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In this episode, Matt & Donovan speak with a health economist, Dr. Lauren Nicholas, whose research investigates how missing a single credit card payment may be a very early indicator of a cognitive issue. Dr. Nicholas is an associate professor in the department of Health Systems Management & Policy at the University of Colorado School of Public Health and has published several studies that examined on how a financial issue might be among the first signs of cognitive decline.

Article Referenced in this Episode

Nicholas LH, Langa KM, Bynum JPW, Hsu JW. Financial Presentation of Alzheimer Disease and Related Dementias [published correction appears in JAMA Intern Med. 2021 Feb 1;181(2):296]. JAMA Intern Med. 2021;181(2):220-227. doi:10.1001/jamainternmed.2020.6432

Transcript

Matt Davis: 

The presence of cognitive impairment can contribute to risky financial decisions, erratic bill payments, and higher susceptibility of financial fraud, something we discussed on an earlier episode. After all, the definition of dementia requires progressive functional decline. And in our world today, managing household finances is perhaps one of the most complex functional tasks of adulting. But we usually think of these financial troubles occurring after the disease has taken hold. Very little is known about financial issues upstream of cognitive decline in the years before identification of dementia. But just the way that mild cognitive impairment may proceed dementia, it's plausible that earlier hints of functional impairment may occur before a diagnosis. In fact, they may even occur before a cognitive issue is even detectable. Not necessarily a financial catastrophe, but more subtle, small issues such as missing a payment or perhaps a few credit card payments that impacted credit rating. 

In this episode, we'll speak with an economist whose research investigated how missing a single credit card payment may be a very early indicator of a cognitive issue. I'm Matt Davis. 

Donovan Maust: 

And I'm Donovan Maust. 

Matt Davis: 

You are listening to Minding Memory, a podcast devoted to exploring research on Alzheimer's disease and other related dementias. 

We're joined today by Dr. Lauren Nicholas. Dr. Nicholas is an associate professor in the Department of Health Systems Management and Policy at the University of Colorado School of Public Health. She's a health economist whose research focuses on the role of public policy in improving health and healthcare quality for older Americans. She's published on a variety of topics including dementia, surgery, and end of life care. Dr. Nicholas is here today to speak with us about her recent study that examined financial issues associated with the onset of dementia. Lauren, welcome to the podcast. 

Dr. Lauren Nicholas: 

Thanks. It's great to be here. 

Matt Davis: 

Dr. Nicholas was the first author of the study titled The Financial Presentation of Alzheimer's Disease and Related Dementias that was published in the journal JAMA Internal Medicine like many other studies of dementia that used Medicare claims data. But here's the interesting part, the study linked Medicare data to consumer credit reports to identify financial issues. 

So to start things off, Lauren, what got you interested in looking at financial issues and dementia? 

Dr. Lauren Nicholas: 

Well, I had attended some conference presentations that we're sort of talking about the challenge of dealing with money management and cognitively impaired older adults, especially those who didn't want to allow other people to take over their finances and sort of the difficulties that this presents for both patients and their families. I think something about being in that room talking about the financial problems and sort of the timeline over which they could occur made me wonder whether these would be detectable in financial data if they were as prevalent as it was sort of sounding like from all of these anecdotal stories. And as we looked into this further, there were a number of news articles talking about kind of one-off situations where a family would discover someone's cognitive impairment because of one of these more catastrophic financial events where you're seeing the notice of eviction for non-payment or losing a family business or putting a new fraudster on your bank accounts and losing a lot of money. 

So I think that it was sort of this potential for really catastrophic financial problems that made us wonder, "How early does this start? Can we catch it? We don't have great ways of dealing with a lot of the clinical presentation, but we might be able to help out on the financial dimensions of dementia." And then it turned into "How do we get the data and actually make this project happen?" Which maybe wasn't the fastest process to think it was going to be a good idea, but I think our findings sort of speak to making the investment worthwhile. 

Matt Davis: 

So it sounds like we knew something about financial issues sort of after dementia has taken hold. And even people write about this in the media and stuff and personal stories and those types of things. So was the idea that you had to look before dementia was identified more like just out of curiosity how far you can go back or more kind of... I mean, what were the motivation to look specifically before it? 

Dr. Lauren Nicholas: 

Yeah, I think it was partly the models that we often use in economics look at what happens before and after a certain event. And so the diagnosis in some ways represents like a natural point to look at are things different at the point of diagnosis or after diagnosis. We are combining the way that we frequently approach these problems with some of the stories that would say, "Oh, people come in for a diagnosis because of one of these financial problems" and wondering how prevalent that is. Is that a normal pathway or is that just these really scary, almost urban legend stories that are actually relatively rare but get a lot of attention because it's so horrifying that this could happen to you and your family? 

So I think we wanted to trace out what this progression looks like because we didn't have great numbers in how early does it start, is it even common enough that we would see it in this type of data. So it was partly very informed by all of these anecdotes, but also sort of a fishing expedition because we didn't have good data on how early does it start and how bad is it. 

Donovan Maust: 

So let's actually talk a little bit about the data. So one of the fun things about this podcast is Matt and I both data. We get to talk to lots of authors who've used all kinds of cool different kinds of data. I think you're the first person who's used financial credit report data, which I've never worked with. I don't know if Matt, if you've ever worked with it. So how have you used these data before? How did you know they existed? What was the process like even to be able to get the credit report data to use? 

Dr. Lauren Nicholas: 

Yeah, so these data have been growing in use and popularity in economics over time. And Joanne Hsu, who is a co-author and sort of co-leader of this project who has also had longstanding interests in the financial implications of dementia was, at the time when we started working on this project, at the Federal Reserve Board in DC, which had already put together a massive credit report data set that was a great starting point for this project. She had a lot of expertise in using the data and we had very good support from the Fed about creating their first data use agreement to have Medicare claims data housed onsite. I think it's a pain that's more familiar to the health researchers and your audience. 

Donovan Maust: 

So can I ask from start to finish, how long did it take you to get this credit data to be able to use? 

Dr. Lauren Nicholas: 

I think we were in the first no-cost extension year of our 21 grant that was funding this when we first combined data sets. So thanks to years of investment from the Fed, before our project started, the credit data were already in a very research accessible format, but getting the permission to combine these data sets and get them both into place was a multi-year effort. 

Donovan Maust: 

So essentially the funded project should have been done before you got your hands on the data? 

Dr. Lauren Nicholas: 

That was what we hoped for when we wrote the grant, yes. We were combining a lot of sensitive data resources. And so I appreciate the lawyers and everyone who made sure that we were keeping everyone kind of optimally protected because I think that is definitely a concern that comes up with a project like this where it's like, "Wow, you know a lot about these people. We don't want just anybody running off with that information." It's a little scary to find out how informative your credit information can be about your health and other non-financial variables. 

Matt Davis: 

It's a good thing that we have these levels of protection, but I think that listeners that may not have experience with working with national data, sensitive data, not only healthcare data, but in this case financial and healthcare data. It's like a double whammy. The process is incredible. 

I mean, for listeners out there, you have this data user agreement thing, which is kind of your agreement to use it which takes months, and like we just said, sort of years. So it can be quite a process. You see the publication like, "Oh sweet, they just grabbed some data and ran with it." But it's like a negotiation agreement contractual thing, which is probably good to keep everything safe and tidy, but it can be quite a process. 

So I guess speaking a little bit more about these two different data sets, this is something that we talk a lot about in my lab and the people that I work with, how can we connect two different data sets that may not have been connected in the past? So to find people across two different data sets, ideally you would have some way to identify them to people out there. I'd imagine just a name or identification number or something. Was that at all an issue with connecting these two data sets? And if so, how did you handle it? 

Dr. Lauren Nicholas: 

Yeah, so in our case we didn't have a common set of exact identifiers and we had to rely on what we could ascertain about household size using household size as determined by the number of people receiving mail at the same address and then small areas of geography and year of birth. So we have people we think we've matched, but there's definitely error in that, right? And that's a factor that's actually going to act against us finding any results. 

So in our current work, we're working on requesting data for randomly generated lists of Social Security numbers so that we will have the same people in- 

Matt Davis: 

Oh wow. 

Dr. Lauren Nicholas: 

... both data sets. And that will allow us to construct a much larger data set, more granular. So within in our study, while our sample size was quite large for the types of information that we were putting together, we were still losing probably 90% of the data that we had access to because people live in apartment buildings and appear to live with hundreds of other people. Or we had a 5% sample of credit report data, so not everybody was going to be in there anyway. Or we'd have error on one side or the other, and so the same person wouldn't match in both data sets. So we'd sort of set these programs running and leave to go to lunch and be like, "We're going to come back and find out that maybe we've been working for two years and we're going to have no actual analytic data set and maybe it's going to be amazing." 

And so luckily, it did work out when we were doing our matching, but I think there were a lot of things that we were basically flying blind designing our initial matching strategy and it luckily worked out and gave us sort of good pilot data to do a more comprehensive version of that. 

Matt Davis: 

The financial data, did that include people just with financial issues or people that may have had or not had a financial issue? 

Dr. Lauren Nicholas: 

We had everyone. 

Matt Davis: 

Everyone. 

Dr. Lauren Nicholas: 

Yeah, the credit bureaus collect many different dimensions of financial data. And so we sort of ultimately focused on some of the more common problems like missing a bill payment. The good news is that older adults on average have pretty good financial outcomes. So that was sort of disappointing as a researcher when you're like, "We need interesting adverse outcomes to look at." And one of the best things you can- 

Matt Davis: 

Oh, it's coming. 

Dr. Lauren Nicholas: 

... do for your credit score is to have your accounts open for a long time. And so if you open an account when you were 25 and now you're 80, you're looking amazing. And the data, also older adults often own their homes outright because they've paid off their mortgage. So we don't see you failing to pay your mortgage and getting foreclosed on, which again is great if you're worried about the financial wellbeing of older adults, but sort of reduced some of our opportunities for investigation on the research side. 

Matt Davis: 

Well, they're coming, right? The financially issued written demographic sway will be there soon probably. 

Donovan Maust: 

So on the topic of financial issues, when you have access to this wealth of information in a credit report, how did you pick which specific outcomes you wanted to look at out of all the information that might have been available in there? 

Dr. Lauren Nicholas: 

Well, we'd started with a list of outcomes that were coming up in some of these anecdotal reports where we thought, "Oh, you hear about people losing businesses or having their homes foreclosed on." And so we were thinking that that might be where some of the mileage was going to be. And then we were also kind of combining that with what's frequently studied in these credit reports studies and we'd present the work in progress and people would have other suggestions. And so I think our list kind of evolved, is we learned more about where you do and don't see movement in credit reports of older adults more generally and what we would specifically expect dementia to be impacting and what's sufficiently common that we could find in a large dataset study like this 

Matt Davis: 

That is a practical consideration in terms of having it be common enough to find something. So in terms of the timeframe, was there anything specific of why you selected... I think it was six years, looking sort of six years back and four years I believe after a diagnosis. Was there any reason that you picked that timeframe? 

Dr. Lauren Nicholas: 

Well, the four years after was directly driven by our data. I think more than half of the sample was dead within four years of a diagnosis. So things just get really noisy after that time period. It sort of made sense that if our coefficients were going to reflect this very selected group that's either getting diagnosed much earlier on average than most folks with dementia are somehow being able to live for a long time with the disease. We didn't know how comparable of those results after four years would be with the results closer to diagnosis where we had a lot more sample to draw on. I think that the six years backwards was driven by just the span of data that we had, although some folks have encouraged us to look even further back- 

Matt Davis: 

Oh wow. 

Dr. Lauren Nicholas: 

... since I guess we're still sort of scratching the surface on how long dementia might impact your financial wellbeing. Often in these studies you would want the same number of periods before and after your key event. In some ways, we were sort of starting with the diagnosis because that's the only time that you have a time period that's clearly defined, but I think we also want to understand how far back these go, right? So I guess seeing anything was kind of surprising to us that, wow, this really is affecting so many people that you can see it in a data set with all of the problems that we've just talked about. So I think when we have our much larger linkage, we'll be able to more robustly estimate especially further back in time. So it's like as you start to learn things, we just get more and more questions that we want to sort of look at next. 

Donovan Maust: 

So we've asked you a lot of questions about the paper, but we haven't actually had you tell the listeners your findings from the paper. So do you want to share the take home messages? 

Matt Davis: 

Drum roll. 

Dr. Lauren Nicholas: 

Sure. I guess in our paper, which was focusing on single person households where we're going to have the strongest connection to your financial capabilities being impacted by an ultimate dementia diagnosis, we found on average symptoms in terms of missed mostly credit account, but other bill payments starting as early as six years prior to diagnosis in the full sample. This was even more pronounced for those who lived in census tracts, which are very small geographic areas with below average rates of education. And we have a lot of evidence in the economics and sort of financial health literature that lower education can be tied to worse abilities at financial management. And so this is going to be kind of an especially vulnerable group. We see either the symptoms are starting earlier or the diagnosis is happening later in disease progression, but I think both concerning for different reasons. This group was also more likely to experience symptoms as well as having them for a longer period of time. 

We also saw that in many cases, financial problems persist after a dementia diagnosis. So we were kind of hoping that if people were having problems and that was leading you to seek a diagnosis, there would be counseling. One of the things dementia impacts is the ability to manage money and a family member should be taking over. So we were sort of hoping that you'd see this break where maybe there were problems before diagnosis and then somebody comes and helps you get the financial house in order, starts making payments on your behalf. That did not happen to the extent that we were hoping. And so that sort of points to a need for more financial counseling in the context of dementia diagnosis. 

Donovan Maust: 

One thing that I thought was super interesting is you all looked at some control conditions, so arthritis, glaucoma, hip fracture, and you didn't see the relationship with financial problems with those conditions. But interestingly, you also looked at myocardial infarction and you saw that ahead of experiencing an MI, there was elevated payment delinquency, which is really interesting because you make the point that MIs can be preceded by periods of intense stress. And so it's just interesting this idea of financial stress contributing to people having heart attacks. I just thought that that was really interesting among the control conditions that you looked at. 

Dr. Lauren Nicholas: 

Yeah, no. There's ano some other papers that look at things like how foreclosure risk impacts rates of heart attack, and that's another place where you do see that financial stress leading directly to adverse health outcomes. And so you're never happy to see bad outcomes happening to people in your data, but we were reassured that this financial stress leading to an MI shows up in our data as it does in some of these other papers that have looked at it. That's much more of an acute stressor. 

So you saw problems for a year or two with heart attack as opposed to the six-year ramp up period with dementia, but it certainly is as we think about like, "Can we use your credit report to help in diagnosis or to help in developing tools for banks to not let you put somebody else on all of your accounts or not let you cash out your 401k?" If there's other signs of cognitive impairment, we would need to be really careful to differentiate between, "Oh, are you just at risk of a heart attack or do you actually have cognitive impairment?" and to tease out all of these different financial clues to your physical and cognitive health. 

Matt Davis: 

Yeah, it is pretty neat that you looked at those different conditions. It brings back memories of papers that my group had under review where the reviewer always asked us, "How does it compare to other conditions?" And we always respond, "That's beyond the scope of what we're going to do in this one," but you actually did it in yours, so good job. 

Dr. Lauren Nicholas: 

Yeah, no, it was sort of interesting to see how flat many of the other conditions were. When we'd be presenting the paper, we'd get questions about, "Isn't this just because people are sick and they forget to pay their bills, or they can't pay their bills because they're in the hospital, or medical care is really expensive and so you don't have money to pay your bills?" Certainly all of those things are true in competing explanations, and so we wanted to be able to really ascertain that dementia is different and has this extended and I think pretty scary property where it can affect your ability to manage money possibly before you're even seeing other symptoms and realize that there's a problem that needs to be addressed. 

Some of Joanne's other work, she has shown that even people who are showing other signs of cognitive impairment, if they were always the financial money manager in the household, they tend to maintain that role. So I think that's also something that we might want to be aware of and worried about. 

Matt Davis: 

I'm going to show how little I know about financial stuff. I think everybody can wrap their heads around missing a credit card payment, that's pretty straightforward. But sort of a dip in credit score rating or whatever, what does that mean? Could it mean different things obviously? Or what? Can you talk a little more about just what that is indicative of? 

Dr. Lauren Nicholas: 

Yes. So each credit report bureau, and there's three major bureaus in the United States that has their own credit score metric, which is basically an assessment of if somebody makes you a loan, how likely are you to pay that off. So are you a good credit risk? The full methodology is proprietary and they don't want you to do too much attempt to reverse engineering that. But basically, it's kind of your score is going to go down from any number of adverse events. Sometimes good things also make a score go down, right? Like if you pay off an auto loan or something like that, you now have less credit extended to you. And so that can cause a short term dip. So there are some weird things like that if you really go down the rabbit hole of how to maximize your credit score. 

But we think most of... For this age group especially where many things are often already paid off, we suspect that many of the declines are both of the missed payments that we see and that we talked about being sort of the most frequent, but also things that are less common in this age group like having a tax lien placed on your property, foreclosure and the subset that is still making home payments and other things along those lines that in our data were sort of too rare to do a good job of studying individually. But when you're kind of accumulating all of these pieces of information, we can still see those credit score impacts. 

We actually saw movement of where you're not just having the little two point dip that comes from you own your car outright and you're not making car payments, but this is moving from basically being a good credit risk to moving into subprime credit where that's that sort of a concerning indicator about financial wellbeing. I think it's typically going to take several adverse events to push you into that territory. 

Matt Davis: 

So thinking a little bit about the implications, and you touched on this a little bit before in one of your comments, but this idea of a financial misstep before a diagnosis, it of course could imply that you're picking up on these really sensitive functional things going on before the disease has really started in terms of clinical identification. But it also could be that people are experiencing a delay in getting a diagnosis. I guess, I mean, obviously there's a lot to unpack there to differentiate the two, but I guess what are your thoughts and the thoughts of your co-authors that worked on this? 

Dr. Lauren Nicholas: 

Yeah, so one thing that we have been able to do is it's hard to tease out who's not seeing other symptoms, who's not getting a diagnosis for other reasons. But many diagnoses for dementia first show up in the context of hospitalization. I think about 40% of our sample is diagnosed during a hospital stay. Or not necessarily diagnosed, but first show symptoms in the claims data. And so we compared those who have a hospitalization when their diagnosis shows up as opposed to those who are only using outpatient care and were more likely to have gone to the doctor with concerning symptoms. 

We see that the outpatient group has both fewer financial symptoms and a shorter sort of lookback period. So there's less time from when the financial symptoms start to present to when a diagnosis happens. The financial problems are both happening more frequently and starting earlier relative to diagnosis for the group that's not being found until hospitalization. And so we interpret that as evidence that these delayed diagnoses are allowing the financial problems to persist for longer and to get worse. If the symptoms are actually bad enough that they are being caught in the context of a hospitalization for something else, they probably should have been caught earlier and the financial signs aren't the only ones present. But we don't know what's happening in the outpatient setting and why this isn't occurring. 

Matt Davis: 

It is good that you had so many... Those six years is a pretty good timeframe in that regard though. 

Donovan Maust: 

If I remember correctly, you stratified your analysis by education. So sort of three part question. First, most listeners might know Medicare claims data don't have education in them. I'm guessing their credit score didn't either. So first, how did you do that or what data did you use to do that? And then why did you do it? And then did it influence your findings? 

Dr. Lauren Nicholas: 

Well, I'll start with the why, which is a great reviewer suggestion. One of our peer reviewers is like, "Oh, we know that education is protective for dementia. We know that education can be informative for both financial resources and ability to manage that money. This is a potential confounder that I'd really like to see addressed." And we got that comment and we were like, "Why didn't we think to do this? This was such an important suggestion." 

Donovan Maust: 

It's nice when peer review is actually helpful, so that's great. 

Dr. Lauren Nicholas: 

Yeah, no, we had a lot of really helpful peer review comments in this paper, but I think that probably sticks out as the biggest, I think. Only one that ended up with its totally new figure. And so in order to do that, as you mentioned, neither of the data sets do contain that information, we incorporated census data. Since we did have pretty small area geographic information that allows you to get down to 600 to a thousand people sharing a census tract, people tend to be very similar to their immediate neighbors so we were able to, at the census tract, level, pull in age and education to basically split areas into those where adult, 65 and older, had fewer years of education than the average American or more years of education than average and compare this above versus below median education groups as sort of our best proxy of anyone's own level of education. 

Donovan Maust: 

And then in terms of your findings? 

Dr. Lauren Nicholas: 

And then as our reviewer I think hypothesized those with the lower education or in the lower education areas, we're much more prone to this concerning financial presentation. So we saw missed payments starting more like eight years in advance of diagnosis. They were much more common among those who developed dementia compared to those who did not in this lower education group. So we saw happening for longer and more likely to happen to any particular dementia patient in the lower education group. So I think definitely this combination of risk factors was making the financial problems worse. 

Matt Davis: 

So to me, this idea that you can find something so small, so far kind of upstream of a diagnosis is kind of surprising, which is why the article is so interesting. But I guess from your perspective, someone who's thought through this a little bit and obviously designed this study and everything, was there anything that surprised you? 

Dr. Lauren Nicholas: 

Well, I mean we went into the study thinking that we would be able to see these things, right? But I think the first couple times we got our results and it was actually there was kind of like, "Oh wow, we actually did this." You're trying to turn New York Times article anecdotes into big data analysis, and I think you never know if that's actually going to pan out. And so I think I'm still always a little bit surprised that it's so persistently there. And when we can look across... We looked at additional measures that don't make it to the paper, but you see a pretty persistent story when you look at some of these things that are just much rarer in credit data. When we do some of these other stratifications, comparing the people with the potentially delayed diagnosis, everything we look at is what you would both expect and fear from this financial presentation. So I think it's always interesting, but both surprising and upsetting to see how pervasive it is. 

Matt Davis: 

That's such an important thing that you just brought up. We find relationships and then they disappear. But when you have something that's persistent, no matter how you slice it and how you look at it, it's such a strong indicator that you're onto something and that it's holding. It's really cool. 

Dr. Lauren Nicholas: 

Yeah. Well, I think whenever I talk to non-researchers about the paper, they're like, "Oh yeah, I totally went through that with my grandmother, with my dad." I think we've been trying to estimate what share of the population is ultimately impacted by views, financial problems in some way. I think it's again where when we think about just regular conversations with everyday people, like a shockingly large share of families have to deal with these problems I think really points to an area where we need better policy protections and sort of better... We probably don't want doctors being like, "Here's how you manage your finances," because they have a lot of other things to deal with. 

Matt Davis: 

No, we don't. 

Dr. Lauren Nicholas: 

We probably don't have time for that in the clinical encounter. We're not training people to do two jobs at once. But having more of a pathway to additional information and counseling I think is really important as we remain a few years away from other treatments and cures. 

Matt Davis: 

So you bring up some really important kind of things to think about in policy, but what do you see in terms of next steps specifically regarding research in this space? 

Dr. Lauren Nicholas: 

We are pretty interested in whether we can create useful tools either to help assist with clinical diagnosis, right? I don't think you want to diagnose anyone entirely based on their credit report, but that could help us figure out like, given limited clinical resources, who should be screened, or can we do something when you're checking in the waiting room that helps go into the diagnostic process, and is there a potential scope for using these more in the financial realm to help put some additional safeguards in place. Because currently if you say, "I want to put this fraudster as a second account holder on all of my bank accounts," the bank has to honor your wishes. And so it's kind of scary, I think, how some of these mistakes could lead to complete depletion of financial assets. 

Donovan Maust: 

So Lauren, you mentioned that in your analysis you identified people who were the only beneficiary at that particular address, living at that address. Have you looked or can you look, like is it moderated by whether or not there's a spouse or caregiver... Like if somebody else lives at that address, do you not see this then under the assumption that that other person is managing their finances so you don't see these financial adverse impacts? 

Dr. Lauren Nicholas: 

We're starting to look at couples in the credit report data and aren't far enough along. To answer you from that standpoint, but I think that's something important that these models would need to account for. Both Joanne and I and some other research I've done with Jing Li at University of Washington has led... We've used the health and retirement study to look at both presence of other family members. And going back to Joanne's dissertation, you do see some family members get involved, but it's certainly not even in the majority of households. And then in Jing's work we do see a number of people with full dementia who are still managing their own money and many of them don't have somebody else in the household available. 

So I think the demographics of who are impacted by dementia, it's happening late in life when many people have already lost a spouse and there isn't necessarily going to be somebody available to take over, or the spouse themselves may be also developing cognitive impairment. So it is sort of a tricky situation. I think especially when you add on top of a lot of what we know about elder financial abuse is the perpetrators are frequently family members, and so we're sort of in this tricky area of you want to recommend, "Oh, have a trusted family member who's available to step in" when that could be totally problematic. 

Matt Davis: 

It seems like you could employ a small army of postdocs to work on all some of this. I can imagine all the types of even technological things that could be done potentially to at least let loved ones and caregivers know about things. Really interesting stuff. 

Dr. Lauren Nicholas: 

Yeah, no, I think there's huge scope for research and we'd love to have more people teaming up. I'm always trying to tell more students to go into aging. I wish this were a more crowded area. 

Matt Davis: 

[inaudible 00:44:31] you have to attach it to an app. So maybe that'll draw them in. This is really important work. We really appreciate you coming on. Lauren, thanks so much for joining us today. 

Dr. Lauren Nicholas: 

Yep. Yeah, thanks for having me. It's fun to talk about. 

Matt Davis: 

If you enjoyed our discussion today, please consider subscribing to our podcast. Other episodes can be found on Apple Podcasts, Spotify, and SoundCloud, as well as directly from us at capra.med.umich.edu, where a full transcript of this episode is also available. On our website, you'll also find links to our seminar series and data products we've created for dementia research. Music and engineering for this podcast was provided by Dan Langa. More information available at www.danlanga.com. Minding memory is part of the Michigan Medicine Podcast Network. Find more shows at uofmhealth.org/podcast. Support for this podcast comes from the National Institute on Aging at the National Institutes of Health, as well as the Institute for Healthcare Policy and Innovation at the University of Michigan. The views expressed in this podcast do not necessarily represent the views of the NIH or the University of Michigan. Thanks for joining us and we'll be back soon.