1051-P: Cost-Effectiveness of Flash CGM Compared with SMBG—A Canadian Private-Payer Perspective
Bibliographic record
Abstract
Introduction & Objective: For people living with diabetes, effective glucose monitoring is recognized as a key component in diabetes care to reduce disease burden, complications, and healthcare utilization. This study aimed to assess the cost-effectiveness of flash CGM, compared with SMBG, from the perspective of a Canadian private payer. Methods: A recently developed patient-level microsimulation model was used to compare flash CGM with SMBG. Time horizons of 40 years (T1DM) and 25 years (T2DM) were used, reflecting the period of typical private payer coverage. Costs and utilities were discounted at 1.5%. Population characteristics, treatment outcomes, and utilities were based on RCTs and RWE studies. Mean age at model entry was 23 years for T1DM and 40 years for T2DM. T2DM treatment assumptions were: 84% non-insulin; 10% basal insulin; and 6% MDI. Costs were taken from Canadian sources, and included flash CGM and SMBG acquisition costs, the costs to private payers of treating diabetes complications, and absenteeism costs. The primary outcome was cost per quality-adjusted life year (QALY). Results: For both T1DM and T2DM, flash CGM provided more QALYs than SMBG while reducing costs (Table). Scenario analyses were consistent with the base-case results. Conclusions: From a Canadian private payer perspective, flash CGM is cost effective compared with SMBG for all people living with diabetes. Disclosure S.B. Harris: Consultant; Abbott. Research Support; Boehringer-Ingelheim. Consultant; Dexcom, Inc. Advisory Panel; Eli Lilly and Company. Consultant; Eli Lilly and Company, Novo Nordisk, Sanofi. Research Support; Novartis AG. Consultant; Bayer Inc. S. Cimino: Consultant; Abbott, Boehringer-Ingelheim, Eisai Inc., Eli Lilly and Company, Merck & Co., Inc., Novartis Canada, Novo Nordisk Canada Inc., Takeda Canada. T. Nguyen: None. Y. Poon: Employee; Abbott. K. Szafranski: Consultant; Abbott, Novo Nordisk, Novartis Canada, CSL Behring, AbbVie Inc., Sanofi.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 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".