Nature published a comprehensive study this week on cardiovascular risk including a total of over 11 million patients that has made a few headlines. The aim was to identify the cause of increased cardiac pathology. It should have been a very simple study comparing four groups:
Not infected and never vaccinated
Not infected and vaccinated
Infected but not vaccinated
Infected and vaccinated
It is hard to believe the authors did not look at these groups, but whatever was found when comparing them remains a mystery.
Instead, the following groups were compared:
Not infected and never vaccinated data from 2017
Not infected, including vaccinated and not vaccinated
Infected but not vaccinated
Infected with vaccinated people included but using modelled adjustments
When studies with huge datasets use modelling and fail to share data prior to their adjustments alarm bells should start ringing. Therefore, I took a deeper dive to see what else was questionable.
Why haven’t lockdowns worked? There are broadly two types of respiratory virus. There are those that spread person to person – like measles – in a continuous chain of transmission, uninterrupted by season and with every susceptible contact falling ill. Then there are those we do not understand so well, like influenza, which are much more complex. Instead of the simplistic close contact model, which assumes Covid spreads like measles, we should perhaps consider an alternative more sophisticated model based on influenza. The influenza virus model is unusual – it is predicated on the majority being exposed to a particular airborne virus but, oddly, only a minority appear to be susceptible to each year’s variant. To complicate matters further, influenza can also spread person to person.
Dr. Clare Craig points out an error by vaccines minister Nadhim Zahawi about what 60% vaccine efficacy means.
- If vaccines have 60% efficacy that does not mean that 60% cannot be infected.
- It means that if 90% of unvaccinated household contacts don’t catch it from index case, then if they were vaccinated that rises to 96%.
- Around 10% of close contacts catch it from an index case. (Source: Public Health England Technical Briefing 15)
- A vaccine with hypothetical 60% efficacy would reduce the proportion who caught it by 60% – to 4%.
- 90% would not catch it in either instance.
- 4% were protected thanks to vaccination.
Diagnostic pathologist Dr Clare Craig: “We have really good evidence that lockdowns don’t work which people find very difficult to accept”.
- Airborne viruses spread and you can’t control the spread, which is why making people hide away doesn’t have the impact that you think it has.
- The data demonstrates lockdowns don’t work and have possibly made things worse.
- We now have examples other than Sweden, such as US states like Florida and Texas, that demonstrate that lifting restrictions make no difference to the virus.
- Florida and Texas prove that the lockdown advice was wrong.
Michael Yeadon was a scientific researcher and vice president at drugs giant Pfizer Inc. He co-founded a successful biotech. Then his career took an unexpected turn.
Briefing paper for MPs authored by:
- Clare Craig BM BCh FRCPath
- Jonathan Engler MBChB LLB
- Mike Yeadon BSc Hons (Biochem-tox) PhD (Pharmacol)
- Christian McNeill LL.B and Dip LP
Stop mass-testing using PCR in the UK and replace with Lateral Flow Tests where required. If we are correct, this single measure alone will cause a sudden drop in “cases” (as seen in Liverpool) and allow the UK to return to normal life within weeks.
Other recommendations as detailed later in this document. It should be noted that legal cases and technical challenges to PC
Clare Craig is a consultant pathologist and expert in diagnostic testing. She has raised concerns that inaccurate Covid test results may be producing a skewed picture of the nature and course of the pandemic – a picture based on overestimates of cases and deaths, and underestimates of immunity levels. spiked caught up with her to discuss what has caused the problems in testing, how they are manifested in the data, and where the government has gone wrong in its Covid strategy.
- There has been so much pressure put on laboratories, there have been flaws in the results of the tests they are doing.
- People who have been diagnosed with Covid who did not have Covid.
- We are testing at such a large scale – over 200,000 tests per day – that even a small percentage of mistakes ends up meaning large numbers of people being affected.
- The SAGE committee has an overrepresentation of physicists, chemists and mathematicians.
- For people from those backgrounds, false-positive test results are usually related to highly precise laboratory equipment. In those cases, the false-positive rate is a stable number.
- It’s not like that in medicine. For the test kits, the false-positive rate is stable. But for the process as a whole, there are all sorts of things that can go wrong. That includes problems with cross-contamination, and problems with cross-reactions with other viruses.
- Things have gone wrong because of the UK’s strategy for testing.
- In an epidemic, there are two strategies that you take, one at the beginning, and then one when you reach peak deaths.
- When you increase the number of tests you do, you start to find milder cases.
- Factors show that Covid has become less severe.
- Normally, we would start to see increasing numbers of influenza cases at this time of year. But influenza seems to have disappeared globally.
Current test results should not be automatically accepted as real
Imagine a world where COVID-19 has been eliminated. To be certain this is true, the government conducts regular tests at random. The number of positive results should be zero, right? Wrong. There will always be a proportion of cases tested that come back with a false positive test result. Thankfully, for COVID-19, the false positive rate is less than one percent of tests done. But it is not zero. It will be impossible for us to ever reach zero. Why? Because COVID-19 cannot be eliminated, even if it is likely to evolve to be more benign and become a seasonal problem like influenza.