Prevalence of child stunting in Sub-Saharan Africa and its risk factors

Quamme, Siri Hundstad; Iversen, Per Ole

doi:10.1016/j.nutos.2022.01.009

Cited by 84 publications

(72 citation statements)

References 45 publications

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“…It is evident that the food available must be distributed to all household members, which raises the risk of under-nutrition in less resourceful households. Living many household sizes together puts a greater strain on caregivers and household resources as compared to a singleton 37 .…”

Section: Discussionmentioning

confidence: 99%

Patterns, prevalence and determinants of stunting among children aged 1-15 years

Wake

Zewotir

2022

Preprint

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Stunting increases morbidity and mortality, hindering mental development and influencing cognitive capacity of children. This study aimed to examine the trends and determinants of stunting from infancy to middle adolescence in four countries: Ethiopia, India, Peru, and Vietnam. A 15-year longitudinal data on the prevalence of stunting were obtained from the Young Lives cohort study. The study includes 38361 observations from 4 countries in between 2002 and 2016. A generalized mixed-effects model was adopted to estimate the determinant of stunting. The patterns of stunting in children from 2002 to 2016 have declined from an estimated 30% in 2002 to 20% in 2016. Stunting prevalence varied among four low- and middle-income countries with children in Ethiopia, India, and Peru being more stunted compared to children in Vietnam. The highest stunted was recorded in India and the lower was recorded in Vietnam. In all four countries, the highest prevalence of severe stunting was observed in 2002 and moderate stunting was observed in 2006. Parents' education level played a significance role in determining child stunting. Children of uneducated parents were shown to be at a higher risk of stunting. The pattern of stunting was reduced in four low- and middle-income countries from 2002 to 2016. Disparities of stunting were observed between- and within-country of low- and middle-income with the highest prevalence recorded in low-income country. Child stunting is caused by factors related to child's age, household wealth, household size, the mother's and father's education level, residence area and access to save drinking water.

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Section: Discussionmentioning

confidence: 99%

Patterns, prevalence and determinants of stunting among children aged 1-15 years

Wake

Zewotir

2022

Preprint

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“…Between 2000 and 2020, the global prevalence of stunting fell from about 30.3% to 22% [ 2 ]. Despite the fall in the magnitude of stunting, the prevalence of stunting has remained high in the sub-Saharan region [ 3 ]. In Zambia, the prevalence of stunting among children under five was estimated to be 35% in 2019 [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

et al. 2022

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Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. About 2327 (34.2%) children were stunted. Thirteen of fifty-eight features were selected for inclusion in the model using random forest. Calibrating the predicted probabilities improved the performance of machine learning algorithms when evaluated using calibration curves. RF was the most accurate algorithm, with an accuracy score of 79% in the testing and 61.6% in the training data while Naïve Bayesian was the worst performing algorithm for predicting stunting among children under five in Zambia using the 2018 ZDHS dataset. ML models aids quick diagnosis of stunting and the timely development of interventions aimed at preventing stunting.

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“…An increase in the number of residents should be anticipated in many areas, including the health sector, especially the problem of stunting. Stunting that often occurs in children causes impaired height and lack of absorption of nutrients [3]. Stunting is a common problem in many countries globally and occurs in 161 million children between 0 and 5 [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of linear model on stunting prevalence with machine learning approach

Mambang

Marleny²,

Zulfadhilah³

2023

Bulletin EEI

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An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

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Prevalence of child stunting in Sub-Saharan Africa and its risk factors

Cited by 84 publications

References 45 publications

Patterns, prevalence and determinants of stunting among children aged 1-15 years

Patterns, prevalence and determinants of stunting among children aged 1-15 years

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia

Prediction of linear model on stunting prevalence with machine learning approach

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