Testing paradox may explain increased observed prevalence of bacterial STIs among MSM on HIV PrEP: A modeling study
Bibliographic record
Abstract
HIV pre-exposure prophylaxis (PrEP) is transforming global HIV prevention, but its implementation coincides with observations of rising bacterial sexually transmitted infection (STI) rates among men who have sex with men, raising questions about whether PrEP is preventing one epidemic while facilitating others. To reconcile this apparent contradiction, we developed a minimal dynamical model of the simultaneous transmission of HIV and chlamydia (as an example of a curable STI). The model integrates three key mechanisms: 1) risk-mediated self-protective behavior, 2) reduction in condom use among PrEP users, and 3) PrEP-related asymptomatic STI screening. We show that these mechanisms can generate a “testing paradox:” True STI prevalence may decline while observed trends rise. This paradox emerges because increased PrEP uptake amplifies screening intensity, which can lower transmission but simultaneously inflate detection. By systematically mapping the parameter space of PrEP uptake, screening frequency, and risk perception, we identify broad and plausible conditions under which the paradox arises. Our findings reconcile conflicting epidemiological evidence and remark that the net effect of PrEP on STI dynamics depends critically on asymptomatic screening strategies. These results highlight the potential dual role of PrEP programs in reducing both HIV and bacterial STI incidence, while emphasizing the need to align screening and treatment policies to maximize benefits and minimize risks, e.g., antimicrobial resistance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".