1046-P: Cost-Effectiveness of Insulin Icodec for the Treatment of Type 2 Diabetes (T2D) in Canada
Bibliographic record
Abstract
Objective: To assess the cost-effectiveness of insulin icodec (IIco), the first once-weekly basal insulin, for the treatment of T2D from the Canadian public payer perspective using the Institute for Health Economics - Diabetes Cohort Model. Three T2D patient groups were analyzed: insulin naïve (IN), basal insulin experienced (BExp), and basal-bolus insulin experienced (BBExp). Comparators included basal insulin analogs currently reimbursed in Canada: insulin glargine (IGlar) U100 & U300, insulin degludec (IDeg) U100 & U200, and insulin detemir (IDet). Comparative data were derived from IIco phase 3 ONWARDS trials and network meta-analyses. Three price points for IIco were considered: equal to originator IGlar U100, equal to IDeg, and 10% higher than IDeg. Outcomes were expressed as quality-adjusted life-years (QALYs), and cost-effectiveness as incremental cost utility ratios (ICUR [cost/QALY]). Results: For the IN and BExp populations, IIco is either dominant or associated with an ICUR ≤ $28,824 in all analyses. For the BBExp population, IIco is either dominant or associated with an ICUR ≤ $45,433 in all analyses, except for the comparison versus IGlar U100 when IIco price is ≥ IDeg, and versus IGlar U300 when IIco price is = IDeg +10%. At a willingness-to-pay threshold of $50,000/QALY gained, IIco is a cost-effective treatment option for T2D versus once daily basal insulin analogues currently reimbursed in Canada. Disclosure D.A. Garcia: Other Relationship; Novo Nordisk Canada Inc. G. Mau: Employee; Novo Nordisk Canada Inc. V. Vuong: Other Relationship; Novo Nordisk Canada Inc. S. Singh: Other Relationship; EVERSANA. M.S. Jensen: Employee; Novo Nordisk A/S. R. Goldenberg: Speaker's Bureau; Bayer Inc., Boehringer-Ingelheim. Advisory Panel; Eli Lilly and Company, HLS Therapeutics Inc., Novo Nordisk, Sanofi.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| 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".