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Background: Hepatitis B virus (HBV) is a major source of disease burden worldwide, with an estimated 296 million individuals living with infections worldwide. Although vaccine programs exist to control infections, certain sub-populations around the world continue to have very high prevalence of HBV infection. Methods: A systematic search of studies of HBV published after 2010 was conducted for India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan. Each paper was independently screened for risk of bias and inclusion. Data were extracted from included studies before being analysed to estimate pooled prevalence, and to conduct sub-group analyses. Random-effects models were used for estimating summary prevalence due to a high level of heterogeneity between studies, and funnel plots were combined with Egger's test to assess publication bias. Meta-regression was conducted to investigate sources of between-study heterogeneity. Results: The pooled prevalence of HBV across all studies was 3% (95% CI 0.02, 0.05). For countries with multiple studies, the pooled prevalence in India was 3% (95% CI 0.02, 0.04), in Pakistan 6% (95% CI 0.03, 0.09), in Bangladesh 5% (95% CI of 0.02, 0.12), and in Nepal 1% (95% CI 0.00, 0.08). There was some evidence of publication bias, and a high level of heterogeneity across studies. Risk of bias analysis found most studies to be of fair or moderate quality. Conclusions: The prevalence of HBV among countries in the sub-continent was higher than the global average, but was not as high as some other regions. Countries with greater numbers of displaced persons had higher prevalence of HBV, with a wide range of prevalence between subpopulations likely reflecting differential uptake, and implementation, of vaccination programs. [ABSTRACT FROM AUTHOR]

Globally, 21 percent of young women are married before their 18th birthday. Despite some progress in addressing child marriage, it remains a widespread practice, in particular in South Asia. While household predictors of child marriage have been studied extensively in the literature, the evidence base on macro-economic factors contributing to child marriage and models that predict where child marriage cases are most likely to occur remains limited. In this paper we aim to fill this gap and explore region-level indicators to predict the persistence of child marriage in four countries in South Asia, namely Bangladesh, India, Nepal and Pakistan. We apply machine learning techniques to child marriage data and develop a prediction model that relies largely on regional and local inputs such as droughts, floods, population growth and nightlight data to model the incidence of child marriages. We find that our gradient boosting model is able to identify a large proportion of the true child marriage cases and correctly classifies 77% of the true marriage cases, with a higher accuracy in Bangladesh (92% of the cases) and a lower accuracy in Nepal (70% of cases). In addition, all countries contain in their top 10 variables for classification nighttime light growth, a shock index of drought over the previous and the last two years and the regional level of education, suggesting that income shocks, regional economic activity and regional education levels play a significant role in predicting child marriage. Given the accuracy of the model to predict child marriage, our model is a valuable tool to support policy design in countries where household-level data remains limited. [ABSTRACT FROM AUTHOR]