National health policies and strategies for addressing chronic kidney disease: Data from the International Society of Nephrology Global Kidney Health Atlas
Bibliographic record
Abstract
National strategies for addressing chronic kidney disease (CKD) are crucial to improving kidney health. We sought to describe country-level variations in non-communicable disease (NCD) strategies and CKD-specific policies across different regions and income levels worldwide. The International Society of Nephrology Global Kidney Health Atlas (GKHA) was a multinational cross-sectional survey conducted between July and October 2018. Responses from key opinion leaders in each country regarding national NCD strategies, the presence and scope of CKD-specific policies, and government recognition of CKD as a health priority were described overall and according to region and income level. 160 countries participated in the GKHA survey, comprising 97.8% of the world's population. Seventy-four (47%) countries had an established national NCD strategy, and 53 (34%) countries reported the existence of CKD-specific policies, with substantial variation across regions and income levels. Where CKD-specific policies existed, non-dialysis CKD care was variably addressed. 79 (51%) countries identified government recognition of CKD as a health priority. Low- and low-middle income countries were less likely to have strategies and policies for addressing CKD and have governments which recognise it as a health priority. The existence of CKD-specific policies, and a national NCD strategy more broadly, varied substantially across different regions around the world but was overall suboptimal, with major discrepancies between the burden of CKD in many countries and governmental recognition of CKD as a health priority. Greater recognition of CKD within national health policy is critical to improving kidney healthcare globally.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".