"Pandemic of the Unvaccinated"?

Never Forget: Millions Were Stigmatized, Abused, Scapegoated, Robbed of Their Livelihoods, and Imprisoned in Their Own Country

Remember when all our world “leaders” were in lock-step with the slogan “Pandemic of the Unvaccinated”?

Well, the data (manipulated though it was), is now being analysed. Guess what? Stigmatizing and abusing your neighbours for their personal medical choices did not help matters at all! But, you knew that already.

There’s a new paper analysing data from Ontario, Canada, which shows the many faulty assumptions, methodologies, study designs, and analyses that were used to try and justify mandating COVID 19 vaccinations (and the stigmatization and abuse of those choosing not to take the vaccines):

The main conclusions of the paper:

Health officials claim that the unvaccinated are driving infection rates and that mandates are necessary to keep from overwhelming hospital capacity. However, a careful inspection of the data indicates that not only were policies based on low quality and seriously biased evidence, but closer examination of the actual data demonstrates the opposite of what the government claimed. Although there was a slight increase in cases among the unvaccinated from August through to mid-October 2021, reflective of increased testing rates, by early December 2021, the majority of cases were among the vaccinated. Moreover, this surge in cases resulted in a higher proportion of vaccinated compared to unvaccinated individuals in the hospital, or admitted to ICU, despite vaccine mandates and high vaccine uptake. Finally, the policy did not adequately weigh the hoped for benefits of vaccination against known risks of injuries and deaths which, in the case of COVID-19 gene therapies far outweigh (adverse events, ARI of 17.9% and 0.5%) any of the claimed benefits from the vaccines (symptomatic cases, ARR of 3.7% and 0.1%) —see Thomas and Moreira et al.(2021). Optimal care should be characterized by scientific discourse and inquiry, reliance on the highest levels of evidence, individualized care, and informed consent (Chow& Gallo et al., 2018; Madden & Bhandari, 2020; Shah & Turrin et al., 2021). Because COVID-19 is no longer a risk to the population at large, due to the arrival of less virulent strains like Omicron, or the latest B.2 variant, and due to widespread population-based immunity, it is time to abandon such outdated vaccine mandates and return to solid medical practices and established standards of care.

Some highlights of the paper:

Because assertions about the COVID-19 unvaccinated have far-reaching consequences for both medical treatments as well as civil rights, there is need for critical evaluation of such claims, particularly given the stigmatization by both the public and government against those who chose not to undergo such vaccination. The current analysis of the Public Health Ontario and Government of Ontario, Ministry of Health COVID-19 databases focuses on four areas: the questionable basis for mass vaccination, the methodology of the studies used to support current vaccination policies, the lack of effectiveness of those same policies, and how all of these have failed to consider vaccine safety.

On invalid assumptions driving failed vaccine policies:

Antibody Efficacy - Most tests of antibody efficacy are based on assessing the extent to which vaccine derived antibodies interfere with the binding of the SARS-CoV-2 spike protein to the host cell ACE2 receptor. In the rapidly emerging COVID-19 variants, mutations within the receptor binding domain may enhance the binding to ACE2, but likely reduce recognition of variants by the specific antibodies that target this region. However, immune responses are directed against many different regions of the spike protein, and binding of antibodies to these regions may also confer significant immune protection. These responses may include facilitation for recognition of the spike protein and the virus by immune cells that feature Fc receptors for the constant region of immunoglobulins as well as recognition by the complement system. In any event, it is misleading to insist, as the media and the OST often do, that mass vaccination with COVID-19 vaccines will be effective over the long term or that they will be a means of achieving “herd immunity” —defined as the scenario when a high percentage of the population has either contracted and recovered from some particular disease (thus gaining natural immunity to it), or has gained sterilizing immunity through specific vaccinations. Assertions placing responsibility for disease transmission or failure to achieve “herd immunity” become even more problematic if failure of the vaccines to block transmission is incorrectly blamed on the unvaccinated.

Mass Vaccination - The practice of mass vaccination is based on the notion of “herd immunity”. If it is achieved, modeling data indicates that the disease cannot spread further and should not be transmitted even to those who have neither sterilizing immunity nor natural immunity after surviving an infection (e.g., unexposed or immunocompromised persons; Dejnirattisai et al., 2022). The thresholds for attainment of herd immunity vary depending on many factors including characteristics of the virus, population, and vaccines. Early modelling of COVID-19 vaccine impacts showed that herd immunity should be achievable with55% of the population vaccinated given a vaccine that is at least 90% effective at blocking transmission under reasonably favorable conditions (Hogan & Winskil et al., 2020; Phillips, 2021). Formerly, herd immunity included both natural and vaccine-derived immunity: Herd immunity occurs when a large portion of a community (the herd) becomes immune to a disease, making the spread of disease from person to person unlikely. As a result, the whole community becomes protected —not just those who are immune. (Mayo Clinic, 1998-2022) However, at the outset of the COVID-19 pandemic the World Health Organization changed the definition of herd immunity to something that is achieved exclusively through vaccination (WHO, 2020). It is also worth noting that historically, vaccination alone has not been routinely successful in providing long lasting herd immunity for some diseases as attributed to secondary vaccine failure (Mathias & Meekison et al., 1989; Edmonson & Addiss et al., 1990; Zhang & Chen et al., 2018;Connell & Connell et al., 2020). The latter may occur because immune responses in terms of antibody levels, T cells, and even memory cells decline over time (Heffernan & Keeling, 2008; Duintjer Tebbens & Pallansch et al., 2013; Vygen& Fischer et al., 2013; Koopman & Henry et al., 2017; Burdin & Handy et al. 2017; Lewnard & Grad, 2018). In addition, the infectious pathogen may undergo extensive mutation that may compromise immune recognition. The long-lasting success of any vaccination campaign in halting disease is almost exclusively dependent on the use of vaccines that confer sterilizing immunity and do not undergo rapid secondary vaccine failure (Hellerstein, 2020; Pollard & Bijker, 2021; Yewdell, 2021; Focosi & Maggi et al., 2022). In contrast, the two doses of novel mRNA COVID-19 vaccines by Pfizer or Moderna or the viral vector vaccine of AstraZeneca, in contrast to those derived from conventional vaccine platforms, seem to exhibit a relatively rapid decline in efficacy, and as such can be characterized as “leaky”, with outbreaks occurring over short time periods even in those with boosted vaccination status (Scott & Richterman et al., 2021). In addition, it is increasingly clear that individuals receiving COVID-19 vaccines can become re-infected or experience infection breakthroughs, as well as transmit the virus to others (Read & Mackinnon, 2008; Keeling & Tildesley et al., 2013; Read & Baigent et al., 2015; Brown & Vostok et al., 2021). Moreover, any efficacy initially promoted for these vaccines was based on PCR-confirmed symptomatic infection against the original Wuhan strain, and we currently lack proper assessment of transmission-blocking efficacy of the vaccine against emerging COVID-19 variants (Collie & Champion et al., 2021; Dejnirattisai et al.; Keegan & Truelove et al., 2021; Lopez Bernal & Andrews et al., 2021; Reis & Barda et al., 2021; Singanayagam & Hakki et al. 2021; Thiruvengadam & Awasthi et al., 2021; Torgovnick, 2021).

On concerns regarding study methodology supporting evidence:

The strength of a medical recommendation should rest on the quality of evidence on which it is based (GRADE Working Group, 2004). Strong recommendations should be supported by the highest level of evidence; a double-blinded, randomized Phase III trial showing benefit against a standard of care in a specific population (Misra, 2012). In contrast, retrospective analyses are considered poor quality evidence as they cannot control for the influence of confounding variables and thus are more effectively used to generate hypotheses that can later be tested in prospective controlled trials. As such, retrospective studies do not provide sufficient evidence to support a strong recommendation for the use of any vaccine and cannot sustain mandatory recommendations (Talari & Goyal, 2020)

Data incompleteness: Population based COVID-19 registries are designed to collect data on COVID status, but not on hospitalization events (see Government of Ontario, 2022; also Government of Ontario, 2021). Presently we know that the Canadian COVID-19 database preferentially captures data on symptomatic individuals and inevitably under-represents those who refuse or are not required to undergo testing, as well as those who are asymptomatic (Public Health Ontario, 2022b). For this reason, any such results must be extrapolated with caution. Conducting an accurate analysis on COVID-19 hospitalization outcomes would require merging the COVID-19 database with one capturing hospital level events. Likewise, data would need to be interpreted in the light of differences in monitoring or sampling across unvaccinated/vaccinated groups. Comparing outcomes across vaccination groups alone, however, assumes they are homogeneous with unvaccinated groups, which is unlikely. For example, it would be expected for the groups to differ in terms of natural immunity status, virus exposure, and risk of severe disease. In Toronto, from January 1st to July 31st 2020, there were just under 1,000 hospitalized COVID-19 cases in the 70+ age group and just over 600 hospitalizations in the 50-69 age group (City of Toronto, 2022). Aside from age, underlying medical conditions such as “obesity, diabetes with complication and anxiety disorders were the strongest risk factors for severe COVID-19 illness” (Kompaniyets & Pennington et al., 2021). Since details about other potentially relevant variables were not provided by Public Health Ontario, these omissions reinforce the view that great caution should be used when ascribing differences in observed outcomes to vaccination status alone.

Terminology - Another factor to consider is the definitions used to describe vaccination status. Public Health Ontario defines vaccination status based on immunity achieved 14 days after any second dose (Ontario Ministry of Health, 2021), contradicting at least one company using an mRNA platform, Pfizer, that in the Phase III trial asserted that immunity can be established after 7 days (Thomas & Moreira et al., 2021). From such discrepancies, it seems likely that fully vaccinated individuals will be considered partially vaccinated up to 14 days after their vaccination, and individuals receiving their first dose will typically be categorized as unvaccinated up to 14 and even 21 days later. These overlapping definitions confound interpretation of outcomes as many vaccinated individuals will be classified as unvaccinated, thus inflating the numbers of the latter and making it difficult to distinguish differences in disease status and health outcomes (e.g., infection, hospitalization, deaths) among vaccinated and unvaccinated patients. It would have been morea ccurate if the unvaccinated category had excluded those who have been vaccinated to any level.

Monitoring Protocols - The main test used to date in Ontario and elsewhere in Canada is the PCR (Polymer Chain Reaction) assay, a test which supposedly detects genetic fragments of the SARS-CoV-2 virus in tested individuals. Of note, if the cycle threshold (Ct) —the number of times a sample of material from a swab taken from a given person is amplified—typically set at 35 or above in COVID-19 testing, the likelihood of false positivity is already too high and likely to create the false impression of an infection where none exists (Mina & Peto et al., 2021). This biasing factor has been acknowledged by the CDC (Centers for Disease Control and Prevention, 2021). A Ct cut-off for positivity at or above 35 cycles (Public Health Ontario officially uses a Ct of 35) (Public Health Ontario, 2021a) may result in up to an 85% false positive rate (Public Health Ontario, 2020). If these Ct levels are then used to describe “cases”, and asymptomatic low probability persons comprise most of tests conducted, the resulting data will inevitably generate inflated case numbers in that population. Moreover, someone with COVID-19 may only be infectious between 4 and 8 days after exposure to the virus, but may test “positive” for about 22 to3 3 days, introducing the serious possibility that a single infection may be recorded as more than one case (Mina & Peto et al., 2021). Given our current vaccine mandate environment, unvaccinated individuals often have to undergo systematic asymptomatic testing by their employers (CBC News, 2021; Government of Ontario Ministry of Health, 2021; Herhalt, 2021; Pringle, 2021). This increase in sampling is likely to result in higher case counts in this population where the standard PCR Ct levels being applied for COVID-19 testing are likely to generate many false positives. In addition, as noted above, poorly designed data collection systems may result in multiple counts of the same infection (Goodfield, 2021; Public Health Ontario, 2021b). Further, it remains unclear whether there are differences in testing requirements between hospitalized individuals based on vaccination status. Added to these constraints, it is well established that SARS CoV-2 is transmitted more readily in communal living environments such as hospitals (Morawska & Tang et al., 2020). For these reasons, when interpreting COVID-19 hospital data, it is essential to distinguish between individuals who were in the hospital for some non-COVID-19 related reason and then happened to test positive for SARS CoV-2 as compared persons who were hospitalized because of a COVID-19 infection.

On study design and analysis:

Retrospective reviews of the COVID-19 database are helpful for identifying potential associations between various events, but because they cannot control for baseline factors, monitoring, or exposure, they are unreliable (Mann, 2003; Ranstam, 2019). Most importantly, the power of retrospective analyses to infer causality is limited, and therefore should not be used to support major health policy decisions.

COVID-19 Risk Factors - There are some well-established factors that increase the risk of severe COVID-19 outcomes. These include older age, obesity, and diabetes co-morbidities (Kompaniyets & Pennington et al., 2021; Kompaniyets & Goodman et al., 2021). Factors that lower the risk of severe disease include naturally acquired prior immunity (Cohen & Linderman et al., 2021) and younger age (Woolf & Champman et al., 2021), as well as access to pharmacological interventions. Because analyses are not always randomized and do not control for baseline factors, differences in such factors in vaccinated and unvaccinated populations could be partially, or even fully, driving the claimed effects of higher unvaccinated cases being causally related to hospitalizations and deaths. For instance, frail elderly individuals are often not vaccinated due to a risk of vaccine injury. For this reason alone, it would not be unreasonable, given the higher levels of co-morbidities associated with this group, to see a greater proportion of these individual in hospitals or ICU units. Such outcomes may be misinterpreted as the unvaccinated being at greater risk of severe COVID-19 outcomes. There has been disagreement about how many COVID-19 “waves” have occurred since the onset of the pandemic in early 2020. For the purposes of this article, although five distinct waves starting with the initial wave in the spring of 2020 have been posited, we focus on the initial four waves. The second wave occurred from Nov 1, 2020 to January 4, 2022 (Figure 1). The second and third peaked above 4,000 cases per day in Ontario and occurred over the colder months beginning in November 2020, declining with warmer summer weather.

Unlike the second and third waves, the fourth began at the height of the warmer summer weather in August, with an initial rise up to 1,000 cases per day in early September 2021, before dramatically shooting up into a fifth wave with an unprecedented 18,000 cases per day by early January 2022, the latter with a more than 4-fold increase over prior peaks. Whether the Omicron B.2 variant is accounting for an emerging sixth wave is presently unclear and not essential to the analysis that follows.

As noted above, herd immunity should be achieved with a highly effective vaccine as soon as about 50% of the population is immune from prior infection or vaccination. In this regard, it should be noted that the third wave was clearly receding before the 50% vaccination threshold had been reached for the first vaccines in early May 2021. Further, the shape of the decline of the curve did not change after this threshold was achieved. Such data indicate that natural immunity rather than vaccine-induced immunity was likely the main driving factor in the decline of the third wave. By early August 2021 at least 65% of the population had been fully vaccinated, according to the definition then in effect, and by mid-November 2021 the number of vaccinated people reached 75% and slowly increased to 78% by early January 2022. Administration of booster (third) doses started in September 2021, with an uptake of about 28% by January 4, 2022. While such high rates of “full vaccination” might explain the minimal rise in daily cases seen from August to September, the rise in cases could more likely be explained by a shift in monitoring and sampling resulting from back-to-work and school policies requiring increased systematic testing of asymptomatic unvaccinated individuals (Government of Ontario Ministry of Health, 2021). It is notable that analyses justifying vaccine mandates reported in the Ontario Science Table were conducted within this time window and were likely heavily influenced by the differential testing policies. It should also be noted that daily case counts began to sharply increase with the drop in temperature in November and December 2021, similar to the seasonal shifts in respiratory disease observed in the previous year and seemingly at variance with increasing levels of vaccination for the overall population. It is precisely these sorts of outcomes that led to claims, in late August and early September, that the unvaccinated were driving the bulk of new infections and threatening ICU capacities and that vaccine passports were required to limit the unvaccinated individuals from entering public spaces to halt further disease spread. These data also led to reports, as cited above, that this was a pandemic of the unvaccinated. However, when cases in the unvaccinated were compared to those who had received the vaccines, the differences in case counts and rates were low and steady leading up to early October 2021, at which point case counts in the two groups began to converge and by early December 2021, a surge in daily cases in the fully vaccinated typical of the infectious peaks of earlier waves was clearly apparent. By early January 2022, at the peak of the time period studied, the number of cases in the vaccinated were 6-fold higher than in the unvaccinated. It is important to note that unvaccinated individuals represent 20% of the population and that children 11 years or younger, representing 15% of the population, are the largest demographic in the unvaccinated group. As COVID-19 vaccines were approved in Canada on November 19, 2021 for children 5 –11 years old for most of the time period studied, this group was then ineligible for vaccination (Government of Canada, 2021b). As case counts were 6-fold higher in the fully vaccinated group compared to the unvaccinated group by mid to late December 2021, the Ontario data does not provide compelling support for policy decisions recommending the vaccination of children younger than 11 years of age. At that time, the proportion of cases in the fully vaccinated had surpassed those of the unvaccinated (7-day average: 87.89 per 100,000 vs 66.15 per 100,000, respectively), reflecting a higher degree of infectivity (Figure 2B). Currently, the vast majority of cases are estimated to be caused by the new Omicron variant: for the week of December 12th-18th 2021, Omicron was estimated to make up 66.1% of Ontario cases, rising to an estimate of 97.5% of positive Ontario cases the week of December 26th 2021–January 1, 2022 (Public Health Ontario, 2022a). It thus seemed likely that the vaccines designed to combat the wild-type strain had become largely obsolete. However, a third dose (the first “booster”) of these vaccines may have provided some initial protection in the face of secondary vaccine failure from the initial two-vaccination schedule. Although increased infection rates in the vaccinated group could be due to them engaging in higher risk activities than the unvaccinated, it is much more likely that these data show vaccine-enhanced disease, a concern raised in the Pfizer Safety Report (Nevada Division of Public and Behavioral Health, 2021)

On hospitalization admissions and death:

Hospitalization admissions peaked at 200 admissions during the second wave, between 300 and 400 admissions for the third, and were lowest for the fourth, and the fifth wave peaked at 148 cases on Jan 4, 2022. Likewise, COVID-19 deaths reaching100 deaths per day during the second wave, were as low as 50 deaths per day in the third wave, rarely exceeded 8 deaths per day in fourth wave, and as of January 4, 2022, deaths for the potential fifth wave have not reached more than 13 deaths per day despite the dramatic surge in cases in December 2021 that are linked to the Omicron variant. As noted above, it is possible that the lower death rates could be attributed to increasing rates of partial vaccination of high-risk populations from March to July 2021, serving (according to traditional medicine) to prevent more severe COVID-19 outcomes, including deaths. We find this interpretation quite unlikely, as only 35% of the population were vaccinated at that point and many were the elderly who are known to be immunosenescent, that is that they have a dysregulated immune system that makes it more difficult for them to develop sterilizing immunity, leaving them more susceptible to infection (Crooke & Ovsyannikova et al., 2019). However, such an uncoupling could also be attributed to a lower net number of individuals who might have been at risk of death from the disease: During the first and second waves as the majority of deaths due to COVID-19 were in long-term care facilities (Marrocco & Coke, 2021; Canadian Institute for Health Information, 2021). Death rates were high in these facilities largely because many facilities were under resourced and ill-equipped to stop aerosol transmission of SARS-CoV-2 among residents (Tang & Mao et al., 2020; Thompson & Barbu et al., 2020; World Health Organization, 2021; Dykgraaf & Matenge et al., 2021; Brown & Jones et al., 2021). These aspects, coupled with the fact that many individuals within these facilities were already at risk of severe disease (Thompson & Barbu et al., 2020; World Health Organization, 2021; Dykraaf & Matenge et al., 2021; Akhtar-Danesh & Baumann et al., 2022), that early treatment was not routinely provided (Sundraram & Nasreen et al., 2021), and that “35% of residents had no hospitalization orders and 80% had do not resuscitate” orders in place, made outbreaks in these facilities more likely to be fatal (Canadian Institute for Health Information, 2021; Morrocco & Coke, 2021). By the beginning of the third wave many of those who were at risk from COVID-19 had either developed immunity or had died, limiting the number of those vulnerable to infection. While many vulnerable residents likely died due COVID-19 infections, others died due to failure to thrive from being isolated for a full year. Another contingent may have died due to vaccine-related injury (Wyller & Kittang et al., 2021). In this regard, it is likely relevant that a CDC report using the VAERS system that was published in early 2021 reported that 69% of all reported possible vaccine deaths were in people living in long-term care facilities and occurred a median of 2 days after vaccination (Gee & Marquez et al., 2021)

On hospitalizations and ICU due to COVID-19 by vaccination status:

The number of patients in hospital peaked at 526 during the early part of the time period studied, but by January 4,2022 the number COVID-19 hospitalizations had reached up to 2,000. Although the numbers had increased by at least 3-fold, these remained considerably below the 21,000 total number of acute care beds available in Ontario (Ontario Hospital Association, 2021). The number of people in ICU remained low and did not surpass more than 200 beds during the time period studied and thus did not come close to approaching the ICU capacity for Ontario of 2,343 beds (Government of Ontario, 2021). Although there were more unvaccinated compared to vaccinated people in the ICU throughout the initial time period studied, the number of admissions in the vaccinated rose progressively, to the point that on January 4, 2022, the number of vaccinated in ICU was very similar to that of unvaccinated (N= 109 vs 100) with ICU admissions still on the rise. The application of vaccines, in the conventional view, may have had a role in conferring some protection from severe disease and death from August through to the end of October 2021. However, such an interpretation does not explain why counts among the vaccinated and unvaccinated were comparably low even with counts only increasing in early November. An alternative, and to us, a more likely explanation is that that differences seen in this time period are instead reflective of changes in vaccination policies requiring systematic testing of asymptomatic unvaccinated individuals. Such differences would be particularly apparent in settings such as hospitals where patients are closely monitored and may have heavily influenced the number of counts seen in this population. Additionally, as COVID-19 is transmitted in shared community living situations such as hospitals, events among the unvaccinated may be a by-product of hospital admission status. In fact, the ministry of health recently indicated that almost half of the COVID-19 hospitalizations were people who were admitted to the hospital with another condition and tested positive with COVID-19 rather than people in the hospital due to COVID-19 (Public Health Ontario, 2022c). The consistently low and comparable rates among the vaccinated and unvaccinated may strongly reflect differences in monitoring and sampling more than differences in actual infection. Another notable observation from these data is that despite surges in total COVID-19 cases, the total number of people in the hospital and the ICU remained relatively low, indicating that the COVID-19 variants are either less virulent, that a clinically meaningful population levels of vaccine and natural-induced immunity had now been achieved, or that there are fewer people at risk of severe disease either due to death or immunity.

On concerns regarding the safety of COVID-19 vaccines and current vaccine policies:

The most significant limitation of retrospective analyses of real-world COVID-19 events is that they usually fail to weigh the potential benefits of vaccination against potential risks of such vaccination. Randomized placebo controlled trials are, supposedly, the most reliable source of safety and efficacy data available. The 6-month data from the Pfizer phase III trial (Table 1) showed that receipt of the BNT1626b2 vaccine increased both the absolute (17.9%) and relative (299.7%) risk of experiencing a vaccine-related adverse event in fully vaccinated adults (Thomas & Moreira et al., 2021). This study also reported relative (74.6%) and absolute (0.5%) increases in severe adverse events, events that affect daily activity, require medical intervention, or require hospitalization. Likewise, there were also relative (9.5%) and absolute (0.05%) risk increases in serious adverse events—ones that required hospitalization, were life-threatening, or resulted in persistent disability, or death. When deaths were considered for both the blinded and open-label periods of the study, there was an increase in deaths on the vaccine arm compared to the placebo arm (20 deaths vs 14 deaths), many of which were cardiovascular in nature (9 deaths vs 5 deaths). Although the Pfizer phase trials reported relative risk reductions in symptomatic COVID-19 cases (90.9%) and severe COVID-19 cases (95.7%) with BNT1626b2 compared to placebo, the absolute risk reductions were modest (mild to moderate 3.7% and severe 0.1%) and lower than the absolute risk increases in adverse events (any 17.8%, severe 0.05%). When the absolute benefit of the vaccine is compared to the absolute risk, it appears that the vaccine, and by extension current vaccine policy, may be doing more harm than good. In Table 1, according to the terminology used in the study report, adult and adolescent populations are defined as ≥16 years old and 12-15 years old, respectively. The significance figures (p-values) there are estimated using the chi-square calculator available at this link. They are obtained without the Yates correction. This procedure was applied following the framework used by Classen (2021)in his analysis of “All Cause Severe Morbidity” based on data from the initial reports of the vaccine Phase III trials. In that study, the authors estimated vaccine efficacy using total surveillance time as the denominator, however, because this value was not always available for all of the events analyzed, in our calculations we used the common statistical definition, i.e., number of events relative to total number of eligible patients for each event analysis reported(Cohen, et al., 2021). This solution, again, is similar to previous analyses of this nature (Classen, 2021; Collie, et al.2021).Although there is a sophisticated system in place for monitoring and tracking COVID-19 events, Public Health Ontario relies on Health Canada’s passive surveillance system, the Canadian Adverse Events Following Immunization Surveillance System (CAEFISS) to monitor adverse events. This system launched decades ago, is burdensome, heavily audited, and provides no financial compensation to clinicians for the considerable time required to complete the reports. Moreover, assertions on the part of Public Health officials claiming that the vaccines are safe and effective is likely to make clinicians and vaccine recipients alike less willing to attribute any suspected injury to the vaccine and thus make them less likely to report actually occurring adverse events. The phase III trials report a solicited adverse event rate of up to 78% and a severe adverse event rate of 5% within 7 days of a given dose (Figure 5). However, the national passive surveillance system reports an event rate of 0.1% for vaccine recipients (Government of Canada, 2021a). These data indicate that the passive reporting system is largely inadequate for monitoring COVID-19 vaccine safety and that there are Canadians experiencing adverse effects from these vaccines that are not being captured by this system and that are not being factored into our policy decision-making.

Keep asking questions. Follow the money. Stay awake.

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Further reading:

Are the Unvaccinated Really a Threat to the Vaccinated?

Stigma as a Public Health Strategy

More Harm Than Good?

Pandemic Mathematics to Combat Misinformation

99.99% Survival Rate

Natural Immunity? Sh!