Global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017

dc.contributor.authorFrank, T. D.
dc.contributor.authorCarter, Austin
dc.contributor.authorJahagirdar, D.
dc.contributor.authorBiehl, Molly H.
dc.contributor.authorDouwes-Schultz, D.
dc.contributor.authorLarson, S. L.
dc.contributor.authorArora, Megha
dc.contributor.authorDwyer-Lindgren, L.
dc.contributor.authorSteuben, K. M.
dc.contributor.authorAbbastabar, Hedayat
dc.contributor.authorAlene, Kefyalew Addis
dc.contributor.authorBin Sayeed, Muhammad Shahdaat
dc.date.accessioned2020-11-06T03:57:08Z
dc.date.available2020-11-06T03:57:08Z
dc.date.issued2019-08-19
dc.date.updated2020-07-06T08:26:34Z
dc.description.abstractBackground Understanding the patterns of HIV/AIDS epidemics is crucial to tracking and monitoring the progress of prevention and control efforts in countries. We provide a comprehensive assessment of the levels and trends of HIV/AIDS incidence, prevalence, mortality, and coverage of antiretroviral therapy (ART) for 1980–2017 and forecast these estimates to 2030 for 195 countries and territories. Methods We determined a modelling strategy for each country on the basis of the availability and quality of data. For countries and territories with data from population-based seroprevalence surveys or antenatal care clinics, we estimated prevalence and incidence using an open-source version of the Estimation and Projection Package—a natural history model originally developed by the UNAIDS Reference Group on Estimates, Modelling, and Projections. For countries with cause-specific vital registration data, we corrected data for garbage coding (ie, deaths coded to an intermediate, immediate, or poorly defined cause) and HIV misclassification. We developed a process of cohort incidence bias adjustment to use information on survival and deaths recorded in vital registration to back-calculate HIV incidence. For countries without any representative data on HIV, we produced incidence estimates by pulling information from observed bias in the geographical region. We used a re-coded version of the Spectrum model (a cohort component model that uses rates of disease progression and HIV mortality on and off ART) to produce age-sex-specific incidence, prevalence, and mortality, and treatment coverage results for all countries, and forecast these measures to 2030 using Spectrum with inputs that were extended on the basis of past trends in treatment scale-up and new infections. Findings Global HIV mortality peaked in 2006 with 1·95 million deaths (95% uncertainty interval 1·87–2·04) and has since decreased to 0·95 million deaths (0·91–1·01) in 2017. New cases of HIV globally peaked in 1999 (3·16 million, 2·79–3·67) and since then have gradually decreased to 1·94 million (1·63–2·29) in 2017. These trends, along with ART scale-up, have globally resulted in increased prevalence, with 36·8 million (34·8–39·2) people living with HIV in 2017. Prevalence of HIV was highest in southern sub-Saharan Africa in 2017, and countries in the region had ART coverage ranging from 65·7% in Lesotho to 85·7% in eSwatini. Our forecasts showed that 54 countries will meet the UNAIDS target of 81% ART coverage by 2020 and 12 countries are on track to meet 90% ART coverage by 2030. Forecasted results estimate that few countries will meet the UNAIDS 2020 and 2030 mortality and incidence targets. Interpretation Despite progress in reducing HIV-related mortality over the past decade, slow decreases in incidence, combined with the current context of stagnated funding for related interventions, mean that many countries are not on track to reach the 2020 and 2030 global targets for reduction in incidence and mortality. With a growing population of people living with HIV, it will continue to be a major threat to public health for years to come. The pace of progress needs to be hastened by continuing to expand access to ART and increasing investments in proven HIV prevention initiatives that can be scaled up to have population-level impact.en_AU
dc.description.sponsorshipBill & Melinda Gates Foundation, National Institute of Mental Health of the US National Institutes of Health (NIH), and the National Institute on Aging of the NIH.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1474-547Xen_AU
dc.identifier.urihttp://hdl.handle.net/1885/214104
dc.language.isoen_AUen_AU
dc.provenanceThis is an Open Access article under the CC BY 4.0 licenseen_AU
dc.publisherThe Lancet Publishing Groupen_AU
dc.rights© 2019 The Author(s).en_AU
dc.rights.licenseCC BY 4.0 licenseen_AU
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_AU
dc.sourceThe Lanceten_AU
dc.titleGlobal, regional, and national incidence, prevalence, and mortality of HIV, 1980-2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017en_AU
dc.typeJournal articleen_AU
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.issue12en_AU
local.bibliographicCitation.lastpageE859en_AU
local.bibliographicCitation.startpageE831en_AU
local.contributor.affiliationFrank, T. D., University of Washingtonen_AU
local.contributor.affiliationCarter, Austin, University of Washingtonen_AU
local.contributor.affiliationJahagirdar, D., University of Washingtonen_AU
local.contributor.affiliationBiehl, Molly H., University of Washingtonen_AU
local.contributor.affiliationDouwes-Schultz, D., University of Washingtonen_AU
local.contributor.affiliationLarson, S. L., University of Washingtonen_AU
local.contributor.affiliationArora, Megha, University of Washingtonen_AU
local.contributor.affiliationDwyer-Lindgren, L., University of Washingtonen_AU
local.contributor.affiliationSteuben, K. M., University of Washingtonen_AU
local.contributor.affiliationAbbastabar, Hedayat, Tehran University of Medical Sciencesen_AU
local.contributor.affiliationAlene, Kefyalew, College of Health and Medicine, ANUen_AU
local.contributor.affiliationBin Sayeed, Muhammad Shahdaat, College of Health and Medicine, ANUen_AU
local.contributor.authoruidAlene, Kefyalew, u5641168en_AU
local.contributor.authoruidBin Sayeed, Muhammad Shahdaat, u6266314en_AU
local.description.notesImported from ARIESen_AU
local.identifier.absfor111706 - Epidemiologyen_AU
local.identifier.absseo920404 - Disease Distribution and Transmission (incl. Surveillance and Response)en_AU
local.identifier.ariespublicationu5786633xPUB1310en_AU
local.identifier.citationvolume6en_AU
local.identifier.doi10.1016/S2352-3018(19)30196-1en_AU
local.identifier.thomsonIDWOS:000500912300019
local.publisher.urlhttps://www.clinicalkey.com.au/en_AU
local.type.statusPublished Versionen_AU

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