| Abstract |
Organ transplantation is a life-saving procedure for patients with end-stage organ disease; delaying transplantation can have life-or-death consequences. Between March and April 2020, in the midst of the COVID-19 pandemic, multiple state governors issued orders to temporarily suspend non-essential surgery. Although such suspensions were not intended for essential surgery (e.g., deceased-donor kidney transplants), the literature on service operations management suggests that these suspensions may have either a positive or a negative spillover effect on essential surgery, depending on whether hospitals maintain or reduce resources in response to such suspensions. Motivated by this dichotomy, we estimate the potential spillover effect of suspending nonessential surgery on deceased-donor kidney transplantation. Through analyzing a dataset of all U.S. kidney transplant procedures, we observe a steep decline in the transplant volume in almost all states during the early months of the pandemic. However, states that suspended nonessential surgery experienced steeper declines than those that did not. Using a difference-in-differences approach, we estimate a state-level suspension of non-essential surgery led to a 23.6% reduction in transplant volume. This negative spillover effect is particularly pronounced for low-efficiency transplant centers with long cold ischemia times (CITs), but less so for high-efficiency centers. Our mediation analysis shows 38.7% of the spillover effect can be attributed to the change in healthcare employment. Our study suggests that in the event of a future public health crisis, policymakers should consider more nuanced approaches to securing the healthcare workforce critical to supporting essential services, especially for transplant centers with long CITs. |
| About the Speaker |
Guihua is an Assistant Professor of Operations Management and a Sydney Smith Hicks Faculty Fellow at the Naveen Jindal School of Management, The University of Texas at Dallas. He obtained his PhD from the University of Michigan, MSc from the Georgia Institute of Technology, MSc and BEng from the National University of Singapore. Prior to his PhD study, Guihua worked as a supervisor of the industrial engineering department at UPS Asia headquartered in Singapore. Guihua’s research focuses on the intersection of empirical econometrics and machine learning with application to personalized healthcare. More specifically, Guihua has developed new empirical machine learning techniques such as instrumental variable forest and first-difference causal tree for heterogeneous treatment effect analysis using observational healthcare data. Guihua’s research has been published at Management Science, Manufacturing & Service Operations Management, Production and Operations Management, Advances in Applied Probability, Surgery, and Annals of Thoracic Surgery, and received media coverage by Associate Press, Crain’s Detroit, Houston Chronicle, Medical Xpress, National Interest, NPR, PRI, Science Daily, Simply Flying, The Conversation, and Yahoo! News. His research was named a finalist of both the Pierskalla Best Paper Award and the MSOM Service Management SIG Best Paper Award, a runner-up of both the Financial Times Responsible Business Education Award and the INFORMS Service Science Best Cluster Paper Award, the winner of the INFORMS Health Applications Society Student Paper Competition, two-time finalists of the MSOM Student Paper Competition, and a finalist of the INFORMS Service Section Best Student Paper Competition. |
