MO549INSIDE ANEMIA OF CKD: MICROSIMULATION MODELLING OF THE IMPACT OF POLICY INTERVENTIONS ON ANAEMIA OF CKD IN CANADA
Bibliographic record
Abstract
Abstract Background and Aims Anaemia is common in patients with chronic kidney disease (CKD) and is associated with increased mortality, cardiovascular complications, reduced quality of life and increased use of healthcare resources. Based on robust epidemiological and clinical data, mathematical modelling is a useful approach for predicting the future burden of disease and the effects of different intervention scenarios, which is essential for health service planning. This analysis uses a microsimulation model, Inside ANEMIA of CKD, to project the impact of a hypothetical intervention scenario that reduces the prevalence of anaemia of CKD on related healthcare costs in Canada from 2020 to 2025. Method A virtual cohort representing the Canadian population was created within the Inside ANEMIA of CKD microsimulation model framework using national demographics and epidemiological data drawn from Statistics Canada and a provincial renal database. In the cohort, virtual individuals were ascribed an age- and sex-stratified CKD status (defined by estimated glomerular filtration rate and albuminuria levels, as per international guidelines) and anaemia status (defined as mild, moderate or severe based on haemoglobin level, as per WHO criteria) based on Canadian prevalence data. Key comorbidities (type 2 diabetes, heart failure and hypertension) were also assigned, reflecting Canada-specific population statistics. Costs related to the treatment of CKD were taken from the published literature, and are shown in Canadian dollars (C$). This modelling analysis evaluated the effects on healthcare costs of a hypothetical intervention scenario in which the prevalence of moderate and severe anaemia is reduced by 20% per year from 2020 to 2025 compared with no intervention (baseline). In each scenario (i.e. intervention or baseline), the modelling analysis estimated CKD-related healthcare costs for patients with moderate or severe anaemia of CKD. The modelling analysis did not adjust for the potential costs of the intervention. Results Preliminary results predict that, with the hypothetical intervention, there could be approximately 435,000 fewer patients with moderate or severe anaemia of CKD in Canada in 2025 compared with no intervention (approximately 497,000 versus 932,000). The intervention is projected to lead to a reduction of C$4.4 billion in annual direct healthcare costs in 2025 for patients with moderate or severe anaemia of CKD compared with no intervention (C$9.1 billion versus C$13.5 billion), assuming that all eligible patients are diagnosed and treated. Conclusion The Inside ANEMIA of CKD microsimulation model predicts that a hypothetical intervention which reduces the prevalence of moderate and severe anaemia of CKD would produce reductions in direct healthcare costs. This suggests that interventions effective at reducing the prevalence of anaemia of CKD would help to reduce the economic burden on healthcare services.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".