Tam in the High Castle

Population denominators and PHAC modelling

Theresa Tam, in all her wisdom, shared the Public Health Agency of Canada’s latest, anemic foray into COVID-19 modelling titled “Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: What could have happened?”

Public health scientists love the counterfactual because they get to make things up. No basis in reality required. All those times you’ve heard a politician thank the vaccine for saving their lives after they got the COVID? Either you are speaking to an omniscient demigod who has peered into the future, or an obtuse demagogue who is listening to the soothsaying of public health bureaucrats.

Outside of the fictional realms, I have no respect for people that use a clearly unhinged set of assumptions to explore a scenario that did not occur. These models are made to look “scientific-y” by attaching numbers, confidence intervals, and graphical representations onto them.

People will, no doubt, eat the graphs up, share them, and spout nonsense about how it was worth it:

• “We dodged a bullet”

• “Thank you Theresa Tam for your strong leadership”

• “Everything was justified”

Of course, there are no counterfactual scenarios that imply we might have had just about the same or worse outcomes despite the fact that much of the recent literature on lockdowns have shown they were at best useless and most likely harmful. Instead, PHAC assumes that early lockdown measures had an even larger effect than their precious vaccine.

Now, the numbers they arrive at are ridiculous on the surface. They are saying that the baseline IFR would be about 1.79% (included asymptomatic persons) if lockdowns and vaccinations did not exist. Nearly half the people that made it into the hospital would die. If there were only vaccinations, then everyone still gets infected, but they imply that the IFR would drop to 0.78%.

The implication of this chart is that lockdowns greatly prevented the spread until vaccinations could come save us eventually lowering the IFR to about 0.1% (or closer to a bad flu season). For columns S5, S6, S7, and S8 in order the IFR drops to 0.65%, 0.38%, 0.15%, and 0.09% respectively thanks to the increase in vaccinations and “less deadly” omicron.

So… it seems like their model allowed the virus to spread freely without constraints, never hitting a roadblock. Without lockdowns, people would never wash their hands and if they were sick they would share drinks and never stay home. Travel measures, which according to their own employees had no policy rationale behind them, turn out to be highly effective in reducing transmission. Interactions were random, so there would never be pockets of people with immunity to lower the risk of spread. Instead, people with the virus would go way out of their way to find that one uninfected person and infect them. Et cetera.

Apparently they also ignored seasonality as they assumed that lockdowns were the prime movers of decreasing cases — they did not seem to consider places that did not lockdown without apocalyptic outcomes. So, from the start, they are operating under the assumption that lockdowns are highly effective and ignoring the wide body of evidence suggesting that lockdown theory is pseudoscience.

Ok.

Ignoring all that, let’s talk about vaccinations. They are using the wrong denominators for the vaccinated population at baseline. I’ve gone through this in so many posts, but needless to say, 99% of the population 80+ in Canada is not vaccinated, nor is 98% 70-79 and so on.

They referenced 2020 population estimates in this report. Those estimates are based on 2016 census data rather than the recently updated 2021 census data, which are not even close in terms of demographic distributions. For example, the 2020 population estimates they are using list 1,668,759 people as older than the age of 80. The 2021 census lists 2,037,905 as older than the age of 80.

Based on their assumptions for vaccination status, this actually lowered the death amounts by quite a lot in the lockdown/vaccination scenarios because they consider the Canadian population to be younger than it really is and more people vaccinated using their false numbers; however, in the no lockdown/no vaccination scenarios, it raises the death rates because the numbers they are basing vaccine effectiveness on are drawn from using the incorrect population numbers in the first place.

• They assume that the vaccine is miraculously 96% effective against death and protection never wanes. The data does not support that. As I noted in my recent Manitoba and British Columbia posts, the unvaccinated have roughly the same outcomes against death. That is even though Manitoba is using an incorrect population denominator for those age groups themselves.

• It also appears like they consider the chance of being infected, hospitalized, or killed to be independent. In other words… a person is 90% less likely to be infected. If infected, then he is 90% less likely to be hospitalized. If hospitalized, then he is 90% less likely to die. Effectively, this counts the benefits thrice.

• They account for waning immunity from the vaccination, yet fabricate the waning periods to make them look far better than they truly are.

• They also consider “full” vaccination to be the same as natural immunity — and boosters are even better than natural immunity.

• The protection for cases in the vaccination scenarios are just flat out made up for delta and omicron.

Meanwhile, despite putting the healthinfo database as a source for unvaccinated mortality rate, the numbers they reference are not there.

• Healthinfo does not differentiate deaths by age and vaccination status, only by age or vaccination status.

• These numbers are also large overestimates due to the with vs from factor.

• The “mortality rate was doubled…” assumption seems to be pulled out of thin air, but since they wrongly assume lockdowns had enormous effects on the amount of people that end up in hospitals, despite 0 evidence of that, it means that in the absence of lockdowns the unvaccinated will be enormously overcounted.

In short, this is just a bad model that relies heavily on the pseudoscience of lockdown theory and fradulent vaccination numbers. Honestly, I wouldn’t be surprised if this report is an attempt for Tam and team to justify their extremely poor pandemic response. Something to point to and say “we saved you” and why not? Most people won’t look at these ridiculous outcomes and wonder why countries that had way less stringest public health measures like Sweden and Norway did better despite being way more densely populated or why countries like South Africa with low vaccination rates didn’t experience anything close to these outcomes.

If people are believed when they make up numbers, no matter how ridiculous, then they will make up numbers and they will be believed.