Re: Normalising vaccine side-effects, as if it has always been like this
Ethical Skeptic ☀
@EthicalSkeptic
I document how the trolls mislead their victims about cancer, here:
https://twitter.com/EthicalSkeptic/s...66903000396096
How The Trolls Lie to You about Excess Cancer Mortality
Inside this article (see image at bottom), we present a comparative case example that vividly illustrates how the CDC Wonder Cancer Mortality Excess signal is conveniently eliminated by less-than-scrupulous analysts inside their 'graphs'. I would call these 'amateur graphs', but they are produced by professional liars, those who have set up corporations or taken compensation for their malicious work on Twitter from various agencies.
The following itemized list outlines specific use cases I have personally observed within their work. The trolls incorporate the majority of these unethical practices into their analyses. None of these items are theoretical, as all are used as a regular part of their praxis.
Highlighted in the article's graphic is a case example demonstrating how the analytics method employed by our notorious trolls can effectively make the excess cancer mortality appear to vanish, or even make the ENTIRE PANDEMIC magically disappear as well!!
The Liar's Method of Pandemic Analytics:
1. Use annualized figures.
2. Lengthen the retrospective horizon to a decade or more.
3. Quash the y-axis until signal and inflection blend into the noise variance.
4. Dilute (per 100K) the older, smaller, high health-risk, and shrinking population which is actually dying, by the whole or young migrant-fed (voting census counts migrants) population growth. Cite this as the 'rate'.
5. Dilute (per 100K) the smaller population bearing the actual morbidity, by the whole, healthy, or young migrant-fed population growth. Cite this as the 'rate'.
6. Extrapolate from a population and population growth rate from an old census, adjusted most recently for migration.
7. Use Age-Standardized Mortality for a single cohort retrospective (this metric is not designed for single cohort retrospective in a black swan event - it is for comparing nations under normal circumstances).
8. Fail to check what calculation method was used to normalize the Age-Standardization.
9. Fail to take into account the depleted death-vulnerable population's eroded future rate of death (pull forward effect), post a black swan event.
10. Use a baseline which includes the pandemic period as part of its projection calculation (paltering the baseline).
11. Use linear regression on inflected and non-linear data.
12. Use linear regression across generational effect data.
13. Blend all age brackets together (Simpson effect) to conceal uptrends in specific age brackets (which show the real impact trend). Never show the age bracket trend data.
14. Reference to a linear baseline without seasonality impacts.
15. Ignore by-week seasonality of the data, and:
16. Add volatility of peak winter mortality periods into the trend regression, and not index off the summer base. That way a rough flu year will raise the baseline artificially to your advantage.
17. Use unadjusted ICD-RXX depleted raw data (most recent 26 weeks of data). This eliminates tens of thousands of cancer and sudden cardiac death records from the data.
18. Use unadjusted 999 depleted raw data (most recent 33 weeks of data). This eliminates tens of thousands of natural cause death records from the data.
19. Use unadjusted Lag period raw data (most recent 13 weeks of data). This eliminates tens of thousands of ICD-code death records from the data.
20. Fail to employ professional charts (such as 'Deviation from Trend' cohort comparatives).
21. Fail to compare the metric signal to corroborating economic, aggregate, and outside-source data.
22. Fail to run 'Non-Covid' versions of natural cause or specific cause mortality. Allow the signal to be hidden inside dry tinder, or use the dry tinder deaths to imply an inflection.
23. Analyze by total or cumulative mortality, rather than excess mortality.
24. Use blended equivocal ICD codes (eg. mixing 'Sudden Cardiac Death' and 'Heart Disease Deaths' to get a 'normal-looking' result when you don't want anyone to see the Sudden Cardiac Death mortality excess).
25. Use 2017 and earlier Wonder data to formulate an inflection chart (data which is inflated by small county effect - see Wonder use notes). This makes trends falsely appear to be going down.
26. Use or blend-in the wrong ICD codes.
27. Use cherry picked or categorical ICD codes when the mortality signal is spread between several ICD codes and must be aggregated.
28. Fail to track and offset the shift in reporting from UCoD to MCoD over the retrospective horizon.
29. Fail to track shifts in reporting UCoD from the ICD code in question, to associated UCoD-ICD codes (cancer to 'failure to thrive').
30. Fail to monitor the weekly data dynamics over a long period of time.
31. Fail to follow-up on their charts at a later time, to see if they were indeed correct.
32. Employ hot takes to create modus absens ('not p' or 'something is not'), instead of a long-tested modus ponens (if p then q) assertion argument. This is a warning flag of pseudoscience.
33. Fail to add in the 'Other and Unspecified' version of an ICD code to determine excess mortality.
34. Target specific individuals, rather than specific ideas, with their work. Small minds (and other parts).
35. Troll, rather than communicate, discuss, or even their pretense of 'disagree'.
No matter their anger, mocking, intimidation, or credential - this is how the unethical among us ply their wares. Be very cautious of such persons.
EVG,
TES
< graph images at X link >
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