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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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This work is licensed under a Creative Commons Attribution 4.0 International License.
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2019-04-04
2024-04-20
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2019.24.14.1800203
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References

  1. World Health Organization (WHO). 90-90-90. An ambitious treatment target to help end the AIDS epidemic. Geneva: WHO. [Accessed 4 Dec 2017]. Available from: http://www.unaids.org/en/resources/documents/2017/90-90-90
  2. World Health Organization (WHO). Global health Observatory data repository. Prevalence of HIV among adults aged 15-49 estimates by WHO region. Geneva: WHO. [Accessed 28 Mar 2019]. Available from: http://apps.who.int/gho/data/view.main.22500WHOREG?lang=en
  3. Public Health Agency of Sweden. Sjukdomsstatistik HIV infection. [Statistics on HIV-infection]. Stockholm: Public Health Agency Sweden. [Accessed 1 Apr 2019]. Swedish. Available from: https://www.folkhalsomyndigheten.se/folkhalsorapportering-statistik/statistikdatabaser-och-visualisering/sjukdomsstatistik/hivinfektion/
  4. Brännström J, Sönnerborg A, Svedhem V, Neogi U, Marrone G. A high rate of HIV-1 acquisition post immigration among migrants in Sweden determined by a CD4 T-cell decline trajectory model. HIV Med. 2017;18(9):677-84.  https://doi.org/10.1111/hiv.12509  PMID: 28444865 
  5. Brännström J, Svedhem Johansson V, Marrone G, Wendahl S, Yilmaz A, Blaxhult A, et al. Deficiencies in the health care system contribute to a high rate of late HIV diagnosis in Sweden. HIV Med. 2016;17(6):425-35.  https://doi.org/10.1111/hiv.12321  PMID: 26559921 
  6. Antinori A, Coenen T, Costagiola D, Dedes N, Ellefson M, Gatell J, et al. Late presentation of HIV infection: a consensus definition. HIV Med. 2011;12(1):61-4.  https://doi.org/10.1111/j.1468-1293.2010.00857.x  PMID: 20561080 
  7. National Quality Registery for HIV (InfCare HIV). English pages. Stockholm: InfCare HIV. [Accessed 28 Mar 2019]. Available from: http://www.kvalitetsregister.se/englishpages/findaregistry/registerarkivenglish/nationalqualityregistryforhivinfcarehiv.2172.html
  8. Gisslén M, Svedhem V, Lindborg L, Flamholc L, Norrgren H, Wendahl S, et al. Sweden, the first country to achieve the Joint United Nations Programme on HIV/AIDS (UNAIDS)/World Health Organization (WHO) 90-90-90 continuum of HIV care targets. HIV Med. 2017;18(4):305-7.  https://doi.org/10.1111/hiv.12431  PMID: 27535540 
  9. Gourlay AJ, Pharris AM, Noori T, Supervie V, Rosinska M, van Sighem A, et al. Towards standardized definitions for monitoring the continuum of HIV care in Europe. AIDS. 2017;31(15):2053-8.  https://doi.org/10.1097/QAD.0000000000001597  PMID: 28906276 
  10. Hamers FF, Phillips AN. Diagnosed and undiagnosed HIV-infected populations in Europe. HIV Med. 2008;9(s2) Suppl 2;6-12.  https://doi.org/10.1111/j.1468-1293.2008.00584.x  PMID: 18557863 
  11. Nakagawa F. Estimation of the size and characteristics of HIV-positive populations in Europe. London: University College London; 2015. Available from: http://discovery.ucl.ac.uk/1471810/
  12. Nakagawa F, van Sighem A, Thiebaut R, Smith C, Ratmann O, Cambiano V, et al. A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model. Epidemiology. 2016;27(2):247-56. PMID: 26605814 
  13. Nakagawa F, Writing Group on HIV Epidemiologic Estimates in Countries With Migrant Populations From High Prevalence Areas. An epidemiological modelling study to estimate the composition of HIV-positive populations including migrants from endemic settings. AIDS. 2017;31(3):417-25. PMID: 27831947 
  14. van Sighem A, Nakagawa F, De Angelis D, Quinten C, Bezemer D, de Coul EO, et al. Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data. Epidemiology. 2015;26(5):653-60.  https://doi.org/10.1097/EDE.0000000000000324  PMID: 26214334 
  15. European Centre for Disease Prevention and Control (ECDC). HIV Modelling Tool (software application). Version 1.3.0. Stockholm: ECDC; 2017. Available from: https://ecdc.europa.eu/en/publications-data/hiv-modelling-tool
  16. Helleberg M, Häggblom A, Sönnerborg A, Obel N. HIV care in the Swedish-Danish HIV cohort 1995-2010, closing the gaps. PLoS One. 2013;8(8):e72257.  https://doi.org/10.1371/journal.pone.0072257  PMID: 23967292 
  17. Sveriges Riksdag. Smittskyddslag (2004:168). [Infectious Diseases Act (2004:168)]. Stockholm: Sveriges Riksdag; 2004. Swedish. Available from: http://www.riksdagen.se/sv/dokument-lagar/dokument/svensk-forfattningssamling/smittskyddslag-2004168_sfs-2004-168
  18. Statistikmyndigheten (SCB). Stockholm: SCB. [Accessed 1 Oct 2015]. Swedish. Available from: www.scb.se
  19. Brown T, Bao L, Eaton JW, Hogan DR, Mahy M, Marsh K, et al. Improvements in prevalence trend fitting and incidence estimation in EPP 2013. AIDS. 2014;28(Suppl 4):S415-25.  https://doi.org/10.1097/QAD.0000000000000454  PMID: 25406747 
  20. Stover J, Andreev K, Slaymaker E, Gopalappa C, Sabin K, Velasquez C, et al. Updates to the spectrum model to estimate key HIV indicators for adults and children. AIDS. 2014;28(Suppl 4):S427-34.  https://doi.org/10.1097/QAD.0000000000000483  PMID: 25406748 
  21. Giardina F, Romero-Severson EO, Axelsson M, Leitner T, Britton T, Albert J. Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers. Preprint. Posted 17 Jun 2018. bioRxiv.
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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