ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has disrupted normal operating procedures at transplant centers. With the possibility that COVID-19 infection carries an overall 4% mortality rate and potentially a 24% mortality rate among the immunocompromised transplant recipients, many transplant centers considered the possibility of slowing down and even potentially pausing all transplants. Many proposals regarding the need for pausing organ transplants exist; however, much remains unknown. Whereas the impact of the COVID-19 pandemic on the overall healthcare system is unknown, the potential impact of pausing organ transplants over a period can be estimated. This study presents a model for evaluating the impact of pausing liver transplants over a spectrum of model for end-stage liver disease-sodium (MELD-Na) scores. Our model accounts for two potential risks of a pause: (1) the waitlist mortality of all patients who do not receive liver transplants during the pause period, and (2) the impact of a longer waiting list due to the pause of liver transplants and the continuous accrual of new patients. Using over 12 years of liver transplant data from the United Network for Organ Sharing and a system of differential equations, we estimate the threshold probability above which a decision maker should pause liver transplants to reduce the loss of patient life months. We also compare different pause policies to illustrate the value of patient-specific and center-specific approaches. Finally, we analyze how capacity constraints affect the loss of patient life months and the length of the waiting list. The results of this study are useful to decision makers in deciding whether and how to pause organ transplants during a pandemic. The results are also useful to patients (and their care providers) who are waiting for organ transplants.
ABSTRACT
Since the outbreak of the COVID-19 pandemic, the effectiveness of stay-at-home orders implemented in many states in the U.S. has been the subject of ongoing debate. Whereas proponents believe these orders help reduce person-to-person contact and therefore the spread of the pandemic, opponents argue these orders are unnecessary and ineffective. In this study, we use eight states that did not implement the orders as a control group and six neighboring states that did implement them as a treatment group to estimate the effectiveness of stay-at-home orders. We find that although residents in both groups were staying at home even before the implementation of any order, these orders reduced the number of new COVID-19 cases by 7.6%. To understand the mechanisms behind these results, we compare the mobility of residents in the control and treatment groups over time. We find stay-at-home orders significantly reduced residents' mobility at grocery stores and pharmacies, transit stations, workplaces, and retail and recreation locations. The results of this study are useful to policymakers in conducting cost-benefit analyses of back-to-work plans versus stay-at-home policies and deciding whether to implement, extend, lift, or reimplement stay-at-home orders amid a pandemic such as COVID-19. Our results are also useful to researchers because we highlight the importance of correcting for potential selection issues. As we illustrate in this study, ignoring potential selection issues would lead to the wrong conclusion that stay-at-home orders increase the number of new COVID-19 cases. This article is protected by copyright. All rights reserved