Statistics

“Excess” Deaths, Vaccines, Iatrogenic & Other Causes Of Death

This took a long time to get to, because we were waiting, patiently, for the CDC to count deaths. They are always eight to sixteen weeks behind. This is of particular concern to the last batch of data presented below. This means we can be confident the data won’t change much for all dates up to about the beginning of 2023.

The point of this post is to explore the limits of what can be known using the CDC’s limited data: that, and nothing more.

Gist: After subtracting deaths attributed to covid, 2020 summer “excess” deaths (above and beyond covid and not flu) were about 91,000, 2021 summer about 88,000 and 2022 summer about 50,000, whereas the maximum summer before this, in 2017, was about 19,000.

OBSERVATIONS

Here are the weekly deaths from all causes over the once United States, as affirmed by the CDC (new data is here; I no longer have a link for the old, but it’s CDC too).

I hope this chart is so plain even an NPR listener can make it out. It should be clear that deaths always bottom in summer and peak in January, because why? Because January is when it’s cold, we all go inside to share bugs, disease burden increases and people, thus put upon, die. I ask you to remind yourself what solution Experts first hit upon to “solve” coronadoom.

The drop off at the end is because it takes a good eight weeks, and sometimes as many as sixteen, and even more, to fully count deaths all across the country.

Some years before 2020 have higher January peaks than others. This is usually attributed to flu being more virulent, and indeed that explanation is surely true, especially because those peaks match up with flu deaths.

Not all deaths are classified as flu, however. The peaks match, flu and all death, but there are still peaks even after removing flu. The reason is causes of death are listed singly and not multiple. So a guy with CHF who gets flu weakens and dies after the flu has cleared has his death is listed as CHF.

The same could be true with coronadoom, of course, except for the zeal to classify any death as covid if the person died with a positive test. And, indeed as we saw, many deaths were classified as the doom even on suspicion, absent any test.

The spikes when the panic hit in 2020 and after are obvious. Too obvious, because we only begin our series in 2009, the data before that is difficult to locate (if anybody has, please send). Meaning we’re not seeing the large relative spikes for the Asian flu of 1957-58 and the Hong Kong flu a decade later, which both killed, the CDC says, at greater rates around the world than the doom. Yes. How soon we forget. There was no panic then, so no one remembers.

All right, let’s look at the same plot, this time subtracting attributed doom deaths:

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Recall the tremendous enthusiasm for attributing doom deaths. Motorcycle crashes, men falling off ladders, really any death in a person who had a positive test was accorded a doom death, and, as said, sometimes even without a test. It became ridiculous. For us what is important is that it is not at all likely doom deaths were under-counted. It is far, far more likely they were over-counted.

Any calculation of “excess” deaths would come from this picture, because these are the deaths from everything else besides covid.

There are two interesting features highlighted using sophisticated arrows drawn by Yours Truly. Keep these in mind. Two spikes in death that seem not to fit the pattern of previous years.

For that is what “excess” deaths are: deaths that do not fit an expected pattern. Expected being the key word. What does is mean? This: all “excess” deaths are in relation to a model. There is no escaping this. “Excess” deaths are always, absolutely always, conditional on a model. And as all models only say what they are told to say, we have to be sure our model is saying the right thing.

The right thing being what is expected. What is expected? Well, deaths with bottom out in summers, peak in Januaries, and with some January peals higher than others, and with summer bottoms pretty much the same, all embedded in a general, probably linear, increase.

All right, let’s look at the first plot again, all deaths including doom deaths, only this time year on year:

The 2020 spike when the panic began is obvious. As is the large bump in late summer early fall of 2021.

Another obvious feature of the first and this graph is the inexorable increase in weekly deaths. This is surely caused by an increasing and, to some extent, aging population. Up until 2020, it appears the year on year increase was fairly steady. Indeed, we make use of this in our “excess” deaths model.

Meanwhile, let’s redo the second graph, all deaths subtracting doom deaths, year on year:

As in the second plot, there’s the strange 2020 bump, which has to be something other than covid deaths, because we are assuming it more likely covid deaths were over-counted. We also see more clearly the summer and fall numbers for 2020, 2021, and 2022 are higher than expected—again, where by “expected” I mean they don’t fit the pattern of previous years.

“EXCESS” DEATHS MODEL

We are now ready to begin our model. I tried two. One, which I won’t show, is a mixed time series model (sine+cosine) with increasing trend. This fit well enough, but over-smoothed the data, and missed that the increase in deaths from 2009-2019 was not quite linear.

The second model, a standard regression on weekly year on year increases, I think is superior because it catches the differences in weekly increases, a model which errs on the side of simplicity. The model builds a separate regression for each week on the years 2009-2019, i.e. a simple linear increase for each week, then predicts the weekly deaths for the years 2020-2023.

(Some will complain “This ignores the correlation between weeks!” Yes. It does.)

The data used is as you see above: All Cause MINUS Covid deaths.

Here is the time series picture of all deaths minus doom deaths, with the model overlaid in green:

It must be noted that 2018 was a big flu year, with many more deaths than previous years. A lot of people who might have died in 2019, which was a very low year, died in 2018. But because 2019 was a low flu year, 2020 was ripe for a larger number of people to die, of whatever cause.

Look from 2009-2019, the model fitting period. The model fits well. It particularly fits pretty in the summer. It nails summers. It also does well in springs and falls, but not quite as well as it did in summers. It doesn’t fit as well in Januaries, smoothing out the spikes; i.e under-predicting in high flu years and over-predicting in low flu years. This is because, of course, the unpredictable variability of flu. The model does get right the average of those peaks (while also accounting for the increasing trend).

The interpretation is this. The pattern is that summers are low, which the model catches nicely, and that winters are high, which the model over-predicts in low flu years. Meaning we can likely trust this model to compute “excess” deaths, i.e. deaths that don’t fit the usual pattern, and understanding the “excess” will be positive when flu years are high in reality, and negative when flu years are low.

And there was no flu starting in spring 2020: it almost disappeared. It has returned only this winter. So negative “excess” deaths when flu is gone will be very low, as we see.

We’ll discuss 2020-2023 in a moment. First, here’s the year on year, all cause minus covid, with the model overlaid, but here as dotted curves matching in color to the years.

That 2020 spike is anomalous. It does not fit the pattern. The deaths are not doom deaths, but something else, as we suppose. The summers and falls of 2020-2022 are also higher than predicted, and we know the model fits well in the summer. These deaths are also anomalous. They are higher than expected.

We finally come to “excess” deaths, shown in this time series:

This in solid black are all non-covid deaths (all cause subtracting covid) minus the prediction. That is, those deaths that are not “expected” (the model is the expectation). The light grey lines are the 50% prediction window. We need some kind of window, because we have a model, and it makes predictions which are uncertain (why 50%? why not? 95% is just silly, anyway the uncertainty is not large). We saw the uncertainty was low in spring through fall, but higher in winter.

Let’s start by looking at 2009-2019, the mode fitting period. The positive spikes correspond to high flu years, and the negative ones to low flu years, as explained above. The spikes are narrow, lasting only a few weeks, which also matches our expectations of seasonal disease and death.

We see again that 2019 was a low-death year, so that “excess” deaths spiked negative.

This was also true in first two months of 2020, the year the panic began. Then came a large spike in spring, which are not, we emphasize, doom deaths, because we’re assuming all doom deaths were counted, or even over-counted.

Then we see large extended positive spring, summer, and fall spikes in 2020–2022. And negative spikes in winter for all these years.

Let’s discuss the negative spikes first. Go back and look at the previous figure. The green line assumed we’d see the “normal” flu pattern. Flu practically disappeared all over the world during these years. So the winter peaks were actually quite low, much lower than expected. recovering somewhat in 2022. (Flu has now returned.) The spikes in winter were also shorter lived than normally seen.

This means the negative “excess” winter deaths are likely put down to missing flu.

What about the positive spring through fall spikes?

The largest positive spike was in spring 2020, right when the panic hit. These deaths, as we’ll see in a moment, were probably to good extent iatrogenic. This was when doctors thought it well to force many patients on ventilators (remember the Great Ventilator Manufacturing Scare?). Treatment was over-aggressive. People were killed. Could some of these deaths be missed doom deaths? That’s a possibility, of course, especially since we were in full fear panic hersteria. However, not all these deaths could have been doom deaths, as we’ll see.

The summer-fall peak in 2020 was larger than the summer-fall peak in 2021. Vaccines did not really get started until 2021. Which means that 2020 “excess” deaths had to have other causes. In 2020, many were likely panic deaths, at least to some extent. People frightened away from seeking treatment for non-doom diseases, that sort of thing. And, yes, it is possible, though not likely, some of these were missing doom deaths.

The 2021 positive spike was larger than the 2020. Assuming the same level of panic, the only new thing were variants of the doom—and the forced vaccinations. Which continued into 2022, which saw the vaccination push and the panic toned down, but not yet called off. (Thanks to Experts at CDC.)

Now it turns out that the “turn”, the time at which the winter peak ends and summer begins is at the ends of May. This last until right around Christmas, after which “excess” deaths peak up (when they do). We guess that the large negative spikes are due to missing flu. Something must account for the larger summer peaks, especially after 2019.

Here, then, is a chart of the “excess” deaths from the last week of May through about Christmas, from 2010 to 2022:

Year Summer-Fall “Excess” Deaths
2010 12,258
2011 -6,601
2012 7,886
2013 -25,096
2014 -9,480
2015 -3,172
2016 14,240
2017 18,725
2018 -10,494
2019 3,639
2020 early 22,630
2020 late 68,692
2020 total 91372
2021 88,431
2022 49,848

The 2020 early is 2020-03-28 to 2020-05-30, and late is the week after through 2020-12-26. The week after this, “excess” deaths turned negative. Thus, after subtracting deaths attributed to covid, 2020 summer “excess” deaths (above and beyond covid and not flu) were about 91,000, 2021 summer about 88,000 and 2022 summer about 50,000, whereas the maximum summer before this, in 2017, was about 19,000.

The early peak of “excess” deaths in 2020 was about 23,000. We guess why in a moment. The late summer-fall peak was about 69,000. There was no vaccine available (in any number) until 2021. “Excess” deaths then were about 88,000. That means the 2021 summer-fall beat 2020 summer-fall by about 20,000 “excess” deaths. Some of those could, of course, be associated with vaccines.

So we have some circumstantial evidence vaccinations were responsible for some deaths. As has been widely noted, deaths are never classified as “vaccine deaths”. So not only is there no direct data on this, there cannot be. We have to guess. Because we are forced to guess does not mean that vaccines could not cause any deaths, nor does it mean they did. It means that everybody, on any side of the debate, is forced to guess. Using data like this.

Now this “excess” death plot also shows the futility of looking at yearly numbers. Summers are not the same as winters in disease characteristic. Yearly sums smear out the signals, and can be misleading.

Before we continue, let’s peek at the official causes of death time series:

Note septicemia (bottom row), which shows the same spike as the spring 2020 deaths. Septicemia means deaths by infection, almost certainly in hospitals. And again, likely from over-aggressive panic treatments of having tubes crammed down peoples’ throats.

You’ll see also with that, as with the other diseases, the expected winter peaks in deaths. Notable is Alzheimer’s, which peaks in winter, too, but which shows a definite downward trend in the peaks. This is surely caused by fewer old people being left alive, as the old were killed at the highest rates by the doom. The weakest were cleared out early.

Flu bottomed out, then returned. Excepting anything to do with breathing, all diseases spiked highest in winter 2022—right after the forced vaccinations, but also after a peak in doom deaths; however, the peak in doom deaths was smaller in January 2022 than 2021. Respiratory diseases also spiked, but even higher in 2023. If doom deaths were under-counted, which does not seem likely, they’d be found here.

Which means the spikes in other diseases, which all happened during the vaccination panic, are curious. They don’t fit the pattern.

Is the winter 2022 spike caused by vaccines? Maybe. We have to guess, and that “maybe” is the best we can do. Worse, I can’t find data at this level of specificity before 2020 from the CDC (if anybody has, email me). So we’re left with considerable uncertainty.

Lastly, here are the non-covid deaths (all cause minus covid) for various age groups. Again, only data from 2020 is available.

Note the changing limits on the vertical axes.

The largest spike is for the older, 45 years and up, in winter 2022. It was not flu. It was not covid, we assume. It had to be something else. What causes were available? Complications from covid are possible, but not likely, given the zealousness to classify deaths are from covid. (The plot above counts across all age groups.)

The younger are different. Their peaks come in summer, which in part is not surprising, given that this is the prime time for accidents of all types, and the young suffer these more. That becomes clearest in those 1 to 34 years old. If there is a vaccine-cause death signal here, it is difficult to see.

So there it is. No definitive answer, and one cannot be had. There is at least good circumstantial evidence vaccines could explain some deaths, especially in those 35 or older. It is just ambiguous enough for those who hold it impossible vaccines can cause harm to dismiss the lot. But neither is the signal so strong that the vaccinated were harmed at large rates, if they were harmed at all.

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Categories: Statistics

26 replies »

  1. So there it is. No definitive answer, and one cannot be had.

    Killjoy. Why don’t you just make up something juicy? Everybody else does.

  2. Excellent post. There will always be *some* iatrogenic associated deaths (we all live as a series of hours). “Vaccines”, improper treatment, etc. Tort lawyers live by exploiting emotions here (both ways). Diagnosis *isn’t* gnosis. They are guesses always; some closer to reality than others. Physicians are human and subject to fads, and external pressures; just like everyone else. What was and is so disturbing to me is the political manipulation of what should be data collected by the most rigorous and apolitical methods possible. This, folks, is how Lysenko happened and it *is* happening in the USA and elsewhere.

  3. Keep in mind the CDC is supposed to investigate excess deaths and find likely cause(s). Crickets from DC.

  4. Seems like this one was aimed primarily at pensioners. Long term fertility and
    zombification trends bear monitoring. I’m becoming increasingly convinced
    operantly this was done to penetrate the blood/brain barrier with ‘something’.
    This would explain the panic and concern of the perpetrators with the unvaccinated.
    Algorithms are being developed with taxpayer funds to ‘track them’. They may need to
    be quarantined in the future because of, well you know ‘the science’..

  5. ‘Doctors fades’ are a quaint notion but we must bear in mind this was the first disease
    in memory with a government bounty for hospitals and doctors on patients that were
    diagnosed, hospitalized, and deceased, ranging up to a hundred thousand dollars
    of taxpayer money; per head.

  6. Interesting and helpful unpacking of the data. A few thoughts come to mind regarding other stats to look at: still births, live births, different causes of death, lifespan. My biggest fear is that the biological agent impacts fertility and long-term health.

  7. What I would like to know is what is the cumulative number of excess deaths – excess relative to the model presented. It seeems to me those wide peaks and throughts after 2020 cancel each other out. As if the entire pattern got shifted sideways or leveled out.

    I’m presuming the dataset is present on CDC’s website.

  8. It doesn’t fit as well in Januaries, smoothing out the spikes; i.e under-predicting in high flu years and over-predicting in low flu years.

    January data have more fluctuation than other months’, therefore not fit as well. Which is a normal result of statistical modelling. What’s more interesting (to me) is the overall fluctuation during covid years.

    Meaning we’re not seeing the large relative spikes for the Asian flu of 1957-58 and the Hong Kong flu a decade later, which both killed, the CDC says, at greater rates around the world than the doom. Yes. How soon we forget. There was no panic.

    Evidence for this statement? What are the effects of so-called panic? Good or bad? Less deaths? Worse economy?

    ”[W]e can likely trust this model to compute ‘excess’ deaths…”

    I guess people have to take your word for it. lol.

    We guess why in a moment.

    Just want to highlight this statement.

  9. So we have some circumstantial evidence…. As has been widely noted, deaths are never classified as “vaccine deaths”. So not only is there no direct data on this, there cannot be. We have to guess. Because we are forced to guess does not mean that vaccines could not cause any deaths, nor does it mean they did. It means that everybody, on any side of the debate, is forced to guess. Using

    You don’t have to guess. Just need to click here.

  10. Briggs, great post! A few questions:

    1). Might you at some point consider translating the excess deaths numbers into percentages above or below expected or percentages of actual total deaths? It’s hard to grok what the raw numbers mean.

    2) Adjacent to this question about vaccines possibly contributing to higher excess deaths is the question about absolute deaths possibly caused by the vaccine. What is your take on VAERS as a data source for that?

    3). Back on the excess deaths question, what’s your take on the data being popularized by Ed Dowd?

  11. I don’t think the whole thing had much to do with actual deaths it was done
    for the lockdown measures to bankrupt small business and destroy the
    economic ability of the average person to resist. It was a first step in normalizing
    complete top down control with digital control of money and future mandated medical
    interventions. The shrill reporting on how deadly the virus was, was done
    to scare people into compliance right down to the refrigerated tractor trailer trucks
    backed up to hospital morgues. That and the outright murder by ventilator and Remdesivir
    to increase the death toll and by blocking any other viable therapeutics. It’s about as
    sinister as it gets and was planned meticulously for a very long time. Anyone that takes the
    time to read their own papers and pronouncements over the past 30 years can figure this out.
    Adjust your tinfoil hats accordingly.

  12. I recently bought Ed Dowd’s book, “Cause Unknown,” regarding the massive (apparent) increase in young people dying, a result surmised as due to the jab. It’s less statistical and more anecdotal than Briggs, certainly, and heavy on the heart-string tugging, but no less revealing for looking at the past three years.

  13. “Thus, after subtracting deaths attributed to covid, 2020 summer “excess” deaths (above and beyond covid and not flu) were about 91,000, 2021 summer about 88,000 and 2022 summer about 50,000 …”

    Do these numbers mean the price of panic, measured in lives, was around 229,000? Comparing this to the total official US death total from Covid (number from “https://www.worldometers.info/coronavirus/country/us/”, the first search result), does this mean that panic increased the death toll by 20%?

    I’m pretty sure I’m misinterpreting the numbers. And another model would be needed for an unpanicked response, since an unpanicked response didn’t actually happen. Or maybe it did in another country? Shutting down big swaths of society was a panic reaction, but did it “save” any lives? I wouldn’t be at all surprised if someone came up with a model that says that the death toll would have been 2x, or 5x, or 10x, had we not panicked.

    One thing I’m sure everyone on all sides will agree on is that Covid-19 was not “the big one”. Maybe that contributed to how easily it was politicized to further an agenda? Will the adults in the room take more responsibility on the next one? Or will the panicked children again run the show?

  14. Graphs too small. Can’t see numbers well enough to read. My eyes not the problem.

  15. What’s with the massive spike in “unclassified” deaths in that set of graphs for death by cause? Why no comments about that? Sorry if regulars already know the answer.

  16. Philip,

    My fault for not mentioning it. These are all the deaths that have no yet been classified, and are part of the delay. They always shrink in time. But the new ones are always high, until they are classified.

  17. Brazilian online civil registry data, updated more or less every other day, confirms the Briggs analysis. Steve Kirsch is out a million bucks.

  18. There was no conclusion so the article did not provide mucch

    Flu deaths are a computer model estimate that may have no relationship to reality,
    Influenza is not a cause of death on a death certificate per CDC guidelines.
    Most doctors believe the CDC guessed flu deaths are too high. Often much too high.

    Covid deaths are deaths from any cause within 28 days
    of a positive PCR test for Covid. Far from a precise methodology

    That leaves all cause deaths.

    They obviously went up a lot in 2020.
    Covid is the most likely explanation

    But 2021 was similar to 2020 in spite of vaccines
    and he fact that many elderly died of Covid in 2020,
    who would have died of other causes in 2021 or 2022.
    The EXPECTED all cause mortality in 2021 was a significant
    reduction. But that did not happen.

    In 2022, Omicron, a coronavirus common cold with
    an infection fatality rate (IFR) similar to other common colds,
    became the dominant disease. Actual Covid19 infections were
    much reduced. Any failure to distinguish between Omicron
    and Covid19, with very different IFRs, is likely to make
    an analysis worthless.

    For three years in a row (2020 through 2023) all cause US mortality was
    15% to 20% above average. Only a very small amount of the increase
    could be explained by an aging population.

    Conclusions:
    Covid vaccines did nt save lives
    They may have caused an increase in deaths

    The truth could only be determined by autopsies

    If Covid b vaccines had a positive effect on all cause mortality,
    I believe there would be lots of autopsies to prove that.

    But there have been very few autopsies of deaths
    to determine whether vaccines are guilty or innocent.

    The obvious conclusion is that some important information
    is being kept from the public.

    And that VAERS reports show Covid vaccines to be
    the most dangerous vaccines in history, by at least 50x.

    And that all cause mortality demonstrated the claim of Covid
    vaccines saving lives is data-free speculation, not a fact.

    The claim that Covid vaccines are safe and effective is a lie.

    We are being lied to by the government … about Covid, Covid vaccines,
    climate change, Nut Zero, Russia, Ukraine, Trump Russia collusion,
    the January 6, 2021 events and many other subjects.

    The lies from government, especially under Bidet, are so frequent
    that even the CDC all cause mortality data could be “fixed”.

    Assuming EVERTHING the government tells you is a lie will get you much closer
    to the truth than blindly believing everything you hear from Washington DC

    Richard Greene
    Bingham Farms, Michigan
    https://honestclimatescience.blogspot.com/

  19. Richard Greene’s comment sure seems like a much more straight-forward, logical conclusion. It sure doesn’t seem like one needs to dig much deeper than that.

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