Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework

Author:

Tong Jiayi12,Shen Yishan123,Xu Alice124,He Xing5ORCID,Luo Chongliang6,Edmondson Mackenzie7,Zhang Dazheng12,Lu Yiwen12,Yan Chao8ORCID,Li Ruowang9,Siegel Lianne10ORCID,Sun Lichao11,Shenkman Elizabeth A5,Morton Sally C12,Malin Bradley A81314,Bian Jiang5ORCID,Asch David A1516,Chen Yong12316ORCID

Affiliation:

1. The Center for Health AI and Synthesis of Evidence (CHASE), Perelman School of Medicine, The University of Pennsylvania , Philadelphia, PA 19104, United States

2. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, The University of Pennsylvania , Philadelphia, PA 19104, United States

3. Applied Mathematics and Computational Science, The University of Pennsylvania , Philadelphia, PA 19104, United States

4. Washington University in St. Louis , St. Louis, MO 63130, United States

5. Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida , Gainesville, FL 32611, United States

6. Division of Public Health Sciences, Department of Surgery, Washington University in St. Louis , St. Louis, MO 63110, United States

7. Biostatistics Division, Merck & Co., Inc. , Rahway, NJ 07065, United States

8. Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, TN 37203, United States

9. Department of Computational Biomedicine, Cedars-Sinai Medical Center , Los Angeles, CA 90048, United States

10. Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota , Minneapolis, MN 55414, United States

11. Department of Computer Science and Engineering, Lehigh University , Bethlehem, PA 18015, United States

12. School of Mathematical and Statistical Sciences, Arizona State University , Tempe, AZ 85287, United States

13. Department of Computer Science, Vanderbilt University , Nashville, TN 37212, United States

14. Department of Biostatistics, Vanderbilt University Medical Center , Nashville, TN 37203, United States

15. Division of General Internal Medicine, University of Pennsylvania , Philadelphia, PA 19104, United States

16. Leonard Davis Institute of Health Economics , Philadelphia, PA 19104, United States

Abstract

Abstract Objectives Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients’ privacy. Materials and Methods We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification). Consisting of 2 modules, dGEM-disparity first provides accurately estimated common effects and calibrated hospital-specific effects by requiring only aggregated data from each center and then adopts a counterfactual modeling approach to assess whether the graft failure rates differ if NHB patients had been admitted at transplant centers in the same distribution as NHW patients were admitted. Results Utilizing United States Renal Data System data from 39 043 adult patients across 73 transplant centers over 10 years, we found that if NHB patients had followed the distribution of NHW patients in admissions, there would be 38 fewer deaths or graft failures per 10 000 NHB patients (95% CI, 35-40) within 1 year of receiving a kidney transplant on average. Discussion The proposed framework facilitates efficient collaborations in clinical research networks. Additionally, the framework, by using counterfactual modeling to calculate the event rate, allows us to investigate contributions to racial disparities that may occur at the level of site of care. Conclusions Our framework is broadly applicable to other decentralized datasets and disparities research related to differential access to care. Ultimately, our proposed framework will advance equity in human health by identifying and addressing hospital-level racial disparities.

Funder

National Institutes of Health

Patient-Centered Outcomes Research Institute

Board of Governors or Methodology Committee

National Science Foundation

Publisher

Oxford University Press (OUP)

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