A systematic review and meta-analysis of the prevalence of hepatitis B virus infection among pregnant women in Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria has a high burden of hepatitis B virus (HBV) infection, commonly acquired through vertical transmission. However, there is a lack of an efficient surveillance system for monitoring and understanding the epidemiology of HBV among pregnant women. Building on a previous review on the prevalence of HBV in Nigeria (2000-2013), we conducted a systematic review and meta-analysis of HBV prevalence among pregnant women in Nigeria. METHODS: Four electronic databases PubMed, Embase, Global Health, and Scopus were systematically searched from January 2014 to February 2021. We also searched the African Journal Online and manually scanned the reference lists of the identified studies for potentially eligible articles. Observational studies that reported the prevalence of HBsAg and/or HBeAg among pregnant women in peer-reviewed journals were included in the study. We performed a meta-analysis using a random-effects model. We defined HBV infection as a positive test to HBsAg. RESULTS: From the 158 studies identified, 20 studies with a total sample size of 26, 548 were included in the meta-analysis. The pooled prevalence of HBV infection among pregnant women across the studies was 6.49% (95% confidence interval [CI] = 4.75-8.46%; I2 = 96.7%, p = 0.001; n = 20). The prevalence of HBV was significantly lower among pregnant women with at least secondary education compared with those with no education or primary education (prevalence ratio = 0.7, 95% CI = 0.58-0.87; n = 10). However, the prevalence of HBV was not significantly different by age, religion, marital status, or tribe. The prevalence of HBV was not significantly different among pregnant women with previous surgery, blood transfusion, multiple lifetime sex partners, tribal marks, tattoos, scarification, or sexually transmitted infections, compared with those without these risk factors. From a total sample size of 128 (n = 7), the pooled prevalence of HBeAg among HBV-infected pregnant women was 14.59% (95% CI = 4.58-27.99%; I2 = 65.5%, p = 0.01). Subgroup analyses of HBV infection by study region and screening method, and meta-regression analysis of the study year, sample size, and quality rating were not statistically significant. CONCLUSIONS: There is an intermediate endemicity of HBV infection among pregnant women in Nigeria. Interventions, such as routine antenatal HBV screening, antiviral prophylaxis for eligible pregnant women, and infant HBV vaccination should be scaled up for the prevention of perinatal transmission of HBV infection in Nigeria.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| 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) | 0.004 | 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".