Why Does Wikipedia Claim a Fifth of Covid Infections Are ‘Severe’?Thu 10:40 am Europe/London, 1 Apr 2021
There’s been a lot of worry about ‘misinformation’ around COVID-19, with numerous calls to suppress anything that doesn’t agree with the WHO’s current line, and news and social media companies all too happy to oblige.
Sometimes, though, the worst offenders are the mainstream sources themselves.
Take Wikipedia. On its main COVID-19 page – a page which cannot be edited by mere mortals as it is “protected to prevent vandalism” – it states the following in the second paragraph:
Of those people who develop noticeable symptoms, most (81%) develop mild to moderate symptoms (up to mild pneumonia), while 14% develop severe symptoms (dyspnea, hypoxia, or more than 50% lung involvement on imaging), and 5% suffer critical symptoms (respiratory failure, shock, or multiorgan dysfunction).
This is claiming that almost a fifth of symptomatic COVID-19 infections are severe, and 1 in 20 are critical. If these are the statistics that people are reading then no wonder they’re scared.
Wikipedia is many people’s first port of call when looking up a subject, and often comes out near the top of internet searches. So the fact that it grossly exaggerates the seriousness of COVID-19 should be concerning. Even more concerning is why it does so.
Where did Wikipedia get its stats from? Alarmingly, the reference is to the U.S. Centers for Disease Control (CDC). In its latest clinical guidance, in a section headed “Illness Severity”, the U.S. federal health agency states:
A large cohort that included more than 44,000 people with COVID-19 from China, showed that illness severity can range from mild to critical:
– Mild to moderate (mild symptoms up to mild pneumonia): 81%
– Severe (dyspnea, hypoxia, or more than 50% lung involvement on imaging): 14%
– Critical (respiratory failure, shock, or multiorgan system dysfunction): 5%
In this study, all deaths occurred among patients with critical illness, and the overall case fatality ratio (CFR) was 2.3%.
These statistics come straight from an early study on the first 44,000 Covid patients in China, published on February 24th 2020. The study does not mention hospital admissions and it appears that all of these cases were in fact hospital patients. At any rate, the figures suggest a sample heavily skewed towards serious illness.
A more accurate estimate of severity comes from the ONS. In the December peak, the ONS estimated around 2% of the population of England were infected with COVID-19 and around 0.04% of the population were being admitted to hospital each week with the virus. This means about 2% of infections were leading to hospital admission, or 1% if we allow for the estimated half of serious infections caught in hospital. This is about 20 times lower than the nearly 20% serious infections in the Chinese study.
Why is the CDC still using this early study as its main source of statistics on the severity of COVID-19 when we’ve found out so much more about the illness since February 2020? Why is Wikipedia featuring these figures at the top of its COVID-19 page? Don’t they realise how misleading and unnecessarily frightening they are?
The CDC has form in sticking with out-of-date and misleading data. Immediately above those severity figures, for instance, it has this to say about asymptomatic infection: “The proportion of SARS-CoV-2 transmission due to asymptomatic or presymptomatic infection compared with symptomatic infection is not entirely clear; however, recent studies do suggest that people who are not showing symptoms may transmit the virus.” These “recent studies” are from February 2020, March 2020 and a Chinese modelling study from May 2020. None of the more recent studies showing that, in common with other similar viruses, asymptomatic spread is not a major driver of transmission are cited.
Similarly, on its “Planning Scenarios” page the CDC states that its best current estimate is that asymptomatic infections are 75% as infectious as symptomatic, which is epidemiologically unheard of. It also states its assumption is of “no pre-existing immunity before the pandemic began in 2019. It is assumed that all members of the U.S. population were susceptible to infection prior to the pandemic”. This is despite the growing evidence of T cell cross-immunity from other coronaviruses, including some varieties of the common cold.
If these are the assumptions that inform the CDC’s public health advice and modelling, no wonder it’s so often useless.
Here, on the other hand, is a proper analysis of the evidence. John P. A. Ioannidis, Stanford Professor of Medicine and Epidemiology, has just published a new review of the global infection fatality rate (IFR) of COVID-19. No, it’s not 2.3%.
Professor Ioannidis estimates the global average IFR to be around 0.15%, over 15 times lower than the Chinese figures quoted by the CDC. He stresses, however, that there are large differences between regions. The IFR in Europe and the Americas is around 0.3%-0.4%, whereas in Africa and Asia it’s about 0.05%. There are also wide differences between countries within regions, especially in Europe.
The differences, he suggests, are driven by “population age-structure, nursing home populations, effective sheltering of vulnerable people, medical care, use of effective (e.g. dexamethasone) or detrimental (e.g. [late treatment] hydroxychloroquine) treatments, host genetics, viral genetics and other factors”.
When U.S. Government agencies and “protected” pages on Wikipedia are the ones spreading falsehoods, you know that the battle against “misinformation” is a lost cause. You also remember why truth is advanced by freedom of speech, not by the dead hand of censorship.