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The Area Deprivation Index in Rural Health Research, with Casey Balio

Date: May 7, 2024
Duration: 25 minutes

Casey Balio. An interview with Casey Balio, PhD, Research Assistant Professor at East Tennessee State University's Center for Rural Health Research and part of the ETSU/NORC Rural Health Equity Research Center. We discuss the origins of the Area Deprivation Index (ADI), its increasing acceptance, and the correlation between ADI and rurality.

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Transcript

Andrew Nelson: Welcome to Exploring Rural Health, a podcast from the Rural Health Information Hub. My name is Andrew Nelson. In this podcast, we'll be talking with a variety of experts about providing rural healthcare, problems they've encountered, and ways in which those problems can be solved.

Joining me today is Dr. Casey Balio, Research Assistant Professor at East Tennessee State University's Center for Rural Health Research. Dr. Balio is also part of the ETSU/NORC Rural Health Equity Research Center. Thank you for joining me today, Casey.

Casey Balio: Thanks for having me.

Andrew Nelson: Through your work with the ETSU/NORC Rural Health Equity Research Center, you recently contributed to a policy brief entitled, “Use of the Area Deprivation Index and Rural Applications in the Peer-Reviewed Literature.” Can you tell us what the Area Deprivation Index is, and how it's calculated?

Casey Balio: Absolutely. So the Area Deprivation Index, or ADI, is a composite measure that was originally created by Dr. Singh at HRSA [the Health Resources and Services Administration] in the early 2000s. It since has been sort of adapted and taken over by Dr. Amy Kind and other researchers at the University of Wisconsin Center for Health Disparities Research. And they've really updated it and validated it at the census block group level and have become the owners of the ADI. The University of Wisconsin team refers to it as an index of neighborhood-level disadvantage. And what it does is combine a number of different measures across what they refer to as the domains of income, education, employment, and housing quality. So the measures that are underlying this are things like the percentage of the adult population with at least a high school diploma, median family income, income disparities, median home values, unemployment rates, and other things like that.

So it takes all of these measures and combines them into this one index measure, or the ADI, which is ultimately calculated as a national percentile of disadvantage or vulnerability. And then there's also a state-level version that's broken up into deciles, or into tenths within each individual state. And it was originally designed by Dr. Singh and correlated with a number of important health outcomes and to be used in policy. So now we see it used quite a bit in policy and practice as a way to account for or adjust for, or even prioritize, areas that may have higher levels of need or higher levels of disadvantage.

Andrew Nelson: Can you tell me a little bit about what concerns led to the ADI's original creation by HRSA?

Casey Balio: So it was originally really designed and used to estimate differences in mortality by age and gender. And this was in the early 2000s, but they looked back at mortality data from the ‘70s through the late ‘90s. And it was meant to accommodate and control for all these differences in the socioeconomic status of individuals and the communities in which they were living. So that's why it was created. The University of Wisconsin team has really taken it over and updated it and validated it with more current data and some more advanced visualization techniques and things like that. So the most current version available uses data from the American Community Survey from 2017 through 2021. And the data and state-level maps and national maps are all available through the University of Wisconsin's website. So it's evolved a little bit over time, some changes in methodology, some changes in data, but ultimately it's really trying to do the same thing that it was originally designed to do.

This study was funded by HRSA's Federal Office of Rural Health Policy [FORHP] to really understand the ADI and all the nuances of how it works in rural communities differentially from urban or just in general. So with this study, we really broke it down into three distinct projects. So the first one is this literature review, which is the policy brief that you've seen, where we tried to understand what do we actually know about the ADI and how it's related to outcomes and how it's been used in literature so we can just have a better baseline understanding of really what is it and how does it behave with those types of outcomes. But then in addition to that, we have done interviews with experts including policy makers and researchers and organizational leaders that were really meant to understand how it's being used in policy and practice and what the strengths and limitations are to it as an index or these indices more generally.

And then the final aim of it is some quantitative analysis looking at how the ADI differs by rurality. We are mostly done with the other two aims, but it hasn't gone through the full approval process yet. So that's sort of the high level. There's a lot in this study, and this is sort of one small piece of it to understand what we know about ADI, so that it can sort of give a stronger foundation for FORHP and others to understand if we are going to use this for policy, what types of outcomes does it actually correlate with that we can think about when we are trying to use it to incentivize providers or reimbursement efforts or things like that.

And we went through a list of inclusion criteria in collaboration with the Federal Office of Rural Health Policy to really narrow in on which studies we should include to understand better how ADI has been studied and what we know about it in relation to rurality. So our inclusion criteria were that the studies had to be peer-reviewed and present original research. They had to be published in English and based in the United States, published since 2015, and they had to have a health outcome related to health status or public health or social determinants of health or healthcare cost, quality outcomes, utilization, things like that.

And then lastly, we required that the studies explicitly used the ADI as designed and provided by the University of Wisconsin team. So there were some studies that did describe replicating the ADI or modifying it, or they used some sort of ambiguous description of an area-level measure of deprivation, and those were not included. So to start with, we had a few thousand studies that came back from the original review, and then after these inclusion criteria were sort of assessed, we ended up with about 220 studies in our final review.

Andrew Nelson: I'm sure that was a lot of data to churn through.

Casey Balio: It was.

Andrew Nelson: It's really cool to see what you eventually came up with. Your policy brief obviously focuses on the use of the Area Deprivation Index. Can you tell us about some of the other tools that can be used to help identify social drivers of health, and tell us why the ADI emerged as something you found was especially useful?

Casey Balio: Sure. There are now many of these different indices that are reflecting sort of similar underlying constructs of vulnerability or deprivation or oppositely framed or more strength-based framing of opportunity or resiliency. They generally use similar measures related to topics like poverty and housing and transportation, social connectedness — all of those types of social drivers or social determinants of health. But there are many of these.

So a few of these examples of indices include the Social Vulnerability index, which is developed by the CDC [Centers for Disease Control and Prevention], the Social Deprivation Index by the Robert Graham Center, the [Rural Health Mapping Tool] Prosperity Index from NORC, and the Baseline Resilience Indicators for Communities from the University of South Carolina. And there have been efforts by others, including those at ASPE [Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services] and RAND and RTI and other researchers to really identify and describe all these different indices and how they compare. So many of these indices use data from the Census Bureau and really similar data, but all of these indices do vary in some really important ways. So even just in the list that I mentioned, some of these are more assets or strength-framed, like the ones that are focused on opportunity or resiliency, while others are really framed around deprivation or vulnerability. But other things that differ are things like the geography.

For example, the ADI is at the census block group, but others are at the census tract level or the county level. And they were created using different methods in weighting. So some used statistical methods like principal component analysis or sort of other statistical techniques, while some used really expert consensus. And some were validated outcomes while others were not. So there's lots of ways that these differ. And one of the reasons why ADI has become the most prominent in many respects is because it has been used so widely in the research. So we know a lot about sort of how it correlates with a lot of important clinical outcomes. It was also really one of the earlier ones on the scene. So I think that contributes to it too. And then lastly, some of these indices do include measures of race and ethnicity directly in the index, but others don't.

So the ADI does not include measures of race and ethnicity, and there's been some discussion on whether race and ethnicity should be included directly in an index or not, and what that means for adoption of these indices, especially by state and federal agencies. So all this really to say that there are many different indices that now exist and are being used, and we know that they do differ in how they were created and developed, but we actually don't know that much about how they can compare to each other in relation to specific outcomes or measures about the community or characteristics of the community like rurality or race/ethnicity.

Andrew Nelson: It seems like thus far, ADI data in some fields of research often ends up getting siloed, and there's a lack of research correlating it with other types of documentation. How have you been working to help develop a consensus in the use of deprivation data?

Casey Balio: Yeah, I think that's correct. The ADI has been really heavily picked up in clinical fields. In our literature review, we found that, especially in surgery and orthopedics for some reason, it seems to be used quite a bit. We see it less used in more traditional health services research or health policy research, although it is heavily important and still there as well, but not as frequently as in other places. I am not really sure exactly why that is. But we have done this review to sort of better understand the ADI in particular and to draw some attention to how researchers can describe it in a way that is more uniform and that we can really get a better understanding of how the ADI and other indices behave and relate to these important health outcomes. The way that ADI is used in this research looks very different from study to study, which has some pretty significant implications for what we know about it and how we may or may not decide to use it in policy or in research.

Andrew Nelson: What are some health outcomes that you've seen that are commonly statistically associated with ADI?

Casey Balio: So in our review, we found that ADI, again, is used pretty often with clinical outcomes in the areas of surgery and orthopedics and cardiovascular conditions and cancer, especially, and some other chronic conditions as well. Within those areas, it does correlate with many important outcomes related to readmission rates and mortality rates, expenditures, rates of chronic conditions and many other factors as well. But it seems to correlate pretty highly with most of the outcomes for which it has been used.

Andrew Nelson: What kinds of lack of consensus or consistency did you find existed between rural applications of the Area Deprivation Index?

Casey Balio: Across the about 220 studies that we found that used the ADI related to a health outcome, one of the biggest lacks of inconsistency that we found across studies was how the ADI was actually operationalized within their analysis. So what I mean by that is how the authors actually measure and use the ADI in their statistical analysis. So, for example, about 40% of studies used that national percentile version of ADI, whereas about 20% use state deciles. Another about 20% dichotomized the ADI, or took it and sort of broke it into two categories of sort of high need and low need and then another 55% use some other type of categorization. And you'll notice that those numbers come to greater than 100, so many studies did use multiple ways to measure ADI.

And even across studies that dichotomized the ADI or just divided it into two categories, they set some threshold for what they consider high or low levels of deprivation. And where that threshold was drawn differed a lot. So, for example, some studies used anywhere from the 25th percentile nationally to the 95th percentile nationally for the cutoff of what's considered sort of higher need or higher vulnerability or deprivation. And those are really substantially different. So when we think about really the upper 75% of census block groups versus the upper 5%, and saying one of those is high-deprivation or high-vulnerability, those are really different. And therefore, when we talk about associations between the Area Deprivation Index and these health outcomes, it's really important to consider what those underlying measures of ADI are in those studies, so we have a better understanding of the nuances of how it relates to certain health outcomes.

So I think that's really the biggest area that we saw a lack of consistency within the studies. One of the other areas that we saw some differences in were the scope of the studies. So a lot of them really relied on individual hospitals or health systems for their data. Because the ADI is at the census block group level, which is really granular and hard to get national- or even state-level data on. So when we're looking only at data within a single health system or a single hospital, you're getting a much more granular look at that area, but it may not really be representative of the country as a whole or of an individual state. So there are some limitations in terms of the generalizability of some of those studies too, and it's helpful that we have so many, so we can sort of piece these together and have a better understanding of what this looks like. But those are important considerations when we try to pull all of this evidence together and see what we actually know about the ADI.

Andrew Nelson: It's now been more than two decades since HRSA's creation of the Area Deprivation Index. Can you tell us about the increasing acceptance of the ADI you've seen by the Assistant Secretary for Planning and Evaluation, ASPE, and CMS, the Centers for Medicare and Medicaid Services?

Casey Balio: Sure. So there are a lot of different uses and adoptions or sort of acceptances of the ADI that are occurring, and ASPE and CMS are two of them. And even within those, there's some variation. So I'll talk about just two of those applications. The first is around resource allocation and reimbursement. So in CMMI [the CMS Innovation Center], there's the ACO REACH program. The Accountable Care Organization Realizing Equity Access and Community Health is what the acronym stands for. So this initiative is using the ADI as one way to adjust reimbursement to align resources with need in areas that may have higher levels of disadvantage. And this is really one of the first federal programs that has used the ADI. And it's using it in conjunction with dual-eligibility status as part of what they're referring to as a health equity benchmark adjustment. So that's the ACO REACH program. And it has been implemented for a little bit.

The second way that I'll talk about is from ASPE, or the office of the Assistant Secretary for Planning and Evaluation. So they commissioned researchers at RAND to really better understand what indices exist to better measure health-related social needs or social determinants of health using data with the goal of informing policy and resource allocations to improve equity. And all of this is really under the HHS [U.S. Department of Health and Human Services] efforts and under the Biden and Harris Administration to focus on health equity. So ASPE really wanted to understand which of these exist and which ones may be better used in policy applications.

And through this work from ASPE and RAND, they identified a number of different indices and they described what they are composed of and how they are related to each other and what they're being used for. And ultimately, ASPE had released this reflections report where they landed on four different measures that they preferred for policy use in the short term with the recognition that all of these do have some limitations. And those are things that we need to consider and we need to improve upon moving forward. So the four that they landed on were the ADI, the Social Deprivation Index, the Social Vulnerability Index, and the Community Resilience Index. And one of the deciding factors that they used, especially in highlighting the ADI as really one of the preferred measures was that it does not include race and ethnicity in the actual index itself. And they described that actual race and ethnicity are inherently not things that can be changed or modified and that the disparities that we see in health outcomes by race and ethnicity are really the products of structural racism and discrimination and all of these underlying constructs. But it's not really race and ethnicity inherently that are relating to these health outcomes. So the ADI really ranked as one of their preferred measures, and we've seen a lot of adoption of it since then as well.

Andrew Nelson: Are there are there any benefits to rural health that you've been able to see from the work you've done with ADI?

Casey Balio: Yes. Most of what we've seen in the literature are studies that look at a health outcome as a function of ADI, and a lot of them do also include rurality in their models or in their analyses. But none of the studies that we found look at the correlation between ADI and rurality or any intersection that may occur there. So that is definitely something that we are looking into more. And as a part of this larger study on ADI that we are wrapping up, we have done some of these analyses too. So we have found that ultimately rural areas on average have higher levels of disadvantage or deprivation as measured by the ADI compared to more urban areas, and that's really independent of which measure of rurality you use. So we do see that in resource allocation efforts, it does really prioritize rural populations.

So there may be some really important rural health benefits that exist there. But we don't really know quite yet what that looks like. And then I also do want to note when we're talking about rurality, that there is an ongoing discussion in the field around some of the potential limitations regarding ADI as it relates to that rural-urban continuum. And there are a set of researchers that have been digging into several of these indices, including some of the ones that I just described. And when looking into the ADI, they have found some areas, especially some highly urban areas that we sort of anecdotally know have high levels of disadvantage, but they actually look pretty okay based on the Area Deprivation Index. So when those folks dig in to those particular areas and really dug into the ADI, they found that this is largely because of the way the ADI is calculated.

And it does not standardize measures including things like housing costs, and then those really high, large numbers sort of dominate the index, particularly in areas that have really high housing costs, but are otherwise considered areas that have high levels of vulnerability or deprivation. So there's this ongoing discussion about how the ADI and these other indices work across the country, including by rurality. So I think we'll see a lot more conversation and a lot more research in this space as well to better understand how these really do differ and perform when we look at individual communities and look at where funds get allocated or where we see changes happening in response to the implementation of these types of indices.

Andrew Nelson: What does your study contribute to the field of rural health and what further research do you think is needed regarding ADI and other similar indices?

Casey Balio: So I think that this study is an important step to better understand these social drivers and social determinants of health and how we are trying to measure these. And ultimately the goal of these is to provide better evidence and better data that can support policy applications that are really trying to promote health equity and really address some of the health disparities that we've seen by rurality and also by race and ethnicity and by many other factors as well. So like I described earlier, this literature review is just one part of this particular project. So we have findings that should be available relatively soon looking at how the ADI differs by rurality and the perspectives of some federal, state, and local folks in terms of their experiences and perspectives on the use of the ADI and other indices. And then this work with the ADI specifically led to another study funded through the Federal Office of Rural Health Policy and CDC's Office of Rural Health, where we are starting to dig into some of these other indices that are specifically available at the county and census tract level.

So we are in the midst of that work now and are really excited to see where that goes and really try to provide better evidence and more complete evidence so that policymakers and researchers and practitioners can really strategically and transparently decide which index to use and how to incorporate them into policy and practice and resource allocation and all of those to really support those goals that they have of addressing inequities and disparities. So we are also just a few of the researchers working in this space. There's now a lot of ongoing work here and then some of these applications and policy and practice that we've talked about. And so other ones are now a few years in and we're starting to see some real-world evidence on what the implications are of using these types of indices. So I think there's a lot to be learned there too, to see how those implementations rolled out and see what worked and what didn't worked and how we can inform future uses.

Andrew Nelson: As you mentioned, there are other groups of people that are also working on this at the same time along similar tracks. Looking ahead, what are some of the next steps you want to make in research in this area?

Casey Balio: I think there are a lot of things that we would like to do here. I think there's some space to really understand if any one of these indices is really the right thing to use nationally, and if it really applies and captures what we're trying to do for all types of communities across the US. There's some conversation around sort of what's the right geographic unit for these types of things, especially when you consider rural and urban areas. In highly urban areas, a very small geographic unit can have even really high heterogeneity, whereas in rural areas we have much larger communities in terms of landmass and fewer people. So what is really the right geographic unit for any given policy application? And how can we best measure things that make sense across the rural-urban continuum, especially around things like housing and transportation.

Those look very different in rural and urban. And those are some of the major components to these types of measures. So I think there's some space there too, to understand how good is the data we have and do these measures really mean the same thing in rural and urban spaces. So those are some of the types of questions that we are starting to think about and starting to explore to ultimately better our understanding of these indices and how we measure for and adjust and use these types of measures and policy.

Andrew Nelson: You've been listening to Exploring Rural Health, a podcast from RHIhub. In this episode, we spoke with Dr. Casey Balio, Research Assistant Professor at East Tennessee State University's Center for Rural Health Research. Dr. Balio is also part of the ETSU/NORC Rural Health Equity Research Center. Look in our show notes for more information about her work and visit ruralhealthinfo.org for all things pertaining to rural health.