Abstract: This paper develops a framework for assessing the impact of changes to the deceased-donor kidney allocation policy taking into account the transplant candidates’ (endogenous) organ acceptance behavior. To be specific, it advances a dynamic structural model of the transplant candidates’ accept/reject decisions for organ offers. Our formulation models the national list (and its geographic structure) which is important for practical implementations, e.g.. for incorporating it in the Kidney Pancreas Simulation Allocation Model (KPSAM). Moreover, it allows various important features of the transplant system such as the degree of tissue matching between the donor and the transplant candidate, changes in the health status of the transplant candidates as they wait on the list, organ quality, geographical sharing and cold-ischemia time of the organs as well as the heterogeneity in transplant candidates’ quality of life scores. Using United Network of Organ Sharing (UNOS) data on transplant candidates, donors, organ offers, and follow up results on transplant outcomes, we first estimate the transplant candidates’ quality of life scores. Our estimates are based on patient’s revealed preferences and yield similar results on average to what is typically assumed in the medical literature. However, they differ significantly when patient and donor characteristics are considered. We then perform various counterfactual studies for assessing the (unintended) consequences of policy changes. In particular, we find that although the current policy increases the total number of transplants by 2.63% and total life years by 4.45%, it decreases total quality adjusted life years by 1.68%. Moreover, it increases the disparity in probability of getting a transplant for patients of different health scores by 69.3%. These happen due to the current prioritization of healthier patients for kidneys of better quality. We also show that geographical redistricting of the transplant system, as done for the liver allocation system, does not change the system performance significantly. However, the brevity matching policy which is a last-come-first served distribution policy based on the health scores of the patients, can further increase the total number of transplants by 1.50%.

Bio: Cem Randa is a Postdoctoral Researcher at UC San Francisco Medical School and a Research Associate at UC Berkeley Haas School of Business. He earned his PhD degree at University of Chicago Booth School of Business in Operations Management. He specializes in solving healthcare operations problems using structural estimation methods and queueing theory, with a focus on improving the nation-wide deceased-donor kidney allocation system.