S04.1 Scaling Up of HIV Treatment Programmes Among the Most At-Risk Populations in Low- and Middle-Income Countries - Introduction
Bibliographic record
Abstract
Although access to antiretroviral therapy (ART) for HIV infection in low- and middle-income countries has tremendously improved over the last decade, it remains far from optimal in most settings when it comes to the treatment of marginalised populations, such as sex workers, injection drug users and men who have sex with men. Small-scale demonstration projects indicate that ART can be successfully implemented among sex workers, with satisfactory immunological and virological responses, good treatment compliance levels, and maintenance or even improvement of safe sex behaviour. Some of the studies carried out so far underline the importance of strongly linking prevention with care through the integration of HIV/STI care services and community-based prevention packages. Scaling-up treatment programmes for marginalised, hard-to-reach populations, will undoubtedly be challenging. Nevertheless, in addition to the health equity issues related to access to ART for such populations, the application of targeted “test and treat” strategies could substantially impact HIV prevention at the general population level, given recent trial data showing that the implementation of this strategy among sero-discordant couples led to a 96% reduction in HIV transmission. Such a strategy should however be carefully evaluated in the target population before implementation and scale-up, and would require specific adherence support and community involvement. It is in this context that this symposium will present practical experiences in implementing ART programmes among female sex workers in the context of generalised, concentrated and mixed epidemics. Modelling studies of the preventive impact of such programmes across different epidemic contexts, and with different criteria for ART initiation (including “test and treat”), will also be presented. A similar study about injection drug users in the context of a concentrated epidemic will complete the presentations in this symposium that should stimulate a fruitful discussion period between the participants and the speakers.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 0.018 |
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".