False Efficacy: the Difference Between Absolute and Relative Risk (Pt. 2 of What to Consider Before Getting a COVID-19 "Vaccine" or Semi-Annual Re-Vaxx)

NOTE: Below is part 2 of What to Consider Before Getting a COVID-19 "Vaccine" or Semi-Annual Re-Vaxx. If you haven't read part 1 yet, you may click the prior-referenced link to read it, which will also include further parts subsequently added.

#2 False Efficacy: the Difference Between Absolute and Relative Risk
As mentioned in part #1, most should be aware that COVID-19 (COVID) vaccines were never tested on human-to-human transmission. The chief medical officer at Moderna, Tal Zaks, excuses this neglect on testing [*1]: “Our trial will not demonstrate prevention of transmission, because in order to do that you have to swab people twice a week for very long periods, and that becomes operationally untenable.” It's now too late to find out whether or not transmission occurs after vaccination, as the original placebo group could get the "real" vaccine after the clinical trials for "emergency use" ended.

The FDA first approved the Pfizer COVID vaccine for emergency use on December 11, 2020 [*2]. But, Pfizer compromised its control group in its trials a month earlier. Nature reports [*3]:

On 10 November [2020], Pfizer sent a letter to participants, seen by Nature, which states that the company is exploring ways to allow interested participants in the placebo group who meet eligibility criteria for emergency access to cross over into the trial’s vaccine arm. A spokesperson told Nature that the company would have “an ethical responsibility to inform all study participants about the availability of an Emergency Authorized Vaccine.”

This not only invalidates future assessments on human-to-human transmission but also those regarding long-term health effects. Lack of data on transmission and long-term effects is why COVID vaccines are approved by the Food and Drug Administration (FDA) for "emergency use" only, and the makers are immune from liability (more on that in a part to follow).

Nonetheless, you've probably read that COVID vaccines are about 95% effective. What does that mean?

In order to measure vaccine effectiveness, we must first determine what the vaccines are "effective" upon. COVID cases are diagnosed using a polymerase chain reaction (PCR) test. A swab of ribonucleic acid (RNA) is collected from deep inside the nose and reverse-transcribed into deoxyribonucleic acid (DNA), which is then used to detect traces of the COVID virus. To be discernible, the sample needs to be amplified in rounds. The number of amplifications is called the cycle threshold (CT). The higher the number, the more sensitive the test is and the more likely it results in a false positive. In Dr. Joseph Mercola's book, The Truth About COVID-19, at pages 78-79 [*4], his co-author, Ronnie Cummins elaborates:

A September 28, 2020, study in Clinical Infectious Diseases revealed that when you run a PCR test at a CT of 35 or higher, the accuracy drops to 3 percent, resulting in a 97 percent false positive rate. Yet tests recommended by the World Health Organization are set to 45 cycles, and the US Food and Drug Administration and the US Centers for Disease Control and Prevention recommend running PCR tests at a CT of 40. The question is why, considering the consensus is that CTs over 35 render the test useless. … 

[A]n April 2020 study in the European Journal of Clinical Microbiology and Infectious Diseases showed that to get 100 percent confirmed real positives, the PCR test must be run at 17 cycles. Above 17 cycles, accuracy drops dramatically. By the time you get to 33 cycles, the accuracy rate is a mere 20 percent, meaning 80 percent are false positives. Beyond 34 cycles, your chance of a positive PCR test being a true positive shrinks to zero. According to a December 3, 2020, systematic review published in the journal Clinical Infectious Diseases, no live viruses could be found in cases where a positive PCR test had used a CT above 24.

Here is the graph from the paper referenced, suggesting a CT of 17 is preferable [*5].

 

Cummins summarizes the problems with PCR testing on page 77 [*6]:

At present, the polymerase chain reaction test is the primary method used to test people for COVID-19. The problem with that is twofold. First of all, the PCR test cannot distinguish between inactive viruses and “live” or reproductive ones. This is a crucial point, since inactive and reproductive viruses are not interchangeable in terms of infectivity. If you have a nonreproductive virus in your body, you will not get sick and you cannot spread it to others. For this reason, the PCR test is grossly unreliable as a diagnostic tool. 

Second, many if not most laboratories amplify the RNA collected far too many times, which results in healthy people testing positive. In order for the PCR test to be of any use whatsoever, in terms of diagnosing COVID-19, labs would need to considerably reduce the number of amplification cycles used.

As I've written before [*7], over the summer of 2020, Florida COVID cases went from 2,000 to 12,000 per day, at the same time multiple testing sites featured a positivity rate of close to 100%, including Orlando Health, which reported 98% positive tests and had to correct its records down to 9.4% [*8]. In a study published by the International Journal of Geriatrics and Rehabilitation, the CDC's COVID test kits were found to give inaccurate results 50% of the time, a 30% false positive rate and 20% false negative [*9].

On January 20, 2021, the day before Joe Biden's inauguration, the World Health Organization (WHO) updated its guidance on PCR tests for COVID to be more cautious of false positives and to ensure "clinical observations" (i.e. symptoms) are considered as well before making a diagnosis [*10]:

WHO guidance Diagnostic testing for SARS-CoV-2 states that careful interpretation of weak positive results is needed (1). The cycle threshold (Ct) needed to detect virus is inversely proportional to the patient’s viral load. Where test results do not correspond with the clinical presentation, a new specimen should be taken and retested using the same or different NAT [nucleic acid testing] technology.

WHO reminds IVD [in vitro diagnostic medical device] users that disease prevalence alters the predictive value of test results; as disease prevalence decreases, the risk of false positive increases (2). This means that the probability that a person who has a positive result (SARS-CoV-2 detected) is truly infected with SARS-CoV-2 decreases as prevalence decreases, irrespective of the claimed specificity.

Most PCR assays are indicated as an aid for diagnosis, therefore, health care providers must consider any result in combination with timing of sampling, specimen type, assay specifics, clinical observations, patient history, confirmed status of any contacts, and epidemiological information [emphasis added].

Still, positive COVID tests have negative ramifications for their victims, regardless of any symptoms detected and regardless of having previously received COVID vaccinations.



Without symptoms, we call a person with a positive PCR test "asymptomatic" and assume he can spread the virus anyway, a supposition with not only no clinical evidence to back it but evidence disproving it [*11]:

In this study, we recorded in detail the hospitalized situation, diagnostic procedure, inspection results, treatment plans and clinical outcome of an asymptomatic SARS-CoV-2 carrier who was laboratory confirmation by RT-PCR assay, but without related symptoms and imaging changes in concert with previous reports. Also, we analyzed epidemiological and clinical data from 455 contacts who had been exposed to the asymptomatic patient. All the 455 contacts were excluded from SARS-CoV-2 infection. Of the 231 quarantined people (196 family members and 35 patients), 229 were removed from medical observation successfully and two died for severe heart failure. New or existing respiratory symptoms were almost appeared in patients, which were considered to be associated with their original disease or complications. A family member complaining of fever was diagnosed as acute tonsillitis ultimately. Unlike COVID-19, normal blood count was found in most contacts. All CT images showed no sign of COVID-19 infection. Unquestionably, all cases tested negative for SARS-CoV-2 nucleic acid. This fact illustrated that there had been no cases of infection in a relatively dense space [emphasis added].

So, how was a COVID case determined in the clinical trials for the vaccines? Because adverse reactions are common in the COVID vaccines, how can we know if a trial subject who exhibited flu-like symptoms, such as myalgia, fever, headache, and chills is suffering from an adverse reaction to the vaccine as opposed to COVID itself or some other illness? Peter Doshi at BMJ, a resource for healthcare professionals, found alarming language in Moderna's trial instructions [*12]. Moderna explains on page 62 [*13]:

It is important to note that some of the symptoms of COVID-19 overlap with solicited systemic ARs [adverse reactions] that are expected after vaccination with mRNA-1273 (eg, myalgia, headache, fever, and chills). During the first 7 days after vaccination, when these solicited ARs are common, Investigators should use their clinical judgement to decide if an NP [nasopharyngeal] swab should be collected. The collection of an NP swab prior to the Day 1 and Day 29 vaccination can help ensure that cases of COVID-19 are not overlooked. Any study participant reporting respiratory symptoms during the 7-day period after vaccination should be evaluated for COVID-19.

Not all symptomatic subjects in the vaccinated groups were PCR tested. And, for those that were tested, what were the CTs for those PCR tests in any the clinical trials? They were not disclosed [*14].

Now that we have a vague idea of what the vaccines must be "effective" against, we must further distinguish between absolute and relative risk of COVID. Absolute risk is the probability one will get COVID when measured against the general population. Relative risk is the difference in getting COVID measured between those getting a COVID vaccine and those getting a saline injection (placebo, control group). When the vaccine makers refer to 95% efficacy, they are referring to relative risk.

Dr. Gilbert Berdine, MD, associate professor of medicine at Texas Tech University Health Sciences Center elaborates [*15]:

The Pfizer study had 43,538 participants and was analyzed after 164 cases. So, roughly 150 out [of] 21,750 participants (less than 0.7 percent) became PCR positive in the control group and about one-tenth that number in the vaccine group became PCR positive. The Moderna trial had 30,000 participants. There were 95 “cases” in the 15,000 control participants (about 0.6 percent) and 5 “cases” in the 15,000 vaccine participants (about one-twentieth of 0.6 percent). The “efficacy” figures quoted in these announcements are odds ratios. 

Of course, Pfizer couldn't be overtaken by Moderna, as shortly thereafter, Pfizer released updated results, analyzed by Peter Doshi, showing Pfizer's "efficacy" now matched Moderna's [*16]: "Pfizer says it recorded 170 covid-19 cases (in 44,000 volunteers), with a remarkable split: 162 in the placebo group versus 8 in the vaccine group." What do you know? Both are equally effective at about 95%.

The difference between 162 in the placebo control group and eight in the real vaccinated group in the Pfizer trials equates to 95% effectiveness (8/162 - 1).The difference between 95 in placebos versus five vaccinated in Moderna also equals 95% effectiveness (5/95 - 1). This is what we call relative risk.

How accurate is this relative risk? Recall, symptomatic subjects in the vaccinated group could be excused from PCR testing based on a doctor's assumption that flu-like symptoms were an adverse vaccine reaction instead of a possible case of COVID. Thus, these instances were excluded from the COVID tally, but all came from vaccine group instead of the control group. Hence, there may have been more COVID cases in the vaccine group than actually reported, which would dramatically lower the relative effectiveness.

A more-useful measure would be against absolute risk, meaning the odds the general public will get COVID, vaccinated versus not, or the number of vaccines needed to prevent a single case. If it takes 167 vaccinations to prevent a single "case" of COVID, one person is saved from the symptoms of the disease while 166 are subject to the risks of the vaccine. Even if the risk of harm from COVID itself is greater than the risk of harm from the vaccine (as we assume), the damage from the vaccine to the general population can still be far greater, when we account for a 167:1 ratio.

Dr. Berdine explains [*17]:

There is no evidence, yet, that the vaccine prevented any hospitalizations or any deaths. The Moderna announcement claimed that eleven cases in the control group were “severe” disease, but “severe” was not defined. If there were any hospitalizations or deaths in either group, the public has not been told. When the risks of an event are small, odds ratios can be misleading about absolute risk. A more meaningful measure of efficacy would be the number to vaccinate to prevent one hospitalization or one death. Those numbers are not available. An estimate of the number to treat from the Moderna trial to prevent a single “case” would be fifteen thousand vaccinations to prevent ninety “cases” or 167 vaccinations per “case” prevented which does not sound nearly as good as 94.5 percent effective. The publicists working for pharmaceutical companies are very smart people. If there were a reduction in mortality from these vaccines, that information would be in the first paragraph of the announcement [emphasis added].

When measuring the odds of getting COVID, whether after being vaccinated or not, it's important to also consider the risk of death from COVID [*18].



Using the "current best estimate" scenario (as of June 8, 2021), even the CDC concedes the 18-49 age demographic has a 99.95% survival rate, while the 50-64 demographic is at 99.4% and the 65+ demographic has a 90% survival rate. This is, of course, if one contracts COVID. And, as discussed prior, since post-vaccination human-to-human transmission wasn't tested and we know vaccinated people are testing positive for COVID anyway, you are taking the vaccine not to protect others but to reduce your personal risk.

But what about your risk of suffering symptoms from COVID? If the vaccines don't prevent transmission, let alone one contracting the virus, it should at least give recipients a milder case of COVID. Right?

The most common rebuttal you'll get from an educated person who gets a COVID vaccine and recognizes that the product is not designed to prevent his asymptomatic transmission of the virus to others: The virus poses a risk of a severe case to me, involving hospitalization or death, and, because I want to mitigate the risk, I'm taking the vaccine to see that, if I get COVID, I'm more likely to get a more-mild case.

Let's assume that and the "asymptomatic carrier" supposition are true. If someone has more-severe symptoms, they are less likely to be "asymptomatic," meaning they are less likely to leave home and spread the virus to others. With less symptoms, a person might not know he has the virus and be unknowingly spreading the virus to others. Thus, he decreases his probability of hospitalization or death to him while increasing that probability to others.

To make matters worse, many businesses are claiming people that are vaccinated don't have to wear face masks. Assuming face masks reduce alleged "asymptomatic transmission," (which I don't believe is true [*19]), the risk to the unvaccinated population is compounded. Thus, to be effective as a tool of public policy, the whole population of a jurisdiction or the global population would need to be vaccinated. Otherwise, a benefit is conferred to the vaccinated at direct cost to the unvaccinated that face an increased risk of injury or death.

But do the COVID vaccines actually reduce severe symptoms? How could the vaccine trials tell us?

Peter Doshi at the BMJ, [*20], highlights a dilemma:

Severe illness requiring hospital admission, which happens in only a small fraction of symptomatic covid-19 cases, would be unlikely to occur in significant numbers in trials. Data published by the US Centers for Disease Control and Prevention in late April reported a symptomatic case hospitalisation ratio of 3.4% overall, varying from 1.7% in 0-49 year olds and 4.5% in 50-64 year olds to 7.4% in those 65 and over.13 Because most people with symptomatic covid-19 experience only mild symptoms,14 even trials involving 30 000 or more patients would turn up relatively few cases of severe disease.

So, when we only have a few hundred cases of COVID in the trials to measure against hospitalizations occurring in around 4% of cases, we'd probably only see eight hospitalizations. To find out how many we'd be keeping out of the hospital, via measuring the vaccinated versus a placebo control group, we'd only have a handful of possible hospitalizations to analyze. How did the trials handle this dilemma?

Doshi elaborates [*21]:

In the trials, final efficacy analyses are planned after just 150 to 160 “events,”—that is, a positive indication of symptomatic covid-19, regardless of severity of the illness. ...

In all the ongoing phase III trials for which details have been released, laboratory confirmed infections even with only mild symptoms qualify as meeting the primary endpoint definition. 9 10 11 12 In Pfizer and Moderna’s trials, for example, people with only a cough and positive laboratory test would bring those trials one event closer to their completion. (If AstraZeneca’s ongoing UK trial is designed similarly to its “paused” US trial for which the company has released details, a cough and fever with positive PCR test would suffice.)

Doshi quotes Moderna's chief medical officer, Tal Zaks [*22], who excuses this practice: 

The trial is precluded from judging [hospital admissions], based on what is a reasonable size and duration to serve the public good here. ... Would I like to know that this prevents mortality? Sure, because I believe it does. I just don’t think it’s feasible within the timeframe [of the trial]—too many would die waiting for the results before we ever knew that.

Doshi concludes: 

Hospital admissions and deaths from covid-19 are simply too uncommon in the population being studied for an effective vaccine to demonstrate statistically significant differences in a trial of 30 000 people. The same is true of its ability to save lives or prevent transmission: the trials are not designed to find out.

Continued in Part 3, Adverse Effects: the Seen and the Unseen. 

All prior and subsequent parts, as they are published, can be read here.

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FOOTNOTES
[*1] https://www.bmj.com/content/371/bmj.m4037
[*2] https://www.fda.gov/news-events/press-announcements/fda-takes-key-action-fight-against-covid-19-issuing-emergency-use-authorization-first-covid-19
[*3] https://www.nature.com/articles/d41586-020-03219-y
[*4] https://www.amazon.com/Truth-About-COVID-19-Lockdowns-Passports-ebook/dp/B08WRDXLVY Mercola, Joseph; Cummins, Ronnie. The Truth About COVID-19 (pp. 78-79). Chelsea Green Publishing. Kindle Edition.
[*5] https://link.springer.com/article/10.1007/s10096-020-03913-9
[*6] https://www.amazon.com/Truth-About-COVID-19-Lockdowns-Passports-ebook/dp/B08WRDXLVY Mercola, Joseph; Cummins, Ronnie. The Truth About COVID-19 (p. 77). Chelsea Green Publishing. Kindle Edition.
[*7] https://stratagemsoftheright.blogspot.com/2021/01/the-broken-thumb-heuristics-in-fall-of.html
[*8] https://www.fox35orlando.com/news/fox-35-investigates-florida-department-of-health-says-some-labs-have-not-reported-negative-covid-19-results
[*9] https://www.msn.com/en-us/health/medical/half-of-cdc-coronavirus-test-kits-are-inaccurate-study-finds/ar-BB16S6M6
[*10] https://www.who.int/news/item/20-01-2021-who-information-notice-for-ivd-users-2020-05
[*11] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219423/
[*12] https://blogs.bmj.com/bmj/2020/11/26/peter-doshi-pfizer-and-modernas-95-effective-vaccines-lets-be-cautious-and-first-see-the-full-data/
[*13] https://www.modernatx.com/sites/default/files/mRNA-1273-P301-Protocol.pdf
[*14] https://mises.org/wire/what-covid-vaccine-hype-fails-mention
[*15] Id.

[*16] https://blogs.bmj.com/bmj/2020/11/26/peter-doshi-pfizer-and-modernas-95-effective-vaccines-lets-be-cautious-and-first-see-the-full-data/

[*17] https://mises.org/wire/what-covid-vaccine-hype-fails-mention
[*18] https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
[*19] https://stratagemsoftheright.blogspot.com/2021/04/the-politics-of-masks-virtuous.html
[*20] https://www.bmj.com/content/371/bmj.m4037
[*21] Id.
[*22] Id.