Prevalence and risk factors for HIV-1 infection in people who use illicit drugs in northern Brazil
Bibliographic record
Abstract
BACKGROUND: People who use illicit drugs (PWUDs) have a high risk of viral infections. To date, there is a paucity of information on HIV infection among PWUDs in remote Brazilian regions. This study determined the prevalence and factors associated with HIV-1 infection among PWUDs in northern Brazil. METHODS: Sociodemographic, economic, drug use and health-related information were collected through interviews from a community-recruited, multi-site sample of 1753 PWUDs. The blood samples collected were tested for the presence of HIV-1 using chemiluminescence immunoassay and PCR or western blotting. Logistic regressions identified factors independently associated with HIV-1 infection. RESULTS: In total, 266 (15.2%) PWUDs were HIV-1 positive. Hepatitis B virus and/or hepatitis C virus nucleic acid was detected in 65 (3.7%) PWUDs infected by HIV-1. The factors associated with HIV-1 infection were male gender, older age, a lower educational level and a lower income, crack cocaine use, a longer drug use history and a history of drug injection and engagement in unsafe sex, sex work and a higher number of sexual partners. CONCLUSIONS: The current study provides unique, initial insights into HIV and co-infection status and pertinent risk factors among PWUDs in northern Brazil, with clear and diverse implications for urgently improved prevention and treatment intervention needs.
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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 | 0.001 |
| 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".