Continuous glucose monitoring for self-management of diabetes in people living with type 2 diabetes mellitus on basal insulin therapy: A microsimulation model and cost-effectiveness analysis from a US perspective with relevance to Medicaid
Bibliographic record
Abstract
BACKGROUND: Reducing the risks of complications is a primary goal of diabetes management, with effective glycemic control a key factor. Glucose monitoring using continuous glucose monitoring (CGM) technology is an important part of diabetes self-management, helping patients reach and maintain targeted glucose and glycated hemoglobin (HbA1c) levels. Although clinical guidelines recommended CGM use, coverage by Medicaid is limited, likely because of cost concerns. OBJECTIVE: To assess the cost-effectiveness of FreeStyle Libre CGM systems, compared with capillary-based self-monitoring of blood glucose (SMBG), in US individuals with type 2 diabetes mellitus using basal insulin. METHODS: A patient-level microsimulation model was used to compare CGM with SMBG for a population of 10,000 patients. A 10-year horizon was used, with an annual discount rate of 3.0% for costs and utilities. Model population characteristics were based on US national epidemiology data. Patient outcomes were based on published clinical trials and real-world studies. Annual costs, reflective of 2023 values, included CGM and SMBG acquisition costs and the costs of treating diabetic ketoacidosis, severe hypoglycemia, and diabetes complications. The effect of CGM was modeled as a persistent 1.1% reduction in HbA1c relative to SMBG based on US real-world evidence. Disutilities were based on published clinical trials and other relevant literature. The primary outcome was cost per quality-adjusted life-year (QALY) gained. Sensitivity analyses were performed to test the validity of the model results when accounting for a plausible variation of inputs. RESULTS: In the base case analysis, CGM was dominant to SMBG, providing more QALYs (6.18 vs 5.97) at a lower cost ($70,137 vs $71,809) over the 10-year time horizon. A $10,456 increase in glucose monitoring costs was offset by a $12,127 reduction in treatment costs. Cost savings reflected avoidance of acute diabetic events (savings owing to reductions in severe hypoglycemia and diabetic ketoacidosis were $271 and $2,159, respectively) and a reduced cumulative incidence of diabetes complications, particularly renal failure (saving $5,292), myocardial infarction (saving $1,996), and congestive heart failure (saving $1,061). Scenario analyses were consistent with the base case results, and the incremental cost-effectiveness ratio for CGM vs SMBG ranged from dominant to cost-effective. In probabilistic analysis, CGM was 100% likely to be cost-effective at a willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: CGM is cost-effective compared with SMBG for US patients with type 2 diabetes mellitus receiving basal insulin therapy. This suggests that state Medicaid programs could benefit from broader coverage of CGM.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".