Disparities in kidney care in vulnerable populations: A multinational study from the ISN-GKHA
Bibliographic record
Abstract
Vulnerable populations, such as the elderly, children, displaced people, and refugees, often encounter challenges in accessing healthcare. In this study, we used data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) to describe kidney care access and delivery to vulnerable populations across countries and regions. Using data from an international survey of clinicians, policymakers, and patient advocates, we assessed the funding and coverage of vulnerable populations on all aspects of kidney replacement therapies (KRT). Overall, 167 countries or jurisdictions participated in the survey, representing 97.4% of the world's population. Children had less access than adults to KRT: hemodialysis (HD) in 74% of countries, peritoneal dialysis (PD) in 53% of countries, and kidney transplantation (KT) in 80% of countries. Available nephrologist workforce for pediatric kidney care was much lower than for adults (0.69 per million population [pmp] vs 10.08 pmp). Refugees or displaced people with kidney failure did not have access to HD, PD, or KT in 21%, 33%, and 37% of the participating countries, respectively. Low-income countries (LICs) were less likely to provide KRT access to refugees compared to high-income countries (HICs): HD: 13% vs 22%; PD: 19% vs 61%; KT: 30% vs 44%. Testing for kidney disease was routinely offered to elderly people in only 61% of countries: LICs (45%), lower-middle-income countries (56%), upper-middle-income countries (54%), and HICs (75%). Equitable access to kidney care for vulnerable people, particularly for children and displaced people, remains an area of unmet need. Strategies are needed to address this issue.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".