Humans are Free
Although people have tragically died from Covid-19, the way the Covid-19 death data is recorded in many countries around the world has produced, and continues to produce, an inflated death toll.
This inflated death toll has then been, and continues to be, used by fascist-style bureaucracies, in conjunction with scientific priesthoods, to terrify the general public into obedience.
One of the most basic laws of statistics is that correlation does not equal causation. Although this may sound complicated, it’s not. It simply means that just because there is a correlation between two variables, or to put this another way, a close relationship between two things in the world, this does not mean that one thing is causing the other thing to happen.
A third factor may be causing the correlation that is observed for instance. As an example, there is usually a correlation in many countries between cold weather and people buying more goods in shops, or online, but this increase in buying is not caused by cold weather.
Instead, it is caused by the Christmas period, when people spend more money, and it just happens to be the case that the weather is usually cold in December in many parts of the world that celebrate Christmas.
So, even though there is a correlation between cold weather and increased buying patterns, cold weather does not cause increased buying patterns, but the Christmas period causes people to buy more goods.
Furthermore, the correlation that is observed between two things in the world may just be a product of random chance.
This has led people to point to some funny correlations, such as the fact that there was a correlation between margarine consumption and divorce rates in the Maine between 2000 and 2009.
There was also a correlation between per capita cheese consumption and the number of people who died by becoming tangled in their bedsheets, or the number of people who drowned by falling into a pool and films Nicholas Cage appeared in. Once again, correlation does not equal causation.
Inflated Death Data
If we turn our attention back to the Covid death data, just because someone has tested positive for Covid-19 and died sometime after (even if we put aside for a second that some tests are known to give false positives), that does not mean that Covid-19 caused that person to die.
Yet, the main figure certain countries around the world are using to express Covid-19 deaths is simply recorded, or coded, as essentially any death involving a positive Covid-19 test within 28 days of death.
Because correlation does not equal causation, simply recording Covid-19 deaths as any deaths involving a positive Covid-19 test within a given period of time is an extremely poor way to measure how many people have died.
For instance, in the UK, the main figure being used for Covid-19 deaths is coded, as stated on the official Coronavirus website, as the “number of deaths of people who had had a positive test result for COVID-19 and died within 28 days of the first positive test.”
This completely ignores the problem of causality, and thus, produces a much larger death toll than there actually is.
For instance, if someone has had an underlying heart condition for 10 years, and has a heart complication and dies, their death was most likely mainly caused by the heart condition that has plagued them for a decade.
However, if that person had tested positive for Covid-19 for the first time within 28 days of them dying, that person could be included as a Covid-19 death in the UK, if all is required to be categorized as a Covid-19 death is simply a positive test result.
For those who understand that the way you code deaths dramatically changes the number of deaths you get, the UK authorities kindly illustrate this for us. There is a second numberrecorded by UK authorities which codes deaths as “people whose death certificate mentioned COVID-19 as one of the causes.”
By coding deaths this way, there are thousands more Covid-19 deaths compared to when deaths are coded as “people who had had a positive test result for COVID-19 and died within 28 days of the first positive test.”
Despite the UK authorities having two ways to code Covid-19 deaths however, none of them are particularly accurate in my opinion.
This is because the positive test figure does not deal with the issue of causality, and the death certificate figure only mentions Covid as needing to be “one of the causes” of death, rather than “the primary cause,” in addition to the death certificate figure not explicitly demanding the need for a positive Covid-19 test result.
US Death Data
If we turn our attention to the United States, we find similar issues with the Covid-19 data.
One of the main figures the Centers for Disease Control and Prevention (CDC) is reporting as the total number of provisional Covid deaths in the United States – which stands at 241,906 deaths at the time I am recording this audio – is presented as “all deaths involving Covid-19.”
If we dig a little deeper, this number is based on “deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1.” If we continue to dig, we can better understand how this number is calculated. The CDC’s website states that:
“The National Center for Health Statistics (NCHS) uses incoming data from death certificates to produce provisional COVID-19 death counts. These include deaths occurring within the 50 states and the District of Columbia… COVID-19 deaths are identified using a new ICD–10 code.
“When COVID-19 is reported as a cause of death – or when it is listed as a “probable” or “presumed” cause — the death is coded as U07.1. This can include cases with or without laboratory confirmation.”
There are many potential problems with coding Covid deaths this way. One problem is again this issue of Covid-19 being listed as “a cause of death,” as opposed to the primary cause of death. If we look at the technical notes, the CDC’s website provides more details:
“Coronavirus disease deaths are identified using the ICD–10 code U07.1. Deaths are coded to U07.1 when coronavirus disease 2019 or COVID-19 are reported as a cause that contributed to death on the death certificate. These can include laboratory confirmed cases, as well as cases without laboratory confirmation.
“If the certifier suspects COVID-19 or determines it was likely (e.g., the circumstances were compelling within a reasonable degree of certainty), they can report COVID-19 as “probable” or “presumed” on the death certificate (5, 6). COVID-19 is listed as the underlying cause on the death certificate in 92% of deaths (see Table 1).”
Even though this 92% of cases where Covid was listed as the underlying cause of death is more compelling, 8% of 241,906 is still a relatively large number, over 19,300 deaths.
Furthermore, if we dig deeper still to understand how robust this data is, we find out from an April report by the NCHS, titled: Guidance for Certifying Deaths Due to Coronavirus Disease 2019 (COVID–19), which is still linked on the CDC’s website where it provides details on its data, that it is acceptable to “report COVID–19 on a death certificate without” the need for the patient to test positive for Covid-19:
“An accurate count of the number of deaths due to COVID–19 infection, which depends in part on proper death certification, is critical to ongoing public health surveillance and response. When a death is due to COVID–19, it is likely the UCOD and thus, it should be reported on the lowest line used in Part I of the death certificate.
“Ideally, testing for COVID–19 should be conducted, but it is acceptable to report COVID–19 on a death certificate without this confirmation if the circumstances are compelling within a reasonable degree of certainty.” (p.2-p.3).
Even though I understand that this report was published in April, surely for a death to be recorded as being due to Covid-19, the patient actually has to test positive for Covid-19.
In my opinion, there needs to be a more robust categorization of what constitutes a Covid-19 death, as the previous, and seemingly current ways of recording Covid-19 deaths are somewhat vague and imprecise, arguably producing an inflated death count.
From my perspective, the main figure countries should use to categorize Covid-19 deaths has to include (1) the need for the patient to test positive for Covid; and (2) the need for a medical professional to examine the patient and conclude that Covid-19 was the primary, or underlying, cause of death.
This should be the main figure that officials and the media then quote, because the average person who hears what the latest death count is on a 2-minute news segment presumes that this figure actually expresses how many people have died of Covid-19 – not with Covid-19, not with suspected Covid-19, but actually of Covid-19.
Countries could have a secondary number of Covid-19 deaths where Covid is recorded as one of many factors in death, but the main death toll has to establish that the individual had Covid-19, and that Covid-19 was the primary, or underlying, cause of death.
From my interpretation, the way many countries have and continue to categorize Covid-19 deaths produces an inflated death count, giving a distorted impression of the scale of Covid-19.
Many would argue that the authorities in various countries around the world are well aware of this issue, and are using statistics to generate fear.
To be clear, I am not a statistician, scientist, or medical doctor, although I did take a few classes in statistics and research methods as part of my degree in Politics at university. But don’t just take my word for it that the Covid-19 data is a mess in various ways. Jamie Jenkins, the former Head of Health Analysis at the Office for National Statistics, has called the Covid-19 data, in the context of Britain, a travesty in various ways.
Additionally, it is important to note that the manipulation of statistics has been a key feature of tyrannical regimes down through history. In the Soviet Union for instance, the Stalinist government constantly supressed or delayed the release of statistics that contradicted their agendas, and only released data that supported their initiatives.
Today, history looks to be repeating itself once again. Governments around the world are selectively using statistics in a way that inflates the scale of the Covid pandemic.
For instance, over the past month or two, there has been a clear shift in the emphasis that the government and the media are placing on the number of positive Covid-19 cases.
Yet with this shift in emphasise, both parties have largely failed to contextualise why this was always going to be the case once mass testing began.
It doesn’t take a rocket scientist to work out that even if the Covid-19 tests being used are 100% accurate, the more tests you conduct, the more positive cases you are going to find. If we take the UK for example, the number of virus tests being conductedhas been increasing month-by-month since May of this year.
On the 1st of May for instance, just under one million virus tests had been conducted in the UK. On the 1stof December, over 40 million virus tests had been conducted. In November alone, approximately 9 million virus tests were conducted in UK.
Therefore, it is no surprise that there were more positive cases in November than there were in May. The number of positive cases only becomes even 1% relevant if there has been a consistent number of tests being conducted over many months, as this gives officials a base to compare too.
Furthermore, what percentage of tests are producing false positives? How sensitive are these tests? What is the margin of error in these cases, as some tests are reportedly picking up fragments of dead viruses from infections months ago that are no longer a potential issue?
There are questions over the validity of the Polymerase Chain Reaction (PCR) test for instance, a popular test used. Kary Mullis, the inventor of the PCR test has said that “quantitative, PCR is an oxymoron.” As John Lauritsen, who quoted Mullis in a 1996 article on the use of PCR tests for HIV patients, wrote:
“PCR is intended to identify substances qualitatively, but by its very nature is unsuited for estimating numbers. Although there is a common misimpression that the viral load tests actually count the number of viruses in the blood, these tests cannot detect free, infectious viruses at all; they can only detect proteins that are believed, in some cases wrongly, to be unique to HIV. The tests can detect genetic sequences of viruses, but not viruses themselves.”
In the context of using the PCR for determining a Covid-19 infection, a spokesperson for Public Health England recently told Reuters that “detecting viral material by PCR does not indicate that the virus is fully intact and infectious i.e. able to cause infection in other people.”
Thus, it is important to ask whether many of these positive Covid test results are merely from some tests picking up fragments of dead viruses that no longer pose a risk of infection? Furthermore, it is important to establish what type of people are testing positive for Covid-19?
In most cases, it is completely irrelevant if a young, healthy person, who is not obese, and who does not have any underlying health conditions, tests positive for Covid-19. This is because statistically, as I understand it as someone who is not a medical doctor, it is extremely unlikelythat a young, healthy person will have a bad reaction to Covid-19, and they may not even know they were ever infected.
Additionally, I would like to bring to your attention a recent story I read in relation to the Covid-19 vaccine. A report in the Independent newspaper states that the UK government has given Pfizer legal indemnity for its vaccine rollout, which protects the pharmaceutical giant from being sued by people who experience any potential issues with the new vaccine. NHS staff who will be administering the vaccine, are also protected.
Furthermore, it was reported that the Department of Health and Social Care has confirmed that the government would add the new coronavirus vaccine to the list of vaccinations covered by the Vaccine Damages Payments Act.
As the Independent notes, this gives “a one-off £120,000 payment to people who are permanently disabled” or injured as a “result of a listed vaccination.” Needless to say, this is a worrying sign – I will link the full article in the description.
If we turn our attention back to the question of statistics, there are clearly major issues with the way the Covid-19 death tolls and positive cases are being calculated and measured. However, the notion that governments around the world that are behaving in a fascist-style manner are using statistics to seemingly control the population is nothing unsurprising to those who understand history.
As George Orwell wrote in his book 1984, where he used historical truths and his own insights to predict how a global dictatorial regime of the future would operate, explained:
“Even the written instructions which Winston received, and which he invariably got rid of as soon as he had dealt with them, never stated or implied that an act of forgery was to be committed: always the reference was to slips, errors, misprints, or misquotations which it was necessary to put right in the interests of accuracy.
“But actually, he thought as he re-adjusted the Ministry of Plenty’s figures, it was not even forgery. It was merely the substitution of one piece of nonsense for another. Most of the material that you were dealing with had no connection with anything in the real world, not even the kind of connection that is contained in a direct lie. Statistics were just as much a fantasy in their original version as in their rectified version. A great deal of time you were expected to make them up out of your head” (Orwell, 2008: 43).
There are many more issues with the Covid-19 data, including the fact that flu deaths in parts of October reportedly decreasedin Britain and the United States compared to weekly and monthly five-year averages, which in part may be because these deaths have been included as Covid-19 deaths. In the interest of time however, the main point to emphasis here is simply that there are lies, damn lies, and then there’s statistics.