Sometimes an article comes along that is so well-written and stunning in its implications that I hesitate to blog about it at all, and simply put it into the "tidbits" category in the hopes that people will read it. This article, shared by Mr. V.T., is one of those articles. Yet I think that if I were to do that people might not read it, and that would be bad. The problem is, however, that anything I might say would mar the implications of the article. The article is long, but the implications are wide and profound. In fact, it's an article that I strongly suspect some of you will save, it's that important, and that good. Nevertheless, I do want to say a few things about the article, both by way of introduction, and my usual commentary on one or two key paragraphs. I would ask that the reader read my blog first, or at least the section "About Meta-Analysis", and then the actual article.
Here's the article:
That the advent of the internet changed everything has become both a mantra and a truism. What is not widely known by the public is that it created a whole new form of data-analysis called "meta-analysis," and a brief overview of this technique is necessary before reading the article, because the main argument of the article depends to a certain extent on this type of analysis, so it is essential to understand it, and what it can do, and cannot do. We all have a general idea of what scientists do when they analyze data. They "crunch the numbers" that result from their experiments, and then publish those results in professional journals, where other people in that field of science read them and debate the conclusions. That, anyway, is more or less how it's supposed to work. The articles are about the hard data. But as the internet arose, and more and more articles were archived and stored digitally, this opened a whole new way of analyzing data, for with this database and the addition of search engines, it became possible to analyze not the data in the articles, but the number of articles published on defined topics.
As a result, it became possible to look not for causes and effects, which would be more or less the purpose of individual articles themselves, but to look for significant and statistically undissmissible, or statistically significant correlations between types of data. In other words, meta-analysis is not about causes, but about correlations. It's saying "Look at this relationship, it's significant, it needs examination to see if there is a causal connection." Meta-analysis suggests causal relations on the basis of data correlations. This is quite the crucial point, as the author of this article, James Lyons-Weiler, Ph.D., does not fall into the trap of deducing causes from correlations. Indeed, as we'll see, he even avoids the trap of saying "Vaccines are bad", but rather suggests that certain compounds and elements found in certain types of vaccines are the real root of the problem (e.g., Thimerosol, aluminum, &c.). One would be hard-pressed to debate that conclusion, for Jenner's small pox vaccine, for example, did not contain Thimerosol and, so far as I'm aware, aluminum or any of the other compounds or elements that have been identified as problematical over the past few years. The technique of vaccination is not the problem; the content of individual vaccines is. With this in mind, we turn to
Lyon-Weiler's Meta-Analysis of Diseases with Unknown Etiology
With these thoughts in mind, here is Lyon-Weiler's basic meta-analysis in his own words:
Crohn’s. Lupus. Autism. ADHD. Food allergies. Celiac disease. Sjögren’s syndrome. Polymyalgia rheumatica. Multiple sclerosis. Anklyosing spondylitis. Type 1 diabetes. Vasculitis. Peripheral neuropathy. The list goes on, and on, and on. We are being increasingly diagnosed with these conditions and diseases of unknown origin, and science has very little to say — why would autoimmune diseases and mysterious diseases of inflammation be so prevalent? When did this increase start?
As an observer and participant in modern biomedical research, and a lover of deep history, I tend to focus not on the immediate or last few years, but look for trends of accumulating risk over longer periods of time. Seeking an answer to the question of “when”, I used Pubmed to estimate, per yer, the number of studies and papers discussing diseases and conditions of unknown origin. I search for the term “unknown causes”, and also for the term “journal” to get some idea of the percentage of studies, papers and editorials discussing disease of unknown causes. I had no idea what to expect.
Lyons-Weiler did two searches, first for articles with the word "journal", and then:
Next I searched for “Unknown Causes”, and calculated the number of articles citing unknown causes per 10,000 articles (again, relative denominator term).
What I found is shocking. Here is a graph of the number of articles per 10,000 discussing “unknown causes” (Y = #articles mentioning “unknown causes” / #articles mentioning “journal”, as in the title of journals).
Because the studies in Pubmed include all sorts of journals studying all sorts of things, the actual number is not as important as the trend. The signature is undeniable. Something changed dramatically in 1976. To the skeptic: the increase is greater if one does not correct for total publications.
Ca. 1975-76, something drastically changed: the number of articles about "new" diseases with unknown origins began a sharp upward rise, as evidenced in Lyons-Weiler's graph, and that trend has continued to this day. The question then, is what happened in that time period that might correlate with this dramatic rise? Lyons-Weiler does not mince words:
What changed was national mass vaccination against influenza.
At this juncture in the article, Lyons-Weiler cites a long excerpt from David Sencer's and J. Donald Millar's "Reflections on the 1976 Swine flu vaccination program." Lyons-Weiler's conclusions tell the whole story:
The “Reflections” article, on the CDC website, shows that knowledge of risk of autoimmune disorders like Guillain-Barré Syndrome and deaths from vaccination was present from the beginning.
Serious side effects in a minority of patients is rationalized by the benefits of the flu vaccine, and vaccine risk denialism perpetuates the regulation of perception necessary for continuation of the view that the benefits outweigh the risks.
But, at a population level, evidence is mounting that, due to numerous reasons, mass influenza vaccination is self-defeating.
He does not leave it there however, and pinpoints specific areas that need careful scientific attention, and I cite only a few of his specific points:
(2) Vaccination with Thimerosal induces immunological damage. Specifically, Thimerosal inhibits the protein ERAP1, which shortens proteins headed for the cell surface of MHC Class 1 [“Stamogiannos et al., 2016 Screening Identifies Thimerosal as a Selective Inhibitor of Endoplasmic Reticulum Aminopeptidase 1″]
(3) Vaccination against Influenza with thimerosal-containing vaccines is associated with an increase in non-influenza respiratory infections [“Increased Risk of Noninfluenza Respiratory Virus Infections Associated With Receipt of Inactivated Influenza Vaccine“]
(4) Repeated vaccination at a young age substantially increases the risk of influenza in older age, by a factor ranging between 1. 2 (vaccination after 50 years) to 2. 4 (vaccination from birth) [“Repeated influenza vaccination of healthy children and adults: borrow now, pay later?“]
(13) The evidence that heterologous immunity and very limited efficacy makes universal vaccination against the flu will create more disease than it prevents is impressive. [Why do people get the flu after getting the flu shot?]
And his conclusions from citing these and other articles is clear and unequivocal:
Repeated calls for addressing these conundrums fall on deaf ears. The explosion of diseases of mysterious origin — the cost of morbidity and mortality — means there is no excuse for sloppy, lazy vaccinology. The changes needed are known, and there is no excuse. Unsafe epitopes that match human proteins must be removed. Thimerosal must be removed. Aluminum exposure must be minimized.
We desperately need a new generation of technologies for artificial immunization, and those products should (a) not be contracted via the CDC at all, (b) subjected to the same rigorous standards of evidence of safety required of drugs with long-term safety outcomes (total health outcome awareness), (c) vaccine risk denialism must be stopped immediately.
To this I can only add that I've seen it with my own eyes, as I've watched family members "get their flu shot," and then become sick. Every year around this time we are subjected to campaigns from various companies to get jabbed with the latest big pharma cocktails; special booths are set up in major retail outlets to jab their customers; some states have now made it mandatory, and are rescinding the various exemptions that once existed to protect people's right of choice over their own health care. And as Lyons-Weiler himself points out, health care professionals are often required to take these things.
I don't know about you, but I feel like printing off copies of this article, and handing them out to the people manning these booths in the stores.
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