Open this publication in new window or tab >>Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden; Department of Veterinary Medicine, College of Agriculture, Animal Science and Veterinary Medicine, University of Rwanda, Nyagatare, Rwanda.
Department of Veterinary Medicine, College of Agriculture, Animal Science and Veterinary Medicine, University of Rwanda, Nyagatare, Rwanda; Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences, Uppsala, Sweden.
College of Medicine and Health Sciences, School of Public Health, University of Rwanda, Kigali, Rwanda; Department of Public Health and Community Medicine, Institution of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Umeå University, Faculty of Medicine, Department of Clinical Sciences. College of Medicine and Health Sciences, School of Public Health, University of Rwanda, Kigali, Rwanda.
Department of Public Health and Community Medicine, Institution of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Pediatrics, Institution of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Department of Public Health and Community Medicine, Institution of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Umeå University, Faculty of Medicine, Department of Clinical Sciences.
Department of Earth and Environmental Sciences, GIS Centre, Lund University, Lund, Sweden.
Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Department of Applied Animal Science and Welfare, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Centre for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali, Rwanda.
College of Medicine and Health Sciences, School of Public Health, University of Rwanda, Kigali, Rwanda.
Department of Veterinary Medicine, College of Agriculture, Animal Science and Veterinary Medicine, University of Rwanda, Nyagatare, Rwanda.
Department of Earth and Environmental Sciences, GIS Centre, Lund University, Lund, Sweden.
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2026 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 21, no 2, article id e0343772Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
Public Library of Science (PLoS), 2026
National Category
Epidemiology Public Health, Global Health and Social Medicine
Identifiers
urn:nbn:se:umu:diva-250759 (URN)10.1371/journal.pone.0343772 (DOI)001702732000002 ()41746957 (PubMedID)2-s2.0-105031140817 (Scopus ID)
Funder
Sida - Swedish International Development Cooperation Agency, 11277
2026-03-122026-03-122026-03-12Bibliographically approved