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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 proporti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Centre for Disease Prevention and Control (ECDC)
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462786/ https://www.ncbi.nlm.nih.gov/pubmed/30968824 http://dx.doi.org/10.2807/1560-7917.ES.2019.24.14.1800203 |
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author | Andersson, Emmi Nakagawa, Fumiyo van Sighem, Ard Axelsson, Maria Phillips, Andrew N Sönnerborg, Anders Albert, Jan |
author_facet | Andersson, Emmi Nakagawa, Fumiyo van Sighem, Ard Axelsson, Maria Phillips, Andrew N Sönnerborg, Anders Albert, Jan |
author_sort | Andersson, Emmi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6462786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | European Centre for Disease Prevention and Control (ECDC) |
record_format | MEDLINE/PubMed |
spelling | pubmed-64627862019-04-24 Challenges in modelling the proportion of undiagnosed HIV infections in Sweden Andersson, Emmi Nakagawa, Fumiyo van Sighem, Ard Axelsson, Maria Phillips, Andrew N Sönnerborg, Anders Albert, Jan Euro Surveill Research 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. European Centre for Disease Prevention and Control (ECDC) 2019-04-04 /pmc/articles/PMC6462786/ /pubmed/30968824 http://dx.doi.org/10.2807/1560-7917.ES.2019.24.14.1800203 Text en This article is copyright of the authors or their affiliated institutions, 2019. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made. |
spellingShingle | Research Andersson, Emmi Nakagawa, Fumiyo van Sighem, Ard Axelsson, Maria Phillips, Andrew N Sönnerborg, Anders Albert, Jan Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title | Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title_full | Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title_fullStr | Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title_full_unstemmed | Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title_short | Challenges in modelling the proportion of undiagnosed HIV infections in Sweden |
title_sort | challenges in modelling the proportion of undiagnosed hiv infections in sweden |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462786/ https://www.ncbi.nlm.nih.gov/pubmed/30968824 http://dx.doi.org/10.2807/1560-7917.ES.2019.24.14.1800203 |
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