The probability that my kid gets cancer in any given year is the same as, for example, the likelihood that you and I pick any date in the 20th century and they will be the exact same date. That’s a small chance (although it’s still 25x larger than winning $25 thousand in Powerball).

I’d be more interested in the probability of serious injury or health defects. Anything awful that could happen to my son. But I have no reliable data on that. And even if I had, the definitions of ‘awful’ will never be as clear-cut as the definition of ‘death.’

In most cases, I believe death is a good enough proxy to general health risk. When 56 toddlers die after being hit by a car each year, then it’s safe to assume there’s a lot more that don’t die after such an accident but suffer permanent consequences like brain damage or permanent disability. Same goes for victims of poisoning, fire, and basically all other causes of death.

Simplification D: Risk is result.

I plot the different causes of death on the y-axis according to the number of reported deaths by each cause. If cause A is twice as high as cause B on the graph, that’s because there are twice as many toddlers that die from cause A than those who die from cause B.

That’s not equivalent to risk, though. It doesn’t automatically follow that cause A is twice as risky as cause B.

For example, let’s have a look at ‘Drowning.’ It’s way above ‘Guns.’ About 175 toddlers drown each year, while “only” 20 are shot to death (mostly unintentionally). Does it mean swimming is riskier than handling a gun?

Of course not. There are two things at play here:

Toddlers, on average, spend much more time around water than around loaded guns.

When people see a kid playing with a loaded gun, they tend to deal with it quickly. When they see a kid playing in the water, they don’t fret.

In other words, if toddlers spent as much time with guns as they do in pools and baths, and if parents were okay with it, then the ‘Gun’ cross would be way above the ‘Drowning’ cross.

Correcting this simplification in a systemic way is hard. I’d have to come up with some kind of coefficient that lifts causes of death that are ‘common-sense-dangerous’ (like guns) and lowers causes that are routine (like water). Such coefficient would be extremely subjective. Instead, I ask you to keep this simplification in mind and correct accordingly.

The different causes of death

I tried to cluster the data into meaningful categories. For example, a category called ‘Demyelinating diseases of the central nervous system’ in one of the source datasets doesn’t really help me make meaningful decisions. I grouped it with ‘Episodic and paroxysmal disorders’ and many other causes into one big ‘Disorders of the nervous system’ category.

You may wonder what’s the difference between ‘Traffic’ and ‘Car crash.’ I put deaths that happened in a moving vehicle (toddler as passenger) into ‘Car crash,’ and deaths that occurred outside any moving vehicle (toddler as pedestrian) into ‘Traffic.’ To me, the distinction is important, because one is about car safety and second is about behavior around the road and in parking lots.

What about adverse effects of vaccination?

There is a category in the datasets for adverse effects of medical & surgical care and drugs. Together, they claim about three toddlers each year. I don’t know if any of these deaths is actually attributable to vaccination but let’s go with the worst case and say all of them are. (To underscore how outlandish this scenario is: this would mean that there are absolutely no deaths from adverse effects of medical and surgical care other than vaccination.)

But, for the sake of argument, let’s say it’s true. Even so, the flu and pneumonia alone kill nine times as many toddlers per year (27 deaths). When you add together all the different bacterial diseases, they claim 73 toddlers each year. That’s over 20 times more than what our paranoid scenario counts for vaccines.