Social disparities in sleep health of African populations: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVES: To document the relationship between socioeconomic status (SES) and sleep health in African populations. METHODS: Observational cross-sectional or cohort studies examining the association between SES indicators and sleep outcomes in participants from African countries were included. The search was performed in the MEDLINE, Embase, and Web of Science Core Collection electronic databases in June 2021. Selection, confounding, attrition/exclusion, detection, and selective reporting bias were assessed using the OHAT Risk of Bias Tool. Random effects meta-analysis was used for summarizing the effect estimates. RESULTS: Forty-three reports were selected, having sampled 153,372 Africans from 26 countries. Education was the most frequent SES indicator and composite measures of sleep quality or disturbances was the most common sleep outcome. Low educational attainment was significantly associated with lower odds of short sleep (odds ratio [OR]=0.65, 95% confidence intervals [0.50, 0.84], p = .001) and higher odds of insomnia (OR=1.53, [1.18, 1.99], p = .001) or poor sleep quality (OR=1.60, [1.17, 2.18], p = .003). Low levels of income/assets were related to higher odds of insomnia (OR=1.38, [1.02, 1.86], p = .04) and low occupational/employment status was linked to lower odds of short sleep duration (OR=0.49, [0.30, 0.79], p = .004). CONCLUSIONS: Socioeconomic disadvantage was a significant predictor of insomnia and poor sleep quality, while it was associated with longer sleep duration. Significant heterogeneity in terms of exposure and outcomes, scarcity of longitudinal designs, lack of objective outcome measurement, and low representation of rural samples and participants from low-income countries limit the quality of evidence.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".