Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method

Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years

BACKGROUND

Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.

METHODS

Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls.

RESULTS

The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities.

CONCLUSION

This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.

Statistical modelling of determinants of child stunting using secondary data and Bayesian networks: a UKRI Global Challenges Research Fund (GCRF) Action Against Stunting Hub protocol paper

INTRODUCTION

Several factors have been implicated in child stunting, but the precise determinants, mechanisms of action and causal pathways remain poorly understood. The objective of this study is to explore causal relationships between the various determinants of child stunting.

METHODS AND ANALYSIS

The study will use data compiled from national health surveys in India, Indonesia and Senegal, and reviews of published evidence on determinants of child stunting. The data will be analysed using a causal Bayesian network (BN)-an approach suitable for modelling interdependent networks of causal relationships. The model's structure will be defined in a directed acyclic graph and illustrate causal relationship between the variables (determinants) and outcome (child stunting). Conditional probability distributions will be generated to show the strength of direct causality between variables and outcome. BN will provide evidence of the causal role of the various determinants of child stunning, identify evidence gaps and support in-depth interrogation of the evidence base. Furthermore, the method will support integration of expert opinion/assumptions, allowing for inclusion of the many factors implicated in child stunting. The development of the BN model and its outputs will represent an ideal opportunity for transdisciplinary research on the determinants of stunting.

ETHICS AND DISSEMINATION

Not applicable/no human participants included.

Feature selection and association rule learning identify risk factors of malnutrition among Ethiopian schoolchildren

Introduction

Previous studies have sought to identify risk factors for malnutrition in populations of schoolchildren, depending on traditional logistic regression methods. However, holistic machine learning (ML) approaches are emerging that may provide a more comprehensive analysis of risk factors.

Methods

This study employed feature selection and association rule learning ML methods in conjunction with logistic regression on epidemiological survey data from 1,036 Ethiopian school children. Our first analysis used the entire dataset and then we reran this analysis on age, residence, and sex population subsets.

Results

Both logistic regression and ML methods identified older childhood age as a significant risk factor, while females and vaccinated individuals showed reduced odds of stunting. Our machine learning analyses provided additional insights into the data, as feature selection identified that age, school latrine cleanliness, large family size, and nail trimming habits were significant risk factors for stunting, underweight, and thinness. Association rule learning revealed an association between co-occurring hygiene and socio-economical variables with malnutrition that was otherwise missed using traditional statistical methods.

Discussion

Our analysis supports the benefit of integrating feature selection methods, association rules learning techniques, and logistic regression to identify comprehensive risk factors associated with malnutrition in young children.

Childhood stunting and subsequent educational outcomes: a marginal structural model analysis from a South African longitudinal study

OBJECTIVE

To examine the association between childhood stunting and grade completion (as educational outcome) in South Africa.

DESIGN

Longitudinal study. Data were obtained using the National Income Dynamics Study over five waves (2008 to 2017). Children were tracked at wave 1 in 2008 until wave 5 in 2017 to determine their total years of schooling. We controlled for time-variant and time-varying confounding with a marginal structural model to estimate the associations between childhood stunting and subsequent grade completion.

SETTING

Nationally representative study of South African households.

PARTICIPANTS

A total of 2629 children aged 2 and 3 years in 2008.

RESULTS

We observed a substantial decrease in the prevalence of stunting between wave 1 (28·2 %) and wave 4 (8·6 %). Our marginal structural model results suggest that childhood stunting was significantly associated with decreased odds (22 % less likely) of grade completion (OR = 0·78; 95 % CI: 0·40, 0·86; P = 0·015), while those who were only stunted during early childhood had a 29 % reduction in the odds of grade completion (OR = 0·71; 95 % CI: 0·51, 0·82; P = 0·020).

CONCLUSION

These findings underscore the fact that stunting is a significant predictor of academic achievement, whose effects might be long-lasting.

Parental Sociodemographic Factors Associated with Stunted Children below 5 Years of Age in Kampar Indonesia

BACKGROUND: The prevalence of stunted children under 5 years in Riau Province exceeds 27.35% and Kampar District contributed the highest prevalence rate (32.05%) compared to other districts in Riau Province. AIM: This study aims to analyze the parental sociodemographic factors of parents associated with stunting children in Kampar District, Riau Province in Indonesia. METHODS: This type of research is a case-control study on stunted children in Kampar Regency aged under 5 years. Control group was selected by matching process include age, gender, residence, and socioeconomic status. Anthropometric measurements performed and calculated using the World Health Organization Anthro (version 3.2.2, October 2020) include weight-for-age z-score (WAZ), height-for-age z-score (HAZ), weight-for-height z-score (WHZ), and body mass index. The analysis carried out includes univariate and bivariate analysis to find the relationship between the independent variable and the dependent variable. RESULTS: Approximately 139 children aged 2 to 59 months consist of stunted (68) and nonstunted (71) groups. Among the 68 stunted children, 31 (41.3%) were very stunted. The stunted group had decreased in WAZ, HAZ, and WHZ, but only HAZ was statistically significant (p < 0.05). Lower mother’s height and education were determined of parental sociodemographic factors associated with stunting and increased risk of stunted children in Kampar (odds ratio [OR] 3.02 and OR 2.50, 95% confidence interval, respectively). CONCLUSIONS: Lower maternal’s height and education were determine parental sociodemographic factors associated with stunting in Kampar.