Background: Estimating the sizes of populations at highest risk for HIV is essential for developing and monitoring effective HIV prevention and treatment programs. We provide several country examples of how service multiplier methods have been used in respondent-driven sampling surveys and provide guidance on how to maximize this method’s use.
Methods: Population size estimates were conducted in 4 countries (Mauritius— intravenous drug users [IDU] and female sex workers [FSW]; Papua New Guinea—FSW and men who have sex with men [MSM]; Thailand—IDU; United States—IDU) using adjusted proportions of population members reporting attending a service, project or study listed in a respondent-driven sampling survey, and the estimated total number of population members who visited one of the listed services, projects, or studies collected from the providers.
Results: The median population size estimates were 8866 for IDU and 667 for FSW in Mauritius. Median point estimates for FSW were 4190 in Port Moresby and 8712 in Goroka, Papua New Guinea, and 2,126 for MSM in Port Moresby and 4200 for IDU in Bangkok, Thailand. Median estimates for IDU were 1050 in Chiang Mai, Thailand, and 15,789 in 2005 and 15,554 in 2009 in San Francisco.
Conclusion: Our estimates for almost all groups in each country fall within the range of other regional and national estimates, indicating that the service multiplier method, assuming all assumptions are met, can produce informative estimates. We suggest using multiple multipliers whenever possible, garnering program data from the widest possible range of services, projects, and studies. A median of several estimates is likely more robust to potential biases than a single estimate.
Findings and suggestions from population size estimations in 4 countries among HIV high-risk populations using the service multiplier method with respondent-driven sampling are presented.
From the *Global Health Sciences, University of California, San Francisco, San Francisco, CA; †Centers for Disease Control and Prevention, Global AIDS Program, Asia Regional Office, Nonthaburi, Thailand; ‡Centers for Disease Control and Prevention, Division of Global HIV/AIDS, Atlanta, GA; §San Francisco Department of Public Health, San Francisco, CA; and ∥Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Disclaimer: The findings and conclusions in this paper are those of the authors and do not necessarily represent those of the Centers for Disease Control and Prevention.
Correspondence: Lisa G. Johnston, PhD, Nieuwezijds Voorburgwal 64F, 1012 SC Amsterdam, the Netherlands. E-mail: Lsjohnston.email@example.com.
Received for publication June 23, 2012, and accepted November 21, 2012.