Epidemiology and burden of chronic insomnia disorder in Europe: an analysis of the 2020 National Health and Wellness Survey
Bibliographic record
Abstract
Introduction Chronic insomnia disorder (CID) is considered a significant worldwide public health concern; however, its exact burden is unknown. We estimate its prevalence across France, Germany, Italy, Spain, and the United Kingdom, and assess the economic and humanistic burden for a broader insomnia population.Methods This retrospective, cross-sectional, observational study used 2020 National Health and Wellness Survey (NHWS) data. Patients reporting insomnia were characterized to define CID. Health-related quality of life (HRQoL), work productivity, and healthcare resource use (HCRU) outcomes were assessed in four cohorts according to insomnia diagnosis and treatment status and examined using multivariable analyses according to Insomnia Severity Index categories.Results Among 62,319 respondents, 9,035 (21.2%) reported experiencing insomnia over the previous 12 months. CID prevalence rates were 5.5% to 6.7% across the five countries and 6.0% overall. HRQoL outcomes were persistently poorer in cohorts of patients diagnosed with insomnia than those with undiagnosed insomnia. Undiagnosed and treated insomnia patients reported the highest work presenteeism and total work productivity impairment and the highest number of emergency room and hospitalization visits than patients with insomnia (either treated or untreated). After adjusting for covariates, patients with severe insomnia reported significantly worse EQ-5D-5L utility scores, higher absenteeism and presenteeism rates, and more healthcare provider visits over the past 6 months than patients without insomnia (all p < 0.01).Conclusions Our prevalence rates for CID align with published literature. A diagnosis of insomnia, use of sleep medications, and severity of insomnia are associated with poor quality of life, loss of work productivity, and higher HCRU, confirming the high unmet need and substantial humanistic and economic burden of CID.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".