Tag: statistics

Government, Health

Crossing The Minefield

Because I have some time on my hands, and I’ve got my tinfoil hat shaped just right, I’m going to add an update to my previous (poorly worded) post on leaky vaccines. This one is about risk assessment. I think we’d all agree that each individual should be able to make an informed decision about the risks of getting or not getting the vaccine or booster. In a perfect world we’d see all the pros/cons and decide what’s right for your particular scenario. But what if you weren’t told all the info?

(adjusts hat) Ok, here we go. It’s starting to look like after your first dose of the mRNA vaccine you go through a roughly two week window of a highly immune suppressed state. The reasons why are not clear yet. The most likely cause seems to be that the mRNA vaccine has to turn off the toll-like receptors so the vaccine can enter the cells without being attacked by the immune system. Other options are a fall in lymphocytes and neutrophils that are seen three days post vaccine. Regardless of cause, it appears likely that you are in a highly immune compromised state for several weeks after your first dose. This does not happen after the second dose.

The impact of this is a significantly increased death rate post first dose. This was seen clearly in the Israel data and now in the Palestinian data since they are just now getting their first doses. Going back to Israel, we see the same corresponding rise in death rate following the booster.

Just to be clear, people are NOT dying from the vaccine. They appear to be briefly in a highly immune compromised state and then get covid or a cancer spreads, etc…

So to steal a brilliant analogy – we have some percentage risk by not taking the vaccine, based upon our age, comorbidities, fitness, etc… We can mitigate some of that risk by taking the vaccine, but in order to do that we have to first cross a minefield. You’d probably want to know what are the odds of stepping on a mine, right? Well, the first study on this shows a 46% increase in suspected covid during that two week period. Even the Pfizer data itself shows a 40% increase.

Don’t you think it would have been good to know that you’d briefly have a nearly 50% increased chance of getting covid and massively increased odds of being hospitalized for several weeks after getting your vaccine? The tradeoff for running across the minefield is a vaccine that is eventually 56% effective (not the 95% we were sold).

Again, I’m not arguing that you shouldn’t get the vax. I think most folks 50 and older, or folks with comorbidities should. BUT, don’t you think folks should have been advised that for 2-3 weeks post first dose you need to self isolate as much as possible to reduce the risk of getting covid? I blissfully went back to work after my first dose, including working the covid floor! And why in gods name would we be pushing the vaccines during peaking cases? From a big picture, public health standpoint, you’d want to be vaccinating during lulls in case rates.

Which brings me back to the previous post. What if we’ve created a bunch of vaccinated asymptomatic superspreaders who are inadvertently causing a spike in case rates? And then in response we push/mandate vaccinations and start boosters. We just potentially put millions of people into that two week risk window with covid on the increase and superspreaders walking around… The hospitalization and death rates will be interesting follow this winter.

Come to think of it, my tinfoil hat is feeling a little snug. Time to take it off and go do something productive outside.

Government, Health, Politics, Solving the wrong problem

I Want The Data

A short one today. The local hardware store in our little town just reimplemented a mask mandate to shop there. The city council is contemplating reinstating the city-wide mask mandate. I’m sure cities across the country are evaluating the same thing with the new delta (sshhh, don’t mention the country) variant of the virus which shall not be named.

For roughly a year every city, county, state, and federal public health office has been collecting extensive data on Covid cases. We know exactly how many new cases we had for every single day in every corner of the country (and world). Every single person in the country has seen multiple instances of the bell-shaped curve graphs showing the current state of Covid case counts.

So here’s my question – with all that data it should be very simple to show a strong correlation between the implementation of a mask mandate and the reduction of case counts, right? The entire point of the mask (as we’ve heard ad nauseum) is that they protect you and others from transmission of the virus. So, across the country the data should easily show the date of a mask mandate and shortly afterwards case counts dropping. Seems like simple science, no?

I have yet to see any data that shows a mask mandate having any impact whatsoever on case counts in any part of the country. Have you? Don’t you think the powers that be would be hammering the news talking heads every night with these charts to prove how effective their mask mandates were? Instead, the CDC’s strongest case for masks seems to be a report on two hairstylists who were positive and saw a bunch of clients. They all wore masks and nobody else was infected. So there you go – based upon two hairstylists, we all have to wear a mask.

We’ve had a real world, year long experiment with extensive amounts of data. Before you force me to wear the damn mouth diaper again, I want to see the data. Not theory, not anecdotes from hairstylists or isolated lab experiments attempting to measure droplet velocities. We know the date we started wearing the damn masks. Can you correlate a drop in case counts afterwards? It doesn’t seem like a hard question, does it?