ArticlePloS one2026
Spatial heterogeneity and spatially varying determinants of childhood stunting in Northern Rwanda: A cross-sectional study to inform targeted interventions.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Despite national progress, stunting remains prevalent in specific regions of Rwanda, highlighting the limitations of coarse-resolution data for effective mapping and intervention planning. This study explored optimal spatial resolution and analytical approach to capture localised dynamics and the multifactorial nature of stunting. A cross-sectional, population-based study was conducted in the Northern Province of Rwanda, focusing on children aged 1-36 months. Data were collected using structured questionnaires covering socio-demographic, economic, health, childcare, livestock factors and anthropometric measurements. Environmental characteristics were obtained from national datasets, while household geographic coordinates were captured using a customized mobile geodata platform (emGeo). After data cleaning, predictors were analysed using univariable and multivariable logistic regression as well as geographically weighted logistic regression (GWLR) to account for spatial heterogeneity. Among 601 children, stunting prevalence was 27% (boys 33.8%; girls 20.9%). GWLR improved model fit, increasing adjusted deviance explained from 34% to 39%. Significant predictors included child age (adjusted OR = 2.46; 95% CI: 1.78-3.39), male sex (OR = 2.83; 95% CI: 1.65-4.86), birthweight (OR = 0.71; 95% CI: 0.54-0.94), maternal autonomy (ability to refuse sexual intercourse; OR = 0.48; 95% CI: 0.27-0.86), inconsistent maternal social support (OR = 2.30; 95% CI: 1.20-4.42), household electricity access (OR = 0.48; 95% CI: 0.27-0.84) and handwashing facilities (OR = 0.21; 95% CI: 0.07-0.67). GWLR revealed substantial spatial heterogeneity in these factors, delineating areas where each factor matters most. This household-level, spatially explicit analysis reveals localised risk patterns often masked by aggregated national data. Prioritising context-specific interventions (such as electrification, hygiene promotion, and enhanced maternal social support), can enhance effectiveness. The proposed analytical workflow provides a model for addressing persistent stunting in other resource-limited settings.
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