A miscarriage of statistics: The thalidomide sequel

Written by Dr Ah Kahn Syed on April 5, 2023. Posted in Current News

Proof that the miscarriage rate after the COVID vaccines is far higher than the real background rate and how the pharma corporations tried to hide it

This story is not going away, however much the pharma companies and their vaccination-in-pregnancy foot soldiers want it to.

The recent revelations of the #Placentagate scandal has brought it to a head, and we are going to keep gnawing away.

Because I know that everybody’s attention span is short I am going to put a summary here for this article.

The normal miscarriage rate of a healthy population is somewhere around 5-6 percent. 
For years, studies have been misrepresenting rates of 15-20 percent so that when new drugs are tested in pregnancy,
a doubling of the miscarriage rate will fly under the radar. 

This is how the COVID vaccine managed to be sold as "safe in pregnancy" when it was far from it.
This article shows you how the data was misrepresented by the CDC and others to cover up the scandal.

In a change of tack I’m also going to try and break up the article into sections with lay summaries at each point. Let me know what you think in the comments.

First, a recap

In the last article I showed you that the pharma companies had been abusing miscarriage statistics for decades. Essentially they ensured that miscarriage rates were recorded as higher than they should have been, using various methods but including “survival analysis” which can overestimate risk.

Why would they do that? Well, in order to confuse you, so that when a drug hit the market with a higher than expected miscarriage rate they can say “oh it’s normal”.

I provided a preliminary model that was able to compare the published Naert 2021 (pre-COVID vaccine era) analysis of miscarriage rates to the CDC’s V-safe (COVID vaccinated cohort) miscarriage rates as discussed in depth here.

Because some people’s minds work pictorially lets put a face to the studies:

You can therefore think of the Naert cohort as the control group and the Zauche dataset as the vaccinated group – of course that’s all we have because the CDC chose not to invite a control group to their study (I wonder why).

The Naert dataset is the biggest (to date) publication of miscarriage data where the authors were able to follow a whole cohort of pregnancies.

Although it is a retrospective study (looking back after collecting data, traditionally susceptible to bias) it attempts to recreate (model) a prospective study on the basis that all patients were included and it draws conclusions about the miscarriage rates as if you presented at 6 weeks of pregnancy.

That’s the closest we have come to a prospective study in pregnancy – in order to do a real prospective study you would need to recruit thousands of women and follow them up for years (which is why it’s never done!).

One criticism levelled against the Naert paper (once the world of Pharma-affiliated doctors realised that it could expose them) is that it ignores pregnancy losses prior to 6 weeks. This is true, and that is because it is impossible to measure these.

These include women who go for medical terminations and those that have a positive pregnancy test one day and then it’s negative the next. These wouldn’t normally be registered as pregnancies in any clinical scenario so they are mostly excluded from any studies.

However, Zauche (who collected the V-safe pregnancy data for the COVID vaccine registry) also removed these patients from her analysis.

The Naert (independent, control) and Zauche (CDC, vaccinated) cohorts should be able to be directly compared with some adjustments as I go through below.

For this analysis I am going to assume that the distribution of miscarriages in the Zauche data is simply due to the way they have been allocated, but we cannot exclude the possibility that the large rise in miscarriages in week 8 & 9 is due to the impact of the COVID vaccine

So let’s get to it. I’ll try and summarise each section as we go…

1. Zauche overestimated the miscarriage rate in her first paper

In Lauren Zauche's first paper she estimated the miscarriage rate at 14.1 percent, 
for 165 miscarriages out of 2217 pregnancies (7.4 percent). How is this possible?

I’ll try and go over this again because it’s difficult to understand why such a simple calculation can be so complicated to present.

Essentially, they are purposefully double-counting.

Here is the table from the early Zauche paper

If you don’t want to do the calculation yourself it’s 165 SAB’s (miscarriages) and the total number of pregnancies in the number at risk at the end of the study period (2052) plus the 165 that miscarried, i.e. 2217.

The “number at risk” in survival analysis, which is the way this was then calculated, is the number of people remaining at any one time, because some people have been lost to follow-up. In the analysis of cancer data that is usually because they have died or recurred. In this case it’s because they have miscarried.

In order to get to a 14.1 percent overall risk of miscarriage what the authors effectively did was add the percentages for each week

To avoid switching between use of “raw risk” and “survival analysis risk estimate” all we have to do to compare the rates directly is treat the cohorts in the same way. Simple eh?

Annoyingly the two cohorts are distributed differently by week but we can assume the following:

Because the rate of miscarriages jumps at week 8-9 it is assumed 2 that the time point at which the miscarriage happens in the Zauche (vaccinated) cohort is the week of gestation in the table.

Conversely, in the Naert (control) analysis it is assumed that the time point at which the miscarriage happens is irrelevant and the miscarriage is attributed to the week that the woman presented for study.

It actually doesn’t matter that the distribution of miscarriages by week (e.g. of 100 miscarriages there were for weeks 6-12: 20,20,15,15,10,10,10 per week or there were 10,10, 30,20,10,10,10 per week) because we are going to match them.

In the analysis below what I have done is taken the whole cohort of pregnancies, and distributed the miscarriages in exactly the same way in accordance with Lauren Zauche’s distribution.

That is, the number of miscarriages remain the same as in the publications but the way they are distributed through the weeks is now made to match up between the cohorts, so there is no confounding due to reporting technique. These are the new “modelled” data sets (in blue and pink):

The eagle-eyed of you will hopefully have noted the green column, which we will get to soon. For now we can plot (as we did before, but with much better matching) the two cohorts on a survival-style curve (remember that I don’t personally approve of this technique here, but we need to match the cohorts)

You should be able to see from this survival analysis-style comparison that the Zauche cohort does a lot worse. In fact we can quantify this with a Hazard Ratio which in this case is 1.5 – roughly that there was a 1.5x increase in miscarriages in the Zauche cohort than the historical Naert cohort.

The p-value on this calculation was 0.001 meaning that it was a one-in-a-thousand chance that this was a random event.

Section summary: 
In a properly matched comparative analysis between a contemporary published historical cohort of miscarriages and the early CDC (post-vaccine) data set
there is a clear increase in the risk of miscarriage in the vaccinated group

2. Zauche never published the follow-up data, but presented it to the CDC

Now comes the interesting bit. As this model is now comparable between cohorts we can again look at Zauche’s later data where she presented to the CDC the miscarriage data some months later. In that presentation there were an estimated 775 miscarriages out of 6,352 first trimester pregnancies 3.

To our knowledge this data has not been formally published. There is no logical explanation as to why this data was not published given that the original Shimabukuro paper came out within weeks of the first pregnant patients being injected.

Plugging these numbers into our model we get a “raw” miscarriage rate of 12.2 percent (775/6352) which you were told was “within historical limits”….

But hang on, that is a “raw” rate not a “survival analysis estimate” rate.

And it’s jumped. A lot.

Just to recap. The "raw" miscarriage rate is the % of total pregnancies that end in miscarriage. 
This should be around 5-6 percent. It's what most people would think of as the miscarriage rate. 

The "survival analysis estimate" rate is an overestimate of the miscarriage rate arrived at by (inappropriately) using cancer survival analysis
methods to miscarriage data. 

The two methods are so different that it is not possible to compare them, but if you wanted to hide a "raw" rate that was 10-14 percent instead
of 5-6 percent you would say it matched "historical rates" whilst forgetting to declare that the historical rates were calculated with the higher
rate method. 

So now what we can do is show you 4 what those figures look like if you make the necessary adjustments to match the cohorts up and do a similar survival analysis. This makes the cohorts directly comparable.

The pink curve is the original Naert (pre-vaccine) cohort and the green curve is the final Zauche CDC (post-vaccine) cohort presented to ACIP in October.

Now you see the green curve falling off much more dramatically than either the Naert historical data or the earlier Zauche preliminary data.

To see how significant the difference is we can compare them all on a forest plot which shows you the comparative “hazard ratios”, i.e the relative risks.

Now this time we see that the “Full” V-safe (COVID vaccine cohort) data, which was delayed and presented by Zauche in October has a hazard ratio (risk of death i.e. miscarriage) of more than double the “historical” data of Naert.

This is taken from a long document. Read the rest here substack.com

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