“Official” excess deaths are determined using a five-year average from previous years. If the average was always pre-pandemic years this would be correct. However, it seems to be standard to include 2021 and 2022 in the five-year average while excluding 2020. So, to determine how many excess deaths there are in 2022, for example, the average of the five years 2016, 2017, 2018, 2019 and 2021 is used as a baseline. This is happening in all three of the UK, Canada and Australia.
This is deceitful because the high excess numbers in 2021 result in excess death figures in 2022 appearing lower than they are. Using the latest excess data from Canada, Professor Norman Fenton how the “official” excess deaths are being manipulated and showed what excess deaths look like when honest methods are used to determine them.
Manipulating Mortality
John Campbell asked me if I could explain what the confidence intervals meant in THESE Canadian excess deaths charts. I [responded and below] is John’s subsequent video where he quotes my summary comments.
It turns out that the latest Canada data on excess deaths is actually much more serious that even suggested in John’s video. THIS website, with thanks to David Dickson, provides continually updated data and exposes multiple problems with the “official” Canada data including the fact that the two main provinces are missing data – Ontario and Quebec. The excess deaths for the years 2020, 2021, and 2022 based on the 2010-2019 10-year average is especially revealing:
We believe that using a 10-year pre-covid (i.e., pre-2020) period is the best way to determine excess deaths, assuming stability and homogeneity in the population and in disease profiles. Many of the excess death figures you see for 2021, 2022 and 2023 from around the world are based on the previous 5 years only; moreover, while most (correctly) exclude the unusual covid year of 2020, it seems to have become standard to include the years 2021 and 2022 which, because of the impact of lockdowns and the vaccines as well as any continuing covid, were certainly not “normal” years in any sense. Thus, for example, for its 2022 figures, the ONS in the UK uses the years 2016, 2017, 2018, 2019, and 2021 for its “baseline” and for 2023 it uses the years 2017, 2018, 2019, 2021, 2022. We believe this is extremely duplicitous, since the high excess numbers in 2021 result in artificially suppressing the excess death figures in 2022, and the high excess numbers in both 2021 and 2022 result in even greater artificial suppression of the excess death figures in 2023.
We see the same in Australia where they estimate 2022 excess deaths using 2017-2019 and 2021 but do not include 2020 because “deaths were significantly lower than expected.” So, by including a year that is higher than expected and excluding a year it is lower than expected the excess is manipulated to look smaller. See Arkmedic’s Substack for details: ‘The Australian Bureau of (Lies, Damned Lies and) Statistics’.
Even with these tricks to downplay the current excess death figures some people are noticing that there is a major problem, as this Daily Mirror article shows:
But of course, if you ignore, as the corporate media does, the possibility that the vaccine may be a contributory factor, then it’s all a mystery as Prof. Coleman in that article suggests. He can’t understand why excess deaths are higher when they should be lower after the pandemic. But he highlighted two key reasons for the excess deaths spike: “Britain’s getting older, and gaining a larger average Body Mass Index.”
Of course, it could be people not taking their statins. Honestly.
Update: Here is David Dickson’s updated analysis of UK excess deaths using the 10-year 2010-2019 average:
About the Author
Norman Fenton is a Professor Emeritus of Risk Information Management at the Queen Mary University of London. He is also a Director of Agena, a company that specialises in risk management for critical systems. He is a mathematician by training whose current focus is on critical decision-making and, in particular, on quantifying uncertainty using causal, probabilistic models that combine data and knowledge (Bayesian networks). The approach can be summarised as “smart data rather than big data.”
He publishes articles together with Professor Martin Neil on a Substack page titled ‘Where are the Numbers?’ which you can subscribe to and follow HERE. You can also visit his website HERE or follow him on Twitter HERE.
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