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A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States

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  • Sounak Chakraborty

    (Department of Statistics, University of Missouri, 209F Middlebush Hall, Columbia, MO 65211, USA)

  • Tanujit Dey

    (Center for Surgery & Public Health, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 2-016, Boston, MA 02120, USA)

  • Lingwei Xiang

    (Center for Surgery & Public Health, Department of Surgery, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 2-016, Boston, MA 02120, USA)

  • Joel T. Adler

    (Division of Transplant Surgery, Department of Surgery and Perioperative Care, Dell Medical School, The University of Texas at Austin, University Station, Mail Stop A3000, Austin, TX 78712, USA)

Abstract

In this study, we employed a novel approach of combining Gaussian processes (GPs) with boosting techniques to model the spatial variability inherent in End-Stage Kidney Disease (ESKD) data. Our use of the Gaussian processes boosting, or GPBoost, methodology underscores the efficacy of this hybrid method in capturing intricate spatial dynamics and enhancing predictive accuracy. Specifically, our analysis demonstrates a notable improvement in out-of-sample prediction accuracy regarding the percentage of the population remaining on the wait list within geographic regions. Furthermore, our investigation unveils race and gender-based factors that significantly influence patient wait-listing. By leveraging the GPBoost approach, we identify these pertinent factors, shedding light on the complex interplay between demographic variables and access to kidney transplantation services. Our findings underscore the imperative for a multifaceted strategy aimed at reducing spatial disparities in kidney transplant wait-listing. Key components of such an approach include mitigating gender disparities, bolstering access to healthcare services, fostering greater awareness of transplantation options, and dismantling structural barriers to care. By addressing these multifactorial challenges, we can strive towards a more equitable and inclusive landscape in kidney transplantation.

Suggested Citation

  • Sounak Chakraborty & Tanujit Dey & Lingwei Xiang & Joel T. Adler, 2024. "A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States," Stats, MDPI, vol. 7(2), pages 1-13, June.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:2:p:31-520:d:1410539
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    2. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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