Sociodemographic Risk Factors of Being Underweight Among Ever-Married Bangladeshi Women of Reproductive Age: A Multilevel Analysis

Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches

BACKGROUND

This paper presents an in-depth examination of malnutrition in women in Bangladesh. Malnutrition in women is a major public health issue related to different diseases and has negative repercussions for children, such as premature birth, decreased infection resistance, and an increased risk of death. Moreover, malnutrition is a severe problem in Bangladesh. Data from the Bangladesh Demographic Health Survey (BDHS) conducted in 2017-18 was used to identify risk factors for malnourished women and to create a machine learning-based strategy to detect their nutritional status.

METHODS

A total of 17022 women participants are taken to conduct the research. All the participants are from different regions and different ages. A chi-square test with a five percent significance level is used to identify possible risk variables for malnutrition in women and six machine learning-based classifiers (Naïve Bayes, two types of Decision Tree, Logistic Regression, Random Forest, and Gradient Boosting Machine) were used to predict the malnutrition of women. The models are being evaluated using different parameters like accuracy, sensitivity, specificity, positive predictive value, negative predictive value, [Formula: see text] score, and area under the curve (AUC).

RESULTS

Descriptive data showed that 45% of the population studied were malnourished women, and the chi-square test illustrated that all fourteen variables are significantly associated with malnutrition in women and among them, age and wealth index had the most influence on their nutritional status, while water source had the least impact. Random Forest had an accuracy of 60% and 60.2% for training and test data sets, respectively. CART and Gradient Boosting Machine also had close accuracy like Random Forest but based on other performance metrics such as kappa and [Formula: see text] scores Random Forest got the highest rank among others. Also, it had the highest accuracy and [Formula: see text] scores in k-fold validation along with the highest AUC (0.604).

CONCLUSION

The Random Forest (RF) approach is a reasonably superior machine learning-based algorithm for forecasting women's nutritional status in Bangladesh in comparison to other ML algorithms investigated in this work. The suggested approach will aid in forecasting which women are at high susceptibility to malnutrition, hence decreasing the strain on the healthcare system.

Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach

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.

Drivers and distribution of the household-level double burden of malnutrition in Bangladesh: analysis of mother-child dyads from a national household survey

OBJECTIVE

The double burden of malnutrition (DBM) has become an emerging public health issue in many low- and middle-income countries. This study aims to provide important evidence for the prevalence of different types of DBM at the national and subnational levels in Bangladesh.

DESIGN

The study utilised data from the latest Bangladesh Demographic and Health Survey (BDHS) 2017-2018. Multivariable logistic regression was performed to identify the sociodemographic factors associated with DBM.

SETTING

Nationally representative cross-sectional survey.

PARTICIPANTS

8697 mothers aged 15 to 49 years with <5 children.

RESULTS

The overall prevalence of the DBM was approximately 21 %, where the prevalence of overweight mother (OWM) & stunted child/wasted child/underweight child (SC/WC/UWC) and underweight mother (UWM) & overweight child (OWC) was 13·35 % and 7·69 %, respectively, with a higher prevalence among urban households (OWM & SC/WC/UWC = 14·22 %; UWM & OWC = 10·58 %) in Bangladesh. High inequality was observed among UWM & OWC dyads, concentration index (CI) = -0·2998, while low level of inequality of DBM were observed for OWM & SC (CI = 0·0153), OWM & WC (CI = 0·1165) and OWM & UWC (CI = 0·0135) dyads. We observed that the age and educational status of the mother, number of children, fathers' occupation, size and wealth index of the household, and administrative division were significantly associated with all types of DBM.

CONCLUSIONS

Health policymakers, concerned authorities and various stakeholders should stress the prevalence of DBM issues and take necessary actions aimed at identifying and addressing the DBM in Bangladesh.