Mohsan Khudri

Using data from the National Longitudinal Study of Adolescent Health, we examine the impact of breastfeeding on the likelihood of obtaining a college degree and attending graduate school. Using a doubly robust estimation technique, we find that having been breastfed is linked to an increased probability of obtaining a college degree, but its association with graduate school attendance is largely insignificant. The effect on college degree attainment is primarily concentrated among males, blacks, and whites. Investigating potential mechanisms, we find evidence that part of these effects is operating through the impact of breastfeeding on adolescents’ cognitive ability.

This paper investigated the determinants of unhealthy BMI categories, namely underweight, overweight, and obesity, for urban and rural women of childbearing age in Bangladesh. Multilevel nested logistic models were estimated using data from multiple waves (2004–18) of the Demographic and Health Surveys of Bangladesh. After controlling for community and regional effects, the underweight status showed an inverse relationship with years of education for urban and rural women. Conversely, the fitted model estimates revealed a positive association between overweight risk and education for both urban and rural women populations. However, the association between obesity and education was significant for only urban women. Employment status appeared to be negatively associated with the risks of being overweight and obese in both population groups. Moreover, the women were found less likely to be underweight and more likely to be overweight and obese when their household socio-economic status improved. Study findings have the potential to inform a wide range of interventions, policies and programs that simultaneously address the adverse consequences of underweight, overweight, and/obese population health conditions in rural and urban Bangladesh.

Background: Although Bangladesh's economy has shown significant improvement over the past two decades, the high population growth rate has hindered development efforts. This study aimed to review the prevalence of different contraceptive methods used among women of reproductive age in Bangladesh, which could help control the population growth rate. Methods: We conducted an extensive literature review and meta-analysis of 82 articles, identifying 20 articles for analysis. The analyses included heterogeneity and publication bias in published papers. Results: The pooled prevalence of various contraceptive methods was as follows: condom use, 7.13%; Female Sterilization, 8.09%; injectables, 12.76%; intrauterine devices (IUDs), 3.76%; male sterilization, 2.34%; periodic abstinence, 6.71%; pills, 33.21%; and withdrawal, 3.27%. Modern contraceptive methods accounted for 62.91% of usage, while traditional methods constituted 8.79%. On average, only 1.95% of women opted for the implant method. The overall prevalence of contraceptive method usage was 59.48%, with 60.59% in urban areas and 54.54% in rural areas. We found statistically significant heterogeneity for all contraceptive methods used by women in Bangladesh. The funnel plot and Egger’s test showed no publication bias for any of the contraceptive methods, except condoms (Z = 2.34, P = 0.0194). The contraceptive methods used by women in rural areas also showed publication bias (Z = −3.04, P = 0.0024). Conclusion: A renewed commitment from government bodies and independent organizations is needed to implement and monitor family planning strategies to ensure adherence to and provision of the most appropriate contraceptive method for couples.

The rural-urban disparity in women’s health outcomes has been largely overlooked in the literature, particularly in the context of developing countries. Applying Oaxaca-Blinder and quantile regression-based counterfactual decomposition methods to the data from the latest five rounds (2004-2018) of Bangladesh Demographic and Health Surveys (BDHS), we identify sources of the sizeable differences in body mass index (BMI), underweight, overweight, and obesity across rural and urban areas among women of childbearing age. We consider socio-economic, healthcare access, health and fertility behaviors, and infrastructure-related variables as potential determinants of this rural-urban gap, including both differing levels and differing returns of these factors across areas. Additionally, our empirical strategy allows us to investigate contributors to the rural-urban gap across the distribution of BMI. Our estimates reveal that disparities in education, healthcare access, infrastructural developments, and inequitable distribution of wealth explain a major fraction of the rural-urban gap in nutritional outcomes. We also find that sizeable gaps are attributable to differential health-related returns to education, institutional births, and wealth status. These findings suggest that experiencing unbalanced economic growth between rural and urban areas drives health disparities in developing countries, highlighting the need for institutionally different policies and strategies in rural and urban settings.

Background: Malnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women’s body mass index (BMI) and malnutrition outcomes (underweight, overweight, and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features.Methods: Data from the 2017–18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach.Results: Study findings demonstrate that the support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen’s kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women’s age, and breastfeeding status are the most important features in predicting women’s nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features, along with the partner’s educational attainment and employment status, significantly predict malnutrition risks.Conclusion: To the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country’s economic burden.

Background: Malnutrition imposes enormous costs resulting from lost investments in human capital and increased healthcare expenditures. There is a dearth of research focusing on the prediction of women’s body mass index (BMI), and the malnutrition outcomes (underweight, overweight and obesity) in developing countries. This paper attempts to fill out this knowledge gap by predicting the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health, and demographic features. Methods: Data from the 2017-18 Bangladesh Demographic and Health Survey and a series of supervised machine learning (SML) techniques are used. Additionally, this study circumvents the imbalanced distribution problem in obesity classification by utilizing an oversampling approach. Results: Study findings demonstrate that support vector machine and k-nearest neighbor are the two best-performing methods in BMI prediction based on coefficient of determination ( R ² ), root mean square error (RMSE), and mean absolute error (MAE). The combined predictor algorithms consistently yield top specificity, Cohen’s kappa, F1-score, and AUC in classifying the malnutrition status, and their performance is robust to alternative standards. The feature importance ranking based on several nonparametric and combined predictors indicates that socioeconomic status, women’s age, and breastfeeding status are the most important features in predicting women’s nutritional outcomes. Furthermore, the conditional inference trees corroborate that those three features along with the partner’s educational attainment and employment significantly predict malnutrition risks. Conclusion: To the best of our knowledge, this is the first study that predicts BMI and one of the pioneer studies to classify all three malnutrition outcomes for women of childbearing age in Bangladesh, let alone in any lower-middle income country, using SML techniques. Moreover, in the context of Bangladesh, this paper is the first to identify and rank features that are critical in predicting nutritional outcomes using several feature selection algorithms. The estimators from this study predict the outcomes of interest most accurately and efficiently compared to other existing studies in the relevant literature. Therefore, study findings can aid policymakers in designing policy and programmatic approaches to address the double burden of malnutrition among Bangladeshi women, thereby reducing the country’s economic burden.

Objectives: This paper examines the associations of socio-economic and demographic correlates with malnutrition among women and investigates education and wealth-related inequalities in malnutrition among women by region.Design: We utilise a two-level mixed-effects logistic regression model to evaluate the associations and employ the concentration, Wagstaff and Erreygers’s correction indices to measure socio-economic inequalities in malnutrition among women.Setting: Bangladesh Demographic and Health Survey data.Participants: Non-pregnant women aged 15–49 years.Results: We find evidence of a significant cluster effect in the data. Women’s age, marital status, total children ever born, education level, husband’s/partner’s education level, residence and wealth index appear to be significantly associated with women underweight and overweight/obesity status. Underweight status is higher among less-educated women and women from poor households, whereas overweight/obesity is more concentrated among higher educated women and women from wealthy households. The southwestern region of the country demonstrates lower education and wealth-related inequalities in malnutrition among women. In contrast, the central and the northeastern areas apparently experience the highest education and wealth-related inequalities in malnutrition among women. The regional differences in predicted probabilities of being underweight shrink at higher education level and the richest quintile, whereas the differences in overweight/obese diminish at the primary education level and lower quintile households.Conclusions: Our findings strengthen the evidence base for effective regional policy interventions to mitigate education and wealth-related inequalities in malnutrition among women. There is a need for developing regional awareness programmes and establishing regional monitoring cells to ensure proper health and nutrition facilities in underprivileged regions.