The methodology of forensic investigation recommended to state and federal agencies will go through iterations. First of all, people didn’t like the TInBUQ acronym for Top-Down Investigation, Bottom-Up Quantification. To make it easier, it’ll just be called the TB Methodology, or TBM. This article contains an excerpt from the document sent on November 26, 2024 to Dr. Ladapo detailing just one of several data paradoxes that most epidemiologists do not take into account. The Prevalence-of-Cause Paradox —>Prevalence-of-Cause Paradox is simple, yet insidious, and is easily managed by dividing the number of deaths of a specific cause-of-death by the number of All-Cause deaths in the same period. The issue manifested when a large number of excess deaths occurred in Massachusetts from March to June 2020 — nearly 9,000 excess deaths in three (3) months. Within each of those 9,000 excess death records were listed multiple causes, not just “Covid.” Some died with pneumonia, some with kidney failure, and many with cardiac arrest, pulmonary embolism, and others. If a cause-of-death truly results from Covid, then one would expect it to be a higher incidence rate, or prevalence, than normal. However, if the prevalence of pulmonary embolism or cardiac arrest within the excess group is no greater than normal within the expected base group, one can argue that it was background noise of an all-cause death spike that inflated cardiac arrest in 2020 and not an attribution to Covid as the cause. Figure 6 depicts “I46” “Cardiac Arrest” in Massachusetts in raw data in the left graph, showing an increase in the year of 2020, and then switching to show an increase in 2021, and not 2020, when shown in the right graph as prevalence-of-cause (PoC), or percentage of all-cause deaths.
Figure 6 clearly depicts the prevalence-of-cause paradox. The great inflection mysteriously switched from 2020 to 2021. While many pedestrian scientists and physicians look at the left graph and emphatically state that Covid caused the increase in “I46” “Cardiac Arrest,” I would say that the prevalence of “I46” deaths among all deaths in 2020 was no greater than is expected based on the 2015-2019 5-year trend. Clearly, the prevalence of “Cardiac arrest” deaths inflected substantially positive in 2021, not 2020. The externality causing “Cardiac arrest” deaths was not Covid in 2020, but rather was from an externality introduced in 2021. This important concept begs more detailed depiction. Figures 7a and 7b depict “N17” “Acute renal failure” in Connecticut. Was there a substantial uptick in “N17” during the first spring 2020 Covid wave?
Figure 7aClearly there is a substantial signal of excess of “N17” in the spring 2020 raw data depicted as a 51-day rolling average in Figure 7a. One would associate this with the big Covid wave. Nearly everyone blamed Covid for these excess “N17” deaths. The Prevalence-of-Cause (PoC) graph in Figure 7b depicts something completely different. “N17” as a percentage of All-Cause deaths is not abnormal in spring 2020. The Covid wave signal is gone — vanished. The “N17” plot then takes off in late November 2020 after the November 2, 2020 CMS.gov financial incentives kicked in for use of NIH recommended treatment protocols. Late November 2020 is when “N17” involved deaths begin to climb in excess. The Covid vaccine also begins around the start of the 2021 year. Either the hospital protocols, the vaccine, both, and/or other factors causing sudden kidney failure must be investigated. “N17” is the greatest loss of life-years in the United States in 100 years except for World War II. See Figure 7b
Figure 7bFigure 7b needs no more explanation. The signal is gone just as it was in the right bar graph for “Cardiac arrest.” Millions of people argue on social media. Scientists use inferential statistical methods and insert graphs in research papers. Yet most of them do not find the paradoxes clearly evinced in this methodology statement [Top-Down Investigation, Bottom-Up Quantification]. [End Prevalence-of-Cause section from Top-Down Investigation, Bottom-Up Quantification] Please understand what is happening out in the world of social media and research papers. People see graphs and data as shiny objects that prove what they dogmatically believe to be true. However, none of the statistics and graphs can prove what caused the ailment or cause-of-death under scrutiny. If pulmonary emboli deaths rose 50% upon the introduction of the Covid vaccine, then that statistical evidence alone can only ever be persuasive. In order to conclusively prove what caused the pulmonary emboli that caused death, there must be an individualized and paricular forensic investigation of samples of records to determine what killed each person. Only after causality has been proven through forensic inspection of individual medical records can one rely upon statistics and graphs to quantify how many people died from the externality proven to be the cause of the excess deaths. Fictional ExampleKidney failure rose 100% upon introduction of a disease. The recommended treatment for the disease involved a cocktail of 7 different drugs never tested in such a combination before. And a novel vaccine was introduced 4 weeks after the drug cocktail was recommended, which was when the disease began. Who thinks it’s the disease causing kidney failure? Who thinks it’s the drug cocktail causing kidney failure? Who thinks it’s the vaccine causing kidney failure? AnswerIt doesn’t matter what you think. You’re not a scientist. Shut up and stop asking questions. It has to be the disease and we refuse to test the bodies for any effects of the drugs or vaccine. Everyone knows vaccines and drugs are safe and effective. That’s why we stopped doing autopsies even when law requires them. There’s no point in doing autopsies because vaccines and drugs are safe and effective. The disease is deadly and must be stopped no matter how many people die to stop it. John 14:6 TRUTHYou're currently a free subscriber to The Real CdC’s Newsletter. For the full experience, upgrade your subscription.
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