Efficacy of Antiretroviral Therapy Programs in Resource‐Poor Settings: A Meta‐analysis of the Published Literature
Bibliographic record
Abstract
BACKGROUND: Despite the advent of effective combination antiretroviral drug therapy (ART) for the treatment of human immunodeficiency virus (HIV) infection, many doubt the feasibility of ART treatment programs in resource-poor settings. We performed a meta-analysis of the efficacy of ART programs in the developing world. We searched the Medline database with the index terms "HIV," "antiretroviral therapy," "CD4 count," "viral load," "experience," and "outcomes." A total of 201 abstracts were reviewed, and 25 articles were selected for detailed review. Ten observational studies with details on patient outcomes were ultimately included in the analysis. METHODS: Three readers independently extracted data from the articles. The details recorded included patient demographic characteristics, baseline CD4 cell counts, baseline HIV RNA viral loads, ART histories, outcomes, and timing of the outcome measure. RESULTS: The proportion of subjects with an undetectable HIV viral load provided the measure of treatment efficacy. A random-effects model weighted the proportion of patients with undetectable viral load at various time points during ART. The proportion was 0.697 (95% CI, 0.582-0.812) at month 6 and 0.573 (95% CI, 0.432-0.715) at month 12 of ART. The provision of medications free of charge to the patient was associated with a 29%-31% higher probability of having an undetectable viral load at months 6 and 12 than was the requirement that patients pay part or all of the cost of therapy. CONCLUSIONS: ART treatment programs in resource-poor settings have efficacy rates similar to those reported for developed countries. The provision of medications free of charge to the patient is associated with a significantly increased probability of virologic suppression at months 6 and 12 of ART.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".