of the three countries that have highest (and surprisingly parallel) slope: japan, south kore, mexico ... it suggests that #visit correlates with LE ... and health coverage is unimportant
but there are countries that have less #visit but more expensive (it suggests patients go to doctor not only for consultation) ... so #visit is an unreliable predictor so is cost
to me it seems the most predictive is location (yes, the country names are data points too) ... if i want to live long, i'll settle in japan and do what the average japanese for their healthy lifestyle
I think the slope is the whole point of the graph and it is supposed represent efficiency although in a very unfortunate way. The angle of the slope is defined by arcsin((life_expectancy - healthcare_costs) / constant). It's a bit like subtracting apples from oranges.
If there's one thing I've learned at engineering school it's that mixing up units of measurement is not a good idea. If you subtract years from dollars the value you get will carry no meaning. And that's why this visualisation is completely meaningless. They should come up with a more clearly defined and sane approach.
You could probably get 90% of the benefit of the Japanese lifestyle by moving to a walkable urban environment like NYC, selling the car, and eating the japanese diet. You'd probably have to visit there first to see what that actually is. Any other East Asian diet would be fine as well - hell, a pre-1970s American diet would probably be fine.
I am curious if those life expectancy figures have been controlled for smoking, though. If not - wow.
but there are countries that have less #visit but more expensive (it suggests patients go to doctor not only for consultation) ... so #visit is an unreliable predictor so is cost
to me it seems the most predictive is location (yes, the country names are data points too) ... if i want to live long, i'll settle in japan and do what the average japanese for their healthy lifestyle