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One possible partial explanation for this is the same reason why the Bill Gates Foundation wasted a bunch of money fostering small high schools. Smaller high sc

by generj 9y ago

One possible partial explanation for this is the same reason why the Bill Gates Foundation wasted a bunch of money fostering small high schools. Smaller high schools were some of the best performing schools...but also some of the worst [0]. The answer is just that small counties have high variance. By chance some small counties will be a lot higher or lower than the national average. I would be interested in seeing a Cox Proportional Hazards Model would show if the remaining changes are related to pollution, meth, economics, etc. [0]http://marginalrevolution.com/marginalrevolution/2010/09/the-small-schools-myth.html http://marginalrevolution.com/marginalrevolution/2010/09/the...

Nacraile 9y ago
Sample-size-based variance is a nice nit to pick, but it doesn't really seem to match the data presented in the article. Outcomes are strongly correlated in adjacent counties across the map, which is not predicted by sample-size (which would produce uncorrelated noise, in inverse proportion to population density). On the other hand, there is substantial correlation between the given geographic distribution of life expectancy changes and geographic distribution of wealth, as observed in the article.
- generj 9y ago
  Yes, this is correct. Especially with how extreme the values are within the clusters (like in Kentucky), there is almost no chance they occurred by sample-size variance. Sample-size variance might explain some of those little counties with blips out in OK, MS, and TN though. As an aside, the color scheme on the map makes any decrease look substantially more massive. I wish we had a map which was recentered on the national average growth of life expectancy as well.
misiti3780 9y ago
i was ready to add that same comment.
flexie 9y ago
But even the small counties have populations in the several hundreds or thousands, right? Outliers that die at 33 or 103 affect the average little when you have a population of 2,000.
- deleted 9y ago
  [deleted]
- generj 9y ago
  You'd think that, but the US has about 724 deaths per 100,000. In a county with fewer than 10,000 people that leaves only about 72 deaths per year. Loving County in Texas has 112 residents. "35 counties have a population under 1,000; 307 counties have a population under 5,000; 709 counties have a population under 10,000" [0]. I haven't read the paper, so I don't know their exact methodology but if they just took a snapshot in 1980 and then in 2014 a county of 10,000 would only be looking at about 145 deaths total. That's small enough that it's pretty possible for outliers to have a big impact. In a county of 1,000 there are only 7 deaths a year, so if you only look at two years that's a total of 14 deaths. It doesn't take a lot to jostle the numbers when you don't have many observations. In any case, this isn't the main contributor here, as pointed out elsewhere. But it is a good thing to be aware of whenever county level statistics are provided. [0]https://en.wikipedia.org/wiki/County_(United_States)#Population https://en.wikipedia.org/wiki/County_(United_States)#Populat...
  - itissid 9y ago
    It seems they are being smart about using counties: "All analyses were carried out at the county level. Counties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110 (eTable 1 in the Supplement). For simplicity, these units are referred to as “counties” throughout." They also use https://en.wikipedia.org/wiki/Small_area_estimation https://en.wikipedia.org/wiki/Small_area_estimation which seems like a way to deal with the counties with small sizes. And from what I can tell they are using hierarchical models that should have some regularizing effect from larger populations.
- 9y ago
PeterisP 9y ago
The largest life expectancy increase is in New York county, which has a population of 1.6 million, comparable to whole smallish countries - no, this is not explained by high variance in small counties.
Perceval 9y ago
Here's a post on how to address trying to map the abnormal while controlling for variance across sample sets: https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865 https://medium.com/@uwdata/surprise-maps-showing-the-unexpec...
aaron695 9y ago
Failure != Wasted

by generj 9y ago

Nacraile 9y ago

Sample-size-based variance is a nice nit to pick, but it doesn't really seem to match the data presented in the article. Outcomes are strongly correlated in adjacent counties across the map, which is not predicted by sample-size (which would produce uncorrelated noise, in inverse proportion to population density). On the other hand, there is substantial correlation between the given geographic distribution of life expectancy changes and geographic distribution of wealth, as observed in the article.

generj 9y ago
Yes, this is correct. Especially with how extreme the values are within the clusters (like in Kentucky), there is almost no chance they occurred by sample-size variance. Sample-size variance might explain some of those little counties with blips out in OK, MS, and TN though. As an aside, the color scheme on the map makes any decrease look substantially more massive. I wish we had a map which was recentered on the national average growth of life expectancy as well.

misiti3780 9y ago

i was ready to add that same comment.

flexie 9y ago

But even the small counties have populations in the several hundreds or thousands, right? Outliers that die at 33 or 103 affect the average little when you have a population of 2,000.

deleted 9y ago
[deleted]
generj 9y ago
You'd think that, but the US has about 724 deaths per 100,000. In a county with fewer than 10,000 people that leaves only about 72 deaths per year. Loving County in Texas has 112 residents. "35 counties have a population under 1,000; 307 counties have a population under 5,000; 709 counties have a population under 10,000" [0]. I haven't read the paper, so I don't know their exact methodology but if they just took a snapshot in 1980 and then in 2014 a county of 10,000 would only be looking at about 145 deaths total. That's small enough that it's pretty possible for outliers to have a big impact. In a county of 1,000 there are only 7 deaths a year, so if you only look at two years that's a total of 14 deaths. It doesn't take a lot to jostle the numbers when you don't have many observations. In any case, this isn't the main contributor here, as pointed out elsewhere. But it is a good thing to be aware of whenever county level statistics are provided. [0]https://en.wikipedia.org/wiki/County_(United_States)#Population https://en.wikipedia.org/wiki/County_(United_States)#Populat...
- itissid 9y ago
  It seems they are being smart about using counties: "All analyses were carried out at the county level. Counties were combined as needed to create stable units of analysis over the period 1980 to 2014, reducing the number of areas analyzed from 3142 to 3110 (eTable 1 in the Supplement). For simplicity, these units are referred to as “counties” throughout." They also use https://en.wikipedia.org/wiki/Small_area_estimation https://en.wikipedia.org/wiki/Small_area_estimation which seems like a way to deal with the counties with small sizes. And from what I can tell they are using hierarchical models that should have some regularizing effect from larger populations.
9y ago

PeterisP 9y ago

The largest life expectancy increase is in New York county, which has a population of 1.6 million, comparable to whole smallish countries - no, this is not explained by high variance in small counties.

Perceval 9y ago

Here's a post on how to address trying to map the abnormal while controlling for variance across sample sets: https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865 https://medium.com/@uwdata/surprise-maps-showing-the-unexpec...

aaron695 9y ago

Failure != Wasted