Association between physical multimorbidity and sleep problems in 46 low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Little is known about the association between multimorbidity (i.e., two or more chronic conditions) and sleep problems in the general adult populations of low- and middle-income countries (LMICs). Thus, we aimed to assess this association among adults from 46 LMICs, and to quantify the extent to which anxiety, depression, stress, and pain explain this association. METHODS: Cross-sectional, predominantly nationally representative, community-based data from the World Health Survey were analyzed. Nine chronic physical conditions (angina, arthritis, asthma, chronic back pain, diabetes, edentulism, hearing problems, tuberculosis, visual impairment) were assessed. To be included in the analysis, sleep problems had to have been experienced in the past 30 days and to have been severe or extreme; they included difficulties falling asleep, waking up frequently during the night or waking up too early in the morning. Multivariable logistic regression and mediation analyses were conducted to explore the associations. RESULTS: Data on 237,023 individuals aged ≥18 years [mean (SD) age 38.4 (16.0) years; 49.2% men] were analyzed. Compared with no chronic conditions, having 1, 2, 3, and ≥4 conditions was associated with 2.39 (95%CI=2.14, 2.66), 4.13 (95%CI=3.62, 4.71), 5.70 (95%CI=4.86, 6.69), and 9.99 (95%CI=8.18, 12.19) times higher odds for sleep problems. Pain (24.0%) explained the largest proportion of the association between multimorbidity and sleep problems, followed by anxiety (21.0%), depression (11.2%), and stress (10.4%). CONCLUSIONS: Multimorbidity was associated with a substantially increased odds for sleep problems in adults from 46 LMICs. Future studies should assess whether addressing factors such as pain, anxiety, depression, and stress in people with multimorbidity can lead to improvement in sleep in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".