How to Tell What's Going to Kill You

In general, the probability of death is pretty simple to calculate. It’s 100 percent. We all die.

But the devil is in the details. Humans fear catastrophe and disaster, and accordingly, tend to worry about horrifying events: gunfire, a terrorist attack, lightning strikes. The fact that such grisly ends rarely come to pass—especially if you stay inside during thunderstorms—doesn’t seem to reduce such concerns.

Every year, the U.S. Centers for Disease Control and Prevention publishes a compendium of how many Americans died the year before. There’s plenty to be learned about freak accidents—two people died in 2014 from “ignition or melting of nightwear”—but the data also shows how exceptionally hardy human beings are.

In any given year of their lives, Americans far more likely to keep chugging along than not. Even at the frail age of 85, you have a 92 percent chance of surviving to the next year.

Pretty good odds! But this is where probability comes in. After all, life is not a single roll of the dice, but thousands. Survival is rarely dependent on a single, cataclysmic moment of chance, but years of smaller risks — the 0.089 percent chance of heart disease at age 50, then 0.098 percent at 51, and 0.109 at 52.

In the end, it’s the additive power of probability that kills us. And at each turn of the calendar, the odds usually go up.

Here’s an experiment. Using CDC data from 2009 through 2014, we coded a program that simulates a person’s lifespan and calculates the odds of dying at any given year. For every year of life, it runs this virtual person through the litany of ways to expire. If their number isn’t called, they advance to the next year.

Of course, one lifetime isn’t very informative. So it repeats the experiment 10,000 times, creating a whole town of clones.

Running this simulation for someone of your own demographic characteristics may prove interesting; comparing your risks to those who have different traits, though, is likely to prove even more illuminating. Different segments of the American population face radically different challenges as they move through life.

There are crucial implications to that simple fact. Every day, policymakers steer resources toward addressing some of these risks, and as a result, away from others. At the same time, Americans who face different risks may back political candidates who address their needs, or donate their time and money to causes that seek to combat the diseases most likely to strike them or those around them. Resources may not end up where they do the most good; often, they wind up devoted to the risks facing segments of the population best-positioned to secure them. Striking the proper balance among these competing demands is among the hardest puzzles facing politicians, policymakers, and the general public.

This isn’t a perfect simulation, as it uses current data across many years of life to generate a new lifespan. For instance, people born today are probably far less likely to die of lung cancer than current 80-year-olds, thanks to the decline in smoking. It uses data on Americans, who face different risks than other populations. And because of CDC data restrictions, the simulation only runs until the virtual person’s 100th birthday.

Even so, the lesson is clear: Lives are defined by the little risks we take, not the big ones. And to paraphrase T.S. Eliot, we’re far more likely to die not with a bang, but a whimper.