Spatial Machine Learning for Exploring the Variability in Low Height‐For‐Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda

Abstract

Abstract Childhood stunting is a serious public health concern in Rwanda. Although stunting causes have been documented, we still lack a more in‐depth understanding of their local factors at a more detailed geographic level. We cross‐sectionally examined 615 height‐for‐age prevalence observations in the Northern Province of Rwanda, linked with their related covariates, to explore the spatial heterogeneity in the low height‐for‐age prevalence by fitting linear and non‐linear spatial regression models and explainable machine learning. Specifically, complemented with generalized additive models, we fitted the ordinary least squares (OLS), a standard geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models to characterize the imbalanced distribution of stunting risk factors and uncover the nonlinear effect of significant predictors, explaining the height‐for‐age variations. The results reveal that 27% of the children measured were stunted, and that likelihood was found to be higher in the districts of Musanze, Gakenke, and Gicumbi. The local MGWR model outperformed the ordinary GWR and OLS, with coefficients of determination of 0.89, 0.84, and 0.25, respectively. At specific ranges, the study shows that height‐for‐age decreases with an increase in the number of days a child was left alone, elevation, and rainfall. In contrast, land surface temperature is positively associated with height‐for‐age. However, variables like the normalized difference vegetation index, slope, soil fertility, and urbanicity exhibited bell‐shaped and U‐shaped non‐linear associations with the height‐for‐age prevalence. Identifying areas with the highest rates of stunting will help determine the most effective measures for reducing the burden of undernutrition.

Keywords

• height‐for‐age
• spatial machine learning
• explainable artificial intelligence
• local nonlinear effect