Impact of screening and doxycycline prevention on the syphilis epidemic among men who have sex with men in British Columbia: a mathematical modelling study
Bibliographic record
Abstract
Background: Men who have sex with men (MSM) in British Columbia (BC) are disproportionately affected by infectious syphilis and HIV. In this study, we developed a co-interaction model and evaluated the impact and effectiveness of possible interventions among different MSM subgroups on the syphilis epidemic. Methods: We designed a deterministic compartmental model, which stratified MSM by HIV status and HIV pre-exposure prophylaxis (HIV-PrEP) usage into (1) HIV-negative/unaware MSM (HIV-PrEP not recommended, not on HIV-PrEP), (2) HIV-negative/unaware MSM with HIV-PrEP recommended (not on HIV-PrEP), (3) HIV-negative/unaware MSM actively on HIV-PrEP, and (4) MSM diagnosed with HIV. We estimated the effect of scaling up syphilis testing frequency from Status Quo to six-, four-, and three-months, increasing the percentage of MSM using doxycycline prevention (Doxy-P) to 25%, 50%, and 100% of the target level, and a combination of both among subgroups (2)-(4). We also assessed the impact of these interventions on the syphilis incidence rates from 2020 to 2034 in comparison to the Status Quo scenario where no intervention was introduced. Findings: Under the Status Quo scenario, with the expansion of the HIV-PrEP program to improve syphilis testing, the syphilis incidence rate was estimated to peak at 16.1 [Credible Interval (CI):14.2-17.9] per 1,000 person-years (PYs) in 2023 and decrease to 6.7 (CI:3.8-10.9) per 1,000 PYs by 2034. The syphilis incidence rate in 2034 was estimated at 0.7 (0.3-1.3) per 1,000 PYs if MSM diagnosed with HIV could be tested every four months, and at 1.5 (0.7-3.0) per 1,000 PYs if HIV-negative/unaware MSM actively on HIV-PrEP could be tested every three months. By achieving 100% of the target coverage of Doxy-P, the syphilis incidence rate was estimated at 1.4 (0.5-3.4) if focusing on MSM diagnosed with HIV, and 2.6 (1.2-5.1) per 1,000 PYs if focusing on HIV-negative/unaware MSM actively on HIV-PrEP. Under the combined interventions, the syphilis incidence rate could be as low as 0.0 (0.0-0.1) and 0.8 (0.3-1.8) per 1,000 PYs, respectively. Interpretation: The HIV-PrEP program in BC plays a crucial role in increasing syphilis testing frequency among high-risk MSM and reducing syphilis transmission among this group. In addition, introducing Doxy-P can be an effective complementary strategy to minimize syphilis incidence, especially among MSM diagnosed with HIV. Funding: This work was funded by the Canadian Institutes of Health Research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".