All-cause mortality is the most reliable data available to scientists.
Why?
Because deaths can be neither hidden nor manipulated. It’s binary. One or zero. Either he died or he didn’t.
And the cool part about all-cause mortality is that it can be split across a few uncompromising data points like
- age,
- location and
- gender.
Meanwhile, making claims about the cause of death quickly becomes complicated due to (lack of) supporting evidence and other epistemological challenges.
For example, if Mike told me that Jenny died from Covid, then my response might be “how do you know?” At which point, Mike is compelled to provide supporting evidence. If he responds with something like “the doctor said so”, then my response might be “how does the doctor know?”.
It becomes messy.
Denis Rancourt’s previous conversations with me were explosive, especially the one in which his all-cause mortality studies clearly revealed absolutely no viral outbreak during 2020 and later.
To be clear, all-cause mortality data is generalised because, err, the mortality data is for all causes. Correlation is not causation, but it’s nevertheless a vital vector towards understanding why so many people “died suddenly“.
In the following conversation, Denis and his team analysed all-cause mortality data following the rollout of the jab.
It is eye-opening.
The studies cited can be found at Denis’ website.
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