Long-Run Public Health Impact of Doxycycline Post-Exposure Prophylaxis and Behavioural Factors on Syphilis Transmission: A Modelling Study in Singapore and England
Bibliographic record
Abstract
Abstract Syphilis remains a significant global public health challenge, particularly among men who have sex with men (MSM). Although penicillin is highly effective for treatment, primary prevention strategies are limited. Recent trials indicate that doxycycline post-exposure prophylaxis (doxy-PEP) has high efficacy in reducing syphilis incidence among MSM; however, its long-run population-level impact, effects on transmission dynamics, and optimal prescribing strategies remain unclear, especially when accounting for real-world behavioural factors such as screening frequency, uptake, adherence, and discontinuation. To address this gap, we developed a behavioural transmission-dynamic model calibrated with Bayesian methods using epidemiological and sexual behavioural data from Singapore and England to characterize transmission dynamics in MSM, quantify the potential long-run public health impact, efficiency, and robustness of alternative doxy-PEP prescribing strategies across different settings (e.g., schools, clinics, age, and risk groups) under varying behavioural patterns and epidemiological settings. Over a 15-year horizon, targeting high-risk MSM at diagnosis emerged as the most efficient approach, averting an estimated 2.50 (0.68 - 5.94) cases per prescription (10000 [95% Credible Interval 1100 - 53200] total cases averted) in Singapore and 4.60 (2.12 - 7.79) cases per prescription (165000 [49300 - 503700]) in England. In contrast, broader strategies such as offering doxy-PEP to all MSM attending sexual health clinics could achieve greater overall reductions (up to 24700 [6800 - 93100] cases in Singapore and 279800 [109200 - 724000] in England), but with substantially lower efficiency, averting as few as 0.02 (0.00 - 0.23) and 0.04 (0.01 - 0.46) cases per prescription, respectively. These findings suggest that untargeted strategies could substantially reduce syphilis incidence but would do so at the cost of over-prescription, increased resource burden, and unnecessary antibiotic exposure. More importantly, our findings remain robust despite variations in behavioural factors and future scenarios. In summary, our results underscore that doxy-PEP prescribing approaches aligned with behavioural risk factors can maximise population-level impact and implementation efficiency, supporting more sustainable syphilis prevention among MSM.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".