Much policy attention has been drawn to the large and persistent geographic variation in healthcare spending – for good reason. The presence of such variation (in the absence of commensurate variation in patient needs or even in health outcomes) suggests that high-intensity practice patterns in some areas signal inefficient resource use. This has led to discussion of policy levers to rein in spending in high-utilization, high-cost areas, such as lowering Medicare payments to providers in those areas. Such policy levers aim to focus on a level of aggregation that captures the local healthcare delivery system – too low a level (such as individual physician payments) could miss the system-level factors that drive some areas to be systematically high-utilization, while too high a level (incorporating multiple systems and markets) could reward or punish utilization well beyond providers’ control and mask substantial heterogeneity.
Analysis has primarily been focused on variation between HRRs – areas defined based on large tertiary facilities and incorporating numerous HSAs. There are advantages to looking at such large areas (they may be large enough to capture more homogeneous patient pools), but the disadvantage of such an exclusive focus is that it can mask substantial heterogeneity at the more local level. This is particularly important when considering the effects of policy levers that aim to act on local practice patterns for primary care or avoidable hospitalizations, for example.
We examined the degree of heterogeneity within HRRs. We found that there is substantial local variation in utilization and spending for both drug and non-drug medical spending and that there is substantial dispersion of local spending within HRRs.
These findings are of course subject to several limitations. First, our analysis is based on the Medicare population. Patterns among the commercially insured may differ. Medicare does, however account for 20% of all national health care spending as of 2010,13
and many of the policy levers discussed apply to Medicare payment rates. Second, our risk-adjusters are imperfect and we do not capture patient preferences.14
To the extent that these vary across localities, they could drive some of the observed patterns of heterogeneity. It is somewhat reassuring on this front that patterns of unadjusted outcomes are quite similar to those with adjusted outcomes. Third, causal connections are inherently difficult to draw from ecological data. While the variation described here (and elsewhere in the literature) is strongly suggestive of inefficient use of resources, it is difficult to use these data to forecast what the effect of different policy levers might be on spending patterns.
Nevertheless these findings do have policy implications. Policies that aim to reduce the spending in high-cost areas by targeting high-spending HRRs may fail on both sensitivity and specificity: about half of the HSAs in the highest spending HRR quintile are not in the highest spending quintile of HSAs; and about half of the HSAs in the lowest spending HRR quintile are not in the lowest HSA quintile. That said, higher spending HSAs are generally in higher spending HRRs. Whether or not this degree of concordance is sufficient to achieving policy aims depends on policy-makers’ tolerance for the ramifications of imperfect targeting.
This does not, however, tell us what the “right” level of aggregation for policy is. There is clearly variation in spending within HSAs – should policy focus on an even more local level? The movement towards Accountable Care Organizations (ACOs) aims to tie payments to the care delivered by provider groups that are large enough to pool risk and abstract from individual-level variation in needs and idiosyncratic outcomes, but small enough to hold the group accountable for the use of resources. In the absence of formal ACOs, payments tied to local area practice patterns aim to accomplish similar goals. This analysis suggests that policies focused exclusively on the hospital referral region may be too blunt to promote the best use of health care resources.