Denominator Problem

Wasted, sacrificed for a new nirvana
Nighttime sends us on our way
--Icicle Works

It seems people at Stanford are among the few asking critical questions related to the accuracy of the 'official' COVID-19 assessment. Earlier we shared the 'evidence fiasco' thoughts of Stanford's John Ioannidis. Today two more Stanford professors of medicine voice their concerns in this WSJ piece.

They suggest what amounts to a massive 'denominator problem' in reported COVID-19 fatality rates statistics. As of this writing, the Johns Hopkins COVID-19 tracking site is reporting 802 deaths in the US from 55,243 total confirmed cases--a 1.45% fatality rate. Many people are using such fatality rates for future projections. If they suppose that 100 million Americans ultimately get infected, then 1.45 million would die using the above fatality rate.

The Stanford profs argue that those estimates are deeply flawed. The true fatality rate depends not on deaths from confirmed positive cases, but on the number of people who get infected. And the total number of people infected with the COVID-19 virus is likely to be far higher than reported cases indicate.

This is because of selection bias in the testing process. Testing for COVID-19 infection has largely been limited to those people who show symptoms of the virus, or who think they might have the virus. As the Stanford profs demonstrate using data from novel population samples from China, Italy, Iceland, and the US, prevalence of COVID-19 infection ranged from 0.9%-2.7% of all individuals in the sample. Applying that prevalence to the US population at large suggests that the number of people that might already be infected with the virus could be more than 10x the reported number of reported cases.

This would make the denominator of the fatality rate calculation much larger than the one currently employed--and the calculated fatality rate much lower. Using our data above, disease prevalence that is 10x the reported case level shifts the decimal point one place to the left for a fatality rate of 0.145%--that's only slightly higher than the flu.

The Stanford profs sense the true death rate may be even lower. The first Chinese COVID-19 cases surfaced in Wuhan in late 2019. Although the first reported US case was reported in mid January, tens of thousands of people travelled from Wuhan to the US prior to that. Assuming a highly transmissible virus that drives infection doubling rate of every three days, and an epidemic seed on January 1 in the US, the researchers estimate 6 million Americans infected by March 9. Assuming a two week lag between infection and death, and a COVID-19 death count of 499 on March 23, then the US mortality rate would be about 0.01%--about one tenth of the mortality rate from the flu.

The researchers suggest that if the true infection and mortality rates approach their estimates, then the epidemic is limited in scale. Measures that focus on reducing risks for older adults and at risk individuals, and on re-allocating health care resources to care for critically ill patients are sensible. Universal quarantines that impose large economic and social costs are not.

Their bottom line: Similar to Dr Ioannidis, we need accurate prevalence and incidence data in order to solve our denominator problem so that we can make intelligent decisions here.