Why are some vulnerable children healthy and others stunted? A case study of vulnerability and resilience among very young children in West Africa

Wednesday, 17 December 2014: 4:24 PM
Kathryn Grace, University of Utah, Salt Lake City, UT, United States and Nicholas Nagle, University of Tennessee, Knoxville, TN, United States
Stunting, when children are shorter than average for their age, poses serious problems for short- and long-term development of individuals, families and communities. Stunting is linked to increase risk or illness or death, reduced educational attainment, reduced earnings and increases the likelihood (for girls) that the next generation of children will be stunted. Stunting occurs as a result of a culmination of inadequate food/calories, experiences with frequent illness, poor care and low weight at birth. Because almost 40% of children under 5 in the developing world suffer from stunting, understanding the community, household and individual components that lead to stunting are vital as these countries aim to improve children’s health and development.

We focus this research on childhood stunting in the neighboring countries of Mali and Burkina Faso, two of the poorest and least developed countries in the world. The populations of both countries are heavily reliant on subsistence farming and the share of children under 5 who are stunted hovers around 30%. In this research we aim to explore child stunting with attention to biology, behavior and environment. Specifically we aim to determine why children in some food insecure communities are stunted while others in the same community are healthy and, as an extension, why some low birth weight babies grow into healthy children and others are stunted.

Because of the significance of food and nutrition on stunting outcomes, and because no micro-level estimates of food production exist, we use high resolution remotely sensed imagery (~1m) combined with coarser resolution landscape data (rainfall, slope, Normalized Difference Vegetation Index) to estimate community level food production for each year of the child’s life. We construct a multi-level analysis through the linking of food production data to other community features gathered from Demographic and Health Survey and smaller scale community surveys gathered by USAID’s WA-WASH. We will include type of water source, distance to a road, presence of a health facility, share of educated individuals, as well as maternal height/weight, parental socio-economic characteristics, household size/assets. Finally, we will evaluate the impact of specific characteristics of the child – age, sex, health history, birth order.