promotes this webinar series using the hashtag CLSA webinar and we invite you to follow us on Twitter at at CLSA underscore ELCV. Thank you again for attending our presentation today. I know we're a couple minutes over. But we look forward to seeing you and Dr. Bedard at the next webinar. Thanks everyone.


My name is Michael. I work for SAP since a couple of the years in the Cloud Foundry team and I would like to talk With you about our experience with admin UI and why we ended up deploying it as our Cloud Foundry application The admin UI is a very useful application for Cloud Foundry operator That allows you to visualize a lot of metrics and operational data For example, you can look for a specific application and see in which Diego sells The application is deployed or on one Diego cell have a list of All the application that are deployed on these cells and many many other useful information our old architecture with admin UI was Bosch release if you're not familiar with Bosch, it's a great tool to manage life cycle for virtual machines so our application was deployed in a virtual machine and we had to keep care about the Ruby Framework actually Bosch release it's open source. It's on the Cloud Foundry community, but it started to be an effort to update and operate as to just update the source code of application. We had to spend a lot of time To create a new Bosch release to test it to deploy it and every deployments takes time as it's a Virtual machine that gets deployed and not just an application So we started to think to use a better Framework a better platform to deploy our application and We started with Bosch because there was already a Bosch release available for the admin UI We are in a team where we deploy Cloud Foundry with Bosch. We deploy concourse with Bosch We deploy our monitoring and logging stack with Bosch so we started using It also for the admin UI, but why not making use of Features of Cloud Foundry also to deploy our application We deliver with our product so Why not on Cloud Foundry? This way we would get The advantage of simple and faster updates and we would stop caring about the underlying framework So we don't need to update Ruby anymore. We don't need to check for vulnerabilities on the Framework we are using What do you have to keep in mind when you want to port the admin UI as a Cloud Foundry application? First thing is that we don't really need persistency. The admin UI has a database, but it's used as a cache So we don't really need that to deploy it on Cloud Foundry What we need is still the same setup of a UAA client So this is as it is described on the Admin UI GitHub project you can put that in a shell script. You can use that on your Automation system like concourse on Jenkins before Pushing the admin UI. We're not going to talk about that We also need a special space on Cloud Foundry. You cannot just deploy the application on a trial developer space Because you need special permission. The admin UI needs to have connection to various Cloud Foundry endpoints to gather its data What we also need is to move the configuration file from a file in the in the file system to Environment variables. So we would like to to Set the configuration of the application Using the Cloud Foundry manifest. We want to use only one place to put all the configuration of the application Not use the manifest for just the Cloud Foundry Related part and then push the application. We've already the configuration inside. We want to keep the source code and the configuration in two different places Let's start with security groups. What do we need to? To run the admin UI on a on a Cloud Foundry space We need a special security group to allow connection to the Cloud Foundry Cloud controller and UAA databases. We need to connect to that to get information about org spaces and more We need to connect to the NATS endpoints. That means we need to Connect to the private IP of NATS VMs in your Cloud Foundry deployment We need to connect to the API endpoints and this means the internal IP of the API virtual machines not the Public one which is exposed by the load balancer. After that the main issue is the configuration file the resolution is not the best, but You can trust me that this is just a YAML file with a list of Configuration parameters you need to set to run the admin UI. One can be the Address of the database, the Cloud controller database, the UAA database and so on So the admin UI is expecting a YAML file in a specific place of a configuration folder But we want to set that through the Cloud Foundry manifest So how can we do that without touching the source code of the admin UI? This is the template of the manifest we are going to use to deploy the application of Cloud Foundry So we see that we pass all the configuration we would like to set as environment variables and then We launch the application with a different command. So you would expect to start immediately the Ruby process of the application, but instead We launch a bash script This allowed us to to read from the environment the configuration. We need a write that to a file This is a really simple bash script that allows you to run ERB to evaluate a template and write the configuration file inside the container This happens inside the container after the application is pushed and then we start the application This is an example of a ERB template So just the configuration file plus the ERB code that allows us to read from the environment and write to the file This is a really simple trick to immediately push an application that needs a configuration file but We don't want to too hard code the configuration into the application. We push we want to pass that as a Environment to the application manifest. This is a suggested way From Cloud Foundry and has other advantages like you can look at the configuration by running CFM and so on So to do that we just need to push our application with two additional files inside The the source code. We just add something. We don't touch the existing code. We add our star script and The ERB template that will read from the environment Let's look at how does it look like on the file system From the bottom we have a star script. We just saw the configuration template the ERB file. We just saw We have a manifest template But we will use to generate all the configuration we need for example We can read that configuration from a Cloud Foundry deployment or from other sources We have a nap YAML file that just includes the URL On GitHub of the admin UI source code and the commit ID So we don't even need to keep the admin UI as a submodule as it was on the Bosch release So if we need to to fork the application for whatever reason we can just change the URL on the YAML In the action folder, we have three files that are just batch scripts That allows us to create the manifest using the template Every one of you Sure, if you deploy a Cloud Foundry application, you will have your own way to to generate the manifest I don't think you just hard code in the manifest all the Credentials, but you just have a template and then you add the credential depending on where your which Cloud Foundry deployment are going to to use We have a deploy script that just will clone The the source code of admin UI and will add the stab folder and then run a CF push We have also pre-deploy script that we'll just check for the existence of the security groups that we need This is the comparison with a Bosch release You can't even read but there are more than 60 files Most of them are generated by Bosch that's true, but we still need to take care on a lot of things One of that is the the Ruby framework this way we make use of Cloud Foundry Ruby build pack and we get for free on updates on every updates of the platform What we gain also is the speed of redeployment of the application Before it was over five minutes to start and recreate especially when we need to update also stem cell the OS of a virtual machine we use to To run the application and it takes time It's a single turn so you cannot even use that during that time and if you want to try to change something it's still An effort to change the Bosch release and to start it again with Bosch instead of just a CF push But we still have a performance issue it's one of a issue that we didn't talk much before and It's about the load of this application that minimize a great tool, but when it's deployed on a Productive landscape we start to get a lot of loads because the application subscribes to Doppler and as an admin user and it gets all the metrics and all the logs all the information from a Cloud Foundry deployment this means that We get one metrics for example as a latency for each and every get requests that hits your landscape On a productive landscape this it means a lot And the admin UI is a Ruby process on a single thread And it's not able to handle all this information so As we are not Ruby experts we started again to think how can we improve the performance without touching the code of application can we put something in Front of that and filter the data. We don't need before it reaches the application and This is the idea to put another Cloud Foundry application written in go Between the admin UI and the Doppler endpoint This way we would be able to Drop all the metrics that are about to reach the application and we would improve the performance So how can we do that? first of all we need to Make the admin UI target our nozzle our application our go application instead of Doppler endpoint in Cloud Foundry Then this nozzle should be able to authenticate to Doppler, but we don't want to write the admin UI token admin UI credential into the nozzle because this is a Cloud Foundry application and it's public So how can we do that? but if we just set the Doppler endpoint on the admin UI to be the nozzle instead of Doppler the admin UI will already Send to the nozzle the authentication and the subscription ID necessary to request data to Doppler This way on the nozzle we can read the subscription ID and the token used by the admin UI that is thinking to It's believing to Connect to Cloud Foundry, but instead it's connecting to our application with that as a proxy Our application we use this data and we connect to the real Doppler endpoint At this point we have two Websock and one client and one server on the nozzle the server will wait for the admin UI connection and The client will connect to the real Doppler When the connection goes through we make all the Swim of events pass through a filter written again in go and this is open source. You can try that and Our filter will look just for value metrics and for container metrics This is the type of information that the admin UI cares about On the value metrics we also filter out all the latency information That is the big chunk of data that is hitting the The admin UI that we don't need at all What are the results of this? nozzle put in between the admin UI and Cloud Foundry Doppler Well, the admin UI on a productive on our productive landscape landscape Took six seconds to load just the first page and this becomes frustrating when you need to look for some data You need to change that up and it takes three four seconds every time After putting the nozzle in between we managed to take that six seconds down to point three seconds Looking at the logs of the nozzle we see that we filter out around 97% of the events that would have hit the admin UI instead On our landscape we have around 800,000 events every 30 seconds and Most of them are useless for the for the admin UI Looking also at the CPU load of the application. The blue one is the Default tab the new I just deployed on Cloud Foundry that is always at a hundred percent But that was exactly the same case also when it was deployed via Bosch on a virtual machine Also, because if you add more cores doesn't it doesn't help as the the application is a single thread Using the nozzle we managed to get the CPU down up to 40 percent. We still have spikes We still hit the second bottlenecked on the on the line, but this way We have better chance to not lose data Because when the admin UI is taking too long to process data from Doppler Doppler we start dropping out information before sending it The nozzle is open source Since one and a half day probably you can look at that you can try it you can deploy that as a Cloud Foundry Operator and you can put that in between the admin UI and your Doppler endpoint. You just need to set The address of a real Doppler endpoint on of your Cloud Foundry installation on the nozzle settings and Tell the admin UI to connect to your nozzle which is a Cloud Foundry application What would be the plans for the future? First of all would be to make the admin UI more Cloud Foundry friendly What does that mean? We'd like to get read of this workaround that we use to wrap the admin UI with a shell script to read the from the environment and write the configuration file it would make sense just to Make a pull request on the application and make that able to read from the environment directly But this way we managed to deploy that in a couple of hours without Asking anyone we are asking the owners of the application to change anything and this is a great thing Would like to try to use a persistent database even if it's not really necessary This could improve a performance as every time we restart the application. We lose the the database and Then takes maybe one minute to to to be populated again The last thing would be how can we scale it up? This is something we still have to start to look into that and would be really interesting because we managed to to to solve a bottleneck, but it's not enough and Getting on bigger and bigger landscapes. We will still have issues in the future actually the nozzle is also the nozzle it's a workaround as Logregator now has announced a way to Filter the metrics to request only what you need But the only thing it's implemented right now I think you can only ask either for logs or for metrics you cannot ask for specific metrics and our issue is that It's the metrics of the issue and we are getting all the metrics and we don't want the latency metrics that are 90% of the metrics we receive and this will Hit the application real hard To conclude We managed to move out the configuration file of the application Into the cloud fund we manifest without touching the application code at all This is not the nicest way to do that, but you can do that in a couple of minutes you can just Add a shell script that will start instead of Directly starting the application and you can read from the environment and write in the configuration file We managed also to greatly improve the performance of the application again almost without touching the source code say almost because The application did not support an override of a topler endpoint So we had to add this as a configuration parameter, and this was a really small pull request, but this way On cloud fund we managed to create a new application on a new a different language that's Improved the performance of an application we don't own and this would have took much more time to try and fix that in the In the ad new eye This is not of course the final solution, but this as well allows you to fix issues in a quick way and Without touching the code of the original code of the application With that you have any question The admin UI is only an application with admin rights So it's for the operator that has an overall view of the whole landscape It's for the platform operator is not for for users Yeah, if you don't have any questions I have one and that would be Why would you deploy the admin UI which is for a cloud fund re operator on cloud fund re does it make sense? You do not deploy something that it's a monitoring tool on cloud fund re to monitor cloud fund re but this Makes sense, but it depends on your usage of this application This is a great application to browse for data to get information of On something, but it's not a monitoring tool. So we have a monitoring stack. We have a login stack, but Chances are if cloud fund re is Experienced several problems. We don't need the admin UI to fix it. This is just when you have to find out Where an application is deployed on which cell what are the application deployed in a cell? That's maybe it's behaving in a strange way and you want to see what are the application affected and check if it's really a problem or not and so on but Everything you can do in the admin UI you can do also Directly from the source of information. It's a really painful to go into the database and look for data to start Getting all the metrics from firewalls and do the query on your own, but you can still do that without the admin UI so in our experience the trade-off of Deploying that for on cloud fund re and the all the time we save for the lifecycle of application. It's fine for us to Have a risk of not having it available when we have Huge issues on cloud fund re as also it's only a single application So we don't have many instances deployed, but that it's fine for our use case I was at that code talk and that was really really similar one thing would be I'm not sure if the top plugin of CLI is Showing all the information or just the top of the column you you're ordering to So on the admin UI you see everything so I can show on the very first slide there is a screenshot of the application and You see that we have 1100 tabs of the application list so we can see everything you can look for everything you can see for Crushed application and so on you can have the all details of what's happening on the landscape. That's why also it's The performance is an issue because it's getting all the data of your cloud fund re deployment Yeah, yeah, okay. Thank you very much and we'll be around here if you need something else


Hey guys, this is Stowe Bishop with Radio Rothbard and I want to let you guys know about the first Mises event of 2024. On February 17th, we will be returning to beautiful Tampa, Florida for an event dedicated to inflation, causes, consequences, and the cure. While the government tries to hide the consequences of inflation and their official statistics, Americans see and feel it every time they visit the grocery store. The state and its media lap dogs try to blame inflation on corporate greed, but the true source of inflation is the Federal Reserve and the banking system. We're going to be tackling this issue with a great lineup of speakers, including Joseph Salerno, Patrick Newman, and our new Mises president, the great Tom DeLorenzo. We have a special code for Radio Rothbard viewers for a 15% discount that's Rothbard 24. And you can find more about this event at Mises.org slash Tampa 2024. Hey guys, this is Stowe Bishop with Radio Rothbard and we've got another great offer for Radio Rothbard listeners. We have a free book that we want to send directly to your doorstep. If you are a fan of this show, you have no doubt heard us discuss Murray Rothbard's classic anatomy of the state, his dive into the mechanics of the state as we know it, what the state fears, what its greatest threats are. It is one of the all-time best Rothbard reads, a personal favorite of both myself and Ryan. You can get your free copy as a Radio Rothbard listener by visiting Mises.org slash Rothpod free. That's R-O-T-H-P-O-D free. You can also find the link in our show notes. Welcome back to Radio Rothbard. I'm Ryan McMakin, Executive Editor with the Mises Institute. My usual co-host though Bishop is under the weather today, so it will be me and my guest, Benjamin Severs. Ben is recently a Mises Institute fellow, that is he was part of our summer fellowship program. He has a bachelor's in economics from Grove City College and is presently a PhD student in economics at West Virginia University where he's just begun that program and we're glad to have him on because he just recently published an article with us on how Arkansas, the state of Arkansas, the government of Arkansas, is forcing a sale of private land that had been owned by Chinese nationals. We'll carry a link to that in the description, but it's called Arkansas's attack on Chinese-owned property is reckless and crony driven. I'm going to talk about this. I invited Ben on, even though this is a thankless topic from the perspective of being an editor at the Mises Institute and that every time we run something about free trade, about letting foreigners do things that MAGA types don't think they should be doing, we get all sorts of nasty emails. And so I fully expect that to happen with this one as well because guess what? We're going to side with free trade and freedom on this and we're going to talk about some of the reasons why the hysteria and the calls for a new Cold War with China are anti-freedom. I certainly have nothing to do with anyone who's going to claim that they're in favor of freedom and free markets. So we'll cover this topic, but we'll of course get plenty of nasty grams about that all from people who claim that they are guided only by reason and logic and never ever affected by emotion while they rage against us. But that's the sort of responses we get to these sorts of topics, but we're going to talk about it anyway. So Ben, why don't you just tell us a little bit about what the specifics are in this case and then we can get into some of the more general principles at work here. But what's going on in Arkansas and what's the deal with this Chinese-owned property? Yeah. And first off, just thank you for having me on, Ryan. It's an honor to be on here. So regarding this recent action by the Arkansas government, they ordered Sinjenta, which is a subsidiary of the Chinese government-owned company. And it's an agricultural firm in Arkansas that owns about 160 acres of land, acres of land. They were ordered to divest of these landholdings. Well, hold on. What's just only a measly 168 acres or 168,000? 160 acres. Yeah. 168? Okay. All right. Go on. Yeah. Yeah, I believe that was the number. But yeah, they were ordered to divest of these landholdings. Yeah, 160 acres of it. They ordered to divest of these landholdings. I think they're given a window of two years because they're violated a law. And now this law that they violated was reporting law. But I believe that the punishments associated with violating such a law were up to the discretion of the government. So they're ordering them to sell this land when they didn't need to. But in this article, I make the case that this is not an ethical way of looking at it. And there's a lot of economic problems with this. So we could get into that, I suppose. But there are some unintended consequences of this law. This is a company that provides a lot of services to American farmers. One such service is research. So they conduct a lot of agricultural research on their land in the United States. And that's something that benefits directly American farmers. So obviously, the people in charge of decision either did not care about the effect that this might have on American farmers, or they have other interests, or they were ignorant of these effects. So it's either unintended consequences or a cronyism. Them choosing to benefit some over others. So yeah, I don't know specifically what you want to get into. But I have a few different arguments I love in this article. Well, yeah, let's get into some of those in this specific case then. And you're right, I just looked at the CNN piece. It's 160 acres, which okay, wow, gee, so much land. And so let's look at some of the assumptions behind this. Okay, first of all, a lot of the people who want to get to kick out all the foreign landholders, it's assumed that these people aren't providing any sort of actual service that the market doesn't want them there, that market participants don't benefit from this relationship in any way. And it's all downside only from foreigners owning land here. And you can see it in some of the calls from the politicians who are grandstanding about it, saying that only American citizens should own land. Of course, we'll see here in a little bit that, well, he's a little late to that show because foreigners have been owning lots of US land from the very beginning. But let's look at the specific Arkansas case. It looks like, I would say that we're facing here a bootleggers and Baptists situation. And if you're not familiar with that concept, listeners, this is an old concept where you get prohibition of alcohol, because A, on one side, you've got the moralists who just don't like alcohol for moral reason. And then on the other side, you've got the bootleggers who want booze to be illegal because they make huge profits from running illegal booze. And so there's a private cynical business interest, and then there's a moralist interest, and they join forces, people who naturally should hate each other, but they join forces in order to benefit themselves either for psychic profit or real monetary profit. And so maybe there's something like that going on here. And you do touch on that on some of that in your article. So yeah, go ahead and get into that now. Yeah. So that's an excellent point, Ryan. I don't specifically say that, but perhaps we've been great to stress that point. It's not going to be one or the other. It's not going to be crony interests or just typical conservative anti-China moralizing. It's not going to be one or the other. So what's some of the crony interests? Well, whenever you do order them to sell the land, who's going to buy it? That's a big question. Now, it could be Bill Gates, who is one of the biggest landowners in the state, who's been acquiring farmland. Could it be him? And he has certainly donated to Republicans, as I found, and looking at his open secrets profile on who he donates to, politics-wise. Now, he hasn't donated directly to Sarah Huckabee Sanders, the current governor. But of course, we know that the money when he donated to a PAC, it can go to her. It can go to other candidates in the state. Or she might realize that, well, look, this isn't the best interest of my party. This is the best interest of the Republicans. So I'm going to take this action. Or she could just realize, well, there's large landowners in the state. And I know they could donate to my campaign. So I'm going to take this action. And I know that they will like it. So there might not even be this direct relationship. But the crony interests are definitely a factor in this, or at least probable. The cronyism explanation has a lot of good explanatory power for a lot of actions in U.S. history. We have Dr. Patrick Newman, who's written an entire book on the topic. And the Public Choice School of Economics has looked into various regulations throughout history and has found that rather than moral motivations for law, crony interests have actually explained more than the typical public welfare theory of regulation. So, so, yeah, it's very, and there's other families, other other large donors, the Tyson family, they're a large landowner in Arkansas as well. So I go through those in the articles a bit. I link to all the open secrets profiles. Some of them may not be as convincing others. They've donated a lot of money to PACS. So, but overall, they come together to kind of paint a picture of, okay, there are these clear benefactors, and they have donated to these campaigns. They have donated campaigns and PACS. So why do they do that? Well, clearly to curry favor. Politicians service their donors. So why would this case be any different? Now, of course, there's this huge movement, or at least conservative movements, anti-China. They don't want foreigners buying land, as you mentioned. Whenever they hear that China is some Chinese company bought land or owns land, they get in a tizzy about it. And we discovered that when, you know, I've received emails. The tweet has, when this article is tweeted, it's received responses. And when I publish a similar article back in July, it also garnered a similar response. But in the end, a lot of the moralizing, a lot of the arguments are not really rooted in what we take to be right, libertarian ethics. So it's like, they say things, they try to mask their faulty economic arguments in moral arguments. So, okay, we're a sovereign people or we're a sovereign country. They're not part of us. It lands as a birthright. Someone said land is a birthright. No, you don't gain land by being born. You gain land through homesteading. You gain land through transforming it. And that's how you bring it into your property. That's how you convert it into your property. You don't gain land by being born in a arbitrarily defined geographic region. That's not how you gain control over land. So, but the people who own this land legitimately, those who gained it through voluntary trade and homesteading, they're the ones that sold it to China, like not China, but they're the ones that sold it to Singenta, the Chinese-owned company. Yeah, let's look at that a little bit more deeply, right? So in the article, yeah, before we move on to the more sort of the deeper principle behind it, just I want to establish a little bit better what the cronious angle is. Now, you have established clearly here that there are interest groups behind this legislation, right? People giving money. There are certain political interests that are using this issue to gain this new government regulation, this government prohibition through the Arkansas State House. Okay, good. Well, why are they doing that? What is the non-moralist, you can see, right? There could be a nationalist element, right? Economic nationalism. But what sort of organizations, let's just think, could benefit from there being prohibitions on this sort of thing? And this is what was always what I found. Every time I read economics in one lesson, what I'm always reminded of is Haslitt is so good in that with taking some sort of economic regulation or attacks and people, a lot of naive people just think, oh, it's because people wanted to do good things and people were motivated by deep principle and stuff. And Haslitt generally shows that no, actually, there's just some sort of lobby here, that there's a lobby behind these labor laws. There's a lobby behind minimum wage. There's a lobby behind these restrictions on trade. These are the reasons those laws exist, not some kind wishes for mankind. And so you note here, for example, that there could be private businesses, say like Tyson Foods, who could face benefit, who would benefit from prohibitions on foreign land. And so am I right in thinking then that, okay, land speculators would benefit, right, from foreigners being able to freely purchase land here, right?  We see that happen a lot in Aspen, Colorado, for example, where it used to be a lot of Russian oligarchs, I don't know who's buying their land at the moment. But they would come in, and this was an agricultural land, of course, but they would just come in and buy huge houses and riverfront property and stuff and massively drive the prices up. And so normal color runs like me avoid Aspen like poison, because it's all just like art galleries and then sufferable rich people, we go to different ski towns, other than Aspen, but that's who's in Aspen. It's a lot of tons of foreigners. Okay, so if you're a real tour there, you're doing great if you're selling to these people. All right, so you can see why those people wouldn't want any sort of ban on, but who would? I guess it would be people who would want to, who are in the business of acquiring land and using it. And so you could see then how other agricultural interests would, would domestic agricultural interests would then benefit from prohibitions on foreign ownership. So you could see absolutely why those people would form, form interests and want to see, want to make sure that it's easier for domestic firms to acquire this land and use it for their own agricultural interests. And so, yeah, there are some big agricultural interests there like Tyson and you could certainly any domestic person who's in the business of, up to as you know, Bill Gates, who's maybe in the business, who is one of the largest owners of farmland in Arkansas, you know, all these people then, why would they want to compete with Chinese buyers? So clearly there's, you can see, you can demonstrate a monetary motivation right there then, can you not? Yeah, yeah, exactly. Now one thing this does do, like Bill Gates is a good example, because he's been up, he's been recent, in recent years have been quite, he's been, he's been progressively acquiring land in the United States, farmland. But they say like the Tyson company, well, why would they want this? They, they're not famed for acquiring land like Bill Gates is. So imagine if they try to acquire this land in a free market in the absence of government interventions, orders to divest to the land holdings, imagine if they tried to acquire it then, well, they'd probably have to pay a higher price. Now whenever you do impose from above the government telling them like, listen, you have to sell this, this makes them a much, this makes them a more eager seller than they were before. So this essentially makes it more cheaply for companies like Tyson or whatever other farmers who are in the market for buying land, it makes it more cheaply for them to integrate or I guess, I guess horizontally integrate with, with Syngenta without merging with the company, they just acquire their assets. And of course, I'm assuming Syngenta just doesn't have farms in the United States, they have headquarters in Arkansas too, like they have buildings, they have other, they have, I'm sure they have countless other assets, they have employees. So who, where are these people going to work? Where is their, what's their next best alternative? If they work for an agricultural firm, I'm sure that they might try to go into the market for, go into the labor market for other agricultural firms, try to get a job there, some other, may Tyson foods, maybe perhaps some other firm. So yeah, it's not just the land holdings that are going to be going to other farmers. It's, it's probably the labor, the capital, the buildings that they own, all the other resources that they have control over. So yeah, it's, it's, it will clearly benefit those who are in the market for acquiring land. You've noted that the article notes that now who they are, that that's a trickier issue. And the point of going through the political contributions is kind of maybe suggested these, the people who have donated the farm, the farming companies that have donated before are the ones who would benefit from this. And it would make sense that the politicians who enact these laws and regulations would be doing so to service those people who have donated to them. Well, and I'm sure it's a winning political issue too. Oh yeah, definitely. Right. I mean, just badmouth foreigners and want to keep them out. And you can just say things like foreigners shouldn't own American land and lots of people nod. And so yeah, it's an easy choice, I think from the politicians point of view. But the next step, I think before we get into the principles here is let's just cover one quick issue here. Now you know it in the article that China owns hardly any farmland in the U.S. It's 0.03, 0.03 percent of total farmland privately owned in the U.S. Now you have, you use a link there that goes to MSN.com. But I looked up a page of the Farmers Bureau. This is November 2023 data. And so who owns who owns this foreign land, right?  Canadian investors are among the, are the foreign national zone. The most foreign held agricultural land in the U.S., they own 31 percent of all foreign held agricultural land. That's 12.8 million acres. And that's 0.97 percent of all U.S. agricultural land overall. And so okay, that's less than 1 percent for Canada. So who else owns all of this land? Let's see. We got, next up is the Netherlands, which owns 0.37 percent of all agricultural land. And then after that, what do we got? Italy, the U.K., Germany. But all this adds up to just not very much at all. Italy 0.21 percent, the U.K. 0.19 percent, and it just keeps getting smaller from there. China is not in the top 10 list here at all. That's all European countries or Canada. And so if you look at the other owners of ag land in the United States, that, all that combined is 0.85 percent. All of these other other countries, including Japan, China, Sweden, et cetera, et cetera. China, which owns less than Japan, 0.03 percent, as you note here in the Farm Bureau's data, a little less than Mexico, 0.05 percent.  Let's compare to how much the U.S. government owns of land in the United States. So when we're looking at the largest land owner, that's 12.8 million acres of ag land. Federal government, 640 million acres, which as we know also is often 50 percent or more, sometimes 80 percent of all the land in certain states, like in Nevada, right? In Colorado, it's about 35 percent. And in a number of Western states, it's over 50 percent. And of course in Alaska, it's huge. And gee, who's the biggest threat to your actual freedom on a daily basis, the U.S. government or the Chinese government? How much does the Chinese government tax you every year, American citizen? How much of your life has the Chinese government controlled, regulated? How many people have been locked up in the United States by the Chinese government? I mean, it's amazing the way people get hysterical over a foreign government has virtually no control over them. And because the United States, of course, is across an ocean, has a nuclear arsenal, a huge Navy, the odds of China having any control over your daily life are about zero. And yet people get really, really worked up about it because of this 0.03 percent of land, which foreigners, of course, have owned land in America forever. The British, of course, used to own huge amounts of land. That's declined over time. Even after the war, the British hung on and the U.S. relied heavily on British investors to keep things going. And that's just been a fact of American life. And as Walter Block points out in his article, right, if you're old like us, you remember all the hysteria over Japan in the late 80s and early 1990s, when Japan was going to take over the United States. And everyone freaked out when Japanese investors bought Rockefeller Center in New York City, which, of course, where they filmed a bunch of NBC shows and it's pretty important building. And it's named after Rockefeller. Chinese nationals ended up selling that. And guess what? They didn't take over America. It didn't turn out that their owning of Rockefeller Center amounted to much of anything at all. So we've been, we've seen this before and the reaction is very similar now to what we saw with all the anti-Japan rhetoric in that time period. And one other question we have to ask, which I don't think has been really mentioned enough at all, is why does China have all these dollars that they want to invest in the United States and buy stuff? It's because Americans buy stuff from China. Americans voluntarily gave those dollars to the Chinese. And now the Chinese need to do something with those dollars. Another reason that they have so many dollars is because of the importance of U.S. Treasury. So the Chinese, they need to have dollars available. They buy a bunch of treasuries because that's low risk because they have all of those dollars. They buy U.S. debt. So no one seems to get particularly worked up about the fact that Chinese own all this debt, although you're starting to hear some people freak it out about that, which is probably a far more important issue than the fact that they own any agricultural land. And so if you're going to hand over tons of dollars to the Chinese, don't get terribly upset when they start buying American stuff. And so what's the answer to that? Oh, to tax Americans more so they don't buy Chinese stuff. Oh, that's a great pro-freedom solution, just raise taxes on imported goods. Great, so that our cost of living goes up even more. Great solution. But that's pretty much the only thing they've got in their holsters, in their toolbox for these people who tell us that that's a problem, they want the Chinese to have fewer U.S. dollars. So that's kind of the level of thinking that the economic nationalists seem to be thinking about on this. Yeah. And to their credit, the same people who are complaining about the debt issue are also the same people who are complaining about the debt issue, like China accumulating debt. But it's rational on their part.  But I think an important question to ask is, what are they trying to influence by doing so? Now, let's assume it's a Chinese conspiracy through and through that they're doing this to screw us over, basically. Let's assume that. I don't think that's true. Let's just assume that. What could they do with this land? And Xinjie, obviously, is a company that they're trying to make profits and so on. But let's assume that China is controlling them through and through. A lot of these acquisitions, a lot of the farmland acquisitions are part of their One Belt, Initiative, which is essentially to control a lot of more of their own food supply.  Who are they going to be harming? They're going to be harming their own people. Their goal is to basically take over some of their own food supply, which is coming from the United States. So, how are Americans being harmed in this? As I did it before, Americans are being benefited, especially in the Xinjie case, who they provide services to American farmers, and to the people who are selling them the land. They're not being forced. They're not having their arms, they're not having their arms behind their back doing this. So, yeah, it's completely ridiculous to say that they're doing this to garner influence over Americans by controlling our food supply, or it's not of any benefit to any Americans whatsoever. It is a benefit to a lot of Americans, and it's a hard case to make that they're doing this to control our food supply. Well, and then let's get into, yeah, these issues of the morals behind seizing property, telling Americans who they can sell their land to, because, yeah, as you noted, right? Someone sold this land to these Chinese investors. Was it an American? Probably was. Based on the statistics we just looked at, there's a very high probability that this land was owned by an American and then sold to the Chinese. Okay, so what you're saying, then, is that we need laws prohibiting Americans from selling stuff who they want to sell it to. Now, I suppose if you're a drug war person, then that makes perfect sense to you, but generally, America was founded on this idea that people should be able to buy and sell what they wanted to do, especially when we're talking about land. And so somebody sold this land to China, and what they're saying is, nope, people should not be allowed to sell to whomever they want, and no foreigners, if you believe some of these politicians, no foreigners at all, can you sell land to? Okay, well, what do I want to sell that land to foreigners? This is the question I was asked to anti-free trade people is, they're like, wow, yeah, you got to control trade or else you lose jobs in America and so on. Well, no law means anything unless you enforce it. So the question always is, how big should the fines be and how long do you want to lock people in cages for trying to import goods from a country that you don't think they should import goods from? So how long will we lock Americans in cages for selling land to the Chinese? That's a question you need to ask, what are the penalties for trying to sell land to a Chinese national or any foreign national if you think that should be illegal and verboten? What are the penalties you propose for that? And what you're telling me is you're in favor of locking people up for engaging in a totally peaceful behavior. And that's the end game of all protectionism. And I haven't heard an answer on that, of course, because they never look at the side of how this controls Americans and what they can do in terms of sales and investment. Yeah, their argument is basically that, okay, we can't have free trade with a country that's fundamentally socialist. Well, I counter that by saying, well, if you're going to put restrictions, you can't put restrictions on a country that's, on trading with a country that's socialist and call that free trade, because that's definitely not free trade. As Rothbard notes that free trade should be unilateral. I mean, who cares what they do? I mean, of course it'd be better if they privatized all their companies, if Sinjento was completely privatized, like all the shares the government owns were sold off or given to some private actor. Of course, it'd be better if that were the case, but that's not the reality we're dealing with.  Well, we've been deprived of the private alternative.  That's even worse, because we're being forced to pay it, and we're being deprived of the private alternative, essentially. So in this case, we're being deprived of private Chinese companies, and plus the Chinese taxpayer, the Chinese, or I guess I don't know if they pay taxes, because the government probably controls a lot of the income, but the Chinese taxpayer, they're the ones that are being also being deprived of a private exporter, somebody who they can work for, who they export and they get US dollars in return or whatever, they're being deprived of a private exporter. So read to say, okay, we need to just completely stop trading with these people, stop trading with these public entities, because we don't have the completely private alternative. That doesn't exist. This doesn't exist in the United States. Countless companies receive privileges either through monetary payments from the government or regulations that benefit them in some way, or they perhaps receive loans that were made possible through Federal Reserve intervention in the economy. So there's plenty of companies in the United States that receive benefits similar to that of Chinese companies. So should we stop trading with those? No, we're being deprived of the private alternative. So I think that the argument that since they're a partially public or a majority public share company that we need to have the government intervene to prevent us from trading with them, that's completely absurd. Any person who believes in the free market should reject that argument. And furthermore, as a libertarian, there's a lot of people who listen to our arguments for privatizing police, privatizing security, who are all on board. But when it comes to the national securities of concerns of China, they believe in government intervention. Why not privatize the response to that as well? If you think that it's insecure to trade with China, don't buy Chinese. But given that so many people do, it's obviously the cost outweigh the benefits of doing so, of cutting off trade for these petty national security concerns. That should either indicate that the national security benefits are very small compared to the detriments of cutting off trade, or they're not existing entirely. So people like Zachary Yost and others, Joseph Mullins, the libertarians, who just published a book on a fake China threat. I haven't got that book yet, but I've seen that he's making the rounds on the podcast circuit. So there's plenty of criticism of this idea that China is a huge threat. And what I see most in conservatives, and you see this a lot of foreign conflicts, especially recently, is that they like to point to the words of our adversaries and say, that's evidence that they're trying to go against us. You see how the way U.S. politicians talk? People, rhetoric's the name of the game. Why should we take words seriously? Shouldn't actions speak louder? Of course, every country has their expansionist measures. Every country engages in unjust actions, acquiring property that shouldn't be theirs. But at the end of the day, what does all this say?  The Soviet Union did too. One of the things they like to cite over the Soviet Union specifically was, oh, that we'll bury you in our output. We're going to bury the United States, but we're really referring to their production, how much they produce. So obviously, these words can be taken out of context very easily and become like the cornerstone of foreign policy. So we have the arguments. We're applying economics to understanding foreign affairs. That is way better than having some romantic view of it that holds the intentions of foreign policymakers above any other consideration. Well, and it's just amazing too to see the asymmetry between U.S. power and Chinese power also when you look at the fact that the U.S. has multiple colonies, bases, basically surrounding eastern China. But we're supposed to be deeply, deeply worried about them owning a few dozen acres of farmland in Arkansas. So I just assume most people just have no idea what the reality is. The U.S. is sailing the Navy through the Taiwan Strait right off the coast of China. I mean, just imagine the hysteria of China were to sail some of their Navy into the Gulf of Mexico, which they never do. And why? Because the U.S. has such a complete total control of the Western Hemisphere. And then the most China can hope for is friendly trade relations and some minor military exercises in Latin America, which pose no real threat to anybody in North America. So it's really quite remarkable just how worked up people get about these. And so we've shown then, of course, that on a practical level, right, China hardly owns any land. China has no rival, no naval power that could even remotely pose any threat to the American mainland. So on a pragmatic level, it's just it's like it's like a nothing issue. But then on a moral level too, it's just you're telling Americans what they can do with their own property as Walter who does not idealize the U.S. Constitution nevertheless asks, hey, where in the text does it say the federal government can control who can or cannot own land? It doesn't. It's not in the enumerated powers. So we certainly can't have any federal regulation on that. Now this would be Arkansas law. So that would be a little bit different. But on a moral level, why should Arkansas be in the business of telling Americans who they can sell their land to? Suddenly, they're all suddenly your state legislator, who's probably never read a book in his life on foreign policy suddenly is an expert on why national security requires a ban on foreign nationals owning land in Arkansas. So that just seems to be where we are. But as we noted, politically, it's they'll be fine. And I can't imagine anyone be punished at the ballot box for this position. The people will be punished will be, you know, usual market participants, anyone who benefits from Syngenta, anyone who might have benefited from these land sales. And yeah, just domestic landowners who could participate in that market and people who might actually like these services from China. But just remember, the reason they have all of these dollars is because Americans gave them to them, and Americans chose to dominate the international currency trade by just pumping so much debt into the international economy through just trillions of dollars in bonds that they keep putting out. And that greatly is what motivates the Chinese to have access to lots of dollars as on top of the trade where they just get flooded with dollars in general. And so that's just that's just the reality of what's going on there. And so you would need to really overhaul the situation in order to change this demand for American goods, but that's just not what's going on right now. Yeah, you hit the nail right on the head with all of that. The situation right now is that you have you have the choice between government intervention, which requires aggression against private property owners and domestically, or just allowing freedom to reign and allowing freedom to reign may allow some publicly owned company to come in. But we're in favor of private property. And if we're in favor of private property, we should be against intervention, regardless whether it's a private Republic actor. Now, some people will take it to the extreme. I saw one person comment. This is why we this is why states should prevent other people in other states from buying land in their own state. But that's completely ridiculous. It's it's it's it's putting authority in the hands of the state in the hands of the governments that preside over us that they should never have had in the first place. And yeah, we there's no libertarian argument for this action. All right. Well, with that, we better wrap up this episode of Radio Ross Barn. Thank you, Benjamin Severs, for being my guest today. And thank you everyone for listening and we'll see you next time.