Abstract
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.
Highlights
As a marker of poor nutrition, stunting in early childhood is strongly associated with numerous short‐term and long‐term consequences, including increased childhood morbidity and mortality (Black et al, 2013), delayed growth and motor development (Grantham‐McGregor et al, 2007), and long‐term educational and economic consequences (Dewey & Begum, 2011)
A more granular assessment of the differences in stunting across India's 640 districts is essential for targeting and planning purposes. We address this knowledge gap with an analysis of a new district‐level data set created to address three research questions: (a) How do stunting prevalence and absolute numbers of stunted children vary across Indian states and districts? (b) Which determinants of stunting are associated with district stunting
High‐stunting districts are characterized by lower levels of immediate and underlying determinants and low levels of nutrition‐specific intervention coverage
Summary
As a marker of poor nutrition, stunting in early childhood is strongly associated with numerous short‐term and long‐term consequences, including increased childhood morbidity and mortality (Black et al, 2013), delayed growth and motor development (Grantham‐McGregor et al, 2007), and long‐term educational and economic consequences (Dewey & Begum, 2011). 2 of 10 bs_bs_banner economic costs of stunting, the Sustainable Development Goals explicitly include reductions in global stunting, and many countries have adopted the World Health Assembly target of achieving a 40% reduction in stunting by 2025 Achieving this reduction on a global scale, requires rapid progress against stunting in India, which accounts for approximately one third of the world's total population of stunted preschoolers (De Onis, Blössner, & Borghi, 2011). We address this knowledge gap with an analysis of a new district‐level data set created to address three research questions: (a) How do stunting prevalence and absolute numbers of stunted children vary across Indian states and districts? India carries a high burden of child stunting, but lack of disaggregated stunting data at the district level has been a challenge for policy and program strategies in a decentralized governance system. Prevalence? and (c) Which determinants account for the variation in stunting observed across high‐ and low‐stunting districts?
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