Sampling and Analysing Spatial Determinants of Undernutrition in Northern Rwanda – A Multidisciplinary Approach

Sampling and Analysing Spatial Determinants of Undernutrition in Northern Rwanda – A Multidisciplinary Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sampling and Analysing Spatial Determinants of Undernutrition in Northern Rwanda – A Multidisciplinary Approach Clarisse Kagoyire, Albert Ndagijimana, Gilbert Nduwayezu, Jean Nepo Utumatwishima, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4751165/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: In Rwanda, various intervention initiatives have led to progress in reducing maternal and child undernutrition. However, the rate of stunting, the chronic form of malnutrition, remains high with about 33% of children under five years stunted countrywide, especially in the Northern Province where the stunting rate is 40.5%. This study aimed to sample and analyse spatially-explicit undernutrition determinants in Northern Rwanda through a multidisciplinary approach. Methods: A multidisciplinary team of experts in public health, geographical information science, epidemiology, animal science and veterinary medicine conducted a population-based cross-sectional study in five districts of the Northern Province of Rwanda. Targeting households with mothers of children aged 1-36 months, we used a structured household-level questionnaire to collect information on socio-demographic and economic factors, child health, childcare practices, presence of violence against children and mothers, maternal health, livestock production and animal health. In addition to child and maternal anthropometric measurements, we collected rectal swabs and blood samples from children to identify potential gastrointestinal pathogens and estimate haemoglobin levels. Data on environmental and physical characteristics of the study area were obtained from existing national datasets. To capture GPS coordinates of each visited household, we customized a web-based geodata management platform into a mobile application for in-field data collection (emGeo). To analyse the determinants underlying stunting, we used the following key techniques: feature selection using random forest to identify 26 most pertinent predictors, univariable and multivariable logistic regression analyses, and geographically weighted logistic regression (GWLR) to account for spatial variation. Results : The multivariable logistic regression accounted for 30% (R 2 = 0.30) of the variation in stunting, with higher odds with increase in child’s age and sex, mother’s decision-making autonomy in major household purchases, having a friend to assist the mother when ill, access to electricity, having a home garden, and handwashing practices. The geographically weighted logistics regression improved explanatory power to 35%, highlighting spatial heterogeneity in the predictors' effects across the study area. Conclusion : A multidisciplinary approach is essential to tackle multifaceted challenges like stunting. Combining logistic regression and geospatial statistics enabled us to identify areas with high stunting risk factors and guide policymakers toward geographically targeted interventions, particularly in limited settings, including Rwanda. undernutrition determinants stunting cross-sectional survey statistics geographically weighted logistic regression Full Text Additional Declarations No competing interests reported. Supplementary Files 1.SummaryStatisticsLogisticregressionGeographicallyweightedlogisticregression.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4751165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355137075,"identity":"a5c254b0-92b9-4b45-9891-4655853530b4","order_by":0,"name":"Clarisse 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Multidisciplinary Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"undernutrition determinants, stunting, cross-sectional survey, statistics, geographically weighted logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-4751165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4751165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e In Rwanda, various intervention initiatives have led to progress in reducing maternal and child undernutrition. However, the rate of stunting, the chronic form of malnutrition, remains high with about 33% of children under five years stunted countrywide, especially in the Northern Province where the stunting rate is 40.5%. This study aimed to sample and analyse spatially-explicit undernutrition determinants in Northern Rwanda through a multidisciplinary approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A multidisciplinary team of experts in public health, geographical information science, epidemiology, animal science and veterinary medicine conducted a population-based cross-sectional study in five districts of the Northern Province of Rwanda. Targeting households with mothers of children aged 1-36 months, we used a structured household-level questionnaire to collect information on socio-demographic and economic factors, child health, childcare practices, presence of violence against children and mothers, maternal health, livestock production and animal health. In addition to child and maternal anthropometric measurements, we collected rectal swabs and blood samples from children to identify potential gastrointestinal pathogens and estimate haemoglobin levels. Data on environmental and physical characteristics of the study area were obtained from existing national datasets. To capture GPS coordinates of each visited household, we customized a web-based geodata management platform into a mobile application for in-field data collection (emGeo).\u003c/p\u003e\n\u003cp\u003eTo analyse the determinants underlying stunting, we used the following key techniques: feature selection using random forest to identify 26 most pertinent predictors, univariable and multivariable logistic regression analyses, and geographically weighted logistic regression (GWLR) to account for spatial variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The multivariable logistic regression accounted for 30% (R\u003csup\u003e2\u003c/sup\u003e = 0.30) of the variation in stunting, with higher odds with increase in child’s age and sex, mother’s decision-making autonomy in major household purchases, having a friend to assist the mother when ill, access to electricity, having a home garden, and handwashing practices. The geographically weighted logistics regression improved explanatory power to 35%, highlighting spatial heterogeneity in the predictors' effects across the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: A multidisciplinary approach is essential to tackle multifaceted challenges like stunting. Combining logistic regression and geospatial statistics enabled us to identify areas with high stunting risk factors and guide policymakers toward geographically targeted interventions, particularly in limited settings, including Rwanda.\u003c/p\u003e","manuscriptTitle":"Sampling and Analysing Spatial Determinants of Undernutrition in Northern Rwanda – A Multidisciplinary Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 18:55:34","doi":"10.21203/rs.3.rs-4751165/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"346b1e73-1a0b-410c-97e8-0f289e2eca13","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-10T07:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-21 18:55:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4751165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4751165","identity":"rs-4751165","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}