Cost-effectiveness of insulin detemir versus insulin glargine for Thai type 2 diabetes from a payer’s perspective
Bibliographic record
Abstract
AIMS: An economic evidence is a vital tool that can inform the decision to use costly insulin analogs. This study aimed to evaluate long-term cost-effectiveness of insulin detemir (IDet) compared with insulin glargine (IGlar) in type 2 diabetes (T2DM) from the Thai payer's perspective. METHODS: Long-term costs and outcomes were projected using a validated IMS CORE Diabetes Model, version 8.5. Cohort characteristics, baseline risk factors, and costs of diabetes complications were derived from Thai data sources. Relative risk was derived from a systematic review and meta-analysis study. Costs and outcomes were discounted at 3% per annum. Incremental cost-effectiveness ratio (ICER) was presented in 2015 US Dollars (USD). A series of one-way and probabilistic sensitivity analyses were performed. RESULTS: IDet yielded slightly greater quality-adjusted life years (QALYs) (8.921 vs 8.908), but incurred higher costs than IGlar (90,417.63 USD vs 66,674.03 USD), resulting in an ICER of ∼1.7 million USD per QALY. The findings were very sensitive to the cost of IDet. With a 34% reduction in the IDet cost, treatment with IDet would become cost-effective according to the Thai threshold of 4,434.59 USD per QALY. CONCLUSIONS: Treatment with IDet in patients with T2DM who had uncontrolled blood glucose with oral anti-diabetic agents was not a cost-effective strategy compared with IGlar treatment in the Thai context. These findings could be generalized to other countries with a similar socioeconomics level and healthcare systems.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".