Gender disparity in minimum dietary diversity failure among currently breastfed children aged 6-23 months in Bangladesh: evidence from Bangladesh Multiple Indicator Cluster Survey, 2019.

Research on children's dietary diversity plays a crucial role in designing effective health interventions. Thus, this study aimed to identify the factors contributing to minimum dietary diversity failure (MDDF) among male and female children aged 6-23 months in Bangladesh. The data for this study was obtained from the Bangladesh Multiple Indicator Cluster Survey, 2019, which included children currently breastfed within a specific age range. Multivariable binary logistic regression was employed to assess the strength and significance of the association. The findings revealed that approximately 59⋅4 % of children in Bangladesh experienced MDDF, with 57⋅8 % of male children and 61 % of female children affected. Proportion test uncovered a significant gender disparity (χ2=6⋅58, P-value = 0⋅01) among children aged 6-23 months. However, the multivariable binary logistic regression analysis revealed that both male and female children shared common risk factors for MDDF, which included child age, maternal educational status, wealth status, number of antenatal care visits, and division. In our study, we observed varied spatial patterns in minimal dietary diversity. Sherpur, Netrokona, Sunamganj, and Sylhet districts showed the highest failure rates. Notably, all are flood-affected areas, impacting food availability and diversity. For targeted regional development programmes, district mapping results may offer valuable insights to policymakers, especially in areas with a high prevalence of dietary diversity failure. By understanding these risk factors, policymakers and stakeholders can implement targeted strategies to improve dietary diversity among children, promoting better health and well-being for the young population in Bangladesh.

Double burden of malnutrition among women of reproductive age in Bangladesh: A comparative study of classical and Bayesian logistic regression approach.

Although the prevalence of undernutrition among women of reproductive age has declined in Bangladesh, the increase in the prevalence of overnutrition remains a major challenge. To achieve Sustainable Development Goal 2.2, it is important to identify the drivers of the double burden of malnutrition on women in Bangladesh. The Bangladesh Demographic and Health Survey, 2017-2018 was used to model the relationship between the double burden of malnutrition among women and the risk factors using a logistic regression model under the classical and Bayesian frameworks and performed the comparison between the regression models based on the narrowest confidence interval. Regarding the Bayesian application, the Metropolis-Hastings algorithm with two types of prior information (historical and noninformative prior) was used to simulate parameter estimates from the posterior distributions. The Boruta algorithm was used to determine the significant predictors. Almost half of reproductive aged women experienced a form of malnutrition (12% were underweight, 26.1% were overweight, and 6.8% were obese). In terms of the narrowest interval estimate, it was found that Bayesian logistic regression with informative priors performs better than the noninformative priors and the classical logistic regression model. Women who were older, highly educated, from rich families, unemployed, and from urban residences were more likely to experience the double burden of malnutrition. This study recommended using the historical prior as the informative prior rather than the flat/noninformative prior to estimating the parameter uncertainty if historical data are available. The double burden of malnutrition among women is a major public health challenge in Bangladesh. This study was to determine the impact of effective risk factors on the double burden of malnutrition among women by applying the Bayesian framework. Using both informative and noninformative priors, "historical prior" was proposed as informative prior information. The main strength is that the proposed prior (historical prior) provided improved estimation as compared to the flat prior distribution.

Rural-urban disparities in nutritional status among ever-married women in Bangladesh: A Blinder-Oaxaca decomposition approach.

This study aims to investigate socioeconomic disparities in nutritional status among ever-married women in Bangladesh and to break down urban-rural differences in the underlying causes of undernutrition. We utilized data from the Bangladesh Demographic and Health Survey 2017-18, a sample size of 18328 ever-married women, including 5170 from urban residences, and 13159 from rural residences. To explore socioeconomic inequality, we employed a concentration indexing measure, while a multiple binary logistic regression model was carried out to identify the determinants associated with the outcome variable. A Blinder-Oaxaca decomposition analysis was performed to decompose the urban-rural gap in women's nutritional status using associated factors. The prevalence of undernutrition among ever-married women in Bangladesh was 12 percent. Notably, this percentage varied by region, with urban residents accounting for 8.6% and rural residents accounting for 13.3%. Our findings confirmed that undernutrition was more prevalent among women with lower wealth indexes in Bangladesh, as indicated by the concentration index (CIX = -0.26). The multivariable analysis investigating the determinants of undernutrition status among ever-married women, with a focus on residence revealed significant associations with respondent age, education, marital status, mass media access, wealth status, and division. According to the Blinder-Oaxaca decomposition and its extension, the prevalence was significantly higher in rural residences of Bangladesh than in urban residences, and the endowment effect explained 86 percent of the total urban-rural difference in undernutrition prevalence. The results of this study indicate that the factors that influence women's nutritional status in rural areas play a significant role in the gap, and the majority of the gap is caused by education and economic position. In order to effectively promote maternal health policies in Bangladesh, intervention techniques should be created that are aimed at the population, that is, the poorest and least educated.

Application of a count regression model to identify the risk factors of under-five child morbidity in Bangladesh.

BACKGROUND: Bangladesh has seen a significant decline in child mortality in recent decades, but morbidity among children <5 y of age remains high. The aim of this analysis was to examine trends and identify risk factors related to child morbidity in Bangladesh. METHODS: This analysis is based on data from four successive cross-sectional Bangladesh Demographic and Health Surveys for the years 2007, 2011, 2014 and 2017-18. Several count regression models were fitted and the best model was used to identify risk factors associated with morbidity in children <5 y of age. RESULTS: According to the results of the trend analysis, the prevalence of non-symptomatic children increased and the prevalence of fever, diarrhoea and acute respiratory infections (ARIs) decreased over the years. The Vuong's non-nested test indicated that Poisson regression could be used as the best model. From the results of the Poisson regression model, child age, sex, underweight, wasted, stunting, maternal education, wealth status, religion and region were the important determinants associated with the risk of child morbidity. The risk was considerably higher among women with a primary education compared with women with a secondary or greater education in Bangladesh. CONCLUSIONS: This analysis concluded that child morbidity is still a major public health problem for Bangladesh. Thus it is important to take the necessary measures to reduce child morbidity (particularly fever, diarrhoea and ARI) by improving significant influencing factors.