Neighborhood Income and Cognitive Health

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Welcome back to Minding Memory! In today’s episode, Lauren & Matt speak with Dr. Laura Zahodne – a professor of psychology at the University of Michigan and an affiliate of the Institute for Social Research. She's a clinical neuropsychologist by training and studies how psychosocial experiences shape late life, cognitive health, and risk of neurodegenerative disease.  Also, a new member of our CAPRA leadership team!   

In this episode, we’ll get to know Laura a little better and talk with her about one of her research studies, the Neighborhood Racial Income Inequality in Cognitive Health, which looks at the association between racial income differences and a variety of cognitive measures. 

More Resources

Laura Zahodne Faculty Profile  

Introduction to the Michigan Cognitive Aging Project 

Articles Referenced in Podcast:

Zahodne LB, Sol K, Scambray K, Lee JH, Palms JD, Morris EP, Taylor L, Ku V, Lesniak M, Melendez R, Elliott MR, Clarke PJ. Neighborhood racial income inequality and cognitive health. Alzheimers Dement. 2024 Aug;20(8):5338-5346. doi: 10.1002/alz.13911. Epub 2024 Jun 27. PMID: 38934219; PMCID: PMC11350017. 

Hu Y, Elliott MR, Meier HCS, Chen L, Walters ME, Sol K, Zahodne LB. The impact of census-tract level mortgage discrimination on cognitive function: accounting for measurement instability in small-area data via joint modeling. Am J Epidemiol. 2025 Nov 4;194(11):3258-3266. doi: 10.1093/aje/kwaf131. PMID: 40522478; PMCID: PMC12634109. 

Transcript

Matt Davis:

Welcome back to another episode and short season of Minding Memory. As many of you know, this podcast is supported by the National Institute on Aging through the Center to Accelerate Population Research in Alzheimer's, what we call CAPRA for short, at the University of Michigan. We're excited to share that CAPRA was renewed, which means we can continue bringing you these conversations. This episode kicks off a four episode series this spring. And next academic year, you can expect a full season of episodes. So, be on the lookout for that in the fall of 2026.

Welcome to Minding Memory, a podcast devoted to exploring research on Alzheimer's disease and other related dementias. Here, we'll discuss some of the most compelling research, and talk with leaders in the field about how their work is improving the detection and treatment of dementia. I'm Matt Davis.

Lauren Gerlach:

And I'm Lauren Gerlach.

Matt Davis:

We're both researchers at the University of Michigan. I have a PhD in data science.

Lauren Gerlach:

And I'm a geriatric psychiatrist who specializes in diagnosis and management of dementia.

Matt Davis:

I'll work to minimize the use of medical jargon in our discussions.

Lauren Gerlach:

And I'll make sure that the research we talk about has practical real-world applications to people living with dementia and their care partners.

Matt Davis:

Thanks for joining us and let's get started. Today, we're excited to be joined by Dr. Laura Zahodne. Dr. Zahodne is a professor of psychology at the University of Michigan and an affiliate of the Institute for Social Research. She's a clinical neuropsychologist by training and studies how psychosocial experiences shape late life, cognitive health, and risk of neurodegenerative disease. In addition to her impressive credentials, we're thrilled to have Dr. Zahodne join the leadership team of CAPRA. In this episode, we'll get to know Laura a bit better and discuss one of her recent studies. Laura, welcome to the podcast, and we should also add, the CAPRA community.

Laura Zahodne:

No, thank you. Glad to be here.

Matt Davis:

Dr. Zahodne's study that we'll be discussing today is titled Neighborhood Racial Income Inequality in Cognitive Health. It was published in the journal, Alzheimer's and Dementia. The study looked at the association between racial income differences and a variety of cognitive measures. We'll include a link to the article for those who want to check it out. So, before we jump into discussing the article, I was wondering, Laura, if you could tell us a little about yourself and what got you interested in studying cognitive health.

Laura Zahodne:

Yeah, happy to. So, like you said, I'm a clinical neuropsychologist. And I've been interested in cognitive aging since I was a teenager, when I was watching my grandfather experience Parkinson's disease. And people really think about Parkinson's disease mostly as a motor disorder, but I remember being really struck by how its non-motor symptoms affected his daily life, including cognitive impairment and later, dementia. So, that's what brought me to the field of clinical neuropsychology. And I studied Parkinson's throughout graduate school. But through coursework, and conferences, and research collaborations, I started getting more and more involved with cognitive aging research more generally, including Alzheimer's disease and other forms of dementia. So, now I really focus on dementia prevention, how people can maintain cognitive health throughout later life.

Matt Davis:

I have an idea that I was thinking about trying today. So, in terms of getting to know each other a little bit better, I was reading this book on communication. Which now that I say that out loud, it sounds kind of weird that I need to read a book on how to communicate, but they came across this survey called Fast Friends. And the idea of this survey was to go through a series of questions to get to know somebody better. And it starts off with superficial questions and it gets into deeper, deeper questions. So, I was thinking that we could maybe just as a trio, maybe try one of these questions. Are people up for that?

Laura Zahodne:

Sure.

Lauren Gerlach:

Sure.

Matt Davis:

Okay. All right. So, here's one of the questions. And like I said, I picked the more simple ones. So, I'm curious, Laura, what would constitute a perfect day for you?

Laura Zahodne:

Well, you're asking me that when it's negative one degree outside. So, that's a really good demonstration of how context matters, which is something that has fueled my research program. But a perfect day for me, I think involves some combination of getting outside and being with people I love. So, one thing that I really enjoy is riding a bike. And unfortunately, I can't do that much in the wintertime, but come March, I'll be doing that a lot more.

Matt Davis:

What about you, Lauren?

Lauren Gerlach:

Yeah. I mean, ditto, I'm staring out my window right now at a lot of snow and ice. And so, I think a perfect day is a day I can be outside. There's some sunshine, needing a few less layers of clothing. And like Laura said, with family or people I love, and maybe nowhere I have to be, and some good coffee to start the day. And that sounds pretty good to me.

Matt Davis:

Actually, when I was thinking about this question a little bit, I would actually do a little bit of work on my perfect day. Isn't that kind of weird? I don't know. I was just thinking my perfect day would be waking up, getting a workout in, and getting a few things done so I feel like I earn the opportunity to relax in the afternoon and just watch a movie, but I might be a little bit lame.

Laura Zahodne:

No. I don't think that's weird at all. I remember reading a study years ago, looking at the relationship between work productivity and satisfaction. And they were comparing undergrads versus grad students in this study. And they found that for undergrads, days where they were more productive with regard to work, they were less happy. But grad students, it was the opposite. Days they were more productive, they were more happy. So, I think what you're describing is pretty common for people in academia.

Lauren Gerlach:

Well, let's turn to your article. So, can you tell our listeners a little bit why look at neighborhood income based on where someone lives in relation to cognitive outcomes? And maybe lay the landscape for us a little bit about what we already know about the association between neighborhood income and cognitive health in general.

Laura Zahodne:

Yeah. Well, I think it's well known that one's own income or socioeconomic status in general is closely connected to one's health, so much so that it's considered a fundamental cause of disease in public health. So, fundamental cause theory tells us that economic resources provide all sorts of benefits that help people avoid health risks and adopt protective measures. And because these economic resources are so flexible, we see these benefits across all sorts of disparate diseases, from asthma to cancer, to dementia. But we also know that health risks are connected not only to your own individual resources, but also to your environmental context, so hazards and amenities in your surrounding area.

So, often more affluent neighborhoods have more amenities, and that's due to investments from businesses and governments. They often have less community violence. They're usually less likely to have polluting infrastructure, like highways and factories. So, the average income among residents in your community can really be thought of as a proxy for all sorts of environmental hazards and resources that can affect health. And I think in the last 10 years or so, we've seen an explosion of studies documenting relationships between neighborhood income and brain health. And these relationships persist even after we control for individual level income.

And I think this is a really exciting area of research, because if we can intervene on neighborhood level factors, we might see a broader and more meaningful impact on population health than we might be able to achieve if we just intervene one individual at a time.

Matt Davis:

So, I'm sure we'll get into mechanisms, but something that I know that you've published actually on, but I'm curious how neighborhoods socioeconomic status differs from personal income when we think about cognitive aging. And then furthermore, how does income inequality come into play?

Laura Zahodne:

Yeah. I mean, I think personal income helps you afford things like better healthcare, and healthier food, and healthier activities. But better neighborhood income just puts you in closer proximity to resources that can support a healthier lifestyle. And you're absolutely right, that our next steps with this project are to look at those mechanisms that link neighborhood socioeconomic status to cognitive aging. So, we're interested in behavioral mechanisms, and social mechanisms, and psychological mechanisms. With regard to neighborhood racial income inequality, I think the psychosocial mechanisms are ones that we're most interested in. So, living in a place that has a high degree of racial income inequality could really erode the social cohesion of the neighborhood.

And that can affect mental health and by extension, cognitive health. And just as an aside, I think one of the reasons cognitive health is such a fascinating outcome to study is because it's not just reflective of biological processes, it's also really sensitive to mental health, psychological processes, social processes. And I think all health outcomes, to some extent, can be thought of in that biopsychosocial model. But cognitive health, I think, is even more so equally affected by our mental health and our biological health.

Lauren Gerlach:

And for your study, how did you define neighborhood racial income inequality?

Laura Zahodne:

Yeah. So, at our study, we operationalized neighborhood racial income inequality as a ratio measure. So, it was a ratio of median income for white versus black residents within a census tract. And previous studies in the US and in Europe had operationalized income inequality more generally with a Gini index or the dispersion of incomes from low to high, but they hadn't considered racial patterning. And especially here in the US, there are stark disparities in income across racial groups. And those racial disparities have stayed consistent since the 1960s. And it's this intersection of racial and socioeconomic inequality that may be particularly impactful, especially because race is often a highly salient identity that can really shape social interactions with others.

Lauren Gerlach:

And can you tell us a little bit the rationale for examining inequality at the census tract level, why that geographic unit?

Laura Zahodne:

Yeah. I think there's a lot of debate about what a neighborhood is and different people can define it in different ways. I think that we used census tract mostly out of convenience. So, that was the smallest geographic unit we had available to us, to readily link with these administrative data sources on median income. But I think especially depending on the area, even smaller units might be more salient to people in defining their own neighborhoods. So, looking at the block group level or something more fine grain might also reveal some more nuance.

Matt Davis:

You mentioned the Gini coefficient, and that's something that I always thought was interesting. Just for our listeners, can you tell people what that is?

Laura Zahodne:

Yeah. It's an indicator of the dispersion of income levels within a geographic region from low to high. And this can be calculated at various geographies, so metropolitan statistical area level, county level, state level, country level. And so, with regard to this research question, there have been studies looking at the Gini coefficient in cognitive aging within the US at the metropolitan statistical area level and the county level, as well as internationally at the country level. And across geographies, what was found is that greater income dispersion was related to worse cognitive health across these really different contexts.

Matt Davis:

I read that it ranges from like zero to one. And zero means perfect equality and one is perfect inequality, where all the wealth is in one household or something. Is that right?

Laura Zahodne:

Yeah, that sounds right. That sounds right. And I don't think any country can really boast perfect equality.

Matt Davis:

So, turning back to your study. I was wondering if you could tell us a little about the data source and the cognitive measures that you used.

Laura Zahodne:

Yeah, absolutely. So, the data sources is really a passion of mine. So, these data come from the Michigan Cognitive Aging Project. This is a cohort study that we established in 2017. It's a community-based longitudinal cohort of adults who are transitioning to late life. So, we target folks between 60 and 65 at baseline, so a relatively age homogenous group. And everybody's living in Southeast Michigan. We run this study out of my lab at the University of Michigan in Ann Arbor. We use address-based recruitment to achieve a representative sample, and we over-sample black adults. And that allows us to have roughly equal sized subgroups of black and white residents, and that helps us answer questions about racial health disparities.

With regard to the cognitive measures, we have a comprehensive neuropsychological battery. It's pretty similar to what clinicians use to assess cognitive impairment, in that it captures cognitive strengths and weaknesses across many different domains, including memory, executive functioning, processing speed, language, and visual spatial ability. So, within each of those domains, we have several tests. And that allows us to get pretty high quality measurement of each of these different domains, as well as global cognition.

Matt Davis:

Is any of the data publicly available or available to the researchers that might be listening?

Laura Zahodne:

Not yet. So, we're in our very first five-year grant cycle. And after we finish that grant cycle, we will be posting the data through ICPSR.

Matt Davis:

And your study also used some data from the National Neighborhood Data Archive, right?

Laura Zahodne:

Exactly. Yeah. So, we took census tract identifiers and used those to link MCAP data to data from NaNDA, the National Neighborhood Data Archive. So, NaNDA included the neighborhood level income data.

Matt Davis:

It's got a wealth of stuff. I'm always amazed by supermarkets and all sorts of things in that data set.

Laura Zahodne:

At the time, such a measure of income inequality wasn't available in NaNDA, so I was really fortunate to team up with them and create this novel measure that I think is now available publicly through NaNDA. I think they now release that as part of their regular neighborhood socioeconomic characteristics. So, that variable, I think, could be readily included into your own data sets and linked to your participant's data.

Matt Davis:

And in your study, I guess big picture, what'd you find? What stood out?

Laura Zahodne:

Yeah. So, I think the primary finding was that participants who lived in neighborhoods with less racial income inequality had better cognitive health. And this was even after controlling for things like individual's own personal income, the overall average income in the neighborhood, and other neighborhood characteristics, like population density and residential segregation. And what stood out the most to me was that this association did not differ by race. It was not statistically significantly different for the white and black subgroups in the sample. But some of the other neighborhood characteristics did seem to have more of an effect on some residents than others.

So, for example, the overall average socioeconomic status of the neighborhood was only associated with cognitive health among black residents. And this was really interesting to us and made us think about how it's possible that white residents on average may have access to other resources, like intergenerational wealth, certain occupations, that might buffer against the negative effects of lacking resources within your neighborhood.

Matt Davis:

Yeah. The persistence across race also is the main thing that kind of jumped out to me in reading your article. And I had to wrap my head around that to understand how, I guess, particularly white individuals would be affected. It was really interesting to me.

Laura Zahodne:

We were also surprised by that. So, we hypothesized that black residents would be more negatively impacted by neighborhood racial income inequality based on how the measure was constructed. But when we went to the literature, it turned out that we shouldn't have been too surprised by this because it's pretty consistent with some other studies that are out there. So, for example, in the Health and Retirement Study, they found that greater income inequality, regardless of race, so just greater dispersion of incomes from low to high was related to worse cognitive health. And that was not moderated by individual socioeconomic status. So, living in a place with more income inequality was bad for cognitive health for everyone in the neighborhood, regardless of their own income and SES.

And similarly, in the REasons for Geographic And Racial Differences in Stroke Study or REGARDS, they found that residential segregation was similarly associated with worse cognitive health among both black and white residents. So, as surprising as the finding initially was, the more we looked at the literature, the more we saw that it might not be such a unique finding. And they really suggest that inequality may be bad for everyone, not just those who are personally disadvantaged in that context. And I think that has really important policy implications, in that addressing these structural inequities has the potential to benefit all residents, not just those who are individually disadvantaged.

Matt Davis:

This might be outside of your area of expertise, but I mean, do you know, does it go beyond cognition to all sorts of other types of health outcomes as well?

Laura Zahodne:

Yeah. That's a great question. So, our study was obviously focused just on cognitive health, so I think we have data available to see what other outcomes it extends to. Again, because cognitive health is so reflective of whole body health, mental and physical, my guess is that we would see similar effects for other health domains, but I think that remains to be seen. And I'll also add that even though the effect didn't differ by race, it's possible that the mechanisms underlying the effect do differ by race. So, in our follow-up work, where we're investigating what are the mechanisms by which neighborhood racial income inequality affects cognitive health, we might find that some mechanisms are more salient for one group versus another. That's still a possibility.

Matt Davis:

So, this next question is a little bit in the weeds, but we do have a fair... this podcast is mostly geared towards researchers. So, in terms of methods, I'm just curious how you made the decision about what individual level factors to control for in your models.

Laura Zahodne:

Yeah. So, this was a cross-sectional observational study, so we can't conclude that these neighborhood factors caused changes in cognitive health. But we did try to statistically control for factors that could have induced an association that wasn't actually causal. So, for example, personal income and educational background could influence both where you live and how healthy you are. So, we wanted to adjust for those individual level socioeconomic variables, but we didn't want to adjust for individual level variables that we thought might explain or mediate the relationship between neighborhood factors and cognitive health, such as physical health conditions.

So, for example, neighborhood factors could influence your likelihood of developing cardiovascular disease, which we know can influence cognitive health. So, if we had controlled for cardiovascular disease, we could've underestimated the total causal effect of these neighborhood factors.

Matt Davis:

That is such a good lesson for trainees that we work with and stuff. You have to resist just dumping things in a model and think about the relationships before you potentially affect the relationship you're trying to measure, which is, I don't know, hard to wrap your head around, takes a little bit of work.

Laura Zahodne:

Oh, yeah. Choosing covariates might be the most important part of your analysis, maybe even more important than how you operationalize your exposure. And a covariate isn't just something that's related to your predictor and your outcome. They can really range from being true confounding variables to mediating variables. And you really have to think that through, think through your conceptual model before you decide what to put in.

Lauren Gerlach:

I know you've spoken to this some, but I was hoping you could maybe walk us through what you think some of the policy implications might be of your work, and how this helps move us forward a little bit mechanistically, and understanding some of these relationships.

Laura Zahodne:

Yeah. Well, first and foremost, I just hope that this work helps to motivate policy to address racial income inequality. I think that dementia risk is such a huge public health issue with really broad support from community members and bipartisan leadership. So, showing that these factors relate to this critical health outcome that we all care about, might help motivate policy to start addressing some of these factors that people might not realize all of the long-term implications of inequality. So, I think the study provides another potential benefit for moving such policies forward in the form of dementia risk reduction. In terms of addressing racial income inequality, I think that can include greater investments in school quality, as income levels are often strongly determined by educational background and our schools are still highly racially segregated.

Addressing inequities in the labor market can also influence income inequality. I'll give a shout-out to Monica Walters, who's one of the scholars currently funded by a CAPRA pilot. She's working really hard to disentangle the relationship between occupational inequalities and dementia inequalities. So, about mechanisms, you asked about mechanisms too. I think additional work revealing mechanisms by which these neighborhood characteristics influence dementia risk is also critical. Because that would identify additional points of intervention, for policy changes or neighborhood investments that could interrupt risk pathways initiated by neighborhood disadvantage or neighborhood racial income inequality.

And those intermediaries may represent even more attractable targets in the shorter term. So, while we're working to eliminate income inequality, are there other ways that we can invest in neighborhoods to make them as supportive and health-promoting for residents as possible?

Matt Davis:

So, what's next for your team?

Laura Zahodne:

So, our follow-up work is really focused on understanding those mechanisms, linking neighborhood factors to cognitive health. We're looking at individual level factors, like stress and health behaviors, as well as interpersonal factors, like how much neighbors trust and rely on each other. Another direction is that we recently quantified these neighborhood factors for our participants' childhood residences, because we want to know how life course neighborhood experiences might have an enduring effect on later life cognitive health. And finally, we are continuing to follow this cohort. So, people are seen every two years in MCAP. And that's hopefully going to let us examine the effects of these neighborhood factors on rates of cognitive change. And that can address some of the limitations related to cross-sectional studies.

Matt Davis:

Is there anything else you'd like for our listeners to know before we wrap things up?

Laura Zahodne:

Well, I think it's always useful to discuss challenges to this work, especially if your listeners include researchers who might be pursuing the nitty-gritty. So, we recently published a methodological paper that was really inspired by some of the challenges we faced with this study. So, that might be of interest to listeners who want to use geographically defined ratio measures of structural racism. So, one challenge that we faced in computing our ratio measure was hyper segregation. That is, some of the census tracts represented in our study are almost 100% black. And since we can't compute a ratio with a zero denominator, we had some measurement instability. So, last year, we worked with a biostats graduate student, Yueying Hu, who published a paper in the American Journal of Epidemiology, exploring a joint modeling approach to address this issue.

So, in her paper, she was taking as her substantive exposure racial differences in mortgage rates within a neighborhood. And her approach simultaneously estimates cognitive outcomes and latent mortgage rates for white versus black households. And she showed that this joint modeling approach improved the ability to detect associations and her simulations further demonstrated that this approach showed lower bias. So, if people are interested in really using these ratio measures, I would recommend checking out Yueying Hu's publication.

Matt Davis:

Very cool. Laura, thanks so much for joining us today.

Laura Zahodne:

Yeah. Thank you so much for having me. It's been a pleasure.

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 other resources we've created specifically for dementia research. Music and engineering for this podcast was provided by Dan Langa. More information is available at www.danlanga.com. Minding Memory is part of the Michigan Medicine Podcast Network. Find more shows at michiganmedicine.org/podcasts.

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.