Rutgers School of Public Health faculty, Dr. Henry F. Raymond, has published a study on “Starfish Sampling” – a novel hybrid approach recruiting hidden populations.
Dr. Raymond along with co-authors Dr. Willi McFarland and Dr. Yea-Hung Chen, sought to leverage the strengths of time location sampling (TLS) and respondent-driven sampling (RDS) for surveys of hidden populations by combining elements of both methods in a new approach, “starfish sampling”. Starfish sampling is a flexible method to sample hidden populations for whom conventional TLS and RDS may not work in theory or practice.
Starfish sampling entails random selection of venue-day-time units from a mapping of the locations where the population can be found, combined with short chains of peer referrals from their social networks at the venue or presenting to the study site later.
Reaching hidden populations for public health research often relies on these populations having venues they attend or having robust social networks. Statistically, methods exist to access these populations through venues or social networks. However, in many geographies and among numerous hidden populations there is a dearth of venues and very small social networks.
Starfish sampling was applied by Dr. Raymond and colleagues in a recent study which used transmen in San Francisco as a case example, recruiting 122 eligible participants: 79 at randomly selected venues, 11 on dating applications, and 32 by referral. Starfish sampling produced one of the largest community-recruited samples specifically for transmen to date.
“As this sampling approach is refined and tested in additional contexts we are hopeful to have another tool in our epidemiologic toolbox to obtain representative data from populations that experience health disparities,” comments Dr. Raymond.
“Starfish Sampling: A Novel, Hybrid Approach to Recruiting Hidden Populations,” was recently published in the Journal of Urban Health.