0404 Americas Prevalence of Insomnia Disorder in Adults: Estimation Using Currently Available Data
Bibliographic record
Abstract
Abstract Introduction Insomnia disorder is a major clinical issue due to its high prevalence and negative health outcomes. While the prevalence of insomnia in developed countries like the United States and Canada is known, its prevalence across the broader Americas region (Northern America, Latin America and the Caribbean) remains unclear. Limited epidemiological studies have been conducted and the majority of the Americas’ population does not have a reliable estimate for insomnia disorder. The purpose of this analysis was to estimate the Americas’ prevalence of adult insomnia disorder using published data. Methods Published nation-specific estimates of the general population prevalence of insomnia disorder were applied to current population estimates for all countries in the Americas. We utilized the following criteria to determine which insomnia prevalence estimates to use: 1) studies that reported prevalence stratified by both age and sex together; 2) studies that reported prevalence by age and sex separately, we used the age-based results; 3) studies that reported prevalence by sex but not age; 4) studies that reported only a single prevalence estimate. Expert opinion was used to select the most methodologically rigorous study when >1 potential reference study existed for a nation. For countries lacking a reference study, we applied the estimates from a large, well-conducted study. Population estimates by sex and age (20yrs+) were sourced from the United Nations World Population Prospects 2022. Results There were 55 countries defined by the United Nations comprising the Americas of which 3 (5.5%) had a suitable prevalence estimate of insomnia. We estimated an Americas’ adult insomnia disorder prevalence of approximately 123 million adults (16.8%), comprised of 73 million females (19.5%) and 50 million males (14.0%). The nations with the greatest burden of insomnia disorder are the United States (37 million), Brazil (29 million), and Mexico (16 million). Conclusion This large burden of disease, ~17% of adults, highlights the importance of comprehensive sleep health initiatives and the need for promotion of sleep health in public health policy. There is a lack of appropriate prevalence studies especially in low- and middle-income countries within the Americas. Support (if any) Study funded by ResMed
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".