Almost 9 years ago, my wife and I adopted a few changes in our lifestyle, one of which was switching to a diet low in carbohydrates.  At the time, low-carb was considered a fad by many nutritionists  and not very well known among the public. What people did tend to have heard of, though, was the paleo diet, so I just said I was doing that. To which many exclaimed “but why would you want to eat like a caveman? They died at 27!”
Which brings me to one of my pet peeves in the (my professional) field of risk – population statistics. Even serious newspapers often either get it wrong, or muddy the issue. Consider this statement from an article in the Guardian from 2019 which criticizes the plans to increase the retirement age to 70 in the UK:
In Glasgow, boys born between 2015 and 2017 have a life expectancy of just 73.3 years – meaning under this plan, many would never reach pensionable age.
On the positive side, the author didn’t say that Glaswegians could only look forward to three years of retirement. The statement is still a weak argument for a number of reasons. For one, we are talking about the year 2085 – in the science fiction genre that wouldn’t even be considered near-future SF anymore. Additionally, 73.3 is an average, which on its own doesn’t tell you much.
Let’s have a look at some life expectancy data :
A quick glance at this chart would seem to tell us that Americans die first among the developed nations I selected (I more or less randomly picked the larger countries where our readers seemed likely to be), and the “primitive” peoples (the three colums on the right) live about half as long. Right? Well, yes and no.
The chart shows e0, which is “average life expectancy at birth”. That’s in principle the same number the Guardian article above mentions. But if Glaswegian boys have an e0 of 73.3, and the UK as a whole has 79.5, some people in the UK have to live to over 85 to even out the average. E0 differs by sex, socioeconomic status, region and a host of other factors. It (obviously) goes up with better medical care. It falls with greater inequality (i.e. worse access to medical care). So e0 on it’s own is an interesting indicator, but won’t tell you how long you have to live.
But the cavemen’s life was still nasty, brutish and short, right? Their e0 is only half of ours, after all?
Again, not quite. Besides being an average calculated from birth, the chart above also doesn’t tell us several other things: when and why people die, and how long they can expect to live if they reach a certain age.
Before the discovery of pathogens and the effects of hygiene, infant and child mortality was extremely high. In 16th century England, 12% of all children born would die in their first year. (Link) That number is around 1% or less today in developed nations. This drop was the leading cause for the steep increase in e0. Looking at “untouched” hunter-gatherer peoples today, i.e. those without much contact with us or other peoples (the !Kung and Hadza on the chart above), on average 57% of the children survive to age 15.
The next big cause of death for caveman was accidents. Hunting a mammoth is dangerous, never mind the competition from saber-tooth tigers and other fun carnivores. So in a caveman mortality table, we would see another dent around the prime hunter ages of 18-25 (for men). For a cavewoman, the major cause of death was childbirth – similar age, similar dent.
The fact that I talked about mortality tables shows that my background is insurance – a life insurer generally wants to know the probability of somebody dying. In population statistics, you mostly use life expectancy tables. Here’s an excerpt of what that looks like for the US:
The first row at Age 0 is the e0 from above, and the longer you survive, the higher your average life expectancy gets. A (US) woman reaching retirement age today can expect to live another 20 years, i.e. to an age of 85.6. And that’s even though when she was born in 1955, her e0 was somewhat lower than it is for a newborn today.
Back to our hypothetical caveman. Again we can use the modern hunter-gatherers as a template for what their life expectancy table would have looked like:
Even “real” prehistoric caveman, once he got past the mortality bumps, could expect to live to an old age comparable to modern humans. The modal age at death e.g. for hunter-gatherers like the Hadza is 76. That number shows the age at which the most individuals in a population die, i.e. the peak of the adult mortality distribution curve. IMO modal age at death is a better indicator of longevity than an average age, which can be skewed by outlier groups.
These hunter-gatherers generally just die of old age – no diseases of civilization like diabetes, Alzheimers or CVD. That’s why I think living like a caveman – combined with sensible hygiene, perks of medicine and some other modern amenities, will get me the most bang out of my life buck.
 Warning: this is a completely left brain post today. I was published a while back on our risklantern blog, and I felt was worth reusing.
 There are still a few who have missed the bus and think it’s a fad. After 9 years I can confidently say a. it works, b. it is sustainable and c. I have never been healthier
 Sources: For the countries: data.oec.org – newest available data (2018 resp. 2017)
for the hunter/gatherer/etc: Gurven, Michael and Hillard Kaplan. “Longevity Among Hunter-Gatherers: A Cross-Cultural Examination”. Gurven Lab. 2007. https://gurven.anth.ucsb.edu/
 I am aware that I am perpetuating the sexist stereotype man=hunter, women=babies. Growing archeological evidence shows that the whole tribe including women and children were involved in the hunt, including the actual killing of game. But that’s a different topic outside the current scope of the blog. Right now I’ll stick with what we see in modern hunter-gatherers.
 The Gurven/Kaplan paper discusses why the prehistoric data, which are based on archeological findings, should be taken with a grain of salt