Current status of health systems financing and oversight for end-stage kidney disease care: a cross-sectional global survey
Bibliographic record
Abstract
OBJECTIVES: The Global Kidney Health Atlas (GKHA) is a multinational, cross-sectional survey designed to assess the current capacity for kidney care across all world regions. The 2017 GKHA involved 125 countries and identified significant gaps in oversight, funding and infrastructure to support care for patients with kidney disease, especially in lower-middle-income countries. Here, we report results from the survey for the second iteration of the GKHA conducted in 2018, which included specific questions about health financing and oversight of end-stage kidney disease (ESKD) care worldwide. SETTING: A cross-sectional global survey. PARTICIPANTS: Key stakeholders from 182 countries were invited to participate. Of those, stakeholders from 160 countries participated and were included. PRIMARY OUTCOMES: Primary outcomes included cost of kidney replacement therapy (KRT), funding for dialysis and transplantation, funding for conservative kidney management, extent of universal health coverage, out-of-pocket costs for KRT, within-country variability in ESKD care delivery and oversight systems for ESKD care. Outcomes were determined from a combination of desk research and input from key stakeholders in participating countries. RESULTS: 160 countries (covering 98% of the world's population) responded to the survey. Economic factors were identified as the top barrier to optimal ESKD care in 99 countries (64%). Full public funding for KRT was more common than for conservative kidney management (43% vs 28%). Among countries that provided at least some public coverage for KRT, 75% covered all citizens. Within-country variation in ESKD care delivery was reported in 40% of countries. Oversight of ESKD care was present in all high-income countries but was absent in 13% of low-income, 3% of lower-middle-income, and 10% of upper-middle-income countries. CONCLUSION: Significant gaps and variability exist in the public funding and oversight of ESKD care in many countries, particularly for those in low-income and lower-middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".