Seroprevalence of select bloodborne pathogens and associated risk behaviors among injection drug users in the Paso del Norte region of the United States – Mexico border
Bibliographic record
Abstract
BACKGROUND: The region situated where the borders of Mexico, Texas and New Mexico meet is known as 'Paso del Norte'. The Paso del Norte Collaborative was formed to study the seroprevalence of select pathogens and associated risk behaviors among injection drug users (IDUs) in the region. METHODS: Respondent-driven sampling (RDS) was used: 459 IDU participants included 204 from Mexico; 155 from Texas; and 100 from New Mexico. Each of the three sites used a standardized questionnaire that was verbally administered and testing was performed for select bloodborne infections. RESULTS: Participants were mostly male (87.4%) and Hispanic/Latino (84.7%) whose median age was 38. In Mexico, Texas and New Mexico, respectively: hepatitis B virus (HBV) was seen in 88.3%, 48.6% and 59.6% of participants; hepatitis C virus (HCV) in 98.7%, 76.4% and 80.0%; human immunodeficiency virus (HIV) in 2.1%, 10.0% and 1.0%; and syphilis in 4.0%, 9.9% and 3.0%. Heroin was the drug injected most often. More IDUs in New Mexico were aware of and used needle exchange programs compared with Texas and Mexico. CONCLUSION: There was mixed success using RDS: it was more successfully applied after establishing good working relationships with IDU populations. Study findings included similarities and distinctions between the three sites that will be used to inform prevention interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".