Bayesian meta-analysis of the effectiveness of implementation science evidence in improving health outcomes for adolescent patients accessing ART in sub-Saharan Africa
Bibliographic record
Abstract
Background: Implementation research (IR) studies and clinical trials have yielded conflicting results on improving treatment outcomes, especially among adolescent patients. To address this, we performed a Bayesian random-effects meta-analysis to evaluate the effectiveness of current IR interventions in improving retention in care and reducing viral suppression among HIV-infected adolescents. Method: A comprehensive search was conducted from 1 January 2000 to 31 December 2020. A Bayesian random-effects meta-analysis was performed using historical evidence from adolescent interventions conducted outside sub-Saharan Africa (SSA) and from adult-derived interventions within SSA. A Bayesian Copas random-effects model was used to account for publication bias and study variations. Power priors were used to weight the contribution of historical data to the analysis. Bayesian meta-analysis was particularly suited for this study since it allowed us to directly include prior assessments from several intervention studies into the pooled intervention data. Results: The pooled results from the 12 studies across eight African countries, involving 19,223 adolescent patients, showed significantly superior retention effects in adolescent-specialized interventions compared to standard care settings [odds ratio (OR) = 3.87; 95% credible interval (CrI): 0.94-10.82]. When 100% of data from eight observational studies on adolescent treatment outcomes were added to the analysis, the resulting OR was 3.02 (95% CrI: 1.01, 6.92). However, inclusion of 100% of historical data from adult randomised control trials (RCTs) reduced the retention effect to OR = 1.24 (95% CrI: 1.03-1.48). Regardless of whether adolescent historical data or adult RCT data were used, the associated posterior probability of benefiting from the intervention remained almost 1. There was no difference between standard care and specialized adolescent care in terms of virological suppression (OR = 1.27; 95% CrI: 0.57-2.32). However, specialized adolescent intervention achieved a superior overall retention rate of 59.7% compared to 52.1% under standard care. Conclusion: Current adolescent-specific interventions are effective in improving retention rates in HIV care. Evidence from adult interventions showed a reduced retention effect, suggesting that while adult-promising interventions may improve adolescent treatment outcomes, they may require modifications.
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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 | 0.139 | 0.266 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".