STI and risk factors among migrants: preliminary data from the Reach Out Project in Southern Italy
Bibliographic record
Abstract
Abstract Sexually transmitted infections (STIs) remain a major public health concern, particularly among vulnerable populations such as migrants and hard-to-reach populations in general. The European “Reach Out” project (reachouteurope.eu), implemented by the NGO INTERSOS in Sicily and Apulia, integrates STI screening and a six-item risk factor questionnaire into primary care through mobile clinics. A multidisciplinary team-including physicians, nurses, social workers, and cultural mediators-conducted field outreach. Between 22 May and 31 October 2024, activities were carried out in four locations: Foggia (Apulia), and Palermo, Agrigento, and Campobello di Mazara (Sicily). A total of 869 individuals were approached; 10.1% were female and 77.6% originated from Sub-Saharan Africa. Overall, 244 individuals (28.1%) underwent STI testing. Prevalence was 0-1% for HIV, syphilis, and hepatitis C, while hepatitis B (HBV) showed a markedly higher positivity rate (7.4%). No co-infections were detected. Self-reported risk factors included previous STIs (7.5%), unprotected sex (35.3%), multiple sexual partnership (16.7%), sharing of potentially contaminated objects (35.3%, e.g., razors, towels), intravenous drug use (2.0%), and current STI symptoms (18.6%). No statistically significant association emerged between these risk factors and any STI positivity; however, prevalence was higher among those reporting a prior STI (12.5% vs. 5.7%; Fisher's exact test p = 0.27). The relatively high HBV prevalence suggests active transmission and underscores the need to prioritise HBV within public health strategies. Enhancing STI prevention, surveillance, and testing-especially for HBV-among migrant and hard-to-reach populations is essential. Efforts should support vaccination uptake and adopt culturally sensitive, community-based, person-centred approaches to mitigate risk behaviours and reduce transmission. Key messages • Mobile clinics are a valuable tool to assess STI prevalence and risk factors in migrants and other hard-to-reach populations. • Among migrants in Southern Italy, hepatitis B prevalence reaches 7.4% and should be a public health priority; HIV, syphilis, and hepatitis C remain below 1%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".