Studies, Schmudies

The latest face mask study du jour comes out of Kansas. On the surface this study would seem to support the use of face masks.

I’m sure Dr. Fauci is holding his breath hoping that you will think that it does.

To the casual consumer of news I’m sure it will reinforce the use of facemasks.

But does it?

Well, we should take a closer look, which is what I did.

I don’t want to bore you with the details but some details have to be presented in order to make an argument.

There are approximately 3 million people in Kansas with 105 counties.

The governor in the state of Kansas issued a non-obligatory mandate to wear facemasks.

24 counties representing 2 million people or 2/3 of the Kansas population opted to enforce the face mask.

The other 81 counties representing 1 million people or 1/3 of the population opted not to enforce the facemask.

The face maskers saw their COVID-19 incidence go from 17 per 100,000 to 16 per 100,000.

The non-face masters saw their COVID-19 incidence go from 6 per 100,000 to 12 per 100,000.

So it would seem that the face maskers won. Dr. Fauci can now tap dance down Broadway singing the psalms of the facemask, right?

Well, maybe not.

You see, the face maskers had a huge advantage working in their favor. Fully 54% (I’ll round it off to 50% for the purposes of a later calculation) of the counties who opted to use the facemask also employed other measures to combat the COVID-19. These measures could include school closings, restaurant restrictions, say at home orders and so forth.

The problem is that the non-facemaskers did not enjoy that advantage. Only 9% of the counties who opted not to use the facemask employed other measures to combat the COVID-19.

What this means is that approximately 1 million face maskers were subjected to other measures whereas only 100,000 non-face maskers were subjected to other measures.

This is a big deal.

It is a big deal because it means that other measures could be primarily responsible for the decline of COVID-19 in face-mask mandated counties.

Of course, it’s much easier to forget all that and to cling to what we want to see, which is what many people will rush to do.

Unfortunately, the virus doesn’t care about statistical studies, and the virus doesn’t care what we want to see.

The virus does whatever it wants to do.

The virus is reality.

That the governor issued the non-obligatory mandate, that the incidence of COVID-19 went up in non-facemask counties indicates that the COVID-19 incidence was going up in general.

So what kind of factors could have resulted in a decrease of COVID-19 in the face masked counties?

School closings. Business shut downs. Stay at home orders.

Stay at home orders in my experience are the most extreme of measures. Stay at home orders do much to increase fear, decrease traffic counts and commingling of people.

Let’s do a simple non-scientific example just to show how the data is skewed in favor of the face-mask mandated counties by virtue of the fact that a larger percentage engaged in other measures.

Remember fully 50% or 1 million people were subjected to other measures in the mandated counties whereas only 10% or 100,000 people were subjected to other measures in the non-mandated counties.

It makes a difference.

Let’s assume that other measures decreased the beginning COVID-19 incidence in half while face mask alone accomplished nothing while allowing the baseline rate to double. The purpose of this exercise is to show what is possible, not what actually happened.

So wipe the spittle from your mouth, MSM Fauci Nazis; it’s just an example. The world is not going to blow up.

I’m going to take the number of people allocated to each category according to mask mandate versus no mask mandate.

Then I will refigure the incidence assuming that a) the mask does nothing to impede the doubling of the incidence, and b) other measures cut the incidence in half.


Mask Mandated Counties

1,000,000 mask and other measures

17 to 8.5

8.5 x 0.5 = 4.25

1,000,000 mask measures alone

17 to 34

34 x 0.5 = 17

Total 17 + 4.5 = 21.5

Non-Mask Mandated Counties

100,000 no mask and other measures

6 to 3.

3 x O.1 = 0.3

900,000 no mask measures alone

6 to 12

12 x 0.9 = 10.8

Total 0.3+ 10.8 = 11.1

In this hypothetical example, one can easily see the disparate impact caused by other measures. It does make a difference what percentage of the population is allowed to engage in other measures, especially if those other measures involve stay at home orders.

Let’s assume that the 50% of the non-mandated counties were allowed to employ other measures such a stay at home orders. What would the numbers look like?

500,000 no mask and other measures

6 to 3.

3 x O.5 = 1.5

500,000 no mask measures alone

6 to 12

12 x 0.5 = 6

Total 1.5 + 6 = 7.5

7.5 is a hell of a lot better than 11.1.

What this simple hypothetical example also shows is the danger of statistical studies, as if we didn’t know that already.

Statistics in the wrong hands is akin to having a little knowledge – dangerous in the wrong hands.

Of course, this will mean nothing to the media. The media picks a conclusion and then fits facts around it.

Fuck you, MSM!


Archer Crosley, MD

Copyright 2020 Archer Crosley All Rights Reserved

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