Prenatal hepatitis B screening, and hepatitis B burden among children, in Ontario: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Ontario is 1 of 5 provinces that immunize adolescents for hepatitis B virus (HBV), despite the World Health Organization recommendation for universal birth dose vaccination. One rationale for not vaccinating at birth is that universal prenatal screening and related interventions prevent vertical transmission. The aims of our study were to evaluate the uptake and epidemiology of prenatal HBV screening, and to determine the number of children in Ontario with a diagnosis of HBV before adolescent vaccination. METHODS: We extracted data from ICES, Public Health Ontario and Better Outcomes & Registry Network (BORN) Ontario databases. We assessed prenatal screening uptake and prevalence of prenatal hepatitis B surface antigen (HBsAg) from 2012 to 2016, as well as subsequent hepatitis B e-antigen (HBeAg) and HBV DNA testing and percent positivity. We used age and region to subcategorize the results. In a separate unlinked analysis, we evaluated the number of children positive for HBV aged 0-11 years who were born in Ontario from 2003 to 2013. RESULTS: From 2012 to 2016, 93% of pregnant women were screened for HBV, with an HBsAg prevalence of 0.6%. Prevalence of HBsAg increased with age, peaking at older than 45 years at 3%. North Toronto had the highest overall prevalence of 1.5%, whereas northern Ontario had the lowest. Of women who were HBsAg positive, HBeAg and HBV DNA tests were subsequently ordered in 13% and 38%, respectively. Of children born in Ontario between 2003 and 2013, 139 of 23 759 tested positive for HBV. INTERPRETATION: Prenatal HBV screening is not universal and subsequent evaluation is poor, limiting optimal intervention and possibly contributing to some Ontario-born children being given a diagnosis of HBV before age 12 years. These findings underscore the limitations of the province's adolescent vaccination strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".