ONS Vaccination Deaths Analysis (part 2)
Another poke at the vital dataset that should be error-free but is not
In part 1 of this series I derived crude mortality for the vaccinated and unvaccinated sub-populations of England over the period Jan 2021 – Dec 2022 using data from table 1 of the file of files. This slide was a bit of a shocker and many Twitterlanders decided to ignore what I’d written. Here are some cautionary words again:
A quick reminder that what we are looking at in this final slide is crude mortality and crude mortality (deaths per 100,000 population) doesn’t take account of important confounding factors like age and health status. We see a difference and that difference may be due to vaccine harm and it may not.
So what we need to do next is consider age standardisation, there being a number of ways we can go about this. The simplest method is not to standardise at all but to look at what is happening within age groups. The ONS, in their wisdom, decided to provide counts for the 18 – 39, 40 – 49, 50 -59, 60 -69, 70 -79 and +80 year groupings. This is a start but the 18 – 39y group is rather coarse and we don’t know anything about deaths in teenagers, kids and nippers despite the vaccine being authorised for use on souls as young as 5 years. A slight oversight there methinks, and it has got to the sorry state that I wonder what they’re hiding on behalf of the State.
The Closet
Two other things that the ONS are hiding are deaths for those who took the fourth booster and beyond (these are not included in the pointless breakdowns) and deaths for those who cannot be linked to the 2021 census data. Here’s screenshot of data note 14:
While you are at it take a peek at data note 17. It sure looks to me like they excluded a whole bunch of people who died shortly after vaccination of which they managed to link only 1,029. So how many others did they leave out who were not linked? We don’t know, and neither does the head ONS honcho who is waiting for that number from her team. Thankfully there are stalwarts like Josh Guetzkow on the case and I recommend you follow him on Twitter.
But there’s more…
What Population Is That, Then?
Back in part 1 I referenced an article by Igor Chudov that revealed excess mortality among the boosted is obscured by under-counting of the unvaccinated. Igor reckons the ONS understates the number of unvaccinated people by about 2x (depending on age category) and that got to me thinking about those person years figures that the ONS provide in tables 1 – 4 in both the first release of this data back on 6 July 2022 as well as the current release dated 21 February 2023 (corrected 22 February 2023). If we add the person years for vaccinated and unvaccinated all cause deaths together for each month we can calculate the percentage of unvaccinated souls and plot out a time series. For reference we can add the percentage derived from the UK GOV coronavirus dashboard variable entitled cumulative people vaccinated 1st dose by vaccination date, and we can add official rates from NIMS. Here’s what all this looks like:
It transpires that the ONS have been using a population cohort that has been falling way short of the mark for both releases of this vital mortality dataset, with significant underestimation of the unvaccinated sub-population. Joel Smalley has commented wisely on this below, pointing out that the ONS database is a specialised subset and not truly representative of the entire nation. This fact alone means the latest results cannot be extrapolated to the national level but, of course, the legacy media types and usual experts aren’t going to let this stop them bellowing “safe, safe, safe!” from all steeples. A quick look in the mortuaries of England will soon dampen that ardour.
As it stands the overall mean for 2022 fetches-up at 22.1% using the UK GOV dashboard estimate, at 27.2% for the NIMS estimate and just 14.9% for the ONS second release. That’s an astonishing extreme difference of 12.3% between the benchmark NIMS and paltry ONS efforts representing a loss of around 7 million unvaccinated people in ONS rate calculations. Igor reckons on a factor of x2 and these calcs suggest x1.8… good one Igor!
Will this swing the results significantly? You betcha! Does it mean that the ASMRs provided are imaginative nonsense? You betcha! Do senior officers at ONS realise this? You betcha! It shouldn’t come as a surprise to learn that officers of the Crown can be directed to tell rather big porky pies on behalf of the State (been there, done that, got the carrier bag).
Tea Break
I’ve been up since 3:59am going through all this, so it’s high time for a tea break. Mrs Dee has just this minute delivered a hot lemon, ginger, raw honey and fresh thyme reviver in my favourite mug so I’m going to sip this whilst planning my next output. I may well have a look at the corrupt ASMR figures the ONS provide and compare these to some home-baked MR figures using deaths and person years. Yes, I know we’ll be back in the land of confusion and confounding biases but it’ll be good to get a grip on what age standardisation has been doing to the crude (person years) mortality rate.
Kettle On!
Thanks for having a look at this! I wonder if it is a waste of time trying to glean anything useful from it given Norman Fenton and Martin Neil's post, in addition to El Gato Malo’s post, on how obviously lame the ONS data is. It seems to me that trying to undo unknown data processing is going to be problematic and could easily lead to imaginary results.
Although, if one could demonstrate the obvious problems with the data as Fenton and Neil did in their letter to the head of statistics, that makes sense because then "we" can write another letter.
I think it's important to note that what this really tells us is that the ONS dataset is biased, i.e. it is NOT a true representation of the population and that's all. It's not that they have underestimated the unvaccinated population, it's that their dataset, given the way it is filtered from the whole population, results in a subset that is not representative. In other words, it cannot be used to make inferences that apply to the general population. As such, it is not really useful for anything and doesn't really show anything one way or another! Unfortunately, this important fact did not make it down to the mainstream media hacks who used it to claim victory over us pesky anti-vaxxers! Meanwhile, people are still dying...