Cost–utility analysis of a flash continuous glucose monitoring system in the management of people with type 2 diabetes mellitus on basal insulin therapy—An Italian healthcare system perspective
Bibliographic record
Abstract
AIMS: To assess the cost-utility of the FreeStyle Libre flash continuous glucose monitoring (CGM) system from an Italian healthcare system perspective, when compared with self-monitoring of blood glucose (SMBG) in people living with type 2 diabetes mellitus (T2DM) receiving basal insulin. MATERIALS AND METHODS: A patient-level microsimulation model was run using Microsoft Excel for 10 000 patients over a lifetime horizon, with 3.0% discounting for costs and utilities. Inputs were based on clinical trials and real-world evidence, with patient characteristics reflecting Italian population data. The effect of flash CGM was modelled as a persistent 0.8% reduction in glycated haemoglobin versus SMBG. Costs (€ 2023) and disutilities were applied to glucose monitoring, diabetes complications, severe hypoglycaemia, and diabetic ketoacidosis. The health outcome was measured as quality-adjusted life-years (QALYs). RESULTS: Direct costs were €5338 higher with flash CGM than with SMBG. Flash CGM was associated with 0.51 more QALYs than SMBG, giving an incremental cost-effectiveness ratio (ICER) of €10 556/QALY. Scenario analysis ICERs ranged from €3825/QALY to €26 737/QALY. In probabilistic analysis, flash CGM was 100% likely to be cost effective at willingness-to-pay thresholds > €20 000/QALY. CONCLUSIONS: From an Italian healthcare system perspective, flash CGM is cost effective compared with SMBG for people living with T2DM on basal insulin.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".