game took a turn in the third quarter. You could see Philadelphia gaining momentum. Ben Simmons leading the way for the 76ers. The Nets did respond and the fourth had a couple of chances to grab the lead. But a Furcon cork maws three pointer big shot for Philly and the Nets fall for the fourth consecutive time with this loss to the sixers. And you can see very, very good teams and even great teams have extra gears and Philly multiple times at the end of quarters into the second quarter into the third quarter. It was like a 10 12 point lead with two minutes ago. Next thing you know, it's a four point lead for the Brooklyn Nets. And then they just kept picking up that defense. Matisse Diable, we talked about him over and over again was everywhere. Ben Simmons started making big defensive plays and the Brooklyn Nets just couldn't get that rhythm again. And once Philly started scoring, it was very, very difficult playing without Kyrie Irving sitting this one out with a hamstring issue. Nets have not been whole for an extended period of time. So as Kenny Atkinson is trying to find some rhythm in his rotation, there have been challenges along the way. Spencer Dinwoody certainly took a lot of the responsibility out there scoring when necessary. Levert provided a big lift off the bench and Nicholas Claxton has been a revelation for the Nets the last couple of games. What this means moving forward remains to be seen, but it's pretty clear that Claxton deserves extra time here for Brooklyn and Claxton was been so impressive.  That's one of the best teams in the NBA playing toe to toe with them all night long. And we talked about in the open that there's a four point differential that the Brooklyn Nets have really struggled in the fourth quarter and here outscored by seven. They're just running out of gas. They're running out of steam. It could be injuries, guys playing elevated minutes, not necessarily having a rhythm, but they're playing the right way. They're playing hard. They just haven't been able to close out games and the schedules are kind to Brooklyn either four game losing streak with the Los Angeles Lakers coming to town during the game. You mentioned that you believe the Lakers are one of three legitimate teams that could win a championship as of this stage, midway point of the season. Some things could change in the second half, but they've been tremendous with LeBron James and the rest of that cast of characters that has fit together nicely for Frank Vogel and company. And one of the things that they've had primarily is health LeBron James being their best player has really only missed. I think one or two games. Anthony Davis has been in and out, but they've had a tremendous amount of health this year. And that's one thing that if you want to be a good team, if you want to have that consistent record, Giannis has not missed many games. You need to have your best players on the floor. And that's something that the Nets have been lacking. Hopefully we'll see what Kyrie's situation is leading up into that game and maybe get a match up between him and LeBron James. Sixers now lead the season series with this win two and one record against the Nets.


There come everybody and I know this is the last session today about the last of the tracks I'm actually very excited to see so many people here when I saw that this is a I'm so late on the third day of a Completely packed program. I thought I'm going to have maybe like 10 people here And then I saw I'm going to go against dr. Nick talking about conqueror So I thought I'm going to have three people here. I'm I'm very delighted to actually see such a big audience here My name is Johannes. I'm a software engineer for pivotal. I've been working on cloud foundry for the last three years Always be working on the logging in metrics team Early on the locker-gator team. I'm very passionate about metrics and logging Whenever I say that it sounds really weird and awkward. I would not recommend using that as a pickup line but It's certainly something that did I I grew very accustomed to working on and this is where they want to advance the product in so naturally then the When the call for papers came along, I thought I would want to talk about application performance monitoring in cloud foundry In order to make my title very buzzword compliant I thought I call it application performance monitoring in the cloud native age Which leads me to the first point. What does it actually mean to be cloud native? I took a tiny little survey asked three people if they could give me a good definition about what it means to be cloud native One of them was able to talk intelligently about it. So that's not I found there's not a lot of information around there So I thought how about actually define it, but before I go ahead and define it I I want to share this one tweet that I saw yesterday by one of my co-workers who taco If you are participating in this game now would be a really good time to have a drink I Just want to make sure that I'm going to mention a cloud native a few times now. So keep your hip flask out So cloud native when I when actually looked it up. I came across this definition here It means you should your application deployment should be container packaged It should be dynamically managed and microservice oriented Now all of these three things have some implication on what that means for for monitoring for example that dynamically managed means Your app is going to be managed by a platform if your app is crashing it might get restarted So do you want to get alerted in the middle of the night whenever your containers restarting? Maybe maybe not because the platform is going to take care of for you You really want to make sure that you get alerted when there's customer impact But that does not necessarily mean anymore when your app is crashing So whenever I approach a topic like application performance monitoring I always try to approach it with those three very important questions. Why and what and how? So this is kind of how I I'd structured my talk So the first why do we want to monitor you all are here? So I assume this is should be very very simple But I want to share this one anecdote, but I think it's actually so Telling and it should be the reason why we are all here who remembers the night capital group It was just four years ago. It's I think known as the Software bug that caused the most amount of money the night capital group 2012 was really big in the stock trading market and they did really well and they had a very intelligent system and So then 2012 for August 1st. They deployed a new version of their software 45 minutes later. They were out of their 45 minutes later. They were out of business This is kind of what happened here They lost in 45 minutes they were running this bad code and they lost $400 million And then they were acquired for a cheap bargain Three months later by another company. They didn't recover from this. The code that was in there just Made the system by a Stock they overpriced and so they lost so much money that they couldn't recover The one thing that I find very interesting about this and how I think this is very related to our topic here I I mean as a software engineer. I have pushed out bucks. I've pushed out bad code So that shouldn't be that interesting here, but they had this code run in a production system for 45 minutes Until it was way too late if they had good monitoring in place Why didn't they catch that after five minutes or ten minutes? They might have been able to recover from that, but they had it run until it was too late And I think this is like the one lesson that yes, please actually makes let's make sure that that we have good monitoring in place So that our company doesn't go out of business And then the other reason why I think monitoring is very important Sharing a little bit of a personal anecdote here at when I just fresh out of college I joined this startup there I was one of the two engineers our monitoring in a learning system typically Looked like this Customer couldn't reach our system customer called the CEO and CEO called me There was made a very unhappy customer made a very unhappy CEO and my wife wasn't very happy either because those calls were typically in the middle of the night so this kind of like was me back then I Was like when I could sleep I should try to get sleep in because I was on call 24 7 and my customers called me Please let's try to not be these people that I don't want to very too much about my sleep I want if monitoring in place it allows me to sleep quietly and sound at night So these are I think some some of the reasons why we monitor Let's try to get good sleep as engineers and also let's make sure that our company stays in business Now next what do we monitor and This is I think a Topic where I might have to disappoint you because unfortunately, I do not have a silver bullet here for you What we have to monitor really depends so much on what kind of application you have in most cases Yes, it might be that latency is exactly what you want to look at Latency has a huge impact on user satisfaction In other cases you might want to look at your error rate or Some completely other metric, but it depends really on what kind of app you have And this is I think where it depends is such a Unsatisfactory answer here, but so let's actually look at some of these metrics you might want to look at so for example, there's latency Mike Villinger from dinatrace Mentioned this in his lightning talk yesterday in the morning Then Amazon's latency drops by 100 milliseconds. They're losing 1% of their conversion rate. So that's Probably a metric they should watch So if I had a Website that's basically a storefront. I probably would watch my latency If you have a marketing website out there and you want to encourage your Customers to download marketing material the bounce rate is very important So maybe that's what you want to monitor if you have a batch processing system that is Processing satellite images. Maybe you just want to look at error rate or your queue length But nevertheless, I think the one thing that is very important Remember the business value of your app Your apps are out there not for technical reasons. I mean latency and and bounce rate and errors They those are all really great Metrics to watch but your your app should generate business value and you should actually keep a handle on Understanding what's the business value of my app and you should watch that very closely because in the end That's why you have your app deployed somewhere in a production environment So the Chunk of my talk is going to be actually spent on how do we want it? How do we monitor? and so they're There are few a few topics that I want to mention So as I said in the beginning being cloud native means microservice oriented Microsoft is oriented means you have a request come to the first server the first server is going to Fire requests to all your other microservices now if the end user complains That the request is taking too long for him you At first don't know which exactly which service exactly is taking so long And it will be your responsibility to figure out which service has to be tuned or which service is faulty which service is responsible for For having negative impact on the user's interaction and this is where request tracing comes in when we talk about request tracing The first thing that always comes to mind is a sipkin sipkin is this project that got started by Twitter actually Was built upon a research paper published by Google About a system called depra that does exactly that in a microservice architecture You look at all your notes and you see exactly where your Latencies is generated So here's a very simple example on how to do request tracing in With with a Cloud Foundry app Imagine you push a spring boot app with spring boot. It's very simple to enable the spring sleuth project which is Includes open sipkin that library You just have to put the library the sleuth library in your class path and you're going to get this all for free so imagine that we have This microservice architecture with the four services The visualization visualization that you're going to get like looks like this year There you have your first request going to service one service one is firing your request to service two Which fires requests to service three and four and you see exactly there is my bike of of the latency generated In when we talk about request tracing, there's typically what we call a trace Which is the one overarching request and then there are spans span is That unit of work that is used by a server to actually process a request So in this case, we have exactly seven spans for for all one trace because that is the service generating requests to other services and processing those requests I Do have a Live demo, but honestly that live demo is not that much more interesting than the screenshots This is an app running on Cloud Foundry and if you look at the URL, this is actually deployed to Pivot web services It is the example straight from the Spring sleuth documentation and so I can just generate a request to my service one here and can search for my request Service one, there's my search button there find traces and you'll see You'll see traces Let's go for news first And so this is a trace that we just generated there you can then dial in look in all the details that you see here So if you have a microservice architecture, this is a very powerful tool this tells you exactly What service has to be tuned what service has bad performance so you can actually really? Assume in on the one thing that you have to improve So my service architecture, please use something like this that allows you to do request tracing and again spring boot and and Cloud Foundry are really your frenzy because they make this in really really easy to get this all up and running Next one of one of the very obvious choices you have an application pushed onto Cloud Foundry You you have to monitor it. They are really really good monitoring solutions out there It's very simple to bind one of their services and restate your app and you will get Out of the box a good interface to actually monitor your app The three that I want to mention here because they are actually part of the Cloud Foundry foundation diner trace New Relic and App Dynamics I was going to try to look for representatives representatives for From those three companies and then wanted to see who's giving the most swag So and then I would use them as a demo here But nobody gave me any swag So I just I ended up using New Relic because honestly that was for me by far the easiest to actually set up It took it took literally a minute. I just went to Pivot web services. I created a New Relic service I bound the New Relic service to my spring music app and the result looks like this year and again just a tiny little Here's my mouse This is then what I what I got as a result Just very simple to provision and and out of the box. I I get this New Relic service now That allows me to really drill into Called as it called traces and and see they exactly my application source code my application is lower Even if you are not on Pivot web services, I I BM Bluemix has a very similar Service that comes out of the boxes. I think it's called Something like Monana something that allows you to monitor your applications We from pivotal actually the team that I'm working on we are working on on a solution for Monitoring your app that comes out of the box So if you are on Pivot web services, you can very simply go from the application manager to just click that link Just look at the metrics of your app You will see very basic information about network traffic on your app and CPU and memory and disk statistics, but that all comes out of the box So the cloud foundry ecosystem is certainly working on trying to make All these things much much easier for you. So If you for some reason cannot use one of these out of the box Monitoring services New Relic and AppDynamics and Dynatrace. They all come with downsides Maybe you don't want to push your monitoring data Through your firewall to a public server or maybe you don't want to pay for an on-prem installation There's still so much data generated just inside the cloud foundry system that you can build a very comprehensive monitoring solution With I want to say very little overhead yourself And so I want to over the next a few minutes I want to highlight some of the things that you can do right now without actually Paying any of these monitoring services And so one of the things that I actually worked with David Zabetti During a hack day a year ago. We were actually looking at the log output generated by an app We wanted to see if if you just look at the at the rate of log lines generated from your app Can you actually get some information out of that and I want to say typically yes If there's an anomaly in your app going on you probably we will see something happening with your log lines that could be Maybe it's just the sheer output of the logs that are generated But also what is the ratio between blocks written to standard out and standard error? What happens if I see suddenly much more standard error output? Maybe something is wrong with my app. And so David and Ivy were working on a CLI plug-in that I want to demo here It is very very simplistic and doesn't look all that great, but I want to show it anyways Because I want to show you how easy it is to To create Something that is of value can be of value And so the CLI plug-in we called it log Eliza So if I start this CLI plug-in I give it my application name and now it's just starting a web server And I'm going to on the web server And see if I find it here Here I'm just refreshing this this page and because my app has no Log output at all yet my log Eliza analyzes automatically and tries to make a judgment call saying yeah, something is not right here There's no log output at all. So let's try to generate some log output and I'm actually going to do this by hitting the application that is hosting my slides because that's my demo and Oh, suddenly I see logs coming in and all looks good because all the logs that are coming in are just going to stand it out And so this is in so down here. You would see The ratio between standard out and standard or my app is not producing any standard or so all is good But I wanted to show this to you that this is something that we packed together in four hours Just using the CLI plug-in architecture a big shout out to the CLI team writing CLI plug-ins is so easy So please give it a try This like writing writing a dashboard like this idea and can put up on your on your build monitor shouldn't take all that long And down here is a bonus. You actually see all the log lines So this is what you can do to just monitor our current log output And again all the credit goes to David's a Betty who actually did most of the work The Next I want to talk about latency and again, I want to show you CLI plug-in that actually wrote this morning and took maybe all of ten minutes to actually get that together because I mostly copied and pasted code from another CLI plug-in a good developer a lazy developer right so Latency when it comes to latency if you don't want to use any of these very great monitoring solutions like app dynamics or dinatrace or Neuralic you can still monitor latency because you get all the data delivered to you from the cloud foundry system and so The one thing that I want to show here is again my CLI plug-in Which I think I have in this window I called it the App nozzle the app nozzle what the app nozzle plug-in is doing It's actually it is Attaching to the firehose, but it's only looking for a message is coming for a specific app that I pass in The on this first question here to ask me what kind of messages are you interested in I'm interested in latency So I'm going to look at HTTP start stop, which is number four. Is it right? I can increase the font yes, and so what you see here I don't see anything right now because there's no request hitting my app So let's again generate some traffic by just going to my slides and Refreshing them a few times and In the back you see you see all these HTTP start stop messages coming in so for every request to my app I'm going to see now an HTTP start stop just Refreshing this page once would have triggered roughly 20 requests because of all the images in the talk and the CSS and JavaScript But so most importantly the one data that we really need to get out of here is there's a start time and a stop time Those obviously the given the difference of those two you can see actually how long it took to see that request The one thing that I want to mention in this in this regard now is when we talk about latency just as a general tip A lot of people are looking at percentiles They want to see oh, what's a 95th percentile form application? What's my what's Given that number they are trying to get a good judgment call on what's a user experience like right now Please be very careful with percentiles. They might not actually mean what you think they mean Like it's one this very simple example already. I can show you one request to my application triggers 20 requests Now if I if I don't really know what I'm doing I might look at the 95th percentile But given that there are 20 requests fired to my app it could very well be that The one main request that's actually loading all the content of the slides all the text is a one request that is Taking the longest the 95th percent I might look totally good because it's just fetching cached images and cached JavaScript and cached CSS that one request that That actually fetches content might take way longer and this is where percentiles can be very tricky If you blow yahoo's front page you're triggering I think hundred requests hundred HTTP requests So I think at that point any user would have I want to I think a 15% chance to be in in the top five slowest requests Rate because that might be one request one of those hundred requests might just be very slow So percentiles are hard. You really have to be very careful about using percentiles to To know what the user experience is like so be careful when you use them The few other things that I've wanted to mention I I'm a Java developer and I go developer So how to actually monitor and debug a Java and go app that's running in Cloud Foundry With Java the the really cool thing that is possible now that I don't think is as widely advertised as it should be Now that we can all SSH into containers. You actually can access The chain X endpoints in your Java app that's running on Cloud Foundry And so actually I don't want to take the credit here Matthew Sykes wrote this up in a very nice blog post very like really dug into see how this How we can get this to work? It is now actually quite simple So you just add these environment variables to your to your Java app that's running on Cloud Foundry And then once you have that running you start an SSH tunnel through the CF SSH command You start the SSH tunnel and so then afterwards you can access your chain X endpoints on port 5000 local host So this is something that is very very useful if you actually want to To monitor your Java applications that they're running Cloud Foundry because most of those monitoring solutions have a connector to chain X and so you can easily Forward then the chain X information to your monitoring solution. There is one interesting caveat here Obviously if your Java app is split across multiple instances, how do you exactly know which instance is coming from? Cloud Foundry as you covered the SSH command allows you to specify which instance you want to SSH to and so you can Actually do exactly exactly what you have to do here just by if you have 10 instances. You just have to start 10 SSH tunnels Yeah, and then not that there's much value in it But then you get a very nice check console picture and it shows you exactly all the things that are happening in your Java container So this is something that a lot of people have been asking for for the last few years and now it's actually really simple and easy to do This very similar you can do something for go apps I've been using go for the last three years. So that's certainly very like my passion is that's my speed spot With go you can very simply enable the deep the P profiling endpoints by just importing One package from from the go libraries from the go standard library and so once you have that imported go will try to Make all these profiling information available to the user It actually was a little bit tricky to get this to work. I have a on my github. There's a An application called P prof on CF you can just go there and Look at the source code what you and so we can't just see what you have to do to to get it to work Just to show you what the information looks like that you can get out of it.  What happens if you have multiple instances for your application The one thing that they've some creative ways to deal with this one would be using sticky sessions with with a session ID So there there are ways to actually make sure that you can get access to information for any for every container the There's only two more things that I want to mention One is Very very dear to me and is if this is the only thing that you retain from this talk, then I'm happy Please make sure that you monitor in all environments not just in your production environment monitor in your dev environment in your test environment again I have to quote what Mike Winning just said from from diner trace because I thought that was actually a very very cool quote And I don't think I get the quote exactly right, but he said something like This new world is not about just pushing out Bad code faster through automation and pipelines So you really want to make sure that the code that you're writing is good And you can do this by having good monitoring in place for your development and your test environment So that you know that before you push to prod your development in your test environment looks all good a Cloud Foundry is actually doing that. I think really well by By monitoring on all stages of the pipeline and there's good monitoring in place the same monitoring that's in place for production environments for Pivot web services is in place for For the stages in the pipeline before it hits Pivot web services And so I I really like like the way how we do it there and I can encourage you to just do the same and Then once you have this all in place You're really good monitoring system. Make sure you use it in the right way by having alerts set up correctly There's a project that I work on right now. That's all about alerting that'll be available for Pivot web services and Pivot Cloud Foundry soonish The very interesting thing about alerting that I want to mention here is Then when you set up your alerts Make sure that you look for anomalies and looking at an animal anomaly detection is not very easy it's actually Really difficult to figure out what your anomalies might be like is it because you might have a spike every Monday morning between 8 and noon because Your app is getting more requests during the time or you might have a spike every Christmas time because you're in the business of selling Christmas decorations or you're in the Tax industry and every tax day the week before the taxes you might get a big spike And so you really want to make sure that you are anticipating those anomalies and they are not necessarily triggering your alerting system Besides that I think Those are all the things that I wanted to share The just one last favor that I have to ask you if you are at all Interested in having your application emit custom metrics through the Locker-gator pipeline. Please let Jim Campbell know the product owner of the locker-gator team PC or somewhere and with that Thanks so much for coming. I really appreciate you all being here. Are there any questions? Yeah, within the platform itself It would be Interest so you mean the Cloud Foundry platform there would be Certainly absolutely possible because they are not very nice go libraries and most of the components are written in go They're very nice go libraries for for the sipkin So I don't think it would be a lot. It's just I think the orchestration effort is going to be Huge because you have to make sure that all these teams are working on the streamlined effort to get All their components instrumented so that they emit locks The logs that sipkin is expecting to the sipkin server So I don't think it would be all that hard but just orchestration and other questions. Thank you


Fruit flies are really interesting pests because in the summertime when it's warm it can speed up its life cycle and complete the whole thing in less than a month. They slow down in the winter time and over winter and so anything found in autumn can just hide away in the winter and then reemerge in the spring. So unfortunately when we find a fly in the autumn we have to make sure our quarantine release extends right through until the spring when we know that that fly is now gone. Overwintering is similar to hibernation. It's when in the cooler months fruit flies slow down their metabolism and they really just hide away and protect themselves from the cold weather. They can still be active if you get a warm period. So if things do warm up in the winter time they still can come out and feed but largely they just hide away and protect themselves and wait for the warm days to come again. We know fruit fly can survive winter and they overwinter quite happily and if we do get warm periods during the winter months they can become active. And in particular all their life stages so the eggs, the larvae and the pupae can still remain active in fruits like lemons and limes and oranges and kumquats and all those fruits that fruit fly like can still harbour fruit fly in the winter months. So we have to make sure we manage that fruit and don't allow fruit fly to spread. So there's lots of things you can do to help prevent fruit fly in your backyard. In particular please pick your fruits. Fruit fly lay their eggs in fruit so if we pick our fruit early we make sure we break that life cycle. Similarly please pick up any fruit underneath your trees and don't allow it to rot underneath because that's just encouraging fruit fly to complete this life cycle. Once you've done that please check it, open it up and have a look and if there's anything wriggling inside, anything that looks like a maggot please call. Call our hotline at 1300 66010 and we can come out to make sure it's not fruit fly. Remember if you live in a red outbreak area it's important you don't move fruit from your property. That's how fruit fly can spread. So please ring cursor and we can make sure that that fruit is managed properly.


Okay, good afternoon everybody to the CLSA webinar series. My name is Ina Rubin, I'm the Managing Director of the Canadian Longitudinal Study on Aging and you're all welcome at today's webinar. And with that I'm going to start with an introduction of Gillian Mulvail. Gillian is an Assistant Professor of Health Policy and Analysis in the DeGroote School of Business at McMass University. Her research focuses on improving care coordination across health professions, sectors and stages of the lifespan through the development of health policy and management frameworks that promote interprofessional person and family-centered care with applications in mental health and primary healthcare. So Gillian derives theoretical approaches from interdisciplinary training in health policy analysis, health economics and health research methods. So and Gillian is the co-author of the recent report on the prevalence and cost of dementia in Canada, a review of the evidence commissioned by the Alzheimer's Society of Canada and she is going to tell us all about that today. So over to you Gillian. Thanks so much Ina, hopefully everyone can hear me okay. It's a pleasure to be here and I really welcome the opportunity to speak with everyone about the recent report as Ina mentioned. Looking at this important question of how do you go about estimating dementia problems and are there opportunities from the CLSA? And we were really tasked with the challenge of how do we understand the various estimates that are out there. Not so much to come up with the one single estimate, but to really shed light on how those estimates are developed and how the various assumptions being made influence those. So I'm sure there's a background as I'm sure everyone on the line is aware the problems of dementia increases sharply with age and some estimates suggest that 15% of the population seven weeks to 74 years of age would have dementia and that when you get into the age category of 90 plus some estimates suggest it could be as high as 40% of the population. And since we know we have an aging population and demographic projections suggest the population age 70 plus is going to double over the next 20 years. Obviously from policy and health care management perspective this is a really important question. And so there's important implications for the future need and cost for health and long-term care services. And so in light of this situation the Alzheimer's Society of Canada and the Public Health Agency of Canada commissioned a report entitled on the problems and costs of dementia in Canada, a review of the evidence. The lead authors were Michelle Grigno and Byron Spencer from McMaster University and Susan Braunskill from Sunnybrook Research Center and Institute for Clinical Evaluated Sciences and myself are also co-authors on that report. And the goal here was really to provide a critical review of the evidence as it relates to current problems of dementia in Canada and costs associated with dementia. Secondly, to assess the methods used to reject these estimates of problems and costs. And this was really the critical piece to really try to understand these estimates. The focus of the overall report was on Canada and selected comparator countries. Here we're going to focus on the estimates for Canada. I'll touch upon the comparator country estimates, but that won't be the focus of today's webinar. And also evidence based on survey and administrative records. Again, we're going to focus on the survey as the source of different estimates. Also note that there was a whole piece of the report around costs. We won't be touching that today, but it's something if you're interested that you can pursue in the report. So measuring dementia. Of course, it's important that we get a definition that's clear. We drew upon Sheehan's definition that dementia is a clinical syndrome characterized by progressive acquired global impairments of cognitive skills and ability to function independently. And I think there's a piece here, and that's the progressive nature of the illness, which is a real challenge when it comes to measurement because it's important to decide at what point does cognitive impairment meet the criteria for dementia. And then within that broad definition of dementia, there can be a variety of levels of severity. And that's important when you try to measure estimates of prevalence. Are you comparing apples and oranges in terms of the severity of the illness when you're looking across jurisdictions, points of time, different estimates? And so Sheehan also did a review of the various methods we could use to assess and measure test for dementia and identified 10 different methods that include a cognitive test for cognitive impairment. Overall, comments were that most of these don't take very long to do and yet perform well, both in terms of sensitivity and specificity to its own gold standard. Within each study, there would be a gold standard they compare again. However, overall, there is not one clear gold standard measurement for dementia. So measuring prevalence and incidence. The other important thing to consider, and I'm sure many of you are aware of this, but just so that we're all on the same page, when we talk about prevalence and incidence, there's an important distinction. Prevalence is a stock measure. In other words, it estimates either the number of cases at a point in time or the rate in a given population group at a point in time. Instead, or in comparison, incidence is a flow measure. So it's the onset of cases in a given time period or the change in rate of prevalence in a time period. So these measures are not always a one-to-one. If we know prevalence, we know incidence, we can just automatically adjust prevalence because we need to understand the differences in survival for different people with various cases of dementia. It's not uniform. And so rates can be measured for the population as a whole with or without age standardization or they can be age standardized rates. And these are very helpful. What they do is they measure prevalence relative to a specific population age distribution. And so it makes it easier to compare rates across time, changes in rates, or across jurisdictions. Also note that when you're being presented with a prevalence rate, it's really important to understand what the denominator is. What is the age group or sex group that it's referring to? So rates can be measured for the population as a whole. This will be much smaller than when they're estimated for, say, the population 65-plus or the population eight years or older. So there's other challenges and measurements that we also need to consider. First, how do we get reliable measures of new cases of incidence? We know that diagnosis can occur at different stages of severity. So someone may meet the criteria for dementia and have a mild case. Well, another person will have a severe case, and yet they would still both be counted as an additional case of dementia. But we may be comparing very different disease states which have important implications for policy and healthcare delivery. Second, severity itself is difficult to measure. So it's easier to identify a case than to identify the severity of that case. And registration of new cases is not something that's mandated. So it's not like we have a directory or a registration that we can just come up with the numbers of all cases that are out there. So in terms of how do we come up with estimates then? Well, there's two main data sources that we look at. Survey estimates and administrative records. And so the survey estimates can be either self-reported, so a survey comes to a person and that person indicates that, yes, they or a family member has received a diagnosis of dementia from a healthcare professional, or the assessment can be done directly by a health professional. And we have examples of surveys that use each of these approaches, the Canadian Community Health Survey or CCHS, and the National Population Health Survey, both use self-reported data in their basic form. And the Canadian Study on Health and Aging used health assessment by a health professional. Now I'll get into more details about each of those surveys a little bit further into the webinar. Administrative records is another approach. And in brief, what this approach does is it analyzes records in administrative databases. So that could be position billing data, for example. And it uses algorithms that examine those records to say, given certain classes of medication that are largely prescribed or targeted to dementia, how many individuals would meet those criteria, also other risk factors are taken into account in these algorithms, persons age, sex, and so on. And so that's another approach to trying to come up with estimates. So if we think about surveys of data sources versus the administrative data, there's advantages and disadvantages. So of course, advantages of surveys is that they can really be targeted to understanding particularly disease categories. Since the survey is being designed, you can ask the kinds of questions you want. You can focus particularly on different disease categories and go into a lot of depth there. However, it's an expensive thing to do, to do a population level survey. It requires a large sample size. And also, it's difficult often to come up with a survey that will capture prevalence rates both in institutionalized and community populations. Often you need a different approach in either who's answering the questionnaire in an institutional setting, it may be a care provider or a staff person. In the community, it's likely to be the person that solves our family member. And then there's issues around the accuracy of the survey. And that depends very much on how the sample is structured, response rates among participants. And then so accurately, they're able to respond to the survey questions. So there may be some discomfort in acknowledging a diagnosis by a physician of dementia for a person or family member or other biases that can result or perhaps misunderstanding by participants in responding to a survey. And then issues around how large is the sample size, the higher the sample size, the more precise the estimates are going to be. And finally, most surveys are cross-sectional. You can have longitudinal surveys, which are year after year surveys of the same participant. Of course, those would be more expensive. So most of them are cross-sectional. And when you have a cross-sectional survey, what that means is you get a snapshot of problems at a point in time, but you don't get information on when did that person first experience the disease or their incidence, I should say, or leave the disease, in this case, at death. And also it can challenge our ability to do comparisons over time and across countries. If we can think about administrative data sources, they too have their advantages and disadvantages. Some of the advantages of administrative data, typically there's information on hospital stays, doctors visits and prescriptions that may be fairly accurate relative to self-recording in a survey. It can also be used to infer the presence of a particular disease. It's a cheap data source because it's usually being already gathered for other purposes, so there's no incremental cost associated with using this administrative data, or I should say relatively low, there's always some cost. They're usually representative because there's often very, very large samples of sample, most people go to see a family physician every year, and so you'll get a really good sample size in that sense, and there's also the opportunity to link to other data, so while there may not be an awful lot of other data perhaps around lifestyle, sometimes we can link administrative data to other survey sources to gather information. However, there's disadvantages here too. They're not really, these databases aren't designed for this purpose, and so how well they capture and estimate presence of dementia is a question. How well do the algorithms perform? There can also be variation, just as there's issues with self-report in a survey, there can be variation in how healthcare providers provide data to these administrative data sources or billing records, for example. Are they consistent in how they classify information that they pass along? And in addition, there's limited additional information to determine the determinants and precursors of the disease, which you may be able to more fully explore in a survey. As I say, this webinar is gonna focus on our findings with regard to survey estimates, and in order to come up with that information, we conducted a targeted literature review, not a systematic review, where we tried to identify the key surveys, published prevalence rates and projections of dementia prevalence in Canada, and a selected group of comparator jurisdictions. So we focused for comparators on the UK, Europe as a whole, and the United States. We conducted a Medline electronic database search over the period January, 1990 to October, 2014, and we used key words related to dementia, prevalence, surveys, and methods, and variations on those. We also carried out Google Scholar and Google searches to capture gray literature. We also explored government and survey websites to look for other reports that may be relevant, and we did reference checking of all of the various literature that we've websites that we examined. And then it was an iterative search. We went back to the literature for published academic publications on the methods used in producing these various estimates and surveys. All together we identified more than 40 publications of these 26 contained prevalence estimates and or projections. We excluded any articles at this round that were focused exclusively on methodological issues. So they came in in the second iteration of the search, or though that reported prevalence rates for countries that weren't on our list of comparators. So clearly this is not a systematic review of every estimate out there, rather a targeted review that reports on what were felt to be the most relevant results for our purposes. So just to give you a bit of an overview of the various national surveys that we found that have a dimension of focus. First, the Canadian Longitudinal Study on Aging Neurological Conditions Initiative. As many of you are aware, this is the developing research program, and so the results in terms of estimates are not yet available. The Canadian Community Health Survey for the cycles in 2010-11, there was a Neurological Conditions Module that was added to the traditional CCHS survey. With a follow-up survey, if the respondents or household members showed or indicated a presence of Alzheimer's or other selective neurological disorders. Third survey was done within institutions, institutional settings, so CCHS is a community setting. Here are the survey of neurological conditions in Canada for 2011 and 2012. And this was a survey done by mail, from mailed out to participants who were long-term care staff, and they were asked about the prevalence of 50 neurological conditions among their residents. There were also some other surveys that we identified, national surveys that were not targeted for producing estimates of prevalence of dementia, but nonetheless have been used for that. And those were the Canadian study on health and aging, the CSHA, and this was a broader study that looked at the health and aging population in general. Within that, there was a significant piece around estimating dementia. And so 1991, 1996, and 2001 were the years of data collection. Also, the National Population Health Survey, which is an ongoing longitudinal survey, whose primary focus is not dementia, but a broad range of health conditions, and the Canadian Community Health Survey, which is a cross-sectional survey, and longitudinal components has been added every other year from 2001 to 2007, and annually since 2008. Again, looking at the broader health of the population and health services utilization, other measures not uniquely focused on dementia. The Canadian study on health and aging, this study in particular has a multi-step neuro-psychological and clinical assessment to really try to accurately capture dementia among participants. The prevalence estimates based on this survey align with the DSM-3, and the core objectives of this survey were to measure incidence and progression of severity of the disease, which requires a longitudinal approach to see how participants are moving over time, so we need these repeated observations on the same sample. And this provides data on the progress of dementia across various stages of severity, starting with dementia at baseline, and for those who don't have dementia at baseline, how do we see over time then progress towards dementia? Comparing this study with the NPHS of the National Population Health Survey and the Canadian Community Health Survey, they have collected dementia data by self-report. So in this case, the person being surveyed or answering the questionnaire would be asked, has a physician ever diagnosed Alzheimer's disease or any other dementia for you or a household member? And they're responding to that question. And the overarching objectives of these surveys are really not, as I mentioned earlier, to understand dementia in particular, but the broader health of the population and its determinants to come up with regional and national estimates. And so it can be useful, but it was not intended for coming up with estimates of dementia problems. And finally, a little bit more detail on the survey of neurological conditions in Canada. As I mentioned, it was a one-time national level survey of long-term care facilities. Questionnaires completed by staff of those facilities. And they indicated the number of residents in total and by sex who have been diagnosed with either Alzheimer's disease, any other dementia and 14 other neurological conditions. Again, by mail survey. And one of the limitations here is that it was very targeted and gave very little associated data to understand covariates and other determinants in circumstances. So this slide, I test to give you a bit of an overview of the various surveys in Canada. So the Canadian Study on Health and Aging, National Population Health Survey has three components. A household, that's the HH component, an institutional component, and one for the North. The CCHS and the institutional survey we just discussed as well, the SNCIC. So you can see when you start to compare surveys, there's a lot of dimensions you need to think about. The year in which it was conducted, the design, was it a longitudinal list, the L or a cross-sectional, or both CS, the setting in which it's carried out. Was it carried out in the community setting or an institutional setting or both? You can see here that only the CSHA was in both. The population that's being surveyed. So CSHA focused on the population 65 and older, whereas the community surveys are focusing on 12 years old and older. Sample size, and not surprisingly, the institutional component has a much smaller sample size. If you can see the CCHS, very large sample size, very broad across the country. The method of the interview, or method of data collection, is it an interview with an examination by a physician? Is it by phone? Is it by in-person or is it by mail survey? And then considerable differences in response rates, although they're all fairly good, but something to take in mind when you're considering the accuracy of the survey, as well as how a case is ascertained or determined cases of dementia. And so CSHA is both a self-assessment or a self-screen and then an assessment by a physician, all of the others were self-reports based on an earlier physician diagnosis. So this slide is looking to provide you with an overview of what we came up with in terms of the prevalence estimates for Canada based on these various surveys. And again, you can see that it's difficult to compare and there's lots of spotty places in terms of being able to compare all of these across the board. So depending on the author, we can have different estimates presented. So for the Canadian Study on Health and Aging 1991 data, if we look at the institutional setting, we have differences from 56.9% from the working group, that's the WGE, the CSHA working group, compared with Graham and it was very difficult. We were not able to really ascertain what the differences that caused those different estimates. But there's some general things we can see here also going on. We can see differences in the population over which these estimates are being generated, differences in the year, and then the setting. Is this estimate overall for the whole population within a particular age group at a point in time? Or is it just for community or just for institution? And we see that only the Canadian Study on Health and Aging gave us an overall for both community and institutional. We also see, and that tends to be in the seven to 8% range, we also see that in the community setting, much lower estimates, so 4.2% from the Canadian Study on Health and Aging, 2% CCHS 2003 for the population 65 plus. We see that go down to 1% for the population 55 plus and so on. So depending, it's up to 4.3 when it's 80, age 80 plus and that makes sense. We expect a higher rate in that different population age group and older population age group. We also see much higher rates in the institutional setting, which is not surprising when we think that about severity of illness. And so we see that any number that's out there that you look at, you're gonna have to think carefully about some of these various determinants of that estimate. On this slide, we look at the data from the Canadian Study on Health and Aging in terms of prevalence by setting and severity of illness among those 65 and older. So within the community, institutional setting and total estimates were provided, here we see that in the community setting, the rates are higher for people with mild dementia and lower for severe. Not surprising, see the opposite, where in the institutional setting, we see 31% have severe dementia, 6.6% of those with mild dementia are in the institutional setting. And so overall, approximately half of those with dementia were living in an institutional setting and this varies by level of severity, 85.4% of those diagnosed with severe dementia were in the institutional setting, compared with 20% of those with mild dementia. So turning now to prevalence projections for Canada, what we found in our review was that the projections often can be based on the same survey estimates, can really differ quite significantly and this largely reflects approach and underlying assumptions. And so there were two main approaches that we identified. The first is where age specific prevalence estimates are assumed to remain constant over time, over the projection period. And so we keep the prevalence rates the same in each age category and forecast out or project out what the population size is going to be in each of those groupings. And so when we do that, we're either assuming that there's going to be no change in prevalence because the incidence rate and the mean length of survival are assumed to be unchanged or any changes in those two variables are offsetting each other and balancing each other out. So the second approach starts with base period prevalence rates and each year adjusts that for predicted changes in number of new cases, so the incidence and the number of deaths or mortality. So two very different approaches. And this graph shows us the prevalence of dementia projections for Canada based on three estimates that we were able to come up with in the literature review. So when we look at these, the blue line and the orange line are both being driven by base prevalence rates from the Canadian study on health and aging. And so one set of estimates is from the CSHA working group and the other was produced by Alzheimer's Society in the Rising Tide report. And so they both began with the CSHA data for 1991 but they take different approaches. The working group assumes that constant age specific prevalence rates and so that's the blue line that's not rising as quickly. And then the Rising Tide uses this forward projection method where it takes estimates of new cases and subtracts out mortality and projects a much higher rate of increase in the prevalence rate. Denton and Spencer, which is the gray line at the bottom much lower, it has a different starting point because it's based on the CCHS, the Canadian Community Health Survey prevalence rates. So it's growing off that lower community based prevalence rate in the first place. And then a few of those prevalence rates by age category stay the same and projects out the population growth for the community based population. And we see much lower increase here. So we aren't going to comment on which of these are right, it's something for you to think about. But in terms of our key findings overall for Canada, the overall prevalence rate for the population aged 65 and older in Canada appears to be in the seven to 8% range based on these findings. The Canadian Study on Health and Aging remains the gold standard for survey based estimates of prevalence and dementia in Canada, although we definitely recognize the information is now quite dated. We say this because of the quality of the algorithm, the large sample size and the fact that it captures both the community and the institutional setting. And so what was also I think somewhat reassuring was that even though the approach seems less rigorous in terms of the case ascertainment, similar prevalence estimates are found using data from the National Population Health Survey and the CCHS, which suggests that if you have a really good baseline prevalence estimate from a survey like the CSHA, these ongoing community based surveys can be helpful to see how does that change over time because there seems to be some overall consistency even though it's a less expensive approach. In terms of what we found from the comparative jurisdictions with regard to prevalence rates, I mentioned I'm not gonna go into a lot of detail here or we'd be on the webinar all day, but just to give you a bit of a flavor, the age standardized rates across Canada and Europe seem to be roughly similar in the range of 6 to 8%. So in general, the European results and the UK results are quite similar to those in Canada. However, the US estimates seem to be much higher than all of those countries. For example, for Alzheimer's disease, the estimates are 11 to 13% prevalence rate. And we were not able to come up with a good understanding of why those US estimates are higher. However, we suspect that it's unlikely, that it's strictly because of differences in risk factors and more likely reflects differences in how dementia is defined and diagnosed. Or it could be that people are going to specialist care more quickly and being captured more quickly and diagnosed more quickly. So I say that's not clear to us, but these are some possible explanations. So looking at the comparative jurisdictions in terms of methods, the Delphi consensus process was another approach that we saw being used in the UK and in several European studies to estimate prevalence. So this was neither strictly a survey nor administrative data. And what this approach involved was looking at published survey-based prevalence estimates, doing systematic reviews of those published estimates and then bringing together expert panels and pooled analysis to try to come up with an overall estimate, particularly in Europe across many countries. The importance of definitions was a clear message that came out of reviewing these studies. So comparative analysis, for example, of UK prevalence estimates over time, was there was a really interesting study by Matthews that suggests that we need to really keep a handle on these overall estimates coming out of the large survey. So it's fine to do these Delphi consensus approaches, but every now and then we do need to gather new data periodically because this study suggested that over time we've seen a significant difference in prevalence estimates and the decline over time in the UK from, let's get the numbers here, from 8.3% in 1991 to 6.5% estimate in 2008. So this may be suggesting that there's a later onset, people are healthier, unclear, but this study really said, you know, it's not enough to just take one estimate and keep it the same over time. We need to take a look at what's going on. It also suggests that in order to understand changes over time, we really need consistent definitions of dementia and the various diagnostic criteria being used. And finally, another message that came true was that we really need to make sure that the exclusiveness of the definition is matched to the purpose of the estimate being produced. So if we're developing programs where we need to really give people assistance with daily living, we aren't so much interested in milder cases of dementia. It's probably the more severe cases. And so we need to make sure that we're matching the estimates to the programming needs. So some of the overall findings that we came across in survey-based estimates. Longitudinal studies emerged as a very important data source in all of the jurisdictions we looked at can either be nationally representative standalone projects like the Canadian Study on Health and Aging or smaller scale studies were used both in Canada and the US which draw upon infrastructure of larger nationally representative studies with broader objectives. Canadian Long Student Study on Aging is looking beyond strictly dementia, but that's an example there or the CCHS or National Population Health Survey. And comparing problems rates based on surveys is challenging due to methodological differences, particularly different age bands and subtypes of dementia. So what about the Canadian Longitudinal Study on Aging and the Neurological Conditions Initiative? Well, the primary objective is to support ongoing population-based research on dementia and selective neurological conditions. One of the challenges we have though when it comes to trying to develop problems estimates is that at the baseline, cognitive impairment is an exclusion criteria. So that means that we're unable to come up with estimates of problems of Alzheimer's disease and dementia because at baseline, we're excluding people with cognitive impairment. However, participants can enter the study and become cognitively impaired during the course of the study and they will remain in the study. This means it can be used for incidents and estimates of incidents. The data, there'll be baseline data from a tracking sample, the first 20,000 participants will be asked about Alzheimer's disease and other dementias as a self-report. So did a physician provide a diagnosis for this for you or a family member? However, we know that's going to be an underestimate because of the exclusion criteria of cognitive impairment at baseline. The in-person survey will come from their more comprehensive sample of 30,000 respondents and here we have a case after team algorithm which I'm just gonna go into on the next slide. So this algorithm was developed by the CLS-18 and incorporates a rigorous neuropsychological battery of tests that will develop composite scores in three domains, memory, executive function and psychomotor speed and that will enable the classification of participants into five different categories, normal, multiple-domain cognitive impairments but not dementia and music, cognitive impairment, single-domain non-memory and dementia. And this becomes important because while the CLSA can't come up with an estimate of prevalence of dementia, it is nonetheless a longitudinal survey and that means the baseline survey will give us some information around vulnerability to dementia and other risk factors and then future ways of the survey will provide estimates of incidents. So new cases and increasing severity is something that it can capture as well. Using this categorized data from several ways may assist us down the road in understanding a progression of symptoms toward dementia over time and enable us to generate transition probabilities across stages of the disorder which will be very helpful for programming and policy objectives. So I'm gonna wrap it up there and say thank you very much to the Alzheimer's Society of Canada and the Public Health Agency of Canada for providing a funding for this study and be happy to take questions and foster discussion among all of you. Okay, so I'll start with the first question to Gillian. And in the meantime, I was quite interested to see why the estimates in the US were so much higher and I know you were speaking about the possible explanations of why that is the case because people get diagnosed earlier but you would think that perhaps then the estimate in Canada would be similar to the estimates in the US. I know that you gave those explanations why you didn't really know but might there also be impact of lifestyle for example and do you know if anybody has ever looked at that and when you think about the US numbers? Well, we've spent some time as a group trying to digest why these would be different. And certainly lifestyle can be part of it. When we look at the estimates from the various studies though it really wasn't clear but I do wonder about specialists in the US being more direct than whether that's an incident to be explored and but honestly it ended up being quite a challenging way for us to try to understand that as a research team. And we've recently presented this information to a number of invested individuals and sort of nobody came up with a better explanation but I'd be certainly interested to hear from other participants that they have other ideas as well. Okay, great, thank you. There's another question for you, Jillian, from Christina. She writes, do you have a concern about to use a self report for estimating the problem? Well, I think in any situation, yes, there's going to be challenges with self-supported data. We know that from survey methodology in general that there's always going to be some questions. Dementia is particularly challenging if we may have difficulties with cognition in the first place, self-report can be challenging. And there's also concerns around social sensitivities with regard to reporting dementia on behalf of a family member and so on. So there's a challenge there and yet we were really struck by the fact when we looked at prevalence estimates from the self-reported data from the surveys and the CCHS and the NPHS that we still were seeing some, at the aggregate level, relatively comparable estimates. And so while there's a concern, we also have concerns with the administrative data. And so we have to work with what we've got. It was somewhat reassuring that self-report from the survey data seemed to perform well given its limitations when we compared that with the Canadian study on health and aging estimates. Okay, great. Another question was answered by Andrew and he asked, what about differences in incidence and prevalence by ethnicity? Is that something that your report has looked at? No, it wasn't part of our mandate to look at that by ethnicity. It's certainly an interesting question and an important one that we need to look at down the road to really get a good picture at this but we were really tasked with kind of more of that overall estimates and comparing across countries. I think part of the motivation here was this dramatic difference as we see in the various forecasts of overall prevalence rates even within Canada, but when you start comparing to other countries and when it comes to health policy and management perspective, these are really, really significant for planning purposes. And so our goal was more to kind of understand the estimates that they said than to come up with one particular estimate or to break that down by ethnicity, but it's certainly something that should be explored down the road. Interesting. You also showed some of the estimates for the European countries. Have you looked at any of the estimates for prevalence of dementia, I'd say, in the Eastern countries like India or Japan that is of interest also when it comes to ethnicity in our own country? Is that something that you looked at as well or are you aware of? I don't know. We didn't explore those, you know, as I say, that was beyond the scope of this particular project. But certainly in future research would be very much of interest as you say. It could help to inform some of the questions around the ethnicity for Canada, but also recognizing some of the differences in how these surveys estimates are being captured across countries as well would also need to be taken into consideration. Okay, question by Christina and it says the CSHA found a high prevalence of cognitive impairment but not dementia and can you Chilean comment on how this could be distinguished from dementia in a self report? Sorry, I don't have an answer, but I think, you know, it could very much depend on the kinds of questions that are being asked to try to distinguish that further in terms of, you know, what is the root cause of the cognitive impairment? I would think that the survey questions could be expanded to include that and to understand that a little bit better. Okay, great. So I don't think I see any more questions and we're getting close to it in time. So unless somebody has another question and you can try really fast and we can answer Chilean, ask Chilean these questions. And then eventually I really want to thank you for this great overview of all the different data that's available to determine dementia prevalence in Canada and beyond and thank you very much for your insights. And I also want to thank our participants for logging on and participating in our webinar. So thank you very much. Thank you.


Able to spot patterns invisible to the human eye, algorithms are learning how to make our lives easier, safer, and more fun. That power is not lost on materials researchers. During the next decade, artificial intelligence or AI-driven research could fundamentally transform how new and better materials are developed. What's more, it might even revamp how materials research itself is carried out, enabling promising new materials and processes to be developed more quickly. Machine learning methods come in a variety of flavors, with some requiring more guidance or supervision from researchers. But generally, a machine learning algorithm designed to discover and understand the behavior of materials looks for patterns connecting the composition, structure, and properties of known materials. Once trained on a sufficiently large dataset and incorporating the latest understanding of materials' behavior, the algorithm makes educated guesses at what unique combinations are most likely to yield better thermoelectric or battery cathode materials, for example. As easy as that process might sound, researchers still face hefty challenges in probing the vast materials universe. For one, machine learning methods are only as smart as their training data allow. Despite their unrivaled computational muscle, machine learning algorithms work best with massive amounts of data to make reliable inferences. Unfortunately for materials researchers, data are hard to come by and bulk, given the numerous ways materials can be combined and processed from the elements. Consider that the entirety of materials research history has produced somewhere between 200,000 and 500,000 inorganic compounds to date. Dazzling, no doubt, but that number represents only a small patch of the full space of possible material combinations. Then of course there's the problem of translating the predictions of these algorithms to the lab. A material that takes minutes to arrive at by computer could take years to synthesize in any useful form. For this second challenge, machine learning could be co-opted to do more than just search for new materials. Algorithms trained on experimental synthesis data could teach researchers how to best go about producing stable materials in the lab, literally generating recipes for success. Other algorithms might help design and conduct new experiments, perhaps in automated loops that barely require human intervention. Continued research will make machine learning algorithms more powerful for material scientists and engineers. Data sets will expand and computational methods will become savvier, but machine reasoning will take a long time, if ever, to substitute for human reasoning. For now at least, materials researchers may look forward to the future of AI as a collaboration between human and machine in pursuit of new and improved materials.