Background: The burden of end-stage kidney disease (ESKD) and kidney transplant rates vary significantly across the United States. This study aims to examine the mismatch between ESKD burden and kidney transplant rates from a perspective of spatial epidemiology. Methods: US Renal Data System data from 2015 to 2017 on incident ESKD and kidney transplants per 1000 incident ESKD cases was analyzed. Clustering of ESKD burden and kidney transplant rates at the county level was determined using local Moran's I and correlated to county health scores. Higher percentile county health scores indicated worse overall community health. Results: Significant clusters of high-ESKD burden tended to coincide with clusters of low kidney transplant rates, and vice versa. The most common cluster type had high incident ESKD with low transplant rates (377 counties). Counties in these clusters had the lowest overall mean transplant rate (61.1), highest overall mean ESKD incidence (61.3), and highest mean county health scores percentile (80.9%, P<0.001 vs all other cluster types). By comparison, counties in clusters with low ESKD incidence and high transplant rates (n=359) had the highest mean transplant rate (110.6), the lowest mean ESKD incidence (28.9), and the lowest county health scores (20.2%). All comparisons to high-ESKD/low-transplant clusters were significant at P value <0.001. Conclusion: There was a significant mismatch between kidney transplant rates and ESKD burden, where areas with the greatest need had 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 county health scores in the study.