Epidemiology of Hepatitis B Virus Infection in Nigeria: A Narrative Review of Prevalence, Transmission, Genotypes, Coinfections, and Mortality
Bibliographic record
Abstract
Background Nigeria has the highest burden of hepatitis B virus (HBV) infection in sub‐Saharan Africa. However, the lack of a robust surveillance system and program data has limited the understanding of the burden and the distribution of HBV across different populations. This narrative review aimed to summarize available data on the epidemiology of HBV in Nigeria and identify research gaps in the existing literature. Methods We searched PubMed, Scopus, and Google Scholar for relevant articles published between January 2000 and June 2025. Primary studies, reviews, and reports that contained data of interest, including prevalence, incidence, mode of transmission, mortality, and genotypes, were included in this review. Where available, we restricted our results to findings from representative surveys (conducted across the six geopolitical zones) or systematic reviews. Prevalence rates < 2%, 2%–7%, and ≥ 8% were described as low, intermediate, and high, respectively. Results Studies on the prevalence of hepatitis B surface antigen (HBsAg) have reported intermediate to high rates in the general population (5.4%–13.6%), with evidence suggesting declining trend. The most recent estimates showed a prevalence of 5.4% in 2022, corresponding to approximately 14.4 million people living with HBV. Available data indicate sociodemographic disparities in HBV prevalence, with higher rates among men, adults (> 18 years), and rural dwellers. Reported prevalence rates among specific subpopulations include blood donors (13.2%–14.0%), pregnant women (5.5%–14.1%), prison inmates (4%–42.2%), people who inject drugs (7%–7.8%), healthcare workers (1.1%–25.7%), female sex workers (0%–17.1%), men who have sex with men (8.4%–11.7%), and transgender women (15.6%). The prevalence of hepatitis B e‐antigen (HBeAg) in the general population ranged from 6.0% to 23.6%. The prevalence of occult HBV infection (OBI) ranged from 0.9% to 17.0%. Genotype E was consistently reported as the predominant HBV genotype. Most studies reported low, intermediate, and high prevalence rates for HBV‐hepatitis C virus (HCV), HBV‐hepatitis D virus (HDV), and HBV‐HIV coinfections, respectively. In 2022, approximately 46,000 deaths were attributed to HBV, translating to a mortality rate of 21 per 100,000 population. Conclusions A wide range of HBV prevalence rates has been observed across various population groups in Nigeria. Key research gaps in HBV epidemiology that must be addressed include modes of transmission, incidence rate, prevalence among key populations, prevalence of OBI in the general population, and spatial distribution of the burden.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".