A once-in-a-century pandemic. But, writes John PA Ioannidis, professor of medicine, of epidemiology and population health, of biomedical data science and of statistics at Stanford University and co-director of Stanford’s Meta-Research Innovation Centre in Stat News, it may also be a once-in-a-century evidence fiasco.
Ioannidis writes:
At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.
Draconian countermeasures have been adopted in many countries. If the pandemic dissipates – either on its own or because of these measures – short-term extreme social distancing and lockdowns may be bearable. How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?
The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the US, lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.
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 World Health Organisation, cause horror – and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.
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 US 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 seven 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%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases – a risk factor for worse outcomes with SARS-CoV-2 infection – than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general US population vary from 0.05% to 1%.
Some worry that the 68 deaths from COVID-19 in the US as of 16 March, will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?
The most valuable piece of information for answering those questions would be 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.
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.
In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40m deaths globally, matching the 1918 influenza pandemic. The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died.
One can only hope that, much like in 1918, life will continue. 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. If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.
[link url="https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/"]Full Stat News report[/link]