Prolific sender-inner Dmitriy T.M. sent in a link to a story at Slate showing increases in rates of U.S. adults of diabetes between 2004 and 2008, as well as the distinct regional variations. You can look at maps individually or watch a time-lapse slide show.

Percent of adults diagnosed with diabetes in 2004:


The change is pretty dramatic for such a short time period. I presume differences in poverty rates, access to health care, and nutritional differences all play a part. Any demographers or public health scholars out there with insights?

The CDCP has the data available for download, and you can play around with different maps (# of adults instead of % and so on).

UPDATE: Reader Arielle says,

Please clarify the type of diabetes this graph is discussing (I assume it’s Type 2). As it stands, you’re perpetuating two myths: one, that all types of diabetes are affected by “poverty rates, access to health care, and nutritional differences” (Type 1 is an autoimmune disease and has nothing to do with lifestyle), and two, that only children are diagnosed with (or have) Type 1 diabetes.

Here’s a problem: neither the CDCP nor the Salon Slate article specify. They say “adult diabetes,” meaning individuals over the age of 18 who are diagnosed with diabetes (so not necessarily adult onset diabetes). I think that would mean either Type 1 or Type 2. But I’d take the data with a little caution since the source doesn’t make that absolutely clear.

UPDATE 2: Chris points out an important element of what might be going on here:

More significantly one of the comments on Slate (not Salon) said that the diagnostic standard for diabetes recently dropped from 140 to 124.  That’s going to add a LOT of people to the second map if the transition occurred between 2004 and 2008.

That would make a lot of sense, because quite honestly, I just couldn’t figure out how the overall numbers had increased so rapidly in just 4 years. This is a great example of the social construction of health and disease: we are constantly defining and redefining what does and doesn’t count as unhealthy, which can dramatically affect statistics on the topic without there necessarily being a large change in the overall state of the population.