Nonsevere Hypoglycemia Episode Clinical and Economic Outcomes: A Comparison between Sulfonylurea and Sodium-Glucose Cotransporter 2 Inhibitor as Add-On to Metformin from a Canadian Perspective
Bibliographic record
Abstract
BACKGROUND: Nonsevere hypoglycemia episodes (NSHEs) are associated with clinically adverse outcomes, lower health-related quality of life, increased burden of disease, and reduced work productivity. OBJECTIVE: To estimate prevalence of NSHEs and associated economic outcomes attributable to sulfonylurea (SU) versus sodium-glucose cotransporter 2 inhibitor (SGLT2i) initiation after metformin over one year for Canadian patients with type 2 diabetes (T2DM). METHODS: Risk difference for NSHEs was calculated for SU and SGLT2i from RCT data. Estimation of NSHEs attributable to SU utilization in Canada was calculated from published data. Both direct and indirect costs associated with NSHEs were obtained from previous published studies in literature. RESULTS: The number of patients with T2DM and exposure to SU in Canada in 2016 was estimated to be 1,246,438. The average underreported NSHEs in clinical settings were estimated at 67.7%. Risk difference for NSHEs for SU versus SGLT2i was estimated at 26.7%. Estimation of excess NSHEs attributable to SU utilization versus SGLT2i in Canada was estimated at 130,434 events per year (sensitivity analysis: minimum 80,680 and maximum 624,465). Total indirect costs including loss-of-work productivity and out-of-pocket costs secondary to excess NSHEs due to SU utilization versus SGLT2i after metformin were estimated at CDN$8.6M (M = millions) for 2016 (sensitivity analysis: minimum CDN$5.3M and maximum CDN$81.2M). CONCLUSION: NSHE, which is a forgotten variable in economic evaluations for healthcare reimbursement models, occurs frequently in real-world clinical settings but is infrequently reported. NSHEs can lead to a significant loss-of-work productivity and out-of-pocket costs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".