Inside CKD: Projecting the Global Clinical Burden of CKD Using Patient-Level Microsimulation
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects ˜10% of the global population and disease progression is associated with increased risk of cardiovascular events, renal replacement therapy (RRT) and premature death. The trajectory of CKD and related costs are critical considerations for public health and policy planning. Using country-specific, patient-level microsimulations, Inside CKD models the global clinical and economic burden of CKD from 2021 to 2026. Methods: We used the Inside CKD microsimulation to project the clinical burden of CKD in Canada, the UK and the US. We constructed a virtual general population for each country using national survey data and relevant published literature. Data inputs included country demographics and the prevalence of CKD, RRT, comorbidities, and complications. CKD stages were defined as discrete health states consistent with Kidney Disease: Improving Global Outcomes (KDIGO) 2012 recommendations. We conducted model validation and calibration using established methods for health economic modelling. Analyses from additional countries in the Americas, Asia-Pacific and European regions are underway. Results: Preliminary results show that the prevalence of CKD stages 1-5 is projected to increase from 13.35% to 14.22% in Canada, from 13.48% to 13.98% in the UK, and from 14.88% to 15.57% in the US from 2021 to 2026 (Table). The number of patients receiving RRT annually is projected to increase from 42 064 to 47 582 in Canada, from 69 796 to 75 051 in the UK, and from 797 638 to 823 050 in the US, between 2021 and 2026 (Table). Conclusions: Inside CKD projects that the prevalence of CKD will continue to rise in Canada, the UK and the US over the period 2021-2026 with a corresponding increase in the annual RRT burden. These data demonstrate that CKD continues to pose a significant global challenge to public health and demonstrates the continued need for national policies aimed at early intervention. Funding: Commercial Support - AstraZenecaProjected increase in CKD stages 1-5 (including undiagnosed) and RRT from 2021 to 2026
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".