Demography

Talking about Risk

A headline read, “What if we could live for a million years?”  My casual answer was “not a chance.”  Even if we could eliminate aging and disease, we still have to consider risk.  The National Safety Council reports 1.25 deaths for every 100 million miles of highway travel.  Assume 10,000 miles per year of highway travel, in the first thousand years of life, you’re up to 10 million miles.  That turns out to be a 1 out of 8 chance of dying – just from highway transportation.  The National Geographic gives odds of being struck by lightning as 1 in 700,000 each year, and one in 3,000 over a lifetime.  Increase the lifetime to that same 1,000 years, and the odds go up.  We live in a risk society, and often ignore that fact. 

I know a guy who survived a grizzly mauling, and I’ve known one who died from wasp stings.  Even in Trego, I encounter more wasps than bears.  CDC stats show 89 people who died from bee stings in 2017.  It isn’t a large risk – but 80% of the fatalities are male.  Bee stings are a greater risk to the male half of the population. 

Just about every endeavor includes misunderstood risks.  In 2018, the police fatality rate was 13.7 per 100,000 (bureau of labor and statistics).  CDC shows the 2017 fatality rate for farmers was 20.4 per 100,000.  BLS shows a rate of 132.7 per 100,000 loggers in 2015.  CDC shows bartenders have a higher on job fatality rate than police.  In some places policing is a relatively risky job – but in farm country and logging country it seems fairly safe.  Here’s the link to more data if this interests you.

When we started looking a Covid-19, we didn’t have the statistical data to calculate risk.  Now, we do have some data – and we’re seeing disagreements, mostly because folks see risks differently.  As I write, RealClearPolitics shows 5,746,272 confirmed cases in the US, and 177,424 deaths, yielding a 3.09% confirmed case fatality rate.  Still, that number goes from 8.25% in New Jersey to 0.79% in Utah.  We have solid numbers – but not numbers that allow us to calculate risk.  When we look at the charts that include age, we can begin to calculate risk – 70 is higher risk than 40.  80 is even higher.  New York numbers suggest that dense populations and public transportation increase risk – but not enough to calculate. 

The numbers let us develop ordinal data about covid risk – ranking things as more or less, rather than develop statistical data.  At 70, I’m at more risk than someone who is 50. The age data is good enough to develop some statistics – but the comorbidities that make it covid fatal mask that. Riding mass transportation is more dangerous than driving a car alone.  The virus doesn’t differentiate between bars and churches – open spaces are safer than crowded enclosed spaces.  So far we know that Covid is a bit more infectious than flu, and less infectious than measles.  Ordinal data. Buzz Hollander, a physician in Hawaii, has a good, readable article on covid at this site