[New York Times] tried to say covid was like the plague, but worse, trust us! They were trying to make the point that in 2020 the death rate in NYC skyrocketed. Seems like they don’t want to show the data after 2020, that will look very bad for the official narrative, and will reinforce the anti-vax sentiment…
However, what really attracted me to this chart is the accidental truth about the so-called “Spanish Flu” that it revealed. Spanish Flu was supposed to be the black death bigger than the black death until the current black death came along… But seems like in reality it was not a big deal. A blip, relatively speaking in comparison to the 1850’s.
Professor Norman Fenton explains why claims of lower mortality rates for vaccinated may be just a statistical illusion. Worth reading in full. (Don’t worry; it’s not too complicated! Here’s a taste…)
In a previous article, we described the concept of survivor bias in studies that claimed better outcomes for covid vaccinated women in pregnancy: since the greatest risk to babies occur early in pregnancy, the babies of women who are vaccinated during pregnancy must already have survived the riskiest period.
In fact, a similar survivor bias more generally affects mortality rates for the vaccinated. If you see a study claiming much higher mortality rates of the ‘never vaccinated’ versus the ‘ever vaccinated’ you need to be sure it’s not just a statistical illusion due to survivor bias. This (7 minute) video provides an animated explanation:
The video shows this particular bias is avoided by using ‘person years in each vaccination category’ rather than people in each category. So a person who first gets vaccinated 6 months into a one year study and lives until the end of the year will be counted as 6 months never vaccinated and 6 months ever vaccinated.
The example is, of course, extremely simplified. Ideally, to calculate the correct number of person years in each category we need to know, for each person in the study, the exact date of each vaccination. And we also need to take account of the varying infection rate at different time intervals. That’s because the survivor bias is further exaggerated if (as was the case in most Western nations for the covid vaccines) the initial vaccine roll-out happened during the winter – meaning that fatality rates would inevitably fall anyway as more people were vaccinated. So, irrespective of the vaccine, more deaths were occuring at a time when more people were unvaccinated. Most of those classified as vaccinated would therefore already have survived the initial death peak when first vaccinated…
May 2020. John Pospichal was on the ball. Multiple graphs, multiple countries… all telling the same story… leading to obvious, penetrating questions… which politicians and mainstream media were NOT asking.
The extracts below are to encourage you to read Part 1 and Part 2 in full – because the questions make most sense in the context of the graphs.
We now have mortality data for the first few months of 2020 for many countries, and, as you might expect, there were steep increases associated with the beginning of the COVID-19 pandemic in each one.
Surprisingly, however, these increases did not begin before the lockdowns were imposed, but after. Moreover, in almost every case, they began immediately after. Often, mortality numbers were on a downward trend before suddenly reversing course after lockdowns were decreed.
This is an astonishing finding. But before I discuss its full import, and pose some questions to those who still defend the utility of lockdowns, I want to present the data that proves it…
You will notice that only after each country (or city) was locked down did the increases begin. Moreover, they began immediately, and in nearly every case, precipitously.
Now let’s examine the data for a few of these countries and cities in greater detail…
… if these vaccines were anything like as efficacious as claimed, dosing them into 70%+ of populations (and 90-95% of high risk of death populations) then they would be bending the covid curves like neutrons stars bend spacetime.
the effects would not be subtle…
yet we see no such signals…
the data to do the really rigorous work is being withheld from us and so, like astronomers unable to see celestial bodies, we must infer or refute their existence by watching how things curve as they travel through space and time.
but try as we might to find it, planet “vaccines stop covid deaths” does not seem to exist…
Age-stratified infection fatality rate (IFR) of COVID-19 in the non-elderly population
The largest burden of COVID-19 is carried by the elderly, and persons living in nursing homes are particularly vulnerable. However, 94% of the global population is younger than 70 years and 86% is younger than 60 years. The objective of this study was to accurately estimate the infection fatality rate (IFR) of COVID-19 among non-elderly people in the absence of vaccination or prior infection… we identified 40 eligible national seroprevalence studies covering 38 countries with pre-vaccination seroprevalence data…
‘Slightly’ sounds innocent. Until you understand the context: it was already well above normal.
Excess mortality in the EU increased slightly in October 2022, after dropping in August and September. This followed a peak of +17% in July, which is the highest value to date in 2022 and unusually high for the month of July. Excess mortality in October 2022 was +10% of the average number of deaths for the same period in 2016-2019, and is +0.5% compared with September 2022.
the area under the curves2People tend to focus on the peaks. But the area under the peak is key. Because the volume, the amount of space enclosed by a curve is key. is much larger than before the injections arrival
Harvey Risch is physician and a Professor Emeritus of Epidemiology at Yale School of Public Health and Yale School of Medicine. His recent critique of The ScienceTM contains so many bombshells, I struggled to pick highlights. But hopefully these snippets will entice you to click through and read the whole thing.1It gets technical at times. But you don’t need to understand all the numbers to understand his points about Randomized Controlled Trials.
… plausible theories are easy to believe, and that is the problem. That is what we have been fed for almost three years of the Covid-19 pandemic. In fact though, we have been fed plausibility instead of science for much longer…
… Matt Ehret speaks with Dr Jessica Rose about the multi-level fraud that is the “Covid Pandemic” with deep dives into her work, her mode of thinking and her penetrating research proving the ugly realities within VAERS and other aspects of the pandemic and the “solutions” which the world was tricked into drinking in response.
Throughout the conversation, the tricky beast known as “statistics” was discussed, which, though useful as a tool, has come to increasingly find their use in the advance of tyranny. Some discussion takes place on the topic of the the electromagnetic components of molecular biology which could serve as tools of great good and great evil, as well as our thoughts on the science of mass stupidity.
The double-jabbed, cardiologist and former TV pundit – whose father (also a cardiologist) died because of a COVID vaccine induced cardiac-death – has switched from countering vaccine hesitancy to warning about vaccine dangers.
public health is declining
overall, medical drug impact is negative
most research can’t be trusted
relative risk reduction is highlighted, absolute risk ignored1Good explanation of difference between Absolute and Relative Risk
‘numbers needed to treat’ are ignored2Number of people one needs to treat to prevent one instance of an illness
consent is not fully informed
most doctors getting their info from media or pharma sources
passing off a model as evidence is tantamount to lying…
so, if it does backfit and your major parameter assumption is wrong, it means the whole rest of your model is garbage.
… it’s pure GIGO1Garbage In, Garbage Out and the minute you assumed “vaccines worked well” then “vaccines saved huge numbers of lives” will pop out.
but if this assumption is wrong (as it appears so clearly to have been in the israeli palestine natural experiment comparison where death rates in the two places were near indistinguishable both before and after vaccination despite wide divergence in vaxx rate) then you’ve just “proven” nothing at all apart from the fact that models express the assumptions of the modeler.
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