Why you can't trust the US government data
In this article, I will show you why government data is not trustable. That is why I personally prefer anecdotes that are verifiable when trying to figure out what is really going on.
Executive summary
The point of this article is to show you why US government data cannot be trusted as being accurate.
I’m going to show you how you can show that the CMS Nursing Home Data, which is supposed to be the gold standard, is crap with just 3 examples.
Basically, the rule I use is if you cannot independently verify the numbers they are telling you are correct, you should not believe the numbers are correct.
This is why I prefer data, such as anecdotes, where you can independently verify the evidence with multiple sources.
Record-level data can often enable you to validate the data is accurate; this is why governments try to avoid releasing this type of data
Do you know why the record level public health safety data on vaccines isn’t publicly available? Until recently, the US government never released record-level data on anything regarding public health other than deaths. But that’s not helpful because you can’t trace it back to any causes like a vaccination.
In general, you only get to see the data summaries they want you to see; you never get to see the “source code” so to speak.
In general, the reason you almost never get to see the record-level data is because it would expose two things:
They are lying to you about the safety and efficacy of the vaccines. There are many nursing homes showing data that clearly shows the vaccines are disaster in nursing home after nursing home where the weekly death rates skyrocketed right after the vaccine rollout.
Some of the underlying data used in their analyses is very unreliable and can completely skew the aggregate numbers, such as COVID deaths > COVID infections or thousands of deaths in a facility with just 50 beds.
In short, it would destroy confidence in their narrative.
When the record-level data is available, you can actually see whether it is self consistent, something that you cannot do with just the summaries that they normally give you
But sometimes they screw up and make the data available. A perfect example is the CMS Nursing Home Data which exposes the data we’ve all been asking for: record level data where we can validate/invalidate the data.
They created a portal for showing COVID-19 cases and deaths, but it also tracked all-cause mortality and deaths and they made it public for everyone to see. Not quite at the record level data of each death, but at the weekly summaries of COVID infections, COVID deaths, and all-cause deaths at each facility.
For me, it’s a target-rich environment. Never before have we been able to see record level data on anything. Now we can.
And as I wrote earlier, the Nursing Home Data shows the COVID vaccine is a disaster.
Today I’m going to poke into the CMS data and show you three examples of why you cannot trust the data they are telling you to trust.
Data transparency
Data transparency is key to better health outcomes.
Sadly, to date, there isn’t a governor or legislator on the planet who believes in data transparency enough to sponsor a bill to force the record-level public health data to be made public.
But there is a guy running for President of the US named Robert F. Kennedy Jr. who believes the public deserves the right to know the truth.
If you want to see the health of America and the world improve drastically, he’s our best hope.
Now for the data that proves my point….
About the CMS data
Download it while you still can:
Medicare Nursing Home data download link (official mortality data for all nursing homes in US)
The 3 examples can be found in this dataset. There were lots more than 3 examples of course.
Example 1: More people die from COVID than got COVID
For provider #315506, from the start of 2021 until 3/8/21 (essentially for the first 2 months of 2021):
20 COVID cases, but 58 COVID deaths.
In other words, there were nearly 3X as many deaths from COVID as there were COVID cases.
That’s impossible: you can’t die from COVID if you don’t have COVID.
But the data passed all QA checks.
Example 2: More people die at a facility than actually reside there
Look at Provider #396122.
There were 131 deaths in the first 3 months of 2021. Yet the number of occupied beds stayed nearly constant at 50 beds over the 3 month period.
So we’d have to believe that the facility had an 83% death rate per month (relative to average occupancy during the period) for 3 months straight and were able to instantly replace anyone who died?
Example 3: Thousands of patients died at a tiny facility in 2020 (just 69 beds with 68% occupancy)
This is my favorite example. Facility #235601.
In the last 7 months in 2020, this facility had 2,584 total deaths which included 9 deaths from COVID (from 48 cases, a 19% death rate from COVID).
Here’s the impressive thing: the average occupancy throughout the period was just 47 beds and it stayed pretty constant throughout the 32 week period. There are just 69 beds in the entire facility.
In other words, on average, everyone living there died, on average, every 4.2 days and they were immediately able to fill the empty beds every week.
This facility passed all the Medicare QA checks.
Single facilities like this with such large death numbers throws the aggregate data off.
And this was not the only example of a small facility with thousands of deaths in 2020!
Summary
In this article, I showed three examples of how data that is supposed to be reliable is anything but reliable. This means it is best to mistrust US government data where we cannot independently validate what is being reported is true.
This is why, for COVID, I prefer to use multiple independent anecdotes that are 100% independently verifiable when trying to figure out what is really going on.
I hold the same perspective as you. Generally speaking, if a phenomena is real and significant, it should demonstrate a large deviation from the expected mean. In many cases, this does not occur (e.g. a drug which reduces your chances of a heart attack by 5% over 20 years or increases your chance of survival from sepsis by 3%) and it requires a lot of data to know for certain if the effect is real. One of my dissenting opinions from the general medical field is that changes which have a relatively small magnitude of importance are not worth prioritizing your focus on; however, our entire medical research apparatus revolves around detecting these effects. Conversely none of that is needed to detect effects with a relatively large magnitude.
In the case of the COVID vaccines, it was clear to me within a week of them being on the market that they were the most dangerous pharmaceutical I had ever come across; I was regularly hearing about adverse reactions to them, whereas in the past with even the most dangerous drugs I know of, I would only periodically hear about severe adverse reactions to them. At that point I assumed everybody would probably lie about the injuries to try to save face, and I was better off gathering anecdotal data rather than attempting to go through the official channels. I essentially started on substack because I compiled all them onto a list that you then promoted to the Internet and made go viral.
https://www.midwesterndoctor.com/p/adverse-reactions-to-covid-vaccines
I spent months of work putting that list together because even though I knew it could never be published, it was the most that I could do in my position and I felt the public needed to have as many early warning signs as possible. At the time and list came out, virtually every single conventional person ridiculed it as being anti-scientific and not at all supported by the data. Now two years later, most of what I put in there is becoming in general knowledge within the medical profession and many others have since reported identical samples to the one I put forward.
It's really sad things like what I did are necessary because science has gone from being about truth to politics, but that's the way things are now. Until we make a fuss about it and stop giving our unconditional trust (and more importantly money) to the scientific community, I don't think it's going change.
If you have a barrel of sewage and add a teaspoon of fine wine, you get sewage.
But, if you have a barrel of fine wine and you add a teaspoon of sewage, you still get sewage.
That ^ is an accurate representation of any information that is contaminated with government data. A raging starving pack of wolves is more trustworthy than anything coming out of with Washington propaganda giga-factory.
With Obama's Repeal of Smith–Mundt DARPA Has Been Running Nonstop PSYOPs On Americans for A Decade: https://bitchute.com/video/1vDlFo8GRqxB [1min]