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Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16
India accounts for approximately one third of the world's total population of stunted preschoolers. Addressing global undernutrition, therefore, requires an understanding of the determinants of stunting across India's diverse states and districts. We created a district‐level aggregate data...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175441/ https://www.ncbi.nlm.nih.gov/pubmed/29797455 http://dx.doi.org/10.1111/mcn.12620 |
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author | Menon, Purnima Headey, Derek Avula, Rasmi Nguyen, Phuong Hong |
author_facet | Menon, Purnima Headey, Derek Avula, Rasmi Nguyen, Phuong Hong |
author_sort | Menon, Purnima |
collection | PubMed |
description | India accounts for approximately one third of the world's total population of stunted preschoolers. Addressing global undernutrition, therefore, requires an understanding of the determinants of stunting across India's diverse states and districts. We created a district‐level aggregate data set from the recently released 2015–2016 National and Family Health Survey, which covered 601,509 households in 640 districts. We used mapping and descriptive analyses to understand spatial differences in distribution of stunting. We then used population‐weighted regressions to identify stunting determinants and regression‐based decompositions to explain differences between high‐ and low‐stunting districts across India. Stunting prevalence is high (38.4%) and varies considerably across districts (range: 12.4% to 65.1%), with 239 of the 640 districts have stunting levels above 40% and 202 have prevalence of 30–40%. High‐stunting districts are heavily clustered in the north and centre of the country. Differences in stunting prevalence between low and high burden districts were explained by differences in women's low body mass index (19% of the difference), education (12%), children's adequate diet (9%), assets (7%), open defecation (7%), age at marriage (7%), antenatal care (6%), and household size (5%). The decomposition models explained 71% of the observed difference in stunting prevalence. Our findings emphasize the variability in stunting across India, reinforce the multifactorial determinants of stunting, and highlight that interdistrict differences in stunting are strongly explained by a multitude of economic, health, hygiene, and demographic factors. A nationwide focus for stunting prevention is required, while addressing critical determinants district‐by‐district to reduce inequalities and prevalence of childhood stunting. |
format | Online Article Text |
id | pubmed-6175441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61754412018-10-19 Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 Menon, Purnima Headey, Derek Avula, Rasmi Nguyen, Phuong Hong Matern Child Nutr Original Articles India accounts for approximately one third of the world's total population of stunted preschoolers. Addressing global undernutrition, therefore, requires an understanding of the determinants of stunting across India's diverse states and districts. We created a district‐level aggregate data set from the recently released 2015–2016 National and Family Health Survey, which covered 601,509 households in 640 districts. We used mapping and descriptive analyses to understand spatial differences in distribution of stunting. We then used population‐weighted regressions to identify stunting determinants and regression‐based decompositions to explain differences between high‐ and low‐stunting districts across India. Stunting prevalence is high (38.4%) and varies considerably across districts (range: 12.4% to 65.1%), with 239 of the 640 districts have stunting levels above 40% and 202 have prevalence of 30–40%. High‐stunting districts are heavily clustered in the north and centre of the country. Differences in stunting prevalence between low and high burden districts were explained by differences in women's low body mass index (19% of the difference), education (12%), children's adequate diet (9%), assets (7%), open defecation (7%), age at marriage (7%), antenatal care (6%), and household size (5%). The decomposition models explained 71% of the observed difference in stunting prevalence. Our findings emphasize the variability in stunting across India, reinforce the multifactorial determinants of stunting, and highlight that interdistrict differences in stunting are strongly explained by a multitude of economic, health, hygiene, and demographic factors. A nationwide focus for stunting prevention is required, while addressing critical determinants district‐by‐district to reduce inequalities and prevalence of childhood stunting. John Wiley and Sons Inc. 2018-05-23 /pmc/articles/PMC6175441/ /pubmed/29797455 http://dx.doi.org/10.1111/mcn.12620 Text en © 2018 The Authors. Maternal and Child Nutrition Published by John Wiley & Sons, Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Menon, Purnima Headey, Derek Avula, Rasmi Nguyen, Phuong Hong Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title | Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title_full | Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title_fullStr | Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title_full_unstemmed | Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title_short | Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16 |
title_sort | understanding the geographical burden of stunting in india: a regression‐decomposition analysis of district‐level data from 2015–16 |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6175441/ https://www.ncbi.nlm.nih.gov/pubmed/29797455 http://dx.doi.org/10.1111/mcn.12620 |
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