Population‐level impact of expanding PrEP coverage by offering long‐acting injectable PrEP to MSM in three high‐resource settings: a model comparison analysis
Bibliographic record
Abstract
INTRODUCTION: Long-acting injectable cabotegravir (CAB-LA) demonstrated superiority to daily tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) for HIV pre-exposure prophylaxis (PrEP) in the HPTN 083/084 trials. We compared the potential impact of expanding PrEP coverage by offering CAB-LA to men who have sex with men (MSM) in Atlanta (US), Montreal (Canada) and the Netherlands, settings with different HIV epidemics. METHODS: Three risk-stratified HIV transmission models were independently parameterized and calibrated to local data. In Atlanta, Montreal and the Netherlands, the models, respectively, estimated mean TDF/FTC coverage starting at 29%, 7% and 4% in 2022, and projected HIV incidence per 100 person-years (PY), respectively, decreasing from 2.06 to 1.62, 0.08 to 0.03 and 0.07 to 0.001 by 2042. Expansion of PrEP coverage was simulated by recruiting new CAB-LA users and by switching different proportions of TDF/FTC users to CAB-LA. Population effectiveness and efficiency of PrEP expansions were evaluated over 20 years in comparison to baseline scenarios with TDF/FTC only. RESULTS: Increasing PrEP coverage by 11 percentage points (pp) from 29% to 40% by 2032 was expected to avert a median 36% of new HIV acquisitions in Atlanta. Substantially larger increases (by 33 or 26 pp) in PrEP coverage (to 40% or 30%) were needed to achieve comparable reductions in Montreal and the Netherlands, respectively. A median 17 additional PYs on PrEP were needed to prevent one acquisition in Atlanta with 40% PrEP coverage, compared to 1000+ in Montreal and 4000+ in the Netherlands. Reaching 50% PrEP coverage by 2032 by recruiting CAB-LA users among PrEP-eligible MSM could avert >45% of new HIV acquisitions in all settings. Achieving targeted coverage 5 years earlier increased the impact by 5-10 pp. In the Atlanta model, PrEP expansions achieving 40% and 50% coverage reduced differences in PrEP access between PrEP-indicated White and Black MSM from 23 to 9 pp and 4 pp, respectively. CONCLUSIONS: Achieving high PrEP coverage by offering CAB-LA can impact the HIV epidemic substantially if rolled out without delays. These PrEP expansions may be efficient in settings with high HIV incidence (like Atlanta) but not in settings with low HIV incidence (like Montreal and the Netherlands).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".