P405 Identifying subgroups at higher risk of infectious syphilis in major Australian cities: Analysis of national sentinel surveillance data 2011–2018
Bibliographic record
Abstract
<h3>Background</h3> In Australia, infectious syphilis notifications have increased 234% in the past decade, from 1318 in 2008 to 4398 in 2017. Although historically concentrated among urban men who have sex with men and remote Indigenous communities, a rise in syphilis notifications among women in major cities and cases of congenital syphilis have been observed. We analysed trends in infectious syphilis positivity among women and heterosexual men in major Australian cities and identified associated risk factors. <h3>Methods</h3> De-identified patient data were extracted from 34 sexual health clinics within a national sentinel surveillance network (2011–18). All women and heterosexual men ≥ 15 years in major cities were included. Infectious syphilis positivity was defined as the proportion of attendees per 6-monthly calendar period with recorded syphilis testing who had recorded clinical diagnoses of infectious syphilis. Poisson regression determined annual trends in positivity and risk factors for infectious syphilis (rate ratios and 95% CIs). <h3>Results</h3> Of 100,230 patients attending the clinics, 50.8% were female (of whom, 96.1% were of childbearing age), 51.2% were aged 15–29 years, 1.7% were Indigenous and 7.4% were from culturally and linguistically diverse (CALD) backgrounds. Modelled positivity for infectious syphilis (2011–18) increased 85% in females from 2.41 (95%CI:1.67–3.14) to 4.48 (95%CI:3.53–5.43) per 1000 patients tested, and 76% in heterosexual males from 4.47 (95%CI:3.44–5.49) to 7.87 (95%CI:6.49–9.24). Factors associated with increased risk of infectious syphilis included: reporting a history of injecting drug use (RR:4.36;95%CI:3.26–5.83), using condoms inconsistently in the past year (RR:2.30;95%CI;1.10–4.79), being male (RR:1.80;95%CI:1.51–2.14), Indigenous (RR:1.70;95%CI:1.02–2.84), from CALD backgrounds (RR:1.90;95%CI:1.46–2.46), and older (30–39 years: RR:1.33;95%CI:1.08–1.65; 40–49 years: RR:2.95;95%CI:2.34–3.73; 50+ years:RR:3.59;95%CI:2.81–4.57). Lower risk was observed in bisexual females (RR:0.53;95%CI:0.30–0.94) and female sex workers (RR:0.43;95%CI:0.29–0.63). <h3>Conclusion</h3> Increasing syphilis in women and heterosexual men in major Australian cities requires enhanced prevention, including integration of sexual and reproductive health care into harm reduction programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".