Preventing Unnecessary Costs of Drug-Induced Hypoglycemia in Older Adults with Type 2 Diabetes in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: The costs of drug-induced hypoglycemia are a critical but often neglected component of value-based arguments to reduce tight glycemic control in older adults with type 2 diabetes. METHODS: An economic (decision-tree) analysis compared rates, costs, quality-adjusted life-years, and incremental costs per quality-adjusted life-year gained associated with mild, moderate and severe hypoglycemic events for 6 glucose-lowering medication classes in type 2 diabetic adults aged 65-79 versus those 80 years and older. The national U.S. (Center for Medicare Services) and Canadian public health payer perspectives were adopted. FINDINGS: Incidence rates of drug-induced hypoglycemia were the highest for basal insulin and sulfonylureas: 8.64 and 4.32 events per person-year in 65-79 year olds, and 12.06 and 6.03 events per person-year for 80 years and older. In both the U.S. and Canada, metformin dominated sulfonylureas, basal insulin and glucagon-like peptide1 receptor agonists. Relative to sulfonylureas, thiazolidinediones had the lowest incremental cost-effectiveness ratios in the U.S. and dominated sulfonylureas in Canada for adults 80 years and older. Relative to sulfonylureas, dipeptidyl peptidase4 inhibitors were cost-effective for adults 80 years and older in both countries, and for 65-79 year olds in Canada. Annual costs of hypoglycemia for older adults attaining very tight glycemic control with the use of insulin or sulfonylureas were estimated at U.S.$509,214,473 in the U.S. and CAN$65,497,849 in Canada. CONCLUSIONS: Optimizing drug therapy for older type 2 diabetic adults through the avoidance of drug-induced hypoglycemia will dramatically improve patient health while also generating millions of dollars by saving unnecessary medical costs.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".