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Sex Transm Infect 82:iii51-iii55 doi:10.1136/sti.2006.020164

Short term estimates of adult HIV incidence by mode of transmission: Kenya and Thailand as examples

  1. E Gouws1,
  2. P J White2,
  3. J Stover3,
  4. T Brown4
  1. 1Department of Policy, Evidence and Partnership, Joint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland
  2. 2Department of Infectious Disease Epidemiology, Imperial College Faculty of Medicine, London, UK
  3. 3Futures Group, Glastonbury, CT, USA
  4. 4Population and Health Studies, East-West Centre, Honolulu, HI, USA
  1. Correspondence to:
 MsE Gouws
 Department of Policy, Evidence and Partnership, Joint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland; Gouwse{at}unaids.org
  • Accepted 5 April 2006

Abstract

Objective: Patterns of transmission of HIV are different among different regions of the world and change over time within regions. In order to adapt prevention strategies to changing patterns of risk, we need to understand the behaviours that put people at risk of infection and how new infections are distributed among risk groups.

Methods: A model is described to calculate the expected incidence of HIV infections in the adult population by mode of exposure using the current distribution of prevalent infections and the patterns of risk within different populations. For illustration the model is applied to Thailand and Kenya.

Results: New infections in Kenya were mainly transmitted through heterosexual contact (90%), while a small but significant number were related to injecting drug use (4.8%) and men who have sex with men (4.5%). In Thailand, the epidemic has spread over time to the sexual partners of vulnerable groups and in 2005 the majority of new infections occurred among the low risk heterosexual population (43%). Men having sex with men accounted for 21% and sex work (including sex workers, clients, and partners of clients) for 18% of new infections. Medical interventions did not contribute significantly to new infections in either Kenya or Thailand.

Conclusions: The model provides a simple tool to inform the planning of effective, appropriately targeted, country specific intervention programmes. However, better surveillance systems are needed in countries to obtain more reliable biological and behavioural data in order to improve the estimates of incidence by risk group.

Footnotes

  • Competing interests: none.

  • Edited by Peter Ghys, Neff Walker, Helen Ward and Rob Miller