Prevalence, incidence and risk factors of syphilis among men who have sex with men in China from 2013 to 2025: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Syphilis has re-emerged in China in recent decades, particularly among men who have sex with men (MSM). We aimed to assess the prevalence, incidence, and associated factors of syphilis among MSM in China. METHODS: We systematically searched major English (MEDLINE via PubMed, Web of Science, Embase, Scopus, Cochrane Library) and Chinese (CNKI, Wanfang, CBM, VIP, Airiti Library) databases for studies on syphilis prevalence or incidence among MSM in China published from January 1, 2013 to March 1, 2025. Study qualities were evaluated using the Hoy et al.'s risk-of-bias tool and the Newcastle-Ottawa Scale. Random-effects meta-analysis models were used to estimate pooled syphilis prevalence (%) and incidence (per 100 person-years, PYs) with 95% confidence intervals (CIs). Meta-regression analyses were performed to assess differences across subgroups. RESULTS: A total of 441 studies (429 prevalence and 33 incidence) were included. The pooled syphilis prevalence among general MSM was 8.8% (95% CI: 8.3-9.4). Study location (R²=0.13) and study year (R²=0.11) each contributed significantly to the high heterogeneity observed (I² = 98.5%) among the general MSM prevalence studies. MSM with high-risk sexual behaviors or related risk factors exhibited higher prevalence. The pooled incidence among all MSM was 7.8 per 100 PYs (95% CI: 6.0-9.8), with similarly high heterogeneity (I² = 96.4%). Both syphilis prevalence and incidence declined over time. CONCLUSION: Syphilis prevalence and incidence remain high among high-risk MSM subgroups in China. More rigorous studies and targeted interventions are needed to obtain more accurate estimates and to further reduce syphilis infection rates.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".