Since CDC Has Admitted PCR Tests Can't Identify Active COVID Infection, What Does It Mean For Those Who Died w/a Positive Test? Did they have an active infection? If not, What is the Real Death Count?
Dr. Joseph Mercola states: What’s the Real Death Count?
The CDC’s belated admission that the PCR test can’t identify active infection raises another question: What does this mean for those who died with a positive test? Did they actually have an active infection? If not, should they have been designated as COVID deaths?
The obvious answer to the last two questions is, of course, no. The vast majority were likely false positives, and the real death toll from COVID-19 considerably lower than we’re led to believe. The CDC undoubtedly knew this all along, seeing how they’ve been relentlessly criticized for their recommendation to run the PCR at a CT of 40. They’re trying to pretend that they just realized this, but that’s simply not believable. [MORE]
Previously Dr. Sucharit Bhakdi and Dr. Karina Reiss wrote the following
How dangerous is the new “killer” virus?
Compared to conventional coronaviruses. Gauging the true threat that the virus posed was initially impossible. Right from the beginning, the media and politicians spread a distorted and misleading picture based on fundamental flaws in data acquisition and especially on medically incorrect definitions laid down by the World Health Organization (WHO). Each positive laboratory test for the virus was to be reported as a COVID-19 case, irrespective of clinical presentation(15). This definition represented an unforgiveable breach of a first rule in infectiology: the necessity to differentiate between “infection” (invasion and multiplication of an agent in the host) and “infectious disease” (infection with ensuing illness). COVID-19 is the designation for severe illness that occurs only in about 10% of infected individuals(16), but because of incorrect designation, the number of “cases” surged and the virus vaulted to the top of the list of existential threats to the world.
Another serious mistake was that every deceased person who had tested positive for the virus entered the official records as a coronavirus victim. This method of reporting violated all international medical guidelines(17). The absurdity of giving COVID-19 as the cause of death in a patient who dies of cancer needs no comment. Correlation does not imply causation. This was causal fallacy that was destined to drive the world into a catastrophe. Truth surrounding the virus remained enshrouded in a tangle of rumours, myths and beliefs.
A French study, published on March 19, brought first light into the darkness(6). Two cohorts of approximately 8,000 patients with respiratory disease were grouped according to whether they were carrying everyday coronaviruses or SARS-CoV-2. Deaths in each group were registered over two months. However, the number of fatalities did not significantly differ in the two groups and the conclusion followed that the danger of “COVID-19” was probably overestimated. In a subsequent study, the same team compared the mortality associated with diagnosis of respiratory viruses during the colder months of 2018–2019 and 2019–2020 (week 47-week 14) in southeastern France. Overall, the proportion of respiratory virus-associated deaths among hospitalised patients was not significantly higher in 2019–2020 than the year before(18). Thus, addition of SARS-CoV-2 to the spectrum of viral pathogens did not affect overall mortality in patients with respiratory disease.
Regarding the number of deaths
How can the aforementioned be reconciled with the official reports of the
horrifying number of COVID-19 deaths? Two numbers must be known if the
danger of a virus is to be assessed: the number of infections and the number of deaths.
How many were infected by the new virus?
Attempts to answer this question were beset by three problems:
How reliable was the test for virus detection?
The virus is present in the nasopharynx for approximately two weeks, during
which time it can be detected. How is this done? Viral RNA is transcribed into DNA and quantified by the so-called polymerase chain reaction (PCR). The first assay for the new coronavirus was developed under guidance of Professor Christian Drosten, Head of the Institute for Virology at the Charité Berlin. This test was used worldwide in the initial months of the outbreak(19). Tests from other laboratories followed(20).
Diagnostic PCR tests must normally undergo stringent quality assessment and be approved by regulatory agencies before use. This is important because no laboratory test can ever give 100% correct results. The quality control requirements were essentially shelved in the case of SARS-CoV-2 because of
declared international urgency. Consequently, nothing was really known regarding test reliability, specificity and sensitivity. In essence, these parameters give an indication of how many false-positive or false-negative results should be expected. The test protocol from the Drosten laboratory were used worldwide, and test results played a key role in political decision-making. Yet, data interpretation was often largely a matter of belief. What did Drosten himself say on Twitter(21)?
Sure: Towards the end of the illness the PCR is sometimes positive and sometimes negative. Here, chance plays a role. When you test a patient twice as negative and discharge him as cured, it is indeed possible that you can have positive test results again at home. But this is still far from being a re-infection.
Several physician colleagues have informed us of similar haphazard results with patients who had been tested repeatedly during their hospitalisation. Is it particularly surprising that goats and papayas tested positive for the virus in
Tanzania? The criticism by the President of Tanzania regarding the unreliability of the test kits was of course immediately dismissed by the WHO(22).
But today it is perfectly clear that the test result is error-prone, as is every
PCR(23,24). How much so, and whether there are significant differences among the presently available tests, cannot be determined because of lack of data.
So let us assume that the PCR test is incredibly good and produces 99.5%
correct results. That sounds, and would indeed be, exceptional – it means that
one can expect only 0.5% false-positives. Now take the cruise ship “Mein Schiff 3”. After a crew member had tested positive for the virus, almost 2,900 people from 73 countries were forced into “ship quarantine”. Many had been on board for nine months. Complaints reached the outside world about the “prison-like” conditions, psychological problems abounded and nerves were frayed(25).
Nine positive cases were reported after testing was completed. One person
who tested positive had a cough, the other eight were without symptoms. Might they have belonged to the 0.5% false-positive cases, as perhaps the very first case had been? Where were the true-positives that must theoretically have been there? Were they possibly tested as false-negatives or were all positive tests false?
In the context of false results, we should consider the following: when the
epidemic subsided (in Germany, in mid-April,) PCR testing became a dangerous source of misinformation because numbers of new cases were derived from the “background noise” of false-positive results. When all 7,500 employees of the Charité Berlin (one of Europe’s largest university hospitals) were tested from April 7 to April 21, 0.33% were positive(26). True or false?
When positive test rates drop below a certain limit, it is senseless to continue
mass screening for the virus in non-symptomatic individuals. And use of
numbers acquired under these circumstances as a reason for implementing any measures should not be tolerated.
Selective or representative? Who was tested?
There is only one way to approximate how many people are infected during an epidemic with an agent that causes high numbers of unnoticed infections: at sites of an outbreak, the population must be tested as extensively as possible. But scientists who called for this during the coronavirus epidemic(27,28) were ignored.
Instead, the Robert Koch Institute (RKI), the German federal government
agency and research institute for disease control, stipulated at the beginning that only selective testing should be carried out – exactly the opposite of what should have happened. And as the epidemic ran its course, the RKI stepwise altered the testing strategy – always in the diametrically wrong direction(29).
At first, only people who had been in a high-risk area and/or had been in
contact with an infected person and also presented with flu-like symptoms were to be tested. At the end of March, the RKI then changed the recommended test criteria to: flu-like symptoms and, at the same time, contact with an infected person. At the beginning of May, the President of the RKI, Professor Lothar Wieler, announced people with even “the slightest symptoms” should be tested(29).
The responsibility for translating these dubious decisions into action lay
entirely within the hands of the local health authorities. A co-worker at our lab was a typical example: the coach of her handball team was coronavirus positive. The players – all from different administrative districts – were sent home on 14-day quarantine. One player developed symptoms with coughing and hoarseness and wanted to get tested but was refused on the grounds that she had no fever. A player from a neighbouring district had no symptoms but the local health authority ordered a test despite this fact.
This resulted in chaos, caused by the appalling ineptitude of the authorities
from top to bottom. What would have been urgently needed instead were
scientifically sound studies to clarify basic issues of virus dissemination. As
many as possible should have been tested in outbreak areas. Antibody responses in those that had tested positively could have subsequently been assessed.
Only a single such study addressing these questions was undertaken in
Germany: the Heinsberg investigation conducted by Professor Hendrik Streeck, Director of the Institute for Virology at the University of Bonn. Aware of the importance of the preliminary data, these were presented at a press conference – where Streeck was torn apart by the disbelieving media(30,31). The fatality rate was ridiculed as being impossible because it was ten times lower than what acknowledged experts and the WHO had been spreading as established facts. After completion of the study, final results essentially confirming the preliminary report were again presented, and again deemed by the media to be flawed and inconclusive. But the results of the study spoke for themselves(32) – and they contradicted the panic propaganda of the media.
3. The number of conducted tests directly influences infection statistics
A third factor added to the statistical mess. Imagine that you wanted to count the number of a migratory bird species in a large lake district. There are hundreds of thousands but your counting device can only count 5,000 per day. Next day, you ask a colleague to help, and together you arrive at 10,000 counts. The day after that, two more colleagues join in and 20,000 birds are counted. In short, the higher the testing capacity/number of tests, the higher the numbers – as long as innumerable unidentified cases abound, as with SARS-CoV-2(16,32–36). The more tests are performed, the more COVID-19 cases are found during the epidemic. This is the essence of a “laboratory-created pandemic”.
Now recall that the test has neither 100% specificity nor 100% sensitivity –
meaning that occasionally you would mistake a log for a bird. Therefore, even
after all our birds have long since moved on, you would still “find” many by just
performing a sufficient number of tests.
In conclusion, no reliable data existed regarding the true numbers of infection at any stage of the epidemic in this country. At the peak of the epidemic, the official numbers must have been gross underestimates – in the order of 10 or even more. At its wane at the end of April in Germany, the numbers must also have been gross overestimates.
Basing any political decisions on official numbers at any stage was fallacy.
How many deaths did SARS-CoV-2 infections claim?
Here, again, we have the dilemma of definition: what is a “coronavirus death”?
If I drive to the hospital to be tested and later have a fatal car accident – just as my positive test results are returned – I become a coronavirus death. If I am diagnosed positive for coronavirus and jump off the balcony in shock, I also become a coronavirus death. The same is true for a sudden stroke, etc. As openly declared by RKI president Wieler, every individual with a positive test result at the time of death is entered into the statistics. The first “coronavirus death” in the northernmost state of Germany, Schleswig-Holstein, occurred in a palliative ward, where a patient with terminal oesophageal cancer was seeking peace before embarking on his last journey. A swab was taken just before his demise that was returned positive – after his death(37). He might equally well have been positive for other viruses such as rhino-, adeno- or influenza virus – if they had been tested for.
This particular case did not need more testing or a post-mortem to determine
the actual cause of death.
However, with the emergence of a new and possibly dangerous infectious disease, autopsies should be undertaken in cases of doubt to clarify the actual cause of death. Only one pathologist ventured to fulfil this task in Germany. Against the specific advice of the RKI, Professor Klaus Püschel, Director of the Institute of Forensic Medicine, Hamburg University, performed autopsies on all “coronavirus victims” and found that not one had been healthy(38). Most had suffered from several pre-existing conditions. One in two suffered from coronary heart disease. Other frequent ailments were hypertension, atherosclerosis, obesity, diabetes, cancer, lung and kidney disease and liver cirrhosis(39).
The same occurred elsewhere. Swiss pathologist Professor Alexander Tzankov reported that many victims had suffered from hypertension, most were overweight, two thirds had heart problems and one third had diabetes(40). The Italian Ministry of Health reported that 96% of COVID-19 hospital deaths had been patients with at least one severe underlying illness. Almost 50% had three or more pre-existing conditions(41).
Interestingly, Püschel found lung embolisms in every third patient(39). Pulmonary embolisms usually arise through detachment of blood clots in deep veins of the leg that are swept into the lungs. Clots typically form when blood flow sags in the legs, as when the elderly spend the day seated and inactive. A high frequency of lung embolisms was already described in deceased influenza patients 50 years ago(42). Thus, we are not on the verge of discovering a unique property of SARS-Cov-2 that would heighten its threat, but we do bear witness to the absurd situation where the elderly seek to protect themselves by obeying the chant that sounds around the world: “Stay at home”. Physical inactivity is pre-programmed, thromboses included? Swedish epidemiologist Professor Johann Giesecke recommended exactly the opposite: As much fresh air and activity as possible. The man knows his job!
The number of genuine COVID-19 fatalities remained unknown outside Hamburg. The situation was no better in other countries. Professor Walter Riccardi, adviser to the Italian Ministry of Health, stated in a March interview with “The Telegraph” that 88% of the Italian “coronavirus deaths” had not been due to the virus(43).
The problem with coronavirus death counts is such that the numbers can be viewed as nothing other than gross overestimates(44). In Belgium, not only fatalities with a positive COVID-19 test entered the ranks but also those where COVID-19 was simply suspected(45).
Scientific competence did not seem to rule the agenda of Germany’s RKI. Fortunately, there are scientists who stand out in contrast. Stanford Professor John Ioannidis is one of the eminent epidemiologists of our times. When it became clear that the epidemic in Europe was nearing its end, he showed how the officially reported numbers of “coronavirus deaths” could be used to calculate the absolute risk of dying from COVID-19(46).
The risk for a person under 65 years in Germany was about as high as a daily drive of 24 kilometres. The risk was low even for the elderly ≥ 80 with 10 “coronavirus deaths” per 10,000 ≥ 80-year olds in Germany (column at the far right).
Calculation of this number is simple. About 8.5 million citizens are ≥ 80 years in Germany. About 8,500 “coronavirus deaths” were recorded in this age group. This leads to an absolute risk of coronavirus death of 10 per 10,000 ≥ 80 year-olds. Now realise that every year about 1,200 of 10,000 ≥ 80-year olds die in Germany (black column, data from the Federal Office of Statistics). Nearly half of them due to cardiovascular diseases (CVD), almost a third from cancer and around 10% (over 100) owing to respiratory infections. The latter have always been caused by a multitude of pathogens including the coronavirus family. It is obvious that a new member has now joined the club, and that SARSCoV-2 cannot be assigned any special role as a “killer virus”.
This is underlined by another observation. Severe respiratory infections are registered by the RKI in the context of influenza surveillance. The vertical line marks the time when documentation of SARS-CoV-2 infections was started. Was there ever any indication for an increase in the number of respiratory infections(47)? No, the 2019/20 winter peak is followed by typical seasonal decline. And note that the lockdown (red arrow) was implemented when the curve had almost reached base level.
CITATIONS
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(22) Australian Associated Press, “WHO Rejects Tanzania Claim Tests Faulty,” Examiner (Launceston), May 8, 2020, https://www.examiner.com.au/story/6749732/who-rejects-tanzania-claim-tests-faulty.
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(47) “GrippeWeb,” Robert Koch-Instituts, https://grippeweb.rki.de.