A Retrospective Observational Study to Understand Medication Utilization and Lines of Treatment in Patients With Insomnia Disorder
Bibliographic record
Abstract
Insomnia is a common sleep disorder, associated with multiple health concerns. Current medications for insomnia are associated with higher safety risks if clinical practice guidelines or monograph recommendations are not followed. This study aims to understand real-world prescribing practices among patients with insomnia in Canada, including medication utilization, potentially inappropriate medication use, cost incurred, and lines of treatment. This retrospective observational study utilized longitudinal drug claims data from 2018 to 2020 from the Canadian IQVIA National Private Drug Plan and Ontario Drug Benefit databases. Patients with any claims for medications approved for insomnia in Canada were identified. Four types of inappropriate medication usage were defined: (1) elevated daily dose; (2) extended duration of use for benzodiazepines (BZD) and/or Z-drugs; (3) combination use; and (4) opioid overlap with BZD and/or Z-drugs. In 2019, 597,222 patients with insomnia were identified; 64% were female, with an average age of 55 years. Inappropriate medication use was noted in 52.5% of adult patients (aged 18-65 years) and 69.5% of senior patients (aged >65 years). Extended duration was the most common inappropriate medication usage category. The annual cost of medications for insomnia was $54.8 million, and $30.3 million (55.2%) met inappropriate medication use criteria. High prevalence of inappropriate medications usage in insomnia raises serious safety concerns for patients suffering from insomnia, particularly seniors, while also placing a substantial burden on the Canadian public and private health systems. This highlights an unmet need for better education regarding current guidelines and more effective and safer treatment options.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".