Population-Level Impact and Cost-effectiveness of Continuous Glucose Monitoring and Intermittently Scanned Continuous Glucose Monitoring Technologies for Adults With Type 1 Diabetes in Canada: A Modeling Study
Bibliographic record
Abstract
OBJECTIVE: Maintaining healthy glucose levels is critical for the management of type 1 diabetes (T1D), but the most efficacious and cost-effective approach (capillary self-monitoring of blood glucose [SMBG] or continuous [CGM] or intermittently scanned [isCGM] glucose monitoring) is not clear. We modeled the population-level impact of these three glucose monitoring systems on diabetes-related complications, mortality, and cost-effectiveness in adults with T1D in Canada. RESEARCH DESIGN AND METHODS: We used a Markov cost-effectiveness model based on nine complication states for adults aged 18-64 years with T1D. We performed the cost-effectiveness analysis from a single-payer health care system perspective over a 20-year horizon, assuming a willingness-to-pay threshold of CAD 50,000 per quality-adjusted life-year (QALY). Primary outcomes were the number of complications and deaths and the incremental cost-effectiveness ratio (ICER) of CGM and isCGM relative to SMBG. RESULTS: An initial cohort of 180,000 with baseline HbA1c of 8.1% was used to represent all Canadians aged 18-64 years with T1D. Universal SMBG use was associated with ∼11,200 people (6.2%) living without complications and ∼89,400 (49.7%) deaths after 20 years. Universal CGM use was associated with an additional ∼7,400 (4.1%) people living complications free and ∼11,500 (6.4%) fewer deaths compared with SMBG, while universal isCGM use was associated with ∼3,400 (1.9%) more people living complications free and ∼4,600 (2.6%) fewer deaths. Relative to SMBG, CGM and isCGM had ICERs of CAD 35,017/QALY and 17,488/QALY, respectively. CONCLUSIONS: Universal use of CGM or isCGM in the Canadian T1D population is anticipated to reduce diabetes-related complications and mortality at an acceptable cost-effectiveness threshold.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".