Paper in Communications Medicine: High-Resolution Spatial Prediction of Anemia Risk Among Children Aged 6 to 59 Months in Low- and Middle-Income Countries

Cite, Read

Anemia remains a significant public health concern, particularly in low- and middle-income countries (LMICs), where it affects millions of children under the age of five. In our latest study, we employ high-resolution Bayesian spatial models to predict anemia risk across 37 LMICs, using data from 750,000 childhood observations collected between 2005 and 2020.

Key Findings

• The prevalence of anemia remains alarmingly high, particularly in sub-Saharan Africa and South Asia.
• Despite some modest improvements, nearly 100 million children in each of these regions were still affected in 2020.
• Our probabilistic modeling approach allows for precise, high-resolution mapping, identifying regional disparities and hotspots of anemia prevalence.
• Socio-economic and environmental factors—such as household wealth, altitude, and temperature—play a crucial role in shaping anemia risk.
• The study provides actionable insights for policymakers to better target health interventions.

Why This Matters

Understanding the spatio-temporal dynamics of anemia is crucial for monitoring progress toward Sustainable Development Goals (SDG 2.2: Ending Malnutrition) and breaking the cycle of poverty and poor health. Our approach provides a robust framework for tracking anemia risk at a granular level, guiding resource allocation and intervention planning.