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Cultural and Contextual Drivers of Triple Burden of Malnutrition among Children in India

This study examines malnutrition’s triple burden, including anaemia, overweight, and stunting, among children aged 6–59 months. Using data from the National Family Health Survey-5 (2019–2021), the study identifies risk factors and assesses their contribution at different levels to existing malnutrit...

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Detalles Bibliográficos
Autores principales: Singh, Shri Kant, Chauhan, Alka, Sharma, Santosh Kumar, Puri, Parul, Pedgaonkar, Sarang, Dwivedi, Laxmi Kant, Taillie, Lindsey Smith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10420920/
https://www.ncbi.nlm.nih.gov/pubmed/37571415
http://dx.doi.org/10.3390/nu15153478
Descripción
Sumario:This study examines malnutrition’s triple burden, including anaemia, overweight, and stunting, among children aged 6–59 months. Using data from the National Family Health Survey-5 (2019–2021), the study identifies risk factors and assesses their contribution at different levels to existing malnutrition burden. A random intercept multilevel logistic regression model and spatial analysis are employed to identify child, maternal, and household level risk factors for stunting, overweight, and anaemia. The study finds that 34% of children were stunted, 4% were overweight, and 66% were anaemic. Stunting and anaemia prevalence were higher in central and eastern regions, while overweight was more prevalent in the north-eastern and northern regions. At the macro-level, the coexistence of stunting, overweight, and anaemia circumstantiates the triple burden of childhood malnutrition with substantial spatial variation (Moran’s I: stunting-0.53, overweight-0.41, and anaemia-0.53). Multilevel analysis reveals that child, maternal, and household variables play a substantial role in determining malnutrition burden in India. The nutritional health is significantly influenced by a wide range of determinants, necessitating multilevel treatments targeting households to address this diverse group of coexisting factors. Given the intra-country spatial heterogeneity, the treatment also needs to be tailor-made for various disaggregated levels.