Is NSW starting to show negative efficacy for Covid vaccines?
3+ dosed over-represented in deaths and hospitalisations
In Australia, Covid hospitalisations and deaths are recorded with Covid, not of Covid, so the data are muddy. Neverthless, working with what we’ve got, NSW Covid-19 surveillance reports might be starting to show nil-to-negative efficacy in the 3+ jabbed population.
In the most recently published NSW epidemiological weeks (24 and 25), the 3+ dosed cohort are over-represented when it comes to Covid deaths. Below, orange indicates the % of the population with 0, 1, 2 or 3+ doses of Covid vaccination. The red columns indicate the % of Covid deaths by dose rate.
The 0 dose rate shows slight-to-moderate overrepresentation in deaths. This is to be expected if Covid vaccines prevent death. Given the catastrophic warnings issued by government and media, I would have expected the over-representation to be much higher in the 0 dose group. But what is really quite stunning in the below graphs is that the 3+ dose group is also over-represented in Covid deaths. In fact, compared to the 0 dose group, they’re faring about equal.
If I had 4 jabs, I’d be wanting my (tax) money back with graphs like this.
This is just two weeks, and they could be the anomaly. So I went back a few more weeks and pulled data from epidemiological weeks 21-25.
The over-representation for the 0 dose group is a tad higher here for Covid deaths, which again, you would expect if the Covid vaccines prevent deaths. But what the heck - deaths in the 3+ group are still over-represented. Aren’t you supposed to get better outcomes the more you vaccinate? If anything, from the below graph and the individual week-by-week graphs above, 2 doses is the magic number.
I also tallied up epidemiological weeks 21-25 in terms of hospitalisation (general + ICU combined) to see who is “clogging up the health system”, to use that ugly phrase that pro-mandate social activists so love to throw around. Turns out, it’s overwhelmingly the 3+ dose group taking hospital beds, over-represented again. By this metric, the 0 dose group is actually the least strain on the health care system, accounting for just 9 out of total 2, 692 hospital cases over weeks 21-25.
As a lay person with no training in public health data analysis, this exercise serves to raise questions moreso than to provide answers.
Some queries that I have include:
How does age stratification interact with dose rate stratification?
What are some possible explanations for the very high representation of the 3+ dose group in Covid hospitalisations and deaths in the past 5 epidemiological weeks?
Why are hospitalisations for the 0 dose group so low?
What is the Unknown category and how does it affect distribution of hospitalisations and deaths across dose rate cohorts?
What are the longer-term trends (going back, say, 3-6 months) for hospitalisation and deaths by dose rate?
How does NSW compare to other states, and what are explanations for similarities or differences?
It would be great to hear some expert opinions on these data. I have attached my excel tables of the relevant data points below for anyone who wants to check it or have a play with it themselves. All data sourced from NSW Health. I used dose rate statics as at 30 June 2022. If you know someone who can add to the conversation, please forward this on.
Good work Rebekah... Bear in mind too that the "unknown" and "no dose" have been swapped int he NSW data. It is not possible that the unknown would have such a low rate of death compared to their ICU and hospitalisation rates. I am not sure when this happened but I have all the reports archived.
The lack of hospitalisations for the no-dose category means these people died at home or in a nursing home. I suspect it means these people were elderly and deemed too frail to withstand the shots. This inflates the no-dose deaths category with people who were going to die anyway. Government health departments have health statistics analysts who must know the shot have failed and are keeping quiet or being suppressed.