Estimating HIV incidence and the undiagnosed HIV population in the European Union / European economic area

ECDC HIV/AIDS Surveillance and Dublin Declaration Networks participants: Portugal - Helena Cortes Martins (INSA).Each year, about 30,000 people are newly diagnosed with HIV in the 31 countries of the European Union/European Economic Area (EU/EEA). We aimed to estimate the number of people living with undiagnosed HIV in the entire EU/EEA and in four sub-regions.N/

Disparities in access to and use of HIV-related health services in the Netherlands by migrant status and sexual orientation: a cross-sectional study among people recently diagnosed with HIV infection

Background Migrants often face barriers to accessing healthcare. We examined disparities in access to and use of HIV-related health services between migrant and non-migrant people recently diagnosed with HIV living in the Netherlands, taken into account sexual orientation. Also, we examined differences in experiences in living with HIV between these groups. Methods We used a questionnaire and clinical data collected between July 2013 and June 2015 among migrant and non-migrant participants of the European cross-sectional aMASE (Advancing Migrant Access to health Services in Europe) study in the Netherlands. Using univariable logistic regression analyses, we compared outcomes on between migrants and non-migrants, stratified by sexual orientation (with non-migrant men having sex with men [MSM] as the reference group). Results We included 77 migrant MSM, 115 non-migrant MSM, 21 migrant heterosexual men, 14 non-migrant heterosexual men and 20 migrant women. In univariable analyses, all heterosexual groups were less likely to ever have had an HIV-negative test before their diagnosis and were more likely to be diagnosed late than non-migrant MSM. All migrant groups were more likely to have experienced difficulties accessing general healthcare in the Netherlands and were less likely to have heard of post-exposure prophylaxis than non-migrant MSM. Migrants frequently reported uncertainty about their rights to healthcare and language barriers. Most (93%) participants visited a healthcare facility in the 2 years before HIV diagnosis but only in 41% an HIV test was discussed during that visit (no statistical difference between groups). Migrant heterosexuals were more likely to have missed appointments at their HIV clinic due to the travel costs than non-migrant MSM. Migrant MSM and women were more likely to have experienced HIV discrimination in the Netherlands than non-migrant MSM. Conclusion Disparities in access to and use of HIV-related health services and experiences exist by migrant status but also by sexual orientation. Our data suggests heterosexual men and women may particularly benefit from improved access to HIV testing (e.g., through provider-initiated testing), while migrant MSM may benefit from improved access to HIV prevention interventions (e.g., pre-exposure prophylaxis)

Challenges in modelling the proportion of undiagnosed HIV infections in Sweden

BACKGROUND: Sweden has a low HIV prevalence. However, among new HIV diagnoses in 2016, the proportion of late presenters and migrants was high (59% and 81%, respectively). This poses challenges in estimating the proportion of undiagnosed persons living with HIV (PLHIV). AIM: To estimate the proportion of undiagnosed PLHIV in Sweden comparing two models with different demands on data availability and modelling expertise. METHODS: An individual-based stochastic simulation model of HIV positive populations (SSOPHIE) and the incidence method of the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool were applied to clinical, surveillance and migration data from Sweden 1980–2016. RESULTS: SSOPHIE estimated that the proportion of undiagnosed PLHIV in 2013 was 26% (n = 2,100; 90% plausibility range (PR): 900–5,000) for all PLHIV, 17% (n = 600; 90% PR: 100–2,000) for men who have sex with men (MSM), 35% in male (n = 300; 90% PR: 200–700) and 34% in female (n = 400; 90% PR: 200–800) migrants from sub-Saharan Africa (SSA). The estimates for the ECDC model in 2013 were 21% (n = 2,013; 95% confidence interval (CI): 1,831–2,189) for all PLHIV, 15% (n = 369; 95% CI: 299–434) for MSM and 21% (n = 530; 95% CI: 436–632) for migrants from SSA. CONCLUSIONS: The proportion of undiagnosed PLHIV in Sweden is uncertain. SSOPHIE estimates had wide PR. The ECDC model estimates were unreliable because migration was not accounted for. Better migration data and estimation methods are required to obtain reliable estimates of proportions of undiagnosed PLHIV in similar settings

Monitoring recently acquired HIV infections in Amsterdam, The Netherlands:The attribution of test locations

Background:  Surveillance of recent HIV infections (RHI) using an avidity assay has been implemented at Dutch sexual health centres (SHC) since 2014, but data on RHI diagnosed at other test locations is lacking. Setting:  Implementation of the avidity assay in HIV treatment clinics for the purpose of studying RHI among HIV patients tested at different test locations. Methods: We retrospectively tested leftover specimens from newly diagnosed HIV patients in care in 2013–2015 in Amsterdam. Avidity Index (AI) values ≤0.80 indicated recent infection (acquired ≤6 months prior to diagnosis), and AI > 0.80 indicated established infection (acquired >6 months prior to diagnosis). An algorithm for RHI was applied to correct for false recency. Recency based on this algorithm was compared with recency based on epidemiological data only. Multivariable logistic regression analysis was used to identify factors associated with RHI among men who have sex with men (MSM).Results: We tested 447 specimens with avidity; 72% from MSM. Proportions of RHI were 20% among MSM and 10% among heterosexuals. SHC showed highest proportions of RHI (27%), followed by GPs (15%), hospitals (5%), and other/unknown locations (11%) (p < 0.001). Test location was the only factor associated with RHI among MSM. A higher proportion of RHI was found based on epidemiological data compared to avidity testing combined with the RHI algorithm. Conclusion:  SHC identify more RHI infections compared to other test locations, as they serve high-risk populations and offer frequent HIV testing. Using avidity-testing for surveillance purposes may help targeting prevention programs, but the assay lacks robustness and its added value may decline with improved, repeat HIV testing and data collection