 Today's webinar, Association Between Shift Work Exposure and Frailty, it will be presented by Dr. Dodana Khan. Dr. Khan recently completed her doctoral degree from the Faculty of Health at York University. Originally, she was trained as a medical doctor. She completed a Master's of Public Health and Health Behavior and Health Promotion from Ohio State University. Her career goals have matured out of a long-held interest in the social determinants of health, which are the conditions in which people are born, grow, live, work, and age. Workplace and labour force conditions are important determinants of health, as we know, and they shape health in powerful ways. Dr. Khan's research focused on job factors, specifically working schedules and their long-term consequences on the health of the working population. So now, I will turn it over to Dr. Khan. Thank you so much, Jennifer, for your kind introduction. It was very kind, and I have attended CLSA webinars, and they're always very beneficial, and I'm very delighted to be part of it today. Let me share my screen. And also, I want to welcome all the audience, and I really want to appreciate your presence here today and your interest in our research. So the article that I'm going to present today is actually one of the projects of my PhD thesis that I recently completed under supervision of Dr. Halatameem and my committee members, Dr. Heather Adgil and Michael Rotondi, and specifically for this project, Dr. Chris, who is an expert on frailty. So we investigated the association between shift work exposure and frailty among middle aged and older adults at three years of follow-up. And we recently published this article in the Journal of Occupational and Environmental Medicine, and it was very exciting that our study has been selected as CME module. But more excitingly, we found that this article has attracted a lot of media attention. About 46 stories has been published so far, but more excitingly, we found that people are coming out and they are sharing their personal stories of how they were exposed to shift work and how shift work has affected their life. There was a lot of social media stories on credit, Twitter, Facebook. And I think that that all makes sense because most of us has exposed to shift work in some point in our lifetimes, or we have seen our loved ones or closed ones, how they were exposed to shift work and how shift work has impacted their life. And I think keeping all these in mind, I think at some point we realized that shift work is very important. It is an integral part of our economy, of our society. We are living in a globalized 24-hour society that never shut down. And the reason behind it is because we all need these continuous services and we all enjoy these continuous services in a form of security, emergency, production and supply. But at the same point, we cannot ignore the negative health outcome of shift work, specifically on workers. And also we cannot just eliminate shift work from our society because we know that it's important. So let's dig deep into what shift work is and how we measure shift work in our study. So shift work has been defined as any work outside the regular hours, that's 9am to 5pm. So any work outside this window is regarded as shift work. In literature, there are different definitions that have been used, but this is the standard definition of shift work. In some countries it is used as 8am to 4pm, but pretty much this is the window that is regarded as a normal daytime work. Shift work has been classified in literature and there are a couple of different types of shift work. Regular evening shift work is when you start work after 3pm and the work end before midnight. Regular night shift work, that's after 11pm and ending before 11am. And then there is rotating shift work. So by rotating shift work, we mean that if the working schedule is changing from day to night or day to evening. So these first three types of shift work are very common in literature and they have been studied a lot. There are some other types of shift work as well like on-call or casual shifts. They don't have prearranged schedules and also there are irregular shifts. Shift work is quite prevalent, it's everywhere, we know that. But specifically if we look at Canadian perspective, we found that one in every four Canadian is working in shift other than regular daytime hours. Two-third of protective service workers like police officers, firefighters and security guards, they are exposed to shift work. 45% of health workers, 40% of sales and service workers and 42% of primary industry workers which included farm workers, miners and forestry workers etc. They are exposed to shift work. So if we look at this prevalent, this is quite prevalent. So shift work is everywhere and the same prevalence is also found in other developing countries like Japan and in different countries of Europe. We found the same prevalence rates. With this much prevalence, keeping this prevalence in mind, one might be thinking that okay, what are the effects of shift work on our health? So in literature, shift work has been linked to some short-term effects like sleep disturbances, accidents and work injuries at workplace and mood disorders. In addition to these short-term effects, there are some long-term health effects that have been linked to shift work. In physical health, we found that shift work has been linked to cardiovascular diseases including hypertension and myocardial infarctions, diabetes, paptic ulcers and cancers. And in addition to that, there are a lot of social health issues which has been linked to shift work and mental health issues including depression and mood disorders. I think next I would like to discuss some mechanisms that have been linked and have been discussed in literature that how shift work is impacting these negative health effects on our body. So the first mechanism that's really important to understand is the circadian timing system in our body. So in our brains, there is a biological internal clock that is located in the hypothalamus. On both sides, there are suprachiasmatic nuclei that act as a biological clock in our brain. And this biological clock is responsible to synchronize the internal and external environment. Now what happens in shift workers? Because shift workers, this mechanism is disrupted and due to interference of this normal circadian rhythm, there could be disturbances at metabolic level, hormonal level and also inflammatory responses are disrupted. Another mechanism that happened discussed in literature is light and dark cycles. So what happens during nighttime when there is dark? So the dark has a signal to the pineal gland which secretes melatonin. Now melatonin is a hormone that is required to induce sleep during nighttime. Now what happens during nighttime when there is light exposure to the workers? So this reduces the circulating melatonin and if sometimes this light is so bright that the levels can completely be suppressed and there will be no melatonin in the body. And melatonin has a very significant effects on our body. In the absence or disruptive melatonin can have negative effects on our body. So that's the second important mechanism. In addition to that, now I want to look into what happened. So there is a cortisol secretions as well. So normally cortisol is secreted in the daytime. So there is more secretion of cortisol in our body and at nighttime the cortisol levels are decreased. And melatonin is reversed to that. Melatonin at nighttime, its secretion increases and in daytime, its secretion decreases. Now what happens in shift workers that this circadian rhythm is disrupted due to which the cortisol is increased during nighttime and melatonin is decreased. So that's where the negative impact came into. And this is how shift work can affect our body. Now in addition to these two mechanisms, there are some behavioral factors that has been linked to shift workers. Shift worker has been found to be involved in unhealthy eating behaviors. There is a link between low physical activity and shift work. And also shift workers were found to be higher incidence of smoking. And among them there's higher intake of alcohol as well. So these factors can add into the pathogenesis of shift workers and can cause disease. So there's social isolation. So now we don't know that whether there is one mechanism that is acting or there is a possibility that all these mechanisms are working together to cause the disease in shift workers. So these are the mechanisms. It's really important to understand the pathogenesis of diseases in shift workers. Now for the rationale of our study was based on a recent International Commission on Occupational Health recent report that they published. It's a consensus statement and they claim that there is strong evidence linking shift work to cardiovascular diseases, gastrointestinal and metabolic disorders like type 2 diabetes and metabolic syndrome. And less consistent evidence was found that linked shift work to mental health problems and reproductive health problems. So we based our rationale on to this consensus statement and we did our literature review to find out the relationship between shift work and frailty. We did find although as we already discussed the shift work has been linked to a lot of physical health issues, diabetes, optical cells and cancers that include prostate cancer, breast cancer, colorectal cancers, a lot of social health issues, mental health issues, but there was a huge gap in literature regarding association of shift work and frailty and we could not find a single study that have looked into it. So to fill that gap, we aimed our study and we aimed to investigate the association between shift work exposure and frailty among middle aged and older adults. So let's discuss the methodology of our study. So we utilized a Canadian longitudinal study on aging database. So there are, so let me explain these cohorts a little bit. Most of us are familiar with that. So there are two cohorts of CLSA tracking and comprehensive. So for our study, we pulled these two together. So we combined these two cohorts. The tracking cohort, there are 20,000 participants and they provide all the information through telephonic interviews, while comprehensive cohort included 30,000 participants and they give in-person interviews and also they also visit data collection sites to provide further other information as well. So for our study, we pulled them together and also we utilize the baseline data that was completed in 2015. And when we were designing our study, we only have first follow-up, that's a three years follow-up that was completed in 2018. So we have two databases, two time points. One is baseline 2015 data and the other is first follow-up that is 2018 data. CLSA inclusion criteria included community dwelling adults. They included those who are cognitively healthy and those who are able to speak and understand English or French. CLSA excluded participants, those who are being a resident of Federal First Nation Reserve or other First Nation settlements in the provinces. Also, those participants were excluded who are being a full-time member of Canadian Armed Forces, not the permanent resident or Canadian citizen. And also those individuals were excluded if they are in long-term care institution that is those providing 24-hour nursing care. Next, I want to discuss that what type of variables were included in our study. And I think it's really important to understand how we measure our primary exposure variable. So for primary exposure variable, we utilized CLSA labor force modules. So there are two type of labor force modules in CLSA. The first one is pre-retirement labor force participation module. And this module collected data from retired participants. The other module that we utilized was labor force module. And this module collected data from currently working population. So what they did, they asked them whether they are working. And if they say yes, they are working. So they asked, okay, what type of schedules they were when they were working. So then there are daytime work, night shift work or rotating shift work. So this is how these two modules were utilized. And we derived three primary shift work exposure variables. So our first exposure variable was ever exposed to shift work. So this variable kind of gave us an overview of shift work exposure. And we categorize into two categories. So the first one is daytime work. That is they are not exposed to shift work. And that's the daffodils group. The other one is ever exposed to shift work. So if any participant, whether they are retired or they are currently working, they are exposed to any type of shift work. Night shift work, rotating shift work, they are included in this variable. Just to give an idea, just like any type of exposure. The second variable we derived was shift work exposure in current job. Now, for this variable, we excluded the retired participants. And we just included those who are currently working. Because we want to get an idea that those who are currently exposed to shift work. And the second and third variable was shift work exposure in longest job. So the categories for them were daytime work. That was considered unexposed because they are working daytime. And night shift work and rotating shift work. So these are the categories. And these are the variables that we utilize to assess our primary shift work exposure. Our outcome variable was frailty. Now, how we define frailty? So we utilized a standard definition, which is defined as medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function that increases an individual's vulnerability and developing for developing increased dependency and death. So that's the definition of frailty. Now, the next step is how we assess frailty. So in literature, a couple of methods have been utilized by different researchers. But we selected a standard procedure that is accumulation of deficit model. So our frailty index score is based on that standard model, that is accumulation of deficit model. And this method or this model has been recently utilized by researchers that created normative frailty values for Canadian population and they utilize CLSA database. So we utilize the same set of variables to create a frailty index score. As I already discussed that we used pool data. So by pool data, I mean that we combine tracking and comprehensive cohorts together. And we utilized 52 items. We included 52 items and we tried our best to include all the variables, all those factors that are significantly related to frailty. And they included self-rated health, vision, and hearing. We included 30 variables that are linked to chronic conditions. Five variables were included that were related to activities of daily living. We included cognitive function variables and also three variables that were linked to mental health. So all these variables are significantly related to frailty and we make sure that we include all of them. So in total, we include 52 variables to create frailty index. And what we did, we transformed this score into zero means no deficit to one means deficit. So deficit that's presence of that factor. And for interval or ordinary variables, which are more than two responses, so we use the fraction of complete deficit. And I will explain it in the coming slides that how we use different codeings when we have different options in different questions. For example, osteoarthritis. So here we asked from the participants that whether doctor ever told you that you have osteoarthritis. So there are now two options, yes and no. So yes means that the deficit is present, so that will be coded as one. No means there is no deficit. So that will be coded as zero. So that's simple, right? There is yes, no, one and two. So if we have two options, yes and no. So that's simple. One means deficit is present, zero means no. Now we have questions like doing housework. So we have three options here, unable, with help or able. So if the participants say that's unable to do housework, that definitely means the deficit is present. So that's one. And if the participant is able to do housework, so it means there is no deficit. So one and two. But we have another option that would help. So we kind of use the fraction of deficit for this option. Then we have some questions in which we have five options, like self-rated health. So and the options are poor, fair, good, very good or excellent. So if somebody is saying that their self-rated health is poor, that means there is presence of deficit. That's one. And if somebody is saying that their health is excellent, it means there is no deficit. But then we have different options. So we use the fraction of that deficit to show each option. So that's how we use the coding zero, one. And we do the coding for all our variables. So the next step is to create frailty index. OK, so now to calculate each participant frailty index score, so we sum the deficit, whatever deficits are present, and we divide them by the total number of deficits that were considered. So we use this formula. In numerator, we have the number of deficits that were present in that individual. And in denominator, we have total number of frailty score that we utilize at 52. So we get the continuous frailty scores, but we want to classify frailty. And then we use that. The final step was the classification of frailty index. So those whose frailty index was less than 0.10, that will be considered non-frail. And those whose frailty index score was from 0.10 to 0.20, that will be considered mild frail. And then those who have frailty index score more than 0.2, that will be considered frail. And that's how we use our classification. I want to give you some examples of how we classify. So for individual one, we found that 15 deficits are present out of total 52. So then we divide 15 by 52, and we get 0.29. So the 0.29, according to our classification, the individual one will be classified as frail. For second individual, we have seven deficits were present out of 52, which make the frailty index of 0.13. And according to our classification, the second individual is classified as mild frail. And the third individual, there are four deficits out of 52. So that's frailty index as 0.07, which means that the third individual is non-frail. So that's how we did our classification. Just to give you an overview of all these study objectives, so we have three independent variables, three primary shift work exposures, and we have dependent variable, we use frailty index score, and we then classify into three categories. We included the following covariates, socio-demographic and lifestyle factors were included like sex, age, ethnicity, marital status, education, household income, and major lifestyle factors like smoking, alcohol intake, retirement status, and baseline frailty. In addition to that, we also included health-related factors like BMI. And for female participants, specifically we controlled for reproductive factors as well in our models, that's badity, number of pregnancies, history of hormone therapy, use of oral contraceptives, menopause classification. So these are all variables that we included in our study. Okay, so next I want to show you the flow chart of our study. So we started with 50,000 participants, as I already discussed this, because we pooled the two dataset together. So we excluded participants if they are frailty, if 10 or more frailty items were missing out of 52. So if some participant has 10 scores, at least missing, so that participant was excluded from our study. Also we excluded participants who reported never worked in any job. We excluded participants who reported working in seasonal or on-call or casual or prearranged schedule because we cannot document their exact schedules. So that's why we excluded those individuals. And also we excluded participants who have missing information related to working schedule. So after applying these excluding criteria, we were left with 47,000 final study sample. And this is the study sample that took part in our study analysis. So at baseline, we have 47,000 participants and we followed them for three years. We excluded at baseline, we measured our shift work exposure at baseline and we also measured all the covariates at baseline. After three years, the outcome variable that's frailty index, we measured it updated score at three years of follow-up. So it's kind of a longitudinal study design in which our exposure was measured at baseline and frailty was measured at three years of follow-up. We found that at baseline, the mean age was 59, about 60 years with standard deviation of 10.15. 51% were women, 95% were white and we found that more than 50% participants reported to be living with partners. They have education of high school to some college level. They are former smokers. They drink at least weekly and they are most of them, more than 50% were in high income group that is 50,000 Canadian dollar and above. Also we found that one in five participants were exposed to ever exposed to shift work. And as far as currently working population is concerned, 4% were exposed to night shift work and 12% are doing rotating shift work. As far as their longest job is concerned, 4% reported to exposed to night shift work and about 16% reported to exposed to rotating shift work. For analysis, we did multinomial regression models and we stratified our analysis on sex. We did three separate models and we generated for three separate models for our three primary shift work exposures. We presented odds ratios and 95% confidence intervals and odds ratio greater than one means that there is increased risk of frailty and odds ratio less than one means that there is decreased risk of frailty. At three years of follow up, we found that 66, about 67% were non-frail, 26% were mild frail and about 7.2% were frail at three years of follow up. This is the table that summarized, I took it from my study and this table summarizes adjusted multinomial regression longitudinal models and odds ratios were presented with 95% confidence interval in brackets. So you can see that there are two columns. The first one is for male and then we have females. So we stratified the analysis for male and female and we did find that. And also I want to mention here that male models were controlled for age, ethnicity, marital status, income, education, retirement status, baseline frailty, smoking, alcohol and VMI categories. And you can see that female models were adjusted for additional covariates that are specific for that, including reproductive health factors. And we did found some significant findings. We found that among males ever exposed to shift workers, they have higher odds of being frail at three years of follow up when we compare them with daytime workers. And similarly for females, we found that ever exposed, if females were ever exposed to shift work, they have higher odds for mild frail and frail at three years of follow up. And interestingly, we found that a rotating shift workers among female workers, we found higher odds of being mild frail and frail as compared to daytime workers among female shift workers. So and we couldn't find any significant results for currently working population. So if I have to summarize the main findings, we found that ever exposed to shift work, both male and female, there's increased risk of frailty at three years of follow up. Rotating shift work in longest job, we found that among female shift workers, there's increased risk of frailty at three years of follow up. And for current job, we couldn't find any significant difference for male or female both. Do our findings have some pathophysiological basis? I would say yes. We are not sure about exact mechanisms, what is happening, but as some of the mechanisms, I have been discussed in the start and there have been discussion in literature that shift work and impaired body level of cortisol, which in turn increase pro and anti-inflammatory proteins like plasma tumor necrosis factor alpha, interleukin 10, C-reactive protein, and they have shown to contribute to ongoing disease process. So we can speculate that the Sarcadian misalignment can disrupt all these mechanisms which can act as a preliminary marker of occurrence of frailty. And in addition to that, there are studies that have shown that sleep deprivation, poor sleep quality, and melatonin depression disruption due to light exposure can contribute to development of frailty. So I think there are some pathophysiological basis for our study findings. And I think it's really important that we should discuss the limitation of study here. We were unable to capture some shift work related information like type and direction of rotating shift because there are studies that have looked into different types of rotating shift as well. There are forward rotation, there's backward rotation, but we were unable to capture that information. And number of consecutive night shifts worked. And number of days off between shifts because this information was not documented in CLSA database. So we were unable to capture some more detail of shift work. And we were unable to capture exact timing of occurrence of exposure. So when we were asking about longest job, so we could not find exact like how previous that exposure was. Was this like a few years back or was this like a 10 years back so we cannot get this information. Also we pulled evening and night shift together because this have done previously in literature. So we did the same because we have a small number of events in our database. So we pulled them together. And also a generalizability of our findings. I think they're because the ethnicity is 95% white. So we cannot say that this is generalizable to all the population. But we still believe that our study has some strength to our knowledge. It's the first study to investigate the association between shift work exposure and frailty. We utilize large Canadian population based longitudinal data and it has a detailed information on shift work. We have included 52 factors for frailty. And we included both current and retired labour force. It was me. And also we included both work schedule night and rotating our shifts. Also, I think CLSA has diverse group of Canadian workers included. So our study is included different types of workers and also CLSA questionnaire utilized a standard measuring tool that are compatible with other international surveys as well. I think our findings are quite significant considering specifically our findings have highlighted the rule of gender in addressing frailty. We have shown that female shift workers are more likely, specifically rotating shift workers are more likely to be at risk of getting frail. So I think future studies or interventions can consider gender when addressing their programs and designing their interventions. Modifiable factors of frailty like shift work exposure among working population. I think it's of clinical relevance and this will assist in extending healthy active life expectancy. Also, I think now the focus should be on designing shift work schedule that are less disruptive to Sarcadian rhythm. It's really important to consider. As a future implication, I think now it's the time that we should focus on educating our workers. I think a worker itself should be educated and employers and also at organizational level there should be counseling tools and also there should be health promotion programs. There should be more awareness of what is shift work and how it can affect workers' health. And also I think focus should be on the health surveillance and work fitness evaluation programs both individual levels and at policy levels. I think the shift scheduling should be organized in an appropriate manner, in a manner that should be less disruptive to the worker. And also I think shift work regulations, there are a couple of regulations, federal regulations regarding shift work. So I think that the focus should be that they are properly implemented. And finally, I think more research is needed as I already discussed that there was no study that was done of finding the association between shift work and frailty. So I think we need more research, more in-depth information related to shift work, scheduling a workplace and personal factors. And I think we only could study the three years of follow-up. So I think that's not enough. We need more extended follow-up periods to study longitudinal relationships. And I think I really want to acknowledge here my supervisor, Dr. Halatami, my committee members, Michael Rotondi, Heather Ethkil for their contributions. And also my co-author, Dr. Chris, who has expertise in frailty and he really helped me understand what frailty is and that was very nice of him. And also I want to thank CLSA team for providing such a great database and all required support. There are some references and thank you so much. Great, well, thank you very much, Dr. Khan. There's lots of questions that have come through the chat. Just a reminder, if anybody has any questions for you to put them into the Q&A box at the bottom, the first question, and I'll just start at the top and go through them, regarding the frailty index threshold values, specifically point one and point two, what is the rationale behind these values? So there is no clear cutoff that we want to classify frailty, but we have used these cutoff which have been already used in literature. And also there is clinical relevance as well because the studies that have utilized these cutoff points. Also, I want to explain like if we say that somebody has frailty index of point one, as we said, like point, if frailty index is less than point one, one zero, so it means that individual is non-frail. So what this point is telling us that the 10%, so just imagine that there are 100 bad things. That's how Dr. Grace explains always, that there are 100 bad things and we are looking at how many an individual has accumulated. So if we say point one zero frailty index, it means that there is 10%, so out of 100, that individual has accumulated 10 bad things. So their chances are very less that they they will have, they are frail. But if, according to our classification, if somebody has more than point two zero frailty index, which means like 20%, so they have accumulated 20 deficits out of 100. So that makes them quite frail. So their possibility or probability in future to get frail increases. So that's how we use these points. Great. The next question is about a reference, which maybe we can follow up, you can follow up by email after, unless it was posted into the chat, I don't think so. So we'll, yeah, maybe if you can follow up, we'll follow up with you and get that out to the webinar participants. The next question, perhaps I overlooked it, but how are the two time points for the outcome variables at baseline and three year follow up utilized in the regression models and why is this approach considered a longitudinal model? Yes, thank you. That's a very good question. And that's, we discussed it with our, one of my committee members is biostatician. So we discussed this approach. So what we did is like we measure our exposure at baseline, that is shift work exposure. And we looked at the frailty at three years of follow up. So we included that in a model, the latest frailty index score. And we adjusted in our models the baseline frailty. So when we adjust our models for the baseline frailty, so that make it a longitudinal design. And that design has been used previously in literature and it has like a statistical basis. Great. And next question, what were the ages, BMI and other demographics of the categories of frailty spectrum outcomes by sex? I have to go back to like the tables and everything, but if you're asking that, like what the BMI categories are, like going to sex, they were not very difficult on the gender. Like they were not very different. So if they're asking like by sex, yes, we categorize them or yeah, so we BMI categories, demographic, I didn't get like what they want to say here. Okay. Well, maybe they can post a follow up or email you directly. Directly, yeah. It just email me and I will share my detailed table that that's also on the published site. Yeah, so I will. And then the next question, what numbers of individuals were current workers? Yeah, so current workers, if you see, I'll share the flow diagram. So I think they were around 29,000 because we excluded, if I quickly share my presentation slide, there was follow a flow diagram because we excluded here. If you see my screen. So here, so we excluded 29,000, those were retired. So then the currently working were 18,000. Yeah. So moving on to the next question, do you have baseline information on frailty status? So baseline prior to follow up? Yes, we measure frailty index at baseline. Yes, so what we did, because we have baseline and then we have follow up. So for our baseline, we have 52 variables and we included all those variables that are present in both cohorts in tracking and comprehensive. So we make sure that and then we calculated frailty index. Excuse me. We calculated frailty index at baseline, 52 variables. And then we for first follow up after three years, we again utilize those 52 variables for follow up one again and then we again calculated all those scores. All right. The next question and which you may or may not know, I may need to answer is, does the CLSA have gender as a variable? Did you use that variable? I know we do have it. So go ahead. Yeah, but we do not use this variable for this study. We just use sex. Yeah. Okay. Moving on, we have a question from Roberta. What should organizations do to mitigate some of the impacts specifically of rotating shift work? Yeah, that's I think is a very good and very important question. So rotating shift work has been in literature it has been regarding more disruptive than the regular shift work. So by regular shift work, that means a person is doing night shift again and again. So body kind of adapt to that work, but rotating shift work, what is happening that a person is continuously changing from day to night and then night to day and then day to evening and then. So the body does not get enough time to adopt those changes. So definitely, yes, we need to focus on rotating shift war. What we can do, there are a couple of recommendations in literature. They're first of all, there are few types of rotating shift workers that were considered, like I can not say healthy, but less disruptive as compared to other rotating shift. Like if you are doing forward rotating shifts. So by forward rotating shift, I mean you start with the day shift, your next shift is evening, and then you do night shift and then you do day again. So that is considered as forward rotating shifts. Forward rotating shift is considered less disruptive towards circadian rhythm as compared to if you start from night, then you do evening and then you do day shift. So these kinds of things have been discussed in literature. And the second things is I think education, the worker should be educated that these are the risks to their health and also in addition to that, I think if the resources are available to, like if there is a healthy options for them and also they should focus on exercises and they should focus on like to rest after their shifts enough to sleep after their shift. So yeah, there are a couple of like individual factors and also I think there should be some, I think there should be some of the implications should be at policy level. Yeah. Great. We have two more and a few minutes left. So did you stratify the analysis based on shift work professions, meaning nurses versus night shift versus nurses versus a night shift security guard? That's a very good question and I wish we could but we cannot. We do not have information regarding the specific like profession of the workers. We had this information but the format of the information was like they are an open-ended question. We cannot utilize that very easily. So we did not but that is something that I think future studies should look at and that's really good question. Yeah, but we cannot stratify in our analysis and no, we didn't stratify. Okay. And what about did you remove people who died during the three-year period from your analysis? No, we did not specifically but we definitely like included those who gave all the information. So automatically those were excluded from our study that died. Yeah. Great. Oh, one more just came in. So from Adeline, are you saying that shift work is sure to add to getting? I think they want to say that shift work like has a risk of arthritis. So like we haven't looked specifically on arthritis but definitely arthritis is included in the frailty index. So what frailty is telling us is that the person is vulnerable to disability and vulnerable to morbidity in future. Definitely, yeah, that's what is telling us and shift workers, those who are doing shift worker they are at higher risk of because they have more frailty, they are frail. So it's telling us that in future they're at higher risk of or they're more vulnerable to disability and morbidity. Yes. And there's one more question I think. Yeah. So have you considered using change in frailty index instead of adjusting for frailty index at baseline we can, the former is a typical longitudinal design. Yes, we understand, but we have like very short follow up period, there are three years but I totally understand that that is that's like an ideal approach. But to adjust again for even the first approach again, in most of the studies, it is considered better to adjust for baseline status as well. And it also happens in a lot of clinical trials as well that they adjust for baseline status to just, yeah. So I think, okay. And I did see a question in the chat which I'll just reiterate, it's not put in the Q&A yet. So you mentioned that you did not exclude those lost to follow up, were you able to avoid the biases of using participants lost to follow up? Yeah, there is, yes, definitely. I think there is lost to follow up at three years of follow up, yes. And were you able to avoid any biases that may have been the result of that? Or for that lost to follow up? Yeah. I think we did like because we adjusted for that. I didn't, I said that's a question. So I guess if you adjusted for it then it would have been, yeah, okay, well that makes sense. But we didn't. Great. Okay, well I think I don't see any other questions. So this is probably a good reminder to everyone to please complete your, for those just starting to leave please complete your survey on the way out. And again, thank you for an excellent presentation. I think we've learned a lot and I know I did about the topic. I'd like to remind everyone that the next deadline for CLSA data access applications is October 4th. So you can 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, like I said before to complete their survey upon exiting today. The next CLSA webinar is actually going to be on risk and protective factors for elder abuse in Canada findings from the CLSA. It will be presented by Dr. David Burns from the Factor Inventosh School of Social Work at the University of Toronto. You can find registration details on our website at CLSA dot sorry www.clsa-elcv. And remember the CLSA promotes the webinar using the hashtag CLSA webinar. And we invite you to also follow us on Twitter at atclsa underscore elcv. So thank you very much everyone for attending today and for Dr. Khan for a great webinar.