Free antiretrovirals as a key tool against the <scp>HIV</scp> pandemic: A systematic review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Access to antiretroviral (ARV) drugs remains a critical challenge in achieving the WHO/UNAIDS 95-95-95 targets, with medication costs representing a substantial barrier. This systematic review evaluates the effect of free ARVs, without out-of-pocket cost to the patient, on the HIV cascade of care: the use of ARV therapy, viral suppression and the use of prophylaxis (PrEP). METHODS: The following databases were searched for publications between 1 January 1996 and 10 July 2024: MEDLINE, Embase, CINAHL, CNKI, Global Index Medicus, the Web of Science, the SciELO Citation Index and grey literature. Publications were eligible if they included people living with or at risk of HIV and compared free access to ARVs with out-of-pocket fees. Reviewers screened publications that focused on the outcomes: being on therapy, being virally suppressed and being on PrEP. The National Heart, Lung and Blood Institute (NHLBI) and Joanna Briggs Institute (JBI) Quality Assessment Tools were used to assess publication quality. RESULTS: A total of 34164 documents were identified, and 407 full-text manuscripts were reviewed. A total of 22 publications met the inclusion criteria. In six of the seven publications reporting on being on therapy, providing free ARVs increased the number of people who received treatment. All four publications reporting on viral suppression showed improvement with free access. Additionally, both publications reporting on PrEP use showed increased utilization with free access. CONCLUSIONS: The review offers valuable insights for countries considering implementing free ARV programmes. It suggests that expanding access to free ARVs helps achieve the global HIV targets and improve health outcomes.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".