Optimizing HIV pre‐exposure prophylaxis implementation among men who have sex with men in a large urban centre: a dynamic modelling study
Bibliographic record
Abstract
INTRODUCTION: Once-daily tenofovir/emtricitabine-based pre-exposure prophylaxis (PrEP) can reduce HIV acquisition in men who have sex with men (MSM), by 44% in the iPrEx trial, and reaching up to 99% with high adherence. We examined the potential population-level impact and cost-effectiveness of different PrEP implementation strategies. METHODS: We developed a dynamic, stochastic compartmental model of HIV transmission among the estimated 57,400 MSM in Toronto, Canada. Parameterization was performed using local epidemiologic data. Strategies examined included (1) uniform PrEP delivery versus targeting the highest risk decile of MSM (with varying coverage proportions); (2) increasing PrEP efficacy as a surrogate of adherence (44% to 99%); and (3) varying HIV test frequency (once monthly to once yearly). Outcomes included HIV infections averted and the incremental cost ($CAD) per incremental quality-adjusted-life-year (QALY) gained over 20 years. RESULTS: Use of PrEP among all HIV-uninfected MSM at 25, 50, 75 and 100% coverage prevented 1970, 3427, 4317, and 4581 infections, respectively, with cost/QALY increasing from $500,000 to $800,000 CAD. Targeted PrEP for the highest risk MSM at 25, 50, 75 and 100% coverage prevented 1166, 2154, 2816, and 3012 infections, respectively, with cost/QALY ranging from $35,000 to $70,000 CAD. Maximizing PrEP efficacy, in a scenario of 25% coverage of high-risk MSM with PrEP, prevented 1540 infections with a cost/QALY of $15,000 CAD. HIV testing alone (Q3 months) averted 898 of infections with a cost savings of $4,000 CAD per QALY. CONCLUSIONS: The optimal implementation strategy for PrEP over the next 20 years at this urban centre is to target high-risk MSM and to maximize efficacy by supporting PrEP adherence. A large health benefit of PrEP implementation could come from engaging undiagnosed HIV-infected individuals into care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".