Status of care for end stage kidney disease in countries and regions worldwide: international cross sectional survey
Bibliographic record
Abstract
OBJECTIVE: To determine the global capacity (availability, accessibility, quality, and affordability) to deliver kidney replacement therapy (dialysis and transplantation) and conservative kidney management. DESIGN: International cross sectional survey. SETTING: International Society of Nephrology (ISN) survey of 182 countries from July to September 2018. PARTICIPANTS: Key stakeholders identified by ISN's national and regional leaders. MAIN OUTCOME MEASURES: Markers of national capacity to deliver core components of kidney replacement therapy and conservative kidney management. RESULTS: Responses were received from 160 (87.9%) of 182 countries, comprising 97.8% (7338.5 million of 7501.3 million) of the world's population. A wide variation was found in capacity and structures for kidney replacement therapy and conservative kidney management-namely, funding mechanisms, health workforce, service delivery, and available technologies. Information on the prevalence of treated end stage kidney disease was available in 91 (42%) of 218 countries worldwide. Estimates varied more than 800-fold from 4 to 3392 per million population. Rwanda was the only low income country to report data on the prevalence of treated disease; 5 (<10%) of 53 African countries reported these data. Of 159 countries, 102 (64%) provided public funding for kidney replacement therapy. Sixty eight (43%) of 159 countries charged no fees at the point of care delivery and 34 (21%) made some charge. Haemodialysis was reported as available in 156 (100%) of 156 countries, peritoneal dialysis in 119 (76%) of 156 countries, and kidney transplantation in 114 (74%) of 155 countries. Dialysis and kidney transplantation were available to more than 50% of patients in only 108 (70%) and 45 (29%) of 154 countries that offered these services, respectively. Conservative kidney management was available in 124 (81%) of 154 countries. Worldwide, the median number of nephrologists was 9.96 per million population, which varied with income level. CONCLUSIONS: These comprehensive data show the capacity of countries (including low income countries) to provide optimal care for patients with end stage kidney disease. They demonstrate substantial variability in the burden of such disease and capacity for kidney replacement therapy and conservative kidney management, which have implications for policy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".