Large cluster outbreaks sustain the HIV epidemic among MSM in Quebec
Bibliographic record
Abstract
OBJECTIVE: HIV-1 epidemics among MSM remain unchecked despite advances in treatment and prevention paradigms. This study combined viral phylogenetic and behavioural risk data to better understand underlying factors governing the temporal growth of the HIV epidemic among MSM in Quebec (2002-2015). METHODS: Phylogenetic analysis of pol sequences was used to deduce HIV-1 transmission dynamics (cluster size, size distribution and growth rate) in first genotypes of treatment-naïve MSM (2002-2015, n = 3901). Low sequence diversity of first genotypes (0-0.44% mixed base calls) was used as an indication of early-stage infection. Behavioural risk data were obtained from the Montreal rapid testing site and primary HIV-1-infection cohorts. RESULTS: Phylogenetic analyses uncovered high proportion of clustering of new MSM infections. Overall, 27, 45, 48, 53 and 57% of first genotypes within one (singleton, n = 1359), 2-4 (n = 692), 5-9 (n = 367), 10-19 (n = 405) and 20+ (n = 1277) cluster size groups were early infections (<0.44% diversity). Thirty viruses within large 20+ clusters disproportionately fuelled the epidemic, representing 13, 25 and 42% of infections, first genotyped in 2004-2007 (n = 1314), 2008-2011 (n = 1356) and 2012-2015 (n = 1033), respectively. Of note, 35, 21 and 14% of MSM belonging to 20+, 2-19 and one (singleton) cluster groups were under 30 years of age, respectively. Half of persons seen at the rapid testing site (2009-2011, n = 1781) were untested in the prior year. Poor testing propensity was associated with fewer reported partnerships. CONCLUSION: Addressing the heterogeneity in transmission dynamics among HIV-1-infected MSM populations may help guide testing, treatment and prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".