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. 2022 Dec;36(12):e14821.
doi: 10.1111/ctr.14821. Epub 2022 Sep 28.

Geographic hot spots of kidney transplant candidates wait-listed post-dialysis

R Blake Buchalter  1   2 , Anne M Huml  3 , Emilio D Poggio  3 , Jesse D Schold  1   2
Affiliations

Geographic hot spots of kidney transplant candidates wait-listed post-dialysis

R Blake Buchalter et al. Clin Transplant. 2022 Dec.

Abstract

Introduction: Preemptive wait-listing of deceased donor kidney transplant (DDKT) candidates before maintenance dialysis increases the likelihood of transplantation and improves outcomes among transplant patients. Previous studies have identified substantial disparities in rates of preemptive listing, but a gap exists in examining geographic sources of disparities, particularly for sub-regional units. Identifying small area hot spots where delayed listing is particularly prevalent may more effectively inform both health policy and regionally appropriate interventions.

Methods: We conducted a retrospective cohort study utilizing 2010-2020 Scientific Registry of Transplant Recipients (SRTR) data for all DDKT candidates to examine overall and race-stratified geospatial hot spots of post-dialysis wait-listing in U.S. zip code tabulation areas (ZCTA). Three geographic clustering methods were utilized to identify robust statistically significant hot spots of post-dialysis wait-listing.

Results: Novel sub-regional hot spots were identified in the southeast, southwest, Appalachia, and California, with a majority existing in the southeast. Race-stratified results were more nuanced, but broadly reflected similar patterns. Comparing transplant candidates in hot spots to candidates in non-clusters indicated a strong association between residence in hot spots and high area deprivation (OR: 6.76, 95%CI: 6.52-7.02), indicating that improving access healthcare in these areas may be particularly beneficial.

Conclusion: Our study identified overall and race-stratified hot spots with low rates of preemptive wait list placement in the U.S., which may be useful for prospective healthcare policy and interventions via targeting of these narrowly defined geographical areas.

Keywords: dialysis; disparities; epidemiology; ethnicity/race; waitlist management.

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Conflict of interest statement

RBB: None; AMH: None; EDP: None; JDS: None

Figures

FIGURE 1
FIGURE 1
Zip code tabulation area race‐stratified hot spots of kidney transplant candidates wait‐listed post‐dialysis, 2010–2020. Hot spots constitute ZCTAs where statistically significant hot spots were identified in all of the three following geospatial clustering methods: Poisson spatial scan statistic, local Moran's I, and local Getis‐Ord Gi*. Alaska, Hawaii, and Puerto Rico not shown due to lack of hot spots.
FIGURE 2
FIGURE 2
Zip code tabulation area hot spots of kidney transplant candidates wait‐listed post‐dialysis, 2010–2020. Hot spots constitute ZCTAs where statistically significant hot spots were identified in all of the three following geospatial clustering methods: Poisson spatial scan statistic, local Moran's I, and local Getis‐Ord Gi*. Alaska and Hawaii not shown due to lack of hot spots.
See this image and copyright information in PMC

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