Investigating the spatial variation and risk factors of childhood anaemia in four sub-Saharan African countries

Abstract

Background: The causes of childhood anaemia are multifactorial, interrelated and complex. Such causes vary from country to country, and within a country. Thus, strategies for anaemia control should be tailored to local conditions and take into account the specific etiology and prevalence of anaemia in a given setting and sub-population. In addition, policies and programmes for anaemia control that do not account for the spatial heterogeneity of anaemia in children may result in certain sub-populations being excluded, limiting the effectiveness of the programmes. This study investigated the demographic and socio-economic determinants as well as the spatial variation of anaemia in children aged 6 to 59 months in Kenya, Malawi, Tanzania and Uganda.

Methods: The study made use of data collected from nationally representative Malaria Indicator Surveys (MIS) and Demographic and Health Surveys (DHS) conducted in all four countries between 2015 and 2017. During these surveys, all children under the age of five years old in the sampled households were tested for malaria and anaemia. A child's anaemia status was based on the World Health Organization's cut-off points where a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11 g/dL. The explanatory variables considered comprised of individual, household and cluster level factors, including the child's malaria status. A multivariable hierarchical Bayesian geoadditive model was used which included a spatial effect for district of child's residence.

Results: Prevalence of childhood anaemia ranged from 36.4% to 61.9% across the four countries. Children with a positive malaria result had a significantly higher odds of anaemia [AOR = 4.401; 95% CrI: (3.979, 4.871)]. After adjusting for a child's malaria status and other demographic, socio-economic and environmental factors, the study revealed distinct spatial variation in childhood anaemia within and between Malawi, Uganda and Tanzania. The spatial variation appeared predominantly due to unmeasured district-specific factors that do not transcend boundaries.

Conclusions: Anaemia control measures in Malawi, Tanzania and Uganda need to account for internal spatial heterogeneity evident in these countries. Efforts in assessing the local district-specific causes of childhood anaemia within each country should be focused on.

Keywords: Adjusted odds ratio; Anaemia; Bayesian; Child; Haemoglobin level; Hierarchical geoadditive model; Spatial effect.

Fig. 1

Potential risk factors of childhood anaemia considered in this study

Fig. 2

Estimated non-linear effects of child’s age in months on the log-odds of anaemia. The posterior mean together with the 95% credible intervals are shown

Fig. 3

Estimated posterior means of the structured spatial effect (left) and the unstructured spatial effect (right) on the log-odds of anaemia (criss-cross pattern indicates water bodies; diagonal lines indicate districts with no data available) This figure is based on the results of this study and makes use of shapefiles freely available from the Spatial Data Repository (https://spatialdata.dhsprogram.com/boundaries)