Record numbers of new diagnoses of HIV infection have been recorded in the UK in recent years. However, whether these are historic infections now being diagnosed or evidence of ongoing HIV transmission is unclear from these data. The Serological Testing Algorithm for Recent HIV Seroconversion (STARHS) is a generic term for .a number of different laboratory techniques that can be used to distinguish recent HIV infections occurring in the 4-6 months prior to sampling from long standing HIV infections. When these data are analysed with appropriate demographic data it is possible to estimate the rate of acquisition ofHIV infection or incidence . . However the method has many .limitations. This thesis identifies and examines the confounding factors that limit the applications of the STARHS technologies or alter its accuracy. It quantifies the degree of misclassification of specimens as recent HIV infections due to use of effective anti-retroviral therapy in patients and proposes uses of ST ARHS data in populations where multiple HIV subtypes circulate. and incidence estimates are difficult to determine. Having excluded confounding factors, the STARHS 'detuned' appr~ach is useq to determine HIV Incidence in men who·have sex with men attending Sexually Transmitted Infection (STI) clinics in the UK as part of an unlinked anonymous HIV prevalence monitoring programme. This demonstrated that ongoing transmission of HIV is occurring in this population and that despite widespread use of anti-retroviral therapies the rate of HIV transmission has shown no decline. This thesis adds new insight to and understanding of the complex mechanisms that limit the application of laboratory techniques for identifying recent HIV infection. Furthermore, it demonstrates that the STARHS teclmique, when used appropriately, is able to provide reliable and sensitive estimates of HIV incidence, thus improving understanding of recent trends in the HIV epidemic.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:504542 |
| Date | January 2008 |
| Creators | Murphy, Gary |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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