Methodological Approaches to Spatiotemporal Analysis in HIV Epidemiology - a protocol for a systematic review

Methodological Approaches to Spatiotemporal Analysis in HIV Epidemiology - a protocol for a systematic review | 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 Method Article Methodological Approaches to Spatiotemporal Analysis in HIV Epidemiology - a protocol for a systematic review Eze-Emiri, Chidinma N., Igwe, Ezinne O., Patrick, Foster A, Olise, Olisedumbi T This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1738032/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 There is a geographic disparity in the epidemiology of HIV infection globally. These differences are interrelated with the sociodemographic and behavioural circumstances applicable to the respective regions. For instance, the HIV-2 strain is mostly found in West Africa. The most significant number of people injecting drugs (PWID) are in East and South-East Asia, North America, and Eastern Europe; they also have the highest prevalence of HIV among PWID. Spatiotemporal analytical methods use geographic and time-related factors to explain observed spatial heterogeneity; thereby, identifying disease patterns, risk factors, and trends relevant to a particular region. Spatiotemporal analytical methods use geographic and time-related factors to explain observed spatial heterogeneity; thereby, identifying disease patterns, risk factors, and trends relevant to a particular region. Objectives This paper presents a protocol for a systematic review that will describe the methods used in the spatial analysis of HIV epidemiology globally. Methods A systematic literature search of geospatial studies of HIV will be conducted using PubMed, EBSCOhost, and Google scholar; with neither language nor date restriction from inception to date. The protocol for this systematic review was prospectively registered in PROSPERO with ID number –CRD42022314604. Results Results will be available once the review is finalised. Discussion This systematic review will identify and report the various spatial analytical methods that have been employed in HIV epidemiology; in addition to highlighting the predominant spatial methodologies applied. Statistical Epidemiology Geographic Information Systems HIV clusters spatial analysis spatiotemporal GIS modelling maps epidemiology Introduction The epidemiology of the human immunodeficiency virus (HIV) varies in distribution, disease patterns, and risk factors among distinct geographic regions. The HIV-1 group M is responsible for the global pandemic associated with HIV; the HIV-2 strain is mainly found in West Africa, Mozambique, Angola, and southwest India (Eberle & Gürtler, 2012 ), and is less pathogenic than the HIV-1 strain. Geographic HIV clusters are driven by different biological and behavioural risk factors (Ying et al., 2020 ). In eastern and southern Africa, HIV prevalence among sex workers is extremely high; adolescent girls and young women account for 30% of new infections, and above 50% of sex workers are living with HIV (UNAIDS, 2020 ). The three geographical subregions with the largest numbers of people who inject drugs (PWID) –East and South-East Asia, North America, and Eastern Europe— together account for 58 per cent of the global number of PWIDS. Coincidentally, these regions also have the highest prevalence of HIV among PWIDs (United Nations, 2020 ). Men who have sex with men (MSM) account majorly for the distribution of new HIV infections (Paraskevis et al., 2019 ); 64% in Western Europe, Central Europe, and the North America region; 44% in Latin America and Asia-Pacific (UNAIDS, 2020 ). HIV infection also exhibits localised geographic clustering that is interrelated to the socio-demographic circumstances around respective regions; consequently, suggesting inconsistency in exposure to HIV risk (Waruru et al., 2018 ). Experiences of stigmatisation, discriminatory attitudes, and criminalisation among people living with HIV (PLHIV) –particularly the key populations— can dampen the quest for pertinent HIV prevention, testing, and treatment; thus, contributing to new infections in susceptible regions (Health Policy Project, 2014 ). Spatiotemporal analytical methods use data that possess elements of space and time (Porta, 2014 ); therefore, they characterise the ‘where’ and ‘when’ of health events in epidemiology (McBride et al., 2019 ). These methods are dependent on both the available data and the purpose of the analysis (Lai et al., 2008 ). Spatial statistical methods are used to uncover relationships between spatiotemporal disease patterns and host (Lawson, 2018 ); visualise the distribution of disease using detailed maps; and, identify clusters or hotspots (Anselin et al., 2010 ). The knowledge of geographical and time-bound factors in HIV research is a vital tool for describing spatial heterogeneity in the epidemiology of HIV; hence, identifying risk factors that lead to increased HIV incidence, and forecasting future health trends in different geographical areas (Stevens & Pfeiffer, 2011 ). The identification of HIV infection clusters reveals the right regions to target priority-tailored HIV interventions while highlighting interventions of utmost priority appropriate for a specific region. Visualisation of HIV surveillance data can help improve existing HIV treatment programmes; and generate theories concerning transmission pathways (Agustí et al., 2020 ). Objectives The overall objective is to review the methods used in the spatial analysis of HIV epidemiology globally. The diverse types of health data employed in the analysis will also be evaluated. Specific review questions include What spatial approaches are used in the identification of HIV clusters? What spatial analytical methods are used in determining the distribution of HIV infection in the human population? What methods are used in the investigation and prediction of HIV temporal trends? Methods Protocol registration The methods for this systematic review were established with the recommendations from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement (Moher et al., 2015 ). This systematic review protocol was registered in PROSPERO with ID number CRD42022314604; a PRISMA-P file is attached. Eligibility criteria Inclusion criteria : Any spatiotemporal characterisation of HIV in the human population from simple descriptive mapping to advanced Bayesian approaches, will be eligible. Studies with pure spatial analysis, time-series analysis, or spatiotemporal analysis will be inclusive. Articles will be included regardless of participant sociodemographic characteristics, geographical location, language, year of publication, or other factors. There will be no restriction based on the spatial unit size, scope or setting, the type of programme activity or intervention, or the study design. Exclusion criteria : Studies will be excluded if the focus of the study was not HIV infection; if opportunistic infection of HIV was the emphasis of the study; and if it was a study on HIV infection in an animal population. Studies that used geographic coordinates solely for study enrolment purposes, for example, to generate sampling frames, will also be excluded. We will exclude HIV phylogenetic studies, unless they involved other spatial analyses, on the basis that such research is complex and specialised enough to warrant its review (Boyda et al., 2019 ). Abstracts, posters, reviews, and commentary pieces will also be excluded. Data source The systematic search strategy will identify peer-reviewed studies that focused on the distribution and determinants of HIV and implemented geographic information systems (GIS) or spatial statistical methods. Studies are to be considered spatial if they employed any spatial techniques or software in the design and analysis of the distribution, determinants, prevention, treatment, and outcomes of HIV (Boyda et al., 2019 ). We will systematically search PubMed, EBSCOhost, and Google Scholar databases from their inception to date using a combination of keywords and medical subject headings (MeSH) relating to our main ideas: HIV and space-time. Additional papers will be identified from the bibliographies of retrieved articles and subject-related databases. Contact with authors for further information will be made when necessary. Search strategy A list of indexed terms and text words used to describe the concept of interest will be used to construct a comprehensive set of possible search terms. These will include but are not limited to the following search term “spatial analysis”, and alternative keywords: “spatial”, “spatial cluster”, “spatial distribution”, “spatiotemporal”, “geographical distribution”, “geographic mapping”, “GIS”, “cluster analysis”, “time-space”, “temporal”, “trends”, “clusters” appearing in the title or abstract in the form of full or truncations. The search strategy was developed by the research team with suggestions from other published strategies. Study Records Data management Search strategies will be implemented; and all identified references will be imported to the EndNote referencing manager (The EndNote Team, 2013 ). All search results from the different electronic databases will be combined in a single EndNote library, with duplicate records of the same reports removed. Data extraction forms will be stored on a Microsoft Excel spreadsheet (Microsoft Corporation, 2022 ). Data selection and extraction Titles and abstracts will be screened against eligibility criteria to identify plausibly inclusive studies, and full-text copies of relevant articles will be retrieved. Three investigators (EEC, IEO and PFA) screened the titles and abstracts of the studies identified by the search strategy of potential articles to include and then removed duplicates. Two independent reviewers (EEC and OOT) will independently assess the full text of the retrieved articles for compliance with our eligibility criteria, and extract the data using an electronic data extraction form. Discrepancies between the reviewers’ judgements will be resolved by consensus. Studies, which appeared relevant but were excluded at this stage will be listed in the table “Characteristics of excluded studies”; where a reason for exclusion will be noted. The reviewers will verify the final list of the included studies. A PRISMA flow diagram of the study selection procedure will be prepared to provide an overview of the decisions made in the data collection process. Data will be extracted to an excel sheet based on the outcomes of the review. To ensure uniformity across reviewers, we will conduct a pre-test standardisation exercise before starting the data extraction process. Each reviewer will extract the themes of interest into an excel sheet. The extracted data items are presented below. Spatial analysis techniques will be categorised as either visualisation (mapping), exploration, cluster identification, spatial modelling, or temporal trends. Data items Data will be extracted on the following: Publication details : Title, journal, author, year in which the study was conducted, and the type of publication. Study details : Study location, objectives, study design, data type and source, study population, and source of funding. Methodology : Unit of analysis, the number of spatial units, the type of spatial statistics performed, tools employed, software used (if any). Outcome measures : Results of the investigations that relate specifically to the use of spatial methods. Commentary : Any comments on the strengths or limitations of the spatial methods used, as reported by the study authors. Risk of bias in individual studies The extent to which bias might have contributed to the identification of clusters will be estimated with reference to the size of spatial units analysed and the type of spatial statistics conducted. Data synthesis To summarise the data in the evidence synthesis reports, a descriptive analysis will be conducted using counts and proportions, and the results presented in tables. A narrative thematic analysis will also be performed, and comparable findings summarised. A comparison of evaluation methods (descriptive, exploratory, trend, predictive) will be performed too. As the aim of this review is not to synthesise the findings of individual studies, no meta-analysis will be conducted. Summary The systematic review will critically examine the global literature relevant to the spatial epidemiology of the burden of HIV infection in the human population. It is intended to explicitly identify and report the various analytical methods applied in the analysis of HIV epidemiology with space and time variables while highlighting the predominant methodologies applied. Declarations CONFLICT OF INTEREST The authors have declared no competing interest –financial or personal that could have influenced the work reported in this paper. FUNDING The authors received no specific funding for this research. References Agustí, C., Font-Casaseca, N., Belvis, F., Julià, M., Vives, N., Montoliu, A., Pericàs, J. M., Casabona, J., & Benach, J. (2020, 2020/10/09). The role of socio-demographic determinants in the geo-spatial distribution of newly diagnosed HIV infections in small areas of Catalonia (Spain). BMC Public Health, 20 (1), 1533. https://doi.org/10.1186/s12889-020-09603-7 Anselin, L., Syabri, I., & Kho, Y. (2010). GeoDa: an introduction to spatial data analysis. In Handbook of applied spatial analysis (pp. 73–89). Springer. Boyda, D. C., Holzman, S. B., Berman, A., Grabowski, M. K., & Chang, L. W. (2019). Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review. PLoS One, 14 (5), e0216388. https://doi.org/10.1371/journal.pone.0216388 Eberle, J., & Gürtler, L. (2012). HIV Types, Groups, Subtypes and Recombinant Forms: Errors in Replication, Selection Pressure and Quasispecies. Intervirology, 55 (2), 79–83. https://doi.org/10.1159/000331993 Health Policy Project. (2014). Capacity Development Resource Guide: Stigma and Discrimination . Futures Group Health Policy Project. https://www.healthpolicyproject.com/pubs/272_StigmaandDiscriminationResourceGuide.pdf Lai, P.-C., So, F.-M., & Chan, K.-W. (2008). Spatial epidemiological approaches in disease mapping and analysis . CRC press. Lawson, A. B. (2018). Bayesian disease mapping: hierarchical modeling in spatial epidemiology . Chapman and Hall/CRC. McBride, K. A., Ogbo, F., & Page, A. (2019). Epidemiology. In P. Liamputtong (Ed.), Handbook of Research Methods in Health Social Sciences (pp. 559–579). Springer Singapore. https://doi.org/10.1007/978-981-10-5251-4_91 Microsoft Corporation. (2022). Microsoft Excel 365. In https://www.office.com/launch/excel?auth=1 Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & Group, P.-P. (2015, 2015/01/01). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4 (1), 1. https://doi.org/10.1186/2046-4053-4-1 Paraskevis, D., Beloukas, A., Stasinos, K., Pantazis, N., de Mendoza, C., Bannert, N., Meyer, L., Zangerle, R., Gill, J., Prins, M., d’Arminio Montforte, A., Kran, A.-M. B., Porter, K., Touloumi, G., & on behalf of the, C. c. o. E. (2019, 2019/01/08). HIV-1 molecular transmission clusters in nine European countries and Canada: association with demographic and clinical factors. BMC Medicine, 17 (1), 4. https://doi.org/10.1186/s12916-018-1241-1 Porta, M. (2014). A dictionary of epidemiology . Oxford University Press. https://doi.org/10.1093/acref/9780199976720.001.0001 Stevens, K. B., & Pfeiffer, D. U. (2011, 2011/09/01/). Spatial modelling of disease using data- and knowledge-driven approaches. Spatial and Spatio-temporal Epidemiology, 2 (3), 125–133. https://doi.org/https://doi.org/10.1016/j.sste.2011.07.007 The EndNote Team. (2013). EndNote. In [64 bit]. Clarivate. UNAIDS. (2020). 2020 Global AIDS Update - Seizing the Moment: Tackling entrenched inequalities to end epidemics . https://aids2020.unaids.org/report United Nations. (2020). World Drug Report 2020 (Book 2: Drug Uses and Consequences, Issue. U. Nations. https://wdr.unodc.org/wdr2020/en/drug-use-health.html Waruru, A., Achia, T. N. O., Tobias, J. L., Ngʼangʼa, J., Mwangi, M., Wamicwe, J., Zielinski-Gutierrez, E., Oluoch, T., Muthama, E., & Tylleskär, T. (2018, 2018/06//). Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya. Journal of acquired immune deficiency syndromes (1999), 78 (2), 144–154. https://doi.org/10.1097/qai.0000000000001652 Ying, R., Fekadu, L., Schackman, B. R., & Verguet, S. (2020). Spatial distribution and characteristics of HIV clusters in Ethiopia. Tropical Medicine & International Health, 25 (3), 301–307. https://doi.org/https://doi.org/10.1111/tmi.13356 Supplementary Files ProtocolcompletedPRISMAPchecklist.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-1738032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":112059420,"identity":"92466ddc-17f1-44fe-befd-77ca12712d93","order_by":0,"name":"Eze-Emiri, Chidinma N.","email":"data:image/png;base64,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","orcid":"","institution":"Department of Epidemiology, School of Public Health, University of Port Harcourt, Rivers State, Nigeria","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chidinma","middleName":"N.","lastName":"Eze-Emiri","suffix":""},{"id":112060454,"identity":"7aaee4a4-70f8-4d56-b9d2-22864b02c2c7","order_by":1,"name":"Igwe, Ezinne O.","email":"","orcid":"","institution":"Faculty of Medicine and Health Sciences, University of Wollongong NSW, Australia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ezinne","middleName":"O.","lastName":"Igwe","suffix":""},{"id":112060455,"identity":"bf3173ab-3a96-4d9a-946a-dbfd37e964fd","order_by":2,"name":"Patrick, Foster A","email":"","orcid":"","institution":"Department of Epidemiology, School of Public Health, University of Port Harcourt, Rivers State, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Foster","middleName":"A","lastName":"Patrick","suffix":""},{"id":112060456,"identity":"2c5d4fe7-b974-4be6-8c7c-7d22765d2be7","order_by":3,"name":"Olise, Olisedumbi T","email":"","orcid":"","institution":"Department of Epidemiology, School of Public Health, University of Port Harcourt, Rivers State, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Olisedumbi","middleName":"T","lastName":"Olise","suffix":""}],"badges":[],"createdAt":"2022-06-08 12:06:51","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-1738032/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1738032/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22422889,"identity":"777174ee-7820-457e-8bea-e21be9869f90","added_by":"auto","created_at":"2022-06-08 16:57:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":247941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1738032/v1/f3d31fee-e865-4a3e-ad6d-2eb6052c23e4.pdf"},{"id":22422888,"identity":"5fe5e3c6-7fc0-423b-9ed3-ab267fcd1f4c","added_by":"auto","created_at":"2022-06-08 16:57:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19089,"visible":true,"origin":"","legend":"","description":"","filename":"ProtocolcompletedPRISMAPchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-1738032/v1/c2aec45850c357b649bbb33b.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMethodological Approaches to Spatiotemporal Analysis in HIV Epidemiology - a protocol for a systematic review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe epidemiology of the human immunodeficiency virus (HIV) varies in distribution, disease patterns, and risk factors among distinct geographic regions. The HIV-1 group M is responsible for the global pandemic associated with HIV; the HIV-2 strain is mainly found in West Africa, Mozambique, Angola, and southwest India (Eberle \u0026amp; G\u0026uuml;rtler, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and is less pathogenic than the HIV-1 strain. Geographic HIV clusters are driven by different biological and behavioural risk factors (Ying et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In eastern and southern Africa, HIV prevalence among sex workers is extremely high; adolescent girls and young women account for 30% of new infections, and above 50% of sex workers are living with HIV (UNAIDS, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The three geographical subregions with the largest numbers of people who inject drugs (PWID) \u0026ndash;East and South-East Asia, North America, and Eastern Europe\u0026mdash; together account for 58 per cent of the global number of PWIDS. Coincidentally, these regions also have the highest prevalence of HIV among PWIDs (United Nations, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Men who have sex with men (MSM) account majorly for the distribution of new HIV infections (Paraskevis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); 64% in Western Europe, Central Europe, and the North America region; 44% in Latin America and Asia-Pacific (UNAIDS, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). HIV infection also exhibits localised geographic clustering that is interrelated to the socio-demographic circumstances around respective regions; consequently, suggesting inconsistency in exposure to HIV risk (Waruru et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Experiences of stigmatisation, discriminatory attitudes, and criminalisation among people living with HIV (PLHIV) \u0026ndash;particularly the key populations\u0026mdash; can dampen the quest for pertinent HIV prevention, testing, and treatment; thus, contributing to new infections in susceptible regions (Health Policy Project, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatiotemporal analytical methods use data that possess elements of space and time (Porta, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); therefore, they characterise the \u0026lsquo;where\u0026rsquo; and \u0026lsquo;when\u0026rsquo; of health events in epidemiology (McBride et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These methods are dependent on both the available data and the purpose of the analysis (Lai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Spatial statistical methods are used to uncover relationships between spatiotemporal disease patterns and host (Lawson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); visualise the distribution of disease using detailed maps; and, identify clusters or hotspots (Anselin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The knowledge of geographical and time-bound factors in HIV research is a vital tool for describing spatial heterogeneity in the epidemiology of HIV; hence, identifying risk factors that lead to increased HIV incidence, and forecasting future health trends in different geographical areas (Stevens \u0026amp; Pfeiffer, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The identification of HIV infection clusters reveals the right regions to target priority-tailored HIV interventions while highlighting interventions of utmost priority appropriate for a specific region. Visualisation of HIV surveillance data can help improve existing HIV treatment programmes; and generate theories concerning transmission pathways (Agust\u0026iacute; et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe overall objective is to review the methods used in the spatial analysis of HIV epidemiology globally. The diverse types of health data employed in the analysis will also be evaluated.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003eSpecific review questions include\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhat spatial approaches are used in the identification of HIV clusters?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat spatial analytical methods are used in determining the distribution of HIV infection in the human population?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat methods are used in the investigation and prediction of HIV temporal trends?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProtocol registration\u003c/h2\u003e \u003cp\u003eThe methods for this systematic review were established with the recommendations from the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement (Moher et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This systematic review protocol was registered in PROSPERO with ID number CRD42022314604; a PRISMA-P file is attached.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003e \u003cem\u003eInclusion criteria\u003c/em\u003e: Any spatiotemporal characterisation of HIV in the human population from simple descriptive mapping to advanced Bayesian approaches, will be eligible. Studies with pure spatial analysis, time-series analysis, or spatiotemporal analysis will be inclusive. Articles will be included regardless of participant sociodemographic characteristics, geographical location, language, year of publication, or other factors. There will be no restriction based on the spatial unit size, scope or setting, the type of programme activity or intervention, or the study design. \u003cem\u003eExclusion criteria\u003c/em\u003e: Studies will be excluded if the focus of the study was not HIV infection; if opportunistic infection of HIV was the emphasis of the study; and if it was a study on HIV infection in an animal population. Studies that used geographic coordinates solely for study enrolment purposes, for example, to generate sampling frames, will also be excluded. We will exclude HIV phylogenetic studies, unless they involved other spatial analyses, on the basis that such research is complex and specialised enough to warrant its review (Boyda et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Abstracts, posters, reviews, and commentary pieces will also be excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe systematic search strategy will identify peer-reviewed studies that focused on the distribution and determinants of HIV and implemented geographic information systems (GIS) or spatial statistical methods. Studies are to be considered spatial if they employed any spatial techniques or software in the design and analysis of the distribution, determinants, prevention, treatment, and outcomes of HIV (Boyda et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We will systematically search PubMed, EBSCOhost, and Google Scholar databases from their inception to date using a combination of keywords and medical subject headings (MeSH) relating to our main ideas: HIV and space-time. Additional papers will be identified from the bibliographies of retrieved articles and subject-related databases. Contact with authors for further information will be made when necessary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eA list of indexed terms and text words used to describe the concept of interest will be used to construct a comprehensive set of possible search terms. These will include but are not limited to the following search term \u0026ldquo;spatial analysis\u0026rdquo;, and alternative keywords: \u0026ldquo;spatial\u0026rdquo;, \u0026ldquo;spatial cluster\u0026rdquo;, \u0026ldquo;spatial distribution\u0026rdquo;, \u0026ldquo;spatiotemporal\u0026rdquo;, \u0026ldquo;geographical distribution\u0026rdquo;, \u0026ldquo;geographic mapping\u0026rdquo;, \u0026ldquo;GIS\u0026rdquo;, \u0026ldquo;cluster analysis\u0026rdquo;, \u0026ldquo;time-space\u0026rdquo;, \u0026ldquo;temporal\u0026rdquo;, \u0026ldquo;trends\u0026rdquo;, \u0026ldquo;clusters\u0026rdquo; appearing in the title or abstract in the form of full or truncations. The search strategy was developed by the research team with suggestions from other published strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy Records\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eData management\u003c/h2\u003e \u003cp\u003eSearch strategies will be implemented; and all identified references will be imported to the EndNote referencing manager (The EndNote Team, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). All search results from the different electronic databases will be combined in a single EndNote library, with duplicate records of the same reports removed. Data extraction forms will be stored on a Microsoft Excel spreadsheet (Microsoft Corporation, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eData selection and extraction\u003c/h2\u003e \u003cp\u003eTitles and abstracts will be screened against eligibility criteria to identify plausibly inclusive studies, and full-text copies of relevant articles will be retrieved. Three investigators (EEC, IEO and PFA) screened the titles and abstracts of the studies identified by the search strategy of potential articles to include and then removed duplicates. Two independent reviewers (EEC and OOT) will independently assess the full text of the retrieved articles for compliance with our eligibility criteria, and extract the data using an electronic data extraction form. Discrepancies between the reviewers\u0026rsquo; judgements will be resolved by consensus. Studies, which appeared relevant but were excluded at this stage will be listed in the table \u0026ldquo;Characteristics of excluded studies\u0026rdquo;; where a reason for exclusion will be noted. The reviewers will verify the final list of the included studies. A PRISMA flow diagram of the study selection procedure will be prepared to provide an overview of the decisions made in the data collection process. Data will be extracted to an excel sheet based on the outcomes of the review. To ensure uniformity across reviewers, we will conduct a pre-test standardisation exercise before starting the data extraction process. Each reviewer will extract the themes of interest into an excel sheet. The extracted data items are presented below. Spatial analysis techniques will be categorised as either visualisation (mapping), exploration, cluster identification, spatial modelling, or temporal trends.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData items\u003c/h2\u003e \u003cp\u003eData will be extracted on the following:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePublication details\u003c/em\u003e: Title, journal, author, year in which the study was conducted, and the type of publication.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eStudy details\u003c/em\u003e: Study location, objectives, study design, data type and source, study population, and source of funding.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eMethodology\u003c/em\u003e: Unit of analysis, the number of spatial units, the type of spatial statistics performed, tools employed, software used (if any).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eOutcome measures\u003c/em\u003e: Results of the investigations that relate specifically to the use of spatial methods.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCommentary\u003c/em\u003e: Any comments on the strengths or limitations of the spatial methods used, as reported by the study authors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eRisk of bias in individual studies\u003c/h2\u003e \u003cp\u003eThe extent to which bias might have contributed to the identification of clusters will be estimated with reference to the size of spatial units analysed and the type of spatial statistics conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eData synthesis\u003c/h2\u003e \u003cp\u003eTo summarise the data in the evidence synthesis reports, a descriptive analysis will be conducted using counts and proportions, and the results presented in tables. A narrative thematic analysis will also be performed, and comparable findings summarised. A comparison of evaluation methods (descriptive, exploratory, trend, predictive) will be performed too. As the aim of this review is not to synthesise the findings of individual studies, no meta-analysis will be conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eSummary\u003c/h2\u003e \u003cp\u003eThe systematic review will critically examine the global literature relevant to the spatial epidemiology of the burden of HIV infection in the human population. It is intended to explicitly identify and report the various analytical methods applied in the analysis of HIV epidemiology with space and time variables while highlighting the predominant methodologies applied.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no competing interest \u0026ndash;financial or personal that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgust\u0026iacute;, C., Font-Casaseca, N., Belvis, F., Juli\u0026agrave;, M., Vives, N., Montoliu, A., Peric\u0026agrave;s, J. M., Casabona, J., \u0026amp; Benach, J. (2020, 2020/10/09). 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BMC Medicine, \u003cem\u003e17\u003c/em\u003e(1), 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-018-1241-1\u003c/span\u003e\u003cspan address=\"10.1186/s12916-018-1241-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorta, M. (2014). \u003cem\u003eA dictionary of epidemiology\u003c/em\u003e. Oxford University Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/acref/9780199976720.001.0001\u003c/span\u003e\u003cspan address=\"10.1093/acref/9780199976720.001.0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens, K. B., \u0026amp; Pfeiffer, D. U. (2011, 2011/09/01/). Spatial modelling of disease using data- and knowledge-driven approaches. 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L., Ngʼangʼa, J., Mwangi, M., Wamicwe, J., Zielinski-Gutierrez, E., Oluoch, T., Muthama, E., \u0026amp; Tyllesk\u0026auml;r, T. (2018, 2018/06//). Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya. Journal of acquired immune deficiency syndromes (1999), \u003cem\u003e78\u003c/em\u003e(2), 144\u0026ndash;154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/qai.0000000000001652\u003c/span\u003e\u003cspan address=\"10.1097/qai.0000000000001652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing, R., Fekadu, L., Schackman, B. R., \u0026amp; Verguet, S. (2020). Spatial distribution and characteristics of HIV clusters in Ethiopia. Tropical Medicine \u0026amp; International Health, \u003cem\u003e25\u003c/em\u003e(3), 301\u0026ndash;307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1111/tmi.13356\u003c/span\u003e\u003cspan address=\"10.1111/tmi.13356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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, clusters, spatial analysis, spatiotemporal, GIS, modelling, maps, epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-1738032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1738032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere is a geographic disparity in the epidemiology of HIV infection globally. These differences are interrelated with the sociodemographic and behavioural circumstances applicable to the respective regions. For instance, the HIV-2 strain is mostly found in West Africa. The most significant number of people injecting drugs (PWID) are in East and South-East Asia, North America, and Eastern Europe; they also have the highest prevalence of HIV among PWID. Spatiotemporal analytical methods use geographic and time-related factors to explain observed spatial heterogeneity; thereby, identifying disease patterns, risk factors, and trends relevant to a particular region. Spatiotemporal analytical methods use geographic and time-related factors to explain observed spatial heterogeneity; thereby, identifying disease patterns, risk factors, and trends relevant to a particular region.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis paper presents a protocol for a systematic review that will describe the methods used in the spatial analysis of HIV epidemiology globally.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA systematic literature search of geospatial studies of HIV will be conducted using PubMed, EBSCOhost, and Google scholar; with neither language nor date restriction from inception to date. The protocol for this systematic review was prospectively registered in PROSPERO with ID number \u0026ndash;CRD42022314604.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResults will be available once the review is finalised.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThis systematic review will identify and report the various spatial analytical methods that have been employed in HIV epidemiology; in addition to highlighting the predominant spatial methodologies applied.\u003c/p\u003e","manuscriptTitle":"Methodological Approaches to Spatiotemporal Analysis in HIV Epidemiology - a protocol for a systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-08 16:57:51","doi":"10.21203/rs.3.rs-1738032/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":"afc05e5b-a983-4a73-863e-3a8b5387bd17","owner":[],"postedDate":"June 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":13140501,"name":"Statistical Epidemiology"},{"id":13140502,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2022-06-08T16:57:51+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-08 16:57:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1738032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1738032","identity":"rs-1738032","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}