ArticleBMC infectious diseases2020
Monitoring progress towards the first UNAIDS 90-90-90 target in key populations living with HIV in Norway.
Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of models used to estimate undiagnosed HIV prevalence in high-income low-prevalence countries and territories.AIDS (London, England) · 2026Pooled it
- Using linked national registry data and the ECDC HIV modelling tool to estimate HIV incidence and the proportion diagnosed in Norway up to 2023.BMC infectious diseases · 2025Article
- Estimating HIV incidence in Türkiye: results from two mathematical models.BMC infectious diseases · 2025Article
- Factors associated with health-related quality of life in people living with HIV in Norway.Health and quality of life outcomes · 2023Article
- Life-time HIV testing among people who inject drugs in Iran: results from the National Rapid Assessment and Response survey.Frontiers in public health · 2023Article
- "They make a difference": a qualitative study of providers' experiences of peer support in outpatient clinics for people living with HIV.BMC health services research · 2022Article
- Length of stay in Denmark before HIV diagnosis and linkage to care: a population-based study of migrants living with HIV, Denmark, 1995 to 2020.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2022Article
- Peer support in an outpatient clinic for people living with human immunodeficiency virus: a qualitative study of service users' experiences.BMC health services research · 2022Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn line with the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 target, Norway aims for at least 90% of people living with HIV (PLHIV) to know their HIV-status. We produced current estimates of the number of PLHIV and undiagnosed population in Norway, overall and for six key subpopulations: Norwegian-born men who have sex with men (MSM), migrant MSM, Norwegian-born heterosexuals, migrant Sub-Saharan Africa (SSA)-born heterosexuals, migrant non-SSA-born heterosexuals and people who inject drugs.
methodsWe used the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool on Norwegian HIV surveillance data through 2018 to estimate incidence, time from infection to diagnosis, PLHIV, and the number and proportion undiagnosed. As surveillance data on CD4 count at diagnosis were not collected in Norway, we ran two models; using default model CD4 assumptions, or a proxy for CD4 distribution based on Danish national surveillance data. We also generated alternative overall PLHIV estimates using the Spectrum AIDS Impact Model, to compare with those obtained from the ECDC tool.
resultsEstimates of the overall number of PLHIV in 2018 using different modelling approaches aligned at approximately 5000. In both ECDC models, the overall number undiagnosed decreased continuously from 2008. The proportion undiagnosed in 2018 was lower using default model CD4 assumptions (7.1% [95%CI: 5.3-8.9%]), than the Danish CD4 proxy (10.2% [8.3-12.1%]). This difference was driven by results for heterosexual migrants. Estimates for Norwegian-born MSM, migrant MSM and Norwegian-born heterosexuals were similar in both models. In these three subpopulations, incidence in 2018 was < 30 new infections, and the number undiagnosed had decreased in recent years. Norwegian-born MSM had the lowest estimated number of undiagnosed infections (45 [30-75], using default CD4 assumptions) and undiagnosed fraction (3.6% [2.4-5.7%], using default CD4 assumptions) in 2018.
conclusionsResults allow cautious confidence in concluding that Norway has achieved the first UNAIDS 90-90-90 target, and clearly highlight the success of prevention strategies among MSM. Estimates for subpopulations strongly influenced by migration remain less clear, and future modelling should appropriately account for all-cause mortality and out-migration, and adjust for time of in-migration.
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