Availability, coverage, and scope of health information systems for kidney care across world countries and regions
Bibliographic record
Abstract
BACKGROUND: Health information systems (HIS) are fundamental tools for the surveillance of health services, estimation of disease burden and prioritization of health resources. Several gaps in the availability of HIS for kidney disease were highlighted by the first iteration of the Global Kidney Health Atlas. METHODS: As part of its second iteration, the International Society of Nephrology conducted a cross-sectional global survey between July and October 2018 to explore the coverage and scope of HIS for kidney disease, with a focus on kidney replacement therapy (KRT). RESULTS: Out of a total of 182 invited countries, 154 countries responded to questions on HIS (85% response rate). KRT registries were available in almost all high-income countries, but few low-income countries, while registries for non-dialysis chronic kidney disease (CKD) or acute kidney injury (AKI) were rare. Registries in high-income countries tended to be national, in contrast to registries in low-income countries, which often operated at local or regional levels. Although cause of end-stage kidney disease, modality of KRT and source of kidney transplant donors were frequently reported, few countries collected data on patient-reported outcome measures and only half of low-income countries recorded process-based measures. Almost no countries had programs to detect AKI and practices to identify CKD-targeted individuals with diabetes, hypertension and cardiovascular disease, rather than members of high-risk ethnic groups. CONCLUSIONS: These findings confirm significant heterogeneity in the global availability of HIS for kidney disease and highlight important gaps in their coverage and scope, especially in low-income countries and across the domains of AKI, non-dialysis CKD, patient-reported outcomes, process-based measures and quality indicators for KRT service delivery.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".