SynthesisRenal failure2025
Health insurance and kidney transplantation outcomes in the United States: a systematic review and AI-driven analysis of disparities in access and survival.
Synthesis in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney transplantation is the preferred treatment for end-stage kidney disease (ESKD) in the United States, yet access and outcomes vary by insurance type, race, and socioeconomic status. This systematic review synthesizes U.S.-based evidence on how insurance coverage influences transplant waitlisting, access, and outcomes. AI-assisted analysis was used to quantify disparities and propose policy recommendations.
methodsA systematic review of MEDLINE, EMBASE, and the Cochrane Database (through November 2024) was conducted to identify studies on insurance-related disparities in U.S. kidney transplantation (PROSPERO: CRD42023484733). AI-assisted synthesis using o3-mini-high (2025) was employed to identify patterns and guide policy development.
resultsAmong 2,163 records, 14 studies met inclusion criteria. Patients with Medicare or Medicaid-particularly racial and ethnic minorities-had lower referral rates and higher transplant waitlist rejection compared to those with private insurance. Socioeconomic barriers such as low income and limited education further impaired access and worsened post-transplant outcomes. Publicly insured recipients had higher post-transplant mortality and graft failure rates. Loss of Medicare after 36 months was associated with reduced immunosuppressant adherence and increased rejection. Disparities were amplified by Medicaid expansion variability and inconsistent transplant center policies. AI-assisted analysis confirmed these disparities and generated policy proposals including standardized referral guidelines, lifelong immunosuppressant coverage, targeted financial aid, equity-linked incentives for transplant centers, and scalable digital health solutions.
conclusionInsurance type, race, and socioeconomic status significantly influence kidney transplant access and outcomes. AI-assisted analysis identified structural inequities and informed targeted policy strategies to advance transplant equity and support broader healthcare reform.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.