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 modelling 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], continuous or intermittently-scanned glucose monitoring [CGM, isCGM]) is not clear. We modelled 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 9 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 $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 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 complication-free and ~11,500 (6.4%) fewer deaths compared to SMBG, while universal isCGM use was associated with ~3,400 (1.9%) more people living complication-free and ~4,600 (2.6%) fewer deaths. Relative to SMBG, CGM and isCGM had ICERs of $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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".