MO518<i>INSIDE CKD</i>: MODELLING THE ECONOMIC BURDEN OF CHRONIC KIDNEY DISEASE IN THE AMERICAS AND THE ASIA-PACIFIC REGION USING PATIENT-LEVEL MICROSIMULATION
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease (CKD) is a debilitating and costly condition, with an estimated global prevalence of approximately 10%. Progression of CKD is associated with end-stage renal disease, cardiovascular events and premature mortality, as well as increased requirement for renal replacement therapies (RRTs), which are associated with significant healthcare costs and resource use. Furthermore, patients with CKD often have additional comorbidities, which are associated with CKD progression and increased costs. The trajectories of CKD prevalence, progression, outcomes and the related costs are therefore critical considerations for public health and policy planning. Using country-specific, patient-level microsimulation, Inside CKD aims to model the global clinical and economic burden of CKD from 2020 to 2025. Method We used the Inside CKD microsimulation to model the economic burden of CKD in the Americas and Asia-Pacific region. We developed a virtual general population for each country using national survey data and relevant data from published literature. Data inputs included country demographics, the prevalence of CKD and RRT, comorbidities and complication rates as well as associated healthcare costs. CKD stages were defined according to Kidney Disease Improving Global Outcomes (KDIGO) 2012 recommendations and patients were categorized according to estimated glomerular filtration rate and albuminuria status. We calibrated the RRT modelling against historical trends from country-specific renal registries. We conducted model validation and calibration using established methods for health economic modelling. Here, we report the results from the US and Canada analyses, with further analyses currently underway for additional countries in the Americas and Asia-Pacific region. Results Initial results for the US and Canada revealed that, between 2020 and 2025, annual healthcare costs associated with CKD will increase linearly from US$232.3B to US$376.2B in the US and from C$21.4B to C$34.1B in Canada (this figure does not include complication costs). In the US, the largest absolute increase in cost was observed in CKD stage 3a ($98.4B); however, CKD stage 4 had the largest relative increase in cost with an approximately three-fold increase (US$7.30B to US$23.3B). In Canada, the largest absolute increase in cost was observed in CKD stage 3a (C$5.84B); whereas CKD stage 5 had the largest relative increase in cost with an approximately five-fold increase (C$0.27B to C$1.41B). By 2025, costs associated with CKD will increase across all age categories (18–34, 35–64 and 65+ years) in both countries. In the US, the 35–64 age group had the largest absolute increase in costs with an increase of $74B (US$58.3B to US$132B). The largest relative increase in cost was observed in the 18–34 age category, with approximately a three-fold increase in costs (US$3.76B to US$10.2B). In Canada, the 65+ age group had the largest absolute increase in costs with an increase of C$7.9B (C$16.4B to C$24.3B). Both the 18–34 and 35–64 age categories had the largest relative increase in costs, with an approximately two-fold increase (C$0.25B to C$0.49B and C$4.77B to C$9.31B, respectively). Conclusion Initial results from Inside CKD demonstrate that CKD poses a significant economic burden over the next 5 years. CKD stage 3a was associated with the most pronounced cost increases in both the US and Canada, likely due to the increased prevalence of this stage. In the US, the largest increase in CKD costs was observed in the 35–64-year-old ‘working’ population, whereas the largest increase in Canada was observed in the 65 years old and over population. Further policy interventions aimed at early diagnosis and proactive management should be considered to slow disease progression, improve patient outcomes and reduce the economic burden associated with CKD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".