Podcast: US health disparities by race

IHME researchers studying health disparities in the US share the impacts of race and ethnicity on life expectancy, COVID-19 outcomes, and more.

 Listen as a podcast

Key takeaways:

• There are large differences in life expectancy across racial and ethnic groups in the US, as well as between different counties. The effects of the COVID pandemic magnified many of the disparities between racial and ethnic groups. Our upcoming research will dive deeper into those impacts.
• Data for our research comes from censuses and surveys, and our work includes adjusting for racial and ethnic identities not represented in those platforms, including individuals who are multiracial. We collect cause of death information from death certificates filed with state governments and are working to include more data on non-fatal health issues.

This transcript has been lightly edited for clarity

Pauline Chiou: Welcome to the Global Health Insights podcast at the Institute for Health Metrics and Evaluation. I’m Pauline Chiou in Media Relations. Thank you for joining us. In this podcast, we’re going to be diving into health disparities across the United States at a county by county level and how the research is done going into this. Dr. Ali Mokdad, a Professor of health metrics sciences at IHME and Chief Strategy Officer of Population Health, is joining us, as well as Dr. Laura Dwyer Lindgren, an Assistant Professor of health metrics sciences at IHME. She also leads the US Health Disparities Team. 

Thank you both for being with us. And let’s start with the exciting news, which is IHME has been awarded a major contract with the NIH. Dr. Mokdad, you want to tell us a little bit more about that?

Dr. Ali Mokdad: We’re so excited and we’re looking forward to this collaboration with NIH. This builds on previous work that we have done with them for the past three years, and now they extended it for another five years and we’re looking at disparities in health outcomes, burden of disease at the county level by race, ethnicity from 1990, all the way.

Now, that’s what GBD does. For this part of work, we’re trying to go back as much as we can, and we are right now going back to 2000. And we’re looking at trends and what’s changing here. What are the areas that have more disparities enhanced by race, ethnicity across the United States?

Pauline Chiou: Dr. Dwyer-Lindgren, take us a little deeper into the picture of health disparities in marginalized communities in the US.

Dr. Laura Dwyer-Lindgren: Sure. So there are all kinds of health disparities in the US. So we focus, for example, on disparities in life expectancy, how long people live. There are gigantic racial and ethnic disparities in life expectancy. So people who are Black, people who are American Indian, Alaska Native, live multiple years less than people who are White, on average. And then similarly, people who are Asian, people who are Latino tend to live longer.

There’s a lot of reasons for that. There are also really substantial spatial differences. So differences among different regions, different states, different counties in the US. And there we’re talking about multi-decade differences in life expectancy among different counties. And when you look at those two dimensions simultaneously, as you look at how for example, Black people in one county and Asian people in another county, how long they live there, the disparities are even bigger.

You just see, again, multiple decades of differences in life expectancy. And for some of the work that we’ve done so far on causes of death, one of the interesting things that we find is that those disparities are essentially ubiquitous. So the exact pattern definitely varies, but we see racial and ethnic disparities across effectively all causes of death.

We see racial disparities across effectively all causes of death. And this is true over time. And in some cases, those disparities are getting worse. There were some cases where the disparities were getting better over the course of the two decades that we looked at. But we also know that in the context of COVID, a lot of that has been reversed.

It’ll be interesting to see how that compares once we add that in as well.

Pauline Chiou: Yeah, we know COVID magnified some of those disparities in a big way. And Dr. Dwyer-Lindgren, you’ve been part of this for several years. And as Dr. Mokdad has mentioned, you’ve looked at that time range from 2000 to 2019. And as we look at the research that’s going to happen for the next five years, you’re going to add years too to that.

And what else will you specifically be researching?

Dr. Laura Dwyer-Lindgren: Yeah, so we’re going to add years. We’ve focused, as you said, on that, what I’m now thinking of is the two decades before COVID and all of that time frame. And obviously, we want to start updating that to the last few years since so many things have changed in the first three years, we also focused not exclusively, but primarily on what we think of as fatal outcomes.

So how long do people live? What are the disparities in longevity, both among counties and among racial and ethnic groups? And also, what are people dying of? And in the next five years, we want to expand into other aspects of health. So what do people suffer from while they are alive? What decreases their health? What causes ill health? So looking at prevalence and incidence of a lot of different conditions.

And then we also want to expand our work on risk factors, things that we know lead to ill health and early death. Another thing that we are looking to expand is for a variety of data challenge reasons. The race and ethnicity groups that we’ve been using so far are aligned with sort of older guidance around how to report on race and ethnicity.

So, for example, we are currently using a combined Asian and Pacific Islander group, which is really no longer the standard. So part of our efforts in the future are going to be to do the very large kind of work required to split those two groups and also to add a new group for individuals who are multiracial.

Pauline Chiou: So that’s very important because in the United States, we’re becoming more and more of a multiracial, multiethnic society. So it’s difficult to just check one box anymore. And so this is very important research. But how exactly are you getting the data? This is county by county. You’re looking at health risks, mortality, death rates. Now, you’re also going to be looking at risk factors.

And that’s the treasure trove of information. So where exactly are you getting all of this from?

Dr. Laura Dwyer-Lindgren: There are a couple of sort of key places that we get data from. So when it comes to data just on how many people are there, which is like the most fundamental thing you can do in terms of starting to think about health, most of that data comes from the decennial census, which happens every 10 years, and then something called the American Community Survey, which is the ongoing approximately 1% sample of the US population.

So that’s where all a kind of basic information about where people living, what is their identity comes from. And then in terms of health outcomes for mortality or fatal outcomes that I mentioned earlier, that information is coming primarily from death certificates. So when somebody dies, their death certificate gets filled out. That’s aggregated at the state level. States submit that data to the federal government, it gets compiled, and then we use that kind of compiled dataset that represents all deaths that occur in the US. So that’s actually reasonably straightforward.

When it comes to estimating non-fatal outcomes that affect people while they’re still alive and cause ill health, that’s much more varied. So we pull from a number of different sources there. In some cases, we can use health surveys. So you’ll have a survey where somebody either shows up at your house or calls you and asks questions that would include things like, “Have you ever been diagnosed with diabetes?” that tell you something about how common the condition is.

We can also leverage certain types of data that come out of clinical encounters. So like claims data, which are relatively common in our hospital. It’s a mix of information that our system we’re looking into kind of what other data sources there are.

We spend a lot of time in the first three years thinking about, okay, among the data sources that exist as part of the GBD, what are the ones that have county detail? That’s what we need for this analysis and also have information on race and ethnicity. And I think going forward, we have to think even more broadly, too, about how do we kind of draw in every potential source of information to really do a good job of this with such a fine level of detail.

Pauline Chiou: And Dr. Mokdad, this is such a huge amount of data. We’ve already looked at more than 3,000 counties, and you’re going to be continuing with that for the next five years. And you’re getting very granular here at the county level. How do you expect all of this rich data to be used.

Dr. Ali Mokdad: I need to clarify one thing with working with NIH, a different institute, but the main institute for working with us on this project is the National Institute on Minority Health and Health Disparities at NIH. So they’re gleaning that, and we have a close relationship working with them.

We at IHME have built an expertise in looking at different sources of data and putting it together in order to get a better picture of health. And we strongly feel if you can’t measure an outcome, you cannot improve it.

And that’s very important for us to get it correct. All that data is coming to us at IHME and we’re putting it together. And in order to get a better picture of health is showing a lot of disparities in the US between different racial groups, and in many instances, that improvement from 2000 to 2019 did not occur or occurred in the first 10 years, but slowed down in the last 10 years for Native Americans, for example – we haven’t seen any improvement at the national level for life expectancy.

And when you look at the disparities between racial groups and between geography and racial and ethnic groups, you have people here in the United States who live longer than anybody else on the planet. And we have people here in the United States with a life expectancy that’s ranked among the lowest countries. And huge disparities in the US for a country with our might and our ability to spend money and deal with problems.

That’s not acceptable. What we hope with this data and with this amount of data provided to everybody, not only for our colleagues at NIH, but for states or counties and for other people who are working in health organizations is basically to point where is the need for interventions. And the fact we are doing this on a continuous basis.

I mean, it allows you also to look and see what kind of impact you are doing if you do it right, because we’re going to repeat it. So this is a baseline from 2000 all the way to now. If there are programs to deal with such risk factors on such diseases in the United States, what will happen?

And the fact that we keep repeating this at IHME will allow you to look at it and evaluate your program. Are you doing the right thing in order to improve outcomes? The bottom line here is we want to provide the best data for decision-makers in order to build on it policies and programs and implement them at the local level to improve health.

Pauline Chiou: All right. And that was a key question that you posed earlier. Where is the need for intervention? And that’s where decision-makers and leaders and the public who are concerned can really start thinking about this as they look at the data? Dr. Dwyer-Lindgren, as you look ahead for this exciting extension of the partnership and really drilling down deeper, what do you see as some of the challenges as a research scientist going forward?

Dr. Laura Dwyer-Lindgren: Yeah, I mean, I think that there’s a lot of just sort of technical challenges with doing this. So I alluded to earlier that we have done a lot of work on fatal outcomes, and that’s because the data are veritably straightforward.

It wouldn’t be easy to work with because they’re not with their comparative history where you have information on all these different causes. You have it comprehensively across location, across time. So it at least is all kind of there.

And once we move into trying to consider non-fatal health outcomes, especially trying to do something really comprehensive, the data get a lot trickier. So you have to deal with a lot more than, Oh, there just isn’t data and every year there isn’t data at every location or here’s a data source that maybe has some detail by race or ethnicity, but not the kind of detail that matches with the groups that we’re actually trying to analyze.

There's a lot more work that needs to be done to figure out how to leverage what information does exist in those data sources, even though it’s not in the preferred format that matches up exactly with the dimensions of our analysis. So I think that’s a big challenge. I think the other challenge just kind of generally for this type of work is that we’re doing these analyses by race and ethnicity because we want to be able to really illuminate racial and ethnic disparities that exist in the US in a very detailed way.

But race in particular is a social construct, and the way that people understand it changes over time. Part of the reason why there are more multiracial individuals in the US is because more people identify as multiracial who didn’t previously identify that way. So I think there are some interpretation challenges as well around what does this mean and what population is this referring to and to what extent are the trends that we’re seeing in part because these populations are changing over time.

So I think there’s sort of that aspect of this as well.

Pauline Chiou: Dr. Mokdad, do you have anything to add in terms of looking ahead and what some challenges might be?

Dr. Ali Mokdad: I don’t like to frame it as challenges. I mean, we have a lot of challenges in the United States. We can keep talking about that. But I’d like to look at opportunities that as you know, in the Global Burden of Disease, we have about 170 diseases that we monitor and about 84 risk factors that we provide data for. Because of the sample size here, we’re looking at the county by race and ethnic group, we will not be able to do a full global analysis.

So a limited set of diseases that we would be able to monitor from 2000 until now at the local level. So that wealth of information that will be available in the United States will create a lot of opportunity. Take, for example, if you provide data for vascular disease or provide data for cancer, that different groups here who need such data in order to act upon it.

The American Heart Association would love to see data for heart disease in order to know where to invest, where to plan for cancer. So it’s very important for us to increase our access to the data, which would make it available for everybody. But to explain how we did it and to make sure people are using it and they own it, we want at the end of the day, the donors should become at the county level, at the state level, at the decision-maker level.

And we are here providing a support role in order to make sure that data can maximize impact.

Pauline Chiou: This is exciting news and thank you so much for your important work, Dr. Ali Mokdad and Dr. Laura Dwyer-Lindgren. It was great speaking with both of you.

Dr. Ali Mokdad: Thank you.