Adherence to Highly Active Antiretroviral Therapy and Its Association with HIV Disclosure status among People Living with HIV/AIDS in Ethiopia: A systematic review and Meta-Analysis.
Bibliographic record
Abstract
Abstract Background:Adherence to Highly Active Antiretroviral Therapy (HAART) medication is the pressing public health problem worldwide. Non-adherence to HAART leads to treatment failure, immunologic failure, and virological failure. Despite different interventions made; still, HAART medication adherence among adult people living with HIV/AIDS (PLWHA) is inconsistent across studies and the effect of HIV disclosure status was not well studied. Therefore this study determines the pooled prevalence of HAART adherence and its relationship with HIV disclosure status among Adult PLWHA.MethodsWe searched 3247 both published and unpublished original articles from January 2016 to November 2019 in Ethiopia using different search engines.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 meta-analysis was performed. Cochran Q statistics and I2 were used to estimate heterogeneity. Eggers and Begg’s test was used to assess the publication bias.ResultsA total of 15 studies for systematic review and four studies for Meta-analysis were used. The pooled prevalence of HAART medication adherence is found to be 81.19% (80.132, 82.248). In the subgroup analysis, the pooled prevalence of HAART adherence 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 SNNPR. HIV disclosure improves HAART adherence by nearly three times compared to non-HIV disclosed HAART users (AOR=2.99, 95%CI: 1.88, 4.77).ConclusionsThe pooled prevalence of HAART adherence among adult PLWHA was found to be low. Having HIV disclosure improved HAART medication adherence.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".