Kontis et al. basically ran every major model type that is used to model age-specific death rates (21 models in all) across 35 industrialised (high-income) countries, and then took a weighted average of those models [*]. The modelling approach also allowed them to make probabilistic forecasts (which is something that Jacques Poot and I have been working on, in terms of population projections, for many years). They looked at life expectancy at birth, and life expectancy (remaining life years) at age 65. I'm just going to focus on the first of those. Here's what they found:
Taking model uncertainty into account, we project that life expectancy will increase in all of these 35 countries with a probability of at least 65% for women and 85% for men, although the increase will vary across countries. There is nonetheless a 35% probability that life expectancy will stagnate or decrease in Japanese women by 2030, followed by a 14% probability in Bulgarian men and 11% in Finnish women...
There is 90% probability that life expectancy at birth among South Korean women in 2030 will be higher than 86·7 years, the same as the highest life expectancy in the world in 2012, and a 57% probability that it will be higher than 90 years... a level that was considered virtually unattainable at the turn of the 21st century by some researchers.So, they are offering better than even odds that life expectancy for South Korean women will exceed 90 years by 2030, a point that has been picked up in the media (see for example here and here). However, something that wasn't picked up (even by the NZ media) was the somewhat surprising projection for male life expectancy in New Zealand. Here's the relevant part of their Figure 3:
There's a lot to commend in this research, and the BMJ article is not too mathy (but I wouldn't recommend reading the online appendix on that score). However, it isn't without its problems. The authors have done a good job of using multiple models and bringing them together using a weighted average.
However, one of the key problems with models is that they are less good at extrapolating beyond the range of data that are inputs into the model. And essentially, that is unavoidable when it comes to projecting life expectancy. No industrialised country has ever had life expectancy before as high as it is in those countries today, let alone projecting future further gains in life expectancy. One of the big remaining questions in human biology is the limits to human lifespan, and this sort of trend extrapolation doesn't really help us to understand that. There may be biological limits to lifespan that we haven't approached yet and maybe we won't even know we have approached them until we hit them. At which point, extrapolations of past trends will not be a good predictor of future gains in life expectancy.
The authors themselves note:
Early life expectancy gains in South Korea, which has the highest projected life expectancy, and previous to that in Japan, were driven by declines in deaths from infections in children and adults; more recent gains have been largely due to postponement of death from chronic diseases.That's not just true in South Korea and Japan, but all industrialised countries. For most of these countries, the future gains in life expectancy at birth that could arise from further reductions in infant and child mortality are limited. The low-hanging fruit of life expectancy gains have already been picked. Further increases in life expectancy now are most likely to arise through reductions in the 'stupid-young-male effect' (e.g. reductions in injury deaths). Remember that life expectancy at birth is measured as the age by which half of that birth cohort will have died (half would still be alive). So, life extending medical technologies that work for the very-old (likely to be those already above the median age for those born in their cohort) will have no effect on measured life expectancy.
Overall, it might be wise to be more cautious in interpreting these projected gains in life expectancy. For New Zealand men, it may be premature to be popping champagne in anticipation of our long-livedness.
[*] What they actually did is called Bayesian model averaging, which essentially means that they weighted the models by how good they are at predicting actual data, with models that are better predictors receiving higher weights.