Unmasking Disparities in Caesarean Section Utilization in Nigeria: A K-Means Cluster Analysis of Obstetric Risk Profiles Using Machine Learning Approach

Unmasking Disparities in Caesarean Section Utilization in Nigeria: A K-Means Cluster Analysis of Obstetric Risk Profiles Using Machine Learning 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 Unmasking Disparities in Caesarean Section Utilization in Nigeria: A K-Means Cluster Analysis of Obstetric Risk Profiles Using Machine Learning Approach Samuel Ayoola AJEBORIOGBON, Abolaji Moses OGUNETIMOJU, Oluwafemi Lawal Bisiriyu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9271827/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Caesarean section rates in Nigeria remain suboptimal, with significant disparities across socioeconomic and geographic strata. The objective of this research is to identify and characterize distinct obstetric risk profiles associated with caesarean section utilization in Nigeria using K-means cluster analysis, and to examine the sociodemographic and geographic factors driving these disparities. We analyzed data from 13,915 women with recent births in the 2024 Nigeria Demographic and Health Survey. Fourteen variables spanning demographics, socioeconomic status, healthcare access, medical history, and geography were used as clustering features. K-Means clustering was performed with optimal cluster selection based on silhouette score, Davies Bouldin index, and Calinski Harabasz index. Bootstrap validation with 100 iterations assessed cluster stability, while chi-square tests and logistic regression examined associations between cluster membership and surgical delivery. Ten distinct clusters were identified with rates ranging from 1.7% to 14.4%, representing an 8.4-fold variation. The highest utilization cluster at 14.4% comprised urban, highly educated, wealthy women with extensive antenatal care averaging 16.5 visits, while the lowest utilization cluster at 1.7% consisted of rural, poorly educated, impoverished women with minimal healthcare access averaging 2.3 visits. Cluster membership was significantly associated with utilization, and bootstrap analysis confirmed cluster stability with a mean silhouette of 0.220. Machine learning based clustering reveals profound disparities in utilization across distinct population subgroups in Nigeria, highlighting the dual challenge of underutilization among disadvantaged rural populations and potential overutilization among urban elites. Targeted interventions addressing geographic, economic, and healthcare access barriers are essential to optimize utilization across all population segments. Caesarean section K-means clustering machine learning health disparities Nigeria obstetric care maternal health Demographic and Health Survey Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 30 Mar, 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. 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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-9271827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633656676,"identity":"4883f75a-07e7-4eb4-8b66-5f9fd6ea3896","order_by":0,"name":"Samuel Ayoola AJEBORIOGBON","email":"data:image/png;base64,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","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":true,"prefix":"","firstName":"Samuel","middleName":"Ayoola","lastName":"AJEBORIOGBON","suffix":""},{"id":633656677,"identity":"2391ac6d-3a89-46a9-92d9-cbea9e7db81f","order_by":1,"name":"Abolaji Moses OGUNETIMOJU","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Abolaji","middleName":"Moses","lastName":"OGUNETIMOJU","suffix":""},{"id":633656678,"identity":"7f0e2b98-808a-4456-bffc-fa05159aee23","order_by":2,"name":"Oluwafemi Lawal Bisiriyu","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Oluwafemi","middleName":"Lawal","lastName":"Bisiriyu","suffix":""}],"badges":[],"createdAt":"2026-03-30 20:39:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9271827/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9271827/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108639145,"identity":"7976920f-f5a9-4cf1-994f-b8185f1ea136","added_by":"auto","created_at":"2026-05-06 18:58:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":658180,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9271827/v1_covered_7d80dccf-c914-43b8-9e1f-281bd1f7765c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unmasking Disparities in Caesarean Section Utilization in Nigeria: A K-Means Cluster Analysis of Obstetric Risk Profiles Using Machine Learning Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Caesarean section, K-means clustering, machine learning, health disparities, Nigeria, obstetric care, maternal health, Demographic and Health Survey","lastPublishedDoi":"10.21203/rs.3.rs-9271827/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9271827/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCaesarean section rates in Nigeria remain suboptimal, with significant disparities across socioeconomic and geographic strata. The objective of this research is to identify and characterize distinct obstetric risk profiles associated with caesarean section utilization in Nigeria using K-means cluster analysis, and to examine the sociodemographic and geographic factors driving these disparities. We analyzed data from 13,915 women with recent births in the 2024 Nigeria Demographic and Health Survey. Fourteen variables spanning demographics, socioeconomic status, healthcare access, medical history, and geography were used as clustering features. K-Means clustering was performed with optimal cluster selection based on silhouette score, Davies Bouldin index, and Calinski Harabasz index. Bootstrap validation with 100 iterations assessed cluster stability, while chi-square tests and logistic regression examined associations between cluster membership and surgical delivery. Ten distinct clusters were identified with rates ranging from 1.7% to 14.4%, representing an 8.4-fold variation. The highest utilization cluster at 14.4% comprised urban, highly educated, wealthy women with extensive antenatal care averaging 16.5 visits, while the lowest utilization cluster at 1.7% consisted of rural, poorly educated, impoverished women with minimal healthcare access averaging 2.3 visits. Cluster membership was significantly associated with utilization, and bootstrap analysis confirmed cluster stability with a mean silhouette of 0.220. Machine learning based clustering reveals profound disparities in utilization across distinct population subgroups in Nigeria, highlighting the dual challenge of underutilization among disadvantaged rural populations and potential overutilization among urban elites. 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