Treatment failure and its associated factors among children receiving highly active antiretroviral therapy in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Over the last decades, large number of children living with human immunodeficiency virus (HIV) have been successfully enrolled in care and initiated treatment. However, treatment failure is still a major challenge in the track, missing far too many children. National-level evidence on antiretroviral therapy failure and its associated factors among children receiving highly active antiretroviral therapy is required to alleviate this challenge. METHODS: PubMed/Medline, EMBASE, CINAHL, Cochrane library, Google, and Google Scholar databases were used to access eligible studies. This meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. In addition, Newcastle-Ottawa Scale quality assessment was applied for critical appraisal. Cochran's Q statistic, funnel asymmetry plot, and Egger's test were used to assess heterogeneity and publication bias. Random effect model was computed to explore the pooled burden of treatment failure and its associated factors among children living with HIV. Odds ratio with 95% confidence interval was considered to identify associated factors. RESULT: The overall pooled prevalence of treatment failure among children living with HIV was 16.6%. Whereas virological, immunological, and clinical failure were 4.49%, 5.41%, and 5.71% respectively, where either of parent is deceased (odds ratio = 2.13, 95% confidence interval: 1.4-3.3), opportunistic infection (odds ratio = 1.67, 95% confidence interval: 1.1-2.5), absence of disclosure of status (odds ratio = 1.6, 95% confidence interval: 1.0-2.5), advanced World Health Organization stage (odds ratio = 4.2, 95% confidence interval: 1.6-10.5), and drug substitution (odds ratio = 2.0, 95% CI: 1.5-2.7) were significantly associated factors. CONCLUSION: The pooled prevalence of treatment failure among children living with HIV in Ethiopia was lower when compared to most African countries. Accordingly, either prevention or early treatment of opportunistic infection and advanced World Health Organization clinical stages, special care for children whose either parents are deceased, advocating disclosure of status, and avoiding drug substitution as much as possible were still needed to prevent treatment failure.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 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 | 0.001 |
| 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".