Spatial Bayesian Modeling of Child Malnutrition with Measurement Error in Demographic Health Survey Data

Spatial Bayesian Modeling of Child Malnutrition with Measurement Error in Demographic Health Survey Data | 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 Spatial Bayesian Modeling of Child Malnutrition with Measurement Error in Demographic Health Survey Data Romuald Daniel BOY-NGBOGBELE, Bertin Dehigbe, Aboubakar Alfa Samuel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9369506/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Childhood malnutrition remains a major public health challenge in low- and middle-income countries, particularly in Sub-Saharan Africa. Conventional statistical models often ignore spatial dependence and measurement error, potentially leading to biased estimates and reduced predictive performance. Methods: This study proposes a spatial Bayesian logistic regression model that incorporates both measurement error and spatial dependence for analyzing pooled cross-sectional data. A simulation study, designed to mimic Demographic and Health Survey (DHS) data, was conducted using multiple clusters and survey periods. Covariates were generated with realistic measurement error, and spatial correlation was introduced through a distance-based covariance structure. The performance of the spatial model was compared with a non-spatial Bayesian model using accuracy, area under the curve (AUC), precision, recall, F1-score, and estimated prevalence. Results: The spatial Bayesian model consistently outperformed the non-spatial model across all evaluation metrics. Improvements were observed in accuracy (approximately 3--5\%), AUC (0.05--0.07 increase), precision, recall, and F1-score. Additionally, the spatial model produced lower and more stable prevalence estimates, indicating improved calibration and reduced bias. These results demonstrate the advantages of incorporating spatial structure in modeling childhood malnutrition. Conclusion: Incorporating spatial dependence and measurement error within a Bayesian framework significantly enhances model performance and provides more reliable estimates of childhood malnutrition. The proposed approach offers a valuable tool for improving health data analysis and supports more effective targeting of public health interventions. Spatial Bayesian modeling Measurement error Childhood malnutrition Demographic and Health Surveys (DHS) Hierarchical logistic regression Spatial epidemiology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Editor assigned by journal 11 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 09 Apr, 2026 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-9369506","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622743505,"identity":"87e82662-f963-44ce-9471-50a9916d82f5","order_by":0,"name":"Romuald Daniel BOY-NGBOGBELE","email":"data:image/png;base64,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","orcid":"","institution":"Pan African University","correspondingAuthor":true,"prefix":"","firstName":"Romuald","middleName":"Daniel","lastName":"BOY-NGBOGBELE","suffix":""},{"id":622743508,"identity":"58f47edf-d70a-4671-b5d5-7df599dac1ae","order_by":1,"name":"Bertin Dehigbe","email":"","orcid":"","institution":"Institute of Mathematics and Physical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bertin","middleName":"","lastName":"Dehigbe","suffix":""},{"id":622743510,"identity":"cfc5cf4c-c62a-4898-bef8-8601ba1fa2e3","order_by":2,"name":"Aboubakar Alfa Samuel","email":"","orcid":"","institution":"Institute of Mathematics and Physical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Aboubakar","middleName":"Alfa","lastName":"Samuel","suffix":""}],"badges":[],"createdAt":"2026-04-09 13:54:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9369506/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9369506/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961870,"identity":"6f0efd5c-d454-48cc-9c26-47717030893c","added_by":"auto","created_at":"2026-04-15 09:27:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1166887,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9369506/v1_covered_a02b31e2-bbb2-413e-b5f1-4cfb275ea962.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Bayesian Modeling of Child Malnutrition with Measurement Error in Demographic Health Survey Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"discover-social-science-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diss","sideBox":"Learn more about [Discover Social Science and Health](https://www.springer.com/journal/44155)","snPcode":"","submissionUrl":"","title":"Discover Social Science and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Spatial Bayesian modeling, Measurement error, Childhood malnutrition, Demographic and Health Surveys (DHS), Hierarchical logistic regression, Spatial epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-9369506/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9369506/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Childhood malnutrition remains a major public health challenge in low- and middle-income countries, particularly in Sub-Saharan Africa. Conventional statistical models often ignore spatial dependence and measurement error, potentially leading to biased estimates and reduced predictive performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This study proposes a spatial Bayesian logistic regression model that incorporates both measurement error and spatial dependence for analyzing pooled cross-sectional data. A simulation study, designed to mimic Demographic and Health Survey (DHS) data, was conducted using multiple clusters and survey periods. Covariates were generated with realistic measurement error, and spatial correlation was introduced through a distance-based covariance structure. 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