COVID vaccination and age-stratified all-cause mortality risk - covid 19 vaccine death count

Columbia Study: True U.S. COVID Vaxx Death Count Around 400,000

Columbia Study pegs U.S. COVID 19 vaccine death count at around 400,000; highlights VAERS gross underreporting of COVID vaxx injuries.
January 12, 2022

A study pre-print titled COVID vaccination and age-stratified all-cause mortality risk, authored by Spiro P. Pantazatos and Hervé Seligmann was published on ResearchGate Oct 2021. Researchers analyzed multiple data sources to provide “Accurate estimates of COVID vaccine-induced severe adverse event and death rates” and calculate a more accurate COVID 19 vaccine death count; showing grossly underreported COVID 19 vaccine death statistics in VAERS, the Vaccine Adverse Event Reporting System responsible for assessing the safety of COVID 19 vaccines.

DOI: 10.13140/RG.2.2.28257.43366

“COVID vaccination and age-stratified all-cause mortality risk suggests the risks of COVID vaccines and boosters outweigh the benefits in children, young adults and older adults with low occupational risk or previous coronavirus exposure.

COVID vaccination and age-stratified all-cause mortality riskRobert W Malone, MD (@RWMaloneMD) December 15, 2021

To that end Dr. Spiro P. Pantazatos and Dr. Hervé Seligmann, along with their team of researchers in columbia, utilized regional variation in vaccination rates to predict “all-cause mortality” and non-COVID deaths in subsequent time periods, based on two independent, publicly available datasets from the U.S. and Europe, providing an accurate COVID 19 vaccine death count.

Table of Contents

Study Synopsis

According to the CDC’s latest COVID-19 vaccine stats, death counts for the vaccines have reached nearly 20,000. However a study by Columbia researchers estimates the actual number of COVID vaccine related deaths is 20x higher; roughly 400,000 deaths due to COVID-19 vaccines.

Researchers noted that accurately tallying vaccine injuries is important to gauge COVID 19 vaccine safety, especially for different age groups:

“accurate estimates of COVID vaccine-induced severe adverse event and death rates are critical for risk-benefit ratio analyses of vaccination and boosters against SARS-CoV-2 coronavirus in different age groups.”

COVID vaccination and age-stratified all-cause mortality risk

According to The Vaccine Adverse Events Reporting System (VAERS) stats for the following COVID-19 vaccine injuries is as follows (Through to Dec 3 2021):

  • 19,886 deaths
  • 102,857 hospitalizations
  • 946,461 total adverse events due to COVID-19 vaccines

Pretty astonishing that according to VAERS, there have already been nearly 1 million COVID-19 vaccine injuries; more then the previous 30 years, of all other vaccines combined!

Results showed that six weeks post-vaccination (after injection), there was a negative correlation with mortality, and within five weeks vaccinated individuals had higher all-cause mortality in nearly every age group. In other words vaccinated persons have a higher risk of death from all causes, compared with someone who is unvaccinated;  with an “age-related temporal pattern consistent with the U.S. vaccine rollout.”

Researchers of the study compared the estimated vaccine fatality rate from their study, against the CDC reported COVID 19 vaccine death count (rate) and found that VAERS deaths are underreported by a factor of 20, which they stated is “consistent with known VAERS under-ascertainment bias.”

In light of this information the researchers suggested an “urgent need to identify, develop and disseminate diagnostics and treatments for life-altering vaccine injuries.”

Abstract

The abstract starts by pointing out the importance of accurate vaccine injury estimates, and states that “existing surveillance studies” currently, do not accurately measure “life threatening” vaccine injuries or fatalities:

Accurate estimates of COVID vaccine-induced severe adverse event and death rates are critical for risk-benefit ratio analyses of vaccination and boosters against SARS-CoV-2 coronavirus in different age groups. However, existing surveillance studies are not designed to reliably estimate life-threatening event or vaccine-induced fatality rates (VFR). Here, regional variation in vaccination rates was used to predict all-cause mortality and non-COVID deaths in subsequent time periods using two independent, publicly available datasets from the US and Europe (month and week-level resolutions, respectively).”

The study found that vaccination increased all-cause mortality, 0-5 weeks after COVID 19 vaccine injection:

Vaccination correlated negatively with mortality 6-20 weeks post-injection, while vaccination predicted all-cause mortality 0-5 weeks post-injection in almost all age groups and with an age-related temporal pattern consistent with the US vaccine rollout.”

In other words, individuals who received the COVID 19 vaccine were found to have an increased risk of death from all causes post-injection.

The study then lists Vaccine Fatality rates by age group, as well as total vaccine-induced deaths from Feb-Aug 2021:

“Results from fitted regression slopes (p<0.05 FDR corrected) suggest a US national average VFR of 0.04% and higher VFR with age (VFR=0.004% in ages 0-17 increasing to 0.06% in ages >75 years), and 146K to 187K vaccine-associated US deaths between February and August, 2021.”

Even more alarming it states that adult vaccination also increased mortality of unvaccinated young:

“Notably, adult vaccination increased ulterior mortality of unvaccinated young (<18, US; <15, Europe).”

In the abstract they then state that the CDC-reported VFR of 0.002% suggests VAERS deaths are grossly underreported:

“Comparing our estimate with the CDC-reported VFR (0.002%) suggests VAERS deaths are underreported by a factor of 20, consistent with known VAERS under-ascertainment bias.”

Finally in the abstract it concludes that when comparing vaccine fatality rates (VFR) to the COVID 19 Infection Fatality Rate (IFR)…the risks of covid vaccines outweigh the benefits especially in children, young and low risk adults:

“Comparing our age-stratified VFRs with published age-stratified coronavirus infection fatality rates (IFR) suggests the risks of COVID vaccines and boosters outweigh the benefits in children, young adults, and older adults with low occupational risk or previous coronavirus exposure. Our findings raise important questions about current COVID mass vaccination strategies and warrant further investigation and review.”

Introduction

In the Introduction they start off again by emphasizing the importance of accurately measuring vaccine injuries and deaths and stating they are ill equipped to assess safety related to vaccine induced deaths:

“Accurate estimates of severe vaccine adverse event rates are critical for cost-benefit ratio analyses of COVID vaccination in various age groups. The vaccine clinical trials (~15-20K participants in each arm) and safety surveillance studies (1) are either underpowered or not designed for adequate safety assessments with respect to vaccine-induced death (see Discussion for brief review).”

It then goes on to highlight some of the faults of the VAERS system, in assessing vaccine safety:

“In the US, real-world vaccine safety signals have relied on the Center for Disease Control (CDC) Vaccine Adverse Events Reporting System (VAERS) database (2). The CDC has used VAERS data to report a vaccine fatality rate (VFR) of 0.002%1, estimated by dividing the number of reported VAERS deaths by the total number of vaccine doses administered in the US. However, the VAERS has several limitations including:

  1. Reported incidents are not independently verified or confirmed to results from vaccination.
  2. It only receives, not collects, reports from individuals and/or health professionals and organizations.
  3. It likely suffers from under-ascertainment/underreporting bias.”

Introduction – Methods

And again states it’s methods for acquiring the data used in the study to calculate vaccine injuries including the US and EU data sources and how they were analyzed:

“Here, two independent, publicly available data sources from the US and Europe were used to test whether region-to-region variation in vaccination rates predicts or correlates with region-to-region variation in future (following weeks or month) mortality rates:

  1. European Data – We asked whether COVID vaccination correlates with deaths at short and long intervals post-injection stratified by 6 age groups (0-14, 15-44, 45-64, 65-74, 75-84, and 85+).
  2. US data – Multiple linear regression was used to test whether we could observe similar short term effects seen in the European data. The US data was stratified by 8 age groups (0-17,18, 29, 30-39, 40-49, 50-64, 64-74, 75-84, and 85+).

These models adjusted for COVID deaths as well as seasonality effects and interregional variation in mortality due to other factors by adjusting for same-month 2020 deaths. Using same month deaths from 2020 (as opposed to 2019 or earlier) also helped control for interregional differences in pandemic public health measures before the vaccination campaigns began.”

In other words by comparing same month 2020 deaths, to 2021 deaths it helped to eliminate factors that could have been attributed to uncertainties in the data such as interregional and seasonality factors.

Secondary Aim

Their secondary aim, was to estimate a US national average Vaccine Fatality Rate VFR stratified by age, to more accurately assess vaccine fatality risk by age:

“Our second aim was to estimate a US national average VFR and age-stratified rates using significant regression slopes for the vaccination term in the regression model.”

It then goes on to explain the differences between the European data vs the US data, used in the study:

  1. European data – “reports age-stratified mortality rates on a weekly basis and allows for higher temporal resolution analyses, but mortality rates are z-scored normalized and hence effect size estimates in real units are not possible.”
  2. US data – units “allow for such estimates since it records raw numbers of administered vaccine doses and death counts in each jurisdiction, but at a lower (monthly) temporal resolution.”

In other words the US data in it’s raw form enables the study to better calculate a US national average primarily because mortality rates aren’t normalized.

The study then compared their VFR estimates against national averages; stratified by age to calculate risk-benefit ratio for COVID-19 vaccinations:

“Finally, we compared our estimates with previously published US national average and age-stratified SARS- CoV- 2 infection fatality rates for risk-benefit ratio analysis of vaccination against COVID-19 stratified by age.”

Results

Results Synopsis

To over simplify…0-5 weeks post jab there is an adverse effect, from 5-15 or so weeks there is a protective effect and then around 15-20+ weeks there is an adverse effect again (roughly you get about 3 months of protection):

most deaths occur within the first weeks after vaccination, and vaccine protection occurs after the sixth week after first dose injection. For age groups 15-44 and 45-64, the overall tendency is that protective vaccine effects (meaning negative associations between mortality and vaccination) disappear about 20 weeks after first injection. After week 20, there might be a tendency for adverse effects of vaccination

To overly simplify the results post injection (please note this is a rough picture of the data):

WeeksAge 0-14Age 15-44 yrs45-64 yrs65-74 yrs75-84 yrs85+ yrs
0-5Adverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse Effect (up to 10 weeks later)
5-15Adverse Effect(2-15) ProtectiveProtectiveProtectiveProtective(10-23) Protective
15-25Adverse Effect (up to week 20)Adverse EffectAdverse EffectProtectiveProtectiveProtective
25+ProtectiveAdverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse Effect
Protective and Adverse effect of vaccines stratified by age and weeks 0-5, 5-15, 15-25, and 25+

Results – EU Data | COVID 19 Vaccine Death Count

In the results it starts out by listing the weekly data obtained for 22 EU countries through 2021 from Our World in Data correlated with varying time lags post-jab, against weekly age-stratified (separated) data from euromomo.eu. This data should be accessible and can easily be verified by anyone skeptical of this study or article.

“For each week since the start of 2021 for 22 European countries, weekly increases in percentages of the total population who received at least one injection were extracted from Coronavirus (COVID) Vaccinations – Statistics and Research – Our World in Data, and correlated with varying time lags (0-28 weeks post vaccination) with weekly age-stratified mortality data extracted from euromomo.eu (see Supplementary Materials and Methods).”

Ages 0-14 were seperated, because that demographic was unvaccinated for the time frame in which the data was extracted.

“The overall description of results requires distinguishing between the age group 0-14 which were unvaccinated during the time period analyzed, and ages above 14.”

Results showed; for the first few weeks of vaccination, there is a positive correlation (adverse effect) between vaccination and mortality, however that tapers off and becomes a negative correlation (protective effect) after 5-6 weeks; until about 15 weeks at which point it returns to a positive correlation (adverse effect) :

“For ages above 14, there is a positive association (correlation) between vaccination and mortality rates during the first few weeks of vaccination (Table 1, lags 0-5 and Figure 1 leftmost yellow peaks). Overall, mortality above age 14 associates near zero or negatively with vaccination for mortality later than 5-6 weeks after vaccination (Table 1, lags 5-20 and Figure 1 middle blue troughs).”

To overly simplify the results post injection (please note this is a rough picture of the data):

WeeksAge 0-14Age 15-44 yrs45-64 yrs65-74 yrs75-84 yrs85+ yrs
0-5Adverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse Effect (up to 10 weeks later)
5-15Adverse Effect(2-15) ProtectiveProtectiveProtectiveProtective(10-23) Protective
15-25Adverse Effect (up to week 20)Adverse EffectAdverse EffectProtectiveProtectiveProtective
25+ProtectiveAdverse EffectAdverse EffectAdverse EffectAdverse EffectAdverse Effect
Protective and Adverse effect of vaccines stratified by age and weeks 0-5, 5-15, 15-25, and 25+

It goes on to state that the estimates found by this study are in line with VAERS data, which helps confirm the validity of the study results:

“These results coincide with known clinical developments of vaccination, as found in the VAERS data: most deaths occur within the first weeks after vaccination, and vaccine protection occurs after the sixth week after first dose injection.”

It states negative associations between mortality and vaccination taper off 20-weeks post-injection (after injection), and after week 20 there may in fact be a chance of adverse events:

“For age groups 15-44 and 45-64, the overall tendency is that protective vaccine effects (meaning negative associations between mortality and vaccination) disappear about 20 weeks after first injection. After week 20, there might be a tendency for adverse effects of vaccination, meaning positive r values between mortality beyond week 20 and vaccination at least 20 weeks before (Table 1, lags >20 and Figure 1 rightmost yellow peaks).”

Effect of Vaccinated Adults on Unvaccinated Children

This is an important dataset, because it highlights an increased risk of all-cause mortality for children of vaccinated individuals up to 18 weeks post-vaccination:

“For the unvaccinated age group 0-14, most associations between mortality and vaccination in adults are positive (among 39 r values with unadjusted two-tailed P < 0.05, 32 are positive and 7 are negative r’s). This tendency for positive correlations increases from the week of vaccination until week 18 after vaccination, then disappears. It indicates indirect adverse effects of adult vaccination on mortality of children of ages 0-14 during the first 18 weeks after vaccination.

Results – US Datac

In this section it goes over the US data on vaccination and mortality, for each state:

“The following analyses used publicly available US data on vaccination, mortality and age stratified population size in each US state. The data were obtained from either the CDC or US Census Bureau (see Data Sources section in Supplementary Materials and Methods).”

The goal here was to compare US data with EU data:

“Our analyses focused on whether we could replicate the higher mortality within the first 5 weeks of vaccination observed in the euromomo.eu data. Since US mortality data were limited to month level resolution, we tested whether monthly vaccination rates predicted mortality during the same month or during the next month.”

It them goes into detail on the methods and formulas used to calculate deaths and how they ruled out potential confounding factors such as COVID case rates and COVID deaths.

COVID vaccination and age-stratified all-cause mortality risk US results methods equation

I’ll skip out on most of the method details, because it is highly technical and really only relevant to scientists or researchers who are peer reviewing the study; please view the PDF file in this article or visit the original study web page…However there are some tidbits of information in this section:

“Prior month or current month vaccinations (# of administered doses) predicted monthly total deaths in most age groups.”

In the US section of the study it yielded in 2021 just under 150,000 vaccine caused deaths:

yielded a total of 146,988 deaths attributed to COVID vaccinations between February and August of 2021 (lower right cell of “Estimated Deaths” in Table 3).”

Table 3 – Model-estimated deaths attributed to COVID vaccination for each age group and month using US CDC data | Significant beta weight coefficients ( ) in Table
2 surviving p<0.05 FDR corrected were used to estimate VFR and total deaths for each age group and month. If a model using same (not previous) month vaccinations was significant and the equivalent models using previous month was not, then death estimates from those models were used instead (light gray boxes). Similarly, if a model using age specific vaccination (i.e. doses administered to people >65 yrs) was significant and the equivalent model using all vaccine doses administered was not, then death estimates from those models were used instead (dark gray boxes). See methods for VFR and aVFR definitions and calculations. ns=not significant at p<0.05 FDR corrected. NA=Not available.

It then explains how the US VFR was calculated, and lists the age-stratified VFR’s:

“Dividing the total number of model-estimated deaths by the total number of vaccine doses administered between January and August yielded an estimated US national average VFR of 0.04% (bottom of Table 2). Age-specific VFRs were estimated by averaging across all months and for all 3 models when thresholding regression slopes at p<0.05 (see methods). These yielded estimated aVFRs of (Table 3):”

  • 0.004% for ages 0-17 years
  • 0.005% for 18-29 years
  • 0.009% for 30-39
  • 0.017% for 40-49
  • 0.016% for 50-64
  • 0.036% for 65-74
  • 0.06% for 75-84
  • 0.055% for 85-plus

Discussion | COVID 19 Vaccine Death Count

The discussion section is an area for researchers to reaffirm the study results, explain the rational building up to, during and after the study…as well to put the study data in the broader context, by discussing how the study fits in with other relevant information; in this case COVID-19, and how other studies work synergistically with the study to provide a more holistic picture of the data

The discussion repeats that “vaccination rates predicts mortality”:

“In this study we find that regional variation in vaccination rates predicts mortality in subsequent time periods.”

It also reaffirms the results regarding the time frames; adverse effect 5-6 weeks post-jab, then a protective period, followed by a decrease in mortality associated with vaccination 6-20 weeks, after which the protection tapers off and returns to an adverse effect:

“The mortality data from euromomo.eu confirms previously known patterns in the vaccinated: a positive association with adverse events, including death, up to 5- 6 weeks after the first injection, followed by a decrease in mortality associated with vaccination 6-20 weeks post-injection.

It theorizes that the decrease in mortality is a result of the protective effect of vaccination, which ends after about 20 weeks; in-line with the generally accepted protective timeline of 4-6 months for COVID 19 vaccines.

“The decrease is presumably due to the protective effect of vaccination, which is known to start 6 weeks after the first injection. The end of the protective vaccine period as observed in our data, about 20 weeks, corresponds approximately with the end of the protective vaccine period as generally accepted, 4-6 months (4).”

It also talks again about the unexpected increase in mortality in unvaccinated children, with adult vaccination rates in the previous period. It states most of these deaths occur in infants as a result of of their pregnant mothers receiving the COVID-19 vaccine:

“The euromomo.eu data also reveal an unexpected increase in mortality in children (which are unvaccinated) with adult vaccination rates in the previous period. It is notable that this indirect adverse vaccination effect was independently observed in both CDC and euromomo.eu datasets. The majority of deaths <18 years age occur in infants <1 years, and a significant effect of vaccination on infant mortality was detected when the US CDC data was restricted to that age group (see Supplementary Results).

This is important information…as throughout 2021 there was allot of media propaganda stating the safety of COVID vaccines for pregnant mothers:

“Note that several important concerns and errors have been raised in response to previously published studies supporting safety of vaccination in pregnant women (see Supplementary Discussion for a brief review).”

Here they discuss the possibility that the increased mortality 0-6 weeks post jab me partly be due to the delayed protection offered by the vaccine; verified by other COVID-19 vaccine studies:

“The increased mortality in the first 0-6 weeks post-injection may be due in part to increased COVID infectivity before vaccine protection takes effect. A re-analysis of a large real world study of vaccine effectiveness (Dagan et al 2021 (5)) suggests infectivity in vaccinated persons increases 3-fold approximately 7 days following the 1st dose of the Pfizer vaccine (17). Figure 2 in (7) suggests a similar pattern with the CoronaVac vaccine.

They then discuss the end of protection around the15-20 week mark, post jab; stating the possibility of Antibody Dependent Enhancement (ADE), also known as Paradoxical Immune Enhancement (PIE); a separate study of note, shows clear evidence of a link between COVID vaccines and PIE / ADE:

Likewise, the euromomo.eu data also suggest a tendency for adverse effects caused by the vaccine in those above age 14 beginning 20 weeks after first injection, potentially indicating that antibody dependent enhancement (ADE) (8–10) or another related effect kicks in after protective vaccine effects dissipate.”

They also denote the possibility these post week 15-20 increases in mortality could be due to short term mortality defects of the booster campaigns (In other words boosters may be causing more deaths). This makes sense since boosters were launched to address the degrading protection offered by vaccines, week 20, post-jab:

Alternatively, the increase in adverse effects observed after week 20 may instead be due to short term mortality arising from booster campaigns which began in late summer or fall. Further analyses are required to disentangle and understand the causes of this effect.

Also It’s important to remind readers that the COVID-19 vaccines were distributed under emergency use authorization, and are still in phase-3 clinical trials (as of Jan 2021). For those that don’t understand what being part of a vaccine trial entails…part of that process is to distribute a saline (placebo) to a control group, this is in order to contrast and prove the effectiveness of the vaccines; therefore it is quite possible that many people whom think they had no reaction to the COVID vaccine, in fact received the placebo, and will incur an adverse event in a subsequent shot or booster.

The study then goes on to compare their estimated VFR of 0.04% with the CDC VFR of 0.002%; suggesting VAERS is underreported by a factor of 20, probably more:

“The US CDC data allowed for estimation of VFR and vaccine-induced deaths. Importantly, our calculations do not rely on VAERS and its associated limitations. Our estimated US national average VFR of 0.04% is 20-fold greater than the CDC reported VFR of 0.002%, suggesting vaccine-associated deaths are underreported by at least a factor of 20 in VAERS.

They point out that VAERS this underreported estimate of 20-fold is a conservative estimate, and it is likely much higher:

The estimate is based only on significant effects detected in our analysis, and hence likely represents a lower bound on the actual underreporting factor.

The study then goes on listing other studies that support the estimation of VAERS being underreported by a factor of 20 or more:

“Interestingly, our estimates of 133K to 187K vaccine-related deaths are very similar to recent, independent estimates based off of US VAERS data through August 28th, 2021 by Rose and Crawford (11) The authors report a range of estimates depending on different credible assumptions about the VAERS underreporting factor and percentages of VAERS deaths definitely caused by vaccination based on pathologists’ autopsy findings. The authors compared a previously reported incidence rate of anaphylaxis in reaction to mRNA COVID vaccine (~2.5 per 10,000 vaccinated) (12) to the number of events reported to VAERS to estimate an underreporting factor for anaphylaxis (41x). This factor, multiplied by the number of reported VAERS deaths and the percentage of VAERS deaths believed to be caused by vaccination based on pathologists’ estimates, yields various estimates with an average around 180K deaths.”

Again they highlight the advantages of not relying solely on VAERS data; by using publicly available data this study contributes to convergent evidence (literally shows how evidence converges together) for calculating COVID vaccine VFR:

“Our estimate does not rely on VAERS data and uses independent and publicly available data, and thus contributes additional convergent evidence for the above estimate of vaccine-induced deaths.”

If you are a scientists or frequently research medical literature, you will understand the importance of distinguishing between association and causation… in this study they evidence a causal link between COVID 19 vaccines and death. In other words this study provides evidence that COVID vaccines directly cause death, on a scale much larger then the CDC reports.

“See Supplementary Discussion for additional reasons why our results evidence a causal link (not just an association) between vaccination and death.”

Spike Protein Travels Past the Injection Site

Here they highlight that some of the deaths caused by COVID vaccines, may be a result of “cytotoxicity of the spike protein”, and the fact that despite what some scientists first though…the spike protein travels past the injection site and circulates throughout the body (they cite several studies as evidence of this fact).:

“Death and severe adverse events to the COVID vaccines appear to be mediated in part by cytotoxicity of the spike protein and its (unintended) cleaving from transfected cells and biodistribution in organs outside the injection site (13–18).”

Protection delay pre-jab and ADE post-jab (after)

Again they suggest COVID vaccine deaths may be partly from the time-delay before vaccination protection kicks in, as well as after full protection wears off, due to Antibody Dependent Enhancement (ADE):

“Vaccination may also contribute to higher COVID IFR before vaccination protection kicks in (and after full protection wears off) due to antibody dependent enhancement (ADE) (8,10,19).”

Enhanced Respiratory Disease and Logistical Quality Control Issues

They suggest the effect may be connected to enhanced respiratory disease, or possibly even damage or taint, due to “quality control issues” in the logistics (storage, handling and distribution specifically), or production of COVID vaccines:

The effect may be related to enhanced respiratory disease observed in preclinical studies of SARS and MERS vaccines (20,21). An additional or alternative mechanism may stem from quality control issues related to production, handling and distribution of the vaccines. A recent analysis of VAERS data suggests only ~5% of the vaccine batches account for the majority (>90%) of adverse reactions, those batches were the most widely distributed (more than 13 states), and reported adverse event rates appear to vary across jurisdictions an order of magnitude (22).

Existing safety and surveillance studies are not designed to reliably estimate COVID vaccine induced death risk | | COVID 19 Vaccine Death Count

Author reported funding by Pfizer

In another study titled The Vaccine Safety Datalink: a model for monitoring immunization safety, they aimed to show the rate of adverse effects from COVID vaccines…however the study was essentially useless because it compared individuals 1-21 days vs 22-42 days. We know due to the data in this study that the protective effect takes place after 5-6 weeks and up until 15-20 weeks. Therefore in order to measure the risk of COVID vaccines effectively, the other study would have to utilize a much longer time frame; this study could in fact mislead readers to draw conclusions about the safety of COVID vaccines and endanger lives:

“A recent safety surveillance analysis of mRNA vaccines against COVID using the Vaccine Safety Datalink (23) found event rates for 23 serious health outcomes were not significantly higher for individuals 1 to 21 days after vaccination compared with similar individuals at 22 to 42 days after vaccination. This is not very informative as the main comparison of interest is the background rate of adverse events in the unvaccinated. If the severe adverse event rate is similar 1-21 days post-vaccination as it is 22-42 days post-vaccination, then no difference in risk (safety signal) will be detected.”

We already established that the study utilized time frames which are incorrect to correctly measure adverse effects. The study also claimed a reduced risk of several adverse effects in the vaccinated 1-21days post-injection:

“The authors include an analysis using an unvaccinated comparator group in Supplementary eTable 6. Surprisingly, the table reports significantly reduced risk of thrombosis with thrombocytopenia syndrome (p=0.004), hemorrhagic stroke (p<0.001), pulmonary embolism (p<0.001), and acute myocardial infarction (p<0.001) in the vaccinated 1-21 days post injection compared to the unvaccinated comparator group.”

Coincidently the exact events the study claims to be lower in the vaccinated, are according to VAERS data, well established as associated with both the adenovirus vectored and mRNA COVID-19 vaccines:

This is intriguing because these adverse events are precisely the events known to be associated with both the viral vector-based and mRNA COVID vaccines based on CDC VAERS data (749 results for “acute myocardial infarction”, 4,579 results for “thrombosis” or “thrombocytopenia”, 98 results for “hemorrhagic stroke”, and 2,395 results for “pulmonary embolism” for mRNA vaccines as of Oct 22nd, 2021) and published case reports (14,24–26). The authors do not devote any discussion on how or why their results provide strong evidence that COVID vaccination appears to protect against the very adverse events that were previously associated with vaccination. We speculate it is more likely the groups were mislabeled due to human or technical error.

COVID-19 Vaccination and Non-COVID-19 Mortality Risk – Seven Integrated Health Care Organizations, United States, December 14, 2020-Jul 31, 2021 | Report

Author reported funding by Pfizer

In another paper by Xu et al. titled COVID-19 Vaccination and Non-COVID-19 Mortality Risk – Seven Integrated Health Care Organizations, United States, December 14, 2020-Jul 31, 2021, they reported similarly to Klein et al, claims of significantly reduced mortality or serious adverse events risk in the vaccinated vs. unvaccinated, theorizing the reason may be differences in risk factors. However this data is also at odds with a recent large survey study showing “vaccine hesitant” groups includes PhD holders…

“A recent paper by Xu et al., also based on the Vaccine Safety Datalink (VSD) cohorts used in Klein et al., reported significantly reduced mortality risk in vaccinated vs. unvaccinated (27). As with Klein et al. that found significantly reduced risk for severe adverse events in vaccinated people (discussed above), the finding of reduced standardized mortality rates (p<0.001) in the vaccinated compared with unvaccined is unexpected, especially since the groups were matched for “similar characteristics” and standardized mortality rates were adjusted for age, sex, race and ethnicity. The authors suggest “This finding might be because of differences in risk factors, such as underlying health status and risk behaviors among recipients of mRNA and Janssen vaccines that might also be associated with mortality risk” (27). However, this does not comport with recent findings from a large survey study that found PhD-holders are among the most vaccine hesitant groups (28,29), as are women looking to become pregnant, religious people, and people who practice yoga/“wellness” culture (30).”

It’s important to note the Xu et al. paper is “based on the same sides/cohorts used used in Klein et al. which made similar claims, and is likely subject to the same errors. Do you honestly think it’s a coincidence that two studies who’s authors reported receiving funding from Pfizer…presented data which made claims for the safety of COVID vaccines, specifically against the exact adverse health effects well established as associated with those same vaccines through multiple other publicly accessible data sources? I’m my frank opinion you’d have to be a naïve fool to to not see a clear bias driven by funding…

“Given that the study is based on the same sites/cohorts used Klein et al. (1), which found significantly reduced risk in the vaccinated for the same severe adverse events that have associated with COVID vaccination in VAERS data and published case reports (see discussion above), we speculate their findings may be due to a technical or human error involving group labeling or coding. Note that the data used for their study is not publicly accessible (in contrast to our study), and two authors report receiving funding from Pfizer.

The authors of both studies / paper, Xu et al. and Klein et al. reported that they received funding from Pfizer. In my eyes that already entirely discredits the study and any evidence found therein as it shows a clear bias.

Vaccine Cost-Benefit Ratio

In this section the study discusses the Infection Fatality Ratio, according to multiple other studies, stratified by age, in order to analyze if the benefits of vaccines, outweighs the adverse effects for different age groups.

“According to a recent meta-analysis of IFR studies, up to 90% of the variation in population-wide coronavirus infection fatality rate (IFR) is explained by age composition and the extent to which older age groups are exposed to the virus (31). The study reports the IFR for:

  • age 10 is 0.002%
  • age 18 years is 0.005%
  • 25 years is about 0.01%
  • 45 years 0.1%
  • 55 years 0.4%
  • 65 years 1.4%
  • 75 years 5%
  • 15% >85 years (31)”

Anyone with nominal intellect and math skill…would question why there is such a fervent push to vaccinate children, when they are at such an extremely low risk of death from COVID-19.

It then goes on to show calculations based on 61 different studies (74 estimates) showing an IFR of around 0.03%-0.3% for all age groups under 70yrs old:

“Calculations based on 61 studies (74 estimates) and eight preliminary national estimates by Ioannides suggest a median of 0.05% and upper bound IFR of 0.3% for ages <70 (32).”

Study results fall in line with similar US national average IFR estimates, stratified by age:

This latter estimate is similar to an estimated US national average IFR of 0.35% based on a Bayesian evidence synthesis model that averaged age-specific IFRs weighted by the fraction of the population in each age group across US states (33).

They conclude what any other person would conclude given the data…with such a low risk of infection for those under 25, the risks of COVID-19 vaccines, outweigh the benefits for that age group:

“Already the numbers clearly show that the benefits of vaccination do not outweigh the risks in anyone aged 25 or under.

Here they point out the association between risk of death from COVID and infection risk; they are not entirely separate, and the risks of both share some commonalities:

“An individual’s overall risk of dying from COVID is also a function of infection risk, which varies based on lifestyle, location, time, occupation, and behavior (i.e. social distancing, effective masking with N95 etc.), as well as the presence of comorbidities

They also note according to clinical trials, that the risk of symptomatic infection (showing symptoms like coughing, sore throat, loss of taste, etc) is extremely low; this may not be the case for the new omicron variant, however multiple studies now show despite it’s increased infectivity, it is the least lethal of the current variants:

In the vaccine clinical trials (when social distancing and masking measures were in place), ~1-2% of the participants contracted symptomatic COVID in the placebo group over a period of a few months (21).

It’s important to point out that in light of COVIDs extremely low mortality rate…many people especially low risk, young and health individuals, experience only minor symptoms, or no symptoms at all, which is referred to as asyptomatic infection. You can still spread COVID-19 infection while asymptomatic…however essentially your body is ‘kicking COVIDs butt’, so to speak, and not experiencing any symptoms, some would argue, means you are effectively ‘not sick’ in the typical sense.

You are also in asymptomatic condition, less likely to cough or sneeze and spread droplets of to the same degree or with the same viral load, as an individual who is exhibiting symptoms. However that does not necessarily mean you are less likely to infect others… primarily because COVID-19 can also spread through aerosol droplets through infected individuals breathing (through the air), and not just smaller droplets on surfaces.

The study then goes into a bit more detail on how the calculations are preformed, showing the association between mortality risk and infection risk:

“Infection risk calculators allow someone to estimate their risk of infection based on attending an event of a certain size (34). For example, a 55 year old attending events over a given time period with a 10% infection risk has a 0.1*0.4%=0.04% chance of dying from COVID, which is similar to the odds of vaccine-induced death (VFR~0.01%).”

The study then lists the two groups which appear to have a positive benefit to risk ratio these are individuals with no previous exposure or natural immunity those a) over 75yrs and b) over 45 years with high occupational risk:

“In individuals with no previous exposure and natural immunity, the benefits of vaccination appear to outweigh the risks in age groups >75 years, where the IFR (>1%) is one or two orders of magnitude greater than the estimated VFR of 0.06% in this age group. The benefits may outweigh the risks in ages >45 with high occupational risk (and no previous coronavirus exposure) where the IFR of 0.1% is an order of magnitude higher than the estimated VFR of
0.01%. (18).”

Implication for Public Health Policy

They emphasize the lack of evidence that vaccines reduce community spread and transmission; we now know they don’t…also many of the vaccine clinical trials used symptomatic, not asymptomatic COVID as an endpoint. In other words patients could very well have had COVID but not exhibited any symptoms, making the vaccine seem more effective, and ultimately rendering the trials entirely inaccurate in gauging the effectiveness of vaccines, to reduce COVID-19 infection or spread:

“There is little to no evidence that vaccines reduce community spread and transmission. The vaccine clinical trials used symptomatic, not asymptomatic COVID, as a clinical endpoint. Since they did not require weekly coronavirus testing in their participants, they were not designed to estimate vaccine efficacy in reducing infection and hence transmission of the virus in pre and/or asymptomatic persons.”

Any study that did not have a clear bias pro-covid vaccine…would require weekly testing through multiple methods as the clinical endpoint; not asymptomatic infection. In fact if you remember above, two of the larger studies to support the protective effects of vaccines, both of their authors admitted receiving funding from Pfizer.

Another example of big pharma corruption, is the article we published regarding factcheck.org which is funded by a company with nearly 2Billion stock in COVID vaccine manufacturer Johnson & Johnson; they are responsible for discrediting hundreds of doctors and medical studies…

Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings – Barnstable County, Massachusetts, July 2021 | Article

They cite another CDC study published Aug 2021 titled Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings – Barnstable County, Massachusetts, July 2021, which showed most COVID infections in the Barnstable, MA area were among fully vaccinated individuals; they also had similar viral loads:

“Indeed a recent July CDC study in Barnstable, MA reported a majority (75%) of COVID infections were among fully vaccinated people in an area with 69% vaccination coverage, with similar viral loads between vaccinated and unvaccinated (35).”

FIGURE 1 SARS-CoV-2 infections (N = 469) associated with large public gatherings, by date of specimen collection and vaccination status — Barnstable County, Massachusetts, July 2021 Abbreviation: MA DPH = Massachusetts Department of Public Health. * Fully vaccinated was defined as ≥14 days after completion of state immunization registry–documented COVID-19 vaccination as recommended by the Advisory Committee on Immunization Practices.

In fact we published another article on a lancet paper showing that vaccinated have similar viral load to unvaccinated and can still become infected with and spread COVID.

They conclude based on the aforementioned information, that vaccines are an inadequate and mandates (in most environments) are an ill advised and ineffective method to reduce COVID 19 community spread:

“Given that vaccines do not appear to reduce community spread and that the risks outweigh the benefits for most age groups, vaccine mandates in workplaces, colleges, schools and elsewhere are ill advised.”

They also mention the monetary benefit of COVID vaccines mandates to the pharmaceutical manufacturers, and little benefit to anyone else:

“We do not see much benefit in vaccine mandates other than increasing serviceable obtainable market (SOM) share for the vaccine companies.”

Those are fighting words…and big pharma doesn’t take kindly to studies or doctors that put their “cash cow”; COVID-19 vaccines at risk, they also have billions of dollars to deal with dissenters and pump out propaganda to discredit scientific dissent…

Supplemental Discussion and Other References

They then cite some other studies / resources showing why mandates are not based on sound science, considering the low risk of severe COVID infection in most healthy youngish adults:

“See (36) and (18) for a more in depth discussion and literature review on why the mandates are not based on sound science given the relatively low COVID risk in healthy middle-aged and young adults and growing evidence base for alternative prevention and early treatment options for COVID. See Supplemental Discussion for more resources where readers can learn about the nature and volume of life-altering COVID vaccine injuries.”

We also published a webpage listing thousands of COVID Vaccine Injuries which you can view to see some of the most horrendous vaccine injuries you will ever witness…There is a “Viewer Discretion is Advised” notice for anyone viewing that page and I do not advise children to watch the videos on that page.

Limitations and future directions | COVID 19 Vaccine Death Count

They mention the importance of autopsies on COVID vaccine deaths to identify causes:

“Future studies that include autopsies on VAERS-reported deaths is required to identify mechanisms of vaccine-induced death”

I stress the importance of autopsies because in many areas doctors have been advised against preforming autopsies on COVID 19 deaths, and in some cases families have been blocked from preforming an autopsy on a loved one that died shortly after receiving a COVID vaccine.

2nd Pathology Conference on Dec. 04, 2021 at 5:00 p.m. in Berlin, Germany

A great example highlighting the importance of autopsies is demonstrated in another paper done by the Pathological Institute in Reutlingen, Germany on September 20, 2021, and again on December 4th:

“A team of top German pathologists and scientists presented findings from a first-ever comprehensive, independent set of autopsies of people who had died unexpectedly within 2 weeks of the COVID vaccine injections. In a shocking assertion, one pathologist calculated that, for a certain broad age group, takers of the shots were 15 times more likely to die from the shots than from COVID.”

The paper was headed by Dr. Burkhardt who has taught at the Universities of Hamburg, Berne and Tübingen. He has published over 150 scientific articles in German and international scientific journals, and has audited and certified institutes of pathology in Germany. Needless to say he is more qualified then most “health experts” to conduct a investigation / study on COVID 19 vaccine injuries and deaths.

“The doctors described what they said were never-before-seen patterns of attack by the immune system on the body’s own organs, discovered through microscopy and mass spectrometry. The doctors described “self-to-self attacks” leading to sudden heart inflammation, blood clotting, rapidly-growing cancers in young people, and many other bizarre findings. The team concluded that 30% to 40% of the bodies they examined, most likely 40%, had died as a direct result of the vaccines. All of the bodies examined were over age 50.”

A strong argument can be made that all deaths that could even be remotely linked to COVID vaccines, should if the family or individual authorizes, be autopsied to analyze the mechanisms of death in COVID vaccines to hopefully help prevent deaths from similar mechanisms, occurring in the future…

Age Stratified Calculations | COVID 19 Vaccine Death Count

They highlight the benefits of age-stratified mortality within the same age groups to increase accuracy of vaccine mortality calculations; the data collected just isn’t organized to account for those variables:

“Ideally, our analyses would use age-stratified vaccination to predict age-stratified mortality within the same age groups. However, the European and Israel vaccination data are not age-stratified, and the US vaccination data only provides some age-specific data starting in later months (i.e. vaccines administered to ages >65, >18, and >12 years). In addition, while the US vaccination and COVID cases are updated daily,
the age-stratified death counts are per-month, thus preventing analyses using shorter time windows
.”

They state that additional data is likely to increase estimated deaths attributed to COVID vaccination:

“The additional information may have increased our sensitivity to detect significant effects in more age groups and time periods. Such a scenario would increase our mortality estimates, in which case the death estimates presented here based only on significant effects (p<0.05 corrected) can be considered a lower bound on the estimated deaths attributed to COVID vaccination.”

They also highlight the importance of future studies to examine to assess potential vaccine associated mortality over longer time frames; protective and long term adverse effects must be measured to estimate long term safety:

“The current study focused on vaccine-attributed deaths within 5-6 weeks of vaccination to estimate age-stratified VFR. Future work should examine later periods to estimate lives saved from vaccination and also potential vaccine associated mortality after protective effects”

Conclusions | COVID 19 Vaccine Death Count

They re-assert the findings in the discussion – conclusions section; following 20 weeks protection wanes:

“In the European and Israeli data, we find that COVID vaccination correlates positively with mortality 0-5 weeks from vaccination, before associating with lower mortality 6-20 weeks from vaccination.

The US National average Vaccine Fatality Rate (VFR) is calculated at 0.04%:

“The US data allowed us to estimate a US national average VFR of 0.04% and age-stratified vaccine-induced fatality rates within 1 month post-vaccination.”

COVID 19 vaccine deaths in the USA tallied at 130k-180K from Feb-Aug 2021:

“Significant regression terms estimate 130K-180K US deaths can be attributed to vaccination between February and August of 2021

This estimate of COVID vaccine deaths is in line with independent estimates on VAERS data showing “VAERS deaths are underreported by a factor of 20”; multiple other studies have calculated only 1%-4% of COVID 19 vaccine injuries are reported in VAERS:

“The estimate converges with independent estimates based on the Vaccine Adverse Events Reporting System (VAERS) and suggests VAERS deaths are underreported by a factor of 20.”

Comparison of age-stratified Vaccine Fatality Rate with age-stratified Infection Fatality Rate shows that the risks of COVID vaccination outweigh the benefits in all but those at extremely high risk; elderly, and those with co-morbidities (pre-existing health conditions such as diabetes, or cancer):

Comparison of our age-stratified VFR and with age-stratified IFR rates suggests the risks of COVID vaccination outweighs the benefits in children, young and middle age adults, and in older age groups with low occupational risk, previous coronavirus exposure, and access to alternative prophylaxis and early treatment options.

This study data warrants further investigations on COVID vaccines strategies; mandates should IMO be abolished in light of this and similar data…:

“Our findings raise important questions about mass COVID vaccinations strategies that warrant further investigation and review”

Please note: Data is publicly available on Github. There are no noted conflicts of Interest. Help and feedback from Eileen Natuzzi.

Tables, Graphs and Supplemental Data

Table 1. Correlations between COVID vaccination rates and mortality as a function of lag (# weeks post-injection) and age group

Each cell summarizes the pearson correlation coefficients between weekly increase in percent vaccinated and weekly mortality in 23 European countries. Top header row: lag=weeks between mortality and injection, n=number of
correlations summarized. Middle matrix (%) shows the percentage of positive correlations for that lag among n correlation.
*=P < 0.05 corrected, sign test. Bottom matrix (P<0.05) shows the number of negative and positive correlation r’s with P <
0.05 uncorrected. Blue: overall protective effect (more injections->lower mortality); yellow: overall adverse effect (more
injections->lower mortality) | COVID 19 Vaccine Death Count
COVID vaccination and age-stratified all-cause mortality risk – Table 1. Correlations between COVID vaccination rates and mortality as a function of lag (# weeks post-injection) and age group. Each cell summarizes the pearson correlation coefficients between weekly increase in percent vaccinated and weekly mortality in 23 European countries. Top header row: lag=weeks between mortality and injection, n=number of correlations summarized. Middle matrix (%) shows the percentage of positive correlations for that lag among n correlation. *=P < 0.05 corrected, sign test. Bottom matrix (P<0.05) shows the number of negative and positive correlation r’s with P < 0.05 uncorrected. Blue: overall protective effect (more injections ->lower mortality); yellow: overall adverse effect (more injections->lower mortality).

Table 2. Regression weights and p-values for the vaccination term predicting same or next month deaths using US CDC data.

COVID vaccination and age-stratified all-cause mortality risk - Table 2. Regression weights and p-values for the vaccination term predicting same or next month deaths using US CDC data. For each month in 2021 and age group, beta weights and uncorrected p-values are listed for the vaccination term ( ) in the fitted equation: | COVID 19 Vaccine Death Count
COVID vaccination and age-stratified all-cause mortality risk – Table 2. Regression weights and p-values for the vaccination term predicting same or next month deaths using US CDC data.

Table 3. Model-estimated deaths attributed to COVID vaccination for each age
group and month using US CDC data.

COVID vaccination and age-stratified all-cause mortality risk Table 3 Model estimated deaths attributed to COVID vaccination for each age group and month using US CDC data | COVID 19 Vaccine Death Count
COVID vaccination and age-stratified all-cause mortality risk – Table 3. Model estimated deaths attributed to COVID vaccination for each age group and month using US CDC data. Significant beta weight coefficients ( ) in Table 2 surviving p<0.05 FDR corrected were used to estimate VFR and total deaths for each age group and month. If a model using same (not previous) month vaccinations was significant and the equivalent models using previous month was not, then death estimates from those models were used instead (light gray boxes). Similarly, if a model using age- specific vaccination (i.e. doses administered to people >65 yrs) was significant and the equivalent model using all vaccine doses administered was not, then death estimates from those models were used instead (dark gray boxes). See methods for VFR and aVFR definitions and calculations. ns=not significant at p<0.05 FDR corrected. NA=Not available.

Figure 1. Graphical representation of Table 1 European data results. Adverse effects

Adverse effects in yellow, above horizontal line, protective effects in blue, below horizontal line. Results of correlation analyses for all age classes and all combinations of weeks, with mortality occurring the same week or after the injection week are plotted. In a) the percent positive correlations between vaccination rates and mortality is plotted against time since 1st injection for 6 age groups (A – 0-14 years, B 15-14 years, C 45-64 years, D 65-74 years, E 75-84 years, and F 85+ years). Percentages >50% are shaded yellow, <50% shaded blue. Asterisk indicates p<0.05 corrected for the sign test (see methods). Pearson correlation coefficients r from these analyses are in Supplementary Table 3. In b) % positive correlations (left column) and numbers of negative and positive r with p<0.05 uncorrected (middle and right columns).

Figure 1. Graphical representation of Table 1 European data results. Adverse effects | COVID 19 Vaccine Death Count
Figure 1. Graphical representation of Table 1 European data results. Adverse effects

Figure 2. Example correlation plot from the European dataset

Z-score of weekly mortality for ages 15-44 in 23 countries on week 14 of 2021 as a function of increase in percent vaccinated in these countries, during week 11 of 2021. For this analysis, the time lag in weeks between injection and mortality is 14-11=3 weeks. The association indicates adverse injection effects during the first weeks after injection.

Figure 1. Graphical representation of Table 1 European data results. Adverse effects | COVID 19 Vaccine Death Count
c

Figure 3. Scatter plots of monthly vaccination doses vs. subsequent month deaths with best fit regression lines from the US CDC dataset

The graph plots log(administered vaccine doses) vs. log(residual July 2021 deaths) after adjusting for log(July, 2020 deaths) for each month (top) and age group (right), for each regression model in which the B2 term survived p<0.05 FDR corrected (see Table 2 and methods) ns=not significant. For a higher resolution image see Supplementary Figure S1, and for the highest resolution plots viewable in a web browser see Supplementary Figure S1 tab
in this link.

Figure 3. Scatter plots of monthly vaccination doses vs. subsequent month deaths with best fit regression lines from the US CDC dataset | COVID 19 Vaccine Death Count
Figure 3. Scatter plots of monthly vaccination doses vs. subsequent month deaths with best fit regression lines from the US CDC dataset

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Supplementary Material for “COVID-19 vaccination and age-stratified all-cause mortality risk”

Supplementary Results

In this section they include some important data that COVID-19 Vaccines administered to pregnant women caused over 36% to experience pregnancy disorders:

“The 0-17 age group is peculiar in that it includes infants <1 years old. Infant deaths comprise the majority of deaths in this age group (1). Since infants are not vaccinated, we hypothesized this effect could be attributed to vaccinations in the mother given a July, 2021 report that found 2,346 VAERS-reported cases were pregnant mothers at time of vaccination, 36% of whom experienced some type of pregnancy disorder (2). To further test this possibility, an additional regression in the <1 years of age group was run, and results were significant for the August model (p<0.05 corrected). The model estimated 667 infant deaths in the US during the month of August, 2021 may be attributed to vaccinations in July, 2021, while 1,227 deaths were estimated overall in the 0-17 age group (see light blue box, Table 2 of main text).”

In light of this data, we must ask why the media and government have pushed so hard to COVID vaccinate pregnant mothers…making strong claims given as to the safety of COVID 19 vaccines for pregnant mothers.

Supplementary Methods and Materials

In this section they include additional raw data and tables from both the EU and US dataset sources used in this study, as well as advanced methods or calculations used to analyze the data…The majority this data is highly technical, and shall be omitted from this article to remain concise and relevant to the average reader; please view the full study for the full supplemental methods and materials data.

EU Data Sources

“Weekly age-stratified mortality data were extracted from euromomo.eu for each week since the start of 2021 for 22 countries covered by euromomo (21 european countries (Austria, Belgium, Cyprus, Danemark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxemburg, Malta, Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, Switzerland, UK (England)) and Israel). These consist of weekly adjusted z-scores, for 6 age groups: 0-14, 15-44, 45-64, 65-74, 75-84 and 80+ years of age. Weekly increases in percentages of the total population who received at least one injection are extracted from Coronavirus (COVID) Vaccinations – Statistics and Research – Our World in Data.”

US Data Sources

“All US data used in the analyses are publicly available and were obtained from either the CDC
or US Census Bureau.
Vaccination rates across time and US states were extracted from the “COVID Vaccinations in the United States,Jurisdiction” spreadsheet (3). Total deaths per month by age group and sex for each US state were extracted from “Provisional COVID Deaths by Sex and Age” spreadsheet (4). Number of COVID cases per month in each US state were extracted from “United States COVID Cases and Deaths by State over Time” (5). Age-stratified total populations per US state in 2019 were obtained from “NC-EST2019-AGESEX” spreadsheet (6). Spreadsheets were accessed during the first week of September 2021 and included data up through September 1st, 2021.”

Supplementary Discussion and References

Errors and concerns raised with vaccines safety studies of pregnant women

Here they go into a bit more detail on data that contradicts the CDCs claim of COVID vaccines being safe for pregnant women; citing several studies that show risk of COVID vaccines on pregnant women, and their unborn offspring:

“Although vaccination during pregnancy is reported as safe by the US CDC (11), a number of issues and concerns have been raised with the studies supporting vaccine safety among pregnant women.”

The Studies and articles which demonstrate risk of COVID-19 vaccines in pregnant women. I’ve provided titled and links to these studies along with a brief excerpt from each:

I also want you to understand that medical studies are very expensive, as is getting drugs FDA approved (it can cost millions). Typically studies are often funded by big pharma. And since big Pharma (of which members include Pfizer, Astrazeneca, J&J and Moderna) esentially runs the COVID show, It’s a wonder that any counter narrative studies are preformed. These doctors put their careers and reputations on the line, and often preform these studies at their own expense simply to save lives. These studies are therefore meticulous scrutinized, and attacked by both Big Pharma and their well funded media campaigns and paid shills. If they can’t find a flaw in the study to discredit it, they will attack the authors reputation, or simply dump a few million into counter studies to counter the data.

Shimabukuro et al. (Original Jun 17 Ver.)

Please Note: There is a Jun 17 2021 Original Version, and an amended Apr 2021 version; where they essentially edited to state “No denominator was available to calculate a risk estimate for spontaneous abortions” however the data remains the same; probably to avoid an expensive attack by big pharma and the media.

“reported a spontaneous abortion rate <20 weeks gestation rate of 12.6% after vaccination, which is similar to previously published background rates. However, their denominator includes ~700 women who were vaccinated after the timeframe for recording the outcome had elapsed (up to 20 weeks of pregnancy). Excluding those participants results in a spontaneous abortion incidence rate that 7-8 times higher (82%-91%) than the originally report rate. Note that the rate seems high because the study only examined completed pregnancies and many participants were yet not followed up on at the time of the report (at early stages the majority of completed pregnancies are expected to be spontaneous abortions). Shimabukuro et al. has since issued correction which now states “No denominator was available to calculate a risk estimate for spontaneous abortions” in the Table footnotes. However, the article abstract, results and discussion still report and discuss the initial findings of the study, including the 12.6% spontaneous abortion rate in those exposed to vaccines before 20 weeks.

COVID vaccination and age-stratified all-cause mortality risk

The following info is pasted directly from the study results:

Among 3958 participants enrolled in the v-safe pregnancy registry:

  • 827 had a completed pregnancy
  • 115 (13.9%) were pregnancy losses
  • 712 (86.1%) were live births (mostly among participants vacci –
    nated in the third trimester).

Adverse neonatal outcomes included:

  • preterm birth (in 9.4%)
  • small size for gestational age (in 3.2%)
  • no neonatal deaths were
    reported.
Brock and Thornley

A study was published Nov 21 titled Spontaneous Abortions and Policies on COVID-19 mRNA Vaccine Use During Pregnancy, which pointed out the flaws and errors in the CDC funded study Shimabukuro et al. (2021), which claimed COVID vaccines were safe for pregnant women.

The study conclusion first states they call into the question the conclusions of the SHimabukuro et al. study and it’s claims for mRNA vaccine safety in pregnant women; a study that has bene the driving force behind the push for vaccinating pregnant women:

“We question the conclusions of the Shimabukuro et al.[4] study to support the use of the mRNA vaccine in early pregnancy, which has now been hastily incorporated into many international guidelines for vaccine use, including in New Zealand.[1]

It continues stating the claims Shimabukuro et al. made supporting mRNA vaccine safety in pregnant women is incorrect becuase it assume exposure in the third trimester represents the effect of exposure throughout pregnancy; it ignores past experiences with other drugs:

“The assumption that exposure in the third trimester cohort is representative of the effect of exposure throughout pregnancy is questionable and ignores past experience with drugs such as thalidomide.[38]”

They argue that based on past experience, in order to estimate the safety of a drug, the studies must follow at least to the perinatal period stage of pregnancy:

“Evidence of safety of the product when used in the first and second trimesters cannot be established until these cohorts have been followed to at least the perinatal period or long-term safety determined for any of the babies born to mothers inoculated during pregnancy.”

Pfizers own statements contradicts the claims of proven mRNA vaccine safety in pregnant women:

“Additionally, the product’s manufacturer, Pfizer, contradicts these assurances, stating: “available data on Comirnaty administered to pregnant women are insufficient to inform vaccine-associated risks in pregnancy”, and “it is not known whether Comirnaty is excreted in human milk” as “data are not available to assess the effects of Comirnaty on the breastfed infant” (page 14).[39]”

The study also cited past experience with exposure to the influenza vaccines containing h1N1pdm09 and links to spontaneous abortion:

“Caution should be exercised in the administration of vaccines in pregnancy, as indicated by the possible association between the exposure to influenza vaccines containing H1N1pdm09 (2010–11 and 2011–12) and spontaneous abortion.[40]”

Finally they take it one step further and state based on the evidence mRNA vaccines should be immediately withdrawn from use in pregnant or lactating women:

we suggest the immediate withdrawal of mRNA vaccine use in pregnancy (Category X)[41] and those breastfeeding, alongside the withdrawal of mRNA vaccines to children or those of child-bearing age in the general population”

References
  1. The Immunisation Advisory Centre. COVID-19 Education. 2021. COVID-19 vaccines in
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  2. Centers for Disease Control and Prevention (CDC). 2021. COVID-19 Vaccines while pregnancy or breastfeeding. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/pregnancy.html Accessed Aug. 3, 2021.
  3. Australian Government Department of Health. 2021. COVID-19 vaccination decision guide for women who are pregnant, breastfeeding or planning pregnancy. https://www.health.gov.au/resources/publications/covid-19-vaccination-shared-decision-making-guide-for-women-who-are-pregnant-breastfeeding-or-planning-pregnancy Accessed Jul. 31, 2021.
  4. Shimabukuro TT, Kim SY, Myers TR, et al. 2021 Preliminary findings of mRNA Covid-19 vaccine safety in pregnant persons. New England Journal of Medicine 384(24): 2273–82. https://www.nejm.org/doi/full/10.1056/nejmoa2104983
  5. McCullough PA, Bernstein I, Jovanovic S, McLeod D, Stricker RB. 2021, Jul. 30. Lack of compelling safety data for mRNA COVID vaccines in pregnant women. https://trialsitenews.com/lack-of-compelling-safety-data-for-mrna-covid-vaccines-in-pregnant-women/
  6. Dugas C, Slane VH. 2021, Jun 29. Miscarriage. StatPearls. Treasure Island (FL): StatPearls Publishing. PMID: 30422585. https://www.ncbi.nlm.nih.gov/books/NBK532992/
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  21. Grobbelaar LM, Venter C, Vlok M, Ngoepe M, Laubscher GJ, Lourens PJ, Steenkamp J, Kell DB, Pretorius E. 2021. SARS-CoV-2 spike protein S1 induces fibrin(ogen) resistant to fibrinolysis: Implications for microclot formation in COVID-19. Bioscience Reports 41(8): BSR20210611. doi: 10.1042/BSR20210611. https://pubmed.ncbi.nlm.nih.gov/34328172/
  22. Mendoza M, Garcia-Ruiz I, Maiz N, Rodo C, Garcia-Manau P, Serrano B, Lopez-Martinez RM, Balcells J, Fernandez-Hidalgo N, Carreras E, Suy A. 2020. Pre-eclampsia-like syndrome induced by severe COVID-19: A prospective observational study. BJOG 127(11):1374–1380. doi: 10.1111/1471-0528.16339. https://obgyn.onlinelibrary.wiley.com/doi/10.1111/1471-0528.16339
  23. European Medicines Agency. 2021, Feb. 19. Assessment report: Comirnaty. https://www.ema.europa.eu/en/documents/assessment-report/comirnaty-epar-public-assessment-report_en.pdf
  24. European Medicines Agency. 2021, Mar. 1. EU Risk Management Plan for COVID-19 mRNA vaccine Moderna. https://www.ema.europa.eu/en/documents/rmp-summary/spikevax-previously-covid-19-vaccine-moderna-epar-risk-management-plan_en.pdf
  25. Mattar CNZ, Koh W, Seow Y, et al. 2021. Addressing anti-syncytin antibody levels, and fertility and breastfeeding concerns, following BNT162B2 COVID-19 mRNA vaccination. medRxiv preprint: 2021, May 25. doi: 10.1101/2021.05.23.21257686. https://www.medrxiv.org/content/10.1101/2021.05.23.21257686v1
  26. National Vaccine Information Centre. 2021. Search the VAERS database. https://medalerts.org/vaersdb/
  27. Guo L, Gu F, Xu Y, Zhou C. 2020, Nov. 17. Increased copy number of syncytin-1 in the trophectoderm is associated with implantation of the blastocyst. PeerJ Life & Environment 8:e10368. doi: 10.7717/peerj.10368. https://peerj.com/articles/10368/
  28. Gallaher B. 2020, Feb. Response to nCov2019 against backdrop of endogenous retroviruses. https://virological.org/t/response-to-ncov2019-against-backdrop-of-endogenous-retroviruses/396 Accessed Aug. 3, 2021.
  29. Langbein M, Strick R, Strissel PL, et al. 2008. Impaired cytotrophoblast cell-cell fusion is associated with reduced Syncytin and increased apoptosis in patients with placental dysfunction. Molecular Reproduction & Development 75(1): 175–83. doi: 10.1002/mrd.20729. https://onlinelibrary.wiley.com/doi/10.1002/mrd.20729
  30. Lokossou AG, Toudic C, Barbeau B. 2014. Implication of human endogenous retrovirus envelope proteins in placental functions. Viruses 6(11): 4609–27. doi: 10.3390/v6114609. https://www.mdpi.com/1999-4915/6/11/4609
  31. Soygur B, Sati L, Demir R. 2016. Altered expression of human endogenous retroviruses syncytin-1, syncytin-2 and their receptors in human normal and gestational diabetic placenta. Histology and Histopathology 31(9): 1037–47. doi: 10.14670/HH-11-735. https://pubmed.ncbi.nlm.nih.gov/26875564/
  32. Köhn FM, Müller C, Drescher D, et al. 1998. Effect of angiotensin converting enzyme (ACE) and angiotensins on human sperm functions. Andrologia 30(4–5): 207–15. doi: 10.1111/j.1439-0272.1998.tb01162.x. https://onlinelibrary.wiley.com/doi/10.1111/j.1439-0272.1998.tb01162.x
  33. Rajput SK, Logsdon DM, Kile B, et al. 2021. Human eggs, zygotes, and embryos express the receptor angiotensin 1-converting enzyme 2 and transmembrane serine protease 2 protein necessary for severe acute respiratory syndrome coronavirus 2 infection. F&S Science 2(1): 33–doi: 10.1016/j.xfss.2020.12.005. https://www.sciencedirect.com/science/article/pii/S2666335X20300616
  34. Verma S, Saksena S, Sadri-Ardekani H. 2020 ACE2 receptor expression in testes: Implications in coronavirus disease 2019 pathogenesis. Biology of Reproduction 103(3): 449–51. doi: 10.1093/biolre/ioaa08 https://academic.oup.com/biolreprod/article/103/3/449/5840520
  35. Virant-Klun I, Strle F. 2021. Human oocytes express both ACE2 and BSG genes and corresponding proteins: Is SARS-CoV-2 infection possible? Stem Cell Reviews and Reports 17(1): 278–84. doi: 10.1007/s12015-020-10101-x. https://link.springer.com/article/10.1007%2Fs12015-020-10101-x
  36. Food and Drug Administration. 2021, Aug. Package insert – Comirnaty. https://www.fda.gov/media/151707/download Accessed Aug. 31, 2021.
  37. Gonzalez DC, Nassau DE, Khodamoradi K, et al. 2021. Sperm parameters before and after COVID-19 mRNA vaccination. JAMA 326(3):273–274. doi: 10.1001/jama.2021.9976. https://jamanetwork.com/journals/jama/fullarticle/2781360
  38. Vargesson N. 2015. Thalidomide-induced teratogenesis: History and mechanisms. Birth Defects Research (Part C) 105(2): 140–56. doi: 10.1002/bdrc.21096. https://onlinelibrary.wiley.com/doi/full/10.1002/bdrc.21096
  39. Pfizer. 2021, Aug. 23. Pfizer-BioNTech COVID- 19 vaccine COMIRNATY® receives full U.S. FDA approval for individuals 16 years and older. https://www.pfizer.com/news/press-release/press-release-detail/pfizer-biontech-covid-19-vaccine-comirnatyr-receives-full Accessed Sep. 5, 2021.
  40. Donahue JG, Kieke BA, King JP, DeStefano F, Mascola MA, Irving SA, Cheetham TC, Glanz JM, Jackson LA, Klein NP, Naleway AL, Weintraub E, Belongia EA. 2017. Association of spontaneous abortion with receipt of inactivated influenza vaccine containing H1N1pdm09 in 2010–11 and 2011–12. Vaccine 35(40):5314–5322. doi: 10.1016/j.vaccine.2017.06.069. https://www.sciencedirect.com/science/article/pii/S0264410X17308666?via%3Dihub
  41. New Zealand Medicines and Medical Devices Safety Authority (MEDSAFE). 2013, Jun. 6. Medicines and use in pregnancy. Prescriber Update 34(2):18–19. https://medsafe.govt.nz/profs/PUArticles/June2013MedsInPregnancy.htm Accessed Aug. 5, 2021.
McCullough et al.

An article was published Jul 30 2021 on trialsitenews by Dr. Peter McCullough titled Lack of Compelling Safety data for mRNA COVID Vaccines in Pregnant Women, which outlined the lack of data supporting the claims of covid mRNA vaccines safety, for pregnant women. Like the Brock and Thornley study, this study also calls into question the Shimabukuro et al. (2021) study, and points out errors that effect it’s ability to accurately assess the safety of mRNA vaccines for pregnant women:

Please note: sab = Sudden Abortion

“In Table 4, the authors report a rate of spontaneous abortion (Sab) in early pregnancy (<20 weeks) of 12.6% (104 Sabs/827 completed pregnancies). This number is misleading, however, as this subset represents only 20.9% of women enrolled in the registry, and 84.6% (n=700) of women received their first vaccine dose in the third trimester. For all other pregnancy outcomes, the authors calculated event proportions by dividing the number of events by the number of participants eligible for that event. However, for Sab they divide the number of events by the entire cohort of completed pregnancies, rendering the statistic meaningless.

Furthermore they state the SAB rates estimated in the study, were incorrect and leads readers to draw wrong conclusion that there is no increased vaccine associated risk:

“Additionally, the authors indicate that the rate of Sabs in the published literature is between 10% and 26%.2-4 However, this range includes clinically-unrecognized pregnancies2,5,6 so the upper limit should be closer to 10% because the study relied on self-reporting that would only detect clinically recognized pregnancies.2,5,7 Reporting a Sab rate of 12.6% in Table 4 may lead some to conclude that there is no increased vaccine-associated risk of Sab in early pregnancy by comparing it to the background rate of 10% to 26%

They also state that the study conclusion that “no obvious safety signals were detected among the pregnant persons who received mRNA COVID-19 vaccines” neglected to incorporate important data:

“Finally, the authors conclude that “no obvious safety signals were detected among pregnant persons who received mRNA COVID-19 vaccines”. However, this does not seem to account for the >12.6% of reported grade 3 adverse events or 8% of women who reported a temperature ≥38 °C among those receiving 2 doses where it is known fever itself can induce miscarriage or premature labor.8-10

Furthermore multiple studies show the spike protein can damage the uterus, placenta and possibly even fetus:

“Additionally, administration of mRNA COVID-19 vaccines results in the production of the spike protein, which has been implicated in pathogenic mechanisms that affect the uterus, placenta, and possibly the fetus.11-21

Studies lack significant data on teratogenicity, oncogenicity and genotoxicity:

“To our knowledge these biologically active agents lack studies of teratogenicity, oncogenicity, and genotoxicity that assure their safety.22-24

They call for the v-safe data to be publicly available and subject to scrutiny and dat analysis to accurately analyze mRNA vaccine safety for pregnant women:

“we request that the authors make the data available for public scrutiny, conduct a temporal association analysis to explore vaccine-related events in pregnant women, and calculate Sab rates based on cohorts at risk of the event, especially when the vaccines are given in early pregnancy.

There is more available on the TrialsiteNews original article.

References
  1. Shimabukuro TT, Kim SY, Myers TR, et al. Preliminary Findings of mRNA Covid-19 Vaccine Safety in Pregnant Persons. N Engl J Med 2021;384:2273-82.
  2. Dugas C, Slane VH. Miscarriage.  StatPearls [Internet]. (https://www.ncbi.nlm.nih.gov/books/NBK532992/; Accessed Jun 21, 2021) StatPearls Publishing LLC; 2021.
  3. Obstetricians ACo, Gynecologists. ACOG practice bulletin no. 200: Early pregnancy loss. Obstet Gynecol 2018;132:e197-e207.
  4. Practice Committee of the American Society for Reproductive Medicine. Evaluation and treatment of recurrent pregnancy loss: a committee opinion. Fertil Steril 2012;98:1103-11.
  5. Wilcox AJ, Weinberg CR, O’Connor JF, et al. Incidence of early loss of pregnancy. N Engl J Med 1988;319:189-94.
  6. Zinaman MJ, Clegg ED, Brown CC, O’Connor J, Selevan SG. Estimates of human fertility and pregnancy loss. Fertil Steril 1996;65:503-9.
  7. Magnus MC, Wilcox AJ, Morken N-H, Weinberg CR, Håberg SE. Role of maternal age and pregnancy history in risk of miscarriage: prospective register based study. BMJ 2019;364:l869.
  8. Dreier JW, Andersen A-MN, Berg-Beckhoff G. Systematic review and meta-analyses: fever in pregnancy and health impacts in the offspring. Pediatrics 2014;133:e674-e88.
  9. Edwards MJ. Hyperthermia and fever during pregnancy. Birth Defects Res Part A: Clin Mol Teratol 2006;76:507-16.
  10. Krubiner CB, Faden RR, Karron RA, et al. Pregnant women & vaccines against emerging epidemic threats: ethics guidance for preparedness, research, and response. Vaccine 2021;39:85-120.
  11. Yu J, Yuan X, Chen H, Chaturvedi S, Braunstein EM, Brodsky RA. Direct activation of the alternative complement pathway by SARS-CoV-2 spike proteins is blocked by factor D inhibition. Blood 2020;136:2080-9.
  12. Kulkarni HS, Atkinson JP. Targeting complement activation in COVID-19. Blood 2020;136:2000-1.
  13. Wang H, Chen Q, Hu Y, et al. Pathogenic antibodies induced by spike proteins of COVID-19 and SARS-CoV viruses. (preprint) 2021;doi:10.21203/rs.3.rs-612103/v1.
  14. Colaco C. Thrombosis, Spike and Complement activation in COVID19 (Response to: Thrombosis after covid-19 vaccination). BMJ 2021;373:n958/rr-6.
  15. Lei Y, Zhang J, Schiavon CR, et al. SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2. Circ Res 2021;128:1323-6.
  16. Biancatelli RC, Solopov P, Sharlow ER, Lazo JS, Marik PE, Catravas JD. The SARS-CoV-2 Spike Protein Subunit 1 induces COVID-19-like acute lung injury in Κ18-hACE2 transgenic mice and barrier dysfunction in human endothelial cells. Am J Physiol Lung Cell Mol Physiol (online ahead of print) 2021;doi:10.1152/ajplung.00223.2021.
  17. Suzuki YJ, Gychka SG. SARS-CoV-2 spike protein elicits cell signaling in human host cells: Implications for possible consequences of COVID-19 vaccines. Vaccines 2021;9:36.
  18. Cines DB, Bussel JB. SARS-CoV-2 vaccine–induced immune thrombotic thrombocytopenia. N Engl J Med 2021;384:2254-6.
  19. Scully M, Singh D, Lown R, et al. Pathologic antibodies to platelet factor 4 after ChAdOx1 nCoV-19 vaccination. N Engl J Med 2021;384:2202-11.
  20. Greinacher A, Thiele T, Warkentin TE, Weisser K, Kyrle PA, Eichinger S. Thrombotic thrombocytopenia after ChAdOx1 nCov-19 vaccination. N Engl J Med 2021;384:2092-101.
  21. Schultz NH, Sørvoll IH, Michelsen AE, et al. Thrombosis and thrombocytopenia after ChAdOx1 nCoV-19 vaccination. N Engl J Med 2021;384:2124-30.
  22. Assessment report: Comirnaty, COVID-19 mRNA vaccine (nucleoside-modified). Procedure No. EMEA/H/C/005735/0000. 2021. (Accessed June 17, 2021, at https://www.ema.europa.eu/en/documents/assessment-report/comirnaty-epar-public-assessment-report_en.pdf.)
  23. Kostoff RN, Briggs MB, Porter AL, Spandidos DA, Tsatsakis A. [Comment] COVID‑19 vaccine safety. Int J Mol Med 2020;46:1599-602.
  24. Vaccines and Related Biological Products Advisory Committee Meeting December 17, 2020. FDA Briefing Document Moderna COVID-19 Vaccine. 2020. (Accessed June 17, 2021, at https://www.fda.gov/media/144434/download.)
  25. Food Drug Administration DHHS. Content and format of labeling for human prescription drug and biological products; requirements for pregnancy and lactation labeling. Final rule. Fed Regist 2014;79:72063-103.
  26. Gruber MF. The US FDA pregnancy lactation and labeling rule–Implications for maternal immunization. Vaccine 2015;33:6499-500.
  27. Zambrano LD, Ellington S, Strid P, et al. Update: characteristics of symptomatic women of reproductive age with laboratory-confirmed SARS-CoV-2 infection by pregnancy status—United States, January 22–October 3, 2020. Morb Mortal Weekly Rep 2020;69:1641.
  28. Fesler MC, Stricker RB. Pre-exposure prophylaxis for covid-19 in pregnant women. Int J Gen Med 2021;14:279.
Kharbanda et al.

The study also in the supplemental – discussions section, cites the Kharbanda et al. study published on Oct 2021 titled Spontaneous Abortion Following COVID-19 Vaccination During Pregnancy, which claimed:

“Among women with spontaneous abortions, the odds of COVID-19 vaccine exposure were not increased in the prior 28 days compared with women with ongoing pregnancies” (15)

Spontaneous Abortion Following COVID-19 Vaccination During Pregnancy

They list several errors that are present in the Kharbanda et al. study:

  1. “article by Cosentino points out that a reanalysis of the frequencies reported in Table 1 shows the crude OR of vaccine exposure in women with spontaneous abortions is 1.07 (95% CI: 1.01-1.14, p = 0.025 by Fisher’s exact test), a result that is apparently fully accounted for by the maternal age group 16-24 y, where the crude OR is 1.37 (95% CI: 1.07-1.75, P = 0.017).”
  2. “Cosentino also points out the arbitrariness of using 28 days as a window. Why not track and report spontaneous abortion rates across all participants up through week 19 gestation? The response by Kharbanda et al. to Cosentino states that their results differ because they controlled for confounding variables, but they do not report statistics for the nuisance terms, making it difficult to assess which nuisances variable accounted correlated with higher spontaneous abortions rates and why.”
  3. “authors’ original analysis DOES report trend level evidence for increased risk of spontaneous abortion (see Table 2, gestation weeks 9-13, OR 1.07, 95% CI 0.99-1.17), but the result is not discussed by the authors elsewhere in the article”
  1. Vaccination predicts mortality in future time periods. Thus our results can not reflect increases in vaccination rates that are caused by increased mortality. Temporal precedence is a basis for inferring causality in i.e. Granger causality analysis.” – In other words because evidence shows vaccination predicts mortality in future time periods, it cant possible infer that vaccination rates are caused by increased mortality.
  2. Our estimates for total deaths due to vaccination are strikingly similar to independent estimates based on a fundamentally different dataset and approach based on the VAERS database that uses data-driven, credible assumptions about the VAERS underreporting bias (16). Our results provide independent, converging lines of evidence for vaccine-induced mortality risk, lending further credence to their accuracy and credibility.” – In other words other studies show similar estimates using very different methods and calculations, on the VAERS underreporting bias.
  3. We are aware of only one variable, COVID cases, that could potentially confound our results. This could happen IF more people get vaccinated as local COVID cases rise and COVID deaths comprise a majority of the deaths in subsequent time periods. Below are the main reasons why COVID case rates do NOT explain our findings:
    1. An additional set of analyses that include COVID case numbers in previous month as a nuisance regressor yielded largely similar results (Supplementary Table S5). – In other words using another data set the study was able to remote nuisance signals, and the results were similar.
    2. A secondary set of analyses that use non-COVID, Influenza, and Pneumonia deaths (non-COVINFPNU) as the dependent variable yielded similar results to analyses that use total deaths, but with larger p-values because there are substantially fewer observations for each regression (Supplementary S6). Note that non-COVINFPNU deaths were not used the primary outcome because the COVID death variable is missing for younger ages (sample size is cut in half for ages 40-49 and below 30 it is about 10-25% of the full sample size when using Total Deaths), and for the younger age groups it is zero for most states that do report a value. – In other words utilizing other daa sets for non-COVID virus infections such as Influenza or Pneumonia deaths yielded similar results to analysis that used total deaths. Death wasn’t the endpoint because COVID deaths in children is so low the data essentially isnt there.
    3. Vaccination rates predict mortality in younger age groups (where COVID deaths are much rarer), providing further support that the effects seen here are not due to COVID. – In other words kids have such a low death rate from covid (almost non existant) that it adds further credence to the assertion COVID was not a factor.
  4. The existing COVID vaccine surveillance studies supporting vaccine safety contain critical errors, issues and limitations (see Discussion, Supplementary Discussion and (17) ). – In other words as you’ve seen throughout this study…they have pointed out numerous errors in other studies which purported the safety of mRNA and COVID vaccines for use in pregnant women and children.
  5. Our results comport (agree) with the volume and nature of responses to social media posts, the FDA dockets for solicited public comments, and websites created to give voice to the vaccine-injured (see Conclusions for sample links and URLs). – In other words numerous stories directly from COVID vaccine injured individuals agree with the data found in this study.
  6. Our US results show an age-related temporal pattern that is consistent with the mass vaccination campaign that first targeted nursing homes and older age groups (i.e. vaccination predicts total deaths in ages older than 75 in early 2021, and then in younger ages later in the year). There appears to be no other explanation for this other than a causal link between vaccination and mortality risk. – In other words US results in this study, follow the timeline of the vaccination campaigns and the rollout first in nursing homes and then older age groups; proves a causal link between COVID vaccines and moratality risk.
  7. Given items 1-6 and the absence of other potential confounding variables, the most logical and reasonable conclusion is that our results reflect a causal effect of COVID vaccination on mortality. – In other words when you factor in the aformentioned information the most logical conclusion is a link between COVID vaccines causing increased mortality.

Life-altering COVID vaccine injuries: real-world evidence through personal testimonials

Here they list a few website / resources which where vaccine injured individuals have shared their COVID vaccine adverse event as well as a Gov website where individuals discuss approval of COVID vaccines for children:

“The post is telling of how injured patients, or those who have lost friends or family to vaccine-induced death, are often ignored by the same major news outlets that encouraged them to be vaccinated.”

  • c19vaxreactions.com
  • nomoresilence.world
  • https://www.regulations.gov/document/FDA-2021-N-1088-0001 “Readers are also encouraged to read the thousands of solicited comments submitted to the public FDA advisory committee meeting held on Oct 26th, 2021 to discuss approval of the COVID vaccines for children ages 5-11”
  • Facebook post by WXYZ-TV Channel 7 “Perusing through over 250K comments left on a Facebook post by WXYZ-TV Channel 7 is also illuminating…The post asked people who had lost an unvaccinated loved one to COVID to contact them for a story, but instead received tens, if not hundreds, of thousands of stories of vaccine injuries or deaths instead.”

I’ll add a several additional COVID-19 vaccine injury resources, to their list:

And here is another post we published…Report shows over 300 athletes who collapsed or suffered cardiac arrest after receiving COVID Vaccine

They conclude this vaccine injuries resource section, by noting that when people realize they’ve made a mistake in endorsing the deadly COVID-19 vaccines, they need to admit their mistake; and I agree with them…instead of people doubling down because they don’t want to look bad, or admit they are complicit in mass murder…for the lives of others, especially the children, they need to admit their mistake and help prevent anyone else from suffering at the hands of these deadly COVID-19 gene based experimental drug injections:

“This is understandable, as no one, especially those with good intentions and high hopes but who were misled by less-than-rigorous science, wants to acknowledge the possibility that the COVID vaccines and their boosters may be causing more harm than good overall. The sooner the taboo surrounding research and discussion of vaccine-induced injury and death is lifted, the sooner public health policy can be adjusted and resources can be mobilized to identify and develop therapies and interventions

I honestly don’t know what it is in humans that makes them adverse to fessing up to their mistakes, but people need to go beyond their own selfish egotistical desires and do whats morally and ethically right; the end results will be millions of lives saved…

Supplementary References

  1. Death rate by age and sex in the U.S. 2018 [Internet]. Statista. [cited 2021 Oct 3]. Available from: https://www.statista.com/statistics/241572/death-rate-by-age-and-sex-in-the-us/
  2. Cotton C. VAERS Data Analysis [Internet]. Available from: https://www.francesoir.fr/sites/francesoir/files/fs_vaers_data_analysis_report-2021-08-08.pdf
  3. COVID-19 Vaccinations in the United States,Jurisdiction | Data | Centers for Disease Control and Prevention [Internet]. [cited 2021 Oct 1]. Available from: https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc
  4. Provisional COVID-19 Deaths by Sex and Age | Data | Centers for Disease Control and Prevention [Internet]. [cited 2021 Oct 1]. Available from: https://data.cdc.gov/NCHS/Provisional-COVID-19-Deaths-by-Sex-and-Age/9bhg-hcku
  5. United States COVID-19 Cases and Deaths by State over Time | Data | Centers for Disease Control and Prevention [Internet]. [cited 2021 Oct 1]. Available from: https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36
  6. Bureau UC. 2019 Population Estimates by Age, Sex, Race and Hispanic Origin [Internet]. The United States Census Bureau. [cited 2021 Oct 1]. Available from: https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html
  7. Vickers AJ. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Med Res Methodol. 2001;1:6.
  8. Clifton L, Clifton DA. The correlation between baseline score and post-intervention score, and its implications for statistical analysis. Trials. 2019 Jan 11;20(1):43.
  9. Van Breukelen GJP. ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies. Journal of Clinical Epidemiology. 2006 Sep 1;59(9):920–5.
  10. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289–300.
  11. CDC. Vaccination Considerations for People Pregnant or Breastfeeding [Internet]. Centers for Disease Control and Prevention. 2021 [cited 2021 Nov 6]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/pregnancy.html
  12. Brock AR, Thornley S. Spontaneous Abortions and Policies on COVID-19 mRNA Vaccine Use During Pregnancy. Science, Public Health Policy, and the Law. 2021 Nov;4:130–43.
  13. Lack of Compelling Safety data for mRNA COVID Vaccines in Pregnant Women [Internet]. TrialSiteNews. 2021 [cited 2021 Nov 6]. Available from: https://trialsitenews.com/lack-of-compelling-safety-data-for-mrna-covid-vaccines-in-pregnant-women/
  14. Shimabukuro TT, Kim SY, Myers TR, Moro PL, Oduyebo T, Panagiotakopoulos L, et al. Preliminary Findings of mRNA Covid-19 Vaccine Safety in Pregnant Persons. New England Journal of Medicine. 2021 Jun 17;384(24):2273–82.
  15. Kharbanda EO, Haapala J, DeSilva M, Vazquez-Benitez G, Vesco KK, Naleway AL, et al. Spontaneous Abortion Following COVID-19 Vaccination During Pregnancy. JAMA. 2021 Oct 26;326(16):1629–31.
  16. Rose J, Crawford M. Estimating the number of COVID vaccine deaths in America [Internet]. Available from: https://downloads.regulations.gov/CDC-2021-0089-0024/attachment_1.pdf
  17. Pantazatos S. Vaccine mandates are not based on sound science: they are harmful and should be lifted as soon as possible. 2021 Aug 23 [cited 2021 Sep 9]; Available from: https://researchers.one/

More on VAERS Pitfalls | Unrelated to Study

Here we at WOKEGuru highlight some of the other negatives of the VAERS system: why it is inadequate to accurately guage COVID vaccine safety, as well as what contributes to the well known ‘underreporting factor’ that sees only a small percentage of COVID-19 vaccine injuries actually get reported in the database:

VEARS System pitfalls

If you look on the VAERS website, when you click to download VAERS Data, under DISCLAIMER. It states the following.

  1. Vaccine providers are encouraged to report any clinically significant health problem following vaccination to VAERS, whether or not they believe the vaccine was the cause.
  2. Reports may include incomplete, inaccurate, coincidental and unverified information.
  3. The number of reports alone cannot be interpreted or used to reach conclusions about the existence, severity, frequency, or rates of problems associated with vaccines.
  4. VAERS data is limited to vaccine adverse event reports received between 1990 and the most recent date for which data are available.
  5. VAERS data do not represent all known safety information for a vaccine and should be interpreted in the context of other scientific information.

The VAERS Disclaimer highlights just how accurate (or rather inaccurate) the system is in collecting data from adverse events related to vaccines. But it misses a few things…

Why the VAERS System is so inadequate

Why might the VAERS system be lacking an accurate number on the true volume of adverse events?

  1. VAERS reports are extremely difficult to fill out, and are usually submitted with the help of a licenced health care practitioner;.
  2. Many doctors find the VAERS application convoluted to fill out, and have difficulty filling it out.
  3. Patients must meet certain criteria to be eligible for a VAERS report. They can submit the report but if found ineligible, the report will not be submitted to the database. The Canada gov website worded it well “A causal relationship does not need to be proven, and submitting a report does not imply causality.” –canada.ca immunization reporting adverse events
  4. The VAERS System only includes US Data. Many other countries either have an even more convoluted reporting system, or no system at all for reporting vaccine adverse events.
  5. A high volume of reports are found ineligible due to patients not meeting the eligibility criteria, lacking sufficient evidence that ties the adverse event to the vaccine, or are not submitted in the alloted time frame after vaccination.

VAERS Application form | USA

COVID 19 Vaccine Injury Rejected by VAERS

After preforming an autopsy on the deceased, it showed that the vaccine contributed to the cause of death. However even after multiple attempts at filling out a VAERS form, the application for Julian Laor’s vaccine injury was rejected.

VAERS Rejects Application of Julian Laor’s vaccine Injury even after Autopsy Evidence was provided.

VAERS Data up to Sept 10 2021 – COVID 19 Vaccine Death Count | Injuries and death

As of September 10th 2021, over 3.1 million injuries have been recorded in VAERS due to the Covid-19 vaccines, alongside 80,337 emergency room visits, 60,565 hospitalizations, 19,210 permanent disabilities, 15,012 life-threatening events, and 14,925 deaths.

This means there have now been more than twice as many deaths recorded shortly after people received a Covid-19 vaccine, during the 9 months since the Covid-19 vaccines were given emergency use authorization, than deaths recorded following all other available vaccines in the last 30 years.

VAERS Data up to Sept 10 2021 – COVID 19 Vaccinne Death Count | Fetal Deaths

There have also now been 1,614 recorded fetal deaths following pregnant women receiving a Covid-19 vaccine despite no pregnant women taking part in a single clinical trial for any of the Covid-19 vaccines. (source)

A search of the VAERS database shows that there have been zero fetal deaths following pregnant women receiving an influenza vaccine during 2021, and there were just 16 fetal deaths following pregnant women receiving an influenza vaccine in 2020. (source)

Yet authorities are still continuing to recommend that pregnant women get a Covid-19 vaccine.

VAERS Data up to Sept 10 2021 – COVID 19 Vaccine Death Count | Child Injuries

There have been 21 deaths, 77 permanent disabilities, 2,019 emergency room visits, 942 hospitalizations, and 165 life threatening events in children under the age of 17 because of the Covid-19 vaccines.

VAERS Data up to Sept 10 2021 – COVID 19 Vaccine Death Count | mRNA vs other Vaccines

search of VAERS for reports made against all other available vaccines from 1st December 2020 to 20th September 2021 shows that there have been just 1 death, 10 permanent disabilities, 51 emergency room visits, 26 hospitalisations, and 9 life threatening events among children between the ages of 12 and 15. (source)

This means the Covid-19 vaccines have caused 21 times more deaths, nearly 8 times more disabilities, 40 times more emergency room visits, 36 times more hospitalizations, and 18 times more life threatening events among children under the age of 17 than all other available vaccines combined.

Other Studies Analyzing Underreporting of VAERS and COVID 19 Vaccine Death Count

Some doctors and studies have stated VAERS is grossly underreported: Other studies have estimated only 1%-4% of cases are submitted to and approved by VAERS; applying doesn’t guarantee approval, and vaccine injuries are quite often rejected due to not meeting the required criteria.

  1. Lazarus study by Harvard researchers estimated VAERS accounted for only one percent of vaccine-induced injuries.
  2. Steve Kirsch, the executive director of the Vaccine Safety Research Foundation, and others conducted an analysis comparing anaphylaxis rates published in a study to rates found in VAERS. They concluded the true death toll from COVID-19 vaccines is 41 times higher.
  3. The website VAERS Analysis used whistleblower data from the CMS, the Centers for Medicare and Medicaid Services, to come up with an estimated underreporting factor of 44.64

FDA Releases first round of documents analyzed for approval of Pfizers Comirnaty COVID-19 vaccine

In another article we showed the first round of 91+ documents, used for the approval of Pfizer’s Comirnaty by the FDA approval, obtained in November through a Freedom of Information lawsuit.

The lawsuit was filed by a group called Public Health and Medical Professionals for Transparency, comprised of more than 30 professors and scientists from universities including Yale, Harvard, UCLA and Brown.

The FDA fought back requesting from a judge that it be given 55 years to release all 329,000 pages of documents related to the Pfizer COVID-19 vaccine requested by the group. The FDA has now modified that request, asking a judge for a delay of 75 years.

Study by Dr. H. Cody Meissner of Tufts University School of Medicine

In a recent, lengthy interview with podcaster Joe Rogan, Dr. Peter McCullough; an outspoken critic of universal COVID-19 vacination, cited a study conducted before COVID by Dr. H. Cody Meissner of Tufts University School of Medicine, which found that about 80 percent of VAERS reports are done by doctors, nurses or other health-care professionals who believe a vaccine caused the problem. Only about 14 or 15 percent of the reports are done by the patients themselves.

McCullough believes CMS data indicates VAERS underreports by a factor of about four or five.

With the CMS data, he said, “you know when someone got the shot and you know when they died.”

The proportion of Medicare and Medicaid patients in the U.S. population is known, he reasons, so an estimate can be obtained through extrapolation.

A factor of five was used in the lawsuit against the FDA, estimating 45,000 U.S. deaths due to COVID-19 vaccines while VAERS reported 9,000 at the time the case was filed.

Important Facts Regarding VAERS

  1. VAERS is described as a “voluntary” reporting system (not mandatory), although the  HHS says that health-care providers “who administer COVID-19 vaccines are required by law after vaccination to report to VAERS” any errors in administering the shots along with, among other things, deaths and life-threatening adverse events.
  2. The U.S. Department of Health and Human Services (HHS) has stated that VAERS is a “passive” system of reporting, and it “receives reports for only a small fraction of actual adverse events.”
  3. Many health care workers have disclosed they are instructed by their superiors not to report to VAERS any harm caused by COVID vaccines.

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