Factors associated with duration of breastfeeding in Bangladesh: evidence from Bangladesh demographic and health survey 2014

Ayesha, Ummay; Mamun, Abu Sayed Md. Al; Sayem, Md. Abu

doi:10.1186/s12889-021-11804-7

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Our bivariate analyses also indicate that BMI is significantly and positively associated with age and educational attainment (of both women and their partners), which is in line with recent studies from Bangladesh [80–82]. We find that breastfeeding women have a lower BMI than their non-breastfeeding counterparts, which (i.e., the protective effect of breastfeeding against high BMI) is also in line with studies in Bangladesh and elsewhere [83–86].…”

Section: Discussionsupporting

confidence: 91%

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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: Discussionsupporting

confidence: 91%

“…that breastfeeding women have a lower BMI than their non-breastfeeding counterparts, which (i.e., the protective effect of breastfeeding against high BMI) is also in line with studies in Bangladesh and elsewhere [83][84][85][86].…”

Section: Discussionsupporting

confidence: 86%

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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“…In addition to the lack of nurseries in the mother's workplace, making it difficult for working women to breastfeed their infants ( 22 ). Previous studies have also reported that maternal employment was negatively associated with exclusive breastfeeding ( 16 , 48 – 50 ). However, this study showed that women living in Sharjah were more committed to EBF than those living in Dubai.…”

Section: Discussionmentioning

confidence: 83%

Determinants of Exclusive Breastfeeding and Mixed Feeding Among Mothers of Infants in Dubai and Sharjah, United Arab Emirates

Sabbah

Assaf²,

Taha³

et al. 2022

Front. Nutr.

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BackgroundBreastfeeding (BF) is considered the ultimate method of infant feeding for at least the first 6 months of life. Exclusive breastfeeding (EBF) is one of the most effective interventions to improve child survival. The main objective of this study was to assess the prevalence and duration of exclusive breastfeeding and the associated factors among women in Dubai and Sharjah, UAE.MethodsA cross-sectional study was conducted in four hospitals and four healthcare centers in Dubai and Sharjah between September 2017 and December 2017. Hospitals and centers are governmental and provide maternal and child health services. A convenience sample of 858 Arab and Emirati mothers with children under the age of 2 years participated in the study. Face-to-face interviews were conducted by using structured questionnaires. The study was approved by the University Ethical Committee and the UAE Ministry of Health before data collection. Descriptive statistics were computed to describe all the questionnaire items. The chi-square test was used to compare the study's categorical variables. A binary logistic regression analysis was used to predict the relationship between BF and its associated factors. Statistical tests with P-values < 0.05 were considered statistically significant.ResultsThe mean age of the participating mothers was 30.6 (SD 5.5) years. Results showed that the prevalence of exclusive breastfeeding among the study participants was 24.4% (31.1% in Sharjah and 22% in Dubai; P = 0.003). The binary logistic regression reported that mother's and father's education, skin-to-skin period, number of children, mothers' health, and place of living were significantly associated with exclusive breastfeeding (P < 0.05). The results reported a significant association between EB and duration of breastfeeding (OR = 6.9, P = 0.002), husband education (OR = 2.1, P = 0.015), mother education (OR = 1.3, P = 0.027), number of children (OR = 7.9, P = 0.045), having any health problem (OR = 1.2, P = 0.045), and living place (OR = 1.4, P = 0.033), and a non-significant positive effect of family size and family income. Furthermore, the result reported a significant association between mixed breastfeeding and duration of breastfeeding (OR = 0.1, P = 0.000), skin-to-skin period (OR = 0.3, P = 0.002), underweight (OR = 4.7, P = 0.034), last infant's sex (OR = 1.6, P = 0.010), having maid at home (OR = 2.1, P = 0.000), number of children (OR = 0.2, P = 0.013), and living place (OR =1.1, P = 0.014), and a non-significant association with family size and family income.ConclusionsTherefore, a health promotion program for exclusive breastfeeding during antenatal health visits, together with initiating health policies in maternal hospitals to encourage the initiation of breastfeeding during the first hour of birth and the introduction of skin-to-skin contact during the first 5 min of birth are highly recommended.

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“…This underscores the necessity of targeted interventions promoting comprehensive sexuality education, delaying childbearing, and ensuring equitable access to family planning services for young people [8]. The negative association between educational attainment and unintended abortion mirrors prior studies in Bangladesh [4,10]. Education empowers women with knowledge about reproductive health and contraceptive options, enabling them to make informed choices about their fertility [8].…”

Section: Discussionmentioning

confidence: 92%

“…Numerous studies have explored the factors associated with unintended abortion in Bangladesh, identifying sociodemographic characteristics, economic factors, and healthcare access as key contributors [4]. However, a complete understanding of the complex interplay between these factors and their relative importance in different contexts is still needed.…”

Section: Introductionmentioning

confidence: 99%

Causes of Unintended Abortion: Evidence from Bangladesh

Islam,

Biswas

2024

Act Sci wom hea

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Background: Unintended abortion remains a critical public health issue globally, necessitating a comprehensive understanding of its determinants, particularly in regions like Bangladesh with high prevalence rates. Aim:The study aims to identify the key sociodemographic, economic, and healthcare-related factors associated with unintended abortion in Bangladesh and to assess the relationship between these factors and the utilization of family planning or birth control methods.Methods: This study used quantitative methods with data from the Bangladesh Demographic and Health Survey 2014. Variables were measured quantitatively, and multiple regression analysis was employed. The sample included 17,300 households, with 3,830 married women who had terminated pregnancies. Sampling was two-staged, resulting in 18,000 households. Multiple linear regression identified factors influencing unintended abortion, detailed in the Results section.Result: Findings show the highest rates among women under 18 (61.31%) and 19-29 (37.61%), declining with age (Table 1), and higher rates among women with secondary or no education (28.5% and 27.2%, respectively) compared to those with higher education (8.0%) (Table 2). Additionally, non-agricultural workers (24.84%) and construction workers (23.59%) experience higher termination rates (Table 3). A U-shaped association is observed between marriage to first birth interval and unintended abortion (Figure 1), while contraceptive users have lower termination rates (23.24%) compared to non-users (76.76%) (Table 4). Regression analysis identifies age (t = -5.010, p < 0.001), type of place (t = 16.494, p < 0.001), and marriage to first birth interval (t = 2.341, p = 0.019) as significant predictors of unintended abortion, with corresponding t-values and significance levels detailed in Table 5. Conclusion:Our study identified key sociodemographic, economic, and healthcare factors associated with unintended abortion in Bangladesh. Younger age, lower education, demanding occupations, and specific birth intervals were associated with higher risk, while consistent contraceptive use was protective.

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Factors associated with duration of breastfeeding in Bangladesh: evidence from Bangladesh demographic and health survey 2014

Cited by 10 publications

References 31 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

Determinants of Exclusive Breastfeeding and Mixed Feeding Among Mothers of Infants in Dubai and Sharjah, United Arab Emirates

Causes of Unintended Abortion: Evidence from Bangladesh

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