Update: The COVID-19 'Evidence Fiasco'
Tuesday, April 14, 2020
Statistician-Epidemiologist John Ioannidis, who sounded an early alarm about our shocking ignorance level in the coronavirus pandemic, summarizes the consequences (so far) of what he calls "exaggerated information and non-evidence-based measures." This paper, published in The European Journal of Clinical Investigation, opens as follows:
Following are twelve points, which I list below. For each point, I have selected a quote. This list is no substitute for reading the whole thing, but it should help the reader understand why I think you should. Each point is in bold, and each quote is in italics:The evolving coronavirus disease 2019 (COVID-19) pandemic is certainly cause for concern. Proper communication and optimal decision-making are an ongoing challenge, as data evolve. The challenge is compounded, however, by exaggerated information. This can lead to inappropriate actions. It is important to differentiate promptly the true epidemic from an epidemic of false claims and potentially harmful actions. [bold added, note omitted]
This old convention poster reminds me of an apt metaphor for much of our pandemic response so far... (Image by Stefan Sagmeister, via the Smithsonian Institution, terms of use: See "b. Other Content -- Usage Conditions Apply").
- Fake News and Withdrawn Papers -- The first report documenting transmission by an asymptomatic individual was published in the New England Journal of Medicine on January 30. However, the specific patient did have symptoms, but researchers had not asked. [There may yet be some asymptomatic spread, but the reference for this one is quite interesting. --ed]
- Exaggerated Pandemic Estimates -- Even after the 40%-70% [percent estimation of infection of the general population] quote was revised downward, it still remained quoted in viral interviews.
- Exaggerated Case Fatality Rate (CFR) -- The most widely quoted CFR has been 3.4%, reported by WHO dividing the number of deaths by documented cases in early March. This ignores undetected infections and the strong age dependence of CFR. [note omitted]
- Exaggerated Exponential Community Spread -- China data are more compatible with close contact rather than wide community spread being the main mode of transmission.
- Extreme Measures -- A systematic review on measures to prevent the spread of respiratory viruses found insufficient evidence for entry port screening and social distancing in reducing epidemic spreading. Plain hygienic measures have the strongest evidence. [note omitted]
- Harms From Nonevidence-Based Measures -- Given the uncertainties, one may opt for abundant caution and implement the most severe containment measures... This reasoning ignores possible harms.
- Misallocation of Resources -- [I]f only part of resources mobilized to implement extreme measures for COVID-19 had been invested towards enhancing influenza vaccination uptake, tens of thousands of influenza deaths might have been averted.
- Lockdowns -- for How Long? -- Maintaining lockdowns for many months may have even worse consequences than an epidemic wave that runs an acute course.
- Economic and Social Disruption -- The global economy and society is already getting a major blow from an epidemic that otherwise (as of March 14) accounts for 0.01% of all 60 million annual global deaths from all causes and that kills almost exclusively people with relatively low life expectancy.
- Claims for Once-in-a-Century Pandemic -- Leaving the well-known and highly lethal SARS and MERS coronaviruses aside, other coronaviruses probably have infected millions of people and have killed thousands. However, it is only this year that every single case and every single death gets red alert broadcasting in the news.
- Comparisons With 1918 -- [T]otal deaths were eventually little affected by early social distancing: people just died several weeks later.
- Learning From Covid-19 -- Even if COVID-19 is not a 1918-recap in infection-related deaths, some coronavirus may match the 1918 pandemic in future seasons. Thus, we should learn and be better prepared.
-- CAV
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