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Water insecurity potentially undermines dietary diversity of children aged 6-23 months: Evidence from India.

Luseadra J McKerracherRoseanne SchusterAlexandra BrewisAmber Wutich
Published in: Maternal & child nutrition (2020)
Dietary diversity is a crucial pathway to child nutrition; lack of diversity may deprive children of critical macro and micronutrients. Though water along with hygiene and sanitation is a known driver of child undernutrition, a more direct role of household water in shaping dietary diversity remains unexplored. Existing literature provides a sound theoretical basis to expect that water could affect dietary diversity among young children. Here, we test the proposition that suboptimal household access to water and low regional water availability associate with lower dietary diversity among young children. Using the nationally representative 2015-2016 India Demographic and Health Survey data, we conducted a probit analysis on the sample of 69,841 children aged 6-23 months to predict the probability that a child achieves minimum standards of dietary diversity (MDD). After controlling for relevant socioeconomic and gender-related covariates, we found that children in household with suboptimal household water access were two percentage points less likely to achieve MDD, when compared with those from households with optimal water access. Children in high water availability regions had nine percentage points greater probability of achieving MDD compared with children from low water availability regions, accounting for household water access. As dietary diversity is central to nutrition, establishing the role of water access in shaping early childhood dietary diversity broadens the framework on how household material poverty shapes child malnutrition-independent of sanitation and hygiene pathways. This provides additional window for nutrition planning and intervention wherein water-based strategies can be leveraged in multiple ways.
Keyphrases
  • young adults
  • randomized controlled trial
  • mental health
  • major depressive disorder
  • systematic review
  • drinking water
  • machine learning
  • big data
  • artificial intelligence
  • deep learning