 Before we begin, I want to acknowledge that today, September 30th is the National Day for Truth and Reconciliation, where we reflect on and honor the lost children and survivors of residential schools, as well as their families and communities. The CLSA National Coordinating Center in Hamilton is located on the traditional territories of the Mississauga and Haudenosaunee nations and within the lands protected by the dish with one spoon wampum agreement. Our presenters, who will be hearing from today are based at Western University, which is settled on the traditional lands of the Anishabek, Haudenosaunee, Lena Pawak, and Atawanda-run peoples. As attendees of the webinar today, I encourage everyone to learn more and to acknowledge the original inhabitants of the lands where we currently have the privilege to do our research, live and work, wherever that may be, and to work towards truth and reconciliation as outlined in the TRC calls to action. Now on to today's webinar. Again, it's entitled Sleep and Health Across the Lifespan, an epidemiological perspective presented by Rebecca Rodriguez and Dr. Severio Stranges. Rebecca is a project coordinator in the Department of Epidemiology and Biostatistics in the Shulich School of Medicine and Dentistry at Western. She focuses on research related to sleep health, mental health, and the social determinants of health. Dr. Severio Stranges is a professor and chair of the Department of Epidemiology and Biostatistics at Shulich School of Medicine and Dentistry at Western, and is cross appointed with the Department of Family Medicine. He is also a scientific advisor in the Department of Population Health at the Luxembourg Institute of Health in Luxembourg. His research focuses on the epidemiology and prevention of chronic diseases and aging, specifically regarding the role of lifestyles, nutrition and social, lifestyles, nutrition and psychosocial factors such as dietary pattern sleep behaviors and social factors in cardiovascular health. So now I will pass it along to our presenters today. Just take your mute off, Severio. Can you hear me? Yes. So good afternoon and thanks for the opportunity to talk about our research on sleep and health across the lifespan on this special day for Canada. I feel the privilege to discuss about this topic on behalf of a larger team at Western University and also in other institutions. And with my co-presenter Rebecca Rodriguez, we will try to discuss some of the most recent findings based also on the Canadian longitudinal study on aging. But my presentation will really try to provide a broad background context to this topic of sleep, an important sleep health across the lifespan. We will discuss about some of the epidemiological trends, the evidence on the association between sleep and chronic disease, some of the issues around transitioning from association to causation and challenges and open questions in this field. Obviously the importance of sleep to general well-being, both physical and mental, has been recognized for a long, long time. Hippocrates used to say in whatever disease, sleep is laborious. It's a deadly symptom, but if you sleep doesn't good, it's not deadly. So we're talking about several years ago, and we're supposed to spend one third of our lives sleeping. So we can assume that if there is an alteration to both the duration of quality of sleep, that may have an impact on our well-being. And also we need to acknowledge that our sleep habits tend to change over time because of adjustment or changes in the circadian rhythm as we get older. It is also important that sleep is a multi-dimensional construct, which is influenced by a range of factors, which are not just individual factors like genetic makeup or other physiological or behavioral factors, but also the social in the environment play a major role in shaping our sleep patterns and therefore also the potential associations with a range of health outcomes. In the last few years, there has been increasing public health concern around the prevalence of sleep disorders and sleep deprivation as an unmet public health issue. There was a report from the Institute of Medicine emphasizing that sleep seems to be still outside the rather in terms of public health priorities. And there is also evidence on the economic and societal impact of sleep problems in income countries in terms of direct indirect consequences to society financially in terms of burden to the health care system and also to the work sector as well. Sleep problems are not just the problem of income countries. There is also evidence on the impact of sleep problems in low middle income countries. This was, for example, a study we did a few years ago with WHO, showing that sleep problems tend to be highly prevalent, also in, for example, African Asian countries. In particular, in this study, we look at self-reported sleep problems across eight different countries. In Asian Africans, there was a high prevalence in some of these countries and also consistent result of higher prevalence self-reported sleep problems among women, which is in line also with evidence from high income countries. So overall, the field of sleep epidemiology is rapidly growing and there is an exponential increase in the number of publications in this field, suggesting that sleep as a behavior should be given sufficient tension alongside with the traditional modifiable risk factors in chronic disease epidemiology such as smoking, poor diet, physical activity, obesity and so on. And as I said before, there is public health concern because the epidemiological data seems to suggest that, for example, duration of sleep may be declining the last few years. There was a study we did a few years ago using data from the Canadian National Population Health Survey where we identify four different categories of sleepers within the Canadian population and there was a consistent trend in declining sleep duration over time across the five time points of the study. Very recently, this was published just a few weeks ago, we also look at the associational sleep problems with other health behaviors including smoking, binge drinking and diet within the Canadian community health service on a very large population. We also found that a large segment of the population in Canada does not meet the recommended sleep duration guidelines and also even over 55% of females and over 40% of males may report sleep problems. We also found associations of binge drinking and smoking with increased risk of sleep problems. On the other hand, increased fruit and vegetable consumption was associated with a lower risk of sleep problems. So the other important aspect in the field of sleep epidemiologists, the overwhelming evidence on the association with sleep problems with a range of chronic diseases. In studies suggesting associations with risk of cardiometabolic conditions and disease with the increased risk of cancer and neurodegenerative disease with mental disorders, multimorbidity and overall and cost specific mortality. In particular in the field of cardiometabolic disease, there is a suggestion that perhaps we should consider poor sleep as an emerging risk factor for cardiovascular disease, given also the evidence on potential mechanisms in terms of biological pathways, which will increase risk of cardiovascular disease, including inflammatory processes or, for example, hormone responses in relation to poor sleep and the potential impact on cardiometabolic health. The American Art Association recently disclosed a statement on the importance of incorporating sleep duration and quality in the assessment of cardiometabolic health. As I said, there is evidence on the impact of sleep problems in terms of mortality risk. This was a study we did in South Africa where people with sleep problems will have reduced or increased risk of mortality and reduced survival. Other studies have suggested U-shaped association in relation to the association between sleep duration and total mortality, whereby both short sleep and long sleep may increase the risk of mortality. Overall, there is a need to look at the potential and implication of sleep in a life course perspective because the implication of sleep problems may occur at very early stage. For example, we found associations between poor sleep and internalizing problems in early adolescence using data from the Canadian longitudinal survey of children and youth. Likewise, we also find associations between poor sleep and risk of multimorbidity from the Canadian longitudinal study which will be discussed by Rebecca in a few minutes. Of course, these epidemiological associations do not necessarily infer that there is a causal association between sleep and range of health outcomes. Therefore, we should also consider poor sleep as a potential risk marker for ill health and there are considerations that need to be done before assessing the causation of this leak. For example, it's important to look at the potential correlates of sleep patterns in terms of socio-demographic profiles and we are trying to do this across different populations. In other words, we will try to identify who are the short and long sleepers or people with a poor quality sleep within the general population in terms of the socio-demographic profile. And also we have assessed geographic variations in terms of sleep patterns across Canada and we also found striking geographic variations both within and across provinces. So what are the open questions in the field of sleep epidemiology, especially in the link between sleep and chronic disease? Well, first of all, we need to distinguish between the evidence on clinically sleep disorders versus behavioral sleep problems in the general population. We also need to consider the range of comorbidities of sleep problems which may account for some of the epidemiological associations we have observed. So, again, need to discuss about bi-directional relationship, reverse causation, and issue of temporality, especially in cross-sectional studies, and trying to capture the multi-level influence by individual social and societal level factors, which obviously influence our sleep patterns. We need to corroborate biological plausibility and one important issue is the assessment of sleep in epidemiological studies, subjective versus objective measure of sleep over time. And this is a critical issue because most of the epidemiological studies, in particular those with the large samples, have relied on self-reporting information. And we know that there are problems in terms of misclassification, recall bias. Of course, there are objective measures such as polysomnography or actigraphy that may be more suitable, but again it's also the feasibility of using those objective measures in large population-based studies. Epidemiological studies should be able to capture changes in sleep patterns over time, and also the question around biomarkers, as at the moment we do not really have reliable biomarkers which can, in a sense, reflect sleep patterns. So in terms of the public health implication of sleep problems, overall there is concern on the high prevalence, both in high-income and low-media-income countries, everyone is exposed. There is a large burden of accidents caused by excessive sleepiness, as we say, the potential increase in burden of several chronic diseases driven by poor sleep, the economic burden in terms of healthcare and society. And we also should acknowledge the little attention to sleep, still in clinical training and clinical practices, also the little attention to sleep in public health circles and primary care. And finally, it is also concerning that poor sleep may contribute to widening health disparities because of the strong association with socio-demographic profiles. These are some of the study populations from which the research we have done come from, and just I would like to acknowledge, again, the large team we have a western universe in particular also, Rebecca Rodriguez will speak next to some of our funding bodies as well as international and international partners. Thank you very much. So thank you Dr. Stranges for the overview of the importance of sleep. So for my presentation, I'm going to share some of our findings on sleep and health among middle-aged and older adults using data from the Canadian longitudinal study on Beijing. And so first off, I just want to acknowledge the team of people who have contributed to the research projects that I'll be presenting here today. So as we just heard from Dr. Stranges, evidence suggests that a healthy lifestyle includes healthy sleep. And sleep problems are highly prevalent globally and here in Canada. So this is data from the Public Health Agency of Canada showing that many Canadians have poor sleep habits with as many as one in three adults age 35 to 64 and one in four age 65 to 79 sleeping fewer hours than the recommended minimum. And as many as one in two reporting for sleep quality. So in the context of an aging population disruptions and sleep duration and sleep quality are highly prevalent among middle-aged and older adults. And this may be due to medical and psychiatric illness changes in lifestyle and social engagement which accompany aging, which may in turn contribute to sleep problems. Given that sleep tends to be an issue among middle-aged and older adults in particular, it's important to understand the mental and physical health correlates of poor sleep in this population, as well as the subgroups of people who are more likely to have poor sleep. So we know that sleep problems are highly prevalent in the Canadian population and middle-aged and older adults. Given that sleep problems such as short and long sleep duration and poor sleep quality are associated with an increased risk for a number of chronic diseases. But associations between sleep patterns and the accumulation of multiple chronic conditions or multi morbidity remains unclear. There's pretty limited evidence on associations with this outcome. And multi morbidity is a particularly relevant health outcome for middle-aged and older adults as chronic conditions accumulate with age. And while regarding sleep and mental health associations, population-based data beyond clinical samples and people with sleep disorders are limited, and particularly in the Canadian context. And associations between sleep problems and health outcomes are further complicated when we consider that not everyone has the same likelihood of sleeping poorly. As Dr. Stratus just discussed, there's a wide range of socio-ecological factors that contribute to sleep disparities. And this is important since sleep disparities are under-recognized contributors to health disparities and disadvantaged groups. So a number of sleep disparities across different socioeconomic groups and different racial ethnic minority groups have been well documented in American settings. So the socioeconomic status is associated with shorter sleep duration, poorer sleep quality, delayed onset, and more fragmented sleep. And one of the most widely studied disparities in the US is between black and white racial groups. So evidence suggests that black people have shorter sleep duration than white people sleeping about 28 minutes less per night according to objective measurements. I know at 15 minutes less according to subjective sleep duration measures. So differences add up to really large gaps in sleep duration over time, as well as Latin, Asian, and Indigenous adults have shorter sleep duration as well. And there is poor sleep quality in these groups relative to white people. But importantly, there really is a lack of Canadian evidence on the subgroups of people more likely to have poor sleep, particularly among middle-aged and older adults. And since that sleep problems are highly prevalent in middle-aged and older adults in Canada, we wanted to explore the associations between sleep problems and different health outcomes that are important in the context of an aging population, including multimorbidity and poor mental health. And upstream of these sleep problems, we were further interested in understanding the subgroups of people more likely to have poor sleep, which would contribute to health disparities. So just to get these aims, we used data from the Canadian longitudinal study on aging. So for those of you attending who may be unfamiliar, the CLSA is a large national and comprehensive study on aging in Canada, with data being collected from over 50,000 Canadians over the age of 45 at baseline, and data collection will span 20 years. This includes two cohorts. There is the tracking cohort of about 20,000 people who were randomly selected within the 10 provinces, and they completed telephone questionnaires. And there's a comprehensive cohort of about 30,000 people randomly selected who live within 25 to 50 kilometres of 11 data collection sites across seven provinces. So you can see on the map here where the data collection sites are located in red. And for the comprehensive cohort interviews were completed in person. So for our study, we used the comprehensive cohort due to the availability of the sleep module questions in the sample. And specifically we used baseline data from the comprehensive cohort, which were collected between 2012 and 2015. So for our work related to sleep and health in the CLSA, we primarily focused on these two measures of sleep. So first we focused on sleep duration since this is widely known to be a problem in the general population. Many people don't get enough sleep. And there's also evidence of associations adverse health outcomes of both short and long sleep as Dr. Strong's discussed earlier. So the sleep duration question in the CLSA asks during the past month on average, how many hours of actual sleep did you get at night? This may be different than the number of hours you spend in bed. And we considered less than six hours to be short duration and normal to be six to eight and long more than eight hours. So the six hour cutoff was selected for a few reasons based on recommendations from the National Sleep Foundation, which states that less than six hours of sleep and health is not recommended. And the eight hour cutoff was selected for long sleep based on the distribution of sleep duration CLSA sample, which you'll see in a minute on the next slide. This group is quite small. And these cutoffs also align with similar previous studies in this area. So what we were interested in was sleep quality, which is a more holistic measure of sleep. And it's also less prone to problems with misclassification between subjective versus subjective measurement as sleep duration is. So the sleep quality question in the CLSA asks how satisfied or dissatisfied are you with your current sleep pattern. And among CLSA participants, the average sleep duration was 6.8 hours per night. So 15% reported short sleep duration to less than six hours and 5% long duration, according to our categorization. So overall short sleep is a more prevalent problem than long sleep in this group. For sleep quality, 27% of CLSA participants reported poor sleep quality has dissatisfaction with the current sleep pattern, whereas 59% were satisfied with their sleep and 15% were neutral. So sleep quality is more of a prevalent problem than abnormal sleep duration. So the other first study examining the sleep health associations in the CLSA was to understand the associations between sleep patterns and multi morbidity among middle-aged and older adults in Canada. And this project was led by Dr. Catherine Nicholson and was published in sleep medicine last year. If you're interested in reading up for more details. So to identify chronic conditions in the CLSA sample to define multi morbidity, we use the series of questions has a doctor ever told you that you have X condition. And we explored multiple definitions of multi morbidity, including a primary care definition, and this includes 17 chronic conditions listed here. So the these conditions were selected based on their relevance to primary care service services and do the impact on affected patients. And we also used a public health definition, which includes nine chronic conditions. And these conditions were selected by an expert working group at the public health agency of Canada. They were selected based on their chronic duration, the high population prevalence in Canada, significant societal and economic impact, and also the amenability to primary prevention. And we further used two cutoffs for each definition. So either two or more chronic conditions from each list and three or more chronic conditions from each list. So for each approach, we conducted a cross sectional analysis of the baseline comprehensive cohort. And in this analysis we modeled sleep variables as independent variables and multi morbidity as the dependent variables. And for sleep duration compared the categories of short and long versus normal and for sleep quality compared satisfied and dissatisfied versus neutral. We also applied with our two measures of multi morbidity and two costs, we have four multi morbidity outcomes. And for the analysis we use modified persona regression with sampling weights to estimate prevalence ratios for the prevalence of multi morbidity in each sleep exposure group. And we adjusted our models with a number of socio demographic variables such as age, sex, education level, some lifestyle variables such as alcohol and tobacco consumption. And physical activity. And we also adjusted for some clinical variables, including BMI and hypertension. And so this was for the public health definition only since those conditions are actually part of the primary care definition of multi morbidity. So shown here is the prevalence of multi morbidity within the CLSA comprehensive cohort participants at baseline. So 28% met the public health definition for multi morbidity, including at least two product conditions. And the smallest group was the three plus group with the public health definition at 10%. So prevalence was much higher for the primary care definition, since this definition includes much wider range of conditions. So we have 63% for two or more chronic conditions and 42% for three or more conditions. And here are the results for the adjusted associations between sleep duration and multi morbidity. So the Y axis shows the adjusted prevalence ratio, in which we modeled the prevalence of each multi morbidity outcome in each sleep duration group, adjusting for socio demographic lifestyle of clinical variables. And the error bars shown here represent the 95% confidence intervals around our parameter estimates. So both short duration in blue and long duration in green were associated with a higher prevalence of multi morbidity across all definitions as compared to normal sleep duration. And in particular the associations were largest with the three plus public health definition with prevalence ratios of 1.3 for short sleep and 1.56 for long sleep compared to normal. So overall we observed a U shaped association between short and long sleep duration and multi morbidity. And this is a trend that has been similarly observed for other chronic disease and adverse health outcomes. So for sleep quality, we again observed significant adjusted associations across all multi morbidity definitions. With dissatisfaction and sleep in blue associated with a higher prevalence of multi morbidity, where satisfaction was sleeping in green was associated with a lower prevalence of multi morbidity. So overall we observed that both measures of sleep abnormal sleep duration and sleep quality were pretty consistently associated with multi morbidity and the CLSA sample. So for the second study in this project, our objective was to understand the associations between sleep patterns and mental health among middle aged and older adults in Canada. We explored a number of different measures of mental health. So we examined self reported mental health with the question. In general, would you say your mental health is excellent, very good, good, fair or poor. And we dichotomized answers as fair or poor versus excellent, very good or good. And we also use the satisfaction of life scale, which is a validated measure of subjective well being. And we dichotomized overall score on the scale as dissatisfied with life versus people who are neutral or satisfied. Similarly, we also use the Kessler psychological distress scale or K 10 with a cut off score of 15 or more to indicate psychological distress. And this cut off has been associated with the best balance of sensitivity and specificity and older adults for screening people for anxiety or effective disorders. So again this was a cross sectional analysis of the baseline comprehensive cohort. And we use the same sleep variables and categories is multi morbidity analysis. Also similar to the multi morbidity analysis, we modeled the sleep measures as independent variables and the mental health measures as dependent variables. And again we use modified some regression with sampling weights to estimate prevalence ratios, modeling the prevalence of poor mental health in each group. And we similarly adjusted for a number of sister demographic lifestyle and clinical covariates as before. So poor mental health and well being within the CLC participants was quite low. So only 6% reported their mental health as poor or fair compared to 94% reporting it as good, very good or excellent. 12% were dissatisfied with life. We're 88% were neutral or satisfied. And 36% of participants had some psychological distress as measured with the K 10. So here are the results for the associations between sleep duration and mental health. So short sleep in blue was associated with a higher prevalence of poor self reported mental health dissatisfaction with life. So psychological distress relative to normal sleep duration. And similarly long sleep duration in green was associated with a higher prevalence of poor self reported mental health and psychological distress, but it was not associated with dissatisfaction with life. So for sleep quality, we observed that dissatisfaction with sleep compared to neutral in blue was consistently associated with higher prevalence of poor mental health across all outcomes with prevalence ratios ranging from 1.46 for poor self reported mental health to 1.18 for psychological distress. So those reporting satisfaction with sleep had a lower prevalence of poor mental health compared to neutral. So overall our findings were generally very consistent with our multimorbidity analysis showing that both short and long sleep duration and sleep quality were associated with poor mental health. So moving on to focus on the factors that are associated with sleep problems in the CLSA. So before our third study was to identify the social determinants of poor sleep health among middle aged and older adults in Canada. So shown here are the specific social determinants we were interested in. And we focused on the individual level social determinants rather than the broad ecological measures as shown in the Billings and colleagues framework that we showed in the introduction. So let's list using multiple sources. So, on the left are the social determinants of health that are important to measure going to Chi High. So this includes sex or gender, but we only have access to sex in the CLSA baseline sample. Age, geographic location, so rural or urban residents, annual household income and educational attainment. And on the right are additional social determinants that have been shown to be important in the sleep literature. So this includes marital status, employment, home ownership, migrant status, racial and ethnic minority groups, and sexual orientation. So for this analysis, again we focused on sleep duration and sleep quality. But in this case we added in an additional measure of sleep to be consistent with a number of prior studies in this area. We also looked at sleep disturbance, which we defined as difficulty initiating or maintaining sleep three times a week or more over the past month. So yet again this is a cross sexual analysis of baseline data from the comprehensive court. And for this analysis we modeled sleep as the outcome variable. So sleep duration we modeled as a continuous variable, rather than categorizing into short normal and long categories. So we used standardized sleep quality and sleep disturbance variables. Social determinant variables were modeled then as independent variables. And we used modified personal regression with sampling weights to model the prevalence of poor sleep quality and sleep disturbance. And we use linear regression to model sleep duration. We used a block wise adjustment approach. So first we adjusted for all social determinants together to identify the key social determinants independently associated with sleep in our sample. And then we adjusted for a number of clinical lifestyle variables to see whether these factors might account for some of the associations we observed. And shown here are the key groups where we saw poor sleep patterns, and at least one indicator. So these are the results from the models where we adjusted for all social determinants together. So females were more likely to report both sleep disturbance and sleep quality compared to males. People who are widowed or divorced were more likely to report sleep disturbance compared to people who are single or never married. People who are employed or unemployed have shorter sleep duration compared to the retired group. And as well, people who are unemployed also were more likely to report poor sleep quality. And among racial ethnic minority groups, we observed significantly shorter sleep duration and black East Asian and other mixed race groups compared to white. So these differences amount to about 20 to 22 minutes less sleep each night in the black and East Asian groups, which is a substantial difference. So this is also consistent with the sleep health inequalities observed in the US, although we did not observe any differences in sleep quality across these groups, as has been noted in the US. So these are the groups where we observed better sleep health or groups where sleep problems were less prevalent. So older age groups had better sleep across all indicators, as compared to the youngest age group of 45 to 54. And there even looked to be some indications of a gradient effect with sleep patterns improving as age groups increase, and in particular with sleep duration. This is a factor that mattered so higher income groups were less likely to report sleep disturbance, poor sleep quality. And we also observed better sleep so longer sleep duration and less sleep disturbance in the groups with higher levels of education. And these associations were larger as levels of education increased. And a few more groups that had better sleep health. People who are homeowners have longer sleep duration and a lower prevalence of sleep disturbance, compared to people who are not homeowners, although these effects are relatively small for this factor. Among racial ethnic minority groups, the South Asian group had a lower prevalence of poor sleep quality relative to the white group so they had better sleep. And the gay bisexual group had longer sleep duration than the heterosexual heterosexual group, which was surprising and we expected to observe poor sleep in this group, based on trends in the literature. So we then adjusted for a number of lifestyle clinical variables, and to see whether or not the associations might be accounted for in lifestyle clinical differences between groups. The associations were attenuated for a couple of factors, so household income and unemployment, but all other associations persisted and didn't really change substantially with adjustment. So who sleeps well in Canada, but we found some groups were less likely to have poor sleep. So people from older age groups above age 55, with higher household income and higher education homeowners, the lesbian gay bisexual group and South Asians. And on the opposite end, on the right, groups that were more likely to have poor sleep were females, relative to males, people who are widowed or divorced versus single and ever married, employed or unemployed versus retired. Black East Asian and other mixed race groups relative to white. In the middle are the groups where we didn't observe any associations with poor sleep patterns. So people living in rural areas compared to urban first generation migrants and Latin American and Arab and Middle Eastern groups compared to white. We didn't observe any, any differences in these groups. There are important limitations to consider with our findings. So the data we used are cross sectional. So you can't for any causality, which is particularly important to consider along with our findings on the associations between sleep, mental health and multimorbidity. So it's all reported measures of sleep, which in particular is an issue for sleep duration, and is prone to misclassification. So people tend to overestimate sleep just sleep duration as compared to objective measures. In any survey, there are some issues with selection bias in the CLSA. So comprehensive cohort includes people living close to the data collection sites, which limits the sample to people living close to these large urban centers and only seven provinces. And so that limits the generalizability of our findings. And the interviews are conducted in English or French. So this likely contributes to an under representation of certain groups, like recent migrants, ethnic minority groups, and people with disabilities, like hearing problems or memory impairment. We found that sleep problems, including short and long sleep duration and poor sleep quality are consistently associated with a higher prevalence of important health outcomes in aging population, including both multimorbidity and for mental health. And these associations are likely bi-directional. So poor sleep can negatively impact physical and mental health, which can in turn negatively impact sleep. And we also observed that sleep health disparities exist among different socioeconomic and racial ethnic minority groups, among middle aged and older adults in Canada, which is a concerning finding and may contribute to or exacerbate existing health inequalities in these groups. The findings, when considered alongside the abundance of longitudinal evidence from the sleep literature as presented earlier by Dr. Strong's suggests that sleep problems do have important public health and clinical implications. So from a public health perspective, as sleep problems are a modifiable risk factor, they might be a potential target for public health interventions and could potentially reduce the risk of chronic disease and poor mental health. And as Dr. Strong has also mentioned in clinical settings, sleep problems aren't given as much attention as other lifestyle factors. And since sleep problems in older adults are under-recognized in clinical settings, opportunities for intervention are the missed. So our findings, again, alongside the mounting evidence of health correlates of poor sleep literature suggests that recognition and intervention of sleep problems, such as an abnormal sleep duration, or poor sleep quality may be important in clinical settings, either to reduce the risk of chronic disease or just as a marker of poor health. So overall, our findings do support the notion that poor sleep warrants increased attention as a public health and clinical problem among middle-aged and older adults in Canada. So there are a number of open questions regarding sleep health associations that we plan to pursue for future projects to build on this work. So first, this includes looking at the longitudinal associations between sleep patterns, multimorbidity, and not to help using follow-up data from the CLSA. And so this will be really important to build on the cross-sectional analysis that I presented today. And we're currently working on a project investigating the neighborhood-level environmental correlates of sleep problems using the CLSA new data linkage. And we also plan to look at the associations between sleep problems and health services and costs using survey data and health administrative data linkages in order to better understand the health system impacts of poor sleep in Canada, which is an under-investigated area. So I'd like to acknowledge all the team members who contributed to this project, in particular Dr. Stranges and Dr. Kelly Anderson, who are the co-PIs on this project, as well as Ray Alonso, who assisted with a number of statistical analyses. And Dr. Catherine Nicholson, who led the multimorbidity analysis. As well, I'd like to acknowledge Lawson and CIHR for funding. So thank you for attending today, and we'd be happy to take any questions. Great. Thank you so much to both of you. I do see we have some questions that have come in. Just a reminder, if you can use the Q&A that is located below the presentation, that just helps us keep track of questions better. But I will go through, there was a few that came in on the chat as well, so I think maybe we'll just start there. First of all, Shirley at the CLSA did post a link to the paper. So thank you very much for doing that. That's in the chat. And one question that came in. First question from Andrea was for Severeo, could you explain about the correlation between sleeping patterns and geographical distribution? Is there any specific factors like weather, demographic kind of jobs in that area? That is an excellent question, and I was pleased to see the future direction slide over back because we are actually looking at that particular aspect with our ongoing catalyst grant and the data linkage with the kind of the data which provides, as you know, a wealth of information on environmental exposures. Obviously, the paper I was mentioning before is based on Canadian community survey data where the information on environmental exposures is quite limited. And as you know, environmental epidemiologists often difficult to capture granular information within current national surveys. So we hope that by looking at these link data from Canoe, we can provide more specific information about, you know, potential environmental factors which may account for the geographic variations. We have served in our studies where the geographic unit was the forward sortation area within each provinces and obviously we accounted also for a range of socio-demographic factors to make sure that that was not, although it's one of the potential, you know, explanation for those geographic variations, but the variations seem to hold even after accounting for socio-demographic differences. So I think the project, the ongoing project will provide more additional information. I don't think in the overall body of evidence there is really consistent findings on a specific, you know, obviously we know that sleep is multi-dimensional and there is a range of environmental factors which are likely to contribute. But I don't think we have definitive evidence in this particular area. Great, thank you. And another question is that research by Sarah Arbor from the UK finds that women who are coupled or more experiencing are often experiencing interrupted sleep because of spouses sleep challenges. Would this contribute to accounting for women's greater sleep issues? Yeah, that's another great question. I'm very pleased. And this is another, you know, area research on sex gender differences in the association between sleep and health outcomes. Certainly the mechanism that this colleague has put forward is extremely plausible. You know, we did a number of studies looking at the association of sleep deprivation, in particular with the cardiometabolic outcomes. And we found, for example, that women seems to have a potential higher risk of hypertension and also cardiovascular disease in general, not necessarily the same, was found in men. And we also found that these associations seems to be stronger, especially in the paleomenopausal period. So, you know, obviously we postulated that, you know, hormonal turmoil or changes in hormones that happened during the menopausal transition may play a role, but certainly there is the influence also social and societal factors in driving some of these differences. And I think there are also many open questions, but the explanation given by our colleague I think is extremely, you know, plausible. Great. And I think we have two questions related to shift work. One is, is whether shift work is if we can identify shift workers in the CLA say, and also do know if the sleep duration measure is valid and shift workers. And then a separate question is the relationship with shift work and your findings. So to answer the first question, I'm, you may know, I'm actually not exactly sure if we ask about shift work in the CLA say, do either of you know. Yeah, I, that's a hard to recall. Lauren Anderson, and I say hello. By the way, I have another project with her. So, yes, we do have information on shift work in the Canadian student study in aging and we have another student actually re alone so also contributed to our analysis always working on the association of shift work with the cognitive. function within the same data set. So we do have some information shift work on, you know, retirement status. Obviously, this is, this is an area which is extremely important in occupational epidemiology and there has been, you know, quite a large body of evidence on the increase risk of a range of health outcomes. Among shift workers and and some of the mediating mechanism can also be disrupted sleep patterns. Obviously, we are, you know, expecting to have the possibility to analyze also the longitudinal data to tease out, you know, the issues that cannot be sorted with the cross sectional, whether or not the sleep duration is a reliable measure among shift workers in this particular data set. I think that's, that's a great question. And obviously, we know all the limitation of self reported information. And, you know, and therefore, I know that there are studies, which have attempt to measure sleep patterns with with that geography, for example, within certain occupational groups. I think the CLSA data set may answer some of the questions around, you know, the important sleep shift workers but there are limitations, which I think, you know, need to be considered when we interpret the findings that we will produce in this particular population. Yeah, there was another question. Jennifer know about shift work or it was just relevant shift work in your particular in your findings. Yeah, they were not including these analysis the Rebecca has discussed that there is a, as I mentioned, an additional project that has produced a manuscript which will be submitted soon so we can we can, you know, we can keep our colleagues posted on that particular aspect. And we do have about five minutes left so I just want to remind participants who are who may need to leave to complete their evaluation and the link to that is posted but we will continue with with questions we do have several more and if we don't get to them we will start with you after. So, the next one from Irving Rootman is anyone studying the recent total withdrawal of sleep apnea machines manufactured by Phillips which are used by people worldwide. Yeah, we know the, you know, the issue and the potential you know concern around cancer risk and not to my knowledge and not within our group. So I am not able to risk, you know, and certainly we are not looking at that that is a specific population subgroup in our analysis generally speaking, we have either accounted for people with the sleep apnea or we have removed that population because the overall evidence we're trying to produce is to really look at the potential impact of behavioral sleep in healthy population so in people without the diagnosis of a clinical sleep disorder, because I think that's where I think the public health message will become even more powerful because as we show, I mean, you know, large segments of the population may have the overall sleep problems which are neglected and not necessarily come to the attention of a professional person. We are focusing really on the population that is not affected by a clinical diagnosis of a sleep disorder. There was another question. There are, there's actually a few more. One is, how are the sleep categories of less than six hours greater than six hours determined it appears that sleep duration is a distribution with the peak at seven to nine hours and the recommended sleep duration cut ups cut offs are arbitrary. What makes slightly shorter sleep duration, 6.5 hours for example, low normal, while less than six hours considered a health risk. I wonder if Rebecca wants to answer that question based on the distribution of the CLSA data. Rebecca, do you want to say anything otherwise I can. Well, we, we've looked at that cut off based on the National Sleep Foundation recommendations. All right, another previous studies that often use that cut off. Yeah, if you have anything to add to that, that would be helpful. Exactly. And you know, obviously, again, this is self reported information we know that there is a system under, I'm sorry, overestimation of sleep duration in general population, although without me really affecting the ranking of individuals. So, because the bias is basically is not differential across, you know, different individuals, but certainly that, you know, obviously, these are, these are categories which have been widely used in the observation epidemiology field. We try to, you know, comply with the with the current guidelines. And, and that, you know, obviously there is also inter individual variability in terms of, you know, sleep duration needs across different individuals. But, as we said, this is observation epidemiology so we need to rely on self reported information. It is one o'clock now so I'm going to wrap up. Unfortunately, we won't get to all the questions we will follow up with participants who didn't have their questions answered directly after. But I do have a few closing things I do want to make sure we we get through. So first of all, thank you again to our presenters we we really appreciate your contributions to these webinar series. This is an important way to promote the CLSA's data, as well as the research that's coming out of the CLSA data platform. I'd like to remind everybody, but everyone that the September deadline for data access has unfortunately passed the next deadline for applications is January 12 of 2022. We also have the CLSA website under data access to review available data, including the COVID-19 questionnaire study data, as well as additional details about the application process. Also, I'd like to remind everyone to complete their anonymous survey upon exiting the zoom session. And for the upcoming CLSA webinar. We'll be in place in October. More details, including the date and registration information will be posted to the CLSA website. We're just confirming presenters for that one and the topic so you can find out more by visiting the website. As a reminder, the CLSA promotes this webinar series using the hashtag CLSA webinar. So we invite you to follow us on Twitter at CLSA underscore at at CLSA underscore ELCV. And I think that's it. So thank you again for attending today's presentation. And again, we will follow up with in regards to the questions directly after. Thank you very much everyone.