The Covid-19 Data Are a ‘Travesty’

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.


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 number recorded 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.


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 (56). 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.

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