Barriers to breastfeeding are shaped by sociocultural context: an exploratory qualitative study in Bangladesh

Uddin, Md. Fakhar; Jabeen, Ishrat; Islam, Mohammad Ashraful; Rahman, Mahfuzur; Chisti, Mohammod Jobayer; Ahmed, Tahmeed; Sarma, Haribondhu

doi:10.1186/s41043-022-00312-y

Cited by 5 publications

(6 citation statements)

References 32 publications

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“…Scaling up breastfeeding: the benefits of breastfeeding for a healthier weight and postpartum weight loss have been well-documented [86,107,108]. However, breastfeeding practice is still not optimum in Bangladesh, and women face a range of barriers at the individual, societal, and system levels that include adverse effects of breastfeeding on maternal health, nutrition and physical appearance [109]. Proven interventions for scaling up breastfeeding in Bangladesh and other Asian countries, which combined intensified interpersonal counseling, mass media campaigns, and community mobilization interventions [110], need to be implemented not only for ensuring nutrition for children but also as an effective approach for healthy BMI for women of childbearing age.…”

Section: Discussionmentioning

confidence: 99%

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

Khudri

Rhee

Hasan

et al. 2022

Preprint

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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 (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 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.

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Section: Discussionmentioning

confidence: 99%

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

Khudri

Rhee

Hasan

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Pilot testing was conducted with 20 participants who were similar to the study's population to ensure the questionnaire's validity and reliability. The results of the pilot testing indicated that the observation sheet was valid and reliable for use [8], [9].…”

Section: Methodsmentioning

confidence: 92%

“…Despite various efforts to raise awareness of the importance of EBI, there are often still obstacles to its practice in the form of sociocultural aspects that cause the implementation of EBI to be hampered [8], [9]. As also happened in one district in Indonesia, the problem of low EBI rates in West Tulang Bawang Regency, Lampung Province, is reflected in the achievement of an EBI of 8.74%.…”

Section: Introductionmentioning

confidence: 99%

Model development of early breastfeeding initiation and exclusive preparation

Indrasari,

Mugiati,

Octaviana

et al. 2024

IJPHS

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The research was conducted in June-September 2022. The early breastfeeding initiation rate (EBI) in one of the districts in Indonesia in 2022 will only reach 8.74%, of the target of 42%. This study aimed to increase the percentage of EBI implementation in birthing mothers and the success of EBI in the first 30 minutes of labor through the development of EBI model. The research method uses a quasi-experimental control group design. The population is 96 people. The sample is the total population, consisting of 48 people closest to pregnant women plus 48 people in the control group. The effect of mentoring shows significant results on the success of EBI because of the value of p=0.002. The mother's educational contribution did not affect the success of EBI between the intervention group and the control group (p=0.774). The family support factor resulted in a significant EBI win between the intervention and control groups (p=0.000). The achievement of EBI provision was 100% of the target of 60%, the achievement of EBI>1 hour was 7.2%, and EBI<1 hour was 92.8%.

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“…Scaling up breastfeeding: the benefits of breastfeeding for a healthier weight and postpartum weight loss have been well-documented [86,114,115]. However, breastfeeding practice is still not optimum in Bangladesh, and women face a range of barriers at the individual, societal, and system levels that include adverse effects of breastfeeding on maternal health, nutrition, and physical appearance [116]. Proven interventions for scaling up breastfeeding in Bangladesh and other Asian countries, which combined intensified interpersonal counseling, mass media campaigns, and community mobilization interventions [117], need to be implemented not only for ensuring nutrition for children but also as an effective approach for healthy BMI for women of childbearing age.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Barriers to breastfeeding are shaped by sociocultural context: an exploratory qualitative study in Bangladesh

Cited by 5 publications

References 32 publications

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

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

Model development of early breastfeeding initiation and exclusive preparation

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

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