{"paper_id":"3030a046-e960-417b-97ee-1b318938daf7","body_text":"A Bayesian Approach to Correcting Measurement Error in Estimating Childhood Malnutrition Prevalence fromPooled Demographic and Health Surveys 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 Article A Bayesian Approach to Correcting Measurement Error in Estimating Childhood Malnutrition Prevalence fromPooled Demographic and Health Surveys Data Romuald Daniel BOY-NGBOGBELE, Raymond Affossogbe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8817211/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 Precise assessment of malnutrition prevalence is essential for successful health policy and the monitoring of Sustainable Development Goals (SDGs). This study seeks to amalgamate repeated cross-sectional Demographic and Health Surveys (DHS) data for Cameroon within a Bayesian frame- work, rectify measurement errors in child stunting indicators, estimate adjusted temporal trends, quantify biases arising from the neglect of errors, and formulate a policy brief framework for Sus- tainable Development Goal (SDG) monitoring. We utilize a Bayesian hierarchical logistic regression model incorporating temporal random effects, applied to aggregated DHS data from Cameroon for the years 2004, 2011, 2018, and 2022. The model explicitly adjusts for misclassification in the bi- nary stunting outcome utilizing validated sensitivity and specificity metrics. Weakly informative priors are established for regression coefficients and variance components. Posterior inference is per- formed using Hamiltonian Monte Carlo. The model’s efficacy is evaluated using extensive simulation simulations that examine bias, mean squared error, and coverage probabilities. The revised model consistently produces higher prevalence estimates (by 3 to 4 percentage points) than uncorrected models across all survey years, demonstrating a systematic underestimating when measurement error is disregarded. Performance indicators indicate considerable enhancements: classification accuracy increased by 4 to 5 percentage points, the Area Under the Curve (AUC) elevated from roughly 0.808 to over 0.86, and precision markedly improved. Simulation analyses validate the model’s resilience in accurately retrieving genuine parameter values under diverse misclassification circumstances. Inte- grating measurement error correction within a Bayesian framework markedly improves the accuracy of child stunting prevalence estimations and trend analysis. This methodology yields more precise ev- idence for health policy development and monitoring of Sustainable Development Goals (SDGs). We advocate for the incorporation of these methodological modifications into national survey reporting systems to enhance data quality and policy legitimacy in resource-constrained environments. Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Child Malnutrition Measurement Error Correction Bayesian Hierarchical Modeling Demographic and Health Surveys (DHS) Prevalence Estimation Health Policy and SDG Monitoring Full Text Additional Declarations No competing interests reported. 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. 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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-8817211\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":618230684,\"identity\":\"a006d18e-e9b6-4c5b-b6dd-33144e98a941\",\"order_by\":0,\"name\":\"Romuald Daniel BOY-NGBOGBELE\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABl0lEQVRIie2SQUsCQRTH3zLgdhjr+hZFv8ITwSUMv0gXZaG9GNnNg8iCoIe2zkpRX6EIPI8srJf1XtDBCLaLB72I1BbNrkpWh65B+z/MzPvzfjNvZh5ArFh/VjUGtFwJAARFQD0MypHFcJ2GGwh9ReTohVM5ikNEsX5FlPYnAt8QvXNygAsqgq7axGovD5nsfkuI2WWzSUPDfZoGoGpd43kMjRJonZBMe6O+ZpMJu7ZHrHfm53MPbnnQ6ztInm/SoA0shQc5C1wDUjw6FA/7yMkBuqvqr0nbqdx0q+Qk+wKlU8CBBSyDZYkkBMjFCtGCJUJsiRxNnbeLpkSO5iiCEDFnFrxLJDteISm+RvjCqVxhFRzFYuEpCRSJsLBqzlLaAlLR/ZGPbotpMvmu7dZY0vLzlHZpYLuO1vP8Ao7ayDR7ctytnBlcs6PC1NOb+0m9mNHV1jXjgXyx89bjeNFo7mwPDR/rwZ6BQ/N6Op2XMjgUGz/DAbYI5HcAbdrhpgasOoH/6BpV3i0AyFrf/NKPzFixYsX6Z/oAsraZ/XsjE1IAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI)\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Romuald\",\"middleName\":\"Daniel\",\"lastName\":\"BOY-NGBOGBELE\",\"suffix\":\"\"},{\"id\":618230685,\"identity\":\"73a59cb6-4cd6-4d14-8b03-be94e759274b\",\"order_by\":1,\"name\":\"Raymond Affossogbe\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institute of Mathematics and Physics\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Raymond\",\"middleName\":\"\",\"lastName\":\"Affossogbe\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-07 17:38:30\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8817211/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8817211/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108005919,\"identity\":\"c789a1c3-8fc6-4d33-98cc-407584be906d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 12:50:51\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":495112,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Article6RomualdDanielSR.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8817211/v1_covered_efc8ecfe-8037-42ed-a950-929c07850c41.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Bayesian Approach to Correcting Measurement Error in Estimating Childhood Malnutrition Prevalence fromPooled Demographic and Health Surveys Data\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Child Malnutrition, Measurement Error Correction, Bayesian Hierarchical Modeling, Demographic and Health Surveys (DHS), Prevalence Estimation, Health Policy and SDG Monitoring\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8817211/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8817211/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003ePrecise assessment of malnutrition prevalence is essential for successful health policy and the monitoring of Sustainable Development Goals (SDGs). This study seeks to amalgamate repeated cross-sectional Demographic and Health Surveys (DHS) data for Cameroon within a Bayesian frame- work, rectify measurement errors in child stunting indicators, estimate adjusted temporal trends, quantify biases arising from the neglect of errors, and formulate a policy brief framework for Sus- tainable Development Goal (SDG) monitoring. We utilize a Bayesian hierarchical logistic regression model incorporating temporal random effects, applied to aggregated DHS data from Cameroon for the years 2004, 2011, 2018, and 2022. The model explicitly adjusts for misclassification in the bi- nary stunting outcome utilizing validated sensitivity and specificity metrics. Weakly informative priors are established for regression coefficients and variance components. Posterior inference is per- formed using Hamiltonian Monte Carlo. The model\\u0026rsquo;s efficacy is evaluated using extensive simulation simulations that examine bias, mean squared error, and coverage probabilities. The revised model consistently produces higher prevalence estimates (by 3 to 4 percentage points) than uncorrected models across all survey years, demonstrating a systematic underestimating when measurement error is disregarded. Performance indicators indicate considerable enhancements: classification accuracy increased by 4 to 5 percentage points, the Area Under the Curve (AUC) elevated from roughly 0.808 to over 0.86, and precision markedly improved. Simulation analyses validate the model\\u0026rsquo;s resilience in accurately retrieving genuine parameter values under diverse misclassification circumstances. Inte- grating measurement error correction within a Bayesian framework markedly improves the accuracy of child stunting prevalence estimations and trend analysis. This methodology yields more precise ev- idence for health policy development and monitoring of Sustainable Development Goals (SDGs). We advocate for the incorporation of these methodological modifications into national survey reporting systems to enhance data quality and policy legitimacy in resource-constrained environments.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\",\"manuscriptTitle\":\"A Bayesian Approach to Correcting Measurement Error in Estimating Childhood Malnutrition Prevalence fromPooled Demographic and Health Surveys Data\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-15 16:59:44\",\"doi\":\"10.21203/rs.3.rs-8817211/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"1ba22c7d-f76f-43bf-b1d6-b2cd09aaac8d\",\"owner\":[],\"postedDate\":\"April 15th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":65785880,\"name\":\"Health sciences/Diseases\"},{\"id\":65785881,\"name\":\"Health sciences/Health care\"},{\"id\":65785882,\"name\":\"Physical sciences/Mathematics and computing\"},{\"id\":65785883,\"name\":\"Health sciences/Medical research\"}],\"tags\":[],\"updatedAt\":\"2026-04-15T16:59:44+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-15 16:59:44\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8817211\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8817211\",\"identity\":\"rs-8817211\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}