Undernutrition in children aged 0-59 months by region and over time: secondary analysis of the Burkina Faso 2012-2018 National Nutrition Surveys

The global burden of undernutrition remains high, responsible for significant under-five mortality in resource-limited settings. Numerous sustainable development goals (SDGs) are linked to nutrition, and nationally representative nutrition surveillance is a key activity to track progress towards SDGs and guide efficient programmes. Objectives The aim of this study is to look at spatial and temporal trends in undernutrition in children under 5years age in Burkina Faso. Setting We used data from annual National Nutrition Surveys using Standardised Monitoring and Assessment of Relief and Transitions methodology (anthropometry, morbidity) over 7years (2012-2018) in Burkina Faso. Participants Children of under 5years from households selected through systemic sampling at countrywide level. Main outcome measures Prevalence of stunting (height-for-age z-score, <-2), underweight (weight-for-age z-score, <-2) and wasting (weight-for-height z-score, <-2) at regional and national. We used general linear mixed models, adjusted by age, survey year, sex, presence of fever and/or diarrhoea, and poverty index to quantify the risk of undernutrition over time and by region of residence. Between 2012 and 2018, decreases were observed overall in the prevalence of growth retardation (stunting) decreased from 33.0% (95% CI 32.3 to 33.8) in 2012 to 26.7% (95% CI 26.2 to 27.3) in 2018. Underweight reduced from 24.4% (95% CI 23.7 to 25.1) to 18.7% (95% CI 18.2 to 19.2) for the same period and wasting decreased from 10.8% (95% CI 10.3 to 11.3) in 2012 to 8.4% (95% CI 8.1 to 8.8) in 2018. However, there was substantial variation across the country, with increased risk of undernutrition in the regions of Sahel, East and Cascades primarily. High-risk regions were characterised by a lower poverty index and limited access to healthcare services. Conclusions Our findings could inform national policymakers in refining and optimising resource allocation based on the identification of high-risk areas.