Adherence to Highly Active Antiretroviral Therapy and Its Association with Serostatus Disclosure among People Living with HIV in Ethiopia: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Abstract Background: Adherence to highly active antiretroviral therapy (HAART) is a public health challenge worldwide. Non-adherence to HAART leads to treatment, immunologic, and virological failure. Despite different interventions made, adherence to HAART among adult people living with HIV (PLWHIV) is still inconsistent across studies, and the effect of serostatus disclosure on adherence to HAART was not studied in Ethiopia. Therefore, the study is aimed to determine the pooled prevalence of adherence to HAART and its relationship with serostatus disclosure among adult PLWHIV in Ethiopia.Methods: We searched 3247 original articles, both published and unpublished on Ethiopia dated from January 2016 to November 2019 by using different search engines. Data were extracted using Microsoft excel. New Castle Ottawa Scale quality assessment tool was used. STATA software version 11 was used for analysis. A random-effects model for meta-analysis was computed. Cochran Q statistics and I2 were used to estimate heterogeneity. Egger’s and Begg’s test was used to assess the publication bias.Results: A total of fifteen articles for systematic review and four articles for meta-analysis were used. The pooled prevalence of adherence to HAART is found to be 81.19% (80.1, 82.3). In the subgroup analysis, the pooled prevalence of adherence to HAART was 79.82% (73.19, 86.45) in the Oromia region, 82.51 %( 73.14, 91.87) in the Amhara region, and 72.7% (63.78, 81.61) in the Southern Nations Nationalities and Peoples’ Region (SNNPR). The serostatus disclosure improves adherence to HAART by nearly three times compared to non-serostatus disclosed PLWHIV (AOR=2.99, 95 %CI: 1.88, 4.77).Conclusions: The pooled prevalence of adherence to HAART among adult PLWHIV in Ethiopia was found to be low compared to WHO antiretroviral treatment recommendations. Having serostatus disclosure improved adherence to HAART.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".