A cost–utility analysis of dulaglutide versus insulin glargine as third-line therapy for Type 2 diabetes in Canada
Bibliographic record
Abstract
AIM: The prevalence of Type 2 diabetes in Canada is estimated to be 7.6% and rising. Given the substantial economic burden associated with Type 2 diabetes treatment, optimizing healthcare expenditure is extremely important. In the present analysis, we evaluated the cost-effectiveness of dulaglutide 1.5 mg, a once-weekly glucagon-like peptide 1 agonist as third-line therapy relative to insulin glargine from the perspective of a Canadian healthcare payer. METHODS: A patient-level cost-utility model of Type 2 diabetes was developed to capture seven microvascular and macrovascular complications and severe and nonsevere hypoglycemia. Cohort characteristics and the relative efficacy of dulaglutide 1.5 mg and insulin glargine were derived from the AWARD-2 head-to-head trial, which was identified by systematic literature review. Cost data were derived from Canadian sources and expressed in 2016 Canadian dollars (CAD), and future cost and quality-adjusted life expectancy (QALE) estimates were discounted at 1.5% per annum. One-way and probabilistic sensitivity analyses were conducted. RESULTS: Based on the AWARD-2 trial, relative to insulin glargine, dulaglutide 1.5 mg was projected to increase QALE by 0.38 quality-adjusted life years and increase costs by CAD 19,773, resulting in an incremental cost-effectiveness ratio of CAD 52,580 per quality-adjusted life year gained. CONCLUSION: A computer simulation analysis showed that dulaglutide 1.5 mg would likely be cost-effective relative to insulin glargine in patients with Type 2 diabetes inadequately controlled on metformin and sulfonylurea 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".