Early marriage is a form of violation of child rights to grow and develop. The Sustainable Development Goals had included early marriage in target 5.3, aiming to eliminate by 2030. This study examines the socio-demographic factors associated with women's early marriage in Bangladesh, Ghana, and Iraq using information extracted from 2019, 2017-2018, and 2018 Multiple Indicator Cluster Surveys (MICSs) of Bangladesh, Ghana, and Iraq, respectively. The chi-square test examined the association between socio-demographic factors and early marriage separately in all three countries. In logistic regression, key factors were primarily evaluated for determining effects on early marriage separately in all three countries. The mean age of the mother at first marriage was found to be 16.86, 20.23, and 20.05 years in Bangladesh, Ghana, and Iraq successively. According to surveys conducted in Bangladesh, Ghana, and Iraq, education levels of household heads and women, wealth status, mass media, number of household members, and residence were significant factors linked to early marriage. The odds of getting married early were significantly higher among women with no formal education and primary education than women with secondary or higher education in all three countries. In terms of economic status, a negative association was found between wealth status and early marriage in both Bangladesh and Ghana. Based on the findings, the study recommended that government take the necessary steps to reduce child marriage in all three countries by raising women's education and campaigning women by media to harmful effects of early marriage, particularly women from low-income families.

Purpose Malnutrition is one of the serious public health problems especially for children and pregnant women in developing countries such as Bangladesh. This study aims to identify the risk factors associated with child nutrition for both male and female children in Bangladesh. Design/methodology/approach This study was conducted among 23,099 mothers or caretakers of children under five years of age from a nationally representative survey named Bangladesh Multiple Indicator Cluster Survey, 2019. This study used chi-square test statistic for bivariate analysis and multinomial logistic regression was used to evaluate the adjusted effects of those covariates on child nutritional status. Findings The prevalence of severely malnourished, nourishment was higher for males than females (5.3% vs 5.1%, 77.4% vs 76.8%) while moderately malnourished were higher for females (18.1% vs 17.4%). The findings from the multinomial model insinuated that the mother’s education level, wealth index, region, early child development, mother’s functional difficulties, child disability, reading children's books and diarrhea had a highly significant effect on moderate and severe malnutrition for male children. For the female children model, factors such as mother’s education level, wealth index, fever, child disability, rural, diarrhea, early child development and reading less than three books were significant for moderate and severe malnutrition. Originality/value There is a solution to any kind of problem and malnutrition is not an exceptional health problem. So, to overcome this problem, policymakers should take effective measures to improve maternal education level, wealth status, child health.

Introduction: The coronavirus disease 2019 (COVID-19) has become a public health concern, and behavioral adjustments will minimize its spread worldwide by 80%. The main purpose of this research was to examine the factors associated with concerns about COVID-19 and the future direction of the COVID-19 scenario of Bangladesh. Methods: The binary logistic regression model was performed to assess the impact of COVID-19 concern in Bangladesh. Based on data obtained through online surveys in November 2020 and to predict the next 40 days daily confirmed and deaths of COVID-19 in Bangladesh by applying the Autoregressive Integrated Moving Average (ARIMA) model. Results: The study enrolled 400 respondents, with 253 (63.2%) were male, and 147 (36.8%) were female. The mean age of respondents was 25.13 ± 5.74 years old. Almost 70% of them were found to be concerned about the COVID-19 pandemic. The result showed that respondents’ education level, knowledge regarding COVID-19 transmits, households with aged people, seasonal flu and HD/respiratory problems, and materials used while sneezing/coughing significantly influenced COVID-19 concerns. The analysis predicted that confirmed cases would gradually decrease for the ARIMA model while death cases will be constant for the next 40 days in Bangladesh. Conclusion: The current study suggested that knowledge about COVID-19 spread and education played a vital role in the decline of COVID-19 concerned. A particular program should focus on creating an awareness of the disadvantages of concerns about the COVID-19 pandemic by augmenting knowledge about COVID-19 spread, enhancing Education in Bangladesh.

Background: The current total fertility rate in Bangladesh is now 2.3 births per woman, which is still above the replacement level of 2.1. Objective: The main objective of this study was to identify potential factors associated with fertility transition in Bangladesh. Methods: This study applied several regression models to find the best-fitted model to determine factors associated with the number of children ever born in Bangladesh and utilize data from the 2019 Bangladesh Multiple Indicator Cluster Survey. Results: Based on the principles of the AIC, BIC, and Vuong tests, the best-fit model was the Hurdle-Poisson regression model compared to other models. Findings based on the Hurdle Poisson regression result revealed that the number of children increases with the increase of women’s age, but the number of children declines if the education status of women as well as their delayed marriage increases. Women who had secondary or higher education were less likely to have children than illiterate women. Similarly, division, residential area, wealth index, women’s functional difficulties, prenatal care, and migration have significantly influenced the number of children ever born. Conclusion: Based on the findings, the study suggests that fertility can be decreased by improving female education, minimizing early marriage, and eliminating poverty for all ever-married women who were particularly live in rural areas of the Chittagong and Sylhet divisions in Bangladesh. Such steps would be the largest contribution to a future reduction in fertility rates in Bangladesh.

Intended pregnancy is one of the significant indicators of women’s well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15–49 were eligible for this study. An independent χ 2 test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen’s Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30–49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.