Ethical Challenges Arising from the Grand Illusion

From the outset, governments were advised that this was a once in a hundred years pandemic, and that the only solution to reduce or prevent mass mortality would be the development of a vaccine. No other solutions were considered, only delaying tactics.

The preliminary results of the mRNA vaccine randomized controlled trials (RCTs) by Pfizer and Moderna were celebrated as spectacularly successful and so governments and the media assumed that the solution had been found. A procession of leaders assured the public that the vaccines were so effective that once injected, you would not become infected or pass infection on to others.

Governments and organizations proceeded to promote universal vaccination and mandate whole classes of workers to be vaccinated on pain of losing their jobs, even though COVID-19 (Covid) overwhelmingly harms the post-working age population.

The ethical justification was not always clearly stated but came down to the argument that universal vaccination was necessary for the public good. The strongest argument was that everyone had an ethical obligation not to harm others by passing the infection on underpinned by the conviction that vaccination would ‘stop the spread,’ both by impeding transmission and by stopping people from getting infected in the first place so they would not have any infection to pass on.

Governments simplify and dumb down their messages to get them across to the public, so the campaign message was the vaccines were both ‘safe and effective,’ period.

But all the underpinning assumptions look increasingly questionable in light of the developing evidence.

Vaccination did not stop the spread in either of the ways mentioned above. Protection against infection and transmission was temporary. The RCTs and subsequent studies base their results on effectiveness during limited windows of time, following up participants for a few months only in many cases, and usually no more than 24 weeks. Extrapolating out from these sample populations and time periods turned out not to be valid. Individuals still succumbed in the intervals between the windows. 

Studies based on longer-term follow-up usually reveal waning of effectiveness over time. This is not reflected in the single point estimates of efficacy such as the 95 percent emanating from the RCTs. Studies or figures based on short follow-up periods have similar validity to opinion polls held twelve months out from an election. You need to know how the story ends. 

Over the two years since the introduction of the vaccines, everyone got infected anyway, multiple times in many cases. According to the recent preprint by experts from Harvard, Yale and Stanford, 94 percent of the US population had been infected by November 2022. 

The logically indisputable conclusion from this is that neither lockdowns nor vaccines nor border controls were able to ‘stop the spread.’ It didn’t work.

So, the argument that everyone should get vaccinated to protect others does not hold. Even the vaccines’ ability to protect the individual is starting to look shaky, especially in the light of the Cleveland Clinic study which showed a dose-response correlation, in which the risk of infection (symptomatic or asymptomatic) progressively increases with the number of doses. It has been clear for some time that diminishing returns set in, and several other sources have indicated that this gives way to negative returns over time; see for example Figure 2 in Tseng et al. These results are consistent with the higher rates of infection for vaccinated people in the tables of Public Health England’s vaccine surveillance reports, tables which were discontinued after it was rebranded as the Health Security Agency.

Immunity builds for some weeks, then declines, eventually below the starting point. Action is followed by reaction. If the measurement period is short enough, you only measure the action phase and miss the reaction.

This may be the first time in history that governments promoted a vaccine for over two years that increased your risk of becoming infected.        

The next line of defense is that vaccination gives longer protection against hospitalization and death. But Figure 1 in Xu et al. shows a steady decline after five weeks until negative effectiveness sets in after about a year.

Further, the ever acute ‘el gato malo’ has been able to download age-stratified data from the Office of National Statistics in the UK of deaths up to May 2022, differentiated between ‘ever vaccinated,’ and unvaccinated. He then calculated the relative risk of death with reference to population data. The results are disturbing, showing 60-70 percent more risk of death for the vaccinated group, and a rising trend. Sure, this is by an anonymous author and not published in a peer-reviewed journal, but the journals are derelict in their duty and are not publishing independent analyses of this data. El gato malo has thrown down the gauntlet – who will refute this bad cat (without statistical trickery)? 

The third line of defense is that protection against hospitalization and death can be restored through boosters. But is this anything more than kicking the can further down the road? Will the effect of boosters also start to wear off after five weeks and turn negative after a year? The short-term results are not translating into medium-term gains generally – why should boosters change that? 

This evidence raises doubts about ‘effective,’ and also encroaches on ‘safe.’ The direct evidence of post-market adverse effects also continues to mount. The most serious of these that agencies should be worrying about and investigating is the possibility that the vaccines could cause a significant number of deaths. 

There is definitive evidence that it is possible for vaccines to lead to deaths in the form of pathology reports, such as this one by Gill et al. and this one by Schwab et al. So, it then becomes a matter of the utmost public importance for governments and their agencies to find out how often this occurs. What is the incidence of death resulting from vaccine injury? 

This information is hard to come by. One line of attack is to calculate the incidence of death from all causes from the point of vaccination to a given end point. This information is also hard to come by, as most studies calculate the incidence of hospitalization and/or death from (or with) Covid, often excluding the first 14 days. 

This is on the grounds that the immune response does not kick in during the first two weeks. But researchers should be giving us information about any adverse outcomes of vaccination from the moment the vaccine enters the body, as this is what the public needs to know in order to make a decision. If the decision is taken out of their hands and is made by governments or employers (which should not happen) then the governments and employers need to know.

Some data can be extracted from a recent study coming out of Qatar, which has been a useful source of information during the pandemic as a microcosm with apparently reliable national records. Butt et al. tell us that 6,928,359 doses were administered in Qatar from 1 January 2021 to 30 June 2022, and 4,413 deaths occurred at any time during the period. There were 138 deaths within 30 days of vaccination, and these are broken down by the probability of association with vaccination: Not Related; Low Probability; Intermediate; Probability; and High.

Before getting on to the methodology, we can observe that the crude rate of mortality 30 days after vaccination overall would be 19.9181 per million doses. Further:

Crude death rates in Qatar for the years 2019, 2020 and 2021 were 6.60, 7.94, and 8.74 per 100,000 population. Death rate among the vaccinated persons with high probability of relationship to SARS-CoV-2 vaccination was 0.34 per 100,000 vaccine recipients, while death rate among the vaccinated persons with either high or intermediate probability of relationship to SARS-CoV-2 vaccination was 0.98 per 100,000 (8 deaths classified as high probability and 15 deaths as intermediate probability among 2,347,635 unique persons who received at least one dose of a vaccine).

The authors argue that the deaths among vaccinated persons are much lower than the crude rates of mortality for 2019, 2020 and 2021. How could Covid vaccination cut your overall risk of dying from all causes by six times or more? This is not plausible.

But the actual number of deaths of vaccinated persons quoted in the paragraph above are the deaths at the 30-day mark, whereas the crude death rates are annual rates (listed in Supplementary Table 2). So, the death rates for vaccinated persons should be multiplied by 12. 

Also, the authors have calculated rates of mortality likely to be related to vaccination using the time-honored method of excluding most of the relevant deaths:

Presence of one or more severe underlying conditions associated with high risk of mortality (e.g., chronic advanced heart failure, pre-existing atherosclerotic heart disease with prior major adverse cardiovascular events) and physician documentation in medical records of those contributing directly to death were used to assign low probability.

So, the very categories of individuals most likely to be pushed over the edge by vaccination were all excluded. By contrast, all these individuals are characteristically included when calculating the numbers of people who die from Covid. In other words, there is a double standard. All the parameters have been selected to justify the desired conclusion.

In a preprint, Day et al. compared Covid reporting rates to the US Vaccine Adverse Events Reporting system (VAERS) with the data on background rates of mortality concluding: ‘For death events within seven days and 42 days of vaccination, respectively, observed reporting rates were lower than the expected all-cause death rates.’

But background death rates are based on the total 100 percent of recorded deaths from all causes, whereas VAERS is based on a subset of deaths and is known to suffer from underreporting. For example, Rosenthal et al. found that the vaccine surveillance reporting rate for deaths following pertussis vaccination was comparable to reporting rates for the disease generally – around 33 percent.

In the case of Covid vaccines, healthcare workers are required by law to report ‘Serious Adverse Events regardless of whether the reporter thinks the vaccine caused the Adverse Event.’ However, as the purpose of VAERS is understood to be to monitor signals of adverse events caused by vaccination, healthcare workers make judgments and report events only when they think it is possible they might be caused by vaccination. Singleton et al. express the normal reality: ‘All persons suspecting a causal relation between the administration of a vaccine and a subsequent adverse event are encouraged to send in a report, including patients or their parents and not just health professionals (as of 1999, <5% of VAERS reports came from parents).’

They would not be reporting all deaths from any cause. It is unlikely that they would report deaths from other infectious diseases such as influenza-like illnesses, for example. They would not report deaths from automobile accidents to a vaccine surveillance system. While the current climate of extraordinary focus on Covid might stimulate more reporting of possible adverse events for these vaccines, on the other hand heavy peer pressure not to encourage ‘vaccine hesitancy’ may discourage healthcare workers from making reports even of relevant adverse events. 

Commentators often attempt to discredit VAERS reports because they can be made by anyone, but 67 percent of the reports to VAERS are submitted by the medical and nursing teams who have direct experience of treating the patient. This testimony should not be lightly dismissed by remote experts working only from the records.

You don’t need a PhD in biostatistics to pick up the logical flaws arising from inappropriate comparisons. Researchers and agencies should be comparing apples with apples instead of with oranges. 

The unadjusted rates from Qatar (which are based on general mortality recording, not vaccine surveillance) are strikingly reminiscent of the rates of mortality reported to VAERS. As I pointed out in this and this previous contribution, the CDC has calculated rates of reported mortality following Covid vaccination over time rising through 21 to 26 per million doses. This is at least 21 times greater than rates of mortality recorded in the literature for previous vaccines in previous years. The CDC has not explained this, and does not discuss it in the published analysis of the first six months of VAERS data, the only such broad review they have published. The analysis does not address the issue of proportionality compared to previous vaccines at all.

Information about proportionality (or rather disproportionality!) has been released as a result of a Freedom of Information Act request, and has been analyzed by Josh Guetkow of the Hebrew University in Jerusalem, who summarizes the results beginning like this:

• CDC’s VAERS safety signal analysis based on reports from Dec. 14, 2020 – July 29, 2022 for mRNA COVID-19 vaccines shows clear safety signals for death and a range of highly concerning thrombo-embolic, cardiac, neurological, hemorrhagic, hematological, immune-system and menstrual adverse events (AEs) among U.S. adults.
• There were 770 different types of adverse events that showed safety signals in ages 18+, of which over 500 (or 2/3) had a larger safety signal than myocarditis/pericarditis.

The release has also been analyzed by Norman Fenton and Martin Neill who comment:

• Another incredibly important statistic is the proportion of deaths (which is only given for the 18+ age group) which is 14% in the covid vaccines (10,169 out of 73,178) compared to just 4.7% (618 out of 13,278) in non-covid vaccines. If the CDC wish to claim that the probability a covid vaccine adverse event results in death is not significantly higher than that of other vaccines the onus is on them to come up with some other causal explanation for this difference.  

In another post, they also found that there was ‘a statistically significant linear relationship between countries that are highly vaccinated and excess deaths.’

The benchmark of 20 or more deaths per million doses is starting to firm up as a baseline, derived from the two diverse sources (VAERS and the Qatar study). By 31 December 2022, 665 million doses had been administered in the US. If we invented criteria to exclude two-thirds of these following the usual approach, that would still leave over 13,000 possible deaths to be accounted for, and the responsible healthcare workers in at least 60 percent of these deaths suspected that they were associated with vaccination. To make all these deaths go away completely would require unprecedented statistical sleight of hand. No doubt it can be done, with sufficient ingenuity.

But why is this risk of thousands of deaths not front-page news?

The answer is that it seems to pale into insignificance besides the claim that vaccination has saved millions of lives, as calculated in this paper published in the Lancet. But this claim is illusory and built on sand. It derives from modeling and the modeling makes assumptions about, for example, the lower death rates following vaccination derived from the short-term studies we have been examining. 

Plug these lower death rates into your model, calculate them out over a population profile, and you find that – vaccination has reduced the number of deaths! But the procedure is entirely circular, and rests on extrapolating out from limited samples as discussed. The underlying systematic review of vaccine efficacy they relied on explicitly states: ‘we do not consider duration of protection in this analysis.’ And again: ‘The duration of follow-up in clinical trials and effectiveness studies is not yet sufficient to robustly estimate the duration of vaccine-induced immunity.’  

The actual medium-term outcomes (over at least 12 months) in the population-level data such as the Health Security Agency and ONS statistics are dramatically different from the modeling based on sampling. Actuals must be preferred over hypotheticals. The ability of mass Covid vaccination to materially change overall population outcomes has not been demonstrated.

The ethical justification for the mass vaccination campaigns rests either implicitly or explicitly on utilitarian ethics, which seeks ‘the greatest good of the greatest number.’ Utilitarian ethicists would argue that it would be justifiable to cause thousands of deaths to save millions. I have discussed these principles in greater detail in The COVID-19 Pandemic: Ethical Challenges and Considerations. 

The first problem with this argument is that such a trade-off has never been accepted before. Pharmaceutical products that cause 50 deaths are taken off the market. Second, agencies have not come clean and disclosed to the public that this trade-off is being made. Third, the terms of the trade-off are not valid – the claim that millions of lives have been saved cannot be validated.

Multiple mass vaccination for Covid has thrown up far too many red flags. It is not ethical for governments and employers to drive past them and continue to promote and mandate vaccination without conducting a proper, impartial, and open investigation of serious adverse events. They are in a state of denial, which must end.

Switching to personal experience, I recently ran into an individual in the extended family about my age (mid-60s). We were shocked to see her on the footpath clutching a walking frame, looking very pale and weak, like an aged care resident. She told us that she had contracted severe Covid, spending weeks in the ICU and nearly dying – after ‘four shots!’ Safe and effective? This is the only individual in my entire circle of acquaintance who has had severe Covid to my knowledge.

Kelly et al. calculate an overall rate of around 9 severe Covid events per 10,000 people over a 24-week period, claiming that this is ‘low.’ But the rates for higher risk groups are twice that, there is no control group to compare with and they adopt a narrow definition of Covid similar to that used by China (Covid pneumonia). They are entirely right that this more conservative definition avoids the inflation of hospitalization numbers by incidental infection, but it also makes it more difficult to benchmark. Individuals are seeking multiple vaccinations because they want to feel safe, against the background where inflated numbers have been used to create their fear. Implicitly comparing deflated numbers with inflated ones is another example of using statistics to mislead. We are led into a maze of mirrors.

In industrial relations in my country, we have a key concept known by the acronym BOOT, which stands for ‘better-off-overall-test.’ Workers can reach agreement with their employers on a deal in which they trade conditions off against wages so long as they are better off overall compared with the legislated minima. Vaccination strategies also involve trade-offs, and we are in desperate need of a rigorous BOOT for them, broken down by risk-group and extending over a reasonable time period.

There is a strong quantitative and qualitative case for agencies to answer that the adverse effects of Covid vaccination exceed the benefits in the medium term. We are still waiting for a fair rebuttal based on broad real-world outcomes over the medium term. If agencies and the research studies they rely on cannot do this – give them the BOOT!