By Ted Belman
Here is just a few of the blogs of stories of vaccination victims in their own words,
or if dead, in the words of a loved one.
Does Israel have such a blog?
This site plays one video after the next of stories.
https://www.bitchute.com/
heartbreaking
https://drive.google.com/file/
horrific
https://t.me/s/
vaccines increase all causes of morbidity
https://www.skirsch.com/covid/
vaccines cause strokes
https://www.strokejournal.org/
other helpful sites: (see page 77 re Israel vs Sweden graph)
THIS slideshow HAS 199 PAGES OF EVIDENCE – Well worth skimming
https://skirsch.com/covid/
http://mykidsmychoice.com/
How to treat and importance of early treatment
https://www.skirsch.io/how-to-
Doctors world wide who will treat with ivermectin
or https://docs.google.com/
The MSM, a cancer in urgent need of a cure. With their propaganda, omissions and various other type of malfeasances (fabrications, falsifications) the MSM are complicit in the death of numerous human beings.
No one will make them accountable.
@Adam
In addition to Dr. James’ claims, there is something that is rarely discussed and that is the time required to complete a VAERS submission. Doctors and nurses are badly understaffed. Their time is very limited. To fill out a VAERS report requires the patient info, the vaccine card(absolutely required) and the time to fill out the report. A single report takes about 30min. So if a patient comes in and develops a rash, that is 30mins to fill out the report. If he develops myocarditis in a couple of days, that’s another 30min to fill out another report. If he develops a blood clot a week later, that is another 30min to fill another report. If he dies 10 days later, that would be another 30min. So with one patient with 5 maturing symptoms, it would take a single doctor or nurse 2 1/2 hrs over a couple of weeks to note these issues. No one in a hospital setting has the time to do this, if they even know how to do it – a common complaint. There is no overtime, and no additional staff are added so these reports can be filed. The fact that over 500,000 reports have been filed is astonishing, quite frankly. Also, there is the political issue again. No one will fill out a VAERS report without certainty that the issue is associated with the injection for fear of retribution with a claim of a false filing which could cost them their license and upto 5yrs in jail. This is likely why 50% of the reports are in the fist 48hrs post-vax and 80% are in the first week post-vax. Longer than that, might lead the clinician to err on the side of self-preservation and not report the event in question. Just some thoughts.
This is from Gateway Pundit.
I keep trying to download a readable version of the key points in this Cambridge University study by 20 or more academic researchers, which is presently in preprint awaiting publication in a medical journal and peer review. I managed to download most of it above, but without the chart that is at the heart of the study, which refuses to download.
Peloni, could you please read ths article in its original pdf setting, using the above link to the Researchgate site where it is has been published in full. I believe it is a very important contribution to the coronavirus vaccine debate.
Also, please “translate” it for us in as simple laymen-oriented language as you can manage, and give us your opinion of it and its conclusions. Many thanks.
I have been unable to post the most important part of Dr. Fenton’s study. This is the chart in which he gives the ttal number of COVID cases and COVID-related deaths in England between January 2021 and June1921. It shows that both the total number of deaths and the rate of deaths was higher for vaccinated than unvaccinated individuals during this period. Although the chart has been published by many people in many places, I cannot get it to copy. When I have a moment I will try to copy the data manually and post it.
Anyone interested in the Civil War within the FDA should read this summary as well as the Lancet article(shorter) where Krause and Gruber launched their offensive against the White House, which has gained significant success with today’s unanimous vote to support their views against the White House. Some may remember a similar civil war within the FDA some 20yrs ago where the defectors were labeled “termites” in which the termites led the way to victory over the interests of the shareholders. Hopefully something similar will be achieved in this current Civil War as well.
https://brownstone.org/articles/the-meaning-of-the-fda-resignations/
Here is the Lancet article(written for any layman to easily read – not by accident):
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02046-8/fulltext
This is from Professor Fenton’s Twitter page. Here he summarizes some the main conclusions of the pre-print study that he has published, together with twenty other mathematicians and scientists. I posted the full study, although poorly edited, above.
Well, that wasn’t suppose to happen… Very surprising move by an advisory committee on the Boosters.
So, the Civil War brewing within the FDA I mentioned the other day is not only real, the breach has just been made that much wider over which the political establishment must now leap in order to pursue the political decision that had been decided weeks ago. The committee unanimously voted against the use of Universal Boosters shots, such as Israel has pursued. The advisory committee is the only body that will make a recommendation on this topic, but it is upto the FDA itself to make the final decision. The question now comes to who will the FDA support, their own advisory panel or the White House which has already, weeks ago, planned for this seminal booster program to being rolled out next week. So, while we await this decision, we are back to that common mantra that keeps being raised. Hope vs Data. Politics vs Science. And still not treatments while millions suffer and 700K US citizens are already dead.
For anyone keeping score: Gruber +1 White House 0
More vaccine horror stories with videos:
https://nomoresilence.world/
I can’t understand what Israel is doing and why in the light of all this evidence.
@Adam
This is a devastatingly pejorative confounding factor. It undermines any positive results of the work that Bear has been sharing with us, for, like the votes in a illicit election, the results are based upon such very important details as to how the results are quantified. If they chose, rather, to create a separate classification for those who died while upto 14day post vax, it would not only serve to solve this self-imposed error to support the vax policy, it would also result in precise evidence displaying to what degree the vax is suppressing the immune system during this critical post-vax period. These facts are well known to these well learned individuals, and their actions to hide such evidence should further concern anyone contemplating the vax, the booster or trusting anything these govt sponsored studies that only display such self-serving data. As Dr. Malone mentioned prior to his trip to Italy, I too have reached a point of belief that these govts and their scientific lackies, who control all the data, are beyond the ability to conduct any inquiry that does not serve their own purposes. Or as Dr. Mackary of John Hopkins more aptly states, they are “cherry-picking” data to support their desired outcomes. They have been shown to earn our distrust, it is best we respond by recognizing these facts and question these remarkable finding with demands of full disclosure of the full data including the vaccination harms.
This is another key finding from thr Fenton report: For example, any person testing positive for Covid19 or dying of any cause within 14 days of their second dose is now classified by the CDC as ‘unvaccinated’ (CDC, 2021). While this definition may make sense for determining effectiveness in preventing Covid19 infections, it may drastically overestimate vaccine safety; this is because most serious adverse reactions from vaccines in general occur in the first 14 days (Scheifele, Bjornson, & Johnston, 1990; Stone, Rukasin, 3 https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated
I had not meant to reprint the entire report, above. But my efforts to edit it failed. The key finding in Dr. Fenton’s report that I wanted to call attention to is the following
PreprintPDF Available
Paradoxes in the reporting of Covid19 vaccine effectiveness: Why current studies (for or against vaccination) cannot be trusted and what we can do about it
September 2021
DOI:10.13140/RG.2.2.32655.30886
Authors:
Norman Elliott Fenton at Queen Mary, University of London
Norman Elliott Fenton
Queen Mary, University of London
Martin Neil at Queen Mary, University of London
Martin Neil
Queen Mary, University of London
Scott Mclachlan at Queen Mary, University of London
Scott Mclachlan
Queen Mary, University of London
Preprints and early-stage research may not have been peer reviewed yet.
Download file PDF
Read file
References (14)
Figures (5)
Abstract and Figures
Given the limitations of the randomized controlled trials (RCTs) for Covid19 vaccines, we must increasingly rely on data from observational studies to determine vaccine effectiveness. But over-simplistic reporting of such data can lead to obviously flawed conclusions due to statistical paradoxes. For example, if we just compare the total number of Covid19 deaths among the vaccinated and unvaccinated then we are likely to reach a different conclusion about vaccine effectiveness than if we make the same comparison in each age category. But age is just one of many factors that can confound the overall results in observational studies. Differences in the way we classify whether a person is vaccinated or is a Covid19 case can also result in very different conclusions. There are many critical interacting causal factors that can impact the overall results presented in studies of vaccine effectiveness. Causal models and Bayesian inference can in principle be used to both explain observed data and simulate the effect of controlling for confounding variables. However, this still requires data about relevant factors and much of these data are missing from the observational studies (and the RCTs). Hence their results may be unreliable. In the absence of such data, we believe the simplest and most conclusive evidence of vaccine evidence is to compare all-cause deaths for each age category between those who were unvaccinated and those who had previously had at least one vaccine dose.
Data from Public Health England, June 2021
Data from Public Health England, June 2021
…
Causal model reflecting the observed data
Causal model reflecting the observed data
…
Causal model as a Bayesian network with probability tables taken from the observed data
Causal model as a Bayesian network with probability tables taken from the observed data
…
Marginal probabilities (Age and Vaccinated are rounded to 0 decimal places)
Marginal probabilities (Age and Vaccinated are rounded to 0 decimal places)
…
Unconfounded impact of vaccination: probability of death decreases from 0.417% to 0.104%
Unconfounded impact of vaccination: probability of death decreases from 0.417% to 0.104%
…
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1 Paradoxes in the reporting of Covid19 vaccine effectiveness Why current studies (for or against vaccination) cannot be trusted and what we can do about it Norman Fenton, Martin Neil and Scott McLachlan Risk Information and Management Research School of Electronic Engineering and Computer Science, Queen Mary University of London 15 Sept 2021 Abstract Given the limitations of the randomized controlled trials (RCTs) for Covid19 vaccines, we must increasingly rely on data from observational studies to determine vaccine effectiveness. But over-simplistic reporting of such data can lead to obviously flawed conclusions due to statistical paradoxes. For example, if we just compare the total number of Covid19 deaths among the vaccinated and unvaccinated then we are likely to reach a different conclusion about vaccine effectiveness than if we make the same comparison in each age category. But age is just one of many factors that can confound the overall results in observational studies. Differences in the way we classify whether a person is vaccinated or is a Covid19 case can also result in very different conclusions. There are many critical interacting causal factors that can impact the overall results presented in studies of vaccine effectiveness. Causal models and Bayesian inference can in principle be used to both explain observed data and simulate the effect of controlling for confounding variables. However, this still requires data about relevant factors and much of these data are missing from the observational studies (and the RCTs). Hence their results may be unreliable. In the absence of such data, we believe the simplest and most conclusive evidence of vaccine evidence is to compare all-cause deaths for each age category between those who were unvaccinated and those who had previously had at least one vaccine dose. creative commons license
2 The randomized controlled trials (RCTs) to establish the safety and effectiveness of Covid19 vaccines produced impressive results (Polack et al., 2020) but were inevitably limited in the way they assessed safety (Folegatti et al., 2020)1 and are effectively continuing (Ledford, Cyranoski, & Van Noorden, 2020; Singh et al., 2021) . Ultimately, the safety and effectiveness of these vaccines will be determined by real world observational data over the coming months and years. However, data from observational studies on vaccine effectiveness can easily be misinterpreted leading to incorrect conclusions. For example, we previously noted2 the Public Health England data shown in Figure 1 for Covid19 cases and deaths of vaccinated and unvaccinated people up to June 2021. Overall, the death rate was three times higher in the vaccinated group, leading many to conclude that vaccination increases the risk of death from Covid19. But this conclusion was wrong for this data because, in each of the different age categories (under 50 and 50+), the death rate was lower in the vaccinated group. Figure 1 Data from Public Health England, June 2021 This is an example of Simpson’s paradox (Pearl & Mackenzie, 2018). It arises here because most vaccinated people were in the 50+ category where most deaths occur. Specifically: a) a much higher proportion of those aged 50+ were vaccinated compared to those aged <50; and b) those aged 50+ are much more likely to die. So, as shown in Figure 2(a), ‘age’ is a confounding variable. While it is reasonable to assume that death is dependent on age, in a proper RCT to determine the effectiveness of the vaccine we would need to break the dependency of vaccination on age as shown in Figure 2(b), by ensuring the same proportion of people were vaccinated in each age category. 1 Some participants and sites were unblinded and non-randomised and others were effectively unblinded when they received paracetamol prior to jab 2 https://probabilityandlaw.blogspot.com/2021/06/simpsons-paradox-in-interepretation.html
3 Figure 2 Causal model reflecting the observed data The Appendix demonstrates how this causal model, and Bayesian inference, can both explain the paradox and avoid it (by simulating an RCT). Using the model in Figure 2 (b), which avoids the confounding effect of age, we conclude (based only on the data in this study) that the (relative) risk of death is four times higher in the unvaccinated (0.417%) than the vaccinated(0.104%), meaning the absolute increase in risk of death is 0.313% greater for the vaccinated. An excellent article by Jeffrey Morris3 demonstrates the paradox in more detail using more recent data from Israel. Clearly confounding factors like age (and also comorbidities) must, therefore, always be considered to avoid underestimating vaccine effectiveness data. However, the conclusions of these studies are also confounded by failing to consider non-Covid deaths, which will overestimate the safety of the vaccine if there were serious adverse reactions. In fact, there are many other confounding factors that can compromise the results of any observational study into vaccine effectiveness (Krause et al., 2021). By ‘compromise’ we mean not just over- or under-estimate effectiveness, but – as in the example above – may completely reverse the results if we fail to adjust even for a single confounder (Fenton, Neil, & Constantinou, 2019). In particular, the following usually ignored confounding factors will certainly overestimate vaccine effectiveness. These include: • The classification of Covid19 deaths and hospitalizations. For those classified as Covid19 cases who die (whether due to Covid19 or some other condition), there is the issue of whether the patient is classified as dying ‘with’ Covid19 or ‘from’ Covid19. There may be differences between vaccinated and unvaccinated in the way this classification is made. The same applies to patients classified as Covid19 cases who are hospitalized. • The number of doses and amount of time since last dose used to classify whether a person has been vaccinated. For example, any person testing positive for Covid19 or dying of any cause within 14 days of their second dose is now classified by the CDC as ‘unvaccinated’ (CDC, 2021). While this definition may make sense for determining effectiveness in preventing Covid19 infections, it may drastically overestimate vaccine safety; this is because most serious adverse reactions from vaccines in general occur in the first 14 days (Scheifele, Bjornson, & Johnston, 1990; Stone, Rukasin, 3 https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated
4 Beachkofsky, Phillips, & Phillips, 2019) and the same applies to Covid19 vaccines (Farinazzo et al., 2021; Mclachlan et al., 2021). There is also growing evidence that people hospitalized for any reason within 14 days of a vaccination are classified as unvaccinated and, for many, as Covid19 cases4. • The accuracy of Covid19 testing and Covid19 case classification. These are critical factors since there may be different testing strategies for the unvaccinated compared to the vaccinated. For example, in the large observation study of the Pfizer vaccine effectiveness in Israel (Haas et al., 2021) unvaccinated asymptomatic people were much more likely to be tested than vaccinated asymptomatic people, resulting in the unvaccinated being more likely to be classified as Covid19 cases than vaccinated5. Even if we wish to simply study the effectiveness of the vaccine with respect to avoiding Covid infection (as opposed to avoiding death or hospitalization) there are many more factors that need to be considered than currently are. To properly account for the interacting effects of all relevant factors that ultimately impact (or explain) observed data we need a causal model such as that in Figure 3. Figure 3 Causal model to determine vaccine effectiveness 4 https://www.bitchute.com/video/lXrcpFe4V4U2/ 5 https://probabilityandlaw.blogspot.com/2021/05/important-caveats-to-pfizer-vaccine.html
5 As in the simple model of Figure 2, the nodes in the model shown in Figure 3 correspond to relevant factors (some of which relate to individuals – like age, and some of which relate to the population – like whether lockdowns are in place) and an arc from one node to another means there is a direct causal/influential dependence in the direction of the arc. For example: younger people – and those who have immunity from previous Covid infection – are less likely to be vaccinated than older people; older people are more likely to have comorbidities and more likely to have symptoms if they are infected. However, while those factors and relationships are widely considered in observational studies, most of the other factors in the model are not. The first thing to note is that the model makes clear the critical distinction between whether a person is Covid19 infected (something which is not easily observable) and whether they are classified as a Covid19 case (i.e. the ones who are recorded as cases in any given study). The latter depends not just on whether they are genuinely infected but also on the accuracy of the testing and whether they are vaccinated. If (as in the Israel study described above) the unvaccinated are subject to more extensive (and potentially inaccurate) testing, then they are more likely to be erroneously classified as a case. The model also makes clear the critical distinction between those who have been vaccinated (at least once) and those classified as vaccinated in the study. The latter depends on the number of doses, time since last dose, and whether the person tests positive. Moreover, whether a person gets more than one dose will depend on whether they suffered an adverse reaction first time; those who do and who do not get a second dose are generally classified as unvaccinated – and this will compromise any studies of risk associated with the vaccine. Indeed, even the results of randomized controlled trials were compromised both by ‘removing’ those who died within 14 days of the second vaccination and ‘losing’ many subjects after the first dose6. The causal model makes clear that a person cannot become infected with the virus unless they come into contact with it. The latter depends not just on age, ethnicity and profession (so young people who live, work and travel in crowded environments are more likely to come into contact with the virus as are any people in a hospital environment) but also on changing population factors like lockdown restrictions in place and current population infection rate. Assuming a person comes into contact with the virus, whether they get infected depends on whether they have natural immunity and whether they are vaccinated. If we had relevant data on all of the factors in the model then, as in the case of the simple model in the Appendix, we can capture the probabilistic dependence between each node and its immediate parents, and then use Bayesian inference to determine the true effect of vaccination. In principle, this enables us to properly explain all observed data, adjust for all confounding factors, and provide truly accurate measures of effectiveness. The problem is that several key variables are generally unobservable directly while many of the easily observable variables are simply not recorded. While we can incorporate expert judgment with observed statistical data to populate the model, this can be extremely complex and subjective. Moreover, if you think the model is already very complex, then it should be noted that it is far from fully comprehensive. Even before we consider all the additional factors and relationships needed to consider the outcomes of hospitalization and death (and the accuracy of reporting these), the model does not take account of: different treatments given; different morbidities and lifestyle choices; seasons over which data are collected; different strains of the virus; and many other factors. Nor does it account for the fact that all observational data are biased (or ‘censored’) in the sense that it only contains information on people who are available for the study; so, for example, studies in particular countries will largely contain people of a specific ethnicity, while all studies will generally exclude certain classes of people (such as the 6 Some of the covid vax trials were unblinded, others were only single-blinded. Yet more were non-randomised and others were accidentally unblinded when the treatment recipients were given paracetamol prior to their covid jab
6 homeless). This means that, while such studies could be useful in determining effectiveness at a ‘local’ level, their conclusions are not generalizable. Indeed, they may are completely unreliable because of another paradox (called collider or Berkson’s paradox) unless we have explicitly adjusted for this as described in (Fenton, 2020). Given the impossibility of controlling for all these factors in randomized trials, and the overwhelming complexity of adjusting for them from observational data there is little we can reliably conclude from the data and studies so far. And we have not even mentioned the general failure of these studies to consider the impact and trade-offs of safety on effectiveness. So, what can we do about this mess? We believe there is an extremely simple and objective solution: if we ignore the cost of vaccination, then ultimately we can all surely agree that the vaccine is effective overall if there are fewer deaths (from any cause) among the vaccinated than the unvaccinated. This combines both effectiveness and safety since it encapsulates the trade-off between them. It is not perfect, because there could be systemic differences in treatments given to vaccinated and unvaccinated7, but it completely bypasses the problem of classifying Covid19 ‘cases’ which, as we have noted, compromises all studies so far. So, provided that we can agree on an objective way to classify a person as vaccinated (and we propose that, for this purpose, the fairest way is to define anybody as vaccinated if they have received at least one dose), then all we need to do is compare all-cause mortality rates in different age categories of the vaccinated v unvaccinated over a period of several months8. A recent analysis does indeed look at all-cause deaths in vaccinated and unvaccinated (Classen, 2021). The study shows that, for all three of the vaccines for which data were available, all-cause deaths is significantly higher in the vaccinated than the unvaccinated. However, this study did not account for age and hence its conclusions are also unreliable. We could immediately evaluate the effectiveness to date of vaccines in the UK by simply looking at the registered deaths since the start of the vaccination programme in December 2020. All we need to know for each registered death is the person’s age and whether they received at least one dose of the vaccine before death. Although a longer period would, of course, be better it is still sufficiently long to show a real effect if the vaccines work as claimed and if Covid19 is as deadly as claimed. Moving forward we should certainly be collecting this simple data, but our concern is that (in many countries) the ‘control group’ (i.e. unvaccinated) may soon not be large enough for such a simple evaluation. 7 There are multiple anecdotal reports that Australian hospitals are now giving ivermectin only to vaccinated patients 8 https://probabilityandlaw.blogspot.com/2021/06/why-all-studies-so-far-into-risks-andor.html
7 References CDC. (2021). COVID-19 Breakthrough Case Investigations and Reporting | CDC. Retrieved September 15, 2021, from https://www.cdc.gov/vaccines/covid-19/health-departments/breakthrough-cases.html Classen, B. (2021). US COVID-19 Vaccines Proven to Cause More Harm than Good Based on Pivotal Clinical Trial Data Analyzed Using the Proper Scientific Endpoint, “All Cause Severe Morbidity.” Trends in Internal Medicine, 1(1), 1–6. Retrieved from https://www.scivisionpub.com/pdfs/us-covid19-vaccines-proven-to-cause-more-harm-than-good-based-on-pivotal-clinical-trial-data-analyzed-using-the-proper-scientific–1811.pdf Farinazzo, E., Ponis, G., Zelin, E., Errichetti, E., Stinco, G., Pinzani, C., … Zalaudek, I. (2021). Cutaneous adverse reactions after m?RNA COVID?19 vaccine: early reports from Northeast Italy. Journal of the European Academy of Dermatology and Venereology, 35(9), e548–e551. https://doi.org/10.1111/jdv.17343 Fenton, N. (2020). Why most studies into COVID19 risk factors may be producing flawed conclusions – and how to fix the problem. ArXiv. https://doi.org/http://arxiv.org/abs/2005.08608 Fenton, N. E., Neil, M., & Constantinou, A. (2019). Simpson’s Paradox and the implications for medical trials. Retrieved from http://arxiv.org/abs/1912.01422 Folegatti, P. M., Ewer, K. J., Aley, P. K., Angus, B., Becker, S., Belij-Rammerstorfer, S., … Oxford COVID Vaccine Trial Group. (2020). Safety and immunogenicity of the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2: a preliminary report of a phase 1/2, single-blind, randomised controlled trial. Lancet (London, England), 396(10249), 467–478. https://doi.org/10.1016/S0140-6736(20)31604-4 Haas, E. J., Angulo, F. J., McLaughlin, J. M., Anis, E., Singer, S. R., Khan, F., … Alroy-Preis, S. (2021). Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: an observational study using national surveillance data. Lancet (London, England), 397(10287), 1819–1829. https://doi.org/10.1016/S0140-6736(21)00947-8 Krause, P. R., Fleming, T. R., Peto, R., Longini, I. M., Figueroa, J. P., Sterne, J. A. C., … Henao-Restrepo, A.-M. (2021). Considerations in boosting COVID-19 vaccine immune responses. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(21)02046-8 Ledford, H., Cyranoski, D., & Van Noorden, R. (2020). The UK has approved a COVID vaccine — here’swhat scientists now want to know. Retrieved from https://www.nature.com/articles/d41586-020-03441-8?utm_source=Nature+Briefing&utm_campaign=597ee8dba8-briefing-dy-20201203&utm_medium Mclachlan, S., Osman, M., Dube, K., Chiketero, P., Choi, Y., & Fenton, N. (2021). Analysis of COVID-19 vaccine death reports from the Vaccine Adverse Events Reporting System (VAERS) Database Interim: Results and Analysis. Retrieved from http://dx.doi.org/10.13140/RG.2.2.26987.26402 Pearl, J., & Mackenzie, D. (2018). The book of why?: the new science of cause and effect. New York: Basic Books. Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., … C4591001 Clinical Trial Group. (2020). Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. The New England Journal of Medicine, 383(27), 2603–2615. https://doi.org/10.1056/NEJMoa2034577 Scheifele, D. W., Bjornson, G., & Johnston, J. (1990). Evaluation of adverse events after influenza vaccination in hospital personnel. CMAJ?: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 142(2), 127–130. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2295029 Singh, J. A., Kochhar, S., Wolff, J., Atuire, C., Bhan, A., Emanuel, E., … Upshur, R. E. G. (2021). Placebo use and unblinding in COVID-19 vaccine trials: recommendations of a WHO Expert Working Group. Nature Medicine, 27(4), 569–570. https://doi.org/10.1038/s41591-021-01299-5 Stone, C. A., Rukasin, C. R. F., Beachkofsky, T. M., Phillips, E. J., & Phillips, E. J. (2019). Immune?mediated adverse reactions to vaccines. British Journal of Clinical Pharmacology, 85(12), 2694–2706. https://doi.org/10.1111/bcp.14112
8 Appendix The prior probabilities based on the study data are shown in Figure 4 Figure 4 Causal model as a Bayesian network with probability tables taken from the observed data This results in the so-called marginal probabilities shown in Figure 5. Figure 5 Marginal probabilities (Age and Vaccinated are rounded to 0 decimal places) By entering observations on Age we can see the overall effect on probability of vaccinated and death as shown in Figure 6.
9 Figure 6 Overall impact of age on probability of vaccinated and death However, the real power of the Bayesian network comes in the backward inference shown in Figure 7 that enables us to determine the impact of vaccination status on age as well as death. Figure 7 Impact of vaccination status on age and death Here we see (as noted in the original data) that the vaccinated are four times more likely to die than the unvaccinated. But this is explained by the vaccinated being much more likely to be 50+
10 Next we use the model to see the impact of vaccination respectively on those aged <50 and those aged 50+. In Figure 8 we see that, for those aged <50 there is a small decrease on probability of death among the vaccinated. Figure 8 Impact of vaccination on those age 50 year-olds, women and those with prior symptomatic/confirmed COVID-19. Adults receiving heterologous schedules on clinical … [Show full abstract]Read more
Preprint
Low seropositivity and sub-optimal neutralisation rates in patients fully vaccinated against COVID-1…
July 2021
Thomas Fox
Thomas FoxAmy A. KirkwoodLouise Enfield[…]Emma Morris
Patients with haematological malignancies are at increased risk of severe disease and death from COVID-19 and are less likely to mount humoral immune responses to COVID-19 vaccination, with the B cell malignancies a particularly high-risk group. Our COV-VACC study is evaluating the immune response to COVID-19 vaccination in patients with B cell malignancies. Eligible patients were either … [Show full abstract]Read more
Last Updated: 17 Sep 2021
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Health and Human Services seems to be taking desperate measures to avoid any competition with the vaccines, at the expense of many lives.
DeSantis hammers Biden admin for limiting Florida’s use of monoclonal antibodies: ‘Very, very problematic’
Therapy was introduced at the same time as the vaccine, but flew under the radar until recently
Houston Keene12 hours ago
DeSantis vows to fight Biden, stand up for freedom and the Constitution
Gov. Ron DeSantis takes aim at President Biden over vaccine mandates and Afghanistan withdrawal while speaking at a political event in Nebraska City, Nebraska
Florida Gov. Ron DeSantis hammered the Biden administration for overhauling the distribution of monoclonal antibodies in a way that will severely hamper the treatment’s availability in several Republican-controlled states.
The Department of Health and Human Services (HHS) alarmed authorities in several southern, red states – where the antibodies are widely used – after announcing Monday that the agency would be changing how the COVID-19 treatment is distributed.
Previously, distribution sites could order the antibody treatments directly from the supplier. Now, the federal government will decide how many doses each state will receive and leave it to state governments to ration it out among locations.
DeSantis warned that HHS’ new equitable distribution plan for monoclonal antibodies (MAB) is “very, very problematic” and warned patients “are going to suffer as a result of this.”
ASSOCIATED PRESS RIPPED AFTER FAUCI TOUTS MONOCLONAL ANTIBODIES: ‘ISN’T THIS THE THING RON DESANTIS PROMOTED?’
“We were happy to see that Biden’s COVID plan announced last week included a 50% increase in monoclonal antibody deliveries to states this month,” DeSantis press secretary Christina Pushaw told Fox News in a Thursday statement.
“So it’s surprising and deeply disappointing that the Biden Admin would break this promise just a week later and cut MAB allocation to Florida, so they aren’t even providing half of the doses of life-saving treatment that COVID patients in Florida will need,” Pushaw continued.
The governor’s spokeswoman added that DeSantis “is committed to ensuring that everyone who needs the treatment” is able to be treated, “even if we can’t count on the Biden administration.”
HHS didn’t return Fox News’ emailed questions for this article.
Florida is one of seven states, alongside Texas and Alabama, making up the majority of demand for the treatment. White House press secretary Jen Psaki said Thursday the seven states made up roughly 70% of the requests.
DeSantis on Thursday argued that it shouldn’t be surprising that the states currently experiencing a seasonal surge are the ones needing the most doses of the treatment.
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The Alabama Medical Association voiced alarm about the HHS changes after Monday’s announcement.
“Alabama’s hospitals are full and under tremendous stress. That’s why physicians are very concerned about federal efforts that will end up limiting our supply and access to this effective treatment,” Dr. Aruna Arora, the association’s president, said in a statement Monday.
“We’re calling on the federal government to help us provide more of this treatment – not less – so we can save lives and keep COVID patients out of the hospital.”
Houston Keene is a reporter for Fox News Digital. You can find him on Twitter at @HoustonKeene.
Many of the responses can be read at the link below:
https://www.worldtribune.com/unexpected-and-heartbreaking-thousands-flood-abc-affiliates-facebook-page-with-vaccination-horror-stories/