Imagine a hypothetical scenario, say, with a fictional country “X”. Let’s imagine this country has a population of 1 million people, 10 thousand of which have a comorbidity (the edge cases) that makes them particularly susceptible to the virus circulating in the population.
The virus affects the population in the following ways:
Transmission occurs at an equal rate regardless of whether individuals have the comorbidity or not;
1 in 10,000 of those without the comorbidity will die of the virus; and,
1 in 20 of those with the comorbidity will die of the virus.
The comorbidity can affect people regardless of age, and for the sake of parsimony we will ignore age. The scientists of X have developed a vaccination, which they claim has efficacy against death but not transmission. But there are a wide array of side effects, which make people with the comorbidity averse to taking the vaccine. In the end, 80% of the group without the comorbidity take the vaccine and only 20% of the group with the comorbidity take the vaccine. So 79.4% of the total population is vaccinated.
Now, let’s say 10% of both groups get the virus, and let’s say it has no effect on deaths. If 10% of the group without the comorbidity get the virus (ie., 99,000), then 9 or 10 (let’s assume 10) will die, and if 10% of the group with the comorbidity get the virus (ie., 1,000), then 50 will die. Since the virus affects people equally, let’s also assume that the proportions of those affected are the same as the proportion of the vaccinated.
So, from the group without comorbidity, 8 vaccinated and 2 unvaccinated will die, and from the group with comorbidity, 10 vaccinated and 40 unvaccinated will die. In total, 18 vaccinated and 42 unvaccinated die in a total population. If we are looking at vaccine effectiveness against death, the numbers suggest the vaccine is 88.8% effective. But we know there is no efficacy and the perceived effectiveness is strictly because of the difference in vaccination levels in the edge cases.
Now, this may look like an extreme example, but at some point when you have two completely different groups within a population, speaking in terms of “effectiveness against death” ceases to become meaningful. In fact, for the vaccines we are using against COVID-19, efficacy against death was never demonstrated in the trials of the most widely used vaccines like the Pfizer vaccine. At the point when omicron came around, the groups (ie., vaccinated and unvaccinated) were so different that it is impossible to tell without a much higher level of granularity what is real versus what is perceived.
Furthermore, there are even subgroups within subgroups here. There are different stages of comorbidities (think the stages of cancer), and comorbidities that are not being accounted for even in the rare case that they are controlled for in studies (ie., vitamin D deficiency). It is nearly impossible outside of a well-run, independent randomized control trial to even understand what is happening here.
We are seeing huge discrepancies in effectiveness against death by country, some countries like in the United Kingdom appear to have negative effectiveness, and some countries have slightly positive effectiveness. This is further complicated because many of the groups that are choosing to remain unvaccinated are at increased risk of having comorbidities, different access to health care, and so on. There are literally hundreds of variables at play here.
Some have noted that testing discrepancies makes hospitalizations and death a better metric for understanding vaccine effectiveness (between countries). I would argue the opposite. There are even worse discrepancies in how hospitalizations and death are accounted for. Even within countries, the standards change. Massachusetts has recently changed how they record deaths from having COVID-19 on a death certificate or having been infected within 60 days to having COVID-19 on a death certificate and having been infected within 30 days. Different states even within the United States account for these differently and maybe even different localities within states.
Even the idea that comorbidities and other predictors of dying with COVID-19 affect the more highly vaccinated age groups is a red herring. There are subgroups within subgroups like age groups. If we control for age, we are missing the fact that individuals with cancer, say, may be more or less likely to get vaccinated in different age groups. Measurement outside of well controlled circumstances is, simply, a mess.
Frankly, it does not make a whole lot of sense to even measure these things. We knew early on that those at risk of dying with the virus were the edge cases. Most people were at a much higher risk of dying with influenza. It never made sense to talk about viral spread within populations that were not at significant risk from the virus, yet, here we are: with a vaccine that has nine pages of side effects that does not stop transmission and was forced on people who were at no risk from the virus in the first place. And, in my country at least, a significant subset of the population wants to keep dosing ad infinitum. Yikes.
You’re absolutely right, everything is impossibly muddied. The stats they generate give the vaccines a degree of credibility they do not deserve.
An interesting change in Ontario data over the last few weeks:
-Ontario now reports cases and deaths using the following categories. Not fully vaccinated, fully vaccinated, and vaccinated with booster.
-their definition of not fully vaccinated is as follows:
—people did not have any vaccine dose
—symptoms started after receiving the first dose of a 2-dose COVID-19 vaccine
—symptoms started between 0 and less than 14 days after receiving the first dose of a single-dose vaccine series (for example, Janssen)
—symptoms started between 0 and less than 14 days after receiving the second dose of a 2-dose vaccine series.
So they’re now blending a huge number of people who had a single shot of Pfizer or Moderna and stopped due to side effects, along with the 14 day periods after first and second shot, and lumping them all in with unvaccinated.
Interestingly the boosted case rate is nearly twice the “not fully vaccinated” rate. I can’t image merging the above groups in with the unvaccinated improves their performance, I assume it makes it worse.
The death rate is inflated solely by the 60+ “not fully vaccinated” group. It’s likely the 80+ causing most of it however they do not allow you to view in 10 year brackets.
The fact that they keep resorting the data suggests they have an accurate set without the 14 day offsets.
Most of these metrics, especially those used in Canada, had the sole purpose of vilifying the unvaccinated. Great article.