So I’m updating my RSS feed library with federal government feeds. You know, so I can keep up with how my tax dollars are being wasted to take away my liberty. Anyway, I’m pulling a feed URL from the Congressional Budget Office, and on the front page of their site, is a link to a PDF entitled “Geographic Variation in Health Care Spending, February 2008″. And there’s a pretty picture. A nice graphic showing “Medicare Spending Per Beneficiary” based on 2005 data. Here’s the graphic:

And what strikes me about this, immediately, is that the darker areas seem to be clustered around population centers — look at California, Texas, New York, etc.
So I skimmed over the report. Very briefly, because I was getting tired. Now, I admit I didn’t go over the report in great detail, but I didn’t see population mentioned as a factor at all.
Explaining Geographic Variation in Health Care Spending
Several researchers have examined explanations for geographic variation in per capita health care spending (see Table 1); most of their studies focus on the Medicare fee-for-service program, largely because better data are available for Medicare than for the private sector. The typical approach has been to measure geographic variation in unadjusted spending per capita and then to measure variation in spending per capita after adjusting for various factors that are believed to affect spending. The contribution of a given factor to geographic variation is measured by the degree to which variation is reduced after adjusting for that factor.
Those factors can be divided into four broad categories, each discussed in detail in the following sections:
- Prices paid for medical services,
- Health and illness status of residents of a given region,
- Regional preferences about the use of health care services (and the determinants of those preferences, such as income), and
- Residual (unexplained) variation.
That last one there seems to me to be double-speak for: “We don’t really know what’s going on.”
The factors listed don’t say anything about population at all. But compare the Medicare spending map above with this map:

This one is a population density map I was able to generate on the US Census Bureau’s FactFinder site. It shows persons per square mile in the US by metropolitan region, using Census 2000 data. Granted, the data is eight years old, and is five years older that the Medicare spending data, but I’m pretty sure that the general population centers haven’t changed that much since the last census.
Look at how closely the two graphics correlate. It’s not perfect, but it’s close enough for me to think that what’s going on in the Medicare sending data is a function of population more than anything else. I think that the population factors are probably the cause for whatever effect is from the three factors listed in the pull quote above (from the report) — discounting, of course, the “Residual (unexplained) variation,” we-just-don’t-know factor.
My conclusion? That the cost of living increases as population increases — which is pretty much exactly what you’d expect.
This actually doesn’t have much to do with anything I’m interested in, except for a little bit of information theory. And Whether the factors in the report are the base causes for the geographic variations in Medicare spending, or are simply secondary causes from a function of population probably doesn’t really matter.
So what’s the point of all this? Not a darn thing. But it’s random crap like this that floats around in my brain and won’t let me sleep until I finish processing it. So I could either suffer through insomnia or post about it.
I posted about it now, and I’m very tired, and it’s almost a half-hour into tomorrow now, so I’m going to sleep. Good night.
p.s.: And guess what? I didn’t finish adding all the RSS feeds that I wanted to add into my reader before I got all tired. Sheesh …