Evaluation of the cost and medical resource use outcomes associated with nasal glucagon versus injectable glucagon for treatment of severe hypoglycemia in people with diabetes in Canada: a modeling analysis
Bibliographic record
Abstract
OBJECTIVES: Treatments for severe hypoglycemia aim to restore blood glucose through successful administration of rescue therapy, and choosing the most effective and cost-effective option will improve outcomes for patients and may reduce costs for healthcare payers. The present analysis aimed to compare costs and use of medical services with nasal glucagon and injectable glucagon in people with type 1 and 2 diabetes in Canada when used to treat severe hypoglycemic events when impaired consciousness precludes treatment with oral carbohydrates using an economic model, based on differences in the frequency of successful administration of the two interventions. METHODS: A decision tree model was prepared in Microsoft Excel to project outcomes with nasal glucagon and injectable glucagon. The model structure reflected real-world decision-making and treatment outcomes, based on Canada-specific sources. The model captured the use of glucagon, emergency medical services (EMS), emergency room, inpatient stay, and follow-up care. Costs were accounted for in 2019 Canadian dollars (CAD). RESULTS: Nasal glucagon was associated with reduced use of all medical services compared with injectable glucagon. EMS call outs were projected to be reduced by 45%, emergency room treatments by 52%, and inpatient stays by 13%. Use of nasal glucagon was associated with reduced direct, indirect, and combined costs of CAD 1,249, CAD 460, and CAD 1,709 per severe hypoglycemic event, respectively, due to avoided EMS call outs and hospital costs, resulting from a higher proportion of successful administrations. CONCLUSIONS: When a patient with type 1 or type 2 diabetes is being treated for a severe hypoglycemic event when impaired consciousness precludes treatment with oral carbohydrate, use of nasal glucagon was projected to be dominant versus injectable glucagon in Canada reducing costs and use of medical services.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 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".