Addressing geographic disparities in liver transplantation through redistricting

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.

Keywords: Broader sharing; Liver Simulated Allocation Model; geographic disparities; liver allocation.

Figure 1. Regions Designed to Reduce Geographic Disparity in Liver Allocation

Based on redistricting integer program designed to minimize DSA-level variance in median MELD at transplantation (see methods). Upper Panel: Optimized Map 1, with 11 regions; Lower Panel: Optimized Map 2, with 11 regions constrained by geographic contiguity.

Figure 2. Distribution of median MELD at transplant across DSAs; by allocation scenario

This mountain plot shows the cumulative distribution of median MELD at transplant across DSAs, folded at the median (i.e. the ascending part of the “mountain” indicates 0–50th percentile, and the descending part of the “mountain” indicates 50–100th percentile) to illustrate the dispersion of the distribution. The three lines show fully regional sharing over the current map (solid black line), Optimized Map 1 as in Figure 1a (dashed blue line), and Optimized Map 2 as in Figure 1b (dashed orange line). Note that Optimized Maps 1 and 2 significantly reduce geographic disparities as shown by the narrower interquartile range (indicated as a double-headed arrow on the figure).

Figure 3. MELDs at transplant comparing Region 1 DSAs with Region 6 DSAs; by allocation scenario

Cumulative distribution of MELDs at transplant, for DSAs in Region 1 (CTOP and MAOB, dashed lines) and in Region 6 (HIOP, ORUO, and WALC, solid lines) (i.e. Regions 1 and 6 as defined by the current map). Four allocation scenarios are shown for each: the current allocation system, black lines; fully regional sharing over current regions, purple lines; fully regional sharing over Optimized Map 1 as in Figure 1a, blue lines; and fully regional sharing over Optimized Map 2 as in Figure 1b, orange lines.