Myth Busting with a simple epidemiology lesson
Feelings and fear are not facts.
The fear machine is on full blast right now. I would encourage you to take a few lessons from basic epidemiological principles, so we can all settle down a bit…
Myth 1 – “all the COVID deaths are in unvaccinated people”
Response: That is literally what a pulmonologist said to me recently. I made a face at him, and said the data totally does not support that (even the main stream media reports don’t support that), but he said all of his patients dying were all unvaccinated.
What this illustrates is sampling bias. Who goes to the hospital with COVID? Very sick people. That does not represent the entire sample of people who have COVID, just the extremely ill sample of people. And considering the survival rate is > 99% in individuals <70 years old, that is quite a biased sample. You cannot conclude “all unvaccinated people die of COVID” from that biased sample. Take that, White House.
Myth 2 – “there is a SURGE of cases”
Response: I actually believe this is a self-fulfilling prophesy. For the past few weeks, the news has been overwhelmed by scary stories about the omicron variant. It also just happens to be cold and flu season. What happens when the media is full of stories about a scary virus and you might have some innocuous symptoms? People get tested. It is well established that most COVID-19 cases are mild, and that asymptomatic people test positive all the time. Then there are reports that stores and testing facilities are running out of tests, there are traffic jams around testing facilities… suddenly getting tested is all the rage like the latest Christmas gift trend.
In other words, this is a surveillance bias. When you increase the number of tests, even with a small false positive rate, you will increase the number of false positive results, and also clinically meaningless positive results.
In a future substack, I will take the opportunity to quote some actual data reports on omicron. Amazing what you learn when you look at the actual data. But for now, it just so happens that as I’m writing this post, this article came up… a main stream media article making the exact point that other epidemiologists have been making. Omicron is going down just as fast as it went up in South Africa.
Myth 3 – “the hospitals are overwhelmed!”
Response: Let’s see, the hospitals have put in place vaccine mandates, and have been firing workers that don’t comply, and/or folks at risk of losing their jobs are just quitting. The correct answer is, hospitals are understaffed.
When you estimate a rate of anything, the denominator is key. In this case, there is a reduced staff problem. Either there are fewer beds available, or the staff that hasn’t been fired are working more or longer shifts. Same effect.
Myth 4 – “pandemic of the unvaccinated”
Response: I addressed this in a previous substack. This is a bias of… not reading the actual data?
In closing, good practice in epidemiology (or any other field of science) is that you don’t take a simple data point and run with it. You question its validity. You ask if there are any other explanations for that other data point other than your initial hypothesis, and you try to prove or disprove those other causes. You actively look for confounding or other sources of bias. Simply stating “all the unvaccinated are dying” because that is all you see is bad practice and intellectually dishonest.
Feel free to comment with some other scary messages you have heard and I can answer them in a future substack!