Characteristics of HIV positive women in Vulindlela and Greater Edendale, KwaZulu-Natal, South Africa, 2014–2015.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3586",leadTitle:null,fullTitle:"Agricultural Chemistry",title:"Agricultural Chemistry",subtitle:null,reviewType:"peer-reviewed",abstract:"The present book is a collection of ten original research articles and reports, associated with selected topics in agricultural chemistry. The discussed issues are organized in four sections: Classification and labeling of active substances in plant protection products, Environmental and stress plant physiology and behavior, Antimicrobial and antioxidant potential of plant extracts, and Pollutants analysis and effects.\nThe information provided in this book should be of interest for academic researchers and for agriculturalists.",isbn:null,printIsbn:"978-953-51-1026-2",pdfIsbn:"978-953-51-5031-2",doi:"10.5772/55163",price:119,priceEur:129,priceUsd:155,slug:"agricultural-chemistry",numberOfPages:224,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"a05731d42f4d1009e8a0f51e0f483081",bookSignature:"Margarita Stoytcheva and Roumen Zlatev",publishedDate:"February 20th 2013",coverURL:"https://cdn.intechopen.com/books/images_new/3586.jpg",numberOfDownloads:42576,numberOfWosCitations:54,numberOfCrossrefCitations:16,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:63,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:133,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 21st 2012",dateEndSecondStepPublish:"November 22nd 2012",dateEndThirdStepPublish:"December 12th 2012",dateEndFourthStepPublish:"December 19th 2012",dateEndFifthStepPublish:"January 29th 2013",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"6375",title:"Prof.",name:"Margarita",middleName:null,surname:"Stoytcheva",slug:"margarita-stoytcheva",fullName:"Margarita Stoytcheva",profilePictureURL:"https://mts.intechopen.com/storage/users/6375/images/1631_n.jpg",biography:"Professor Margarita Stoytcheva graduated from the University of Chemical Technology and Metallurgy of Sofia, Bulgaria, with titles of Chemical Engineer and Master of Electrochemical Technologies. She has a Ph.D. and DSc. degrees in chemistry and technical sciences. She has acted in research and teaching in several Universities in Bulgaria, Algeria and France. From 2006. to the present she has participated in activities of scientific research, technological development and teaching in Mexico at the University of Baja California, Institute of Engineering, Mexicali, as a full time researcher. Since 2008. she has been a member of the National System of Researchers of Mexico. Her interests and areas of research are analytical chemistry and biotechnology.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"10",institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"120830",title:"Dr.",name:"Roumen",middleName:null,surname:"Zlatev",slug:"roumen-zlatev",fullName:"Roumen Zlatev",profilePictureURL:"https://mts.intechopen.com/storage/users/120830/images/system/120830.jpg",biography:"Dr. Zlatev is a fulltime researcher in the Engineering Institute of the Autonomous University of Baja California (UABC) at Mexicali, Mexico awarded as Academic Merits of the same University, where he heads the Laboratory of Applied Electrochemistry. Dr. Zlatev obtained his Bachelor’s and Master’s Degrees from the University of Chemical Technology and Metallurgy of Sofia, Bulgaria and his Ph.D. Degree from the National Polytechnic Institute of Grenoble (INPG), France. Until 1992 he was a fulltime researcher in the Bulgarian Academy of Science and a part-time professor in Sofia University. In 2005 Dr. Zlatev founded the Electrochemistry Laboratory ZENIT in Sofia, Bulgaria and worked as its director until January 2005 when he accepted the position of fulltime senior researcher in UABC.\nDr. Zlatev is a member of the Mexican National System of Researchers (SNI) and Regular Member of the Mexican Academy of Sciences. He participates in research projects in France, Germany and Mexico. Dr. Zlatev is the author of more than 170 publications, book chapters and reports in scientific congresses and holds 14 patents in the field of the electrochemical and spectroscopic methods of analysis, corrosion and materials, electrochemical and analytical instrumentation.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"487",title:"Agricultural Chemistry",slug:"agricultural-chemistry"}],chapters:[{id:"43058",title:"Report of the Workshop on Harmonized Classification and Labelling (CLH) of Active Substances in Plant Protection Products Held in Berlin on 12 and 13 April 2011",doi:"10.5772/55603",slug:"report-of-the-workshop-on-harmonized-classification-and-labelling-clh-of-active-substances-in-plant-",totalDownloads:3033,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:null,signatures:"Roland Solecki, Abdelkarim Abdellaue, Teresa Borges,\nKaija Kallio-Mannila, Herbert Köpp, Thierry Mercier, Vera Ritz, Gabriele Schöning and José Tarazona",downloadPdfUrl:"/chapter/pdf-download/43058",previewPdfUrl:"/chapter/pdf-preview/43058",authors:[{id:"156055",title:"Dr.",name:"Roland",surname:"Solecki",slug:"roland-solecki",fullName:"Roland Solecki"}],corrections:null},{id:"43080",title:"Grain Yield Determination and Resource Use Efficiency in Maize Hybrids Released in Different Decades",doi:"10.5772/55287",slug:"grain-yield-determination-and-resource-use-efficiency-in-maize-hybrids-released-in-different-decades",totalDownloads:4873,totalCrossrefCites:4,totalDimensionsCites:15,hasAltmetrics:0,abstract:null,signatures:"Laura Echarte, Lujan Nagore, Javier Di Matteo, Matías Cambareri, Mariana Robles and Aída Della Maggiora",downloadPdfUrl:"/chapter/pdf-download/43080",previewPdfUrl:"/chapter/pdf-preview/43080",authors:[{id:"164811",title:"Dr.",name:"Laura",surname:"Echarte",slug:"laura-echarte",fullName:"Laura Echarte"},{id:"165595",title:"Dr.",name:"Maria",surname:"Nagore",slug:"maria-nagore",fullName:"Maria Nagore"},{id:"165596",title:"BSc.",name:"Javier",surname:"Di Matteo",slug:"javier-di-matteo",fullName:"Javier Di Matteo"},{id:"165598",title:"BSc.",name:"Mariana",surname:"Robles",slug:"mariana-robles",fullName:"Mariana Robles"},{id:"165599",title:"MSc.",name:"Aída",surname:"Della Maggiora",slug:"aida-della-maggiora",fullName:"Aída Della Maggiora"},{id:"167765",title:"Dr.",name:"Matias",surname:"Cambareri",slug:"matias-cambareri",fullName:"Matias Cambareri"}],corrections:null},{id:"43060",title:"Ecophysiology of Wild Plants and Conservation Perspectives in the State of Qatar",doi:"10.5772/55305",slug:"ecophysiology-of-wild-plants-and-conservation-perspectives-in-the-state-of-qatar",totalDownloads:6821,totalCrossrefCites:3,totalDimensionsCites:12,hasAltmetrics:0,abstract:null,signatures:"Bassam T. 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The magnitude of the effect of interventions that modify one Quality Adjusted Life Years (QALYs) will be compared, and the transferability of these interventions globally will be investigated by comparison between different socio-economic countries with different government structures (from socialist to autocratic). The reproducibility and cost of the lifestyle interventions (e.g., exercise, sleep, nutritious diet, national child care, pollution limitations) on QALYs will also be documented. The ratio of (QALY/Cost) weighted by reproducibility and transferability should give a rank-ordered list of actions humans can take to increase the quality years of human consciousness. Differences in the optimized list of rank-ordered interventions to maximize the quality of life between nation-states with varying GDP and government types (i.e., the lack of transferability) will be discussed. 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Perez",fullName:"Maria Cristina Florian Perez",slug:"maria-cristina-florian-perez"}]},{id:"39661",title:"Experimental Sepsis Models",slug:"experimental-sepsis-models",signatures:"Safiah Mai, Momina Khan, Patricia Liaw and Alison Fox-Robichaud",authors:[{id:"81157",title:"Dr.",name:"Patricia",middleName:null,surname:"Liaw",fullName:"Patricia Liaw",slug:"patricia-liaw"},{id:"81981",title:"Ms.",name:"Safiah",middleName:null,surname:"Mai",fullName:"Safiah Mai",slug:"safiah-mai"},{id:"143605",title:"Dr.",name:"Alison",middleName:null,surname:"Fox-Robichaud",fullName:"Alison Fox-Robichaud",slug:"alison-fox-robichaud"},{id:"150039",title:"Ms.",name:"Momina",middleName:null,surname:"Khan",fullName:"Momina Khan",slug:"momina-khan"}]},{id:"39660",title:"Sepsis, the Liver and the Gut",slug:"sepsis-the-liver-and-the-gut",signatures:"Neville Azzopardi, Manuel Fenech and Tonio Piscopo",authors:[{id:"147466",title:"Dr.",name:"Nevil",middleName:null,surname:"Azzopardi",fullName:"Nevil Azzopardi",slug:"nevil-azzopardi"},{id:"151611",title:"Dr.",name:"Manuel",middleName:null,surname:"Fenech",fullName:"Manuel Fenech",slug:"manuel-fenech"},{id:"154379",title:"Dr.",name:"Tonio",middleName:null,surname:"Piscopo",fullName:"Tonio Piscopo",slug:"tonio-piscopo"}]},{id:"39637",title:"Nerve Growth Factor and Sepsis",slug:"nerve-growth-factor-and-sepsis",signatures:"Ayse Belin Ozer and Mustafa Bayar",authors:[{id:"141270",title:"Prof.",name:"Mustafa",middleName:"Kemal",surname:"Bayar",fullName:"Mustafa Bayar",slug:"mustafa-bayar"},{id:"145572",title:"Dr.",name:"Ayse Belin",middleName:null,surname:"Ozer",fullName:"Ayse Belin Ozer",slug:"ayse-belin-ozer"}]},{id:"39631",title:"Nicotinamide Phosphoribosyltransferase in Sepsis",slug:"nicotinamide-phosphoribosyltransferase-in-sepsis",signatures:"Peixin Huang, Ding You Li, Daniel P. Heruth, Li Qin Zhang, Dmitry N. Grigoryev and Shui Qing Ye",authors:[{id:"102472",title:"Dr.",name:"Shui",middleName:null,surname:"Ye",fullName:"Shui Ye",slug:"shui-ye"}]},{id:"39635",title:"Neural Reflex Control of Inflammation During Sepsis Syndromes",slug:"neural-reflex-control-of-inflammation-during-sepsis-syndromes",signatures:"Ricardo Fernandez and Claudio Acuña-Castillo",authors:[{id:"142894",title:"Dr.",name:"Ricardo",middleName:null,surname:"Fernandez",fullName:"Ricardo Fernandez",slug:"ricardo-fernandez"},{id:"150418",title:"Dr.",name:"Claudio",middleName:null,surname:"Acuña-Castillo",fullName:"Claudio Acuña-Castillo",slug:"claudio-acuna-castillo"}]},{id:"39640",title:"Early Detection of Sepsis, MOF and Outcome Prediction in Severely Traumatized Patients",slug:"early-detection-of-sepsis-mof-and-outcome-prediction-in-severely-traumatized-patients",signatures:"Zeljko Lausevic and Mirjana Lausevic",authors:[{id:"145609",title:"Prof.",name:"Željko",middleName:null,surname:"Laušević",fullName:"Željko Laušević",slug:"zeljko-lausevic"},{id:"146319",title:"MSc.",name:"Mirjana",middleName:null,surname:"Lausevic",fullName:"Mirjana Lausevic",slug:"mirjana-lausevic"}]},{id:"39630",title:"New Biomarkers for Sepsis",slug:"new-biomarkers-for-sepsis",signatures:"Lixin Xie",authors:[{id:"143073",title:"Dr.",name:"Lixin",middleName:null,surname:"Xie",fullName:"Lixin Xie",slug:"lixin-xie"}]},{id:"39632",title:"The Role of Procalcitonin in the Early Diagnosis of Septic and Non-Septic Complications in the Immediate Postoperative Setting After Orthotopic Liver Transplantation",slug:"the-role-of-procalcitonin-in-the-early-diagnosis-of-septic-and-non-septic-complications-in-the-immed",signatures:"Aristotelis Perrakis, Volker Müller,\nWerner Hohenberger and Roland S. Croner",authors:[{id:"81912",title:"Dr.",name:"Roland S",middleName:null,surname:"Croner",fullName:"Roland S Croner",slug:"roland-s-croner"},{id:"145627",title:"Dr.",name:"Aristotelis",middleName:null,surname:"Perrakis",fullName:"Aristotelis Perrakis",slug:"aristotelis-perrakis"},{id:"145630",title:"Dr.",name:"Volker",middleName:null,surname:"Müller",fullName:"Volker Müller",slug:"volker-muller"},{id:"145631",title:"Prof.",name:"Werner",middleName:null,surname:"Hohenberger",fullName:"Werner Hohenberger",slug:"werner-hohenberger"}]},{id:"39633",title:"Microdialysis Monitoring of Biomarkers for Early Recognition of Intestinal Ischemia",slug:"microdialysis-monitoring-of-biomarkers-for-early-recognition-of-intestinal-ischemia",signatures:"Radha Swathe Priya M. and Emma P. Córcoles",authors:[{id:"145647",title:"Dr.",name:"Emma",middleName:null,surname:"Córcoles",fullName:"Emma Córcoles",slug:"emma-corcoles"},{id:"145689",title:"Ms.",name:"Radha",middleName:null,surname:"Swathe Priya M",fullName:"Radha Swathe Priya M",slug:"radha-swathe-priya-m"}]},{id:"39663",title:"Challenges in the Diagnosis of Sepsis of the Neonate",slug:"challenges-in-the-diagnosis-of-sepsis-of-the-neonate",signatures:"Bernhard Resch, Nora Hofer and Wilhelm Müller",authors:[{id:"66173",title:"Prof.",name:"Bernhard",middleName:null,surname:"Resch",fullName:"Bernhard Resch",slug:"bernhard-resch"},{id:"149705",title:"Dr.",name:"Nora",middleName:null,surname:"Hofer",fullName:"Nora Hofer",slug:"nora-hofer"},{id:"149706",title:"Prof.",name:"Wilhelm",middleName:null,surname:"Müller",fullName:"Wilhelm Müller",slug:"wilhelm-muller"}]},{id:"39629",title:"Relevance of Procalcitonin for the Diagnosis of Early and Late-Onset Sepsis in Newborns",slug:"relevance-of-procalcitonin-for-the-diagnosis-of-early-and-late-onset-sepsis-in-newborns",signatures:"C. Savagner, B. Leboucher, G. Gascoin-Lachambre and C. Gras-Leguen",authors:[{id:"118185",title:"Dr.",name:"Christele",middleName:null,surname:"Gras-Leguen",fullName:"Christele Gras-Leguen",slug:"christele-gras-leguen"},{id:"141483",title:"Dr.",name:"Christophe",middleName:null,surname:"Savagner",fullName:"Christophe Savagner",slug:"christophe-savagner"},{id:"161526",title:"Dr.",name:"Bertrand",middleName:null,surname:"Leboucher",fullName:"Bertrand Leboucher",slug:"bertrand-leboucher"},{id:"161527",title:"Dr.",name:"Géraldine",middleName:null,surname:"Gascoin-Lachambre",fullName:"Géraldine Gascoin-Lachambre",slug:"geraldine-gascoin-lachambre"}]},{id:"39627",title:"Catheter-Related Bloodstream Infections in Critical Care",slug:"catheter-related-bloodstream-infections-in-critical-care",signatures:"Efraín Riveros Pérez",authors:[{id:"141259",title:"Dr.",name:"Efrain",middleName:null,surname:"Riveros-Perez",fullName:"Efrain Riveros-Perez",slug:"efrain-riveros-perez"}]},{id:"39636",title:"Beware of Unusual Organisms Masquerading as Skin Contaminants",slug:"beware-of-unusual-organisms-masquerading-as-skin-contaminants",signatures:"Marilynn R. Fairfax and Hossein Salimnia",authors:[{id:"148092",title:"Dr.",name:"Marilynn",middleName:null,surname:"Fairfax",fullName:"Marilynn Fairfax",slug:"marilynn-fairfax"},{id:"148094",title:"Dr.",name:"Hossein",middleName:null,surname:"Salimnia",fullName:"Hossein Salimnia",slug:"hossein-salimnia"}]},{id:"39628",title:"Blood Culture Systems: From Patient to Result",slug:"blood-culture-systems-from-patient-to-result",signatures:"Andries William Dreyer",authors:[{id:"144868",title:"Dr.",name:"Andries",middleName:null,surname:"Dreyer",fullName:"Andries Dreyer",slug:"andries-dreyer"}]},{id:"39639",title:"Sepsis: Antimicrobial Therapy",slug:"sepsis-antimicrobial-therapy",signatures:"Renato Finkelstein and Ilana Oren",authors:[{id:"144450",title:"Prof.",name:"Renato",middleName:null,surname:"Finkelstein",fullName:"Renato Finkelstein",slug:"renato-finkelstein"},{id:"147443",title:"Dr.",name:"Ilana",middleName:null,surname:"Oren",fullName:"Ilana Oren",slug:"ilana-oren"}]},{id:"39664",title:"Infections in Leukemia",slug:"infections-in-leukemia",signatures:"Rekha Chandran, Morgan Hakki and Stephen Spurgeon",authors:[{id:"148243",title:"Dr.",name:"Stephen",middleName:null,surname:"Spurgeon",fullName:"Stephen Spurgeon",slug:"stephen-spurgeon"},{id:"149975",title:"Dr.",name:"Rekha",middleName:null,surname:"Chandran",fullName:"Rekha Chandran",slug:"rekha-chandran"},{id:"149976",title:"Dr.",name:"Morgan",middleName:null,surname:"Hakki",fullName:"Morgan Hakki",slug:"morgan-hakki"}]},{id:"39634",title:"Continuous Renal Replacement Therapies in Patients with Severe Sepsis and Septic Shock",slug:"continuous-renal-replacement-therapies-in-patients-with-severe-sepsis-and-septic-shock",signatures:"F.J. García-Miguel",authors:[{id:"147092",title:"PhD.",name:"F Javier",middleName:null,surname:"García Miguel",fullName:"F Javier García Miguel",slug:"f-javier-garcia-miguel"}]},{id:"39626",title:"Induction Agents for Endotracheal Intubation in Severe Sepsis and Septic Shock",slug:"induction-agents-for-endotracheal-intubation-in-severe-sepsis-and-septic-shock",signatures:"Marie Mullen",authors:[{id:"146536",title:"Dr.",name:"Marie",middleName:null,surname:"Mullen",fullName:"Marie Mullen",slug:"marie-mullen"}]}]}],publishedBooks:[{type:"book",id:"802",title:"Severe Sepsis and Septic Shock",subtitle:"Understanding a Serious Killer",isOpenForSubmission:!1,hash:"7ac9a759048ec9ec47c283813a4131cc",slug:"severe-sepsis-and-septic-shock-understanding-a-serious-killer",bookSignature:"Ricardo Fernandez",coverURL:"https://cdn.intechopen.com/books/images_new/802.jpg",editedByType:"Edited by",editors:[{id:"76827",title:"Dr.",name:"Ricardo",surname:"Fernandez",slug:"ricardo-fernandez",fullName:"Ricardo Fernandez"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1737",title:"Emergency Medicine",subtitle:"An International Perspective",isOpenForSubmission:!1,hash:"937b8698946595f200066b643e153534",slug:"emergency-medicine-an-international-perspective",bookSignature:"Michael Blaivas",coverURL:"https://cdn.intechopen.com/books/images_new/1737.jpg",editedByType:"Edited by",editors:[{id:"106834",title:"Dr.",name:"Michael",surname:"Blaivas",slug:"michael-blaivas",fullName:"Michael Blaivas"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2583",title:"Sepsis",subtitle:"An Ongoing and Significant Challenge",isOpenForSubmission:!1,hash:"85121c8c358a97497c254ca2832be903",slug:"sepsis-an-ongoing-and-significant-challenge",bookSignature:"Luciano Azevedo",coverURL:"https://cdn.intechopen.com/books/images_new/2583.jpg",editedByType:"Edited by",editors:[{id:"72846",title:"Prof.",name:"Luciano",surname:"Azevedo",slug:"luciano-azevedo",fullName:"Luciano Azevedo"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2621",title:"Hyperthermia",subtitle:null,isOpenForSubmission:!1,hash:"e3557de8bc852fa2c1a8b17e026cd37c",slug:"hyperthermia",bookSignature:"Nagraj Huilgol",coverURL:"https://cdn.intechopen.com/books/images_new/2621.jpg",editedByType:"Edited by",editors:[{id:"146901",title:"Dr.",name:"Nagraj",surname:"Huilgol",slug:"nagraj-huilgol",fullName:"Nagraj Huilgol"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5851",title:"Sepsis",subtitle:null,isOpenForSubmission:!1,hash:"140438644d49079e214fd82e9fe5e5ac",slug:"sepsis",bookSignature:"Vijay Kumar",coverURL:"https://cdn.intechopen.com/books/images_new/5851.jpg",editedByType:"Edited by",editors:[{id:"63844",title:"Dr.",name:"Vijay",surname:"Kumar",slug:"vijay-kumar",fullName:"Vijay Kumar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[{type:"book",id:"802",title:"Severe Sepsis and Septic Shock",subtitle:"Understanding a Serious Killer",isOpenForSubmission:!1,hash:"7ac9a759048ec9ec47c283813a4131cc",slug:"severe-sepsis-and-septic-shock-understanding-a-serious-killer",bookSignature:"Ricardo Fernandez",coverURL:"https://cdn.intechopen.com/books/images_new/802.jpg",editedByType:"Edited by",editors:[{id:"76827",title:"Dr.",name:"Ricardo",surname:"Fernandez",slug:"ricardo-fernandez",fullName:"Ricardo Fernandez"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},onlineFirst:{chapter:{type:"chapter",id:"82367",title:"Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa",doi:"10.5772/intechopen.105547",slug:"spatial-variation-and-factors-associated-with-unsuppressed-hiv-viral-load-among-women-in-an-hiv-hype",body:'In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) set ambitious 90–90-90 HIV testing and treatment target to achieve the 73% composite viral suppression target by the year 2020 towards ending the epidemic by year 2030 [1]. While few countries like Australia and Botswana achieved this target [2, 3], the global public health community failed to achieve this target [4]. Therefore, in 2021 the UNAIDS Global AIDS strategy raised the targets to 95–95-95 with an overall viral suppression of 86% to be met by 2025 including prioritising sexual reproductive health and rights for women living with HIV (WLHIV), with the aim of controlling the epidemic by the year 2030 [5]. The “first 95” represents 95% of people living with HIV knowing their HIV status; the “second 95” represents 95% of people who know their HIV-positive status and are on antiretroviral therapy (ART); and the “third 95” represents 95% of HIV positive people who know their HIV status are on ART and are virally suppressed [1, 4]. At the country and global level, commitment, and resources to meet these indicators has been prioritised as the strategy was expected to prevent onward transmission of HIV and reduce HIV incidence [5, 6, 7].
In 2020, globally, 36 million adults over the age of 15 were living with HIV [4]. Out of these, 84% knew their status, 73% were accessing treatment and 66% were virally suppressed [4]. South Africa contributes approximately 22% of the global HIV burden [4, 8], and KwaZulu-Natal province is the epicentre [9, 10], where the UNAIDS targets has not been met [11, 12]. Whilst South Africa has substantially scaled-up ART provision, having the largest HIV treatment programme globally, has resulted in reducing number of HIV related death [8]. However, country level HIV prevalence (14.0%) with an estimated of 231,100 new infection remains persistently high [13], and almost a fourth of women in their reproductive ages (15–49) were HIV positive at the end of 2020 [8]. KwaZulu-Natal has the highest HIV burden with prevalence of 18.1% compared to Western Cape with a prevalence of 6.8% [14]. Heterosexual sex is the key path to HIV transmission and acquisition in this region [15], where women of reproductive age are disproportionately affected [16, 17], thus increasing the potential of mother to child transmission (MTCT) of HIV during pregnancy, childbirth, or breastfeeding [13, 18]. Thus, viral suppression is critically important among this key population for the prevention of mother-to-child transmission (PMTCT) of HIV [18, 19] and transmission to sexual partners.
Small area location-based approaches have been recommended for targeted interventions, scale up of treatment and identify spatially distributed structural and behavioural risk factors towards achieving the UNAIDS targets and to help to reduce the overall HIV burden [20]. Evidently, their exist geographic variation in the complexity of HIV epidemiological measures [21]. Therefore, spatial analysis and modelling accounting for the presence of spatial autocorrelation between observation and residual must be considered [20, 21]. Failure to account for spatial heterogeneity and possible causes could result in misleading epidemiologist, public health institutions, and policy makers. The national HIV prevalence survey among pregnant women that also examined socioeconomic factors associated with unsuppressed viral load did not account for the possible linear effect of continuous covariates or mapped the spatial effect [22]. Therefore, the aim of this study was to determine factors associated with unsuppressed HIV viral load among women living with HIV while accounting for possible linear effects of some continuous covariates and mapping spatial risk effect using Bayesian inference. Furthermore, study assessed the progress towards UNAIDS indicators, examine the prevalence, and hotspots of unsuppressed HIV viral load among women in a hyperendemic area of KwaZulu-Natal, South Africa. This study applied the Bayesian hierarchical Geoadditive model technique to identify risk factors associated with unsuppressed HIV viral load and mapping the spatial areas in KwaZulu-Natal, South Africa.
This analysis was based on data from HIV Incidence Provincial Surveillance System (HIPSS) that monitored HIV related measures of HIV prevalence and incidence in association with the programmatic scale of HIV prevention and treatment efforts in a “real world” non-trial setting. The study undertook two sequential cross-sectional surveys with the first survey from June 2014 to 18 June 2015 (2014 Survey) and the second survey from 8 July 2015 to 7 June 2016 (2015 Survey). All study participants provided written informed consent and or assent, completed a face-to-face questionnaire to obtain socio-demographic, behavioural, knowledge of HIV testing, sexually transmitted infections (STI) and tuberculosis (TB) history and biological information. From a total of 600 Enumeration Areas (EAs), 591 EAs with more than 50 households were systematically selected at random, of which 221 were drawn for the 2014 Survey and 203 were drawn for the 2015 Survey. Households were randomly selected using multi-stage random sampling, were geo-referenced and one individual per household, within the age range 15–49 years old was randomly selected and invited to participate in the study. In the 2014 Survey a total of 9812 participants were enrolled, of whom 6265 were women, whilst in the 2015 Survey a total of 10,236 participants were enrolled, of whom 6341 were women. All enrolled participants had HIV antibody and viral load testing undertaken. In the 2014 Survey, 2955 were HIV positive and 2946 had viral load measurement, whilst 9 participants had missing viral load measurement. In the 2015 Survey, 2947 women were HIV positive and 2946 had viral load measurements, whilst 1 participant had missing viral load measurement.
HIPSS study was conducted in accordance with the approval by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (Reference number BF269/13), the KwaZulu-Natal Provincial Department of Health (HRKM 08/14), and the Associate Director of Science of the Centre for Global Health (CGH) at the United States Centre for Disease Control and Prevention (CDC) in Atlanta, United States of America (CGH 2014–080). Details about HIPSS study design, objectives and study and data collection procedures have been described elsewhere [10, 11].
HIPSS was conducted in a geographically defined region of rural Vulindlela and peri urban Greater Edendale areas in the Msunduzi municipality, uMgungundlovu district of KwaZulu-Natal province in South Africa. Whilst this community has basic access to water, electricity and free health facilities, the area is characterised by high rates of unemployment, poverty, and HIV. The EAs are located between 29°39’ South and 30°17 East of KZN, covers a total of 33 wards in the Msunduzi and a part of uMngeni municipalities, in uMgungundlovu district.
The primary outcome variable was HIV viral load status among women living with HIV (WLHIV) in this community, which was categorised as binary outcome:
This threshold was used in accordance with the country revised ART treatment guideline [23, 24] as well as evidence from several studies on transmission potential at this cut off [25, 26]. Unsuppressed viral load calculation and definition was based on the composite viral suppression of all WLHIV irrespective of being on ART or not.
Initial data exploration to identify potential factors associated with unsuppressed viral load was established using multiple correspondence analysis and random forest analysis [27]. The explanatory variables considered in the study comprised of socio-demographic, behavioural, knowledge of HIV status and HIV testing, medical history, and biological variables. These included age, marital status, education level, community duration, migration history, monthly income, accessing health care, meal cut, income loss, place of residence, number of household members, sex last 12 months, number of sexual partners last 12 months, number of total lifetime sex partners, forced first time sex, ever consume alcohol, ever tested for HIV, number of lifetime HIV test, knowledge of HIV status, perceived risk of contracting HIV, exposed to TB last 12 months, ever diagnosed of TB, had any STI symptoms, ever diagnosed of STI, ever pregnant, currently on antiretrovirals (ARV) and current CD4 cell count. The variance inflation factors (VIF) was used to check for collinearity among continuous independent variables and all variables with VIF < 4 was assumed that multicollinearity was not significantly present. Also, non-linear effect of all continuous variables was also examined, of which only age, household size, number of lifetime HIV test and total number of children ever born displayed a significant non-linear effect and were considered in the fitted model while the remaining independent variables were included as linear fixed effect.
To account for the complex multilevel sampling design, weighted percentage and frequency were used to describe and summarise the study characteristics across both surveys. Progress towards each of the 95-95-95 indicators and composite viral suppression was estimated. Comparisons of weighted proportion of viral load status was estimated with associated 95% confidence intervals (CIs) and p values using Taylor series methods. Initial non-spatial bivariate survey logistic regression was used to test association between each background characteristics and the dependent variable using Rao-Scott chi-square test. Statistical analyses were performed using SAS (SAS Institute, Cary, North Carolina) version 9.4. Covariates with significant association at 5% significant level for each study year was included in the multivariate model.
Suppose
Eq. 1 is a semi-parametrical model, where
The argument is that spatial effect is the proxy of most unobserved influence, under which spatial structure assumption must be followed. The structured spatial effect accounts for the assumption that location close in proximity are more likely to be correlated in respect of their outcome. While the unstructured spatial effect accounts for the spatial variation because of the effects of interminable district-level factors that are not related spatially [31, 32, 33].
The study utilised a fully Bayesian inference, hence all parameters and functions were considered as random variables and thus assigned with appropriate prior. Parameter
Where
Independent and identically distributed random variable (i.i.d) Gaussian priors were assigned to the unstructured spatial effect to account for the unobserved covariates that are inherent within the districts, denoted as:
where the variance
Lastly, the posterior distributions of all the parameters
This is a high dimensional model and analysis which sometimes require good knowledge of advance mathematical and statistical computation. So, Markov chain Monte Carlo (MCMC) algorithm is required to generate samples from this distribution which comes with much computational difficulties. To circumvent this problem and difficulties, the Integrated Nested Laplace Approximation (INLA) was used to obtain the estimate [38, 39]. The outmost goal is to estimate marginal posterior distribution for the latent Gaussian model which was used to compute the summary statistics of interest like posterior mean, standard deviation, and 95% credible interval.
Three models were considered for comparison namely:
Deviance information criterion (DIC) of each model were compared. The final Geoadditive model was selected based on smallest DIC which was considered as good predictive performance and best fit model [40, 41]. The summary results give the posterior mean estimates with associated credible interval as well as the spatial effect map. The enumeration area shapefile was created in ArcGIS using the geographic attributes. Bayesian inference was analysed using INLA package in R software [37, 42].
Table 1 shows the sample size and characteristics of HIV positive women in rural and peri urban areas of KwaZulu-Natal, South Africa. Almost half (45.2%) of the women had unsuppressed viral load in 2014 and about one third (38.1%) in 2015. Majority of WLHIV were aged between 20 and 44 years; 86.9% in 2014 and 85% in 2015 with median age and interquartile range (IQR) of 31 [25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38] in 2014 and and 32 [26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]) years in 2015. Majority of the women were never married; 84.6% in 2014 and 81% in 2015. More than half had incomplete high school education 53.5% in 2014 and 57.3% in 2015. Most women had always lived in the community; 76.5% in 2014 and 54.8% in 2015 whilst never being away from home in the last 12 months was 89.8% in 2014 and 92.7% in 2015. In 2014 75.5% and in 2015, 53.0% of women reported a monthly income of ≤R2500 More than half of women sampled (57.6%) in 2014 were from rural area whilst the majority (63.8%) in 2015 were from urban areas. Overall 77.2% in 2014 and 83.5% in 2015 had engaged in sex in the last 12 months, whilst 46.7% in 2014 and 22.1% in 2015 reported having had two or more number of sex partners in the last 12 months. Overall, the majority; 77.8% in 2014 and 84.3% in 2015 reported having had two or more lifetime sex partners. Almost all the women were not forced to have sex at their first-time sex encounter. Regarding their HIV testing knowledge and perception, 88.9% of women in 2014 and 98.9% in 2015 reported having had an HIV test with 62.7% in 2014 and 69.8% had HIV test more than twice in their lifetime. In 2014, 21.5% had a perception of not likely to contract HIV, while only 14.1% in 2015. Overall, 79.6% of women in 2014 and 88.2% in 2015 had reported having been pregnant in their lifetime. Less than half, 48.8% in 2014 reported to be on ART, though this increased to 59.8% in 2015. More than half of the women 55.8% in 2014 and 58.6% in 2015 had a current CD4 cell counts of ≥500 cells per μL and 23.1% in 2014 and 21.6% in 2015 had CD4 cell counts of <350 per μL.
Characteristics | 2014 Surveyα | 2015 Surveyβ |
---|---|---|
Total | 2955 | 2948 |
Age median (IQR) | 31 [25–37] | 32 [26–39] |
15–19 | 131 (4.6) | 133 (4.9) |
20–24 | 436 (14.3) | 337 (11.2) |
25–29 | 578 (20.3) | 606 (20) |
30–34 | 561 (20.7) | 674 (21.1) |
35–39 | 517 (18.4) | 510 (18.8) |
40–44 | 426(13.2) | 431 (13.9) |
45–49 | 306 (8.5) | 257 (10.2) |
Never married | 2468 (84.6) | 2364 (81) |
Ever married | 478 (15.4) | 584 (19) |
No Schooling | 137 (2.9) | 37 (1.4) |
Incomplete High School | 1525 (53.5) | 1688 (57.3) |
Complete high school | 1293 (43.7) | 1223 (41.2) |
Always | 2290 (76.7) | 1504 (54.8) |
Moved here less than 1 year ago | 88 (2.3) | 122 (3.8) |
Moved here more than 1 year ago | 577 (21.0) | 1322 (41.4) |
Yes | 311 (10.2) | 217 (7.3) |
No | 2644 (89.9) | 2731 (92.7) |
No income | 602 (17) | 50 (1.3) |
≤ R2500 | 2177 (75.5) | 1657 (53.0) |
> R2500 | 169 (7.5) | 1241 (45.8) |
Yes | 682 (24.5) | 1477 (50.8) |
No | 2273 (75.5) | 1469 (49.2) |
Yes | 606 (22.1) | 1330 (46.2) |
No | 2342 (77.9) | 1616 (53.8) |
Yes | 1216 (44.9) | 2168 (73.3) |
No | 1732 (55.1) | 778 (26.7) |
Rural | 1009 (57.6) | 954 (36.2) |
Urban | 1946 (42.4) | 1994 (63.8) |
Yes | 2218 (77.2) | 2501 (83.5) |
No | 737 (22.8) | 447 (16.5) |
1 partner | 1178 (53.3) | 1824 (72.9) |
2 or more partners | 1034 (46.7) | 677 (27.1) |
1 partner | 533 (22.2) | 455 (15.7) |
2 or more partners | 1844 (77.8) | 2429 (84.3) |
No response | 879 | 64 |
Yes | 72 (2.5) | 100 (3.3) |
No | 2831 (95.9) | 2833 (96.1) |
Do not remember | 52 (1.6) | 15 (0.5) |
Yes | 390 (11.5) | 536 (18.5) |
Never | 2564 (88.5) | 2412 (81.5) |
Yes | 2513 (88.9) | 2868 (96.9) |
No | 442 (11.1) | 80 (3.1) |
Never | 442 (11.1) | 81 (3.1) |
1 time | 769 (26.2) | 765 (27.1) |
2 or more times | 1744 (62.7) | 2102 (69.8) |
Yes | 1870 (65.6) | 2219 (73.7) |
No | 1085 (34.4) | 729 (25.3) |
Likely to acquire HIV | 573 (19.3) | 461 (16) |
Not likely to acquire HIV | 686 (21.5) | 405 (14.1) |
I am already infected | 1696 (59.2) | 2082 (70) |
103 (3.6) | 162 (5.9) | |
2853 (96.6) | 2786 (94.1) | |
Yes | 251 (9.6) | 363 (12.7) |
No | 2704 (90.4) | 2585 (87.3) |
Yes | 219 (9.0) | 449 (14.9) |
No | 2736 (91.0) | 2499 (85.1) |
Yes | 1245 (47.1) | 1689 (57.4) |
No | 1710 (52.9) | 1259 (42.6) |
Yes | 163(4.5) | 80(2.9) |
No | 2792(95.5) | 2868(97.1) |
Yes | 213 (9.1) | 320 (11.3) |
No | 2742 (90.9) | 2628 (88.7) |
Yes | 2346 (79.6) | 2595 (88.1) |
No | 600 (20.4) | 353 (11.9) |
Yes | 1346 (48.8) | 1775 (59.8) |
No | 1600(51.2) | 1172 (40.2) |
Single/fixed | 1079 (86.3) | 1580 (88.5) |
Multiple | 172 (13.7) | 196 (11.5) |
<350 cells per μL | 696 (23.1) | 634 (21.7) |
350–499 cells per μL | 639 (21.1) | 576 (19.7) |
≥500 cells per μL | 1593 (55.8) | 1729 (58.6) |
Characteristics of HIV positive women in Vulindlela and Greater Edendale, KwaZulu-Natal, South Africa, 2014–2015.
Participants missing for: a = 7, and f = 27 in 2014; b, c and d = 2, f = 9 in 2015. No response: e: = 879(64) for 2014(2015). Missing data were excluded from percentage calculation. ZAR = South African Rand (ZAR15 ∼ US$1). TB = tuberculosis, STI = sexually transmitted infections, ARV = antiretroviral drugs, ART = antiretroviral therapy, Ever had any STI symptoms = any symptoms of abnormal vaginal discharge, burning or pain when passing urine or presence of any genital warts/ulcers.
Figure 1 provides the status on the UNAIDS 95–95-95 indicators. Of the 2955 women in 2014 and 2948 in 2015 who tested positive for HIV, 9 and 1 participants respectively had no viral load measurement. Thus, 2946 women in 2014 and 2947 women in 2015 had viral load measurements. In 2014, to meet the “first 95”, 65.5% (95% CI, 62.9–68.2) (n = 1890/2955) were aware of their HIV positive status and for the “second 95”, 74.2% (95% CI, 71.6–76.8 (n = 1348/1870) had initiated ART and for the “third 95”, 82.9% (95% CI, 80.4–85.4) (n = 1105/1346) had achieved viral suppression, and overall viral suppression among all HIV positive women was 54.8% (95% CI, 52.0–57.5) (n = 1574/2946). While in 2015, progress towards 95–95-95 targets were: 74.7% (95% CI, 72.7–76.6) (n = 2219/2948) were aware of their HIV status; 80.0% (95% CI, 78.1–82.0) (n = 1777/2219) of these had initiated ART and 88.2% (95% CI, 86.6–89.9) (n = 1551/1777) of those on ART had achieved HIV viral suppression, resulting in the overall viral suppression among all HIV positives to be 61.9% (95% CI, 59.7–64.1) (n = 1828/2947).
Progress of the UNAIDS 95–95-95 indicators by age group and overall, among HIV positive women (2014–2015). (A). First 95: Women living with HIV who know they are HIV positive. (B). Second 95: Women who knew they were HIV positive and were taking ART. (C). Third 95: Women who knew their HIV positive, were on ART and had achieved HIV viral suppression at HIV viral load <400 copies/ml. (D). UNAIDS composite measure towards achieving HIV viral suppression among all HIV positive women.
Disaggregated by age groups, Figure 1a shows the progress towards the “first 95” Knowledge of HIV status increased from 65.6% in 2014 to 74.7% in 2015, and across age groups, with highest achieved among 35–39 (86.5%), 40–44 (82.4%) and 45–49 (82.4%) in 2015. Highest increase in the knowledge of HIV positive status was in the age group 15–29, increasing from 25.8% in 2014 to 46.7% in 2015. Figure 1b shows the progress towards the “second 95”. Overall proportion of women who knew their HIV positive status and were on ART increased from 74.2% in 2014 to 80.0% in 2015. The uptake of ART varied across age groups, uptake was high in the 15–19 years age group at 74.8% in 2014 and 75.9% in 2015; in ages 30–34 uptake was 77.2% in 2014 and 80.5 in 2015; in ages 35–39 years uptake was 77.8% in 2014 and 85.6% in 2015; in ages 40–44 years uptake was 77.1% in 2014 and 84.6% in 2015 and in age 45–49 uptake was 79.1% in 2014 and 84.2% in 2015. However, ART uptake in the age group 20–24 years was lowest at 62.4% in 2014 and 62.8% in 2015. Figure 1c shows the progress towards the “third 95”, that is the proportion of HIV positive women who knew their HIV positive status, were on sustained ART and who had achieved viral suppression of <400 copies per mL. Proportion varied across ages group; HIV viral suppression was lowest at 66% among 20–24 years old in 2014 and increased to 74.4% in 2015. Viral suppression of 92.9% was achieved among 45–49 years old and 91.7% among 40–44 years old and 91.8% among 35–39 years old in 2015. Figure 1d shows the overall UNAIDS 95–95-95 composite measure of achieving viral suppression of 86% among all HIV positive women. Overall, 54.8% of women in 2014 and 61.9% in 2015 had achieved HIV viral suppression of <400 copies per mL. Substantial variation existed across the age groups, with 27% among 15–19 years in 2014 and increased to 46% in 2015. Highest achievement was observed with 76% among 45–49 years old.
Table 2 shows the overall prevalence of unsuppressed HIV viral load was 45.2% (95 CI, 42.5–48.0), (n/N = 1372/2946) in 2014 and 38.1% (95% CI, 35.9–40.3), (n/N = 1119/2947) in 2015. Viral suppression increased by 7.1% over the study period with high viral load prevalence decreasing as age increased and it was 72.9% (95% CI, 62.7–83.2), (n = 95/130) in 15–19 years age group, 68.2% (95% CI, 62.4–73.9), (n = 290/433) in the 20–24 years age group, 47.3% (95% CI, 41.9–52.7), (n = 299/577) in 25–29 years age group, 43.1% (95% CI, 37.9–48.3), (n = 248/561) in 30–34 years age group, 32.5% (95% CI, 26.6–38.4), (n = 185/513) in 35–39 years age group, 36.5% (95% CI, 30.6–42.4), (n = 153/426) in 40–44 years age group, 33.0% (95% CI, 26.6–39.3), (n = 102/306) in 45–49 years age group. In 2015, prevalence also decreased by age and it was 56.0% (95% CI, 43.8–64.1), (n = 74/133); 65.1 [59.5–70.7], (n = 210/337); 46.5 [41.4–51.5], (n = 279/606); 36.4% (95% CI, 32.1–40.8), (n = 244/674); 25.2% (95% CI, 20.9–29.4), (n = 125/509); 29.1% (95% CI, 24.0–34.2), (n = 120/431); 24.0% (95% CI, 18.3–29.8), (n = 67/257) in the 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years age categories (
Characteristics | 2014 Survey | 2015 Survey | ||||
---|---|---|---|---|---|---|
≥400 copies per mL | 1372/2946 | 45.2 [42.5–48.0] | 1119/2947 | 38.1 [35.9–40.3] | ||
<400 copies per mL | 1574/2946 | 54.8 [52.0–57.5] | 1828/2927 | 61.9 [59.7–64.1] | ||
Age median (IQR) | 31 [26–39] | 32 [26–39] | ||||
Socio-demographic characteristics | ||||||
Age groups (years) | ||||||
15–19 | 95/131 | 72.9 [62.7–83.2] | <0.0001 | 74/133 | 56.0[43.8–64.1] | <0.0001 |
20–24 | 290/433 | 68.2 [62.4–73.9] | 210/337 | 65.1 [59.5–70.7] | ||
25–29 | 299/577 | 47.3 [41.9–52.7] | 279/606 | 46.5 [41.4–51.5] | ||
30–34 | 248/561 | 43.1 [37.9–48.3] | 244/674 | 36.4 [32.1–40.8] | ||
35–39 | 185/513 | 32.5 [26.6–38.4] | 125/509 | 25.2 [20.9–29.4] | ||
40–44 | 153/426 | 36.5 [30.6–42.4] | 120/431 | 29.1 [24.0–34.2] | ||
45–49 | 102/306 | 33.0 [26.6–39.3] | 67/257 | 24.0 [18.3–29.8] | ||
Never married | 1188/2468 | 46.4 [43.6–49.2] | 0.03 | 948/2364 | 40.6 [38.1–43.2] | <0.0001 |
Ever married | 184/478 | 39.0 [32.3–45.6] | 171/583 | 27.4 [23.4–31.4] | ||
No schooling | 63/137 | 44.1 [34.8–53.4] | 0.09 | 10/37 | 30.2 [12.8–47.6] | 0.03 |
Incomplete High School | 672/1521 | 43.2 [39.3–47.0] | 605/1688 | 35.7 [32.8–38.6] | ||
Complete High School | 637/1288 | 47.9 [44.4–51.4] | 504/1222 | 41.7 [38.0–45.3] | ||
Always | 1078/2282 | 45.5 [42.7–48.4] | 0.53 | 585/1504 | 39.0 [35.8–42.1] | 0.04 |
Moved here less than 1 year ago | 44/88 | 48.4 [34.3–62.4] | 60/122 | 48.4 [38.1–58.7] | ||
Moved here more than 1 year ago | 250/576 | 43.9 [38.3–49.5] | 474/1321 | 36.0 [33.1–39.0] | ||
Yes | 152/311 | 50.1 [41.9–58.3] | 0.21 | 113/217 | 50.2 [42.6–57.7] | 0.01 |
No | 1220/2635 | 44.7 [41.9–47.5] | 1006/2730 | 37.2 [34.9–39.4] | ||
No income | 285/601 | 46.4 [41.0–51.7] | 0.29 | 17/50 | 34.4 [19.8–49.0] | 0.89 |
≤R2500 | 1017/2170 | 45.6 [42.4–48.8] | 627/1656 | 38.0 [35.3–40.7] | ||
> R2500 | 67/168 | 38.8 [30.2–47.4] | 475/1241 | 38.3 [34.9–41.7] | ||
Yes | 318/682 | 47.0 [41.2–52.7] | 0.04 | 549/1477 | 37.7 [34.7–40.8] | 0.02 |
No | 1051/2257 | 44.7 [41.8–47.5] | 569/1468 | 38.5 [35.4–41.6] | ||
Yes | 285/606 | 47.4 [41.5–53.3] | 0.38 | 499/1330 | 37.3 [34.0–40.5] | 0.45 |
No | 1084/2333 | 44.6 [41.8–47.5] | 619/1615 | 38.8 [35.9–41.7] | ||
Yes | 488/1212 | 41.7 [37.4–46.0] | 0.03 | 769/2167 | 35.5 [33.0–38.1] | <0.0001 |
No | 881/1727 | 48.1 [44.4–51.8] | 349/778 | 45.2 [41.2–49.2] | ||
Rural | 457/1006 | 44.5 [40.3–48.7] | 0.51 | 340/954 | 36.3[32.1–40.4] | 0.25 |
Urban | 915/1940 | 46.2 [43.4–49.1] | 779/1993 | 39.1 [36.7–41.7] | ||
Yes | 1052/2212 | 45.8 [42.8–48.8] | 0.29 | 965/2501 | 38.8[36.4–41.3] | 0.14 |
No | 320/734 | 43.3 [38.9–47.7] | 154/446 | 34.4[28.9–39.8] | ||
1 partner | 908/1178 | 46.1 [43.0–49.2] | 0.02 | 860/1824 | 38.2[35.6–40.7] | 0.03 |
2 or more partners | 464/1034 | 43.6 [39.1–47.4] | 259/677 | 37.9[33.6–42.3] | ||
1 partner | 265/532 | 48.3 [42.0–54.6] | 0.01 | 185/455 | 39.4[34.7–44.1] | 0.05 |
2 or more partners | 828/1839 | 43.7 [40.7–46.8] | 910/2428 | 37.9[354–40.2] | ||
Yes | 31/72 | 38.8 [26.1–51.5] | 0.28 | 33/100 | 34.7[23.7–45.7] | 0.74 |
No | 1314/2822 | 45.2 [42.4–48.0] | 1082/2832 | 38.3[36.0–40.6] | ||
Do not remember | 27/52 | 56.1 [39.4–72.9] | 04/15 | 30.4[1.1–59.7] | ||
Yes | 229/390 | 58.8 [51.7–65.9] | <0.0001 | 249/536 | 44.4[39.4–49.5] | <0.0001 |
No | 1143/2556 | 43.9 [40.6–46.3] | 870/2411 | 36.6[34.2–39.1] | ||
Yes | 1086/2505 | 42.4 [39.5–45.3] | <0.0001 | 1066/2867 | 37.3[35.1–39.5] | <0.0001 |
No | 286/441 | 68.1 [63.2–73.0] | 53/80 | 63.4[51.4–75.4] | ||
Yes | 619/1865 | 31.2 [28.1–34.3] | <0.0001 | 591/2218 | 25.8[23.6–28.0] | <0.0001 |
No | 753/1081 | 72.0 [68.5–75.6] | 528/729 | 74.3[70.7–77.8] | ||
Likely to Acquire HIV | 364/572 | 67.4 [62.1–72.6] | <0.0001 | 309/461 | 69.0[64.0–74.0] | <0.0001 |
Not likely to Acquire HIV | 478/682 | 70.7 [66.1–74.7] | 281/405 | 70.2 [64.8–75.5] | ||
I am already infected | 530/1692 | 28.9 [25.8–31.9] | 529/2081 | 24.6[22.3–26.9] | ||
Never | 286/441 | 68.1 [63.2–73.0] | <0.0001 | 54/81 | 63.7[51.8–75.6] | <0.0001 |
1 time | 330/764 | 41.6 [37.1–46.2] | 225/765 | 29.1[25.2–33.1] | ||
2 or more times | 756/1741 | 42.7 [39.1–46.3] | 840/2101 | 40.4[37.7–43.2] | ||
Yes | 41/102 | 41.2 [27.8–54.6] | 0.56 | 44/159 | 22.4[14.9–29.9] | <0.0001 |
No | 1331/2844 | 45.4 [42.6–48.2] | 1075/2786 | 39.0[36.8–41.3] | ||
Yes | 77/251 | 30.9 [23.2–38.6] | <0.0001 | 82/362 | 19.7[15.6–23.9] | <0.0001 |
No | 1295/1295 | 46.8 [44.0–49.5] | 1037/2585 | 40.8[38.4–43.2] | ||
Yes | 48/217 | 20.6 [13.6–27.6] | <0.0001 | 84/449 | 16.7[12.7–20.7] | <0.0001 |
No | 1324/2729 | 47.6 [45.0–50.3] | 1035/2498 | 41.8[39.4–44.3] | ||
Yes | 417/1242 | 32.6 [28.8–36.4] | <0.0001 | 463/1688 | 27.1[24.7–29.5] | <0.0001 |
No | 955/1704 | 56.5 [53.4–59.6] | 656/1259 | 52.9[49.5–56.3] | ||
Yes | 65/162 | 37.3 [28.1–46.4] | 0.08 | 30/80 | 37.4[23.0–51.9] | 0.92 |
No | 1307/2784 | 45.6 [42.9–48.4] | 1089/2867 | 38.1[35.8–40.4] | ||
Yes | 98/213 | 43.7 [35.0–52.4] | 0.71 | 155/320 | 47.7[41.5–53.9] | 0.001 |
No | 1274/2733 | 45.4 [42.5–48.3] | 964/2627 | 36.9[34.5–39.3] | ||
Yes | 1029/2346 | 42.5 [39.6–45.5] | <0.0001 | 947/2595 | 36.6[34.3–38.9.5] | <0.0001 |
No | 343/596 | 59.4 [53.7–65.1] | 169/352 | 49.3[42.6–55.9] | ||
Yes | 241/1346 | 17.1 [14.6–19.6] | <0.0001 | 220/1775 | 11.8[10.2–13.5] | <0.0001 |
No | 1131/1600 | 72.1 [68.9–75.3] | 899/1172 | 77.3[74.4–80.2] | ||
Single/fixed | 127/1077 | 11.5 [8.9–14.0] | <0.0001 | 170/1579 | 24.4(17.1–31.7) | <0.0001 |
Multiple | 36/172 | 21.2 [12.3–30.1] | 50/196 | 10.2(8.9–11.7) | ||
<350 per μL | 493/695 | 69.3 [64.8–73.8] | <0.0001 | 430/633 | 68.5[64.4–72.5] | <0.0001 |
350–499 per μL | 315/638 | 49.1 [43.2–55.0] | 247/576 | 42.0[37.4–46.6] | ||
≥500 per μL | 546/1591 | 33.2 [29.9–36.5] | 437/1729 | 25.6[23.2–28.0] |
Prevalence of unsuppressed viral load by study characteristics among women in Vulindlela and Greater Edendale, KwaZulu-Natal, South Africa, 2014–2015.
A total of nine women in 2014 survey and one woman in 2015 survey were missing viral load data. Participants missing for: a = 7, and e = 27 in 2014; b, c and d = 2, e = 9 in 2015.
Whilst unsuppressed viral load prevalence was similar across most variables decrease in the trends over the study years was observed. In 2014 unsuppressed viral load prevalence was 50.1%, (n = 152/311) and declined to 20.2%, n = 113/217) in 2015, among women that were away from home in the last 12 months (compared to those that were never away from home; 44.7%, (n = 1220/2635) in 2014 and 37.2%, n = 1006/2730) in 2015. Among those that ever-consumed alcohol 58.8%, (n = 229/390) in 2014 and declined to 44.4%, n = 249/536) in 2015 compared to those that never consumed alcohol and 43.9%, (n = 1143/2556) (36.6%, n = 870/2411) also among those that never had HIV test 68.1%, (n = 286/441) (63.4%, n = 53/80). Among those that ever had an HIV test 42.4%, (n = 1086/2505) in 2014 and 37.3%, n = 1066/2867) in 2015 had unsuppressed viral load. Similarly, among women who did not know their HIV status 72.0%, (n = 753/1081) in 2014 and 74.3%, (n = 528/729) in 2015 compared to those who knew their status 31.2%, (n = 619/1875) in 2014 and 25.8%, n = 591/2218) in 2015 had unsuppressed viral load. Women who perceived they are not likely to contact HIV 70.7%, (n = 478/682) in 2014 and 70.2% (n = 281/405) in 2015 compared to those who already perceived they had been infected 28.9%, (n = 530/1692) (24.6%, n = 529/2081), also women who have ever been diagnosed of STI 43.7%, (n = 98/213) (47.7%, n = 155/320), among women who had never been pregnant 59.4%, (n = 343/600) (49.3%, n = 169/352) compared to those that has ever been pregnant 42.5%, (n = 1029/2346) (36.6%, n = 947/2595), likewise among WLHIV and not on ART 72.1% (n = 1131/1600) (77.3%, n = 899/1172) in comparison with those on ART 17.1% (n = 241/1346) (11.8%, n = 220/1775). Prevalence was higher among women whose current CD4 cell count were < 350 count per μ/L, 69.3%, (n = 493/695) (68.5%, n = 430/633), and those with CD4 cell count of between 350 and 499 count per μ/L 49.1%, (n = 315/638) (42.0%, n = 247/576) compared to those 500 count per μ/L 33.2%, (n = 546/1591) (25.6%, n = 437/1729) in 2014(2015) respectively.
Figure 2 shows the observed prevalence map of unsuppressed viral load. Highest prevalence was observed in the north and south of Vulindlela and east part of Greater Edendale in 2014, while in the north part of Vulindlela and the south part of Greater Edendale in 2015. The north area (Mpophomeni) showed a consistently high prevalence across both surveys.
Observed prevalence maps of unsuppressed viral load among women (a) 2014 and (b) 2015 in Vulindlela and Greater Edendale area in uMgungundlovu district, KwaZulu-Natal province, South Africa.
Table 3 shows values of the deviance information criterion (DIC) and effective numbers of parameters (pD) for each of the fitted model. Unstructured model has the minimum values (DIC = 2593.26 and 2087.70) for 2014 and 2015 respectively, thus attesting as the best fit model for the data sets, while GAM model offers the least fit. Besides, the unstructured model is of actual interest because it contains all the variables considered, and account for spatial autocorrelation and between clusters heterogeneity, failure to do so would have produced misleading and overfitting results. Thus, further results of this study are based on the unstructured model.
2014 Survey | 2015 Survey | |||||
---|---|---|---|---|---|---|
DIC | 2768.42 | 2597.64 | 2593.26 | 2097.27 | 2089.95 | 2087.70 |
DIC saturated | 2998.34 | 2992.52 | 3004.69 | 2992.61 | 2987.95 | 2998.66 |
44.92 | 48.88 | 49.23 | 46.53 | 46.50 | 47.55 |
Model diagnostic.
DIC: Deviance Information Criteria. pD: effective numbers of parameters.
Figure 3 shows the non-linear effect of continuous covariates after accounting for other variables. The results shows that current age, number of household members, total number of children ever born and total number of lifetime HIV test, had a non-linear significant effect on women being virally unsuppressed in this study area. Furthermore, in Figure 3a and e shows a slight increase in effect among ages 15 to 20 in 2014 and sharp increase in 20 to 25 in 2015, after which the effect declined. Younger age 15 to 29 have higher risk of being virally unsuppressed compared to ages 30 above. Figure 3b and f shows that risk of unsuppressed viral load decreases with higher number of household members from 5 members. Also Figure 3c and h shows that the effect of total number of children ever born decreases the risk of being virally unsuppressed in 2014 but increases in 2015. Similarly, Figure 3d and g showed that the risk of unsuppressed viral load increased as the number of lifetime HIV tests increased in 2014, whilst in contrast unsuppressed viral load decreased as the number of lifetime HIV tests increased in 2015.
Nonlinear effect of continuous covariate.
Table 4 displays the adjusted posterior mean estimates with their 95% credible intervals of the linear fixed effect from the multivariable model. If these intervals contain the number zero (0), then the parameter (estimate of the mean beta) is not significant; otherwise, it is significant. Factors associated with unsuppressed viral load across both years were knowledge of HIV status, low perceived risk of contracting HIV, ARV treatment and current CD4 cell counts. Women with no prior knowledge of their HIV status were more likely to be virally unsuppressed than those that knew their status. Women with either unlikely or likely perception of contracting HIV, not on ARV, and for those on ARV having multiple tablets of ARV had the highest risk of being virally unsuppressed compared to their reference categories. Additionally, in 2014 those that ever consumed alcohol were also at higher risk of having unsuppressed viral load. While in 2015, we also found that women that reported being away from home in the last 12 months, had a meal cut, being with two or more sexual partners in one’s lifetime, ever tested with TB and ever diagnosed with STI had the highest risk of being virally unsuppressed compared to their counterparts.
2014 Survey | 2015 Survey | |||||
---|---|---|---|---|---|---|
Variables | Posterior mean | Posterior SD | 95 Credible intervals | Posterior mean | Posterior SD | 95% credible intervals |
0.405** | 0.075 | (0.255, 0.551) | 0.407** | 0.076 | (0.257, 0.557) | |
Never married | 0.032 | 0.017 | (−0.002, 0.067) | 0.005 | 0.021 | (−0.035, 0.045) |
Incomplete high school | 0.015 | 0.021 | (−0.015, 0.045) | −0.005 | 0.014 | (−0.032, 0.022) |
Moved here less than 1 year ago | −0.016 | 0.042 | (−0.099,0.068) | 0.044 | 0.033 | (−0.022, 0.109) |
Moved here more than 1 year ago | 0.002 | 0.019 | (−0.034, 0.039) | 0.002 | 0.014 | (−0.025, 0.029) |
Yes | 0.002 | 0.024 | (−0.045, 0.048) | 0.056 | 0.025 | (0.007, 0.105) |
Yes | 0.016 | 0.026 | (−0.035, 0.067) | 0.011 | 0.018 | (0.024, 0.045) |
No | 0.018 | 0.016 | (−0.013, 0.049) | 0.014 | 0.015 | (−0.016, 0.044) |
2 or more partners/no res | 0.024 | 0.019 | (−0.013, 0.061) | 0.002 | 0.019 | (−0.036, 0.039) |
2 or more partners | 0.037 | 0.027 | (−0.016, 0.091) | 0.049 | 0.058 | (0.102, 0.162) |
Yes | 0.058 ** | 0.022 | (0.016, 0.101) | 0.029 | 0.017 | (−0.005, 0.063) |
No | −0.058 | 0.034 | (−0.120, 0.014) | −0.022 | 0.045 | (−0.110, 0.066) |
No | −0.142 | 0.030 | (−0.201, −0.084) | −0.200 | 0.030 | (−0.259, −0.142) |
Likely | 0.095 | 0.025 | (0.045, 0.144) | 0.062 | 0.028 | (0.008, 0.116) |
Not Likely | 0.103 | 0.027 | (0.050, 0.156) | 0.074 | 0.029 | (0.017, 0.131) |
Yes | −0.034 | 0.018 | (−0.069, 0.001) | −0.070 | 0.015 | (−0.099, −0.041) |
Yes | 0.061 | 0.041 | (−0.019, 0.142) | −0.022 | 0.029 | (−0.079, 0.035) |
Yes | 0.008 | 0.028 | (−0.048, 0.064) | 0.005 | 0.022 | (−0.038, 0.048) |
Yes | −0.052 | 0.029 | (−0.108, 0.004) | −0.027 | 0.019 | (−0.064, 0.010) |
Yes | 0.034 | 0.033 | -(0.031, 0.100) | −0.077 | 0.039 | (−0.084, 0.069) |
Yes | 0.042 | 0.028 | (−0.014, 0.097) | 0.059 | 0.021 | (0.018, 0.099) |
No | −0.065 | 0.019 | (−0.103, 0.028) | 0.001 | 0.018 | (−0.156, 0.059) |
No | 0.321** | 0.030 | (0.262, 0.379) | 0.511 | 0.031 | (0.451, 0.571) |
Multiple | 0.251** | 0.027 | (0.199, 0.303) | 0.242 | 0.018 | (−0.163, −0.093) |
350–499 | −0.184 | 0.021 | (−0.226, −0.143) | −0.157 | 0.020 | (−0.197, −0.118) |
≥ 500 | −0.319 | 0.018 | (−0.354, −0.285) | −0.287 | 0.016 | (−0.319, −0.254) |
Adjusted posterior means, standard deviation (SD) and 95% credible intervals for the best fitted model.
Significant at 5% level of significance.
Figure 4 shows the chloropleth spatial effect maps based on model 3, shows both positive and negative effects with predicted high and low risk areas of unsuppressed viral load. The colours on the chloropleth maps show the log-odds scale, indicating each area contribution to the odds of unsuppressed viral load in women. Predicted high risk areas are shaded in yellow and gold brown (0.00015 to 0.00020), in 2014, two distinct locations were in the north-east and south-west, while 2015 shows a clustered area in the south-east. Predicted lower risk areas are shaded in royal to dark blue (−0.00015 to −0.00025), the south-west in 2014, with both north and west in 2015. Evidently their exist spatial variation of unsuppressed viral load in this hyperendemic community.
Estimated posterior mean of the unstructured spatial effect map on the log-odds of unsuppressed viral load among women in uMgungundlovu district, KwaZulu-Natal province, South Africa (2014–2015). (a) 2014, (b) 2015.
This analysis examined factors associated with unsuppressed viral load among women ages 15–49 years in peri-urban Greater Edendale and rural Vulindlela areas in the uMgungundlovu district, KwaZulu-Natal, South Africa between 2014 to 2015 while accounting for possible nonlinear effect of some continuous variables and mapping the unstructured spatial effects. We fitted hierarchical Bayesian Geoadditive multivariate model while controlling for the confounding effects of the explanatory variables. Bayesian spatial approach have numerous advantages over frequentist statistics, such as ability to account for and measure uncertainty in a model, minimise bias in complex data, ability to produce smoothed risk map, increased prediction accuracy, just to name a few [39, 43, 44]. Due to the strength of this approach many studies have emanated in investigating risk factors of anaemia in Sub Saharan Africa [33, 45], of HIV variation in Kenya [32], viral suppression [46] and other infectious disease globally [47]. Application of Bayesian spatial modelling therefore helped in identifying predictors and high-risk location of unsuppressed viral load among women in a small enumeration area. This enhancement of strategically identifying areas of key population is highly recommended as part of the global AIDS strategy to end inequality in resources allocation and provide localised HIV intervention in hyperendemic communities.
Our study found that knowledge of HIV status, perceived risk of contracting HIV, not on ART, and ART dosage were consistent significant factors associated with higher odds of being virally unsuppressed across both years. Having a CD4 cell count of >350 cells per μL was more likely to be associated with viral load <400 copies per mL Additionally, alcohol consumption was significant in 2014 while meal cut, total number of lifetime sex partner, ever tested for TB and ever diagnosed with STI were factors associated with unsuppressed viral load in 2015. These revealed the heterogeneity and need for continuous surveillance of HIV and its measures, as the predictors of this outcome are dynamic. Although fewer studies on women have been conducted in the country and other developing countries. Similar findings on the association of higher number of sexual partners were also reported among women in uMkhanyakude district of north KZN [48]. The use of ART and dosage of ARV was also found to be significant which is similar to past studies [49, 50]. Furthermore, similar studies have found higher CD4 cell count of >350 cells per μL to be predictive of being virally unsuppressed [51, 52]. We also found similar results on alcohol [53] from Western cape, South Africa and history of TB or STI in Kenya and Uganda [49, 54]. Alcohol use has been found to be associated with non-adherence to treatment in people living with HIV [55] and prevalence of which leads to high risk of transmission [56]. Several studies have shown relationship between TB and virological non-suppression [51, 57]. Similarly, concurrent ART and TB/STI treatment has been shown to increase the risk of virological non-suppression due to impaired treatment adherence and pharmacokinetics drug interaction [49].
The nonlinear effects of age, household size, total number of children ever born and total lifetime HIV test were considered. Across both years, risk of being virally unsuppressed decreases as age increases, with younger age 15–20 having a higher risk and being older associated with reduced risk of unsuppressed viral load. This is similar to past studies [48, 58]. Also risk of being virally unsuppressed decreases with increasing size of the family. Having 5 or lower number of family member showed a higher risk of being unsuppressed. This revealed that larger family members could bring more support to WLHIV. In contrast unsuppressed viral load decreases as number of children ever born increases in 2014 while the inverse was observed in 2015 (risk increases as number of birth increases). Recent study among pregnant women revealed significant association of currently breastfeeding with increase odd of viral load non-suppression [22, 59].
The unstructured spatial effect and observed prevalence map revealed the existence of localised positive spatial variation of unsuppressed viral load among women of reproductive age in this hyperendemic community. While higher prevalence was observed in the north area from both surveys and southern area in 2015. The predicted risk map revealed that in 2014 north-east and south-west as well as south-west in 2015 have an increase likelihood of being virally unsuppressed. This evidently shows that there are regional/district specific factors contributing to unsuppressed viral load with substantial spatial variation. Spatial variation in HIV and its measures has been reported by previous studies [16].
Among women in this community progress on 95–95-95 target was 65.5%, 74.2%, 82.9% in 2014 and 74.7%, 80.0%, 86.6% in 2015. The largest shortfall was in the first target, which is the entry point to health care system. None of the UNAIDS targets were met among this key population. Although, the country has made significant progress but has not achieved the UNAIDS 95–95-95 and 86% composite viral suppression target [14]. However, significant increase in viral suppression of 7.1% over a one year period was seen in this study, while ages 35 to 49 contributed to this increase, which could be attributed to the country commitment and effort in ART scaleup and intervention [27]. However, judging by the 90–90-90 indicator, our findings showed that the “third 90” target was met among age group 35–39, 40–44, 45–49 (91.8%, 91.7%, 92.9%) in 2015.
The key strengths of our study were the robustness of the study design, high participation rate, available of spatial variables and conducting the survey in a real time setting.
This was a cross sectional population based study and not a randomised clinical trial with limited ART data available. Therefore, no causal effect could be established between unsuppressed viral load and women characteristics. The results are not generalisable to older individuals or children as study only accessed men and women aged 15–49 years.
Spatial effects in the model act as a representative of the unobserved predictors which strengthen the result. Identifying high risk areas could help policy maker, epidemiologist, and public health institutions to develop develop strategies and interventions that are suitable for women in the area, thus increasing the impact of allocated resources as well as effective monitoring to improve the health status of women in the community. Increase in progress of the 95–95-95 targets over time showed that the target is achievable in this community among this key population, with intensive HIV testing service, eradication of stigmatisation, ending inequality and increasing uptake of ART treatment. Knowledge of HIV status is a proxy and entry point to achieving the other indicators, generally women are more likely to test than men and receive optimum health care especially during pregnancy.
The likelihood of being virally unsuppressed was higher among younger age group, highlighting public health implication of sustained risk of HIV transmission. Aside clinical factors, family support cannot be underestimated as part of the factors that could help in achieving undetectable viral load among women of reproductive age. Right perception and knowledge of HIV positive status, being on ART and having a higher CD4 cell count contributed to achieving viral suppression. Thus, these remain multi-factorial and important public health priority to attain viral suppression towards the goal to end the epidemic by 2030.
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