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Measuring the size of populations most at risk of HIV, such as men who have sex with men (MSM), female sex workers (FSWs), and injection drug users presents significant challenges, as these populations are often hidden or hard to reach.1 2 Among the most popular methods to estimate the size of populations are those based on capture-recapture, where multiple samples (or data sources) are combined, and the population size estimated based on the concept that individuals are more likely to be sampled multiple times from small populations than large populations. This principle, originating as far back as the 17th century,3 was embraced later by wildlife researchers4 and epidemiologists5 and is advocated in the WHO/UNAIDS ‘Guideline on Estimating the Size of Populations Most at Risk to HIV’.6
Respondent-driven sampling (RDS)7 8 is increasingly being used to estimate HIV prevalence within hidden populations, by utilising social networks of the target population to facilitate sampling.9 Although RDS does not directly estimate population size, by weighting individuals according to their number of acquaintances, sampling bias can be controlled for to produce estimates of quantities such as disease prevalence that are similar to simple random (uniform) sampling (SRS), albeit with larger variance.7 8 10
In this issue, Paz-Bailey et al11 describe the combined use of RDS and capture-recapture in order to estimate the number of FSWs and MSM in El Salvador. Relatively few studies have combined RDS with capture-recapture (cf,12 13), and the study by Paz-Bailey et al is unusual in the use of an active ‘capture’ stage, where key chains were distributed to the members of …
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