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Abstract

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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/content/10.2807/1560-7917.ES.2019.24.14.1800203
2019-04-04
2024-12-22
/content/10.2807/1560-7917.ES.2019.24.14.1800203
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Challenges in modelling the proportion of undiagnosed HIV infections in Sweden
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Emmi+Andersson&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Andersson Emmi</a>, <a href="/search?value1=Fumiyo+Nakagawa&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Nakagawa Fumiyo</a>, <a href="/search?value1=Ard+van+Sighem&option1=author&noRedirect=true" class="nonDisambigAuthorLink">van Sighem Ard</a>, <a href="/search?value1=Maria+Axelsson&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Axelsson Maria</a>, <a href="/search?value1=Andrew+N+Phillips&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Phillips Andrew N</a>, <a href="/search?value1=Anders+S%C3%B6nnerborg&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Sönnerborg Anders</a>, <a href="/search?value1=Jan+Albert&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Albert Jan</a></span>. Challenges in modelling the proportion of undiagnosed HIV infections in Sweden. <a href="/content/ecdc">Euro Surveill.</a> 2019;24(14):pii=1800203. <a href="https://doi.org/10.2807/1560-7917.ES.2019.24.14.1800203" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2019.24.14.1800203</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 18 Apr 2018;&nbsp;&nbsp; <span class="generated">Accepted</span>: 28 Nov 2018 </span> </p>
/content/10.2807/1560-7917.ES.2019.24.14.1800203
/content/10.2807/1560-7917.ES.2019.24.14.1800203
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