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Ben

@USMortality

May 30

💥 Bombshell: German RKI Admits No Negative Controls in Viral Genome Sequencing

The German RKI has confirmed they conducted no negative controls during genome sequencing of SARS-CoV-2 and measles viruses.

Negative controls—where identical methodology is applied to samples without the target virus—are essential for validating results. Instead, they relied solely on unvalidated PCR testing to confirm assumed presence prior to sequencing.
Full FOIA:

https://x.com/USMortality/status/1840501046910550277

This directly contradicts findings from my CDC FOIA request, which revealed that no sample has ever been sequenced containing only purified viral genetic material.

https://x.com/USMortality/status/1840501046910550277

Read 10 tweets

Ben

@USMortality

Mar 25

By 2020, ~90% of U.S. teens (12–17) were vaccinated against HPV — with no meaningful difference by race.

But look at cervical cancer mortality since the vaccine’s rollout:

🔺 Asian women: mortality increased
➖ Native American women: unchanged
🔻 Black women: biggest decline

Same vaccine coverage. Different outcomes.

If HPV vaccination drove the drop in deaths, we’d expect similar trends across groups.

This strikes at the core of today’s vaccination debate. Even if we accept—despite questionable evidence, such as the lack of decline among Asian populations—that there has been a reduction, it amounts to just 1.5 fewer cervical cancer deaths per million white females..

... since introduction. Yet nearly 900,000 per million are treated, reflecting a ~90% vaccination rate.

Read 7 tweets

Ben

@USMortality

Mar 4

🔥 MEGA ANALYSIS on #Measles #Outbreak: The Recent “Measles Outbreak” Scare Appears to Be a Big Pharma Campaign, Unsupported by Data!

Let's look at actual data & studies. Notably, before the introduction of the first measles vaccine ...
Mega Thread 🧵 1/n

... by Enders et al. in 1963, measles deaths had already declined by 97.2%, from 12,992 in 1919 to 364 in 1963—without vaccination. While deaths dropped to typically less than 10/year.

Proving causality of efficacy would require long-term placebo-controlled RCT's. The pre-vaccination trend from '49-'62 shows that both cases & deaths follow the expected trajectory -> the decline might have continued without vaccination.

Read 22 tweets

Ben

@USMortality

Feb 27

I’ve recently updated German data to use more user-friendly 10-year age groups instead of the previous 5-year intervals. The difference was surprisingly large, so I decided to investigate—digging deep into the code for about three days to verify everything. Here’s what I found:

Overview of Mortality Watch Data Handling

Mortality Watch integrates multiple data sources and can handle different temporal resolutions (yearly, monthly, weekly). A few months ago, I refined the logic to select the best available data for each resolution—e.g., using yearly data for yearly resolutions.

For these recent updates, I also added 5-year resolution yearly data:
• Yearly Deaths: These match exactly with the underlying dataset from Destatis, which provides official yearly figures.
• Yearly Population: This is reported as of December 31st each year. The simplest approach would be to join the data by year, but that would be inaccurate. Since the beginning of Mortality Watch, I’ve always calculated daily population estimates—breaking down deaths and population into daily values and interpolating as needed. This ensures consistency, as summing up daily data correctly matches the totals.

When comparing Mortality Watch population data with the Destatis source, Mortality Watch reports the mean of daily interpolated data. This may seem counterintuitive at first, but it provides a more accurate representation.

Verifying the Accuracy of Mortality Watch

To ensure my calculations were correct, I conducted a deep dive and even discovered a small bug. The issue, which had only a minor impact, affected age-stratified CMR calculations:
github.com/MortalityWatch…

To further validate the methodology, I created an example script demonstrating different approaches to calculating ASMR. The results vary significantly based on the method used:

1️⃣ Simple year joins (joins deaths of 2020 to population of 12/31/2020), using Mortality.org age groups (0–14, 15–64, 65–74, 75–84, 85+)
2️⃣ Simple year joins, using 10-year age groups (0–10, …, 80+) – Currently used on Mortality Watch
3️⃣ Daily year joins (interpolated daily population joined on daily death data (evenly split)), using 10-year age groups (0–10, …, 80+)
4️⃣ Simple year joins, using single-year age groups (0–85)
5️⃣ Daily year joins, using single-year age groups

As you can see, the red values match MortalityWatch figures identically, (besides 2023 🥴).
It’s good news that there aren’t any bugs in the calculations. It’s also important to be aware of the sensitivity of the methodology used, and that we should always use the highest resolution possible. This analysis demonstrates how different age groupings and interpolation methods can have a significant impact on ASMR calculations.

So here are the chart with the highest precision.

Since 2000. 3y reference and confidence interval with variance 1970-2019.

Read 4 tweets

Ben

@USMortality

Nov 27, 2024

🛠️ GUIDE: How to Spot Vaccine Fraud 💉

>> 5 Key Steps to Evaluate Any Trial Study <<

🧵A thread...

Evaluating the safety and effectiveness of vaccines requires careful scrutiny of clinical trial methodologies. Here are five essential steps to identify potential flaws or fraud in vaccine studies.

1. Double-Blinded, Placebo-Controlled Randomized Trials
These trials are the gold standard for eliminating bias and ensuring reliable results. Participants and researchers don’t know who receives the vaccine or placebo, reducing the influence of expectations on outcomes.

Read 17 tweets

Ben

@USMortality

Aug 20, 2024

💥💥💥 An official CDC FOIA response confirms that the validation of the SARS-CoV-2 genome has not been completed to scientific standards! 💥💥💥

CDC Unable to Scientifically Verify Full SARS-CoV-2 Genome, Leaving Potential for Semi-Random Construct.

🧵 A thread...

The response:

CDC has responded to my FOIA request, in which I have asked for records related to these four points:

1. Records on single virion sequencing of SARS-CoV-2 that ensured the virion was physically isolated from any other genetic material before sequencing.

2. Records of a single sequencing (long-)read from the first position [..] to the last position [..] of the genome. [..]

Read 17 tweets

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