The Healthy User Bias Dismantles the Vaccine Effectiveness Debate

Shattering the Efficacy Illusion, Part 1

"The single biggest problem in communication is the illusion that it has taken place." -George Bernard Shaw

Articles from the Vaccine Wars can be found here.

My original plan was to publish this as one massive article. I’ve been working on it for quite a while, but new information and new data keeps finding me, adding to the story. The current text spans just shy of 60 pages, and there are still numerous gaps to fill in, and graphs to produce. So, I’ve made the decision to publish this in chapters. When it’s done, I’ll make some edits and publish it in one complete article to make sharing it easier. Often, over so many pages, RTE readers have pointed to evidence I haven’t seen or made additional points worth including. After that, I’ll consider whether or not it can be expanded into a book on its own.

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Dismantling the Vaccine Effectiveness Debate

This is one of the most important articles to read for anyone interested in understanding whether or not the biological therapeutics usually referred to as COVID-19 vaccines are effective in preventing COVID-19. This is one you should forward (actually, maybe wait for the full article!) to those who meet that description, regardless of where they fall on the debate.

If you have come here, but do not fall on the same side of these debates as I do, understand that, for me, this is an open invitation. This article is long, and I'd ask that you read sections, then meditate on the information. Try to relax your mind away from medical partisan bickering thrust at us like a firehose so that you can test the reasonableness of claims made for and against the arguments I present with the calm and deliberate side of your mind.

For those who want a video primer on this conversation, I like at the bottom of the article to a presentation that I gave to Pandata in January that focuses primarily on the Healthy User Bias (HUB). Just know that this article goes into far greater detail, and I'd rather that most people battle through that detail because it goes much deeper than my presentation.

Here is the punchline up front: Using the CDC's data for all 3.141 U.S. counties, the only correlation between vaccine uptake and COVID-19 mortality is fully explained by the fact that healthier people are more vaccinated.

This chart shows income correlation, not "health", but we'll get to why wealth is health…

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What is the Healthy User Bias (HUB)?

"The eyes only see what the mind is prepared to comprehend." -Robertson Davies, Tempest-Tost

If you read Wikipedia's overly brief article, you get a vague story that HUB "can damage the validity of epidemiologic studies" due to the fact that "subjects that take up an intervention, including by enrolling in a clinical trial, are not representative of the general population." The single citation and several additional links for further reading betray the importance of the HUB, and the HUB has perhaps never been more important to understand than during the COVID-19 quasi-vaccine rollout.

I'll take a crack at a more succinct statement:

The Healthy User Bias (HUB) is the bias observed in medical intervention studies due to the recipients being healthier on average.

One confusing aspect of the HUB is that it often gets broken down into two other biases:

• Healthy vaccinee bias, and

• Confounding by indication.

This makes it harder to search and absorb the full array of literature on the topic. Vaccine manufacturers probably aren't working hard to sort any of that out for watch dogs.

The two component forms of bias sound almost nothing like one another, but they describe something very similar with respect to vaccine effectiveness research:

• Prior health variable: The healthy vaccinee bias describes the fact that (in aggregate) healthier people are more likely to seek vaccination.

• Subsequent health variable: Confounding by indication describes the fact that people who seek a health intervention (vaccine or other) are more likely to take better care of their health moving forward.

The difference between these data bias types is whether health effects from before or after the point of intervention confound (bias) the results. If you're thinking that these variables probably have substantial overlap, you're most certainly correct. Fortunately, that won't affect our primary observation.

One more definition:

The Zero Effectiveness Hypothesis (ZEH): the hypothesis that the so-called COVID-19 vaccines confer zero benefit with respect to COVID-19, and may result in increased cases or mortality.