Economic Value of Improved Accuracy for Self-Monitoring of Blood Glucose Devices for Type 1 Diabetes in Canada
Bibliographic record
Abstract
OBJECTIVE: The objective was to simulate and compare clinical and economic outcomes of self-monitoring of blood glucose (SMBG) devices along error ranges and strip price. METHODS: We programmed a type 1 diabetes natural history and treatment cost-effectiveness model. In phase 1, using past evidence from in silico modeling validated by the Food and Drug Administration, we associated changes in SMBG error to changes in hemoglobin A1c (HbA1c) and separately, changes in severe hypoglycemia requiring an inpatient stay. In phase 2, using Markov cohort simulation modeling, we estimated clinical and economic outcomes from the Canadian payer perspective. The primary comparison was a SMBG device with strip price $0.73 Canadian dollars (CAD) and 10% error (exceeding accuracy requirements by International Organization for Standardization (ISO) 15197:2013) versus a SMBG device with strip price $0.60 CAD and 15% error (accuracy meeting ISO 15197:2013). Outcomes for the average patient, were quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICERs), and budget impact. RESULTS: Assuming benefits translate into HbA1c improvements only, the ICER with 10% error versus 15% was $11 500 CAD per QALY. Assuming the benefits translate into reduced severe hypoglycemia requiring an inpatient stay only, an SMBG device with 10% error dominated (ie, less costly, more effective) an SMBG device with 15% error. The 3-year budget impact findings ranged from $0.004 CAD per member per month for HbA1c improvements to cost-savings for severe hypoglycemia reductions. CONCLUSIONS: From efficiency (cost-effectiveness) and affordability (budget impact) payer perspectives, investing in devices with improved accuracy (less error) appears to be an efficient and affordable strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".