Abstract:
Background
Malnutrition among under-five children is a common public health problem and it is one of
the main cause for the mortality of under-five children in developing countries, including Ethiopia. Therefore, lack of evidence about geographic heterogeneity and predictors of undernutrition hinders for evidence-based decision-making process for the prevention and control
programs of under-nutrition in Ethiopia. Thus, this study aimed to address this gap.
Methods
The data were obtained from the Ethiopian Demographic and Health Survey (EDHS) 2016.
A total of 9,384 under-five children nested in 645 clusters were included with a stratified twostage cluster sampling. ArcGIS version 10.5 software was used for global, local and ordinary
least square analysis and mapping. The spatial autocorrelation (Global Moran’s I) statistic
was held in order to assess the pattern of wasting, stunting, and underweight whether it was
dispersed, clustered, or randomly distributed. In addition, a Bernoulli model was used to
analyze the purely spatial cluster detection of under-nutrition indicators through SaTScan
version 9.6 software. Geographically weighted regression (GWR) version 4.0 software was
used to model spatial relationships in the GWR analysis. Finally, a statistical decision was
made at p-value<0.05 with 95%CI for ordinary least square analysis and geographically
weighted regression.
Main findings
Childhood under-nutrition showed geographical variations at zonal levels in Ethiopia.
Accordingly, Somali region (Afder, Gode, Korahe, Warder Zones), Afar region (Zone 2),Tigray region (Southern Zone), and Amhara region (Waghmira Zones) for wasting, Amhara
region (West Gojam, Awi, South Gondar, and Waghmira Zones) for stunting and Amhara
region (South Wollo, North Wollo, Awi, South Gondar, and Waghmira zones), Afar region
(Zone 2), Tigray region (Eastern Zone, North Western Zone, Central Zone, Southern Zone,
and Mekele Special Zones), and Benshangul region (Metekel and Assosa Zones) for underweight were detected as hot spot (high risk) regions. In GWR analysis, had unimproved toilet facility for stunting, wasting and underweight, father had primary education for stunting
and wasting, father had secondary education for stunting and underweight, mothers age
35–49 years for wasting and underweight, having female children for stunting, having children eight and above for wasting, and mother had primary education for underweight were
significant predictors at (p<0.001).
Conclusions
Our study showed that the spatial distribution of under-nutrition was clustered and high-risk
areas were identified in all forms of under-nutrition indicators. Predictors of under-nutrition
were identified in all forms of under-nutrition indicators. Thus, geographic-based nutritional
interventions mainly mobilizing additional resources could be held to reduce the burden of
childhood under-nutrition in hot spot areas. In addition, improving sanitation and hygiene
practice, improving the life style of the community, and promotion of parent education in the
identified hot spot zones for under-nutrition should be more emphasized
