Why didn't pandemic data include us all?

Policy that affects everyone shouldn't be based on how it affects almost no one

In the aftermath of this COVID-19 pandemic, Canada’s political and public health leaders must resolve to never again make decisions and policy that affect everyone, based solely on how it affects practically no one.

That’s what’s been happening for the past 18 months in Canada. And, it’s hurt millions of people. People who did not need to be hurt.

In part, this great suffering was caused by our collective inability to understand and appreciate risk. Because COVID-19 is more contagious than the seasonal flu and because some people who get infected get sicker than the seasonal flu, and because some people who get sick, die more often than with the seasonal flu – we collectively overestimated the risk of COVID-19 to the average person. But, that’s a topic for another column on another day.

The greater cause of needless pandemic-related suffering over the past year and half in Canada, has been the penchant for our leaders to make decisions based on incomplete data. That’s happened consistently throughout the pandemic.

Where is the comparative data? Why aren’t there other lines on the charts?

Most of the pandemic counter-measures Canada has introduced, including the horrifically draconian and devastating Lockdowns, have been based on a willful ignorance of the whole set of facts on the ground.

I’ve opined many times on the quality of public health expertise available to Canadian political leaders and won’t belabour it again, except to reiterate that the official public health advice provided to elected officials, and the freelance advice offered to news media, has been focused very narrowly on the health of those infected by the virus.

I’ve also criticized the quality of the modelling. I won’t belabour that either. Instead, I’m going to point out something so basic, so obvious and so overlooked that I haven’t talked about it before.

Data presented to decision makers rarely reflects the whole picture

As an example, let’s look at one particular data set at the core of Ontario’s pandemic crisis.

Chart 1 below is data published by Ontario on its website showing the cumulative number of COVID-19 cases since the pandemic began in January 2020. It is data like this that has formed the basis of advice provided to provincial decision-makers in every province.

Chart 1 – Cumulative number of COVID-19 cases in Ontario during the pandemic.

It’s pretty stark, isn’t it? An exponential growth curve in COVID-19 infections. Clearly, there is and was a compelling need for urgent and drastic action!

Based on this type of data, decision makers took unprecedented action – ordering extensive lockdowns that had horrific effects on the population. Residents were ordered to stay home for weeks at a time. Businesses were closed – some completely shuttered for almost a year. All restricted significantly in how many patrons they could serve. In every case, these closures cost people jobs and their families incomes.

Of course, the chart doesn’t show how many cases were resolved. Nor, does it show how many Ontarians have never been touched by the virus. If we were to add the latter figures (no contact with COVID-19) into the chart, it tells a radically different story. Chart 2 does this.

Chart 2 – Cumulative number of COVID-19 cases in Ontario during the pandemic PLUS the number of Ontarians unaffected by the virus.

In this second chart, the blue line represents the same data presented in Chart 1 – over the same time frame. But, we’ve added the orange line which represents the number of Ontarians who have never been infected by COVID-19, on the same scale.

This graph puts the COVID-19 pandemic in an entirely different perspective. It’s a bit harder to feel quite so panicked about the virus looking at this data. But, it’s exactly the same data.

Where Chart 1 looks only at the people who’ve contracted the virus, Chart 2 looks at the entire population of Ontario. Which is a more appropriate frame of reference for decision makers making policy that affects everyone?

Sure, decision makers are well aware of how many people live in Ontario and can undoubtedly do the math for themselves: 14,500,000 less half a million infected, yada, yada. They don’t need that information on the chart. Or, do they?

Presenting the “non-infected” information side-by-side with the “infected” numbers makes the point immediately. Perhaps, the advisors didn’t want the point to be quite so obvious. Perhaps, that’s why they chose to present the data on a scale and in a format that made it look more desperate than it actually was.

One can ask similar questions of all the data and all the charts presented to decision makers to help them make (or influence) their decisions.

Why weren’t decision makers presented with all the relevant data?

The data presented to decision makers during this pandemic has been almost entirely focused on the fraction of the population (3.7%) that has been infected with the COVID-19 virus. As if, they’re the only people who matter.

Shouldn’t the charts also show how many people are unaffected by the virus – but who will be directly affected by the decisions made by officials? Without that comparative data, the vast majority of Ontario residents were all but forgotten when crisis management decisions were made.

Shouldn’t decision makers also see how many people are suffering from mental illnesses, terminal cancer diagnoses, poverty, and reduced years of life expectancy resulting from their policies? After asking dozens of times, I have yet to receive any answers from any official, anywhere, that suggests these people and the impacts on their lives were ever even recognized – let alone considered when decisions were made.

What rational human being would choose to make a decision that affects everyone – without even considering how many people will suffer from their choices?

Mental health issues are real and often deadly. Poverty has been proven to cause decreased health outcomes and shortened life spans. Delayed diagnosis for serious illnesses and cancers lead to higher mortality. Only this last issue appears to have ever been even considered by those who advise Ontario’s decision makers.

Why weren’t data for all these other factors presented to decision makers considering lockdowns and other pandemic mitigation measures?

Excluding these data from consideration is like demanding someone take action to reduce flooding in the basement by cutting off the water supply to the building, without mentioning the building is on fire.

In a word, it’s unconscionable.