Intersecting Risk Factors Associated With High Syphilis Seroprevalence Among a Street-Involved Population in Canada
Bibliographic record
Abstract
Background: Syphilis has reemerged as a global public health concern. In Ontario, Canada's most populous province, a 340% increase in infectious syphilis cases was observed between 2013 and 2023. This surge was accompanied by a demographic shift, with women emerging as the fastest-growing at-risk group. We examined intersecting risk factors associated with syphilis seropositivity among a street-involved population. Methods: Data were collected from the Syphilis Point of Care Rapid Test and Immediate Treatment Evaluation (SPRITE) study-an outreach model of care implemented by 8 public health units (PHUs) across Ontario between 2023 and 2024. Reactive treponemal antibodies defined syphilis seroprevalence. A mixed-effects regression with a log-binomial distribution was used to evaluate the association between risk factors and seropositivity. Adjusted prevalence ratio (aPR) controlled for age and sex and clustering by PHUs. Results: A total of 630 participants, 42% women, with a median age of 38, were included; 19.1% of participants reported having sexual risk factors, using illicit drugs, and being un(der)housed. Overall, syphilis seroprevalence was 7.6% (95% confidence interval 5.5-9.7), with significant heterogeneity across the province and higher among those reporting 3 risk factors (19.2% [11.2-29.7]) compared with 1 risk factor (4.8% [1.8-10.1]). Seropositivity was higher among women (aPR 1.62 [.94-2.80]) and people who use illicit drugs (aPR 2.30 [.93-5.50]), particularly those who use crystal methamphetamine (aPR 2.88 [1.31-6.33]). Conclusions: Syphilis is heightened at the intersection of sexual risk factors, illicit drug use, and housing instability among equity-deserving populations. Targeted outreach models of care are necessary to reach this emerging at-risk population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".