Relationship of cosmetic procedures and drug use to hepatitis C and hepatitis B virus infections in a low-risk population
Bibliographic record
Abstract
We conducted an anonymous cross-sectional seroprevalence study of a population with a low frequency of injection drug use to determine whether persons with a history of cosmetic procedures, such as tattooing and body piercing, or intranasal drug use were at increased risk for hepatitis C virus (HCV) or hepatitis B virus (HBV) infection. Students 18 years and older from eight college campuses in Houston, Texas, were invited to participate in the study. Of the 7,960 who completed a self-administered questionnaire and provided a blood sample, 5,282 U.S.- or Canadian-born participants were analyzed. Their median age was 21, 62% were female, 42% were white, 26% black, 22% Hispanic, and 10% Asian or other. Two percent reported injection drug use, 13.7% intranasal drug use, 21.2% body piercings, and 25.2% tattoos. The overall prevalence of HCV infection was 0.9% and of HBV infection was 5.2%. Higher HCV prevalence was independently associated with increasing age (odds ratio [OR] per year = 1.11; 95% confidence interval [CI] = 1.08-1.14), history of injection drug use (OR = 18.24; 95% CI = 7.74-42.92), blood transfusion before 1991 (OR = 3.21; 95% CI = 1.02-10.12), and incarceration (OR = 3.48; 95% CI = 1.45-8.37). Among 5,066 students who denied injecting drugs, HCV prevalence was 0.8% in those who reported intranasal drug use and 0.6% each in those who reported tattoos and those who reported body piercing. Increased HBV prevalence was associated with high-risk sexual behaviors and black or Asian race. In conclusion, there was no increased risk for HCV or HBV infection in low-risk adults based solely on history of cosmetic procedures or snorting drugs. However, proper infection control practices for cosmetic procedures should be followed, illegal drug use discouraged, and hepatitis B vaccination provided to adolescents and sexually active adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".