Disaggregated level child morbidity in Zambia: an application of small area estimation method.

Abstract

Background: High rates of child morbidity and developmental challenges among children under five remain critical challenges in sub-Saharan Africa. Despite Zambia's progress in reducing under-five morbidity, the rates remain high, with provincial-level disparities. These disparities are likely to be more pronounced at finer geographic levels, such as districts. However, demographic health surveys, designed for national and provincial estimates, lack sufficient data to produce reliable district-level morbidity statistics.

Objective: This study investigates the geospatial distribution of child morbidity prevalence across disaggregated administrative units using small area estimation (SAE) methods.

Data and methods: Data from the 2018 Zambia Demographic and Health Survey and the 2010 Zambian Census were used to derive direct estimates of child morbidity for small domains cross-classified by district and age group. A hierarchical Bayesian SAE model was developed to account for spatial and unobserved heterogeneity at provincial and district levels, including cross-classifications by age group.

Results:  Model-based estimates show lower standard errors compared to the direct estimates and significant differences in morbidity levels within and between districts and provinces. Under-five morbidity prevalence remains high at 25%, with the highest rates in Luapula (approximately 40%) and Western provinces (around 35%) and among children aged 11-23 months (nearly 40%). SAE estimates at the district and district-by-age levels were numerically consistent when aggregated to higher levels, such as province or child age group.

Conclusion: These data-driven detailed level estimates provide critical insights into the spatial distribution of child morbidity, supporting targeted interventions and informed policymaking at disaggregated levels.