Landscape of kidney replacement therapy provision in low- and lower-middle income countries: A multinational study from the ISN-GKHA
Bibliographic record
Abstract
In low- and lower-middle-income countries (LLMICs), delivering equitable kidney care presents substantial challenges, resulting in significant disparities in disease management and treatment outcomes for people with kidney failure. This comprehensive report leveraged data from the International Society of Nephrology-Global Kidney Health Atlas (ISN-GKHA), to provide a detailed update on the landscape of kidney replacement therapy (KRT) in LLMICs. Among the 65 participating LLMICs, reimbursement for KRT (publicly funded by the government and free at the point of delivery) was available in 28%, 15%, and 8% for hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT), respectively. Additionally, while 56% and 28% of LLMICs reported the capacity to provide quality HD and PD, only 41% reported accessibility to chronic dialysis, defined as >50% of the national population being able to access KRT, and a mere 5% LLMICs reported accessibility to KT. Workforce shortages in nephrology further compound these challenges. Kidney registries and comprehensive policies for non-communicable diseases and chronic kidney disease care were limited in LLMICs. A comprehensive and cost-effective approach is crucial to address these challenges. Collaboration at global, regional, country, and individual levels is essential to enhance the quality of kidney care across LLMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".