O1-S06.02 Impact of pill sharing on drug-resistance and population-level effectiveness of a wide-scale oral PrEP intervention in resource-constrained settings
Bibliographic record
Abstract
Background In 2010 two randomised trials suggested that pre-exposure prophylaxis (PrEP) products based on tenofovir, an antiretroviral drug either administered daily orally (oral PrEP) or applied topically (vaginal microbicides), significantly reduced HIV acquisition among adherent users. Behavioural studies also suggest that some PrEP users are compelled to share product with sex partners, family, or friends. Pill sharing (PS) decreases the adherence levels of the intended PrEP users and creates an uncontrolled environment for the development of drug-resistance. However PS effects on the expected population-level impact of PrEP interventions have never been assessed. Thus, we aim to evaluate the potential impact of PS on the PrEP effectiveness to prevent HIV transmission and the spread of drug-resistance in heterosexual populations in resource-constrained settings. Methods A transmission dynamic model was used to assess the population-level impact of oral PrEP in a variety of intervention scenarios and high HIV prevalence settings. The cumulative fractions of new HIV infections prevented (CPF) and transmitted drug-resistance (TDR) are evaluated over fixed time periods under various epidemiological conditions. The influence of different factors (eg, acquisition rate, PrEP coverage, rates of resistance development) on CPF and TDR is studied through simulations, using parameter sets sampled from ranges representative of countries in Sub-Saharan Africa. Results Without PS, a 70% effective oral PrEP intervention used by 60% of the population prevents about 52% (95% CI 49.6% to 53.8%) of all new HIV infections over 10 years (10 years CPF) if adherence is 100%. CPF increases by 7% in populations with 10% PS, assuming no efficacy reduction for those who share PrEP and reduces by 2% if the efficacy reduction for sharers (prescribers or untracked users) is 50%. However, the fraction of transmitted drug-resistance (TDR) increases 2- to 6-fold in all scenarios investigated. It depends on the success in preventing PrEP usage by HIV infected individuals. Conclusions PS may increase the PrEP coverage level achieved in the population but it also affects the PrEP efficacy for the users who do not follow the prescribed dosing. It creates a pool of untracked users who do not receive counselling, remain hidden and unreached by the effort to avoid the PrEP usage by HIV infected individuals. This increases substantially the potential risk of drug-resistance development.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.382 | 0.045 |
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".