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Some Observations On the Efficacy of Masks in a #COVID19 World

By Kevin Kilty | Watts Up With That? | March 16, 2021

Some weeks ago, Pat Frank suggested that I might consider writing an essay about the efficacy of masks and mandates to wear masks during this pandemic. I hesitated doing so at first, but March 8th I noticed another research effort on the part of the CDC to justify masks as a prophylactic strategy.[1] This effort seems very deficient in my view and so this essay resulted. What I write here is a summary of a much larger work in progress.

Lincoln Moses and Frederick Mostellar long ago suggested that public policy be organized as experiments so that we might learn of its effectiveness, or lack thereof, and avoid successive failures.[2] When the COVID-19 pandemic arrived last spring, I wrote that we didn’t need to go through successive battles with exponential processes, but that we appeared not ready to gather useful data and evidence about the effectiveness of social distancing and other advice in this battle.[3] Considering the tendency of people to don a mask against all sorts of bad air is so universal that even screen writers employ it to add realism to a disaster scene, one would think we would know something about their effectiveness.[4] We do and we don’t. While I am told by some people employed in medicine along with many amateurs that masks are essential to controlling spread of SARS-COV-2; highly reputable authorities, many of them, thousands of them, make much more modest and even opposite claims.[5]

How might we analyze these competing claims? I see three avenues of attack: First, we can examine theoretical reasons for and against masks from a mechanical perspective. Second, there are limited experiments known as randomized clinical trials available, all of which have some deficiencies and limited pertinence. Third, we can examine observations of the progress of this epidemic as shown by cases in the light of local mandates. These observations and the methods used to evaluate them are quite deficient in many ways, but they do tend toward similar conclusions.

Mechanical Considerations

The CDC, WHO, and local departments of health have issued a variety of advisories about masks which they update periodically. A typical advisory begins as follows:

“Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission.”

As a rationale for masks this fails because it does not mention a necessary prior element. In order to work, masks have to attenuate the guilty aerosols. The individual aerosols involved could be only a micrometer or few micrometers in size. The individual virions are in the range of 50-130 nanometers.[6] I have looked at a number of cloth masks that one can purchase and found their pore sizes to be 0.05 to 0.15 millimeters. This is 1000 times larger than virions and hundreds of times larger than small aerosols. No wonder these packages of masks should come with disclaimers. Adding to this issue of excessive pore size is that cloth masks are not made of certified materials, are manufactured to no standard, are often ill-fitting displaying gaps aside the nose and on the cheeks, or pulled down below the nose, and sometimes placed over a beard. Flat surgical masks do better at times with the excessive pore size problem but still present issues with poor fit and gaps.

There is a mask that corrects most of these deficiencies. The N-95 mask is made of qualified materials and manufactured to a standard. These masks attenuate 95% of particles in the size range of 0.3 to 0.5 micrometers. However, they still require attention to fit to reduce gaps, and they are not guaranteed to halt very small aerosols the size of individual virions. A news article last summer in the Japanese newspaper, The Asahi Shimbun,[7] summarized measurements that researchers made on particle attenuation of cloth, gauze, and N-95 masks, supports what I have summarized here. Cloth and gauze masks have zero effectiveness; while N-95 masks perform to specification, but only if fitted and worn properly. And even then there is no guarantee they prevent the transmission of disease.

There is one more mechanical aspect to ponder. Often in a crisis people will offer what expertise they can – they recycle their expertise. Something I am doing here. Recently a number of researchers in the field of fluid dynamics have weighed in with measurements and simulations (as one would expect) using computational fluid dynamics (CFD). The AIP journal Physics of Fluids produced a special issue in October 2020 highlighting the physics of masks. One study uses CFD to model persons wearing masks inside and outside, in various conditions of air flow, to address ability of masks to attenuate aerosols ejected from a cough or a sneeze.[8] They state in conclusion…

“… our results suggest that, while in indoor environments wearing a mask is very effective to protect others, in outdoor conditions with ambient wind flow present wearing a mask might be essential to protect ourselves from pathogen-carrying saliva particulates escaping from another mask wearing individual in the vicinity.”

This means, I presume, that masks are useful in a situation when all around are sick, and sneezing, wheezing, and coughing — in other words, in a Covid ward of a health care facility. What does “very effective” mean? If it means a very great attenuation of particles, greater than 95% say, then this still has to be interpreted in the light of findings that as few as 300 virions can lead to disease.[9] However, one would think that if coughing and sneezing are the issue, then covering a cough or sneeze should do as well, or perhaps even better when one considers the problem of ill-fit and aerosol escaping through gaps. My experience since March 2020 is that I never encounter anyone in public who are so sick that they are simply sneezing and coughing with abandon.

This computational fluid dynamics approach to determining the efficacy of masks resembles the equivalent modeling approach to climate change. They imply that models define reality when, in fact, it should be that observations and measurements do. There is no means to turn CFD models into clinical outcomes.

In summary, there are mechanical reasons to suppose that masks could reduce the spread of virus in some settings, but none appear pertinent to the materials used to construct masks, or to the ways the public wear them in about 98% of situations. Opposed to supposing that masks might work, or modeling how they might work, we can only learn what efficacy they have by making experiments or observations.

Experiments

The closest thing I have found to true experiments regarding masks are a small number of randomized clinical trials (RCTs). A surprisingly few RCTs involving masks and respirators have been done.[10] I will summarize only two of these. Of these one is pre-COVID-19 and not controversial, and the other is post COVID-19 and subject to controversy and censorship.

There are many respiratory diseases which circulate in the human population. The recent epidemics of MERS, SARS, Ebola and influenza provoked a search for effective non-pharmaceutical interventions. In one example, a group of doctors became interested in how well cloth masks performed for preventing infection in hospitals because such masks are in wide use in the developing world. This trial involved 1607 volunteers at 14 hospitals in Hanoi, Vietnam working in high-risk wards. There were three arms in this RTC: cloth masks, surgical masks, and a control arm of “standard practice” which involved some mask usage but at about one-half the compliance rate of the two treatment arms. The study took place over a four week period, and was to the authors’ knowledge, the first RCT involving cloth masks. Among their findings were that particle attenuation was virtually nil in the cloth masks (97% infiltration), and surprisingly poor in these particular medical masks (44% infiltration). The rate of infection in the cloth mask wearers was double that in the medical mask wearers; medical masks showed some effectiveness, but this contradicted earlier studies showing no efficacy to the medical masks.[11] The researchers conclude that cloth masks should not be advocated for health-care workers, at least until a much better design of such is produced.[12]

The second RCT was performed in Denmark last spring and was subject to censorship by our social media as well as facing some publication resistance.[13] It involved 4862 participants who completed the study. It is more pertinent to this essay because it addressed the efficacy of masks outside of a health care setting. Participants were divided into a control group asked to refrain from wearing masks when out of their home and a treatment arm asked to wear a mask when out of the home for three hours per day. Both groups were ask to follow other social distancing guidelines in order to prevent confounding of masks and distancing which have similar if not identical effects. The primary measured outcome was the number of participants showing SARS-CoV-2 or other respiratory viral infections after one month as determined from PCR testing or hospital diagnosis.

The outcome produced an infection rate of 2.1% in the control arm against 1.8% in the treatment arm. However, the confidence interval of odds ratio (CI of 0.53 to 1.23) included a value of 1.0 almost at its center, suggesting no significant difference in outcomes. If one were to yet insist that the small difference in attack rate (42/2392=1.8% versus 53/2470=2.1%) is nonetheless an important risk reduction, the absolute risk reduction implied (0.003) translates into 30,000 hours (90 hours/0.003) of mask wearing to prevent one case of COVID-19 when community prevalence is around 2.0%. Take that as you may.

There is an interesting series of response letters to this study that are published along with it. These make some legitimate points about design deficiencies. It is certainly true that a study involving masks cannot be a “true RCT” because one cannot blind a study involving masks to a clinical end. The wearer knows they are wearing a mask, and so does the rest of the public. I won’t belabor this point by describing what can go wrong in an unblinded study. Another criticism focuses on using PCR tests, with their false positives and negatives, to measure outcome – a problem which will return in the next section about observations. However despite some criticism, one might note that the outcome of the CHAMP study, in which U.S. Marine Corps recruits were subjected to rigorous social distancing, hygiene and mask wearing resulted in just about the same attack rate as found in this study.[14]   I doubt it is possible in the present politicized and hysterical atmosphere to do an RCT on any non-pharmaceutical intervention that could satisfy critics, but none that I know of have shown significant effectiveness of masks.[15]

Observations

Before launching into a discussion of what observations concerning the epidemic may mean, a brief segue into the incubation period and other influences on reporting is instructive. The incubation period of Sars-CoV-2 is probably ten or fourteen days long. Following exposure there is a probability on each successive day of someone becoming a case with half of the ultimate cases developing by day five or six.[16] The process behaves like a low pass filter with a delay. Figure 1 shows this. One-hundred exposures on day zero, presuming all result in cases, produces rising numbers until 19 cases occur on day five. Then they decline to zero.

This has two important considerations. First, it smooths the results of any factor producing a change to R, the reproductive ratio, and makes such changes harder to detect. That is, it reduces resolution. Second, it produces a correlation of cases day to day, so that counts of cases on successive days are not independent of one another, and this has the effect of reducing the degrees of freedom in observational data.[17]

Add to this the distortions resulting from common graphing options like 7 to 21 day averaging done with one-sided (causal) filters; and distortions which resulted from switching from clinical diagnosis to “lab confirmed” cases resting on PCR tests, and what one has is a mess. It is easy to reach a point where what a graph shows today is what might have happened three weeks earlier.

Figure 1. From a single exposure event cases climb for many days afterward in the incubation period. This behaves like a low-pass filter with a delay.

One does not have to search extensively to find evidence suggesting that epidemics proceed unhindered despite all sorts of mandates. I know of no epicurve showing a clear effect. Figure 2, using data drawn from the Covid Tracking Project, for example, shows a comparison among Colorado, New Mexico, and Utah. Despite mandates of various rigor, introduced at different times, the epicurves are virtually the same.[18] The Swiss Policy Research Group produced a nice twelve-paned panel, found here, which makes comparisons among various countries, with the same result – masks have no obvious benefit. A more detailed time series of cases in four German cities during April, 2020 also shows no benefit;[18] however, I would criticize these time series as being of such short duration following the mandatory mask order as to have possibly missed the period of greatest effect, if there is one, just over incubation delay.

Figure 2. Comparison of epicurves from three neighboring states, with timing of mask mandates shown. This was done by @ianmSC on Twitter using data drawn from the Covid Tracking Project.

The global data firm Dynata reported that by the first of July mask wearing in Houston and south Florida was likely to be 80% even before mandates; yet these places saw multiple large waves of infection thereafter.[20] California and New York applied rigorous mask mandates, yet still went through several large waves in the summer and autumn. The USA as a whole, in which 39 states imposed mask mandates in April or before, exhibits an epicurve almost identical, except for vertical scale, to Wyoming, the smallest state, even though Wyoming applied no state-wide mandate until November 9. The CDC reported that most people contracting COVID had worn masks, although self-reporting is notoriously inaccurate.[21]

There are many problems with our observational data. Death counts have been biased by incentives provided to hospitals over payments for COVID-19 deaths.[22] While many states tried to build useful epicurves by placing cases on date of symptom onset, many publically available data sets were built by date of case report and become dominated by the cycle of bureaucratic testing and reporting rather than by characteristics of the disease. To see how these differ Figure 3 shows Colorado data from 08/02/20. The difference is stark with a dominant seven day cycle which some people have confused with a dynamic of the disease and which disappears in the date of onset rendition. A subtle effect like mask usage is likely to be lost in these extraneous influences.

Figure 3. Comparison of epicurves by date of onset vs. report date.

The case data is a mess because when it began early in 2020 cases were confirmed through symptoms or at least a probable contact with another case, but eventually became dominated by mass testing of people without symptoms using PCR tests. Once this mass testing took hold even states trying to maintain an epicurve by date of onset could no longer do so. Figure 4 shows the curve for the state of Wyoming which became dominated by the weekly cycle of PCR testing which began at the University in Laramie in mid-august, but really took effect with return of students around September 1. Because so many of the “lab confirmed” cases had no associated symptoms a full one-third of cases remained always under investigation and the date of report became the de facto date of onset.[23]

This university provides an interesting case study in itself. The total number of cases from the start of the epidemic to the 31st of August in the entire county was134 – less than one case per day. The university instituted a very rigorous set of rules for reopening including mask wearing in all settings inside and out, rules for limiting number of persons in university vehicles, foot traffic patterns inside buildings, dedicated entrances and exits, periodic sanitation of all surfaces, social distance guidelines and even a web site to report persons not following rules. I did a few informal surveys around campus in September and October and thought mask compliance was between 80 and 90%.

Nevertheless by October 15, six weeks later, the county had added 780 cases of which 551 (71%) were connected to the U.W. campus. The rules and masks appeared to present no barrier to the spread of our mini-epidemic.[24]

Figure 4. Confirming cases using lab PCR tests caused the appearance of a seven day period in the epicurve.

Evidence provided to support mask mandates consisted mainly of a single study.[25] There have been many criticisms of this study, including one which suggested it be retracted.[26] However, ignoring its controversy for the moment, let’s just focus on what the authors have to say.

They state, first of all, that masks may have effectiveness as large as 85%, but that this estimate has low confidence – precise number but narrow confidence interval. Second, they notice a diminished effectiveness between N95 respirators on the one hand and cloth masks with 12 to 16 plies on the other. No one wears cloth masks with even one-fourth as many plies. Thus, this can’t be an endorsement of cloth masks. No one has unlimited access to N95 respirators,[27] and couldn’t because there is not enough manufacturing capacity to supply them to the public in general. Thus, this “essential” study does no more than reiterate what the other sources of information, including the measurements of particle attenuation reported in the Asahi Shimbun article, have to say. Its recommendations are not pertinent to reality of mask wearing by the general public. This is an unscientific rationale.

A more recent effort to promote masks as essential to controlling the pandemic appears to me to have many shortcomings.[28] This is a retrospective study of the history of the epidemic on a county level, referenced to timing of mask mandates and orders to close or limit restaurant traffic between March 2020 and October 2020. It is what economists would call an “event study”.[29] Problems with the study include:

  1. The event involved in an event study should be independent of the data. It is not in this case. Mask mandates were generally applied through political pressure during a pandemic wave. Often applied when the wave had begun to wane.
  2. Mask mandates are probably hopelessly confounded with other orders such as closure of restaurants. According to the researchers themselves, the mask mandates began in April in 39 states, and restaurant closures began in 49 states in March and April. Two influences atop one another. The claim to having a mask measurement unconfounded by closures cannot be true, or there was a lot of data sorting involved which becomes another confounder.
  3. The paper is missing details about the statistical methods and calculation of significance.
  4. Even if significant in a statistical sense, the effect seems very small.

The worst flaw seems to me to be a subtle one. The underlying data of the CDC study are curves of cumulative cases and deaths, which I have already explained are flawed to begin with. However, the typical cumulative curve, being a logistic curve, has a particular shape that begins as an almost exponential rise but quickly passes through an inflection with constantly diminishing slope as it approaches a horizontal asymptote. Such a curve will display a long sequence of days in which the case rate declines. An average of daily changes over segments of this decline, even with noise added, which are then referred to an earlier time period, will produce results just like those in the CDC study. No matter what the cause of the limit to an epidemic, the result is the same. What has happened is the CDC has chosen a statistic having a nearly perfect expectation to the characteristics of a logistic curve from any limiting influence, and cannot draw a distinction between the null hypothesis and a particular alternative. It is like circular logic.

Conclusions

There are situations, health care settings mainly or situations of extreme community prevalence with a lot of coughing and sneezing in public, where masks serve a useful purpose. Yet, people who insisted last spring that the epidemic would go away with mask mandates could not have been more wrong. Every consideration shows this.

Nearly all the masks we see people wearing are constructed to no standard, made of varying sorts of cloth, are poorly fitting, are worn with near complete disregard for effectiveness, reused who knows how many times, used for what else we know not, and are often completely open at the cheeks, nose, chin and beard. They appear mainly useful for making a person touch their face constantly.

How about experimental or observational evidence from the present pandemic? The only experimental evidence is consistent with the benefits being so small they cannot be distinguished from occurrence by chance. Probably no new experimental evidence will become available for the following reason: People have probably changed their behavior drastically during this pandemic leading to too many confounding factors to identify the effect of just one. As the epidemic wanes recruiting sufficient subjects for RTCs becomes difficult.

Masks mandates are not a risk free intervention. They have a poor effect of civil society, they absorb resources, they possibly carry health risks of their own, and they certainly contribute to mistaken notions of safety and risk. Masks seem to me like a solution to a political problem which should alone raise skepticism about all claims.


References/Notes:

1- Gery P. Guy,Jr. et al, Association of State-issued Mask Mandates and Allowing On-Premises Dining with County-level COVID-19 Case and Death Growth Rates, https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e3.htm?s_cid=mm7010e3_w, last accessed 3/8/2021.

2-Lincoln Moses and Frederick Mostellar,  Experimentation: Just do it!, In Statistics and Public Policy, Bruce D. Spencer Ed., Oxford U Press, 1997.

3-Futile Fussings: A history of Graphical Failure from Cattle to #coronavirus https://wattsupwiththat.com/2020/03/31/futile-fussings, last accessed 03/13/2021.

4-Close Encounters of the Third Kind, for example.

5-I have a collection including about three-dozen essay, opinion pieces, and research papers, discussing the topics of social distancing, mask mandates, lockdowns, school closures. These include contributions by Dr.s Scott Atlas, John Ioannidis, Paul Alexander, Donald Henderson, Jay Battacharya, Sunetra Gupta, Carl Henehgan, Tom Jefferson, Martin Kulldorff, and others; and almost all of these have been ignored, scorned, or censored in some way.

[6]-Individual virions are mentioned as having various sizes ranging from 50 to 130 nanometers in various internet sources. Corona viruses are pleomorphic which means they have a variety of shapes.

7- Cloth face masks offer zero shield against virus, a study shows, Nayon Kon, The Asahi Shimbun, July 7, 2020.

8-Ali Khosronejad, et al, Fluid Dynamics simulations show that facial masks can suppress the spread of COVID-19 in indoor environments, AIP Advances 10, 125109, (2020); https://doi.org/10.1063/5.0035414;

9-Referenced in Imke Schroeder, COVID-19: A Risk Assessment Perspective, J Chem Health Saf., 2020 May 11: acs:chas.0c00035

10-Tom Jefferson, and Carl Heneghan, Masking lack of evidence with politics, Center for Evidence Based Medicine, July 23, 2020. In particular the authors note the surprisingly small number of RTCs considering the great importance of controlling respiratory disease.

11-C. Raina MacIntyre, et al, A cluster randomized trial of cloth masks compared with medical masks in healthcare workers. BMJ Open 2015;5;e006577. doi.org/10.1136/bmjopen-2014-006577. Two earlier studies conducted in China by same group found no effectiveness for medical masks.

12-By significant in this context the authors mean a 95% confidence interval that does not enclose a relative risk of infection of 1.0, but is entirely above or below 1.0.

13-Henning Bundgaard, et.al. Effectiveness of adding a mask recommendation to other public health measures to prevent SARS-CoV-2 infection in Danish mask wearers, Annals of Internal Medicine, 18 November 2020. https://doi.org/10.7326/M20-6817

14-Andrew G. Letizia, et al, SARS-CoV-2 Transmission among Marine Recruits during Quarantine, N Engl J Med 2020; 383:2407-2416. DOI: 10.1056/NEJMoa2029717

15- Not finding significant protection, significant in the statistical sense, does not mean masks are completely ineffective, or counter-effective, but rather that their effect was not so large that it could be distinguished from a chance outcome at some level, usually 95%, of confidence.

16-P.E. Sartwell, The distribution of incubation periods of infectious disease, Amer. Jour. Hyg., 1950, 51:310-318. Sartwell lists coronaviruses as having a log mean of 0.4 (2.5 days) and dispersion of 1.5. However, a recent training class stated a median of 5-6 days for SARS-CoV-2. I used 5 days for purposes of producing Figure 1.

17-swprs.org/2018/10/01/covid-19-intro/ search for the English language version.

18- This panel of four German city graphs can be found at swprs.org/face-masks-evidence/ last accessed on 3/12/2021

19-This is well known, but see for example, chaamjamal, Illusory Statistical Power in Time Series Analysis, April 30, 2019, https://tambonthongchai.com/2019/40/30/illusory-statistical-power-in-time-series-analysis/ last accessed 1/18/2020

20-WSJ July 29, 2020.

21-CDC report referenced in article at The Federalist, CDC Study Finds Overwhelming Majority Of People Getting Coronavirus Wore Masks, October 12, 2020 https://thefederalist.com/2020/10/12/cdc-study-finds-overwhelming-majority-of-people-getting-coronavirus-wore-masks/

22-Payments for covid deaths, but not for others is incentive enough to bias results.

23-My attempts to learn how many cycles were being employed to report PCR results revealed that no one at any responsible agency in my state knew. All they would do is refer me to a misleading and wrong page at the supplier of the tests. However, a news item reported that researchers at Wayne State University a variety of cycle numbers are used to report results nationally including numbers from 25 to above 37. Viral Loads In COVID-19 Infected Patients Drop, Along With Death Rate, Study Finds Researchers find “a downward trend in the amount of virus detected.” Joseph Curl, DailyWire.com, Sep 27, 2020

24-UW to implement enhanced covid-19 testing program Monday, UW press release, Oct. 15. Data from this also mentions the university expects to perform 15000 tests per week. Yet my asking questions revealed that no one seemed to know what to expect from false positive and negative results. Amazingly few people recognize that interpreting the outcomes of PCR tests is a matter of conditional probability and cannot be done reliably without other information. Even one-half of the faculty and students at Harvard medical school did not know this according to an example from Julian L. Simon in his book “Resampling: The New Statistics, 1997.”

25-Derek K Chu, MD, et al, Physical distancing, face masks, and eye protection to prevent person to person transmission of SARS-CoV-2 and COVID-19: a systematic

review and meta-analysis, The Lancet,  v 395, issue 10242, p1973-1987, June 27, 2020 https://doi.org/10.1016/S0140-6736(20)31142-9

26-For example, the Center for Evidence Based Medicine (CEBM) at Oxford University objects to its social distancing conclusions.

27-The term “N95 Respirator” is ambiguous. These respirators are designed to be tight fitting, but most N95s are manufactured for construction, while there are N95s specifically manufactured to prevent disease transmission. Unfortunately the studies cited do not present a clear picture of which N95s were employed.

28-Refer to note #1 above. But in addition to my concerns listed here more were raised in Paul E. Alexander, The CDC’s Mask Mandate Study: Debunked, AIER, March 4, 2021 https://www.aier.org/article/the-cdcs-mask-mandate-study-debunked/ last accessed 3/13/2021

29-John Staddon, Scientific Research: How Science Works, Fails to Work, and Pretends to Work, Routledge, 2018, p. 124.

March 17, 2021 Posted by | Science and Pseudo-Science, Timeless or most popular | , , | 1 Comment

CDC IN COLLUSION WITH VACCINE MANUFACTURERS (SINCE 2004 AT LEAST!)

Amazing Polly | March 2, 2021

Have you heard of the 7-Step Recipe for Generating Interest In, And Demand For Flu (or any other) Vaccination? Back when journalists did some real work, HuffPo’s Laurence Solomon wrote a fascinating expose on the CDC colluding with vaccine makers.

This video is an edited version of my 41 minute expose with much more information. Please watch it here: https://www.bitchute.com/video/JR8gw6GLwug/

To support my work you can find my contact information on my website Amazing Polly St George here: https://amazingpolly.net/contact-support.php

References for this video can be found on the original.

March 8, 2021 Posted by | Deception, Science and Pseudo-Science, Timeless or most popular, Video | , , | 3 Comments

The CDC’s Mask Mandate Study: Debunked

Paul E Alexander MSc PhD | AIER | March 4, 2021

The US Centers for Disease Control and Prevention (CDC) recently published a February 2020 MMWR report entitled “Decline in COVID-19 Hospitalization Growth Rates Associated with Statewide Mask Mandates — 10 States, March–October 2020.” This report focused on 10 sites that had been included in the Covid-19 Associated Hospitalization Surveillance Network.

This CDC report described a decrease in hospitalization rates of growth of up to 5.6% in adults (18-65 years old) and attributed this to the use of masking and/or the introduction of mask mandates in the various sites. These rates were compared to those obtained from a 4-week period of time prior to the introduction of mask mandates. In so doing, and by way of regression analysis, the reduced rates of hospitalization were attributed to the introduction of statewide mask mandates.

Firstly, the initial publication by the CDC (February 5/February 12th, 2021) was plagued with important inaccuracies that were then fortunately addressed in an updated erratum (February 26th 2021). We applaud the CDC for taking the steps required to correct these errors. Reporting done by the CDC, which is generally considered as the premier public health agency in the US, must be of the highest quality, particularly since advice rendered by the CDC is also relied upon worldwide.

En face, CDC’s conclusion on mandates might appear to make sense unless one is familiar with the scientific data pertaining to the ineffectiveness of masking for prevention of the spread of Covid-19 (e.g. references 123456789101112131415) in which case the findings in fact contradict most of what is now known. The CDC’s conclusion might have made more sense if the real-world evidence we have about mandates did not actually exist (e.g. references 1234).

Does the CDC really think that masks prevent the wearer from getting Covid, or from spreading it to others? The CDC admits that the scientific evidence is mixed, as their most recent report glosses over many unanswered scientific questions. But even if it were clear – or clear enough – as a scientific matter that masks properly used could reduce transmission, it is a leap to conclude that a governmental mandate to wear masks will do more good than harm, even as a strictly biological or epidemiological matter. Mask mandates may not be followed; masks worn as a result of a mandate may not be used properly; some mask practices like double masking can do harm, particularly to children; and even if a mask mandate results in some increased number of masks being worn and worn properly, the mandate and the associated publicity may reduce the public’s attention to other more effective safeguards, such as meticulous hygiene practices.

Thus, it is not surprising that the CDC’s own recent conclusion on the use of nonpharmaceutical measures such as face masks in pandemic influenza, warned that scientific “evidence from 14 randomized controlled trials of these measures did not support a substantial effect on transmission…” Moreover, in the WHO’s 2019 guidance document on nonpharmaceutical public health measures in a pandemic, they reported as to face masks that “there is no evidence that this is effective in reducing transmission…” Similarly, in the fine print to a recent double-blind, double-masking simulation the CDC stated that “The findings of these simulations [supporting mask usage] should neither be generalized to the effectiveness … nor interpreted as being representative of the effectiveness of these masks when worn in real-world settings.”

Just look at the data from Jonas F. Ludvigsson that is emerging from Sweden in children 16 years old and under when preschools and schools were kept open and there were no face masks though social distancing was fostered. The result was zero (0) deaths from COVID-19 in 1.95 million Swedish children across the study period. The number of infections was exceedingly low, the number of hospitalizations was exceedingly low, and there were no deaths in children with COVID-19, all this despite not wearing masks due to no schoolwide mask mandate. Is this merely a perfunctory and legally prudent warning by the CDC that “your mileage may vary?” Or is it more like a hot mutual fund telling you that “past performance is no guarantee of future results.” What is the CDC really trying to say about face masks and why so much confusion?

We have reservations about the methodology employed and conclusions drawn in the CDC double mask study which we will address in a separate discussion but again their disclaimer as noted above: “The findings of these simulations should neither be generalized to the effectiveness … nor interpreted as being representative of the effectiveness of these masks when worn in real-world settings” seeds thoughts of doubt in relation to the value of this report. Why then, would the CDC even bother to publicize these findings? What is the public health impact? What is the benefit?

Moreover, the CDC even indicated in the double mask study that there are harms e.g. impediments to breathing, due to double masking. Indeed, the harms (e.g. reference 12345678910) are very real when face masks are used yet are often dismissed and not even discussed by the media medical establishment or government bureaucrats.

In relation to this, Dr. Anthony Fauci of the NIAID created appreciable confusion by initially suggesting and encouraging the use of double masks instead of one. Dr. Fauci then reversed his statements on the use of double masks. Dr. Fauci’s advisories took on a form of double peak which has an appearance of randomness or worse, capriciousness. This can only distort the desperately needed advice by the public at large; unsound advice can be very damaging on several levels. This random form of advice-giving was not reflective of a single event. For example, while touting vaccines as the only way for society to emerge back to normal from the pandemic, Dr. Fauci is now advising that in fact, even with vaccinations, people should still not attend public gatherings and restaurants, and that such restrictions could be in place until end of 2021. While changes in advice are required when new data emerge, we hold that this was definitely not the case with respect to masking (or vaccination for that matter).

Below are the main scientific shortcomings or analytical ambiguities in the CDC’s most recent MMWR report on mask mandates:

  1. The CDC’s main evidence, a regression study based on selected sites in ten states with masking mandates from March through October 2020, did not include the four-month period from November through February 2021 (which might have controlled for other possibly contributing factors such as sunlight and vitamin D) and did not appear to take into account the possible effects of such factors as school closures or changes in social distancing practices. We point out that during the period of March 22, 2020 to October 12, 2020 this is actually representative of the spring, summer and early fall seasons when outdoor activity increases. Of course, this leads to more exposure to sunlight with the attendant generation of active vitamin D metabolites, while at the same time there are marked reductions in confinement within enclosed spaces which would necessarily reduce the opportunities for transmission of disease. A more stringent approach to the analyses, including the use of all available data (i.e. not excluding a full 4-month period of time), might have led conceivably to a conclusion that there was in fact no significant effect of mask mandates on disease or case rates. And in concert with the CDC’s disclaimers noted above, the CDC indicated in their own report that the conclusions described in the study in favour of masking were, at best, only moderately reliable.
  2. The CDC analyzed changes in hospitalizations, but did not compare infection, disease, or death rates between states with and without masking mandates. Available evidence of that nature suggests that the course of the pandemic was not affected by state masking mandates.
  3. The CDC used a least squares fit regression analysis (OLS) (using “x” as mask wearing and the dependent/outcome to the “y” variable which is the number of Covid cases) despite the fact that simple regression is not the optimal approach and, we believe, should be replaced with Orthogonal Distance Regression (ODR) which would yield more reliable findings.
  4. Based on the reporting, it appears that the CDC’s regression analysis was based on data from limited sites within a state, and not the entire state.
  5. The CDC report failed to address/discuss recent potent research data based on high-quality case-controlled analyses, as well as a high-quality Danish randomized controlled trial study published in the Annals of Internal Medicine which found no statistically or clinically significant impact of mask-use in regard to the rate of infection with SARS CoV-2, or a recent NEJM publication (prospective cohort CHARM study) where researchers studied SARS-CoV-2 transmission among Marine recruits at Parris Island (n=1,848) who volunteered, underwent a 2-week quarantine at home that was followed by a second 2-week quarantine in a closed college campus setting. The predominant finding was that despite the very strict and enforced quarantine, including 2 full weeks of supervised confinement and then enforced social distancing and masking protocols, the rate of transmission was not reduced and in fact seemed to be higher than expected, despite the strong experimental design and the rigor associated with carrying out the study.
  6. The CDC report does not address and contextualize substantial “real world” experience showing that adding mandates where there is already substantial mask wearing has little effect, and that mask mandates that were followed can be correlated with increased case counts (e.g. references 1234). This obviously may not be cause and effect, but the same criticism can be levied against correlations or regressions going in the opposite direction.

Based on our assessment of this CDC mask mandate report, we find ourselves troubled by the study methods themselves and by extension, the conclusions drawn. The real-world evidence exists and indicates that in various countries and US states, when mask mandates were followed consistently, there was an inexorable increase in case counts. We have seen that in states and countries that already have a high frequency of mask wearing that adding mandates had little effect. There was no (zero) benefit of adding a mask mandate in Austria, Germany, France, Spain, UK, Belgium, Ireland, Portugal, and Italy, and states like California, Hawaii, and Texas. Importantly, we do not ascribe a cause-effect relationship between the implementation of mask mandates and the rise in case rates, but we also demand the same approach when it comes to claiming some sort of causal relationship between the introduction of mask mandates and likely claims by the CDC that their findings could support their implementation countrywide.

We think that inclusion of such evidence on the failures of masks mandates globally and states within the US would have made for more balanced, comprehensive, and fully-informed reporting. Specifically, when we consider the evidence on mask mandates, “in states with a mandate in effect, there were 9,605,256 confirmed Covid-19 cases, which works out to an average of 27 cases per 100,000 people per day. When states didn’t have a statewide order—including states that never even had mandates, coupled with the period of time states with mandates still didn’t have a mandate in place—there were 5,781,716 cases, averaging 17 cases per 100,000 people per day. In other words, protective-mask mandates have a poor track record insofar as fighting this pandemic. States with mandates in place produced an average of 10 more reported infections per 100,000 people per day than states without mandates.” The blind acceptance of the current unsupported dogma has become so entrenched that if cases do go up, the experts wedded to the universal use of masks then claim that this is good news and infer that the masking mandate prevented even more cases from occurring. This is a fine example of tautology and defies reason. We are very troubled by this type of scientific reporting and inference, for it is based on assumptions, supposition, and speculation.

Masks for the general population as they are currently used (surgical masks and the cloth masks), are ineffective (particularly when used without other mitigation) and the body of evidence (see AIER) is clear. A recent op-ed in the Washington Post spoke to mask wearing by everyone during the 1918 flu pandemic, with the conclusion that masks were useless. We embrace fully the contention by Klompas in the NEJM that “what is clear, however, is that universal masking alone is not a panacea. A mask will not protect providers caring for a patient with active Covid-19 if it’s not accompanied by meticulous hand hygiene, eye protection, gloves, and a gown. A mask alone will not prevent health care workers with early Covid-19 from contaminating their hands and spreading the virus to patients and colleagues. Focusing on universal masking alone could, paradoxically, lead to more transmission of Covid-19 if it diverts attention from implementing more fundamental infection-control measures.” We are particularly alarmed by the harms of masking and the failure by top US agencies and leadership (as well as the media and ‘media’ medical experts) to discuss or highlight harms in any discourse on masking.

We end by imploring the CDC to take our critique in the spirit in which it was generated. We welcome continued, rigorous scientific examination of these important societal lockdowns, school closures, and masking and broader mask mandate issues by CDC and others. We are entirely willing to consider any evidence that contradicts what we have seen which suggests that societal lockdowns and school closures are not effective, and as presented here, suggests that mask mandates are ineffective. Most importantly, to maintain the validity of scientific research as a tool, and the public’s confidence in such research, reports on the results of such research should more comprehensively address the weakness or ambiguities that exist, as well as the conclusions the reporting agency supports.

Trusting the science means relying on the scientific process and method and not merely ‘following the leader.’ It is not the same as trusting, without verification, the conclusory statements of human beings simply because they have scientific training or credentials. This is especially so if their views and inquiry have become politicized. Dr. Martin Kulldorff of Harvard’s Medical School has recently commented on the present Covid-19 scientific and research environment by stating, “After 300 years, the Age of Enlightenment has ended.”

Sadly, we must agree, that it’s not just that the age of enlightenment has come to an end, but indeed, that the science itself has been politicized and severely corrupted.

Contributing Authors

  • Paul E Alexander MSc PhD, McMaster University and GUIDE Research Methods Group, Hamilton, Ontario, Canada elias98_99@yahoo.com
  • Howard C. Tenenbaum DDS, Dip. Perio., PhD, FRCD(C) Centre for Advanced Dental Research and Care, Mount Sinai Hospital, and Faculties of Medicine and Dentistry, University of Toronto, Toronto, ON, Canada
  • Ramin Oskoui, MD, CEO, Foxhall Cardiology, PC, Washington, DC  oskouimd@gmail.com
  • Dr. Parvez Dara, MD, MBA, daraparvez@gmail.com

March 5, 2021 Posted by | Deception, Science and Pseudo-Science, Timeless or most popular | , , , | 1 Comment

Tony Fauci and the Swine Flu hoax; betrayal of trust

By Jon Rappoport | NoMoreFakeNews | March 5, 2021

In my current series of articles, I’ve taken apart the Ebola and Zika hoaxes.

Now I take you back to the summer of 2009, when the CDC and the World Health Organization were hyping the “deadly H1N1 Swine Flu pandemic.”

They were, of course, also urging people to take the new Swine Flu vaccine. On that subject, here is an excerpt from Robert Kennedy Jr.’s Children’s Health Defense (3/27/20):

“For example, [Dr. Anthony] Fauci once shilled for the fast-tracked H1N1 influenza (‘swine flu’) vaccine on YouTube, reassuring viewers in 2009 that serious adverse events were ‘very, very, very rare.’ Shortly thereafter, the vaccine went on to wreak havoc in multiple countries, increasing miscarriage risks in pregnant women in the U.S., provoking a spike in adolescent narcolepsy in Scandinavia and causing febrile convulsions in one in every 110 vaccinated children in Australia—prompting the latter to suspend its influenza vaccination program in under-fives.”

However, that is only half the Swine Flu story. The other half—which involves an astounding hoax—was surely something Fauci was aware of at the time.

Fauci was, in fact, recommending a highly dangerous vaccine for protection against AN EPIDEMIC THAT DIDN’T EXIST AT ALL.

His friends and professional colleagues at the CDC were creating the hoax.

Let me run it down for you.

In the summer of 2009, the CDC was claiming there were thousands of Swine Flu cases in the US. But behind these statistics lay an unnerving secret. A major crime, considering the CDC’s mandate to report the truth to the American people:

Secretly, the CDC had stopped counting cases of Swine Flu.

What? Why?

CBS investigative reporter, Sharyl Attkisson, discovered the CDC secret; and she found out why.

The routine lab testing of tissue samples from the most likely Swine Flu patients was coming back, in the overwhelming percentage of cases, with: NO SIGN OF SWINE FLU OR ANY OTHER KIND OF FLU.

Attkisson wrote an article about this scandal, and it was published on the CBS News website. However, the next, bigger step—putting out the story on CBS television news—was waylaid. No deal. And CBS shut down any future investigation on the subject. Attkisson’s article died on the vine. No other major news outlet in the world picked up her article and ran with it deeper into the rabbit hole.

Here is what Attkisson told me when I interviewed her:

Rappoport: In 2009, you spearheaded coverage of the so-called Swine Flu pandemic. You discovered that, in the summer of 2009, the Centers for Disease Control, ignoring their federal mandate, [secretly] stopped counting Swine Flu cases in America. Yet they continued to stir up fear about the “pandemic,” without having any real measure of its impact. Wasn’t that another investigation of yours that was shut down? Wasn’t there more to find out?

Attkisson: The implications of the story were even worse than that. We discovered through our FOI efforts that before the CDC mysteriously stopped counting Swine Flu cases, they had learned that almost none of the cases they had counted as Swine Flu was, in fact, Swine Flu or any sort of flu at all! The interest in the story from one [CBS] executive was very enthusiastic. He said it was “the most original story” he’d seen on the whole Swine Flu epidemic. But others pushed to stop it [after it was published on the CBS News website] and, in the end, no [CBS television news] broadcast wanted to touch it. We aired numerous stories pumping up the idea of an epidemic, but not the one that would shed original, new light on all the hype. It was fair, accurate, legally approved and a heck of a story. With the CDC keeping the true Swine Flu stats secret, it meant that many in the public took and gave their children an experimental vaccine that may not have been necessary.

So… fake pandemic, CDC crimes, and a damaging vaccine.

But that wasn’t end of it. The CDC wanted to commit another crime. About three weeks after Attkisson’s findings were published on the CBS News website, the CDC, obviously in a panic, decided to double down. If one lie is exposed, tell an even bigger one. A much bigger one.

Here, from a November 12, 2009, WebMD article is the CDC’s response:

“Shockingly, 14 million to 34 million U.S. residents — the CDC’s best guess is 22 million — came down with H1N1 swine flu by Oct. 17 [2009].” (“22 million cases of Swine Flu in US,” by Daniel J. DeNoon).

Are your eyeballs popping? They should be.

Fast forward to 2020. Who in his right mind, armed with a little history, would believe anything the CDC is saying about COVID-19? The discovery of a new coronavirus. The case and death numbers, the accuracy of the diagnostic tests, the need for lockdowns and economic devastation, the safety and importance of a vaccine, the fear porn? Who would believe any of it?

And who would believe anything coming out of the mouth of Dr. Anthony Fauci?

Only a fool.


SOURCES:

[1] https://blog.nomorefakenews.com/2021/03/02/ebola-the-new-fake-outbreak/

[1a] https://blog.nomorefakenews.com/category/ebola/

[2] https://blog.nomorefakenews.com/2021/03/04/zika-was-a-warm-up-for-covid-it-didnt-fly/

[2a] https://blog.nomorefakenews.com/category/zika/

[3] https://childrenshealthdefense.org/news/dr-fauci-and-covid-19-priorities-therapeutics-now-or-vaccines-later/

[3a] https://web.archive.org/web/20200328080313/https://childrenshealthdefense.org/news/dr-fauci-and-covid-19-priorities-therapeutics-now-or-vaccines-later/

[4] https://www.cbsnews.com/news/swine-flu-cases-overestimated/

[4a] https://web.archive.org/web/20140101163355/https://www.cbsnews.com/news/swine-flu-cases-overestimated/

[5] https://www.cdc.gov/media/transcripts/2009/t091009.htm

[6] https://www.webmd.com/cold-and-flu/news/20091112/over-22-million-in-us-had-h1n1-swine-flu#1

[6a] https://web.archive.org/web/20100105035212/https://www.webmd.com/cold-and-flu/news/20091112/over-22-million-in-us-had-h1n1-swine-flu

March 5, 2021 Posted by | Deception, Science and Pseudo-Science, Timeless or most popular | , , | Leave a comment

Annual Flu Deaths Scam Unwittingly Exposed and Replaced by the COVID Deaths Scam

Actual text from the CDC website (which used to be here) regarding annual flu deaths.
By Brian Shilhavy | Health Impact News | February 28, 2021

During the past 10 years that Health Impact News has been publishing the truth about vaccines and exposing the corruption and lies in the pharmaceutical industry and their marketing branch, the U.S. Centers for Disease Control (CDC), regarding the annual flu statistics, we have usually run stories around this time of year explaining to people that the number of people dying from the flu according to the CDC is false, and that the CDC themselves have always admitted that they do not know the exact number of people who die from the flu each year, but instead base their data on “estimates.”

For years this was published very clearly on the CDC website for all to see, at least for those who bother to “fact check” the CDC’s claims regarding annual flu deaths.

Many others over the years have exposed this scam as well.

Here is an excerpt from an article written in 2014 by Lawrence Solomon in the Huffington Post:

The CDC’s decision to play up flu deaths dates back a decade, when it realized the public wasn’t following its advice on the flu vaccine.

During the 2003 flu season “the manufacturers were telling us that they weren’t receiving a lot of orders for vaccine,”Dr. Glen Nowak, associate director for communications at CDC’s National Immunization Program, told National Public Radio.

Flu results in “about 250,000 to 500,000 yearly deaths” worldwide, Wikipedia tells us.

“The typical estimate is 36,000 [deaths] a year in the United States,” reports NBC, citing the Centers for Disease Control.

“Somewhere between 4,000 and 8,000 Canadians a year die of influenza and its related complications, according to the Public Health Agency of Canada,” the Globe and Mail says, adding that “Those numbers are controversial because they are estimates.”

“Controversial” is an understatement, and not just in Canada, and not just because the numbers are estimates. The numbers differ wildly from the sober tallies recorded on death certificates — by law every certificate must show a cause — and reported by the official agencies that collect and keep vital statistics.

According to the National Vital Statistics System in the U.S., for example, annual flu deaths in 2010 amounted to just 500 per year — fewer than deaths from ulcers (2,977), hernias (1,832) and pregnancy and childbirth (825), and a far cry from the big killers such as heart disease (597,689) and cancers (574,743).

The story is similar in Canada, where unlikely killers likewise dwarf Statistics Canada’s count of flu deaths.

Even that 500 figure for the U.S. could be too high, according to analyses in authoritative journals such as the American Journal of Public Health and the British Medical Journal.

Only about 15-20 per cent of people who come down with flu-like symptoms have the influenza virus — the other 80-85 per cent actually caught rhinovirus or other germs that are indistinguishable from the true flu without laboratory tests, which are rarely done.

In 2001, a year in which death certificates listed 257 Americans as having died of flu, only 18 were positively identified as true flus. The other 239 were simply assumed to be flus and most likely had few true flus among them.

“U.S. data on influenza deaths are a mess,” states a 2005 article in the British Medical Journal entitled “Are U.S. flu death figures more PR than science?

This article takes issue with the 36,000 flu-death figure commonly claimed, and with describing “influenza/pneumonia” as the seventh leading cause of death in the U.S.

Read the full article.

As you can see from Mr. Solomon’s 2014 article, he quoted sources dating all the way back to the early 2000s where this scam was exposed. It just wasn’t published in the pharma-controlled corporate media, so those spoon-fed the propaganda from this corporate media lined up every year to get their flu shots, as Big Pharma raked in huge profits from producing over 300 million doses of the flu vaccine each year.

Dr. David Brownstein is another doctor who regularly exposed this scam, although the government has now stepped in and censored his writings scrubbing his blog clean, but we have preserved many of his articles on this topic.

In October of 2018, he wrote:

The Centers for Disease Control and Prevention estimated that the 2017-2018 flu season killed 80,000 and hospitalized 900,000 Americans.  Of course, the mainstream media reported this as fact as shown in this September 27, 2018 article in the Washington Post.

The Powers-That-Be, including the CDC and the mainstream media, are using these estimates to promote the flu shot for the upcoming flu season.

Keep in mind, the 80,000 deaths and 900,000 hospitalizations are ESTIMATES. And, I can state, with authority, that they are very poor estimates.

You see, deaths from flu are always estimates because if the Powers-That-Be reported the true numbers of deaths from actual influenza infections, the numbers would be much lower and people would not be so inclined to receive a flu shot.

How does the CDC overestimate the number of flu deaths? The CDC accomplishes this by reporting a combined pneumonia and influenza death rate. Every time I try to analyze this data, I know I will have to spend at least an hour searching for the true number who died from influenza because the CDC tries to hide that data.

Why does the CDC do this? The answer is easy: The more people that receive the flu vaccine, the more money the CDC makes. You see, the CDC holds patents on many vaccines including the flu vaccine. (1)

Perhaps I could tolerate the CDC combining pneumonia with flu deaths IF the flu vaccine prevented both. However, the flu vaccine has never been shown to have any impact on the number of deaths from pneumonia.

In fact, for the vast majority who receive it, the flu vaccine has little impact on preventing the flu, but I digress.

In 2001 the CDC reported that 62,034 died from influenza and pneumonia. That year, I would bet that CDC proclaimed that flu killed over 50,000 Americans. After a painful hour of searching the CDCs database, I found the true 2001 numbers: 257 died from influenza and 61,777 died from pneumonia. Keep in mind, any death from the flu is tragic, but those numbers are out of a population of over 300 million. In 2010 (the latest year data are available) there were 55,227 deaths due to pneumonia and flu. Flu killed 4,605 while pneumonia killed the rest. (2)

So, let’s go back to last year’s flu season. The flu season lasts about six months.  80,000 deaths would lead one to conclude that 13,333 died per month (80,000/6 months) from the flu.  If we further divide that number by 50 (the number of states), we can conclude that there were 267 people dying each month in every state from the flu.  Since the internet provides 24-hour news cycles, I think we all would have heard that about 9 people (267/30 days per month)  in every state dying daily from the flu. (3)

I have five practitioners in my office.  We have over 100 years of experience in treating patients.  None of us has can recall a single patient dying from the flu.

In fact, I can guarantee you that if 9 people were dying in my state daily from the flu, my partners and I would hear about it.  In fact, there are always headlines on the internet when one person dies from the flu.

Studying the past CDC data shows that each year a few hundred to a few thousand die from the flu.

80,000 died last year? I say, “Fake News!”

See:

Did 80,000 People Really Die from the Flu Last Year? Inflating Flu Death Estimates to Sell Flu Shots

This annual flu death scam continued through 2019, as again Dr. Brownstein wrote:

The headline in the January 5, 2019 edition of the Wall Street Journal reads “Six Feet, 48 Hours, 10 Days: How to Avoid the Flu.”

This article, like nearly all main stream media flu articles was written to scare the reader into getting the flu vaccine. As with most mainstream medical articles about the flu, it is filled with fake news.

Let’s analyze the article.

The author starts off by writing,

“After a slow start, the flu season has taken off. Between Christmas and New Years Day, there was a marked rise in flu illnesses across the U.S.”

So far, no fake news to report.

However, as with most main stream media influenza stories, the writer misstates the true numbers of Americans who die from the flu.

“In a mild year, influenza, a highly contagious viral infection of the respiratory system, kills as many as 12,000 people in the U.S., and in a bad year, it could be as many as 56,000.”

I have two words to state here:

FAKE NEWS!

Folks, that is a blatant LIE. Over the last 38 years, neither twelve nor fifty-six thousand deaths from influenza infections occurred. In fact, the deaths from influenza are not even close to those numbers.

Why would the mainstream media and the Powers-That-Be continually lie about the numbers of people that die from the flu?

It is not hard to understand why—they want to scare the public in order to increase the number of people vaccinated with the flu vaccine.

The Centers for Disease Control and Prevention keeps annual death statistics. When searching through that data, it is easy to find the first Table (Table B) which lists the number of deaths from the top fifteen causes of death  (https://www.cdc.gov/nchs/data/nvsr/nvsr67/nvsr67_05.pdf -page 6).

In fact, every year that the CDC reports the final data for deaths, the CDC combines influenza and pneumonia together as one of the top 15 causes of death. No other separate illnesses are combined, so why combine pneumonia and influenza which are two separate illnesses?

The answer is easy: the CDC artificially inflates the numbers of deaths from influenza to scare us into getting the flu shot. You see, if a very small percentage of Americans died yearly from the flu vaccine, why would so many of them want to get vaccinated for influenza?

The CDC has a direct financial interest in vaccinating the entire population since it holds multiple patents on vaccines including the flu vaccine.

In order to frighten the public to get the flu vaccine, the CDC’s scare tactics include annual statements that the flu kills 36,000 Americans per year.

See:

Medical Doctor Calls Out Mainstream Media for Reporting Fake Numbers of Flu Deaths in Order to Sell More Flu Vaccines

The Great Flu Reset 2020-21: COVID Deaths Take Over to Sell New Novel COVID “Vaccines”

So now we come to the 2020-21 flu season, where we are being told the flu has “vanished.” Hardly anyone is getting the flu. Nobody is dying from the flu.

And if you still get your news only from the pharma-owned corporate media, you are being told that the lockdowns, social distancing, handwashing, and of course masks, have worked to eliminate all those horrible flu cases and deaths.

To believe this, of course, one has to overlook that these measures did nothing to slow down COVID deaths, as according to CDC statistics we have seen more COVID deaths in January and February than all the rest of the months since the “pandemic” started combined.

The “logic” of this is so absurd, that they even hired an editorialist at the New York Times to explain to people that they should not try to figure this out, because “Critical thinking, as we’re taught to do it, isn’t helping in the fight against misinformation.”

ZeroHedge News covered this issue today:

Despite all those warnings from Dr. Anthony Fauci about COVID-19 and the flu joining forces in 2020 and 2021 to create some kind of super-deadly double-whammy viral pandemic, it’s no longer a secret at this point that worries about a super-charged flu season simply never came to pass. We’ve reported on the phenomenon of falling flu cases before.

February is usually the peak of flu season, when doctors’ offices and hospitals are packed with patients. But that’s not the case this year. Instead, the flu has virtually disappeared from the US, with reports coming in at far lower levels than the world has seen in decades. Some areas, like San Diego, have seen such low numbers, health authorities have demanded audits of COVID-positive patients to see whether some might have been misdiagnosed.

According to the CDC, the cumulative positive influenza test rate from late September into the week of December 19th was just 0.2%, compared to 8.7% from a year before.

Hospitals say the expected army of flu-sickened patients never materialized, and that nationally “this is the lowest flu season we’ve had on record,” according to a surveillance system that is about 25 years old.

One source from Maine Medical Center in Portland, the state’s largest hospital, said “I have seen zero documented flu cases this winter,” said Dr. Nate Mick, the head of the emergency department.

Ditto in Oregon’s capital city, where the outpatient respiratory clinics affiliated with Salem Hospital have not seen any confirmed flu cases.

The phenomenon isn’t unique to the US.

In the UK, data released this week show that the number of active flu cases in the country has fallen to zero. (Source.)

Of course for those of us who have followed this issue for more than a decade now, we know that the flu deaths have not gone anywhere, because they were never there to begin with.

What we actually had, based on the Department of Justice quarterly reports on settlements paid out from the National Vaccine Injury Compensation Program, is many people being injured and killed by the flu shots.

You can see this for yourself by reading their quarterly reports for the past several years here. More people were injured and killed by the annual flu shot than all the other FDA approved vaccines combined.

The CDC simply stopped estimating and inflating the flu deaths, and concentrated on COVID deaths instead, to support the TRILLIONS spent to fast-track experimental COVID vaccines which Big Pharma is now rushing to manufacture and distribute.

So has this “Great Reset” simply replaced one scam for another one?

I’ll let Dr. Scott Jensen, a medical doctor and Senator from Minnesota, tell you in his own words:

This is from our Rumble channel, and it is also on our Bitchute channel (still processing at time of publication).

See also:

Minnesota Doctor and Senator Speaks Out on Fox News Regarding Coronavirus “Padded” Death Statistics for Financial Gain

March 1, 2021 Posted by | Deception, Science and Pseudo-Science, Timeless or most popular, Video | , | Leave a comment

COVID-19 Fatalities 16.7 Times Too High Due to ‘Illegal’ Inflation

GreenMedInfo Research Group | February 1, 2021

In March 2020, the CDC changed the way COVID-19 deaths are reported on death certificates, resulting in a dramatic — and possibly illegal — inflation of fatalities that drove restrictive public health policies threatening health freedom

Only 6% of COVID-19 deaths include only COVID-19 as the cause on the death certificate, according to the U.S. Centers for Disease Control and Prevention. This means for the other 94%, additional causes are listed, with an average of 2.9 additional conditions or causes of death included.[i]

“This is the most important statistical revelation of this crisis,” according to a study by the Institute for Pure and Applied Knowledge (IPAK), as it reveals that many “COVID-19 deaths” may have been due to other causes. In fact, the CDC published new guidelines on March 24, 2020, which alter the way deaths are recorded exclusively in cases of COVID-19.

The guidelines were published without peer-review or opportunity for public comment, and resulted in a dramatic and misleading inflation in “COVID-19” deaths, which would have been deemed due to other causes using the CDC’s longstanding system of data collection and reporting established in 2003. As IPAK’s report questioned:[ii]

“Why would the CDC decide against using a system of data collection & reporting they authored, and which has been in use nationwide for 17 years without incident, in favor of an untested & unproven system exclusively for COVID-19 without discussion and peer-review?”

CDC Changed Death Certificate Recording Rules for COVID-19 Only

IPAK’s report reveals a historical timeline of events showing how a number of incidents conspired to inflate COVID-19 fatality data and, in turn, justify restrictive public health policies like lockdowns, quarantines, business closures and social distancing. One key issue has to do with the way cause of death is recorded in the case of comorbidities.

In 2003, the CDC published the “Medical Examiners’ and Coroners’ Handbook on Death Registration and Fetal Death Reporting” and “Physicians’ Handbook on Medical Certification of Death.” Part I of a death certificate includes the immediate cause of death, listed in order from the official cause of death (a) down to underlying causes that contributed to death (in descending order of importance, as b, c, d).

Part II of the death certificate includes other significant conditions that are not related to the underlying causes in Part I. According to the report:[iii]

“Comorbid conditions have been listed on Part I of death certificates as causes of death per the CDC Handbook since 2003 to ensure accurate reporting can be developed. Comorbidities are seldom placed in Part II. Part II is typically the section where coroners and medical examiners can list recent infections as underlying, initiating factors.

Prior to the CDC’s March 24th decision, any co-morbidities would have been listed in Part I rather than Part II and initiating factors such as infections including the SARS-COV-2 virus, would have been listed on the last line in Part I or more commonly in Part II.”

After the March 2020 guideline change, however, comorbidities were to be listed in Part II, which meant COVID-19 could be listed exclusively in Part I:[iv]

“This has had a significant impact on data collection accuracy and integrity. It has resulted in the potential false inflation of COVID-19 fatality data and is a potential breach of federal laws governing information quality.”

New CDC Guidelines Inflate COVID-19 Deaths by at Least 16.7-Fold

The report examined COVID-19 fatalities through August 23, 2020 and compared them using the CDC’s guidelines that had been in place since 2003 and those put into place in March 2020 for COVID-19. You can see the results in their figure below, which shows, “Had the CDC used the 2003 guidelines, the total COVID-19 [fatalities would] be approximately 16.7 times lower than is currently being reported.”[v]


Image source: IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020, Figure 9

‘This Leaves Me Speechless’

On Twitter, investigative health journalist Nicolas Pineault wrote, “If this is accurate, this leaves me speechless.”[vi] Indeed, not only did the CDC leave no records as to how it made the decision to change how deaths are reported, but some estimates suggest they may have resulted in an inflation of COVID-19 fatalities of over 90%, while violating U.S. law:[vii]

“Previous reports detailed the substantial changes on how causes of death were forcibly modified by the CDC through the NVSS, and how together, both federal agencies inflated the actual number of COVID-19 fatalities by approximately 90.2% through July 12th, 2020.

We believe this deliberate decision by the CDC and NVSS [National Vital Statistics System] to deemphasize pre-existing comorbidities, in favor of emphasizing COVID-19 as a cause of death, is in violation of 44 U.S. Code 3504 (e)(1)(b), which states the activities of the Federal statistical system shall ensure ‘the integrity, objectivity, impartiality, utility, and confidentiality of information collected for statistical purposes.'”

The public health implications of an artificial inflation of COVID-19 deaths are immense, as rates of anxietydepression[viii] and suicidal thoughts[ix] are on the rise — a direct result of restrictive COVID-19 health policies.

Only with accurate data can individuals and health officials make decisions to truly protect health, and as the report noted, “It is concerning that the CDC may have willfully failed to collect, analyze, and publish accurate data used by elected officials to develop public health policy for a nation in crisis.”[x] It’s also one more reason why now is more important than ever to take a stand for health freedom.


References

 

[i] U.S. CDC January 27, 2021 https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm#ExcessDeaths

 

[ii] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

[iii] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

[iv] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

[v] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

[vi] Twitter, Nick Pineault October 15, 2020 https://twitter.com/nickpineault1/status/1316744440917250049

 

[vii] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

[viii] University of Wisconsin, The Impact of School Closures and Sport Cancellations on the Health of Wisconsin Adolescent Athletes https://cdn1.sportngin.com/attachments/document/33fe-2195426/McGuine_study.pdf#_ga=2.138358896.1736658140.1612045938-245521230.1612045938

 

[ix] BMJ 2020;371:m4095 https://www.bmj.com/content/371/bmj.m4095

 

[x] IPAK PHPI, COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Perspective October 12, 2020 https://t.co/nRoW2TGdK7?amp=1

 

© 2021 GreenMedInfo LLC. This work is reproduced and distributed with the permission of GreenMedInfo LLC.

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February 19, 2021 Posted by | Science and Pseudo-Science, Timeless or most popular, Video | , | 1 Comment

The CDC’s double mask mannequin ‘study’ is lunacy dressed up as science

A new “public health” low

By Jordan Schachtel | The Dossier | February 10, 2021

The CDC has released a new “study” by the government health institution that claims to support the thesis that double-masking — or further sealing your mask in order to make it more difficult to breathe — will work to prevent the spread of the coronavirus.

The “study,” which occurred in January, was nothing more than a handful of experiments on mannequins in a contained environment. Here are some photos from the CDC “study” that was published today:

No human beings were involved in this study. And yes, it was that simple. The CDC sprayed aerosols at mannequins and slapped a science™ label on their experiments.

There are endless amounts of clear, immediate, obvious issues with this “study” that causes a rational-minded person to send it to the dumpster.

First and foremost, it is not a completed study at all. These are mere experiments conducted on mannequins, not humans. A proper study on the efficacy of masks needs to be a randomized controlled trial involving human beings in their normal settings — such as the Danish mask study that showed there is no evidence that masks do anything to prevent COVID-19 — and not mannequins in a laboratory.

Second, as you can see on the double masked mannequin, the lifeless object is barely able to “see” over its double mask.

Third, these masks are very tightly sealed and secured to the face of the mannequins. It is not exactly rocket science to “discover” that it is more difficult to breathe in particles from outside of a contained environment when you fully seal something over your face.

However, this is unsustainable, as it would make breathing in oxygen (which, you know, is a thing that humans need to do) very difficult, and cause severe discomfort for regular use. Mannequins don’t have to worry about breathing or seeing, but humans do.

There are so many more potential variables and side effects involving mask-wearing, and how human behavior cannot be replicated through mannequin experiments. For more on this, check out the feed of cognitive scientist Mark Changhizi on Twitter @MarkChanghizi.

When you read the fine print of the “study,” even the CDC seems to acknowledge the aforementioned paradoxes in the following paragraph of their report:

“Finally, although use of double masking or knotting and tucking are two of many options that can optimize fit and enhance mask performance for source control and for wearer protection, double masking might impede breathing or obstruct peripheral vision for some wearers, and knotting and tucking can change the shape of the mask such that it no longer covers fully both the nose and the mouth of persons with larger faces.”

The CDC concludes its remarks by stating:

“Continued innovative efforts to improve the fit of cloth and medical procedure masks to enhance their performance merit attention.”

Not exactly much of a bombshell, but that’s not how the media and Big Tech interpreted it in order to advance their agenda.

The absurd CDC mannequin study has already been promoted by countless legacy media publications and propped up by social media sites as if it’s the gospel.

Twitter has promoted the “mask study” to #1 in its curated list, claiming, without evidence, that the CDC has “confirmed” the efficacy of double mask wearing.

Eric @IAmTheActualET

Hi @TwitterSupport I’d like to report @Twitter for spreading misinformation

February 10th 2021

77 Retweets

There is no real, functional experiment-based science behind single-masking, so it shouldn’t be particularly surprising that the “public health experts,” media stenographers, and power drunk politicians are now promoting double-masking as the “new science” to “stop the spread” of COVID-19.

February 14, 2021 Posted by | Fake News, Mainstream Media, Warmongering, Science and Pseudo-Science | , , | Leave a comment

BEHIND THE CURTAIN OF THE CURRENT CON

BRAGGING ABOUT THEIR MANIPULATION

AN ENTIRE INDUSTRY OF PAID LIARS

I think we are the only source to talk about this:

The vast communications network that is being used to sell the current Con.

Watch this for a glimpse of how it works and the people involved.

ABOUT THE PEOPLE WHO MAKE FINE LIVINGS HYPING EPIDEMICS FOR PHARMA

Click here to support Brasscheck

February 12, 2021 Posted by | Deception, Mainstream Media, Warmongering, Science and Pseudo-Science, Timeless or most popular, Video | , , , | Leave a comment

Study: CDC Broke Federal Law by Manipulating COVID Death Statistics

By Brian Shilhavy | Health Impact News | February 3, 2021

A study published in the journal Science, Public Health Policy & the Law recently claims that the CDC violated federal law by inflating COVID-19 fatality statistics.

The study is titled “COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Retrospective.”

From the Abstract:

According to the Centers for Disease Control and Prevention (CDC) on August 23, 2020, “For 6% of the deaths, COVID-19 was the only cause mentioned. For deaths with conditions or causes in addition to COVID-19 , on average, there were 2.6 additional conditions or causes per death.”

For a nation tormented by restrictive public health policies mandated for healthy individuals and small businesses, this is the most important statistical revelation of this crisis. This revelation significantly impacts the published fatalities count due to COVID-19.

More importantly, it exposes major problems with the process by which the CDC was able to generate inaccurate data during a crisis.

The CDC has advocated for social isolation, social distancing, and personal protective equipment use as primary mitigation strategies in response to the COVID-19 crisis, while simultaneously refusing to acknowledge the promise of inexpensive pharmaceutical and natural treatments.

These mitigation strategies were promoted largely in response to projection model fatality forecasts that have proven to be substantially inaccurate.

Further investigation into the legality of the methods used to create these strategies raised additional concerns and questions.

Why would the CDC decide against using a system of data collection & reporting they authored, and which has been in use nationwide for 17 years without incident, in favor of an untested & unproven system exclusively for COVID-19 without discussion and peer-review?

Did the CDC’s decision to abandon a known and proven effective system also breach several federal laws that ensure data accuracy and integrity?

Did the CDC knowingly alter rules for reporting cause of death in the presence of comorbidity exclusively for COVID-19? If so, why? (Full study.)

Patrick Howley, writing for National File, reported:

The groundbreaking peer-reviewed research…asserts that the CDC willfully violated multiple federal laws including the Information Quality Act, Paperwork Reduction Act, and Administrative Procedures Act at minimum. (Publishing Journal – Institute for Pure and Applied Knowledge / Public Health Policy Initiative)

“Most notably, the CDC illegally enacted new rules for data collection and reporting exclusively for COVID-19 that resulted in a 1,600% inflation of current COVID-19 fatality totals,” the watchdog group All Concerned Citizens declared in a statement provided to NATIONAL FILE, referring to the Institute for Pure and Applied Knowledge study.

The research demonstrates that the CDC failed to apply for mandatory federal oversight and failed to open a mandatory period for public scientific comment in both instances as is required by federal law before enacting new rules for data collection and reporting.

“The CDC is required to be in full compliance with all federal laws even during emergency situations. The research asserts that CDC willfully compromised the accuracy and integrity of all COVID-19 case and fatality data from the onset of this crisis in order to fraudulently inflate case and fatality data,” stated All Concerned Citizens.

On March 24th the CDC published the NVSS COVID-19 Alert No. 2 document instructing medical examiners, coroners and physicians to deemphasize underlying causes of death, also referred to as pre-existing conditions or comorbidities, by recording them in Part II rather than Part I of death certificates as “…the underlying cause of death are expected to result in COVID-19 being the underlying cause of death more often than not.”

This was a major rule change for death certificate reporting from the CDC’s 2003 Coroners’ Handbook on Death Registration and Fetal Death Reporting and Physicians’ Handbook on Medical Certification of Death, which have instructed death reporting professionals nationwide to report underlying conditions in Part I for the previous 17 years.

This single change resulted in a significant inflation of COVID-19 fatalities by instructing that COVID-19 be listed in Part I of death certificates as a definitive cause of death regardless of confirmatory evidence, rather than listed in Part II as a contributor to death in the presence of pre-existing conditions, as would have been done using the 2003 guidelines.

“The research draws attention to this key distinction as it has led to a significant inflation in COVID fatality totals. By the researcher’s estimates, COVID-19 recorded fatalities are inflated nationwide by as much as 1600% above what they would be had the CDC used the 2003 handbooks,” stated All Concerned Citizens.

Then on April 14th, the CDC adopted additional rules exclusive for COVID-19 in violation of federal law by outsourcing data collection rule development to the Council of State and Territorial Epidemiologists (CSTE), a non-profit entity, again without applying for oversight and opening opportunity for public scientific review.

On April 5th the CSTE published a position paper Standardized surveillance case definition and national notification for 2019 novel coronavirus disease (COVID-19) listing 5 CDC employees as subject matter experts.

“This key document created new rules for counting probable cases as actual cases without definitive proof of infection (section VII.A1 – pages 4 & 5), new rules for contact tracing allowing contact tracers to practice medicine without a license (section VII.A3 – page 5), and yet refused to define new rules for ensuring that the same person could not be counted multiple times as a new case (section VII.B – page 7),” stated All Concerned Citizens.

By enacting these new rules exclusively for COVID-19 in violation of federal law, the research alleges that the CDC significantly inflated data that has been used by elected officials and public health officials, in conjunction with unproven projection models from the Institute for Health Metrics and Evaluation (IHME), to justify extended closures for schools, places of worship, entertainment, and small businesses leading to unprecedented emotional and economic hardships nationwide.

“A formal petition has been sent to the Department of Justice as well as all US Attorneys seeking an immediate grand jury investigation into these allegations,” All Concerned Citizens stated.

Read the full article here.

Where are the 2020-2021 Influenza Statistics? “Influenza has been renamed COVID” According to Epidemiologist

Epidemiologist Dr. Knut Wittkowski. Image source.

Daniel Payne, writing for Just the News, interviewed epidemiologist Dr. Knut Wittkowski regarding the disappearing flu statistics this year.

Dr. Knut Wittkowski is the former head of biostatistics, epidemiology and research design at Rockefeller University. He holds two doctorates in computer science and medical biometry, and one of his videos on YouTube last year had amassed over 1 million views before YouTube took it down, because he was critical of the lockdowns and its ineffectiveness on stopping the spread of COVID-19.

Just the News reports:

The Centers for Disease Control and Prevention’s weekly influenza surveillance tracker reports that the cumulative positive influenza test rate from late September into the week of Dec. 19 stands at 0.2% as measured by clinical labs. That’s compared to a cumulative 8.7% from a year before.

The weekly comparisons are even starker: This week one year ago, the positive clinical rate was 22%, where now it stands at 0.1%.

Those low numbers continue trends observed earlier in the year in which flu rates have remained at near-zero levels. The trend is not limited to the U.S. Worldwide, health authorities have all reported sharply decreased influenza levels throughout what is normally peak flu season in the northern hemisphere. Rates in the southern hemisphere were also low this year.

Where have all the flu cases gone?

Epidemiologist Knut Wittkowski thinks he can answer the riddle.

“Influenza has been renamed COVID in large part,” said the former head of biostatistics, epidemiology and research design at Rockefeller University.

“There may be quite a number of influenza cases included in the ‘presumed COVID’ category of people who have COVID symptoms (which Influenza symptoms can be mistaken for), but are not tested for SARS RNA,” Wittkowski told Just the News on Thursday.

Those patients, he argued, “also may have some SARS RNA sitting in their nose while being infected with Influenza, in which case the influenza would be ‘confirmed’ to be COVID.” (Read the full article.)

Is the CDC Hiding and Manipulating Data Regarding Overall Death Rates for 2020?

As we were nearing the end of 2020, we reported on some analysis projections for 2020 that were shaping up to have about as many total deaths for the year as previous years, based on the CDC’s own statistics. See:

Statistics Show that the Number of People who Died in the U.S. in 2020 will be the SAME as Previous Years, in Spite of COVID

A subscriber to Health Impact News recently sent me some screen shots that she allegedly saved at the end of December, 2020, from the CDC website, including a page that was reportedly available during most of 2020 tracking COVID deaths and deaths due to all causes (see above).

This page allegedly used to be at this URL: https://www.cdc.gov/nchs/nvss/covid_weekly/index.htm

However, when you go this page now, you get this notice:

Resource Not Available

“The page you requested cannot be found at this time. It may be temporarily unavailable or it may have been removed or relocated.”

This is NOT the standard 404 error code which you get if you mistype a page address, because on the CDC website the 404 error code looks like this:

So this is a page that used to exist, and according to the screenshot that this user sent to me, on December 30, 2020 this page stated that the total deaths from all causes in 2020 was 2,902,664.

Here is a copy of page 9 of the National Vital Statistics Reports, Vol. 68, No. 6, June 24, 2019, which lists total deaths for 2016 and 2017:

There were 2,744,248 recorded deaths from all causes in 2016, and 2,813,503 recorded deaths from all causes in 2017, according to the CDC.

So if the alleged CDC numbers for deaths from all causes in the screen capture from December 30, 2020 is correct, with 2,902,664 on December 30th, it is right in line with what we would expect, without the additional deaths allegedly attributed to COVID-19.

The only way this number for total deaths could be accurate, along with the deaths attributed to COVID, would be if deaths due to all other causes that were not COVID, drastically decreased. Is it possible that deaths due to heart disease, cancer, etc. – all decreased so that the total deaths would be on par for what would be expected if there was no Coronavirus pandemic?

So what happened to this page on the CDC website?

What is the CDC now reporting as the total deaths for 2020 here in 2021?

If you go to: https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm – and go down to Table 1, and click on “Yearly,” it will produce this chart showing 3,320,435 deaths for 2020:

So which version is correct?

Only the CDC would know the answer to that question, since they control all the data.

Is the CDC Trustworthy?

I have published this information in several articles the past few weeks, but it obviously bears repeating in this article, since the CDC is supposed to be supplying accurate information and statistics, especially now with regards to the new non-FDA approved experimental COVID mRNA injections.

The CDC is the largest purchaser of vaccines in the world, allocating over $5 BILLION in their budget (supplied by American taxpayers) each year to purchase and distribute vaccines from Big Pharma. See:

Should the CDC Oversee Vaccine Safety When They Purchase Over $5 Billion of Vaccines from Big Pharma?

Do you think this might be a conflict of interest?

Secondly, the CDC owns over 56 patents on vaccines, and many of their scientists earn royalties from the sale of vaccines. (Source.)

Do you think this might be a conflict of interest?

CDC Fraud Corruption

The CDC has a long history of corruption, and over the years many of their own scientists have tried to blow the whistle on this corruption only to be silenced. See some of our previous coverage on CDC corruption:

CDC Scientist Whisteblowers Confirm Corruption Within the CDC

CDC Whistleblower: CDC Covered Up MMR Vaccine Link to Autism in African American Boys

The CDC’s History of Research Fraud Regarding Vaccines and Autism

Can We Trust the CDC? British Medical Journal Reveals CDC Lies About Ties to Big Pharma

In addition, many of the directors running the CDC go on to work for Big Pharma after they complete their term at the CDC. See:

Former CDC Director that Approved Gardasil Vaccine and Became Head of Merck’s Vaccine Division Named “Woman of the Year”

Dr. Scott Gottlieb was the former Food and Drug Administration (FDA) Commissioner. He joined the board of directors of Pfizer, Inc.—the world’s largest pharmaceutical company and second largest manufacturer of vaccines, in 2019 just shortly after he left the FDA. Pfizer, which posted total revenues of $53.7 billion in 2018, announced Dr. Gottlieb’s election to the board on June 27, 2019.

On July 22, 2020 President Trump’s “Operation Warp Speed” project awarded $1.95 BILLION to Pfizer and BioNTech for 100 million doses of their mRNA-based COVID-19.

So what do you think? Can we trust the CDC and the FDA? Are they actually concerned about Public Health, or are they simply the marketing branches of Big Pharma trying to protect their products?

February 3, 2021 Posted by | Deception, Science and Pseudo-Science | , , | Leave a comment

Exposed: Fauci and CDC clash; can’t keep their story straight

By Jon Rappoport | January 12, 2021

Once more, dear reader, I venture into the insane world where experts falsely claim they’ve proved SARS-CoV-2 exists. Within that world, they contradict themselves. They just can’t keep their story straight.

So let’s begin with Tony Fauci. We have him on video making the following statement: “… In all the history of respiratory borne viruses of any type, asymptomatic transmission has never been the driver of outbreaks… Even if there’s a rare asymptomatic person that might transmit [the virus], an epidemic is not driven by an asymptomatic carrier.” [1]

Fauci is emphatic. People with no symptoms who are carrying a virus? Not a problem. They don’t spread the virus to other people. They don’t cause or maintain an epidemic.

Now let’s turn to the CDC. Jay Butler, CDC deputy director for infectious diseases just told the Washington Post, “The bottom line is controlling the COVID-19 pandemic really is going to require controlling the silent pandemic of transmission from persons without symptoms.” [2] [3]

Just the opposite of what Fauci said.

So now we have this:

ONE: People who carry the virus but have no symptoms don’t cause or maintain an epidemic.

TWO: Those very people ARE a major problem, and the epidemic can’t be controlled without controlling them—with masks, distancing, and lockdowns.

Follow the science? What science?

On the back of this gibberish, nations all over the world are seeing their economies destroyed, and hundreds of millions of lives ruined.

It’s a freak show, and the freaks are running it.

Of course, the experts can lie their way out of this. They can say, “Well, this is the FIRST TIME in human history that people with no symptoms are driving an epidemic. We’ve never seen it before…”

Right. This is a special case. Astounding.

If you believe that, I have condos for sale on the far side of the moon.

The truth is, the experts are starting backwards from an unexpressed premise, which is: WE WANT TO LOCK DOWN THE PLANET AND WRECK ITS ECONOMY, AS THE FIRST STEP TO CREATING A BRAND NEW WORLD OF TECHNOCRATIC CONTROL. NOW, WHAT DO WE HAVE TO SAY IN ORDER TO MAKE THAT HAPPEN?

This is how official science operates. It’s political and totalitarian, and it pretends to be objective.

So Jay Butler, the CDC deputy director, rounds off his statement to the Washington Post with this: “The community mitigation tools that we have [masks, distancing, lockdowns] need to be utilized broadly to be able to slow the spread of SARS-CoV-2 from all infected persons, at least until we have those vaccines widely available.”

Translation: We have to keep lying, to keep the global population under lock and key. Putting the Chinese model of control in place, in Western countries, takes time. Buy the con for another few years and we’ll have an iron grip on the population.


SOURCES:

[1] https://youtu.be/JIOzN03ZWXY

[2] https://www.foxnews.com/health/more-than-half-coronavirus-cases-spread-asymptomatic-carriers-cdc-model

[3] https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774707

Jon Rappoport is the author of three explosive collections, THE MATRIX REVEALED, EXIT FROM THE MATRIX, and POWER OUTSIDE THE MATRIX, Jon was a candidate for a US Congressional seat in the 29th District of California. He maintains a consulting practice for private clients, the purpose of which is the expansion of personal creative power.

January 12, 2021 Posted by | Deception, Timeless or most popular | , , | 1 Comment

In Unprecedented Move CDC Stops Tracking Influenza for 2020-21 Flu Season

By Brian Shilhavy | Health Impact News | November 4, 2020

I have been covering the fraud that happens every year with how the CDC tracks incidents and deaths due to the annual influenza for almost a decade now.

The numbers used each year to scare the public into getting the flu vaccine are based not on actual data, but estimates of number of people who die from the flu according to the CDC. Basically, anyone dying from “influenza-like” symptoms are all lumped together into supposed flu deaths each year. Autopsies are seldom performed to prove cause of death.

The CDC has admitted publicly in the past that these numbers are just “estimates.” If the real number of those infected with the influenza virus, and resulting deaths, were vastly lower than what the CDC reports based on their “estimates,” the public would have no way of knowing it.

So this has presented quite a dilemma for the CDC for the first couple of weeks of the 2020-21 flu season, which have just passed.

Because “flu-like” symptoms could also be attributed to COVID-19, and they have the now widely known ineffective COVID PCR test to back up these claims, which also kicks in federal funding for hospitals to treat COVID patients.

As one might expect, with the media widely reporting that cases of COVID are now increasing just as flu season starts, reports of flu cases have dropped dramatically during the same time period last year. Across the globe, it has been reported that incidents of influenza have dropped by about 100%. (Source.)

Source

Whoops! How did the CDC allow these numbers to be published?

In an apparent response to media reports about the fast declining flu cases here at the beginning of the 2020-21 flu season, the CDC did what any corrupt agency would do which doesn’t want the public to know the truth: They decided to “suspend data collection for the 2020-21 influenza season.” (Source.)

To my knowledge, this is unprecedented, and has never happened before.

There is a screen shot here in case they take this down due to public awareness (thanks to Patrick Wood).

Correlation Between Flu Shot and Senior Deaths Allegedly due to COVID

It is important to remember that most of the deaths in the U.S. attributed to COVID have occurred among those over 70 years old, with co-morbidity factors.

Another factor to consider is that seniors over 65 in the U.S. get a different flu shot than everyone else each year, one that is much stronger.

Most of the initial deaths attributed to COVID in early 2020 occurred in nursing homes or assisted care facilities for the elderly, where the flu vaccine is routinely given every year as a matter of policy.

Deaths in these facilities are common every year just after administering the flu vaccine, but never reported in the corporate media.

Health Impact News had a nurse whistleblower contact us in 2014 to report that 5 seniors in a Georgia assisted care facility died the same week the flu shot was given. We were threatened with a lawsuit for reporting this. See:

5 Seniors Die after Flu Shot at Assisted Care Center in Georgia

A recently published study out of Mexico confirmed the correlation between senior flu shots and COVID deaths.

recently published study in PeerJ  by Christian Wehenkel, a Professor at Universidad Juárez del Estado de Durango in Mexico, has found a positive association between COVID-19 deaths and influenza vaccination rates in elderly people worldwide.

According to the study, “The results showed a positive association between COVID-19 deaths and IVR (influenza vaccination rate) of people ≥65 years-old. There is a significant increase in COVID-19 deaths from eastern to western regions in the world. Further exploration is needed to explain these findings, and additional work on this line of research may lead to prevention of deaths associated with COVID-19.”

To determine this association, data sets from 39 countries with more than half a million people were analyzed. (Read the full article.)

Verified Death Statistics Will Tell the True Story

Once 2020 is complete, it will probably be seen that total deaths that have been recorded will be similar to previous years.

The difference will be the number of deaths attributed to COVID to justify all the government fear and tyrannical actions, as deaths by other causes will drop so that the end result will be about the same.

These kinds of stats are becoming more and more difficult to find, but here is one projected total compared with total deaths from the previous 3 years.

I am not sure of the original source of this graph (it is most likely a compilation of available health statistics), but the graph was published here.

November 4, 2020 Posted by | Science and Pseudo-Science | , | 3 Comments

RFK Jr. Sues Facebook, Zuckerberg and So-Called ‘Fact-Checkers’ for Vaccine Censorship

Children’s Health Defense | August 18, 2020

Washington, DC — Children’s Health Defense (CHD) filed a lawsuit on Monday in San Francisco Federal Court charging Facebook, Mark Zuckerberg, and three fact-checking outfits with censoring truthful public health posts and for fraudulently misrepresenting and defaming CHD. CHD is a non-profit watchdog group that roots out corruption in federal agencies, including Centers for Disease Control and Prevention (CDC), the World Health Organization (WHO), and the Federal Communications Commission (FCC), and exposes wrongdoings in the Pharmaceutical and Telecom industries. CHD has been a frequent critic of WiFi and 5G Network safety and of certain vaccine policies that CHD claims put Big Pharma profits ahead of public health. CHD has fiercely criticized agency corruption at WHO, CDC and FCC.

According to CHD’s Complaint, Facebook has insidious conflicts with the Pharmaceutical industry and its captive health agencies and has economic stakes in telecom and 5G. Facebook currently censors CHD’s page, targeting its purge against factual information about vaccines, 5G and public health agencies.

Facebook acknowledges that it coordinates its censorship campaign with the WHO and the CDC. While earlier court decisions have upheld Facebook’s right to censor its pages, CHD argues that Facebook’s pervasive government collaborations make its censorship of CHD a First Amendment violation. The government’s role in Facebook’s censorship goes deeper than its close coordination with CDC and WHO. The Facebook censorship began at the suggestion of powerful Democratic Congressman and Intelligence Committee Chairman Representative Adam Schiff, who in March 2019 asked Facebook to suppress and purge internet content critical of government vaccine policies. Facebook and Schiff use the term “misinformation” as a euphemism for any statement, whether truthful or not, that contradicts official government pronouncements. The WHO issued a press release commending Facebook for coordinating its ongoing censorship campaign with public health officials. That same day, Facebook published a “warning label” on CHD’s page, which implies that CHD’s content is inaccurate, and directs CHD followers to turn to the CDC for “reliable, up to date information.” This is an important First Amendment case that tests the boundaries of government authority to openly censor unwanted critique of government

Attorneys Robert F. Kennedy, Jr., Roger Teich, and Mary Holland represent Children’s Health Defense in the litigation.

The lawsuit also challenges Facebook’s use of so-called “independent fact-checkers” – which, in truth, are neither independent nor fact-based – to create oppositional content on CHD’s page, literally superimposed over CHD’s original content, about open matters of scientific controversy. To further silence CHD’s dissent against important government policies and its critique of Pharmaceutical products, Facebook deactivated CHD’s donate button, and uses a variety of deceptive technology (i.e. shadow banning) to minimize the reach and visibility of CHD’s content.  In short, Facebook and the government colluded to silence CHD and its followers. Such tactics are fundamentally at odds with the First Amendment, which guarantees the American public the benefits to democracy from free flow of information in the marketplace of ideas. It forbids the government from censoring private speech—particularly speech that criticizes government policies or officials. As Justice Holmes famously said, “the best test of truth is the power of the thought to get itself accepted in the competition of the market.” The current COVID pandemic makes the need for open and fierce public debate on health issues more critical than ever.

Mark Zuckerberg publicly claims that social media platforms shouldn’t be “the arbiters of truth.” This case exposes Zuckerberg for working with the government to suppress and purge unwanted critiques of government officials and policies.

The court will decide whether Facebook’s new government-directed business model of false and misleading “warning labels,” deceptive “fact-checks,” and disabling a non-profit’s donate button, passes muster under the First and Fifth Amendments, the Lanham Act, and RICO. Those statutes protect CHD against online wire-fraud, false disparagement, and knowingly false statements.

CHD asks the Court to declare Facebook’s actions unconstitutional and fraudulent, and award injunctive relief and damages.

August 20, 2020 Posted by | Civil Liberties | , , , , | Leave a comment