All-cause mortality is the most reliable scientific data for analysing the ‘Covid pandemic‘ – or any so-called pandemic – because it doesn’t discriminate and it has no bias.
It is pretty much the gold standard.
This is because it encapsulates the ultimate outcome: death.
All-cause mortality data is king
Unlike other metrics that are subject to bias, misdiagnosis, or reporting inconsistencies, death is definitive and universally recorded. A death is a death.
It’s binary and no nuance is required.
It cuts through the noise of varying definitions and diagnostic criteria. All-cause mortality data ignores how the person died.
When time, age, and region are used as additional variables, the correlations become (nearly) bulletproof, as noted by Rancourt.
Another important paper
His team previously authored a huge paper in which they concluded that all-cause mortality data shows no viral outbreak in 2020.
There was no pandemic, in other words.
It was a campaign of fear and mass compliance.
He has co-authored yet another big paper, COVID-Period Mass Vaccination Campaign and Public Health Disaster in the USA.
Summary of the paper
Denis and his team analysed American all-cause mortality data during the ‘pandemic’, but they correlated it with the ‘vaccination‘ rollout.
It dissects data until early February 2022, contrasting mortality against vaccination stats by time, age, and state (which is a feature of all-cause mortality data).
The aim of the study was to spot any temporal links.
The results show that the jab didn’t cut all-cause mortality in the US. Not even a little bit. No deaths were prevented by the injection, while high excess mortality persisted throughout.
Deaths were due to poverty and other socio-economic factors (like lockdowns, stress, isolation, and more).
For a detailed breakdown of the study, I recommend watching the presentation conducted by Denis and his co-authors (Marine Baudin and Jérémie Mercier).
Here’s my conversation with Denis.