A new organ transplantation location–allocation policy: a case study of Italy

Abstract

In this paper, we propose a location model for the optimal organization of transplant system. Instead of simulation approach, which is typical when facing many health care applications, our approach is distinctively based on a mathematical programming formulation of the relevant problem. In particular, we focus on the critical role of time in transplantation process as well as on a spatial distribution of transplant centers. The allocation of transplantable organs across regions with the objective of attaining regional equity in health care, is the aim of this paper. Our model differs from previous modeling approaches in that it considers the nationwide reorganization of the transplant system, identifying system barriers that may impair equity and efficiency. The demolition of these barriers may leads on a reduction of waiting lists and of wasted organs. We provide the basic structure and the properties of the model, and validate it on a real case study. The experimental validation of the model demonstrates the effectiveness and robustness of our proposal.

Optimizing the facility location design of organ transplant centers

“…Most work related to organ donations and organ transplants focuses on policies to allocate organs to recipients (see [6,24], and references therein). Within this domain, kidney and liver transplants are most frequently studied, since they constitute the vast majority of transplanted organs (Belgium is no exception, as can be observed from Table 1).…”

“…Bruni et al [6] and Kong et al [18] are, as far as we know, the only papers in location theory related to organ transplants. Bruni et al [6] present a mixed integer linear programming (MILP) model that selects the optimal location of recipient centers, donor centers and organ procurement organizations (OPOs).…”

Simulation models for socioeconomic inequalities in health: A systematic review

“…Depending on the situation, a characteristic of the system may be more or less important to model (e.g., modeling a dynamic system with active individuals may be particularly interesting when studying the impact of a policy on health behavior, because this impact depends on individuals' adaptation and may vary over time). Some studies reported in their limitations that their model lacked dynamic [56,62], stochastic [57,75] or individual heterogeneity [25]. These concerns reflect the different considerations that must be balanced when developing any simulation model: the accuracy of the model, its validity and its applicability.…”

“…Nearly all reported models included more than one level of factors, e.g., cold-ischemia time of an organ transplant (endogenous), waiting time of the patient (individual), location of the health center (neighborhood) and allocation rules (policy) [57].…”

Matching patient and physician preferences in designing a primary care facility network

“…A solution that compromises these conflicting objectives may help a central decision maker in developing effective and sustainable public health-care systems. In this context, Bruni et al (2006) propose a binary integer programming model to locate transplant centres by using an objective function incorporating both total travel distance and maximum waiting list size, which is the equity criterion forced to decrease by the model. Furthermore, Gu et al (2010) analyse a bi-objective model with the objectives of maximizing coverage and maximizing participation.…”