A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data statnews.com/2020/03/17/a-f… via @statnews
The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.
This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the WHO, cause horror — and are meaningless.
Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes.
The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers.
The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers.
The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.
Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%.
But since this estimate is based on extremely thin data — there were just 7 deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%).
Adding extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
If 0.05% to 1% is the true rate, locking down the world with potentially tremendous social & financial consequences would be totally irrational.
It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.
It would be valuable to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have.
In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates.
Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory. The visual shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.
Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated.
If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse:
One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health.
Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.
Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.
