Dr. David Katz Discusses COVID-19
Rick Weissenstein, Cowen Washington Research Group’s Health Care Services and Pharmaceutical Policy analyst speaks with Dr. David L. Katz, author of the op-ed “Is Our Fight Against Coronavirus Worse Than the Disease?” in the New York Times on March 20, 2020. Dr. Katz proposes strategies to reopen the economy using risk based vertical interdiction to minimize harm caused by COVID-19. They also discuss a possible timeline to restart elective medical procedures in hospitals and patient access to COVID-19 therapies if supplies are not sufficient to meet demand.
They are joined by Charles Rhyee, Cowen’s Health Care Technology analyst and Ritu Baral, Cowen’s Biotechnology analyst.
Press play below to listen to their conversation.
Cowen Hosts:
Rick Weissenstein, Managing Director, Washington Research Group – Health Care Services & Pharmaceutical Policy
Charles Rhyee, Managing Director, Health Care – Health Care Technology
Ritu Baral, Managing Director, Health Care – Biotechnology
Guest:
Dr. David L. Katz
David L. Katz, MD, MPH, FACPM, FACP, FACLM is a Preventive Medicine specialist and globally recognized authority on lifestyle medicine. He is the founding director of Yale University’s Yale-Griffin Prevention Research Center (1998-2019); Past President of the American College of Lifestyle Medicine; President and Founder of the non-profit True Health Initiative; and Founder and CEO of Diet ID, Inc. Katz is a Fellow of the American College of Preventive Medicine; the American College of Physicians; the American College of Lifestyle Medicine; and Morse College, Yale University. He earned his BA at Dartmouth College; his MD at the Albert Einstein College of Medicine; his MPH from the Yale University School of Public Health; and completed sequential residencies in Internal Medicine and Preventive Medicine/Public Health, earning Board certification in both disciplines. He practiced clinical medicine for 25 years, first in primary care internal medicine, subsequently in a model of evidence-based, integrative medicine team care he developed. The recipient of numerous awards for teaching, writing, and contributions to public health, Katz was a 2019 nominee for a James Beard Foundation Award in health journalism, and has received three honorary doctorates. He holds multiple US patents; has over 200 peer-reviewed publications; has published many hundreds of on-line and newspaper columns; and has authored/co-authored 17 books to date including multiple editions of leading textbooks in both nutrition and preventive medicine. Recognized by colleagues as the “poet laureate of health promotion,” Katz has given keynote addresses in all 50 United States and in countries on 6 continents. He has appeared on most major media outlets and reaches a social media following of nearly one million. His 18th book- How to Eat- co-authored with food writer and New York Times columnist, Mark Bittman, is available now.
Speaker 1: Welcome to Cowen Insights, a special look at the coronavirus and its effects on sectors across the economy as well as the policy arena. You will hear the latest insights from leading experts about where things stand and what’s around the corner.
Rick: [00:00:30] Well, thank you. Thanks everyone for joining us. As I’m sure you all know, our speaker today is Dr. David Katz, someone whose writing I’ve followed for a long time and have always been impressed with for a number of reasons. One, it’s always well thought out. Two, it’s always done in a way that even a numskull like myself can understand, which is not at all an easy task to do. As an ex-journalist, you realize sometimes that’s the hardest part of the whole thing.
Dr. Katz [00:01:00] today is going to join us for 45 minutes. So I’m going to jump in here. Dr. Katz, thank you so much for joining us. We really appreciate that.
Dr. David L. Ka…: My pleasure, and thank you for the kind words of intro. Explaining things so that everybody understands them so we can all get involved in the dialogue, that’s definitely an aspiration. So I appreciate that.
Rick: Well, you do a great job of it, that’s for sure. The genesis of this was a piece that you wrote an op-ed in The [00:01:30] New York Times kind of early in the COVID situation. At that point you made a compelling case for why the medical resources should be better targeted to the most vulnerable people. I think at that point there was something like 200 deaths. We’re now well over 2,000, and it doesn’t seem like that sort of targeting has really happened in any systemic way. So I guess I’d ask you, is it now too late to do? If so, [00:02:00] is there a way to expand the definition of vulnerable population based on what we know now? I think at that time it was really just old people. We’ve certainly had more younger people dying.
What do you think of some of the other approaches? I guess, I’ll just add to that then, what do you think of some of the other approaches like Sweden, which sort of has tried to do that? Is that a good balance, for example? So maybe you can kind of run through that and talk to about where we are now and whether that’s still a viable possibility.
Dr. David L. Ka…: Sure. [00:02:30] Thank you. I’ll kind of interpret the full scope of that question in a way that lets me run through a set of answers that I think assemble into something useful. My initial argument was based on looking at global data. We had obviously data out of Wuhan, and we had an impressive data-gathering effort out of South Korea. South Korea remains in the vanguard. South Korea, Iceland, Germany have arguably [00:03:00] done the best jobs of obtaining population level data. I’ll come back to that issue of population level data, what we have, what we need here in the United States to inform policy and really to help guide thinking about where does this take us next, what does it mean for business and so forth.
But in some sense my writing, and again, I appreciate the kind words about being easily understood … In some sense it was misunderstood because [00:03:30] both The New York Times when my piece ran and when Tom Friedman did a follow-up column about my column, the headlines for both of those pieces kind of emphasized the economy and jobs. I’m a public health physician. I care about the economy, I care about jobs, but really my primary interest is lives. I’m a humanist, not an economist.
My thinking was that there’s more than one way for this contagion to hurt people [00:04:00] and kill people. One is directly. It can infect them, make them very sick and cause them to die. But the other is indirectly. If we have a misguided policy response to this, if we shut down the economy in a haphazard way, if we lay people off, if we disrupt critical goods and services and supply chains, there’s an awful lot of public health fallout from that. There’s potential for food insecurity, hunger, there’s depression, anxiety, [00:04:30] addiction, suicide, domestic violence. All kinds of really bad things happen if the national policy is, “Oh dear God, the sky is falling. Everybody hunker in a bunker indefinitely and await further instructions.” There’s a calamity in the making there.
In the follow-up column by Tom Friedman, I invited a colleague, Steve Woolf from Virginia Commonwealth University to join the discussion, and Dr. Woolf is an expert in the social [00:05:00] determinants of health, the way things like poverty and food insecurity and all manner of adverse circumstance can translate into pretty dire health outcomes. So my goal, and I think I’ve managed to articulate it better since than in that early going, my goal really was total harm minimization. What do we need to do so that we protect people most at risk of severe coronavirus infection from being exposed? But how [00:05:30] do we balance that against essentially protecting the rest of the population may be at greater risk of indirect harms secondary to our policy, what do we do to best protect them? That’s going to be some balance between sheltering those who need sheltering and allowing some portion of us to stay out in the world.
The opportunity to do that from the start … And I called that approach vertical interdiction. Essentially interdiction would be anything directed at interrupting [00:06:00] the transmission of this virus. Horizontal interdiction is one-size-fits-all. Keep everybody away from everybody else, let’s try to avoid letting anybody get this infection. The problem with that, of course, is you don’t have the same level of need for all people in the population because risks vary and the unintended consequences are essentially the spillover of shutting everything down. We’ve all seen how calamitous that could be [00:06:30] in terms of unemployment, and there actually has been a surge in domestic violence and addictions seem to be rising and all the things that we might’ve predicted.
So the alternative would be vertical interdiction, where you do different things for different people based on risk. So you asked me, “Is it still viable?” When I first wrote my op-ed, and March 20th was when it ran in The New York Times. That seems like several lifetimes ago now because every day in a pandemic is a week and [00:07:00] every week is a year or a decade. Anyway, it seems like a very long time ago. I actually wrote my piece 10 days before that, March 10th, and it took 10 days to run the gauntlet of review and editing and fact checking at The New York Times before it saw daylight. That was a lifetime too.
When I wrote it, we had not yet made the fateful decision in the US to send all of the nation’s college students back home to their far- [00:07:30] flung households … Without testing them by the way … We hadn’t yet laid off a lot of young, healthy workers in big cities and sent them home. Since we did that … We didn’t test anybody wouldn’t even take anybody’s temperature … For all we know, we kind of took coronavirus and inoculated it from sea to shining sea in one fell swoop. It was misguided.
So when I first was writing this, I was thinking, “Wait a minute, deep breath. It’s not clear that, that’s the safest [00:08:00] policy. Maybe the best place for college students to be is on college campuses where they’re in a population of young, healthy people who seem, based on South Korean data, to be at very, very low risk of severe infection or hospitalization. Maybe the last thing we want to do is send them home to their 50 something parents or in a multi-generational home, their 70 something grandparents to potentially infect them.” That ship sailed between the time I first wrote and the time my op-ed so daylight [00:08:30] in The New York Times. So we had already essentially sent them home. We’re starting to shut everything down.
At that point the issue became, “Okay, fine. We’re clearly not organized enough. We’re clearly not maybe thoughtful enough to start out in the United States with vertical risk-based interdiction. Our initial phase is going to be horizontal. Fine. Let’s pivot to vertical interdiction,” and that absolutely remains viable now. So as we [00:09:00] think about what we could do next, we should be getting very clear indications from our government that the relevant data collection is well underway. We should have a very clear telescoping of how the data will be used, and we should be anticipating a phased return to normalcy. We’ll come back to other countries, Sweden, Netherlands, UK, various experiments around the world.
Instead of doing [00:09:30] that though, let me tell you what I think the ideal situation would be for the US and frankly for the policy response by any country because I haven’t seen any country do this in the way that I would consider ideal. It’s not too late for us to do it in an ideal way. First of all, all major public policy should be data informed. Otherwise, you’re guessing, and in this situation [00:10:00] we would be guessing about lives and deaths. There’s no reason to do that when you can make data-driven decisions.
So the data we need, I think may be best characterized as a data pyramid. Although it would be lovely to do massive population-wide testing for both coronavirus infection and immunity, it’s not really necessary. What we need to inform decisions, and this could be any given population by the way … This could be private sector [00:10:30] if you’re talking about a given workforce. Major league baseball wants to know what’s going on with their players, they could do it. But from my point of view as a public health physician, ideally this would be conducted through the Centers for Disease Control. It would be nationally representative here in the US. Similar projects could be undertaken anyplace else around the world.
We want a representative random sample of the population of interest. So it could be again, the United States. 5, [00:11:00] 000 people, 10,000 people, not a huge number, something that, basically we’re talking about a project that would take 72 hours. We want to test this representative random sample. So, distribution of people, different settings, urban, suburban, rural, affluent and indigent, people of different ethnicities, all different parts of the country, different ages, different health status. These methods by the way, are not rocket science. This is done routinely [00:11:30] by the CDC. There’s a national project called the Behavioral Risk Factor Surveillance System, BRFSS. You can just Google that, but it’s a representative random sampling project the CDC engages in all the time.
What we would want to know from the sample of, let’s just say, arguably 5,000 people, is your age, your sex, your height, your weight. Do you have diabetes? Yes or no. Do you have heart disease? Yes or no. Do you have any of the leading apparent risk factors for COVID? [00:12:00] Yes or no. Then we need blood or saliva to see are you infected now, and do you have antibodies because you were infected and maybe didn’t even know about it and have now achieved immunity. From that we can build the data pyramid. Essentially with a representative random sample, you can extrapolate to the population of interest.
Again, let’s say that, that’s the population of the US. We now say, okay, we have pretty good estimates now. We have pretty [00:12:30] tight 95% confidence intervals to say what the range of people in the country already infected with this is. We have pretty tight estimates around the percent of the total infected who are now immune. We’re able to say what proportion of that population got sick enough to seek medical attention at all. What proportion of that group got sick enough to need hospitalization? What proportion of that group got sick enough to need the ICU [00:13:00] or a ventilator? And what proportion of that population died?
You’ve heard lots of discussion about the case fatality rate, and I contend we have absolutely no idea what the case fatality rate is. You don’t make it through epidemiology 101, in fact, you don’t really make it through the first week of epidemiology 101 without learning to ask, what is the denominator. Case fatality rate is the ratio of deaths from a specific cause to the number of people who [00:13:30] have that condition. If you don’t know the number of people who have the condition, you have absolutely no idea what the case fatality rate is. That’s one of the reasons we’ve seen estimates range from 0.3% or lower to 3.7% or higher, a full order of magnitude or more because we don’t know. We don’t know the total numbers infected.
It’s interesting now to start to see around the world the various thoughts of phased returns to [00:14:00] normalcy, rebooting the world as it were, getting economies going again. Sweden’s obviously looking at that and other countries are too. I’ve yet to see any country build this data pyramid based on representative random sampling. I think we could, and I think we should. Again, I think we can do this as a nation, but it could be done in a population specific manner. So if we’re not inclined to wait for the Feds to get this job done, [00:14:30] potentially these are exercises that could be coordinated is an industry specific way in the private sector. Certainly they could be done through state governments, but I think they ought to be done. I’m not aware yet that any country is predicating its policy on the complete population of this data pyramid.
I’ll pause there and let you ask the next question because I think, frankly, in some ways that may preempt some [00:15:00] of what I might say about specific national responses because I think this is the way to do it. By the way, we’re already in this. If we hadn’t shut everything down and you were asking me, “Do we need to shut everything down while we gather data,” I might’ve given a different answer. But now it’s just a case of in for penny and for a pound. We’re already hunkering in our bunkers. We’re talking about a difference of just a few days. Waiting to have a vaccine for something like 18 months, that’s pretty daunting, [00:15:30] but saying, “Look, we’re going to spend the next 72 hours gathering all of the essential data we need to make truly informed decisions so we get this right, and we risk stratify based on more than just a guess that an age cutoff at 70 is important.” I don’t think a few days are going to make the difference in terms of economic recovery.
Rick: Thank you for that backdrop. I think your plan makes sense. [00:16:00] So kind of where we are now and looking forward a bit, I mean, it seems like certainly all the talk at this point is trying to get back to “normal,” whatever that is. Life as usual, whatever that is. I think that’s the word you use in your piece. The Trump administration is talking [inaudible 00:16:19] business leaders. You have states trying to potentially go their own way on this. What do you think is a realistic timeline to get back to “life as usual?” [00:16:30] I hesitate to say this, but I mean, it certainly doesn’t seem like they’re moving in the direction that you’re espousing. Based on what you know about what they’re doing, what do you think is a realistic timeline for us to get back to that normalcy, whatever that is?
Dr. David L. Ka…: Yeah. Yeah. So I coined the term, SERLAWKI, selective early returners to life as we knew it. So whatever normalcy is, and it may be a little different after this than it was before. But I don’t think it should be very different.
[00:17:00] Let’s operationalize this notion of getting back to life as we knew it, rebooting the economy, restoring the society we knew just a few lifetimes ago before the pandemic began. I think there are essentially four objectives that derive from our policy approaches, and then they tell us a lot about the timeline. The first is we need to keep the highly [00:17:30] vulnerable people away from this infection. So, anybody who is likely to get severely ill and need an ICU bed and potentially at risk of dying from SARS CoV-2, we want to keep them and the virus apart. We have to do that for as long as the virus is circulating.
The second thing we want to do is minimize the adverse toll, public health and economic, of our policy responses. We don’t want [00:18:00] to hurt people with food insecurity and hunger and all the other things that can spill out to. So again, total harm minimization encompasses those first two.
The third thing we want to do is make sure that whatever we do to return people to the world, doesn’t … Even, let’s say, for example, you’re talking about younger, healthier people where the frequency of severe coronavirus infection is low, but it’s still possible that if you [00:18:30] return people to the world in one fell swoop, the total number that got sick all at one time might still be more of a demand than the medical system in a given place could readily manage. We want to use what we know about hospital demand to make sure we do not allow for hospital overload. So not overloading the medical system is the third objective.
But then there’s a fourth. I think people kind of lose their [00:19:00] way here. I believe the fourth objective is herd immunity. I don’t think we want to live in and out of bunkers for 18 months or longer. 18 months is generally considered the most optimistic timeline for the development and deployment of a highly effective vaccine. So this is the quirky part of all this. We actually want people to get exposed to the virus. We actually want people to get this [00:19:30] infection and get over it. That’s how pandemics end, and again, you can end them with a vaccine, but then you have to wait for the vaccine. What that means is we are highly vulnerable to this happening over and over again.
We either never come out of hiding or if we come out, because the virus goes away over the summer but then comes back in the fall, then this all starts again in the fall. We’re basically throwing the economy in and out of activity, and that seems pretty daunting too. I think we want [00:20:00] the large pool of people who can safely afford to get this infection, get over it, make antibodies and be immune, to get it. I think the phased return to normalcy can begin almost immediately. In fact, we could guess now and say it’s pretty clear that people under 50 without a major chronic disease are at very low risk of severe infection. We could just say, every household where you’ve got [00:20:30] nobody over age 50 and nobody has heart disease or diabetes, back to the world. Back to school, back to work. We could potentially do that.
I argue, why should we? We’re in this far. Take the extra few days, build the data pyramid and do a more refined assessment than that. Let’s make sure we fully understand risk differentials, not just based on data from around the world, but homegrown data. United States health is not terrific. We have a lot of obesity [00:21:00] and it affects a lot of young people. Do we need to know more about risk differentials here to do this safely? Might we want to stratify the population into more than just two risk tiers, low risk, high risk. Do we want to say lowest risk, slightly higher, slightly higher, intermediate, and then a highest risk category? Then do we essentially define the group that most needs to stay away from this virus? [00:21:30] Their job basically is to honor the policies of interdiction until we achieve herd immunity.
That’s the other thing we need the data to know. You’ve probably all heard or read reference to something called the R-Naught, which is an epidemiologic term, essentially the number of people who are going to get infected because of each person who’s already infected. How readily the virus transmits itself. If we don’t know the population levels of infection, [00:22:00] that’s just a guess too. We do want to know that because it’s a derivative of that R-Naught, the percent of the population that needs antibodies before you achieve herd immunity.
Let me just quickly define herd immunity. It’s the idea that when some critical mass of us has made antibodies to a given infection, we represent a dead-end for the pathogen. It can’t set up shop in us, our immune system knocks it down too effectively, [00:22:30] and so we can not transmit it. So if I’m immune, I protect you because I’m a dead-end. Maybe you’re exposed to me, maybe you’re still vulnerable, but I can’t give you the virus because it can’t replicate inside of me because my immune system has made antibodies and is prepared to deal with it. When enough of us represent those dead-ends, the vulnerable are protected. Essentially that’s when everybody’s grandparents get to once again come out and hug their grandkids.
[00:23:00] I think the first phase, returning to work, returning to normalcy, is days away. It shouldn’t be more than days away. By the way, for backup material here including quite elaborate risk models, visit truehealthinitiative.org. True Health Initiative. That’s my 501(c)3. We collated all these materials there. So we have a whole range of risk models from experts around the world. They’re all aimed [00:23:30] at this goal of total harm minimization. You’ll also see that one of the things we do, kind of this high level model that looks at both the risks of the workforce based on age and health and the priority of the work based on its role in supply chain, services, goods, and economic importance.
You can essentially devise a strategy for returning any given population to the world early based on the confluence [00:24:00] of those two. So where you’ve got highest priority work and lowest risk workers, that group should be back to the world very soon. Again, from my point of view, you could say, “Hey, let’s start doing it right now,” because we already know quite a bit about risk differentials. My preference would be let’s wait, again the three days, roughly 72 hours thereabouts, three days, five days, it would take to build that data pyramid. The next priority really ought to be the construction [00:24:30] of the data pyramid. If the Feds don’t do it, again, private sector certainly could.
Rick: Okay, thank you very much. Obviously showing my bona fides as a numskull, I said 2,000 deaths across the country. Of course, it’s more than 20,000. If it were only 2,000, that would be so much more wonderful. So with that, I’m going to stop. Charles Rhyee, you want to take over with your question?
Charles Rhyee: Yeah. Great. Dr. Katz, thanks for joining us today. Maybe moving more specifically [00:25:00] into the healthcare field. Can you talk about the situation with hospitals? You mentioned about capacity and not over burdening capacity. Clearly we’re having that in pockets of the country today. An issue related to that is obviously in response we have canceled the bulk of elective procedures across the country. But there are expectations now returning to some normal pattern at some point, perhaps as early as late summer. When do you think hospitals could be safe to resume elective procedures [00:25:30] and what are the conditions you think we need to see to get started? Does that include the need for rapid testing for every patient and provider in order for that to get underway?
Dr. David L. Ka…: Good questions, Charles. Just to refer back to Rick’s comments, yeah, so the deaths in the United States now are just under 24,000. But honestly, if getting that number wrong is being a nincompoop, I’ve been there many times because we’re watching these numbers change day by day. Charles, [00:26:00] before I pivot to hospitals, let me just say something about that. There is, and this is important, I don’t need to tell everybody on this call that the coronavirus pandemic is a big deal. It’s completely disrupted our lives. It’s arguably the greatest crisis in public health in living memory. We all know it’s a big deal. But I can go the other way and tell you it’s not as big a deal as it looks like because we are in fact scrutinizing these mortality numbers every day and every minute [00:26:30] another person died. The simple fact is we’re doing it for this one cause, and we’re not doing it for anything else. So under 24,000 people have died of coronavirus in the United States today. That number will of course go up.
But so far it is significantly less than half the deaths from flu this year alone. Kind of people roll their eyes at you when you say that, but that’s the truth. 650,000 people will die in the United States this year from heart [00:27:00] disease that hides in plain sight and that’s largely preventable. Those are mostly premature deaths and we don’t pay much attention to that, and it goes on and on. So there is an element of risk distortion. Even though that number, Rick, is bigger than 2,000, it’s 24,000, because we’re just scrutinizing that one number we do tend to lose perspective.
You may want to keep this in mind, and this will pivot to your question, Charles, about other issues that we’re potentially neglecting, 8,000 people die [00:27:30] of miscellaneous causes in the United States every day. Some of that’s tragic, some of that is just the circle of life, if you will. We get old and we die. We’re mortal. But about 8,000 people a day. So the total SARS CoV-2 mortality in the US today is about three days of average mortality from miscellaneous causes. These are real people. These are real families. This is real grief. I’m not trying to belie any of that [00:28:00] by using these statistics. I regret that statistics can conceal real human suffering, but the reality is that people die every day of many causes, and by focusing just on this one cause we tend to overlook that.
So that does have implications for hospital function. There has been concern, and there have been reports that people with other conditions that need treatment for cancer or that have chest pain [00:28:30] that ought to be checked out are very reluctant to go to the hospital, and that there may in fact be an indirect mortality toll from the coronavirus because of people neglecting other health problems. Hospitals are struggling to be able to take care of everybody not knowing infectious status. So Charles, I think we’re going to pivot out of that very quickly. I think that’s a matter of really just weeks. Honestly, I’ll have a better perspective on that when I’m looking at this from the inside out. I volunteered as a physician [00:29:00] in the beleaguered New York City hospitals. I started signing up a couple of weeks ago, took a while to get through the bureaucracy. I’m going to be deployed finally in the Bronx this weekend. So I’ll kind of have an insider’s look at that.
But I know they are struggling now to try and keep potentially infected patients apart from patients who are there for other reasons. That will get much better when we have population level data. That will get much better when we can do rapid testing. That will get much [00:29:30] better when we have effectively allowed a lot of the population to be out in the world, potentially exposed to the virus and over it because rates of transmission will drop. So, all of these things are linked.
If we agree that we could gather the data we need to make truly well-informed policy decisions in a span of a few days, and if after that span of a few days, we use the data to make those decisions and say, “Okay, the phased return to normalcy and the world as we knew it begins now,” that [00:30:00] means that lots of people are going to be exposed who are not going to get severe infection, who are not going to need the hospital. The people most likely to get severe infection and need hospital care are not going to be exposed, and so hospitals will start to shift in the direction of the world as they knew it before, where most people are not coming in with coronavirus infection, but for other things. So, honestly, I think the return to relatively normal operations in our hospitals could play out [00:30:30] over a span of just upcoming weeks. I don’t think this is a long-term issue. I think we can get there pretty quickly.
Charles Rhyee: Dr. Katz, you just mentioned as part of your idea of getting back to normalcy, it kind of requires this idea of data pyramid as well as rapid testing, both of which we don’t have yet. The question would be how quickly could that get ramped up and coordinated? To a certain extent, [00:31:00] a lot of these edicts on canceling elective procedures are coming from governmental officials. Obviously you have to get states like, let’s say New York. Governor Cuomo, to reverse his decision.
So in that sense, what would you think is actually a realistic timeline then? Obviously, maybe in theory, you could get it done in weeks or we could start within weeks, but more practically speaking in terms of the bureaucracy that probably underlies it as well, what do you think is more likely? And [00:31:30] then secondly, is there capacity to go above normal capacity? During like after natural disasters, hospitals will ramp up, let’s say 150, maybe even up to 200% of normal capacity on elective procedures to try to make up the volume. But here, when we potentially face a resurgence in the fall, can hospitals responsibly ramp up volume in the back half of the year, knowing that they potentially [00:32:00] have a resurgence of COVID in the fall?
Dr. David L. Ka…: All right. So lots of questions there. First, on the matter of timelines and bureaucracy, obviously I don’t have particular expertise in the decision-making bureaucracy at New York State and Florida and how readily shortcuts can be devised to deal with the current situation. But I would argue, it seems [00:32:30] like we’ve been in pandemic world forever, doesn’t it? It’s hard to remember life before this, and yet it’s only been a few weeks. So, when I talk about things happening a few weeks, I don’t know whether that’s fast or slow because the last few weeks have taken up a lifetime. I think in some ways it’s both. I think it’s a generous amount of time, and we’ve seen massive decisions navigate through bureaucracy in very, very short periods of time. I mean, after all, we shut everything down, which is a huge [00:33:00] policy decision. We did that in decisions that played out over just days.
So the idea that we potentially could be returning to significant elements of normalcy in hospital function, everything else, in a period of weeks, I don’t think that’s unrealistic. I think Governor Cuomo is eager to reverse the emergency decisions he’s forced to make by what’s going on right now. But the reality is that the surge capacity in New York City hospitals [00:33:30] right now greatly exceeds need. Essentially they operated on some of the worst case projections. They commissioned the Javits Center, which I understand is not more than 10% full. I just, again, had a conversation with one of the New York City hospitals this morning where I’m going to be working and they have significantly more ICU beds than they’re using. This is at the very peak of it. No one’s expecting things to get much worse in New York City [00:34:00] than they are right now, and yet we actually have excess capacity.
The issue of a resurgence in the fall … See, that all depends on whether or not we allow exposure to happen now. I favor, and this is why the data pyramid is so important because we really need a good understanding of what are the rates at which different populations get mild infection versus severe infection. If in young, healthy people, a severe [00:34:30] infection needing a hospital bed is one in 10,000, we take risks like that every day. We take risks like that when we cross the street. So, I think people would be willing to take their chances with that. What that means is hospitals won’t be overwhelmed by any means, but a lot of us can build antibodies. Then there won’t be a resurgence in the fall because before this bug comes back around again, we will be substantially immune.
Maybe it’ll come back around and it will be mild and it’ll be more like a seasonal [00:35:00] flu, and maybe we’ll barely notice, but it’s not as if this will start in again. The only way this starts in again is if we keep everybody away from the virus as best we can until the virus goes away. That’s a longer timeline. Maybe that’s a month, maybe it’s two. I don’t know. Maybe that’s weather related. We have to wait for summer. But however long it is, the virus is dictating the timeline. It’s really not a matter of policy. We’re tracking whether or not transmission [00:35:30] is occurring, and as long as it is, everybody stays in their bunkers.
Then we come back out when we’re not at risk of exposure, and when the returns, we’re all still vulnerable. I don’t favor that approach. I don’t want to spend the rest of my life, or however long it is between now and a vaccine, living that way. I don’t want to have to worry that my 80-year-old parents on any given day might get exposed and suffer the consequences of this. I think a far better strategy is to get a clear handle [00:36:00] on how many cases are mild, what segments of the population in fact ought to be exposed to this thing, because they’ll be fine for the most part and they will get us to the all-clear in a safe way. And then, assuming you do that, I don’t think there’s all that much worry about surge capacity at hospitals in the fall.
That said, I think we should build error bars around all of our policy decision-making, hope for the best, plan for the worst. Based [00:36:30] on the data and how many people are immune now, we ought to have some notion of how many people do remain vulnerable if this infection comes back around again in the fall. Some of what we’ve put in place now, which we’ve had to scramble to do, the surge capacity for various hospital systems, we ought to keep it more proximally accessible. This time around, we were totally unprepared and we had to manufacture surge capacity out of the ether, but we just did [00:37:00] it. So essentially we archive all of this as basically surge capacity protocol one, and you just pull that trigger again and you keep those resources more proximal. Again, there are all sorts of operational details attached to that, but the basic idea that we figured out how to do it now at a time of crisis, we certainly can keep the capacity to do it again readily available so that [00:37:30] we’re able essentially to reactivate the same plan.
Charles Rhyee: Dr. Katz, I’m going to turn it over to my colleague in a second. Just to follow up though. You talk about herd immunity, but can we follow this plan when it doesn’t seem 100% certain that infected patients gain immunity with an antibody response? It seems like there’s some [crosstalk 00:37:51]
Dr. David L. Ka…: Yeah. There’s so much stuff written on coronavirus and everybody knows, I’m sure, [00:38:00] the game that the media play. So if it bleeds, it leads. Comfort the afflicted, afflict the comfortable. So, if we’re starting, whatever our anxieties may be, if we’re starting to think, “Okay, yes, but,” at least some of us are going to get over this and be immune, “Ah, not so fast. Here’s a case where somebody got reinfected.” Some of that’s valid and a lot of that is if it bleeds, it leads and afflict the comfortable. As soon as you start getting comfortable with something you think you know about [00:38:30] coronavirus, there’s got to be a media story to tell you, “Wait a minute. Not so fast.”
So frankly, with all of the viruses in this family, immunity is the norm. If you survive the infection, you make antibodies. You’re immune either completely or at least partially. It may not be lifelong, but it lasts long enough to prevent you from getting this thing again soon. That’s almost certainly the case here too, both because it’s kind of an intrinsic [00:39:00] property of the immune system, it’s an intrinsic property of this kind of virus. The cases that have been reported of reinfection occur in the context, and we all have heard these reports too, that the testing is not very reliable. So in other words, somebody tests negative, and then they test positive. Well, is that reinfection or is that the first test failed? We’ve had a whole bunch of test kits where their ability, their sensitivity, their ability to find the disease when it’s there, has been quite poor. [00:39:30] So to date, we have no truly reliable indications of anybody getting promptly reinfected.
There are some anecdotal reports that raise that as a possibility. I suspect it’s a possibility because in almost any medical circumstance, it’s ill-advised to say never or always. There’re always going to be exceptions. So I just said always, but that’s one [00:40:00] that’s pretty reliable. There’s almost always an exception to any given rule. But it’s unlikely. It’s almost certain to be rare. Most of what we’ve heard reported in the media may simply be wrong. It may be that these are not cases of reinfection. It was just an inadvertent conclusion that infection had come and gone when it really never had. So I think it’s overwhelmingly likely that we will make antibodies and be reliably immune to some degree for some period [00:40:30] of time.
Charles Rhyee: With that, let me turn it off to my colleague, Ritu.
Ritu: Thanks, Charles. In our last few minutes, Dr. Katz, can you talk a little bit about risk stratification as it pertains to potentially therapies and care? Does this same risk vulnerability interdiction paradigm apply to when we may have treatments, which of course will be in short supply initially, or vaccines when [00:41:00] they are in short supply?
Dr. David L. Ka…: I think so. Certainly the vaccine, and these things are hard to predict. There does not appear to be a shortage of azithromycin. I think the supplies of hydroxychloroquine are ramping up. Some of the biologic agents that are being used may be in a more rate-limiting supply. Corticosteroids for the most part, which are used to treat acutely ill patients, tend to be readily available. So some of the [00:41:30] supplies are really quite good. I haven’t heard major issues with most of the drugs that are being considered. And the vaccine is unpredictable. It’s going to depend on the methods involved in producing an effective vaccine, which will then dictate the laboratory methods required to scale up production. But yes, I would say across the board to the extent that there are limitations … Obviously if you’re talking about treatment, it’s no longer [00:42:00] so much a matter of risk because risk for severe infection becomes moot when you’re dealing with severe infection.
So I would say your highest priority would be people who are fighting for their lives, obviously, and whatever treatments can be used to increase the likelihood of survival in those circumstances get directed there. Then you might back up in terms of priority and say, if you detect infection in somebody in a high-risk group, you would use treatment there to try [00:42:30] and obviate the progression to severe life-threatening infection. So absolutely risk would be relevant there. If, for any reason, the production of the vaccine ran into sort of supply limitations, same thing there. So, essentially the group at highest risk of severe infection would be the group that would line up first for the vaccine. So people over 70, people with chronic illnesses. All of these same data that we are gathering [00:43:00] now absolutely, or should be gathering now to make our policy decisions, absolutely could be used to optimize the allocations of both the various treatments and the vaccine.
Ritu: I know we’re at time, but I just want to ask you one more follow-up to your own published vertical interdiction. You stratified, I believe on your website, you’ve got high-risk service providers to high risk, intermediate risk and low average risk. Do you [00:43:30] have an idea as to how the population is segmented percentage wise within your own vertical interdiction paradigm? Also, intriguingly for the high risk you’ve got an other in there. So beyond chronic lung disease, heart disease, diabetes, what are the other stratifications that we should be keeping in mind?
Dr. David L. Ka…: Yeah. So the second question first. [00:44:00] There are all kinds of conditions that may have a major impact on the immune system. It’s kind of a long catalog, and there they’re much less common than things like heart disease and diabetes. But you would really worry that somebody who is being treated with high-dose steroids, for example, for severe rheumatoid arthritis or lupus or multiple sclerosis or sarcoid, that the condition itself [00:44:30] may be affecting the immune system. But perhaps even more importantly, the treatment. Advanced HIV, where there is a significant need for immune-altering drug treatment may be a consideration. There are others too. At the population level, these are less of a numerical concern than obesity, diabetes, heart disease, but for a given individual, they might be extremely important. [00:45:00] So the other would encompass all of that.
Then in terms of the prevalence in the population, this is why actually it’s a very good question. Colleagues and I have a pre-publication research paper online now where this is what we looked at. What is the percent of the whole US population that appears to be at elevated risk for severe coronavirus infection based on health status? Sadly, those numbers are not great. [00:45:30] About 45% of the whole population has one of these predisposing conditions. Obesity, if you put that on the list, extremely prevalent. Hypertension is very prevalent. Type 2 diabetes and some form of heart disease. However, what we don’t know, and this is why all roads lead back to that data pyramid to say, “Okay, we’re sort of speculating that we know age is a factor. We know health status is a factor, and we’re going to do the best we can to kind of guesstimate who’s [00:46:00] at elevated risk here,” but it may be that the tonic of youth is robust enough that it offsets some of these other risk factors.
So yes, obesity increases risk, but with a BMI below X and people under age Y, rates of severe infection are still very low. I think we can do a better job of partitioning even within risk groups and say, not everybody with the risk factor is at the same risk. Even the risk factor, the [00:46:30] influence of it varies with age, it varies with severity. Maybe well-controlled type 2 diabetes exerts a very different influence than poorly controlled type 2 diabetes. We’re only going to be able to answer these more refined questions with a diligent effort at data acquisition, and then frankly, whatever data we collect will be partial. We’ll need to subject our policy decisions to empirical reality checking by monitoring data over time and being prepared [00:47:00] to adjust. It may turn out that some people we thought were at lower risk are at higher, and some people we thought were at higher are at lower. But if we get this basically right at the start, these would be relatively modest adjustments to major policy decisions.
Ritu: Understood. Do you think that geography and one of the things in at least some of the literature recently has been speculation about viral load of first [00:47:30] contact. Do you think those should factor into your risk tiers?
Dr. David L. Ka…: Yeah, so I’m actually I’m having that conversation right now with my nervous family, which would rather that I not go work in a New York City hospital. But I feel like I have to. So we’re talking that over and the fact that in the healthcare setting, for example, first, certain kinds of activities like bronchoscopy or [00:48:00] intubating patients in the ICU puts you at greater risk because you’re more intimately exposed to the body fluids where the virus is concentrated. Then it appears that the magnitude of exposure may influence how sick you get, and so that does become a consideration in the mix. That kind of makes sense.
The analogy I use for this all the time to help people understand is [00:48:30] you and your associates are defending yourselves from an ambush. If the ambush comes at you in relatively small numbers and you’re reasonably well defended, you defend your perimeter successfully. If that same ambush comes with orders of magnitude more numbers than you have, your perimeter’s overwhelmed and your defenses don’t hold up. That’s the immune system. The ambush is the virus. Your defenses are the immune system. You can actually be exposed [00:49:00] to this germ and not get acutely ill if the dose of exposure is relatively modest. Your immune system sees it, you can maybe make antibodies and never really get very sick. That may be part of why some people have asymptomatic infection. On the other hand, if your initial exposure is to a very high concentration of this, then it may overwhelm your defenses before you can protect your perimeter. Essentially that’s what leads to symptomatic infection or severe infection.
[00:49:30] That dose matters. That’s a risk we ought to really be carefully interdicting in the clinical setting. I’m reassured I will be getting all manner of appropriate personal protective equipment. I certainly hope that’s true. That’s where everybody wearing a mask has been helpful. It doesn’t eliminate viral circulation in the air, but it cuts it way down. That may still make sense for a while. These are things, again, where I would [00:50:00] say we can make pretty good decisions based on data collection now, but then we want to monitor the real world experience and maybe adjust our decision-making. Maybe we would decide, actually selective use of masks for a while is a good idea because it doesn’t prevent transmission of the virus, but it reduces the dose of exposure. Maybe that facilitates more people having asymptomatic infection and making antibodies. I’m making this up. We don’t know, but it’s the sort of thing we could figure [00:50:30] out over time that would be useful to know.
Ritu: Great. I’m going to turn it over to my colleague, Mark.
Mark: Thanks Dr. Katz, and thanks for all the answers. Maybe to follow up on one of the answers you gave to Ritu a minute ago, you thought maybe 45% of the population in the US has one of these kind of immediately apparent risk factors. You’re recognizing that maybe with more granular data and the interplay of risk factors, we can bring that population down to something [00:51:00] smaller, but then you also have a population that doesn’t have a risk factor, but lives with that population. So how do you implement taking that maybe 55% of people who aren’t at risk and bringing them back to work when such a large portion of the US maybe has to stay home?
Dr. David L. Ka…: Great [crosstalk 00:51:20] Yeah, it’s a great question, and it’s come up a lot in the conversations I’ve been having. I always give [00:51:30] the same answer, and that is there is some degree of devilry in the operational details of any complex public policy. If you start asking the, “Okay, what about a household where,” and you’ve got older people living and taking care of their grandchildren. So it’d be okay for the grandkids to go back to school, but it’s not okay for the grandparents to get exposed to the virus and on and on it goes. I think, first of all, the complete answer we don’t have time for here. It’s a 1, [00:52:00] 200-page policy manual. Secondly, I have not written it. I’m aware of the need for that sort of thing. In any complex situation, you start with high-level decisions, and then when you get into the implementation details, it’s a lot of writing. Essentially it’s an explode function. You get this massive decision tree, if this, then that.
Maybe the best way to get there from here, it’s the sort of thing large consulting firms, large industries could potentially [00:52:30] do, but it’s also the sort of thing, again, if I ran the zoo, would have been a perfect excuse to convene a whole lot of experts at Camp David really or virtually and say, “Okay, here are the marching orders. By close of business tomorrow, I want the 1,200-page manual that accounts for every variation on the theme of what about this, what about that.” At a high level, again, I think it’s fairly simple. We have lots of households where young healthy people live on their own. We have lots [00:53:00] of households were relatively healthy, young to early middle-aged parents live with their kids. You could potentially cover a large portion of the population by saying any household where there’s nobody over age 70 and no one has heart disease or diabetes, or maybe one of the other conditions on this list, check below is okay to return to the world. That’s going to be a big number.
The other thing is that you can, again, map [00:53:30] that against priorities in the economy. One of the models that we have posted on our website is that approach. The overlap of, again, low risk workers, high priority work. The other thing we can do, everybody who’s able to be working digitally from home has started doing that. This can be a phased return where there may be some people who return to the physical workforce, and some people with more complex [00:54:00] home situations are given waivers where they’re allowed to continue contributing virtually because of that higher risk situation.
So again, I think there is a lot of subtlety to that. I think some of it does potentially get complicated. In the early going, I said, “Look, we’ve basically shut down the hospitality industry. We’ve got a lot of hotels doing nothing. Potentially they could be really nice places to concentrate people at higher risk of exposure where they can [00:54:30] be well-tended and well fed and kept away from this thing until we have herd immunity.” I think potentially a redeployment of some of our resources that are lying fallow at the moment could help with all of this. But I think really the best answer for me to give is to say I think it is a very lengthy detailed, nuanced, what about this, what about that operational manual. While I can kind of get my mind around the generalities of it, I have [00:55:00] not written it.
Mark: Okay. I realize we’re coming to the end of the hour here. I’m not sure if you need to go, but if you can take one more question. Maybe thinking from the other side of the similar question of how do you implement this from the employer perspective of if you have only some percent of your workforce is able to come back. Does the government kind of step in and support the rest of it still? [00:55:30] Does that business need to keep its jobs open for the people when we do get to herd immunity eventually? How do you help them out on that side? Your thoughts? What would you advocate in that way?
Dr. David L. Ka…: Yeah. So again, I really don’t think the timelines here need to be all that long. I don’t think we can know for sure what they are going to be absent the data collection that I advocated at the start because if we find out that there’s [00:56:00] already been very widespread exposure to the virus, and a lot of people have already made antibodies, we may be 80% of the way there already. Then everything happens very, very quickly. Honestly, a lot of what I’m seeing kind of pushes me in that direction. It’s not as if we did a really terrific job right at the start in the United States of flattening the curve. We kind of dithered. [00:56:30] There may have been an awful lot of viral exposure even going as far back as December, but certainly January, February and the early going in March before we started getting really good at containing spread.
We may be a lot further along toward herd immunity than we thought, and that would alleviate the difficulties in all this decision-making. But assuming we have weeks or even a matter of a couple [00:57:00] of months to achieve herd immunity, I think to the extent that businesses themselves can accommodate and say, “Initially, we’re identifying the workforce we consider to be at low risk, but we allow for people having complex home situations, so you can either join the first wave back to the workforce, or we offer waivers and people can continue working remotely.” Obviously, there’s some industries where you can’t [00:57:30] work remotely. You can’t do construction remotely and so forth, so this is going to have to be industry specific. Ideally yes, the government would continue to provide the supports necessary so that people who are furloughed can retain their jobs and come back when the epidemiologic circumstance allows for it.
I think some of the work that shifted to online, maybe it’s never going to come back all the way. Maybe there will be more virtual [00:58:00] meeting forever after. Maybe that’s a good thing for the environment. Maybe it’s a good thing for productivity as well. Maybe there will be combinations of in-person and virtual work that alter the workplace, and maybe that enhances productivity and offers environmental advantages. Again, I think a lot of this is really very industry specific, but I do think that a sizable enough [00:58:30] portion of the general population will prove to be at relatively low risk, that an awful lot of what matters most across a range of industries can get going. I don’t think it’s one fell swoop. I don’t think it’s everybody back into the water all at once.
I do think this has to be a phased sequence of return to normalcy. But again, right now we’re kind of making it through [00:59:00] a sequence of days with everything pretty much shut down and compensating as best we can. I would argue that any return to in-person work as we knew it before should be easier than what we’re doing right now. The greater the alacrity with which we can return people in ways to life as we knew it formerly, the better. But it ought to get easier from here, not harder. [00:59:30] I don’t think granular decision-making about who best to return first should greatly complicate our lives. Again, I think there’s some subtleties in thinking about how best to handle that. But I think there ought to be latitude to accommodate the needs of specific industries, and the government’s policies ideally, would be flexible enough to adapt to those needs.
Mark: All right. Thank [01:00:00] you very much, Dr. Katz, for the expansive answers. Unfortunately we’re out of time, so we’ll let everyone go back to their day. But thank you very much for joining, and thanks everybody who joined and listened in on the call as well.
Dr. David L. Ka…: My pleasure. Everybody, stay safe.
Mark: Yes. You as well.
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