Poor sleep quality and associated factors among people attending antiretroviral treatment clinics in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Sleep disturbances are frequently reported among people living with HIV infection. In Ethiopia, approximately half of people living with HIV/AIDS experience mental health issues, which further degrade sleep quality. This systematic review and meta-analysis aims to assess the national prevalence of poor sleep quality and identify key determinants. Methods A systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, focusing on English-published studies. The search spanned Google Scholar, HINARI, Scopus, PubMed, EMBASE, Web of Science, and AJOL from April 4, 2023, to May 15, 2023. Three reviewers independently extracted data and evaluated study quality using a modified Newcastle‒Ottawa scale for cross-sectional studies. Stata version 11 was used for the meta-analysis, employing a random-effects model to estimate poor sleep quality. Study heterogeneity was assessed using I2 and Cochran's Q test. Results A total of 6,070 articles regarding poor sleep quality and/or associated factors among people attending antiretroviral treatment clinics in Ethiopia were retrieved. The pooled estimate of poor sleep quality among people living with HIV in Ethiopia was 52.64 (95% CI: 44.08, 61.20). Depression (AOR = 4.61; 95% CI: 1.15, 18.51), a CD4 count < 200 cells/mm 3 (AOR = 1.83; 95% CI: 0.33, 10.18), a viral load > 1000 copies (AOR = 1.42; 95% CI: 0.19, 10.61), and anxiety (AOR = 17.16; 95% CI: 4.47, 65.91) were identified as factors associated with poor sleep quality. Conclusion A systematic review and meta-analysis found that about half of people living with HIV/AIDS in Ethiopia experience poor sleep quality. Key factors contributing to poor sleep quality include CD4 count, viral load, depression, and anxiety. Policymakers and relevant organizations should address these issues to improve sleep quality and manage the factors affecting it.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".