The Virus Stats that Cost Everyone a Lot

By Martin Cohen

This is a post about statistics. Not that I’m actually a great mathematician, let alone a statistician, but I do at least appreciate that the power of numbers to influence debates. And a debate in particular where statistics have been thrown around since the beginning of the Covid one. 

So, travel with me back nearly two years to the origins, and take a second look at some key metrics that have been tossed about ever since. One problematic measure has been the “Case Fatality Rate”. This was officially put by the United Nations at just under 1%, making Covid a very deadly virus.

The bit we can agree on is the definition. The CFR is the number of deaths from Covid divided by the number of “confirmed cases”.

The problem is that deciding who actually died “from Covid” is very murky. A typical report is that 95% of people dying from Covid have other co-morbidities. This means that they may actually have died from these rather than from Covid. The issue is exacerbated when you see that the typical age of someone dying “from Covid” is pretty much the age at which anyone dies.

So the numerator part of this crucial figure is HIGHLY debatable - and the denominator part, the number of cases is too. For starters, one problem is that what you, me and Joe Public understand as “a confirmed case” is someone who has symptoms and goes to hospital and is tested and found to have the virus. That would all make sense. But in fact, a case is simply someone who has the virus. And again, it is agreed that the great majority of people who encounter the virus never have any symptoms. These people are often not counted. This is why the number of cases a country has depends essentially on how much testing the government chooses to do.

To make matters worse, it depends on the criteria used for the test. The benchmark test, the so called PCR (polymerase chain reaction ) test, considered “the gold standard” for detecting Covid. The test amplifies genetic matter from the virus in cycles; the more cycles used, the greater the amount of virus, or viral load, found in the sample. Crucial to the test, then, is how many cycles are used -and that, perhaps surprisingly, is not a medical decision but a political one. In Europe, for example, The European Centre for Disease Prevention and Control does not recommend a specific maximum amplification cycle threshold for PCR tests. However, it does recommend that if the values are high, e.g. > 35, “repeated testing should be considered”. In other words, it recognises the results are unsafe.

Yet that decision on the number of cycles is not even communicated when a “positive test” is returned. As Angela Rasmussen, a virologist at Columbia University in New York told the New York Times, “It’s just kind of mind-blowing to me that people are not recording the C.T. values from all these tests — that they’re just returning a positive or a negative.”

Ultimately, there is no standard cycle threshold value that is agreed upon internationally. The U.S. Food and Drug Administration currently gives laboratory manufacturers autonomy in determining how many cycles are needed to determine whether a sample is positive or negative.

How accurate the test is matters, because everyone admitted to hospital, for whatever reason - maybe they had a heart attack - is routinely tested for Covid. If they are considered positive, and later on die, they will be counted as a Covid fatality, as “dying within 21 days of a positive test”.

So that’s three rather big question-marks lurking in the Covid data. But the next one, I think, is worse. This statistic purports to show vaccines save people from the worst effects of the disease.

It’s the statistic that led the CDC in the US to say:

“COVID-19 vaccines are highly effective at preventing severe COVID-19 and death.”

And you can read that in all the papers, in all the fact-checkers, and so you “might” think it must be true. However, statisticians at Queen Mary College in London, looked at the UK data (which is representative of other countries too) and concluded:

“Official mortality data for England suggest systematic miscategorisation of vaccine status and uncertain effectiveness of C19 vaccination”

They noticed that the official statistics showed that, following vaccination, there was a sudden surge in the numbers of UNVACCINATED people dying. The so-called ‘healthy vaccine’ effect. A less cheery explanation was that vaccines might actually be killing people - but if the deaths occurred within 21 days, as most side-effects do - being classified as deaths of “unvaccinated”.

This is a possibility, and adverse effects databases like the European EudraVigilance database and US VAERs ones currently report alarmingly high numbers, in apparently compelling detail – however the miscategorisation does not need to mean that vaccines are killing a lot of elderly people. Rather, fragile people are prioritised for vaccination, and thus skew the figures. However, by grouping vulnerable people together statistically to be vaxed and then … calling this group the unvaccinated, the authorities have very conveniently created an apparently miraculous positive effect for vaccines. That it is not really there is indicated that the positive effect – “vaccines save lives” - is not only for Covid but for ALL CAUSE mortality!

This is known. Yet far from accepting the statistics mislead, governments and drug companies surmise that the treatments may have unexpected general positive effects.

In reality, the statistical anomaly is large because in countries like the UK, the NHS Guidelines explicitly state that the most critically ill people are the ones who must be prioritised for vaccination in each age group.

Let me try to sum it all up in three sentences! Vaccine data shows most of the advantage from the jab in the first few months. Because Covid vaccination programs prioritise very ill people, a significant number of whom die in the following 21 days - not from the vax necessarily, just because they were, well, vulnerable. Whatever the reason, again under the official guidelines, these deaths are classed as ‘unvaccinated’, creating the ‘bad news’ for the unvaccinated and the amazing, parallel, health boost for those who are.

So there you have it. Some examples of how duff statistics alone, not anything more secretive let alone worrying, could have created a Ten Trillion Dollar “pandemic” that maybe never was. Worse still, they could have led to policies that really have been killing people.

The Myth of the Global Cow

Posted by Martin Cohen

Data crunchers have started to attack farms on the basis of statistical creations such as ‘The Global Cow’. Of course, there’s no such thing. The sublimation of differences in concepts like the average cow, leaves cows and sheep who are helpfully and quietly grazing grass suddenly accused of inefficiently expropriating vast tranches of valuable land, while farmers keeping animals fed soya in sheds can be reinvented and presented as efficient and ‘climate friendly’. And yet summarised and simplified messages creatively abstracted from the data itself construct a global picture, skewed by preconceived ideas, and designed to influence policy decisions.

    • The idea of ‘the Earth's average temperature’ is also an exercise in mental gymnastics - which parts of the oceans are included - or of the atmosphere? Does it make sense to have hypothetical data points in uninhabited regions? Even NASA and the Met Office cannot agree. 

   • Food policy in particular always seem to consist of sharp, Manichean (good versus evil), divisions even as most things are nuanced and a matter of detail - and degree. Missing from both types of thinking is any acknowledgement that the experts behind the expert consensus are also political and ideological subjects, and the vast majority of respected science (or any research) is produced from a mainstream and shaped by the policy objectives of funders.   

But let’s just take up that idea of a ‘global cow’. Even small farms can be completely different in terms of differing habitats and differing good or really bad practices in one place. Last year I had a series of email exchanges with a Welsh couple in the Brecon Beacons (on the England/ Wales border) about their efforts to graze farm animals ‘sustainably’. The two explained how they have mountain grazing rights on the Brecon beacons and have cattle grazing an ancient hill fort, to preserve the archaeology from the incursion of scrub and to enhance the diversity of the grassland untouched by a plough for millennia, if at all. All their fields are natural pasture kept in a grazing rotation. One of the fields is an iron age enclosure and has never been ploughed in modern times! Yet now the call everywhere is to shun animal farming and rely solely on crops. 

The couple keep grassfed (Dexter breed, as in  the picture above) cattle and sheep and rare-breed pigs, all raised outdoors and supplemented  by a range of non-soya concentrates, and farm amazingly sustainably. They firmly believe that the sheer complexity of their farm demonstrates that the global environmentalist models about ‘Norm’ cannot possibly map onto reality anywhere on the planet. 

Instead, their farm is a case study in how the new ‘plant-based food’ movement risks upturning delicate relationships between humans and nature but also a more anthropological study in how apparently deeply-entrenched attitudes towards long-established activities and traditions can be rapidly changed by elite groups using sophisticated control of public information.