Prevalence of hepatitis B virus infection among pregnant women in Africa: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Africa exhibits a considerably high prevalence of the hepatitis B virus among pregnant women. Furthermore, there is a discernible lack of a well-established surveillance system to adequately monitor and comprehend the epidemiology of the hepatitis B virus, particularly among pregnant women. The eradication efforts of the virus in Africa have been impeded by the significant disease burden in the region, and there is a lack of evidence regarding the pooled prevalence of the hepatitis B virus in Africa. Consequently, this systematic review and meta-analysis aims to determine the prevalence of hepatitis B virus infection among pregnant women in Africa. METHODS: We conducted a systematic literature search using reputable databases such as PubMed, Advanced Google Scholar, Scopus, and the Cochrane Library. The search spanned from July 2013 to July 2023 and included all relevant articles published within this period. To identify potentially eligible articles, we conducted a comprehensive manual review of the reference lists of the identified studies. Our review encompassed articles from the African Journal Online. The analysis focused on observational studies published in peer-reviewed journals that reported the prevalence of hepatitis B surface antigen-positive testing among pregnant women. We utilized the Newcastle-Ottawa critical appraisal checklist to assess the methodological quality of each paper. Finally, a meta-analysis was conducted using a random-effects model. RESULTS: Out of the 774 studies identified, 31 studies involving 33,967 pregnant women were selected for the meta-analysis. According to the random-effects model, the combined prevalence of hepatitis B virus among pregnant women was 6.77% [95% CI: 5.72, 7.83]. The I2 statistic was calculated to be 95.57% (p = 0.00), indicating significant heterogeneity among the studies. The high I2 value of 95.57% suggests a substantial degree of heterogeneity. A subgroup meta-analysis revealed that factors such as time-dependent bias, sample size dependence, or individual variation among study participants contributed to this heterogeneity (p-difference < 0.05). CONCLUSION: According to the findings of this study, the pooled prevalence of hepatitis B infection among pregnant women in Africa was found to be intermediate-high. It is recommended that policymakers implement hepatitis B virus immunization programs targeting pregnant women and their new-born babies at higher risk of exposure.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.045 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".