Measuring well-being across the US

Race, gender, and where you live can have a profound impact on your well-being. In a first-of-its kind analysis, new data shows significant disparities in individual well-being as measured by lifespan, education, and income. As researchers point out, these disparities are not merely statistics, but a call for action. We discuss the data with study author IHME Associate Professor Laura Dwyer-Lindgren. 

The transcript has been lightly edited for clarity

Rhonda Stewart: Welcome to Global Health Insights, a podcast from IHME, the Institute for Health Metrics and Evaluation. I’m Rhonda Stewart. In this episode, we’ll hear from IHME Associate Professor Laura Dwyer-Lindgren as she discusses an analysis that’s the first of its kind looking at well-being as measured by lifespan, income, and education. Researchers found significant disparities by race, ethnicity, sex, and age group. Where you live also impacts your well-being. What makes this analysis unique is that IHME researchers adapted the UN Development Programme’s Human Development Index, or HDI, and combined that with data from the American Community Survey to produce data at the individual level rather than the group level. As Professor Dwyer-Lindgren points out, the disparities uncovered in the data are not merely statistics, but a call for action. The research is part of a new series on health and health policy in the US that will be published in The Lancet. The study examines well-being as measured by life expectancy, lifespan, and income. Tell us about what makes this type of analysis unique.

Laura Dwyer-Lindgren: So let me start with a little bit of background and history on this. The study draws on an existing measure of well-being that’s known as the Human Development Index, or HDI. And HDI was developed by the United Nations Development Programme several decades ago as a way of measuring and comparing well-being among countries. It’s a composite metric that encompasses these three different dimensions: One of them is a long and healthy life, which in the original form is measured by a population’s life expectancy. The second is knowledge, which is measured by a population’s mean years of schooling. And then the third is standard of living, which is measured by the gross national income per capita. And that version of HDI is really widely used and really well suited for comparing among countries, but it doesn’t tell us very much about inequality within countries, and it doesn’t tell us anything at all about who within a given country is doing poorly. And identifying who within a given country is doing poorly matters, because one strategy for improving well-being overall, and especially for improving equity in well-being, is focusing on the challenges of those who are currently the worst off. For this analysis, we adapted the HDI metric to measure well-being at an individual level in the US, and to do so we kept the same kind of three dimensions, but we modified the way that these dimensions were measured to use measures that make sense for individuals rather than for countries. So for the long and healthy life dimension, we measured what we’re calling expected lifespan, which is an individual’s current age, the number of years that they’ve already lived, summed with their remaining life expectancy based on their age and sex and location within the US. For knowledge, we just look at the number of years of education that an individual reports having completed. And then for standard of living, we looked at household consumption, which is essentially household income, but adjusted for the size of the household. And we did this using the American Community Survey, which is a very large annual survey of the US population that’s done by the US Census Bureau. That does not allow us to measure HDI for literally every individual in the US, but it allows us to do so for a representative sample. And that’s useful because then we can look at patterns in well-being, and we can focus specifically on those who are worst off and examine, for example, the demographic characteristics of these people and also what regions of the US these folks tend to live in.

Rhonda Stewart: And what are some of the key findings of the study?

Laura Dwyer-Lindgren: So first and foremost, when you look at this at the individual level versus at the country level, you find a lot of variation in HDI among individuals within the US. There are people within the US who just have vastly different life circumstances. And that really pops out looking at it with this frame. We paid particular attention to the folks who have the lowest HDI and specifically the bottom decile, or the bottom 10% of the population when ranked by HDI. And one thing that jumps out when you consider the lowest HDI decile is that there are these really marked disparities by race and ethnicity and then also by sex. Now, remember, we’re looking at the 10% of the population with the lowest HDI scores. So if everything was sort of even across racial and ethnic groups and across sex, every group would have 10% of its population in this lowest group. But that’s not at all what we see. Fully half of American Indian males are in the lowest decile – so it’s five times as many as what you would expect if there were no racial and ethnic disparities. 40% of Black males were in the lowest decile, as well as a little over 20% of Latino males and American Indian females. There’s also more than 10%, although not by quite as much, of Black and Latina females in this group. So in terms of the probability that somebody will be in the lowest HDI decile, the main story is about racial and ethnic inequalities, and particularly for males. There’s another way of looking at these data, however, which is to say, what is the composition of the lowest decile? And that is impacted both by the likelihood that different groups are in the lowest decile, but also the relative population size of those different groups. And when you look at the composition of the lowest decile, the single largest population group is White males, who make up 27% of the population. And then Black males are second at 22%, and Latino males are third at 15%.

Rhonda Stewart: And unfortunately, disparities by race, ethnicity, gender are not unique. So what does this analysis add to the conversation on disparities?

Laura Dwyer-Lindgren: The US has a long and horrible history with colonialism and racism, and consequently, racial and ethnic disparities are far more common than not. You see them across just a huge range of different outcomes. And with that in mind, it’s just not at all surprising to find racial and ethnic disparities in well-being as measured as HDI. And that’s really what I expected when we set out to do this analysis. But I think that in part because we do see racial and ethnic disparities across so many different outcomes, I think there’s value in summarizing that in one general metric of well-being such as HDI, because it provides a relatively simple way to communicate just how egregious and also fundamental to people’s everyday lives these disparities are. And so we hope that by making this a little bit easier to communicate and to understand, we can help inspire action among those who might otherwise just not be paying as much attention.

Rhonda Stewart: And what kinds of geographic differences did you find?

Laura Dwyer-Lindgren: Quite large differences geographically. So again, if we focus primarily on the lowest decile, the lowest 10% of the population – and keep in mind that if everything was even, you’d expect to see 10% of the population in every location in the lowest decile. That’s, of course, not at all what we see in many locations throughout the South and in Appalachia and in Rust Belt states, as well as a few locations in other parts of the country – we see more than 10% of the population in the lowest HDI decile. And there’s even a handful of locations in Appalachia in the Lower Mississippi Valley, where more than 40% of the population is in the lowest HDI decile. So there are these really large geographic disparities as well.

Rhonda Stewart: The study also found differences by age group. Tell us about those.

Laura Dwyer-Lindgren: In addition to looking at the racial and ethnic and sex disparities in HDI, we also considered how these disparities differed between four age groups – so ages 25 to 45, 45 to 65, 65 to 85, and 85 and older. And these age groups don’t match exactly specific generations the way that we think about that generally. But given how broad the age groups are, you can think of the differences between these age groups as intergenerational, long-term differences in patterns of disparity in HDI. So if you recall from earlier, I mentioned that about half of AIAN males and about 40% of Black males overall were in the lowest HDI decile. And once you look at this by age, it becomes apparent that this is a particularly acute issue for younger individuals. For example, almost two-thirds of AIAN males in the age group 25 to 44 are in the lowest decile as compared to about 20% among those ages 65 and older. And we see a similar pattern for Black males, where nearly half of Black males ages 25 to 44 are in the lowest HDI decile, but only about 20% – which is still overrepresented, but just not by as much – among those age 65 and older. And you know, the other thing that I think is really interesting when you look at this by age is I’ve spent a lot of time thus far talking about results for males because males generally are overrepresented in the lower HDI deciles. When you pay a lot of attention there, you see a lot of results come out for males. But when you break things up by age, and particularly when you look at the older age groups, you start to see some real disadvantages for females as well. So, for example, only about 7% of Latina females ages 25 to 44 are in the lowest decile, so actually a little bit underrepresented. But around 35% of Latina females ages 65 and up are in the lowest HDI decile. The other thing that’s really different by age is sort of this composition of the lowest HDI decile, which again is impacted both by the likelihood that each group is in the lowest HD cell, but also the relative size of the populations. And this varies a lot by age. And the most striking thing here, I think, is the differences by sex. So if you look at the youngest age group, those who are 25 to 44, three-quarters of that population in the lowest HDI decile is male, whereas that’s almost completely reversed in the oldest age group, or about 70% of the lowest HDI decile is female.

Rhonda Stewart: Okay, and what are some of the trends you found with respect to education, lifespan, and household consumption?

Laura Dwyer-Lindgren: That’s a really interesting question because of course those are the things that are driving what’s going on for HDI just by consumption. In terms of the time trends, I think the two most interesting things are with respect to education and to lifespan. But for education over the study period, which is from 2008 to 2021, we saw a gradual increase in the average years of education for all of the racial and ethnic groups and also for both sexes. However, within each racial and ethnic group, we typically saw a slightly bigger increase for females than for males. And in some cases that means that the gap – because in most cases males had higher education at the beginning of the period – sort of closed a bit. And for the White population, it actually means that it swapped. So males had slightly higher education at the beginning and females had slightly higher education at the end. For lifespan, the big story is really about COVID. Again, there was a gradual increase in expected lifespan from 2008, which is the beginning of the study period, to 2019. And then there was this huge decline as a consequence of COVID in 2020. Trends in 2021, depending on the continuing experience with COVID, the size of the decline was really inequitable. So certain groups, and particularly minoritized racial and ethnic groups, were just impacted much, much, much more dramatically by COVID, although everybody had a negative experience with COVID.

Rhonda Stewart: And how can this information be used to inform policymaking and long-term planning and investment in health?

Laura Dwyer-Lindgren: So I mentioned at the beginning the importance of identifying who is worst off as a motivating factor for this analysis. This study provides insights into the demographic characteristics of those who are worst off and also points to some specific geographic locations where there are especially large numbers of people who are doing poorly. I think it’s really critical that we act on this in an intentional and systematic way. And the other thing I want to highlight is that the three components of HDI are really interconnected. That’s not really a result of this analysis, but we know that from lots of other research. So, for example, there’s an obvious connection between higher education and higher income. There’s also a huge amount of evidence that higher education, higher income is related to better health outcomes. And so it makes sense to me to act on all three of these factors, which could create a sort of synergy that has the potential to make a really big difference in people’s lives.

Rhonda Stewart: And finally, what’s the main thing that people should take away from the study?

Laura Dwyer-Lindgren: I think the main thing is that there’s more work to be done. I would argue, and I think a lot of people would agree, that everyone deserves to live a long and healthy life. And I also think that they deserve sufficient education and income to live the life that they want. And what we’re showing here is that this is just not a reality for many Americans at this point. But, you know, I think there’s something we can do about that. In fact, we probably need to do lots of things about that. I don’t think there’s a single silver bullet. But it’s a matter of effort. It’s a matter of attention. It's a matter of resources, and it’s probably a matter of time. So I think, again, we need to do those things to make a difference here.

Rhonda Stewart: Great. Thanks so much, Laura. Thank you. Details about the HDI study can be found at www.healthdata.org.