Use of peers to improve adherence to antiretroviral therapy: a global network meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: It is unclear whether using peers can improve adherence to antiretroviral therapy (ART). To construct the World Health Organization's global guidance on adherence interventions, we conducted a systematic review and network meta-analysis to determine the effectiveness of using peers for achieving adequate adherence and viral suppression. METHODS: We searched for randomized clinical trials of peer-based interventions to promote adherence to ART in HIV populations. We searched six electronic databases from inception to July 2015 and major conference abstracts within the last three years. We examined the outcomes of adherence and viral suppression among trials done worldwide and those specific to low- and middle-income countries (LMIC) using pairwise and network meta-analyses. RESULTS AND DISCUSSION: Twenty-two trials met the inclusion criteria. We found similar results between pairwise and network meta-analyses, and between the global and LMIC settings. Peer supporter+Telephone was superior in improving adherence than standard-of-care in both the global network (odds-ratio [OR]=4.79, 95% credible intervals [CrI]: 1.02, 23.57) and the LMIC settings (OR=4.83, 95% CrI: 1.88, 13.55). Peer support alone, however, did not lead to improvement in ART adherence in both settings. For viral suppression, we found no difference of effects among interventions due to limited trials. CONCLUSIONS: Our analysis showed that peer support leads to modest improvement in adherence. These modest effects may be due to the fact that in many settings, particularly in LMICs, programmes already include peer supporters, adherence clubs and family disclosures for treatment support. Rather than introducing new interventions, a focus on improving the quality in the delivery of existing services may be a more practical and effective way to improve adherence to ART.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.014 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".