Impact of pre-exposure prophylaxis on HIV-1 drug resistance and phylogenetic cluster growth in British Columbia, Canada
Bibliographic record
Abstract
Oral HIV pre-exposure prophylaxis (PrEP) effectively prevents infection when taken during periods of risk, however, its population-level effectiveness is hindered by incomplete uptake, adherence, and retention. Since PrEP became available free-of-cost in British Columbia (BC), Canada, in January 2018, uptake has been rapid among gay, bisexual, and other men who have sex with men (GBM). Epidemiological evidence suggests adding PrEP onto a background of generalized access to free antiretroviral therapy, under the BC Treatment as Prevention (TasP) strategy, had a synergistic effect on reducing new infections. Here, we sought to evaluate the impact of PrEP on HIV transmission and drug resistance in phylogenetic clusters. In a retrospective cohort study, we evaluated whether baseline HIV drug resistance and phylogenetic clustering were more likely among newly diagnosed PrEP users in BC (n=39) compared to non-PrEP users (n=566) during the same diagnosis period from October 23, 2018 to December 5, 2022. Subsequently, we evaluated heterogeneity in PrEP-related reductions of the effective reproduction number (R e ) across BC, key populations, and phylogenetic clusters. Stochastic branching processes of phylogenetic clusters informed by R e preceding PrEP were used to estimate diagnoses averted via PrEP. Newly HIV diagnosed PrEP users were significantly more likely than non-PrEP users to join phylogenetic clusters and carry baseline nucleoside-analogue reverse transcriptase inhibitor (NRTI) resistance-associated mutation M184I/V. Despite reductions in growth rate and R e in the GBM population overall and in 50% of active GBM-predominant clusters following PrEP availability, we highlight predominantly GBM and PWID clusters with high or increasing R e . Across active phylogenetic clusters and non-clustered new diagnoses, we estimate PrEP averted approximately 20 new HIV diagnoses per year in BC since 2018. These findings highlight how PrEP has reduced HIV burden, while illuminating groups that could benefit from prioritized PrEP education, access, and retention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".