A global overview of health system financing and available infrastructure and oversight for kidney care
Bibliographic record
Abstract
BACKGROUND: Governance, health financing, and service delivery are critical elements of health systems for provision of robust and sustainable chronic disease care. We leveraged the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) to evaluate oversight and financing for kidney care worldwide. METHODS: A survey was administered to stakeholders from countries affiliated with the ISN from July to September 2022. We evaluated funding models utilized for reimbursement of medications, services for the management of chronic kidney disease, and provision of kidney replacement therapy (KRT). We also assessed oversight structures for the delivery of kidney care. RESULTS: Overall, 167 of the 192 countries and territories contacted responded to the survey, representing 97.4% of the global population. High-income countries tended to use public funding to reimburse all categories of kidney care in comparison with low-income countries (LICs) and lower-middle income countries (LMICs). In countries where public funding for KRT was available, 78% provided universal health coverage. The proportion of countries that used public funding to fully reimburse care varied for non-dialysis chronic kidney disease (27%), dialysis for acute kidney injury (either hemodialysis or peritoneal dialysis) (44%), chronic hemodialysis (45%), chronic peritoneal dialysis (42%), and kidney transplant medications (36%). Oversight for kidney care was provided at a national level in 63% of countries, and at a state/provincial level in 28% of countries. CONCLUSION: This study demonstrated significant gaps in universal care coverage, and in oversight and financing structures for kidney care, particularly in in LICs and LMICs.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".