The Impact of Socioeconomic Factors on Kidney Transplantation: A Systematic Review of Low- and Middle-Income Countries
Bibliographic record
Abstract
Kidney transplantation (KT) is a preferred treatment for end-stage renal disease (ESRD) because it offers better long-term survival and cost-effectiveness compared to dialysis. Significant global disparities persist in access to KT, particularly in low- and middle-income countries (LMICs). This study aims to assess the epidemiology and outcomes of KT in LMICs while examining the relationship between a country’s income level and its KT prevalence. A systematic review of the literature was conducted, with searches of PubMed, Scopus, and Web of Science from inception to 31 May 2024. Relevant articles reporting on the epidemiology and outcomes of KT or ESRD patients undergoing kidney replacement therapy (KRT) in LMICs were included. A total of 8054 articles were identified, with 972 articles selected for full-text screening after initial title and abstract review. Following full-text screening, 35 articles met the inclusion criteria. The data showed significant variation in KRT and KT prevalence across different geographical locations. Higher-income countries within LMICs tended to have higher KT prevalence rates. Barriers such as inadequate healthcare infrastructure, limited financial resources, and insufficient organ donation frameworks were identified as contributing factors to the low KT rates in these regions. The study highlights the disparities in KT access and prevalence in LMICs, underscoring the need for targeted interventions and international collaboration to address these gaps. Efforts to increase both living and deceased donor transplants, expand health system capacity, and incorporate KT in healthcare planning are needed to close this gap. Global partnerships spearheaded by organizations such as The Transplantation Society (TTS) and the International Society of Nephrology (ISN) are crucial for improving KT rates and outcomes in LMICs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".