The difficulty in calculating the benefit/risk of the Vax

We are bombarded with government-sponsored propaganda telling us to get (and possibly threatening us if we don't get) the COVID Vaccine.  I'm a firm believer that people who want to take it should, and people who don't want to shouldn't.  But as someone trained in the Scientific Method back at State U (Electrical Engineering, thanks for asking), it would be nice to have some better data to help make this decision.  What are the (quantifiable) risks, and what are the (quantifiable) benefits?

First, a discussion on quantifiability.  Isegoria (you do read him every day, don't you?) posts something that I'd never heard about but sure wish I had:

NNT is an abbreviation for “number needed to treat.” In other words: How many patients must be treated with the drug in order for a single patient to get the desired benefit?

When you read about drugs in the news — or even in most medical journals — you will almost never be explicitly given the NNT (which I will explain in more detail below). Instead, you’ll get relative risk reduction, a metric that a Michigan State med school dean once told me “is just another way of lying.”

[…]

Here’s a fictional example:

You read that a new drug reduces your chance of dying from Ryantastic syndrome by 40 percent. Here’s what that means in practice: if 10 in 100,000 people normally die from Ryantastic syndrome, and everyone takes the new drug, only 6 in 100,000 people will die from Ryantastic syndrome. Now let’s think about it from an NNT perspective.

For 100,000 patients who took the new drug, four deaths by Ryantastic syndrome were avoided, or one per 25,000 patients who took the drug. So the NNT is 25,000; that is, 25,000 patients must take the drug in order for one death-by-Ryantastic to be avoided. Ideally, you also want to know the NNH, or “number needed to harm.”

Let’s say that 1 in 1,000 patients who take the new drug suffer a particular grievous side effect. In that case, the NNH is 1,000, while the NNT is 25,000. Suddenly, the decision seems a lot more complicated than if you’re just told the drug will lower your chance of dying from Ryantastic syndrome by 40 percent.

So one patient in 25,000 will see a benefit and one in 1000 will see a harm.  Thank you very much, but I'll take a hard pass on this hypothetical scenario.

So what are the NNT and NNH for the COVID Vaccine?  The data are elusive here.  As far as I can tell the NNT data are entirely opaque - there doesn't seem to be any way at all to calculate this from published sources (please post links to sources you know in the comments).  But Peter shows that there are very troubling data on NNH.  Very troubling:

A recent analysis by researchers at Queen Mary University in London found that even in senior citizens, about 85% of deaths reported to VAERS were definitively, likely or possibly caused by the vaccine. Moreover, due to significant under-reporting, the true number of vaccine-related deaths may already be significantly higher, possibly in the range of 10,000 to 50,000 deaths in the US alone.

Indeed, despite very few covid deaths, there continues to be unexplained excess all-cause mortality in all US age groups below the age of 75, with all-cause mortality having reached record levels in age groups below 45 since the beginning of the vaccination campaign.

. . .

Below a certain age, covid-related mortality is so low that covid vaccines are bound to kill or severely injure more healthy people than they save. In the US, this age threshold may be close to 40 years, while in some Western European countries, it may be as high as 60 years (for healthy people).

It has been argued that vaccination against covid may at least prevent “long covid” or multi-system inflammatory syndrome (MIS) in children and young adults; however, new reports from Israel and the US indicate that, to the contrary, covid vaccines may themselves cause MIS as well as “long covid”-like conditions, often lasting for months or possibly even longer

It is often said that the plural of "anecdote" is not "data", but all of these sure point in the same direction, towards a higher NNH - and in particular, a higher NNH for age groups below 75.  The younger the age group, the seemingly higher the NNH.

I find the NNH concept to be extremely useful in making a benefit/riskdecision, even in the absence of solid NNT data because you are holding the (unknown) numerator (the NNT) constant while you increase the denominator (the NNH).  What you know with some certainty - at least if you have some confidence in the Harm data from the VAERS database is that the benefit/risk number is declining as you do this.  Likely it declines by a LOT, particularly for younger age groups.

Now, a note about NNT for Covid.  We have seen many studies comparing case rate between regions that had severe government-imposed lockdowns and regions that had none.  We saw this comparison between different US States as well as different member countries in the EU.  There was no correlation at all between lockdown harshness and case rate.  None.

I would love to see a similar comparison between vaccination rate and death rate.  My guess is that there is little or no correlation, and may in fact be negative.  That's a guess based on the elevated excess death rate but some or all of this could be due to non-Covid impacts from the lockdown: elevated suicide or overdose rates from people forced into house arrest.

I highly encourage you to read both Isegoria's and Peter's posts.  As I have been pointing out (for a dozen years) on Climate Change, the data will set you free but it can be some work to unpack the folded, spindled, and mutilated data sets.  The analogy with Covid is uncomfortably close: for both Global Warming and Covid the governments seem to be highly motivated to use these "emergencies" to increase their control over the economy.  In both cases the data are at least partially suppressed, and are certainly obfuscated.  In both cases the government's allies in Big Tech are actively censoring discussions deviating from the government's position.

But VAERS seems to be both hard to censor (and any censorship would be highly suspicious indeed) and extremely helpful towards calculating NNH.

UPDATE 27 OCTOBER 2021 11:24: More interesting data: Covid hospitalizations for children down 56% since schools opened.  This suggests pretty strongly that the vax NNT for children is a lot lower than Dr. Fauchi and the CDC are telling us.