Dear editor,
On September 30, 2021, the Los Alamos Daily Post published an article by Richard Skolnik entitled, “Skolnik: COVID Update For Week Ending Sept. 27, 2021.” In this article, Mr. Skolnik summarized an Arizona study, on the CDC website, by stating, “Schools in two of Arizona’s most populous counties were 3.5 times more likely to have COVID-19 outbreaks if they did not have a mask requirement at the start of school compared with schools that required universal masking on day one.” I found the article he referenced on the CDC website. It was entitled, “Association Between K-12 Mask Policies and School-Associated COVID-19 Outbreaks – Maricopa and Pima Counties, Arizona, July – August 2021.”
This study provides a lot of useful information, but I realized that the authors’ analyses of the data do not appear to take the size of the schools into consideration. According to the study, a COVID outbreak is defined as: “two or more laboratory-confirmed COVID-19 cases among students or staff members at the school within a 14-day period… .” Under this condition, the size of the school is important, because it is more likely to have two cases occur in a large school than in a small school.
In the Arizona study, 52% of the schools requiring masks had less than 850 students and 33% had over 1200 students; whereas, in the schools not requiring masks, 13% of the schools had less than 850 students and 66% of the schools had over 1200 students. This is a big difference in school size. It is so much of a difference that masking may have no effect at all. So, I undertook a probability analysis of my own, using their data, to account for the size of the schools involved and determine if there is a mask related effect.
To do this analysis, the background probability of having COVID has to be determined. This is ascertained by the case rate. For the zip codes involved, 96%, 91%, and 99% of the unmasked, masked, and late-masked schools, respectively, had a case rate of over 1 per 1000 people. So a background probability of 1.1 cases per 1000 people was assumed. A Poisson distribution was used, with this probability, to determine the likelihood of finding two cases in schools of the sizes in these counties. For the schools of less than 850 students I assumed the average school had 600 students. For the schools ranging from 850 to 1,199 students I assumed an average size of 1025 students. For the schools ranging from 1200 to 1649 students I assumed an average size of 1425 students. For the schools with more than 1650 students I assumed the average size was 1650 students.
The results of the analysis predicted that the group of schools with no masking mandate would have 113 outbreaks. The actual number of reported outbreaks was 113. Wow! Dead-on! For the late-masked case, 64 outbreaks were predicted and 62 occurred. This is a small over prediction. For the masked case, 37 outbreaks were predicted and 16 were reported. Whoah! The number of cases was over-predicted by a factor of 2.3. What caused the difference? The most likely cause is the effect of masking. Notice the reduction factor is 2.3 not 3.5 as reported in the CDC article.
How many people are affected in this study? In the masked case, the total number of students was about 205,000. In the unmasked case there were about 630,000. The reduction in the number of cases for the masked case ranged from 21 to about 50. This is a factor of about 1 to 3 people in 10,000. It is hard to get feel for these kinds of numbers, so I will give an illustration: A piece of copier paper is about 0.0035 inches thick. Therefore, a stack of 10,000 sheets of copier paper would be 35 inches tall (roughly 3 feet tall). So, the effect of reducing COVID cases by a factor of 1 to 3 people out of 10,000 people is comparatively the same as reducing the 35 inch tall stack of copier paper by 1 to 3 sheets. This an imperceptible change. However, are each of those 21 to 50 lives important? Yes they are. But, other lives are involved also.
Looking at the COVID data on healthdata.org, the masking rate in Arizona at the time this study began (mid-July) was 25%. Assuming this was the case for the students prior to the masking mandate, then 51,000 students chose to wear masks before the mandate. After the mandate about 154,000 additional students had to wear masks. So, 154,000 students changed their lifestyle to cause an effect of 1 to 3 out of 10,000. Are everyone one of the individual 154,000 lives important? Yes they are, and there may be health effects associated with wearing a mask for at least 7 hours per day that affect these 154,000 people.
In the past year, I have had a strep infection, a sinus infection, and numerous eye infections – all directly related to wearing masks. Strep infections are quite contagious and can lead to other diseases that are very serious. The sinus infection was not contagious, but was quite painful and lasted about two weeks. The eye infections were not contagious, but were quite painful and eye infections can result in serious eye damage. Fortunately, all of these were treated with therapeutics. I am probably in the minority regarding infections like these, but I am probably not alone. Maybe some of those 154,000 students also had health effects. Other effects have been reported. A study published in the June 30, 2021 Pediatric Journal of the American Medical Association shows that increased carbon-dioxide content of the air being breathed as a mask is worn, can cause hypercapnia in children. There are negative health effects caused by wearing masks, but these are not reported. Therefore, no one knows about them.
To put all of this into a practical perspective: At the time the article by Richard Skolnik was written, a state-wide masking mandate had been enacted. His data showed that there were 20 COVID cases in Los Alamos at that time. Since there are about 20,000 residents of Los Alamos, the COVID case rate was 1 per 1000. Using my analysis, if I were to walk into a room of 20 fully masked people there would be a 98% chance of no one in the room having COVID. If, however, there had been no masking mandate, this analysis shows that the case rate would have been 2 per 1000, and there would be a 96% chance of no one in the room having COVID. To me, in practical terms, these odds are the same.
In summary, the Arizona study reveals the level of effect that massive masking requirements has on COVID case-reduction. The reduction is there, but it is very small. That explains why there is no correlation between mask usage and COVID cases in the data reported at the healthdata.org website. This is because there are other, far more influential factors at play. In view of my own analyses and examples summarized in this letter, it seems reasonable that the people should have the freedom to choose their own form of COVID mitigation methods; and if someone does contract COVID it should be treated with known therapeutics as is done with other diseases.
Sincerely,
Robert D. Day