sibly make an adjustment for insurance status and then to compare that outcome with some measure of economic resources. But in this cost model, baseline risk would be a function of claim types. Utilization of health care services is certainly influenced by insurance status, so the uninsured, for example, will have fewer claims. The baseline level of risk for the uninsured, before one compares that with a comparable risk of the insured person, is therefore going to be somewhat of a muddied measure at the baseline. He wanted to know if Meier and Wolfe had thought about that, or how they addressed it in their paper.

Wolfe responded that one approach is just to estimate these risk levels for people who are insured. In fact, if there were sufficient observations, one could do it for people who have insurance similar to what one has in mind as the appropriate benefit package. That way would avoid including those with so much coverage that they are using care that may not have much of an effect, as well as dealing with the underinsured or the uninsured problem.

John Czajka (Mathematica Policy Research) observed that one of the issues the study panel has to deal with is the intersection between income poverty and this risk index. One can certainly imagine people who are poor by the SPM income measure who would not be considered to have a risk of getting worse. He asked: Is that the right way to think about these two indices?

Short responded that yes, that is what she was thinking. The first part, the SPM-like piece that is really about certain spending and certain income, does not include risk. And the second part is really a measure of risk—if one sets the definition of a catastrophe at the poverty threshold from the first index, then the question really becomes what is the probability of uninsured expenses that would cause a person to fall below that threshold or fall down relative to that standard.

She also responded to Meier’s comments, stating that she might have misunderstood what Short meant in speaking about the cost equivalent of someone being at risk, because they are complementary. If someone is at risk of being poor—and that could be a yes/no indicator or a how much at risk indicator—one way of quantifying that is to determine how much money it would take to eliminate that risk. And that is kind of the way she thinks about it in an insurance framework.

Kenneth Finegold (Office of the Assistant Secretary for Planning and Evaluation, HHS) mentioned one point that comes up in the context of what spending is necessary: the Dartmouth Atlas–inspired theme that there is unjustified variation in spending geographically, suggesting that overall a substantial portion of medical spending in the country is not necessary and in fact could be avoided (see And some places have found a way to avoid it. The ACA provisions try to adopt some of those methods.

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