Research Article District Effect Appraisal in East Sub-Saharan Africa: Combating Childhood Anaemia Danielle J. Roberts and Temesgen Zewotir School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa Correspondence should be addressed to Danielle J. Roberts; robertsd@ukzn.ac.za Received 25 July 2019; Revised 26 September 2019; Accepted 23 October 2019; Published 13 November 2019 Academic Editor: Duran Canatan Copyright © 2019 Danielle J. Roberts and Temesgen Zewotir. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Anaemia in children is a significant health problem that receives little attention. is study aimed at determining the factors significantly associated with anaemia in children aged 6 to 59 months in Kenya, Malawi, Tanzania, and Uganda while accounting for the spatial heterogeneity within and between the districts of the four countries. In addition, the performance of the districts with regard to their impact on anaemia was assessed and ranked. Methods. A generalised additive mixed model with a spatial effect based on the geographical coordinates of the clusters was used. A district-level random effect was included to further account for the heterogeneity as well as to rank the performance of the districts based on the best linear unbiased prediction (BLUP). Results. e results depicted significant spatial heterogeneity between and within the districts of the countries. After accounting for such spatial heterogeneity, child-level characteristics (gender, malaria test result, and mother’s highest education level), household-level characteristics (household size, household’s wealth index Z-score, the type of toilet facility available, and the type of place of residence), and the country of residence were found to be significantly associated with the child’s anaemia status. ere was a significant interaction between the type of place of residence and the country of residence. Based on the BLUP for the district-level random effect, the top 3 best- and worst-performing districts within each country were identified. Conclusion. e ranking of the performance of the districts allows for the worst-performing districts to be targeted for further research in order to improve their anaemia control strategies, as well as for the best-performing districts to be identified to further determine why they are performing better and then to use these districts as role models in efforts to overcome childhood anaemia. 1. Introduction Identifying significant factors associated with an in- creased risk of anaemia in children is relevant to de- veloping appropriate and effective interventions. Such studies aid in identifying subpopulations that are most at risk, which assists in creating a more efficient delivery system of limited national resources [1]. However, studies identifying these factors should account for spatial het- erogeneity and spatial autocorrelation in the observations. Failure to do so may produce inaccurate estimates and thus misleading results and ineffective anaemia control programs [2, 3]. Spatial autocorrelation arises when observations close in proximity tend to be more alike than those further apart and is present even if the observations have been recorded in a standardised way [4]. Spatial heterogeneity refers to the spatial variation or uneven distribution of attributes across a region [5]. Climatic and environmental factors, such as temperature, rainfall, and proximity to waterbodies, among others, are largely responsible for such spatial heterogeneity as its effects are usually only partially explained by the covariates that are available in a model [4]. Indeed, many other factors that vary geographically can also contribute to spatial heterogeneity in observations, such as the availability and distance to quality child health care, access to a rea- sonable transport system, culture, and the cost of living, all of which may not always be fully explained by the available covariates. Various methods of accounting for spatial au- tocorrelation and spatial heterogeneity have been well Hindawi Anemia Volume 2019, Article ID 1598920, 10 pages https://doi.org/10.1155/2019/1598920