 So now it's my pleasure to introduce to you Dr. Lauren Griffith. She's an associate professor in the Department of Health Research Methods Evidence and Impact at McMaster University. She holds the McLaughlin Foundation Professorship in Population and Public Health, and she's a co-PI of the Canadian longitudinal study on aging and the lead of the Hamilton data collection site and Privilege to work with her and her research interests include physical functioning frailty and aging as well as the harmonization of longitudinal data. Lauren, please Just make sure I know how to use this. I didn't realize when I Made my talk that I was going to have the feel-good talk of our panel talking about COVID-19 impacts on community living older adults, but I think one of the things that is Important in this talk is thinking about this evidence base to help inform moving forward And I think studies like the CLSA are going to be extremely important in that regard So I'm going to talk about the CLSA talk a little bit about it as just a brief introduction of the of what it is I'm going to talk about the COVID studies that were embedded in the CLSA and primarily a survey study And then I'm going to just hit on some of the findings that we found that I think are Important and these are findings from our entire research group and Marla presented some today But I think they're really important things in terms of understanding COVID So I'm going to be looking at symptoms mental health and frailty So just in terms of the CLSA as a research platform We call it a research platform because it really is we collect the data But it's available to all researchers that want to study health and aging There's over 50,000 people that were recruited at baseline They were 45 to 85 when they were recruited and then about 40 percent of them were a random sample from the 10 provinces In Canada and they provide data through telephone interviewing and about 60 percent are from more geographically restricted areas around 11 data collection sites across Canada and they provide their data very similar questionnaire data in in-person interviews But they also come to one of the data collection sites like the one here at MIP and They provide clinical measures as well as conduct physical tests They provide blood and urine and here you can see we started recruitment in 20 to 20 or 2010 to 2015 But every three years we start a new wave of data collection and of course in the middle of follow-up to is when COVID happened and One of the things about CLSA that actually Helped us in terms of COVID and adjusting is that there was a really robust IT Infrastructure and there was a lot of accommodation strategies already in place So we were able I think within two weeks to start collecting data again but the other thing was this opportunity to Actually understand the importance of having these population based Based data and these pre-existing cohort in terms of understanding COVID and So we launched a COVID survey study This gives you an idea of what they what the actual study looked like But I think as Parminder mentioned yesterday within a month of COVID happening we were launching our survey study So we had two longer Interviews we had a baseline interview where we collected kind of socio-demographic some socio-demographic information information on symptoms and COVID status and but a number of other things like social variables lifestyle variables population the public health impacts and and and and as well looking at You know healthcare Access and things like that and then then there was the weekly bi-weekly surveys Those were mostly just kind of looking at symptoms and outcomes along the way and then we had another similar exit interview where we had a number of things that were from the baseline but we also introduced some things like loneliness and Symptom persistence things that we kind of learned that were important along the way and that happened at kind of end of September to the end of December 2020 so the first study I'm going to talk about is COVID symptoms and why is it important to to look at COVID symptoms in terms of in a population-based study and one of the things we could do here is we know a lot about the symptoms people were Presenting with when they had COVID but we don't really know necessarily what the background rates were what we would expect in the in the Community if we didn't have COVID so we were able to look at that and we were also Able to look at persistent symptoms. So we were able for the people that had COVID We were able to look to see what were their persistence or how often they had persistence of symptoms what they were and As well, what were the pre-pandemic factors that were associated with symptom persistence? So the pre-pandemic factors were from a follow-up one for me, and then the symptoms were from the COVID exit survey So here it's a bit of a busy slide, but I think it really shows you in the in the community So to the right side are the symptoms that we saw and people that reported having COVID The left were the burden of symptoms and people that did not have COVID these symptoms that were related to COVID and Clearly you could see the dark part of these lines are people that had reported either severe or Moderate symptoms and so clearly there was a lot more symptoms reported in our COVID group and a lot more in terms of the Moderate and severe but one thing about having CLSA is we have a very large sample size too so we can dig down a little bit deeper and Look at what those symptoms looked like by age and here you could see I think we keep hearing the things about the the age effect in COVID it was in terms of the people getting it was more common in the younger age groups But here you can see the symptoms were actually the burden was higher in the younger age groups But in the older age groups you could see that there was more times Moderate or severe symptoms, so we can start understanding this a little bit better And in terms of persistent symptoms, it's actually interesting in our community base survey We found similar prevalence of Persistent symptoms as we were seeing in the hospitals at the time so people that had less severe cases We're still having these persistent symptoms And so it was about two-thirds of the people had symptoms at one year or sorry one year more than one month and About half the people reported having the symptoms more than three months later And again, it's a bit of a busy slide, but for both one month and three months We had the female sex was related to so the females tended to have a higher risk of having persistent symptoms in males both at one month and three months and As well having more chronic conditions was associated with have more likelihood Higher likelihood of having persistent symptoms, but when we looked at the three month Time having persistent symptoms for more than three months It was also this subjective social status that came in and that's kind of an interesting measure This is one where you you picture a ladder people are asked to picture a ladder that have ten rungs That is really representing the social standing in a committee in your community And the tenth rung is like the highest social standing the first rung is the lowest So we asked people to put themselves which rung are you on and what we found that an increase of one rung Was actually associated with about a 15 percent decrease in the risk of reporting Persistent symptoms at three months. So again having this rich contextual data outside of COVID actually helps us to understand some of these things better and So in terms of mental health, we looked at depression that was led by dr. Raina and Loneliness which was led by dr. Kirkland from Dalhousie and here again We were able to actually look at trajectories because for depression We had data from baseline to follow-up one and then COVID the first COVID Interview and then COVID exit and for loneliness that was introduced a little bit later So we had the follow-up one and the COVID exit And what we found this is a Globe and Mail article that is reporting on Dr. Raina's nature aging paper, but some of the things that we expected to see here Clearly you could see that the the first column the kind of burgundy one is the pre-covid The kind of greenish one is at the beginning of COVID and the more salmon-colored one is at the exit Or sorry at the one-year point and here you could see there's a big increase in the prevalence of depression from Pre-covid to the beginning of COVID and again There's a bit of an increase for some groups, but not quite so high in terms of between the the spring and and the end of year one for the pandemic But what we found were things that we thought would we would find in terms of living alone was much more It was important. There was a higher prevalence of Depression and females had a higher depression prevalence of depression than males But one of the kind of interesting things was when we look at income so total household income Clearly the prevalence of depression Is highest for the people that had the lowest level of income But you can also see the impact from pre-pandemic to pandemic was much greater for the people that had higher levels of income but one of the other things that came from this article is the strong association with pre-pandemic loneliness and depression and so We dug a little bit deeper into this in our loneliness study and again. This is a Figure that's looking at the predicted probability of loneliness. It's adjusted for all sorts of other factors and What we see here is again the black squares of the pre-pandemic the red circles are At the end of the first year and we could see that there is a big increase in terms of loneliness for all groups, but it tends to be a Bigger step like we saw in depression for those with high income But one of the kind of interesting things here is if we actually look it seems that The age effect which again we found the highest levels of loneliness in our our youngest age group, but you could see the Association with age is much stronger in the low income group than in the high income group and again having these large sample sizes allows you to Sort some of these things out So the last thing I'm going to present is Looking at frailty in health care access challenges and one of the things we were interested in doing a lot of the public health Directives and and as well even thinking about getting vaccinations. It was all age-based But we were kind of interested in looking at frailty because we know that even in older adults It's a very heterogeneous group. So we wanted to take a look at that And here we use the frailty index as described earlier and we looked at people who reported having Challenges with access to health care. So specifically, I'm just presenting the primary care specialist care Diagnostic diagnostic tests and screening But again, we were able to look at pre-pandemic levels of frailty and the other other variables that we adjusted for and then looked at What they reported in terms of access challenges And what was kind of interesting is that we found Overall that people with higher levels of frailty had more reported challenges with access to health care Which in ways make a sense because you would think that there's a higher need for health care as well But when we broke this out then by age We found that the oldest age group actually had the least Impact in terms of frailty. So here we looked at frailty quartile So four would be the people with the highest levels of frailty and you could see the younger age groups With the highest level of frailty were the ones that were actually reporting the most most challenges in terms of access and in primary care There was there was a clear divergence between the oldest and the other three age groups but you can really see in terms of specialist care and As well diagnostic tests and screening tests that there's really this difference in this youngest age group. So here we're seeing that age may in terms of level of frailty actually was associated with getting less Having more challenges with access to health care and this was after adjusting for all sorts of other factors So I want to just end with This advertisement slide because I have to because permanenters here and but essentially I I really think data sets like the CLSA are going to be critically important in terms of Understanding COVID now tracking things as we move forward because as we've been talking about It's not just a thing that was where it's going to happen here And we're just going to move on these are things that we need to be able to track over time and for the long term and Having data like the CLSA are critically Important in being able to do this so we have our questionnaire survey that I described today we also have a seroprevalence study and There are the data will be available to all researchers so if you're thinking about Projects and thinking about better understanding things that we can use for knowledge translation the next date of the Data access application deadline is September 14th So I encourage you all to think about things that we can do to inform our way forward