Spatial Distribution and Determinant Factors of Anaemia Among Adults Aged 15-59 in Ethiopia; Using Mixed-effects Ordinal Logistic Regression Model

Abstract

Background: Anaemia is a global public health problem particularly in developing countries. Assessing the geographical distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anaemia. Thus, the current study aimed to assess the spatial distribution and determinant factors of anaemia among adults aged 15-59 in Ethiopia.Methods: A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anaemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anaemia.Result: The spatial distribution of anaemia among adults age 15-59 was found to be clustered in Ethiopia (Global Moran’s I = 0.81, p value < 0.0001). In the multivariable mixed-effect ordinal regression analysis; being females [AOR = 1.53; 95% CI: 1.42, 1.66], never married [AOR = 0.86; 95% CI: 0.77, 0.96], higher educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anaemia among adults.Conclusions: A significant clustering of anaemia among adults aged 15-59 were found in Ethiopia and the significant hotspot areas with high clusters of anaemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, gender, marital status, educational level, place of residence, region, wealth index and body mass index (BMI) were significant predictors of anaemia. Therefore, effective public health intervention and nutritional education should be designed in the identified hotspot areas and risk groups to decrease the incidence of anaemia.