4 ms·

The undeveloping world. Widespread obesity, poor diet, sedentary lifestyles, the uniquely inefficient health care system, wealth inequality, etc, will likely c

by notadoc 10y ago

The undeveloping world. Widespread obesity, poor diet, sedentary lifestyles, the uniquely inefficient health care system, wealth inequality, etc, will likely cause overall expectancy to drop further. Related and interesting: > "As of 2010, the average, upper-income 50-year-old man was expected to live to 89. But the same man, if he's lower income, would live to just 76, according to the report." https://www.washingtonpost.com/news/wonk/wp/2015/09/18/the-government-is-spending-more-to-help-rich-seniors-than-poor-ones/?utm_term=.2ff0f4046c66 https://www.washingtonpost.com/news/wonk/wp/2015/09/18/the-g...

astrodust 10y ago
Unimproving: Infrastructure in the US is falling apart, often literally. Nobody wants to pay for bridges, roads, sewers, or flood protection. It's like a return to the 1800s before any of this stuff existed. Uneducating: The "fake news" problem is the tip of the iceberg. People are celebrating their ignorance as if it's a good thing, that being educated is to lose touch with the real world. It's telling that the flat-earth community, filled with offensive levels of pride in stupidity, thrives in the US. Unearning: Children are often earning far less than their parents because they're forced into crappy part-time jobs or minimum wage temp work. Those lifetime jobs that parents got in a union or a big enterprise are all but extinct.
yummyfajitas 10y ago
While life expectancy is strongly correlated with income, it's actually uncorrelated with inequality and access to health care. https://jamanetwork.com/journals/jama/article-abstract/2513561 https://jamanetwork.com/journals/jama/article-abstract/25135...
- coldtea 10y ago
  >While life expectancy is strongly correlated with income, it's actually uncorrelated with inequality and access to health care. Or so says one published study, but scientific consensus develops across many studies, responses to them, meta-studies, and over a long period. Here is a counter result for example: https://www.ncbi.nlm.nih.gov/pubmed/15650149 https://www.ncbi.nlm.nih.gov/pubmed/15650149
- xaa 10y ago
  It's difficult to interpret this because the paper defines income throughout the text in terms of income quantile (not absolute, COL-adjusted levels). And oddly, in the FT, they show a significant negative correlation b/t Gini index and life expectancy in the upper quartile, and not the lower quartile (you'd expect the opposite). In the lower quartile, it is p=0.11, r=0.2; i.e., a nonsignificant positive association between income inequality and income quartile. This suggests the effects of income inequality might actually affect LE in different directions for high and low earners. This would explain why they say in the abstract they don't see a LE-Gini correlation for the distribution as a whole. The story I would spin is that, within one geographic region, a high Gini actually means that rich and poor are living in proximity and sharing social structures. If you have low Gini in a compact region it likely means everyone there is (uniformly) poor. IOW, they very likely have a correlation b/t Gini and income levels they aren't controlling for. This is why we use multivariate regression with interactions, not just run a Pearson between every pair of variables. The statistics in this article are really bad. JAMA strikes again.

by notadoc 10y ago

astrodust 10y ago

Unimproving: Infrastructure in the US is falling apart, often literally. Nobody wants to pay for bridges, roads, sewers, or flood protection. It's like a return to the 1800s before any of this stuff existed. Uneducating: The "fake news" problem is the tip of the iceberg. People are celebrating their ignorance as if it's a good thing, that being educated is to lose touch with the real world. It's telling that the flat-earth community, filled with offensive levels of pride in stupidity, thrives in the US. Unearning: Children are often earning far less than their parents because they're forced into crappy part-time jobs or minimum wage temp work. Those lifetime jobs that parents got in a union or a big enterprise are all but extinct.

yummyfajitas 10y ago

While life expectancy is strongly correlated with income, it's actually uncorrelated with inequality and access to health care. https://jamanetwork.com/journals/jama/article-abstract/2513561 https://jamanetwork.com/journals/jama/article-abstract/25135...

coldtea 10y ago
>While life expectancy is strongly correlated with income, it's actually uncorrelated with inequality and access to health care. Or so says one published study, but scientific consensus develops across many studies, responses to them, meta-studies, and over a long period. Here is a counter result for example: https://www.ncbi.nlm.nih.gov/pubmed/15650149 https://www.ncbi.nlm.nih.gov/pubmed/15650149
xaa 10y ago
It's difficult to interpret this because the paper defines income throughout the text in terms of income quantile (not absolute, COL-adjusted levels). And oddly, in the FT, they show a significant negative correlation b/t Gini index and life expectancy in the upper quartile, and not the lower quartile (you'd expect the opposite). In the lower quartile, it is p=0.11, r=0.2; i.e., a nonsignificant positive association between income inequality and income quartile. This suggests the effects of income inequality might actually affect LE in different directions for high and low earners. This would explain why they say in the abstract they don't see a LE-Gini correlation for the distribution as a whole. The story I would spin is that, within one geographic region, a high Gini actually means that rich and poor are living in proximity and sharing social structures. If you have low Gini in a compact region it likely means everyone there is (uniformly) poor. IOW, they very likely have a correlation b/t Gini and income levels they aren't controlling for. This is why we use multivariate regression with interactions, not just run a Pearson between every pair of variables. The statistics in this article are really bad. JAMA strikes again.