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COVID-19 much less deadly than previously thought, major study finds
Will Jones
The Daily Sceptic
Mon, 17 Oct 2022 12:53 UTC
COVID-19 is much less deadly in the non-elderly population than
previously thought, a major new study of antibody prevalence surveys has
concluded.
The study was led by Dr. John Ioannidis, Professor of Medicine and
Epidemiology at Stanford University, who famously sounded an early
warning on March 17th 2020 with a widely-read article
in Stat News, presciently arguing that "we are making decisions without
reliable data" and "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".
In the new study,
which is currently undergoing peer-review, Prof. Ioannidis and
colleagues found that across 31 national seroprevalence studies in the
pre-vaccination era, the average (median) infection fatality rate of
COVID-19 was estimated to be just 0.035% for people aged 0-59 years and
0.095% for those aged 0-69 years. A further breakdown by age group found
that the average IFR was 0.0003% at 0-19 years, 0.003% at 20-29 years,
0.011% at 30-39 years, 0.035% at 40-49 years, 0.129% at 50-59 years, and
0.501% at 60-69 years.
The study states that it shows a "much lower pre-vaccination IFR in non-elderly populations than previously suggested".
A breakdown by country reveals the wide range of IFR values across different populations.
The
significantly higher values for the top seven suggest some of the
difference may be an artefact of, for example, the way Covid deaths are
counted, particularly where excess death levels are similar. Note also
that the antibody studies datefrom various points during the first year of the pandemic, most of them
prior to the large winter wave of 2020-21, when levels of spread and
numbers of deaths were more varied than later in the pandemic as
subsequent waves caused countries to converge.
The reason some countries had much lower values and some much higher is
not completely clear. The authors suggest that "much of the diversity in
IFR across countries is explained by differences in age structure", as
per the plot below.
However,
the age breakdown by country suggests that the IFR differed for each
age group in each country, casting doubt on that suggestion. (In the
chart below, note the logarithmic scale, and ignore the zig-zag lines,
which are due to small countries having low numbers of deaths.)
Why
are countries seeing differing IFRs even for the same age groups? The
authors suggest a number of explanations, including data artefacts (e.g.
if the number of deaths or seroprevalence are not accurately measured),
presence and severity of comorbidities (for example, obesity affects
42% of the U.S. population, but the proportion of obese adults is only
2% in Vietnam, 4% in India and under 10% in most African countries,
though it affects almost 40% of South African women), the presence of
frail individuals in nursing homes and differences in management,
healthcare, overall societal support and levels of drug problems.
Prof. Ioannidis has previously published a number of papers
estimating COVID-19's IFR using seroprevalence surveys. He and his team
conclude that their new estimates provide a baseline from which to
assess further IFR declines following the widespread use of vaccination,
prior infections and evolution of new variants such as Omicron.
Comment: See also:
- John Ioannidis: Another shutdown would do more harm than good
- Modelers were 'astronomically wrong' in COVID-19 predictions, says epidemiologist Dr. John Ioannidis - And the world is paying the price
- Stanford researcher Dr. John Ioannidis says coronavirus might not be as deadly as flu
- Ioannidis releases new medical analysis of 12 global coronavirus studies finding 9 of 12 show COVID-19 fatality rates similar to seasonal flu
- Stanford U. epidemiologist John Ioannidis lambasts the media for panicking the public over Covid-19
- World's leading epidemiologist John Ioannidis on COVID-19 fiasco: 'We are making decisions without reliable data'
