Understanding the unmet needs in insomnia treatment: a systematic literature review of real-world evidence
Bibliographic record
Abstract
The objective of this study was to define and characterize the unmet needs in the pharmacological management of insomnia. A systematic literature review was conducted to identify relevant literature reporting real-world evidence in insomnia, published from January 2009 to April 2020. Pharmacological treatments – both prescription (benzodiazepines, ‘Z-drugs’ and suvorexant) and off-label (antidepressants, antipsychotics, and antihistamines) – were considered. Overall, 108 publications describing the humanistic (n = 59) and economic burden (n = 20) of insomnia, off-label treatment patterns (n = 28) and factors influencing treatment adherence or persistence (n = 8) were identified. A high prevalence of comorbid conditions was reported in patients with insomnia resulting in significantly lower health-related QoL compared to those with insomnia or a comorbidity alone. Current treatment options were associated with adverse events, including reduced sleep quality and next-day somnolence. An increased risk of accidents/injuries was also associated with insomnia and its treatment. Furthermore, safety concerns and perceived lack of efficacy for approved treatments have led to frequent off-label prescribing, despite a lack of clinical evidence of risk/benefit ratios. Safety concerns associated with benzodiazepines include risk of dependence, leading to prolonged treatment persistence and exacerbated adverse events, making them unsuitable for use in patients with chronic insomnia. Finally, the substantial economic burden of insomnia was evident, with reduced work productivity demonstrated in patients with insomnia compared to the general population. This review highlights a clear unmet need for insomnia therapies that improve sleep quality without resulting in next-day impairment and/or dependence.
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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 | 0.030 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".