Cost-effectiveness of insulin icodec for the treatment of type 2 diabetes in Canada
Bibliographic record
Abstract
AIM: . This study aimed to determine the cost-effectiveness of insulin icodec, first once-weekly basal insulin, compared with long-acting basal insulins for improving glycemic control in adults with T2D in Canada. MATERIALS AND METHODS: The Swedish Institute of Health Economics T2D Cohort Model was used to analyse three T2D groups: insulin naïve (IN), basal insulin experienced (BIE), and basal-bolus insulin experienced (BBIE). Comparators included insulin glargine, insulin detemir, and insulin degludec. Comparative efficacy was informed by the phase 3 ONWARDS trials of insulin icodec and network meta-analyses (NMA). Time horizon was 40 years. Analyses considered disutilities resulting from vascular complications, age, gender, diabetes duration, hypoglycemia, and injection burden. A 1.5% annual discount rate was used for costs and effects. The average cost per unit for each treatment, accounting for the market share of available formats and biosimilars was calculated. Outcomes were expressed as quality-adjusted life-years (QALYs) and cost-effectiveness as incremental cost utility ratios (ICUR [cost/QALY]). RESULTS: In all analyses, insulin icodec dominated insulin degludec and insulin detemir. Compared to insulin glargine U100 and U300, insulin icodec was associated with ICURs of $17,876, $20,844, and $73,253; and $6,439, $8,623, and $45,433 in the IN, BIE, and BBIE populations, respectively. LIMITATIONS: Limitations of this economic evaluation include the lack of data for some treatments in certain NMAs, uncertainty regarding the use of the NMA results for a 40-year time horizon, heterogeneous sources of disutilities, and uncertainty regarding the HRQoL benefits of reduced injection frequency. CONCLUSIONS: Insulin icodec is a cost-effective treatment option for adult patients with T2D versus once daily basal insulin-analogues publicly reimbursed in Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".