Amid a nation under partial house arrest, restrictions on God-given individual unalienable rights continue to plague the people. All of this done at the hands of government because of an increasingly dangerous “virus” with a high mortality rate, according to the World Health Organization (WHO), the Centers for Disease Control (CDC), and Dr. Anthony Fauci – head of the National Institute of Allergy and Infectious Disease (NIAID) and dubbed “America’s infectious disease expert”. However, one doctor is questioning how the data on Con-VID-19 was presented to Congress.
PJMedia.com has the story.
More and more voices are now speaking out on the lockdowns, widespread testing, and other policies associated with the COVID-19 response. Now a doctoral candidate in epidemiology from the University of Waterloo in Ontario, Canada, Ronald B.Brown, Ph.D., is questioning how mortality data was presented to Congress early in the pandemic.
This hearing was held on March 11, 2020, in front of the House Oversight Committee. During the questioning, Dr. Anthony Fauci asserted that the mortality rate for COVID-19 was 3%. He then extrapolated to claim that if you added in the mildly symptomatic cases, it would probably be about 1%, which is ten times more deadly than the seasonal flu.
Brown asserts that this testimony is what launched campaigns for social distancing, lockdowns, and shelter-in-place orders to varying degrees nationwide. He then goes on to explain that fatality rates are classified in two different ways, while mortality rates are a timebound calculation.
In the medical profession, there should be no “assumptions” or “presumptions” when dealing with illness and disease. But, it appears these two “no-nos” were the driving force behind the house arrest of Americans, mask mandates, social distancing, and the destruction of the American economy.
Brown’s Abstract reads:
In testimony before U.S. Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was ten times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission. [Emphasis Mine.]
Brown’s entire thesis is worthy of a full read and full disclosure to the public and the medical community. However, it is unlikely that will happen due to the iron fist Dr. Anthony Fauci has wielded against the American public after Trump appointed him as the “expert” on infectious disease Con-VID-19. Fauci, Birx, and head of the CDC Robert Redfield have intentionally engaged in various biases to promote a national political agenda that leads to a global one.
The CDC defines mortality rate as the frequency of deaths within a time period for a well-defined population. To calculate the mortality rate for women under 40 from breast cancer for 2019, you would divide the number of deaths from breast cancer for women under 40 by the total population of women under 40 during that specific calendar year.
A case fatality rate (CFR) is calculated as the number of people who die of a disease divided by the total number of confirmed cases of the disease during a period of time. This calculation is a measure of disease severity. Using the example above, the CFR for breast cancer in women under 40 would be divided by the number of confirmed breast cancer cases under 40.
By contrast, an infection fatality rate (IFR) is the number of deaths from an infection divided by the prevalence of that infection in the population. As we know, with COVID-19, there are a significant number of asymptomatic and mild infections. The number of infections is estimated based on representative samples of blood tests looking for an immune response. The CDC estimated that for COVID-19 the number of infections in the population was ten times the number of confirmed cases in July of 2020.
Doesn’t anyone else find it odd that Con-VID-19 has been miscalculated? Even worse, medical professionals are instructed to “trust” the CDC, WHO, and other government agencies dealing with health care, taking the information received as factual, reproducible, and worthy of action. Research indicates these entities cannot be trusted because of their entanglement in the political machine to bring about political results. Yet, the majority of health care professionals are still relying on entities concerned with a political agenda over the duty to provide accurate information to aid in the health care of Americans.
For COVID-19, all of these calculations are already a little muddy. The National Center of Health Statistics (NCHS) relaxed the guidelines for classifying a death as COVID-19. It did not require a confirmed test or any other medical documentation to use the COVID-19 ICD-10 code on the death certificate. The latest update encourages testing wherever possible, but still does not require a lab-confirmed test.
Combine the relaxed stance from the NCHS with the financial incentives provided to hospitals for COVID-19 care through the CARES Act, and it’s not a conspiracy theory to think death rates may be overstated. This assertion is reinforced by states like New York periodically reclassifying deaths after the fact.
Because these statistics can vary widely, Brown insists it is essential they are not confused; however, he believes that is exactly what happened with Con-VID-19.
On March 11, 2020, the U.S. Congress House Oversight and Reform Committee received information from the National Institute of Allergy and Infectious Diseases (NIAID) concerning the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and coronavirus-disease 2019 (COVID-19).3 Based on the data available at the time, Congress was informed that the estimated mortality rate for the coronavirus was ten-times higher than for seasonal influenza, which helped launch a campaign of social distancing, organizational and business lockdowns, and shelter-in-place orders.
Previous to the Congressional hearing, a less severe estimation of coronavirus mortality appeared in a February 28, 2020 editorial released by NIAID and the Centers for Disease Control and Prevention (CDC). Published online in the New England Journal of Medicine (NEJM.org), the editorial stated: “…the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza (which has a case fatality rate of approximately 0.1%).”4 Almost as a parenthetical afterthought, the NEJM editorial inaccurately stated that 0.1% is the approximate case fatality rate of seasonal influenza. By contrast, the World Health Organization (WHO) reported that 0.1% or lower is the approximate influenza infection fatality rate,5 not the case fatality rate.
Because different types of fatality rates can vary widely, it is imperative to not confuse fatality rates with one another; else misleading calculations with significant consequences could result. Interestingly, a search of the keyword term “infection fatality rate” on the CDC website returned no matching results, nor was the epidemiological term located in the 511-page CDC publication, Principles of Epidemiology in Public Health Practice. This terminology omission, in conjunction with questionable use of fatality rate terminology in the NEJM editorial, raises red flags—warning of possible inaccuracies in the coronavirus mortality estimation presented to Congress.
Text from the February 2020 NEJM.org editorial and video of Congressional testimony are compared with reliable informational texts from the WHO and CDC. Inconsistencies, inaccuracies, biases, utilization, and consequences of the coronavirus mortality estimation are discussed.
In NIAID testimony before the House Oversight and Reform Committee Hearing on Coronavirus response, Day 1, 3 the Committee learned that mortality from seasonal influenza is 0.1%. Additionally, it was reported to Congress that the overall coronavirus mortality of about 2–3% had been reduced to 1% to take into account infected people who are asymptomatic or have mild symptoms. The adjusted mortality rate from coronavirus of 1% was then compared with the 0.1% mortality rate from seasonal influenza, and the conclusion was reported to the House Committee that the coronavirus was ten-times more lethal than seasonal influenza. In a comparative analysis with WHO and CDC documents, the coronavirus mortality rate of 2–3% that was adjusted to 1% in Congressional testimony is consistent with the coronavirus CFR of 1.8–3.4% (median 2.6%) reported by the CDC.12 Furthermore, the World Health Organization reported that the CFR of the H1N1 influenza virus is also 2–3%,13 similar to the unadjusted 2–3% CFR of the coronavirus reported in Congressional testimony, with no meaningful difference in mortality. As previously mentioned, the World Health Organization also reported that 0.1% is the IFR of seasonal influenza,5 not the CFR of seasonal influenza as reported in the NEJM editorial.
Confusion between case fatality rates and infection fatality rates may seem trivial, and it is easy to overlook at first, but this confusion may have ultimately led to an unintentional miscalculation in coronavirus mortality estimation. [Emphasis mine.]
Brown gives a form of “benefit of the doubt” to individuals who definitely know the difference between the various fatality rates – there was no confusion – and intentionally miscalculated the mortality estimation of Con-VID-19 for a political agenda. What has happened with this Con-VID-19 debacle is more than “information bias in epidemiological research”. This entire contrived “cooking” of the numbers is geared to instilling fear, using media to propagandize the American public, running a massive psy-op on the people, and encouraging a hive mind mentality to better control the public. This is sinister and criminal. And, it all is done with malice.
How bad is the misleading of the American public and Congress? Brown explains.
A comparison of coronavirus and seasonal influenza CFRs may have been intended during Congressional testimony, but due to misclassifying an infection fatality rate as a case fatality rate, the comparison turned out to be between an adjusted coronavirus CFR of 1% and an influenza IFR of 0.1%. Had the adjusted coronavirus mortality rate not been lowered from 3% to 1%, fatality comparisons of the coronavirus to the IFR of seasonal influenza would have increased from 10-times higher to 20- to 30-times higher. By then, epidemiologists might have been alerted to the possibility of a miscalculation in such an alarming estimation. [Emphasis mine.]
These numbers from Fauci are intentional in order for the public to believe that “mitigation measures” work to keep the public in the “hive mind” in order to accept any additional “instructions” from government entities and authorities. All of these measures implemented to mitigate Con-VID-19 were implemented by government authorities without minimal supporting evidence of effectiveness. Moreover, the violations of fundamental principles of science and logic resulted in the “mistaken assumption the correlation implies causation”. Again, Brown give the benefit of the doubt to those who do not deserve it and explains.
For example, the public’s belief that mitigation measures were responsible for reducing coronavirus mortality may be a post hoc fallacy if lower mortality was actually due to the overestimation of coronavirus deaths. Furthermore, implementing the unconfirmed hypothesis that mitigation measures save lives in vulnerable populations, and rejecting the null hypothesis that assumes no life-saving effect exists, is a type I error in hypothesis testing. The null hypothesis does not assume a priori knowledge. Therefore, before implementing mitigation measures that incur severe costs, the onus is on mitigation proponents to formally reject the null hypothesis by justifying claims of lifesaving benefits. Additionally, education in principles of basic research methods is essential for consumers of public health research, and there is a need to increase instruction in the science and logic of research methods in general education curricula.
Brown indicates scientists warned against making public health decisions on unreliable data on the infection prevalence in the population. The lack of date resulted in statistical modelling methods relying upon speculative “assumptions” that produced “fearful predictions of increased mortality which have often proved unreliable.”
Again, read Brown’s research thesis.
The estimation provided by Dr. Fauci was used to communicate with the public, by the media, and to determine public policy. It has also led to the word “cases” being redefined to mean people who are sick with COVID-19, as well as people who display no symptoms. A positive test puts you in that category even though the test is so sensitive that as often as 90% of the time it picks of virus incapable of causing transmission of infection.
Even worse, the media still does not delineate the risks to various segments of the population. They also don’t discuss where the majority of new cases are occurring. This metric is vital to assess health system resources. If the majority of cases are in people under 40, the risk to the system remains low.
So, thanks to a false comparison, we closed schools, colleges, and pretty much everything else. Many areas are still having reopening debates. Ultimately, the CDC placed the IFR for COVID-19 at 0.26 in June. That is a rate between two and three times the WHO estimate of IFR for the flu and is significantly concentrated in patients over 65 with preexisting conditions. This knowledge should have caused a significant shift in public policy nationwide, but we still have states and cities that have draconian restrictions.
The conflation of CFR and IFR at the outset of the pandemic was a mistake that likely cost billions of dollars and an untold number of lives when we consider increases in suicide and deaths from undetected diseases, which will lag. The public health apparatus owes the country an answer on this and an assurance it won’t happen again. [Emphasis mine.]
The public health apparatus owes this nation more than an answer and an assurance it won’t happen again. It provides proof that unconstitutional entities need dismantling. Moreover, those entities, Fauci, Birx, Redfield, and any public health official at State and local levels should be charged with crimes for the harm that has been inflicted upon the population – increase in suicides, domestic violence, child abuse, alcohol abuse, emotional distress, spiritual distress, etc. Left to the current political apparatus, no one will be held accountable for the damage that has occurred because of Trump’s appointment of Fauci and Birx as the “face” of Con-VID-19.
It’s time for citizens to convene a grand jury in order to exact justice for these crimes. Moreover, it’s time to stand up and confront local and State public health officials, holding them accountable also to conduct independent research, in order to verify what is being presented to them as accurate. If alternative media journalists and citizen journalists can locate this information, public health officials should be able to do the same. Shame on you officials who have not verified what you have been told and for supporting these draconian measures that have not been proven to be effect.
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