To whom should we prescribe doxy-PEP? A retrospective review of public health surveillance data for bacterial STI diagnoses among gay, bisexual, and other men who have sex with men in Ottawa, Canada
Bibliographic record
Abstract
Introduction: Increases in bacterial sexually transmitted infections (bac-STIs), such as gonorrhoea, chlamydia and syphilis, have affected gay, bisexual, and other men who have sex with men (gbMSM). A new strategy to prevent bac-STIs involves giving doxycycline as postexposure prophylaxis (doxy-PEP), and research shows that this intervention can prevent chlamydia and syphilis by 70% and gonorrhoea by 33%-50%. Consequently, the US CDC released guidelines in 2024 recommending doxy-PEP for gbMSM with ≥1 bacterial STI diagnosis in the previous 12 months. Methods: We reviewed public health STI surveillance data between 1 January 2021 and 31 December 2024 for gbMSM in Ottawa, Canada, and analysed the number of infections and episodes of infections per person. We estimated the number of individuals who would need doxy-PEP to prevent a single bac-STI episode. Results: During the study period, 1819 unique gbMSM experienced 2834 positive bac-STI testing episodes (PTE), during which 3114 bac-STIs were diagnosed. Consistently, three-quarters of gbMSM did not have a subsequent infection, whether they were diagnosed with 1 infection, 2 infections or ≥3. Considering the average effectiveness of doxy-PEP in this study population, the average number needed to treat (NNT) if doxy-PEP were prescribed to all gbMSM to prevent a first PTE would have been 60. The NNT among those with their first PTE to prevent a second PTE was 8; among those with their second, the NNT was 7. Conclusions: Based on these data, and in alignment with the CDC guidelines, we conclude that doxy-PEP would likely have the most balanced population-level bac-STI prevention effect if given to gbMSM with ≥1 bac-STI diagnosis within the preceding 12 months. Providing doxy-PEP to all gbMSM would likely result in an overuse of antibiotics, and providing doxy-PEP only after a second PTE would result in fewer infections averted for the same proportion treated.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".