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Publications Summary


Document Type
Further Analysis
Publication Topic(s)
Geographic Information, Nutrition, Wealth/Socioeconomics
Country(s)
Mali
Language
English
Recommended Citation
Benedict, Rukundo K., Benjamin K. Mayala, Jean de Dieu Bizimana, Ibrahima Cisse, Idrissa Diabaté, Kissia Sidibe. 2019. Geospatial Modelling of Changes and Inequality in Nutrition Status among Children in Mali: Further Analysis of the Mali Demographic and Health Surveys 2006-2018. DHS Further Analysis Reports No. 137. Rockville, Maryland, USA: ICF.
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Publication ID
FA137

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Abstract:

Background: Strengthening Mali’s multisectoral nutrition policies is key to achieving the Sustainable Development Goals for child malnutrition by 2030. Although the decline in stunting and wasting prevalence over the last decade is promising, we need to understand the reasons for the decline in order to create future policies and programmes. The aim of this study was to examine factors associated with stunting and wasting in Mali in 2006 and 2018 by using geospatial modelling techniques. Methods: We used Demographic and Health Survey data from 2006 and 2018, and converted key child, maternal, and household variables into geospatial covariates. These variables, together with selected environmental and demographic geospatial covariates, were used in a Bayesian geospatial model to provide estimates for stunting and wasting at the subnational level for 2006 and 2018, respectively. We computed the difference in malnutrition estimates between 2006 and 2018 and then determined the covariates associated with stunting and wasting. Results: The cercles-level maps show how stunting and wasting estimates varied across Mali and how the estimates changed between the two survey years. Results from the stunting models in 2006 and 2018 showed that children’s minimum dietary diversity, mother’s education, and mother’s body mass index were the leading factors associated with stunting. Children’s minimum dietary diversity and aridity were associated with wasting in both years. Conclusions: With a Bayesian geospatial modelling approach, this study generated subnational estimates of stunting and wasting in Mali, and identified key factors associated with undernutrition. This approach allows the Government of Mali to target programmatic efforts at lower administrative levels and to focus resources on complementary feeding, women’s nutrition, and environmental factors related to child undernutrition.

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