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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...

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Autores principales: Andersson, Emmi, Nakagawa, Fumiyo, van Sighem, Ard, Axelsson, Maria, Phillips, Andrew N, Sönnerborg, Anders, Albert, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Centre for Disease Prevention and Control (ECDC) 2019
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.
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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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