Supplementary materials: The cost-effectiveness of real-time continuous glucose monitoring versus intermittently scanned continuous glucose monitoring in individuals with insulin-treated Type 2 diabetes mellitus in Canada
Bibliographic record
Abstract
These are peer-reviewed supplementary materials for the article 'The cost-effectiveness of real-time continuous glucose monitoring versus intermittently scanned continuous glucose monitoring in individuals with insulin-treated Type 2 diabetes mellitus in Canada' published in the Journal of Comparative Effectiveness Research. Appendix 1: ECHO-T2DM DescriptionSupplementary figure 1: ECHO-T2DM simplified flow diagramAppendix 2: Validation of ECHO-T2DMAppendix Table 1: ECHO-T2DM Cross-Validation Results=Appendix Table 1: ECHO-T2DM Cross-Validation ResultsAppendix Table 2: ECHO-T2DM External ValidationECHO-T2DM AdViSHE AssessmentPart B: Input Data ValidationPart C: Validation of the Computerized ModelPart D: Operational ValidationPart E: Other Validation TechniquesAppendix 3: Detailed Input DataAppendix Table 3: Comparison of DIAMOND T2D and REPLACEAppendix Table 4: Unit Costs for Micro- and Macrovascular Complications (Inflated to 2023 CAD)Appendix Table 5: QALY Disutility WeightsAppendix 4: Detailed ResultsAppendix Table 6: Rates for First Event (Per 100 Patient-Years)Appendix Table 7: Base Case Sources of QALY DisutilityAppendix Table 8: Results of Sensitivity Analysis Comparing rtCGM with isCGM in patients with T2DMAppendix Figure 2: Estimated HbA1c TrajectoryAppendix Figure 3: Estimated SurvivalAppendix Figure 4: Model ConvergenceAppendix 5: Checklist of Reporting Model Input in Diabetes Health Economics Studies and CHEERS ChecklistReferencesConsolidated Health Economic Evaluation Reporting Standard (CHEERS) 2022 ChecklistAim: Continuous glucose monitoring (CGM) supports glycemic control and reduces diabetes complications. CGM systems include intermittently scanned CGM (is-CGM) and real-time CGM (rt-CGM). While rt-CGM may provide better outcomes than is-CGM, it costs more upfront and its cost-effectiveness in Canada has not been established. We assessed the cost-effectiveness of rt-CGM versus is-CGM in people with insulin-treated Type 2 diabetes mellitus (T2DM) from a Canadian healthcare payer perspective. Materials & methods: We used the ECHO-T2DM microsimulation model to estimate incremental lifetime health outcomes and costs of rt-CGM versus is-CGM. Clinical inputs came from an indirect treatment comparison; cost and utility data were drawn from published sources. Sensitivity analyses tested robustness. Results: Rt-CGM was more effective and less costly than is-CGM, yielding 0.346 additional quality-adjusted life-years and CAD 2237 in savings over 30 years. Benefits stemmed primarily from better glycemic control and fewer complications, reductions in glycemic events, and reduced fear of hypoglycemia. Although rt-CGM incurred CAD 3867 higher acquisition costs, these were more than offset by avoided complications. Deterministic analysis showed dominance in 14 of 18 scenarios, and cost-effectiveness in the remaining four. Uncertainty analysis showed rt-CGM had an ICER below CAD 50,000 in 98% of simulations. Discussion: Rt-CGM is potentially a cost-saving alternative to is-CGM among people with insulin-treated T2DM in Canada. This finding was strengthened by rigorous sensitivity analysis. Study strengths include use of a validated microsimulation model and adoption of conservative assumptions. Limitations include absence of head-to-head trial evidence and indirect use of time in and out of range. Conclusion: Rt-CGM is a potentially cost-saving option for managing insulin-treated T2DM in Canada, with implications for clinical practice and reimbursement policy.
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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 | 0.004 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.304 | 0.010 |
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".