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Updated: May 9, 2025
Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
Published on: April 12, 2021
Sociodemographic Barriers to Starting the Kidney Transplantation Evaluation Process and Waitlisting in the Ohio River
Catherine E Kelty1, Jade Buford2, Kelsey M Drewry2,3
1Division of Nephrology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Introduction:
Individuals with end-stage kidney disease face barriers and delays in access to kidney transplantation, but little is known about access before waitlisting due to the lack of national data on pre-waitlisting measures. The Early Steps to Transplant Access Registry (E-STAR) captures referral and evaluation data in four US regions, including the Ohio River Valley, and this study utilizes E-STAR data to describe sociodemographic factors associated with starting the transplant evaluation and waitlisting in this region.
Methods:
Adults referred to a transplant center for evaluation within the Ohio River Valley during 2015-2021 and captured within E-STAR were included. Linked E-STAR, US Renal Data System, and American Community Survey data were used to assess the association between sociodemographic (age, sex, race or ethnicity, insurance status), clinical, and neighborhood factors and time from referral to evaluation start and time from evaluation start to waitlisting by Cox proportional hazards analyses.
Results:
Among 15,673 referred adults, the mean age was 55 years, and the majority were male (61.4%) and had public insurance (56.6%), while 21.3% were preemptively referred. Compared to individuals aged 18-29, all other age groups had a lower likelihood of starting the evaluation in the adjusted model. Black adults (vs. White; adjusted hazard ratio: 0.89 [95% CI: 0.81-0.98]), and those with Medicaid or Medicare were less likely to start the evaluation (vs. employer-sponsored, 0.58 [0.50-0.66]; 0.66 [0.66-0.82], respectively). Among individuals who started the evaluation, those with Black (vs. White) race, and Medicaid or Medicare (vs. employer-sponsored) were less likely to be waitlisted in the adjusted analysis.
Conclusion:
Associations between age, sex, race, and economic characteristics and access to evaluation start and waitlisting were observed. Future research investigating underlying causes and points of intervention in this region is warranted.
