Podcast: COVID resiliency in the US

Which states fared the best in the fight against COVID? And which political, social, and economic factors had the biggest impact on their outcomes?

Health researchers Tom Bollyky, Emma Castro, and Joe Dieleman join Pauline Chiou to discuss the findings from their recently published research on COVID resiliency in the US.

 Listen as a podcast

Key takeaways:

• Researchers found huge variation in COVID outcomes between states – up to 4 times higher mortality rates in some states compared to others. The states with the lowest mortality rates were Maine, New Hampshire, and Hawaii and the highest were Arizona, DC, and New Mexico.
• Why did some states have better pandemic outcomes than others? Factors like high rates of poverty, low rates of interpersonal trust, and low access to quality health care contributed to worse outcomes.
• States with larger Hispanic, Black, or Native American populations tended to have higher infection rates, likely due to historic inequities blocking access to preventive measures like vaccination, as well as treatment for infections.
• Our policy recommendations:
  • Address structural inequities that prevent access to quality health care.
  • Support community-specific programs that provide avenues to treatment and build effective dialogues between governments and people.
  • Be transparent about which policy measures work and which don’t, to build trust in government.

This transcript has been lightly edited for clarity

Pauline Chiou: Welcome and thanks for joining us on this podcast for Global Health Insights at the Institute for Health Metrics and Evaluation. I’m Pauline Chiou in Media Relations. We have fascinating new research to tell you about that’s a state-by-state analysis of how each state in the United States fared during the first two years of COVID and what the driving forces were.

The paper is now published in The Lancet. Our research team is with us to talk about this. Dr. Joe Dieleman is corresponding author and Associate Professor of Health Metrics Sciences at IHME. Emma Castro is co-lead author and researcher at IHME, as well as a doctoral student. And Tom Bollyky joins us as well. He’s co-lead author and Director of the Council on Foreign Relations’ global health program.

So thank you, all three of you, for joining us for this discussion. Joe, let me start with you. There are so many layers to this research and so many themes to think about. What would you like somebody to walk away with in terms of the key takeaways?

Joe Dieleman: Yeah, Thanks, Pauline, and thanks for asking. You know, this is a big paper. It was a long time in the making. And we have a really great research team that we got to work with on it, and it has a lot of key pieces. But where we really started was just acknowledging the enormous variation across the 50 US states and Washington, DC, for that matter.

The fact that the mortality rate in some states was three or four times as high as other states, which is really remarkable, suggests that in those states with the highest mortality rates, they could have somehow achieved the lower mortality rates of the highest achieving states. They could have seen 25% as many deaths as they saw. That’s just a remarkable and really important characteristic.

And so as a bunch of researchers, we felt like it was really important to not only report on those differences, but also try to standardize and say, well, are there some things that states just couldn’t have done to achieve a better or a lower mortality rate? And then to ask the question really of everything that’s left, what are the things that we can use to explain that tremendous variation?

What are the factors that explain why states like Maine and New Hampshire and Hawaii did so much better than other states, particularly Arizona, Washington, DC, and Texas. And then that led us down this track of trying to understand characteristics that influence the pandemic that were established before the pandemic, things like the kind of socioeconomic conditions within a state health care system.

Then we went on and looked at the policy response, what were the mandates, what were the messages that were coming away from the governor of the states? And then finally, how did people respond? What were the vaccination rates like? What kind of mask use existed within the state? And then finally, the very last piece was to ask the question of how did these behaviors and policies impact other things we care about, and particularly how do they impact the economy, how do they impact employment rates?

And to some degree, was it detrimental that it did? Our policy response to the pandemic led to worse economic outcomes, higher levels of unemployment, as well as reductions in student learning, which we quantified by looking at changing test scores over time. So again, this very large paper looked at a lot of different things and came to, I think, some really important conclusions.

Pauline Chiou: Yeah, these are excellent big questions. And the research revolved around a concept of the syndromic framework. Can you explain to us laypeople what that is and how to think about this when we’re thinking of some of those big questions that Joe just posed?

Emma Castro: Yeah. So that term comes from two words synergistic epidemic, and it’s just a framework that helps to draw attention to some of these upstream context factors that can play a role in how diseases tend to cluster among populations that are more vulnerable.

And I think one example that I like to point to that helps people understand why that framework is useful: One of our key findings is that states that had higher rates of poverty tended to have worse outcomes. And, examining the issue of race, we see states that have higher rates of individuals identifying as either Hispanic, Black, or Native American, those states also tended to have higher infection rates.

But let’s remember, there’s no biological reason why someone of a certain race or ethnicity should be more at risk of being infected. Those reasons are entirely structural. And so this endemic framework can help us look for the upstream social, economic, and political factors that are causing COVID to disproportionately impact certain communities, particularly communities of color.

And I mentioned at the beginning that the notion that we found that poverty was associated with worse outcomes as well as interpersonal trust and access to quality health care. So you can see we can start to unpack some of those conditions, the characteristics of a state, and see why that might be related. Regarding poverty, someone who’s living in poverty is more vulnerable to the disease in that they’re less able to socially distance.

Perhaps they’re reliant on public transportation rather than owning a car. Perhaps the job where they’re employed is considered essential work, and they weren’t able to work remotely during the pandemic. Or perhaps that paid leave, sick leave isn’t an option, and unpaid leave is not an economic reality. And so you can see how there’s all of these contextual factors that make a huge difference in terms of someone’s risk of being infected.

And it’s really important, as we’re trying to explain that variation that Joe was telling us about, that we consider all of that, not just the biological factors like, for example, population density. There’s many, many other contextual factors that we need to be considering.

Pauline Chiou: Tom, let’s talk about race, because that’s a factor that you looked at in this research and you discovered that the states that performed the worse had larger Black and Hispanic communities.

Can you tease out why there was this disparity and how it might be different from our previous discussions about racial disparities in terms of access?

Tom Bollyky: So the reasons for the racial disparities in this pandemic differ. Let’s start first with the result that ties to states with a disproportionate number of people that identify as Black in the 2020 US census. What we find there is that most of these individuals do reside in the Southeastern states, and in those states you still see for that population in particular, that Black Americans are perishing from COVID-19 at a rate about 1.6 times as frequently as Whites.

This seems to be tied to historical inequities, in particular in those states. You see a cluster of socioeconomic characteristics that you can comment to those states, in particular high rates of poverty, lower access or less access, rather, to quality health care, lower rates of interpersonal trust. And those factors seem to really combine to drive the disparities we see in terms of people getting vaccinated or their ability to get the treatment and care that they require.

Pauline Chiou: So if we push this forward into what policymakers can do and they look at these racial inequities in preparation of the next pandemic, what would some policy recommendations be?

Tom Bollyky: So for the states where you have these historical inequities, socioeconomic inequities, this is not new. So to answer your question from before, these inequities exist across a number of challenges, from maternal mortality to HIV death rates also tend to cluster in the same states that are in the Southeast and have disproportionately large populations that identify as Black.

Progress really depends on addressing those inequities. And it’s past time as a society that we do, that there are other strategies we can employ in terms of having community-specific programs that can make it easier for people to get vaccinated in a crisis, and make it easier for people to obtain the treatment, to have a dialogue between affected populations and policymakers.

But it really is on that level on the Hispanic side, to be clear, there we’re largely talking about Southwestern states, states like New Mexico, Nevada, Arizona, and there it seems largely driven by a different set of factors, largely around high rates of uninsured. So those are states that don’t have the same levels of socioeconomic inequities overall that you do in in the Southeast.

But there are still barriers to people getting access to care. And while a lot of things were provided by the federal government at low cost or free during this pandemic, like vaccines, tests, masks, that’s generally not true for treatment and generally not true for the care that people received in hospitals. And that’s still remained a barrier for populations. And that seems to have played a role in why we see outsized poor results for Hispanic populations in this pandemic.

Pauline Chiou: So as we start to think about these factors and the structural reasons, Emma, let’s just stay with you for this. Can you help us understand why a state like Hawaii or New Hampshire fared better than Georgia, for example, and Nevada?

Emma Castro: Well, so those are states that exhibit differences in those key traits that I identified. The top performers tended to have lower rates of poverty, higher general educational attainment, and higher rates of interpersonal trust and better access to quality care.

And the converse is true for the states that struggled in the pandemic. Those were really the four main factors that we saw mattered.

Pauline Chiou: Joe, let’s talk about some of the trade-offs that you saw in education and economic trade-offs. Some of them might be surprising when you look at the mandates. Can you talk a little bit about what you found and what was particularly interesting to you?

Joe Dieleman: So again, we kind of looked at three different pieces here. One was general economic productivity. So the production levels within the state. Then we also looked at employment rates, and then we looked at changes in student test scores in fourth and eighth grade. And starting with general health of the economy and the ability for a state to be producing.

What we found is that there, most, if not all states really struggled during the pandemic. That goes without saying. The economy was was in dire straits, especially in 2020, with some obvious rebound in 2021. But we found that was really important and interesting is that there was really no relationship between economic productivity and how a state did relative maybe to their neighbor and the amount of policy mandates that they put in place or the behavior of the people that were living in the state.

So to be really clear, the states that maybe had longer lockdowns or had schools closed for a longer period had mask mandates. Those states didn’t do any worse than the states that maybe had less of the kind of policy response, maybe fewer restrictions for the people living in the states. So that’s the first point. And I think it’s really important and surprises some people because it does speak to a kind of thinking about the next pandemic and what kind of policies we might be able to put in place and really ultimately how robust the US economy is in general.

The second point was employment. And here we did see more of a reaction. We saw that the states that did put in place more policy mandates, more restrictions related to masking, closing restaurants, for example. Those are states where there was a larger reduction in employment. And consequently, we saw that in some ways there’s kind of a trade-off. The states that have the lowest infection rates for COVID-19 are the states that had the lowest reductions in employment.

And so, you know, again, from a policy perspective, there is to some degree a choice of asking, do we want policies in place that we know or can restrict the transmission of this disease, but at the expense of potentially leading to more unemployment? And again, I think thinking for the next pandemic, that really poses the question of in a pandemic, what can we do to protect the unemployed or what are the systems that we can put in place if we know that some of the policy response is going to lead to unemployment?

What are the systems that we can put in place to care for those that may be losing their jobs in the midst of a crisis, the health crisis? And then the last piece we looked at was education rates and changes in test scores for fourth and eighth graders. And here we found a relationship that wasn’t quite as rock solid as looking at employment.

I think we believe there is a relationship, but it was weaker evidence and more variation. Remember, our analysis is at the state level and there’s so much variation within schools, within school districts, and within states that it’s hard to capture these things. In addition schools responded to school closures in very different ways as far as what services are still available to students.

We did find weak evidence that the states that essentially had the lowest infection rates had slightly higher reductions in fourth grade math scores. And this is consistent with some of the other findings that that researchers have pointed to. But again, I think as far as trade-offs are concerned, where we found evidence that really was the story revolves around employment and infection rates.

Pauline Chiou: Tom, you also looked at political affiliation, and this was a very interesting observation in this research that the states that fared the worst were states that voted for the 2020 Republican presidential candidate. Why was it important to look at political affiliation? Because some people who read this may say, look, COVID doesn’t care if you’re male or female, if you’re Black, Hispanic, or White, or if you’re Republican or Democrat.

So why was political affiliation important?

Tom Bollyky: So the virus does not care about the political affiliation of its victims. It does respond to mitigation measures designed to slow the spread of the virus. It does respond to the availability of effective treatment. And these are policy choices, policy choices that may differ by party. That’s certainly the perception. And there are some articulated differences between members of those parties in terms of how they pursued this pandemic.

So we looked into it. We looked into it both in terms of what was the political affiliation of the governor or a highest-level executive in that state or territory, as well as looking at what the vote share was. And we looked at it both ways, both for Democrats and for Republicans in terms of vote shares. Now, on the governor’s side, we found no affiliation at all.

In fact, in terms of the top 10 performing states in this pandemic, they’re evenly split between states that are led by Republicans, which includes states like Nebraska and Ohio in the top 10 as well as states that are led by Democratic governors. So an even split there. We see no affiliation overall. That said, for the states that voted most heavily for the 2020 Republican presidential candidate, you do see a difference.

That difference seems to be driven by two things, or at least we’ve identified two pathways for it. One is by virtue of the fact that we saw less use of policy mandates like business closures or mask mandates or vaccine mandates in states that voted heavily for the 2020 Republican candidate. And we do find that overall, those mandates affected vaccination rates, and particularly in the case of vaccine mandates, affected death rates.

The second pathway by which it seems to have an effect is that the strength of your health system matters a lot. And states that voted heavily for the Democratic candidate in the 2020 election in the sense that the stronger your health system, the more health spending you have, the higher the vaccination rates, you see in those blue or Democratic-leaning states.

The opposite is true for Republican-leaning states where it does not seem to have mattered, the strength of your health system did not necessarily lead to higher vaccination rates. And that’s a pretty stark finding from this study.

Pauline Chiou: Tom, thank you so much for joining us with your insight. I know that you have to drop off from the podcast right now, but I want to thank you, and we’ll continue the conversation with Emma and Joe.

Tom Bollyky: It was my great pleasure. Thanks for having me.

Pauline Chiou: Emma, I wanted to tie into some of these comparisons with the issue of trust. You looked at community trust and government trust. How does it play into this and piggyback off of what Joe had talked about in terms of some of the trade-offs resulting from the mandates?

Emma Castro: Yeah, and you know, that also comes back to your question about recommendations for the future.

In building trust, which is something that our paper tells us is lacking and is something that’s important in terms of protecting states against worse COVID outcomes in building trust, it’s important to be transparent about what policy measures work, which ones don’t, and what the trade-offs are. And I think that’s really the whole goal of our paper, is to lay it all out there, the good and the bad, what worked, what didn’t, and what are the compromises that states have to make if they want to take certain actions.

And so we hope that by laying out all of this information, we can start to communicate that and build trust around this issue, around pandemic preparedness. And what should we do in the future? One of the interesting observations when it comes to education was we found that in certain states with higher infection rates, the test scores were better.

So how does that make sense? And how would a policymaker be able to take that and create a policy if this happens again? Well, so, interestingly,  we thought we might find an association between primary school closures and test scores. You could imagine why if schools were closed for more time, the scores might suffer.

We didn’t necessarily see that. And as Joe mentioned, part of that could be that there’s so much nuance that’s happening at the district level and our analysis is at the state level. And perhaps that’s why. But based on what we’ve seen in the paper at the state level, the way that we’ve conceptualized our findings in terms of trade-offs with test scores is that the states that were more cautious in terms of implementing more policies to protect their citizens from being infected likely also has a population that is extra cautious when it comes to COVID.

And so you might imagine that the parents living in these states perhaps kept their children in remote learning longer than maybe was even required at the state level. And so that could be one possible explanation why we’re seeing this trade. It’s important to remember, as Joe said, that there are negative impacts across all the US. I guess I should say the scores suffered unanimously.

We saw declines almost in every state. So I guess the question becomes, how do we minimize those declines? And now that we’re moving beyond the pandemic, how can we get back on track?

Pauline Chiou: And finally, yes, Joe.

Joe Dieleman: I just want to add to what I was saying about interpersonal trust and trust in governments. This is a topic we’ve explored with this pandemic previously and found that trust in governments in particular was one of the most important things internationally for reducing adverse outcomes for COVID, so reducing mortality and reducing infection.

So this concept of how does trust have impacts on health and specifically COVID mortality has been looked at a fair amount. And I think it’s really important for people to understand that the amount of trust that people have in government officials and elected officials, but also people in public health as well as their neighbors, this is interpersonal trust that we look at.

Those things change over time. And by and large, in the United States, which used to have some of the highest levels of trust, there’s been a real reduction over the last three decades that has led to much lower levels of trust in a pandemic. Levels of trust evolve relatively quickly. And this is where I think what Emma was saying about very clear communication from public health officials and other elected officials is tremendously important.

Taking some of the ambiguity out of a very scary situation instead of contradicting each other, speaking with a single voice. So that, again, it isn’t a political issue, it isn’t a socioeconomic issue, but people have the opportunity. They know what they’re supposed to do and do their best to do that. And again, that’s where I think the public health system really has a place to intervene and say this is what needs to happen.

And for those people that will have a harder time acting upon our recommendations, we need additional support. We need to intervene in different ways.

Pauline Chiou: So building that trust now and building that communication now before the next pandemic is critical. I know you’ve written about this in the paper. Dr. Joe Dieleman and Emma Castro, thank you so much for joining us on this podcast.

Emma Castro: Thank you.

Joe Dieleman: Thanks so much.

More COVID-19 research focused on the USA:

• Research paper: Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis
• Largest US state-by-state analysis of COVID-19 impact reveals the driving forces behind variations in health, education, and economic performance