LONGITUDINAL CHANGES IN THE PREVALENCE OF THE EXCESSIVE DAYTIME SLEEPINESS IN TWO SASKATCHEWAN FIRST NATIONS COMMUNITIES
Bibliographic record
Abstract
Background: Excessive daytime sleepiness (EDS) is a major public health issue that can reduce individuals’ work productivity and could be a sign for sleep disordered breathing such as obstructive sleep apnea (OSA). Objectives: 1) To determine predictors associated with EDS at baseline and follow-up; and 2) to investigate the longitudinal changes in the prevalence of EDS assessed by Epworth Sleepiness Scale (ESS) in two First Nations communities. Methods: The First Nations Sleep Health Project (FNSHP) was conducted in two phases in 2018 and 2022 in Saskatchewan. The survey collected information on demographics, socioeconomics, environment, and determinants of health. Two separate cross-sectional data analysis using baseline (n=573) and follow-up (n=346) surveys was conducted. For longitudinal analysis (n=919), multivariable logistic regression based on generalized estimating equations to account for within subject correlation due to repeated measurements at baseline and follow-up was employed. Results: Women made up 56% of respondents with an average age of 41 years (±15), while men comprised 44% with an average age of 39 years (±14.5). At baseline, individuals with heart disease had significantly increased odds of experiencing EDS (OR = 2.67; 95% CI: 1.37–5.23; p < 0.004). Ever-smokers who reported trouble sleeping due to coughing or snoring had significantly higher odds of EDS (p = 0.02), suggesting a confounding role of smoking in this relationship. At follow-up, ever-smokers had increased odds of EDS (OR = 3.39; 95% CI: 0.87–13.20). Among housing-related variables, higher crowd index was significantly associated with higher odds of EDS (OR = 3.38; 95% CI: 1.20–9.49). Obesity appeared to amplify the effect of depression on EDS, with obese individuals experiencing notably higher odds of EDS when also reporting depressive symptoms (OR = 4.67; 95% CI: 1.03–21.16). Longitudinal data analysis revealed that age, BMI, and smoking status were not significantly associated with EDS. Participants with heart conditions had 2.1 times higher odds of EDS compared to those without. Trouble sleeping due to coughing/snoring was also significantly associated with EDS (OR = 2.19; 95% CI: 1.43–3.35; p < 0.001). Significant interaction effects were observed between time and both sex and depression. At follow-up, males had significantly higher odds of EDS compared to females. Participants with depression at follow-up had substantially higher odds of EDS (OR = 4.09; 95% CI: 1.67–10.00) compared to those without depression at baseline. Conclusion: This study observed a modest increase in the prevalence of EDS over a three-year period. Longitudinal analysis revealed that changes in EDS prevalence were most strongly associated with male sex and co-morbid conditions, including heart disease, trouble sleeping due to coughing or snoring, and depression. These findings highlight the multifactorial nature of EDS and underscore the importance of considering both biological and environmental influences over time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".