Cost-effectiveness of insulin analogues for diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Insulin analogues may be associated with fewer episodes of hypoglycemia than conventional insulins. However, they are costly alternatives. We compared the cost-effectiveness of insulin analogues and conventional insulins used to treat type 1 and type 2 diabetes mellitus in adults. METHODS: We conducted a cost-effectiveness evaluation of insulin analogues versus conventional insulins using the Center for Outcomes Research Diabetes Model. We compared rapid-acting analogues (insulin aspart and insulin lispro) with regular human insulin, and long-acting analogues (insulin glargine and insulin detemir) with neutral protamine Hagedorn insulin. We derived clinical information for the comparisons from meta-analyses of randomized controlled trials. We obtained cost and utility estimates from published sources. We performed sensitivity analyses to test the robustness of our results. RESULTS: For type 1 diabetes, insulin aspart was more effective and less costly than regular human insulin. Insulin lispro was associated with an incremental cost of Can$28,996 per quality-adjusted life-year. The incremental cost per quality-adjusted life-year was Can$87,932 for insulin glargine and Can$387,729 for insulin detemir, compared with neutral protamine Hagedorn insulin. For type 2 diabetes, insulin aspart was associated with an incremental cost of Can$22,488 per quality-adjusted life-year compared with regular human insulin. For insulin lispro, the incremental cost was Can$130,865. Compared with neutral protamine Hagedorn insulin, insulin detemir was less effective and more costly. Insulin glargine was associated with an incremental cost of Can$642,994 per quality-adjusted life-year. The model was sensitive to changes in the effect size of hemoglobin A(1c) and to decrements applied to utility scores when fear of hypoglycemia was included as a factor. INTERPRETATION: The cost-effectiveness of insulin analogues depends on the type of insulin analogue and whether the patient receiving the treatment has type 1 or type 2 diabetes. With the exception of rapid-acting insulin analogues in type 1 diabetes, routine use of insulin analogues, especially long-acting analogues in type 2 diabetes, is unlikely to represent an efficient use of finite health care resources.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".