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. 2007:115:103-140.
doi: 10.1086/512249.

PRODUCTIVITY SPILLOVERS IN HEALTHCARE: EVIDENCE FROM THE TREATMENT OF HEART ATTACKS

Amitabh Chandra  1 , Douglas O Staiger
Affiliations

PRODUCTIVITY SPILLOVERS IN HEALTHCARE: EVIDENCE FROM THE TREATMENT OF HEART ATTACKS

Amitabh Chandra et al. J Polit Econ. 2007.

Abstract

A large literature in medicine documents variation across areas in the use of surgical treatments that is unrelated to outcomes. Observers of this phenomena have invoked "flat of the curve medicine" to explain these facts, and have advocated for reductions in spending in high-use areas. In contrast, we develop a simple Roy model of patient treatment choice with productivity spillovers that can generate the empirical facts. Our model predicts that high-use areas will have higher returns to surgery, better outcomes among patients most appropriate for surgery, and worse outcomes among patients least appropriate for surgery, while displaying no relationship between treatment intensity and overall outcomes. Using data on treatments for heart attacks, we find strong empirical support for these and other predictions of our model, and reject alternative explanations such as waste or supplier induced demand, for geographic variation in medical care.

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Figures

Figure 1
Figure 1
Multiple Equilibrium (Panel A), versus Single Equilibrium (Panel B) Characterizations of Area Variations.
Figure 2
Figure 2. Graphical Illustration of Roy Model with Productivity Spillovers
Panel A describes the relationship between two alternative ways to treat patients within an area. The survival production possibilities frontier describes the best treatment for a patient of given clinical appropriateness. The model predicts that the returns to intensive management are increasing in patients’ appropriateness for such interventions. Panel B contrasts the care across two areas that differ in their surgical intensity. The productivity spillover results in patients appropriate for intensive management being better off in the surgically intensive areas, whereas patients appropriate for non-intensive management being worse off in such areas.
Figure 3
Figure 3. Relation Between Average Patient and Marginal Patient Receiving Cardiac Catheterization
For each of the 306 HRRs we graph the average propensity to receive cardiac catheterization (amongst patients who actually received it) against the log of the area risk-adjusted CATH rate. Using local-regression, we estimated the relationship between the average propensity and the risk-adjusted CATH rate and the slope of this line at each point. These estimates were then used to plot average (upper line) and marginal patient (lower line and estimated as the local difference in the average) receiving treatment.
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