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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
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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"/>
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34<meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2026-01-28"/>
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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 1–36 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 Moran’s 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 (Moran’s I = −0.005). 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."/>
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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&#039;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"/>
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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;
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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"/>
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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 1–36 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 Moran’s 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 (Moran’s I = −0.005). 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&#039;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
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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 Association—LISA. Geograph Anal 27:93–115."/>
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 26:77–104."/>
98<meta name="citation_reference" content="Archer KJ, Kimes RV, 2008. Empirical characterization of random forest variable importance measures. Computat Stat Data Analysis 52:2249–60."/>
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 53:46–53."/>
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 20:40–9."/>
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 621:550–7."/>
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 10:143–87."/>
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, 371:243–60."/>
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 382:427–51."/>
106<meta name="citation_reference" content="Breiman L, 2001. Random Forests. Machine Learning 45:5–32."/>
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 55:155–78."/>
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 58:11–15."/>
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 34:1353–77."/>
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 6−23 months in northwest Rwanda: The role of care practices related to child feeding and health. Br J Nutrition 12
1106:1203–14."/>
111<meta name="citation_reference" content="Ekholuenetale M, Okonji OC, Nzoputam CI, Edet CK, Wegbom AI, Arora A, 2023. Socioeconomic disparities in Rwanda’s under-5 population’s growth tracking and nutrition promotion: Findings from the 2019–2020 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 &amp; 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 23:1025–44."/>
115<meta name="citation_reference" content="Fotheringham AS, Yang W, Kang W, 2017. Multiscale Geographically Weighted Regression (MGWR). Ann Am Assoc Geographers 107:1247–65."/>
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, 37:491–6."/>
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 10:1002–19."/>
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) 1026–1034."/>
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 &amp; D. Blei (Eds), Proceedings of the 32nd International Conference on Machine Learning 37:448–56."/>
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. 69–134). 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 577:231–4."/>
125<meta name="citation_reference" content="Klein N, Kneib T, Marra G, Radice R, 2020. Chapter 5—Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition. In Y. Fan, D. Nott, M. S. Smith, &amp; J.-L. Dortet-Bernadet (Eds), Flexible Bayesian Regression Modelling Academic Press. pp. 121–152."/>
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), 978–983."/>
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 32:9–32."/>
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 28:6587–99."/>
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 113:1501–15."/>
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 26:349–56."/>
132<meta name="citation_reference" content="McMahan LD, Sprague C, 2024. The varied perspectives of organisational effectiveness: What’s 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 621:568–76."/>
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 89:226–42."/>
135<meta name="citation_reference" content="Moonga G, Böse-O’Reilly 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 6–59 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], &amp; 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 555:41–47."/>
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 193:101–9."/>
141<meta name="citation_reference" content="Prechelt L, 1998. Automatic early stopping using cross validation: Quantifying the criteria. Neural Networks, 11:761–7."/>
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 42:49–61."/>
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, 2006–2018. Sci Rep 11:1–12."/>
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 8:63–84."/>
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, &amp; 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 371:340–57."/>
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, &amp; World Bank Group. (2018). Nurturing care for early childhood development: A framework for helping children survive and thrive to transform health and human potential."/>
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161<meta name="citation_reference" content="Wu Y, 2021. Can’t ridge regression perform variable selection? Technometrics 63:263–71."/>
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460						<a href="https://doi.org/10.4081/gh.2026.1491">https://doi.org/10.4081/gh.2026.1491</a>
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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>
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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.
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517                	<b>Received:</b> 28 January 2026<br/>
518	<b>Accepted:</b> 29 March 2026<br/>
519	    <b>Published:</b> 14 September 2026<br/>
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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>
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589								    <a href="https://www.geospatialhealth.net/gh/search?authors=Clarisse Kagoyire"><i class="fa fa-fw fa-user"></i> Clarisse Kagoyire</a>
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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 1–36 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 Moran’s 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 (Moran’s I = −0.005). 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
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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 Association—LISA. Geograph Anal 27:93–115.
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 26:77–104.
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 52:2249–60.
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 53:46–53.
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 20:40–9.
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 621:550–7.
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 10:143–87.
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, 371:243–60.
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 382:427–51.
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 45:5–32.
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 55:155–78.
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 58:11–15.
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 34:1353–77.
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 6−23 months in northwest Rwanda: The role of care practices related to child feeding and health. Br J Nutrition 12
8296:1203–14.
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 Rwanda’s under-5 population’s growth tracking and nutrition promotion: Findings from the 2019–2020 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 &amp; 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 23:1025–44.
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 107:1247–65.
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, 37:491–6.
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 10:1002–19.
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) 1026–1034.
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 &amp; D. Blei (Eds), Proceedings of the 32nd International Conference on Machine Learning 37:448–56.
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. 69–134). 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 577:231–4.
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 5—Bayesian mixed binary-continuous copula regression with an application to childhood undernutrition. In Y. Fan, D. Nott, M. S. Smith, &amp; J.-L. Dortet-Bernadet (Eds), Flexible Bayesian Regression Modelling Academic Press. pp. 121–152.
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), 978–983.
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 32:9–32.
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 28:6587–99.
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 113:1501–15.
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 26:349–56.
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: What’s 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 621:568–76.
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 89:226–42.
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-O’Reilly 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 6–59 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], &amp; 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 555:41–47.
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 193:101–9.
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, 11:761–7.
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 42:49–61.
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, 2006–2018. Sci Rep 11:1–12.
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 8:63–84.
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, &amp; 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 371:340–57.
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, &amp; 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 35:582–608.
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. Can’t ridge regression perform variable selection? Technometrics 63:263–71.
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>
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1114    </div>
1115
1116									<button class="accordion">
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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>
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1133
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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. (2026). <i>Geospatial Health</i>, <i>21</i>(2). <a href="https://doi.org/10.4081/gh.2026.1491">https://doi.org/10.4081/gh.2026.1491</a></div>
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1160											>
1161												ABNT
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1163										</li>
1164																			<li>
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1171												ACM
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1173										</li>
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1175											<a
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1203										</li>
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1210											>
1211												Harvard
1212											</a>
1213										</li>
1214																			<li>
1215											<a
1216												aria-controls="citationOutput"
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1221												IEEE
1222											</a>
1223										</li>
1224																			<li>
1225											<a
1226												aria-controls="citationOutput"
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1233										</li>
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1241												Turabian
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1251												Vancouver
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1255																	<h4 style="margin:1em;">Download Citation</h4>
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1257																					<li>
1258												<a href="https://www.geospatialhealth.net/gh/citationstylelanguage/download/ris?submissionId=1491&amp;publicationId=1295&amp;issueId=50">
1259													<span class="fa fa-download"></span>
1260													Endnote/Zotero/Mendeley (RIS)
1261												</a>
1262											</li>
1263																					<li>
1264												<a href="https://www.geospatialhealth.net/gh/citationstylelanguage/download/bibtex?submissionId=1491&amp;publicationId=1295&amp;issueId=50">
1265													<span class="fa fa-download"></span>
1266													BibTeX
1267												</a>
1268											</li>
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1278																		Copyright (c) 2026 the Author(s)
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1301		</div><!-- .main_entry -->
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1338<div class="pkp_block block_custom" id="customblock-indexing">
1339	<h2 class="title pkp_screen_reader">indexing</h2>
1340	<div class="content">
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1342<ul>
1343<li><a href="http://www.ncbi.nlm.nih.gov/pubmed/?term=%22Geospat+Health%22%5Bjour%5D" target="_blank" rel="noopener">PubMed</a></li>
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1356		Categories
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1361													<li class="category_1">
1362								<a href="https://www.geospatialhealth.net/gh/catalog/category/news">
1363									Contents
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1367								<a href="https://www.geospatialhealth.net/gh/catalog/category/articles">
1368									Original Articles
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1371													<li class="category_3 is_sub">
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1373									Corrections
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1377								<a href="https://www.geospatialhealth.net/gh/catalog/category/editorials">
1378									Editorials
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1380							</li>
1381													<li class="category_6 is_sub">
1382								<a href="https://www.geospatialhealth.net/gh/catalog/category/GIS">
1383									Gnosis(GIS) symposium
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1388									Reviews
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1396<div class="pkp_block block_custom" id="customblock-vhealths">
1397	<h2 class="title pkp_screen_reader">vhealths</h2>
1398	<div class="content">
1399		<div class="title">vHealth Section</div>
1400<p align="center"><img src="/public/site/images/mikimos/logo_vhealth1.png"></p>
1401<p align="center"><strong>Geospatial Health</strong><br>opens the new vHealth section. <a href="http://www.geospatialhealth.net/gh/vhealth"><strong>Discover vHealth</strong></a></p>
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