Background: Key populations, including female sex workers (FSWs), are at a disproportionately high risk for HIV infection. Estimates of the size of these populations serve as denominator data to inform HIV prevention and treatment programming and are necessary for the equitable allocation of limited public health resources.
Objective: This study aimed to present the respondent-driven sampling (RDS) adjusted reverse tracking method (RTM; RadR), a novel population size estimation approach that combines venue mapping data with RDS data to estimate the population size, adjusted for double counting and nonattendance biases.
Methods: We used data from a 2014 RDS survey of FSWs in Windhoek and Katima Mulilo, Namibia, to demonstrate the RadR method. Information from venue mapping and enumeration from the survey formative assessment phase were combined with survey-based venue-inquiry questions to estimate population size, adjusting for double counting, and FSWs who do not attend venues. RadR estimates were compared with the official population size estimates, published by the Namibian Ministry of Health and Social Services (MoHSS), and with the unadjusted RTM.
Results: Using the RadR method, we estimated 1552 (95% simulation interval, SI, 1101-2387) FSWs in Windhoek and 453 (95% SI: 336-656) FSWs in Katima Mulilo. These estimates were slightly more conservative than the MoHSS estimates-Windhoek: 3000 (1800-3400); Katima Mulilo: 800 (380-2000)-though not statistically different. We also found 75 additional venues in Windhoek and 59 additional venues in Katima Mulilo identified by RDS participants' responses that were not detected during the initial mapping exercise.
Conclusions: The RadR estimates were comparable with official estimates from the MoHSS. The RadR method is easily integrated into RDS studies, producing plausible population size estimates, and can also validate and update key population maps for outreach and venue-based sampling.
Keywords: human immunodeficiency virus; population density; sex workers; social networking; vulnerable populations.
©Paul Douglas Wesson, Rajatashuvra Adhikary, Anna Jonas, Krysta Gerndt, Ali Mirzazadeh, Frieda Katuta, Andrew Maher, Karen Banda, Nicholus Mutenda, Willi McFarland, David Lowrance, Dimitri Prybylski, Sadhna Patel. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.03.2019.