Literature DB >> 18686336

Using mathematical modelling to help explain the differential increase in HIV incidence in New South Wales, Victoria and Queensland: importance of other sexually transmissible infections.

Alexander Hoare1, David P Wilson, David G Regan, John Kaldor, Matthew G Law.   

Abstract

BACKGROUND: Since 1999 there has been an increase in the number of HIV diagnoses in Australia, predominantly among men who have sex with men (MSM), but the magnitude of increase differs between states: approximately 7% rise in New South Wales, approximately 96% rise in Victoria, and approximately 68% rise in Queensland.
METHODS: Epidemiological, clinical, behavioural and biological data were collated into a mechanistic mathematical model to explore possible reasons for this increase in HIV notifications in MSM. The model was then used to make projections to 2015 under various scenarios.
RESULTS: The model suggests that trends in clinical and behavioural parameters, including increases in unprotected anal intercourse, cannot explain the magnitude of the observed rise in HIV notifications, without a substantial increase in a 'transmission-increasing' factor. We suggest that a highly plausible biological factor is an increase in the prevalence of other sexually transmissible infections (STI). It was found that New South Wales required an approximately 2-fold increase in other STI to match the data, Victoria needed an ~11-fold increase, and Queensland required an approximately 9-fold increase. This is consistent with observed trends in Australia for some STI in recent years. Future projections also indicate that the best way to control the current rise in HIV notifications is to reduce the prevalence of other STI and to promote condom use, testing for HIV, and initiation of early treatment in MSM diagnosed during primary infection.
CONCLUSIONS: Our model can explain the recent rise in HIV notifications with an increase in the prevalence of other STI. This analysis highlights that further investigation into the causes and impact of other STI is warranted in Australia, particularly in Victoria.

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Year:  2008        PMID: 18686336     DOI: 10.1071/sh07099

Source DB:  PubMed          Journal:  Sex Health        ISSN: 1448-5028            Impact factor:   2.706


  3 in total

Review 1.  Mathematical models for the study of HIV spread and control amongst men who have sex with men.

Authors:  Narat Punyacharoensin; William John Edmunds; Daniela De Angelis; Richard Guy White
Journal:  Eur J Epidemiol       Date:  2011-09-20       Impact factor: 8.082

2.  Trends in detectable viral load by calendar year in the Australian HIV observational database.

Authors:  Matthew G Law; Ian Woolley; David J Templeton; Norm Roth; John Chuah; Brian Mulhall; Peter Canavan; Hamish McManus; David A Cooper; Kathy Petoumenos
Journal:  J Int AIDS Soc       Date:  2011-02-23       Impact factor: 5.396

3.  Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes.

Authors:  Yen T Duong; Reshma Kassanjee; Alex Welte; Meade Morgan; Anindya De; Trudy Dobbs; Erin Rottinghaus; John Nkengasong; Marcel E Curlin; Chonticha Kittinunvorakoon; Boonyos Raengsakulrach; Michael Martin; Kachit Choopanya; Suphak Vanichseni; Yan Jiang; Maofeng Qiu; Haiying Yu; Yan Hao; Neha Shah; Linh-Vi Le; Andrea A Kim; Tuan Anh Nguyen; William Ampofo; Bharat S Parekh
Journal:  PLoS One       Date:  2015-02-24       Impact factor: 3.240

  3 in total

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