 Now, today's webinar is the Early Retiree Divestor Workforce, a quantitative analysis of early retirement among health professionals using CLA-SA data. So let me introduce our speaker for today, Dr. Sarah Yuko. Dr. Sarah Yuko is an assistant professor in the Department of Applied Health Sciences at the University of Prince Edward Island. She's a registered dietician with more than 10 years of clinical experience. Her primary interest is in human resources, and in her program of research, she seeks to better understand what keeps Canadian allied health professionals in their jobs and the impact of allied health professionals' continuity on patient outcomes. So over to you, Dr. Yuko. Thank you very much. All right. So welcome, everybody. Thank you for coming to listen to my webinar talking about my thesis research, which I used the Canadian Longitudinal Study on Aging Data for. And as already mentioned, I was looking at how the early retiree can divest the workforce and quantitatively exploring early retirement and involuntary retirement a bit among health professionals. So the members of my committee for my thesis were Dr. Trish Ray at the Alberta School of Business at the University of Alberta, and Dr. Carol Esther Brooks and Dr. Rita Cummings both at the University of Alberta in the Faculty of Nursing. I did complete my PhD there in the Faculty of Nursing, despite being a registered dietician. I do have no disclosures for non-financial or financial interests in the subject matter of this research and neither do any of my committee members. So just to give you an idea of the structure for this presentation, I'm going to be first talking a bit about the study objectives, the methods and then the results, the so what and the next steps and then any questions you may have. You can ask at the end. I did have four manuscripts that were incorporated into my paper-based thesis. And I have cited them throughout, so several of them have already been published. The first has been published in the Canadian Journal on Aging and that's the development of the conceptual models that I used and tested. The second paper is a descriptive paper and it's in press with health care policy. And the third is an early retirement paper that was published in Human Resources for Health and the fourth on involuntary retirement has yet to be submitted. But basically what the way I'm going to do the presentation is I'm going to sort of talk about all of the objectives for the entire study and then the methods for the entire including all four papers and ongoing like that, just to make it a little less disjointed. So my question when I started my study was sort of why do health professionals specifically registered nurses and allied health professionals retire early? Report from the Global Health Workforce Alliance and World Health Organization had estimated a deficit of 12.9 million skilled health professionals by 2035, which would be a 79% increase to the current deficit at the time the report was written. So it seemed as though prolonged labor force participation among health professionals was becoming increasingly necessary. And nurses specifically are known as really a staple of the health care system and without them other health care professionals really struggle to deliver any health services. And for the first time in 20 years in 2014, the supply of registered nurses in Canada had declined. Allied health professionals, which I define in my study as baccalaureate degree prepared as a minimum health professionals, including speech language pathologists, pharmacists, dieticians, physiotherapists, occupational therapists and clinical social workers, they all provide really key services across the health care continuum and their particular value in the prevention, treatment and management of chronic diseases. Comparatively, both RNs and health professional, allied health professionals require a baccalaureate level of education at a minimum and both groups are female dominated and can be found in diverse health care settings. Their professional services are often provided in shared workspaces and neither are likely to be the most responsible practitioner as this is the typical purview of the physician. The differences between the two groups are most apparent when we consider the content and scheduling of their work with nursing tasks frequently being more physical and more demanding on the body than those of allied health professionals and nurses being more likely to work rotating elongated shifts on evening nights and weekends. So, for definitions for this, all of my study, early retirement was defined as retirement before the age of 65 and retirement was considered involuntarily if the individual who'd retired considered their retirement to have been involuntary as this was how it was measured in the Canadian longitudinal study on aging. And there was no real literature indication of how frequently involuntary retirement occurred among registered nurses and allied health professionals. So going to the objectives, I started by developing and validating conceptual models of early and involuntary retirement among registered nurses and allied health professionals. And then using the CLA data after that, I was able to identify and compare factors that were reported to influence retirement decisions among registered nurses and allied health professionals. Following that, I looked to explore the relative importance of factors that influence early retirement and on time or late retirement, so that being 65, later than 65 years, among publicly employed nurses and allied health professionals. And then I quantitatively tested conceptual models of early and involuntary retirement in that same population. Last, two objectives I obsessed or obsessed the model fit and the association of identified variables with either early or involuntary retirement across occupational groups. So trying to look at differences in the testing between the RNs and the HPs. And last, tried to identify and discuss some implications for RN and HP workforce policy. So I started off with a theory to guide my methods. Many theories and conceptual frameworks have been developed and applied to enhance understanding of retirement and retirement decisions. The life course as a key concept in the life course perspective is defined as the coalescing of age-graded trajectories, which includes family pathways and career paths, which are contingent on changes and conditions and the availability of future options and short-term transitions, such as exits from education and retirement. And I selected the life course perspective because, firstly, employment-related decisions intertwine significantly with many other decisions during the life course. Second, because the perspective is inherently interdisciplinary, so it incorporates concepts and learnings from economics, anthropology, developmental psychology, demography, and sociology. And this was important to me, both being sort of an interdisciplinary scholar, so being a registered dietitian who did a Masters of Health Administration and then ended up in PhD in nursing, but also because I was looking at multiple different professions who would come from, you know, very different theoretical and didactic forms of education. And last, the life course perspective encouraged looking at micro, meso, and macro-level factors, which was important to me. So even though I may not have been able to look at all those factors, with the Canadian Longitudinal Study on Aging Data, it did help me to develop what I hope are comprehensive conceptual models that can be tested later, potentially, and have more accommodation for some of the, particularly the meso-level factors. And that's what this graph, which I developed for my study, is supposed to just demonstrate that over time, an individual has their own career trajectory towards retirement and they may retire on time, as we call it, or late early or late. It may be voluntary or involuntary and it may be a partial or a full retirement and factors at the macro-level and the micro-level and the meso-level, which is right in the middle, where that trajectory is, will all have an impact on what types of decisions they make about their retirement. So I won't talk too much about the Canadian Longitudinal Study on Aging, as many of you are probably very familiar with it, but it was developed to provide an infrastructure for state-of-the-art interdisciplinary population-based research. And for this study, I accessed baseline data for all respondents in both cohorts, which the comprehensive cohort, which included 30,000 people who had complete face-to-face interviews and site visits, and 20,000 in the tracking cohort who completed telephone interviews. And the questions, as you probably know, there was many questions on the survey related to demographics, health status, health behaviors, physical ability, psychological mental health, socioeconomic status and participation in the workforce. And specifically for my purposes, they did ask about occupation and settings of employment. So as far as my steps go, I began with a literature review and I conducted this search in multiple electronic databases. I ended up including 23 studies sort of in developing the conceptual model. There was a real absence of relevant studies related to retirement among allied health professionals in particular. And there was a limited scope and fairly low quality of existing reviews on registered nurse retirement. And then there was multiple meta-analysis and reviews related to sort of retirement in the broader population, which obviously would include health professionals. So I elected to sort of include results from high quality reviews that from the broader population, individual studies and reviews on registered nurse retirement and then any individual studies that existed on allied health professional retirement. So after developing the models, where all the factors identified as contributing to retirement were sort of added into the models and into the appropriate model, whether early or involuntary retirement. And last, I conducted interviews with 14 current and former health professionals between 45 and 85 years old to sort of validate the models to find out if they appear to be, you know, valid in line with their experience. And I'll talk a bit more about that on the next slide. So the goal, these were the demographics of people who participated in my qualitative interviews. So as you can see, I tried to get a range from across the Canadian longitudinal study on aging age range from 45 to 85. And I tried to have one registered nurse represented or represented in each category. And then the allied health professionals divided across the categories fairly equally. And 50 percent of them were already retired and the other were not, which means that they were able to give me sort of there. They were in that age range, but still able to give me their perceptions of what would impact on their decision making. I was able to get different provinces or representation from different provinces and from many of the allied health professionals, which actually made me quite satisfied to, especially some people like radiation therapists, where there aren't as many of them. It was nice and speech language pathologists to get representation in my interviews. So ideally, I was trying to achieve sort of a face validity of my conceptual models, and I tried to recruit them based on the principles of maximum variation sampling. So prior to the interview, I sent them the conceptual models, so both the early and involuntary retirement conceptual models, which we'll see later. And I conducted the interviews that are over telephone or Skype and one took place in person. And they were asked explicitly if the model appeared clear, logical and relevant to the retirement of registered nurses and allied health professionals. Additionally, I asked them if they had any changes they would suggest to improve the model. I analyzed the registered nurse and allied health professional responses separately to sort of see if there was any comparison across the professional groups in terms of differences. And then I applied those results to refine the models. And if I thought that factors factors were added or adjusted in the model, if more than two people who had participated in the qualitative interviews identified it as an issue. So this was a big step for me, the data cleaning, as you might have guessed from the way of described who I included. I didn't use all of the participants in the CLA. I used only those in the occupations of interest. So originally, my understanding had been that the CLA was going to code occupation and setting of employment. So I was kind of informed that that was decided they decided not to do that. So I ended up reviewing free text entries for both occupation and setting of employment for all 50,000 respondents and pulling out only those who were publicly employed, registered nurses and allied health professionals. So this was complicated by the fact that a lot of nurses reported themselves as simply a nurse. And this could have meant registered nurse, licensed practical nurse or health care aid. So for those who didn't specifically identify themselves as registered nurses, I filtered out those who had less than a baccalaureate level degree. So as a result, I'm sure that there may have been some diploma paired registered nurses who had identified themselves only as a nurse and I might have missed them even though they were registered. It's also possible that some licensed practical nurses or health care aides had undergraduate degrees in other fields and weren't actually registered nurses. As far as setting goes, this was also free text. And so I was looking mainly at publicly employed, where that was my intention. So I removed participants who reported self-employment, retail employment or government employment from the sample, as my objective was to sort of look at people to try to give information of relevance for health policy makers and administrators in the public health care system, inclusive of hospitals, regional public health centers, primary care centers and provincially run long term care facilities. So in some cases, that setting was really unclear. So if it was simply health care, I included them in the sample. So it is definitely possible that some respondents were self-employed or employed outside the public health care system. So the type of model testing I used was logistic regression. What this did mean is that everyone who was included in my model testing had to be retired already. So because the binary was for the logistic regression, the outcome was either early retirement or not early retirement or involuntary or nonvoluntary, but the premises that you've retired already. So this meant that anyone who wasn't already retired but who met my criteria for a profession couldn't be included in my regression models. So I did do some exploratory data analysis looking for outliers, distribution and variance. And this was definitely an appeal of the CLSA data is that there was very minimal missing data, especially probably the one with the most was income, but even then it was very low. One issue was just that the voluntariness of retirement. So whether or not retirement was voluntary was only asked of one of the cohorts and this significantly limited my sample size for testing the model of involuntary retirement. So I did conduct correlations, colonnuity and I looked at variance inflation factor before conducting non-stepwise unconditional multivariate logistic regression. For the early retirement model, I had enough enough people to run a separate model for the registered nurses and allied health professionals. And that allowed me to compare them. But for involuntary retirement, I wasn't able to have to do a blended sample and use occupation as a variable instead of separating by profession, which again was because only one cohort answered the question about the voluntariness of their retirement. So this descriptive in my descriptive paper, I basically showed because I was able to use the full sample for this paper of people who hadn't yet retired and people who had retired in each of the professions. I was able to look at age of retirement, the mean and the standard deviation for and then the plan to retirement age and the mean and standard deviation for that for registered nurses, pharmacists, social workers, dieticians, occupational therapists, physiotherapists, SLPs and other allied health professionals, which included audiologists, radiation therapists and child life specialists. So the age of retirement did range from about 55.8 among SLPs to 60.5 among other allied health professionals with a close sort of second oldest being pharmacists. And the planned retirement age for those people who hadn't yet retired range from 61.6 among nurses to 62.8 among pharmacists. And what I think is most important to me to take away from this is that all of these are pretty well below the age of 65. So even the planned retirement ages were all below the age of 65 and the average age of retirement for nurses in particular is 58.1 and was statistically significantly different than allied health professionals average age of retirement as a group. OK, so in this table, which is also in the descriptive paper, which is in press, I was able to compare factors contributing to retirement. So not all people, so basically people who hadn't yet retired weren't necessarily asked which factors they thought might contribute to their retirement in all the all the cohorts. But I was able to see sort of which factors of the factors contributing to retirement were more significant for early retirees versus on time or late and which were more significant for RNs versus HPs. So just as I don't know if it's a reminder, but the question people are asked were just sort of like, did you financial possibility? Was this a contributor to your decision to retire? Yes or no. So it was sort of a binary and people could choose more than one of these factors contributing to their retirement. So the only significant difference there was between early retiring RNs and allied health professionals was that RNs were less likely to report a desire to pursue hobbies as contributing to their retirement decision. Whether it was early or on time or late, which I found really interesting and I still haven't really found a reason to say why that may be. And then as far as differences between early and on time or late retirees, regardless of profession, early retirees were more likely to indicate that financial possibility, requirement for caregiving and organizational restructuring had contributed to their retirement decision. Whereas on time or late retirees were more likely to indicate that a desire to stop working contributed to their decision to retire. So I know this is a really busy slide, but this is basically the conceptual model for early retirement that I came up with after, you know, the literature review and and had validated or did face the legity with participants. So none of the people interviewed. I didn't end up having or two or more people recommend a change to this particular model in terms of what impacts on early retirement. Both registered nurses and Alley Health professionals felt that the model was clear, logical and relevant. So obviously, I mean, it may not be obvious, but I wasn't able to test all these variables. Not all of them are in the CLSA and some of them are in the CLSA, but were not included for both cohorts. So there's different reasons why I might not be able to include them in my testable model. So and the another reason was limited sample size. So I ended up selecting variables for inclusion in my tested model based on the highest correlations with early retirement and those that were factors speaking specifically to retirement decision making. So those questions of was, you know, financial possibility of factoring your decision to retire, just because those were most most related to my question and also most temporarily focused because a lot of the questions asked are true to the current times of the time when they were originally asked by the CLSA sort of like what is your income or those types of questions. Whereas was this a factor in your decision to retire puts it at the time and place of their retirement decision. OK, so what you see here is my results of my logistic regression. So you can see the RN model and the Alley Health professional model on the right side and the RN model on the left. And basically for RN, there was several significant odds ratios with financial possibilities, a factor contributing to retirement. Basically, there was a 2.49 greater odds of having retired early. If somebody indicated that was one of the factors. Organizational restructuring was associated with a 3.94 greater odds of having retired early and 7.60 greater odds of having retired early was related to caregiving responsibilities. And then on the other side for, you know, a decreased odds of retiring early being tired of work was associated with an odds ratio of 0.49. So basically people who identified as being tired of work were half as likely to have retired before the age of 65. Organizational restructuring was also predictive of early retirement among Alley Health professionals and they had a slightly higher odds ratio than nurses with 5.59. And that was basically the only factor that was significantly predictive of early retirement in both groups. So this is the model of involuntary retirement. Involuntary retirement is much less studied. And so it's not surprising. It wasn't surprising that there was far fewer articles that specifically explored this and the candidates of people who reviewed it for me thought that it was a clear, logical and relevant model. We did end up modifying it or I didn't modify it because more than one person indicated that they thought caregiving responsibilities may be necessary or should be included in the model as contributing to involuntary retirement in this group. So as I said, the sample size was a great issue here. And if you're familiar with logistic regression, you'll know that cases is how you determine the number of, you know, the power of your model or how much power you need. So because in involuntary retirement. So the issue being not only that one cohort didn't answer this question, but also that involuntary retirement is actually fairly rare in this population. That meant I had very few cases. So I was only able to test a very limited number of variables in my model. So I tested only self-rated general health, chronic diseases, which is basically a continuous number of chronic diseases. Caregiving as a factor for retirement and occupation were included in the model. So here's the model results. This is the paper. The manuscript is still in preparation. If anyone has any tips of where a journal might be interested in this type of study, I'd be very interested in hearing. So as you can see, only 8 percent of variants in involuntary retirement was explained by the model. So this may be partially obviously that I didn't get to test all the different variables that I had indicated, but it may also be that a lot is not understood about involuntary retirement as of yet. The only thing that was significantly predictive in this population of involuntary retirement was self-reported health. So in this case, it's sort of counterintuitive, but a one-digit increase in self-reported health is actually associated with a poor sense of health. So basically someone with a very poor health rating, which was self-reported, would have had or did have 6.3 greater odds of involuntary retirement than someone with a very good health rating. Allied health professionals, as compared to registered nurses, had a far lower odds of involuntary retirement with an odds ratio of 0.24. But this should be interpreted with caution just because there was such a small number of allied health professional respondents that reported in voluntary retirement the majority of the people in this sample were registered nurses. So as far as the results go, in word form, I guess, essentially registered nurses and allied health professionals were largely in agreement regarding the clarity, logic, and relevance of the conceptual models that I had developed. The average age of our in retirement of 58.1 years is significantly lower than that of allied health professionals from a statistical perspective. Financial possibility and desire to stop working are among the most frequently reported factors contributing to early and on-time or late retirement among registered nurses and allied health professionals. 85% and 77% of allied health professionals do retire early. The model of early retirement has tested explained a variance of 25%, or a maximum of 25% of variance. And I believe this is largely because a lot of the meso-level variables were not included and many of the macro as well. The only real sort of macro one was looking at province of residence, which could give an indication of how provincial policies affect retirement decision making. RNs and HPs whose retirement decision had been influenced by organizational restructuring were more likely to have retired early. Registered nurses with caregiving responsibilities were more likely to retire early and only 8% of variation in involuntary retirement was explained by the tested model. And it's somewhat surprising even that things like chronic disease were not predictive of involuntary retirement. Some of the key limitations to the study are related to this, my study related to the timing of the CLSA survey in terms of, so some questions that were asked that I would have included in my early retirement model, like things related to marital status, household income and dependent kids living at home are reflective of the individual's current status and they may not reflect the status that they had at the time of the retirement. So they may have been married at retirement and not married now or vice versa. So that was one factor. And then even questions about, you know, did this contribute to your decision to retire may be subject to recall bias because there are some people who may have retired in the sample as many as 20 years before that question was being asked to them. So their memory may not be completely accurate. So in conclusion, registered nurses and allied health professionals do consider many factors when they're contemplating early retirement. There's much that remains to be known about publicly employed, are in an allied health professional pathways to retirement, particularly involuntary. And the conceptual models have only been partially tested. Further quantitative testing is definitely needed. Some of the administrative and policy implications, strategies to reduce rates of early retirement among registered nurses and allied health professionals might include reducing the frequency of restructuring in healthcare or at a minimum sort of improving the implementation and the management of these restructuring efforts, a potentially legislated expansion of pay lead policies to people providing informal care and subsidization of caregiving support for would-be caregivers who wish to remain in the workforce. As far as involuntary retirement goes, work-based interventions, there are some that have been proven to improve self-rated health and self-rated health was the only factor of protective and voluntary retirement. So maybe some of these interventions could improve self-rated health which reduces the risk of involuntary retirement in this population. And I do want to add just to make it, because I haven't explicitly stated this to make it clear, is that I don't think necessarily that the goal would be to even have everybody work until 65 or to increase the average to 65, but just based on the volume of professionals, particularly registered nurses. If we were able even to increase the retirement age by six months on average, the mean retirement age, this could have a significant impact on work, you know, supply and demand in the country of Canada and help us to maybe stave off some shortages or predicted shortages in these populations or these professional populations. So as far as next steps go, I am hoping to deepen my understanding of publicly employed pathways to early and involuntary retirement. I am hoping to get some funding to get a larger sample of allied health professionals because I would like to make comparisons across allied health professions. I believe that there could be very significant differences in the way, for instance, that a pharmacist approaches retirement than an occupational therapist. And this is knowing that it's one reason why I think there may not have been as many significant results in relation to allied health professionals in that model where RNs had a few, and that might be just because there's some of that difference within the group across professions. Additionally, I'm interested in the question of involuntary retirement as a concept because there are, and there's been some papers talking about this too, and involuntary retirement is sometimes measured differently in different studies where certain reasons are attributed as being considered involuntary, such as having to retire for poor health. And I feel that some people in the study, you know, it may be if I didn't hand in my resignation letter, then for some people that would mean they didn't consider it involuntary or if they, yeah, essentially if they did sorry, hand in the resignation letter. So for instance, if you had to give up your job to take care of an elderly caregiver and you submitted your resignation, there may be some people who consider that an involuntary retirement and others who don't because they were still the ones who initiated it. And lastly, which I just found out today and I'm very excited about it, I am going to have the opportunity to do comparative analysis using the Irish longitudinal database on aging to be able to see how this compares with them. I'm sure small sample size is going to be an issue there as well, especially with allied health professionals, but I'm really looking forward to that and seeing how there's a difference across countries. And that's it for now, so I look forward to hearing your questions. Thank you so much, Dr. Yuko, for your excellent presentation. I'd now like to open it up for questions. Just a reminder for the participants that muting will remain on, but you can enter your questions into the chat box in the bottom right corner of the WebEx window. So there is a first question, and you can also see it in the chat box. The question is, what are your thoughts on the implications of R&S caregivers in a both paid and unpaid setting, on the job and then retiring to be caregivers for loved ones? This transition is very interesting that one would retire from their professional caregiving job to an unpaid one. What are your thoughts about that? I mean, it's a big question. I think that it's because I didn't do qualitative interviews around sort of the experiences of people who've retired and how they go from their job to maybe a job afterwards in terms of taking care of loved ones. I did certainly, I can't really speak to it broadly, but I definitely did look at the literature to see sort of what the patterns are, and certainly it's a very gendered thing, and we are a very female-dominated professions, allied health professionals and registered nurses. And in general, women are more likely to give up work or to reduce their hours of work to take care of loved ones, and actually, at least in general, are more likely, as the evidence indicates, to work more or work longer in order to pay for caregivers. So it's sort of an interesting, I think, partly tied up with the roles that women are expected to play, especially women in caring professions. Okay, great. I guess I had another question that came to my mind, is that the sample size that you used would cover all the different provinces in Canada, with the healthcare system being provincially determined. Did you see or was your sample size large enough to look at differences between people working in different provinces on their factors, for example, influencing early retirement? So the sample size, unfortunately, wasn't large enough to look at differences in factors, but in early sort of descriptive analysis, it was definitely clear that rates and also the age of retirement were significantly different across provinces with, I believe, Newfoundland had the lowest age of retirement in many of the professions, and I think the province of Saskatchewan, I feel like, might have had the highest age of retirement. So certainly there were differences cross-provincially in terms of age of retirement. Yeah, and I'm sure that also with provincial elections or governments turning over in different times and perhaps implementing new changes to this system, it will make it very challenging to look at it with a small sample size. The other question that somebody had was to, you looked mostly at public employees. Have you had a chance to kind of think about how it would be different from private employees or health professionals that work in private settings such as private, long-term care homes, for example, and what differences you would expect to see? Yeah, so the evidence that I looked at it anyway indicates that people working privately do work longer, and that's typically more connected to sort of private practice. So pharmacists in particular, one of the reasons I suspect that they were the highest in terms of average age of retirement and expected age of retirement is because they probably had some privately employed pharmacists in there and they do tend to work longer because they are often owners of a business and there's not always a pension. I think one of the biggest things is definitely pension. So if you are self-employed, you don't necessarily have a pension and so that decision might be different depending on how much money you've saved. The other reason I think it might be different in self-employment is it's a lot easier to make adjustments to your work hours and the type of work that you do when you're the one deciding on your own employment. So if you want to do this type of work or you don't, and I think that that's one of the flexibilities, one thing that makes working later in life more tolerable, I guess, so that would be my suspicion. As far as people working in private sector, long-term care facilities, those sometimes would be unionized. So I'm not sure what the difference would be in those particular situations. I think to me it has more to do with sort of unionization versus non-unionization. Okay, great. You also mentioned that you are looking at the Irish or Tilda, the Irish Longitudinal Study on Aging. And there's lots of other cohorts available. Is there any reason why you chose to look at Tilda, the Irish Longitudinal Study on Aging? Because the Health and Retirement Study, I'm sure has lots of information as well in the U.S. about retiring and health professionals. Yes, the main reason is because there's a confederation of Ireland and Canada University Foundation, I believe it's called, and they had awards basically that were available to do studies that looked at particularly connections and I guess similarities between the Maritimes and Ireland. And so I applied for that just because I work at a university in the Maritimes. So it was more convenient. But I am certainly interested in Ireland in particular and hope maybe eventually to be able to do more comparative analysis with bigger samples and in other countries. Yeah, okay. Okay, thank you, Dr. Yuko. For any of the participants that are online, please feel free to enter questions in the chat box. And I will continue to close the session, but if any questions pop up, I will interrupt that and take that question. So Dr. Yuko, thank you again for such an excellent presentation. It was really good to see for myself as the CELSA managing director how well your presentation was put together and how you've used the data of the CELSA. So we really appreciate that and of course we appreciate your participation in the CELSA webinar series. I'd like to remind everyone that CELSA data access request applications are ongoing. The next deadline for application is February 12th, 2020. And please visit our CELSA website under data access to review available data and further information and details about how you can apply, specifically for a graduate student, there are fee waivers available. I also like to remind everyone to complete their survey located under the polling option. If you don't see beside the chat button on your screen, please click the drop down arrow and then we'll define it. Okay, so our next seminar will take place on Wednesday, February 19th, at noon. Dr. Alexandra May, a scientist working with the CELSA at McMaster University will present sarcopenia in the CELSA, the impact of diagnostic criteria on the agreement between definitions and your association of sarcopenia with falls. And registration for that is open right now. Finally, graduate students and postdoctoral fellows with an interest in longitudinal studies at aging are encouraged to save the date for SPA 2020. Now, this is not a real SPA, but this is the summer program in aging that's funded by CIHR. This innovative five-day training program will take place next June at the Hockley Valley Resort in southwestern Ontario. And applications will open in February on CHR's research net. So if you're interested in that, please keep that in mind. And remember, the CELSA promotes this webinar series using the hashtag CELSA Webinar and we also invite you to follow us on Twitter and the handlers at CELSA underscore ELCV. So there are no further questions. So again, Dr. Yuko, thank you so much very much for your presentation and to all the participants, thank you for attending today's CELSA Webinar. Thank you.