Okay, we're back with Mark Hill, who's the Director of IT Operations at Digital River. Mark, welcome to theCUBE, good to see you. Oh, thanks for having me, I really appreciate it. Hey, tell us a little bit more about Digital River. People know you as a payment platform, you've got marketing expertise. How do you differentiate from other e-commerce platforms? Well, I don't think people realize it, but Digital River responded about 27 years ago, primarily as a one-stop shop for e-commerce, right? And so we offered site development, hosting, order management, fraud, expert controls, tax, physical and digital fulfillment, as well as multi-lingual customer service, advanced reporting and email marketing campaigns, right? So it was really just kind of a broad base for e-commerce. People could just go there, didn't have to worry about anything. What we found over time as e-commerce has matured, we've really pivoted to a more focused API offering, specializing in just our global seller services. And to us, that means payment, fraud, tax and compliance management. So our global footprint allows companies to outsource that risk management and expand their markets internationally very quickly and with low cost of entry.  Which I think talks to the fact that incumbents can evolve, they can reinvent themselves from a technology perspective. I wonder if you could first paint a picture of how you use the cloud, you use AWS, I'm sure you got S3 in there, maybe you could talk about that a little bit. Yeah, exactly. So when I think of a cloud native business, you kind of go back to the history, well, 27 years ago there wasn't a cloud, right? There wasn't any public infrastructure. It was, we basically stood our own data center up in a warehouse. And so over our history, we've managed our own infrastructure and co-located data centers over time through acquisitions and just how things worked. There was over 10 data centers globally. For us, it was expensive, both from a software hardware perspective, as well as getting the operational teams and expertise up to speed too. So, and it was really difficult to maintain and ultimately not core to our business, right? Nowhere in our mission statement doesn't say that our goal is to manage data centers. So about five years ago, we started the journey from our hosted into AWS. It was 100% lifted and shift plan and we were able to complete that migration a little over two years, right? Amazon really just fit for us. It was a natural place for us to land and they made it really easy here for us too. Not to say it wasn't difficult, but. But once in the public cloud, we really adopted a cloud-first vision, meaning that we'll not only consume their infrastructures to service, but we'll also purposely evaluate and migrate to software as a service. So I come from a database background. So an example would be migrating from self-deployed and managed relational databases over to AWS RDS Relational Database Service. You're able to utilize the backups, the standby and the patching tools automatically with a click of the button and that's pretty cool. And so we moved away from the time-consuming operational tasks and really put our resources into revenue-generating products, like pivoting to an API offering. I always like to say that we stopped being busy and started being productive. I love that. That's really what the cloud has done for us. Is that what you mean by cloud native? I mean, being able to take advantage of those primitives and native APIs. What does that mean for your business? Yeah, exactly. I think, well, the first step for us was just to consume the infrastructure, right? But now we're looking at targeted services that they have in there too. So, you know, we have our data stream of services. So log analytics, for example, we used to put it locally on the machine. Now we're just dumping it into an S3 bucket and then we're using Kinesis to consume that data, put it in Elastic and go from there. And none of the services are managed by DigiRiver. We're just utilizing the capabilities that AWS has there too. So. And as an e-commerce player, retail company, were you ever concerned about moving to AWS as a possible competitor or did you look at other clouds? Well, can you tell us about that? Yeah, and so I think e-commerce is really mature, right? And so we got squeezed out by the Amazon's of the world. It's just not something that we're doing, but we had really a good area of expertise with our global seller services. But so we evaluated Microsoft, we value the AWS as well as Google. And, you know, back when we did that, Microsoft was Windows based. Google was just coming into the picture really didn't fit for what we're doing, but Amazon was just a natural fit. So we made a business decision, right? It was financially really the best decision for us. And so we didn't really put our feelings into it, right? We just had to move forward and it's better than where we're at. And we've been delighted actually. Yeah, makes sense, best cloud, best tech. Yeah. Yeah, I want to talk about Chaos Search. A lot of people describe it as a data lake for log analytics. Do you agree with that? You know, what does that even mean? Yeah, well, from our perspective, because our self-managed solutions were costly and difficult to maintain. You know, we had older versions of self-deployed using Splunk, other things like that too. So over time, we made a conscious decision to limit our data retention in generally seven days. But in a lot of cases, it was zero. We just couldn't consume that log data because of the cost in maintaining it. So because of this limit, you know, we've lost important data points used for incident triage, problem management, trending and other things too. So Chaos Search has offered us a manageable and cost-effective opportunity to store months or even years of data that we can use for operations as well as trending automation. And really the big thing that we're pushing into is an adventure of an architecture so that we can proactively manage our services. Yeah, you mentioned Elastic. So I know, I've talked to people who use the Elk Stack, they say there's these exponential growth in the amount of data, so you have to cut it off at whatever, I think you said seven days or less. Yeah. You're saying you're not finding that with Chaos Search? Yeah, yeah, exactly. And that was one of the huge benefits here too. So, you know, we were losing out if there was a lower priority incident, for example, and people didn't get to it till eight, nine days later, well, all the breadcrumbs are gone. So it was really just kind of the best guess or the incident really wasn't resolved, we didn't find a real cause. Yeah, like my video camera down at my, you know, my other house when somebody breaks in, I don't find out for two weeks and the video's gone. So it's the kind of same thing. So how do you, can you give us some more detail on how you use your data lake and Chaos Search specifically? Yeah, yeah, so there's many different areas, but what we found is we were able to easily consolidate data for multiple regions into a single pane of glass to our customers, so internal and externally. And it really does serve that operational support for the data extract transformation low process, right? It offered us also a seamless transition for the users who were familiar with Elasticsearch, right? It wasn't difficult to move over. And so these are a lot of selling points, benefits. And so now that we have all of this data that we're able to capture and utilize, it gives us an opportunity to use machine learning, predictive analysis, and like I said, you know, driving to an event driven architecture. So that's really what it's offered and it's been a huge benefit. So you're saying you can speak the language of Elastic, you don't have to move the data out of an S3 bucket and you can scale more easily, is that right? Yeah, yeah, absolutely. And so for us, just because we're running in multiple regions to drive more high availability, having that data available from multiple regions in a single pane of glass or a single way to utilize it is a huge benefit as well, just not to mention actually having the data. What was the initial catalyst to sort of rethink what you were doing with log analytics? Was it cost? Was it flexibility scale? There was, I think all of those went into it. One of the main drivers, so last year we had a huge project. So we have our Elk stack and it's probably from a decade ago, right? And version point or two or something, anyways, it's very old. And we went through a whole project to get that upgraded and migrated over and it was just, we found it impossible internally to do, right? And so this was a method for us to get out of that business to get rid of the security risks, the support risk and have a way for people to easily migrate over. And it was just a nightmare here, consolidating the data across regions. And so that was a huge thing. But yeah, it was also then the cost, right? It was, we're finding it cheaper to use Chaos Search and have more data available versus what we're doing currently in AWS. Got it. I wonder if you could share maybe any stories that you have or examples that underscore the impact that this approach to analytics is having in your business, maybe your team's everyday activities, any metrics you can provide or even just anecdotal information? Yeah, yeah. And I think one, coming from an Oracle background here, so did your ever historically has been an Oracle shop, right? And we've been developing or reporting an analytics environment on Oracle and that's complicated and expensive, right? We had to use advanced features in Oracle like partitioning materialized views and bring in other supporting software like Informatica, Hyperion S-Base, right? And all of these required a large team with a wide set of expertise into these separate focus areas, right? And the amount of data that we were pushing into Chaos Search would simply have overwhelmed this legacy method for data analysis and a relational database, right? And not to mention the human toll of the stress of supporting that Oracle environment into 24 by 7 by 365 environment, which requires literal or no downtime. So just that alone is a huge thing. So it's allowed us to break away from Oracle. It's allowed us to use new technologies that make sense to solve business solutions. You know, Chaos Search is really interesting company to me. I'm sure, like me, you see a lot of startups. I'm sure they're knocking on your door every day. And I always like to say, okay, where are they going after? Are they going after a big market? How are they getting product market fit? And it seems like Chaos Search has really looked at hard at log analytics and kind of maybe disrupting the Elk stack. But I see, you know, other potentially use cases, you know, beyond analyzing logs. I wonder if you agree. Are there other use cases that you see in your future? Yeah, exactly. So I think there's one area would be Splunk, for example. We have that here too. So we use Splunk versus, you know, flat file analysis or other ways to capture that data just because from a PCI perspective it needs to be secured for our compliance and certification, right? So Chaos Search allows us to do that. There's different types of authentication, really a hodgepodge of authentication that we used in our old environment, but Chaos Search has a more easily usable one, one that we can set up, one that can really segregate the data and allows to satisfy our PCI requirements too. So, but Splunk, but I think really, you know, deprecating all of our elastic search environments are homegrown ones, but then also taking a hard look at what we're doing with relational databases, right? 27 years ago, there was only relational databases, Oracle and SQL Server. So we've been logging into those types of databases and that's not cost effective, it's not supportable. And so really getting away from that and putting the data where it belongs and it's easily accessible in a secure environment and allowing us to push our business forward. And when you say where the data belongs, it sounds like you're putting it in the BitBucket S3, leaving it there, is it's the most cost effective way to do it and then sort of adding value on top of it. That's what's interesting about Chaos Search to me. Yeah, exactly.   So, yeah. Yeah, copycrate, you're moving data around, it gets expensive. It's funny what you say about databases, it's true. Database used to be such a boring market, now it's exploded, then you had the whole no SQL movement and SQL became the killer app, you know, it's like a full circle, right? Yeah, yeah, exactly. Well, anyway, good stuff Mark, really appreciate you coming on theCUBE and sharing your perspectives. Would love to have you back in the future. Oh yeah, yeah, no problem. Thanks for having me, I really appreciate it. Okay, so that's a wrap. You know, we're seeing a new era in data and analytics. For example, we're moving from a world where data lives in a cloud object store and needs to be extracted, moved into a new data store, transformed, cleanse, structured into a schema and then analyzed. This cumbersome and expensive process is being revolutionized by companies like Chaos Search that leave the data in place and then interact with it in a multilingual fashion with tooling that's familiar to analytic pros. You know, I see a lot of potential for this technology beyond just log analytics use cases, but that's a good place to start. You know, really, if I project out into the future, we see a trend of the global data mesh really taking hold where a data warehouse or a data hub or a data lake or an S3 bucket is just a discoverable node on that mesh and that's governed by automated computational processes. And I do see Chaos Search as an enabler of this vision. You know, but for now, if you're struggling to scale with existing tools or you're forced to limit your attention because data is exploding at too rapid a pace, you might want to check these guys out. You can schedule a demo just by clicking the button on the site to do that or stop by the Chaos Search booth at AWS re-invent, theCUBE is going to also be there. We'll have two sets, a hundred guests. I'm Dave Vellante. You're watching theCUBE, your leader in high tech coverage.


Good afternoon everybody. My name is Marco Ramis and I'm an Associate Scientific Director with the Canadian Longitude and the Study on Aging and I'm also the moderator for the CLSA's webinar series. I hope all of you can hear me. If you can hear me, somebody please just type yes or no in the chat so I can confirm that the volume is working. Today it gives me fantastic. Lots of people are saying yes, they can hear me great. So today it gives me great pleasure to introduce Dr. Susan Kirkland. Susan is one of the principal investigators of the Canadian Longitude and the Study on Aging along with Dr. Parminder Reina and Dr. Christina Wilson. Susan has quite a distinguished career in the field of epidemiology here in Canada. She is currently a professor in the Departments of Community Health Epidemiology and Medicine at Dalhousie University and she's the Associate Director of Population Studies of the Geriatric Medicine Research Unit at Dalhousie. She's trained as an epidemiologist and her areas of expertise lie in aging, chronic disease epidemiology, health services utilization and women's health. She has been involved in a great many large population-based epidemiologic studies besides CLSA including the Canadian Multicenter Osteoporosis Study or KMOS, the Nova Scotia Health Survey and the Canadian Community Health Survey follow-up studies. Dr. Kirkland has been involved with the CLSA since its inception and she has played a major role in the development of the study design, its content, study measures, governance structure and the study's implementation plans. She also takes a lead on many of the ethics-related issues as they pertain to the CLSA. So today Susan will be speaking about an update to the research community on CLSA. Where are we now and what's next for Canada's premier study on aging? So just before I pass the microphone over to Susan, just a quick word on the format of the webinar, Susan will take approximately the first 30 to 40 minutes to present. And after the presentation is over we will entertain questions from the listening audience because if everybody activates their microphones we can get a lot of feedback. We mute everyone but if you look at the lower left hand portion of your screen you'll see a chat feature. Please just type your questions into the chat feature and Susan will respond to them orally. That's just again to avoid the feedback. So without further ado I will stop talking and now pass the mantle over to Susan for her presentation. So welcome and thank you very much for agreeing to present today. Thanks Mark. It's my pleasure.  And I give this presentation on behalf of principal investigators Tina Wilson and Parminder Raina as well as the much larger CLSA research team. You understand that this is a very large initiative and cannot be done with one or two or three people alone. So there's a very large team behind us. Just to give you an idea of the things I'm going to talk about today, I know many of you are familiar with the design of the CLSA but some of you may not be so I'm just going to review that very briefly. I apologize to the people who know this very well. But then I will get into some new information. So I'm going to talk a little bit about the renewal funding, the recruitment milestones that we've achieved to date. I'm going to give you a very small snapshot of the CLSA participants at baseline. And then I'm going to talk about the first follow-up which we're moving into now. So I'm going to talk about some of the protocol and content changes, some of the accommodation and retention strategies that we're using to engage our participants. And as Mark said, I'm involved with a lot of the ethical, legal and social issues in the study. So I'm going to talk to you about some of those. I'm sure you're all interested in data access, so I will talk about that. And I will definitely leave time for questions at the end. So just to get you started, the CLSA is a strategic initiative of CIHR and it really has been on the research agenda since the inception of CIHR. It is a very large initiative that we have in addition to the three principal investigators, more than 160 co-investigators. We have a large number of units, a large number of enabling units, a large number of staff right the way across the country. It's truly a multidisciplinary study and it really is the largest study of its kind to date in Canada for both depth and breadth. Our vision is that of a research platform and it's particularly important that this is a research platform. It really does give us the infrastructure to enable state-of-the-art interdisciplinary population-based research and evidence-based decision-making that ultimately will lead to better health and better quality of life for Canadians as they age. As I said, this has been an initiative that has been on the go for a while now and you can see that there have been various phases as we've gone through the years. So we are getting pretty old in the tooth as a study even though we've only been in the fields for three or four years. So you can see the development that has occurred along the way. Those little yellow boxes that say IR indicate that we went through international review at each point. And we took a brief pause when we got CFI funding in 2011 because that enabled us to fill the physical infrastructure to undertake the study. But you can see that in late 2015 we have just finished baseline recruitment and data collection and we're just beginning our first follow-up. And again, that's how I'll spend my time talking about what I'll spend my time talking about today. Just briefly, the study design and methodology. Our intent was to follow 50,000 men and women who were aged 45 to 85 at baseline. And we did that in two ways. One is a group that we call the tracking and those were 20,000 people who were randomly selected from across 10 provinces. And then another group we call the comprehensive who were randomly selected from within 25 to 50 kilometers of 11 sites in seven provinces. In the group that is the tracking, we follow them by questionnaire or using questionnaires by telephone. So we use a CAFE process. On the comprehensive side, we use a similar process, but we do it in person and we go into the home to capture that information. In addition to the comprehensive group, we have people come to a data collection site and we do extensive physical assessments at the site. And they also have the option of providing blood and urine. We go back to people every three years for a full follow-up. We do a maintaining contact interview with them in between the three years. And we intend to follow participants for 20 years. And then another unique feature of the study is that we intend to link to administrative data and people have provided their health card number for us to be able to do that. This slide just gives you an idea of the scope. You can see that people who participate in telephone interviews come from all 10 provinces, unfortunately not the territories. And then you can see the data collection sites that are marked with the red dots. And the cities that are sort of in purple indicate that there's also a chatty site at that location. We use a number of sample frames for recruitment. The first thing that we did was we partnered with Statistics Canada on the Canadian Community Health Survey. We used Cycle 4.2 where we worked with Statistics Canada to create a survey on healthy aging. And for that survey they used the 2006 census as an area frame. And we had some questions about agreeing to be contacted by the CLSA. And we recruited a number of participants in that way. We also partnered with many of the provincial ministries of health and utilized the health card registration databases. And the ministries would do a mail out on our behalf. And then people who were interested in hearing more about the CLSA returned a consent to contact form. And then we also supplemented this with random digit dialing. And when we did random digit dialing, we called initially in a pre-recruitment phase. This was essentially cold calling. We told people about the study and we asked them whether they would agree to be recontacted to hear more about it. And if they agreed to be recontacted, then we called them back and talked to them about the study in greater detail. The interesting thing about the CLSA is that it is completely standardized despite the fact that it takes place in multiple sites across the country. And it's completely paperless. So once people are pre-recruited, then we actually recruit them into the study and they give us their consent. And they agree to provide questionnaire data. Now, that questionnaire data for the tracking cohort is done by telephone. If it's done in the comprehensive, it's done by home interview. The people who come to the data collection site get an extensive physical and neurological exam. They also provide blood and urine and we do the processing of that on site. And then we send it to be stored at the biorepository and bioanalysis center at McMaster. Also at LEAP, all of the data is cleaned and stored at the statistical analysis center where it is available for dissemination to researchers. I think that you're probably aware that there is a large amount of information collected in the CLSA. This just gives you an indication of some of the modules that are within the CLSA based on questionnaire information. So you can see there's a wide range of demographic information, information related to health behaviors, health status, physical status, psychological status, and social aspects of health. We also collect a fair amount of physical information done in assessments.  So you can see that in addition to height, weight, BMI, we have things like blood pressure, we do spirometry, carotid ultrasound to get carotid intima medial thickness. We do an ECG, a 12-leave ECG, so that we can get heart rate and rhythm. We have a DEXA, so that we can get bone mineral density, body composition, as well as aortic calcification. We do hearing tests, visual acuity tests, grip strength. We do a funded photograph that gives us an idea of blood flow. We do a tenometer, which gives us an idea of ocular pressure, as well as an extensive neuropsychological battery that examines both memory, executive functions, and reaction time. And then we have a number of performance measures, things like timed up and go, balanced 4-meter walk, chair ride, and then participants have the option of providing blood and urine. This slide just gives you an indication of what goes on at the data collection site. You can see that there are a number of different stations. Again, everything is completely standardized right the way across the country. And you see on the left-hand side, we use a barcode to identify our participants, and that barcode is used with every single test so that there's no opportunity for mix-ups. The biospecimen processing is relatively complicated. We do some basic CBC blood count stuff on site, but the remainder is processed and frozen within two hours. And eventually it goes to the Biorepository and Bioanalysis Center. The other thing that we do is we have a number of algorithms that we've developed for disease outcomes. This is an epidemiologic study, it's not a clinical study, so there's virtually no clinical assessment of disease outcomes. So we do it in a number of ways. We have a lot of questions related to self-reported diagnosis, or sorry, self-reported physician diagnosis. So people say that they've been told by a physician that they have a particular disease outcome. And then we also have the various tests that we do at the data collection site. We have a large amount of information on medications, and it's the confidence of this information that goes together to create algorithms. Ultimately, we'll have information linked from administrative health databases as well, but for the CLSA itself, we have these algorithms in place. So that's just a brief outline of the study itself. What I'd like to do now is just walk you through some key milestones that we've achieved over the last little while and let you know where we are today. As I said, we're hoping to recruit 50,000 participants into the CLSA, but we have actually achieved those goals and actually extended them a little bit. So we have 21,241 participants who are being followed every three years by phone. And we have 30,111 participants who are being followed in their homes and at the data collection site. And we exceeded our original numbers for a couple of reasons. One is because we wanted to ensure that we had a good representation of the range of socioeconomic status. And so once we saw that we were perhaps a little bit low on people in low socioeconomic status groups, we went back and targeted people who lived in specifically identified low SES census areas. And with respect to doing the maintaining contact questionnaire and interview, we're almost finished. We've gone through almost 41,000 maintaining contacts and hope to be finished at the end of this year. So that brings us to a grand total of 51,352 participants recruited into the CLSA. Something that I'm sure you can understand is a major achievement. We certainly feel that it is. We also have been able to release the first set of data to the research community. In fact, we've had two releases now. This is the first set of data from the telephone interviews. And the first set of data included only just the questionnaire-based information. The second set of data now includes the cognition data. So we have all of that data available for use by the research community. I'd also like to bring you up to date on the renewal funding. As you know, this is a strategic initiative of CIHR. And although being a strategic initiative in theory, the funding will last for the duration of the CLSA. We are re-awarded the funding every five years based on an international review. So we have gone through our international review after the baseline, and we have been awarded our Phase II renewal directed grant. And this covers the five-year period from 2015 to 2020. And the amount of this funding from CIHR is 41,000- 41.6 million. And that accounts for 86% of the operating cost. So it actually caused us more to run the base, the core of the study. And it is the responsibility of the scientific management team to raise the additional 14% of the funds for the study. What that 41.6 million covers is data collection for our first follow-up, as well as two-thirds of the data collection for our second follow-up. In addition, what we're able to do with that funding is to analyze the baseline biological samples. Not for everything, but what we are able to do is analyze some key biomarkers and do some genetic and epigenetic analyses. And I'll tell you a little bit more about that later. The other thing has to do with the CFI funding, and we've had this funding for infrastructure. We've been very pleased with the opportunity to create both the space that we need and renovate the space that we need, as well as by the equipment that we need. And as part of the amendment, we have added a caddy site. We've been able to buy some equipment to use in the home, as well as replace some of the very large, to be able to have some large pieces of replacement equipment, as well as make some renovations to the National Coordinating Center. So that moves me into talking about a snapshot of the CLSA participants. And although these are snapshots, these are not CLSA participants. But they could be. Just to give you an idea about recruitment and how participants were recruited from the various sampling frames, you can see that almost 4,000 participants were recruited by the Canadian Community Health Survey on Healthy Aging. These were only tracking participants because they were our first participants when we first started the study. You can see that we got almost 8,000 participants by a Provincial Health MALO, and that the bulk of our participants come from utilizing random digit dialing. We also had the opportunity to add on a number of participants from the new life study in Quebec, and these were particularly older participants, and that leads us to our grand total. I'm not going to provide many extensive analyses at this point. I would encourage you to go and visit the data preview portal because you can see a number of statistics on that portal. But it does only have the tracking data at this point in time. So what's interesting here is that I can present to you both the tracking and the comprehensive data, and just show you some basic figures. But what I think this is unleaded data, and the reason I'm showing you this is to give you an idea of frequency. So if you're interested in a particular age group or you're interested in a particular sex, or if you're interested in some other particular piece of information, you can get an idea of the absolute numbers of participants in the CLSA. So you can see the age distribution across participants. This is really no accident because we collected participants within certain age and sex straight up, and so this was predetermined essentially by us. You can see that we have a lot of participants in each of the age categories, and that we have a pretty much 50-50 split between men and women in the study. We were also able to collect data in both English and French. Unfortunately, we could not accommodate data collection in other languages. And so you can see that almost 19% of CLSA participants participate in the CLSA in French. You can see that the majority of participants are born in Canada, 84%. It's slightly higher in the tracking participants than in the comprehensive participants. Again, this gives you an idea of how participants fall out by province. You can see that in the comprehensive, there are three provinces where we do not have data collection sites. This had to do with a number of things, but mostly because of population density, as well as the ability to host a site in that particular province. You can see that some of the larger provinces also have the greatest number of participants, and some provinces, as you will have noticed on the previous slide, have more than one data collection site in them. But you can see that it ranges from a high of over 11,000 participants in Ontario to a low of just over 1,100 participants in Prince Edward Island. So for some questions, you might be able to analyze the data by province. In either cases, you will have to analyze the data by region, or you might have to actually pull together the entire data set in order to do it. Again, I thought I would just introduce some of the chronic conditions that people self report. Again, these are chronic conditions that have been diagnosed by a physician or health professional. And you can see that there's a range of conditions that occur here. These are just some of the conditions that we have information on. So you can see that, for example, about 17% of the population in the CLSA has diabetes, and about approximately 38% report having hypertension.  So if you look at the things that are highlighted in red, you can see that there are fairly distinct sex differences between men and women. And the things that are highlighted in blue indicate that there are quite dramatic age differences. So for example, if you look at cataracts, you can see that in the younger age groups, the proportion of people who report having cataracts is 12.3% for men and 16.3% for women. But then if you look in the group at 65 and older, it jumps to 30% for men and 37% for women. And if you look at something like heart disease, you can see that there are both age and sex differences. The other thing that I thought I would just show you here is related to self-rated health. So you can see that a large majority of CLSA participants report their health as either excellent or very good or good. A much smaller proportion report their health as fair or poor. And again, it's things like this that will be very, very interesting for us to be able to track over time. You can also see how people reported satisfaction with life. And I did something that I should never do as an epidemiologist. You'd think I'd know better, but I actually switched the way the tables are presented here. So now I'm presenting at the top people who are dissatisfied with their health or report neutral health. And again, it's about the same proportion as self-rated general health. But overall, about 85% of the participants were satisfied with their life. So that was just a very brief introduction to some of the descriptive statistics of the participants. And I do hope that you have the opportunity, as I said, to go to the portal and look at that further. I'd just now like to walk you through the first follow-up because this is where we are and where we will spend our time for the next three years. So right now, we will be doing in the three-year period our first follow-up with the comprehensive group that started in July.  We will be going into the field in October for the tracking group. That's the group that's followed by phone. And again, we'll be recontacting that group for their follow-up telephone interviews. And as well, we will be doing a maintaining contact. So everyone will be recontacted at about 18 months by phone. As you can imagine, there has been a flurry of activity getting ready for the first follow-up. One of the challenges we have in the CLSA is that we collect data over the entire wave. Each wave is three years. And once we finish one wave, we move right into the next wave. So at every single point, we are furiously preparing. Just to let you know some of the modifications that we've made to the protocol. Obviously, we made some revisions to the questions. Some questions don't need to be asked every single time. Some questions had to be changed to reflect the time window, but we also added some new content. As well, we would like to add information from someone who can talk about the participants last three months of life should they have died. And we have also accommodations for a proxy. So we developed a proxy questionnaire. There's a number of accommodation strategies that we've also developed, including an in-home visit for the data collection site. And the other thing that we have done is switch the timing when the content of the maintaining contact question is actually administered. So I'll talk about each of those briefly. This is the new content. It comes from our working groups and also with our partners. So we have added modules on elder abuse, childhood maltreatment, gender identity, unmet health care needs, preventive health behaviors, workability, loneliness. We ask questions now about subjective cognitive decline. And again, as I said, we're adding decedent questions around the three months prior to death, particularly around health service use and also living arrangements. Despite this, we really haven't changed dramatically the overall length of time that it takes to administer the various questionnaires. The key thing is that rather than actually doing a full data collection at the maintaining contact point, which is at 18 months, what we've now done is move that. So with the group who are followed by phone, we're actually going to conduct or get that information in two interviews that are done very closely together. In the comprehensive, what we've arranged is that we've spread the content over the in-home visit and the data collection site visit. And what this means is it's important because it allows us to have all of the information collected in a much tighter timeframe. But also it allows us to collect all of the information within the three-year wave because before what we were doing is having to extend over to the next wave with the maintaining contact. When we go back to participants, we get verbal consent over the phone. But there are a number of things that we're going back to participants for that we didn't clearly identify at baseline or we have added in sent. And we will get signed consent for those. So we will get signed consent for being able to do retrospective linkage by a health card number, which we've had only done prospectively before. Also to inform people that if they die, we will ask the next of kin to provide a decedent questionnaire. And also to indicate to people that should they become institutionalized, we'll do our best to follow them into those institutions. A key factor for the first follow-up is really around retention. And so a key strategy for retention is to create accommodation strategies. And so really what we're doing there is we're trying to make sure that people can stay in the study as long as they'd like to stay in the study as their circumstances change. And so this could be things like they move out of area. It's not so much a problem for people who are followed by someone, but if they're at a data collection site, we need to be able to think about how we can accommodate them if they move away.  And we've come up with a number of strategies to accommodate people who are hard of hearing. People will also have challenges related to mobility and travel. And that's why we have developed the DCS at home visit. And again, as people move into institutions, we want to be able to follow them there as well. And the other thing that we have always been very clear on is that obviously in a study of aging, there's the potential for cognitive decline over time. And we've developed as many adaptation strategies as we can to maintain participants in the face of cognitive decline. So these things allow for flexible participation. And some of the things that were exclusion criteria baseline, things like cognitive competency or being cognitively able, no longer apply. This just gives you a very busy slide of some of the strategies that are developed. And there's all kinds of standard operating procedures and guidance provided around these to our staff. I just want to talk very briefly about the DCS at home. So obviously the ideal situation is someone comes for a full visit at the DCS. But if that's not possible, if they really are unable to travel or really have mobility issues, then we will think about how we can accommodate them. It also could be that they are away for an extended period of time. If we know that the participant can come back for a full DCS visit in the next wave, what we'll try and do is the DCS by phone. So then we won't switch permanently how we collect that data. But if it's really not possible for people to continue to come in, we will actually come to them. And you can see here this is the equipment that we have tested and will utilize for a data collection site visit in the home. And as I said earlier, there were a number of ethical, legal, and social issues that we have to deal with. These are also accommodation strategies. But there are more sensitive strategies that have been developed with a lot of input, primarily from the CHR, CHR, CLSA, Ethical and Legal Social Issues Committee, which is a CHR level committee that advises the CLSA. So when participants in the study turn 70, they're given information, an information package and consent about a proxy. And so they're given the opportunity to identify a proxy decision maker and a proxy information provider. And this could be two different people or it could be one in the same person. The consent also includes an advanced directive to indicate their preferences for future participation in the event that they can't make decisions on their own or they can't provide information on their own. This is not a legal document, but it's simply an indication of their wishes. The participant is asked to inform the proxy of their role. But when we go back to the participant at the first follow-up, we verify this information with the participant. But we don't actually contact the proxy decision maker or the proxy information provider until we actually need to use them. Again, all of these documents were developed with advice from ELSI, and we are now in the process of implementing the proxy questionnaire. So these would be questions that the actual proxy would then answer about the participant. This gives you an idea what the participant actually consents to about a proxy. So it says in the consent package, should I become unable in the future to take part in the CLSA on my own? And this is for everyone. The question is, I would like my proxy information provider to continue to answer the research questions asked by an interviewer on my behalf. And then for the comprehensive, there's a number of additional features. So if people had originally agreed to do, or they had the option of agreeing to continue to do the physical test as long as it's feasible, and if they had given their blood and urine, they can indicate whether they'd like to continue to do that. If they'd given their health card number, they can indicate whether they would like to continue to do that. And again, this is not a legal document, but what it does do is it provides some guidance to the proxy in the case that it's needed. The other thing that we have implemented is a way of flagging cognitive decline. Now, this is not to indicate that we're flagging cognitive impairment, but just that there are some kind of cognitive difficulties that could impact completing the questionnaire. So we are using a six-item screener for all of the participants who are 17-older. And it just asks them for delayed recall of three words, penny, table, apple, and what year is it, what month is it, what month is it, and what day of the week is it.  And again, this is not to necessarily implement the proxy process, it's just to have a conversation with them about their level of comfort. And then we can decide that they want to continue and that's perfectly fine. And then there's also the option, as the interview is going along, for both the interviewer and the interviewee to say, you know, it's not working and initiate that whole proxy script all over again. And the thing that we have worked out is the process of withdrawal. And in a longitudinal study as complex as the CLSA, this is really a very complex process. There's a lot of things that need to be taken into consideration, not just about whether they don't want to provide information anymore, but what they would like around their options for future use of data and samples and healthcare linkage. So in the tracking cohort, we have to consider whether they want to provide information anymore, but also do they want the linkage? For the comprehensive, there's also the more complex issue of samples. The other thing that is that withdrawal can occur at any point. So there's a number of options, but essentially the default option is a D-link option. And the D-link option permanently destroys the link between a participant's identifying information and their data. I've got my eye on the time here and I see that it's going quickly, but I know that this is an important element, so I will do my best to cover it well. So in terms of data and biospecimen access, the fundamental tenets are that obviously we have to respect the right and privacy and consent of participants at all times, that the confidentiality and security has to be safeguarded, and also that these are unique resources that have to be used optimally, particularly the biosamples. With regards to the tracking information, both the questionnaire data and the cognition modules are now available. There will be another release that codes occupation and medication, and we'll have the MCQ or the Maintaining Contact Questions available in early 2016. For the comprehensive, you'll note that I haven't put any timelines here specifically, but if you remember, we won't be finished data collection for the comprehensive... Or sorry, we are finished data collection for the comprehensive now. And so the questionnaire data will be the first data that's released because it's fairly easy to clean and I would imagine that would be somewhere in early 2016 again. And then there will be a rollout of increasingly complex data. So the simple clinical data will be available before the complex clinical data will be available, and then the biosamples and biospecimen data will be available last. This just gives you an idea of what will be available for the biomarker and epigenetic analysis. We currently have a complete blood count, and the tentative release for that would be early 2017 because actually I... Oh no, sorry, that's right, because that data has already been collected. But if you'll look here, you'll see that there is a number of biomarker analyses that will occur as well as genotyping and epigenetic analyses. And those will be done over the three-year wave in 2000 or over the next three years. And so it won't be available for release until 2018. The steps to access data, again, the application process can be found on the data preview portal. What happens is once you complete an application, it goes through administrative review first just to make sure that everything's in there. Once that's been done, then it goes to the data and sample access committee for review. And once that has been done, a recommendation is then made to the scientific management team. The scientific management team signs off on it, and then we notify the applicant. So those four steps take approximately three to four weeks. Where we can't guarantee the timeline is what happens after that is that there's a CLSA data access agreement that is prepared for the applicant's institution. And that requires institutional review and signing. And that process itself is much more unpredictable, and it's also out of our control. So it could take longer. I think that as institutions become more familiar with signing these contracts, it will speed up significantly. But I just point that out to you. And then once that's signed, then the raw data is provided to the investigator. And the way in which you submit your application is through the website access at clsa.e or slash elcd.ca. I'm sure you're all interested in cost, and I wish I had some really definitive news for you. What I can tell you is that for the baseline tracking data, we operate on a cost recovery basis, and we charge $1,000 for the baseline tracking data set. If you are a graduate student using the CLSA data for your thesis, there is no cost. When it comes to the comprehensive, it's much more complex data. And the guidelines are currently under development. And then there's increasing layers of complexity. So the questionnaire data in the CLSA is much more extensive than in the tracking. There's the addition of a vast amount of clinical data. Some of that clinical data will eventually be all from the numeric, but some will be images. And then there will be the biosamples as well. There's both biomarker analysis and genetic analysis. And there will be different costing for accessing the biological samples themselves as raw material versus accessing the analyzed alphanumeric or other information that comes from them. So if you are planning a grant submission that uses particularly the comprehensive data, including the clinical data and the biosamples, it's really important that you do consult with us early enough that we can help you with that. And the other thing that's important to know is that there are quarterly submission deadlines to the data collection site. I'm sure you're also interested in the role of ancillary studies. And by ancillary studies, I mean actual add-ons of data to the CLSA. And the answer is yes, but the problem is that, well, it's not a problem, but anything that's added to the participant data collection has to be incorporated as a core component of the CLSA. And it's just give people, participants contact information, and they go and collect the data. The other thing is that we really only can add elements at the beginning of each follow-up wave. And because we can't add a huge amount of information at each follow-up, it has to be vetted through the working group. So really, it has to be a priority. It has to be logistically feasible, and there has to be funding associated with it. The other thing that has to be understood is that there is no exclusive right to the data, even if you bring money to the table. This is the way that we work as a platform, and it applies to the principal investigators as well as all other investigators. We are developing an ancillary study policy, and it will be posted on the website in 2016. I would encourage you to visit the data preview portal. There's lots of very, very interesting information on it, as well as extensive information on access. But you can also actually look at some of the data from the CLSA. So please do have a look at it if you get a chance. I would like to acknowledge the research team, and these are the researchers who take a major role in the CLSA at either data collection sites or caddy sites or at the enabling units, and also acknowledge the scientific working groups and the larger co-investigators. A picture is sometimes worth a thousand words. This is the operations committee and scientific leads. These are many members of the CLSA team, and these are some of our participants who obviously could not do the study without. And lastly, I'd just like to acknowledge our funders and our partners. Great. Wonderful. Excellent presentation. Thank you very much, Susan, for giving us this extremely informative overview of the past, present, and the future of CLSA. So if there are any questions, please type your questions into the chat feature. We have one question that has already come through, and I'll ask that question in a second. But I've got one quick question, Susan. People often ask me when I talk about CLSA, what might happen if we get participants who drop out after baseline or after the first follow-up? Are they going to be replaced, or will we just say, you know, they just have one time point of data? So how is that going to be handled? That's a good question, Mark. Thanks. At this point in time, we haven't set up the CLSA as a panel study. And so a panel study would mean, you know, that we would continuously replace participants. We have allowed for attrition over time, and there may become a point in time when we consider re-recruitment of a group. But at this point in time, it hasn't been... We'll start with some of the questions. From Mike, do you have any contact with family doctors either to collect data or to inform them if you discover abnormalities? That's a great question. Do not collect data by a family doctor. And this was a decision that we made very early on in the design because it's extremely... There are a number of different ways in which we could collect data, but we decided that we would use primary data collection with our participants. We have a policy that we don't release results directly to anybody, to any third party, except to the participant themselves. The participant themselves is free to, obviously, take their results to a family physician should they want to. And they can also... So there's a number of... Excuse me. A number of tests that we release the results to participants after their visit. There's a number of tests that we don't release. But it's very clear that the participant's information is their information. They own it. They can request to access it. And so they can access any information about themselves that they would like to take to their family physician. We do have guidelines and policies around... Well, obviously around adverse events, but that's different. And we also have policies around incidental findings as well. And in the case of incidental findings, which is a finding that comes up that puts the participant at risk, that we weren't actually collecting information on or we became aware of sort of vicariously, then we will work with... With... We have physicians who are consultants at each of the data collection sites. We also have a clinical working group. And we also have a mechanism via that to contact the person's family physician. And Sandra, is there also accelerometer data available on any participants? Very good question. There isn't currently... We have submitted a number of grants to add accelerometer data to the CLSA, but so far, none of them have been funded. It is something that is on our radar. It is something that we would like to add to the CLSA. And we think that we can do it relatively easily because we have this window where we can set it up in the in-home visit and they can bring it back to us at the data collection site. But it does require funding. Does the comprehensive group all attend a data collection site or are these participants a subgroup? And what do you do with genetic incidental findings? All of the participants who are recruited into the comprehensive all come to the data collection site. So all 30,000 come to a data collection site. And they're spread in about 3,000 participants per data collection site. And it takes us the full three years to see all 3,000 participants at each data collection site. The genetic incidental findings is a very good question. At the current time, we do not release any genetic findings. We actually don't have any genetic analyses done at this point in time. And that's something that's important to note is that other than the analyses that I told you about that have been funded, we actually don't have funding within the CLSA to do these analyses. So what we do is we eliquid them, we store them in multiple ways so that they can be used by the research community but we don't actually analyze them. And what this can mean is that these data can be stored for a very long period of time before they actually get utilized. And in fact, some of them, the intent is that they be stored for a very long time before they are utilized because we want to be able to look at some of the changes. I think the second question about the genetics and your answer in terms of the fact that the CLSA isn't funded to do any analyses is an important take-home message for the audience. The CLSA is a platform of research. So CLSA isn't driven by any specific hypothesis or research question on its own. It is a collection of data and it's up to the research community to access the data to address various research questions of interest. I think that's something that often gets lost when people who aren't familiar with the study listen to the CLSA because I've gotten questions myself. What are your results? And we don't have results because it's not our responsibility to produce results. It's our responsibility to make data available for researchers to analyze. Okay. I see that the next question is, any intention to reflect ethnicity in the future or is this too complex to do? And again, this is a very good question. This is something that we grappled with a lot when we were designing the CLSA. And ultimately, we chose to both have a random sample of participants within province and then select participants for the comprehensive around data collection sites. So we chose not to address ethnicity specifically, but we do address ethnicity indirectly and part of our rationale in terms of some of the larger metropolitan areas was so that we could get a broader range of ethnic participants. The very real disadvantage is that you have to be able to speak English for French in order to be able to participate in the CLSA. And what we hope in the future is that we can collaborate and participate with other groups who are particularly interested in ethnic populations to be able to interface. And this applies to the Aboriginal community as well because it's not something that we can take on in the CLSA, but we would love to have a partner who took it on, used all the CLSA materials, utilized the design and everything, but applied it to specific ethnic groups. Great.  Another excellent question. We don't currently. I have been championing adding the addition of information on sexual activity. We respond to participants a lot to indicate that this is an intimacy and sexual function and sensuality is an important part of aging and they would like to see it reflected in the CLSA. And I hope that it will be in the next wave, but that will be, it won't come until the second follow-up. Part of it was because we were very, there's a lot of information and there is a lot of sensitive information and there were two issues when we first mounted the study. One is that we wanted to be able to get it through ethics and the other is that we wanted to be able to develop a rapport with our participants and we didn't want to defend them with really sensitive information off of that. But what we see is that our participants want to provide this kind of information. So I think that will make it a lot easier for us as we go forward. Great, so we'll take one last question and that is, your high volume of telephone questionnaires means you must address hearing loss. What strategies have you adopted for these participants? Right. Well, we've adopted some strategies that range from very, very simple to more complex. But essentially the most straightforward scenario is that if someone's having difficulty hearing a person on the phone, we make sure that we have someone who has a deep voice do the interview with them, typically a male and we have the person slow down considerably. This does help, but it doesn't alleviate the problem as someone's hearing deteriorates over time. We have improved our voice over internet protocol system and we are also developing alternate strategies. So we will have in the future a web based questionnaire that people can complete. It's virtually impossible to mail the questionnaire because of the complex gift patterns, but we have the possibility of someone having a helper who can repeat the question to them, to their face and repeat the answer back. And then in the case where that simply isn't feasible, we also can move to a proxy. Great. So there's a question, what was the greatest challenge in developing the data access which is quite involved, maybe Susan, you might have a quick answer to that? No, I don't. It's all been challenging. The data access is probably one of the most in theory simplest things, but in practice hardest things. I think the biggest thing to be honest is that it takes up a lot of resources and it takes up well, actually that's even not true. It's developing the policies and even the issue around costing is a very big issue for us and how we make it accessible to the research community, but still manage to be able to do it with the funding that we have. You know, it is clearly something that we put as a high priority, but we're still working out the kinks. Great. And last question, are you aware of anyone analyzing longitudinal data available for participants in both CCHS 4.2 and the CLSA baseline? That's a very good question. Actually, these are all great questions. I think that it's currently structured is that it's not possible to link CCHS 4.2 and CLSA 1. That said, I'll let me think that through. No, it's not possible. And I see there's a question here about the slides being circulated. I don't think we can circulate them because of the actual volume, but they will be posted on the CLSA website. Yes, that's right. You are able to go to, I'm just looking at it now, CLSA webinar videos. If you type that into a search engine, it will bring you to the slides and you'll be able to watch the slides on YouTube. So you will have some access to the slides. Anyway, we're a little bit over, but I think that just is reflective of the extent to which there is so much interest in this study. Susan, once again, thank you very much for presenting this informative overview. And we look forward to getting many, many, many more follow-ups on CLSA in the future. It's my pleasure. Thanks. Great. And so just before we log off, our next webinar will be held on October 19th from 12 to one eastern time. The speaker will be Dr. Brent Richards and he's going to be talking about the genetics of osteoporosis and aging related disease and he's going to link some of that to the CLSA and we'll be talking about how his research links into some of the genetic possibilities within CLSA. So that should be interesting. We'll see you all back on October 19th. Thank you for joining us all of you. Thank you for the wonderful questions. Thanks, Susan. And have a great afternoon.


Digital smile design is an important tool for aesthetic planning and dentistry as it allows for easier communication between professionals and patients. It involves drawing a smile curve, measuring the space between teeth, creating a simulated image of the smile, and making diagnostic wax models. This process enables better patient contact with the final results, as seen in this case study. This article was authored by Savio Morado de la Serta Gantijo, Felipe Marc Morgado, Leniana Santos-Nevis, and others.