We mentioned these two items weeks ago, but it bears repeating.
I watch local TV news since I no longer read a local newspaper. They still report daily new cases of COVID-19 and never define a “case.” I suspect a case is simply a lab test positive for COVID-19. An unknown number of those cases are false positives, meaning the test is wrong, there is no infection. Another unknown number of cases is folks who harbor the virus but aren’t sick at all, and may or may not become ill in the near future. Two of my first-degree relatives, one quite elderly, were diagnosed with COVID-19 in the pre-delta era; their illnesses were like a head cold or mild flu. Should we care much about the aforementioned “cases”?
Remember that, at least early-on in the pandemic, the diagnostic tests were criticized for being too sensitive (cycle threshold set too high), leading to excessive false positives. I hope that problem has been minimized, but don’t know. Around a third of head colds are caused by coronaviruses. I wonder if my relatives had non-COVID-19 coronavirus infections.
One, the only two outcomes—and it’s really just one—worth studying are illness severity and death. All other derived measures are always a clue you are being fooled.
“Cases” are NOT an illness severity measure. Ignore ALL studies which invoke “cases”, whether they are on “our side” or theirs. “Cases” are NOT cases, but a combination of testing level (still at ridiculous levels), testing sensitivity (still too high), and multiple disease characteristics.
Look at hospitalizations for (and not after-admission-for-something-else-first either) the [COVID-19], or look at deaths of the [COVID-19]. Nothing else.
Two, we cannot examine any study of efficacy without having removed from the data those people with prior infection who recovered.
How often is this done? Something close to never.
Parker here again.
One reason recovery from prior COVID-19 infection is important in studies of vaccine efficacy, is that recovery confers immunity from future infection that is at least as good as immunity gained via vaccination, if not better. So if you’re studying vaccine efficacy in a population, comparing outcomes of vaccinees to the unvaccinated, you won’t know if a better outcome was due to the vaccine or to natural immunity. One way around that would be to ensure equal numbers of “naturally immune” in both study groups. But why muddy the water and increase expense?
I don’t know if Briggs is legit or not. Maybe he’s a dog pawing at a computer in his owner’s basement. Mr Briggs describes himself:
“I am a wholly independent vagabond writer, statistician, scientist and consultant. Previously a Professor at the Cornell Medical School, a Statistician at DoubleClick in its infancy, a Meteorologist with the National Weather Service, and a sort of Cryptologist with the US Air Force (the only title I ever cared for was Staff Sergeant Briggs).
My PhD is in Mathematical Statistics, though I am now a Data Philosopher (I made that up), Epistemologist, Probability Puzzler, Unmasker of Over-Certainty, and (self-awarded) Bioethicist. My MS is in Atmospheric Physics, and Bachelors is in Meteorology & Math.”
Steve Parker, M.D.