An epidemic scenario based approach to assessing global HIV epidemics among MSM in low and middle income countries (LMIC)

Published: July 20, 2010

An epidemic scenario based approach to assessing global HIV epidemics among men who have sex with men (MSM) in low and middle income countries (LMIC)

C. Beyrer1, S.D. Baral1, F. Sifakis1, A.L. Wirtz1, B. Johns2, I. Semini3, R. Oelrichs3, D. Walker2

1Johns Hopkins Bloomberg School of Public Health, Epidemiology, Baltimore, United States, 2Johns Hopkins Bloomberg School of Public Health, International Health, Baltimore, United States, 3The World Bank, Global HIV/AIDS Program, Washington D.C., United States

Background: HIV epidemic dynamics vary greatly across LMIC in terms of HIV prevalence and incidence, attributable fraction by risk population, government and non-governmental HIV prevention, treatment, and care programs, and policy responses. To develop mathematical models to predict cost-effectiveness of prevention programs for MSM in LMIC as well as costs of inaction, we sought to develop classifications of epidemics with more inputs than are currently used.

Methods: We systematically reviewed 138 HIV prevalence studies from 54 LMIC from published and conference abstracts available after January 1, 2000; studies were required to have a sample size of greater than 50 MSM. We further reviewed 1679 unpublished reports describing populations or programs for MSM in LMIC and gathered data on HIV prevalence among reproductive-age MSM, the general population, sex with women, and data on people who inject drugs. Data were analyzed to identify trends across LMICs.

Results: HIV epidemics among MSM in LMIC may be classified into five Epidemic Scenarios as follows:

(1) MSM are the predominant contributor to HIV;
(2) MSM transmission occurs within epidemics driven by people who inject drugs;
(3) MSM transmission occurs within well-established heterosexual epidemics;
(4) where sexual and parenteral transmission both contribute significantly to HIV transmission; and
(5) where sociologic data suggest the presence of MSM populations but epidemiologic data are too sparse to model.

Conclusions: These scenarios do not replace the use of generalized or concentrated classifications but allow for more precise understanding of HIV epidemics among MSM and their relationship with the general population. Mapping these scenarios may help predict effective research and intervention strategies by epidemic scenario, as well as facilitating the development of models to predict costs associated with these strategies, and costs associated with the lack of effective and comprehensive prevention and care services for MSM.

Leave a Reply