Literature DB >> 19031304

Late diagnosis of HIV in Europe: definitional and public health challenges.

A Adler1, S Mounier-Jack, R J Coker.   

Abstract

With universal access to antiretroviral therapy (ART), people can access effective treatment but are only able to benefit from these advances if they are aware of their status and are effectively accessing testing services. Although it was anticipated in the mid-1990s that the availability of ART would lead to earlier testing, this trend has not been observed in practice, with stagnant or even increasing rates of late diagnosis in Europe. Ahead of a gathering of key European stakeholders in Brussels in November 2007, we reviewed definitions of late diagnosis and approaches to surveillance of late HIV diagnosis in Europe. We found that there is no common or consistent reporting of late diagnosis across Europe and that the multiplicity of definitions for late diagnosis is likely proving a hindrance to providing information on the magnitude of the problem, determining trends, and informing understanding of reasons for changes in trends. We also show that existing evidence points to high rates of late diagnosis across Europe - between 15 and 38% of all HIV cases - and concur that trends that are increasing or at best stagnant. We identify risk factors that are associated with individuals being more likely to present late and we explore the reasons for late presentation. We reflect on the need to review surveillance and testing policies, notably in relation for population groups that are heavily represented in late presenters and make recommendations for a coherent, cross-European approach to surveillance and monitoring in order to support improvements in service provision and, ultimately, public health.

Entities:  

Mesh:

Year:  2009        PMID: 19031304     DOI: 10.1080/09540120802183537

Source DB:  PubMed          Journal:  AIDS Care        ISSN: 0954-0121


  33 in total

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Journal:  AIDS Res Hum Retroviruses       Date:  2015-10-15       Impact factor: 2.205

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Journal:  Infection       Date:  2013-06-01       Impact factor: 3.553

10.  Using a machine learning approach to explore predictors of healthcare visits as missed opportunities for HIV diagnosis.

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Journal:  AIDS       Date:  2021-05-01       Impact factor: 4.177

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