243.5: Chronic kidney disease: Measuring global burden and effectiveness of treatment.
Bibliographic record
Abstract
Introduction: As dialysis and transplantation programs work to address the increasing prevalence of chronic kidney disease (CKD, >850 million worldwide in 2023), it is crucial to identify reliable measures to monitor their effectiveness and progress. Existing studies use inconsistent metrics of mortality and have not assessed treatment effectiveness on a population level. We therefore aimed to compare different metrics of mortality and their relationship with national income level, CKD prevalence, and dialysis and transplant volumes. Methods: From the Institute for Health Metrics and Evaluation, the International Society of Nephrology, and the Global Observatory on Donation and Transplantation, we aggregated country-level data on World Bank income level, per-capita gross domestic product adjusted for purchasing power parity (GDP), CKD prevalence, chronic dialysis prevalence, kidney transplantation (KT) incidence, and CKD-specific mortality (CKD mortality). We used uni- and multivariable generalized linear models (gamma with log-link function, alpha = 0.05) to examine relationships among the variables. Results: Data were available for 176 (90.2%) of the world’s countries: 24 low-income (LICs), 45 lower-middle income (LMICs), 43 upper-middle income (UMICs), and 64 high-income countries (HICs). Data completeness averaged 69.3%, 83.2%, 88.6%, and 86.8%, respectively. CKD prevalence, dialysis prevalence, and KT incidence increased with income level (Table 1). CKD mortality, however, had an inverse relationship with income level depending on the metric used: measured as a proportion of all deaths, CKD mortality increased with income level, while age-standardized and dialysis prevalence-adjusted CKD mortality decreased with income level.Unadjusted CKD mortality increased with CKD prevalence across all income levels. Prevalence-adjusted mortality, however, increased with CKD prevalence among HICs but decreased with CKD prevalence among LICs. CKD mortality decreased with unadjusted dialysis prevalence and KT incidence. After GDP adjustment, CKD mortality only decreased with KT incidence, not with dialysis prevalence (Figure 1).Conclusions: On a global scale, the relationship between CKD mortality and income level varies depending on the mortality metric used. Regardless of GDP, however, countries with higher kidney transplant volumes have lower CKD mortality. Our findings stress the importance of deliberate, standardized selection of metrics for researchers evaluating population-level CKD outcomes and for policymakers allocating resources in constrained environments. National Institutes of Health T32 grant in implementation science (5R25HL126146-09).
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".