Estimation of the global prevalence and burden of insomnia: a systematic literature review-based analysis
Bibliographic record
Abstract
Insomnia is common, is associated with major adverse medical and mental health outcomes, has a negative impact on quality of life, and has significant economic consequences. However, little is known about the global insomnia burden. This systematic review estimated the global prevalence of insomnia in adults. PubMed and Embase were searched (terms "insomnia," "prevalence," and "general population") to identify relevant peer-reviewed studies (final search 2-3 Sep 2024). Included studies had the highest data quality and lowest risk of bias, and reported clinically relevant insomnia prevalence in the general population. Insomnia prevalence estimates were applied to United Nations (UN) population data using a country-specific study (if available) or the highest-quality study (if no country-specific study). Of 1651 potential records, 18 studies (262,582 participants) were included. Thirty-one of 237 UN/World Bank-recognized countries/territories had a suitable nation-specific adult insomnia prevalence estimate. 852,325,091 adults (95 % confidence interval 830,354,161-874,309,252) were estimated to have insomnia (global prevalence: 16.2 %) and 414,967,941 were estimated to have severe insomnia (7.9 %). Insomnia and severe insomnia were more prevalent in females versus males across all age groups. The high global prevalence of insomnia disorder reinforces the need for comprehensive public health and clinical sleep health initiatives worldwide. REGISTRATION: PROSPERO: CRD42024581410.
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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".