» Articles » PMID: 23837931

Addressing Geographic Disparities in Liver Transplantation Through Redistricting

Overview
Journal Am J Transplant
Publisher Elsevier
Specialty General Surgery
Date 2013 Jul 11
PMID 23837931
Citations 52
Authors
Affiliations
Soon will be listed here.
Abstract

Severe geographic disparities exist in liver transplantation; for patients with comparable disease severity, 90-day transplant rates range from 18% to 86% and death rates range from 14% to 82% across donation service areas (DSAs). Broader sharing has been proposed to resolve geographic inequity; however, we hypothesized that the efficacy of broader sharing depends on the geographic partitions used. To determine the potential impact of redistricting on geographic disparity in disease severity at transplantation, we combined existing DSAs into novel regions using mathematical redistricting optimization. Optimized maps and current maps were evaluated using the Liver Simulated Allocation Model. Primary analysis was based on 6700 deceased donors, 28 063 liver transplant candidates, and 242 727 Model of End-Stage Liver Disease (MELD) changes in 2010. Fully regional sharing within the current regional map would paradoxically worsen geographic disparity (variance in MELD at transplantation increases from 11.2 to 13.5, p = 0.021), although it would decrease waitlist deaths (from 1368 to 1329, p = 0.002). In contrast, regional sharing within an optimized map would significantly reduce geographic disparity (to 7.0, p = 0.002) while achieving a larger decrease in waitlist deaths (to 1307, p = 0.002). Redistricting optimization, but not broader sharing alone, would reduce geographic disparity in allocation of livers for transplant across the United States.

Citing Articles

The role of an integrated referral program for patients with liver disease: A network between hub and spoke centers.

Germani G, Ferrarese A, DArcangelo F, Russo F, Senzolo M, Gambato M United European Gastroenterol J. 2023; 12(1):76-88.

PMID: 38087960 PMC: 10859718. DOI: 10.1002/ueg2.12475.


A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors.

Li C, Jiang X, Zhang K J Biomed Inform. 2023; 149:104545.

PMID: 37992791 PMC: 11619923. DOI: 10.1016/j.jbi.2023.104545.


Removing geographic boundaries from liver allocation: A method for designing continuous distribution scores.

Mankowski M, Wood N, Segev D, Gentry S Clin Transplant. 2023; 37(9):e15017.

PMID: 37204074 PMC: 10657628. DOI: 10.1111/ctr.15017.


Disparities in the Effects of Acuity Circle-based Liver Allocation on Waitlist and Transplant Practice Between Centers.

Nagai S, Ivanics T, Kitajima T, Shimada S, Shamaa T, Collins K Transplant Direct. 2022; 8(10):e1356.

PMID: 36176726 PMC: 9514831. DOI: 10.1097/TXD.0000000000001356.


Increased Logistical Burden in Circle-based Kidney Allocation.

Wood N, VanDerwerken D, Segev D, Gentry S Transplantation. 2022; 106(10):1885-1887.

PMID: 36173652 PMC: 11561894. DOI: 10.1097/TP.0000000000004127.


References
1.
Thompson D, Waisanen L, Wolfe R, Merion R, McCullough K, Rodgers A . Simulating the allocation of organs for transplantation. Health Care Manag Sci. 2005; 7(4):331-8. DOI: 10.1007/s10729-004-7541-3. View

2.
Massie A, Caffo B, Gentry S, Hall E, Axelrod D, Lentine K . MELD Exceptions and Rates of Waiting List Outcomes. Am J Transplant. 2011; 11(11):2362-71. PMC: 3229963. DOI: 10.1111/j.1600-6143.2011.03735.x. View

3.
Stahl J, Kong N, Shechter S, Schaefer A, Roberts M . A methodological framework for optimally reorganizing liver transplant regions. Med Decis Making. 2005; 25(1):35-46. DOI: 10.1177/0272989X04273137. View

4.
Washburn K, Pomfret E, Roberts J . Liver allocation and distribution: possible next steps. Liver Transpl. 2011; 17(9):1005-12. DOI: 10.1002/lt.22349. View

5.
Roberts J, Dykstra D, Goodrich N, Rush S, Merion R, Port F . Geographic differences in event rates by model for end-stage liver disease score. Am J Transplant. 2006; 6(10):2470-5. DOI: 10.1111/j.1600-6143.2006.01508.x. View