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Perpetuating Disparity: Failure of the Kidney Transplant System to Provide the Most Kidney Transplants to Communities with the Greatest Need
Robert M. Cannon1, Douglas Anderson1, Paul MacLennan1, Babak Orandi1, Saulat Sheikh1, Vineeta Kumar2, Michael Hanaway1, Jayme Locke1
1Surgery, University of Alabama at Birmingham, Birmingham, Alabama, United States, 2Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States

Background: The burden of end stage kidney disease (ESKD) and kidney transplant (KT) rates vary significantly across the US. This study aims to examine the mismatch between ESKD burden and KT rates from a perspective of spatial epidemiology, which has been only sparsely applied in the transplant literature.

Methods: US Renal Data System data on ESKD burden, waitlisting, and kidney transplant from 2015 onward were analyzed. County level ESKD burden was defined as incident ESKD cases per 100,000 population, while transplant rate was defined as transplants per 1,000 incident ESKD cases. ESKD and transplant rates were smoothened using Empirical Bayes methodology. Clustering of ESKD burden and kidney transplant rates at the county level determined using local Moranís I and correlated to a composite measure of community health (community health score, CHS). Clusters were defined as high-high (high transplant rate, high ESKD burden), high-low (high transplant rates, low ESKD burden), low-low (low transplant rate, low ESKD burden), and low-high (low transplant rate, high ESKD burden). CHSís were determined using a modified version of the Robert Wood Johnson Foundationís County Health Rankings (CHR) and ranked into percentiles at the national level. Lower percentile CHS indicated better overall community health.

Results: Significant clusters of high ESKD burden tended to coincide with clusters of low KT rates, and vice versa. The most common cluster type (figure) was low-high (377 counties), followed by high-low (359 counties). Counties in low-high clusters had the lowest overall mean transplant rate (61.1) and highest overall mean ESKD burden (61.3). By comparison, counties in high-low clusters had the highest overall mean transplant rate (110.6; p<0.001 vs. low-high) and lowest overall mean ESKD burden (28.9; p<0.001 vs. low-high). The highest mean CHS percentile was present in counties belonging to low-high clusters (80.9%), followed by high-high clusters (64.0%), low-low clusters (34.3%), and high-low clusters (20.2%). Counties not belonging to significant clusters had a mean CHS percentile of 49.4%. All comparisons between mean cluster type CHS percentiles were significant at p<0.001.

Conclusion: The kidney transplant system in the United States has failed to provide the most kidneys for transplant to the areas with the greatest need, as there is a significant mismatch between kidney transplant rates and ESKD burden. Areas with the greatest need, defined by ESKD burden, also have the lowest transplant rates. This pattern exacerbates pre-existing disparities, as disadvantaged high ESKD regions already suffer from worse access to care and overall community health, as evidenced by the highest CHS scores in the study.

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