Climate predictors of child undernutrition: Insights from a machine learning model

Climate variability is increasingly recognized as a driver of child undernutrition, yet the non-linear relationships between specific climatic variables and nutrition remain unclear. This study uses machine learning to identify and quantify key climatic predictors of undernutrition among children.

In a mixed-method approach, a cross-sectional study assessed nutrition and child health outcomes in May 2024, while retrospective climate data was assessed spanning January 2022 to December 2023. The cross-sectional study recruited two hundred and seventy (270) children aged 6-23 months from rural areas in the Bosomtwe district. Anthropometry, hemoglobin concentrations, and food frequency were assessed using standard procedures. The collected data was standardized and subjected to principal component analyses to identify dietary patterns. Household food security was assessed using the USDA Household Food Security questionnaire, while the climate data was obtained from ERA5 reanalysis. Random forest algorithms were employed to evaluate the relative importance of various climatic factors in predicting undernutrition and morbidity. Decision trees were then derived from the models to examine interactions and thresholds.

Rainfall emerged as the most critical climate predictors of stunting, while severe acute malnutrition was more sensitive to shortwave radiations. Temperature was the top predictor of fever, anaemia and diarrhoea. Low rainfall and high temperature substantially increased the possibility of undernutrition and morbidity. Threshold effects showed that rainfall below 4.78 mm and temperature under 23.40°C, increased stunting risk, especially when SW radiation drops below 6.01 W/m2. For severe acute malnutrition, rainfall below 5.53 mm and temperatures above 27.67°C significantly increased the risk.

This study finds significant influences of climate variables on child undernutrition, highlighting the importance of integrated climate health strategies that account for compound climate effects. These findings can inform the development of early warning systems and targeted interventions to mitigate climate-related health risks in vulnerable populations.