People with Multiple Tattoos and/or Piercings Are Not at Increased Risk for HBV or HCV in The Netherlands
Bibliographic record
Abstract
BACKGROUND: Although published results are inconsistent, it has been suggested that tattooing and piercing are risk factors for HBV and HCV infections. To examine whether tattooing and piercing do indeed increase the risk of infection, we conducted a study among people with multiple tattoos and/or piercings in The Netherlands who acquired their tattoos and piercings in The Netherlands and/or abroad. METHODS: Tattoo artists, piercers, and people with multiple tattoos and/or piercings were recruited at tattoo conventions, shops (N = 182), and a biannual survey at our STI-outpatient clinic (N = 252) in Amsterdam. Participants were interviewed and tested for anti-HBc and anti-HCV. Determinants of HBV and HCV infections were analysed using logistic regression analysis. RESULTS: The median number of tattoos and piercings was 5 (IQR 2-10) and 2 (IQR 2-4), respectively. Almost 40% acquired their tattoo of piercing abroad. In total, 18/434 (4.2%, 95%CI: 2.64%-6.46%) participants were anti-HBc positive and 1 was anti-HCV positive (0.2%, 95%CI: 0.01%-1.29%). Being anti-HBc positive was independently associated with older age (OR 1.68, 95%CI: 1.03-2.75 per 10 years older) and being born in an HBV-endemic country (OR 7.39, 95%CI: 2.77-19.7). Tattoo- and/or piercing-related variables, like having a tattoo or piercing in an HBV endemic country, surface percentage tattooed, number of tattoos and piercings etc., were not associated with either HBV or HCV. CONCLUSIONS: We found no evidence for an increased HBV/HCV seroprevalence among persons with multiple tattoos and/or piercings, which might be due to the introduction of hygiene guidelines for tattoo and piercing shops in combination with the low observed prevalence of HBV/HCV in the general population. Tattoos and/or piercings, therefore, should not be considered risk factors for HBV/HCV in the Dutch population. These findings imply the importance of implementation of hygiene guidelines in other countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".