There follows a guest post by the academic economist who contributes regularly to the Daily Sceptic, who has looked in more detail at data from vaccine pioneer Israel and found indications that the vaccines are not protecting against serious disease and death as well as is generally thought.
Israel remains the gold standard when it comes to macro data for vaccine efficacy. As many know, it started its vaccine program early and its roll-out was remarkably quick. Over 60% of the population is fully vaccinated, while rates of vaccination in vulnerable groups are upwards of 90%.
It is no secret that despite these numbers, Israel is experiencing another wave of the virus. This can be seen clearly in the positive test rate data (data from Our World in Data).
We have all come to accept that the vaccines have little impact on infection – this even though we were once told it was 95% effective against infection. But no matter – be sure to throw that down the memory hole. The latest ‘mandatory science opinion’ is that the vaccine prevents cases from evolving into severe cases that require hospitalisations and, eventually, death.
Given the data in Israel we can now test this hypothesis.
Let’s start with hospitalisations. If the vaccine is preventing hospitalisations, then the rate of hospitalisations per positive test rate should have fallen dramatically relative to history. Has it? Here is the data.
One of the most surprising things to emerge from the pandemic, at least from a lockdown sceptic’s point of view, is how overwhelmingly the British public has backed the lockdowns. For example, a YouGov poll taken in March of 2020 found that 93% of people supported the first lockdown. Another poll taken in January of 2021 found that 85% of people supported the third lockdown.
While some lockdown sceptics claim these polls can’t be trusted, I suspect they’re not too far off the mark. And even if they do overstate support for lockdowns (due to unrepresentative samples or social desirability bias) the true number is unlikely to be more than 10 or 20 percentage points lower.
The high level of public support for lockdowns may explain why they’ve lasted as long as they have. Politics is notoriously short-sighted, so why would the Conservatives ease up on a policy that’s kept them ahead in the polls for most of the last 14 months?
Aside from the public’s longstanding reverence for the NHS, an obvious reason why support for lockdown is so high is that millions of people have been paid 80% of their wages to stay at home. In the absence of the Government’s unprecedented furlough scheme, many of these people would be out of work, and presumably much less supportive of lockdowns.
However, there might be a more important reason why support for lockdown is so high: the public overestimates the risks of COVID-19, especially the risks to young people. Let’s review the evidence.
In July of 2020, the consultancy Kekst CNC ran a poll asking Britons what percentage of the population has died of COVID-19. The correct answer at the time was around 0.1%. However, the median answer among respondents was 1%, and of those who ventured a guess (rather than saying “don’t know”) one in five said at least 6% of the population had died.
Last year, Gallup ran a poll for Franklin Templeton in which they asked Americans what percentage of people who’ve been infected with COVID-19 need to be hospitalised. Less than 20% of respondents gave the correct answer of “1–5%”. And a staggering 35% said at least half of those infected need to be hospitalised. Interestingly, Democrats were much more likely than Republicans to overestimate the risk of hospitalisation, as this chart reveals:
It should be noted that the poll also revealed some underestimation of risks on the part of Republicans. For example, 41% incorrectly stated that flu causes more deaths than COVID-19. This shows that results can vary depending on exactly which question you ask. (Notice that Republicans did overestimate the risk of hospitalisation; just to a lesser extent than Democrats.)
Likewise, a survey carried out by Ipsos MORI for Kings College London asked Britons what are the chances of needing hospital treatment if you catch coronavirus. The median answer among respondents was 30%, and of those who ventured a guess, one in four said the chances are at least 50%.
In March and April of last year, the economist Arthur Attema and colleagues carried out two surveys of the French population: one two weeks after the first lockdown began, and the other two weeks before it ended. They asked respondents, “Out of 100 people who are infected with the Coronavirus, how many of them die from the disease?”
In both surveys, the average answer was 16 (whereas the correctanswer for Western populations is less than 1). The fact that the average in the second survey was no lower than the average in the first indicates that people’s understanding of the risks did not improve over time, despite more evidence accumulating that the IFR is less than 1%.
Members of the public seem to have a particularly skewed perception of the risk COVID-19 poses to younger people. The aforementioned Gallup poll asked Americans what percentage of those who’ve died were aged 24 and under. The correct answer at the time was around 0.1%, yet the average answer among Republicans was 8%, while the average among Democrats was 9%.
Likewise, a poll taken by Ipsos MRBI for The Irish Times asked people what percentage of those who’ve died were under the age of 35. The correct answer was around 1%, yet the average among respondents was 12%.
In November of 2020, Savanta ComRes ran a poll on behalf of The Conservative Woman and asked Britons to guess the average age of people who’ve died after testing positive for COVID-19. The correct answer is around 82. However, the median answer among respondents was 65.
Incidentally, one problem with asking people to estimate very small quantities (like the percentage of people who’ve died from COVID-19) is that humans have a tendency to revise small percentages upwards when they’re not sure. This “uncertainty-based rescaling” probably accounts for some of the overestimation in the surveys mentioned above.
However, taking all the evidence together, people – particularly in Britain – do seem to overestimate the risks of COVID-19. And this may help to explain their high level of support for lockdowns.
Johan Giesecke, an advisor to the Director General of the WHO, former Chief Scientist of the EU Centre for Disease Control, and former state epidemiologist of Sweden, returned to UnHerd yesterday to resume his discussion with editor Freddie Sayers, adjourned a year ago. He was one of the first major figures to come out against lockdowns last spring, saying they are not evidence-based, the correct policy is to protect the old and the frail only, and the Imperial College modelling was “not very good”.
While he admits he made some mistakes, he believes that history will judge him kindly, and says: “I think I got most of the things right, actually.”
He gives a solid defence of the outcome in Sweden, ably batting away the “neighbour argument” that says Sweden failed because Norway and Finland did better.
The differences between Sweden and its neighbours are much bigger than people realise from the outside – different systems, different cultural traditions…If you compare Sweden to other European countries [such as the UK, France, Spain, Italy, Belgium] it’s the other way round. On the ranking of excess mortality, Sweden is somewhere in the middle or below the middle of European countries. So I think it’s really Norway and Finland that are the outliers more than Sweden. … They’re more sparsely populated. There are less people per square kilometre in these two countries. There are also much fewer people who were born outside Europe living in these two countries.
He is also rightly dismissive of the charge that Sweden is currently the worst for infections in Europe. While positive cases are up, so is testing, and besides on the most important metric, excess deaths, Sweden has been far below average since the start of February.
Giesecke is direct in his unflattering comparison of the UK’s outcome with Sweden’s:
They’re very similar. And yet one of the countries has had three severe lockdowns and the other has only had voluntary or mostly voluntary measures. That tells us something I think. That lockdowns may not be a very useful tool in the long run.
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:
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?
In its latest “reality check” the BBC attempts to rebut seven of the “most frequently-shared” “false and misleading claims”.
It’s written by Jack Goodman, a “producer, newsreader and reporter at BBC Radio Derby”, and Flora Carmichael, a “journalist and producer with a strong track record of developing media partnerships and managing international projects and teams”.
So you can see why they would be well-qualified to set straight Oxford’s Professor Sunetra Gupta, Harvard’s Professor Martin Kulldorff, Stanford’s Professor Jay Bhattacharya and other eminent sceptics.
Let’s take each of the seven “myths” in turn.
1. “Here we are a year later – the world shut down for a 99.97% survival rate”
Verdict: This figure and similar figures being widely shared, are incorrect.
That might not seem like a big difference, but it means that about 70 in 10,000 people are expected to die – not three in 10,000.
The death rate is much higher for older and more vulnerable people.
The “fact check” does not cite any sources for the claims it is debunking so it’s hard to know what the full context is. However, a search on Twitter brings up a number of recent tweets claiming that Covid has a 99.97% survival rate. While taken by itself this is not in line with current best estimates, a number of the tweets claim this is the survival rate once the over-65s have been vaccinated, though without citing a source. One tweet uses data from Minnesota to estimate a survival rate for the under-60s of 99.97%.
The BBC quotes 99.3% (IFR 0.7%) from the Cambridge MRC Biostatistics Unit, but it’s worth bearing mind that this is the same modelling team that produced the notorious projection of more than 4,000 deaths a day by the start of December, modelling which was already wrong on the day it was presented to the public by Witless and Unbalanced.
Professor John Ioannidis has estimated the global IFR for the WHO at 0.23% overall (survival rate 99.77%) and, for people under-70, 0.05% (survival rate 99.95%).
The BBC’s “fact-checked” IFR of 0.7% is therefore on the high side, and if the 99.97% claim refers to the under-60s (or to a scenario where all the over-60s have been vaccinated) then it would be within the ballpark of current data.
The wider point though is that the death rate has been greatly exaggerated, especially for those who are young and without underlying conditions. The median age of death with Covid is 83, and only 388 people under 60 with no underlying conditions died with Covid in English hospitals in 2020. Sweden, a country which did not implement strong restrictions (retail, hospitality and most schools remained open, there were no limits on private gatherings and no mask mandate) experienced only 1.5% excess age-adjusted mortality in 2020.