District Effect Appraisal in East Sub-Saharan Africa: Combating Childhood Anaemia.

Danielle J Roberts,Temesgen Zewotir

doi:10.1155/2019/1598920

Abstract

Background Anaemia in children is a significant health problem that receives little attention. This 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 The 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. There 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 The 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.

Highlights

Identifying significant factors associated with an increased risk of anaemia in children is relevant to developing appropriate and effective interventions
All children under the age of five years in the sampled households were tested for malaria and anaemia with the consent of a parent or guardian. e final dataset consisted of 18247 children from 1595 clusters with valid geographical coordinates within 370 administrative areas, which consisted of all 47 counties or districts for Kenya; 26 out of 28 districts for which data were available for Malawi; 176 out of 184 districts for which data were available for mainland Tanzania; and 121 out of 122 districts for which data were available for Uganda. ese administrative levels were chosen based on the levels for which public health decisions are made within each country
Out of the continuous covariates considered in this study, only the child’s age in months displayed a significant nonlinear effect on the logodds of anaemia; it was the only nonlinear effect incorporated into the generalised additive mixed models (GAMMs), with the rest of the covariates entered into the model as linear fixed effects

Summary

Introduction

Identifying significant factors associated with an increased risk of anaemia in children is relevant to developing 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]. 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.

Methods

Results

Discussion

Conclusion