Sero-prevalence and risk factors for hepatitis B virus infection among pregnant women attending antenatal clinics in Adigrat General Hospital, Eastern Tigrai, Northern Ethiopia
Bibliographic record
Abstract
Abstract OBJECTIVES Hepatitis B virus (HBV) infection is one of the most common health problems worldwide and is associated with high mortality and heavy economic burdens. The aim of this study was to determine the prevalence of hepatitis B surface antigen (HBsAg) and associated risk factors among pregnant women attending antenatal clinics in Adigrat General Hospital in Northern Ethiopia. METHODS An institutional-based cross-sectional study was conducted from January to March, 2024, among 385 pregnant women. Participants were selected through a systematic random sampling method. Sociodemographic and associated factor data were collected using a structured questionnaire, and 5 mL blood samples were collected. The data were subsequently entered into EPI Info and analyzed using SPSS version 25. Descriptive statistics were computed. Bivariate and multivariate regression analyses were employed to measure associations, and values < 0.05 were considered to indicate statistical significance. RESULTS The overall seroprevalence of HBsAg among the 385 study subjects enrolled was 10.4% (n = 40). HBsAg was common in all age groups. The prevalence of infection was greater in those who had a history of multiple sexual partners (22.7%), early piercing (13.4%), or abortion (27.6%), as was the history of delivery (26.3%) and genital mutilation (35.7%). According to multivariate logistic regression, patients were unmarried (AOR 8.57; 95% CI 3.20-22.93), illiterate (AOR 12.06; 95% CI 3.07–47.33), had a history of ear piercing (AOR 5.66; 95% CI 1.65–19.45), a history of abortion (AOR 8.16; 95% CI 3.18–20.95), a history of home delivery (AOR 6.69; 95% CI 1.26–35.53) and a history of genital mutilation (AOR 9.77; 95% CI 2.64–36.18) for acquiring HBV infection compared to their counterparts. Conclusions The results showed that HBV was highly prevalent in our study area. Being unmarried, having a low educational level, having an ear piercing, having an abortion, having a home delivery and having genital mutilation were significantly associated with HBV infection. Therefore, these findings suggest that health education programs should be provided to the community to increase awareness among mothers.
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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 | 0.000 | 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".