Cost–Utility Analysis of Glucagon-Like Peptide-1 Agonists Compared with Dipeptidyl Peptidase-4 Inhibitors or Neutral Protamine Hagedorn Basal Insulin as Add-On to Metformin in Type 2 Diabetes in Sweden
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess the costs and benefits of three alternative second-line treatment strategies for Swedish patients with type 2 diabetes mellitus (T2DM) who fail to reach glycated hemoglobin (HbA1c) ≤ 7% with metformin treatment alone: glucagon-like peptide-1 (GLP-1) receptor agonists, dipeptidyl peptidase-4 (DPP-4) inhibitors, and neutral protamine Hagedorn (NPH) insulin. METHODS: A previously developed cohort model for T2DM was applied over a 35-year time horizon. Data on T2DM patients on metformin monotherapy with HbA1c > 7% were collected from the Swedish National Diabetes Register. Treatment effects were taken from published studies. Costs and effects were discounted at 3% per annum, and the analysis was conducted from a societal perspective. The robustness of the results was evaluated using one-way and probabilistic sensitivity analyses. RESULTS: Treatment with GLP-1 agonists was associated with a discounted incremental benefit of 0.10 and 0.25 quality-adjusted life years (QALYs) and higher discounted costs of Swedish Krona (SEK) 34,865 and SEK 40,802 compared with DPP-4 inhibitors and NPH insulin, respectively. Assuming willingness-to-pay (WTP) of SEK 500,000 per QALY, treatment strategy with GLP-1 agonists was a cost-effective option with incremental cost-effectiveness ratios of SEK 353,172 and SEK 160,618 per QALY gained versus DPP-4 inhibitors and NPH insulin, respectively. The results were most sensitive to incidence rate of moderate/major hypoglycemia and disutilities associated with insulin treatment, body mass index (BMI), and hypoglycemia. CONCLUSION: Assuming a WTP of SEK 500,000 per QALY, treatment strategy with GLP-1 agonists is a cost-effective strategy in comparison to DPP-4 inhibitors and NPH insulin among T2DM patients inadequately controlled with metformin alone in a Swedish setting.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".