The Darmouth Atlas of Health Care Studies: Are We Misunderstanding the Data?

Readers will know that one of my pet peeves that I inflict upon you, dear readers, is my dislike of misinterpreting data. In the healthcare debate, one of the key studies that many have relied on, including the Obama Administration, is the Darmouth Atlas of Health Care Studies. A major finding of the DAHCS is that there were tremendous regional differences in costs for various procedures. Some, including the Obama Administration, have argued that these regional differences mean that many doctors and hospitals overcharge and overtreat, and that massive savings could be realized if these inefficiencies were removed. This assumes, however, that these regional differences in costs are due to medical decisions, and not medical necessity. One critic, Dr. Richard Cooper, disagrees (italics mine):

The fundamental problem with studying geographic differences is that poverty is geographic, and poverty is the major factor that influences population health, health care costs and outcomes. Low-income patients are sicker, they cost more and their outcomes are worse.
The Dartmouth group uses three different levels of analysis. One is hospitals, and we know that some serve poor populations. A second one is states, and we know that there are rich states, like Massachusetts, and poor ones, like Mississippi. But it’s more complicated than that. Some states, like New York and California, are wealthy on average but include areas of dense poverty.

The third level is made up of about 300 hospital referral regions in which most of the patients use hospitals in the region most of the time. These are the building blocks of the Dartmouth Atlas. But averages can be misleading.
For example, if you average a city like Detroit, one of the poorest, with the adjacent Oakland County, one of the richest, you get average.
…you can’t talk about income inequality if you take an area like Detroit and Oakland County and average it. In fact, overall it’s rich. But there is a tremendous use of resources in Detroit because of the poor population. That’s where all the utilization is.
There’s another important point. The relationship between lower income and higher utilization is not linear. It zooms up at very low income. So, whether in a hospital referral region, a state or a hospital, if the population of patients is averaged to obtain a single value for income, utilization and outcomes, small numbers of poor patients contribute to high utilization and poor outcomes, while small numbers of wealthy patients raise the average income to a higher level.
There could be real differences in the way care is given, but the income effect is so large, they’re impossible to discern. On the other hand, if you believe that there are no differences due to income, or you think you have corrected for them, then all of the differences that are observed are interpreted as due to practice differences. And that’s exactly what the Dartmouth group does.

Then there’s the issue of what exactly is being measured (italics mine):

There was another problem with these studies, and it was pointed out in papers by Gerald Neuberg at Columbia, Michael Ong and his associates in California and, most recently, Peter Bach in the New England Journal of Medicine. It has to do with what is measured. The Dartmouth group only measures costs in the last two years of life, and because everyone had died.
They point out on their Web site that “The study focused on patients who died, so we could be sure that patients were similarly ill across hospitals. By definition, the prognosis was identical–all were dead. Therefore, variations cannot be explained by differences in the severity of illnesses.”
Now, that is patently absurd. Everyone knows it. In fact, Peter Bach showed that length of stay in various hospitals correlates strongly with the predicted risk of death of patients on admission. Poorer and sicker patients stay longer.
So if you look at hospital readmission rates in the inner cities of places like Detroit or Milwaukee, two very segregated cities, the rate of admission for common diseases, heart failure and asthma, are five- and six-times higher in the poorer areas than in the rest of the community.

There are big savings to be realized, but they’re not geographic, they’re economic:

In fact, some care is unnecessary, and large amounts of necessary care are not being delivered. The system is imperfect. But this is not a geographic problem. It’s true everywhere and in all income groups. What is geographic and draws on a lot of extra resources is poverty. The “big bucks” are in the care that is necessarily given to patients who are poor. Their care is necessary. Their poverty is not.

Dirty Fucking Hippie.

This entry was posted in Economics, Healthcare. Bookmark the permalink.

1 Response to The Darmouth Atlas of Health Care Studies: Are We Misunderstanding the Data?

  1. hipparchia says:

    damn, i missed this when you first posted it.
    yeah, the dartmouth atlas is my favorite example of bad science being used to craft bad public policy.

Comments are closed.