The determinants of growth failure in children under five in 25 low- and middle-income countries

Jiang, Stephen; Sung, Jennifer; Sawhney, Rakshat; Cai, Jinxuan; Xu, Hengyong; Ng, Shu‐Kay; Sun, Jing

doi:10.7189/jogh.13.04077

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…A Bayesian geostatistical analysis done in Ethiopia found that wealth status as a significant predictor of stunting among under-five children [ 13 ]. The underlying reasons for the increased prevalence of growth failure in children from low-income households can be attributed to insufficient food intake, a higher likelihood of illness, and limited access to essential healthcare services [ 58 , 59 ]. It is widely acknowledged that children from low-income households are at a greater risk of growth failure due to inadequate nutrition, which has a direct impact on their overall development and well-being.…”

Section: Discussionmentioning

confidence: 99%

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis

Seifu,

Tesema,

Fentie

et al. 2024

PLoS ONE

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Introduction Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. Method The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. Result The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6–23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother’s age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. Conclusion In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

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Section: Discussionmentioning

confidence: 99%

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis

Seifu,

Tesema,

Fentie

et al. 2024

PLoS ONE

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“…Additionally, many studies find that infant morbidities like diarrhea or pneumonia can impair infant growth 6 , 21 . Current evidence indicates that infant feeding patterns such as longer duration of exclusive or partial breastfeeding tended to be associated with healthier growth patterns during infancy (i.e., slower growth rate and earlier peak BMI in developed settings 7 , 22 )) and a reduced risk of overweight and obesity at ages 2 years and older 23 . Similarly, some but not all observational studies of term infants fed HM have reported enhanced brain development (through imaging studies), higher intelligence quotient scores and increased cognitive and behavioral outcomes (using validated scales) compared to formula-fed infants 8 , 24 – 28 , although this benefit has not been linked to any particular HM component.…”

Section: Introductionmentioning

confidence: 99%

Multimodal machine learning for modeling infant head circumference, mothers’ milk composition, and their shared environment

Becker,

Fehr,

Goguen

et al. 2024

Sci Rep

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Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e−05) and maternal intake of fish (p = 4.1e−03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.

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Malnutrition: A Longstanding Challenge in India

Saini,

Bajpai

2024

CNF

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According to the World Health Organization, “malnutrition is the “deficiencies, excesses, or imbalances in a person’s intake of energy and/or nutrients”. Indian population has the highest rate of adolescent malnutrition. Deficiency, Illness, Small level of education, and a range of other factors contribute to India's malnutrition crisis. Undernutrition, micronutrient deficiency conditions, and overnutrition are all forms of malnutrition. A malnourished always lags in every aspect of life which will eventually affect the productivity of the country. In the 2023 Global Hunger Index, India is positioned at 111th among 125 countries, reflecting a serious level of hunger with a score of 28.7. India is grappling with a substantial population affected by anemia, malnutrition, and obesity. Other than this Indian population is also affected by several other micronutrient deficiencies. The present article provides an overview of the triple burden of malnutrition in India by applying the literature review method and the data were collected from online sources such as government survey reports, articles, and publications related to nutrition, healthcare status, healthcare indicators, nutritional indicators, and healthcare survey reports, etc. A brief introduction of the strategies followed by the Government as well as global health organisations to combat the issue is also discussed.

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The determinants of growth failure in children under five in 25 low- and middle-income countries

Cited by 3 publications

References 39 publications

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis

Multimodal machine learning for modeling infant head circumference, mothers’ milk composition, and their shared environment

Malnutrition: A Longstanding Challenge in India

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