Modeling the lifetime costs of insulin glargine and insulin detemir in type 1 and type 2 diabetes patients in Canada: a meta-analysis and a cost-minimization analysis
Bibliographic record
Abstract
BACKGROUND: Two basal insulin analogues, insulin glargine once daily and insulin detemir once or twice daily, are marketed in Canada. OBJECTIVE: To estimate the long-term costs of insulin glargine once daily (QD) versus insulin detemir once or twice daily (QD or BID) for type 1 (T1DM) and type 2 (T2DM) diabetes mellitus from a Canadian provincial government's perspective. METHODS: A cost-minimization analysis comparing insulin glargine (IGlarg) to insulin detemir (IDet) was conducted using a validated computer simulation model, the CORE Diabetes Model. Lifetime direct medical costs including costs of insulin treatment and diabetes complications were projected. T1DM and T2DM patients' daily insulin dose (T1DM: IGlarg QD 26.2 IU; IDet BID 33.6 IU; T2DM: IGlarg QD 47.2 IU; IDet QD 65.7 IU or IDet BID 80.4 IU) was derived from a meta-analysis of randomized trials. All patients were assumed to stay on the same treatment for life. Costs were discounted at 5% per annum and reported in 2010 Canadian Dollars. RESULTS: The meta-analysis showed T1DM and T2DM patients had similar HbA(1c) change from baseline when receiving IGlarg compared to IDet (T1DM: 0.002%-points; p = 0.97; T2DM: -0.05%-points; p = 0.28). Treatment of T1DM patients with IGlarg versus IDet BID resulted in lifetime cost savings of $4231 per patient. Treatment of T2DM patients with IGlarg resulted in lifetime cost savings of $4659 per patient versus IDet QD and cost savings of $8709 per patient versus IDet BID. CONCLUSIONS: Similar HbA(1c) change from baseline can be achieved with a lower IGlarg than IDet dose. From the perspective of a Canadian provincial government, treatment of T1DM and T2DM patients with IGlarg instead of IDet can generate long-term cost savings. Main limitations include trial data were derived from multi-country studies rather than the Canadian population and self-monitoring blood glucose costs were not included.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".