Hepatitis B and C in Immigrants and Refugees in Central Brazil: Prevalence, Associated Factors, and Immunization
Bibliographic record
Abstract
Introduction: Eliminating hepatitis B and C in immigrant and refugee populations is a significant challenge worldwide. Given the lack of information in Brazil, this study aimed to estimate the prevalence of infections caused by hepatitis B and C viruses and factors associated with hepatitis B in immigrants and refugees residing in central Brazil. Methods: An observational, cross-sectional, and analytical study was conducted from July 2019 to January 2020 with 365 immigrants and refugees. Hepatitis B was detected by a rapid immunochromatographic test, enzyme immunoassay, and chemiluminescence, and hepatitis C by rapid immunochromatographic test. Multiple analysis was used to assess factors associated with hepatitis B infection. Results: Of the participants, 57.8% were from Haiti and 35.6% were from Venezuela. Most had been in Brazil for less than 2 years (71.2%). The prevalence of HBV infection and exposure was 6.6% (95% CI: 4.5–9.6%) and 27.9% (95% CI: 23.6–2.8%), respectively, and 34% had isolated anti-HBs positivity. Reporting a sexually transmitted infection was statistically associated with HBV infection (OR: 7.8; 95% CI: 2.3–26.4). No participant with positive anti-HCV serology was found. Conclusions: The study showed that participants were outside the reach of prevention and control actions for hepatitis B. Therefore, public health strategies must be designed to reach, inform, and vaccinate this group.
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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 | 0.001 |
| 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".