Evaluation of the Long-Term Cost-Effectiveness of the Dexcom G6 Continuous Glucose Monitor versus Self-Monitoring of Blood Glucose in People with Type 1 Diabetes in Canada
Bibliographic record
Abstract
BACKGROUND: The Dexcom G6 real-time continuous glucose monitoring (RT-CGM) system is one of the most sophisticated RT-CGM systems developed to date and became available in Canada in 2019. A health economic analysis was performed to determine the long-term cost-effectiveness of the Dexcom G6 RT-CGM system versus SMBG in adults with type 1 diabetes (T1D) in Canada. METHODS: The analysis was performed using the IQVIA Core Diabetes Model. Based on clinical trial data, patients with mean baseline HbA1c of 8.6% were assumed to have a HbA1c reduction of 1.0% with RT-CGM versus 0.4% reduction with SMBG. RT-CGM was also associated with a quality of life (QoL) benefit owing to reduced incidence of hypoglycemia, reduced fear of hypoglycemia (FoH) and elimination of fingerstick testing. Direct medical costs were sourced from published literature, and inflated to 2019 Canadian dollars (CAD). RESULTS: Dexcom G6 RT-CGM was projected to improve mean quality-adjusted life expectancy by 2.09 quality-adjusted life years (QALYs) relative to SMBG (15.52 versus 13.43 QALYs) but mean total lifetime cots were CAD 35,353 higher with RT-CGM (CAD 227,357 versus CAD 192,004) resulting in an incremental cost-effectiveness ratio (ICER) of CAD 16,931 per QALY gained. Sensitivity analyses revealed that assumptions relating to the QoL benefit associated with reduced FoH and the elimination of fingerstick testing with RT-CGM as well as SMBG usage and change in HbA1c were the key drivers of cost-effectiveness. CONCLUSION: For adults with T1D in Canada, RT-CGM is associated with improved glycemic control and QoL benefits owing to a reduced FoH and elimination of the requirement for fingerstick testing and over a lifetime time horizon is 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".