YES, and even when they occasionally release an "analysis" comparing vaccinated vs. unvaccinated, it is always an absurdly cherry-picked snapshot (never a full population for the full post-vaccine time period) that always has many obvious flaws. And what's REALLY telling is that they tend to be very quiet and cautious when releasing these "vaccines saved lives" analyses, instead of releasing them loudly and proudly as one would expect, which is quite a giveaway that they know those analyses won't stand up to any serious scrutiny!
EXCELLENT response and analysis- far more factual/accurate, balanced, and to-the-point than almost all of what I've seen so far. Thank you very much for this.
A few tidbits from an "interloper" to add to for discussion: My take is Denis Rancourt's team, due to years of experience analyzing health and death data, have a different view of what occurred.
1) Prof Arne Burkhardt in Germany said in his last interview (2 hrs long, find it), that families approached him begging that he investigate as they did not believe what the coroner wrote as "cause of death" of their loved ones. As you know, several doctors in Canada reported having a slew of "cause unknown" listed as cause of death.
2) In the US, due to the Brix/CDC change in how to fill out death certificates, the FDA advising no autopsies be performed or the virus might spread (my note: instead the shots shed). Regardless, this change meant the CDC insured we will never know what happened. Little to no closure is possible for many of those families either. Albert (Welcome the Eagle) seems to have read some of VAERS death reports, mentioned he saw inconsistencies as well.
3) John Beaudoin Jr. (coquin de chien substack) said in his 14 minute testimony in New Hampshire showing straight analysis of Massachusetts death data he obtained, that the reason for death was often carefully elaborated in the text, not in the boxes at the top. So death data may never been picked up by "the system". If inconsistent across the state, can we also assume also across the US?
And the sidestep? No one seems to be paying attention to estimates that lockdowns alone may have caused 80 million lives around the world, not including China. When supply chains are broken, people starve to death. Does the food supply magically recover?
Yes, the shots are all toxins with no benefits. Yes, these were overlapped with deadly hospital protocols. Yes, this was planned, hardly a pandemic. My conjecture is death data has been so skewed by WHO protocols being pushed to cause environmental and societal damage in advance of toxic shots; that may have killed far more. Nevermind some hospitals in so-called developed countries were also dehydrating, starving, venting and killing their patients for govt kickbacks.
Always want to keep in mind that even before covid came to town, iatrogenic interventions are listed as the third leading cause of death in the US. Its a wonder anyone can attribute deaths solely to shots. Its well-known that suppressed immune systems come from many directions and with age. Its also known that shooting toxins into the elderly, who already accumulated toxins over a lifetime, is a particularly nasty way to kill old people.
As an independent sanity check, I just did the following:
- Computed excess deaths using a modern boosted tree forecasting approach--shown in recent years to be near state of the art in several Kaggle competitions which I won or placed very highly in. I used the same STMF mortality data as Ioannidis et al. (https://www.mortality.org/File/GetDocument/Public/STMF/Outputs/stmf.csv) with years 2016-2019 as training to forecast total deaths for 2020-2023 using 4-year lag and week index features.
- Graphs of model fit and forecasts look sensible for all 37 STMF countries.
- This method estimates a total of 2.25 million excess deaths for 2021-2023 for the 37 countries, whose total population is around 1 billion. Multiplying by 7.888 to get a world estimate results in 17.8 million. Rancourt's 17 million estimate is within this upper bound.
- For additional reference, the method estimates 968,000 excess deaths in the US for 2021-2023 and 9132 for Denmark (nicely close to your 10K estimate). Please let me know if you'd like to see other countries or collaborate further.
Kirsch claims that there were about 10,000 excess deaths in New Zealand in 2021 to 2023, because he used the average number of deaths in 2017 to 2019 as the baseline.
I have shown him why his average baseline is inaccurate, and I have tried to teach him how to calculate the baseline by doing linear regression in R, but he seems to be stuck with Excel. So maybe you could run your code on the STMF data from New Zealand and show the results to Kirsch.
Thanks Mongol. I'm estimating only 2528 excess deaths for NZ over 2021-2023, #35 out of 37 and ahead of only Iceland (66) and Luxembourg (-197). It's important to correct for trend as you have done.
Kudos to Kirsch for putting himself prominently out there and calling for better data and transparency, but he does not appear to be receptive to any criticisms of analyses he is vested in. Very few folks overall appear to be pursuing legit causal inference on vax data. I appreciate all of the solid work you have done in pursuit of the truth.
Thank you Ben and good point about using intervals.
All I effectively did was substitute in a better forecasting model, since we can do way better than a simple line. Line 17 of excess.r is: model <- lm(deaths ~ year, data = df_train). The idea is to swap in a time series forecast instead, which should be a much improved baseline from which to compute excess. This requires using monthly or weekly data.
I used XGBoost in JMP Pro software to forecast, which involves more manual steps but likely gives a little better point forecasts than ARIMA. Prediction intervals are more work. There is an XGBoost wrapper in R.
Really appreciate all of your great and transparent work with code!
And 2023 is looking more similar to 2022 than earlier years. Probably around 37k deaths (as compared to ~30k from 2010 to 2020). Need to see and compare to population figures as well. There has been an increase in population in the last 4 years but nevertheless I don’t think that the increase is in the over 60s.
Excel or R there’s not enough variables being considered.
- Their main source of power apart from sin-empowered demons? NOT a coincidence that the USA left dollar convertibility to gold in 1971, precisely triggering the exponential government deficit coupled with the trade deficit and inflation.
This is the Achilles’ heel of all nations: masonic and satanic secret societies counterfeit paper money and launder trillions with which they buy Banks, seats in the Federal Reserve (the only private run Central Bank in the world), political careers and parties, puppeticians, listed corporations, media, healthcare corporations and organizations, universities, foundations, judges, etc.:
Confessions of illuminati, David Rockefeller (finest quotes):
1. Create an easy system for real money: private currencies/warrants based on real assets, goods, services, etc. (gold, corn, oil, distance/volume/weight transportation, labor human hour/minute, etc.)
2. Ban legal tender. Let the free markets decide which real-currencies/valuables/warrants they prefer to trade with
3. Ban paper-backed currencies (unlike real-backed ones of point 1.)
4. Enforce a Legal Banking Reserve of 100% of deposits (so banks don't create money based on air) and therefore there's no excuse for a Central Bank, because there would be no risk of bank-runs since all their loans are fully backed with deposits
Plllllease, on my knees, don’t believe me, just do your own homework by searching the following in yandex.com, mojeek.com (includes crawl date filter and substack search), gigablast.com, startpage.com, duckduckgo.com (not Google, Bing, Yahoo censors). The key terms to test them? Child Satanic Ritual Abuse, Child Satanic Ritual Murder.
President John Quincy Adams: “Masonry ought forever to be abolished. It is wrong - essentially wrong - a seed of evil, which can never produce any good.”
If you are a mason or know a mason, ask him to ask his 33° master to put in writing and sign it, who is "the great architect" and that he is not Lucifer. If he refuses, then he’ll know who he is really serving, Satan: tell him to get out of masonry NOW. Sooner or later he’ll be required to trample on a cross to get to a higher degree.
Confessions of a former mason (Serge Abad-Gallardo):
first interesting view on monetary policy which not considered so interesting to ponder upon, thanks...
then comment that from a science of evil perspective, evil appears as not that smart and shows signs of being limited to derive more by trial and error...
then from a soulless money blind ignore the externalities business type of evil this evil wants to more cause sickness that it can profit off of at an exponential increasing rate...
then from here the biggest factor desiring mass death is the shadowed projected aggregate fear of overpopulation...
"Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
I stopped reading after that--Unbelievable how many people-- and how much they get paid and how credentialed they are-- to cherry pick data.
I have been in the field for over 10 years and this BS is rampant. You get pressured. Why go through 2 or 5 years of rigorous graduate school training if you are going to make shit up? Just get ChatGPT/AI do it for you, or whatever.
Jeffrey Morris wrote: "BTW, a lot of people raised questions about our primary analysis focusing on events 28d after vaccination or index date (for unvaccinated). The reason for this was because as a medical records study, some individuals may have had covid tests at the index visit and were found to be infected, so would not be valid for the primary comparison." (https://twitter.com/jsm2334/status/1744762762226794661)
But they also didn't check, all-cause mortality or any clinical diagnosis such as CLI, didn't they? Why not, why stick with biased COVID-19 attribution, if we know it's unreliable?
Those two quotes above, in consequence, amounts to trolling. There so many assumptions, contradictions and irrelevant/vague statements in there that trying unpack it all really just amounts the purpose of trolling... sometimes called intellectually dishonest.
Thanks for the message Mongol--The take away is that a significant proportion of vaccine induce deaths occur within the first few weeks of getting vaccinated. Thus, excluding these already dead people from the study will bias the results.
That is pretty much the end of the story and why I stopped reading after "Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
We don't even have to get into the well known, highly incentivized process attributing covid deaths using the highly flawed PCR test and dying "with" covid, etc.
Regarding the first quote, if it doesn't change the results, why not leave them in? Why are they adding unnecessary complexity while claiming the additional complexity isn't adding any value? Huge red flag
Adding unnecessary complexity as a substitute for rigor should always raise some red flags to dig deeper into footnotes, methodologies, assumptions, the data, etc. Which is a good segue way into your second quote.
2nd quote: I have no idea how this statement relates to building a case for excluding people up to 28 days after vaccination and only COVID attributed deaths. Probably if I squint my eyes and stare intently I might find some hair thin logic in there somewhere. But it is clear, in it's lack of clarity, that there is no there there.
It sounds like what it is trying to say, if I read it literally, because it is a "medical records" (if it was non-medical records could we include up to 28d? ) study, then some people "may" have had a PCR test and "may" have been "found" to be "infected". Thus we need to exclude anybody up to 28d after vaccination. That's a couple of pretty big jumps there dude....
My main take-away is that these kinds of analyses are not as straightforward as they might seem. I was able to follow Rancourt's presentation, and from my non-expert perspective, it seemed bulletproof. You've made me aware, there are more layers to the puzzle.
I don't see these spikes on Euromomo ("... a European mortality monitoring activity, aiming to detect and measure excess deaths related to seasonal influenza, pandemics and other public health threats.") for Denmark under https://www.euromomo.eu/graphs-and-maps/#z-scores-by-country
You wrote this about Denmark: "It is evident, that excess mortality only rose starting in mid 2021, thus after the COVID-19 vaccine intervention." However you only included yearly plots and a plot which used a 52-week moving average. But on a monthly scale, Mortality Watch shows that there was about 8% seasonality-adjusted excess ASMR in Denmark in December 2020: https://www.mortality.watch/explorer/?c=DNK&t=asmr_excess&ct=monthly&df=2019+Jul&p=1&v=2. Other Northwestern European countries also had a spike in deaths in late 2020 which coincided with spikes in PCR positivity rate and COVID deaths: https://i.ibb.co/CmjyKHD/denmark-sweden-netherlands-germany.png.
---
You wrote: "My 'before vs. after' intervention' analysis of the top 20 most vaccinated countries shows increasing excess mortality after the intervention in each country". However there were actually three countries in your analysis which had higher excess mortality in 2020 than in 2021 or 2022, which were Portugal, Spain, and Italy. And also out of the 20 countries and subnational jurisdictions in your analysis, only 9 reached 100 COVID deaths per million people in 2020: https://sars2.net/nopandemic.html#Twitter_thread_about_excess_mortality_in_20_most_vaccinated_high_HDI_countries. Australia, New Zealand, and Hong Kong remained mostly free of COVID until 2022. And even in the case of countries that got COVID in 2020, your total excess mortality percent for 2020 includes the months before COVID.
---
You wrote: "'Vaccine deaths'" may not be immediate (only in the very frail) but may be systemic over time, thus causing a higher level of mortality. This may be observed in Germany, where you can see normal seasonal effect encircled, and a systemic increase after intervention".
However according to the calculation used at Mortality Watch, in 2020, 2021, 2022, and 2023, the month when Germany had the highest seasonality-adjusted excess ASMR was always December: https://www.mortality.watch/explorer/?c=DEU&t=asmr_excess&ct=monthly&v=2. And there's negative excess ASMR in the February and March of each year. So why would the vaccines follow a regular pattern where each year they're killing people in December but not in February? And also the highest peak in monthly excess ASMR is in December 2022, when only a small number of new vaccine doses were given, and there was no sudden spike in the number of new vaccine doses which would've coincided with the sudden spike in deaths.
---
You wrote this about the recent paper where one of the authors was Jeffrey Morris: "Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
However on pages 57 to 60 of their supplementary PDF, they included the results of an alternate analysis where they didn't exclude people for the first 28 days after vaccination: https://www.acpjournals.org/doi/suppl/10.7326/M23-1754/suppl_file/M23-1754_Supplement.pdf. It shows that for example in the Delta study in adolescents, the vaccine efficacy percentage was calculated as about 97.3% against documented infection, 98.1% against mild COVID-19, 98.2% against moderate or severe COVID-19, and 99.0% against ICU admission with COVID-19. When they excluded people for the first 28 days, the corresponding percentages were about 98.4%, 99.0%, 98.7%, and 99.0%, so there wasn't that much difference.
> BTW, a lot of people raised questions about our primary analysis focusing on events 28d after vaccination or index date (for unvaccinated).
> The reason for this was because as a medical records study, some individuals may have had covid tests at the index visit and were found to be infected, so would not be valid for the primary comparison.
> But realizing some distrustful people would suspect this is hiding something, we also provided the following table showing how many were excluded from the study because of infections in first 28d, and there were far more in the unvaccinated (0.005%, 1.05% and 1.58% in the 3 study cohorts) than vaccinated (0.002%, 0.25%, and 0.11%, respectively).
> We also in section 7 of the supplement repeated our vaccine effectiveness analysis starting at day 0, not day 28, and including those early infections - and the results are given below.
And also in the record-level data that Kirsch has published from New Zealand, the United States, and the Maldives, there is a reduced number of deaths during the first month after vaccination. Morris wrote: "By the way, it is clear from all data that death rates in the first 2 weeks after shots are way lower than the baseline date in the population because of something called the temporal 'healthy vaccinee effect' since people close to death will tend not to get a shot at that moment. So even though I haven't seen anyone actually define deaths within 14d of vaccination as unvaccinated, if they did they'd just be making unvaccinated look even better and vaccines worse." (https://twitter.com/jsm2334/status/1744178364355690857)
Not a single country will willfully disclose their record level data. I wonder why??? Hmmmm
Besides all the harm, the main issue IMO is that it may undermine their future "pandemic preparedness".
YES, and even when they occasionally release an "analysis" comparing vaccinated vs. unvaccinated, it is always an absurdly cherry-picked snapshot (never a full population for the full post-vaccine time period) that always has many obvious flaws. And what's REALLY telling is that they tend to be very quiet and cautious when releasing these "vaccines saved lives" analyses, instead of releasing them loudly and proudly as one would expect, which is quite a giveaway that they know those analyses won't stand up to any serious scrutiny!
Mistakes were NOT made.....They have been at this for decades..... we need to educate everyone..... Back in 1969, Dr Richard Day made some astonishing predictions about where the world would be today https://henrithibodeau.wordpress.com/2015/06/08/back-in-1969-dr-richard-day-made-some-astonishing-predictions-about-where-the-world-would-be-today/
How to Fake Pandemics in 4 Easy Steps
A masterclass by the DOD showman, James Giordano.
https://sashalatypova.substack.com/p/how-to-fake-pandemics-in-4-easy-steps
...hard to believe that this level of evil exists in our world....
EXCELLENT response and analysis- far more factual/accurate, balanced, and to-the-point than almost all of what I've seen so far. Thank you very much for this.
A few tidbits from an "interloper" to add to for discussion: My take is Denis Rancourt's team, due to years of experience analyzing health and death data, have a different view of what occurred.
1) Prof Arne Burkhardt in Germany said in his last interview (2 hrs long, find it), that families approached him begging that he investigate as they did not believe what the coroner wrote as "cause of death" of their loved ones. As you know, several doctors in Canada reported having a slew of "cause unknown" listed as cause of death.
2) In the US, due to the Brix/CDC change in how to fill out death certificates, the FDA advising no autopsies be performed or the virus might spread (my note: instead the shots shed). Regardless, this change meant the CDC insured we will never know what happened. Little to no closure is possible for many of those families either. Albert (Welcome the Eagle) seems to have read some of VAERS death reports, mentioned he saw inconsistencies as well.
3) John Beaudoin Jr. (coquin de chien substack) said in his 14 minute testimony in New Hampshire showing straight analysis of Massachusetts death data he obtained, that the reason for death was often carefully elaborated in the text, not in the boxes at the top. So death data may never been picked up by "the system". If inconsistent across the state, can we also assume also across the US?
And the sidestep? No one seems to be paying attention to estimates that lockdowns alone may have caused 80 million lives around the world, not including China. When supply chains are broken, people starve to death. Does the food supply magically recover?
Yes, the shots are all toxins with no benefits. Yes, these were overlapped with deadly hospital protocols. Yes, this was planned, hardly a pandemic. My conjecture is death data has been so skewed by WHO protocols being pushed to cause environmental and societal damage in advance of toxic shots; that may have killed far more. Nevermind some hospitals in so-called developed countries were also dehydrating, starving, venting and killing their patients for govt kickbacks.
Always want to keep in mind that even before covid came to town, iatrogenic interventions are listed as the third leading cause of death in the US. Its a wonder anyone can attribute deaths solely to shots. Its well-known that suppressed immune systems come from many directions and with age. Its also known that shooting toxins into the elderly, who already accumulated toxins over a lifetime, is a particularly nasty way to kill old people.
As an independent sanity check, I just did the following:
- Computed excess deaths using a modern boosted tree forecasting approach--shown in recent years to be near state of the art in several Kaggle competitions which I won or placed very highly in. I used the same STMF mortality data as Ioannidis et al. (https://www.mortality.org/File/GetDocument/Public/STMF/Outputs/stmf.csv) with years 2016-2019 as training to forecast total deaths for 2020-2023 using 4-year lag and week index features.
- Graphs of model fit and forecasts look sensible for all 37 STMF countries.
- This method estimates a total of 2.25 million excess deaths for 2021-2023 for the 37 countries, whose total population is around 1 billion. Multiplying by 7.888 to get a world estimate results in 17.8 million. Rancourt's 17 million estimate is within this upper bound.
- For additional reference, the method estimates 968,000 excess deaths in the US for 2021-2023 and 9132 for Denmark (nicely close to your 10K estimate). Please let me know if you'd like to see other countries or collaborate further.
That's awesome.
Yes, I'd be intrigued to see your code and/or collaborate. D you use R?
Please reach out via email, usmortality@protonmail.com if you like. Thanks!
Kirsch claims that there were about 10,000 excess deaths in New Zealand in 2021 to 2023, because he used the average number of deaths in 2017 to 2019 as the baseline.
I have shown him why his average baseline is inaccurate, and I have tried to teach him how to calculate the baseline by doing linear regression in R, but he seems to be stuck with Excel. So maybe you could run your code on the STMF data from New Zealand and show the results to Kirsch.
Thanks Mongol. I'm estimating only 2528 excess deaths for NZ over 2021-2023, #35 out of 37 and ahead of only Iceland (66) and Luxembourg (-197). It's important to correct for trend as you have done.
Kudos to Kirsch for putting himself prominently out there and calling for better data and transparency, but he does not appear to be receptive to any criticisms of analyses he is vested in. Very few folks overall appear to be pursuing legit causal inference on vax data. I appreciate all of the solid work you have done in pursuit of the truth.
Your estimate seems to be in the ballpark- "Stats NZ" seems to say about 3642 in 2022.
https://www.nzherald.co.nz/nz/new-zealand-records-biggest-increase-in-registered-deaths-in-100-years/BQERSTKIANCKRNNA7IL42RD52U/
My site's Mortality.Watch mid-point estimate for NZ from March 2020 - October 2023, comes in at: 2,829
If you start from the point of vaccination in Feb 2021, you get 4,416.
The key point however is to also calculate the prediction intervals.
This is currently not yet supported by Mortality.watch, but I've written this script, which shows that 10k lays within the 95% PI,
Oct 2020-Sept 2023
[1] "Actual deaths: 110050, Expected: 103971, 95%PI[98606,109336]"
[1] "Excess deaths: 6079, 95%CI[714,11444]"
So with 95% certainty, we can say that excess deaths in NZ, using a 10y pre-vaxx trend is witin 714 to 11,444 people.
https://github.com/USMortality/charts/blob/nz/covid19/nz/excess.r
https://www.mortality.watch/explorer/?c=NZL&t=deaths_excess&ct=monthly&df=2020+Mar&ce=1&sl=0&v=2
Thank you Ben and good point about using intervals.
All I effectively did was substitute in a better forecasting model, since we can do way better than a simple line. Line 17 of excess.r is: model <- lm(deaths ~ year, data = df_train). The idea is to swap in a time series forecast instead, which should be a much improved baseline from which to compute excess. This requires using monthly or weekly data.
A classic approach is an ARIMA model with trend and seasonal components. One reasonable implementation in R is prophet: https://facebook.github.io/prophet/docs/quick_start.html#r-api and see https://facebook.github.io/prophet/docs/uncertainty_intervals.html to get intervals.
I used XGBoost in JMP Pro software to forecast, which involves more manual steps but likely gives a little better point forecasts than ARIMA. Prediction intervals are more work. There is an XGBoost wrapper in R.
Really appreciate all of your great and transparent work with code!
Yes, I've been using R package fable, and the accompanying book: https://otexts.com/fpp3/toolbox.html
It's great and also allows for TSLM. In this case we are talking about yearly data, hence a linear method should be sufficient.
But the thing about that is that’s all in 2022.
And 2023 is looking more similar to 2022 than earlier years. Probably around 37k deaths (as compared to ~30k from 2010 to 2020). Need to see and compare to population figures as well. There has been an increase in population in the last 4 years but nevertheless I don’t think that the increase is in the over 60s.
Excel or R there’s not enough variables being considered.
Great analysis! Now, here's the motive:
Depopulation or EXTERMINATION? (finest quotes):
https://scientificprogress.substack.com/p/depopulation-or-extermination
COVID was designed as a primer for even more lethal COVID haccines:
https://scientificprogress.substack.com/p/the-real-covid-timeline
https://scientificprogress.substack.com/p/not-vaccine-not-gene-therapy-just
https://scientificprogress.substack.com/p/what-do-bioweapons-have-to-do-with
Weaponization of vaccines:
https://scientificprogress.substack.com/p/vaxxed-v-unvaxxed/
https://scientificprogress.substack.com/p/depop-vaccines-no-myth
- Their main source of power apart from sin-empowered demons? NOT a coincidence that the USA left dollar convertibility to gold in 1971, precisely triggering the exponential government deficit coupled with the trade deficit and inflation.
This is the Achilles’ heel of all nations: masonic and satanic secret societies counterfeit paper money and launder trillions with which they buy Banks, seats in the Federal Reserve (the only private run Central Bank in the world), political careers and parties, puppeticians, listed corporations, media, healthcare corporations and organizations, universities, foundations, judges, etc.:
Confessions of illuminati, David Rockefeller (finest quotes):
https://scientificprogress.substack.com/p/david-rockefeller-illuminati
Confessions of ex-illuminati Ronald Bernard (all lodges obey the same master, Satan):
http://youtu.be/JAhnCdXqPww
The way out of this mess:
1. Create an easy system for real money: private currencies/warrants based on real assets, goods, services, etc. (gold, corn, oil, distance/volume/weight transportation, labor human hour/minute, etc.)
2. Ban legal tender. Let the free markets decide which real-currencies/valuables/warrants they prefer to trade with
3. Ban paper-backed currencies (unlike real-backed ones of point 1.)
4. Enforce a Legal Banking Reserve of 100% of deposits (so banks don't create money based on air) and therefore there's no excuse for a Central Bank, because there would be no risk of bank-runs since all their loans are fully backed with deposits
Anything else you might think of?
Now, are you really ready for this?:
The full PLAN exposed:
https://scientificprogress.substack.com/p/the-plan-revealed
16 laws we need to exit Prison Planet
https://scientificprogress.substack.com/p/laws-to-exit-planet-prison
Plllllease, on my knees, don’t believe me, just do your own homework by searching the following in yandex.com, mojeek.com (includes crawl date filter and substack search), gigablast.com, startpage.com, duckduckgo.com (not Google, Bing, Yahoo censors). The key terms to test them? Child Satanic Ritual Abuse, Child Satanic Ritual Murder.
President John Quincy Adams: “Masonry ought forever to be abolished. It is wrong - essentially wrong - a seed of evil, which can never produce any good.”
If you are a mason or know a mason, ask him to ask his 33° master to put in writing and sign it, who is "the great architect" and that he is not Lucifer. If he refuses, then he’ll know who he is really serving, Satan: tell him to get out of masonry NOW. Sooner or later he’ll be required to trample on a cross to get to a higher degree.
Confessions of a former mason (Serge Abad-Gallardo):
https://www.ncregister.com/interview/confessions-of-a-former-freemason-officer-converted-to-catholicism
Confession of 33rd degree master mason - Masons worship deities/demons
https://rumble.com/v294ksc-words-from-33rd-degree-master-mason-rare-video-masons-worship-all-sorts-of-.html
Masonry's Satanic Connection
https://odysee.com/@HiddenTruths:c/Masonry's-Satanic-Connection:4
Masonry's Satanic Doctrine | From Their Own Books
https://rumble.com/v2wg24a-masonrys-satanic-doctrine-from-their-own-books.html
Do Freemasons Worship Lucifer？ Evidence They Don't Want You To See
https://odysee.com/@John_4-14:a/Do-Freemasons-Worship-Lucifer%EF%BC%9F-Evidence-They-Don't-Want-You-To-See-%EF%BD%9C-Hidden-Agendas---Walter-Veith:0
Satanic Ritual Abuse and Secret Societies [1995] [VHS]
https://odysee.com/@thisworldworks:1/satanic-ritual-abuse-and-secret-societies-1995:3
Satanic Pedophilia Torture and Blood - Dark Satanic Secrets Revealed
https://odysee.com/@Gmail.com:52/822821884_Satanic-Pedophilia-Torture-and-Blood---Dark-Satanic-Secrets-Revealed:4
UNITED NATIONS LUCIFER AND THE LUCIFER TRUST
https://odysee.com/@dynosarus:c/UNITED-NATIONS-LUCIFER-AND-THE-LUCIFER-TRUST:4
Freemasonry: 100% incompatible with Christ
https://rumble.com/vs9mxb-heres-why-christianity-is-totally-incapatable-with-freemasonry.html
first interesting view on monetary policy which not considered so interesting to ponder upon, thanks...
then comment that from a science of evil perspective, evil appears as not that smart and shows signs of being limited to derive more by trial and error...
then from a soulless money blind ignore the externalities business type of evil this evil wants to more cause sickness that it can profit off of at an exponential increasing rate...
then from here the biggest factor desiring mass death is the shadowed projected aggregate fear of overpopulation...
Speaking of which: Depopulation or EXTERMINATION? (finest quotes):
https://scientificprogress.substack.com/p/depopulation-or-extermination
"Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
I stopped reading after that--Unbelievable how many people-- and how much they get paid and how credentialed they are-- to cherry pick data.
I have been in the field for over 10 years and this BS is rampant. You get pressured. Why go through 2 or 5 years of rigorous graduate school training if you are going to make shit up? Just get ChatGPT/AI do it for you, or whatever.
On pages 57 to 60 of the supplementary PDF, they included the results of an alternate analysis where they didn't exclude people for the first 28 days after vaccination, but it didn't change the results that much: https://www.acpjournals.org/doi/suppl/10.7326/M23-1754/suppl_file/M23-1754_Supplement.pdf.
Jeffrey Morris wrote: "BTW, a lot of people raised questions about our primary analysis focusing on events 28d after vaccination or index date (for unvaccinated). The reason for this was because as a medical records study, some individuals may have had covid tests at the index visit and were found to be infected, so would not be valid for the primary comparison." (https://twitter.com/jsm2334/status/1744762762226794661)
But they also didn't check, all-cause mortality or any clinical diagnosis such as CLI, didn't they? Why not, why stick with biased COVID-19 attribution, if we know it's unreliable?
Agreed Ben--So obvious!
Those two quotes above, in consequence, amounts to trolling. There so many assumptions, contradictions and irrelevant/vague statements in there that trying unpack it all really just amounts the purpose of trolling... sometimes called intellectually dishonest.
Thanks for the message Mongol--The take away is that a significant proportion of vaccine induce deaths occur within the first few weeks of getting vaccinated. Thus, excluding these already dead people from the study will bias the results.
That is pretty much the end of the story and why I stopped reading after "Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
We don't even have to get into the well known, highly incentivized process attributing covid deaths using the highly flawed PCR test and dying "with" covid, etc.
Regarding the first quote, if it doesn't change the results, why not leave them in? Why are they adding unnecessary complexity while claiming the additional complexity isn't adding any value? Huge red flag
Adding unnecessary complexity as a substitute for rigor should always raise some red flags to dig deeper into footnotes, methodologies, assumptions, the data, etc. Which is a good segue way into your second quote.
2nd quote: I have no idea how this statement relates to building a case for excluding people up to 28 days after vaccination and only COVID attributed deaths. Probably if I squint my eyes and stare intently I might find some hair thin logic in there somewhere. But it is clear, in it's lack of clarity, that there is no there there.
It sounds like what it is trying to say, if I read it literally, because it is a "medical records" (if it was non-medical records could we include up to 28d? ) study, then some people "may" have had a PCR test and "may" have been "found" to be "infected". Thus we need to exclude anybody up to 28d after vaccination. That's a couple of pretty big jumps there dude....
Anyway...whatever...
wow nice this is central relevant at least from my more Jung biased perspective...
in that in brings to the table of discussion the concept of conformation bias...
like Rancourt here our current example or not and the more important question...
yet even if someone is a bit of a loose cannon still they are applying both wisdom and knowledge...
this to legitimate their path to, in this example, a proposed number of significance...
so a path of what we find value and meaningfulness in...
which valuates for us what we think we know, meaningful predictability, within the rational...
so this bias is like built in to some degree especially to the degree of lacking a healthy relationship with uncertainty...
Thank you for this analysis.
My main take-away is that these kinds of analyses are not as straightforward as they might seem. I was able to follow Rancourt's presentation, and from my non-expert perspective, it seemed bulletproof. You've made me aware, there are more layers to the puzzle.
Thanks again.
I don't see these spikes on Euromomo ("... a European mortality monitoring activity, aiming to detect and measure excess deaths related to seasonal influenza, pandemics and other public health threats.") for Denmark under https://www.euromomo.eu/graphs-and-maps/#z-scores-by-country
What could be the explanation?
euromomo is using a "heavily processed" method, z-scores. Check out the crude deaths here: https://www.mortality.watch/explorer/?c=DNK&t=deaths&ct=weekly&v=2
Thanks for explaining the reason.
You wrote this about Denmark: "It is evident, that excess mortality only rose starting in mid 2021, thus after the COVID-19 vaccine intervention." However you only included yearly plots and a plot which used a 52-week moving average. But on a monthly scale, Mortality Watch shows that there was about 8% seasonality-adjusted excess ASMR in Denmark in December 2020: https://www.mortality.watch/explorer/?c=DNK&t=asmr_excess&ct=monthly&df=2019+Jul&p=1&v=2. Other Northwestern European countries also had a spike in deaths in late 2020 which coincided with spikes in PCR positivity rate and COVID deaths: https://i.ibb.co/CmjyKHD/denmark-sweden-netherlands-germany.png.
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You wrote: "My 'before vs. after' intervention' analysis of the top 20 most vaccinated countries shows increasing excess mortality after the intervention in each country". However there were actually three countries in your analysis which had higher excess mortality in 2020 than in 2021 or 2022, which were Portugal, Spain, and Italy. And also out of the 20 countries and subnational jurisdictions in your analysis, only 9 reached 100 COVID deaths per million people in 2020: https://sars2.net/nopandemic.html#Twitter_thread_about_excess_mortality_in_20_most_vaccinated_high_HDI_countries. Australia, New Zealand, and Hong Kong remained mostly free of COVID until 2022. And even in the case of countries that got COVID in 2020, your total excess mortality percent for 2020 includes the months before COVID.
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You wrote: "'Vaccine deaths'" may not be immediate (only in the very frail) but may be systemic over time, thus causing a higher level of mortality. This may be observed in Germany, where you can see normal seasonal effect encircled, and a systemic increase after intervention".
However according to the calculation used at Mortality Watch, in 2020, 2021, 2022, and 2023, the month when Germany had the highest seasonality-adjusted excess ASMR was always December: https://www.mortality.watch/explorer/?c=DEU&t=asmr_excess&ct=monthly&v=2. And there's negative excess ASMR in the February and March of each year. So why would the vaccines follow a regular pattern where each year they're killing people in December but not in February? And also the highest peak in monthly excess ASMR is in December 2022, when only a small number of new vaccine doses were given, and there was no sudden spike in the number of new vaccine doses which would've coincided with the sudden spike in deaths.
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You wrote this about the recent paper where one of the authors was Jeffrey Morris: "Not only is his entire study extremely biased, as it looks only at COVID-19 attributed deaths and not all-cause, but it also excludes people up to 28 days after vaccination."
However on pages 57 to 60 of their supplementary PDF, they included the results of an alternate analysis where they didn't exclude people for the first 28 days after vaccination: https://www.acpjournals.org/doi/suppl/10.7326/M23-1754/suppl_file/M23-1754_Supplement.pdf. It shows that for example in the Delta study in adolescents, the vaccine efficacy percentage was calculated as about 97.3% against documented infection, 98.1% against mild COVID-19, 98.2% against moderate or severe COVID-19, and 99.0% against ICU admission with COVID-19. When they excluded people for the first 28 days, the corresponding percentages were about 98.4%, 99.0%, 98.7%, and 99.0%, so there wasn't that much difference.
Morris wrote the following (https://twitter.com/jsm2334/status/1744762762226794661):
> BTW, a lot of people raised questions about our primary analysis focusing on events 28d after vaccination or index date (for unvaccinated).
> The reason for this was because as a medical records study, some individuals may have had covid tests at the index visit and were found to be infected, so would not be valid for the primary comparison.
> But realizing some distrustful people would suspect this is hiding something, we also provided the following table showing how many were excluded from the study because of infections in first 28d, and there were far more in the unvaccinated (0.005%, 1.05% and 1.58% in the 3 study cohorts) than vaccinated (0.002%, 0.25%, and 0.11%, respectively).
> We also in section 7 of the supplement repeated our vaccine effectiveness analysis starting at day 0, not day 28, and including those early infections - and the results are given below.
And also in the record-level data that Kirsch has published from New Zealand, the United States, and the Maldives, there is a reduced number of deaths during the first month after vaccination. Morris wrote: "By the way, it is clear from all data that death rates in the first 2 weeks after shots are way lower than the baseline date in the population because of something called the temporal 'healthy vaccinee effect' since people close to death will tend not to get a shot at that moment. So even though I haven't seen anyone actually define deaths within 14d of vaccination as unvaccinated, if they did they'd just be making unvaccinated look even better and vaccines worse." (https://twitter.com/jsm2334/status/1744178364355690857)
1) Yes, and there was a 11% excess in March 2018, local excess has always been normal.
2) Covid-19 attributed deaths have been shown to be too biased for any meaningful analysis.
3) No I was referring to a systemic increase. You have to "ignore" the normal seasonal patterns.
4) Same as 2), why has he not included any clinical or all-cause endpoints?