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Sexually Transmitted Diseases:
doi: 10.1097/OLQ.0b013e31827fd650
Original Study

Incorporating the Service Multiplier Method in Respondent-Driven Sampling Surveys to Estimate the Size of Hidden and Hard-to-Reach Populations: Case Studies From Around the World

Johnston, Lisa G. PhD*; Prybylski, Dimitri PhD†‡; Raymond, H. Fisher DrPH; Mirzazadeh, Ali MD, PhD; Manopaiboon, Chomnad MA; McFarland, Willi MD, PhD

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Abstract

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.

© Copyright 2013 American Sexually Transmitted Diseases Association

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