Mathematical modelling of the influence of serosorting on the population-level HIV transmission impact of pre-exposure prophylaxis
Bibliographic record
Abstract
OBJECTIVES: HIV pre-exposure prophylaxis (PrEP) may change serosorting patterns. We examined the influence of serosorting on the population-level HIV transmission impact of PrEP, and how impact could change if PrEP users stopped serosorting. DESIGN: We developed a compartmental HIV transmission model parameterized with bio-behavioural and HIV surveillance data among MSM in Canada. METHODS: We separately fit the model with serosorting and without serosorting [counterfactual; sero-proportionate mixing (random partner-selection proportional to availability by HIV status)], and reproduced stable HIV epidemics with HIV-prevalence 10.3-24.8%, undiagnosed fraction 4.9-15.8% and treatment coverage 82.5-88.4%. We simulated PrEP-intervention reaching stable pre-specified coverage by year-one and compared absolute difference in relative HIV-incidence reduction 10 years post-intervention (PrEP-impact) between models with serosorting vs. sero-proportionate mixing; and counterfactual scenarios when PrEP users immediately stopped vs. continued serosorting. We examined sensitivity of results to PrEP-effectiveness (44-99%; reflecting varying dosing or adherence levels) and coverage (10-50%). RESULTS: Models with serosorting predicted a larger PrEP-impact than models with sero-proportionate mixing under all PrEP-effectiveness and coverage assumptions [median (interquartile range): 8.1% (5.5-11.6%)]. PrEP users' stopping serosorting reduced PrEP-impact compared with when PrEP users continued serosorting: reductions in PrEP-impact were minimal [2.1% (1.4-3.4%)] under high PrEP-effectiveness (86-99%); however, could be considerable [10.9% (8.2-14.1%)] under low PrEP effectiveness (44%) and high coverage (30-50%). CONCLUSION: Models assuming sero-proportionate mixing may underestimate population-level HIV-incidence reductions due to PrEP. PrEP-mediated changes in serosorting could lead to programmatically important reductions in PrEP-impact under low PrEP-effectiveness. Our findings suggest the need to monitor sexual mixing patterns to inform PrEP implementation and evaluation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".