Effects of extreme air pollution and El Niño Southern Oscillation on insufficient sleep: a cross-sectional study
Bibliographic record
Abstract
Abstract Background While there are many well understood clinical risk factors on sleep patterns, the associations of environmental factors, specifically air pollution on insufficient sleep remains understudied. This study investigates the association between insufficient sleep and particulate matter 2.5 (PM 2.5 ) among adults in the United States. There is also a need to determine whether various El Niño Southern Oscillation (ENSO) phases are effect modifiers in this relationship. Method A cross-sectional observational analysis using annual survey data from 3100 United States counties for adult (≥ 18 years) age-adjusted insufficient sleep prevalence from 2017 to 2024. Annual average county-specific PM 2.5 data was categorized into three categories [low (< 5 µg/m 3 ), moderate (5–11 µg/m 3 ), extreme (≥ 11 µg/m 3 )]. The annual average ENSO index was used to determine if the year was either El Niño, La Niña, or neutral. Adjusted associations were conducted using Poisson regression and were stratified by various phases of ENSO. Adjusted associations were reported as rate ratio (RR). Results From 2017 to 2024, the United States annual insufficient sleep is 34% [range min to max: 23–49%]. With respect to low PM 2.5 ; moderate and extreme PM 2.5 levels were associated with an increased risk of insufficient sleep by 1.03 (95% CI 1.02–1.05, P < 0.001) and 1.11 (95% CI 1.09–1.12, P < 0.001), respectively. The interaction between PM 2.5 and ENSO was significant ( P < 0.001) on insufficient sleep. The magnitude in associations between extreme PM 2.5 and insufficient sleep differed by various ENSO phases. Conclusion Long-term (i.e. annual) effects of air pollution can pose a risk on adult sleep. While El Niño and La Niña phases were found to be a significant effect modifier, yet during the neutral phase the risks for extreme PM 2.5 were observed to be the strongest on insufficient sleep. Further investigations are needed to recognize the environmental effects on sleep deprivation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".