Cost-effectiveness of dapagliflozin versus sulfonylurea in combination with metformin in patients with type 2 diabetes in Vietnam: Analysis from the Payer’s Perspective
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) poses a substantial economic burden on both patients and healthcare systems. Dapagliflozin, an SGLT2 inhibitor, has shown effective glycemic control and reduced risk of hypoglycemia in T2DM patients; however, its higher treatment cost compared with other antidiabetic agents raises concerns about its economic value. This study assessed the cost-effectiveness of dapagliflozin versus sulfonylurea, both in combination with metformin, in patients with T2DM inadequately controlled on metformin monotherapy, from the Vietnamese healthcare payer’s perspective over a lifetime horizon. A model-based cost-utility analysis using the Cardiff Diabetes Model—a patient-level microsimulation—was conducted. Results indicated that dapagliflozin reduced the cumulative incidence of complications, particularly hospitalization for heart failure and chronic kidney disease. The incremental cost and quality-adjusted life-years (QALYs) gained with dapagliflozin were estimated at 15,941,900 VND and 0.52 QALY, respectively, yielding an incremental cost-effectiveness ratio (ICER) of 30,865,504 VND/QALY, well below Vietnam’s per capita GDP threshold (~114.2 million VND in 2024). Sensitivity analyses confirmed the robustness of these findings, with dapagliflozin remaining cost-effective across various assumptions. In conclusion, dapagliflozin is a cost-effective and clinically beneficial alternative to sulfonylurea as a first-line therapy in Vietnam, supporting its inclusion in reimbursement schemes and clinical practice guidelines for long-term T2DM management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".