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BACKGROUND: Stunting among children under five years old is associated with long-term effects on cognitive development, school achievement, economic productivity in adulthood and maternal reproductive outcomes. Accurate estimation of stunting and tools to forecast risk are key to planning interventions. We estimated the prevalence and distribution of stunting among children under five years in Somalia from 2007 to 2010 and explored the role of environmental covariates in its forecasting. METHODS: Data from household nutritional surveys in Somalia from 2007 to 2010 with a total of 1,066 clusters covering 73,778 children were included. We developed a Bayesian hierarchical space-time model to forecast stunting by using the relationship between observed stunting and environmental covariates in the preceding years. We then applied the model coefficients to environmental covariates in subsequent years. To determine the accuracy of the forecasting, we compared this model with a model that used data from all the years with the corresponding environmental covariates. RESULTS: Rainfall (OR = 0.994, 95 % Credible interval (CrI): 0.993, 0.995) and vegetation cover (OR = 0.719, 95 % CrI: 0.603, 0.858) were significant in forecasting stunting. The difference in estimates of stunting using the two approaches was less than 3 % in all the regions for all forecast years. CONCLUSION: Stunting in Somalia is spatially and temporally heterogeneous. Rainfall and vegetation are major drivers of these variations. The use of environmental covariates for forecasting of stunting is a potentially useful and affordable tool for planning interventions to reduce the high burden of malnutrition in Somalia.

Original publication

DOI

10.1186/s12889-016-3320-6

Type

Journal article

Journal

BMC Public Health

Publication Date

28/07/2016

Volume

16

Keywords

Forecasting, Malnutrition, Somalia, Stunting, Bayes Theorem, Child, Preschool, Cross-Sectional Studies, Environment, Family Characteristics, Female, Forecasting, Growth Disorders, Humans, Male, Nutrition Surveys, Prevalence, Rain, Risk Factors, Somalia, Spatio-Temporal Analysis