Geospatial Analysis of HIV Prevalence in KwaZulu Natal, South Africa: Bayesian Spatial Hierarchical Models

Geospatial Analysis of HIV Prevalence in KwaZulu Natal, South Africa: Bayesian Spatial Hierarchical Models | 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 Geospatial Analysis of HIV Prevalence in KwaZulu Natal, South Africa: Bayesian Spatial Hierarchical Models Exaverio Chireshe, Retius Chifurira, Jescca Batidzirai, Knowledge Chinhamu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5361952/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 : Globally, South Africa has the highest number of people living with the human immunodeficiency virus (HIV) and the province of KwaZulu Natal (KZN) has the highest number of individuals who are HIV positive. However, a better understanding of the spatial heterogeneity of provincial estimates of HIV prevalence, specifically at localised level, could help advance HIV treatment and prevention strategies. The aim of the study was to assess the spatial heterogeneity of HIV prevalence and examine the individual-level characteristics of the prevalence of HIV at localised level using the Bayesian hierarchical spatial modelling technique. Methods: This was an analysis of data collected from 9812 men and women aged 15- 49 years participating in the HIV Incidence Provincial Surveillance System (HIPSS) from June 2014 to July 2015. To fit the Bayesian hierarchical spatial model to the HIV prevalence data, the integrated nested Laplace approximation (INLA) numerical method was employed. Results: Results revealed that there was a positive spatial autocorrelation in the wards. The Kulldorf’s spatial scan statistic identified one hot-spot cluster around Nadi, KwaMbanjwa and Zayeka areas and one cold-spot cluster around the Greater Edendale area. Gender, age group, education level, source of income and marital status, along with behaviours like alcohol use and having multiple sexual partners, were significantly associated with HIV prevalence. Also, being diagnosed with sexually transmitted infections (STIs) and TB increased the chances of getting infected with HIV. Conclusion : The detection of HIV hotspot cluster, the predictors of HIV transmission and the spatial distribution of HIV infection in uMgungundlovu Municipality is crucial for focused mitigations, outreach efforts, and resource allocation to populations in need, eventually advancing the efficiencies and integrity of public health schemes. HIV Prevalence Bayesian modelling Kulldorf’s spatial scan statistics odds ratios spatial clustering global Moran’s index Getis-Ord statistic 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. 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-5361952","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374866707,"identity":"a43e8526-1a3b-4cde-aa06-c9a8e908b820","order_by":0,"name":"Exaverio Chireshe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACHsYGZgYDIEOCgfEBiM9HihZmEMXDRlgLAwMzmCHBwCYBoglq4ec53Pi5oOCenPzs5mOVX3PsZNgYmB8+uoFHi2RvY7P0DINiY4M7x9Juy25LBjqMzdg4B48Wg/OMbcw8BgmJGyRyzG5LbmMGauFhk8anxR6qpX7+jPxvxZLb6glrMeBtBGtJYLiRw8b4cdthwlokzhxslgZqMdxwI81YmnHbcR42ZgJ+4e9Jf/iZ50+CvPyM5Icff26rtudnb374GJ8WFMDMAyaJVQ4CjD9IUT0KRsEoGAUjBgAAZeFAjhitVk4AAAAASUVORK5CYII=","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":true,"prefix":"","firstName":"Exaverio","middleName":"","lastName":"Chireshe","suffix":""},{"id":374866709,"identity":"a18a3dd4-5075-46ec-846a-4c466592ad5e","order_by":1,"name":"Retius Chifurira","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Retius","middleName":"","lastName":"Chifurira","suffix":""},{"id":374866712,"identity":"59dc7201-7169-414c-b957-e1eefa1257bf","order_by":2,"name":"Jescca Batidzirai","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Jescca","middleName":"","lastName":"Batidzirai","suffix":""},{"id":374866715,"identity":"86bb361f-071e-46d1-9140-a4318256196f","order_by":3,"name":"Knowledge Chinhamu","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Knowledge","middleName":"","lastName":"Chinhamu","suffix":""},{"id":374866718,"identity":"e4be3516-1a7b-443e-9136-793e99ececb4","order_by":4,"name":"Ayesha B.M Kharsany","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"B.M","lastName":"Kharsany","suffix":""}],"badges":[],"createdAt":"2024-10-30 15:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5361952/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5361952/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93380880,"identity":"3cbb6932-af0e-45be-9a00-d4ce778aafb0","added_by":"auto","created_at":"2025-10-13 08:47:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":934745,"visible":true,"origin":"","legend":"","description":"","filename":"ARTICLE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361952/v1_covered_332bfd6d-9f33-4d4b-8a84-361220081c90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Analysis of HIV Prevalence in KwaZulu Natal, South Africa: Bayesian Spatial Hierarchical Models","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":"HIV Prevalence, Bayesian modelling, Kulldorf’s spatial scan statistics, odds ratios, spatial clustering, global Moran’s index, Getis-Ord statistic","lastPublishedDoi":"10.21203/rs.3.rs-5361952/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5361952/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Globally, South Africa has the highest number of people living with the human immunodeficiency virus (HIV) and the province of KwaZulu Natal (KZN) has the highest number of individuals who are HIV positive. 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