 So, hello everyone. Thank you for joining the webinar today. As previously mentioned, my name is Vanessa Derubis and I recently completed my PhD in health research methodology at McMaster University, working under the supervision of Dr. Laura Anderson. Today, I will be talking about the work we completed using data from the Canadian longitudinal study on aging to understand experiences of stress during the COVID-19 pandemic among Canadian adults. So, the COVID-19 pandemic is unlike any other disaster in recent history, given the wide scale implications across the globe. However, similar to other disasters, in addition to the direct impacts of the COVID-19 pandemic, such as the morbidity or mortality associated with the virus. There are several indirect consequences that may impact population health and have lasting effects on the health and well-being of people. At the start of the pandemic, little was known about the potential long-term health outcomes that could be associated with the COVID-19 pandemic. So, to fill this gap, our research team was interested to determine what has previously been studied in terms of the association between disasters and long-term outcomes specifically on cardiometabolic health. So, we aim to conduct a systematic review to determine the impact of disasters, including pandemics on cardiometabolic outcomes across the life course. So, we did this by conducting a systematic search in May of 2020 on two electronic databases, which were M-Base and Medline. We were interested in any study assessing the association between a population level disaster and any cardiometabolic outcome that occurred at least one month following the disaster. We did not restrict studies on age, year of publication, country, or population. So, after we ran the search, we identified 58 studies that were eligible to be included in this review. The majority of these studies were published in North America in between 2010 and 2020. 71% of the studies utilize a cohort or longitudinal study design. We classified the period of exposure to disaster as either pregnancy or childhood or adulthood exposure, and 41% of the studies investigated exposure to disaster that occurred during pregnancy or childhood, and the remaining 59% of studies investigated adulthood exposure. We also classified the type of disaster as either natural or human-made disaster, and about 60% of the included studies explored the impact of a natural disaster, which included hurricanes, tsunamis, the Ebola epidemic, or the 1918 influenza pandemic. The remaining 40% of studies investigated human disasters, which included the World Trade Center disaster or the Holocaust. As I previously mentioned, we were interested in any cardiometabolic outcome, with the majority of studies exploring the impact on cardiovascular disease, and only about 21% of studies exploring the impact on obesity or BMI. So moving on to the results. So overall, 47 out of the 58 studies reported an increase in cardiometabolic risk, which includes increased cardiovascular disease, diabetes, and obesity. So this only left 11 studies that reported no increased risk or unclear findings. However, given the variation in the studies, it was actually quite difficult to both quantitatively and qualitatively synthesize the results in a consistent way, and therefore we cannot conduct a meta-analysis. So an example of a study that assessed exposure to disaster during pregnancy and subsequent adulthood disease risk was conducted by Mazumba at all. So this study found that those who were in utero during the 1918 influenza pandemic had a 36.7% excess risk of diabetes compared to those who were born after the pandemic. Another example of a study but now looking at exposure to disaster during adulthood and subsequent adulthood disease risk was conducted by Anne at all. So this study found that people who had higher perceived stress following a hurricane had a higher average BMI compared to those who reported lower perceived stress, and this was found to be statistically significant. So the findings of this review suggests that the burden of disasters extends beyond the known direct harm and attention is needed on the detrimental indirect long-term effects on cardiometabolic health, which includes obesity. Findings from this review may inform public health prevention strategies to mitigate the impact of disasters, including the COVID-19 pandemic on future cardiometabolic risk. Although it was not a main objective of this study, we did note that mechanisms underlying the association between disasters and cardiometabolic outcomes were not well studied. However, one hypothesized pathway was stress. So taking the findings from this review and given and what we've seen in the literature, several factors, sorry. Given that there's been a dramatic change in daily functioning throughout the pandemic with several factors such as limited access to physical activity facilities or closures, it may have led to an increase in incidence of chronic diseases. So it also has been hypothesized that chronic stress or prolonged exposure to stress may also influence disease development. So like other disasters, the COVID-19 pandemic can be viewed as a stressful event as it has completely altered the daily functioning of individuals across the globe. So as identified in our systematic review and what we've seen in the literature, a possible indirect consequence that may be a result of the COVID-19 pandemic, which subsequently may lead to long-term health outcomes is stress. So a systematic review from early in the pandemic found a high prevalence of stress, which varied by different factors including sex, age, unemployment status, presence of chronic conditions and mental health problems. However, in this review, it was noted that there were limitations with these studies such as a small sample size. There was also limited research specifically exploring how obesity may lead to differences in experiences during the pandemic. And it is possible that people with obesity had greater experiences of stress throughout the pandemic. So this increase in stress may be related to obesity being identified as a risk factor for increased morbidity and mortality associated with COVID-19. Once this was identified as a risk factor, it led to an increase in weight stigma and bias, which subsequently may have increased stress. So the literature did note that the identification of obesity as a risk factor for COVID-19 was not completely well understood and that there was stigmatization present on different social media platforms and other media outlets towards people with obesity. So when we think about stress and obesity during the pandemic, applying this framework by Vander Volk et al. allows for a better understanding of the complex relationship that they share. So it has been found that obesity and stress are constantly influencing each other, meaning that they share of cyclical bi-directional association. So as shown in this figure, stress and obesity impact each other, which can be through several different mechanistic pathways, including lifestyle or behavioral factors, genetics, or a stress response. Stress can take many forms across the life course. So for instance, a major stressor in a person's life is a diversity that occurs during childhood. So adverse childhood experiences or ACEs for short include a wide range of negative events that occur during childhood and adolescence, such as abuse or neglect. It has been found that ACEs influence the development of obesity later in life. So as I previously mentioned, and as we all know, another example of a major stressful event is the COVID-19 pandemic. The COVID-19 pandemic altered daily functioning across the globe. And in addition to the direct impacts of the pandemic, it has also been found to increase stress. So understanding this relationship is particularly of interest given that about 26% of Canadian adults are said to have obesity. It is also important as obesity is not only a disease, but is also a risk factor for several other diseases such as cardiovascular disease or diabetes and can increase the risk of stress. So we also see in this figure that the association between stress and obesity is impacted by several factors at different levels of influence. So some examples of each level include age and sex at the micro level, interactions with family and friends at the MISO level, and economic supports provided by the government for families at the macro level. So there really has been limited research that has taken a comprehensive approach to explore both proximal and distal risk factors for stress that was experienced during the pandemic. If we think back to that cyclical association between stress and obesity, it helps to identify potential factors that may play a role in differential experiences throughout the pandemic, which include individual characteristics, adverse childhood experiences, and adulthood obesity. So that brings us to the overall objective of this webinar. So in this webinar today, I'll discuss and explore how experiences of stress during the COVID-19 pandemic varied among Canadian adults. So to meet this objective, I'll be describing two separate manuscripts, which each had their own unique objectives. The objective of the first manuscript was to describe stress during the COVID-19 pandemic by socioeconomic factors, and the objective of the second manuscript was to determine how ACEs and obesity impacted stress during the COVID-19 pandemic. So to answer these objectives and for both of the manuscripts that I'll discuss today, we use data from the Canadian longitudinal study on aging. Many of you may know the CLSA is a national prospective cohort study that has collected data on over 50,000 adults age 45 years and older at the time of recruitment. The CLSA has collected data at baseline, which is from 2011 to 2015, at follow-up one, which is from 2015 to 2018. And in April of 2020, the CLSA COVID-19 questionnaire study was launched to collect pandemic related data over nine months. So the CLSA is a unique data set given the comprehensive data available measuring stress during the pandemic, as well as the ability to link to previous CLSA surveys to access other data on socioeconomic variables or chronic diseases such as obesity. The CLSA COVID-19 questionnaire study had exit survey, sorry, had about 24,000 participants that were included. However, for our studies, we excluded some participants who were missing data on either our exposures or our outcomes of interest, which left us with a sample of just over 23,000 people. So just over half of the sample were female and had a household income of $50,000 to $150,000. The majority of the participants were age 65 to 96 years of white racial background, had a post-secondary degree or diploma, or lived in an urban setting. 61% of the people reported one or more adverse childhood experience and about 31% of the sample were found to have obesity. So just as a note, the participants who were included in CLSA COVID-19 questionnaire study was a subset of the base CLSA sample, and the CLSA COVID-19 questionnaire study participants were found to be comparable to the base CLSA sample. However, it was found that they had a slightly higher income and were slightly more educated. So at the CLSA COVID-19 exit survey, participants were asked how they were experiencing the pandemic. So these measures were then used to determine the experiences of stress in both of the manuscripts I plan to discuss today. So these measures have been used in previous disaster research and are adapted and modified from gold standard measurement tools. So the first measure of stress was an objective measurement where participants were asked to report if they had experienced any of the 12 stressors that you can see listed on the slide since the start of the pandemic. So these included things like if a participant was ill, if they had lost their income, if there was an increase in conflict or a breakdown in family relationships. Each of these individual stressors were treated as a binary outcome since participants could report if they had experienced the stressor or not. Next, we summed these 12 stressors together creating a cumulative or a total score which ranged from 0 to 12. So as you can see on the figure on the bottom half of the slide it shows the distribution of the number of stressors participants reported. And as you can see highlighted in the dark blue box, the majority of participants did report experiencing one or more stressors since the onset of the pandemic. So next we classified the 12 stressors into different domains and these domains were the health, resources, relationships and caregiving domain. So each of these domains were treated as a separate count outcome and the range varied. So for instance, looking at the health domain, three different stressors fell within this domain, meaning that this, this outcome range from zero to three, whereas the caregiving domain only two stressors fell within this domain. So this, this outcome range from zero to two. If you look at the bottom half of the slide the proportion it shows the proportion of people who reported experiencing one or more stressor within each of the domains. So we see that just over half of participants reported at least one stressor within the relationships domain, and about 30% of people reported at least one stressor within both the health and resources domains. So the last measure of stress was the perceived consequence of the pandemic. So this measure was developed based on the transactional model by Lazarus and folkmen, which states when a person experiences a stressful event they complete a cognitive appraisal where they determined if there was a threat, a threat associated with the stressful event. If the threat is perceived and they are unable to cope, then stress is said to be perceived in regards to that event. So for this measurement participants were asked to report how they would describe the consequence of the pandemic on them in their household with response options ranging from very negative to very positive. However, very few people were responded to the extreme options of very negative and very positive. So this led us to collapse very negative with negative and positive with very positive. And then we further collapse neutral with positive and very positive since we were most interested in negative outcomes. So that brings us to the first manuscript that I'm going to discuss today and this was titled stressors and perceived consequences of the COVID-19 pandemic among older adults which was a cross sectional study using data from the CLA say, and this paper is currently published in CMAJ open. So the objectives of this paper were just to describe the prevalence of stressors and the perceived consequences reported by older adults during the COVID-19 pandemic, and to evaluate how they differed by socioeconomic factors. So for this specific study and to answer these objectives we used a cross sectional study design, and this was because the outcomes of interest were assessed at one time point which was the CLA COVID-19 exit survey. So we're interested in several different socioeconomic factors and these included sex, age, urban real status, region of residence, essential worker status, household income, marital status, racial background and education. So all of these variables were taken from the various CLA surveys. So for instance, we took data on age and region of residence from the COVID-19 questionnaire study to reflect the most up to date information since this was most recently collected. Whereas variables like household income and marital status were most recently collected at follow up one because they were not collected during the COVID questionnaire. Finally, education was taken from CLA baseline since this was the only time point that this was collected at. So to evaluate the association between the various socioeconomic factors and stress, we ran three separate models using both logistic and negative binomial regression to estimate prevalence ratios and 95% confidence intervals. We adjusted all models for all the variables that are listed on the slide. So this first figure I have here shows the prevalence of total stressors by age group. So on the x-axis we have the total number of stressors reported. And as you can see on the slide, we've collapsed five or more together since relatively few people reported this many stressors. On the y-axis we have the prevalence or the percentage of people who reported the number of stressors. As you can see just over 30% of adults age 75 to 96 years reported no stressors, which was the greatest proportion relative to the other age groups. We also see that people age 50 to 64 had the highest proportion of people who reported five or more stressors with about 7% of people reporting five or more stressors, which was the most relative to the other age groups. So this next figure shows the prevalence of people who reported each individual stressor by sex. So you can see that the bar shaded in gray represent the proportion of males and the darker bars represent the proportion of females. Across the x-axis we see each of the 12 stressors participants were asked about if they had had experience. On the y-axis we see the prevalence of the proportion of people who reported experiencing each of the stressors. So if we look across the stressors, for almost all stressors aside from loss of income, females were more likely to report that they had experienced a stressor relative to males. So the final figure that I plan to show shows the perceived consequence of the pandemic, which is also stratified by sex. So across the x-axis is the perception of the consequence of the pandemic, and similar to the previous figures, the y-axis shows the prevalence for the percentage of people who reported these experiences. So we see on the right side of the plot that females had the highest proportion who reported experiences of the pandemic to be very negative or negative. It's surprising that we see that almost 17% of males and almost 20% of females reported the consequence of the pandemic to be neutral, positive, or very positive. So now I will highlight some key findings from our regression analysis where we explore the association between various socioeconomic factors and stress during the COVID-19 pandemic. So just as a note, as I move through the results tables, all statistically significant values are bolded. So first are the results for the associations between each socioeconomic characteristic with each individual stressor. So on this first slide, I have the first six stressors. So as you can see at the top of the table, they're labeled from one to six, and I'll just highlight some interesting findings. So first, we can see that almost across all stressors that are listed on this slide, individuals in the oldest age groups were less likely to report that they experienced a stressor compared to those who were aged 50 to 64 years of age. Another interesting finding was that individuals who were divorced or separated were 35% more likely to have issues accessing necessary food or supplies compared to those who are married or in a common law relationship. So next on this slide, we have the results for the remaining six stressors. So as you can see at the top of the table, they're labeled seven to 12. And we see somewhat similar findings that some of the socioeconomic factors lead to an increased risk of experiencing some of the stressors. So looking at the region in which participants resided, we see that for people residing in Ontario, the prairies in British Columbia, they were typically more likely to report experiencing the stressors compared to those in the Atlantic provinces. However, there were some interesting findings for people residing in Quebec. We see for the stressors unable to access usual prescriptions, separation from family and increased caregiving, people residing in Quebec were less likely to experience these stressors compared to those in the Atlantic provinces. Whereas for increased conflict and breakdown in family relationships, people in Quebec were more likely to experience these stressors. We also see that females relative to males were more likely to report being separated from family increase in caregiving, unable to care for those who required assistance and breakdown in family relationships, compared to males. So next on this slide, we have the adjusted association between socioeconomic characteristics and the total number of stressors score. So once again, we see an interesting finding by sex where females were 20 times more likely to report an additional stressor relative to males. And we also see another interesting finding by age group. So people in the 65 to 74 and 75 to 96 age groups were less likely to report an additional stressor relative to those who were age 50 to 64 years. So finally, we have the results for the perceived consequences of the pandemic. And once again, we see interesting differences across the regions of Canada. So for instance, people who resided in Quebec, relative to those in the Atlantic provinces, were less likely to perceive the pandemic as negative or very negative. Whereas people in Ontario, the prairies in British Columbia, were more likely to perceive the consequence of the pandemic as negative or very negative compared to those in the Atlantic provinces. The findings from this paper were that adults across Canada experience stress and perceive the consequence of the pandemic as negative, which found were found to vary by socioeconomic factors and geography, highlighting inequalities and experiencing stress. Future research will be needed to determine the impacts of stress during the pandemic on future health outcomes and how this varies by different socioeconomic factors. So that brings us to the second manuscript that I plan to discuss today. And this was titled obesity and adverse childhood experiences in relation to stress during the COVID-19 pandemic, which was an analysis of the Canadian longitudinal study on aging. And this paper is currently published in the International Journal of Obesity. So for this study, we aim to evaluate the associations between both ACEs and obesity and stress during the pandemic, and to examine if the association between obesity and stress during the pandemic was modified by ACEs. So to address these objectives, we use the longitudinal study design. And this was because both of our exposures, adverse childhood experiences and obesity were taken from follow up one, and our outcomes which were stressed during the pandemic was measured at CLSA COVID-19 exit survey. So participants were asked to recall eight adverse childhood experiences that occurred before the age of 18. So this included parent divorce, abuse, parent death, intimate partner violence, neglect and family health problems. We created a cumulative or a total score where we summed together these different adverse childhood experiences, which ranged from zero to eight. So since relatively few people reported five or more experiences, we actually collapsed four to eight together. So for obesity, participants either self-reported their height and weight or it was measured by trained research assistants. So to combine the measured and the self-reported values, we applied a correction factor, which helps to overcome biases associated with self-reported height and weight. We then used height and weight to calculate body mass index and applied standard cutoffs from the World Health Organization to define obesity and more specifically severe obesity class one, two and three. So that brings us to the statistical analysis for this paper. So for objective one, we ran separate models for the two exposures which were adverse childhood experiences and obesity with each of the different stress outcomes. We used Poisson, negative binomial and logistic regression to estimate relative risks in 95% confidence intervals. We ran both unadjusted and adjusted models and we adjusted for the variables that you can see here on the slide. For objective two, we wanted to determine if the association between obesity and stress was modified by ACEs. So prior to this analysis, we decodamized our ACEs variable. So this means that we grouped people who experienced one or more ACE versus those who experienced zero. So we assessed interaction by ACEs on both the multiplicative and the additive scales. So to evaluate the multiplicative scale, we estimated the ratio of relative risks, which is also known as the RRR. And to evaluate the additive scale, we calculated the relative excess risk due to interaction, which is also known as the RERI. So here are the results for objective one where we evaluated the associations between adverse childhood experiences and obesity with the stressor domains and the total number of stressor score. So for the sake of time for this presentation, I have left off the results for our outcome that was the perceived consequences of the pandemic. So we found a dose response association for ACEs and all stress outcomes. So as you can see on the slide, as the number of adverse childhood experiences increases, the risk of reporting an additional stressor also increased for all stressor domains as well as the overall total number of stressor score. So for instance, people who reported four to eight ACEs relative to zero were 53% more likely to report an additional stressor in the resources domain and 44% more likely to report an additional stressor within the caregiving domain. We see somewhat similar results for the association between obesity and the total number of stressor score and for the resources and health domains, whereby as obesity level increases, the risk of reporting an additional stressor also increases. So for instance, people with severe classroom obesity were 25% more likely to report an additional stressor within the health domain, or 38% more likely to report an additional stressor within the resources domain, compared to those who had normal weight. So that brings us to the objective, to the results for objective two where we were interested in determining if ACEs modified the association between obesity and stress during the pandemic. So we did not find consistent evidence of interaction by ACEs on either the additive or the multiplicative scales. However, we did identify one multiplicative interaction to be statistically significant. And this was between class three obesity and ACEs for stressors within the health domain. So as you can see on the slide, because the RERI is less than zero, and the RRR is less than one. This indicates that among people with adverse childhood experiences compared to those without, they were, sorry, so they were less likely to report an additional stressor within the health domain. And this was among people with severe three, severe class three obesity. So that brings us to the key findings of this paper. So it was evident that experiences across the life course, including obesity or adversity during childhood were associated with increased stress during the COVID-19 pandemic, which confirms subgroup of people, subgroups of people are more susceptible to stress associated with a stressful event. So it'll be important for future research to determine the long-term effects that were experienced, the long-term effects of stress that were experienced during the pandemic, and how this may vary by different subgroups of people. So the two studies we conducted using the CLSA provided evidence that suggests people had different experiences during the COVID-19 pandemic, which were related to both proximal and distal factors, including factors like age and sex, adversity during childhood, and adulthood obesity. Both of the studies that I discussed today has several different strengths and limitations. So first, these studies were one of the first to explore experiences of stress during the pandemic in Canada, using a large nationally generalizable sample with a population-based sampling strategy. Data were collected using surveys that were administered by both phone and web, which allowed for inclusion of participants that had limited internet access. The CLSA COVID-19 questionnaire study collected in-depth pandemic-related data, which can be linked to future waves of data being collected by the Canadian Longitudinal Study on Aging, allowing for longitudinal research on how the experiences of stress during the pandemic impact both short and long-term health outcomes. So with these strengths, we also identified different limitations. So one limitation was that the CLSA sample is primarily of white racial background, which limits the representativeness of the results. So this means we can only really generalize findings to comparable samples or populations. Another limitation was that the list of stressors in the CLSA, sorry, and it was developed quite early on in the pandemic. So as we all know, stressors' experience may have changed during the pandemic, or these may not have comprehensively included all possible stressors that participants may have experienced. So we also did not have data that was collected using a validated perceived stress scale. However, the measures we used were used in previous disaster research and were adapted and modified from gold standard measurement tools. Finally, the last limitation was that the CLSA COVID-19 questionnaire study was administered during the first two waves of the pandemic, which was back in 2020. And as we all know, we experienced several subsequent waves of the pandemic in Canada, which unfortunately we did not have data on, so we couldn't really comprehensively assess changes in stress throughout the different waves of the pandemic. So after conducting these studies, we identified several areas for future research. So the first includes further exploration and understanding of the mechanistic pathways between both obesity and stress and stress and obesity. Having an understanding of these pathways allows for targeted interventions and prevention strategies to be developed. Secondly, as we all know, several unanswered questions remain surrounding the lasting effects of the COVID-19 pandemic. Future research will be needed to determine how experiences of stress during the pandemic may impact future health outcomes, including obesity. So as I previously mentioned, using the data that is planned to be collected by the CLSA will allow for this work to be conducted, which can be informed by the methodology and the results from the work our team has conducted that I presented here today. So as I mentioned earlier, both all of the papers that I presented today are published. So if you are interested in reading more, I've put the different, the abstracts for each of the papers as well as the DOI and the QR code if you'd like to read the full paper. So I'd like to thank my PhD supervisor, Dr. Laura Anderson, my thesis committee members, Dr. Lauren Griffith, Dr. Jean-Marie Theride and Dr. Andrew Gonzalez. I would also like to thank my supervisors from the Public Health Agency of Canada, Doctors Margaret DeGro and Yang Jiang and all collaborators who helped throughout these projects. This research was made possible using the data collected by the Canadian Longitudinal Study on Aging and funding was received from the Public Health Agency of Canada and the Canadian Institute for Health Research. Thank you for joining this webinar today and listening to my presentation and I look forward to any questions that you may have. Well, thank you very much for the very informative session today. I don't see any questions so far, but I just want to remind everyone if you wanted to provide a question, please type it into the Q&A box. We usually do have some, just also checking if there's any in the chat box. Maybe just to start off with a little bit of a question on my part. I guess you did talk about the, you know, things changing in the pandemic and not all, you know, we could have, we could have asked, or there could have been different stressors asked about early on in the pandemic. I'm just thinking if you could actually do the study now, what other stressors would you ask about to sort of fill that, to fill that information gap in the study? Yeah, that's a great question. So as we all know throughout the different waves of the pandemic, I think each wave introduced a new set of problems. So there weren't really any questions or stressors surrounding the closure of physical activity facilities or the religious places of worship. So I think including different stressors surrounding that would have allowed for maybe a more comprehensive assessment of different stressors that people may have experienced. I think the list of 12 stressors that we included was quite detailed, but I do think individuals may have experienced different stressors that we may not have captured, and I think it would have been difficult to really try to identify every stressor that people may have experienced. So I think we broadly did include a good amount of them, but I do think maybe exploring different stressors would have maybe allowed for a better assessment of potential stressors that people did experience throughout the pandemic. Great. And we do have a question about the differences by region. Do you have any explanations for the differences in number of stressors by region? Yeah, so the region was really interesting. So we saw that, for example, Quebec, they had lower perception, there was less perceptions of the pandemic to be negative. And we saw that even the individual stressors people, they were less likely to report experiencing them. And when I was preparing for my thesis back in January, I was reviewing what has been found in the literature. And I found a really interesting article stating that Quebec had quite intense restrictions that we didn't see across all of Canada, like curfew, and they did have high rates of COVID-19. So that made the results in my work even more interesting because it was kind of unexpected. But in this article, it really said that people of lower socioeconomic status tended to fair worse during the pandemic. And these restrictions actually created the most burden for this group of people. And if we look at the demographics of the people within the CLSA, we may have missed those people who really had those worse experiences. So we may not have been capturing that. So that could be one explanation of why we may have seen such great differences with Quebec versus the rest of Canada. And as we all know, the public health preventative strategies that were implemented did vary across Canada and the proportion of people getting sick from COVID-19 also varied across Canada. So I think those are also reasons that we may have saw those differences in people recording the number of stressors. Okay. And then the only other, I had a, the other question that I had was, oh, it popped into my head and then pop back up. Oh, just in terms of the follow up and sort of future studies. So are you already planning on sort of taking what you did here you're at sort of at the point in your career where you're starting to really build a program of research. So do you think you'll request additional CLSA and data and also try to sort of go back and revisit this on in a long, more longitudinal way. Yeah, I would, I would love to continue exploring how the stressor in the pandemic does impact future health outcomes and, and I am currently working on a project looking at adherence to public health preventative measures that across Canada and we're trying to create sort of a cumulative score of how people adhere to the pandemic and, and we're looking at how people with different chronic conditions actually had better or worse adherence to, to these different public health preventative measures. So I am sort of continuing to work on using CLSA data related to the COVID-19 pandemic it's not exactly the same as this work here but in the future I would like to continue exploring this work. Great. Well, we look forward to seeing more related papers. I don't see any more questions. So I think maybe we'll start to wrap it up and oh actually wanted to come in. There we go. I knew as soon as I said that we get a couple more. So before we wrap it up. But actually, while I started that if people are going to start leaving just a reminder to complete your exit questionnaire, but we will continue with the questions in the meantime. So, Gloria's question is the percent reporting aces seems high can you comment on that. We found that about 61% of people reported at least one adverse childhood experience and it is relatively high but this is comparable to other population based surveys from Canada and from outside of Canada. And if you think about what the adverse childhood experiences are there are a range of different types so we have some related to maltreatment and some related to family dysfunction so it is a cumulative score so it doesn't really speak to maybe what types of adverse childhood experiences people did experience but this was comparable to what we've seen in the literature related to the prevalence of adverse childhood experiences in Canada so it does seem high which is alarming but it is similar to what we've seen in the literature. And the next question from the adora is what strategies would you use to encourage participation of more diverse cultures. I'm not sure if that's referring to in the actual code with questioners. A good question or the CLSA answer it. So the CLSA the sampling strategy was developed based on other population based samples such as Community Health Canadian Community Health Survey aging, healthy aging studies so it was created and it is a population based sampling strategy but we do see that the characteristics of the of the participants in the study aren't really representative of the Canadian population. So I think maybe it's important to ensure that different participants are represented in these studies so it may come down to when you're recruiting participants on ensuring that they feel comfortable and safe to participate in the study and and we also know that it is time consuming to dedicate time to actually to be in these surveys so maybe different forms of reimbursement or different methods to ensure that we do recruit these diverse samples. So, yeah, we it is difficult and I'm definitely not an expert in the area but we do see that the way the CLSA was created but was following a really great sampling strategy but the characteristics, as I said aren't aren't representative of the Canadian population but we still can take these findings and generalize them to comparable population so that is important. Great. Well, we also have a comment here from Dr. Anderson just saying what a great job you did and how timely this work is given the pandemic is now three years past us and hopefully never to be returned again so thank you very much. And best of luck in your future research in this area. I think we will wrap up now since the questions have had again died down. If you do have additional questions you're more than welcome to follow up with us and we will make sure that we get the questions to Dr. I'd like to remind everyone that the next deadline for data access application is April 12 of 2023 so coming up. Please visit the CLSA website under data access to review available data, as well as additional details about the application process. I'd also like to remind everyone to complete their anonymous survey before exiting the session today. And for the next CLSA webinar, which is actually going to be just in a couple weeks in two weeks. It's called it's entitled applications to the CLSA and considerations for access and use of my indigenous identified data, and it will be presented March 22 by Dr. Jennifer Walker. The registration for the details for this webinar posted on our website. Also just to remind you that the CLSA promotes this webinar series using the hashtag CLSA webinar so we invite you all to follow us on Twitter. And thank you again for your for your participation today and for everybody for attending. So thank you. If anybody if you wanted to stay on actually we if anybody there are input question that came in. Maybe we could just address it while a few people are still on. It was related to wasn't posted in the chat. But Audrey products, Audrey you have to post it in the Q&A next time. She says I was really interested in the difference in stress and perceived negativity between men and women. Does the CLSA. Does the CLSA include variables that could allow us to understand if this is an effect of predisposition to stress reactions or possibly due to different levels of burden experienced such as caregiving. Great question, Audrey. And, and so when we were looking at if this association between obesity and stress during the pandemic was modified by adverse childhood experiences we were really trying to explore a potential cumulative stress across the life course because we know that adversity during childhood is a stressful event and in chronic exposure to stress could really could could lead to maybe predisposition predisposition to stress in the future. So that was in the second paper but then in the first papers where we saw those differences by males and females and, and I really did spend a lot of time trying to figure out why we saw these differences and it's always hard to know without sort of that qualitative aspect to studies but I think one potential reason could be related that maybe females are just more. We didn't have measures of how often people were caregiving before the pandemic so maybe females were had higher caregiving responsibilities prior to the pandemic and then this just increased. And once the pandemic began so it we didn't have those measures and, and I'd have to look back into the CLC to determine if we could actually explore that but yeah the differences we saw especially between males and females was really interesting and, and it would require definitely a little bit more digging to figure out why exactly that is