Cost-utility of real-time continuous glucose monitoring versus self-monitoring of blood glucose in people with insulin-treated Type 2 diabetes in Canada
Bibliographic record
Abstract
Aim: Clinical trials and real-world data for Type 2 diabetes have shown that real-time continuous glucose monitoring (rt-CGM) lowers glycated hemoglobin (A1c) and reduces hypoglycemia relative to self-monitoring of blood glucose (SMBG). This analysis examined the long-term health and economic outcomes associated with using rt-CGM versus SMBG in people with insulin-treated Type 2 diabetes in Canada. Materials & methods: Clinical data were sourced from a real-world study, in which rt-CGM reduced A1C by 0.56% versus continued SMBG. The analysis was performed using the IQVIA Core Diabetes Model, from a Canadian payer perspective over a lifetime horizon for a cohort aged 65 years with an A1C of 8.3% at baseline. Future costs and clinical outcomes were discounted at 1.5% annually. Results: Projected total mean lifetime costs were CAD 207,466 for rt-CGM versus CAD 189,863 for SMBG (difference: CAD 17,602) and projected mean quality-adjusted life expectancy was 9.97 quality-adjusted life years (QALYs) for rt-CGM versus 9.02 QALYs for SMBG (difference: 0.95 QALYs), resulting in an incremental cost-utility ratio (ICUR) of CAD 18,523 per QALY gained for rt-CGM versus SMBG. Findings were sensitive to changes in the A1C treatment effect, annual cost and quality of life benefit associated with using rt-CGM, SMBG frequency, and baseline age, but ICURs remained below CAD 50,000 per QALY in all analyses. Conclusion: For people in Canada with insulin-treated Type 2 diabetes and poor glycemic control, use of rt-CGM is likely to be cost-effective relative to SMBG.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".