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Published on: January 19, 2024
District Effect Appraisal in East Sub-Saharan Africa: Combating Childhood Anaemia
Danielle J Roberts1, Temesgen Zewotir1
1School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Insights
Childhood anaemia in Kenya, Malawi, Tanzania, and Uganda is linked to spatial factors and child/household characteristics. Identifying best and worst performing districts can improve anaemia control strategies.
Area of Science:
- Public Health
- Epidemiology
- Spatial Analysis
Background:
- Childhood anaemia is a neglected global health issue.
- Significant spatial heterogeneity exists in anaemia prevalence within and between East African countries.
- Understanding contributing factors is crucial for effective intervention.
Purpose of the Study:
- To identify factors associated with childhood anaemia (6-59 months) in Kenya, Malawi, Tanzania, and Uganda.
- To account for spatial heterogeneity in anaemia prevalence.
- To assess and rank district-level performance in anaemia control.
Main Methods:
- A generalized additive mixed model with spatial effects was employed.
- Geographical coordinates of clusters were used to model spatial heterogeneity.
- District-level random effects and Best Linear Unbiased Prediction (BLUP) were used for ranking.
Main Results:
- Significant spatial heterogeneity in anaemia prevalence was observed.
- Child (gender, malaria status, maternal education), household (size, wealth, toilet, residence type), and country of residence were significant factors.
- A significant interaction between residence type and country was found.
Conclusions:
- District performance ranking aids targeted interventions for anaemia control.
- Best-performing districts can serve as models for improving anaemia strategies.
- Further research in worst-performing districts is recommended to enhance anaemia control.
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.
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