Attrition one year after starting antiretroviral therapy before and after the programmatic implementation of HIV “Treat All” in Sub-Saharan Africa: a systematic review and meta- analysis
Bibliographic record
Abstract
Abstract Introduction: Evidence on the real-world effects of “Treat All” on attrition has not been summarised. We conducted a systematic review and meta-analysis to compare attrition 12 months after antiretroviral therapy (ART) initiation, before and after “Treat All” was implemented in Sub-Saharan Africa. We also describe predictors of attrition. Methods: We searched Embase, Google Scholar, PubMed, and Web of Science in July 2020 and created alerts up to the end of March 2022. We also searched for preprints and conference abstracts. Two co-authors screened and selected the articles. Risk of bias was assessed using the modified Newcastle-Ottawa Scale. We extracted and tabulated data on study characteristics, attrition 12 months after ART initiation, and predictors of attrition. We estimated a pooled estimate for attrition using random-effects meta-analysis. Results : Seven articles and one conference abstract (eight studies) out of 5782 screened records were included in the meta-analysis. The random-effects adjusted pooled risk ratio (RR) comparing attrition before and after “Treat All” 12 months after ART initiation was not significant [RR = 1.08 (95% Confidence interval (CI): 0.97–1.21)] with 82% heterogeneity (I2). Being a pregnant or breastfeeding woman, starting ART with advanced HIV and within the same week were reported as risk factors for attrition both before and after “Treat All”. Conclusions: We found no significant difference in attrition before and after “Treat All” one year after ART initiation. While “Treat All” is being widely implemented, differentiated approaches to enhance retention should be prioritised for those subgroups at risk of attrition. PROSPERO Number: CRD42020191582
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.054 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".