Spatial pattern and determinants of anaemia in Ethiopia

• Published in: PloS one Vol. 13; no. 5; p. e0197171
• Main Authors: Ejigu, Bedilu Alamirie, Wencheko, Eshetu, Berhane, Kiros
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.05.2018; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Age; Analysis; Anemia; Anemia - epidemiology; Biology and Life Sciences; Blood cells; Body mass; Computer and Information Sciences; Data analysis; Data processing; Demographic aspects; Demographics; Determinants; Earth Sciences; Erythrocytes; Ethiopia - epidemiology; Female; Females; Geospatial data; Health care; Hemoglobin; Humans; Malaria; Male; Males; Malnutrition; Medicine and Health Sciences; Middle Aged; Modelling; Neighborhoods; Nutrition; Parasites; People and Places; Plasmodium falciparum; Pregnancy; Prevalence; Preventive medicine; Public health; Red blood cells; Risk Factors; Rural areas; Social aspects; Social Sciences; Spatial Analysis; Spatial distribution; Statistical analysis; Studies; Surveys; Variables; Womens health; Young Adult; United States; Ethiopia; United States > US; Africa; Addis Ababa Ethiopia
• DOI: 10.1371/journal.pone.0197171
• ISSN: 1932-6203

Anaemia is a condition in which the haemoglobin concentration falls below an established cut-off value due to a decrease in the number and size of red blood cells. The current study aimed (i) to assess the spatial pattern and (ii) to identify determinants related to anaemia using the third Ethiopian demographic health survey. To achieve these objectives, this study took into account the sampling weight and the clustered nature of the data. As a result, multilevel modeling has been used in the statistical analysis. The analysis included complete cases from 15,909 females, and 13,903 males. Among all subjects who agreed for hemeoglobin test, 5.22% males, and 16.60% females were anemic. In both binary and ordinal outcome modeling approaches, educational level, age, wealth index, BMI and HIV status were found to be significant predictors of anaemia prevalence. Furthermore, this study applied spatial methods to generate maps at regional level which could be useful for policy makers where great efforts should be concentrated to reduce the prevalence of anaemia. As revealed by Moran's I test, significant spatial autocorrelation were noted across clusters. The risk of anaemia was found to vary across different regions, and higher prevalences were observed in Somali and Affar regions.