Spatial distributions and determinants of anaemia among adolescent girls in Ethiopia: a secondary analysis of EDHS 2016 – a cross-sectional study

OBJECTIVE: This study aimed to investigate the spatial distributions and determinants of anaemia among adolescent girls in Ethiopia. Exploring the spatial epidemiology of anaemia and identifying the risk factors would inform policymakers to come up with evidence-based prevention strategies for anaemia, especially in adolescent girls, who are the most affected segment of the population. METHODS: Secondary analysis of the Ethiopian Demographic and Health Survey 2016 was conducted. A total of 3172 adolescents were included in the analysis. The Bernoulli model was fitted using SaTScan V.9.6 to identify hotspot areas and the geospatial pattern and prediction of anaemia were mapped using ArcGIS V.10.8. A multilevel logistic regression model was fitted to identify factors associated with anaemia among adolescent girls. Adjusted OR with 95% CI was calculated and variables having a p value less than 0.05 were statistically significant factors of anaemia. RESULT: The overall prevalence of anaemia among adolescent girls in Ethiopia was 23.8 (22.4 to 25.3), with significant spatial variations across the country. The SaTScan analysis identified a primary cluster in the eastern, northeastern and southeastern parts of Ethiopia (loglikelihood ratio=39, p<0.001). High anaemia prevalence was observed in eastern parts of the country. In the multivariable multilevel logistic regression analysis, no formal education (adjusted OR (AOR)=1.49, 95% CI 1.05 to 2.12), Afar (AOR=3.36, 95% CI 1.87 to 6.05), Somali (AOR=4.63, 95% CI 2.61 to 8.23), Harari (AOR=1.90, 95% CI 1.32 to 4.10), Dire Dawa (AOR=2.32, 95% CI 1.32 to 4.10) and high cluster altitude (AOR=1.37, 95% CI 1.03 to 1.82) were significantly associated with anaemia. CONCLUSION: The national distributions of anaemia varied substantially across Ethiopia. Educational status, region and cluster altitude were significantly associated with anaemia in the multivariable logistic regression model. Thus, targeted public health interventions for adolescent girls should be implemented in the hotspot areas.