Have Fatality Numbers From New York Already Debunked the Santa Clara Serology Study?

Signs instruct citizens to remain 6 feet apart and observe social distancing measures in Mission Bay, San Francisco, California during an outbreak of the COVID-19 coronavirus, March 26, 2020. (Photo by Smith Collecti... Signs instruct citizens to remain 6 feet apart and observe social distancing measures in Mission Bay, San Francisco, California during an outbreak of the COVID-19 coronavirus, March 26, 2020. (Photo by Smith Collection/Gado/Getty Images) MORE LESS
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Since the first reports in January of a novel coronavirus spreading out of control in China, people around the globe have been trying to figure out just how lethal the disease is. As the pandemic has ravaged the United States and shuttered large sections of the national economy the question has only become more controversial and politicized. An infected individual’s chances of dying or becoming gravely ill from COVID19 are not only important in themselves. They directly inform what costs society should be willing to incur to slow or halt the spread of the disease.

That question is now engaged again in the furious public debate over when or how quickly to restart economic life in the country. We’ve seen the crazy talk and denial on Fox News and other pro-Trump media. But I want to discuss a version of this debate being carried on by real doctors and public health scientists, with very direct impacts on what Americans do next as they combat COVID19.

As I noted earlier today, the grim events of the last month in New York City allow us to set a hard lower bound on COVID19 mortality. That lower bound is immediately relevant to evaluating the news antibodies study from Santa Clara County, California which got a lot of media attention (including here at TPM) last week. That study suggested that COVID19 exposure might be far more widespread than we think. Everybody gets that there are many more infections than those confirmed by a lab test. The question is the multiple. The Santa Clara County study suggested that the number exposed might be 50 to 80 times greater than the official numbers.

The team behind the Santa Clara study is a group of doctors and researchers from Stanford University, two of whom are what we might call COVID19 severity skeptics. To be clear, I’m not talking about crazies or conspiracy theorists. These are credentialed medical specialists who’ve argued that COVID19 is likely more widespread than we know and thus less lethal than we fear. Because of this they have argued that the public health measures employed in the US are too severe.

One of the studies’ authors is John Ioannidis, a physician-scientist at the Stanford School of Medicine, who wrote an editorial in Statnews on March 17th arguing that America was launching into a damaging national lockdown with too little data about how deadly COVID19 actually was. That was followed the next day by a reply by Marc Lipsitch, a professor of epidemiology at the Harvard T.H. Chan School of Public Health, who said we knew more than enough already to take dramatic action. The two have been debating the matter ever since.

Another author is Dr. Jay Bhattacharya, also of Stanford School of Medicine. Bhattacharya has also been outspoken in focusing on the very real costs of the the national lockdown the country has been in for almost a month. Ioannidas, Bhattacharya and a handful of others make up the team that is behind the Santa Clara Study and another released for LA County, which showed comparable rates of infection. My understanding is that they have a few other such studies underway in other parts of the country.

These two studies have garnered a fair amount of criticism from other epidemiologists and public health experts who say that they’ve taken insufficient account the failure rate of the tests themselves. The details are technical. But the gist is this: the current serology tests have relatively high percentages of false positives, ones which may be higher than the prevalence of COVID19 in the population itself. Here’s how to understand this. If you have a test that produces 5% false positives and your study finds an infection rate of 3% in the population it’s possible all your positives are false positives. You really don’t know what your results are telling you. There are also questions about how representative the samples were (a difficult task in these initial studies.) But the accuracy of the tests is the key issue, especially in populations where on a tiny fraction has been exposed.

So let’s bring this back to the question of the lethality of COVID19. Finding out how many people have been exposed to COVID19 is critical to knowing how lethal it is. If you know ten people died out of 100 people who were exposed that’s one thing – 10% mortality. If it’s ten out of 10,000 people that’s totally different – .1% mortality. Much of the news coverage of the Stanford serology studies has focused on how many more infections they seem to show than the official lab confirmed numbers – ranging from 50 to 90 times the number in the LA County study. But these studies also speak directly to how lethal the disease is. They suggest that the actual death rate is just over .1%.

Here is a portion of an interview on Uncommon Knowledge, a web-based interview show from The Hoover Institution, which is also affiliated with Stanford.
A first appearance by Dr. Bhattacharya last month went viral. In his second appearance last week he explained that the Santa Clara Country study suggests an infection fatality rate (IFR) of between .1% and .2%.

Bhattacharya has also been making the rounds of conservative media. Here he is on Tucker Carlson a week ago saying that the COVID19 death rate is ‘likely orders of magnitude lower’ than previously thought.

The problem with this estimate is that, as I explained here, the actual mortality data out of New York City seems to make that estimate all but impossible. As of two days ago, the COVID19 mortality rate in New York City for the entire population was between .11% and .16%, depending on whether you count only lab confirmed COVID19 fatalities or those diagnosed on the basis of symptoms alone. To put that differently, for something in the range of Bhattacharya’s IFR to be accurate, literally the entire population of New York City would have to have been infected already.

The numbers are straightforward: as of two days ago, there were 9,101 lab confirmed cases and 4,582 presumptive diagnosed cases for a total of 13,683 fatalities In New York City. The population of New York City is 8,398,748. That comes to either .11% or .16% depending on which death toll number is used.

I do not think anyone thinks 100% exposure is at all possible. Even if we assume what I think most experts would consider the highly unlikely possibility that 50% of New Yorkers have been infected with COVID19 that would mean a .33% IFR. To be generous, let’s say a third of the population of New York City had already been infected with COVID19 – very high but not inconceivable. That would mean a IFR of .49%.

Needless to say, I’m no epidemiologist and I’m no statistician. I can’t tell you what the actual infection fatality rate is. But the actual death toll from New York City appears to place a hard lower bound on the numbers that is significantly higher than what the Stanford group’s serology studies suggest.

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