Estimating the Causal Effect of Liver Allocation Policy Reflecting the Heterogeneity from Age and Geography

Project Summary / Abstract An optimal organ allocation policy for liver transplantation remains a critical yet unmet need. Fair and efficient organ allocation are often incompatible goals. Although the transplant community has made incremental changes to allocation policies over the years, disparities including, but not limited to, geographic region, age,

and disease type persist. This is problematic, as organs represent severely limited resources (e.g., In 2020, median pretransplant mortality rate remained high [12.2 per 100 waiting list-years]) that are allocated in a life- and-death context. One major challenge in this arena is that randomized controlled trials are virtually

impossible. Nevertheless, evidence-based policy making is necessary, making it difficult to accurately assess the causal effects of policies. Thus, it is essential to estimate the causal effects of these policies using observational data and rigorous methods that emulate the randomized trial. This Career Development Award

aims to prepare the applicant for a career as a health policy/services researcher focusing on causal inference applied to chronic liver diseases / transplantation and whose work addresses a variety of health disparities. This will be strengthened by the applicant’s solid clinical domain expertise in chronic liver disease and liver

transplantation. This goal will be accomplished within the infrastructure of the University of Iowa College of Medicine and College of Public Health through (a) specific graduate coursework, (b) a Mentorship Advisory Committee, (c) carefully selected conferences and workshops, and (d) a mentored research plan. The specific

aims of the proposed studies are to estimate (1) the causal effects of the fundamental liver allocation policies (the Share-35 and the Acuity Circle policies), which designate different geographic boundaries and cutoffs for the criterion (Model of End-stage Liver Disease score; MELD) that is used to determine medical priority for

allocation, (2) the causal effect of existing liver allocation priority rules in pediatric and adolescent candidates and (3) the potential causal impact of formulating a policy proposing to match the age of donors and recipients (donor-recipient age matching) and the cost-effectiveness of the potential policy implementation, all in the

United States (U.S.). These estimates will be computed rigorously using natural- and quasi-experiments with an overlying causal inference framework, using a well-established nationwide observational transplant cohort. At the end of this Career Development Award, the candidate will be well-prepared to become an independent

scientist with expertise in health policy, health services, epidemiology, and causal inference frameworks/econometrics focused on various disparities in healthcare (i.e., race, gender, age, geography, socioeconomic factors, and disease type), with specific expertise in chronic liver disease including liver

transplantation. Our study findings will inform revisions to the current liver allocation policy in the U.S. and future studies designed to understand the causal effects of health policies guiding chronic liver disease/liver transplantation, which will form the foundation of the candidate's first R01 submission.