## Where the fat folks live

Since it’s after Thanksgiving and I’m feeling bloated, I figure a follow up to the post on obesity and diabetes might be apropos. I want to focus on obesity. I have the raw county-by-county data, but obviously it isn’t broken down by race. But, I do have the proportions for reach race by county, and, the CDC provides state-by-state breakdowns of the proportion of obese by race. So I decided to “estimate” the proportion of whites obese by county.

1) By “white,” I mean “Non-Hispanic white.” I’m going to say “white” from now on exclusive of Hispanics.

2) Some states, such as Vermont, do not have a large enough sample to estimate the obesity proportion of blacks. I just used a neighboring state to fill in the numbers. This guesstimate is really not much of an issue because the proportion of blacks is so low in the states I had to estimate that the estimate of obesity for whites and estimate of obesity for all races is the same in these counties anyhow.

3) Simple algebra. Total Obesity Percent In County = (Obesity Percent Whites) X (Percent Whites) + (Obesity Percent Blacks) X (Percent Blacks) + (Obesity Percent Latinos) X (Percent Latinos)

For the obesity percent of blacks and Latinos I only have state level data, so this is going to be a rough estimate. And it’s going to result in the variation exhibiting state-to-state discontinuities, since the county variable is dependent on a state level variable. Also, I discarded some counties where the usage of state level data caused really big distortions. Along the Mexican border Latinos are not nearly as obese as they are further into the United States, so I end up with numbers where whites have negative obesity percentages to make the math work out. These are counties which are 90% or more Latino with relatively low obesity numbers.

I did the map shading the way I normally do. Blue is above the median value, and red below the median value, with the scale being set to their max and mins respectively. Unfortunately this causes a problem in the scaling in terms of an asymmetry because one side of the distribution will tend to have a more extreme outlier (usually the above median is where the skew is).

Here’s the map with all the populations:

This is basically the earlier map except shaded differently. Here are the summary statistics for obesity by county:

min = 12.40
1st quartile = 26.60
median = 28.40
mean = 28.25
3rd quartile = 30.20
max = 43.70

Now for my estimate of whites only:

As you can see, the use of state level is causing some distortions. Also, you see something peculiar in the summary statistics:

1st quartile = 25.54
median = 27.62
mean = 26.71
3rd quartile = 29.47
max = 58.11

These averages don’t align with the CDC values aggregated. But that’s because I’m looking at county level data, and not weighting by population. Lots of low density counties with few people have many obese people. Instead of looking at national averages, we’re looking at regional variations.

On the estimates, Texas probably jumps out at you. To my surprise it turns out that whites in Texas are a touch lighter than the national average for whites! For me the big thing that sticks out is that Appalachia seems to be split in two, along the Appalachian Trail (I feel funny mentioning the Appalachian Trail….). Some areas, such as New England, Colorado and California do not surprise in terms of whites who are below the national median. But again there is a pattern of some pockets in the Upper Midwest being relatively under the norm in the proportion of obesity. Some of you might be surprised by the Pacific Northwest, but this region is characterized by urban-rural polarization.

What are the correlations by ethnicity? Here are the correlations with white obesity in terms of ancestral proportion (the proportion of ethnicity X as a proportion of whites):

English = -0.17
German = -0.02
American = 0.07
Scots Irish = -0.13
Irish = -0.19

These are very modest correlations. Probably mostly explained by geography. How about voting?

Obama vote = -0.21

Again, modest. Median Family Income? Only -0.14! That surprised me. Interestingly, Median Home Value had a -0.26 correlation with obesity. Of course the “Dirt Gap” tracks this; in places where people are thinner property values are higher, and rose higher in the past decade. The proportion who have a college degree is like home value, a correlation of -0.25.

None of this is really surprising, on the aggregate level you know that wealthier and more educated people are thinner. So I might as well do something that’s not totally predictable. Most of the variance of obesity on the county level isn’t predicated by educational levels, but a non-trivial fraction is. I decided to fit a loess curve to the plot of obesity (white) who are college educated. Then I simply took the residuals above and below the line and shaded them blue and red respectively. In other words, blue areas have a lot of fat people for the number of college graduates, while red areas have relatively few fat people for the number of college graduates.

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1. Running to Google, I plugged in ‘Why is Colorado…’, and Google offered 5 search completions. Two being: ‘Why is Colorado the thinnest state’ and ‘Why is Colorado the least obese state’.

New to me.

2. Mountain states have low obesity (including 5 of the 10 states with the least childhood obesity), and also have high IQs, so this may fit in with agnostic’s theory about pathogens influencing general Mountain State health.

But the sharp contrast between neighboring counties at the border of Colorado and Kansas gives me pause. Why would white children in Prowers County, CO be skinnier than white children in adjacent Hamilton County, KS? I doubt the people are different. State level school lunch differences?

3. The population of Hamilton Co,KS, is less than 3,000. So it could be that the uniformity of obesity levels across these western Kansas counties in contrast with the uniformly lower rates across Colorado’s eastern counties could be an artifact of the estimation method, which uses state level data to estimate county level data where the statistical sampling is low.

4. Thanks for the link, Ziel. I see now Zeeb noted that in the last post. That certainly must explain it.

And the plains people in Eastern Colorado are fatter like Kansans, while the mountain people in Western Colorado are thinner, as per the Mountain State profile.

5. some state borders, like kansas/colorado, seem to have an outsized effect even without my estimate:

http://www.cdc.gov/obesity/data/trends.html

some hypotheses:

1) different state level protocols and reporting or collecting data on these traits, or estimates in the methods like i did

2) population centers really far from each other within counties on opposite sites of the borders are very different

3) different state level public health policies effecting obesity and diabetes rate (e.g., school lunches?)

i think #1 is the most plausible. the state-line effect is too pervasive for #2, and i’m skeptical of #3.

6. One factor that probably explains part of the higher rate of obesity in the South is the level of religious participation. Purdue University sociologist Kenneth Ferraro published a study in 1998 showing that religious people had higher rates of obesity that others, and that this differed by denomination, with Baptists being the most likely to be obese.

This might also help to explain part of the east-west divide in the Appalachian region. Western Virginia, for example, is not as heavily dominated (no pun intended) by Baptists as the areas to the west.

One reason that has been suggested to explain this tendency is that it is related to the evangelical Protestant tendency to forbid the use of alcoholic beverages. Because they abstain, many Baptists and other evangelicals might tend to overeat where others might turn to alcohol.

7. One reason that has been suggested to explain this tendency is that it is related to the evangelical Protestant tendency to forbid the use of alcoholic beverages. Because they abstain, many Baptists and other evangelicals might tend to overeat where others might turn to alcohol.

interesting. but what about mormons? i’ve seen the data. in general conservative “sectlike” protestant groups have less health and wealth that more mature and establishment denominations. but i would tend to be of the opinion that this is a matter of sorting and the types of “services” that different classes of individuals might need.