Thomas J. Bollyky, senior fellow for global health, economics, and development and director of the Global Health program at CFR, leads a conversation on observations and lessons learned from states’ public health responses to the COVID-19 pandemic.
FASKIANOS: Thank you and welcome to the Council on Foreign Relations State and Local Officials Webinar. I’m Irina Faskianos, vice president for the National Program and Outreach here at CFR. We’re delighted to have participants from fifty-one states and U.S. territories for today’s conversation. Thank you for taking the time to join us for this discussion, which is on the record.
CFR is an independent and nonpartisan membership organization, think tank, publisher, and educational institution, focusing on U.S. foreign and domestic policy. CFR is also the publisher of Foreign Affairs magazine. As always, CFR takes no institutional positions on matters of policy. Through our State and Local Officials Initiative, CFR serves as a resource on international issues affecting the priorities and agendas of state and local governments, by providing analysis on a wide range of policy topics.
We are pleased to have Tom Bollyky with us for today’s conversation on public health and lessons learned from the COVID-19 pandemic. We’ve shared his bio with you, so I will just give you a few highlights. Thomas Bollyky is the senior fellow for global health, economics, and development at CFR, and the director of CFR’s Global Health program. He’s also an adjunct professor of law at Georgetown University, and a senior consultant to the Coalition for Epidemic Preparedness Innovations. Mr. Bollyky is also the author of the book Plagues and the Paradox of Progress: Why the World is Getting Healthier in Worrisome Ways, and the founder and editor of Think Global Health, an online magazine that examines the ways health shapes economies, societies, and everyday lives around the world.
So, Tom, thanks very much for being with us today. You recently co-authored a report on COVID-19 pandemic policies and behaviors. I thought you could talk us through the differences in public health responses that influenced states’ infection and mortality rates, and what you came away through this research for recommendations for future pandemics.
BOLLYKY: Great. Well, thank you, Irina, for the kind invitation to be here and that nice introduction. It is—this is, I think, my third time, maybe fourth time, speaking to the State and Local Officials network. And it is one of my favorites in terms of a resource at the Council. I always learn as much from these discussions as I think I am able to impart, so I’m really looking forward the chance—to the chance to speak with all of you. And congratulate Irina and team for pulling together such a useful network.
What the thing we’re here to talk about today is—it is—Irina, are you making faces? Is my internet causing trouble?
FASKIANOS: Yeah, your internet—I was like, oh, no, his internet is freezing. So—
BOLLYKY: Hmm, ah. Well, let’s keep going.
FASKIANOS: Let’s keep going.
BOLLYKY: And perhaps at some point I will turn off my video and do it just with the audio if it remains a problem. But apologies for that.
Again, this paper appeared in Lancet six weeks ago. It’s a year-long study, the product of five different institutions. And I had the pleasure of co-leading that group. And it—what it was meant to look at is what explains the very large differences we have seen between how states, U.S. states, performed in the pandemic. And I think it’s been underreported, but perhaps not a surprise to this group, that while the U.S. overall struggled in the COVID-19 pandemic, not all U.S. states struggled equally. There is, in fact, a nearly four-fold difference in cumulative total COVID deaths from the worst to best performing U.S. states, even once you adjust for all the relevant biological factors—differences in the age of the population or key preexisting health conditions.
For most of the pandemic, states like New Hampshire, Vermont, or Washington have actually posted COVID-19 death rates that are comparable to countries in Scandinavia, like Denmark, or in Europe, like Germany. While mortality rates from some other states have actually rivaled the worst-performing countries in the world during the pandemic—Russia, Bulgaria, and Peru. That difference, between top performers and poor performers, is large in health standards, even by American standards. For instance. The U.S. states with the shortest average lifespans come nowhere close to matching Chad, Nigeria, or the worst countries in the world on that measure of longevity.
The state variation, though, is a reason for hope. Because if poorly performing U.S. states could more closely match their more successful counterparts when the next health crisis emerges, many lives might be saved. One estimate we have from that Lancet study is that if every state had performed as well as New Hampshire, the second—state with the second-lowest COVID mortality rate—there would have been 504,000 fewer U.S. deaths from COVID-19 during—just during our study period. That would have made the U.S. again, in terms of overall death tolls, very similar to other high-income countries, as opposed to one of the worst-performing countries, which is sadly where we were.
I’m going to pull out just four specific themes about what drove those differences, and then I’m going to save most of the discussion about what do we do for this, because I really do intend for those to be mostly a conversation about looking forward and how do we respond to this. But four themes that came out from our analysis in the Lancet. One is—or, theme number one is that the role of health equity, socioeconomic and racial disparities, loomed very large in this pandemic. Larger than it does in many other—even larger than it does in many other U.S. health measures.
So what we—what we saw was a cluster of factors—low educational attainment, limited access to high-quality health care, the percentage of people living below the poverty line—had a strong association both with differences between states, and their infection rates, and in their COVID-19 death rates. In many ways, this reaffirms what we’ve seen in the past, that these disparities played a large role in H1N1 and the response there. These disparities combined with racial disparities, which were also significantly associated with state variations in our study, also play a role in differences in seasonal flu vaccination. It’s not just in pandemics or infectious disease. Of course, in other health crises, you see these social, economic, and racial disparities loom large. And it will be important to proactively seek to mitigate these differences ahead of the next emergency. And we can talk a little bit about in the discussion of ways to do that.
All right. Theme number two that came out from here. Trust, interpersonal trust in particular, played a large role in this pandemic in the U.S. Interpersonal trust, if that term isn’t familiar to you, is the trust we have in one another. And it is actually a finding that has been shown also in many international studies. For example, we did a—the same group did a study in the Lancet the year earlier on the global level. And we were unable in that study to find any connection between country variation and COVID outcomes in many of the leading theories or pet theories of what made a difference in the pandemic—like economic inequality, or pandemic preparedness metrics, or democracy, what have you. We didn’t find any links. But interpersonal trust had a very large and significant association with differences in how countries did.
We see the same thing in the U.S. context, that the trust—how we feel about one another, the trust we have in one another, is tied to vaccination rates and adoption of health-protective behaviors. And that, in the end, has a large tie to the outcomes and how states did. Meaning that when confronted by contagious novel virus, government—most effective ways for governments to protect their citizens is ultimately by convincing them to protect themselves. And their willingness to do that, particularly in free societies and in U.S. states, depends on the trust we have in one another. And that’s going to be important to foster stronger in the future in thinking about how we respond to these things.
Theme number three: The role of politics was nuanced in this pandemic. There is a perception political parties mattered a great deal in the response to this pandemic. But at least from our study, there is no association between the party of the leading state official, or state governor, or, in Washington, D.C.’s context, mayor, and COVID deaths. In fact, out of top ten states that did best, half—five of them are Republican and—five of them are led by Republican governors and five of them are led by Democratic governors. That said, there is certainly a role for politics in this pandemic. And the degree to which states voted for a particular candidate in the last election does seem tied to the adoption of health-protective behaviors, and vaccination rates, and the application of mandates. And that does seem to have had some effect.
Which brings me to the last theme to draw out, which is mandates. And by mandates, I mean bar and restaurant closures, gathering restrictions, mandates around vaccination use—or, vaccination, or mask use, or stay-at-home orders. What we find in this study is that the package of mandates, or the broader use—because states tended to use many of them together, and nearly all states used some mandates in this pandemic, usually for roughly—for about a sixteen-month period. And what we found is they were generally associated with fewer infections. But it was vaccine mandates that had the largest effect on deaths.
And there’s been a discussion around tradeoffs in this pandemic. We did find some. There weren’t any tradeoffs between overall economy and the adoption of health protective measures, but there were some tradeoffs particularly on restaurant closures and employment and there were some tradeoffs on educational performance in this pandemic. It will be important in the future to adopt—to apply these mandates in a way that they target the most vulnerable and are designed in a manner that it promotes getting back to work and getting into schools as soon as possible. They will also be important to combine with mitigation measures for the period in which they are in place. We can, again, talk a little bit about that.
But those are the four themes to start us off, to draw us out. I’m really interested to hear about the experiences you all have had in the pandemic, and questions you might have about this study. And I will put the link to the study in the chat, if it’s not already available to attendees. Irina, do they have it already? Sorry, you’re on mute.
FASKIANOS: Oh, can you hear me now?
FASKIANOS: Great. Yes. They do have a link to the report. So we did send it out in advance.
FASKIANOS: So that’s great. I’m going to—now it’s great to turn to all of you. Again, this is a forum to share best practices, ask questions, and whatnot. And I want to go first to Dr. Jonathan Ballard, who’s the chief medical officer in New Hampshire—the New Hampshire Department of Health and Human Services in the office of the commissioner, since, Tom, you mentioned New Hampshire being in second—the second good story. I guess Hawaii was number one. So it would be great if you could just react and maybe share your thinking of what you—what else you will do in the future, Dr. Ballard. And if you accept the unmute prompt, that would be great. There you go.
Q: Thank you, Irina. Thank you.
So the question I have is around health equity and the diversity of the population. So some of the questions I have, particularly around your study, is does this study adequately adjust for the disparities in—related to health equity that we see between New Hampshire? New Hampshire’s one of the healthiest states in the country. And so, you know, the theory is that, well, you’re already a healthy population, you do not have obesity to the degree, you have lower smoking rates, you have high rates of physical activity in New Hampshire.
And so is that—was that taken into account already into your study about why some states are performing well? Was it the underlying population was already healthy or not? I would conjecture that it’s not—it’s not simply that underlying fact, because there are several states in your report that are just as—nearly as health or heathier than New Hampshire but did not have the same outcome with the mortality rate. And I think that there several things that New Hampshire did do that was quite protective and did kind of go against the strain of what the national guidance was.
Each time there was a recommendation that came out from the CDC or any other national body, we did look at it carefully, and noting particularly the recommendations around the vaccination priority populations. New Hampshire did not follow the national guidance on vaccinating frontline workers. We did a different approach. We looked at social vulnerability index and vaccinated those who had the highest risk of social vulnerability—of vulnerability, but then also looked at—made a big effort to vaccinate the other vulnerable populations, those in congregant facilities, nursing facilities, and other locations.
And New Hampshire was the first to get to kind of whatever number you would—each state would get to with its vaccination rate. We had a lot of emphasis on speed, on delivery of the vaccines, and very seldom had any in reserve during the early months. They were all used. And I think a lot of that relates to what you talked to around the interpersonal trust, resulting in us being fastest to get the vaccines out. New Hampshire’s known as a—you know, the live free or die state, and individual liberty, individualism.
But we didn’t have a lot of the culture wars. We’re a purple state. We have split government as far as state government versus our federal delegation. And we just didn’t see vaccines getting caught up in that, especially early on. So I just wanted to stop there and, Thomas, would be appreciative of your response on the was—what were the adjustment rates that you used, and did it account for just these healthier states did better, or not?
BOLLYKY: Great. So the first one it’s a relatively quick answer, fortunately, which is the adjustment, it does, in fact, account for BMI, it accounts for rates of diabetes, cardiovascular disease. Really, an expansive view of the key comorbidities that might have made a difference in how states performed in the pandemic. So it is adjusted for that. And, of course, it is adjusted for age.
I would draw out a couple things at least from our study, but obviously you lived the experience so I take your insights more—as seriously. But, you know, New Hampshire, as a state, is a little healthier than other states. Though New Hampshire’s average life expectancy at birth actually only ranks twenty-third in the U.S. out of states. So it’s around the middle. And its performance in this pandemic was better than that metric might have suggested. There are a few—about New Hampshire. It does have the lowest poverty rate, or percentage of population under the poverty line. It has the highest levels of interpersonal trust in the country. It has relatively few uninsured. Reasonably—among the top ten in terms of access to quality health care.
It is also, you know, not a—as states go—not a particularly diverse state in terms of its racial makeup. But the—what people identify as in the U.S. Census. However, as you rightly pointed out, one of the things we’ve—in a follow-up piece that we wrote—pointed out that to the extent that New Hampshire does have social, economic, or racial disparities, the state was quite aggressive about addressing them in its vaccination program. And that seems to have made a large difference as well. In terms of our research, or talking to local officials, also they reaffirmed the view that you had put forward about a strong partnership between states and local communities in terms of enabling some of the local actors to have some agency to respond to what they were seeing as well.
But we highlight New Hampshire, in terms of an example because, of course, unlike Hawaii it is not an island. But there is a lot—you know, New Hampshire has many advantages but again, as we pointed out, the health circumstances has some challenges too. And through aggressively addressing some of those challenges, the state did well in this pandemic. And hopefully more states are able to match it in the future.
FASKIANOS: Thank you.
So we have two questions on interpersonal trust, which I will—I will ask together. So the first one is from Colorado State Representative Parenti. How were levels of interpersonal trust measured? And then, from Alder Regina Vidaver in Madison, Wisconsin, she asked: What are evidence-based approaches to improving interpersonal trust?
BOLLYKY: Great. So two fantastic questions. I will start with how we—the data sources we used for interpersonal trust in this study, and then I’ll just briefly reference how it can be measured more broadly. So the short answer is surveys. We have a set of surveys reflecting nine thousand respondents throughout the country, all conducted in 2019. Those surveys asked the question: Do you—how often do you trust others to do the right thing? The responses coded for most of the time being high levels of interpersonal trust.
This would seem like a subjective question, but surveys—social scientists have been actually asking that question since the 1950s internationally. And you would be amazed how stable the values are for countries and communities. So that is the way people measure interpersonal trust through surveys. There are also, of course, experiments people do to measure them in a community, or they look to proxy behaviors that are suggestive of interpersonal trust. We use for this—for this study surveys.
Now, what would you do with it? Well, a couple of things. Or, what’s the evidence-based interventions for interpersonal trust? First thing I will say is the government of Denmark actually monitored trust at the community level throughout the pandemic and adjusted its public health interventions to reflect those changing levels of trust. That’s just running a survey at the community level. Not cheap, but not impossibly expensive. But to give you an idea, for instance—because they convinced people that they will not the only who is vaccinated, that there won’t be holdouts.
But in low-trust populations, they have the opposite effect, where they tend to inculcate hostility and a reaction. So that was used to tailor public health policies for different populations, just to give one example. As a general matter of how you build trust, and how you identify where there is low trust, and what you need to do differently to respond to that in the future. But hopefully that gives at least a start of the conversation around trust.
FASKIANOS: Thanks. All right, the next question, we’ll take an oral question from Pennsylvania Representative Arvind Venkat.
Q: Hello. My name is Arvind Venkat. I’m a state representative in Pennsylvania. I’m also an emergency physician.
I had two questions. One is on did you distinguish in a large state like Pennsylvania, when you’re looking at it, between urban—or, among urban, suburban, and rural areas? Because the response in all of these area was very different in our state during the height of the pandemic. And the second question is, what specific legislative recommendations do you have coming out of your study? Thank you.
BOLLYKY: Great. We did look at population density, but we only looked at population density at the state level. So the study in general functions at the state level. We don’t look at whether it’s at the ZIP code level or the community level. So that will have to be a future study. I will say population density, as the pandemic progressed, was less meaningful in terms of having a tie to either infection rates or deaths. And perhaps that might make sense from what we—what you’ve seen, what others—what we all have seen in the rural communities and how the pandemic experience has changed in those over time.
In terms of legislative approaches, I think there are a few. I do think it’s important for states with high rates of uninsured, or states that have not extended Medicaid use or are reversing those policies. The study suggests that rates of uninsured did have a significant association with how states performed in this pandemic. Perhaps not surprisingly, and high death rates. So those are one area. Another is we did see an association between states that had adopted more generous family leave policies, or personal leave policies, and infection rates as well. And it will be important, whether they’re adopted on an ongoing basis or adopted in a manner that allows them to be expeditiously exercised in a health crisis, or extended in a health crisis. It’ll be important to have those structures in place.
As I mentioned, whether it’s on politics or on social, economic, and racial disparities it’s really important to have ongoing community engagement, or to build these partnerships between state officials and community organizations or faith-based organizations. That’s perhaps less of a legislative matter, but certainly a matter of appropriations. And it’ll be important to have those partnerships established ahead of a crisis, because it is difficult to build them and use them and harness them effectively once the crisis has begun. But great questions. Thanks for participating in today’s call.
FASKIANOS: So the next question is a written question from police chief Patrick Finlon, who’s in Village Cary, Illinois. And I’m not sure that this is in your area, Tom, but I will ask it: What were your findings related to the ability/desire to use/exercise governmental authority related to the shutting down of businesses and the application of constitutional provisions? I’m in law enforcement, and our risk management provider advised us not to close businesses for fear of a potential civil rights violation.
BOLLYKY: Well, in terms of—what I can use on the use of mandates, in general, is there—although underreported—there is actually a surprising level of uniformity across states. There’s a perception that some states locked down and other states didn’t, and that that tends to vary politically. As an initial matter, lockdowns or use of mandates, rather, at the state level really over occurred over a sixteen-month period. Virtually all states from March until June of 2020 used some policy mandates. Where really you started to see the big differences in the outset of the Omicron wave, between some states reimposing them and others doing less so. But there’s a lot of uniformity to that at the state level.
I will—I will forgo the—opining on the legal merits of the adoption of these, but there have been, of course, a good number of cases that have worked their way through the courts, some of which have gone to the Supreme Court, and they point to a few lessons on, you know, public health authorities/powers, and where they draw from and what they extend to. But, again, I will save that for a more legal discussion.
FASKIANOS: Thanks. We’ll take the next question, raised hand, from Georgia Representative Imani Barnes.
Q: Hello. Thank you for having me. I don’t think I can turn my camera on.
But I was wondering, what type of educational data did you gather from this study? I was wondering the data compared to New Hampshire with other states that—I wanted to understand the disparities, educational disparities, that you gathered—the data that you gathered for educational disparities. And what suggestions do you have to mitigate the learning loss that the children experienced during virtual learning?
BOLLYKY: Great. So the educational data we used for is average educational attainment. Again, like our metrics in the study, it is statewide. So by disparities, we’re talking the difference between states, and that average level of educational attainment. It does—didn’t matter a great deal in terms of showing differences between how states performed in this pandemic. Levels of—or access to high-quality health care or percentage of people below the poverty line does seem to have a pathway through vaccination rates, that states with lower rates of—or, a lower average of educational attainment had lower vaccination rates, by and large. So that’s the way we address that.
On the learning gap question, I think the real answer is people don’t know as of yet, in terms of we haven’t really had a disruption of this duration and length before. So there are theories of what matters, from tutoring to, you know, more extended engagement or programs with students that fell behind. What I can say from our study is that the tradeoffs on the educational side were significant. All states suffered from an educational standpoint in this pandemic. Some states suffered more than others. It is unfortunately true that the same racial disparities and socioeconomic disparities we see in educational attainment, by other studies that have been done, suggests those were exacerbated in this pandemic. So it will be important to redouble and be aggressive about addressing those gaps.
FASKIANOS: I’m going to take the next written question from Crystal Goodwin, who is with the Texas Council for Developmental Disabilities, and serves as a public health and disability integration specialist: If this were something that—if this was something that the study looked at, did the findings show any difference among states based on disability status or disability services offered? Something we found during the pandemic, and studies show, that individual with intellectual and developmental disabilities as a comorbid condition were in the top three of deaths here in Texas.
BOLLYKY: I wish it was something our study looked at. It’s an important issue, and I really appreciate you raising it as something that deserves more attention, both by my colleagues and I but others in the future. So thank you for raising the question and, unfortunately, it was not in our study. But I wish it had been.
FASKIANOS: It can be the subject of your next study.
FASKIANOS: Let’s go next to W. Abdullah Brooks with a raised hand.
Q: Hello. This is—yeah, I’m W. Abdullah Brooks. I’m actually standing in for a representative from the state of Maryland, Scott Phillips. In full disclosure, I’m a faculty at the Bloomberg School of Public Health at Johns Hopkins, and with a background in infectious disease and global public health.
First of all, congratulations on a brilliant study. And I haven’t had time to go into a deep dive, but I had just two questions that maybe you could elaborate on, if they’re not in your paper. One is, you talked about the correlation with employment and health outcomes. And given the structure of health care access in the U.S. often being tied to employment status, I’m wondering if you adjusted for access through, for example, those who have public assisted health access. Just to look at the question of health equity or equity in health outcomes, and whether or not there was any difference between those who are on public assistance, had access to public—to health access, hospital, and so forth, versus those who only had access through private insurance. That’s one question, just getting at the issue of equity of outcomes.
The second, you have a reference to interpersonal trust. And during the beginning part of the COVID pandemic, the American Society of Tropical Medicine and Hygiene held a series of discussions around this and looked at specifically the issue of trust towards health experts—trust or distrust. And I’m wondering whether or not your paper looks at this specifically with regard to health communicators and health communications, and whether or not you gleaned any insights into messaging. And, you know, whether there were better or worse strategies with respect to trying to get messages regarding, you know, responses to the pandemic, and access to things such as vaccines. Thank you.
BOLLYKY: Great. Thank you for such a rich group of questions. And thank you for the kind words about the study.
On the employment side, the employment results are fascinating in the study, in that by and large most of the use of policy mandates are not associated with differences in employment. There is an association, in particular, with restaurant closures, which perhaps not surprisingly, given that sector. But there is an association between higher infections and higher employment. And that actually reaffirms what we’ve seen in other studies of the economic impacts of the pandemic. That it may have been less a matter of policy in terms of differences in economic impacts, and more in the responses of the population.
So, meaning people that stayed home more cautiously, whether the state ordered you or not, had broader economic impacts. As a general matter, economically what you see in the pandemic is often a fair amount—and this is perhaps why the GDP levels aren’t shifted—or, have no association with the degree of public health response—is that you’re largely shifting economic activity between sectors. So less activity in restaurants and bars means more grocery. And you see some of that shift where all states suffered in the pandemic economically, but it tends to net out, to some degree, in terms of the various sectors positively and negatively affected.
In terms of equity in the private and public insurance, we do include both public, private, and out-of-pocket spending—estimates of out-of-pocket spendings in our measure of health spending. We, unfortunately, do not break them down and see how the results might be different depending on the level of spending between each. But that too, like Irina suggested before on the disabilities, would make for an interesting follow-up analysis. So thank you for proposing it.
On the trust in health experts, we do look at trust in government. Now, that is not—and we also looked at trust in science in the studies. Both of them also the product of surveys. As you rightly perhaps intimated, you know, trust in government does tend to vary by agency and area. There have been some good studies that have come out that have looked at trust in health authorities. And what you have seen are declines, particularly in trust in state governors, trust in federal health authorities. What I’ve—from what I’ve seen from multiple surveys or studies of this kind, what has really held up are your family physician.
Local hospitals, local health clinics still enjoy high trust. They enjoy it across political lines. And that too may be something we can seek to leverage in the future but would be a different lesson than we’ve had in the past, where we have really emphasized having one voice speak in a pandemic, having it be at the federal level, perhaps having it be CDC. What the lessons of this pandemic suggest is that we need more community and local engagement, engaging trusted health sources of information.
FASKIANOS: Thank you. I’m going to take the next written question from Commissioner Keith Baker from Colorado.
Was the level of interagency—county, municipalities, healthcare, school districts, et cetera—coordination and collaboration evaluated in your report? And were there any lessons drawn from that? So we have another question too on this, about, you know, measurement of the level of intergovernmental cooperation and outcomes.
BOLLYKY: Great. Thank you for the good question. No. I haven’t seen a good standardized data source of measuring the cooperation that occurred in the pandemic. There are different measures of polarization people have looked at, but they typically look at the legislature, state legislature, or surveys of the population and how polarized they are on particular issues, or politically. But the interagency cooperation’s an interesting question. But I have unfortunately not seen it well measured, particularly across U.S. states.
FASKIANOS: All right. So the next question I will take a written—I see no more raised hands, so I will continue to go for our written questions.
Next one from Vice Chair Mary Alford from the Alachua County Board of Commissioners, in Alachua County, Florida: Was good information found in states like Florida, where information shared was of questionable accuracy? How was that information treated—margin of error, sampling from other sources, et cetera?
BOLLYKY: Great. So in terms of our study, we do—these are estimated death rates and infection rates. They do tend to be backed up by a variety of sources, including both state-reported data but also zero-prevalence studies and peer-reviewed data, is what we used from that. So that’s how we tried to adjust for the fact that some states may not have been reporting as actively or as rigorously as others.
FASKIANOS: All right.
Next question from Ellyan Veronica from the Puerto Rico Senate: What data did you find regarding unvaccinated people who suffered violations and interference in educational, medical, or other services by their vaccinated status? Not sure—Senator Martinez, do you want to ask your question? Maybe clarify it a bit? OK. Don’t think—oh, if you unmute yourself, you can clarify. No, that is not working. OK.
FASKIANOS: Oh, good. Thank you.
Q: OK. Yes.
I’m referring that what is the data did you find regarding the unvaccinated people who suffer interference with their educational, medical, and other services because they didn’t want to be vaccinated? Did you study that matter?
BOLLYKY: We did, actually. So we look at vaccine mandates for state employees and vaccine mandates for school employees, and both their association with health outcomes, infection rates, and death rates, as well as whether they have any tie to shifts in employment or in lower educational performance, particularly for fourth graders. We used NAEP test scores.
On infections and deaths, they are very much associated with lower rates of both. State employees, of course, it will not surprise people on this call, represent millions of people in the United States. So it’s not a small group. And you do see a strong association with fewer deaths from the use of those mandates. We did not find any tie between the use of those mandates and lower state GDP or lower employment. So nothing on the economic side.
You do see an association with lower math test scores. However, almost all mandates were associated with lower math test scores. And what our theory there—so this includes things that have, you know, restaurant or bar closures—. And so the hypothesis is that association reflects the caution in the population. People who were less likely to send their children to in-person schooling, those children tended to—or, those states where that was happening at a greater rate—to do more poorly educationally. Because math is something that, I can say as a parent myself, parents don’t teach as well as the school settings do. So it really does seem to be a stronger tie between in-person schooling and better math test performance, at least for fourth graders.
Sorry, that’s a long-winded answer. But most of what you could say is, no, I don’t see any educational, economic, or deleterious health outcomes from those vaccine mandates.
FASKIANOS: So I’m going to take the next written question from Dawn Gresham, who is a community liaison in Senator Liz Krueger’s office of New York Senator Liz Krueger: It seems as though it would have been helpful if messaging had communicated that there would be saves in community infection levels requiring additional safety measures to be followed at times, and relaxing safety measures where possible. Because this did not happen, it made it more difficult to discuss reinstating certain measures when it would have been helpful. Can you share thoughts on best practices for handling communication?
And, Tom, I’m going to add onto that. I think we’ve seen some backlash against other vaccines because of the experience of COVID-19, which could be potentially alarming for things that we have not had problems with, because vaccinations have been measles, and whatever, and how we deal with that. So can you talk about messaging and vaccines going forward for other diseases?
BOLLYKY: Great. So on the communication side, I completely agree with the questioner on the premise that we struggled to educate the population on the fact that this was likely to evolve and to change. That is actually—there have been a relatively large literature on communication in this regard. And this ties to the earlier question we got about trust. In addition to monitoring levels of trust to try to tailor programs to low-trust communities, we do have good research on communication strategies that preserve the levels of trust you already have. So less on how do you build it in crisis, and more about how you slow its erosion.
And one of them is—or, two of them are related to your question. One is transparency. So saying the quiet part out loud. For instance, there is a great study that looked at—they presented two groups of individuals with—or, two groups of individuals, rather, with information about a hypothetical vaccine. One of those groups received information about—that was vague about the side effects but suggesting that there may be some but somewhat vague about what they were. Another was very specific about the range of things you might find in those circumstances with the vaccine.
And what you found in that is not that you had a higher rate of people willing to take the vaccine between those two populations, but the population that received more detailed and complete information expressed higher levels of—or, more sustained levels of trust in the health authorities that provided it. Suggesting, again, that transparency is important, but also—and this is the second lesson—trusting the population. In order to be trusted, governments have to be trustworthy, but they also need to trust the population to be able to understand what they’re communicating. And that is something we struggled with throughout in this pandemic.
FASKIANOS: Thank you. I’m going to go next to a raised hand from Paul Rotello from the city of Danbury in Connecticut.
Q: Thank you. Yeah. Paul Rotello, City Council, Danbury, Connecticut.
Connecticut, in terms of geography, is one of the smaller states. In terms of population, it’s relatively moderate. I think it’s about thirtieth. Both Vermont and New Hampshire are not particularly big when it comes to geography, but they’re much bigger than Connecticut. Their populations are quite a bit smaller. So I was just curious as to what—there seems to be a little bit more elbow room, or maybe a lot more elbow room, in Vermont and New Hampshire, compared to Connecticut. I was curious as to what density played in your statistics and your analysis. And how would you even go about figuring that, because while you can live in a somewhat agrarian community, you may spend a lot of time in town at diners, and post offices, and things like that, or even at jobs? How do you tease that out? And were you able to tease that out? And did you see a difference? Thank you.
BOLLYKY: Great. Well, I’m happy to get the question. I actually grew up in Stamford, Connecticut. So I know Danbury quite well. I went to high school in Fairfield. And so it’s nice to meet you and have this engagement on this.
Connecticut, as a state, actually does well in our study also. It is ranked seventh in terms of standardized deaths. So, again, adjusting for the biologically relevant factors. We did not see a strong tie between population density and infection or deaths in this study. The reason why is over time—in the beginning, it mattered, in terms of the spread of the virus to communities in the initial wave of the pandemic. But over time it was more around economic geography. Congregant housing, people—percentage of essential health workers, people with a greater ability to avoid people that are infected or isolate on their own is tied more to economic geography than the population density. So there are some fairly rural states that don’t do well in this study because of, we suspect, these broader questions of economic geography.
FASKIANOS: I’m going to take the last question from Alison Despathy, who has raised her hand, from Vermont. You need to unmute yourself.
Q: Thank you.
FASKIANOS: There we go.
Q: OK, good. All right, thank you so much.
So I’m here in Vermont. And my question relates to, back to the trust issue. And this is also sort of stemming from some of the swine flu history and what we saw go on there with a bit of the sort of marketing and propaganda around the safety and effectiveness of vaccines. So with regards to the trust, did you see any data or results surface around the fact that the COVID vaccines were originally sold as safe and effective, and included the ability to prevent COVID and prevent transmission? So there was clearly a level of propaganda, not necessarily intended. But many heard that, you know, this is the pandemic of the unvaccinated.
So as actual vaccine impact surfaced vis-à-vis safe and the failure of COVID vaccines to prevent infection and transmission, did you assess the role of propaganda, marketing of pharmaceutical products, and any—? And thank you.
BOLLYKY: Great. So we did not assess the role of mis- or disinformation in the study, other than trust levels. The trust levels that we had, of course, they had to, for the study to work, predate the pandemic. So we looked at levels of trust in 2019, the situation, effectively, the virus found us in. So we did not assess ways that might have changed over the course of the pandemic. Other studies certainly have. I will say that levels of trust declined everywhere, even in countries like Denmark or Scandinavia, famously high levels of interpersonal trust. The question is, how quickly and to what degree. And, you know, some of the good communication practices that we’ve talked about, and I’m happy to communicate more about with people via email, do seem to have been effective in slowing that erosion. But we didn’t look at the mis- or disinformation and how that changed trust in the United States.
FASKIANOS: Thank you. Unfortunately, we are out of time. I’m sorry we couldn’t get to all of the questions. But I just want to ask you, Tom, to take just thirty seconds to talk about Think Global Health, since we have so many health commissioners and medical officers on this call. If you could talk a little bit about your magazine and what you’re doing there.
BOLLYKY: Great. I will do that in twenty seconds, because in ten seconds I want to say that health crises are fought at the state and local level. And I am grateful to all of you for what you did during the pandemic, and what we will need to rely on you for in future health emergencies. I don’t think we’re getting enough attention on what states and localities need to succeed in the future. And hopefully, this study can help spotlight that.
Now, that said, on Think Global Health, it’s an online magazine that’s meant to look at how health affects economies, societies, and everyday lives. It’s been up for about three years. It has been—it’s analysis has really been picked up everywhere, from the New York Times to the Atlantic to Fox, across the aisle. More than eight hundred pieces published, from authors from sixty countries around the world. We would welcome state and local members of this network contributing. And it’s ThinkGlobalHealth.org. And thanks, again, for your time today.
FASKIANOS: Thank you. And thanks to all of you. We will disseminate the link to this webinar recording and the transcript. We will circulate again the report that Tom Bollyky authored—co-authored, as well as the link to ThinkGlobalHealth.org. We’ve also dropped those links in the chat. You can follow Tom on Twitter at @tombollyky. And, as always, we encourage you to visit CFR.org, ForeignAffairs.com and, of course, ThinkGlobalHealth.org for more expertise and analysis. You can also email [email protected] to let us know how CFR can support the important work that you are doing. And we do recognize all the hard work that you are doing. As Tom does go—not enough attention is given to it. So thank you for all you’re doing. Thank you for being with us. And thank you to Tom Bollyky for your efforts.