Dialysis Patients' Social Networks and Living Donation Offers

Avrum Gillespie; Jonathan Daw; Riley Brown; Jamie Cappiello; Briana Eugene Lee; Edward L Fink; Heather M Gardiner; Peter P Reese; Crystal A Gadegbeku; Zoran Obradovic

doi:10.1016/j.xkme.2023.100640

Dialysis Patients' Social Networks and Living Donation Offers

Kidney Med. 2023 Apr 15;5(6):100640. doi: 10.1016/j.xkme.2023.100640. eCollection 2023 Jun.

Authors

Affiliations

¹ Division of Nephrology, Hypertension, and Kidney Transplantation, Department of Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania.
² Department of Sociology and Demography, College of Liberal Arts, Penn State, Philadelphia, Pennsylvania.
³ Department of Social & Behavioral Sciences, College of Public Health, Temple University, Philadelphia, Pennsylvania.
⁴ Department of Communication and Social Influence, Klein College of Media and Communication, Temple University, Philadelphia, Pennsylvania.
⁵ Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
⁶ Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio.
⁷ Center for Data Analytics and Biomedical Informatics, Temple University, Philadelphia, Pennsylvania.

Abstract

Rationale & objective: Most living kidney donors are members of a hemodialysis patient's social network. Network members are divided into core members, those strongly connected to the patient and other members; and peripheral members, those weakly connected to the patient and other members. We identify how many hemodialysis patients' network members offered to become kidney donors, whether these offers were from core or peripheral network members, and whose offers the patients accepted.

Study design: A cross-sectional interviewer-administered hemodialysis patient social network survey.

Setting & participants: Prevalent hemodialysis patients in 2 facilities.

Predictors: Network size and constraint, a donation from a peripheral network member.

Outcomes: Number of living donor offers, accepting an offer.

Analytical approach: We performed egocentric network analyses for all participants. Poisson regression models evaluated associations between network measures and number of offers. Logistic regression models determined the associations between network factors and accepting a donation offer.

Results: The mean age of the 106 participants was 60 years. Forty-five percent were female, and 75% self-identified as Black. Fifty-two percent of participants received at least one living donor offer (range 1-6); 42% of the offers were from peripheral members. Participants with larger networks received more offers (incident rate ratio [IRR], 1.26; 95% CI, 1.12-1.42; P = 0.001), including networks with more peripheral members (constraint, IRR, 0.97; 95% CI, 0.96-0.98; P < 0.001). Participants who received a peripheral member offer had 3.6 times greater odds of accepting an offer (OR, 3.56; 95% CI, 1.15-10.8; P = 0.02) than those who did not receive a peripheral member offer.

Limitations: A small sample of only hemodialysis patients.

Conclusions: Most participants received at least one living donor offer, often from peripheral network members. Future living donor interventions should focus on both core and peripheral network members.

Keywords: Constraint; ESKD; density; hemodialysis; kidney transplant; living donation; living donor; social determinants of health; social network; transplantation.