1<!DOCTYPE html> 2<html lang="en" xml:lang="en"> 3<head> 4 <meta charset="utf-8"> 5 <meta name="viewport" content="width=device-width, initial-scale=1.0"> 6 <title> 7 Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models 8 | Geospatial Health 9 </title> 10 11 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.7.2/css/all.min.css"> 12 13 14<link rel="icon" href="https://www.geospatialhealth.net/public/journals/1/favicon_en.png" /> 15<meta name="csrf-token" content="BOMwui92rFbqNVh7izehMBKxFeLSKvAxDyWovoxW" /> 16<meta name="generator" content="Open Journal Systems 3.5.0.5" /> 17<meta name="keywords" content="Geospatial Health, Geographical Information Systems (GIS), Remote Sensing, Global Positioning Systems (GPS), Spatial Statistics, Geospatial Tools, Human Health, Veterinary Health, Epidemiology, Spatial Analysis, Health Mapping, Disease Surveillance, Environmental Health, Public Health, Health Informatics, Health Geography, Spatial Epidemiology, Health Risk Assessment, Geospatial Data, Mapping Technologies, Geographic Patterns, Health Disparities, One Health, Data Visualization, Geostatistics, , Veterinary Health, Parasite Health, Geospatial Health, Spatial Statistics, Geospatial Tools, Animal Epidemiology, Parasite Mapping, Disease Surveillance, Spatial Analysis, One Health Approach, Veterinary Epidemiology, Environmental Impact on Animal Health, Health Risk Assessment in Animals, Livestock Health Monitoring, Vector-Borne Diseases, Spatial Distribution of Parasites, Animal Welfare and Health, Geospatial Data Analysis, Mapping Animal Diseases, Zoonotic Diseases, Geographic Patterns in Veterinary Health, Data Visualization in Veterinary Science, , Veterinary Epidemiology , Environmental Impact on Animal Health , Health Risk Assessment in Animals, Livestock Health Monitoring, Vector-Borne Diseases , Spatial Distribution of Parasites , Animal Welfare and Health , Geospatial Data Analysis , Mapping Animal Diseases , Zoonotic Diseases , Geographic Patterns in Veterinary Health, Data Visualization in Veterinary Science " /> 18<meta name="robots" content="all" /> 19<meta name="revisit-after" content="10" /> 20<meta name="language" content="en-US" /> 21<meta name="charset" content="us-ascii" /> 22<meta name="copyright" content="PAGEPress" /> 23<meta name="author" content="PGP" /> 24<meta name="reply-to" content="[email protected]" /> 25<link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> 26<meta name="DC.Contributor.Sponsor" xml:lang="en" content="This study was funded by the Swedish International Development Cooperation Agency (SIDA): SIDA contribution No. 11277."/> 27<meta name="DC.Creator.PersonalName" content="Clarisse Kagoyire"/> 28<meta name="DC.Creator.PersonalName" content="Gilbert Nduwayezu"/> 29<meta name="DC.Creator.PersonalName" content="Rachid Oucheikh"/> 30<meta name="DC.Creator.PersonalName" content="Jean Pierre Bizimana"/> 31<meta name="DC.Creator.PersonalName" content="Petter Pilesjö"/> 32<meta name="DC.Creator.PersonalName" content="Ali Mansourian"/> 33<meta name="DC.Date.created" scheme="ISO8601" content="2026-09-14"/> 34<meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2026-01-28"/> 35<meta name="DC.Date.issued" scheme="ISO8601" content="2026-07-23"/> 36<meta name="DC.Date.modified" scheme="ISO8601" content="2026-09-14"/> 37<meta name="DC.Description" xml:lang="en" content="Childhood stunting remains a major public health concern in low- and middle-income countries, with persistent sub-national disparities. This study examined spatially varying associations between Height-for-Age z-scores (HAZ) and selected child, maternal, and household factors, and compared Geographically Weighted Regression (GWR), Multi-scale GWR (MGWR) and Geographically Neural Network Weighted Regression (GNNWR) in their ability to capture spatial heterogeneity and non-linearity. We analysed data from a cross-sectional survey conducted in December 2021 in the Northern Province of Rwanda. The survey covered 615 households, with analyses performed for 601 children aged 1â36 months. After imputation, multicollinearity screening, and feature selection, HAZ was modelled using Ordinary Least Squares (OLS), GWR, MGWR and GNNWR. Model performance was asse
37ssed using coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike Information Criterion/AIC Corrected (AIC/AICc), and Moranâs I of residuals. Overall stunting prevalence was 27.1%. Spatially varying associations were observed for child age, sex, birthweight, underweight status, selected childcare practices, maternal support, and household living conditions. GNNWR achieved the best training performance (R2 = 0.66; RMSE=0.74) followed by MGWR (R2 =0.51; RMSE=0.88) and GWR (R2 =0.43; RMSE=0.95). Validation performance declined for all models, although GNNWR retained a higher validation (R2=0.28) with negligible residual spatial autocorrelation (Moranâs I = â0.005). GNNWR provided the strongest overall fit among the compared models and highlighted local spatial variation in associations with HAZ. However, the modest validation performance indicates that the findings should be interpreted cautiously. The workflow offers a portable framework for analysing DHS-like geocoded household datasets, but broader applicability should be assessed using spatial cross-validation, and sensitivity analyses."/> 38<meta name="DC.Format" scheme="IMT" content="application/pdf"/> 39<meta name="DC.Format" scheme="IMT" content="application/pdf"/> 40<meta name="DC.Format" scheme="IMT" content="text/xml"/> 41<meta name="DC.Identifier" content="1491"/> 42<meta name="DC.Identifier.DOI" content="10.4081/gh.2026.1491"/> 43<meta name="DC.Identifier.URI" content="https://www.geospatialhealth.net/gh/article/view/1491"/> 44<meta name="DC.Language" scheme="rfc5646" content="en"/> 45<meta name="DC.Rights" content="Copyright (c) 2026 the Author(s)"/> 46<meta name="DC.Rights" content="https://creativecommons.org/licenses/by-nc/4.0"/> 47<meta name="DC.Source" content="Geospatial Health"/> 48<meta name="DC.Source.ISSN" content="1970-7096"/> 49<meta name="DC.Source.Issue" content="2"/> 50<meta name="DC.Source.Volume" content="21"/> 51<meta name="DC.Source.URI" content="https://www.geospatialhealth.net/gh/gh"/> 52<meta name="DC.Subject" xml:lang="en" content="Ethical clearance for this study was obtained from the University of Rwanda College of Medicine and Health Sciences Institutional Review Board (reference No. 181/CHMS IRB/2021). All required permissions were also received from the National Institute of Statistics of Rwanda (reference No 0971/2020/10/NISR and No 0295/2021/10/NISR), the Ministry of Local Governance (reference No 0779/07.01), and the Ministry of Health (reference No 20/4184/DPMEHF/2021 and No NHRC/2020/PROT/047). All participants were fully informed about the study's aims, procedures, and measures to ensure confidentiality and privacy, in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. Interviews were conducted privately to safeguard participant confidentiality, and a rigorous data anonymization protocol was applied to ensure data could not be traced back to participants during analysis or in published results."/> 53<meta name="DC.Subject" xml:lang="en" content="Child height-for-age"/> 54<meta name="DC.Subject" xml:lang="en" content="childhood stunting"/> 55<meta name="DC.Subject" xml:lang="en" content="spatial heterogeneity"/> 56<meta name="DC.Subject" xml:lang="en" content="geographically weighted regression"/> 57<meta name="DC.Subject" xml:lang="en" content="Rwanda"/> 58<meta name="DC.Title" content="Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models"/> 59<meta name="DC.Type" content="Text.Serial.Journal"/> 60<meta name="DC.Type.articleType" content="Original Articles"/> 61<meta name="gs_meta_revision" content="1.1"/> 62<meta name="citation_journal_title" content="Geospatial Health"/> 63<meta name="citation_journal_abbrev" content="Geospat Health"/> 64<meta name="citation_issn" content="1970-7096"/> 65<meta name="citation_author" content="Clarisse Kagoyire"/> 66<meta name="citation_author_institution" content="Department of Earth and Environmental Sciences, Lund University, Lund, Sweden; Centre for GIS and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali"/> 67<meta name="citation_author" content="Gilbert Nduwayezu"/> 68<meta name="citation_author_institution" content="Department of Earth and Environmental Sciences, Lund University, Lund, Sweden;
68 Department of Civil, Environmental and Geomatics Engineering, College of Science and Technology, University of Rwanda, Kigali"/> 69<meta name="citation_author" content="Rachid Oucheikh"/> 70<meta name="citation_author_institution" content="Department of Earth and Environmental Sciences, Lund University, Lund"/> 71<meta name="citation_author" content="Jean Pierre Bizimana"/> 72<meta name="citation_author_institution" content="Department of Spatial Planning, School of Architecture and Built Environment, College of Science and Technology, University of Rwanda, Kigali"/> 73<meta name="citation_author" content="Petter Pilesjö"/> 74<meta name="citation_author_institution" content="Lund University, Lund, Sweden"/> 75<meta name="citation_author" content="Ali Mansourian"/> 76<meta name="citation_author_institution" content="Department of Earth and Environmental Sciences, Lund University, Lund"/> 77<meta name="citation_title" content="Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models"/> 78<meta name="citation_language" content="en"/> 79<meta name="citation_date" content="2026/09/14"/> 80<meta name="citation_volume" content="21"/> 81<meta name="citation_issue" content="2"/> 82<meta name="citation_doi" content="10.4081/gh.2026.1491"/> 83<meta name="citation_abstract_html_url" content="https://www.geospatialhealth.net/gh/article/view/1491"/> 84<meta name="citation_abstract" xml:lang="en" content="Childhood stunting remains a major public health concern in low- and middle-income countries, with persistent sub-national disparities. This study examined spatially varying associations between Height-for-Age z-scores (HAZ) and selected child, maternal, and household factors, and compared Geographically Weighted Regression (GWR), Multi-scale GWR (MGWR) and Geographically Neural Network Weighted Regression (GNNWR) in their ability to capture spatial heterogeneity and non-linearity. We analysed data from a cross-sectional survey conducted in December 2021 in the Northern Province of Rwanda. The survey covered 615 households, with analyses performed for 601 children aged 1â36 months. After imputation, multicollinearity screening, and feature selection, HAZ was modelled using Ordinary Least Squares (OLS), GWR, MGWR and GNNWR. Model performance was assessed using coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike Information Criterion/AIC Corrected (AIC/AICc), and Moranâs I of residuals. Overall stunting prevalence was 27.1%. Spatially varying associations were observed for child age, sex, birthweight, underweight status, selected childcare practices, maternal support, and household living conditions. GNNWR achieved the best training performance (R2 = 0.66; RMSE=0.74) followed by MGWR (R2 =0.51; RMSE=0.88) and GWR (R2 =0.43; RMSE=0.95). Validation performance declined for all models, although GNNWR retained a higher validation (R2=0.28) with negligible residual spatial autocorrelation (Moranâs I = â0.005). GNNWR provided the strongest overall fit among the compared models and highlighted local spatial variation in associations with HAZ. However, the modest validation performance indicates that the findings should be interpreted cautiously. The workflow offers a portable framework for analysing DHS-like geocoded household datasets, but broader applicability should be assessed using spatial cross-validation, and sensitivity analyses."/> 85<meta name="citation_keywords" xml:lang="en" content="Ethical clearance for this study was obtained from the University of Rwanda College of Medicine and Health Sciences Institutional Review Board (reference No. 181/CHMS IRB/2021). All required permissions were also received from the National Institute of Statistics of Rwanda (reference No 0971/2020/10/NISR and No 0295/2021/10/NISR), the Ministry of Local Governance (reference No 0779/07.01), and the Ministry of Health (reference No 20/4184/DPMEHF/2021 and No NHRC/2020/PROT/047). All participants were fully informed about the study's aims, procedures, and measures to ensure confidentiality and privacy, in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. Interviews were conducted privately to safeguard participant confidentiality, and a rigorous data anonymization protocol was a
85pplied to ensure data could not be traced back to participants during analysis or in published results."/> 86<meta name="citation_keywords" xml:lang="en" content="Child height-for-age"/> 87<meta name="citation_keywords" xml:lang="en" content="childhood stunting"/> 88<meta name="citation_keywords" xml:lang="en" content="spatial heterogeneity"/> 89<meta name="citation_keywords" xml:lang="en" content="geographically weighted regression"/> 90<meta name="citation_keywords" xml:lang="en" content="Rwanda"/> 91<meta name="citation_pdf_url" content="https://www.geospatialhealth.net/gh/article/download/1491/1610"/> 92<meta name="citation_pdf_url" content="https://www.geospatialhealth.net/gh/article/download/1491/1611"/> 93<meta name="citation_reference" content="Amegbor PM, Zhang Z, Dalgaard R, Sabel CE, 2020. Multilevel and spatial analyses of childhood malnutrition in Uganda: Examining individual and contextual factors. Sci Rep 10:20019."/> 94<meta name="citation_reference" content="Amir-ud-Din R, Fawad S, Naz L, Zafar S, Kumar R, Pongpanich S, 2022. Nutritional inequalities among under-five children: A geospatial analysis of hotspots and cold spots in 73 low- and middle-income countries. Int J Equity Health 21:135."/> 95<meta name="citation_reference" content="Amoroso CL, Nisingizwe MP, Rouleau D, Thomson DR, Kagabo DM, Bucyana T, Drobac P, Ngabo F, 2018. Next wave of interventions to reduce under-five mortality in Rwanda: A cross-sectional analysis of demographic and health survey data. BMC Pediatr 18:27."/> 96<meta name="citation_reference" content="Anselin L, 1995. Local Indicators of Spatial AssociationâLISA. Geograph Anal 27:93â115."/> 97<meta name="citation_reference" content="Anselin L, Bera AK, Florax R, Yoon MJ, 1996. Simple diagnostic tests for spatial dependence. Regional Sci Urban Econ 26:77â104."/> 98<meta name="citation_reference" content="Archer KJ, Kimes RV, 2008. Empirical characterization of random forest variable importance measures. Computat Stat Data Analysis 52:2249â60."/> 99<meta name="citation_reference" content="Atsbeha DM, Nayga RM, Rickertsen K, 2015. Can prolonged breastfeeding duration impair child growth? Evidence from rural Ethiopia. Food Policy 53:46â53."/> 100<meta name="citation_reference" content="Azur MJ, Stuart EA, Frangakis C, Leaf PJ, 2011. Multiple imputation by chained equations: What is it and how does it work? Int J Methods Psychiatr Res 20:40â9."/> 101<meta name="citation_reference" content="Benjamin-Chung J, Mertens A, Colford JM Jr, Hubbard AE, van der Laan MJ, Coyle J, Sofrygin O, Cai W, Nguyen A, Pokpongkiat NN, Djajadi S, Seth A, Jilek W, Jung E, Chung EO, Rosete S, Hejazi N, Malenica I, Li H, Hafen R, Subramoney V, Häggström J, Norman T, Brown KH, Christian P, Arnold BF; Ki Child Growth Consortium, 2023. Early-childhood linear growth faltering in low- and middle-income countries. Nature 621:550â7."/> 102<meta name="citation_reference" content="Binagwaho A, Scott KW, Harward SH, 2016. Early childhood development in Rwanda: A policy analysis of the human rights legal framework. BMC Int Health Human Rights 16:1."/> 103<meta name="citation_reference" content="Biswas M, 2022. Identifying geographical heterogeneity in associations between under-five child nutritional status and its correlates across Indian districts. Spatial Demography 10:143â87."/> 104<meta name="citation_reference" content="Black RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, Ezzati M, Mathers C, Rivera J, 2008. Maternal and child undernutrition: Global and regional exposures and health consequences. Lancet, 371:243â60."/> 105<meta name="citation_reference" content="Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, De Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R, 2013. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382:427â51."/> 106<meta name="citation_reference" content="Breiman L, 2001. Random Forests. Machine Learning 45:5â32."/> 107<meta name="citation_reference" content="Comber A, Brunsdon C, Charlton M, Dong G, Harris R, Lu B, Lü Y, Murakami D, Nakaya T, Wang Y, Harris P, 2023. A route map for successful applications of geographically weighted regression. Geograph Anal 55:155â78."/> 108<meta name="citation_reference" content="Daelmans B, Manji SA, Raina N, 2021. Nurturing care for early childhood development: global perspective and guidance. Indian Pediatr 58:11â15."/> 109<meta name="citation_reference" content="Du Z, Wang Z, Wu S, Zhang F, Liu R, 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. Int J Geograph Informat Sci 34:1353â77."/> 110<meta name="citation_reference" content="Dusingizimana T, Weber JL, Ramilan T, Iversen PO, Brough L, 2021. An empirical study of factors associated with height-for-age z-scores of children aged 6â23 months in northwest Rwanda: The role of care practices related to child feeding and health. Br J Nutrition 12
1106:1203â14."/> 111<meta name="citation_reference" content="Ekholuenetale M, Okonji OC, Nzoputam CI, Edet CK, Wegbom AI, Arora A, 2023. Socioeconomic disparities in Rwandaâs under-5 populationâs growth tracking and nutrition promotion: Findings from the 2019â2020 demographic and health survey. BMC Pediatrics 23:467."/> 112<meta name="citation_reference" content="Eryando T, Sipahutar T, Budhiharsana MP, Siregar KN, Nur Aidi M, Minarto D, Utari DM, Rahmaniati M, Hendarwan H, 2022. Spatial analysis of stunting determinants in 514 Indonesian districts/cities: Implications for intervention and setting of priority. Geospat Health 17:1055."/> 113<meta name="citation_reference" content="Fotheringham AS, Brunsdon C, Charlton M, 2003. Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons."/> 114<meta name="citation_reference" content="Fotheringham AS, Wong DWS, 1991. The modifiable areal unit problem in multivariate statistical analysis. Environ Planning A: Economy Space 23:1025â44."/> 115<meta name="citation_reference" content="Fotheringham AS, Yang W, Kang W, 2017. Multiscale Geographically Weighted Regression (MGWR). Ann Am Assoc Geographers 107:1247â65."/> 116<meta name="citation_reference" content="Getis A, 2007. Reflections on spatial autocorrelation. Regional Science and Urban Economics at 35. A Retrospective/Prospective Special Issue, 37:491â6."/> 117<meta name="citation_reference" content="Hailu BA, Bogale GG, Beyene J, 2020. Spatial heterogeneity and factors influencing stunting and severe stunting among under-5 children in Ethiopia: Spatial and multilevel analysis. Sci Rep 10:16427."/> 118<meta name="citation_reference" content="Haque S, Price A, Mengersen K, Hu W, 2025. Evaluating the impact of the Modifiable Areal Unit Problem on ecological model inference: A case study of COVID-19 data in Queensland, Australia. Infect Dis Modelling 10:1002â19."/> 119<meta name="citation_reference" content="He K, Zhang X, Ren S, Sun J, 2015. delving deep into rectifiers: surpassing human-level performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV) 1026â1034."/> 120<meta name="citation_reference" content="Ioffe S, Szegedy C, 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In F. Bach & D. Blei (Eds), Proceedings of the 32nd International Conference on Machine Learning 37:448â56."/> 121<meta name="citation_reference" content="James G, Witten D, Hastie T, Tibshirani R, Taylor J, 2023. Linear regression. In An introduction to statistical learning: With applications in python (pp. 69â134). Springer."/> 122<meta name="citation_reference" content="Kalinda C, Phiri M, Simona SJ, Banda A, Wong R, Qambayot MA, Ishimwe SMC, Amberbir A, Abebe B, Gebremariam A, Nyerere JO, 2023. Understanding factors associated with rural-urban disparities of stunting among under-five children in Rwanda: A decomposition analysis approach. Mat Child Nutr 19:e13511."/> 123<meta name="citation_reference" content="Khan J, Mohanty SK, 2018. Spatial heterogeneity and correlates of child malnutrition in districts of India. BMC Public Health 18:1027."/> 124<meta name="citation_reference" content="Kinyoki DK, Osgood-Zimmerman AE, Pickering BV, Schaeffer LE, Marczak LB, Lazzar-Atwood A, Collison ML, Henry NJ, Abebe Z, Adamu AA, Adekanmbi V, Ahmadi K, Ajumobi O, Al-Eyadhy A, Al-Raddadi RM, Alahdab F, Alijanzadeh M, Alipour V, Altirkawi K, ⦠Local Burden of Disease Child Growth Failure Collaborators. (2020). Mapping child growth failure across low- and middle-income countries. Nature 577:231â4."/> 125<meta name="citation_reference" content="Klein N, Kneib T, Marra G, Radice R, 2020. Chapter 5âBayesian mixed binary-continuous copula regression with an application to childhood undernutrition. In Y. Fan, D. Nott, M. S. Smith, & J.-L. Dortet-Bernadet (Eds), Flexible Bayesian Regression Modelling Academic Press. pp. 121â152."/> 126<meta name="citation_reference" content="Kramer MS, Moodie EEM, Dahhou M, Platt RW, 2011. Breastfeeding and infant size: evidence of reverse causality. Am J Epidemiol 173(9), 978â983."/> 127<meta name="citation_reference" content="Kuse KA, Debeko DD, 2023. Spatial distribution and determinants of stunting, wasting and underweight in children under-five in Ethiopia. BMC Public Health 23:641."/> 128<meta name="citation_reference" content="Leung Y, Mei C-L, Zhang W-X, 2000. Statistical tests for spatial nonstationarity based on the geographically weighted regression Model. Environ Planning A 32:9â32."/> 129<meta name="citation_reference" content="Luo Y, Yan J, McClure S, 2021. Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis. Environ Sci Pollut Res 28:6587â99."/> 130<meta name="citation_reference" content="Ma Z, Huang Z. 2023. A Bayesian implementation of the multiscale geographically weighted regression model with INLA. Ann Am Assoc Geographers 113:1501â15."/> 131<meta name="citation_reference" content="Marquis GS, Habicht JP, Lanata CF, Black RE, Rasmussen KM, 1997. Association of breastfeeding and stunting in Peruvian toddlers: An example of reverse causality. Int J Epidemiol 26:349â56."/> 132<meta name="citation_reference" content="McMahan LD, Sprague C, 2024. The varied perspectives of organisational effectiveness: Whatâs at stake for early childhood development programmes in Rwanda? Global Public Health 19
132:2377280."/> 133<meta name="citation_reference" content="Mertens A, Benjamin-Chung J, Colford JM Jr, Coyle J, van der Laan MJ, Hubbard AE, Rosete S, Malenica I, Hejazi N, Sofrygin O, Cai W, Li H, Nguyen A, Pokpongkiat NN, Djajadi S, Seth A, Jung E, Chung EO, Jilek W, Subramoney V, Hafen R, Häggström J, Norman T, Brown KH, Christian P, Arnold BF; Ki Child Growth Consortium, 2023. Causes and consequences of child growth faltering in low-resource settings. Nature 621:568â76."/> 134<meta name="citation_reference" content="Miconi D, Beeman I, Robert E, Beatson J, Ruiz-Casares M, 2018. Child supervision in low- and middle-income countries: A scoping review. Children Youth Serv Rev 89:226â42."/> 135<meta name="citation_reference" content="Moonga G, Böse-OâReilly S, Berger U, Harttgen K, Michelo C, Nowak D, Siebert U, Yabe J, Seiler J, 2021. Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach. PLOS ONE 16:e0255073."/> 136<meta name="citation_reference" content="Muche A, Gezie LD, Baraki AG, Amsalu ET, 2021. Predictors of stunting among children age 6â59 months in Ethiopia using Bayesian multi-level analysis. Sci Rep 11:3759."/> 137<meta name="citation_reference" content="NISR [Rwanda], Ministry of Health (MOH) [Rwanda], & ICF. (2021). Rwanda Demographic and Health Survey 2019-20 Final Report. Available from: https://dhsprogram.com/pubs/pdf/FR370/FR370.pdf"/> 138<meta name="citation_reference" content="Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, Graetz N, Kinyoki DK, Ray SE, Bhatt S, Browne AJ, Burstein R, Cameron E, Casey DC, Deshpande A, Fullman N, Gething PW, Gibson H, Henry NJ, ⦠Hay SI, 2018. Mapping child growth failure in Africa between 2000 and 2015. Nature 555:41â47."/> 139<meta name="citation_reference" content="Oshan TM, Li Z, Kang W, Wolf LJ, Fotheringham AS, 2019. mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS Int J Geo-Information 8:269."/> 140<meta name="citation_reference" content="Perkins JM, Kim R, Krishna A, McGovern M, Aguayo VM, Subramanian SV, 2017. Understanding the association between stunting and child development in low- and middle-income countries: Next steps for research and intervention. Soc Sci Med 193:101â9."/> 141<meta name="citation_reference" content="Prechelt L, 1998. Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks, 11:761â7."/> 142<meta name="citation_reference" content="Puri P, Khan J, Shil A, Ali M, 2020. A cross-sectional study on selected child health outcomes in India: Quantifying the spatial variations and identification of the parental risk factors. Sci Rep 10:6645."/> 143<meta name="citation_reference" content="Quamme SH, Iversen PO, 2022. Prevalence of child stunting in Sub-Saharan Africa and its risk factors. Clinical Nutrition Open Sci 42:49â61."/> 144<meta name="citation_reference" content="Quiñones S, Goyal A, Ahmed ZU, 2021. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep 11:6955."/> 145<meta name="citation_reference" content="Ruiz-Casares M, Nazif-Muñoz JI, Iwo R, Oulhote Y, 2018. Nonadult supervision of children in low- and middle-income countries: results from 61 national population-based surveys. Int J Environ Res Public Health 15:1564."/> 146<meta name="citation_reference" content="Samuel VW, 2023. miceforest: Fast Imputation with Random Forests in Python. Available from: https://github.com/AnotherSamWilson/miceforest/"/> 147<meta name="citation_reference" content="Seboka BT, Hailegebreal S, Mamo TT, Yehualas
147het DE, Gilano G, Kabthymer RH, Ewune HA, Kassa R, Debisa MA, Yawo MN, Endashaw H, Demeke AD, Tesfa GA, 2022. Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: A geographically weighted regression analysis. J Health Population Nutr 41:28."/> 148<meta name="citation_reference" content="Siqi J, Yuhong W, Ling C, Xiaowen B, 2023. A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models. Urban Climate 47:101390."/> 149<meta name="citation_reference" content="Ssentongo P, Ssentongo AE, Ba DM, Ericson JE, Na M, Gao X, Fronterre C, Chinchilli VM, Schiff SJ, 2021. Global, regional and national epidemiology and prevalence of child stunting, wasting and underweight in low-and middle-income countries, 2006â2018. Sci Rep 11:1â12."/> 150<meta name="citation_reference" content="Striessnig E, Bora JK, 2020. Under-five child growth and nutrition status: spatial clustering of Indian districts. Spatial Demography 8:63â84."/> 151<meta name="citation_reference" content="Sun Y, Jia W, Zhu W, Zhang X, Saidahemaiti S, Hu,T, Guo H, 2022. Local neural-network-weighted models for occurrence and number of down wood in natural forest ecosystem. Sci Rep 12:6375."/> 152<meta name="citation_reference" content="Tamir TT, Tekeba B, Mekonen EG, Zegeye AF, Gebrehana DA, 2024. Spatial heterogeneity and predictors of stunting among under five children in Mozambique: A geographically weighted regression. Front Public Health 12:1502018."/> 153<meta name="citation_reference" content="UNICEF, WHO, & World Bank. 2023. Levels and trends in child malnutrition: Key findings of the 2023 Edition of the Joint Child Malnutrition Estimates. New York: UNICEF and WHO."/> 154<meta name="citation_reference" content="Utumatwishima JN, Mogren I, Elfving K, Umubyeyi A, Krantz G, 2025. Association between poor mental health in mothers and child stunting: A population-based cross-sectional study in Rwanda. BMJ Open 15:e101117."/> 155<meta name="citation_reference" content="Uwiringiyimana V, Osei F, Amer S, Veldkamp A, 2022. Bayesian geostatistical modelling of stunting in Rwanda: Risk factors and spatially explicit residual stunting burden. BMC Public Health 22:159."/> 156<meta name="citation_reference" content="Vaivada T, Akseer N, Akseer S, Somaskandan A, Stefopulos M, Bhutta ZA, 2020. Stunting in childhood: An overview of global burden, trends, determinants, and drivers of decline. Am J Clin Nutrition 112:777S-91S."/> 157<meta name="citation_reference" content="Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, Sachdev HS, 2008. Maternal and child undernutrition: Consequences for adult health and human capital. Lancet 371:340â57."/> 158<meta name="citation_reference" content="WHO, 2006. WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development."/> 159<meta name="citation_reference" content="WHO, UNICEF, & World Bank Group. (2018). Nurturing care for early childhood development: A framework for helping children survive and thrive to transform health and human potential."/> 160<meta name="citation_reference" content="Wu S, Wang Z, Du Z, Huang B, Zhang F. Liu R, 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. Int J Geograph Informat Sci 35:582â608."/> 161<meta name="citation_reference" content="Wu Y, 2021. Canât ridge regression perform variable selection? Technometrics 63:263â71."/> 162<meta name="citation_reference" content="Yi H, Li M, Dong Y, Gan Z, He L, Li X, Tao Y, Xia Z, Xia Z, Xue Y, Zhai Z, 2024. Nonlinear associations between the ratio of family income to poverty and all-cause mortality among adults in NHANES study. Sci Rep 14:12018."/> 163<meta name="citation_reference" content="Zhao Z, Xu Z, Hu C, Wang K, Ding X, 2024. Geographically weighted neural network considering spatial heterogeneity for landslide susceptibility mapping: A case study of Yichang City, China. CATENA, 234:107590."/> 164<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
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464 465 </div> 466 467 <h1 class="page_title">Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models</h1> 468 469 470 <div class="item cover_image"> 471 <div class="sub_item"> 472 <img src="https://www.geospatialhealth.net/public/journals/1/submission_1491_1295_coverImage_en.webp" alt=""> 473 </div> 474 </div> 475 476 <div class="discl_content_col_dx"> 477 <b><i class="fa-solid fa-circle-info"></i> Publisher's note</b><br /> 478 All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. 479 </div> 480 481 <div class="item galleys"> 482 <div id="container_galleys_flex"> 483 484 485 486 487<a class="obj_galley_link pdf" href="https://www.geospatialhealth.net/gh/article/view/1491/1610"> 488 489 PDF 490 491 </a> 492 493 494 495 496<a class="obj_galley_link pdf" href="https://www.geospatialhealth.net/gh/article/view/1491/1611"> 497 498 Supplementary Matrials 499 500 </a> 501 502 503 504 505<a class="obj_galley_link file" href="https://www.geospatialhealth.net/gh/article/view/1491/1613"> 506 507 HTML 508 509 </a> 510 </div> 511 </div> 512 513 514 <div class="item published"> 515 <section class="sub_item"> 516 <div class="content"> 517 <b>Received:</b> 28 January 2026<br/> 518 <b>Accepted:</b> 29 March 2026<br/> 519 <b>Published:</b> 14 September 2026<br/> 520 521 <!-- <b>Received:</b> <span>28 January 2026</span><br /> 522 <b>Accepted:</b> <span>29 March 2026</span><br /> 523 <b>Published:</b> <span>14 September 2026</span> 524 --> 525 </div> 526 </section> 527 </div> 528 529 <div class="container_stats"><!--FLEX--> 530 <div class="content_stats"> 531 532 <div class="item_metrics"> 533 <div class="item_metrics_number">180</div> 534 <div class="item_metrics_text">Views</div> 535 </div> 536 <div class="item_metrics"> 537 <div class="item_metrics_number">89</div> 538 <div class="item_metrics_text">Downloads</div> 539 </div> 540 <div class="item_metrics"> 541 <div class="item_metrics_number">0</div> 542 <div class="item_metrics_text">HTML</div> 543 </div> 544 </div> 545 </div> 546 547 <section class="categories" title="Categories"> 548 <div class="content"> 549 <a href="https://www.geospatialhealth.net/gh/catalog/category/articles"> 550 Original Articles 551 </a> 552 </div> 553 </section> 554 555 <section class="keywords" title="Keywords"> 556 <div class="content"> 557 <a href="https://www.geospatialhealth.net/gh/search?query=Child height-for-age">Child height-for-age</a>, <a href="https://www.geospatialhealth.net/gh/search?query=childhood stunting">childhood stunting</a>, <a href="https://www.geospatialhealth.net/gh/search?query=spatial heterogeneity">spatial heterogeneity</a>, <a href="https://www.geospatialhealth.net/gh/search?query=geographically weighted regression">geographically weighted regression</a>, <a href="https://www.geospatialhealth.net/gh/search?query=Rwanda">Rwanda</a> </div> 558 </section> 559 560 561 <section class="keywords" > 562 <div class="content_badges"> 563 <div class="items_badges"> 564 <!--<h3 class="label">##plugins.themes.RIOManuscript.dimension_badge##</h3>--> 565 <span class="__dimensions_badge_embed__" data-doi="10.4081/gh.2026.1491" data-hide-zero-citations="true" data-style="small_circle"></span>
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574 575 <a href="https://plu.mx/plum/a/?doi=10.4081/gh.2026.1491" class="plumx-plum-print-popup" data-hide-when-empty="true" data-popup="left"></a> 576 </div> 577 </div> 578 </section> 579 580 <!-- INIZIO MAIN PAGE --> 581 <div class="row"> 582 <div class="main_entry"> 583 <section class="item authors"> 584 <h2 class="pkp_screen_reader">Authors</h2> 585 <div class="authors"> 586 <div class="content_author"> 587 <div class="content_nome_mail"> 588 <div class="nome"> 589 <a href="https://www.geospatialhealth.net/gh/search?authors=Clarisse Kagoyire"><i class="fa fa-fw fa-user"></i> Clarisse Kagoyire</a> 590 </div> 591 <div class="mail"> 592 <span class="primary_contact"><i class="fa-regular fa-envelope" style="color:var(--main-color);font-size:12px;"></i> <a href="mailto:[email protected]" >[email protected]</a></span> 593 </div> 594 <!-- --> 595 <span class="orcid"> 596 <svg class="orcid_icon" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"> 597 <path fill-rule="evenodd" clip-rule="evenodd" d="M29.474 16c0 7.442-6.032 13.474-13.474 13.474S2.526 23.442 2.526 16 8.558 2.526 16 2.526 29.474 8.558 29.474 16Z" fill="#fff"/> 598 <path fill-rule="evenodd" clip-rule="evenodd" d="M32 16c0 8.837-7.163 16-16 16S0 24.837 0 16 7.163 0 16 0s16 7.163 16 16ZM16 29.474c7.442 0 13.474-6.032 13.474-13.474S23.442 2.526 16 2.526 2.526 8.558 2.526 16 8.558 29.474 16 29.474Z" fill="#7FAA26"/> 599 <path fill-rule="evenodd" clip-rule="evenodd" d="M18.22 10.973h-4.547v11.6h4.569c3.8 0 6.133-2.82 6.133-5.8 0-1.365-.469-2.815-1.478-3.925-1.013-1.115-2.557-1.875-4.676-1.875Zm-.177 9.732h-2.347v-7.864h2.264c1.521 0 2.603.46 3.304 1.167.703.709 1.046 1.688 1.046 2.765 0 .654-.2 1.641-.83 2.46-.621.808-1.677 1.473-3.437 1.473Zm-.083-8.073c3.13 0 4.558 1.898 4.558 4.141 0-2.243-1.429-4.141-4.558-4.141h-2.472 2.472Zm6.205 4.017.001.124c0 2.869-2.242 5.591-5.924 5.591h-4.36V11.182v11.182h4.36c3.682 0 5.924-2.722 5.924-5.591l-.001-.124ZM9.5 11.005v11.588h2.024V11.005H9.5Zm1.815.208v11.172-11.172H9.71h1.606ZM10.512 10.15c.7 0 1.262-.575 1.262-1.263 0-.687-.561-1.262-1.262-1.262-.7 0-1.262.563-1.262 1.262 0 .688.561 1.262 1.262 1.262Zm.614-.408a1.044 1.044 0 0 0 0 0Z" fill="#7FAA26"/> 600</svg> 601 602 <a href="https://orcid.org/0000-0002-3141-7568" target="_blank">https://orcid.org/0000-0002-3141-7568 (unauthenticated)</a> 603 </span> 604 </div> 605 <div class="content_affiliazione"> 606 <span class="affiliation"> 607 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Department of Earth and Environmental Sciences, Lund University, Lund, Sweden; Centre for GIS and Remote Sensing, College of Science and Technology, University of Rwanda, Kigali, Rwanda.</span> 608 </span> 609 </div> 610 </div> 611 <div class="content_author"> 612 <div class="content_nome_mail"> 613 <div class="nome"> 614 <a href="https://www.geospatialhealth.net/gh/search?authors=Gilbert Nduwayezu"><i class="fa fa-fw fa-user"></i> Gilbert Nduwayezu</a> 615 </div> 616 <!-- --> 617 <span class="orcid"> 618 <svg class="orcid_icon" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"> 619 <path fill-rule="evenodd" clip-rule="evenodd" d="M29.474 16c0 7.442-6.032 13.474-13.474 13.474S2.526 23.442 2.526 16 8.558 2.526 16 2.526 29.474 8.558 29.474 16Z" fill="#fff"/> 620 <path fill-rule="evenodd" clip-rule="evenodd" d="M32 16c0 8.837-7.163 16-16 16S0 24.837 0 16 7.163 0 16 0s16 7.163 16 16ZM16 29.474c7.442 0 13.474-6.032 13.474-13.474S23.442 2.526 16 2.526 2.526 8.558 2.526 16 8.558 29.474 16 29.474Z" fill="#7FAA26"/> 621 <path fill-rule="evenodd" clip-rule="evenodd" d="M18.22 10.973h-4.547v11.6h4.569c3.8 0 6.133-2.82 6.133-5.8 0-1.365-.469-2.815-1.478-3.925-1.013-1.115-2.5
62157-1.875-4.676-1.875Zm-.177 9.732h-2.347v-7.864h2.264c1.521 0 2.603.46 3.304 1.167.703.709 1.046 1.688 1.046 2.765 0 .654-.2 1.641-.83 2.46-.621.808-1.677 1.473-3.437 1.473Zm-.083-8.073c3.13 0 4.558 1.898 4.558 4.141 0-2.243-1.429-4.141-4.558-4.141h-2.472 2.472Zm6.205 4.017.001.124c0 2.869-2.242 5.591-5.924 5.591h-4.36V11.182v11.182h4.36c3.682 0 5.924-2.722 5.924-5.591l-.001-.124ZM9.5 11.005v11.588h2.024V11.005H9.5Zm1.815.208v11.172-11.172H9.71h1.606ZM10.512 10.15c.7 0 1.262-.575 1.262-1.263 0-.687-.561-1.262-1.262-1.262-.7 0-1.262.563-1.262 1.262 0 .688.561 1.262 1.262 1.262Zm.614-.408a1.044 1.044 0 0 0 0 0Z" fill="#7FAA26"/> 622</svg> 623 624 <a href="https://orcid.org/0000-0001-5505-1276" target="_blank">https://orcid.org/0000-0001-5505-1276 (unauthenticated)</a> 625 </span> 626 </div> 627 <div class="content_affiliazione"> 628 <span class="affiliation"> 629 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Department of Earth and Environmental Sciences, Lund University, Lund, Sweden; Department of Civil, Environmental and Geomatics Engineering, College of Science and Technology, University of Rwanda, Kigali, Rwanda.</span> 630 </span> 631 </div> 632 </div> 633 <div class="content_author"> 634 <div class="content_nome_mail"> 635 <div class="nome"> 636 <a href="https://www.geospatialhealth.net/gh/search?authors=Rachid Oucheikh"><i class="fa fa-fw fa-user"></i> Rachid Oucheikh</a> 637 </div> 638 <!-- --> 639 <span class="orcid"> 640 <svg class="orcid_icon" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"> 641 <path fill-rule="evenodd" clip-rule="evenodd" d="M29.474 16c0 7.442-6.032 13.474-13.474 13.474S2.526 23.442 2.526 16 8.558 2.526 16 2.526 29.474 8.558 29.474 16Z" fill="#fff"/> 642 <path fill-rule="evenodd" clip-rule="evenodd" d="M32 16c0 8.837-7.163 16-16 16S0 24.837 0 16 7.163 0 16 0s16 7.163 16 16ZM16 29.474c7.442 0 13.474-6.032 13.474-13.474S23.442 2.526 16 2.526 2.526 8.558 2.526 16 8.558 29.474 16 29.474Z" fill="#7FAA26"/> 643 <path fill-rule="evenodd" clip-rule="evenodd" d="M18.22 10.973h-4.547v11.6h4.569c3.8 0 6.133-2.82 6.133-5.8 0-1.365-.469-2.815-1.478-3.925-1.013-1.115-2.557-1.875-4.676-1.875Zm-.177 9.732h-2.347v-7.864h2.264c1.521 0 2.603.46 3.304 1.167.703.709 1.046 1.688 1.046 2.765 0 .654-.2 1.641-.83 2.46-.621.808-1.677 1.473-3.437 1.473Zm-.083-8.073c3.13 0 4.558 1.898 4.558 4.141 0-2.243-1.429-4.141-4.558-4.141h-2.472 2.472Zm6.205 4.017.001.124c0 2.869-2.242 5.591-5.924 5.591h-4.36V11.182v11.182h4.36c3.682 0 5.924-2.722 5.924-5.591l-.001-.124ZM9.5 11.005v11.588h2.024V11.005H9.5Zm1.815.208v11.172-11.172H9.71h1.606ZM10.512 10.15c.7 0 1.262-.575 1.262-1.263 0-.687-.561-1.262-1.262-1.262-.7 0-1.262.563-1.262 1.262 0 .688.561 1.262 1.262 1.262Zm.614-.408a1.044 1.044 0 0 0 0 0Z" fill="#7FAA26"/> 644</svg> 645 646 <a href="https://orcid.org/0000-0001-9996-9759" target="_blank">https://orcid.org/0000-0001-9996-9759 (unauthenticated)</a> 647 </span> 648 </div> 649 <div class="content_affiliazione"> 650 <span class="affiliation"> 651 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Department of Earth and Environmental Sciences, Lund University, Lund, Sweden.</span> 652 </span> 653 </div> 654 </div> 655 <div class="content_author"> 656 <div class="content_nome_mail"> 657 <div class="nome"> 658 <a href="https://www.geospatialhealth.net/gh/search?authors=Jean Pierre Bizimana"><i class="fa fa-fw fa-user"></i> Jean Pierre Bizimana</a> 659 </div> 660 <!-- --> 661 </div> 662 <div class="content_affiliazione">
663 <span class="affiliation"> 664 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Department of Spatial Planning, School of Architecture and Built Environment, College of Science and Technology, University of Rwanda, Kigali, Rwanda.</span> 665 </span> 666 </div> 667 </div> 668 <div class="content_author"> 669 <div class="content_nome_mail"> 670 <div class="nome"> 671 <a href="https://www.geospatialhealth.net/gh/search?authors=Petter Pilesjö"><i class="fa fa-fw fa-user"></i> Petter Pilesjö</a> 672 </div> 673 <!-- --> 674 </div> 675 <div class="content_affiliazione"> 676 <span class="affiliation"> 677 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Lund University, Lund, Sweden, Sweden.</span> 678 </span> 679 </div> 680 </div> 681 <div class="content_author"> 682 <div class="content_nome_mail"> 683 <div class="nome"> 684 <a href="https://www.geospatialhealth.net/gh/search?authors=Ali Mansourian"><i class="fa fa-fw fa-user"></i> Ali Mansourian</a> 685 </div> 686 <!-- --> 687 <span class="orcid"> 688 <svg class="orcid_icon" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg"> 689 <path fill-rule="evenodd" clip-rule="evenodd" d="M29.474 16c0 7.442-6.032 13.474-13.474 13.474S2.526 23.442 2.526 16 8.558 2.526 16 2.526 29.474 8.558 29.474 16Z" fill="#fff"/> 690 <path fill-rule="evenodd" clip-rule="evenodd" d="M32 16c0 8.837-7.163 16-16 16S0 24.837 0 16 7.163 0 16 0s16 7.163 16 16ZM16 29.474c7.442 0 13.474-6.032 13.474-13.474S23.442 2.526 16 2.526 2.526 8.558 2.526 16 8.558 29.474 16 29.474Z" fill="#7FAA26"/> 691 <path fill-rule="evenodd" clip-rule="evenodd" d="M18.22 10.973h-4.547v11.6h4.569c3.8 0 6.133-2.82 6.133-5.8 0-1.365-.469-2.815-1.478-3.925-1.013-1.115-2.557-1.875-4.676-1.875Zm-.177 9.732h-2.347v-7.864h2.264c1.521 0 2.603.46 3.304 1.167.703.709 1.046 1.688 1.046 2.765 0 .654-.2 1.641-.83 2.46-.621.808-1.677 1.473-3.437 1.473Zm-.083-8.073c3.13 0 4.558 1.898 4.558 4.141 0-2.243-1.429-4.141-4.558-4.141h-2.472 2.472Zm6.205 4.017.001.124c0 2.869-2.242 5.591-5.924 5.591h-4.36V11.182v11.182h4.36c3.682 0 5.924-2.722 5.924-5.591l-.001-.124ZM9.5 11.005v11.588h2.024V11.005H9.5Zm1.815.208v11.172-11.172H9.71h1.606ZM10.512 10.15c.7 0 1.262-.575 1.262-1.263 0-.687-.561-1.262-1.262-1.262-.7 0-1.262.563-1.262 1.262 0 .688.561 1.262 1.262 1.262Zm.614-.408a1.044 1.044 0 0 0 0 0Z" fill="#7FAA26"/> 692</svg> 693 694 <a href="https://orcid.org/0000-0001-6812-4307" target="_blank">https://orcid.org/0000-0001-6812-4307 (unauthenticated)</a> 695 </span> 696 </div> 697 <div class="content_affiliazione"> 698 <span class="affiliation"> 699 <span><i class="fa-solid fa-building-columns" style="color:var(--main-color);"></i> Department of Earth and Environmental Sciences, Lund University, Lund, Sweden.</span> 700 </span> 701 </div> 702 </div> 703 </div> 704 </section> 705 706 <button class="accordion" id="defaultOpen"><i class="fa-solid fa-folder-open"></i> Abstract</button> 707<div class="acc_panel"> 708 <section class="item abstract"> 709 <p>Childhood stunting remains a major public health concern in low- and middle-income countries, with persistent sub-national disparities. This study examined spatially varying associations between Height-for-Age z-scores (HAZ) and selected child, maternal, and household factors, and compared Geographically Weighted Regression (GWR), Multi-scale GWR (MGWR) and Geographically Neural Network Weighted Regression (GNNWR) in their ability to capture spatial heterogeneity and non-linearity. We analysed data from a cross-sectional survey conducted in December 2021 in the Northern Province of Rwanda. The survey covered 615 households, with analyses performed for 601 children aged 1â36 months. After imputation, multicollinearity screening, and feature selection, HAZ was modelled using Ordinary Least Squares (OLS), GWR, MGWR and GNNWR. Model performance was asse
709ssed using coefficient of determination (R2), Root Mean Square Error (RMSE), Akaike Information Criterion/AIC Corrected (AIC/AICc), and Moranâs I of residuals. Overall stunting prevalence was 27.1%. Spatially varying associations were observed for child age, sex, birthweight, underweight status, selected childcare practices, maternal support, and household living conditions. GNNWR achieved the best training performance (R2 = 0.66; RMSE=0.74) followed by MGWR (R2 =0.51; RMSE=0.88) and GWR (R2 =0.43; RMSE=0.95). Validation performance declined for all models, although GNNWR retained a higher validation (R2=0.28) with negligible residual spatial autocorrelation (Moranâs I = â0.005). GNNWR provided the strongest overall fit among the compared models and highlighted local spatial variation in associations with HAZ. However, the modest validation performance indicates that the findings should be interpreted cautiously. The workflow offers a portable framework for analysing DHS-like geocoded household datasets, but broader applicability should be assessed using spatial cross-validation, and sensitivity analyses.</p> 710 </section> 711 </div> 712 713<button class="accordion"><i class="fa-solid fa-chart-column"></i> Metrics</button> 714<div class="acc_panel"> 715 <div class="container_other_info"> 716 <!--<div class="cross_scopus_badge"> 717 <h3 class="label">Altmetrics</h3> 718 </div>--> 719 <div class="grafico_downloads"> 720 <h3 class="label">Downloads</h3> 721 722 723 724 <section class="item downloads_chart"> 725 <div class="value"> 726 <canvas class="usageStatsGraph" data-object-type="Submission" data-object-id="1491"></canvas> 727 <div class="usageStatsUnavailable" data-object-type="Submission" data-object-id="1491"> 728 Download data is not yet available. 729 </div> 730 </div> 731 </section> 732 </div> 733 <!--<div class="cross_scopus_badge"> 734 <h3 class="label">Citations</h3> 735 736 </div>--> 737 </div> 738</div> 739 740 741<button class="accordion"><i class="fa-solid fa-pen-to-square"></i> References</button> 742<div class="acc_panel"> 743 <p> 744 Amegbor PM, Zhang Z, Dalgaard R, Sabel CE, 2020. Multilevel and spatial analyses of childhood malnutrition in Uganda: Examining individual and contextual factors. Sci Rep 10:20019. 745 DOI: <a href="https://doi.org/10.1038/s41598-020-76856-y">https://doi.org/10.1038/s41598-020-76856-y</a> 746 747 </p> 748 <p> 749 Amir-ud-Din R, Fawad S, Naz L, Zafar S, Kumar R, Pongpanich S, 2022. Nutritional inequalities among under-five children: A geospatial analysis of hotspots and cold spots in 73 low- and middle-income countries. Int J Equity Health 21:135. 750 DOI: <a href="https://doi.org/10.1186/s12939-022-01733-1">https://doi.org/10.1186/s12939-022-01733-1</a> 751 752 </p> 753 <p> 754 Amoroso CL, Nisingizwe MP, Rouleau D, Thomson DR, Kagabo DM, Bucyana T, Drobac P, Ngabo F, 2018. Next wave of interventions to reduce under-five mortality in Rwanda: A cross-sectional analysis of demographic and health survey data. BMC Pediatr 18:27. 755 DOI: <a href="https://doi.org/10.1186/s12887-018-0997-y">https://doi.org/10.1186/s12887-018-0997-y</a> 756 757 </p> 758 <p> 759 Anselin L, 1995. Local Indicators of Spatial AssociationâLISA. Geograph Anal 27:93â115. 760 DOI: <a href="https://doi.org/10.1111/j.1538-4632.1995.tb00338.x">https://doi.org/10.1111/j.1538-4632.1995.tb00338.x</a> 761 762 </p> 763 <p> 764 Anselin L, Bera AK, Florax R, Yoon MJ, 1996. Simple diagnostic tests for spatial dependence. Regional Sci Urban Econ 26:77â104. 765 DOI: <a href="https://doi.org/10.1016/0166-0462(95)02111-6">https://doi.org/10.1016/0166-0462(95)02111-6</a> 766 767 </p> 768 <p> 769 Archer KJ, Kimes RV, 2008. Empirical characterization of random forest variable importance measures. Computat Stat Data Analysis 52:2249â60. 770 DOI: <a href="https://doi.org/10.1016/j.csda.2007.08.015">https://doi.org/10.1016/j.csda.2007.08.015</a> 771 772 </p> 773 <p> 774 Atsbeha DM, Nayga RM, Rickertsen K, 2015. Can prolonged breastfeeding duration impair child growth? Evidence from rural Ethiopia. Food Policy 53:46â53. 775 DOI: <a href="https://doi.org/10.1016/j.foodpol.2015.03.010">https://doi.org/10.1016/j.foodpol.2015.03.010</a> 776 777 </p> 778 <p> 779 Azur MJ, Stuart EA, Frangakis C, Leaf PJ, 2011. Multiple imputation by chained equations: What is it and how does it work? Int J Methods Psychiatr Res 20:40â9. 780 DOI: <a href="https://doi.org/10.1002/mpr.329">https://doi.org/10.1002/mpr.329</a> 781 782 </p> 783 <p> 784 Benjamin-Chung J, Mertens A, Colford JM Jr, Hubbard AE, van der Laan MJ, Coyle J, Sofrygin O, Cai W, Nguyen A, Pokpongkiat NN, Djajadi S, Seth A, Jilek W, Jung E, Chung EO, Rosete S, Hejazi N, Malenica I, Li H, Hafen R, Subramoney V, Häggström J, Norman T, Brown KH, Christian P, Arnold BF; Ki Child Growth Consortium, 2023. Early-childhood linear growth faltering in low- and middle-income countries. Nature 621:550â7. 785 DOI: <a href="https://doi.org/10.1038/s41586-023-06418-5">https://doi.org/10.1038/s41586-023-06418-5</a> 786 787 </p> 788 <p> 789 Binagwaho A, Scott KW, Harward SH, 2016. Early childhood development in Rwanda: A policy analysis of the human rights legal framework. BMC Int Health Human Rights 16:1. 790 DOI: <a href="https://doi.org/10.1186/s12914-016-0076-0">https://doi.org/10.1186/s12914-016-0076-0</a> 791 792 </p> 793 <p> 794 Biswas M, 2022. Identifying geographical heterogeneity in associations between under-five child nutritional status and its correlates across Indian districts. Spatial Demography 10:143â87. 795 DOI: <a href="https://doi.org/10.1007/s40980-022-00104-2">https://doi.org/10.1007/s40980-022-00104-2</a> 796 797 </p> 798 <p> 799 Black RE, Allen LH, Bhutta ZA, Caulfield LE, De Onis M, Ezzati M, Mathers C, Rivera J, 2008. Maternal and child undernutrition: Global and regional exposures and health consequences. Lancet, 371:243â60. 800 DOI: <a href="https://doi.org/10.1016/S0140-6736(07)61690-0">https://doi.org/10.1016/S0140-6736(07)61690-0</a> 801 802 </p> 803 <p> 804 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, De Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell R, Uauy R, 2013. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382:427â51. 805 DOI: <a href="https://doi.org/10.1016/S0140-6736(13)60937-X">https://doi.org/10.1016/S0140-6736(13)60937-X</a> 806 807 </p> 808 <p>
809 Breiman L, 2001. Random Forests. Machine Learning 45:5â32. 810 DOI: <a href="https://doi.org/10.1023/A:1010933404324">https://doi.org/10.1023/A:1010933404324</a> 811 812 </p> 813 <p> 814 Comber A, Brunsdon C, Charlton M, Dong G, Harris R, Lu B, Lü Y, Murakami D, Nakaya T, Wang Y, Harris P, 2023. A route map for successful applications of geographically weighted regression. Geograph Anal 55:155â78. 815 DOI: <a href="https://doi.org/10.1111/gean.12316">https://doi.org/10.1111/gean.12316</a> 816 817 </p> 818 <p> 819 Daelmans B, Manji SA, Raina N, 2021. Nurturing care for early childhood development: global perspective and guidance. Indian Pediatr 58:11â15. 820 DOI: <a href="https://doi.org/10.1007/s13312-021-2349-5">https://doi.org/10.1007/s13312-021-2349-5</a> 821 822 </p> 823 <p> 824 Du Z, Wang Z, Wu S, Zhang F, Liu R, 2020. Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity. Int J Geograph Informat Sci 34:1353â77. 825 DOI: <a href="https://doi.org/10.1080/13658816.2019.1707834">https://doi.org/10.1080/13658816.2019.1707834</a> 826 827 </p> 828 <p> 829 Dusingizimana T, Weber JL, Ramilan T, Iversen PO, Brough L, 2021. An empirical study of factors associated with height-for-age z-scores of children aged 6â23 months in northwest Rwanda: The role of care practices related to child feeding and health. Br J Nutrition 12
8296:1203â14. 830 DOI: <a href="https://doi.org/10.1017/S0007114520004961">https://doi.org/10.1017/S0007114520004961</a> 831 832 </p> 833 <p> 834 Ekholuenetale M, Okonji OC, Nzoputam CI, Edet CK, Wegbom AI, Arora A, 2023. Socioeconomic disparities in Rwandaâs under-5 populationâs growth tracking and nutrition promotion: Findings from the 2019â2020 demographic and health survey. BMC Pediatrics 23:467. 835 DOI: <a href="https://doi.org/10.1186/s12887-023-04284-8">https://doi.org/10.1186/s12887-023-04284-8</a> 836 837 </p> 838 <p> 839 Eryando T, Sipahutar T, Budhiharsana MP, Siregar KN, Nur Aidi M, Minarto D, Utari DM, Rahmaniati M, Hendarwan H, 2022. Spatial analysis of stunting determinants in 514 Indonesian districts/cities: Implications for intervention and setting of priority. Geospat Health 17:1055. 840 DOI: <a href="https://doi.org/10.4081/gh.2022.1055">https://doi.org/10.4081/gh.2022.1055</a> 841 842 </p> 843 <p> 844 Fotheringham AS, Brunsdon C, Charlton M, 2003. Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons. 845 846 </p> 847 <p> 848 Fotheringham AS, Wong DWS, 1991. The modifiable areal unit problem in multivariate statistical analysis. Environ Planning A: Economy Space 23:1025â44. 849 DOI: <a href="https://doi.org/10.1068/a231025">https://doi.org/10.1068/a231025</a> 850 851 </p> 852 <p> 853 Fotheringham AS, Yang W, Kang W, 2017. Multiscale Geographically Weighted Regression (MGWR). Ann Am Assoc Geographers 107:1247â65. 854 DOI: <a href="https://doi.org/10.1080/24694452.2017.1352480">https://doi.org/10.1080/24694452.2017.1352480</a> 855 856 </p> 857 <p> 858 Getis A, 2007. Reflections on spatial autocorrelation. Regional Science and Urban Economics at 35. A Retrospective/Prospective Special Issue, 37:491â6. 859 DOI: <a href="https://doi.org/10.1016/j.regsciurbeco.2007.04.005">https://doi.org/10.1016/j.regsciurbeco.2007.04.005</a> 860 861 </p> 862 <p> 863 Hailu BA, Bogale GG, Beyene J, 2020. Spatial heterogeneity and factors influencing stunting and severe stunting among under-5 children in Ethiopia: Spatial and multilevel analysis. Sci Rep 10:16427. 864 DOI: <a href="https://doi.org/10.1038/s41598-020-73572-5">https://doi.org/10.1038/s41598-020-73572-5</a> 865 866 </p> 867 <p> 868 Haque S, Price A, Mengersen K, Hu W, 2025. Evaluating the impact of the Modifiable Areal Unit Problem on ecological model inference: A case study of COVID-19 data in Queensland, Australia. Infect Dis Modelling 10:1002â19. 869 DOI: <a href="https://doi.org/10.1016/j.idm.2025.05.003">https://doi.org/10.1016/j.idm.2025.05.003</a> 870 871 </p> 872 <p> 873 He K, Zhang X, Ren S, Sun J, 2015. delving deep into rectifiers: surpassing human-level performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV) 1026â1034. 874 DOI: <a href="https://doi.org/10.1109/ICCV.2015.123">https://doi.org/10.1109/ICCV.2015.123</a> 875 876 </p> 877 <p> 878 Ioffe S, Szegedy C, 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In F. Bach & D. Blei (Eds), Proceedings of the 32nd International Conference on Machine Learning 37:448â56. 879 880 </p> 881 <p> 882 James G, Witten D, Hastie T, Tibshirani R, Taylor J, 2023. Linear regression. In An introduction to statistical learning: With applications in python (pp. 69â134). Springer. 883 DOI: <a href="https://doi.org/10.1007/978-3-031-38747-0_3">https://doi.org/10.1007/978-3-031-38747-0_3</a> 884 885 </p> 886 <p> 887 Kalinda C, Phiri M, Simona SJ, Banda A, Wong R, Qambayot MA, Ishimwe SMC, Amberbir A, Abebe B, Gebremariam A, Nyerere JO, 2023. Understanding factors associated with rural-urban disparities of stunting among under-five children in Rwanda: A decomposition analysis approach. Mat Child Nutr 19:e13511. 888 DOI: <a href="https://doi.org/10.1111/mcn.13511">https://doi.org/10.1111/mcn.13511</a> 889 890 </p> 891 <p> 892 Khan J, Mohanty SK, 2018. Spatial heterogeneity and correlates of child malnutrition in districts of India. BMC Public Health 18:1027. 893 DOI: <a href="https://doi.org/10.1186/s12889-018-5873-z">https://doi.org/10.1186/s12889-018-5873-z</a> 894 895 </p> 896 <p> 897 Kinyoki DK, Osgood-Zimmerman AE, Pickering BV, Schaeffer LE, Marczak LB, Lazzar-Atwood A, Collison ML, Henry NJ, Abebe Z, Adamu AA, Adekanmbi V, Ahmadi K, Ajumobi O, Al-Eyadhy A, Al-Raddadi RM, Alahdab F, Alijanzadeh M, Alipour V, Altirkawi K, ⦠Local Burden of Disease Child Growth Failure Collaborators. (2020). Mapping child growth failure across low- and middle-income countries. Nature 577:231â4. 898 DOI: <a href="https://doi.org/10.1038/s41586-019-1878-8">https://doi.org/10.1038/s41586-019-1878-8</a> 899 900 </p> 901 <p> 902 Klein N, Kneib T, Marra G, Radice R, 2020. Chapter 5âBayesian mixed binary-continuous copula regression with an application to childhood undernutrition. In Y. Fan, D. Nott, M. S. Smith, & J.-L. Dortet-Bernadet (Eds), Flexible Bayesian Regression Modelling Academic Press. pp. 121â152. 903 DOI: <a href="https://doi.org/10.1016/B978-0-12-815862-3.00011-1">https://doi.org/10.1016/B978-0-12-815862-3.00011-1</a> 904 905 </p> 906 <p> 907 Kramer MS, Moodie EEM, Dahhou M, Platt RW, 2011. Breastfeeding and infant size: evidence of reverse causality. Am J Epidemiol 173(9), 978â983. 908 DOI: <a href="https://doi.org/10.1093/aje/kwq495">https://doi.org/10.1093/aje/kwq495</a> 909 910 </p> 911 <p> 912 Kuse KA, Debeko DD, 2023. Spatial distribution and determinants of stunting, wasting and underweight in children under-five in Ethiopia. BMC Public Health 23:641. 913 DOI: <a href="https://doi.org/10.1186/s12889-023-15488-z">https://doi.org/10.1186/s12889-023-15488-z</a> 914 915 </p> 916 <p> 917 Leung Y, Mei C-L, Zhang W-X, 2000. Statistical tests for spatial nonstationarity based on the geographically weighted regression Model. Environ Planning A 32:9â32. 918 DOI: <a href="https://doi.org/10.1068/a3162">https://doi.org/10.1068/a3162</a> 919 920 </p> 921 <p> 922 Luo Y, Yan J, McClure S, 2021. Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis. Environ Sci Pollut Res 28:6587â99. 923 DOI: <a href="https://doi.org/10.1007/s11356-020-10962-2">https://doi.org/10.1007/s11356-020-10962-2</a> 924 925 </p> 926 <p> 927 Ma Z, Huang Z. 2023. A Bayesian implementation of the multiscale geographically weighted regression model with INLA. Ann Am Assoc Geographers 113:1501â15. 928 DOI: <a href="https://doi.org/10.1080/24694452.2023.2187756">https://doi.org/10.1080/24694452.2023.2187756</a> 929 930 </p> 931 <p> 932 Marquis GS, Habicht JP, Lanata CF, Black RE, Rasmussen KM, 1997. Association of breastfeeding and stunting in Peruvian toddlers: An example of reverse causality. Int J Epidemiol 26:349â56. 933 DOI: <a href="https://doi.org/10.1093/ije/26.2.349">https://doi.org/10.1093/ije/26.2.349</a> 934 935 </p> 936 <p> 937 McMahan LD, Sprague C, 2024. The varied perspectives of organisational effectiveness: Whatâs at stake for early childhood development programmes in Rwanda? Global Public Health 19
937:2377280. 938 DOI: <a href="https://doi.org/10.1080/17441692.2024.2377280">https://doi.org/10.1080/17441692.2024.2377280</a> 939 940 </p> 941 <p> 942 Mertens A, Benjamin-Chung J, Colford JM Jr, Coyle J, van der Laan MJ, Hubbard AE, Rosete S, Malenica I, Hejazi N, Sofrygin O, Cai W, Li H, Nguyen A, Pokpongkiat NN, Djajadi S, Seth A, Jung E, Chung EO, Jilek W, Subramoney V, Hafen R, Häggström J, Norman T, Brown KH, Christian P, Arnold BF; Ki Child Growth Consortium, 2023. Causes and consequences of child growth faltering in low-resource settings. Nature 621:568â76. 943 944 </p> 945 <p> 946 Miconi D, Beeman I, Robert E, Beatson J, Ruiz-Casares M, 2018. Child supervision in low- and middle-income countries: A scoping review. Children Youth Serv Rev 89:226â42. 947 DOI: <a href="https://doi.org/10.1016/j.childyouth.2018.04.040">https://doi.org/10.1016/j.childyouth.2018.04.040</a> 948 949 </p> 950 <p> 951 Moonga G, Böse-OâReilly S, Berger U, Harttgen K, Michelo C, Nowak D, Siebert U, Yabe J, Seiler J, 2021. Modelling chronic malnutrition in Zambia: A Bayesian distributional regression approach. PLOS ONE 16:e0255073. 952 DOI: <a href="https://doi.org/10.1371/journal.pone.0255073">https://doi.org/10.1371/journal.pone.0255073</a> 953 954 </p> 955 <p> 956 Muche A, Gezie LD, Baraki AG, Amsalu ET, 2021. Predictors of stunting among children age 6â59 months in Ethiopia using Bayesian multi-level analysis. Sci Rep 11:3759. 957 DOI: <a href="https://doi.org/10.1038/s41598-021-82755-7">https://doi.org/10.1038/s41598-021-82755-7</a> 958 959 </p> 960 <p> 961 NISR [Rwanda], Ministry of Health (MOH) [Rwanda], & ICF. (2021). Rwanda Demographic and Health Survey 2019-20 Final Report. Available from: <a href="https://dhsprogram.com/pubs/pdf/FR370/FR370.pdf">https://dhsprogram.com/pubs/pdf/FR370/FR370.pdf</a> 962 963 </p> 964 <p> 965 Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, Graetz N, Kinyoki DK, Ray SE, Bhatt S, Browne AJ, Burstein R, Cameron E, Casey DC, Deshpande A, Fullman N, Gething PW, Gibson H, Henry NJ, ⦠Hay SI, 2018. Mapping child growth failure in Africa between 2000 and 2015. Nature 555:41â47. 966 DOI: <a href="https://doi.org/10.1038/nature25760">https://doi.org/10.1038/nature25760</a> 967 968 </p> 969 <p> 970 Oshan TM, Li Z, Kang W, Wolf LJ, Fotheringham AS, 2019. mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS Int J Geo-Information 8:269. 971 DOI: <a href="https://doi.org/10.3390/ijgi8060269">https://doi.org/10.3390/ijgi8060269</a> 972 973 </p> 974 <p> 975 Perkins JM, Kim R, Krishna A, McGovern M, Aguayo VM, Subramanian SV, 2017. Understanding the association between stunting and child development in low- and middle-income countries: Next steps for research and intervention. Soc Sci Med 193:101â9. 976 DOI: <a href="https://doi.org/10.1016/j.socscimed.2017.09.039">https://doi.org/10.1016/j.socscimed.2017.09.039</a> 977 978 </p> 979 <p> 980 Prechelt L, 1998. Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks, 11:761â7. 981 DOI: <a href="https://doi.org/10.1016/S0893-6080(98)00010-0">https://doi.org/10.1016/S0893-6080(98)00010-0</a> 982 983 </p> 984 <p> 985 Puri P, Khan J, Shil A, Ali M, 2020. A cross-sectional study on selected child health outcomes in India: Quantifying the spatial variations and identification of the parental risk factors. Sci Rep 10:6645. 986 DOI: <a href="https://doi.org/10.1038/s41598-020-63210-5">https://doi.org/10.1038/s41598-020-63210-5</a> 987 988 </p> 989 <p> 990 Quamme SH, Iversen PO, 2022. Prevalence of child stunting in Sub-Saharan Africa and its risk factors. Clinical Nutrition Open Sci 42:49â61. 991 DOI: <a href="https://doi.org/10.1016/j.nutos.2022.01.009">https://doi.org/10.1016/j.nutos.2022.01.009</a> 992 993 </p> 994 <p> 995 Quiñones S, Goyal A, Ahmed ZU, 2021. Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA. Sci Rep 11:6955. 996 DOI: <a href="https://doi.org/10.1038/s41598-021-85381-5">https://doi.org/10.1038/s41598-021-85381-5</a> 997 998 </p> 999 <p> 1000 Ruiz-Casares M, Nazif-Muñoz JI, Iwo R, Oulhote Y, 2018. Nonadult supervision of children in low- and middle-income countries: results from 61 national population-based surveys. Int J Environ Re
1000s Public Health 15:1564. 1001 DOI: <a href="https://doi.org/10.3390/ijerph15081564">https://doi.org/10.3390/ijerph15081564</a> 1002 1003 </p> 1004 <p> 1005 Samuel VW, 2023. miceforest: Fast Imputation with Random Forests in Python. Available from: <a href="https://github.com/AnotherSamWilson/miceforest/">https://github.com/AnotherSamWilson/miceforest/</a> 1006 1007 </p> 1008 <p> 1009 Seboka BT, Hailegebreal S, Mamo TT, Yehualashet DE, Gilano G, Kabthymer RH, Ewune HA, Kassa R, Debisa MA, Yawo MN, Endashaw H, Demeke AD, Tesfa GA, 2022. Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: A geographically weighted regression analysis. J Health Population Nutr 41:28. 1010 DOI: <a href="https://doi.org/10.1186/s41043-022-00309-7">https://doi.org/10.1186/s41043-022-00309-7</a> 1011 1012 </p> 1013 <p> 1014 Siqi J, Yuhong W, Ling C, Xiaowen B, 2023. A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models. Urban Climate 47:101390. 1015 DOI: <a href="https://doi.org/10.1016/j.uclim.2022.101390">https://doi.org/10.1016/j.uclim.2022.101390</a> 1016 1017 </p> 1018 <p> 1019 Ssentongo P, Ssentongo AE, Ba DM, Ericson JE, Na M, Gao X, Fronterre C, Chinchilli VM, Schiff SJ, 2021. Global, regional and national epidemiology and prevalence of child stunting, wasting and underweight in low-and middle-income countries, 2006â2018. Sci Rep 11:1â12. 1020 DOI: <a href="https://doi.org/10.1038/s41598-021-84302-w">https://doi.org/10.1038/s41598-021-84302-w</a> 1021 1022 </p> 1023 <p> 1024 Striessnig E, Bora JK, 2020. Under-five child growth and nutrition status: spatial clustering of Indian districts. Spatial Demography 8:63â84. 1025 DOI: <a href="https://doi.org/10.1007/s40980-020-00058-3">https://doi.org/10.1007/s40980-020-00058-3</a> 1026 1027 </p> 1028 <p> 1029 Sun Y, Jia W, Zhu W, Zhang X, Saidahemaiti S, Hu,T, Guo H, 2022. Local neural-network-weighted models for occurrence and number of down wood in natural forest ecosystem. Sci Rep 12:6375. 1030 DOI: <a href="https://doi.org/10.1038/s41598-022-10312-x">https://doi.org/10.1038/s41598-022-10312-x</a> 1031 1032 </p> 1033 <p> 1034 Tamir TT, Tekeba B, Mekonen EG, Zegeye AF, Gebrehana DA, 2024. Spatial heterogeneity and predictors of stunting among under five children in Mozambique: A geographically weighted regression. Front Public Health 12:1502018. 1035 DOI: <a href="https://doi.org/10.3389/fpubh.2024.1502018">https://doi.org/10.3389/fpubh.2024.1502018</a> 1036 1037 </p> 1038 <p> 1039 UNICEF, WHO, & World Bank. 2023. Levels and trends in child malnutrition: Key findings of the 2023 Edition of the Joint Child Malnutrition Estimates. New York: UNICEF and WHO. 1040 1041 </p> 1042 <p> 1043 Utumatwishima JN, Mogren I, Elfving K, Umubyeyi A, Krantz G, 2025. Association between poor mental health in mothers and child stunting: A population-based cross-sectional study in Rwanda. BMJ Open 15:e101117. 1044 DOI: <a href="https://doi.org/10.1136/bmjopen-2025-101117">https://doi.org/10.1136/bmjopen-2025-101117</a> 1045 1046 </p> 1047 <p> 1048 Uwiringiyimana V, Osei F, Amer S, Veldkamp A, 2022. Bayesian geostatistical modelling of stunting in Rwanda: Risk factors and spatially explicit residual stunting burden. BMC Public Health 22:159. 1049 DOI: <a href="https://doi.org/10.1186/s12889-022-12552-y">https://doi.org/10.1186/s12889-022-12552-y</a> 1050 1051 </p> 1052 <p> 1053 Vaivada T, Akseer N, Akseer S, Somaskandan A, Stefopulos M, Bhutta ZA, 2020. Stunting in childhood: An overview of global burden, trends, determinants, and drivers of decline. Am J Clin Nutrition 112:777S-91S. 1054 DOI: <a href="https://doi.org/10.1093/ajcn/nqaa159">https://doi.org/10.1093/ajcn/nqaa159</a> 1055 1056 </p> 1057 <p> 1058 Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, Sachdev HS, 2008. Maternal and child undernutrition: Consequences for adult health and human capital. Lancet 371:340â57. 1059 DOI: <a href="https://doi.org/10.1016/S0140-6736(07)61692-4">https://doi.org/10.1016/S0140-6736(07)61692-4</a> 1060 1061 </p> 1062 <p> 1063 WHO, 2006. WHO child growth standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. 1064 1065 </p> 1066 <p> 1067 WHO, UNICEF, & World Bank Group. (2018). Nurturing care for early childhood development: A framework for helping children survive and thrive to transform health and human potential. 1068 1069 </p> 1070 <p> 1071 Wu S, Wang Z, Du Z, Huang B, Zhang F. Liu R, 2021. Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. Int J Geograph Informat Sci 35:582â608. 1072 DOI: <a href="https://doi.org/10.1080/13658816.2020.1775836">https://doi.org/10.1080/13658816.2020.1775836</a> 1073 1074 </p> 1075 <p> 1076 Wu Y, 2021. Canât ridge regression perform variable selection? Technometrics 63:263â71. 1077 DOI: <a href="https://doi.org/10.1080/00401706.2020.1791254">https://doi.org/10.1080/00401706.2020.1791254</a> 1078 1079 </p> 1080 <p> 1081 Yi H, Li M, Dong Y, Gan Z, He L, Li X, Tao Y, Xia Z, Xia Z, Xue Y, Zhai Z, 2024. Nonlinear associations between the ratio of family income to poverty and all-cause mortality among adults in NHANES study. Sci Rep 14:12018. 1082 DOI: <a href="https://doi.org/10.1038/s41598-024-63058-z">https://doi.org/10.1038/s41598-024-63058-z</a> 1083 1084 </p> 1085 <p> 1086 Zhao Z, Xu Z, Hu C, Wang K, Ding X, 2024. Geographically weighted neural network considering spatial heterogeneity for landslide susceptibility mapping: A case study of Yichang City, China. CATENA, 234:107590. 1087 DOI: <a href="https://doi.org/10.1016/j.catena.2023.107590">https://doi.org/10.1016/j.catena.2023.107590</a> 1088 1089 </p> 1090 </div> 1091 1092<button class="accordion"><i class="fa-solid fa-check-double"></i> Information</button> 1093<div class="acc_panel"> 1094 1095 <div class="content"> 1096 <h3 class="label">Ethics Approval</h3> 1097 <div style="margin-left:1em;">Ethical clearance for this study was obtained from the University of Rwanda College of Medicine and Health Sciences Institutional Review Board (reference No. 181/CHMS IRB/2
1097021). All required permissions were also received from the National Institute of Statistics of Rwanda (reference No 0971/2020/10/NISR and No 0295/2021/10/NISR), the Ministry of Local Governance (reference No 0779/07.01), and the Ministry of Health (reference No 20/4184/DPMEHF/2021 and No NHRC/2020/PROT/047). All participants were fully informed about the study's aims, procedures, and measures to ensure confidentiality and privacy, in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. Interviews were conducted privately to safeguard participant confidentiality, and a rigorous data anonymization protocol was applied to ensure data could not be traced back to participants during analysis or in published results.</div> 1098 </div> 1099 1100 <div class="content"> 1101 <h3 class="label">CRediT authorship contribution</h3> 1102 <div style="margin-left:1em;"><p>Clarisse Kagoyire and Ali Mansourian conceptualized and designed the study. Clarisse Kagoyire carried out the literature review, developed the methodology, formal analysis, writing and visualisation, under Ali Mansourian supervision. Ali Mansourian critically revised and edited the manuscript. Gilbert Nduwayezu and Rachid Oucheikh contributed to the methodology. Jean Pierre Bizimana and Petter Pilesjö critically revised the manuscript. All authors revised, read and approved the final manuscript.</p></div> 1103 </div> 1104 1105 <div class="content"> 1106 <h3 class="label">Supporting Agencies</h3> 1107 <div style="margin-left:1em;">This study was funded by the Swedish International Development Cooperation Agency (SIDA): SIDA contribution No. 11277.</div> 1108 </div> 1109 1110 <div class="content"> 1111 <h3 class="label">Data Availability Statement</h3> 1112 <div style="margin-left:1em;"><p>The datasets used and/or analysed during the current study are not publicly available due to the sensitive nature of the information they contain, which includes geo-located data on surveyed households, as well as detailed records on gender-based violence and mental health. These datasets contain personal and other highly sensitive information, necessitating restrictions to protect the privacy and confidentiality of the participants. Access to the data will be granted upon reasonable request to the corresponding author, subject to compliance with appropriate ethical standards and data sharing agreements.</p></div> 1113 </div> 1114 </div> 1115 1116 <button class="accordion"> 1117 <span><i class="fa-solid fa-list"></i> Author Biography</span> 1118 </button> 1119<div class="acc_panel"> 1120 <div id="biographies" class="tabcontent"> 1121 <section class="item author_bios"> 1122 <div class="sub_item"> 1123 <div class="label"> 1124 Petter Pilesjö, Lund University, Lund, Sweden 1125 </div> 1126 <div class="value" style="margin-left:1em;"> 1127 <p>Department of Physical Geography and Ecosystem Science.</p> 1128 </div> 1129 </div> 1130 </section> 1131 </div> 1132</div> 1133 1134<button class="accordion"><i class="fa-solid fa-link"></i> Citation / <i class="fa-solid fa-copyright"></i> Copyright</button> 1135<div class="acc_panel"> 1136 <div class="item citation"> 1137 <section class="sub_item citation_display"> 1138 <h3 class="label"> 1139 How to Cite 1140 </h3> 1141 <br /><br /> 1142 <div class="value"> 1143 <div id="citationOutput" role="region" aria-live="polite"> 1144 <div class="csl-bib-body"> 1145 <div class="csl-entry">Household-level spatial modelling of child height-for-age in Northern Province, Rwanda: comparison of geographically weighted and neural network weighted regression models. 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