Prevalence of hepatitis B surface antigen and hepatitis B core antibody among adults with latent tuberculosis infection
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence of hepatitis B surface antigen (HBsAg) positive or hepatitis B core antibody total (anti-HBc) among adults with latent tuberculosis infection (LTBI) in the USA. METHODS: Using data from the National Health and Nutrition Examination Survey 1999-2000 and 2011-2012 cycles, US adults with LTBI (identified by positive tuberculin skin test or positive QuantiFERON-TB Gold In-Tube test) were evaluated to determine prevalence HBsAg and anti-HBc. Survey-weighted data was used to determine prevalence estimates of HBsAg or anti-HBc, which were further stratified by sex, race/ethnicity, country of birth and age. Trends were analyzed by regressing the outcome over time. Between-group comparisons used chi-squared testing. RESULTS: Overall prevalence of LTBI was 4.2% [95% confidence interval (CI), 3.5-5.1]. Among individuals with LTBI, HBsAg prevalence was 0.9% (95% CI, 0.4-2.1) and anti-HBc prevalence was 12.9% (95% CI, 9.8-16.8), both of which remained stable between 1999-2000 and 2011-2012. While no significant differences in HBsAg prevalence were observed by sex, race/ethnicity, country of birth, age, anti-HBc prevalence was significantly higher in men vs. women (16.8 vs. 7.9%, P < 0.05), blacks vs. non-Hispanic whites (22.9 vs. 5.9%, P < 0.05), non-US born vs. US-born (15.9 vs. 7.2%, P = 0.01) and highest in the oldest age group (age ≥65 years: 17.5%, 95% CI, 10.5-27.8). CONCLUSION: Among US adults with LTBI, overall prevalence of HBsAg was 0.9%. One in eight individuals with LTBI had prior HBV exposure. Effective HBV screening among individuals with LTBI may allow changes in clinical practice to prevent drug-induced liver injury from anti-TB therapies.
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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 | 0.002 |
| 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".