Virological suppression rate and its predictors among HIV/AIDS patients on antiretroviral therapy before and during the COVID-19 pandemic in Ethiopia: Systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Achieving viral load suppression is crucial for the prevention of complications and deaths related to HIV infection. Ethiopia has embraced the worldwide 90-90-90 target, but there is no national representative information regarding virological suppression. Therefore, this review aims to determine the pooled virological suppression rate and identify the pooled effect of contributing factors of viral suppression for HIV-positive patients on antiretroviral therapy in Ethiopia. Method We systematically searched websites and databases, including online repositories, to obtain primary studies. We assessed the quality of the included articles using the Newcastle‒Ottawa Scale appraisal checklist by two reviewers. Publication bias was checked using Egger’s regression test, the heterogeneity of the studies was assessed using I2 statistics and Q statistics, and sensitivity analysis was performed to identify any outlier results in the included studies. The Der Simonian Laird random-effects model was used to estimate the overall proportion of viral suppression, and STATA 17 statistical software was used for all types of analysis. Result A total of 21 eligible articles were used for this quantitative synthesis. The overall pooled virological suppression was 71% (95% CI: 64%, 77%). The pooled effects of poor adherence to ART (AOR = 0.33; 95% CI: 0.28, 0.40), body mass index (18.5–24.9 kg/m2) (AOR = 1.8; 95% CI: 1.37, 2.36), disclosure AOR = 1.41; 95% CI: 1.05, 1.89), absence of opportunistic infection (AOR = 1.68; 95% CI: 1.43, 1.97), and high baseline viral load (AOR = 0.65; 95% CI: 0.52, 0.81) were identified as significant predictors of viral suppression. Conclusion The overall pooled percentage of virological suppression was low compared with the global target of viral suppression. Poor adherence, normal body mass index, disclosure, absence of opportunistic infection and high viral load were contributing factors of viral suppression in Ethiopia. Responsible stakeholders should maximize their effort to achieve the global target of virological suppression by addressing significant predictors. Prospero registration number and google link: CRD42023434248.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".