Skip to main content
SpringerLink
Log in

“Starfish Sampling”: a Novel, Hybrid Approach to Recruiting Hidden Populations

  • Published:
Journal of Urban Health Aims and scope Submit manuscript

Abstract

We sought to leverage the strengths of time location sampling (TLS) and respondent-driven sampling (RDS) for surveys of hidden populations by combing elements of both methods in a new approach we call “starfish sampling.” 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. Using the population of transmen in San Francisco as a case example, we recruited 122 eligible participants using starfish sampling: 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. Starfish sampling is a flexibility method to recruit and sample hidden populations for whom conventional TLS and RDS may not work in theory or practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Parry C, Petersen P, Dewing S, Carney T, Needle R, Kroeger K, et al. Rapid assessment of drug-related HIV risk among men who have sex with men in three South African cities. Drug Alcohol Depend. 2008;95(1–2):45–53.

    Article  PubMed  Google Scholar 

  2. Watters JK, Biernacki P. Targeted sampling: options for the study of hidden populations. Soc Probl. 1989;36(4):416–30.

    Article  Google Scholar 

  3. MacKellar D, Valleroy L, Karon J, Lemp G, Janssen R. The Young Men’s Survey: methods for estimating HIV seroprevalence and risk factors among young men who have sex with men. Public Health Rep. 1996;111(Suppl 1):138–44.

    PubMed  PubMed Central  Google Scholar 

  4. Heckathorn D. Respondent-driven sampling: a new approach to the study of hidden population. Soc Probl. 1997;44(2):174–99.

    Article  Google Scholar 

  5. MacKellar D, Gallagher KM, Finlayson T, Sanchez T, Lansky A, Sullivan PS. Surveillance of HIV risk and prevention behaviors of men who have sex with men: a national application of venue-based, time-space sampling. Public Health Rep. 2007;122:39–47.

    Article  PubMed  PubMed Central  Google Scholar 

  6. de Sousa Mascena Veras MA, Calazans GJ, de Almeida Ribeiro MC, de Freitas Oliveira CA, Giovanetti MR, Facchini R, et al. High HIV prevalence among men who have sex with men in a time-location sampling survey, São Paulo, Brazil. AIDS Behav. 2015;19(9):1589–98.

    Article  PubMed  Google Scholar 

  7. Lansky A, Abdul-Quader AS, Cribbin M, Hall T, Finlayson TJ, Garfein RS, et al. Developing an HIV behavioral surveillance system for injecting drug users: the National HIV Behavioral Surveillance System. Public Health Rep. 2007;122(Suppl 1):48–55.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bastos FI, Bastos LS, Coutinho C, Toledo L, Mota JC, Velasco-de-Castro CA, et al. HIV, HCV, HBV, and syphilis among transgender women from Brazil: assessing different methods to adjust infection rates of a hard-to-reach, sparse population. Medicine (Baltimore). 2018;97(1S Suppl 1):S16–24.

    Article  Google Scholar 

  9. Gustafson P, Gilbert M, Xia M, Michelow W, Robert W, Trussler T, et al. Impact of statistical adjustment for frequency of venue attendance in a venue-based survey of men who have sex with men. Am J Epidemiol. 2013;177(10):1157–64.

    Article  PubMed  Google Scholar 

  10. Leon L, Jauffret-Roustide M, Le Strat Y. Design-based inference in time-location sampling. Biostatistics. 2015;16(3):565–79.

    Article  PubMed  Google Scholar 

  11. Sommen C, Saboni L, Sauvage C, Alexandre A, Lot F, Barin F, et al. Time location sampling in men who have sex with men in the HIV context: the importance of taking into account sampling weights and frequency of venue attendance. Epidemiol Infect. 2018;146(7):913–9.

    Article  PubMed  CAS  Google Scholar 

  12. Heckathorn DD. Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Soc Probl. 2002;49(1):11–34.

    Article  Google Scholar 

  13. Salganik MJ, Heckathorn DD. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Methodol. 2004;34:193–239.

    Article  Google Scholar 

  14. Volz E, Heckathorn DD. Probability based estimation theory for respondent driven sampling. J Off Stat. 2008;24(1):79–97.

    Google Scholar 

  15. Gile KJ, Handcock MS. Respondent-driven sampling: an assessment of current methodology. Sociol Methodol. 2010;40(1):285–327.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gile K. Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. J Am Stat Assoc. 2011;106(493):135–46.

    Article  CAS  Google Scholar 

  17. Kendall C, Kerr LR, Gondim RC, Werneck GL, Macena RH, Pontes MK, et al. An empirical comparison of respondent-driven sampling, time location sampling, and snowball sampling for behavioral surveillance in men who have sex with men, Fortaleza, Brazil. AIDS Behav. 2008;12(4 Suppl):S97–104.

    Article  PubMed  Google Scholar 

  18. Kral AH, Malekinejad M, Vaudrey J, Martinez AN, Lorvick J, McFarland W, et al. Comparing respondent-driven sampling and targeted sampling methods of recruiting injection drug users in San Francisco. J Urban Health. 2010;87(5):839–50.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wei C, McFarland W, Colfax GN, Fuqua V, Raymond HF. Reaching black men who have sex with men: a comparison between respondent-driven sampling and time-location sampling. Sex Transm Infect. 2012;88(8):622–6.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Arayasirikul S, Cai X, Wilson EC. A qualitative examination of respondent-driven sampling (RDS) peer referral challenges among young transwomen in the San Francisco Bay Area. JMIR Public Health Surveill. 2015;1(2):e9.

    Article  PubMed  PubMed Central  Google Scholar 

  21. McFarland W, Wilson EC, Raymond HF. HIV prevalence, sexual partners, sexual behavior and HIV acquisition risk among trans men, San Francisco, 2014. AIDS Behav. 2017;21(12):3346–52.

    Article  PubMed  Google Scholar 

  22. Berchenko Y, Rosenblatt JD, Frost SDW. Modeling and analyzing respondent-driven sampling as a counting process. Biometrics. 2017;73(4):1189–98.

    Article  PubMed  Google Scholar 

  23. Cooley LA, Oster AM, Rose CE, Wejnert C, Le BC P-BG. Increases in HIV testing among men who have sex with men—National HIV Behavioral Surveillance System, 20 U.S. Metropolitan Statistical Areas, 2008 and 2011. PLoS One. 2014;9(9):e104162.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Holt M, Lea T, Asselin J, Hellard M, Prestage G, Wilson D, et al. The prevalence and correlates of undiagnosed HIV among Australian gay and bisexual men: results of a national, community-based, bio-behavioural survey. J Int AIDS Soc. 2005;18:20526–33.

    Article  CAS  Google Scholar 

  25. Burt RD, Theide H. Reduction in needle sharing among Seattle-area injection drug users across 4 surveys, 1994–2013. Am J Public Health. 2016;106(2):301–7.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mirandola M, Gios L, Sherriff N, Pachankis J, Toskin I, Ferrer L, et al. Socio-demographic characteristics, sexual and test-seeking behaviours amongst men who have sex with both men and women: results from a bio-behavioural survey in 13 European cities. AIDS Behav. 2017;21(10):3013–25.

    Article  PubMed  Google Scholar 

  27. McFarland W, Wilson E, Fisher RH. How many transgender men are there in San Francisco? J Urban Health. 2018;95(1):129–33.

    Article  PubMed  Google Scholar 

  28. Census US. American Fact Finder 2010. Available from: https://factfinder.census.gov/faces/nav/jsf/pages/community_facts.xhtml. Accessed 13 October 2018.

  29. Crissman HP, Berger MB, Graham LF, Dalton VK. Transgender demographics: a household probability sample of US adults, 2014. Am J Public Health. 2017;107(2):213–5.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge research support from the National Institute for Child Health and Human Development with the National Institutes of Health under award number R21HD071765. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding source had no role in the design of this study; collection, analysis, and interpretation of the data; writing of the report; or the decision to submit the paper for publication.

Author information

Authors and Affiliations

  1. School of Public Health, Rutgers University, Piscataway, NJ, USA

    H. Fisher Raymond

  2. University of California, San Francisco, CA, USA

    H. Fisher Raymond, Yea-Hung Chen & Willi McFarland

  3. San Francisco Department of Public Health, 25 Van Ness, Suite 700, San Francisco, CA, 94102, USA

    Yea-Hung Chen & Willi McFarland

Authors
  1. H. Fisher Raymond
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yea-Hung Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Willi McFarland
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Willi McFarland.

Ethics declarations

The study protocol was reviewed and approved by the Internal Review Board of the University of California San Francisco. Participants provided written informed consent.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raymond, H.F., Chen, YH. & McFarland, W. “Starfish Sampling”: a Novel, Hybrid Approach to Recruiting Hidden Populations. J Urban Health 96, 55–62 (2019). https://doi.org/10.1007/s11524-018-0316-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11524-018-0316-9

Keywords

Search

Navigation