 Can you hear me? Great. So glad to see so many faces here. This is really exciting. Welcome to Engaging and Aging, a CLSA question-and-answer session. It's really our privilege to have you here today, and it honors us that so many of you have shown up. So thank you very much for your participation. My name is Susan Kirkland. I'm a professor in the Department of Community Health and Epidemiology here at Dalhousie, and I'm also one of the three principal investigators for the Canadian Longitudinal Study on Aging, and I have here with me today a number of guests and both local guests and guests from across the country who are here to talk to you about the CLSA today and to give you a little bit of a flavor of the kinds of things that we're studying and the kinds of things that we're finding. But I'm going to introduce you to them in a sec, but first I'd like to be able to introduce you to them. So maybe you can just tell me for a moment just by raising your hands, how many of you are CLSA participants? Oh my gosh. Oh, that's just fabulous. So how many of you are participants who are followed by phone? Oh, fabulous to see, and those of you who come into a data collection center as well? Lovely. Fantastic. Great. And how many of you are situated in Halifax? Great. And anybody here from outside of Halifax? Oh fabulous. Welcome. The other thing that I'd like to know is how many of you here are a family member or a spouse of a CLSA participant? Excellent. Wonderful. And thank you for your support as well. That's really great to see. So as you will see from the program, we have a number of presenters and we're going to try and stick to the program, but I'm going to tell you right now we'll do our best attempt. And if we get a little bit off and you're getting bored, you just get up and move around or do what you need to do. But I promise you we will leave plenty of time for questions. I'm going to introduce you to the panelists now. Dr. Lauren Griffith is here from McMaster. She's an associate professor in the Department of Health Evidence and Impact and Lauren is also the associate scientific director of the Canadian Longitudinal Study on Aging. And like me, Lauren has been involved for a very long time in the CLSA and we're really, really grateful for her participation here today. So welcome Lauren. And we also have two local investigators who are using the CLSA, but are also CLSA co-investigators as well. So they also contribute to the direction of the study. First, I'm going to introduce you to Dr. Yukiko Asada, and Dr. Asada is also in the Department of Community Health and Epidemiology here at Delhousie and she co-leads the Halifax site with me of the CLSA and she does a number of research activities related to the CLSA. So welcome Yukiko. And lastly, we have Dr. Melissa Andrew and Melissa is an associate professor of medicine and a consultant at the Geriatric Medicine Unit at the QE2 Health Sciences Center and Melissa is also a CLSA co-investigator and she has a large program of research in dementia and in other aspects. She's also very interested in sex and gender and health as it relates to aging and she's going to talk to us today about caregiving. So thank you all for being here. As you know, and I'm not going to get into my thank you's yet, but I would like to be able to introduce you to a number of staff members or who are here with us today. And those of you who come into the data collection site, they're going to be much more familiar to us to you than the faces that are up here today. But we have Katarina McIntyre here who is the manager of the data collection site. She's right over there. And we also have Lindsey McDonald who is there as well. And I think we have Luke Martelli. Luke is here somewhere. Oh, over there. And we have Sue Nesto with us and also Leah McIntyre at the back. Great. And then for those of you who are followed by phone, you don't get the benefit of seeing the person who you're interacting with. But we have William Martin here from the computer-assisted caddy site, the computer-assisted telephone interviewing site, which stands for caddy. So you can welcome William as well. We also have a number of staff at the back who have helped organize this event and work with me on a day-to-day basis for the CLSA. So Ashley-Ann Marcotte is right there. Kirstie Smith is at the back. And Tim Cron is also here as well. So all of those people are well involved in the CLSA. And if you have any questions that you don't get to answer or you would like to just chat with, they are more than willing to talk to you. What we've also done is saved some time at the end of the program so that you can just ask us any questions that you want. We hope that we will have time to answer all of your questions, but the possibility is that we might not. What we've done is on the back of your program, you'll see that there's a space where you can write down questions. You may think of questions as you go along and you can write them down there and we'll ask them afterwards. We also are live streaming this event. So right now there are other people all the way across the country who may be watching and we welcome the live streamers as well. And for those of you who are live streamed, I'm sorry we can't accommodate your questions here right now, but if you send them into the website, we can also develop a question and answer brief for you as well. Before we get started, I just want to give you the logistics. We are going to have a we're going to have a series of short presentations, I hope. We'll save a couple of minutes after each presentation to maybe ask some specific questions. But then what we'll do is we'll have a little bit of a brief intermission and we'll go into just an open question and answer session. And there are mics that we will be able to rotate around the room for you to ask questions. The washrooms are out this door and to your right. We finally do have water at the back of the room. So there's water and there's coffee and tea and there's cookies. So help yourselves. And again, you know, if you want to get up and walk around you feel free to do that. That's everything I had to say before we get started. Thank you. Great. Thank you. So what I'm going to do is give you the sort of backdrop of the CLSA and, you know, how it came to be and why we decided to do it. And then just a little bit of information about you, actually, and then we'll go on. And we all have both interest in sort of the design and how the studies run. And that's what I'll focus on primarily. And then we also are researchers who are interested in the findings and generating findings. And the other presenters will mostly focus on those findings for you. So I want to just set the stage for you when we think about aging. And aging is a global phenomenon. And these are what are called population pyramids. And you can see the first pyramid is generated in 1970. The second pyramid is generated in 2015. The third pyramid is what is predicted by 2060. And what you notice is the shape of those pyramids, right? So in 1970, there were a lot of people who were young and there were very few people who were old. And then in 2015, which is more or less roughly right now, you see that the shape of the pyramid, and this is worldwide, is dramatically changing. And by 2060, it doesn't even look like a pyramid at all. And that's really a remarkable shift. In Canada, the impact of this is quite severe. And so what we see in this graph right here, and this was reported in 2016. And for the first time, these two lines crossed. So the top line that goes down is the number of people who are under the age of 15. And the bottom line that goes up is the number of people who are 65 and older. And for the very first time, those two lines crossed. And all of a sudden, the proportion of people who are over 65 in Canada is larger than the proportion of people who are under age 15. And that has an impact on a number of different things, you know, social programs and all kinds of issues. There's a number of metaphors that people use about aging demographics. You very often hear people talk about the gray tsunami. And I don't like that term tsunami because when I think about tsunami, I think about damage. And I don't think that what we're here for is damage. We also talk about, you know, it's a fast moving train, a hurtling bullet. But I also don't think that's accurate either. And I love this metaphor, which is actually a very maritime metaphor. And it's about the ebb and flow of the tide. And if you think back to those pyramids and the projections, what you see is not that we didn't know it was coming. We could see it. We knew it was coming. And what we need to do is prepare. But we also need to understand that this is not for all time. That pyramid is going to continue to shift over many, many years. And the point is that we need to understand and be prepared for what that means to our society. In Canada, what it means is that people are living longer. Our life expectancy for women is 83.3 years from birth. And our life expectancy for men is 78.8 years. But what's really interesting, and you don't think about this very often, is if you live to age 65, your life expectancy is actually longer. Because a lot of the life expectancy, a lot of the way that they calculate life expectancy counts in, you know, things that you die of at birth. Or, you know, the fact that young people die in car accidents and things like that. So if you make it to age 65, your life is actually extended beyond the period of birth. So women who make it to age 65 can expect to live another 21.6 years, or to age 86.6. And men who reach 65 can expect to live another 18.6 years, or to 83.5. And so what that means is we really need to think about how we handle this. Because it's not good enough to just live long. We want to live well as well. And what that does is it also changes how we think about studying aging. So we really need to get away from just saying, okay, what did somebody die of? Or did they die? And, you know, what diseases do they have? And what's their life expectancy? And really start to think about the things that matter to people. So, you know, how are people able to function? What's their ability or disability? What are their needs? How is their well-being? What is their quality of life? How are they maintaining autonomy and independence? And those are the kinds of things that we need to be thinking about. And those are the kinds of things that we need to be tracking when we're doing research in order to understand what people's needs are and how we can make it a better place for people as they age. And that leads me to the Canadian Longitudinal Study on Aging. So hopefully after I've told you all that it's a no-brainer that we would need really good, high-quality data where we followed people over time and we understood what the trajectories of aging are like, what it means for people to age, how critical periods ebb and flow, and really understand those things like function and like quality of life and the things that are really important to people as they age on a day-to-day basis. This is a study that is a strategic initiative of the Canadian Institutes of Health Research, which is our major health funding body in the country. And it is not a single-handed effort. There are three principal investigators, and you can see the other two principal investigators in that picture. There is Parminder Reina, who is at McMaster University, and Christina Wolfson, who is at McGill. And the three of us have been responsible for running the study since its initiation. But we also have a large number of co-investigators that we work with. And we work with researchers across 26 universities across the country. And they really do cover all ranges of discipline, because if you think about it, if you're going to study aging, really what you're studying is life. Like, what don't you want to think about when you're thinking about aging? You want to think about your social health. You want to think about your mental health. You want to think about your physical health. You want to think about your economic health. You want to think about your health services. You want to think about the broad range of factors that influence you and how those things work together to create the environment and the outcomes that you have. So really, this study was designed with two things in mind. One is to really get at the science of aging and really be at the forefront of the science of aging. And the other was to provide evidence so that policymakers and governments can actually use this information to create good public policy. And the last thing is that there has been a real dearth of public health infrastructure and population-based infrastructure in the research community across the country up until now. And the Canadian Longitudinal Study on Aging really does serve that niche because we've been able to create a national initiative that really addresses the health of the population. And I think I'd also like to have you be aware as participants that this is one of the largest studies to date in Canada, both for its numbers but its depth and its breadth. And it really is acquiring a very strong international reputation. So we're really excited about that. But what we realize is that the CLSA is only as strong as its participants. It's really the high quality information that you give us that we can then use to translate that into science. This is what the structure of the CLSA looks like. And we talked about those of you who are followed by phone. And in our jargon, we call that the tracking group. And then there's the group who come into the data collection site. And we call that the comprehensive group. But the important thing is is that we have a whole range of the same information on over 50,000 people. And we can really use that in a very big way to not just advance science but change policy as well. For those of you who come into a data collection site, as you know, there's also additional information that gets collected. And again, that really contributes to the science. The CLSA has been funded for, in theory, we always have to go through scientific review in order to get the next slot of funding. But because it has been put forward as a strategic initiative of the Canadian Institutes for Health Research, it's intended to last until 2033 and cover seven waves of data collection. So as you know, we've finished the baseline data collection. We've finished collection for follow-up one. And we're now partway through follow-up two. And you can see that there are CLSA participants in every province. And those of you who are followed by phone are randomly selected to participate within age and sex group within province. And for those of you who are in a comprehensive site, you're selected from a specific area around the site so that you can travel to it. And you can see the various data collection sites across the country. I don't need to tell you about the amount of information that we collect because you've experienced it yourself. You know that we collect a wide range of information on sociodemographic factors, health factors, social factors, psychological factors. And we also have people come to the data collection site where we collect a lot more clinical information that is used. And you also provide, for those of you who agree, provide blood and urine to us. And I just want you to know that your 3 tablespoons of blood that you provide actually gets divided up into 42 aliquots that are covered in different ways that researchers can use for various studies and investigation. And if you ever wondered where your biospecimens go, this is where they're stored. They're stored at the Biorepository and Bioanalysis Center at McMaster. And these are nitrogen freezers. And we have 31 of these nitrogen freezers and the capability of storing 5 million samples. The other thing that I'd like to let you know, and I hope you know this, is that the study is funded as a platform. So that means that the information that you provide to us is not just for the CLSA co-investigators. It's made available to the research community across Canada. But it's very important to us that we safeguard your data and that we protect your confidentiality at all times. But the data is available to researchers and to trainees to use. And the way in which they gain access to it is they have to submit their proposals and be approved by a data and sample access committee and also a university-based ethics committee. And we have approved over 200 teams to use the CLSA data to date. So just to give you a snapshot of what you all look like, the majority of people who are in the CLSA identify as white and were born in Canada, and most often speak English at home. 4% self-identify as indigenous. And that includes North American Indian, Métis, and Inuit. 64% state that their religion is Christian. 21% say that they have no religion. And 24% attend religious activities on a regular basis. But these are overall statistics. And what becomes really interesting is when you start to break them down and look at them in a little bit more detail. So for example, when it comes to income, participants who are in the CLSA are relatively well off in comparison to the Canadian population. The majority of CLSA participants have a household income of between $50,000 and $100,000. However, there are 6% of the sample who report that their household income is less than $20,000. But if you break it down and you look at various age and sex groups, for example, if you look at women who are aged 75 to 85, that rate is double. So 12% of women who are 75 to 85 say that they live on less than $20,000 as a household income. This just gives you an idea. And I don't expect you to be able to see these. And I don't want you to worry about it. If you can't, I'm just going to tell you what's on there. Overall, there are a number of people from the Atlantic region in total. The Atlantic makes up 20% of the sample of the CLSA and almost 11,000 participants. So we're very strong in the Atlantic, and we like to stay that way. The CLSA includes a number of special interest groups, and these were not specifically designed. When we designed the study, it wasn't specifically designed that we could look at this. What we find is when we have 50,000 people, we get large groups of subpopulations. So we have a large group of veterans who are in the CLSA. We can look at Aboriginal peoples. We have a Francophone population. So 20% of the CLSA participants do the CLSA interview in French. We can study some urban groups. We have urban-rural differences where there are people who are living with a number of different chronic diseases. We have people who are both caregivers and care receivers and a large retiree population. When we finished the baseline recruitment and data collection, we worked with the Public Health Agency of Canada to generate a report on the baseline findings. And there are some cards made up at the back there that you can go onto the website and look at the actual report. And even if you don't have a card, if you go to the CLSA website, you'll be able to find it. But this was a really important time for us. And we actually had a day on the hill with all of the politicians where we released the report and it was a very big, splashy event. And we hope to be able to do that each time we finish a wave of data collection. There's some interesting things and I'm going to go through them quickly. But I just want to leave you with a few little messages. So not unexpectedly, a large proportion of the participants are married. And you can see these bars, and I don't want you to worry about the individual bars, but those dark bars that are the highest bars, that indicates that people are married and the other bars are widowed and separated and divorced. And they're divided out by age group and by sex. And you can see that all of those bars look the same except one. And guess what? It's women aged 75 to 85. And you see for women aged 75 to 85, the pattern is reversed. By then, a lot of those women are widowed so that you can see that lighter blue bar is the highest proportion. When it comes to retirement status, again, this is broken down by sex and also by age group. And all I want you to really notice is the light blue bars are people who are still working. The dark blue bars are people who have retired. And you can see that the proportions of people who are working are still fairly high until they reach age 65. And then there's a big switch. There's a big flip that occurs. But what's really interesting and what we found is that a quarter of people say that they retire for health reasons. But 20% of women and 30% of men actually, we've called it unretired. But at some point, after they've formally retired, they decide to go back to work either part-time or full-time for various reasons. And it'll be really interesting as we follow through that to see what that pattern looks like. Loneliness is a big deal. We're concerned more and more about loneliness. In an age where we're more and more connected, we also experience more and more loneliness. Here, the thing that I'd like you to note is, again, that there's differences by age and sex. So these bars are not very well differentiated. But the green bars are women. And the blue bars are men. And so what you'll notice, and the first set of bars is people who are married. The second set of bars is people who are single. The third set of bars is people who are widowed or separated. And the third set of bars is divorced or separated. And the last set of bars is widowed. So for those last three bars, you can see in every group men are higher. So if you're widowed or you're divorced or you're single or you're separated, men tend to be lonelier than women. But if you look at the first set of bars, which is married, you see an opposite effect. And so you'll see that the highest bars are the green bars. So among people who are married, women are lonelier than men. I'm not going to read anything into that at this point. I'm just reporting it. In these slides, we look at hearing loss and vision loss. And the first group is hearing loss. And the second group is vision loss. And the dark green bars are men. And the light green bars are women. So you can see for hearing, for both women and men, hearing loss increases over time. That's not surprising. But what is surprising is that it's remarkably different between women and men, that it's higher for men in terms of hearing loss. And when you look at vision, you see a different pattern. For vision, it's women who experience more vision loss over time. Again, so this is about activities of daily living, which are general things that you do in life, like bathing and eating and dressing and those kinds of things. And also instrumental activities of daily life, which cover things like doing your banking and more complex tasks. And here what we see is that when we combine those two things together, that women over time have more difficulty in activities of daily living. But one of the things that you have to think about, and especially in the older age groups, is what you get is survivor bias. So you can't have more problems with activities of daily living if you've already died. And the people who live, so those are the kinds of things that we have to think about as researchers when we're interpreting these findings. This is a very busy slide, but I know that all of you, many of you, are very interested in your cognitive function. And some of you are concerned about your cognitive decline. And I hear all the time that you don't like the cognitive component because it makes you feel like you can't remember anything. But if it makes you feel any better, I struggled for two hours yesterday to remember the name of McNabbs Island when I was taking Lauren on a tour. So honestly, but what's really interesting about this? So these are bars of men and women, and they're by English and French. And what you see is that for both men and women, for both immediate recall and for delayed recall, there's a decrease over time. But what's really interesting is that this is the first time we've ever had this kind of information. We've never really known in a population what normal cognitive decline looks like. And so in a study like the CLSA, we're starting to be able to say, OK, this is what is normal when you age. And I also want to reassure you that these are just two elements of cognitive decline or cognition. There's a number of other elements of cognition when, in fact, we don't see this pattern of decline when it stays across the board. So the CLSA is really doing groundbreaking work in this area. And as much as I know you don't like doing those tests, we really appreciate that you do them because it's telling the world a lot. My last slide is going to talk about self-rated health. And the thing that I want to leave you with here is that participants in the CLSA generally rate their general health and their mental health as very high. And this is something that is rewarding and it's nice to see. But we were also worried that maybe you guys were just happier than the rest of the world or the rest of Canada. So we checked other studies to see. And in fact, no, it's quite consistent across other studies as well that as people age, they tend to maintain high levels of self-reported general health and also self-reported high levels of mental health. So I'm going to leave you now and pass it on to our next presenters. But I do want to, again, reinforce that this is not a solo effort. There are many, many people involved. And these are some of the central people involved in the CLSA. This study is also not a cheap study to run. And we have many partners and many funders. And the key Canadian institutions that have funded us are the Canadian Institute for Health Research and the Canada Foundation for Innovation. But the provinces and the universities and private industry has also contributed to this study. And I show this slide every single presentation I give. And it's an acknowledgment and thanks to the CLSA participants. But I have the honor of thanking you in person today. So thank you. Excellent. And I want to thank you as well and echo everything that Susan said about how much we appreciate all of the amazing willingness to participate and participate over a long period of time. Because one of the things that we struggle with in longitudinal studies is keeping people in and engaged. And it really has been amazing in terms of the number of people who have stayed and keep contributing in a very, very important way to our study and to our research. And again, here today is one of the really exciting times for us because we get to talk about some of our preliminary findings. And I wanted to talk a little bit about some different ways that we've used the same sort of data. So I started it with calling it why so many questions. Because as you know, there are a lot of questions in the CLSA. But these things really give us important context. But not only that, it also gives us a way to look at the data in multiple ways. So there's all sorts of different lenses that we can look at with the data that we collect. And so I'm going to just organize myself a little bit with why are we interested in these areas, what information is collected in the CLSA, and then how are the data being used. And I also wanted to acknowledge my co-investigators and my collaborators, some of the work that I'm going to show today is things that they are actually leading. So I want to acknowledge Alexander Mayhew, Marla Beauchamp, and Aishe Kuspinart. So I'm going to talk a little bit about chronic conditions. And there are a lot of chronic conditions that we ask you about in the CLSA. And I know that Susan showed a little bit of the data. But one thing we can see is we can look at the proportion of people who have different types of chronic conditions. So for example, the most common one was actually arthritis. So over a third of people reported having arthritis, and 16% reported having diabetes. And we can compare these chronic conditions to people in Canada and around the world. And again, there's all sorts of things that we've looked at. We've looked at angina, we've looked at dementia, we've looked at migraine headaches. But what we know is most people actually have more than one chronic condition at a time. And that's one of the issues that's very difficult to deal with in our health care system that's kind of focused around single conditions. And so one of the areas that I've been studying is multimorbidity. And so one of the biggest reasons that we want to study multimorbidity, or essentially just having two or more chronic conditions at the same time, is that it's associated with a higher risk of death, disability, functional status, how we're able to live our lives and our quality of lives. And as well, adults with multimorbidity account for about 2 thirds of the health care spending. So it's a very important area of research. But one of the things that we found, and when you think about multimorbidity, it is kind of generic. So you have two or more chronic conditions, but two or more of what list of chronic conditions. And so what we've been able to do with the CLSA data is look at some of these lists of chronic conditions. We've been able to look to see what sorts of chronic conditions are included in them. Some of them have, for example, diseases that we're used to seeing, heart disease or diabetes. But some have risk factors, like hypertension or even obesity, and some have symptoms, like pain, those sorts of things. And so what we've been able to do with CLSA, because we have all of the comprehensive data, is to actually break these down. And what we found is that the list really matters. When we look at multimorbidity, which kind of makes sense, if you start including things like risk factors, like hypertension, the prevalence goes way up. But when you look at the associations with really important outcomes, the sorts of things that we want to be able to identify, like self-reported health and disability and social participation, it's really not as strong. But when you start including symptoms in these lists, it actually makes a difference. So in terms of this particular study, it was really informative in terms of how these lists should be constructed in terms of doing further research in multimorbidity, but also about interventions. So starting to understand this better is allowing us to make better interventions. So this is kind of one area where it was very general, or generated this study that really spoke to research. But as some of you know, I know that probably about two thirds said that you're part of the comprehensive cohort. When you come to the data collection site, there's a number of performance tests that you do. So you do the gate speed, so you do a four meter walk, a timed up and go, where you kind of get up out of a chair and walk and a balance, where you stand on one leg, and a chair rise, where you go up and down from the chair a couple of times, and grip strength. So there's these five tests, but they're five, and I think you could attest to this, they're fairly simple tests, they don't take a lot of time, but they're also very informative in terms of clinicians wanting to assess things like falls risk. And what's interesting is, again, falls, very, very important. It's the biggest cause of injury related hospitalizations for older adults. And the other part is, you have what you think of the injury in the fall, but there's really this also social and psychological context, where many people who fall develop a fear of falling start to limit their activities and their social connections, and so it can lead to social isolation. So it's really, really important. But what we've found is, clinicians like to use these performance measures to create fall risk tools, but there really is no agreement in terms of what is the right test to use and what are the right cut points to use. And one of the reasons that there's not this sort of information out there is a lot of these recommendations have been made on very small populations. And so we have these huge, heterogeneous populations of older adults where we can start working on identifying what is the best test to use. Again, they're simple, they don't take a lot of time. And what are the cutoffs that are gonna help us to really inform who can use fall's interventions? And so these are things that will actually be used. This is work that was led by Marla Beauchamp and Ayushé Cuspinard, but these are things that will actually be used in clinical practice as we move along. And this is actually a lot of the data for CLSA is able to be used in this way, and it's very, very important. So just the last bit, I wanted to show, this is some of the work that is being led by Alex Mayhew. And again, when we think about physical limitations and physical functioning, we have those performance measures, but what we're really interested in why we wanna look at those is they seem to predict disability. So you have restrictions in performance start to impact the way that we live our lives. So here we have the gentleman with the cane who has some sort of physical limitation, but he uses an assistive device and a cane. It doesn't mean he necessarily is disabled. It means that he has some sort of limitation. And we hope to make sure that he's able to live his life and do all the sorts of things that we need to do to be able to live on our own and to be autonomous. So when we talk about disability, as Susan alluded to, we look at these things we called activities of daily living and instrumental activities of daily living. So essentially the things that you need to do to live your life autonomously. And what we found is we have, although we measure physical functioning differently in the comprehensive cohort, you come in and do your performance measures. In the tracking, we actually ask a number of questions. So we have the five performance measures in tracking or in comprehensive, but we have these 14 questions in tracking. And they really kind of link on to three main domains. So upper body, lower body and dexterity. So we have kind of three domains in tracking and we have these five performance measures in comprehensive. But what was really interesting, what we found is although they measured in a very different way, again, we have another bar chart, but this one is a little bit different. So here for the comprehensive, it's in blue, and you can see the bars represent people who have one, two, three, four, or five of the performance measures on which they are performing in the lowest quartile. So lower in the performance measures. And so the way you can interpret these bars is compared to people who have none of the performance measures in the lowest quartiles. What is the odds or what is the possibility? What is the chance of having disability? And you can see if you have one, two, three, it increases. The more of these performance measures that are for which it's a lower level and up to the point where there's five. And what we find it's in tracking, we have very similar results. If you have one domain, two domains, or three domains where there is impairment, then you're much more likely to have some sort of issues in terms of the activities of daily living. So here at this point, if you have the three of the domains or five of the measures, you're actually over 10% or 10 times higher in terms of your chance of having activities of daily living limitations. So here it's actually, it's interesting because we have a huge amount of data, but we're able to actually find similar results looking at the data in two different ways. So with the questionnaires and with the actual performance measures. So in summary, again, I'm trying to be cognizant of time. I know we're all very excited about the research that we're doing. But again, we owe a great debt to all of the CLSA participants. And the CLSA data are being used by research as well as to inform practice, which is I think very important and policy, as Susan was saying. And again, the richness of the CLSA data is really what allows us to look at these things with many lenses. So they can be used in many different ways. And thank you. Okay, how is everybody doing okay? It's kind of mixed stage, yeah. Okay, thank you. My name is Yukiko Asada. So I'm going to tell you about the research I'm doing using CLSA. Let me start with the personal story. So I've just come back from Japan, where I'm originally from. And one of the highlights always to go back to Japan is to see my grandmother. She just turned 92 this month, and she's doing well. But when I think about her, her aging, I think about whether her needs, basic needs are met, how about her social life, and how about her health. And then I think you think about successful aging of yourself, your family, and friends. We can think about successful aging of a population or society as a whole. And when you want to do that, it's not just a single person, there are many people in there. And then there, we need to worry about two things. One is on average, how we are doing. So like Susan's presentation, we heard that on average, people expected to live this years. But then we know that it's not everybody's experience. They're just a little disfigure. All of these people probably have different experience. So we have to also worry about everybody. And this concept of thinking about average and then everybody is not new. And probably the most familiar is the income. This is not the CLSA data, but it's from Statistics Canada data. So in 2011, in Canada, after tax, average income is $44,500. We know that this is not everybody has that income. And then here, the top 20% is 87,100, bottom 20% is $16,000. So there is a gap. And we worry about those things. And this is not just one way to look at the income gap. For example, when we know that an income is actually, has a relationship with a kind of social disadvantage or prestige. So we know that the people with disability tend to have lower income or indigenous population tend to have lower income. We worry about it. So that's an income story. But with my colleagues, Susan is also in my team for this, we think about maybe for health because we value health. We look at an average health, but everybody's health. Then this health inequality can be used as a kind of barometer of a successful aging of a society. So how Canada is aging? Is it successfully aging? And we need to worry about everyone. So that's what we are doing. And the interesting thing about health is that unlike income, income you can count its money, but health is many aspects to it. And then I don't need to tell you that you have to spend a lot of time to answer a lot of questions for CLSA. So there are many aspects of health and which one to use. We decided that for our purpose, we want to focus on overall health. And we found three candidates in the CLSA data. So first one is self-rated health. You might remember, this is in general, would you say your health is, and you have five options. Excellent, very good, good, fair or fair. And in research, a lot of researchers put it in two different categories, good health and poor health. So this is kind of interesting subjective measure of you assess your own health and tell us. You might think that we might be a bit more objective, so there's objective measure called frailty. And frailty is a researchers try to measure frailty in different ways, but in a one common way to do is developed in this university actually by Dr. Ken Rockwood, our group. And it's there, you look at different aspects of health. So from CLSA, we picked 44 different aspects of health. And then we say, we ask this question, how many of them are deficient? So for example, one of the questions, maybe related to whether the participant had certain chronic condition, like a cancer or diabetes. And if that person had that answer is yes, then we count is a one deficiency, right? So you can see that we make that way. And then we make a total, then we can say in this frailty index, we can say in that person, what is a percentage of deficiency? And if it is zero, there's no deficiency. If it is one, everything is deficient. And in our research, we know that the people actually cannot sustain life to have all deficient but alive. Usually there's at some point that then it breaks down so much so that then it's death, okay? In our research group, we flipped over this because somehow we felt that the larger number is better health is more intuitive to think about it. So we consider that the net percent optimal health. So that zero means that all deficient, one is no deficiency and optimal. One last health measure we used to look at is grip strength. So you might imagine that in a grip strength is tend to be stronger among men and younger person and a larger body size. But if you look among your peers, so meaning that if I compare my grip strength with other female and my age group, which I'm not gonna tell you which age group, and then also same body size. And if I happen to have strong grip strength than my peers, then I have a better future health prospect. So I'm less likely to die. I'm less likely to have cognitive problem. So this is kind of interesting study now coming out. It's not what we discovered is in other researchers in the world is talking about. So what we can say is that maybe it doesn't matter than how strong you are, but after those things considered the peer effect, then we can kind of calculate future health predicting grip strength, meaning that if you have that and if that is strong, then you have a better future health prospect. That's something you might wanna have. So we use those and three things as the health indicator and we wanted to look at the inequality of it. So what we found is that when we look at the different aspect of health, actually we get the different stories about health inequalities. And so I'm gonna show you just one result. As I said earlier, those inequality and distribution can be looked at in many different ways. But then I'm gonna show you about association with income. So how does the health is related to how much household income people have? So here is the reporting good health. So compared to people in the lowest household income, that is less than $20,000. If people have the $20,000 to $49,000, it's hard to say, but it's like a lowest, next lowest level, blue. Those people are 1.5 times more likely to report good health. Next income level is the 2.3 times more likely to report good health. And then it goes on and on. So the basic method here is that then if you have more income, you're more likely to report good health. Okay. For that frailty, it's the story is quite similar compared to the people in the lowest household income. The percentage of the optimal health increase in a higher level, right? But when you look at grip strength, we couldn't see much of the difference in the 1, 2, 3. So the four bottom differences, those income group, they don't see any differences we can say for certainty. Only at the very top income level, we can say those people have the future health predicting grip strength. So they are stronger, so they have a better prospect. So these are different stories about health inequalities. And we are actually quite surprised about that. And then again, we started out its overall health. We've not even looked at a cognitive health or a function physical health. We didn't divide them up. So maybe they're very different stories. So which one to choose? Because originally I said that I want to use that as a kind of barometer for how successfully aging Canadian society is. And maybe we can use, you know, when we talk about income, GDP, gross domestic product in Canada, it's increasing this year, but income inequality goes up. Isn't it nice to have that same health indicator like life expectancy went up, but health inequality went down? You know, so those are the information that we can make, we can use to make a health policy for aging society. But now we are getting different answers. So here, ultimately, the answer to the question which one to choose is what aspect of health do we wish to distribute fairly in society? So it's not just kind of number game. It's like what you value. So I find that that's very interesting that, you know, I'm doing those statistical analysis in front of the computer, but it comes down to what means to us. So I would like to finish with this presentation with the pictures, smiley picture of the oldest member of my family. I hate aging well. And then also you, participants. So I hope that and I gave you a little snippet of your personal information comes to, you know, the data, but then that comes back to the very important question that you might answer differently, what you value to distribute fairly, right? And then of course, I'm not doing this alone. It's a difficult research. And then I'm very grateful for my colleagues and Susan Kirkland is one of them. And then Jelly Hurley and Michelle Groningen at McMaster University are close collaborators on this project. So thank you very much. I have a barometer of how everybody's doing. Yes, you may. Yeah, so you can think about for each, oh yeah, sorry. So I said that to make a frailty index. I used a 44. Let me just pull up this slide. It might be helpful. Okay, here. So I said I used a 44 aspects and why the number became zero and one. Okay, so could I explain a little bit? So you look at the 44. So this at the bottom, you see that the denominator, so you have 44, right? And then in the numerator side, you have how many of 44, you have deficiency. So let's say it's 22, you have deficiency. So 22 over 44 is 0.5. Then you can say 50% deficient. Okay, so that's how we summarized. Sorry if I went too quickly, but does it make sense now? Yeah, okay, thank you. Yes, does anybody else feel like going to practice their grip strength now? Yes. It's good to practice. So this part of research is not my own thinking. It's done by the Ken Rockwood, Dr. Ken Rockwood group. So I asked that question. Many people actually ask the question that then some aspect of health is more important than the other. And maybe we should wait. We should give more value, more importance to those aspects. And for which I got the answer that no, that's not the point of this measure. So what they are thinking of is more like a system kind of understanding. So it doesn't really matter. But you need to look at more than 30 different aspects of health. And you wait equally. And then somehow we come up with some very interesting result. That is a very, it's meaningful when we compare populations across times. It gives us some information about how the body is doing underneath. All right, yeah, it's an interesting question. Comes up a lot. I work with that frailty measure too. And one way to think about it is that it's self waiting. So that if you have bad enough arthritis that also gives you pain and functional deficiencies in other areas, then you get three points instead of just the one. So we're really, there's many ways to think about it. Great, so thanks so much for the opportunity. Are you getting tired at all? Anybody wanna stand up, stretch? We are gonna have a break, I think, right after me. So I'll try not to hold you up. So I'm gonna be talking about another example of a study that we can do with the very rich data that you're providing as part of the CLSA. And Yukiko started with a personal story. And I guess my personal story is I found out my mother-in-law is here. So I figured I'd better do a good job. And then I found out that my parents are watching online from PEI. So then I had to dress up, okay? So it's a real great pleasure to be here and to be sharing some of what we're doing. So this is a project that I've been working on with Susan Kirkland and Christy Smith, who's here as part of the CLSA team. Just one example of many that we can do with your data. So I'm gonna talk about, so far we've been hearing about the participants themselves and health measures. And right now we're gonna shift a little bit to thinking about caregiving and relationships with other people. So we'll talk a bit about caregiving in the general population in Canada and what we've found from your baseline data. And also start looking at some of the implications or how health or other factors or socioeconomic factors are associated with caregiving amongst CLSA participants. So just as a bit of background, in Canada we think of ourselves as having a universal healthcare system and we cover a lot of things including home care and long-term care. So we think that caregiving is really covered by the system, but those of us who are on the ground, including you, will know that that's not the case. So the most care is actually provided outside of the system. We call that informal, that's just one word for it, but it means done by family and friends, as in the people such as yourselves who are participating in our study. And this is really critical because it allows somebody who's receiving care to remain more independent, living in their own home and not have to, for example, move to a long-term care facility if they don't want to. And so we do know that the experience with caregiving is quite varied, though. So if you just say, are you a caregiver or not, or are you receiving care or not, it doesn't capture the richness of the experience. So what we aim to try to do is to try to figure out how long are people providing care, like the duration? Is it just short-term when somebody has a broken leg, for example, that heals up and then they're not needing that care, or is it long-term for something like dementia, for example, that is progressive? And also try to think about the intensity. So how many hours per week, for example, the person's providing care? And also, do they live with the person or not, and what's the relationship? I know from a clinical point of view, I talk to a lot of people and their caregivers, and there's such a variety of experiences, whether the person lives with them or whether they have to drive two hours each way to make sure that the person hasn't fallen on the floor, for example, it's a really big difference. Okay, so just among the baseline CLSA, what we found is that almost 40% or four out of 10 participants are providing care, so what we would call caregivers, and about 8% are care recipients or receiving care, but there's some overlap there too, so there's about 6% who are both giving care and receiving care, and I think that's a really important message. We see that all the time, actually. We think sometimes that we think of seniors as receiving care and needing a lot of help, but really most of what we see is that seniors are providing support or it goes both directions, so it's really enriching for both parties in many cases. When we break it down by who's providing care, receiving care, and both giving and receiving, it is more often women than men, although there's a good chunk of men who are doing a lot of care giving, so we don't want to forget about them. So if we look at it by age, I suppose not surprisingly, there are more people, so the care receivers are in the light blue, so there's more people receiving care or that increases as people get older, but certainly there's some of our younger participants who are receiving care, and there are many of our oldest participants who are giving care, right? So there's a trend, association with age, but it's not universal, so we need to think about that, and so a lot of this has relevance for clinical practice, so how do we best support care givers and care recipients, but also on a policy level? How does our government, home care, long-term care system support caregivers? So I mentioned that we were gonna be looking at it by intensity too, so we have broken it down into different groups, so we have the non-care givers compared to people who give care or provide care, and then we have low intensity, defined as less than five hours a week, medium intensity is five to 19, and high intensity is more than 20 hours a week, and that actually aligns with some of the programs that are in place in the Maritimes, where if one wants to be eligible for certain supports for caregivers, usually it's for more than 20 hours a week, and then we also looked at the length of time of caregiving, so is it short term, so less than 12 weeks, or is it longer term? So the short term is for more of a self-limited condition or someone who's perhaps at the very end of life would be another example, but longer term would be someone who needs a lot of assistance over time. So the slides are a little busy, but the idea is to get a visual impact there, right? So the whole CLSA population is the black bar on the top, so that's the 51,000 people. The caregivers I said are about four out of 10, so that's the sort of reddish, and then within the caregivers we can look at short and long term, so it's about half and half, so it's not just short term care that people are providing. And then when we think about intensity, there's a fair number of people, over 4,000 who are providing high intensity care, so that's the more than 20 hours per week, and over a long period of time. We have an Atlanta, Canada focus with their audience now today, so it's interesting to just look at some differences between the provinces, so Nova Scotia's up at the top, and we have PEI, New Brunswick, Newfoundland, and then Atlantic Canada all together. So the patterns are roughly similar, so we would see it's around 40% in each of the provinces who are providing care, although Newfoundland is a bit lower than that, could have to do with age structure and who's there and who's out working somewhere else, for example, and then within each of the provinces, it's roughly similar. And then we wanted to look just briefly as a high level overview of how the long term and high intensity caregivers are faring, because that might be the group that we'd be most concerned about supporting from a policy and clinical care standpoint. So we looked at a few different analyses, so one is retirement status, income, like Yukika was just talking about, relationship to the care recipient, the living situation, and then some measures of satisfaction with life and mental health. So caregivers who are doing long term and high intensity care are more likely to be retired, makes sense because they do have the more than 20 hours a week that's required for the high intensity. I know, we haven't looked at this exactly, but some of the people probably retired in order to be caregivers, which is an important thing to think about when we think about supporting them in long term for that choice, which may have an impact on their pensions and their own socioeconomics into the future. Caregivers and income, long term high intensity caregivers are more likely to report low income, and that may be again, part of this reverse causation where they've had to cut back on their work in order to do the caring. This is useful from a policy standpoint because in the maritime provinces, at least in Nova Scotia, we do have a caregiver benefit where people can apply for and receive financial compensation for caregiving in the high intensity. They used to have one in New Brunswick, they just cut it apparently. I don't know what the PEI situation. The relationships of the care recipient, the people who are doing the long term, high intensity caregiving, not surprisingly are more likely to be caring for a spouse or partner, so it's about 40% than the other groups. And they're also more likely to be living with the care recipient, so that's about 60% of them are living together, which kind of makes sense they're there to be able to provide that intensity of care. It's interesting though that many of them aren't, so the other 40% are not living together, but yet they're still trying to provide that degree of care. And then getting to the depression and mental health, I like to think about it in the flip side first, to say that most caregivers are doing well with their mental health, they're not having depressive symptoms, minority are, and unfortunately it's the people who are doing the long term, high intensity caregiving, who tend to have more of a hit to their mental health based on what we've seen in these analyses. And satisfaction with life is similar, so like we've heard, as people get older, they tend to report better satisfaction with life, and that's one of the reasons I love working in geriatrics, but we do find minority people who are having more struggles in that domain, and it is more likely that the people who have long term, high intensity caregiving are struggling in that way than the other people who are doing less intense caregiving. So that's really all I wanted to say for now, just a brief, high level example of one of the ways that your data is being used, and we're planning to carry this forward into the future including to try informed policy and clinical practices, as we've said. So thank you for your attention. That's a great question. So the question is what counts as caregiving, and an example is does it have to be face to face? And in many cases, to qualify for the benefits, it does have to be face to face, and then the person who's receiving the care has to have a certain degree of dependence and need for assistance, maybe cognitive impairment, that type of thing, but the question is, well, what about other types of care? So like looking after somebody's house if they're in the hospital for a long term, making sure their pets are cared for, home maintenance. So these are all really important pieces of caregiving, and it is very true that our systems, the formal supports, don't do as well across all the different levels or all the different domains, but that is something that we need to be thinking of, and so that type of question, hopefully we'll be able to look at, and maybe not that specific one, depends what we have in your questionnaires, but that's the type of thing that we really need more information on, definitely. Yeah, so the question is it about being a volunteer neighbor, and being a caregiver, you can have different relationships with the care recipient. Usually there is some sort of relationship, but I've been pleasantly surprised over the years of how many caring neighbors actually do so much for people to help keep them independent in their communities, so volunteers and neighbors are a critical piece of the puzzle too? Yes, mm-hmm, wow, yeah. Yes, I would say that it is. Now, for programs with the different levels of government, sometimes they'll have different definitions, so it would be more if the person is an adult or not a child, or a child with disabilities, they would tend to count it more for access to programs, but in terms of what we're talking about, what matters to us, yes, that would be caregiving, because that's certainly something that has impacted your life in important ways, yeah. Okay, everyone, it appears that there's no shortage of questions, so what I would like to do now is bring us back in a formal way and start to ask some of these questions and hear answers from all of us. So if I could ask you to take your seats again, and what I would like to say is if you don't want to ask your question in public, if you do have the sheets with you, feel free to drop off your questions at the back. We can answer them for you and we'll put them on the website. The other thing is if you want to put your personal email address on there, that's fine. I can't promise that I will respond to you individually in a short period of time, but we will respond to you in some way. But I would encourage you if you have a question to ask it in the public sphere, because usually what I find is that if one person has a question, at least 10 people have that same question, and it's always really good to have them answered in public. I would also like to bring Kat Arena McIntyre and William Martin up to the stage. As I said to you before, Kat is the manager of the data collection site, and William is the manager of the computer-assisted telephone interviewing site. And you might have questions specifically for them as well. And the other thing that I'd like to say is that when I was pointing out all of the staff that was here, I forgot Nick Dinkowski. So Nick is with us as well, so please acknowledge him. We do have microphones, so if you want to put up your hand, we'll run to you with a microphone and then we'll respond with a microphone answer on this end. Okay, here's our microphone. Is it working? We've all got microphones, but I don't, can you hear me? Okay, so if you have a question, put your hand up high. Somebody will come to you with a microphone, and I see someone with a microphone now. Hi. Hi, you've referred a couple of times to the importance of income and how it relates to health outcomes. But what early on you mentioned that with this study, that the income appeared at least to me to be kind of skewed to the high end. Is that pose a problem when you're analyzing your data? It's a really good question. This is typical of all studies, is that the people who tend to respond do tend to have a higher income than the general population. The advantage of the CLSA is that you couldn't, not anybody could just participate. You were asked to, invited to participate. And so you were specifically chosen within an age and sex range within your province so that, and we knew where you came from, so we can then use you as one individual to scale you up to the general population. And that's a really big deal to be able to wait to the general population to make the inferences that we make representative. But there is a caveat, and that is we can never fully overcome some of those deficits where you're not equal to the general population. One of the things that we did is when we were recruiting people at about three quarters of the way along, we looked at the data and we said, okay, this is a group who's very well educated and this is a group who is, has a high income. And so we specifically looked for low income postal code categories and we targeted our last recruitment there. And you'll notice that we have actually over 50,000 people in the study and that's because we were trying to beef up the number that the end in the lower income group. We've never been able to completely even it out. And the way that we get around it is by making very clear when we make inferences that there are these limitations and making sure that we also reference it to other population-based studies. Could I add, please? So yes, it's a very important point and then Susan's response to how CLSA compares to general population is important one. But the other aspect of it is that it used to be, we thought that the poverty is the problem. So if you're not in poverty, you should be fine. But the recent studies, including the ones I showed you, is that it's not just a bottom income or income sufficiency, but it looks like a relationship is graded. It's like a rudder, if you have a little bit more income, your health is a little bit better. And a little bit income, health isn't even further better. So that graduation is a kind of a consistent finding so we saw in CLSA except the grip strings. But that's what we are finding so that that pose very important policy question that we cannot just worry about whether people have enough sufficient of poverty income, but it's the distribution of income that might matter. Okay, I'm gonna put more microphones out in the field. Please do. Okay, I noticed that the study says from 65 to 85. Now I'm going to be 85 in August. So does that mean I'm finished? You don't need me anymore? Great question. You think you're gonna get off the hook that easily? No way. So when we were designing the CLSA, it was really interesting because we looked at all of the studies that had been done worldwide. And what we found is that the majority of studies looked at people who were over the age of 65. And what happens when you do that is you are already, and no offense, and I don't mean to say that people who are over the age of 65 rolled because I don't mean to say that. But what happens is you're only looking at a truncated point in life. And what we thought was really important is to look at the trajectory of aging and to understand the factors that impact how you end up as an older adult and really look at the things that occur. And if we had our way, we would go right back to birth but we stopped at midlife. So we recruited people who were 45 to 85 at the baseline. And we will follow people for 20 years or until death. And we will follow you even if you become cognitively incompetent if you choose. And so that's how many of you have been asked. Once you reach age 70, we ask you if you would be willing to identify a proxy in the event that you are no longer able to participate on your own. And we ask you to tell us how you would like to participate in the future if you're no longer able to do it on your own. And we ask you to identify who you might like to have be the person who answers questions for you. So we're really interested in being able to follow you for as long as possible. But the real disadvantage in studies of aging is the important things that happen happen to you. The outcomes happen at the end of life. We're following all of the things that are gonna impact that outcome. And the sad thing is if we don't actually get to understand what that outcome is if we lose you before that outcome because then we lose really valuable and important information. So that's why it's so important to us that you stay in the study and you stay in the study as long as possible. Thanks. Yes. My question's probably most best directed to Dr. Griffith. But I have a small preliminary question which would make it easier. Do you know about the adverse childhood experiences study? I do not. Okay. Well, I'll just explain that very simply. This was a study done in the 1990s by Kaiser Permanente on over 17,000 people. And they looked at 10 adverse childhood experiences that they experienced. I'm not gonna give you all 10, I know them but that's not what's relevant. What's relevant is that by increasing number, not frequency of an experience but by increasing numbers of these, you ended up with a histogram that very clearly showed that more number of adverse experiences were associated with more comorbidities and earlier death with diseases, behaviors that affected health and disorders. Now what I'm aware of in one of the more recent questionnaires is that some of those questions started to appear on it. Now I admit I prompted it because I wrote a letter and said you've got a wonderful opportunity here to kind of duplicate that information and look at what's the role of adverse childhood experiences in health of your longitudinal sample. And you did ask some of that and I'm wondering if anyone has reviewed that yet. Well, it's a good question and it's actually, it was very exciting because so far all of the research that we've been able to do is on the baseline CLSA data. And as you say, this was asked of the first follow up. And so those data are now available as of... June the first. And so I find that completely fascinating. I wasn't aware of the, but I know exactly of the whole life course approach to looking at some of these things. And I think it's absolutely relevant and it is something that we will absolutely do. I know there's a number of outcomes that are related to that. And again, that's one of the really interesting things with the CLSA is that we can look at a number and look at this with a number of different lenses. And in terms of asking it a little bit later, and Susan can probably speak to this as well, it was really difficult to figure out what content to include because we wanted to have as much breadth as possible. And one of the things that we introduced, and there's a couple of I think historical items that were introduced at follow up one, because that was something that we thought once people are in and then we have maybe a bit more time because some of the demographic things we don't have to ask again, then we can ask some of these historical things. So in terms of using these data, I think there definitely are people myself included that are interested in using the, it's called the childhood maltreatment survey and it's a stats can one that was developed by a number of people in who are working with CLSA. So absolutely. Is there a chance to contribute to the mental health of children at the same time if you find this connection? No, absolutely. Thank you. Many of us give blood to you people and the number 42 was mentioned earlier. Could you indicate something of the use to which you put it and would it be possible at some stage to make the numbers known to us, the participants so that we can compare with whatever the numbers showed earlier? I realize that it wouldn't be appropriate for all 42 but possibly for some of the numbers. It might be interesting for us to see how we are progressing or not. Thank you. That's a very interesting question. So, you know, collecting blood and urine was something that we really wanted to do but we were worried about doing it and we have been reassured by you and by your response to it. So what we do with that blood and urine is there's a number of analyses that we do right on site and they're basic hematology markers and we have about 10 of those and those are available for use right away. Then what we have is another 30,000 where we have a chemistry panel done and those are things like cholesterol and like ALT and a number of markers, body markers. Then there's a number of genetic studies that we're doing and this is why the 42 alquots were collected and why they were collected in different ways because each type of analysis requires that the blood is stored in a different kind of medium and so now we're in the process of doing what's called a genetic wide analysis study or GWAS with your genetic data and we've done the first 10,000 and we will eventually do all 30,000. We are also doing metabolomics which looks at metabolites and we're also doing epigenetics. So these are really new forms of using genetic data. The reason why we didn't talk about them today is because those data have not yet been released to researchers. The genetic data, the GWAS data on the first 10,000 has been released as of June the first and we will start to see some of the genetic findings coming out. Now one of you and we will hopefully be able to provide you with some of the biochemistry markers back to you but one of the things that we've said is that we really will not give you your individual genetic information back at this point and the reason is because there's nothing that you can do with it and it's collected for research purposes and it's meant on a population wide basis and until there is something, like there's no point in telling you that you have a genetic disease that you cannot do anything about or you have a marker for a genetic disease that you cannot do anything about. One of the facets that we've said is that we had to make decisions about what information we would return to you and yes that information is your own property but here's the thing. We've said if it is something that is actionable we will give it back to you. If there's something that has population based norms so that we can tell you where your result fits in relation to the population we'll give it back to you and we'll give it back to you if it's feasible for us to give it back to you without increasing the cost of the study dramatically. So it's not like we're trying to withhold things from you but there are things that we don't feel are, they're not used in a clinically diagnosable way thank you. Okay so if you remember when you first joined the study and there was a consent form we asked you if you would be interested in having us be able to link to the data that's collected from you on an administrative basis by the province. So the province collects information on the number of times you've been hospitalized, what you were hospitalized for, how many times you went to the physician whether you saw a specialist. It's not your personal medical records, it's just administrative medical records. And when we asked you that it was at the beginning of the study and we asked you if from the time you entered the study could we collect that information. And so now what happened is it's really hard to access that information by the province and we have to go to every single province and get permission from every single province and every single province has to agree and we're not allowed to cross jurisdiction so we can never put it all together and look at it in a national study. But there is an institute that collects hospitalizations across the country and it's called CHIHAI, the Canadian Institute for Health Information. And they have national level data on hospitalizations. And so we said okay, well that makes sense, let's go to one organization and get this data rather than trying to deal with all 10 provinces. But when we went to the national organization they said great but then their legal team looked at it and said no, you can't do it because you didn't get permission from the participants to get the national level data. So what we decided to do is come back to you and ask two things. One is could we also add in CHIHAI which is a national level organization to get the data from. And also we asked to go back 10 years from the start because if you think about it and it's like the same rationale as starting in midlife and following you through to old age, there's a lot of things that happen in your health records that could actually help us answer some outcomes right now rather than waiting 10 or 20 years into the future. So if we can go back 10 years, it'll help us see the pattern in the health trajectory and be able to get research out more quickly. So that was the reason for coming back to you for those two things. It's protected in exactly the same way. So we never release your health card number to anybody. And even when we try to link with these various databases you should see the rigmarole that we have to go through. We have to, it's very complex but we never release that data to individual researchers ever. Actually it's a great question but when we, because we need to contact you of course to invite you back to the study but the actual contact information, any identifying information is actually in a physically different location than the data that is distributed to researchers. So only de-identify, we're very, very careful about that. And in terms of these linkage sorts of things the link, all the information that would be used to be linked is actually in a separate place. Hi, scattered throughout the presentations today I saw two words, right? And I didn't really get a definition of the two words, right? One was successful and the other one was unsuccessful. Successful aging versus unsuccessful aging. So unsuccessful aging in my estimation is dying before 65. Successful aging is living beyond 65. So so far I'm successful, which is a good thing. I'm on the right side of the grass, right? But really with all the data you've got and everything you're looking at and salaries and everything else I'd be curious to see what your definition as researchers are as successful versus unsuccessful. Oh, you asked such a good question. She paid you to ask this. So here's the thing, again, when we were designing the study we looked at the literature and there's a number of terms that are used in the literature. People use successful aging, they use healthy aging, they use optimal aging, they use productive aging and they mean different things. But ultimately we thought about this long and hard. And if you talk about successful aging, as you say, the opposite is unsuccessful aging and it sounds like a judgment call. It sounds like you're being judged about whether you've aged all right or not. And we really didn't like that term because people can age in various ways and you can have a disability and still age well. You can have some kind of disadvantage and overcome it. People adapt all the time and that's the biggest thing that we're interested in learning about aging is how people adapt to the circumstances that they find themselves in. So generally we shy away from using the term successful aging, but not always. There's sometimes when it gets the point across quickly but we tend to talk about healthy aging and when we talk about healthy aging we do not mean simply physical health. We're talking about physical, social, psychological health and the broad spectrum of things that go into making up your overall health. But what's really interesting in the study that I'm doing, which I think is absolutely fascinating is you know that we've asked you this open-ended question about what does healthy aging mean to you. And it's really interesting because we thought it was, it's the only question that's not scripted. Everything else, we take off a box but in that question we write down verbatim everything you say. But what's really interesting is that it becomes really hard to use that information because it's not qualitative information, it's not quantitative information, there's 50,000 responses and we've looked at it and the shortest response is one word and the longest response is 312 words. And how do you make sense of that? But what we said is the whole reason we ask that is because we're always willing to stand up here as researchers and say, what do you think healthy aging is? But why don't we ask the people who have lived experience and understand healthy aging from their perspective? So what we're now doing, I have a team working with some computer scientists and we're looking at ways in which we can use all of this data to come up with lay definitions or population-based definitions of healthy aging. And we're right in the thick of it, we're using what's called natural language processing and machine learning techniques to try and make use of that very interesting data and we'll certainly report it back out to you. Yes. Yeah, I have a question, the woman in the front kind of made me think about a question she basically said like, once I'm 85, am I done? So my question is, you said the endpoint is 2033, is that correct? Yes. Yeah, so it makes me think back to years ago in paramedic school, I had to learn about the Framingham Heart Study, it was just boring stuff I just thought I had to learn to get through part of my courses, but it makes me realize now that started like well over, I think last year celebrated 70th birthday and my question is, with the endpoint, with this program, the study, like with the Framingham Heart Study, it was long enough for 70 years, they said like in the 60s we understand how cigarette smoking affected cardiac health. In the 80s we understand how cholesterol affects it and in the 80s it started to understand better how like blood pressure affects cardiac health. I guess my question is, is a longitudinal study long enough, are you confident it'll be long enough so that these things will actually trickle down to the general population, become like common knowledge? People today know you shouldn't smoke, it's bad for you. Watch your cholesterol, it's bad for you. You know, watch your blood pressure, it's bad for you. Are you guys confident enough that this will be longitudinal enough that it'll trickle down to the general population and those will become common things that people will accept how you should watch your health and well-being? Well here's a secret. We don't intend to end the study in 2033. We haven't told our funders yet that in so many words but what we intend to do is demonstrate the value of this study and by 2033 I think they will understand quite clearly that by ending the study now it will be a significant loss to Canadians and I'm hoping that it won't be hard to make the argument to that point. You know, it's really interesting because when we go and present our study internationally people are amazed and they're amazed for a number of different reasons. They're amazed at the quality of the information that we collect. They're amazed at the way we collect it because it's completely standardized. Right the way across the country every single one of you completes exactly the same questionnaire in exactly the same way. It's totally standardized. It's very, very high caliber study. And the other thing that we hear is even though studies like Framingham that have gone on for 70 plus years or the birth cohort studies that have gone on in Europe they've never been funded for any longer than five years at a time. And they've never known that the funding was going to continue and they've had to reapply for funding every single time. And when we tell them that the CIHR has adopted this as a strategic initiative and essentially set aside money for a long period of time. Now we have to make sure we have to go through international review and submit a new protocol and it has to be approved to get the funding. But the fact that there was some foresight to understand that this was a study that needed to be funded in the long term is actually quite unique. And I forgot that we're on a webcast. But I guess the secret's out. Thank you very much. I think my question is a little bit of a follow on but maybe refining it. You are in a study that's been going on for about 10 years. I recognize tonight that I have statistically got another 15 years to go. And you may not have drawn any conclusions but for those of us with that statistical opportunity are there factors that the 85 and 95 year olds are now telling you that have reached the physically fit, mentally fit age both of that that could be useful to people who are in early 70s to start zeroing in on things that will increase that statistical chance. Yeah, that's a really interesting question. We haven't actually done that yet but there are a number of studies who have sort of put people into quartiles, let's say. And again, how you define what successful is varies from thinker to thinker. But people have looked at quartiles and said, okay, for the people who are in the highest quartile what does life look like for them? What does that trajectory look like for them? And how does it differ from the trajectories of others? And that's certainly something we can do in the CLSA. No, there's no data because that's the kind of thing that really requires a little bit more of a longitudinal approach. About a year ago, I heard about how a doctor stayed in a social situation that according to him there were two things that governed your longevity. One was your genetics, your genes, and the other one was the amount of stress you were under. And he said, I don't care how many vitamins you take or how much you exercise, if you're under stress you're gonna go before your time. Is stress part of your study? Yes, there's a number of questions that get at stress. But stress is a very elusive thing to measure to be perfectly honest. It's a very difficult thing to measure. So we look at the way in which the questions are asked can be combined in numerous ways to get at stress. So we understand about family relationships, we understand about income, we understand about chronic conditions. We do have scales that talk about distress. We have scales that get at anxiety and depression. And we have scales that look at sleep which is also a very important indicator. No, that's not part of the 44 factors. Well, they go into the 44 factors but we can also look at them in a different context. So I would be very surprised if researchers don't take up looking at some of those stress factors. And the interplay, and stress is one aspect but it's the interplay between genes and environment. When I say environment is genes and everything else basically. But it's really the interplay between those two that is important. Thank you. I'll add something on that topic. I don't know if this is turned on. As another example of a project we're working on we've taken the work on the frailty index that Yukiko was talking about measuring up the number of deficits or health and functional problems somebody has. And then we have another index measure of people's social circumstances and social vulnerability. So it's like a whole bunch of social related factors from income and housing to do you feel lonely, isolated? How do you feel about your life in those social ways? But then we're adding in a third one which is about resilience factors and health and protective behaviors. And so we're trying to tease out the differences between your health and your function is one thing and then what your social situation is is another thing and then how do you be resilient? So I think in the coming years you will see more information coming out of the CLSA about that. Yes. We're great. Look at them, don't look at me. Thank you. Hi, thank you very much. I wanted to ask a question pertaining to alcohol use and cannabis use or non-medicated use of different things. I always, I'm not originally from Nova Scotia but I've been here for most of my life but I always felt that Nova Scotia's really and I didn't realize when the cannabis came out last October that we also were gonna get a rating very high in the use of cannabis compared to the rest of the ratio compared. Is that something you look at as part, I think there's a question or somebody asked me that at some point but is that something you're finding out that pertains more so to this group maybe not so much then or yeah, thank you. It's an interesting question and there's a very interesting sort of West to East gradient around health behaviors in general in Canada and sadly because we're the East we're on the wrong side of the gradient it's just because we live on the East Coast. But what we have done is look at the alcohol variable and alcohol in the CLSA is a very interesting variable and it doesn't work the way it usually does. In general, and this is not for the Atlantic region this is for all Canadians, the more you drink the better off your health is. That's what it shows in the data. I don't know what to say. We're a little bit flummoxed but right now we're gonna until we break that down a little bit further and understand it a little bit better. That's what the data shows. Maybe all the socializing that goes with the drinking. Absolutely. And the exercising and getting out. We haven't, we have not yet asked about cannabis use but I suspect that that will be on the next questionnaire. How did you come up with the number 50,000? Why not 100,000 for instance? Why not which number? 100,000 participants. Well here's the thing, when we were designing this study we had the Cadillac version and we had the Saturn version and we had the Volkswagen version. And the Cadillac version had more than 100,000 people in it. Ultimately what we, where we landed on 50,000 was because we wanted to be able to study everybody across the country and that was the 20,000 that we follow by phone because we're not limited to having them within a data at data collection site. But really where we're going to make brown clothes breaking research is with all of the additional clinical and physical and genetic information combined with the very rich information on social and physical and mental health. And so we looked at the various end points that we would be interested in and some of them had to do with function and some of them had to do with disease and some of them had to do with whether you were institutionalized or not. And really it came down to, we didn't have the power, the statistical power to do everything but we could get enough statistical power with 50,000 and we just had to make that decision because of the funding that we had to end up negotiating. Although it seems like the CLSA is a very expensive study, it's one of the most efficient studies in the world. Most studies, if you look at the per dollar cost per participant is much, much higher. Good value for money. We did not have a hard time getting 50,000. We, it took us a long time to get 50,000 just because we were, and it was interesting because the way that the funding works, it was really strange. So we got the funding to operate the study from the Canadian Institutes for Health Research but we got the infrastructure like the building and all the equipment from the Canada Foundation for Innovation and the two organizations aren't linked. So we got the funding for, to operate the study first but we didn't have the infrastructure funding. So we had to say, hang on get the funding for the infrastructure, get the infrastructure built and then get the study going. So that was what caused the delay in the initial part. We started off early with the people who we could by phone and we had to wait till all the building was finished before we could bring in the comprehensive group. Yes. That's a great question. No, it is not automatically destroyed. What happens is when you turn 70, you will be asked some specific questions and we'll come to you and we'll say, there could be a point in the future when you may no longer be able to participate on your own whether it's because you are no longer cognitively able to answer the questions or there may be some other health circumstances. And in that case, what would you like to have happen? And there, you can tell us how you would like to participate in the future if you're not able to. And if you would like to participate in certain, if you would like to continue to keep participating then we ask you to identify a proxy. And the proxy is somebody who can make decisions on your behalf and who can answer questions on your behalf. And then when you get to that point we just switch over and the proxy continues. You do have the option to withdraw at any point and there are a number of withdrawal options. But here's the thing, a longitudinal study is only good if we can look at the data longitudinally. So for us, if you stay in the study for 20 years and then you withdraw and we have to withdraw your data, that's a major failure for us. But also too, because we release the data to researchers over time, we actually can't destroy your previous data. It's already been used. So that is something that needs to be understood when people are in the study is that it's almost impossible to destroy longitudinal data. Oh, yes, sorry. I will say something about the decedent interview. So if a person dies, what we will do is we have what's called a decedent interview. So we will either go to the person that you've identified as your proxy or we will go to somebody who is your next of kin that is in the database. And we will ask them if they would be interested in completing the decedent questionnaire. And what it asks is about the things that happened to you over the last year of your life. And it asks about your use of health services. It asks about whether you died quickly or if it was a long trajectory, the type of death you had. And it also asks some issues around quality of death and dying and whether they think you died in the way that you wanted to die. And it also does ask now about medically assisted death. Yes. Hi. As the candidates, basically the population die off. Are you gonna add new people for this study if it goes for the longer run? That's also a great question. We haven't made that decision yet. Once we're midway, I think. So here's one of the things that's happened is not as many people have dropped out of the study as we thought. We had to develop a trajectory of what we thought how people were going to drop out of the study over time and more of you have stayed in the study than we thought. It's actually got a real impact on our budget. But it's a good problem to have. However, there is discussion about having what's known as a panel where at some point, we would replenish the people that we've lost either through attrition or through death. It's under discussion. Okay, and the other one is just a curiosity question. Why do you think higher income people live longer? Oh, that's a very good question. I'm passing that to you, Kiko. There are different theories. One is about materials. So about you have house, you have all the means you need, you can eat well, and all those kind of things. Other theories talk about psychosocial issues. So it's not that you're suffering those are material things, but just because you compare yourself to someone who has more, and that relates to earlier question of stress that maybe that is causing certain stress. And that psychosocial thing is really interesting. It's not just humans. There are interesting studies about bonobos, and so high alpha male, and low ranking bonobos and how it goes. And then it's chimpanzees, and then we have those things. But then it's very complicated. It's not just, and it's better if you're on the top, but then it's actually depending on what kind of society you live, and if it is, you know, the value is more cooperation, then that's not better to be in a top ranking, and all those kind of things. So the short answer is we don't know, but there's just like when we talk about aging and in health, it's not just one thing. There are many things that comes into, and then so that relationship with an income and health is also very multifaceted. I'm aware that we're beyond our time, and I'm happy to continue to answer questions, but I do want to say to those of you who want to leave or need to leave, please do feel free to get up. But before we do that, I would just like to really acknowledge your participation, and thank you so much for being here with us today. If you still have questions, we're still here to answer them. Yes, at the back. We are asked questions to determine our cognitive skills, and we're asked pretty close to the same questions the next time we're interviewed. And I think there may be a little learning curve there. I know that I'm better now at listing off a number of words that start with a certain letter in the alphabet than I was the first time I attended one of your get-togethers. You're practicing. Well, it's just natural. It is natural, and if you think you're the only one that's doing it, you're wrong. Do you think that that is causing a bias in the reduction in cognitive skill over time? No, actually it does cause a bias, but we're aware of the bias. So we can compensate for it. Yes, understood. One other thing, the four to four factors. Is there any weighting put on those at all? Because I'm sure that they're not all equal. But here's the thing, and this is the basis of this approach, is if you take those 44 factors that come from your system wide, it's not about what they are, it's about the number. And because a lot of them are small subclinical factors. They don't show up. They won't show up in a day-to-day basis, but we'll look at, and there's all kinds of little things that go into it. And what we found over and over and over again is it doesn't matter which ones are in there. It's the number that counts. Yeah, but some of them may have five times the response, the value. But we've looked at it over and over and over again, and it does not matter. We can, so we can take 100 factors, and we can randomly select 25, 10 different times. And we can predict, we can look at how they relate to mortality, how they relate to hospitalization, how they relate to institutionalization. And they predict exactly the same way every single time. It's just the number. So the statistics smooth it out. It's very cool, actually. There's a lot of math and even theoretical physics and now machine learning that goes into it. And the replication has been so powerful. One thing also to think about is when a lot of the risk scores, when they go to weight things, like to say, you know, like cancer counts as 3.2 and like a broken leg counts as 0.8 or whatever. It's just very specific to that one study that they found those numbers and then you go to try to generalize it somewhere else and it doesn't work as well. So what we have found is, and it's well supported by the math and the physics, that it doesn't need to be weighted. And it also does the self-weighting, as I talked about earlier, where if you have a really severe illness, say a cancer or something, you get the point for the cancer, but you also get the point for the pain, the shortness of breath, the low mood and the functional impact. So it self-weights in that way for things that have a big impact on multiple domains. So if you have one factor, you're probably gonna have four more anyway, associated with it. Thank you very much for the presentation. Thank you very much all. 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