A State of Fear: How the UK government weaponised fear during the Covid-19 pandemic by Laura Dodsworth (feel good novels .TXT) 📕

The new system counted deaths within 28 days of a positive Covid-19 test. This would still include people who died from other causes – not all of these deaths were ‘from’ Covid either – but it was an improvement and resulted in the immediate removal of 4,149 deaths from the 15 July death count.

As David Paton said, ‘The metrics always seem to err on the side of maximising numbers and it takes time to undo that, like with the Public Health England death counting. The BBC continued to report PHE death data even though the government had officially suspended it, so that had the effect of people thinking deaths were higher than they were. There is also a case for saying we don’t report daily cases and deaths for flu, so just reporting by day is damaging in itself.’

And on a stronger note, the anonymous scientist who advises at Whitehall told me, ‘The higher the death toll, the more draconian the measures you can bring in. The plan would be to go with the big numbers and then say there was a problem with the figures.’

THE BMA AND MASKS

Some data has crumbled like icing sugar at the merest whiff of a challenge. Sadiq Khan quoted some quite astonishing figures: that someone not wearing a face covering had a 70% risk of transmitting the virus, but by wearing a mask the risk was reduced to 5%, dropping to 1.5% if both parties were wearing masks.16 The source was the British Medical Association (BMA). I contacted the BMA, which claimed that their Medical Academic Staff Committee and Public Health Medicine Committee had produced the calculations. Seven emails, two tweets and one phone call later, it turned out these figures had not been calculated by the BMA, but were ‘based on a presentation by Chinese infectious disease specialist Professor Wenhong Xhang in March’.17

The BMA withdrew its claims, but by then the figures had been published on national broadcast and print media and in Twitter memes shared by Sadiq Khan, and are all still in circulation now. Associated Press Factcheck came to the same conclusion as me and labelled the claims ‘partially false’.18 The strange thing is that memes were circulating in other languages at around the same time and earlier, making BMA’s claim to have produced the calculations even more spurious.

The effort to counter misinformation online has certainly seemed one-sided.

HOSPITALISATIONS

Sir Simon Stevens, the Chief Executive of the NHS, said on 29 December 2020 that 20,426 people were being treated for Covid in hospitals in England, which was higher than the previous peak of about 19,000 in April.

Comparing two absolute numbers was problematic. During the winter we always have more patients than we do in April. Numerically there were more patients in December, but just presenting that as a crude statistic is disingenuous because we also had more beds nationally overall than we did in April and without the occupancy figures as a percentage it’s impossible for anyone to understand what the inpatient numbers mean. Further breaking down occupancy into overall total, ICU, beds with oxygen and mechanical ventilation beds for Covid and non-Covid would provide more insight.

In April 2020, all the Covid patients were there because they were truly ill with Covid. Later, we were routinely testing people in hospitals regardless of why they were admitted, which was a sensible measure for infectious control, but meant some people were classed as Covid patients in the government dashboard figures but were actually in hospital for different reasons. The absolute number of 20,426 includes those admitted with Covid and diagnosed with Covid in a hospital setting. So, what was the total split by people who go into hospital because they had Covid, nosocomial infections (acquired in hospital) and those who were routinely tested when they were in for something else and had a positive test result? Answers to those questions would have revealed how much of a problem community versus nosocomial infection was and helped guide decisions about the value of restricting liberties in the community.

An NHS England data scientist who must remain nameless for the sake of their job, shared some confidential information with me. We looked at the data for the south-east and London when I was writing to my MP about the tier restrictions at the time. At that point my MP told me that local hospitals were overwhelmed with Covid admissions. In fact, in mid-December in the south-east and London only 20% of the total hospital admissions were patients actually admitted with Covid. About 25% of ‘hospital admissions’ had caught Covid in hospital. And the remaining 55% had been tested while in for another matter and found to be positive. By January 2021 the government had admitted (albeit in a very low key no-fanfare way) that test results could be positive ‘long after’ someone is infectious.19 Which means that of those 55%, an unspecified but significant number will be ‘false positives’. All this paints a different picture. In addition, approximately 30% of the most recent admissions were from care homes, therefore also nosocomial infections, as they were acquired in a care setting.

Another important consideration is staffing levels. Talking about absolute numbers of in-patients requires the context of occupancy percentages and also staff available to look after them.

A responsible government and NHS would be providing this context. The use of absolutes was not untruthful, but it obscured more important facts. It seemed like a false flag, designed to create alarm and therefore soften us up for the next tranche of emergency restrictions. A BBC article20 reporting on Stevens’s statement only gave a modicum of context and led straight into a scientist’s call for more lockdown, in what had become a familiar government-media lockstep.

The use of alarmist data is, well, alarmist, and the elision of detailed data is suspicious. Combined, this erodes trust in leaders and the media.

I emailed NHS England’s media team with a request for all of this data. I followed