The debate on easing or extending lockdown restrictions in the wake of COVID-19 is a matter of “lives versus lives”, and not of “lives versus livelihoods” as it is currently being framed, Jay Bhattacharya, professor of medicine at Stanford University, says. The global economic depression caused by continued lockdowns will kill large numbers of people, Bhattacharya, a PhD in economics, and a senior fellow at the Stanford Institute for Economic Policy Research, tells us in this interview.
A pre-print serological study of 3,300 people in California’s Santa Clara, by Bhattacharya and others at Stanford University, estimated that the prevalence of COVID-19 was 50 to 85 times more than the number of confirmed cases. Widespread seroprevalence tests to ascertain the actual spread of the disease will help determine the mortality rate, and help us make better decisions, he added.
India is currently under a countrywide lockdown, set to end on May 3, 2020.
Edited excerpts from a video interview:
You are a professor of medicine at Stanford (you did an MD) and did a PhD in Economics. Tell us what made you go into economics after doing medicine?
I have always been interested in medicine—I loved science. In school, I encountered economics and I was amazed to find that you could use the tools of economics to understand questions in medicine--in a very different, but a very effective way. And that is why I picked both.
And you focus on the economics of healthcare as a theme around the world. Why is it that you believe that we are overstating what could happen if we were not to have a lockdown?
I want to correct the record slightly—I actually believe that the epidemic is more widespread than we believe, not less widespread. That is my hypothesis. I would like to see that hypothesis tested, basically, everywhere. And the reason I believe this is that there are many people—we are seeing this from studies around the world—who have been infected with the virus, cleared it (sometimes with no symptoms, sometimes with mild symptoms) and never got tested. So the case numbers are all cases where there has been a test done to check whether the virus is active in you.
And in India, we are testing only if there are symptoms to start with.
Right, that is similar to all around the world… because why would you go get a test if you have no symptoms, right? But that means that we are missing, most likely, large numbers of people who have not had symptoms and have not been tested, and yet have had the virus. The only way to check for that is by doing antibody testing.
Antibodies form in response to the virus and provide evidence that you had previously been infected. Only by doing that kind of testing do we understand how far along in the epidemic we are. And as I said, my hypothesis is that we are very far along, much further along, than we can tell from just looking at the case reports.
To understand the death rate from the virus, we need to understand the size of the number of cases. We have some sense of how many people died in the numerator, we also need to know how many people have been infected in the denominator. And that is the piece we do not know. If my hypothesis is right, and that number is much larger than we realise, then the death rate per case might also be lower than we realise.
Why would you assume that there are far more people either in the United States or elsewhere carrying this virus already or for a much longer period of time?
That is a good question. Obviously, this is a novel virus, there is a lot that we do not know about it. The hypothesis is primarily driven by the work that I did during the H1N1 flu epidemic in 2009, and actually just following that literature. In that literature, the early case reports reported very high fatality rates. And the reason was, they looked at the identified cases in the denominator just like we have been doing now. And 1-2% of the population that were infected with H1N1 in the early days of the epidemic died.
A year later, people started to do these antibody tests and found out that very large numbers had been infected and never knew it. The fatality numbers went from 1% of the cases to 0.01%. That transformation of our view happened over a year and half.
It is not unreasonable to say that that same kind of situation may be happening now. Now, this is a hypothesis—it needs to be tested. It might be wrong, it might be right; but the only way to find out is to do widespread population-level seroprevalence testing.
Anecdotally, however, if you were to look at New York, or New Jersey, hospitals are flooded with patients. In India, it is the opposite. I am talking to a lot of pulmonologists and critical care specialists and we are not seeing a surge in cases, even assuming we ignore the data completely. So that did not happen in H1NI. You were not seeing a surge of patients in hospitals.
I am sorry… I should be completely clear. This is different from H1NI, absolutely, it behaves differently. And it is very clear also that the mortality from this virus--it is not just the biology of the virus, but the situation in which the people who get very sick with it are treated. In overwhelmed systems, it seems very likely, based on the data, that the mortality rate will be higher per case than in places where the hospital system is not overwhelmed. It is both the biology and the health economics, sort of acting together to determine the mortality rate of this virus.
And that is why I think it is important not just to say, I have done a study in Santa Clara and let’s pretend we know the numbers everywhere. We need to study in India, to understand the virus, how deadly it is, how widespread it is in India; we need a study in Sweden, in Switzerland, in New York—separate from Santa Clara. These are all very different environments in which people get taken care of, and without understanding the denominator, we are not going to have any chance of understanding how this virus is behaving and how deadly it is in various situations.
In Santa Clara, your study said that the actual number of people infected could be 50-85 times what is presently known. So, that seems like a dramatic number.
There are about 2 million people in Santa Clara. Our estimates of prevalence range from 2-4% depending on statistical assumptions. The day we did the study, there were roughly 1,000 identified cases. At the low end, our numbers suggest something like 50,000 cases in Santa Clara. On the high end, it suggests more. That means, let’s say you take the low end, there’s at least 49,000 people who had the virus in Santa Clara that were never identified with a PCR test.
The other number that one hears, including from the medical profession, is that for 85% of the people who are infected, nothing really happens. And it is the 10-15% we are worried about, maybe the 3-4% who go into intensive care units and into ventilation, and then things go south quite dramatically after that. So is your theory in some ways reflecting that—that a lot of us may carry it… by now we know people who contracted it, only exhibited mild symptoms or severe symptoms of flu and then it dissipated.
I completely agree with that. The only thing I would add to that is, we don’t know it’s 85% of what, because we do not know the denominator. But it does seem to have, in many cases, no symptoms at all. In Chelsea, Massachusetts, there was a study done just a couple of days ago suggesting very large numbers of people with antibodies, studies now done in prisons, where they find the virus active in people and most of the prisoners do not have any symptoms at all.
On the other hand, the virus is also deadly, as you say, in many many cases. It presents with a severe viral pneumonia, especially in older people or people who are vulnerable. So, it is not that we should take the virus less seriously. We should take it more seriously. We should better understand who is at risk from it, how extensive it is, and then make better decisions as to what to do about it as opposed to now. I think, to some extent, we are making decisions in the dark, in fear. That is my main call, let us shine the light of science on this. Let us actually get better numbers, so that we make better decisions.
How do you explain some of this--you talked about that study in Massachusetts, or the study from prisons--and how people are behaving quite dramatically and differently. What explains that?
As I said earlier, there is obviously still a lot we do not understand about the virus. But it is very clear that it is just not the biology of the virus that makes it deadly. The circumstances by which you get it, the circumstances of the healthcare system that is managing people, all those things combined determine what happens to people when they get sick with the virus.
So, your path forward is to test and then find out. So how do you test and what is the scale of testing you propose that America or other countries should do?
India should do this too. I think a lot of people, when they talk about the testing, they are talking about the active virus in you--it’s called the polymerase chain reaction (PCR) test. Actually, the virus is an RNA virus. The first step is to do a reverse transcriptase, and then put PCR. (Read our explainer on the test here.) That test just checks if you have it right now. After you clear the virus, you will be negative. What you need is the antibody test. But for this kind of population surveillance, you do not need to test everybody. You do not need a billion-plus people tested in India for this surveillance. What you need is random sampling of the population.
If you look at areas, let us take Mumbai, we divide up Mumbai into various parts and then randomly pick people from each part. And, it does not take very large samples. With a few thousand samples, you can get a very good idea about the spread of the virus in populations that reflect millions. The tests themselves are inexpensive--basically, many of them just require a finger-prick blood sample.
There has been a lot of controversy over the accuracy of these tests. But, there are two kinds of accuracies in these kinds of tests and it is really important to understand the difference. The first kind of accuracy is called specificity. What that means is the rate at which a negative case (someone who is truly negative) ends up negative. So, it is kind of related to the false positive rate. That has to be very low.
There is also another number called sensitivity, that says if you are positive how likely is it I am going to find you to be positive. The cheap tests are very very specific, but only somewhat sensitive. But that is fine. As long as you are specific, it is great for epidemiologic work.
I will just give you an example. Suppose there is a test that is only 50% sensitive. That means for every positive I see, there is another out there that I did not see. But I can adjust for that statistically, very easily. So, these tests are ideal--these antibody tests that are cheap. It is actually inexpensive to do this kind of population survey work relative to trying to test the entire population or to shut down an economy or something. We should just do these. There is no good reason not to have these studies basically done everywhere.
And then the economic conclusion would be that we should not be fretting too much about lockdowns and quarantines, and open things up?
I do not know the optimal policy until I know the numbers--it is really hard to say. One outcome I am hoping from this work is, we will quell the fear. I believe that when I get the virus or if I get the virus, I have a 3% chance of death, I am going to be very scared. That is essentially what the World Health Organization said, 3% mortality. On the other hand, if it is 1 out of a 1,000, or 2 out of a 1,000, I am going to be much less scared. I think making policy in the midst of fear is really a bad idea.
Now, there may be reason to fear. As I said, it is a hypothesis, we need to see these numbers everywhere. But if you are going to reason and think about policy in fear, it better be well-rounded fear, not fear based on not knowing what the number we can very easily get is.
In a way, you represent both worlds to answer the next question. In India, the Prime Minister has said it is lives versus livelihoods, and it is the same debate everywhere
It is not just livelihoods though, it is lives. People talk about economics as if it were a secondary thing. But it is actually lives. Poor countries are deadly, especially for the poor. The deaths from shutdown and lockdown policies worldwide will create (we are already in it) a devastating global economic depression. That depression will kill people--large numbers of people. It’s only a question of who will die relative to the COVID deaths. On both sides of this policy, there are deaths. It is not dollars for lives. It is lives for lives.
Are we any closer to knowing on which side the balance is tilting today?
I need numbers. I need seroprevalence numbers. I have been saying this from the beginning--we cannot really answer the questions you are asking me with any confidence until we have those numbers. It is the start of a reasoned policy.
If we shut down the world economy, it almost certainly saves lives from COVID, absolutely. It slowed down the rate of growth of the disease. It is probably one of the reasons why the Indian numbers are as low--they may sound horrifically high---but as low as they are. They would have been higher if the lockdown had not happened. Absolutely.
So we know to some extent the benefits of the policy. But we have not made any effort whatsoever to measure the costs. I am not talking of the economic dollar costs; I mean the lives costs of the global economic depression that is about to hit.
This is a novel virus, and as you put it, and it has crept into our lives, our existence, silently, much earlier perhaps--not so easily quantifiable by saying 400,000 people flew in from mainland China to the United States and therefore that is how it spread. So, what does it tell us about viruses, about pandemics and lives as we will lead them or likely to lead them, in the coming months and years?
We face the risk of death from a vast number of pathogens every single day. It is just the nature of human existence. This is going to be one more, I think, at the end of the day, that we will just have to cope with. Hopefully we will learn more about it, so we could treat it better--treat people who get it, better. We can learn how to prevent it, maybe there will be a vaccine. So, I think all those things are good, they are coming. My fellow scientists have been doing amazing work and I look forward to that.
But in the meantime, you should not destroy the world economy and kill—I saw a story in the New York Times the other day, about half a million deaths from starvation in children worldwide, projected from the global economic loss. I mean do we really want to act so that I can protect myself, at the cost of these half a million children, without having the numbers to say, to reason about it correctly? That is really the case I am making. Let us get the numbers, the real numbers, and then decide what the right thing to do is.
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