Statistics

Quick Post Today: New Zealand Vax Data Analysis In Progress: Your Ideas?

I’ve finished an analysis of the New Zealand vax data. But since tempers are hot on this, and the subject is important, I want to be sure I haven’t missed something glaringly obvious. Which would be just like me. I was going to run the analysis today, but I decided to wait a day or two to think.

That being so, I have no other post.

Here’s a link to (one of) Steve Kirsch’s words on the data. It was Steve who asked me (a few weeks back) to look at the data.

Let me ask this: what do you, dear readers, see in any of the analyses out there? Strengths and weaknesses. Of the analyses alone.

We don’t here care about the politics of the thing. Yes, NZ arrested the whistleblower, and, I read, were originally denying him bail. I gather they might have weakened that. But whatever. That NZ is a tyrannical Expertocracy we already knew. Remember when their Regime claimed to be the only source of Truth? This arrest has no direct bearing on the data itself, except to suggest it is genuine.

In any case, like all statisticians, we take the data as it is given to us. And all analyses, like all probability, is conditional on the assumptions made. Data are always part of these assumptions. Data are the model.

Here’s Normal Fenton on the data: “The New Zealand vaccine data: what I actually saw and analysed and what the limitations are”.

My data, unlike Fenton’s, is not summary, but is record level: the whole data set (anonymized). Each date of each shot, and each date of each death (if any), for each person (identified by only by record numbers). There is also age and some other information on batch number and specific vaccines. But there is no no-shot data. That is, this is data only on people who have had a least one dose. People who no shots are not represented. The data spans about a two and a half year span, and all during the time frame of the panic.

There are no causes of death given: just death date for those who died.

There are many threads arguing about the data and the analysis, which are easy to find. Here’s one.

When I woke up this morning, too early, it was thinking of a particular feature of the data I do not think I explained adequately. Maybe. Or maybe I did.

So I decided to sit on this for a bit to try and see if I did anything idiotic. No small chance of that, my friends. As regular readers know, I do not care who I make happy or who I make angry with any opinion. Except myself. I hate to be wrong. Especially on something for which I have a blind spot but which is obvious to everybody else.

Meanwhile, I am eager to see if any of you have already considered any of this.

Regular posting recommences tomorrow.

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Categories: Statistics

23 replies »

  1. What are the implications of having no no-shot data? Wouldn’t that be the baseline against which you’re comparing excess death and adverse reaction rates to?

  2. I can’t comment in detail on Kirsch’s analysis (I’m sure it’s fine; he’s careful and rolls back when he’s wrong -he’s only been wrong in magnitude so far, but it’s a good signal), but I’ve been tracking excess deaths since Covid started. One of the things which bothers me is Sweden. Well, it didn’t bother me back in ~2021, that’s what I expected, but they’ve subsequently gotten vaxxed up and persist in not dying.

    https://ourworldindata.org/grapher/excess-mortality-p-scores-average-baseline?country=SWE~USA

    I guess one possibility here is early exposure to virums cancels out whatever baleful effect the vaccines have. It’s pretty hand wavey, but this really ain’t my department.

  3. An interesting development reported at The Daily Skeptic:

    “Kevin McKernan Loses Entire Database of Research Worth $200,000 After New Zealand Health Service Obtains Injunction to Prevent Sharing of Leaked Covid Vaccine Data”

    https://dailysceptic.org/2023/12/05/kevin-mckernan-loses-entire-database-of-research-worth-200000-after-new-zealand-health-service-obtains-injunction-to-prevent-sharing-of-leaked-covid-vaccine-data/

    “It appears that McKernan’s account was deleted by MEGA in response to an urgent injunction granted to New Zealand’s (NZ) Ministry of Health (MOH) to prevent the sharing of anonymised data leaked by whistleblower Barry Young.”

  4. The only 3 (2 + 1) things I have thought of since reading this a minute or so ago.. are:

    1 – if the data covers an extended period during which shots were administered you might be able to fake a control group (the no-shot people) by using the people who got their first shot late in the process – and therefore were unshot during the early part of the period for which you have data. Now if the later patients weren’t really different from earlier ones except for being later and their shot-to-dying times are similar…

    2 – how about comparing NZ overall pop demographics (cohort life expectancy) before covid to after covid/vax?

    sort of 3 – 1 additional death per thousand is actually pretty good – remember when Trump authorized the moon-shot effort to develop this he was told it would kill between 1 in 700 and 1 in 1000 (but that no vax would have death rates in the 50/1000 range).

  5. Paul Murphy: “1 additional death per thousand is actually pretty good”

    It is only pretty good if it prevents substantially more than 1 death in 1,000. Does it?

  6. regarding the shots killing off old people more, my friend was just telling me his uncle, 74, just died.

    he said he was always healthy. but he bought up the “news” and was getting every covid shot offered and then last week he went in for the flu/covid booster combo and a day or two later he felt like shit then he died.

    my friend’s dad (who stopped after the 3rd shot) said that he has been telling people their suspicious about what happened and he’s had like a dozen plus people say the same thing. their relative was older but healthy got the booster shot and then they got some sort of malady and/or died.

  7. Paul Murphy wrote:

    “1 additional death per thousand is actually pretty good – remember when Trump authorized the moon-shot effort to develop this he was told it would kill between 1 in 700 and 1 in 1000 (but that no vax would have death rates in the 50/1000 range).”

    There are lots of things I can force people to consume that only causes 1 extra death per thousand, Paul. That alone doesn’t make it “pretty good”. Think about it some more.

  8. Cloud buster and Yancey

    I do not think it saved more than it hurt – What seems to have happened is that the expectations about the vaccine were more accurate than the expectations about the disease. The virus itself mutated down (in lethality) very quickly and that was not expected. Had the disease not mutated You might well have seen the 5% predicted death rate even in a high healthcare country like the US. In that case the vaccine might very well have been an enormous success. Please read my entirely imaginary brief history of Covid on my winface.com website for more.

    Bear in mind, please, that we should evaluate decisions in terms of the information available at the time not in terms of hindsight. In hindsight, the entire public health response was wrong and should have been stopped by June or July 20 20. it wasn’t but that’s politics not science.

  9. I suggest using Survival Analysis, maybe distribution free Proportional Hazards with a Cox test, but always visualize the data first. Also, check the assumptions, such as the Excess Death Model, which frankly sucks. I mean, what if someone got the jab and died in a car wreck on the way home from the Jabitarium? Is that or is that not a vaxx-caused death?

  10. The virus was never particularly lethal. The CDC itself noted that 95% of the deaths assigned to Covid were among folks with over 3 comorbidities including motorcycle accidents.

    If it is true the Swedes are vaxxed and not dying, perhaps they were sent saline shots. They may be a setup to show the shots were safe. Hmmmm.

  11. Glad you’re on the case, WMB. Clearly, NZ.gov is in a panic over this. If data shows jab safe why not release all data? “We’re concerned about potential breech of privacy”, NZ.gov claims, which is an obvious and laughable lie. Maybe the data is a nothing burger. But that’s not how they’re acting. Why so frightened?

  12. Paul Murphy, do you have any links for this: “[Trump] was told it would kill between 1 in 700 and 1 in 1000…”. I searched, couldn’t find anything. Thanks.

  13. Available from Brasil we find both record level vax data base, and record level all-cause mortality data base:

    https://opendatasus.saude.gov.br/dataset/covid-19-vacinacao – (jan 2021 to present)
    https://opendatasus.saude.gov.br/dataset/sim – (1979 to April 2023)

    OF COURSE, no direct linkage is possible, as that would be TOO EASY! You can risk using a proxy index, by combining date of birth, sex, race, municipality of origin.

    Anyway, there´s enough info available to show that the VAX WAS TOTALLY USELESS, just by time-series plotting all the doses and all the obits.

    Population data is still pending from the 2022 census, but yearly estimates are available by age and sex and municipality of residence for year 2020. For example, in my home state, at least 109 percent of all 80+ year-olds were vaccinated! Talk about efficiency!

    As to the VAX deadliness, THERE IS NO OBVIOUS EVIDENCE, though many interesting things are observable,
    particularly if you use another data base, the SARDS, which actually has record level vax status for hospitalization. It shows what a fine job the Pfizer Pediatric Vax does in sending young children to the hospital! But it doesn´t seem to kill them:

    https://opendatasus.saude.gov.br/dataset/srag-2021-a-2023

    I´m totally incompetent to analyze this stuff. Anybody interested?

  14. P.S. Like I said, I´m totally incompetent, and in terms of math power, no match for Kirsch (or Briggs). But philosophically speaking, in my opinion, Kirsch is overblowing the DANGER aspect. I see it as a matter of SCALE. If you forced the entire world to eat SHRIMP NOODLES, you might easily same the same mortality spikes (except in places were shrimp noodles are yummy).

    We should focus more on the WASTE OF MONEY.

  15. “I predict I make no one happy.”

    That you don’t lie for an agenda makes some people happy. Kirsch seems to think he has a solid case. Others are urging caution. I like that Kirsch wanted your naysay. Let the chips fall where they may.

  16. After viewing Kirsch’s presentation, I can see why you’d want to make sure you don’t have any blind spots.

    He’s got a huge one that I thought I was catching throughout the presentation, but wasn’t plainly obvious until the very end.

  17. Governments keep measuring in “deaths” and deaths saved. An absolutely mad* measure. All lives are lost; there are no exceptions. The only differences are when. So then the proper measure is Quality-Adjusted Life-Years lost. When I thought of this metric, at the start of the fraud, I thought to myself ” I bet that measure already exists.” so I looked it up and of course it did. It was, of course, already a real thing. So why has it not been used to measure impacts of ALL governmental actions? Death is not the only, or in the last 3.7 years the major destroyer of QALY. In New Zealand the loss of QALY due to the governmental crimes is over 100 times that which would have occurred from the government doing nothing except to say “If feeling ill stay home.”.

    * Not really mad just a measure to further the Ardern agenda.

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