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.
