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What if there was a tool that could break down a neighborhood’s socioeconomic measures, like income, education, employment and housing quality, to give us a sense of how those factors influence overall health, and maybe even inform where to target health resources and social interventions.

On today’s podcast we talk with Dr. Amy Kind from the University of Wisconsin School of Medicine and Public Health, who developed that tool, the Neighborhood Atlas. The Neighborhood Atlas uses the “Area Deprivation Index,” which includes 17 measures of education, housing quality and poverty, and can be used free by anyone by going to the Neighborhood Atlas website (

In addition to talking with Amy about the Atlas, we discuss some of the following questions:

  • What is neighborhood disadvantage and what health outcomes is it linked to?
  • How should providers use neighborhood disadvantage when caring for patients?
  • How should health care systems use neighborhood disadvantage?
  • How does the Atlas also identify areas of resilience in communities?

So take a listen and if you want to read more about it, check out Amy’s NEJM article on the topic –

Eric: Welcome to the GeriPal podcast. This is Eric Widera.

Alex: This is Alex Smith.

Eric: And Alex, who do we have with us today?

Alex: Today we’re delighted to be joined by Amy Kind, who is professor of medicine and geriatrics and director of the Center for Health Disparities Research at the University of Wisconsin. Welcome to the GeriPal podcast Amy.

Amy: Thank you so much, Alex. Thank you so much, Eric. It’s just an immense pleasure to be here.

Eric: I am really excited. We’ve heard a lot over the last year, especially with COVID, about social determinants of health and also about neighborhood disadvantage. So we’re going to be talking to Amy about this, who is really an international leader about this subject, but before we do, Amy do you have a song request for Alex?

Amy: I do have a song request and you know when I grew up, I was a big fan of The Clash, but we weren’t sure that that would translate over into an acoustic guitar. So I thought perhaps Bob Dylan’s Blowin’ in the Wind, given the themes that are brought up and the importance of taking a stand and moving our society forward. So Blowin’ in the Wind Alex.

Alex: Great choice. Although next time you’re on let’s do The Clash. [laughter].

Alex: (singing)

Eric: Lovely Alex.

Amy: Very impressive.

Eric: So Amy we’ve got a lot to talk about. Last night I was playing with the Neighborhood Atlas. We’ll talk about that a little later on. But I always like to start, how did you get interested in thinking about social determinants of health, and importantly thinking about the neighborhood deprivation… Not neighborhood deprivation, although that’s a word, right..that people use?

Amy: That is a word. It’s more of a British term, but it is absolutely a word. Neighborhood disadvantage for the US.

Eric: Neighborhood disadvantage.

Amy: Yes.

Eric: So how did you get interested in this as a subject to research?

Amy: Yeah, that’s such a great question, Eric. Thank you for that. For me, I’ve always been interested in the intersection of poverty and health. I grew up in an area in which socioeconomic disadvantage or poverty was very common and continues to be common. It was an area that was quite rural, quite poor, and it used to have industry, but a lot of the industry has since left.

Amy: And so as I was growing up, seeing kind of the intersection of how poverty intermingles with all aspects of life and ultimately results in differences in health and wellbeing, it was something that was clearly demonstrated each and every day of my childhood. And so I was very fortunate to get a scholarship to be able to go to college. Otherwise, I’m not sure I would have been able to afford it.

Amy: But I was able to go to undergrad and then pursue my interests in health and healthcare, spending some time in health policy in Washington and ultimately moving forward and being able to integrate health disparities in all aspects of my work.

Amy: So in my philosophy, I always think back to kind of where I grew up and the many neighborhoods that are like the place I grew up, and the many neighborhoods that echo some of those qualities and maybe in inner cities or in other corners of our country, that haven’t benefited from many of the policies that might move forward, and certainly from the healthcare infrastructure that is missing in many of those places.

Amy: So for me those areas are the areas I care about quite deeply. And so it was a natural continuation of my upbringing to be able to continue forward in the area of the social determinants of health. So thinking about those factors that influence our health wellbeing across the spectrum of our lives and how they ultimately result in wellness and disease.

Alex: When we talk about neighborhood disadvantage, what does that mean? I could think of a number of potential ways that a neighborhood might be disadvantaged. Is there sort of a established, agreed upon definition about what makes a neighborhood disadvantaged?

Amy: Yeah, that’s a great question to Alex. So we tend to align with some of the definitions that have been established in Europe around these constructs. So in the UK, for instance, they first started measuring the construct of neighborhood deprivation or neighborhood disadvantage back in the 1980s, and neighborhood disadvantage in those philosophies is a measure of the social determinants of health at a very discreet kind of geographic unit, like a neighborhood.

Amy: So the social determinants that are often measured in these areas are things like income, education, housing quality, and employment. And they come together into sometimes a single score that then can be used to more effectively and efficiently allocate resources.

Amy: One of the first ones of these measures of neighborhood disadvantage came forward by a gentleman by the name of Sir Brian German, and he was the president of the British Medical Society back in the ’80s and just a brilliant, brilliant person.

Amy: And he had this real challenge in which in the UK system they were having difficulty recruiting providers into areas that were highly disadvantaged. So he pioneered some of these philosophies, some of these techniques of using geographic indices in order to measure neighborhood disadvantage, and then was able to convince Margaret Thatcher and her administration to change the way doctors were paid to be able to recruit more providers into these highly disadvantaged construct areas of the UK.

Amy: And this was back in the 1980s. And since then, these types of indices have gone through many different iterations. There’s many different flavors of these things that are used there. They’re often used as a cornerstone of many of the different European health programs in order to inform how best to be efficient in resource allocation. But they’re are also used in non-universal payer systems, like in Germany, for instance.

Amy: Germany has a very similar system to ours in which it’s primarily insurance-based, but they can use these types of metrics in order to inform the decision-making that happens at the policy level, in a way that that allows for more efficient and effective use of the resources that they have.

Amy: So the construct of neighborhood disadvantage itself kind of is born from these European indices, and the measures that we use to measure neighborhood disadvantage within the United States echo the statistical methods that are used in Europe. So they again include income, education, housing quality, and employment on a very, very tiny level, something called a census block group, which is about 1500 persons population, and it’s very population sensitive.

Amy: So in rural areas, these areas can be really, really big and encompass a huge area of geography, but in urban areas that can be very tiny indeed, but they are important. And as you know, and as I’m sure we’ll talk about they are strongly associated with health outcomes.

Alex: Yeah, I guess that’s a key conceptual idea that many of our listeners may not be immediately obvious to them. Why look at neighborhood level factors? Because our listeners are overwhelmingly clinicians who care for older adults or care for people with serious illness. And they see the patient in front of them who has this level of educational attainment and this level of accumulated wealth and income and this level of burden of disease. And has engaged in these sorts of health behaviors.

Alex: Why move from that patient level factors, who neighbor had level disadvantage and how should… Like to the practicing clinician, how should they be thinking about this?

Amy: Oh, that’s such a great question, Alex. And I have to say, I did not write your questions, but it almost feels like I could have, these are such great questions.

Amy: So we know from decades of social science research that yes, individual level factors, kind of someone’s individual characteristics, the aspects about them that are unique to them. So just as you said, the education, their income, their particular job, that these are important influences on health and wellbeing.

Amy: But we also know that independently of the individual factors, the neighborhood level factors have a profound impact on health. And sometimes when we look at different types of studies and there’s been a lot of decades of social science research that have looked at this, sometimes the contextual factors are actually more important in terms of health outcomes than the individual factors.

Amy: So, one great example of this was a study that was conducted by researchers at the University of Colorado… Excuse me, University of Chicago back in the 1990s, early two 2000s. And what had happened then is that back in the Clinton Administration.

Amy: So back in the ’90s, there was an experiment that was done through the Department of Housing and Urban Development in which specialized housing vouchers were available to families to apply to potentially move out of typical Section 8 housing and move into neighborhoods that were just a little bit, perhaps slightly more wealthy than the typical Section 8 housing neighborhoods.

Amy: And this was a lottery system that families in five cities across the United States could apply to. And certain ones of these families received these vouchers, certain families did not obviously. But these researchers at the University of Chicago quickly realized that this was a natural experiment.

Amy: So they’ve been following these families forward over the course of many decades now, so now we’re in the 2020s, of course. And this is called the moving to opportunity study, and it has published… There’s been all sorts of publications that have come on it.

Amy: Initially the hope was that this would be a cure for poverty. That somehow by moving these individuals to different contexts, that their individual socioeconomic status would improve, that they would ultimately not be poor anymore.

Amy: But what the study found after many years of follow-up, is that individuals who have moved to these less disadvantaged contexts were just as poor, those families were just as poor as when they started, yet, their health was substantially improved.

Amy: And this health improvement was something that wasn’t necessarily expected to be seen. These were sociologists and economists that were following these families forward and really looking at poverty, looking at kind of financial wellbeing, and yet what they found was impact on health.

Amy: So the family’s mental health improved, the family’s cardiovascular health improved, diabetes metrics improved. These were individuals that although their individual characteristics from what was measured were largely the same in terms of the typical social demographics, their health was better.

Alex: This is fascinating. Yeah.

Amy: There’ve been a bunch of papers on this that have come out in New England Journal and science. And this is what we find in our own work too. That the context, that individual factors play a role in the health associations that we see, but context itself also plays an independent role.

Alex: What’s a story for our clinical audience? And how are they to understand… Like, it’s easier to understand how some of these individual level factors might influence health. How is it that the contextual factors influence health? I don’t know if you can draw a particular example?

Amy: Well, I think one great example of this is that many of these system level factors like neighborhood disadvantage, echo structural racism and systematic inequities, that populations experience across a large population. So let’s take the example of redlining for instance.

Amy: So just in case your readership or your listenership is not familiar with redlining, redlining were policies that were enacted by the banking sector and other financial sectors, that basically allowed certain areas, geographic areas, to have easier access to financial resources like home loans than other areas. And they would draw the areas that did not have access to these financial resources, outlined them in red. And that’s where the term redlining comes from.

Amy: Well, unfortunately, many of these redline neighborhoods were predominantly African-American or minority neighborhoods. And when we look at metrics of neighborhood disadvantage to this day, they echo some of those redlining characteristics.

Amy: So if you have these, you have these areas that have lacked systematic investment because of structural racism factors that have limited someone’s access to financial resources, you can imagine how that could echo throughout generations.

Amy: In addition, many of the neighborhoods that we see that show up as highly disadvantaged on our score, are also areas that have might have alignment with other negative experiences that an individual might have.

Amy: So one example of this is food deserts, not being able to access healthy food, having increased risk of experiencing pollution, be it lead in the water, in Flint, or air pollution within the inner city, also increased or excuse me, decreased access to different types of health infrastructure, like pharmacies or decreased access to healthcare clinics and other facilities. It may be difficult to walk in these neighborhoods and engage in behaviors that promote healthy exercise.

Amy: So all of the things hat individuals who live in more advantaged context might take for granted, like walking down to their pharmacy and picking up a prescription ordered by their physician, could be 10 times as hard in these highly disadvantaged context. But it moves us towards thinking about population level health, and thinking about our tool and our lever for action here is resource allocation in social policy, which is very exciting.

Amy: So for that individual clinician, if they have a patient that’s coming forward to them from one of these most disadvantaged areas, could it trigger additional resources, to be provided for that encounter? Could it trigger those additional questions? Could it trigger the social services or the community partners, or community health workers that could be involved and then help optimize the health and wellbeing of that individual patient?

Eric: So I got a question. When we’re thinking about kind of looking at this from a systems’ perspective, targeting in that way, versus looking at it from an individual perspective right in front of you. For example, I was just looking at the Neighborhood Atlas. We will talk about that. Like you have Palo Alto and you have East Palo Alto, like one is in the top decile, the other’s in the bottom decile. They’re right next to each other, separated by a freeway.

Eric: But then you also have places that are in very… People who live in very wealthy areas, but may actually have a lot of poverty. I hear what you’re saying, that actually there may be some mitigation around health. But when we’re dealing with that person in front of us, how much should we be looking at this population data versus the individual data of this person that’s sitting in front of us?

Amy: Eric, that’s such a great question. And hopefully you’ll have an opportunity to ask the individual in front of you about the social determinants of health, but unfortunately in our clinical encounters, and certainly from my own research and that of others, too often that doesn’t happen.

Amy: The conversations about someone’s different levels of social burden, perhaps there is not time that’s always open for those types of conversations and the encounter in the typical 15-minute primary care encounter. There may not be time to really delve into the fact that getting a new prescription from a pharmacy is a huge, huge burden for a whole host of reasons.

Amy: But because of that, when we think at the population level, it gives us another tool to identify need, and to potentially prompt some of those additional discussions that otherwise, to be honest with you, they should happen, but they aren’t happening within our healthcare system in a universal way.

Eric: And if I’m a provider, I want to know a little bit more about kind of their neighborhood disadvantage. There’s a tool out there, right? That I can go to look this stuff up?

Amy: Yes, there is, there is, and it’s completely free. So this is a tool called the Neighborhood Atlas. It is funded by the National Institutes of Health, and it is housed and curated by our team, our Center for Health Disparities research here at the University of Wisconsin, School of Medicine and Public Health. Anyone can go to it. The address is

Eric: And we’ll have a link to that on our podcast.

Amy: Oh, wonderful.

Eric: On GeriPal, so you can actually just go to the show notes.

Amy: Wonderful. And so anyone can go there and either use the data or take a look at the maps. And, one of the fascinating things about this tool is that it shows metrics, that we have been using for decades, sharing openly for decades, but it shows them in a map-based format that anyone can access. And that was our goal here.

Amy: So if someone can use a smartphone app to find their way using Google Maps, for instance, they can use the Atlas to look at the disadvantage in their particular neighborhood. So the app-

Eric: So the metric that you’re using is the area deprivation index, right?

Amy: Yup. It’s the English label. And it was originally created by HERSA the Health Resources and Services Administration back in the 1980s, right around the same time German was creating his synthesis back in the UK. But it wasn’t ready for prime time. And it had some differences in its construction that made it different from the UK policy ready indices.

Amy: And so we took this index, which had been created at the county level for HERSA, and we refined it down to this much more UK aligned block group level, which is exactly the exact same level that the UK uses and other European countries use to decide on health allocation and resources,

Amy: Because when you look at that county, and I’m so glad you brought up this suggestion of the City of Palo Alto, what you would find if you used a county level metric, is that none of that difference would be seen. You would not see those differences along that highway corridor, because using the much more discreet metrics, we are able to pinpoint disadvantage very specifically.

Amy: And so anyone can use these metrics on the Atlas, and the use has been really profound. We’re approaching a half a million hits now. We’ve had tens of thousands of downloads of the data. The data are freely available and easily linkable to research data, but 75% of our users are outside of the research field.

Amy: So we have groups that are working with health systems, for instance, that have downloaded this data, or using the mapping for accountable care organizations to better manage their populations. And to think about, again, this more efficient allocation of resource to need the targeting of the social resources.

Amy: We’ve had groups in the Obama Administration that were using the metrics to use them as an eligibility characteristic for some types of pilot CMMI programs. We’ve had groups that have been using these metrics for allocation of other types of community resources. So community groups who might… We had a group, for instance, that was very interested and reached out to us about Baby Box delivery within highly disadvantaged areas.

Amy: And what they could do is take their workers, look at the map and plan out where all their delivery location sites would be, targeting those areas that were most disadvantaged within their particular region.

Alex: What’s a Baby Box?

Amy: A Baby Box is kind of a kit, a starter kit for new moms that may not have the resources to have all of the infrastructure that they need and all of that stuff that you need when you have a baby. And so it also provides a safe place to sleep as well.

Alex: I thought it was a box with a baby in it. [laughter]

Amy: No. No it is not a box with a baby in it. [laughter] But I’ve been really, really fascinated and I’ve loved the response we’ve had, because it’s such a wide array of different activities that have gone forward. And of course, as you mentioned with COVID right now, some of the greatest interest in the tool is that it’s been being used in certain areas for preferential allocation of COVID vaccines.

Amy: So being able to do outreach efforts at the ground level, into highly disadvantaged contexts that we know increase risk for COVID, that’s been studied again using our metrics over and over again. But then you can respond by allocating vaccine and setting up your vaccine administration areas, right within those highly disadvantaged neighborhoods, to bring the resources to individuals who need it the most.

Eric: And then what components go into the area deprivation index?

Amy: So the area deprivation index that’s again shown on the metric are those same social determinants of health at the contextual level that are being used in other countries. So income, education, employment, and measures of housing quality.

Amy: The measures themselves, the raw data that we use to construct these metrics using the complicated statistics that we use, is pulled from the US Census. So these metrics reflect the information that the US Census obtains on the full US population. So it’s a kind of metric that it carries the same limitations as the US Census does. The census to under count certain groups, but it’s the best thing we have in terms of population representativeness for the United States.

Amy: And so what it allows us to do is it takes us beyond a traditional research metric where you have to survey a certain population, but you’re limited by your finances, and you can only include certain groups as we all know, and we’ve worked with some of those really wonderful surveys.

Amy: But this gives us a measure that could potentially be employed for any individual within the United States. So because of that, it becomes incredibly policy-relevant. It becomes a metric that anyone in Congress could take and say, “Oh, maybe we’re interested in targeting additional resources to certain areas of need.”

Amy: And so we can use this, and it would apply to individuals in Alaska, just as well as Florida, Puerto Rico, California, all across the United States. From a research perspective, this is important too, because it allows us to do something called data harmonization, and that’s a fancy term for basically saying we’ll have measures that can be compared across large swaths of the United States.

Amy: And then of course, because it’s based on methodologies that are widely used in other countries in Europe, suddenly you can do cross national comparisons as well.

Eric: Yeah, I guess another question is, I think 2020 has really brought also to light a lot of issues around systemic racism and disparities of care. Race and ethnicity not part of the area deprivation index, is that right?

Amy: So the area deprivation index does not include measures of race and ethnicity because it’s aligned with the European metrics. So it includes those four areas that I talked about in the social determinants of health. One of the reasons this becomes really, from a research perspective, and this might get a little bit too technical for some, but from a-

Eric: We’ll allow you to get a little bit wonky right now.

Amy: Get a little bit wonky. It allows us to isolate the effect of these social determinants and then put race into the model as an independent effect, which allows us to really examine racism in a much more specific way, if that makes sense.

Amy: So we can look at the interaction much more specifically. If we have all of these things in, and we weren’t just with the one construct, we limit our ability to measure some of the effects that we’re most interested in, because we have taken these metrics now, and we have, through this open data platform, all sorts of other research groups are using them as well.

Amy: Our group is interested in Alzheimer’s disease. So we’ve connected these metrics to many different measures of brain health and brain health outcomes. But other groups have shown as well, that living in a highly disadvantaged neighborhood is associated with changes in the epigenetic signature genome, to suggest premature aging, increased risk of other types of diseases as well.

Amy: If you live in a highly disadvantaged neighborhood by these metrics, you have less chances to access chemotherapy. You’re more apt to die more quickly after a cabbage surgery. You have changes in your brain structure, even from age two, they’ve seen in some of the work coming out of New York.

Amy: So it is fundamental and being as clean as we can in terms of isolating the effect, it allows us as a research community, to really look at the whole spectrum of other factors that go into creating mechanistic focused research that allows us to be a little bit more specific when we design interventions.

Amy: So I know that got really wonky very quickly, but there are good reasons for it. And it’s something that doesn’t… It absolutely does not mean that others cannot add those factors. We’re not taking that away at all. We’re just giving a very, very specific construct of measure.

Alex: Yeah. I worry, if you included race in the model, and you said that neighborhoods that had a high proportion of minorities, this minority population were disadvantaged. That language concerns me. And I see you’re shaking your head, definitely concerns you as well.

Alex: The language is important here, and I like that you’ve moved from the area deprivation index, which suggests that certain neighborhoods are deprived where they may have their own inner resources and strengths that aren’t recognized by this measure, to the Neighborhood Atlas.

Alex: And I wonder, A, if you’re building in measures of resources that aren’t captured, maybe they have a strong faith based community, for example, and then B, I also wanted to hear, the words that you’re using now, and you’ve used the word social exposome, we’d love to hear about that too. That’s a lot of questions, so you could do whatever you want.

Amy: Oh yeah. This is all great. This is all great. Well, let’s first talk about this issue of resiliency, because it’s so important, right? So by measuring these constructs across the full US, what it gives us an opportunity to do is identify areas that are highly resilient, more resilient than we would anticipate in their health outcomes, given the level of challenge that these particular areas might face.

Amy: And so, as researchers and as communities, it gives us an opportunity to dive in, to find out how the resiliency happens. What is the mechanism of that resiliency? Is it a social policy? Is it a cultural practice? Is it some sort of individual behavior. Exactly what is it? And then can we take that and replicate it in other places?

Amy: Can we take something that was really, really effective and built a lot of resiliency in Michigan, for instance, and translate it to California? So it opens the door for a whole host of research like that, because you’re absolutely right. This is metric that is just those four constructs has a lot of other numbers that go into it, but ultimately it’s all those four constructs.

Amy: And so, it only measures that one piece of experience, there’s so many other things, social cohesion, political activity, the different responsiveness and the access to decision-making power within a community who’s driving the car forward in those areas. And is there opportunity that could be harnessed in new ways?

Amy: So you’re absolutely right. The metric, we hope to start building some more of those pieces. The ADI itself will likely always because it’s been validated as just these four core constructs, will likely always be that. But then on the Atlas, hopefully in the future providing more measures of the social exposome as you say, which is…

Amy: So the social exposome is this idea of all of the exposures, all of the experiences that an individual might have that are outside of their human body, although the microbiome, which is inside the body is now being thought of as part of the human exposome, but it’s technically outside of the biologic human.

Amy: And we follow those across the life course, and understand how dosage and timing to different experiences along the social exposome might influence health in completely new ways. So thinking about, for instance, the works that’s being done in New York, by the Ramphal group, looking at childhood brain development within early life in these highly disadvantaged neighborhoods.

Amy: We see a window of susceptibility there, that is absolutely… Well, it’s heartbreaking, but it’s also absolutely perhaps intervenable, right?

Alex: Mm-hmm (affirmative).

Amy: And trying to build these new interventions in order to carry our society forward towards a much more healthy opportunities for all, so that we can get healthier as a country. We certainly spend enough on health and health resources, but we’re not spending it in the way we should in order to best promote health.

Amy: So the social exposome is important. It is a relatively newer construct. It’s being used a lot in linking cells to communities. And in thinking about how some of the basic science processes that we’ve been studying for decades as a medical community might be influenced by the context around those processes.

Eric: So, when I think about, let’s say a randomized control study or anything, I’m looking at like a table one, it has age, gender, some other basic demographics like race or ethnicity, sometimes, maybe some socioeconomic status like core tiles. Should this be also a standard in a table one?

Amy: I think it should. We’ve worked on a lot of secondary clinical trial data or other partners have, or other individuals who have downloaded the data from my Atlas. And what we find resoundingly is that the individuals who are actually randomized in those clinical trials are not from disadvantaged neighborhoods. They’re from highly wealthy contexts, in many, many cases. So on the map, on the Atlas, it uses a heat map format in which the red areas are more disadvantaged than the blue at least. And in many of these clinical trials, we find that there’s this massive skew towards blue.

Alex: Mm-hmm (affirmative). Can I ask you about that color scheme?

Amy: Oh yeah, sure, sure, sure.

Eric: If you look at the elections and the red, blue Democrat, Republican split, there’s actually quite a bit of correlation. I was just eyeballing California. I was like, “I know that that’s a heavily Republican area of California.” It’s also the reddest area on this map, which corresponds to the lowest ranking in the neighborhood Atlas, in terms of relative advantage. Was that intentional or was that just the way it came out to be?

Amy: That’s the way it came out to be. We have no data in there whatsoever on voting behavior. This is purely, and I am not a political scientist, although I’m very interested in policy, which I like to think of as slightly different from politics, but perhaps interwoven. But in this case, no, this is purely housing, education, income, and employment. And so, what you’re seeing is that, and again, I’m not a political scientist, so I have not conducted any sort of analysis to look at voting patterns across the US, and how they correlate with ADI.

Amy: But what we see, certainly what I can say is that individuals who live in highly disadvantaged contexts are having very different experiences than individuals who live in the more advantaged context. And some of these experiences may be driving some of the voting behavior we see. We can also say that the most disadvantaged neighborhoods in the United States are that when we look at the very top decile in inner city core areas. These inner city cores of red that you see surrounded by the circles of blue.

Alex: Yeah.

Amy: And then in the highly rural areas, particularly in native American reservations. And so, you see these patterns of disadvantage across the United States that are clearly not equally distributed at all. We have certain states in the US that have more than their fair share of disadvantage as compared to other states as well. And certainly, I’ll let your listenership take a look at the neighborhood Atlas, if they’re interested in looking at some of those patterns for themselves.

Amy: So again, I’m not a political scientist. I can’t say why the voting behavior is potentially correlated with that, or even if it is correlated that. But certainly we can say say the experience is different in those areas.

Alex: Right. And it was just an eyeball of that. And there are certainly examples as you point out that are clearly back at that overall trend. It just sort of striking it. Many of the rural areas end up being highly disadvantaged as you know.

Amy: They’re highly disadvantaged. Well, and certainly, I’m an example of that. I grew up in an extremely disadvantaged area first-generation college. And I can tell you, at least from our own story, from our own anecdotal story from the area I grew up in, and it’s beautiful, absolutely beautiful. But it used to have in the ’50s, huge mining infrastructure, which of course all left.

Amy: And then it used to have huge paper mill infrastructure, which is slowly but surely leaving. And so you had these areas and I’m sure the same stories can be told across the United States and many other rural areas where farming or different types of produce and factory work that was reliant on the natural resources of the land of those areas are slowly but surely going away.

Amy: And of course, with those businesses going away, the jobs go away, the opportunity sometimes goes away and we see this echoed in our rural hospitals, right? I don’t know if you’ve ever had someone on here talking about the crisis of critical access hospitals in rural areas, but they’re closing at huge rates. And I think some of this is policy of course, the way we pay for healthcare. But some of it is also the way the infrastructure of the country has changed in terms of where our raw and natural resources are coming from in terms of our different technologies and things.

Eric: Yeah. You know, I also see this from a palliative care perspective, the access to palliative care significantly drops in more rural areas. There are some people trying to address it, and doing really high quality palliative care in those areas. Like we had Michael Fratkin on our podcast, but even access to things like hospice becomes exceptionally hard as you go into the rural areas of California.

Amy: Yup. Absolutely. I mean, my own mother was an associate degree nurse, associate degree. And she was put into the role of almost a psychiatrist in many cases because there was no psychiatric services up there when I was growing up. So she would be going into the prisons and doing intakes and the intersection of some of these social challenges with health and particularly mental health is just so profound.

Amy: Access to resources in areas like that particularly health aligned resources is not easy, even something as simple as a Meals on Wheels program, maybe out of reach. And you can’t… Certainly where I grew up to, internet is not reliable at all. The idea of having some sort of a video conference like we’re having right now is well out of reach that does not happen because the infrastructure is not there. And this is the story of many rural areas across the country.

Amy: But yes, they are highly red. When I look at my hometown on the Atlas, it’s as red as it can be, as it’s in the most disadvantaged SL. We have opportunities as a country to think about bringing together our policies and making them perhaps more aligned to these constructs, the social determinants of health, for instance, that can be measured across wide swaths of the population and enact different policy actions that could potentially not only help individuals in rural areas like we were just talking about, but individuals in inner city, urban areas as well.

Amy: It’s not too many metrics that bridge that gap, I think. But I think it also gets to the core of some of the challenges that exist in those areas, when you start talking about social determinants of health in particular, they echo across the human experience in so many ways.

Eric: Well, what’s next for you, Amy?

Amy: What’s next for me? So, we continue the work, obviously with the Atlas, we’ll continue updating it. We are so thrilled to be able to continue doing our work with the social exposome. Again, we’re linking neurons to neighborhoods and understanding how dosage and timing of disadvantage exposure across the life course influences brain health and influences Alzheimer’s disease risk.

Amy: So we were just recently awarded as a very large social exposome study from the National Institute on Aging, which I will be leading. This is called The Neighborhood Study. It involves 22 sites, 22 Alzheimer’s disease research centers across the United States, and will be one of the largest studies of its kind ever funded. So, it will keep me very busy in the coming time.

Amy: I’m thrilled that UCSF is one of our groups we’re working closely with your ADRC as part of this. And it will open up new doors and provide new perspectives for us to understand how the social exposome, that social environment influences health and wellbeing across the life course in terms of brain health. And hopefully, our research will be very policy aligned. And that thinking about some of the questions that we ask, hopefully would provide data and additional evidence to encourage our policy makers to make decisions that can help the health of our country.

Eric: Well, my last question is, and we like to ask the magic wand question. If you had a magic wand to make kind of one change, either from a healthcare systems perspective, provider perspective, or policy perspective, what would that be?

Amy: I would pick the policy perspective and I would say, to help our country use metrics like these geographic based metrics to allocate resources to need, in a way that is much more efficient and aligned with health and health outcomes. I do think that we, as a country are much too focused on investments in expensive technologies and not enough focused on aligning with some of the bread and butter care that is needed and not funded.

Amy: So perhaps my wand would bring social services and health services together under the same umbrella in the United States, and think of it all as healthcare-and fund it.

Alex: That’s great.

Eric: Well Amy, a very big thank you for joining us, but before we end, Alex, you want to do a little bit more Dylan?

Alex: (singing).

Amy: Oh, that was just beautiful. Thank you so much for having me. I’m just completely honored and it’s always so much fun to hear Alex play the guitar.

Eric: I want to send you a big thank you for joining us for this podcast, I certainly learnt a lot.

Alex: And congratulations for all you’ve done. Your next grant, it’s tremendous. Love this work that you’re doing. So important.

Amy: Oh, thank you so much.

Eric: And I’d really encourage all of our listeners, check out The Neighborhood Atlas. Just google Neighborhood Atlas, it’s like the first thing that came up for me, and we’ll have a link to it on our show notes at GeriPal. Also, thank you Archstone Foundation for your continued support and to all of our listeners for supporting the GeriPal Podcast. Goodnight everybody.

Alex: GoodNight.

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