Medicaid/Medicare Medicare

What can Geographic Variation in Health Care Spending Tell Us About Efficiency?

In a project with the Institute of Medicine (IOM), I examined the sources of regional variation in Medicare and Medicaid spending and spending growth. The IOM wisely concluded that policymakers should target decision-makerss rather than geography when attempting to improve the efficiency of the healthcare system.

I recent paper by the Louise Sheniner of the Brookings institute claims that patient characteristics may drive most of the regional variation in healthcare spending. Her conclusion is:

But the paper has several findings that suggest that the variation in Medicare spending does not represent wasteful spending that could be easily eliminated without significant effects on the health system. First, population characteristics have more explanatory power for Medicare spending than measures of social capital, indicating that the variation in patient characteristics is more important than variation in provider characteristics. Second, health measures are significantly more correlated at the state level than at the individual level, making it likely that state level regressions do a better job of controlling for unobserved variation in population health. Third, there does not seem to be a significant relationship between the use of “preference-sensitive procedures” and the level Medicare spending. Fourth, states with high levels of Medicare spending tend to have lower levels of non-Medicare spending. Providers in these states may face greater financial difficulties, and may “volume shift” to Medicare patients in order to cover costs.

First, I would agree that population characteristics do make up a large share of the differences across states. However, they cannot explain all the differences. Since Dr. Sheiner relies on state-level data rather than individual level data, and the Dartmouth Group wisely points out the issue of an ecological fallacy of assigning individual-level inference based on group level data. However, the examination of average patient characteristics misses the point. In our research for IOM, we found that variation in median spending across regions is much less than regional variation in average spending. In essence, it is how difference physician practices treat the sickest patients in the tails of the distribution that determines whether an area is low or high cost; regional policies to treat low or medium cost patients have little affect on an areas relative spending ranking due to the skewed nature of health care data.

This finding is consistent with Dr. Sheiner’s conclusion that preference-sensitive procedures are not driving the regional variation in spending. Just because one area is more likely to give hip or knee replacements than another, this practice pattern will be dwarfed by how a region treats end of life patients or patients with multiple serious comorbidities.

Overall, however, I do agree with Dr Sheiner’s conclusion that geography is a straw man here. We need to focus on how policymakers treat the sickest patients and identify treatments that are most cost-effective to help improve their health and the nations’ fiscal bottom line.


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