Power of Narratives

Making a Pandemic: A Simple Meat & Potato Recipe

Gleb Lisikh
June 30, 2023
Two of the basic things governments and public health officials needed to know to gauge the dangers of Covid-19 and plan the response were: how many transmissible cases were out there, and how lethal the virus really was. It turns out they didn’t really know either. Perhaps, gripped by their narrative of panic, they didn’t want to know. Gleb Lisikh reveals how skewed interpretation of the standard Covid-19 test combined with public health policies of inflating Covid-19 death numbers led to massive overestimation of the pandemic’s scale. If there is a next time, then before they shut down the world economy and destroy personal freedoms, they might want to make sure they get the numbers right.
Power of Narratives

Making a Pandemic: A Simple Meat & Potato Recipe

Gleb Lisikh
June 30, 2023
Two of the basic things governments and public health officials needed to know to gauge the dangers of Covid-19 and plan the response were: how many transmissible cases were out there, and how lethal the virus really was. It turns out they didn’t really know either. Perhaps, gripped by their narrative of panic, they didn’t want to know. Gleb Lisikh reveals how skewed interpretation of the standard Covid-19 test combined with public health policies of inflating Covid-19 death numbers led to massive overestimation of the pandemic’s scale. If there is a next time, then before they shut down the world economy and destroy personal freedoms, they might want to make sure they get the numbers right.
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If a new global viral outbreak is declared – and judging by some credible warnings, this is not too far off – its official status as “pandemic” will be determined through metrics and tools very similar to those used for Covid-19. Now that we have sufficient data available, especially data from 2020 uncluttered by the effects of vaccines, we should evaluate those metrics and tools to see how well they served us and whether they should be reused, adjusted or possibly abandoned. This is of obvious importance since the declaration of a pandemic and global emergency is, in addition to the actual disease effects, costly on all levels and disrupts virtually every aspect of modern life.

While many things elevated the Covid-19 pandemic from mere disease into world-altering narrative – statistics and models, media hype, the visuals of masking and social distancing, lockdowns, hospital utilization, peculiar treatments – the narrative largely rested on two basic metrics. These were cases, determining the scale of the outbreak, and deaths, driving public understanding of its severity. These in turn were measured predominantly by two tools: PCR testing and death certificates, respectively.

Those are the “Meat & Potato” of the Covid-19 pandemic. They are the two essential ingredients and their use (or misuse) provides a base recipe for moving a future viral outbreak into the category of pandemic. Accordingly, they are prime subjects of a post-mortem analysis.

Potato: PCR, the Gold Standard?
Jonathan Gubbay (left), a microbiologist with Public Health Ontario, hailed the PCR test for its ability to reliably identify the Covid-19 virus; but the PCR test only detects molecule-sized bits of genetic material – it does not measure viral load.

“In Ontario, we use PCR as the gold standard of testing for Covid-19 because it is able to successfully detect tiny amounts of the virus (sensitivity) with a low chance for error (accuracy) compared to other types of lab tests.” So said Jonathan Gubbay, a medical microbiologist with Public Health Ontario (PHO), in an online explainer the ministry published in February 2021. This was a typical expression of how public health organizations around the world explained the polymerize chain reaction (PCR) test to the public. And the public had no reason to disbelieve its accuracy and reliability.

Gubbay did not give the PCR technology quite enough credit, however. It can detect not only tiny amounts of virus but tiny amounts of virus parts, right down to molecules. PCR was an amazing invention, for which Kary Mullis, the eccentric American biochemist and writer who developed the process while working at a private company, was recognized through the Nobel Prize in Chemistry in 1993. PCR revolutionized the field of molecular biology and genomics by enabling efficient DNA/RNA amplification. It became fundamental to many procedures used in genetic testing and biomedical research, including analysis of ancient DNA samples, criminal forensics, tissue typing (vital for organ transplants) and many others.

“It does not tell you that you are sick”: American biochemist Kary Mullis (left), who won the Nobel Prize in 1993 for the invention of the PCR process, cautioned about its use for diagnosis. Mullis did not live to see the Covid-19 panic. (Sources of photos: (left) Dona Mapston, licensed under CC BY-SA 3.0; (right) Boldie, licensed under CC BY-SA 4.0)

It would be fascinating to hear Mullis’ opinion on the “gold standard” in the context of today’s PCR application. Sadly, he died in 2019 just before the Covid-19 disaster was announced. Mullis had previously sounded a cautionary note, however, saying that, “With PCR, if you do it well, you can find almost anything in anybody.” In particular, he warned, “It does not tell you that you are sick.” While he was speaking in the context of HIV, his observation casts doubt on (or at least raises the possible limitations of) PCR-based diagnosis for any virus, including SARS-CoV-2.

But why would there be even a slight controversy for such an amazing technology, without which such achievements as the Human Genome Project would be hardly possible? Addressing this requires understanding the issue of what it means to diagnose someone as “sick” with Covid-19.

For someone to get sick, he or she needs to have viral culture in loads sufficient to evoke symptoms and transmissibility. A mere detection of viral parts is (or should be) irrelevant to proclaiming someone as “having Covid.” In this guidance document from 2021, the Government of Canada gives us a very good perspective on that point: “The genetic fingerprint of the virus can be picked up long after the virus is no longer infectious. PCR can be positive for over 100 days or more after infection.”

Misdiagnosis can have all sorts of negative consequences, many obvious, others not so obvious. A false positive might keep you away from work, require you to isolate for two weeks or prevent important visits with loved ones. Some people even took advantage of this unfortunate loophole in the PCR test for easier travel during the pandemic. Many countries worldwide recognized that a certified recovery from Covid-19 rendered Covid-19 testing meaningless for a period of time. In Canada, for around $150, an online doctor could issue a “proof of recovery from Covid” and this would permit the person to travel to many other countries and provide 180 days immunity from Covid-19 testing. (Many countries, though not Canada, also recognized such a recovery as functionally and legally equivalent to vaccination, as discussed in this C2C essay.)

A vivid example of “would be” inappropriate PCR application for clinical diagnosis can be given using the analogy of varicella-zoster virus (VZV). VZV causes chickenpox and shingles. After a person recovers from chickenpox, the virus remains in the body (dormant) and can later cause shingles if it reactivates (for example, if the person’s immune system becomes compromised). Shingles are worse than Covid-19 for most people, and chickenpox is roughly three times more contagious. But while many people have VZV “living” inside them (and it, too, is best detected by PCR), it would be ludicrous to suggest they are sick or contagious. Yet that’s what would have to be assumed if PCR testing were applied and interpreted in the same manner as it was done, and still is done, for Covid-19 clinical diagnosis.

“The gold standard”: Public health officials lauded the PCR test for its sensitivity and accuracy; the public had no reason to disbelieve this claim, nor the implication that a “positive” result meant you were actually sick with Covid-19. (Source of photo: Senado Federal, licensed under CC BY 2.0)

So, how does PCR testing distinguish a viable viral culture from harmless molecular debris? To humbly correct PHO’s Gubbay, the PCR method does not detect viruses per se. Instead, it deals with DNA or RNA molecules. Through a series of temperature changes, the PCR test makes copies of RNA molecules of interest (in the case of Covid-19 tests, targeting regions in the SARS-CoV-2 gene).

Each temperature cycle doubles the number of RNA molecules present, until their amount reaches a certain detectable threshold. The number of cycles it takes for the PCR machine to reach such a threshold is called the cycle threshold (abbreviated as Ct or CT). The more initial RNA of interest there is, the fewer cycles are required to detect that significant amount. A low Ct number means that the target molecule was present in high amounts, while a high Ct number means it was present in low amounts.

The conclusion whether a viral culture is present is a probabilistic estimate done by limiting the number of PCR cycles not to exceed a certain predetermined value. If the generated RNA amount reaches the significant threshold within those cycles, it is assumed that the viral culture is likely there, and the test is “positive.” The PCR test, accordingly, requires strict discipline in its use, and it is not hard to see how, in an unusual public atmosphere of panic where everyone is looking for positive cases, it becomes vulnerable to simple error, misuse or abuse.

In the PCR process, the Ct value is the number of temperature cycles RNA molecules are put through to become detectable. The higher the number, the fewer the molecules present, and the greater the likelihood of a false positive. How the “right” Ct values were selected was typically shrouded in secrecy or ambiguity. (Source of graph: cebm.net)

The unique aspect of the PCR process results in different opinions and opposing views on the use of Ct in clinical diagnostics, specifically its cut-off value. Just how unsettled this subject is can be seen by the following two reputable sources arguing respectively for and against paying attention to and reporting Ct values in PCR testing:

  • “CT values could also help clinicians flag patients most at risk for severe disease and death.” (From Science magazine in September 2020.)
  • “The utilization of Ct values to guide patient management is discouraged. Correlation with viral load, viral burden, or infectivity has not been established for qualitative SARS-CoV-2 tests.” (From the American Association for Clinical Chemistry in December 2021.)

Another PHO explainer, “An Overview of Cycle Threshold Values and their Role in SARS-CoV-2 Real-Time PCR Test Interpretation,” helps a lot with the Ct numbers dilemma. It states that “specimens with Ct values > 24 were viral culture negative. However, the US CDC has reported that they were able to culture virus from specimens with Ct values up to the low 30s (unpublished data).” So, starting at a Ct of 25, things become quite problematic for declaring infection.

A French research group’s extensive study on this subject corroborates the PHO’s message:

  • At a Ct of 25, PCR testing resulted in 30 percent of false positives (i.e., 30 percent of samples that tested positive were not infectious);
  • At a Ct of 30, 80 percent of false positives;
  • At a Ct of 35, 97 percent of false positives; and
  • At a Ct above 35, no sample remained infectious in a cell culture (100 percent of false positives).

With that established, what is the Ct cut-off value used in Canadian labs? The answer is not an easy find. The aforementioned PHO article has a whole section entitled, “Why aren’t cycle threshold (sic) reported on test results?” Apparently, according to the article, “It is not recommended to provide Ct values on test results in Ontario (and Canada).” This is allegedly because “Ct values are not directly comparable from one PCR test kit to the next, and can change with increased transportation times, sample storage conditions, and sample collection method.”

The PHO article does, however, give us a hint of what has been going on in Ontario (and perhaps other provinces): “At PHO, Ct values are available to health care professionals upon request, and low level detected results (Ct value 35 to 38) are indicated on the laboratory report (since November 2020).” This means the Ontario labs run PCR tests at least up to Ct=38. Assuming that Canadian testing labs’ practices were similar to those in the U.S., the Ct cut-off is probably 40, as this would conform to CDC guidelines (see page 39).

It is clear, then, that a positive result at Ct over 30 is almost guaranteed to be a non-infectious “case.” So what proportion of all reported Canadian “cases” fell within the Ct >30 bucket, wherein virtually no test results even represent a viral culture that might cause disease? Put another way, how many of Canada’s reported total of 4.7 million Covid-19 “cases” (using the Government of Canada’s most recent update on June 27) were not cases in any meaningful, disease-inducing or transmitting way at all?

We will likely never know, because PHO admits that the Ct information is not collected: “How commonly this [false positive] occurs in the province is not known, as individual reports that are corrected are not centrally documented.” Still, the organization rather blithely claims that “the rate of false positive tests that are later determined to be negative requiring results to be corrected is extremely low.” It purports a false-positive rate “of less than 0.01%.”

Canada and the U.S. performed PCR tests with Ct’s of 38-40, even though the accuracy of PCR tests plunges at Ct values above 24; yet Public Health Ontario insisted the number of false-positives was “extremely low,” an optimism severely undermined by a German study that found the ratio of false positives to be 50-75 percent. Meanwhile, political leaders like UK Prime Minister Boris Johnson (above) kept urging people to get tested. (Source of photo: UK Prime Minister, licensed under CC BY-NC-ND 2.0)

These contradictory PHO statements stand in stark contrast with a German scientific study published in the Journal of Infection in May 2021 which re-analyzed PCR tests of more than 160,000 people and concluded, “Our results confirm the findings of others that the routine use of ‘positive’ RT-PCR test results…fails to reflect the fact that 50-75% of the time an individual is PCR positive, they are likely to be post-infectious.” False positives at a rate of 50-75 percent! And that’s in Germany, where labs work with lower Ct thresholds of 30 to 40 cycles.

It is worth noting that the share (and hence probability) of false positives was measured against a single test per individual. But many people had multiple tests, especially in “high risk” settings like hospitals (daily testing) and long-term care homes (weekly testing). This only multiplied the odds of bad information being collected – and influencing the narrative.  If we assume, for discussion purposes, an initial false positive test probability of 60 percent, then mathematically speaking, the chances of a healthy person being diagnosed with Covid-19 after the third test rises to 94 percent!

A Lancet study looked into rather obvious consequences of the false positives. The list is long but here’s just a handful of issues:

  • Unnecessary treatment cancellation or postponement;
  • Exposure to infection following a wrong pathway in hospital settings as an in-patient;
  • Overestimating Covid-19 incidence and the extent of asymptomatic infection; and
  • Misdirection of policies regarding lockdowns and school closures.

Let’s pause for a moment to consider the enormous, disastrous consequences embedded in these four brief statements. The first represents hundreds of thousands, possibly millions, of needless deaths worldwide – many of them still to come since many of the diseases in question take years or decades to kill. The second represents thousands of preventable deaths from Covid-19 that the victims caught needlessly while in hospital. The third helped to justify masking, lockdowns and other restrictions. The fourth caused trillions of dollars in economic damage and untold personal misery.

The grim consequences: Prodigious false positives enabled a global misreading of Covid-19’s severity, which led to possibly millions of deaths from postponed treatment for other conditions, to masking mandates and massive lockdowns, to untold misery and to trillions in economic damage. (Sources of photos (clockwise from top left): Pexels; novarihealth.com; Famartin, licensed under CC BY-SA 4.0; Pexels; Tim Dennell, licensed under CC BY-NC 2.0)

As we are lacking the actual data, what real-world observations might have been indicative of potential PCR Covid-19 misdiagnosing? Before 2020 Canada was reporting approximately 50,000 cases of influenza annually. But according to the Government of Canada FluWatch report, in the entire 2020-2021 season there were only 69 influenza detections.

Such near-elimination of the flu in 2020-21 is sometimes credited to Covid-19 containment measures. Yet countries with a much lighter approach to containment, such as Sweden, had a very similar flu elimination picture. While an argument can still be made that influenza viruses have been displaced by the more infectious novel coronavirus, having the case data stratified by Ct numbers would be very helpful to validate the displacement hypothesis against what might have been an entirely different cause: habitually misdiagnosing influenza as Covid-19.

A lot more can be said about PCR and other methods of testing, their setup deficiencies, dubious interpretation of results, and the very definition of a Covid-19 “case.” But the main point of this article is not to go into minutiae but instead to probe for the foundational aspects of the method and its role in counting cases and, in turn, shaping the scale of the Covid-19 outbreak in earning its ominous (and, it turned out, catastrophic) “pandemic” label.

Meat: Death Certificate or Licence to Kill?

Do you remember how the pandemic scare started in the West? It was the news and images coming from northern Italy that generated something akin to mass panic in Canada, from Ontario’s profusely sweating premier on down: hospital corridors overflowing with catatonic patients, desperate relatives screaming for news, military trucks carting bodies off to mass graves or crematoria or who knows where, “experts” extrapolating millions of deaths as the disease threatened to sweep its way down Italy’s boot.

How the scare started: Horrifying images from northern Italy – hospital corridors crammed with catatonic patients, rows of coffins, military trucks carting off dead bodies – triggered panic throughout the West. (Sources of screenshots: (top to bottom) Sky News/YouTube, Bloomberg Quicktake/YouTube, FRANCE 24 ENGLISH/YouTube)

And yet facts available at the time (kept obscure, but nonetheless discoverable) provided the context required for a more restrained reaction from Canada’s opinion leaders. Such context was almost entirely provided in a report released on March 20, 2020 by the Italian public health agency. Among its findings was this revealing statement: “For 2 patients under the age of 40 years, no clinical information is available; the remaining 7 had serious pre-existing pathologies (cardiovascular, renal, psychiatric pathologies, diabetes, obesity).” Very few people actually perused the report. Those who did and then expressed concerns were ridiculed and ostracized. Did they have reason to be concerned?

Defining a person’s cause of death is a tricky but important business. There are well-established rules for medical professionals to follow when determining the “underlying cause of death.” According to the Canadian Medical Association Journal, “The underlying cause is the antecedent cause that initiated the sequence of events leading to death. In other words, the underlying cause starts the chain of events, and the immediate cause is the final link in the chain.” This is important – and highly relevant to understanding the Covid-19 pandemic narrative – because, “The underlying cause of death is the cause selected for coding and tabulation of the official cause-of-death statistics. [emphasis added]

The Covid-19 pandemic brought in quite revolutionary changes to certifying death, which were documented in the World Health Organization’s (WHO) International Guidelines for Certification and Classification (Coding) of Covid-19 as Cause of Death. Those guidelines requested participating member states to attribute death to Covid-19 whenever the disease could be thought of as a contributor to death: “Covid-19 should be recorded on the medical certificate of cause of death for ALL decedents where the disease caused, or is assumed to have caused, or contributed to death. [emphasis in original]”

Succumbing to the scare: Political leaders throughout the West, including in Canada Ontario Premier Doug Ford (top) and Alberta Premier Jason Kenney (bottom), propounded the prevailing Covid-19 narrative, even though there was discoverable – if obscure – information suggesting a far more restrained approach. (Sources of photos: (top) The Canadian Press/Nathan Denette; (bottom) The Canadian Press/Jeff McIntosh)

The WHO also advised countries to do the same for “probable” Covid-19 infections: “A Covid-19 death is defined for surveillance purposes as a death resulting from a clinically compatible illness in a probable or confirmed Covid-19 case, unless there is a clear alternative cause of death that cannot be related to Covid-19 disease (e.g. trauma).”

It would be interesting to compare Covid-19 fatality rates between countries that used the WHO’s guidelines and those that did not. Out of 195 countries in the world today, however, 193 are WHO member states, which renders such comparison practically unfeasible. Still, some observations can be made based on what the revised death certification practice might lead to.

An obvious concern about such an unprecedented promotion of a particular illness as the underlying cause of death is that it would absolve severe comorbidities to the whims of PCR and other (less reliable) testing, or to mere suspicion of Covid-19 infection. Indeed, for someone in a critical condition (cancer, trauma, heart attack), any, however minor, stress (think of a paper cut) can be conceived as initiating the chain as opposed to being the straw that broke the camel’s back or, as the Canadian Medical Association put it, the “final link in the chain.”

And in the case of suspected Covid-19 infection, under the new guidelines, what goes on the death certificate as “underlying cause” becomes heavily biased toward Covid-19. The amended certification resulted in ridiculous situations where people injured in traffic accidents, shot by guns (Colorado and Florida), or falling off ladders (Croatia) were pronounced dead of Covid-19 (seemingly but not necessarily contrary to the WHO’s guidelines). 

Pushing Covid to the front of the death queue: World Health Organization guidelines requested Covid-19 be listed as the cause of death for “ALL decedents where the disease caused, or is assumed to have caused, or contributed to death.” This was revolutionary change, upending decades of accepted medical practice. (Source of table: World Health Organization)

Still, even with such bias (justified for “surveillance purposes”), according to Statistics Canada, in 2020 Covid-19’s contribution was only 5 percent of overall Canadian mortality. This was on par with accidents and quite behind two leading causes: malignant neoplasm (cancer) and heart diseases, which respectively accounted for 26 percent and 17 percent of deaths in Canada.

It is worth noting that in the same year the contribution of cancer to overall mortality dropped by nearly 2 percent (significantly above its regular year-over-year fluctuation of less than 1 percent). Considering that it has been widely stated and acknowledged that cancer, due to its adverse effects on the immune system, is a serious Covid-19 comorbidity, it would be reasonable to expect more cancer deaths in 2020 – not fewer. But if the underlying cause (and hence reporting) were switched to be the viral infection itself in some cases (in full accord with WHO’s guidelines), that’s how it should look indeed.

In 2020, cancer’s contribution to overall mortality in Canada actually dropped by 2 percent; given its effect on the immune system, cancer is a serious Covid-19 comorbidity and it would have been reasonable to expect more cancer deaths, not fewer. (Source of table: Statistics Canada)

Although widely hyped as threatening the entire population (why else would we mask kids and close schools?), Covid-19 is almost entirely an illness of the old and vulnerable. Yet dying “from old age” is not even a thing when it comes to official death causes. When an elderly person dies, it has to be attributed to something other than age, even though it should be obvious that, in many cases, 80+ age fragility renders the cause of death determination a nominal exercise. While there are many fit and healthy 80-year-olds, nobody lives forever and it is undeniable that people become more vulnerable with age. This leads us to another interesting observation.

The average age of the 15,600 Canadians who died of Covid-19 in 2020 was 83.8 years. Of those 15,600, about two-thirds were over the Canadian life expectancy of 82 years. By taking those numbers at face value, it follows that elderly Canadians with Covid-19 live longer than average! In reality, of course, many people in that age group have multiple comorbidities, plus age fragility, meaning that almost any additional health problem could provide that “final link in the chain.” So why, then, speaking from a dry statistical point of view (while not taking into account individual health conditions), should those two-thirds PCR-diagnosed Covid-19 deaths be even meaningfully counted toward Covid-19?

Mixing and Cooking

As we have seen, combining biased death attribution with opportunistic testing can result in a pandemic outlook for an otherwise conventionally manageable (if serious) infectious outbreak, especially if it is spiced up by overhyped media attention and the willing participation of political leaders.

Consider this hypothetical scenario. According to the WHO, it is estimated that around 67 percent of the global population under age 50 carries HSV-1 (one of the herpes viruses). In developed countries like Canada, the prevalence can be higher, reaching over 90 percent in certain groups. These percentages are obtained as statistical estimates by sampling and PCR-testing for herpes in a primarily asymptomatic population. Because people merely carrying the virus aren’t considered “sick,” there are no resulting panicked public policies.

Combining biased death attribution with opportunistic testing – plus media hype and overreaching governments – can transform a manageable (if serious) infectious outbreak into a “global pandemic” ostensibly warranting states of emergency. (Source of photo: waltarrrrr, licensed under CC BY-NC-ND 2.0)

But if we were to start chasing down individuals with the same PCR test used as a clinical diagnostic tool for Covid-19 that assumed any positive case was “concerning,” then we would end up with more than half of the population “sick” and subject to various measures and mandates. And then linking deaths to those detected infections might give some people reason to panic.

It would be quite difficult to ramp this up in public through the media, because HSV-1 is a well-known nuisance and people might not be scared enough by its widespread presence alone. But what if a novel variant (let’s call it “HSV-5”) were discovered, (supposedly) highly contagious and (allegedly) dangerous? Who would then blame medical authorities for expanded and scrupulous herpes testing that erred on the side of caution in order to protect the vulnerable? And why not instruct doctors and nurses to skew the records on death certificates for HSV-5 surveillance purposes? That would make the media job so much easier. And if this simple recipe worked, it should be recognizable in the next pandemic or even epidemic local to Canada or any other country.

Gleb Lisikh is a researcher and IT management professional, and a father of three children, who lives in Vaughan, Ontario and grew up in various parts of the Soviet Union.

Source of main image: reginaviarum, public domain.

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